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# 股票数据分析工具
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这是一个用于采集和分析股票数据的Python工具。主要功能包括：
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1. 从雪球网获取股票数据并存储到数据库
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2. 分析涨幅超过1.5%的个股
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3. 按板块统计上涨股票的分布情况
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4. 识别在多个热门板块中出现的股票
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5. 对个股进行AI驱动的基本面分析并建立企业画像
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## 环境要求
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- Python 3.8+
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- MySQL数据库
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## 安装依赖
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```bash
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pip install -r requirements.txt
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```
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## 配置数据库
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在运行脚本之前，请确保：
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1. MySQL数据库已经启动
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2. 创建了名为 `db_gp_cj` 的数据库
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3. 数据库中存在 `stock_changes` 和 `gp_gnbk` 表
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## 使用方法
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1. 修改数据库连接信息：
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在 `src/scripts/stock_analysis.py` 中修改 `db_url` 变量为你的数据库连接信息
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2. 运行脚本：
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```bash
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python src/scripts/stock_analysis.py
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```
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## 主要功能
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1. `fetch_and_store_stock_data`: 从雪球网获取股票数据并存储到数据库
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2. `get_high_performance_stocks`: 分析涨幅超过1.5%的股票及其板块分布
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3. `get_top_industries_and_stocks`: 分析在多个热门板块中出现的股票
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## 基本面分析与企业画像
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系统通过AI驱动的分析，对个股进行多维度基本面分析，构建全面的企业画像。每个维度分析结果都以文本形式保存，并提取关键指标形成量化评估，存储在数据库的`extra_info`字段中。
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### 实控人和管理层持股 (management_ownership)
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#### 持股稳定性 (shareholding)
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- `1`: 减持比例低 - 近三年减持比例低于2%，减持次数少于5次，减持后持股比例仍高于5%
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- `0`: 减持适中 - 减持比例在2%-5%之间，减持次数不超过10次，减持后持股比例仍高于5%
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- `-1`: 减持比例高 - 减持次数较多或比例较高，大股东持股比例已减持到5%以下
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#### 管理层能力 (ability)
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- `1`: 领军人物 - 实控人或高管中存在行业领军人物
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- `0`: 一般水平 - 没有明显的行业领军人物
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- `-1`: 存在风险 - 实控人或高管存在被证监会处罚情况或司法犯罪可能性
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### 财务报表分析 (financial_report)
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#### 财务报表质量 (financial_report_level)
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- `2`: 优秀 - 边际向好，最新财报没有任何风险
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- `1`: 良好 - 边际变化波动不高较为稳定，风险很小
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- `0`: 一般 - 波动不高较为稳定，但存在一定财报隐患
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- `-1`: 较差 - 财报波动较大，且存在一定财报隐患
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- `-2`: 风险高 - 财报波动较大，且存在较大财报隐患
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### 行业竞争格局 (industry_competition)
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#### 行业发展空间 (industry_space)
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- `2`: 高增长 - 市场环境、阶段和竞争格局符合未来企业高速增长
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- `1`: 稳定增长 - 市场环境、阶段和竞争格局符合未来企业稳定经营
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- `0`: 不确定性 - 市场环境、阶段和竞争格局存在较大不确定性
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- `-1`: 不利环境 - 市场环境、阶段和竞争格局不利于企业正常经营
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### 重大订单和项目进展 (recent_projects)
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#### 项目进展质量 (major_events)
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- `1`: 超预期 - 项目进展顺利，且订单交付/建厂等超预期
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- `0`: 符合预期 - 进展顺利，但没有超预期
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- `-1`: 低于预期 - 进展不顺利，或按照进度进行但仍在验证中
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### 股吧讨论分析 (stock_discussion)
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#### 市场情绪 (emotion)
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- `1`: 乐观 - 股吧讨论情绪偏乐观
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- `0`: 中性 - 股吧讨论情绪偏中性
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- `-1`: 悲观 - 股吧讨论情绪偏悲观
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### 产业链合作动态 (industry_cooperation)
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#### 合作动态质量 (collaboration_dynamics)
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- `2`: 高质量 - 近期有较多且质量高的新合作动态，具备新业务拓展能力，可体现在近一年财报中
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- `1`: 一般 - 半年内合作动态频率低或质量一般，在原有业务上合作关系的衍生，对财报影响一般
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- `0`: 低质量 - 没有合作动态或质量低
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- `-1`: 负面 - 有负面合作关系，如解除合作或业务被其他厂商瓜分
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### 券商研究与目标价 (target_price)
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#### 券商评级 (securities_rating)
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- `2`: 买入 - 券商评级以"买入"居多
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- `1`: 增持 - 券商评级以"增持"居多
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- `0`: 中性 - 券商评级以"中性"居多或无评级
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- `-1`: 减持 - 券商评级以"减持"居多
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- `-2`: 卖出 - 券商评级以"卖出"居多
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#### 上涨/下跌空间 (odds)
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- `1`: 上涨空间大 - 上涨空间大于下跌空间
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- `0`: 平衡 - 上涨空间和下跌空间差不多
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- `-1`: 下跌空间大 - 下跌空间大于上涨空间
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### 投资建议 (investment_advice)
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#### 投资建议类型 (investment_advice_type)
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- `短期`: 近期(1-3个月内)有明确利好因素、催化事件或阶段性业绩改善
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- `中期`: 无明确短期利好，但中期(3-12个月)业绩面临向上拐点或行业处于上升周期
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- `长期`: 公司具备长期稳定的盈利能力、行业地位稳固、长期成长性好
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- `不建议`: 存在风险因素、基本面恶化、估值过高或行业前景不佳
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## 企业画像应用
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基于上述多维度的量化指标，系统可以综合评估企业基本面状况，用于：
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1. 筛选优质投资标的
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2. 评估投资风险
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3. 识别行业中的领先企业
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4. 预判企业未来发展趋势
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5. 辅助投资决策
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企业基本面画像可以通过PDF报告形式输出，便于投资者快速了解企业综合情况。
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## 企业筛选方法
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基于基本面画像的量化指标，系统支持以下类型企业的筛选：
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### 1. 高成长潜力企业
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- 行业发展空间 (industry_space) = 2
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- 财务报表质量 (financial_report_level) ≥ 1
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- 项目进展质量 (major_events) = 1
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- 合作动态质量 (collaboration_dynamics) ≥ 1
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- 券商评级 (securities_rating) ≥ 1
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- 上涨/下跌空间 (odds) = 1
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筛选逻辑：选择处于高增长行业、财务状况良好、项目进展超预期、有高质量合作动态、券商看好且上涨空间大的企业。
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### 2. 稳定型龙头企业
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- 管理层能力 (ability) = 1
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- 持股稳定性 (shareholding) ≥ 0
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- 财务报表质量 (financial_report_level) ≥ 1
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- 行业发展空间 (industry_space) ≥ 1
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- 合作动态质量 (collaboration_dynamics) ≥ 1
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- 券商评级 (securities_rating) ≥ 0
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筛选逻辑：选择管理层能力强、持股稳定、财务状况良好、行业地位稳固、有稳定合作关系的企业。
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### 3. 短期投资机会
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- 项目进展质量 (major_events) = 1
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- 市场情绪 (emotion) = 1
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- 券商评级 (securities_rating) ≥ 1
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- 上涨/下跌空间 (odds) = 1
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- 财务报表质量 (financial_report_level) ≥ 0
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筛选逻辑：选择近期有超预期项目进展、市场情绪乐观、券商看好且上涨空间大的企业。
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### 4. 价值型投资标的
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- 财务报表质量 (financial_report_level) = 2
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- 持股稳定性 (shareholding) = 1
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- 行业发展空间 (industry_space) ≥ 0
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- 合作动态质量 (collaboration_dynamics) ≥ 0
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- 券商评级 (securities_rating) ≥ 0
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筛选逻辑：选择财务状况优秀、持股稳定、行业环境稳定、合作关系正常的企业。
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### 5. 困境反转机会
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- 财务报表质量 (financial_report_level) = -1
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- 项目进展质量 (major_events) = 1
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- 行业发展空间 (industry_space) = 1
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- 市场情绪 (emotion) = 0
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- 券商评级 (securities_rating) = 0
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筛选逻辑：选择当前财务状况较差但项目进展超预期、行业环境改善、市场情绪企稳的企业。
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### 6. 风险规避标的
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- 管理层能力 (ability) = 1
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- 持股稳定性 (shareholding) = 1
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- 财务报表质量 (financial_report_level) ≥ 1
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- 行业发展空间 (industry_space) ≥ 0
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- 合作动态质量 (collaboration_dynamics) ≥ 0
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- 券商评级 (securities_rating) ≥ 0
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筛选逻辑：选择管理层能力强、持股稳定、财务状况良好、行业环境稳定、合作关系正常的企业。
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### 7. 创新驱动型企业
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- 行业发展空间 (industry_space) = 2
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- 合作动态质量 (collaboration_dynamics) = 2
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- 项目进展质量 (major_events) = 1
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- 财务报表质量 (financial_report_level) ≥ 0
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- 券商评级 (securities_rating) ≥ 1
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筛选逻辑：选择处于高增长行业、有高质量新业务合作、项目进展超预期、财务状况稳定、券商看好的企业。
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### 8. 行业整合机会
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- 行业发展空间 (industry_space) = 1
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- 合作动态质量 (collaboration_dynamics) = 2
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- 财务报表质量 (financial_report_level) ≥ 1
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- 券商评级 (securities_rating) ≥ 1
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- 上涨/下跌空间 (odds) = 1
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筛选逻辑：选择行业环境稳定、有高质量新业务合作、财务状况良好、券商看好且上涨空间大的企业。
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## 数据库查询示例
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以下SQL查询示例展示了如何从数据库中筛选特定类型的企业：
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```sql
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-- 筛选高成长潜力企业
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SELECT stock_code, stock_name, dimension, ai_response, extra_info
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FROM analysis_results
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WHERE dimension = 'industry_competition'
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AND JSON_EXTRACT(extra_info, '$.industry_space') = 2
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AND stock_code IN (
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SELECT stock_code
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FROM analysis_results
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WHERE dimension = 'financial_report'
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AND JSON_EXTRACT(extra_info, '$.financial_report_level') >= 1
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)
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AND stock_code IN (
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SELECT stock_code
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FROM analysis_results
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WHERE dimension = 'recent_projects'
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AND JSON_EXTRACT(extra_info, '$.major_events') = 1
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)
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-- ... 其他条件
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-- 筛选稳定型龙头企业
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SELECT stock_code, stock_name, dimension, ai_response, extra_info
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FROM analysis_results
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WHERE dimension = 'management_ownership'
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AND JSON_EXTRACT(extra_info, '$.ability') = 1
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AND JSON_EXTRACT(extra_info, '$.shareholding') >= 0
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AND stock_code IN (
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SELECT stock_code
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FROM analysis_results
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WHERE dimension = 'financial_report'
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AND JSON_EXTRACT(extra_info, '$.financial_report_level') >= 1
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)
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-- ... 其他条件
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```
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注意：实际查询时可能需要根据数据库表结构调整SQL语句。建议根据具体需求组合使用不同的筛选条件。
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@ -0,0 +1,17 @@
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flask==2.0.3
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werkzeug==2.0.3
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flask-cors==3.0.10
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sqlalchemy==1.4.46
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pymysql==1.0.3
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tqdm>=4.65.0
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easy-spider-tool>=0.0.4
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easy-twitter-crawler>=0.0.4
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python-dateutil>=2.8.2
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numpy>=1.21.0
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matplotlib>=3.4.0
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volcengine-python-sdk[ark]
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openai>=1.0
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reportlab>=4.3.1
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markdown2>=2.5.3
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# 1. 按下 Win+R ，输入 regedit 打开注册表编辑器。
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# 2. 设置 \HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem 路径下的变量 LongPathsEnabled 为 1 即可。
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|
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@ -0,0 +1,832 @@
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# 股票企业画像 API 文档
|
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这个 API 提供了基于企业画像筛选股票的功能。
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## 基础信息
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- 基础 URL: `http://localhost:5000/api`
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- 所有响应均为 JSON 格式
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## 接口列表
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### 1. 健康检查
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检查 API 服务是否正常运行。
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- **URL**: `/health`
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- **方法**: `GET`
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- **响应示例**:
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```json
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{
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"status": "ok",
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"message": "Service is running"
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}
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```
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### 2. 获取企业画像类型列表
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获取所有可用的企业画像筛选类型。
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- **URL**: `/stock_profiles`
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- **方法**: `GET`
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- **响应示例**:
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```json
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{
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"profiles": [
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{"id": 1, "name": "高成长潜力企业", "method": "high_growth"},
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{"id": 2, "name": "稳定型龙头企业", "method": "stable_leaders"},
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// ... 其他画像类型
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]
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}
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```
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### 3. 根据企业画像类型筛选股票
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根据预定义的企业画像类型筛选股票。
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- **URL**: `/screen/{profile_type}`
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- **方法**: `GET`
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- **参数**:
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- `profile_type`: 企业画像类型，对应企业画像列表中的 `method` 字段
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- **可用的 profile_type 值**:
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- `high_growth`: 高成长潜力企业
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- `stable_leaders`: 稳定型龙头企业
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- `short_term`: 短期投资机会
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- `value_investment`: 价值型投资标的
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- `turnaround`: 困境反转机会
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- `risk_averse`: 风险规避标的
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- `innovation_driven`: 创新驱动型企业
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- `industry_integration`: 行业整合机会
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- `recommended`: 推荐投资企业
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- **响应示例**:
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```json
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{
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"status": "success",
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"profile_type": "recommended",
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"count": 2,
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"stocks": [
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{"code": "000001", "name": "平安银行"},
|
||||
{"code": "600000", "name": "浦发银行"}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### 4. 自定义条件筛选股票
|
||||
|
||||
使用自定义条件筛选股票。
|
||||
|
||||
- **URL**: `/screen/custom`
|
||||
- **方法**: `POST`
|
||||
- **请求体**:
|
||||
```json
|
||||
{
|
||||
"conditions": [
|
||||
{
|
||||
"dimension": "industry_competition",
|
||||
"field": "industry_space",
|
||||
"operator": "=",
|
||||
"value": 2
|
||||
},
|
||||
{
|
||||
"dimension": "financial_report",
|
||||
"field": "financial_report_level",
|
||||
"operator": ">=",
|
||||
"value": 1
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
- **条件字段说明**:
|
||||
- `dimension`: 分析维度（例如：`industry_competition`, `financial_report`等）
|
||||
- `field`: 维度中的字段名（例如：`industry_space`, `financial_report_level`等）
|
||||
- `operator`: 操作符（`=`, `!=`, `>=`, `<=`, `>`, `<`, `in`）
|
||||
- `value`: 比较值
|
||||
- `required`: 是否必须满足（默认为`true`）
|
||||
- **响应示例**:
|
||||
```json
|
||||
{
|
||||
"status": "success",
|
||||
"count": 3,
|
||||
"stocks": [
|
||||
{"code": "000001", "name": "平安银行"},
|
||||
{"code": "600000", "name": "浦发银行"},
|
||||
{"code": "601288", "name": "农业银行"}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### 5. 获取推荐投资企业
|
||||
|
||||
获取符合推荐投资条件（第9个企业画像）的企业列表。
|
||||
|
||||
- **URL**: `/recommended_stocks`
|
||||
- **方法**: `GET`
|
||||
- **响应示例**:
|
||||
```json
|
||||
{
|
||||
"status": "success",
|
||||
"count": 2,
|
||||
"stocks": [
|
||||
{"code": "603690", "name": "至纯科技"},
|
||||
{"code": "688053", "name": "思科瑞"}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### 6. 批量生成股票投资报告
|
||||
|
||||
为指定的股票列表生成PDF投资报告。
|
||||
|
||||
- **URL**: `/generate_reports`
|
||||
- **方法**: `POST`
|
||||
- **请求体**:
|
||||
```json
|
||||
{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
]
|
||||
}
|
||||
```
|
||||
- **响应示例**:
|
||||
```json
|
||||
{
|
||||
"status": "success",
|
||||
"message": "处理了 3 个股票的报告生成请求",
|
||||
"reports": [
|
||||
{
|
||||
"code": "603690",
|
||||
"name": "至纯科技",
|
||||
"report_path": "/path/to/603690_report.pdf",
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "688053",
|
||||
"name": "思科瑞",
|
||||
"report_path": "/path/to/688053_report.pdf",
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "300750",
|
||||
"name": "宁德时代",
|
||||
"status": "error",
|
||||
"error": "数据获取失败"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### 7. 分析股票列表并推荐投资企业
|
||||
|
||||
分析指定的股票列表，为每个股票生成投资建议，并筛选出符合推荐投资条件的企业。
|
||||
|
||||
- **URL**: `/analyze_and_recommend`
|
||||
- **方法**: `POST`
|
||||
- **请求体**:
|
||||
```json
|
||||
{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
]
|
||||
}
|
||||
```
|
||||
- **响应示例**:
|
||||
```json
|
||||
{
|
||||
"status": "success",
|
||||
"total_input_stocks": 3,
|
||||
"investment_advices": [
|
||||
{
|
||||
"code": "603690",
|
||||
"name": "至纯科技",
|
||||
"advice": "中期持有...",
|
||||
"reasoning": "基于行业前景...",
|
||||
"references": [...],
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "688053",
|
||||
"name": "思科瑞",
|
||||
"advice": "短期关注...",
|
||||
"reasoning": "考虑到公司近期...",
|
||||
"references": [...],
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "300750",
|
||||
"name": "宁德时代",
|
||||
"status": "error",
|
||||
"error": "数据获取失败"
|
||||
}
|
||||
],
|
||||
"recommended_stocks": {
|
||||
"count": 1,
|
||||
"stocks": [
|
||||
{"code": "688053", "name": "思科瑞"}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 8. 综合分析接口 - 组合多种功能和参数
|
||||
|
||||
综合接口，可以组合不同的功能和参数，包括股票列表、是否生成PDF以及企业画像筛选参数。
|
||||
|
||||
- **URL**: `/comprehensive_analysis`
|
||||
- **方法**: `POST`
|
||||
- **请求体**:
|
||||
```json
|
||||
{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
],
|
||||
"generate_pdf": false,
|
||||
"profile_filter": {
|
||||
"type": "recommended" // 使用预定义画像
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
或者使用自定义筛选条件:
|
||||
|
||||
```json
|
||||
{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
],
|
||||
"generate_pdf": false,
|
||||
"profile_filter": {
|
||||
"type": "custom",
|
||||
"conditions": [
|
||||
{
|
||||
"dimension": "financial_report",
|
||||
"field": "financial_report_level",
|
||||
"operator": ">=",
|
||||
"value": 1
|
||||
},
|
||||
{
|
||||
"dimension": "industry_competition",
|
||||
"field": "industry_space",
|
||||
"operator": "=",
|
||||
"value": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
- **参数说明**:
|
||||
- `stocks`: 股票列表，每个股票为 [代码, 名称] 格式
|
||||
- `generate_pdf`: 是否生成PDF报告，布尔值 (true/false)
|
||||
- `profile_filter`: 企业画像筛选条件，可选
|
||||
- `type`: 筛选类型，可以是预定义画像名称或 "custom"
|
||||
- 预定义画像: `high_growth`, `stable_leaders`, `short_term`, `value_investment`, `turnaround`, `risk_averse`, `innovation_driven`, `industry_integration`, `recommended`
|
||||
- 自定义条件: `custom`
|
||||
- `conditions`: 当 type="custom" 时的自定义筛选条件数组
|
||||
- `dimension`: 分析维度
|
||||
- `field`: 字段名
|
||||
- `operator`: 操作符 ('=', '!=', '>=', '<=', '>', '<', 'in')
|
||||
- `value`: 比较值
|
||||
|
||||
- **响应示例**:
|
||||
```json
|
||||
{
|
||||
"status": "success",
|
||||
"total_input_stocks": 3,
|
||||
"investment_advices": [
|
||||
{
|
||||
"code": "603690",
|
||||
"name": "至纯科技",
|
||||
"advice": "中期持有...",
|
||||
"reasoning": "基于行业前景...",
|
||||
"references": [...],
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "688053",
|
||||
"name": "思科瑞",
|
||||
"advice": "短期关注...",
|
||||
"reasoning": "考虑到公司近期...",
|
||||
"references": [...],
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "300750",
|
||||
"name": "宁德时代",
|
||||
"status": "error",
|
||||
"error": "数据获取失败"
|
||||
}
|
||||
],
|
||||
"filtered_stocks": {
|
||||
"count": 1,
|
||||
"filter_type": "high_growth",
|
||||
"stocks": [
|
||||
{"code": "688053", "name": "思科瑞"}
|
||||
]
|
||||
},
|
||||
"pdf_reports": {
|
||||
"count": 3,
|
||||
"reports": [
|
||||
{
|
||||
"code": "603690",
|
||||
"name": "至纯科技",
|
||||
"report_path": "/path/to/603690_report.pdf",
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "688053",
|
||||
"name": "思科瑞",
|
||||
"report_path": "/path/to/688053_report.pdf",
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "300750",
|
||||
"name": "宁德时代",
|
||||
"status": "error",
|
||||
"error": "生成PDF失败"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 错误处理
|
||||
|
||||
当发生错误时，API 将返回带有错误详情的 JSON 响应：
|
||||
|
||||
```json
|
||||
{
|
||||
"status": "error",
|
||||
"message": "错误详情"
|
||||
}
|
||||
```
|
||||
|
||||
## 使用示例
|
||||
|
||||
### 使用 curl 获取高成长潜力企业
|
||||
|
||||
```bash
|
||||
curl -X GET http://localhost:5000/api/screen/high_growth
|
||||
```
|
||||
|
||||
### 使用自定义条件筛选股票
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:5000/api/screen/custom \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"conditions": [
|
||||
{
|
||||
"dimension": "investment_advice",
|
||||
"field": "investment_advice_type",
|
||||
"operator": "!=",
|
||||
"value": "不建议"
|
||||
}
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
### 获取推荐投资企业列表
|
||||
|
||||
```bash
|
||||
curl -X GET http://localhost:5000/api/recommended_stocks
|
||||
```
|
||||
|
||||
### 批量生成投资报告
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:5000/api/generate_reports \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
### 分析股票列表并筛选推荐投资企业
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:5000/api/analyze_and_recommend \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
### 综合分析 - 生成投资建议、PDF报告并筛选高成长企业
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:5000/api/comprehensive_analysis \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
],
|
||||
"generate_pdf": true,
|
||||
"profile_filter": {
|
||||
"type": "recommended"
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
接口文档说明：
|
||||
# 综合分析接口文档
|
||||
|
||||
## 接口信息
|
||||
|
||||
- **接口名称**: 综合分析接口
|
||||
- **接口路径**: `/api/comprehensive_analysis`
|
||||
- **请求方法**: POST
|
||||
- **Content-Type**: application/json
|
||||
|
||||
## 接口用途
|
||||
|
||||
综合分析接口提供了一站式解决方案，可以组合多种功能和参数来完成以下任务：
|
||||
|
||||
1. 为指定的股票列表生成投资建议
|
||||
2. 根据企业画像条件筛选符合特定投资标准的股票
|
||||
3. 选择性地为股票生成 PDF 投资报告
|
||||
|
||||
该接口高度灵活，允许用户根据需求自由组合不同参数，实现个性化的股票分析和筛选功能。
|
||||
|
||||
## 请求参数
|
||||
|
||||
### 请求体(JSON格式)
|
||||
|
||||
```json
|
||||
{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
],
|
||||
"generate_pdf": false,
|
||||
"limit": 500,
|
||||
"profile_filter": {
|
||||
"type": "recommended"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 参数详细说明
|
||||
|
||||
| 参数名称 | 类型 | 是否必须 | 描述 |
|
||||
|---------|------|---------|------|
|
||||
| `stocks` | 数组 | 是 | 要分析的股票列表，每个股票为一个包含两个元素的数组 `[股票代码, 股票名称]` |
|
||||
| `generate_pdf` | 布尔值 | 否 | 是否为每个股票生成 PDF 投资报告，默认为 `false` |
|
||||
| `profile_filter` | 对象 | 否 | 企业画像筛选条件，用于从股票列表中筛选符合特定条件的股票 |
|
||||
|
||||
#### `stocks` 参数详情
|
||||
|
||||
二维数组，每个内部数组必须包含两个元素：
|
||||
- 第一个元素：股票代码（字符串）
|
||||
- 第二个元素：股票名称（字符串）
|
||||
|
||||
示例：`[["603690", "至纯科技"], ["688053", "思科瑞"]]`
|
||||
|
||||
#### `generate_pdf` 参数详情
|
||||
|
||||
- `true`：为每个股票生成 PDF 投资报告
|
||||
- `false`：不生成 PDF 报告（默认值）
|
||||
|
||||
#### `profile_filter` 参数详情
|
||||
|
||||
筛选条件对象，包含以下字段：
|
||||
|
||||
1. 使用预定义画像类型筛选：
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "recommended"
|
||||
}
|
||||
```
|
||||
|
||||
或者（兼容旧版用法）：
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "predefined", // 或任何非"custom"的值
|
||||
"profile_name": "recommended"
|
||||
}
|
||||
```
|
||||
|
||||
| 字段名称 | 类型 | 是否必须 | 描述 |
|
||||
|---------|------|---------|------|
|
||||
| `type` | 字符串 | 是 | 预定义的企业画像类型或"custom"(自定义条件) |
|
||||
| `profile_name` | 字符串 | 否 | 当`type`不是"custom"时可选，用于指定具体的预定义画像类型。如果不提供，则使用`type`的值作为画像类型 |
|
||||
|
||||
预定义画像类型可选值：
|
||||
- `high_growth`: 高成长潜力企业
|
||||
- `stable_leaders`: 稳定型龙头企业
|
||||
- `short_term`: 短期投资机会
|
||||
- `value_investment`: 价值型投资标的
|
||||
- `turnaround`: 困境反转机会
|
||||
- `risk_averse`: 风险规避标的
|
||||
- `innovation`或`innovation_driven`: 创新驱动型企业
|
||||
- `integration`或`industry_integration`: 行业整合机会
|
||||
- `recommended`: 推荐投资企业
|
||||
|
||||
2. 使用自定义条件筛选：
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "custom",
|
||||
"conditions": [
|
||||
{
|
||||
"dimension": "financial_report",
|
||||
"field": "financial_report_level",
|
||||
"operator": ">=",
|
||||
"value": 1
|
||||
},
|
||||
{
|
||||
"dimension": "industry_competition",
|
||||
"field": "industry_space",
|
||||
"operator": "=",
|
||||
"value": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
| 字段名称 | 类型 | 是否必须 | 描述 |
|
||||
|---------|------|---------|------|
|
||||
| `type` | 字符串 | 是 | 必须为 `"custom"` |
|
||||
| `conditions` | 数组 | 是 | 自定义筛选条件数组 |
|
||||
|
||||
##### `conditions` 数组中的每个条件对象：
|
||||
|
||||
| 字段名称 | 类型 | 是否必须 | 描述 |
|
||||
|---------|------|---------|------|
|
||||
| `dimension` | 字符串 | 是 | 分析维度 |
|
||||
| `field` | 字符串 | 是 | 维度中的字段名 |
|
||||
| `operator` | 字符串 | 是 | 比较操作符 |
|
||||
| `value` | 多种类型 | 是 | 比较值 |
|
||||
| `required` | 布尔值 | 否 | 是否必须满足该条件，默认为 `true` |
|
||||
|
||||
**分析维度(`dimension`)可选值**：
|
||||
- `financial_report`: 财务报告分析
|
||||
- `industry_competition`: 行业竞争分析
|
||||
- `industry_cooperation`: 行业合作分析
|
||||
- `management_ownership`: 管理层持股分析
|
||||
- `recent_projects`: 近期项目分析
|
||||
- `stock_discussion`: 股票讨论分析
|
||||
- `target_price`: 目标价格分析
|
||||
- `investment_advice`: 投资建议分析
|
||||
|
||||
**字段名(`field`)可选值**（根据不同维度）：
|
||||
- 财务报告: `financial_report_level`
|
||||
- 行业竞争: `industry_space`
|
||||
- 行业合作: `collaboration_dynamics`
|
||||
- 管理层持股: `ability`, `shareholding`
|
||||
- 近期项目: `major_events`
|
||||
- 股票讨论: `emotion`
|
||||
- 目标价格: `securities_rating`, `odds`
|
||||
- 投资建议: `investment_advice_type`
|
||||
|
||||
**操作符(`operator`)可选值**：
|
||||
- `=`: 等于
|
||||
- `!=`: 不等于
|
||||
- `>`: 大于
|
||||
- `<`: 小于
|
||||
- `>=`: 大于等于
|
||||
- `<=`: 小于等于
|
||||
- `in`: 包含于（此时 `value` 应为数组）
|
||||
|
||||
**比较值(`value`)类型**：
|
||||
- 当操作符为 `in` 时，`value` 为数组
|
||||
- 其他情况下，根据字段类型可以是数字或字符串
|
||||
|
||||
## 响应内容
|
||||
|
||||
### 成功响应
|
||||
|
||||
```json
|
||||
{
|
||||
"status": "success",
|
||||
"total_input_stocks": 3,
|
||||
"investment_advices": [
|
||||
{
|
||||
"code": "603690",
|
||||
"name": "至纯科技",
|
||||
"advice": "中期持有，结合目前的情况...(详细内容)",
|
||||
"reasoning": "基于行业前景和公司近期表现...(详细内容)",
|
||||
"references": ["财报数据", "行业研报"],
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "688053",
|
||||
"name": "思科瑞",
|
||||
"advice": "短期关注，考虑到...(详细内容)",
|
||||
"reasoning": "考虑到公司近期的产品进展...(详细内容)",
|
||||
"references": ["公司公告", "行业数据"],
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "300750",
|
||||
"name": "宁德时代",
|
||||
"status": "error",
|
||||
"error": "数据获取失败，请稍后重试"
|
||||
}
|
||||
],
|
||||
"filtered_stocks": {
|
||||
"count": 1,
|
||||
"filter_type": "high_growth",
|
||||
"stocks": [
|
||||
{"code": "688053", "name": "思科瑞"}
|
||||
]
|
||||
},
|
||||
"pdf_reports": {
|
||||
"count": 3,
|
||||
"reports": [
|
||||
{
|
||||
"code": "603690",
|
||||
"name": "至纯科技",
|
||||
"report_path": "/reports/603690_20240328.pdf",
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "688053",
|
||||
"name": "思科瑞",
|
||||
"report_path": "/reports/688053_20240328.pdf",
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"code": "300750",
|
||||
"name": "宁德时代",
|
||||
"status": "error",
|
||||
"error": "生成报告时出错：数据不完整"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 响应字段说明
|
||||
|
||||
| 字段名称 | 类型 | 描述 |
|
||||
|---------|------|------|
|
||||
| `status` | 字符串 | 响应状态，成功为 `"success"` |
|
||||
| `total_input_stocks` | 整数 | 输入的股票总数 |
|
||||
| `investment_advices` | 数组 | 每个股票的投资建议结果 |
|
||||
| `filtered_stocks` | 对象 | 筛选结果（仅当请求中包含 `profile_filter` 时返回） |
|
||||
| `pdf_reports` | 对象 | PDF报告生成结果（仅当 `generate_pdf=true` 时返回） |
|
||||
|
||||
#### `investment_advices` 数组中的每个对象
|
||||
|
||||
成功情况：
|
||||
| 字段名称 | 类型 | 描述 |
|
||||
|---------|------|------|
|
||||
| `code` | 字符串 | 股票代码 |
|
||||
| `name` | 字符串 | 股票名称 |
|
||||
| `advice` | 字符串 | 投资建议详细内容 |
|
||||
| `reasoning` | 字符串 | 投资建议的推理过程 |
|
||||
| `references` | 数组 | 参考资料列表 |
|
||||
| `status` | 字符串 | 处理状态，值为 `"success"` |
|
||||
|
||||
失败情况：
|
||||
| 字段名称 | 类型 | 描述 |
|
||||
|---------|------|------|
|
||||
| `code` | 字符串 | 股票代码 |
|
||||
| `name` | 字符串 | 股票名称 |
|
||||
| `status` | 字符串 | 处理状态，值为 `"error"` |
|
||||
| `error` | 字符串 | 错误详情 |
|
||||
|
||||
#### `filtered_stocks` 对象
|
||||
|
||||
| 字段名称 | 类型 | 描述 |
|
||||
|---------|------|------|
|
||||
| `count` | 整数 | 筛选出的股票数量 |
|
||||
| `filter_type` | 字符串 | 使用的筛选类型 |
|
||||
| `stocks` | 数组 | 筛选出的股票列表 |
|
||||
|
||||
`stocks` 数组中的每个对象：
|
||||
| 字段名称 | 类型 | 描述 |
|
||||
|---------|------|------|
|
||||
| `code` | 字符串 | 股票代码 |
|
||||
| `name` | 字符串 | 股票名称 |
|
||||
|
||||
#### `pdf_reports` 对象
|
||||
|
||||
| 字段名称 | 类型 | 描述 |
|
||||
|---------|------|------|
|
||||
| `count` | 整数 | 处理的报告总数 |
|
||||
| `reports` | 数组 | 报告生成结果列表 |
|
||||
|
||||
`reports` 数组中的每个对象：
|
||||
|
||||
成功情况：
|
||||
| 字段名称 | 类型 | 描述 |
|
||||
|---------|------|------|
|
||||
| `code` | 字符串 | 股票代码 |
|
||||
| `name` | 字符串 | 股票名称 |
|
||||
| `report_path` | 字符串 | 生成的PDF报告路径 |
|
||||
| `status` | 字符串 | 处理状态，值为 `"success"` |
|
||||
|
||||
失败情况：
|
||||
| 字段名称 | 类型 | 描述 |
|
||||
|---------|------|------|
|
||||
| `code` | 字符串 | 股票代码 |
|
||||
| `name` | 字符串 | 股票名称 |
|
||||
| `status` | 字符串 | 处理状态，值为 `"error"` |
|
||||
| `error` | 字符串 | 错误详情 |
|
||||
|
||||
### 错误响应
|
||||
|
||||
```json
|
||||
{
|
||||
"status": "error",
|
||||
"message": "错误详情说明"
|
||||
}
|
||||
```
|
||||
|
||||
| 字段名称 | 类型 | 描述 |
|
||||
|---------|------|------|
|
||||
| `status` | 字符串 | 响应状态，失败为 `"error"` |
|
||||
| `message` | 字符串 | 错误详情描述 |
|
||||
|
||||
## 使用示例
|
||||
|
||||
### 示例1：基本使用 - 仅生成投资建议
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:5000/api/comprehensive_analysis \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"]
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
### 示例2：生成投资建议和PDF报告
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:5000/api/comprehensive_analysis \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"]
|
||||
],
|
||||
"generate_pdf": false
|
||||
}'
|
||||
```
|
||||
|
||||
### 示例3：使用预定义画像筛选符合条件的股票
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:5000/api/comprehensive_analysis \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
],
|
||||
"profile_filter": {
|
||||
"type": "recommended"
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
### 示例4：使用自定义条件筛选股票
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:5000/api/comprehensive_analysis \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
],
|
||||
"profile_filter": {
|
||||
"type": "custom",
|
||||
"conditions": [
|
||||
{
|
||||
"dimension": "financial_report",
|
||||
"field": "financial_report_level",
|
||||
"operator": ">=",
|
||||
"value": 1
|
||||
}
|
||||
]
|
||||
}
|
||||
}'
|
||||
```
|
||||
Binary file not shown.
|
|
@ -0,0 +1,795 @@
|
|||
import sys
|
||||
import os
|
||||
|
||||
# 添加项目根目录到 Python 路径
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from flask import Flask, jsonify, request
|
||||
from flask_cors import CORS
|
||||
import logging
|
||||
|
||||
# 导入企业筛选器
|
||||
from src.fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||||
|
||||
# 设置日志
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 创建 Flask 应用
|
||||
app = Flask(__name__)
|
||||
CORS(app) # 启用跨域请求支持
|
||||
|
||||
# 创建企业筛选器实例
|
||||
screener = EnterpriseScreener()
|
||||
|
||||
@app.route('/api/health', methods=['GET'])
|
||||
def health_check():
|
||||
"""健康检查接口"""
|
||||
return jsonify({"status": "ok", "message": "Service is running"})
|
||||
|
||||
@app.route('/api/stock_profiles', methods=['GET'])
|
||||
def get_stock_profiles():
|
||||
"""获取企业画像筛选结果列表"""
|
||||
return jsonify({
|
||||
"profiles": [
|
||||
{"id": 1, "name": "高成长潜力企业", "method": "high_growth"},
|
||||
{"id": 2, "name": "稳定型龙头企业", "method": "stable_leaders"},
|
||||
{"id": 3, "name": "短期投资机会", "method": "short_term"},
|
||||
{"id": 4, "name": "价值型投资标的", "method": "value_investment"},
|
||||
{"id": 5, "name": "困境反转机会", "method": "turnaround"},
|
||||
{"id": 6, "name": "风险规避标的", "method": "risk_averse"},
|
||||
{"id": 7, "name": "创新驱动型企业", "method": "innovation_driven"},
|
||||
{"id": 8, "name": "行业整合机会", "method": "industry_integration"},
|
||||
{"id": 9, "name": "推荐投资企业", "method": "recommended"}
|
||||
]
|
||||
})
|
||||
|
||||
@app.route('/api/screen/<profile_type>', methods=['GET'])
|
||||
def screen_stocks(profile_type):
|
||||
"""根据企业画像类型筛选股票
|
||||
|
||||
Args:
|
||||
profile_type: 企业画像类型 (method 字段值)
|
||||
"""
|
||||
try:
|
||||
# 解析limit参数
|
||||
limit = request.args.get('limit', None, type=int)
|
||||
|
||||
# 处理别名
|
||||
if profile_type == 'innovation_driven':
|
||||
profile_type = 'innovation'
|
||||
elif profile_type == 'industry_integration':
|
||||
profile_type = 'integration'
|
||||
|
||||
# 映射画像类型到筛选方法
|
||||
method_map = {
|
||||
'high_growth': screener.screen_high_growth_stocks,
|
||||
'stable_leaders': screener.screen_stable_leaders,
|
||||
'short_term': screener.screen_short_term_opportunities,
|
||||
'value_investment': screener.screen_value_investment_stocks,
|
||||
'turnaround': screener.screen_turnaround_opportunities,
|
||||
'risk_averse': screener.screen_risk_averse_stocks,
|
||||
'innovation': screener.screen_innovation_driven_stocks,
|
||||
'integration': screener.screen_industry_integration_stocks,
|
||||
'recommended': screener.screen_multi_term_investment_stocks
|
||||
}
|
||||
|
||||
# 检查请求的画像类型是否有效
|
||||
if profile_type not in method_map:
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"Invalid profile type: {profile_type}"
|
||||
}), 400
|
||||
|
||||
# 调用相应的筛选方法
|
||||
stocks = method_map[profile_type](limit=limit)
|
||||
|
||||
# 转换结果格式
|
||||
result = []
|
||||
for code, name in stocks:
|
||||
result.append({
|
||||
"code": code,
|
||||
"name": name
|
||||
})
|
||||
|
||||
return jsonify({
|
||||
"status": "success",
|
||||
"profile_type": profile_type,
|
||||
"count": len(result),
|
||||
"stocks": result
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"筛选股票失败: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"Failed to screen stocks: {str(e)}"
|
||||
}), 500
|
||||
|
||||
# 条件筛选接口，接受自定义的筛选条件
|
||||
@app.route('/api/screen/custom', methods=['POST'])
|
||||
def screen_custom():
|
||||
"""使用自定义条件筛选股票"""
|
||||
try:
|
||||
# 从请求体获取筛选条件
|
||||
data = request.get_json()
|
||||
|
||||
if not data or 'conditions' not in data:
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": "Missing conditions in request body"
|
||||
}), 400
|
||||
|
||||
# 调用通用筛选方法
|
||||
stocks = screener._screen_stocks_by_conditions(data['conditions'])
|
||||
|
||||
# 转换结果格式
|
||||
result = []
|
||||
for code, name in stocks:
|
||||
result.append({
|
||||
"code": code,
|
||||
"name": name
|
||||
})
|
||||
|
||||
return jsonify({
|
||||
"status": "success",
|
||||
"count": len(result),
|
||||
"stocks": result
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"自定义筛选股票失败: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"Failed to screen stocks with custom conditions: {str(e)}"
|
||||
}), 500
|
||||
|
||||
@app.route('/api/generate_reports', methods=['POST'])
|
||||
def generate_reports():
|
||||
"""为指定的股票列表生成PDF投资报告
|
||||
|
||||
请求体格式:
|
||||
{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
]
|
||||
}
|
||||
"""
|
||||
try:
|
||||
# 从请求体获取股票列表
|
||||
data = request.get_json()
|
||||
|
||||
if not data or 'stocks' not in data or not isinstance(data['stocks'], list):
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": "请求格式错误: 需要提供股票列表"
|
||||
}), 400
|
||||
|
||||
# 检查股票列表格式
|
||||
stocks = data['stocks']
|
||||
if not all(isinstance(item, list) and len(item) == 2 for item in stocks):
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": "股票列表格式错误: 每个股票应该是 [代码, 名称] 格式"
|
||||
}), 400
|
||||
|
||||
# 导入 PDF 生成器模块
|
||||
try:
|
||||
from src.fundamentals_llm.pdf_generator import generate_investment_report
|
||||
except ImportError:
|
||||
try:
|
||||
from fundamentals_llm.pdf_generator import generate_investment_report
|
||||
except ImportError as e:
|
||||
logger.error(f"无法导入 PDF 生成器模块: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: PDF 生成器模块不可用, 错误详情: {str(e)}"
|
||||
}), 500
|
||||
|
||||
# 生成报告
|
||||
generated_reports = []
|
||||
for stock_code, stock_name in stocks:
|
||||
try:
|
||||
# 调用 PDF 生成器
|
||||
report_path = generate_investment_report(stock_code, stock_name)
|
||||
generated_reports.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"report_path": report_path,
|
||||
"status": "success"
|
||||
})
|
||||
logger.info(f"成功生成 {stock_name}({stock_code}) 的投资报告: {report_path}")
|
||||
except Exception as e:
|
||||
logger.error(f"生成 {stock_name}({stock_code}) 的投资报告失败: {str(e)}")
|
||||
generated_reports.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
})
|
||||
|
||||
# 返回结果
|
||||
return jsonify({
|
||||
"status": "success",
|
||||
"message": f"处理了 {len(stocks)} 个股票的报告生成请求",
|
||||
"reports": generated_reports
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理报告生成请求失败: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"Failed to process report generation request: {str(e)}"
|
||||
}), 500
|
||||
|
||||
@app.route('/api/recommended_stocks', methods=['GET'])
|
||||
def recommended_stocks():
|
||||
"""获取系统推荐的投资股票
|
||||
|
||||
该接口会返回系统基于基本面分析推荐的股票列表，具有较高投资价值
|
||||
|
||||
可以通过以下参数进行过滤:
|
||||
- profile_type: 企业画像类型，例如 'high_growth', 'stable_leaders' 等
|
||||
- limit: 限制返回的股票数量
|
||||
"""
|
||||
try:
|
||||
# 解析请求参数
|
||||
profile_type = request.args.get('profile_type', 'high_growth')
|
||||
limit = request.args.get('limit', 10, type=int)
|
||||
|
||||
# 导入筛选器模块
|
||||
try:
|
||||
from src.fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||||
except ImportError:
|
||||
try:
|
||||
from fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||||
except ImportError as e:
|
||||
logger.error(f"无法导入企业筛选器模块: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: 企业筛选器模块不可用, 错误详情: {str(e)}"
|
||||
}), 500
|
||||
|
||||
# 创建筛选器实例
|
||||
screener = EnterpriseScreener()
|
||||
|
||||
# 根据类型获取推荐股票
|
||||
try:
|
||||
if profile_type == 'recommended':
|
||||
stocks = screener.screen_multi_term_investment_stocks(limit=limit)
|
||||
elif profile_type == 'high_growth':
|
||||
stocks = screener.screen_high_growth_stocks(limit=limit)
|
||||
elif profile_type == 'stable_leaders':
|
||||
stocks = screener.screen_stable_leaders(limit=limit)
|
||||
elif profile_type == 'short_term':
|
||||
stocks = screener.screen_short_term_opportunities(limit=limit)
|
||||
elif profile_type == 'value_investment':
|
||||
stocks = screener.screen_value_investment_stocks(limit=limit)
|
||||
elif profile_type == 'turnaround':
|
||||
stocks = screener.screen_turnaround_opportunities(limit=limit)
|
||||
elif profile_type == 'risk_averse':
|
||||
stocks = screener.screen_risk_averse_stocks(limit=limit)
|
||||
elif profile_type == 'innovation' or profile_type == 'innovation_driven':
|
||||
stocks = screener.screen_innovation_driven_stocks(limit=limit)
|
||||
elif profile_type == 'integration' or profile_type == 'industry_integration':
|
||||
stocks = screener.screen_industry_integration_stocks(limit=limit)
|
||||
elif profile_type == 'custom' and 'conditions' in request.args:
|
||||
# 解析自定义条件
|
||||
try:
|
||||
import json
|
||||
conditions = json.loads(request.args.get('conditions'))
|
||||
stocks = screener.screen_by_custom_conditions(conditions, limit=limit)
|
||||
except Exception as e:
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"解析自定义条件失败: {str(e)}"
|
||||
}), 400
|
||||
else:
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"不支持的企业画像类型: {profile_type}"
|
||||
}), 400
|
||||
except Exception as e:
|
||||
logger.error(f"筛选股票时出错: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"筛选股票时出错: {str(e)}"
|
||||
}), 500
|
||||
|
||||
# 格式化返回结果
|
||||
formatted_stocks = []
|
||||
for stock_code, stock_name in stocks:
|
||||
formatted_stocks.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name
|
||||
})
|
||||
|
||||
# 返回结果
|
||||
return jsonify({
|
||||
"status": "success",
|
||||
"profile_type": profile_type,
|
||||
"count": len(formatted_stocks),
|
||||
"stocks": formatted_stocks
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取推荐股票失败: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"获取推荐股票失败: {str(e)}"
|
||||
}), 500
|
||||
|
||||
@app.route('/api/analyze_and_recommend', methods=['POST'])
|
||||
def analyze_and_recommend():
|
||||
"""分析指定企业列表，生成投资建议，并筛选推荐投资的企业
|
||||
|
||||
请求体格式:
|
||||
{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
],
|
||||
"limit": 10 // 可选，限制返回推荐股票的数量
|
||||
}
|
||||
"""
|
||||
try:
|
||||
# 从请求体获取股票列表
|
||||
data = request.get_json()
|
||||
|
||||
if not data or 'stocks' not in data or not isinstance(data['stocks'], list):
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": "请求格式错误: 需要提供股票列表"
|
||||
}), 400
|
||||
|
||||
# 检查股票列表格式
|
||||
stocks = data['stocks']
|
||||
if not all(isinstance(item, list) and len(item) == 2 for item in stocks):
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": "股票列表格式错误: 每个股票应该是 [代码, 名称] 格式"
|
||||
}), 400
|
||||
|
||||
# 解析limit参数
|
||||
limit = data.get('limit', 10)
|
||||
if not isinstance(limit, int) or limit <= 0:
|
||||
limit = 10
|
||||
|
||||
# 导入必要的聊天机器人模块
|
||||
try:
|
||||
# 首先尝试导入聊天机器人模块
|
||||
try:
|
||||
from src.fundamentals_llm.chat_bot import ChatBot as OnlineChatBot
|
||||
logger.info("成功从 src.fundamentals_llm.chat_bot 导入 ChatBot")
|
||||
except ImportError as e1:
|
||||
try:
|
||||
from fundamentals_llm.chat_bot import ChatBot as OnlineChatBot
|
||||
logger.info("成功从 fundamentals_llm.chat_bot 导入 ChatBot")
|
||||
except ImportError as e2:
|
||||
logger.error(f"无法导入在线聊天机器人模块: {str(e1)}, {str(e2)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: 聊天机器人模块不可用，错误详情: {str(e2)}"
|
||||
}), 500
|
||||
|
||||
# 然后尝试导入离线聊天机器人模块
|
||||
try:
|
||||
from src.fundamentals_llm.chat_bot_with_offline import ChatBot as OfflineChatBot
|
||||
logger.info("成功从 src.fundamentals_llm.chat_bot_with_offline 导入 ChatBot")
|
||||
except ImportError as e1:
|
||||
try:
|
||||
from fundamentals_llm.chat_bot_with_offline import ChatBot as OfflineChatBot
|
||||
logger.info("成功从 fundamentals_llm.chat_bot_with_offline 导入 ChatBot")
|
||||
except ImportError as e2:
|
||||
logger.warning(f"无法导入离线聊天机器人模块: {str(e1)}, {str(e2)}")
|
||||
# 这里可以继续执行，因为某些功能可能不需要离线模型
|
||||
|
||||
# 最后导入基本面分析器
|
||||
try:
|
||||
from src.fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
|
||||
logger.info("成功从 src.fundamentals_llm.fundamental_analysis 导入 FundamentalAnalyzer")
|
||||
except ImportError as e1:
|
||||
try:
|
||||
from fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
|
||||
logger.info("成功从 fundamentals_llm.fundamental_analysis 导入 FundamentalAnalyzer")
|
||||
except ImportError as e2:
|
||||
logger.error(f"无法导入基本面分析模块: {str(e1)}, {str(e2)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: 基本面分析模块不可用，错误详情: {str(e2)}"
|
||||
}), 500
|
||||
except Exception as e:
|
||||
logger.error(f"导入必要模块时出错: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: 导入必要模块时出错，错误详情: {str(e)}"
|
||||
}), 500
|
||||
|
||||
# 创建基本面分析器实例
|
||||
analyzer = FundamentalAnalyzer()
|
||||
|
||||
# 为每个股票生成投资建议
|
||||
investment_advices = []
|
||||
for stock_code, stock_name in stocks:
|
||||
try:
|
||||
# 生成投资建议
|
||||
success, advice, reasoning, references = analyzer.query_analysis(
|
||||
stock_code, stock_name, "investment_advice"
|
||||
)
|
||||
|
||||
if success:
|
||||
investment_advices.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"advice": advice,
|
||||
"reasoning": reasoning,
|
||||
"references": references,
|
||||
"status": "success"
|
||||
})
|
||||
logger.info(f"成功生成 {stock_name}({stock_code}) 的投资建议")
|
||||
else:
|
||||
investment_advices.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"status": "error",
|
||||
"error": advice
|
||||
})
|
||||
logger.error(f"生成 {stock_name}({stock_code}) 的投资建议失败: {advice}")
|
||||
except Exception as e:
|
||||
logger.error(f"处理 {stock_name}({stock_code}) 时出错: {str(e)}")
|
||||
investment_advices.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
})
|
||||
|
||||
# 导入企业筛选器
|
||||
try:
|
||||
from src.fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||||
except ImportError:
|
||||
try:
|
||||
from fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||||
except ImportError as e:
|
||||
logger.error(f"无法导入企业筛选器模块: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: 企业筛选器模块不可用，错误详情: {str(e)}"
|
||||
}), 500
|
||||
|
||||
# 创建筛选器实例
|
||||
screener = EnterpriseScreener()
|
||||
|
||||
# 筛选符合推荐投资条件的股票
|
||||
# 获取传入的所有股票代码
|
||||
input_stock_codes = set(code for code, _ in stocks)
|
||||
|
||||
# 获取所有符合推荐投资条件的股票
|
||||
recommended_stocks = screener.screen_multi_term_investment_stocks(limit=limit)
|
||||
|
||||
# 筛选出传入列表中符合推荐投资条件的股票
|
||||
recommended_input_stocks = []
|
||||
for code, name in recommended_stocks:
|
||||
if code in input_stock_codes:
|
||||
recommended_input_stocks.append({
|
||||
"code": code,
|
||||
"name": name
|
||||
})
|
||||
|
||||
# 返回结果
|
||||
return jsonify({
|
||||
"status": "success",
|
||||
"total_input_stocks": len(stocks),
|
||||
"investment_advices": investment_advices,
|
||||
"recommended_stocks": {
|
||||
"count": len(recommended_input_stocks),
|
||||
"stocks": recommended_input_stocks
|
||||
}
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析和推荐股票失败: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"分析和推荐股票失败: {str(e)}"
|
||||
}), 500
|
||||
|
||||
@app.route('/api/comprehensive_analysis', methods=['POST'])
|
||||
def comprehensive_analysis():
|
||||
"""综合分析接口 - 组合多种功能和参数
|
||||
|
||||
请求体格式:
|
||||
{
|
||||
"stocks": [
|
||||
["603690", "至纯科技"],
|
||||
["688053", "思科瑞"],
|
||||
["300750", "宁德时代"]
|
||||
],
|
||||
"generate_pdf": true,
|
||||
"limit": 10, // 可选，限制返回推荐股票的数量
|
||||
"profile_filter": {
|
||||
"type": "custom", // 可选值: 预定义的画像类型或 "custom"
|
||||
"profile_name": "high_growth", // 当 type = 预定义画像类型时使用
|
||||
"conditions": [ // 当 type = "custom" 时使用
|
||||
{
|
||||
"dimension": "financial_report",
|
||||
"field": "financial_report_level",
|
||||
"operator": ">=",
|
||||
"value": 1
|
||||
},
|
||||
// 其他条件...
|
||||
]
|
||||
}
|
||||
}
|
||||
"""
|
||||
try:
|
||||
# 从请求体获取参数
|
||||
data = request.get_json()
|
||||
|
||||
if not data or 'stocks' not in data or not isinstance(data['stocks'], list):
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": "请求格式错误: 需要提供股票列表"
|
||||
}), 400
|
||||
|
||||
# 检查股票列表格式
|
||||
stocks = data['stocks']
|
||||
if not all(isinstance(item, list) and len(item) == 2 for item in stocks):
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": "股票列表格式错误: 每个股票应该是 [代码, 名称] 格式"
|
||||
}), 400
|
||||
|
||||
# 解析其他参数
|
||||
generate_pdf = data.get('generate_pdf', False)
|
||||
profile_filter = data.get('profile_filter', None)
|
||||
limit = data.get('limit', 10)
|
||||
if not isinstance(limit, int) or limit <= 0:
|
||||
limit = 10
|
||||
|
||||
# 导入必要的聊天机器人模块
|
||||
try:
|
||||
# 首先尝试导入聊天机器人模块
|
||||
try:
|
||||
from src.fundamentals_llm.chat_bot import ChatBot as OnlineChatBot
|
||||
logger.info("成功从 src.fundamentals_llm.chat_bot 导入 ChatBot")
|
||||
except ImportError as e1:
|
||||
try:
|
||||
from fundamentals_llm.chat_bot import ChatBot as OnlineChatBot
|
||||
logger.info("成功从 fundamentals_llm.chat_bot 导入 ChatBot")
|
||||
except ImportError as e2:
|
||||
logger.error(f"无法导入在线聊天机器人模块: {str(e1)}, {str(e2)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: 聊天机器人模块不可用，错误详情: {str(e2)}"
|
||||
}), 500
|
||||
|
||||
# 然后尝试导入离线聊天机器人模块
|
||||
try:
|
||||
from src.fundamentals_llm.chat_bot_with_offline import ChatBot as OfflineChatBot
|
||||
logger.info("成功从 src.fundamentals_llm.chat_bot_with_offline 导入 ChatBot")
|
||||
except ImportError as e1:
|
||||
try:
|
||||
from fundamentals_llm.chat_bot_with_offline import ChatBot as OfflineChatBot
|
||||
logger.info("成功从 fundamentals_llm.chat_bot_with_offline 导入 ChatBot")
|
||||
except ImportError as e2:
|
||||
logger.warning(f"无法导入离线聊天机器人模块: {str(e1)}, {str(e2)}")
|
||||
# 这里可以继续执行，因为某些功能可能不需要离线模型
|
||||
|
||||
# 最后导入基本面分析器
|
||||
try:
|
||||
from src.fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
|
||||
logger.info("成功从 src.fundamentals_llm.fundamental_analysis 导入 FundamentalAnalyzer")
|
||||
except ImportError as e1:
|
||||
try:
|
||||
from fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
|
||||
logger.info("成功从 fundamentals_llm.fundamental_analysis 导入 FundamentalAnalyzer")
|
||||
except ImportError as e2:
|
||||
logger.error(f"无法导入基本面分析模块: {str(e1)}, {str(e2)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: 基本面分析模块不可用，错误详情: {str(e2)}"
|
||||
}), 500
|
||||
except Exception as e:
|
||||
logger.error(f"导入必要模块时出错: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: 导入必要模块时出错，错误详情: {str(e)}"
|
||||
}), 500
|
||||
|
||||
# 创建基本面分析器实例
|
||||
analyzer = FundamentalAnalyzer()
|
||||
|
||||
# 为每个股票生成投资建议
|
||||
investment_advices = []
|
||||
for stock_code, stock_name in stocks:
|
||||
try:
|
||||
# 生成投资建议
|
||||
success, advice, reasoning, references = analyzer.query_analysis(
|
||||
stock_code, stock_name, "investment_advice"
|
||||
)
|
||||
|
||||
if success:
|
||||
investment_advices.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"advice": advice,
|
||||
"reasoning": reasoning,
|
||||
"references": references,
|
||||
"status": "success"
|
||||
})
|
||||
logger.info(f"成功生成 {stock_name}({stock_code}) 的投资建议")
|
||||
else:
|
||||
investment_advices.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"status": "error",
|
||||
"error": advice
|
||||
})
|
||||
logger.error(f"生成 {stock_name}({stock_code}) 的投资建议失败: {advice}")
|
||||
except Exception as e:
|
||||
logger.error(f"处理 {stock_name}({stock_code}) 时出错: {str(e)}")
|
||||
investment_advices.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
})
|
||||
|
||||
# 生成PDF报告（如果需要）
|
||||
pdf_results = []
|
||||
if generate_pdf:
|
||||
try:
|
||||
# 导入PDF生成器模块
|
||||
try:
|
||||
from src.fundamentals_llm.pdf_generator import generate_investment_report
|
||||
except ImportError:
|
||||
try:
|
||||
from fundamentals_llm.pdf_generator import generate_investment_report
|
||||
except ImportError as e:
|
||||
logger.error(f"无法导入 PDF 生成器模块: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: PDF 生成器模块不可用, 错误详情: {str(e)}"
|
||||
}), 500
|
||||
|
||||
# 生成报告
|
||||
for stock_code, stock_name in stocks:
|
||||
try:
|
||||
# 调用 PDF 生成器
|
||||
report_path = generate_investment_report(stock_code, stock_name)
|
||||
pdf_results.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"report_path": report_path,
|
||||
"status": "success"
|
||||
})
|
||||
logger.info(f"成功生成 {stock_name}({stock_code}) 的投资报告: {report_path}")
|
||||
except Exception as e:
|
||||
logger.error(f"生成 {stock_name}({stock_code}) 的投资报告失败: {str(e)}")
|
||||
pdf_results.append({
|
||||
"code": stock_code,
|
||||
"name": stock_name,
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error(f"处理PDF生成请求失败: {str(e)}")
|
||||
pdf_results = []
|
||||
|
||||
# 应用企业画像筛选
|
||||
filtered_stocks = []
|
||||
if profile_filter:
|
||||
try:
|
||||
# 导入企业筛选器
|
||||
try:
|
||||
from src.fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||||
except ImportError:
|
||||
try:
|
||||
from fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||||
except ImportError as e:
|
||||
logger.error(f"无法导入企业筛选器模块: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"服务器配置错误: 企业筛选器模块不可用，错误详情: {str(e)}"
|
||||
}), 500
|
||||
|
||||
# 创建筛选器实例
|
||||
screener = EnterpriseScreener()
|
||||
|
||||
# 根据筛选类型获取推荐股票
|
||||
filter_type = profile_filter.get('type', 'custom')
|
||||
|
||||
# 如果是自定义条件筛选
|
||||
if filter_type == 'custom':
|
||||
conditions = profile_filter.get('conditions', [])
|
||||
if conditions:
|
||||
print(f"使用自定义条件筛选: {len(conditions)} 个条件")
|
||||
all_stocks = screener.screen_by_custom_conditions(conditions, limit=limit)
|
||||
else:
|
||||
# 如果没有提供条件，使用默认的high_growth类型
|
||||
print("未提供自定义条件，使用默认的high_growth类型")
|
||||
all_stocks = screener.screen_high_growth_stocks(limit=limit)
|
||||
# 如果是预定义类型
|
||||
else:
|
||||
# 直接使用type作为画像类型，如果用户同时提供了profile_name也兼容处理
|
||||
profile_name = profile_filter.get('profile_name', filter_type)
|
||||
|
||||
print(f"使用预定义画像类型: {profile_name}")
|
||||
|
||||
if profile_name == 'high_growth':
|
||||
all_stocks = screener.screen_high_growth_stocks(limit=limit)
|
||||
elif profile_name == 'stable_leaders':
|
||||
all_stocks = screener.screen_stable_leaders(limit=limit)
|
||||
elif profile_name == 'short_term':
|
||||
all_stocks = screener.screen_short_term_opportunities(limit=limit)
|
||||
elif profile_name == 'value_investment':
|
||||
all_stocks = screener.screen_value_investment_stocks(limit=limit)
|
||||
elif profile_name == 'turnaround':
|
||||
all_stocks = screener.screen_turnaround_opportunities(limit=limit)
|
||||
elif profile_name == 'risk_averse':
|
||||
all_stocks = screener.screen_risk_averse_stocks(limit=limit)
|
||||
elif profile_name == 'innovation' or profile_name == 'innovation_driven':
|
||||
all_stocks = screener.screen_innovation_driven_stocks(limit=limit)
|
||||
elif profile_name == 'integration' or profile_name == 'industry_integration':
|
||||
all_stocks = screener.screen_industry_integration_stocks(limit=limit)
|
||||
elif profile_name == 'recommended':
|
||||
all_stocks = screener.screen_multi_term_investment_stocks(limit=limit)
|
||||
else:
|
||||
# 如果类型无效，使用默认的high_growth类型
|
||||
logger.warning(f"无效的画像类型: {profile_name}，使用默认的high_growth类型")
|
||||
all_stocks = screener.screen_high_growth_stocks(limit=limit)
|
||||
|
||||
# 获取传入的所有股票代码
|
||||
input_stock_codes = set(code for code, _ in stocks)
|
||||
|
||||
# 筛选出传入列表中符合条件的股票
|
||||
for code, name in all_stocks:
|
||||
if code in input_stock_codes:
|
||||
filtered_stocks.append({
|
||||
"code": code,
|
||||
"name": name
|
||||
})
|
||||
|
||||
logger.info(f"筛选出 {len(filtered_stocks)} 个符合条件的股票")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"应用企业画像筛选失败: {str(e)}")
|
||||
filtered_stocks = []
|
||||
|
||||
# 返回结果
|
||||
response = {
|
||||
"status": "success",
|
||||
"total_input_stocks": len(stocks),
|
||||
"investment_advices": investment_advices
|
||||
}
|
||||
|
||||
if profile_filter:
|
||||
response["filtered_stocks"] = {
|
||||
"count": len(filtered_stocks),
|
||||
"stocks": filtered_stocks
|
||||
}
|
||||
|
||||
if generate_pdf:
|
||||
response["pdf_results"] = {
|
||||
"count": len(pdf_results),
|
||||
"reports": pdf_results
|
||||
}
|
||||
|
||||
return jsonify(response)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"综合分析失败: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": f"综合分析失败: {str(e)}"
|
||||
}), 500
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(host='0.0.0.0', port=5000, debug=True)
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,501 @@
|
|||
序号,类别名称,示例原始数据
|
||||
1,光伏组件,"光伏组件、太阳能电池组件"
|
||||
2,光伏玻璃,"光伏玻璃、超白光伏玻璃"
|
||||
3,光伏支架,"光伏支架、光伏支架产品"
|
||||
4,光伏电池片,"光伏电池片、单晶电池片"
|
||||
5,光伏胶膜,"光伏胶膜、EVA胶膜"
|
||||
6,电力电缆,"电力电缆、电气装备用电线电缆"
|
||||
7,风电塔筒,"风电塔筒、风电产品"
|
||||
8,风电主轴,"风电主轴"
|
||||
9,锂电池,"锂电池、锂离子电池"
|
||||
10,动力电池系统,"动力电池系统、电动汽车类电池"
|
||||
11,储能电池,"储能电池、储能系统"
|
||||
12,煤炭,"煤炭、精煤"
|
||||
13,焦炭,"焦炭、冶金焦炭"
|
||||
14,天然气,"天然气、天然气销售"
|
||||
15,液化天然气（LNG）,"液化天然气、LNG"
|
||||
16,蒸汽,"蒸汽、对外蒸汽销售"
|
||||
17,电力销售,"电力销售、售电收入"
|
||||
18,水力发电,"水力发电、调峰水电"
|
||||
19,热力,"热力、供热"
|
||||
20,电解铜,"电解铜、阴极铜"
|
||||
21,电池材料,"电池材料、锂电池材料"
|
||||
22,负极材料,"负极材料"
|
||||
23,正极材料,"正极材料、锂电池正极材料"
|
||||
24,电解液,"电解液、电解液添加剂"
|
||||
25,充电桩,"充电桩、充电服务"
|
||||
26,太阳能电池,"太阳能电池、电池片"
|
||||
27,电力设备,"电力设备、输变电产品"
|
||||
28,核电设备,"核电设备、核能设备"
|
||||
29,燃气发电,"燃气发电"
|
||||
30,生物质发电,"生物质发电、垃圾发电"
|
||||
31,燃料油,"燃料油销售、成品油销售"
|
||||
32,汽油,"汽油、0#柴油"
|
||||
33,柴油,"柴油"
|
||||
34,煤化工产品,"煤化工产品、煤化工收入"
|
||||
35,氢能产品,"氢能产品、氢气"
|
||||
36,电能表,"电能表、智能电表"
|
||||
37,变电站设备,"箱式变电站、环网柜"
|
||||
38,电力熔断器,"电力熔断器"
|
||||
39,铅酸电池,"铅酸电池、铅酸蓄电池"
|
||||
40,储能系统,"储能系统、储能设备"
|
||||
41,光伏电站,"光伏电站、光伏电站运营"
|
||||
42,风力发电,"风力发电、风电"
|
||||
43,油气开采,"油气开采、油气销售"
|
||||
44,绿色电力证书,"绿色电力证书收入"
|
||||
45,电力工程,"电力工程、电力工程建设"
|
||||
46,燃气设备,"燃气设备、燃气运营"
|
||||
47,热电产品,"热电、热电板块"
|
||||
48,抽水蓄能,"抽水蓄能"
|
||||
49,供电服务,"供电、供电业务"
|
||||
50,油田服务,"油田技术服务、钻井"
|
||||
51,氯化钾,"氯化钾、钾肥-氯化钾"
|
||||
52,烧碱,"烧碱、烧碱类"
|
||||
53,聚氯乙烯（PVC）,"聚氯乙烯、PVC树脂"
|
||||
54,尿素,"尿素、氮肥产品"
|
||||
55,甲醇,"甲醇、甲醇贸易"
|
||||
56,铜箔,"铜箔、电解铜箔"
|
||||
57,铝箔,"铝箔、高端铝箔类"
|
||||
58,改性塑料,"改性塑料、其他改性塑料"
|
||||
59,橡胶助剂,"橡胶助剂、橡胶增塑剂"
|
||||
60,钛白粉,"钛白粉、金红石型钛白粉"
|
||||
61,硫酸,"硫酸、磷酸"
|
||||
62,纯碱,"纯碱、小苏打"
|
||||
63,磷酸,"磷酸、磷酸一铵"
|
||||
64,碳酸锂,"碳酸锂、锂盐"
|
||||
65,聚酯薄膜,"聚酯薄膜、聚酯产品"
|
||||
66,涤纶长丝,"涤纶长丝、FDY"
|
||||
67,化工原料,"化工原料、基础化工产品"
|
||||
68,精细化工产品,"精细化工产品、特种化学品"
|
||||
69,染料,"染料、活性染料"
|
||||
70,涂料,"涂料、防水涂料"
|
||||
71,胶黏剂,"胶黏剂、聚氨酯胶"
|
||||
72,硅片,"硅片、单晶硅片"
|
||||
73,氧化铝,"氧化铝、高纯铝"
|
||||
74,高分子材料,"高分子材料、高分子新材料"
|
||||
75,复合材料,"复合材料、碳纤维"
|
||||
76,钢材,"钢材、热轧板"
|
||||
77,铝合金,"铝合金、铝锭"
|
||||
78,锌精矿,"锌精矿、锌锭"
|
||||
79,铁精粉,"铁精粉、铁矿石"
|
||||
80,玻璃纤维,"玻璃纤维、玻璃钢制品"
|
||||
81,树脂,"树脂、不饱和聚酯树脂"
|
||||
82,水泥,"水泥、水泥制造业"
|
||||
83,商品混凝土,"商品混凝土、混凝土"
|
||||
84,覆铜板,"覆铜板"
|
||||
85,炭黑,"炭黑、白炭黑"
|
||||
86,聚氨酯,"聚氨酯、聚氨酯新材料"
|
||||
87,磷酸铁锂,"磷酸铁锂、磷酸铁"
|
||||
88,催化剂,"催化剂、脱硝催化剂"
|
||||
89,农药,"农药、除草剂"
|
||||
90,化肥,"化肥、新型复合肥"
|
||||
91,饲料添加剂,"饲料添加剂、酶制剂"
|
||||
92,表面活性剂,"表面活性剂、特种表面活性剂"
|
||||
93,塑料制品,"塑料制品、塑料件"
|
||||
94,包装材料,"包装材料、包装容器"
|
||||
95,黄金,"黄金、金锭"
|
||||
96,白银,"白银、银锭"
|
||||
97,铅产品,"铅产品、电解铅"
|
||||
98,钴产品,"钴产品、四氧化三钴"
|
||||
99,镍产品,"镍产品、镍中间品"
|
||||
100,稀土氧化物,"稀土氧化物"
|
||||
101,石英砂,"石英砂、高纯石英砂"
|
||||
102,工业气体,"工业气体"
|
||||
103,氢气,"氢气"
|
||||
104,氧气,"氧气"
|
||||
105,氩气,"氩气"
|
||||
106,硝酸,"硝酸"
|
||||
107,黄磷,"黄磷"
|
||||
108,丙烯,"丙烯、丙烯酸及酯"
|
||||
109,合成氨,"合成氨"
|
||||
110,钼精粉,"钼精粉"
|
||||
111,锡精粉,"锡精粉、锡锭"
|
||||
112,锰产品,"锰锭、电解金属锰"
|
||||
113,钒制品,"钒制品"
|
||||
114,锂云母精矿,"锂云母精矿"
|
||||
115,萤石,"高品位萤石块矿"
|
||||
116,无水氟化氢,"无水氟化氢、氟化氢"
|
||||
117,氟制冷剂,"氟制冷剂"
|
||||
118,苯酚,"酚酮相关产品"
|
||||
119,乙二醇,"乙二醇"
|
||||
120,双氧水,"双氧水"
|
||||
121,有机硅,"有机硅、有机硅产品"
|
||||
122,氯化铵,"氯化铵"
|
||||
123,工业盐,"工业盐"
|
||||
124,食用盐,"食用盐、小工业盐"
|
||||
125,元明粉,"元明粉"
|
||||
126,氨基酸,"饲料氨基酸、赖氨酸"
|
||||
127,维生素,"维生素、B族维生素"
|
||||
128,石灰石,"石灰石"
|
||||
129,聚丙烯,"聚丙烯"
|
||||
130,聚醚,"聚醚、聚醚单体"
|
||||
131,糊树脂,"糊树脂、PPVC糊状树脂"
|
||||
132,甲烷氯化物,"甲烷氯化物"
|
||||
133,电石,"电石、BDO及电石"
|
||||
134,废盐渣,"废盐渣资源化利用处置"
|
||||
135,阻燃剂,"阻燃剂、阻燃剂及中间体"
|
||||
136,光刻胶,"光刻胶、光刻胶配套试剂"
|
||||
137,电子化学品,"电子化学品、电子材料产品"
|
||||
138,超净高纯试剂,"超净高纯试剂"
|
||||
139,合成香料,"合成香料、天然香料"
|
||||
140,润滑油,"润滑油、车用润滑油"
|
||||
141,尾气处理液,"尾气处理液"
|
||||
142,植物提取物,"植物提取物"
|
||||
143,生物柴油,"生物柴油"
|
||||
144,环保新材料,"环保新材料"
|
||||
145,全生物降解材料,"全生物降解材料产品"
|
||||
146,纤维板,"纤维板、刨花板"
|
||||
147,磁性材料,"磁性材料、钕铁硼"
|
||||
148,陶瓷材料,"陶瓷材料"
|
||||
149,电熔氧化锆,"电熔氧化锆"
|
||||
150,铸造改性材料,"铸造改性材料"
|
||||
151,化学药制剂,"化学药、化学制剂"
|
||||
152,中药制剂,"中药、中药饮片"
|
||||
153,原料药,"原料药、化学原料药"
|
||||
154,医药中间体,"医药中间体、中间体"
|
||||
155,诊断试剂,"诊断试剂、体外诊断试剂"
|
||||
156,医疗器械,"医疗器械、手术耗材"
|
||||
157,疫苗,"疫苗、流感疫苗"
|
||||
158,血液制品,"血液制品、人血白蛋白"
|
||||
159,抗生素,"抗生素、抗感染类"
|
||||
160,抗肿瘤药物,"抗肿瘤药物、抗肿瘤类产品"
|
||||
161,保健品,"保健品、功能性保健品"
|
||||
162,兽药,"兽药、禽用疫苗"
|
||||
163,注射剂,"注射剂、大输液"
|
||||
164,片剂,"片剂"
|
||||
165,胶囊,"胶囊、软胶囊"
|
||||
166,软膏,"膏剂、软膏"
|
||||
167,输液,"输液、冻干粉针剂"
|
||||
168,生物制品,"生物制品、重组蛋白"
|
||||
169,中药材,"中药材、参茸滋补药材"
|
||||
170,医疗服务,"医疗服务、诊疗服务"
|
||||
171,检测服务,"检测服务、医学诊断服务"
|
||||
172,康复器械,"康复器械、伤口护理类"
|
||||
173,耗材,"耗材、注射器"
|
||||
174,试剂,"试剂、试剂销售"
|
||||
175,药用辅料,"药用辅料"
|
||||
176,妇科用药,"妇科用药"
|
||||
177,补益用药,"补益用药"
|
||||
178,肾病药物,"肾病及免疫调节类"
|
||||
179,肠外营养药物,"肠外营养类"
|
||||
180,麻醉药物,"麻醉系列"
|
||||
181,导尿产品,"导尿类"
|
||||
182,呼吸系统药物,"呼吸系统药物、呼吸系统用药"
|
||||
183,消化系统药物,"消化系统类、消化类"
|
||||
184,泌尿系统药物,"泌尿系统类"
|
||||
185,抗菌药物,"抗菌类"
|
||||
186,痛风药物,"痛风类"
|
||||
187,肝病药物,"肝病类"
|
||||
188,喹诺酮,"喹诺酮"
|
||||
189,珍药商品,"珍药商品销售(工业)"
|
||||
190,外购药品,"外购药品销售(商业)"
|
||||
191,灵芝孢子粉,"灵芝孢子粉类产品"
|
||||
192,铁皮石斛,"铁皮石斛类产品"
|
||||
193,益生菌制品,"益生菌制品"
|
||||
194,消毒产品,"消毒清洁类"
|
||||
195,防护用品,"防护类、防护用品"
|
||||
196,创可贴,"创可贴、敷贴"
|
||||
197,药学研究服务,"药学研究服务、临床研究服务"
|
||||
198,医药批发,"医药批发、药品批发"
|
||||
199,医药零售,"药品零售、医药零售"
|
||||
200,医疗美容服务,"医疗美容、医疗美容服务"
|
||||
201,商品房,"商品房、商品房销售"
|
||||
202,房屋租赁,"房屋租赁、房产租赁"
|
||||
203,工程施工,"工程施工、建筑施工"
|
||||
204,钢结构,"钢结构、高端钢结构"
|
||||
205,物业管理,"物业管理、物业服务"
|
||||
206,建筑装饰,"建筑装饰、装饰工程"
|
||||
207,幕墙,"幕墙、幕墙工程"
|
||||
208,门窗,"门窗、定制木门"
|
||||
209,瓷砖,"瓷砖"
|
||||
210,地板,"地板、锁扣地板"
|
||||
211,防水材料,"防水材料、防水卷材"
|
||||
212,砂石,"砂石、砂石骨料"
|
||||
213,混凝土外加剂,"混凝土外加剂"
|
||||
214,园林绿化,"园林绿化、园林工程"
|
||||
215,市政工程,"市政工程、市政环卫"
|
||||
216,玻璃门体,"玻璃门体"
|
||||
217,装配式装修,"装配式装修"
|
||||
218,整体卫浴,"整体卫浴"
|
||||
219,建筑材料,"建筑材料、建材"
|
||||
220,管道管件,"PE管材管件、PPR管材管件"
|
||||
221,保温材料,"保温产品"
|
||||
222,声屏障系统,"声屏障系统"
|
||||
223,金属围护系统,"金属围护系统"
|
||||
224,建筑光伏一体化,"建筑光伏一体化"
|
||||
225,石膏板,"石膏护面纸"
|
||||
226,预应力机械,"预应力机械"
|
||||
227,养护工程,"养护工程施工"
|
||||
228,周转材料,"周转材料及设备租赁销售"
|
||||
229,地基处理,"地基处理"
|
||||
230,桩基工程,"桩基及围护业务"
|
||||
231,路桥工程,"路桥工程施工"
|
||||
232,房屋销售,"房屋销售、住宅销售"
|
||||
233,商业租赁,"商业租赁、商业物业出租"
|
||||
234,投资物业,"投资物业及相关服务"
|
||||
235,园区租赁,"园区租赁、园区物业租赁"
|
||||
236,写字楼出租,"写字楼出租"
|
||||
237,大厦出租,"大厦出租"
|
||||
238,商铺租赁,"商铺租赁、商铺及车位"
|
||||
239,物业开发,"物业开发与销售"
|
||||
240,公路养护,"公路养护决策咨询"
|
||||
241,上衣,"上衣、衬衫、T恤"
|
||||
242,裤子,"裤子、西裤、男裤"
|
||||
243,外套,"外套、茄克"
|
||||
244,鞋类,"鞋类、皮鞋、男鞋"
|
||||
245,内衣,"内衣、文胸"
|
||||
246,童装,"童装、婴童服饰"
|
||||
247,白酒,"白酒、高档白酒"
|
||||
248,啤酒,"啤酒、啤酒酿造设备"
|
||||
249,葡萄酒,"葡萄酒、有机葡萄酒"
|
||||
250,饮料,"饮料、功能饮品"
|
||||
251,乳制品,"乳制品、液体乳"
|
||||
252,肉制品,"肉制品、泡椒凤爪"
|
||||
253,食品添加剂,"食品添加剂、味精"
|
||||
254,调味品,"调味品、酱油"
|
||||
255,糕点,"糕点、面包"
|
||||
256,休闲食品,"休闲食品、炒货"
|
||||
257,化妆品,"化妆品、护肤系列"
|
||||
258,洗护用品,"洗护用品、香皂"
|
||||
259,家电,"家电、消费电子"
|
||||
260,空调,"空调、空调器"
|
||||
261,冰箱,"冰箱、电冰箱"
|
||||
262,洗衣机,"洗衣机"
|
||||
263,电视机,"电视机、彩电"
|
||||
264,小家电,"小家电、电动剃须刀"
|
||||
265,家具,"家具、休闲家具"
|
||||
266,沙发,"沙发、功能沙发"
|
||||
267,床垫,"床垫、记忆绵床垫"
|
||||
268,橱柜,"橱柜、整体厨柜"
|
||||
269,珠宝首饰,"珠宝首饰、K金珠宝类"
|
||||
270,日用百货,"日用百货、商品百货"
|
||||
271,纸制品,"纸制品、生活用纸"
|
||||
272,卫生陶瓷,"卫生陶瓷"
|
||||
273,包装容器,"包装容器、二片罐"
|
||||
274,宠物食品,"宠物食品、宠物零食"
|
||||
275,文具,"文具、教育装备"
|
||||
276,玩具,"玩具、益智玩具"
|
||||
277,香精,"香精、食用香精"
|
||||
278,烟草制品,"烟标、卷烟商标"
|
||||
279,茶叶,"茶叶、茶叶及调味品"
|
||||
280,食用油,"食用油、植物油"
|
||||
281,面粉,"面粉"
|
||||
282,速冻食品,"速冻调制食品、速冻面米制品"
|
||||
283,冷饮,"冷饮产品系列"
|
||||
284,咖啡制品,"咖啡制品"
|
||||
285,粽子,"粽子系列"
|
||||
286,蛋制品,"蛋制品"
|
||||
287,水刺非织造布,"水刺非织造材料制品"
|
||||
288,墙布,"墙布、窗帘及相关产品"
|
||||
289,皮革制品,"皮革产品、皮具"
|
||||
290,毛巾,"湿巾"
|
||||
291,被芯,"被芯"
|
||||
292,枕芯,"枕芯"
|
||||
293,婴童护理产品,"婴童护理系列产品"
|
||||
294,驱蚊产品,"驱蚊系列产品"
|
||||
295,精油,"精油系列产品"
|
||||
296,按摩椅,"按摩椅、按摩椅椅身"
|
||||
297,办公椅,"办公椅"
|
||||
298,休闲椅,"休闲椅"
|
||||
299,电动床,"电动床"
|
||||
300,晾衣架,"晾衣架产品"
|
||||
301,厨卫电器,"厨卫电器、集成灶"
|
||||
302,水家电,"水家电"
|
||||
303,智能坐便盖,"智能坐便盖"
|
||||
304,浴室柜,"浴室柜"
|
||||
305,五金洁具,"五金洁具"
|
||||
306,面包机,"空气炸锅、空气烤箱"
|
||||
307,油炸锅,"油炸锅"
|
||||
308,吸尘器,"高效分离吸尘器"
|
||||
309,扫地机器人,"智能扫地机器人"
|
||||
310,电动牙刷,"电动牙刷"
|
||||
311,电吹风,"电吹风"
|
||||
312,鼻毛器,"鼻毛器"
|
||||
313,电动理发器,"电动理发器"
|
||||
314,消毒柜,"商用展示柜"
|
||||
315,西厨设备,"西厨设备"
|
||||
316,自助餐设备,"自助餐设备"
|
||||
317,商用制冷设备,"商用餐饮制冷设备"
|
||||
318,木门,"木门、模压门"
|
||||
319,桌椅,"桌几类、椅架类"
|
||||
320,床组,"床组类"
|
||||
321,高档酒,"高档酒"
|
||||
322,中档酒,"中档酒"
|
||||
323,普通酒,"普通酒"
|
||||
324,果汁,"苹果清汁、梨汁"
|
||||
325,矿泉水,"矿泉水"
|
||||
326,蜂蜜,"蜜饯及果蔬"
|
||||
327,酱腌菜,"竹笋"
|
||||
328,鸡翅,"鸡翅"
|
||||
329,猪皮晶,"猪皮晶"
|
||||
330,火腿,"火腿、火腿及其制品"
|
||||
331,冲泡饮品,"冲泡类"
|
||||
332,即饮饮品,"即饮类"
|
||||
333,餐食,"餐食系列"
|
||||
334,糖制品,"糖制品及食品"
|
||||
335,膨化食品,"膨化食品"
|
||||
336,豆制品,"豆制品"
|
||||
337,高强瓦楞纸,"高强瓦楞纸"
|
||||
338,涂布白板纸,"涂布白板纸"
|
||||
339,双胶纸,"双胶纸"
|
||||
340,白卡纸,"白卡纸"
|
||||
341,汽车零部件,"汽车零部件、配件"
|
||||
342,轮胎,"轮胎、半钢轮胎"
|
||||
343,整车,"整车、乘用车"
|
||||
344,车灯,"车灯、汽车照明"
|
||||
345,汽车电子,"汽车电子、汽车电子产品"
|
||||
346,刹车片,"刹车片"
|
||||
347,减震器,"减震器、汽车减震器"
|
||||
348,发动机,"发动机、销售发动机"
|
||||
349,变速器,"变速器、汽车变速器"
|
||||
350,轮毂,"轮毂、轮毂组件"
|
||||
351,座椅,"座椅、车辆座椅"
|
||||
352,电池托盘,"电池托盘、汽车电池托盘"
|
||||
353,内饰件,"内饰件、内饰功能件"
|
||||
354,外饰件,"外饰件、内外饰件"
|
||||
355,汽车玻璃,"汽车玻璃"
|
||||
356,轴承,"轴承、轴承保持架"
|
||||
357,齿轮,"齿轮、汽车齿轮"
|
||||
358,密封件,"密封件、橡胶密封件"
|
||||
359,汽车空调,"汽车空调、汽车空调压缩机"
|
||||
360,线束,"线束、汽车线束"
|
||||
361,汽车传感器,"汽车传感器"
|
||||
362,涡轮壳,"涡轮壳、涡轮壳及其装配件"
|
||||
363,中间壳,"中间壳、中间壳及其装配件"
|
||||
364,同步器总成,"同步器总成"
|
||||
365,差速器,"差速器"
|
||||
366,结合齿,"结合齿"
|
||||
367,汽车视镜,"汽车视镜"
|
||||
368,保险杠,"汽车保险杠"
|
||||
369,汽车声学产品,"汽车声学产品"
|
||||
370,车轮总成,"车轮总成分装"
|
||||
371,油泵,"发动机油泵、输油泵"
|
||||
372,凸轮轴,"凸轮轴"
|
||||
373,变速箱油泵,"变速箱油泵"
|
||||
374,分动箱油泵,"分动箱油泵"
|
||||
375,悬架系统,"悬架系统零部件、空气悬架"
|
||||
376,踏板总成,"踏板总成"
|
||||
377,连杆,"连杆"
|
||||
378,气门嘴,"气门嘴"
|
||||
379,TPMS,"TPMS"
|
||||
380,汽车金属管件,"汽车金属管件"
|
||||
381,驱动系统,"驱动系统类"
|
||||
382,车载电源,"车载电源集成产品"
|
||||
383,DC/DC变换器,"车载DC/DC变换器"
|
||||
384,燃料分配管,"燃油分配管"
|
||||
385,空调管路,"汽车空调管路"
|
||||
386,万向节,"万向节"
|
||||
387,锻铝控制臂,"锻铝控制臂"
|
||||
388,汽车连接器,"汽车连接器"
|
||||
389,热交换系统,"汽车热交换系统管路及附件"
|
||||
390,EGR冷却器,"汽车废气再循环(EGR)冷却器零部件"
|
||||
391,集成电路,"集成电路、芯片"
|
||||
392,软件开发,"软件开发、定制软件"
|
||||
393,系统集成,"系统集成、智能制造系统"
|
||||
394,显示模组,"显示模组、液晶显示模组"
|
||||
395,传感器,"传感器、汽车传感器"
|
||||
396,LED芯片,"LED芯片、LED"
|
||||
397,印刷电路板（PCB）,"印刷电路板、PCB"
|
||||
398,电容器,"电容器、电力电容器"
|
||||
399,电阻器,"电阻器"
|
||||
400,继电器,"继电器、继电器产品"
|
||||
401,变压器,"变压器、油浸式变压器"
|
||||
402,控制器,"控制器、智能控制器"
|
||||
403,智能终端,"智能终端、智能终端产品"
|
||||
404,通信设备,"通信设备、通讯类产品"
|
||||
405,光纤光缆,"光纤光缆、光缆"
|
||||
406,电源适配器,"电源适配器、LED驱动电源"
|
||||
407,存储设备,"存储设备、固态硬盘"
|
||||
408,消费电子,"消费电子、消费电子产品"
|
||||
409,手机,"手机、智能手机"
|
||||
410,平板电脑,"平板电脑"
|
||||
411,智能家居,"智能家居、智能家居产品"
|
||||
412,机器人,"机器人、服务机器人"
|
||||
413,无人机,"无人机、智能无人飞行器"
|
||||
414,云服务,"云服务、云计算"
|
||||
415,网络安全产品,"网络安全产品、信息安全产品"
|
||||
416,数据中心设备,"数据中心产品、IDC服务"
|
||||
417,半导体材料,"半导体材料、半导体硅片"
|
||||
418,电子元器件,"电子元器件、自产被动元器件"
|
||||
419,光学元件,"光学元件、光学组件"
|
||||
420,激光器,"激光器"
|
||||
421,检测设备,"检测设备、智能检测设备"
|
||||
422,自动化设备,"自动化设备、工业自动化设备"
|
||||
423,仪器仪表,"仪器仪表、专业仪表"
|
||||
424,智能电表,"智能电表、用电信息采集终端"
|
||||
425,光伏设备,"光伏设备、光伏专用设备"
|
||||
426,电池管理系统,"电池管理系统"
|
||||
427,充电器,"充电器"
|
||||
428,电线电缆,"电线电缆、裸电线"
|
||||
429,开关设备,"开关设备、成套开关设备"
|
||||
430,连接器,"连接器、射频同轴连接器"
|
||||
431,背光模组,"背光模组、背光显示模组"
|
||||
432,触摸屏,"电容式触摸屏"
|
||||
433,功率器件,"功率器件、功率IC"
|
||||
434,分立器件,"分立器件"
|
||||
435,NOR Flash,"NORFlash产品"
|
||||
436,微波器件,"微波器件"
|
||||
437,电解液添加剂,"电解液添加剂"
|
||||
438,液晶显示屏,"液晶显示屏及模块"
|
||||
439,智能卡,"智能卡"
|
||||
440,信号链芯片,"信号链芯片"
|
||||
441,红外热像仪,"红外热像仪"
|
||||
442,视频监控系统,"视频监控系统"
|
||||
443,音频设备,"电声器材及扬声器"
|
||||
444,智能头显,"智能头显与智能穿戴类"
|
||||
445,无线通信模组,"无线通信模组"
|
||||
446,基站天线,"基站天线"
|
||||
447,嵌入式计算平台,"嵌入式与融合计算平台"
|
||||
448,网络可视化设备,"网络可视化基础架构"
|
||||
449,轨道扣件,"轨道扣件"
|
||||
450,高频头,"高频头及相关配件"
|
||||
451,射频电缆,"射频电缆类产品"
|
||||
452,显示器件,"显示器件、光电显示器件"
|
||||
453,触控模组,"触控显示模组"
|
||||
454,虚拟现实产品,"虚拟现实产品"
|
||||
455,专业视听产品,"专业视听产品"
|
||||
456,照明应用产品,"照明应用产品及相关"
|
||||
457,电子制造服务,"电子制造服务"
|
||||
458,光传输设备,"光传输设备"
|
||||
459,ODN设备,"ODN网络物理连接及保护设备"
|
||||
460,屏蔽罩,"手机精密屏蔽罩及结构件"
|
||||
461,精密连接器,"精密连接器及组件"
|
||||
462,模具治具,"模具治具"
|
||||
463,智能检测分选设备,"智能检测分选装备"
|
||||
464,智能包装设备,"智能包装装备"
|
||||
465,软管配件,"清洁电器软管及配件"
|
||||
466,清洁电器整机,"清洁电器整机"
|
||||
467,刀具产品,"刀具产品"
|
||||
468,滑轨,"滑轨"
|
||||
469,电能计量箱,"电能计量箱"
|
||||
470,智能水表,"智能水表"
|
||||
471,膜式燃气表,"膜式燃气表"
|
||||
472,点胶设备,"点胶设备"
|
||||
473,特种光纤,"特种光纤"
|
||||
474,光学膜,"光学膜"
|
||||
475,功能件,"功能件"
|
||||
476,墙板,"墙板"
|
||||
477,行业专机,"行业专机"
|
||||
478,传输类设备,"传输类设备"
|
||||
479,宽带网络设备,"宽带网络类设备"
|
||||
480,油气类商品,"油气类商品"
|
||||
481,游戏,"游戏、移动网络游戏"
|
||||
482,数字内容,"数字内容"
|
||||
483,数字营销服务,"数字营销服务、全域数字营销"
|
||||
484,视频会议系统,"视频会议系统"
|
||||
485,公共安全产品,"公共安全产品"
|
||||
486, sedia,"电子商务、电商代运营"
|
||||
487,平板显示设备,"平板显示模组设备"
|
||||
488,物联网产品,"物联网产品"
|
||||
489,AI视频平台,"AI视频大数据平台"
|
||||
490,融合媒体平台,"融合媒体运营平台项目"
|
||||
491,工程机械,"工程机械、土方机械"
|
||||
492,农业机械,"农业机械"
|
||||
493,数控机床,"数控机床、机床"
|
||||
494,压缩机,"压缩机、压缩机(组)"
|
||||
495,泵类,"泵类、水泵"
|
||||
496,阀门,"阀门、闸阀"
|
||||
497,电机,"电机、直驱轮毂电机"
|
||||
498,铸件,"铸件、其他铸件"
|
||||
499,模具,"模具、精密模具"
|
||||
500,精密零件,"精密零件、精密结构件"
|
||||
|
|
|
@ -0,0 +1,656 @@
|
|||
光伏组件
|
||||
光伏玻璃
|
||||
光伏支架
|
||||
光伏电池片
|
||||
光伏胶膜
|
||||
电力电缆
|
||||
风电塔筒
|
||||
风电主轴
|
||||
锂电池
|
||||
动力电池系统
|
||||
储能电池
|
||||
煤炭
|
||||
焦炭
|
||||
天然气
|
||||
液化天然气（LNG）
|
||||
蒸汽
|
||||
电力销售
|
||||
水力发电
|
||||
热力
|
||||
电解铜
|
||||
锂电池核心材料
|
||||
锂盐及化合物
|
||||
电池材料-三元与前驱体材料
|
||||
电池材料-电池隔膜与膜材料
|
||||
电池材料-磷酸铁及其衍生材料
|
||||
电池材料-导电与包覆材料
|
||||
电池材料-其他新能源材料
|
||||
电池材料-特殊电池材料
|
||||
电池材料-金属及合金材料
|
||||
负极材料
|
||||
正极材料
|
||||
电解液
|
||||
充电桩
|
||||
太阳能电池
|
||||
核电设备
|
||||
输配电设备
|
||||
变压器与电抗器
|
||||
电机与驱动系统
|
||||
发电设备与配件
|
||||
电源与电能质量设备
|
||||
配用电自动化与智能电网
|
||||
压力容器与换热设备
|
||||
新能源与清洁能源设备
|
||||
智能电控与监测系统
|
||||
风机与通风设备
|
||||
电工与配电电器
|
||||
绝缘子与电瓷产品
|
||||
轨道交通电力设备
|
||||
工业专用设备
|
||||
电梯与相关设备
|
||||
电气成套与高低压设备
|
||||
其他电力设备与服务
|
||||
有机化工中间体
|
||||
功能性助剂
|
||||
特种化学品
|
||||
染料与颜料中间体
|
||||
油墨与涂料化学品
|
||||
民爆化学品
|
||||
表面活性剂与乳化剂
|
||||
水处理与净水剂
|
||||
含氟化学品
|
||||
定制软件开发
|
||||
企业管理软件
|
||||
行业应用软件
|
||||
大数据与智能分析软件
|
||||
人工智能软件
|
||||
技术开发与服务
|
||||
软件销售与维护
|
||||
智慧城市与政务软件
|
||||
金融与商业软件
|
||||
移动互联网与SaaS服务
|
||||
图形与多媒体软件
|
||||
工业与制造软件
|
||||
教育与办公软件
|
||||
软件基础设施与中间件
|
||||
其他软件开发与服务
|
||||
聚合物添加剂
|
||||
纺织与皮革助剂
|
||||
医药与化妆品化学品
|
||||
农药与植保化学品
|
||||
油脂与润滑化学品
|
||||
新材料化学品
|
||||
酸酐与酯类化合物
|
||||
其他精细化工产品
|
||||
电能计量与校验设备
|
||||
燃气发电
|
||||
生物质发电
|
||||
燃料油
|
||||
汽油
|
||||
柴油
|
||||
煤化工产品
|
||||
氢能产品
|
||||
电能表
|
||||
变电站设备
|
||||
电力熔断器
|
||||
铅酸电池
|
||||
储能系统
|
||||
光伏电站
|
||||
风力发电
|
||||
油气开采
|
||||
绿色电力证书
|
||||
工程总承包与EPC
|
||||
工程设计与咨询
|
||||
新能源工程服务
|
||||
电力安装与维护
|
||||
其他电力工程服务
|
||||
燃气设备
|
||||
热电产品
|
||||
抽水蓄能
|
||||
供电服务
|
||||
油田服务
|
||||
环境监测设备
|
||||
工业检测与测试设备
|
||||
汽车与新能源检测设备
|
||||
科学分析与实验室仪器
|
||||
其他检测设备
|
||||
钾系列产品
|
||||
烧碱
|
||||
聚氯乙烯（PVC）
|
||||
尿素
|
||||
甲醇
|
||||
铜箔
|
||||
铝箔
|
||||
改性塑料
|
||||
橡胶助剂
|
||||
钛白粉
|
||||
硫酸
|
||||
纯碱
|
||||
磷酸
|
||||
碳酸锂
|
||||
聚酯薄膜
|
||||
涤纶长丝
|
||||
染料
|
||||
涂料
|
||||
胶黏剂
|
||||
硅片
|
||||
氧化铝
|
||||
合成橡胶与弹性体
|
||||
聚烯烃与塑料制品
|
||||
化学纤维与纺织材料
|
||||
功能性膜与薄膜材料
|
||||
电缆与绝缘材料
|
||||
特种高分子与新材料
|
||||
其他高分子制品
|
||||
碳纤维与纤维复合材料
|
||||
金属基复合材料
|
||||
硬质合金与超硬材料
|
||||
其他复合材料制品
|
||||
钢材
|
||||
铝合金
|
||||
锌精矿
|
||||
铁精粉
|
||||
玻璃纤维
|
||||
树脂
|
||||
水泥
|
||||
商品混凝土
|
||||
覆铜板
|
||||
炭黑
|
||||
聚氨酯
|
||||
磷酸铁锂
|
||||
催化剂
|
||||
农药
|
||||
化肥
|
||||
饲料添加剂
|
||||
表面活性剂
|
||||
塑料制品
|
||||
包装材料
|
||||
黄金
|
||||
白银
|
||||
铅产品
|
||||
钴产品
|
||||
镍产品
|
||||
稀土氧化物
|
||||
石英砂
|
||||
工业气体
|
||||
氢气
|
||||
氧气
|
||||
氩气
|
||||
硝酸
|
||||
黄磷
|
||||
丙烯
|
||||
合成氨
|
||||
钼精粉
|
||||
锡精粉
|
||||
锰产品
|
||||
钒制品
|
||||
锂云母精矿
|
||||
萤石
|
||||
无水氟化氢
|
||||
氟制冷剂
|
||||
苯酚
|
||||
乙二醇
|
||||
双氧水
|
||||
有机硅
|
||||
氯化铵
|
||||
工业盐
|
||||
食用盐
|
||||
元明粉
|
||||
氨基酸
|
||||
维生素
|
||||
石灰石
|
||||
聚丙烯
|
||||
聚醚
|
||||
糊树脂
|
||||
甲烷氯化物
|
||||
电石
|
||||
废盐渣
|
||||
阻燃剂
|
||||
光刻胶
|
||||
电子化学品
|
||||
超净高纯试剂
|
||||
合成香料
|
||||
润滑油
|
||||
尾气处理液
|
||||
植物提取物
|
||||
生物柴油
|
||||
环保新材料
|
||||
全生物降解材料
|
||||
纤维板
|
||||
磁性材料
|
||||
陶瓷材料
|
||||
电熔氧化锆
|
||||
铸造改性材料
|
||||
化学药制剂
|
||||
中药制剂
|
||||
原料药
|
||||
医药中间体
|
||||
诊断试剂
|
||||
医疗器械
|
||||
疫苗
|
||||
血液制品
|
||||
抗生素
|
||||
抗肿瘤药物
|
||||
保健品
|
||||
兽药
|
||||
注射剂
|
||||
片剂
|
||||
胶囊
|
||||
软膏
|
||||
输液
|
||||
生物制品
|
||||
中药材
|
||||
检验与测试服务
|
||||
智慧医疗与数字健康
|
||||
眼科诊疗服务
|
||||
健康管理与体检服务
|
||||
临床研究与专科服务
|
||||
其他医疗服务
|
||||
环境监测与治理服务
|
||||
医学与生物检测服务
|
||||
汽车与工业品检测
|
||||
电子与半导体检测
|
||||
工程与技术咨询服务
|
||||
其他检测服务
|
||||
无线通信与网络设备
|
||||
光通信与接入设备
|
||||
卫星与专网通信
|
||||
通信终端与组件
|
||||
康复器械
|
||||
耗材
|
||||
试剂
|
||||
药用辅料
|
||||
妇科用药
|
||||
补益用药
|
||||
肾病药物
|
||||
肠外营养药物
|
||||
麻醉药物
|
||||
导尿产品
|
||||
呼吸系统药物
|
||||
消化系统药物
|
||||
泌尿系统药物
|
||||
抗菌药物
|
||||
痛风药物
|
||||
肝病药物
|
||||
喹诺酮
|
||||
珍药商品
|
||||
外购药品
|
||||
灵芝孢子粉
|
||||
铁皮石斛
|
||||
益生菌制品
|
||||
消毒产品
|
||||
防护用品
|
||||
创可贴
|
||||
药学研究服务
|
||||
医药批发
|
||||
医药零售
|
||||
医疗美容服务
|
||||
商品房
|
||||
房屋租赁
|
||||
工程施工
|
||||
钢结构
|
||||
物业管理
|
||||
建筑装饰
|
||||
幕墙
|
||||
门窗
|
||||
瓷砖
|
||||
地板
|
||||
防水材料
|
||||
砂石
|
||||
混凝土外加剂
|
||||
园林绿化
|
||||
市政工程
|
||||
玻璃门体
|
||||
装配式装修
|
||||
整体卫浴
|
||||
建筑材料
|
||||
管道管件
|
||||
保温材料
|
||||
声屏障系统
|
||||
金属围护系统
|
||||
建筑光伏一体化
|
||||
石膏板
|
||||
预应力机械
|
||||
养护工程
|
||||
周转材料
|
||||
地基处理
|
||||
桩基工程
|
||||
路桥工程
|
||||
房屋销售
|
||||
商业租赁
|
||||
投资物业
|
||||
园区租赁
|
||||
写字楼出租
|
||||
大厦出租
|
||||
商铺租赁
|
||||
物业开发
|
||||
公路养护
|
||||
上衣
|
||||
裤子
|
||||
外套
|
||||
鞋类
|
||||
内衣
|
||||
童装
|
||||
白酒
|
||||
啤酒
|
||||
葡萄酒
|
||||
饮料
|
||||
乳制品
|
||||
肉制品
|
||||
食品添加剂
|
||||
调味品
|
||||
糕点
|
||||
休闲食品
|
||||
化妆品
|
||||
洗护用品
|
||||
家电
|
||||
空调
|
||||
冰箱
|
||||
洗衣机
|
||||
电视机
|
||||
小家电
|
||||
家具
|
||||
沙发
|
||||
床垫
|
||||
橱柜
|
||||
珠宝首饰
|
||||
日用百货
|
||||
纸制品
|
||||
卫生陶瓷
|
||||
包装容器
|
||||
宠物食品
|
||||
文具
|
||||
玩具
|
||||
香精
|
||||
烟草制品
|
||||
茶叶
|
||||
食用油
|
||||
面粉
|
||||
速冻食品
|
||||
冷饮
|
||||
咖啡制品
|
||||
粽子
|
||||
蛋制品
|
||||
水刺非织造布
|
||||
墙布
|
||||
皮革制品
|
||||
毛巾
|
||||
被芯
|
||||
枕芯
|
||||
婴童护理产品
|
||||
驱蚊产品
|
||||
精油
|
||||
按摩椅
|
||||
办公椅
|
||||
休闲椅
|
||||
电动床
|
||||
晾衣架
|
||||
厨卫电器
|
||||
水家电
|
||||
智能坐便盖
|
||||
浴室柜
|
||||
五金洁具
|
||||
面包机
|
||||
油炸锅
|
||||
吸尘器
|
||||
扫地机器人
|
||||
电动牙刷
|
||||
电吹风
|
||||
鼻毛器
|
||||
电动理发器
|
||||
消毒柜
|
||||
西厨设备
|
||||
自助餐设备
|
||||
商用制冷设备
|
||||
木门
|
||||
桌椅
|
||||
床组
|
||||
高档酒
|
||||
中档酒
|
||||
普通酒
|
||||
果汁
|
||||
矿泉水
|
||||
蜂蜜
|
||||
酱腌菜
|
||||
鸡翅
|
||||
猪皮晶
|
||||
火腿
|
||||
冲泡饮品
|
||||
即饮饮品
|
||||
餐食
|
||||
糖制品
|
||||
膨化食品
|
||||
豆制品
|
||||
高强瓦楞纸
|
||||
涂布白板纸
|
||||
双胶纸
|
||||
白卡纸
|
||||
底盘与悬架系统
|
||||
制动系统零部件
|
||||
传动系统零部件
|
||||
发动机与动力系统
|
||||
热管理与冷却系统
|
||||
电气与电控系统
|
||||
内饰与座椅系统
|
||||
外饰与车身部件
|
||||
轮毂与轮胎系统
|
||||
安全系统零部件
|
||||
空调与环境系统
|
||||
燃油与流体系统
|
||||
轻量化与结构件
|
||||
后处理与进气系统
|
||||
转向与操控系统
|
||||
整车与专用车辆
|
||||
配件与服务
|
||||
其他汽车零部件
|
||||
轮胎
|
||||
整车
|
||||
车灯
|
||||
汽车电子
|
||||
刹车片
|
||||
减震器
|
||||
发动机
|
||||
变速器
|
||||
轮毂
|
||||
座椅
|
||||
电池托盘
|
||||
内饰件
|
||||
外饰件
|
||||
汽车玻璃
|
||||
轴承
|
||||
齿轮
|
||||
密封件
|
||||
汽车空调
|
||||
线束
|
||||
汽车传感器
|
||||
涡轮壳
|
||||
中间壳
|
||||
同步器总成
|
||||
差速器
|
||||
结合齿
|
||||
汽车视镜
|
||||
保险杠
|
||||
汽车声学产品
|
||||
车轮总成
|
||||
油泵
|
||||
凸轮轴
|
||||
变速箱油泵
|
||||
分动箱油泵
|
||||
悬架系统
|
||||
踏板总成
|
||||
连杆
|
||||
气门嘴
|
||||
TPMS
|
||||
汽车金属管件
|
||||
驱动系统
|
||||
车载电源
|
||||
DC/DC变换器
|
||||
燃料分配管
|
||||
空调管路
|
||||
万向节
|
||||
锻铝控制臂
|
||||
汽车连接器
|
||||
热交换系统
|
||||
EGR冷却器
|
||||
工业机器人与自动化系统
|
||||
智能物流与仓储设备
|
||||
激光与精密加工装备
|
||||
锂电池智能制造设备
|
||||
新能源汽车自动化装备
|
||||
传统汽车智能制造装备
|
||||
通用工业自动化设备
|
||||
运动控制与伺服系统
|
||||
智能输送与装配生产线
|
||||
电子与半导体自动化设备
|
||||
智能装备核心部件
|
||||
其他行业专用自动化设备
|
||||
处理器与智能芯片
|
||||
电源管理与功率芯片
|
||||
存储与通信芯片
|
||||
其他集成电路产品
|
||||
精密机械零部件
|
||||
精密金属结构件
|
||||
航空航天精密零件
|
||||
传动系统精密件
|
||||
紧固件与连接件
|
||||
锻件与冷锻零件
|
||||
压缩机与液压精密零件
|
||||
减速器及其配件
|
||||
机床与刀具零件
|
||||
粉末冶金与MIM零件
|
||||
其他精密加工件
|
||||
显示模组
|
||||
行业解决方案与系统集成
|
||||
智慧城市与交通系统
|
||||
智能制造与工业自动化
|
||||
建筑智能化系统
|
||||
IT基础设施与服务
|
||||
金融与支付系统集成
|
||||
大数据与物联网解决方案
|
||||
轨道交通与仿真系统
|
||||
政务与公共服务系统
|
||||
智能监控与指挥系统
|
||||
数字化转型与企业应用
|
||||
智慧医疗与教育系统
|
||||
网络安全与信息工程
|
||||
其他系统集成服务
|
||||
传感器
|
||||
LED芯片
|
||||
印刷电路板（PCB）
|
||||
电容器
|
||||
电阻器
|
||||
继电器
|
||||
变压器
|
||||
控制器
|
||||
智能终端
|
||||
光纤光缆
|
||||
电源适配器
|
||||
存储设备
|
||||
消费电子
|
||||
手机
|
||||
平板电脑
|
||||
智能家居
|
||||
机器人
|
||||
无人机
|
||||
云服务
|
||||
网络安全产品
|
||||
数据中心设备
|
||||
半导体材料
|
||||
光学元件
|
||||
被动元器件
|
||||
连接器与继电器
|
||||
光电与显示器件
|
||||
功率与电源管理器件
|
||||
传感器与执行器
|
||||
其他电子元器件
|
||||
激光器
|
||||
芳烃与烯烃类原料
|
||||
无机化工原料
|
||||
有机化工原料
|
||||
氯碱与氟化工原料
|
||||
磷化工与矿石原料
|
||||
石化与炼化产品
|
||||
粘胶与聚酯原料
|
||||
金属化工原料
|
||||
其他化工原料
|
||||
仪器仪表
|
||||
智能电表
|
||||
光伏设备
|
||||
电池管理系统
|
||||
充电器
|
||||
电线电缆
|
||||
开关设备
|
||||
连接器
|
||||
背光模组
|
||||
触摸屏
|
||||
功率器件
|
||||
分立器件
|
||||
NOR Flash
|
||||
微波器件
|
||||
电解液添加剂
|
||||
液晶显示屏
|
||||
智能卡
|
||||
信号链芯片
|
||||
红外热像仪
|
||||
视频监控系统
|
||||
音频设备
|
||||
智能头显
|
||||
无线通信模组
|
||||
基站天线
|
||||
嵌入式计算平台
|
||||
网络可视化设备
|
||||
轨道扣件
|
||||
高频头
|
||||
射频电缆
|
||||
显示器件
|
||||
触控模组
|
||||
虚拟现实产品
|
||||
专业视听产品
|
||||
照明应用产品
|
||||
电子制造服务
|
||||
光传输设备
|
||||
ODN设备
|
||||
屏蔽罩
|
||||
精密连接器
|
||||
模具治具
|
||||
智能检测分选设备
|
||||
智能包装设备
|
||||
软管配件
|
||||
清洁电器整机
|
||||
刀具产品
|
||||
滑轨
|
||||
电能计量箱
|
||||
智能水表
|
||||
膜式燃气表
|
||||
点胶设备
|
||||
特种光纤
|
||||
光学膜
|
||||
功能件
|
||||
墙板
|
||||
行业专机
|
||||
传输类设备
|
||||
宽带网络设备
|
||||
油气类商品
|
||||
游戏
|
||||
数字内容
|
||||
数字营销服务
|
||||
视频会议系统
|
||||
公共安全产品
|
||||
平板显示设备
|
||||
物联网产品
|
||||
AI视频平台
|
||||
融合媒体平台
|
||||
工程机械
|
||||
农业机械
|
||||
数控机床
|
||||
压缩机
|
||||
泵类
|
||||
阀门
|
||||
电机
|
||||
铸件
|
||||
模具
|
||||
银行贷款业务
|
||||
证券与投资业务
|
||||
利息收入（非贷款）
|
||||
手续费及佣金收入
|
||||
财富管理与金融服务
|
||||
|
|
@ -0,0 +1 @@
|
|||
# fundamentals_llm package initialization
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,308 @@
|
|||
import logging
|
||||
from openai import OpenAI
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import random
|
||||
from typing import Dict, Any, Optional
|
||||
|
||||
# 设置日志记录
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 默认配置
|
||||
DEFAULT_API_KEYS = [
|
||||
"28cfe71a-c6fa-4c5d-9b4e-d8474f0d3b93", # 默认测试密钥
|
||||
]
|
||||
|
||||
DEFAULT_MODELS = {
|
||||
"online_bot": "bot-20250325102825-h9kpq", # 联网智能体ID
|
||||
"offline_model": "ep-20250326090920-v7wns", # 免费推理50W
|
||||
"doubao": "doubao-1-5-pro-32k-250115", # 豆包模型
|
||||
"deepseek": "deepseek-r1-250120", # Deepseek模型
|
||||
}
|
||||
|
||||
# 获取项目根目录的绝对路径
|
||||
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
# 尝试导入配置
|
||||
try:
|
||||
# 添加项目根目录到 Python 路径
|
||||
sys.path.append(os.path.dirname(ROOT_DIR))
|
||||
sys.path.append(ROOT_DIR)
|
||||
|
||||
# 导入配置
|
||||
try:
|
||||
from scripts.config import get_random_api_key, get_model
|
||||
logger.info("成功从scripts.config导入配置")
|
||||
except ImportError:
|
||||
try:
|
||||
from src.scripts.config import get_random_api_key, get_model
|
||||
logger.info("成功从src.scripts.config导入配置")
|
||||
except ImportError:
|
||||
logger.warning("无法导入配置模块，使用默认配置")
|
||||
|
||||
# 使用默认配置的实现
|
||||
def get_random_api_key():
|
||||
return random.choice(DEFAULT_API_KEYS)
|
||||
|
||||
def get_model(model_type):
|
||||
return DEFAULT_MODELS.get(model_type, DEFAULT_MODELS["offline_model"])
|
||||
except Exception as e:
|
||||
logger.error(f"导入配置时出错: {str(e)}，使用默认配置")
|
||||
|
||||
# 使用默认配置的实现
|
||||
def get_random_api_key():
|
||||
return random.choice(DEFAULT_API_KEYS)
|
||||
|
||||
def get_model(model_type):
|
||||
return DEFAULT_MODELS.get(model_type, DEFAULT_MODELS["offline_model"])
|
||||
|
||||
class ChatBot:
|
||||
def __init__(self, model_type: str = "online_bot"):
|
||||
"""初始化聊天机器人
|
||||
|
||||
Args:
|
||||
model_type: 要使用的模型类型，默认为联网智能体
|
||||
"""
|
||||
try:
|
||||
# 从配置获取API密钥
|
||||
self.api_key = get_random_api_key()
|
||||
# 从配置获取模型ID
|
||||
self.model = get_model(model_type)
|
||||
|
||||
logger.info(f"初始化ChatBot，使用模型: {self.model}")
|
||||
|
||||
# 初始化OpenAI客户端
|
||||
self.client = OpenAI(
|
||||
base_url="https://ark.cn-beijing.volces.com/api/v3/bots",
|
||||
api_key=self.api_key
|
||||
)
|
||||
|
||||
# 系统提示语
|
||||
self.system_message = {
|
||||
"role": "system",
|
||||
"content": """你是一个专业的股票分析助手，擅长进行深入的基本面分析。你的分析应该：
|
||||
|
||||
1. 专业严谨
|
||||
- 使用准确的专业术语
|
||||
- 引用可靠的数据来源
|
||||
- 分析逻辑清晰
|
||||
- 结论有理有据
|
||||
|
||||
2. 全面细致
|
||||
- 深入分析问题的各个方面
|
||||
- 关注细节和关键信息
|
||||
- 考虑多个影响因素
|
||||
- 提供详实的论据支持
|
||||
|
||||
3. 客观中立
|
||||
- 保持独立判断
|
||||
- 不夸大或贬低
|
||||
- 平衡利弊分析
|
||||
- 指出潜在风险
|
||||
|
||||
4. 实用性强
|
||||
- 分析结论具体明确
|
||||
- 建议具有可操作性
|
||||
- 关注实际投资价值
|
||||
- 提供清晰的决策参考
|
||||
|
||||
5. 及时更新
|
||||
- 关注最新信息
|
||||
- 反映市场变化
|
||||
- 动态调整分析
|
||||
- 保持信息时效性
|
||||
|
||||
请根据用户的具体需求，提供专业、深入的分析。如果遇到不确定的信息，请明确说明。"""
|
||||
}
|
||||
|
||||
# 对话历史
|
||||
self.conversation_history = [self.system_message]
|
||||
except Exception as e:
|
||||
logger.error(f"初始化ChatBot时出错: {str(e)}")
|
||||
raise
|
||||
|
||||
def format_reference(self, ref):
|
||||
"""格式化参考资料"""
|
||||
try:
|
||||
if isinstance(ref, str):
|
||||
return ref
|
||||
elif isinstance(ref, dict):
|
||||
title = ref.get('title', '')
|
||||
summary = ref.get('summary', '')
|
||||
url = ref.get('url', '')
|
||||
publish_time = ref.get('publish_time', '')
|
||||
|
||||
formatted_ref = []
|
||||
if title:
|
||||
formatted_ref.append(f"标题：{title}")
|
||||
if summary:
|
||||
formatted_ref.append(f"摘要：{summary}")
|
||||
if url:
|
||||
formatted_ref.append(f"链接：{url}")
|
||||
if publish_time:
|
||||
formatted_ref.append(f"发布时间：{publish_time}")
|
||||
|
||||
return "\n".join(formatted_ref)
|
||||
else:
|
||||
return str(ref)
|
||||
except Exception as e:
|
||||
logger.error(f"格式化参考资料时出错: {str(e)}")
|
||||
return str(ref)
|
||||
|
||||
def chat(self, user_input: str) -> Dict[str, Any]:
|
||||
"""与AI进行对话
|
||||
|
||||
Args:
|
||||
user_input: 用户输入的问题
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 包含以下字段的字典：
|
||||
- response: AI的回答内容
|
||||
- reasoning_process: 推理过程
|
||||
- references: 参考资料列表
|
||||
"""
|
||||
try:
|
||||
# 添加用户消息到对话历史
|
||||
self.conversation_history.append({
|
||||
"role": "user",
|
||||
"content": user_input
|
||||
})
|
||||
|
||||
# 调用API（流式输出）
|
||||
stream = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=self.conversation_history,
|
||||
stream=True
|
||||
)
|
||||
|
||||
# 收集完整的回复
|
||||
full_response = ""
|
||||
print("\nAI: ", end="", flush=True)
|
||||
|
||||
# 用于存储参考资料和推理过程
|
||||
references = []
|
||||
reasoning_parts = []
|
||||
|
||||
for chunk in stream:
|
||||
# 检查是否有参考资料
|
||||
if hasattr(chunk, "references"):
|
||||
refs = chunk.references
|
||||
if refs:
|
||||
references.extend(refs)
|
||||
|
||||
if not chunk.choices:
|
||||
continue
|
||||
|
||||
# 输出内容
|
||||
if chunk.choices[0].delta.content:
|
||||
content = chunk.choices[0].delta.content
|
||||
print(content, end="", flush=True)
|
||||
full_response += content
|
||||
time.sleep(0.02)
|
||||
|
||||
# 收集推理过程
|
||||
elif hasattr(chunk.choices[0].delta, "reasoning_content"):
|
||||
reasoning = chunk.choices[0].delta.reasoning_content
|
||||
if reasoning:
|
||||
reasoning_parts.append(reasoning)
|
||||
|
||||
# 如果有推理过程，格式化并显示
|
||||
if reasoning_parts:
|
||||
print("\n\n推理过程：")
|
||||
reasoning_text = "".join(reasoning_parts)
|
||||
print(reasoning_text)
|
||||
print("-" * 50)
|
||||
|
||||
# 如果有参考资料，格式化并显示
|
||||
if references:
|
||||
print("\n参考资料：")
|
||||
for ref in references:
|
||||
formatted_ref = self.format_reference(ref)
|
||||
print(f"\n{formatted_ref}")
|
||||
print("-" * 50)
|
||||
|
||||
# 从回答中提取推理过程和参考资料
|
||||
main_response = full_response
|
||||
reasoning_process = ""
|
||||
|
||||
if "推理过程：" in full_response:
|
||||
parts = full_response.split("推理过程：")
|
||||
main_response = parts[0].strip()
|
||||
if len(parts) > 1:
|
||||
reasoning_parts = parts[1].split("参考资料：")
|
||||
reasoning_process = reasoning_parts[0].strip()
|
||||
|
||||
# 添加AI回复到对话历史
|
||||
self.conversation_history.append({
|
||||
"role": "assistant",
|
||||
"content": main_response,
|
||||
"reasoning_process": reasoning_text,
|
||||
"references": references
|
||||
})
|
||||
|
||||
return {
|
||||
"response": main_response,
|
||||
"reasoning_process": reasoning_text,
|
||||
"references": references
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"对话失败: {str(e)}")
|
||||
return {
|
||||
"response": f"抱歉，处理您的请求时出现错误: {str(e)}",
|
||||
"reasoning_process": "",
|
||||
"references": []
|
||||
}
|
||||
|
||||
def clear_history(self):
|
||||
"""清除对话历史"""
|
||||
self.conversation_history = [self.system_message]
|
||||
print("对话历史已清除")
|
||||
|
||||
def run(self):
|
||||
"""运行聊天机器人"""
|
||||
print("欢迎使用AI助手！输入 'quit' 退出，输入 'clear' 清除对话历史。")
|
||||
print("该版本支持联网搜索，可以回答实时信息。")
|
||||
print("-" * 50)
|
||||
|
||||
while True:
|
||||
try:
|
||||
# 获取用户输入
|
||||
user_input = input("\n你: ").strip()
|
||||
|
||||
# 检查退出命令
|
||||
if user_input.lower() == 'quit':
|
||||
print("感谢使用，再见！")
|
||||
break
|
||||
|
||||
# 检查清除历史命令
|
||||
if user_input.lower() == 'clear':
|
||||
self.clear_history()
|
||||
continue
|
||||
|
||||
# 如果输入为空，继续下一轮
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
# 获取AI回复
|
||||
self.chat(user_input)
|
||||
print("-" * 50)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n感谢使用，再见！")
|
||||
break
|
||||
except Exception as e:
|
||||
logger.error(f"运行错误: {str(e)}")
|
||||
print(f"发生错误: {str(e)}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 设置日志级别
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
# 创建并运行聊天机器人
|
||||
bot = ChatBot()
|
||||
bot.run()
|
||||
|
|
@ -0,0 +1,203 @@
|
|||
import logging
|
||||
from openai import OpenAI
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import random
|
||||
|
||||
# 设置日志记录
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 获取项目根目录的绝对路径
|
||||
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
# 尝试导入配置
|
||||
try:
|
||||
# 添加项目根目录到 Python 路径
|
||||
sys.path.append(os.path.dirname(ROOT_DIR))
|
||||
sys.path.append(ROOT_DIR)
|
||||
|
||||
# 导入配置
|
||||
try:
|
||||
from scripts.config import get_model_config
|
||||
logger.info("成功从scripts.config导入配置")
|
||||
except ImportError:
|
||||
try:
|
||||
from src.scripts.config import get_model_config
|
||||
logger.info("成功从src.scripts.config导入配置")
|
||||
except ImportError:
|
||||
logger.warning("无法导入配置模块，使用默认配置")
|
||||
|
||||
# 使用默认配置的实现
|
||||
def get_model_config(platform: str, model_type: str):
|
||||
return {
|
||||
"base_url": "https://ark.cn-beijing.volces.com/api/v3/",
|
||||
"api_key": "28cfe71a-c6fa-4c5d-9b4e-d8474f0d3b93",
|
||||
"model": "ep-20250326090920-v7wns"
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"导入配置时出错: {str(e)}，使用默认配置")
|
||||
|
||||
# 使用默认配置的实现
|
||||
def get_model_config(platform: str, model_type: str):
|
||||
return {
|
||||
"base_url": "https://ark.cn-beijing.volces.com/api/v3/",
|
||||
"api_key": "28cfe71a-c6fa-4c5d-9b4e-d8474f0d3b93",
|
||||
"model": "ep-20250326090920-v7wns"
|
||||
}
|
||||
|
||||
class ChatBot:
|
||||
def __init__(self, platform: str = "volc", model_type: str = "offline_model"):
|
||||
"""初始化聊天机器人
|
||||
|
||||
Args:
|
||||
platform: 平台名称（volc/doubao/deepseek），默认为volc
|
||||
model_type: 要使用的模型类型，默认为offline_model
|
||||
"""
|
||||
try:
|
||||
# 从配置获取API配置
|
||||
config = get_model_config(platform, model_type)
|
||||
self.api_key = config["api_key"]
|
||||
self.model = config["model"]
|
||||
|
||||
logger.info(f"初始化ChatBot，使用平台: {platform}, 模型: {self.model}")
|
||||
|
||||
# 初始化OpenAI客户端
|
||||
self.client = OpenAI(
|
||||
base_url=config["base_url"],
|
||||
api_key=self.api_key,
|
||||
timeout=600
|
||||
)
|
||||
|
||||
# 系统提示语
|
||||
self.system_message = {
|
||||
"role": "system",
|
||||
"content": """你是一位经验丰富的专业投资经理，擅长基本面分析和投资决策。你的分析特点如下：
|
||||
|
||||
1. 分析风格：
|
||||
- 专业、客观、理性
|
||||
- 注重数据支撑
|
||||
- 关注风险控制
|
||||
- 重视投资性价比
|
||||
|
||||
2. 分析框架：
|
||||
- 公司基本面分析
|
||||
- 行业竞争格局
|
||||
- 估值水平评估
|
||||
- 风险因素识别
|
||||
- 投资机会判断
|
||||
|
||||
3. 输出要求：
|
||||
- 简明扼要，重点突出
|
||||
- 逻辑清晰，层次分明
|
||||
- 数据准确，论据充分
|
||||
- 结论明确，建议具体
|
||||
|
||||
请用专业投资经理的视角，对股票进行深入分析和投资建议。如果信息不足，请明确指出。"""
|
||||
}
|
||||
|
||||
# 对话历史
|
||||
self.conversation_history = [self.system_message]
|
||||
except Exception as e:
|
||||
logger.error(f"初始化ChatBot时出错: {str(e)}")
|
||||
raise
|
||||
|
||||
def chat(self, user_input: str) -> str:
|
||||
"""处理用户输入并返回AI回复"""
|
||||
try:
|
||||
# # 添加用户消息到对话历史-多轮
|
||||
self.conversation_history.append({
|
||||
"role": "user",
|
||||
"content": user_input
|
||||
})
|
||||
|
||||
|
||||
# 调用OpenAI API（流式输出）
|
||||
stream = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=self.conversation_history,
|
||||
temperature=0,
|
||||
stream=True,
|
||||
timeout=600
|
||||
)
|
||||
|
||||
# 收集完整的回复
|
||||
full_response = ""
|
||||
print("\nAI: ", end="", flush=True)
|
||||
|
||||
for chunk in stream:
|
||||
if not chunk.choices:
|
||||
continue
|
||||
content = chunk.choices[0].delta.content
|
||||
if content:
|
||||
print(content, end="", flush=True)
|
||||
full_response += content
|
||||
# 添加一个小延迟，使输出更自然
|
||||
time.sleep(0.02)
|
||||
|
||||
print() # 换行
|
||||
|
||||
# 添加AI回复到对话历史
|
||||
self.conversation_history.append({
|
||||
"role": "assistant",
|
||||
"content": full_response
|
||||
})
|
||||
|
||||
return full_response
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"聊天失败: {str(e)}")
|
||||
error_msg = f"抱歉，发生错误: {str(e)}"
|
||||
print(f"\n{error_msg}")
|
||||
return error_msg
|
||||
|
||||
def clear_history(self):
|
||||
"""清除对话历史"""
|
||||
self.conversation_history = [self.system_message]
|
||||
print("对话历史已清除")
|
||||
|
||||
def run(self):
|
||||
"""运行聊天机器人"""
|
||||
print("欢迎使用AI助手！输入 'quit' 退出，输入 'clear' 清除对话历史。")
|
||||
print("-" * 50)
|
||||
|
||||
while True:
|
||||
try:
|
||||
# 获取用户输入
|
||||
user_input = input("\n你: ").strip()
|
||||
|
||||
# 检查退出命令
|
||||
if user_input.lower() == 'quit':
|
||||
print("感谢使用，再见！")
|
||||
break
|
||||
|
||||
# 检查清除历史命令
|
||||
if user_input.lower() == 'clear':
|
||||
self.clear_history()
|
||||
continue
|
||||
|
||||
# 如果输入为空，继续下一轮
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
# 获取AI回复
|
||||
self.chat(user_input)
|
||||
print("-" * 50)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n感谢使用，再见！")
|
||||
break
|
||||
except Exception as e:
|
||||
logger.error(f"运行错误: {str(e)}")
|
||||
print(f"发生错误: {str(e)}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 设置日志级别
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
# 创建并运行聊天机器人
|
||||
bot = ChatBot()
|
||||
bot.run()
|
||||
|
|
@ -0,0 +1,595 @@
|
|||
import logging
|
||||
from typing import List, Dict, Tuple
|
||||
from fundamentals_llm.fundamental_analysis_database import get_db, get_analysis_result
|
||||
from sqlalchemy import text
|
||||
|
||||
# 设置日志记录
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class EnterpriseScreener:
|
||||
"""企业筛选器"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化筛选器"""
|
||||
self.db = next(get_db())
|
||||
|
||||
def _get_stock_info(self, stock_code: str) -> Tuple[str, str]:
|
||||
"""获取股票信息
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
Tuple[str, str]: (股票代码, 股票名称)
|
||||
"""
|
||||
try:
|
||||
# 从任意维度获取股票信息
|
||||
result = get_analysis_result(self.db, stock_code, "company_profile")
|
||||
if result:
|
||||
return stock_code, result.stock_name
|
||||
return stock_code, "未知"
|
||||
except Exception as e:
|
||||
logger.error(f"获取股票信息失败: {str(e)}")
|
||||
return stock_code, "未知"
|
||||
|
||||
def _screen_stocks_by_conditions(self, conditions: List[Dict], limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""通用的股票筛选方法
|
||||
|
||||
Args:
|
||||
conditions: 筛选条件列表，每个条件是一个字典，包含：
|
||||
- dimension: 分析维度
|
||||
- field: extra_info中的字段名
|
||||
- operator: 操作符 ('=', '!=', '>=', '<=', 'in')
|
||||
- value: 比较值
|
||||
- required: 是否必须满足（默认True）
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
try:
|
||||
# 构建SQL查询条件
|
||||
where_conditions = []
|
||||
having_conditions = []
|
||||
for i, condition in enumerate(conditions):
|
||||
dimension = condition['dimension']
|
||||
field = condition['field']
|
||||
operator = condition['operator']
|
||||
value = condition['value']
|
||||
required = condition.get('required', True)
|
||||
|
||||
# 构建JSON提取表达式
|
||||
json_extract = f"JSON_EXTRACT(fa.extra_info, '$.{field}')"
|
||||
if operator in ('=', '!='):
|
||||
json_extract = f"JSON_UNQUOTE({json_extract})"
|
||||
elif operator in ('>=', '<=', '>', '<'):
|
||||
json_extract = f"CAST({json_extract} AS SIGNED)"
|
||||
|
||||
# 构建比较条件
|
||||
if operator == 'in':
|
||||
value_str = ', '.join(str(v) for v in value)
|
||||
where_condition = f"{json_extract} IN ({value_str})"
|
||||
else:
|
||||
where_condition = f"{json_extract} {operator} '{value}'"
|
||||
|
||||
# 添加到WHERE子句
|
||||
where_conditions.append(f"(fa.dimension = '{dimension}' AND {where_condition})")
|
||||
|
||||
# 添加到HAVING子句
|
||||
if required:
|
||||
having_conditions.append(f"WHEN fa.dimension = '{dimension}' AND {where_condition} THEN {i+1}")
|
||||
|
||||
# 构建完整的SQL查询
|
||||
sql = f"""
|
||||
SELECT fa.stock_code, fa.stock_name
|
||||
FROM fundamental_analysis fa
|
||||
WHERE 1=1
|
||||
AND ({' OR '.join(where_conditions)})
|
||||
GROUP BY fa.stock_code, fa.stock_name
|
||||
HAVING COUNT(DISTINCT CASE
|
||||
{' '.join(having_conditions)}
|
||||
END) = {len(having_conditions)}
|
||||
"""
|
||||
|
||||
# 添加LIMIT子句
|
||||
if limit is not None:
|
||||
sql += f"\nLIMIT {limit}"
|
||||
|
||||
# 打印SQL查询语句到控制台
|
||||
print("\n===================== SQL查询 =====================")
|
||||
print(sql)
|
||||
print("==================================================\n")
|
||||
|
||||
# 执行查询
|
||||
cursor = self.db.execute(text(sql))
|
||||
qualified_stocks = []
|
||||
for row in cursor:
|
||||
stock_code = row[0]
|
||||
stock_name = row[1] if row[1] != None else self._get_stock_info(stock_code)[1]
|
||||
qualified_stocks.append((stock_code, stock_name))
|
||||
|
||||
print(f"查询结果: 找到 {len(qualified_stocks)} 个符合条件的股票")
|
||||
|
||||
return qualified_stocks
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"通用筛选方法失败: {str(e)}")
|
||||
print(f"SQL查询执行失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def screen_multi_term_investment_stocks(self, limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""筛选推荐投资企业
|
||||
|
||||
Args:
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
conditions = [
|
||||
{
|
||||
'dimension': 'investment_advice',
|
||||
'field': 'investment_advice_type',
|
||||
'operator': '!=',
|
||||
'value': '不建议'
|
||||
}
|
||||
]
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def screen_high_growth_stocks(self, limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""筛选高成长潜力企业
|
||||
|
||||
Args:
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
conditions = [
|
||||
{
|
||||
'dimension': 'industry_competition',
|
||||
'field': 'industry_space',
|
||||
'operator': '=',
|
||||
'value': 2
|
||||
},
|
||||
{
|
||||
'dimension': 'financial_report',
|
||||
'field': 'financial_report_level',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'recent_projects',
|
||||
'field': 'major_events',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_cooperation',
|
||||
'field': 'collaboration_dynamics',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'securities_rating',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'odds',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
}
|
||||
]
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def screen_stable_leaders(self, limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""筛选稳定型龙头企业
|
||||
|
||||
Args:
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
conditions = [
|
||||
{
|
||||
'dimension': 'management_ownership',
|
||||
'field': 'ability',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'management_ownership',
|
||||
'field': 'shareholding',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
},
|
||||
{
|
||||
'dimension': 'financial_report',
|
||||
'field': 'financial_report_level',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_competition',
|
||||
'field': 'industry_space',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_cooperation',
|
||||
'field': 'collaboration_dynamics',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'securities_rating',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
}
|
||||
]
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def screen_short_term_opportunities(self, limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""筛选短期投资机会
|
||||
|
||||
Args:
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
conditions = [
|
||||
{
|
||||
'dimension': 'recent_projects',
|
||||
'field': 'major_events',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'stock_discussion',
|
||||
'field': 'emotion',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'securities_rating',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'odds',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'financial_report',
|
||||
'field': 'financial_report_level',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
}
|
||||
]
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def screen_value_investment_stocks(self, limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""筛选价值型投资标的
|
||||
|
||||
Args:
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
conditions = [
|
||||
{
|
||||
'dimension': 'financial_report',
|
||||
'field': 'financial_report_level',
|
||||
'operator': '=',
|
||||
'value': 2
|
||||
},
|
||||
{
|
||||
'dimension': 'management_ownership',
|
||||
'field': 'shareholding',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_competition',
|
||||
'field': 'industry_space',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_cooperation',
|
||||
'field': 'collaboration_dynamics',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'securities_rating',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
}
|
||||
]
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def screen_turnaround_opportunities(self, limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""筛选困境反转机会
|
||||
|
||||
Args:
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
conditions = [
|
||||
{
|
||||
'dimension': 'financial_report',
|
||||
'field': 'financial_report_level',
|
||||
'operator': '=',
|
||||
'value': -1
|
||||
},
|
||||
{
|
||||
'dimension': 'recent_projects',
|
||||
'field': 'major_events',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_competition',
|
||||
'field': 'industry_space',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'stock_discussion',
|
||||
'field': 'emotion',
|
||||
'operator': '=',
|
||||
'value': 0
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'securities_rating',
|
||||
'operator': '=',
|
||||
'value': 0
|
||||
}
|
||||
]
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def screen_risk_averse_stocks(self, limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""筛选风险规避标的
|
||||
|
||||
Args:
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
conditions = [
|
||||
{
|
||||
'dimension': 'management_ownership',
|
||||
'field': 'ability',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'management_ownership',
|
||||
'field': 'shareholding',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'financial_report',
|
||||
'field': 'financial_report_level',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_competition',
|
||||
'field': 'industry_space',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_cooperation',
|
||||
'field': 'collaboration_dynamics',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'securities_rating',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
}
|
||||
]
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def screen_innovation_driven_stocks(self, limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""筛选创新驱动型企业
|
||||
|
||||
Args:
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
conditions = [
|
||||
{
|
||||
'dimension': 'industry_competition',
|
||||
'field': 'industry_space',
|
||||
'operator': '=',
|
||||
'value': 2
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_cooperation',
|
||||
'field': 'collaboration_dynamics',
|
||||
'operator': '=',
|
||||
'value': 2
|
||||
},
|
||||
{
|
||||
'dimension': 'recent_projects',
|
||||
'field': 'major_events',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'financial_report',
|
||||
'field': 'financial_report_level',
|
||||
'operator': '>=',
|
||||
'value': 0
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'securities_rating',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
}
|
||||
]
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def screen_industry_integration_stocks(self, limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""筛选行业整合机会
|
||||
|
||||
Args:
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
conditions = [
|
||||
{
|
||||
'dimension': 'industry_competition',
|
||||
'field': 'industry_space',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'industry_cooperation',
|
||||
'field': 'collaboration_dynamics',
|
||||
'operator': '=',
|
||||
'value': 2
|
||||
},
|
||||
{
|
||||
'dimension': 'financial_report',
|
||||
'field': 'financial_report_level',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'securities_rating',
|
||||
'operator': '>=',
|
||||
'value': 1
|
||||
},
|
||||
{
|
||||
'dimension': 'target_price',
|
||||
'field': 'odds',
|
||||
'operator': '=',
|
||||
'value': 1
|
||||
}
|
||||
]
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def screen_by_custom_conditions(self, conditions: List[Dict], limit: int = None) -> List[Tuple[str, str]]:
|
||||
"""根据自定义条件筛选企业
|
||||
|
||||
Args:
|
||||
conditions: 筛选条件列表，每个条件是一个字典，包含：
|
||||
- dimension: 分析维度
|
||||
- field: extra_info中的字段名
|
||||
- operator: 操作符 ('=', '!=', '>=', '<=', 'in')
|
||||
- value: 比较值
|
||||
- required: 是否必须满足（默认True）
|
||||
limit: 限制返回结果数量
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, str]]: 符合条件的股票列表，每个元素为(股票代码, 股票名称)
|
||||
"""
|
||||
print("\n================== 自定义条件筛选 ==================")
|
||||
print(f"条件数量: {len(conditions)}")
|
||||
for i, condition in enumerate(conditions):
|
||||
print(f"条件 {i+1}: 维度={condition.get('dimension')}, 字段={condition.get('field')}, " +
|
||||
f"操作符={condition.get('operator')}, 值={condition.get('value')}, " +
|
||||
f"必须满足={condition.get('required', True)}")
|
||||
print("====================================================\n")
|
||||
|
||||
return self._screen_stocks_by_conditions(conditions, limit)
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
# 设置日志级别
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
# 创建筛选器实例
|
||||
screener = EnterpriseScreener()
|
||||
|
||||
# 测试选项
|
||||
print("\n请选择要筛选的企业类型：")
|
||||
print("1. 高成长潜力企业")
|
||||
print("2. 稳定型龙头企业")
|
||||
print("3. 短期投资机会")
|
||||
print("4. 价值型投资标的")
|
||||
print("5. 困境反转机会")
|
||||
print("6. 风险规避标的")
|
||||
print("7. 创新驱动型企业")
|
||||
print("8. 行业整合机会")
|
||||
print("9. 推荐投资企业")
|
||||
print("10. 退出")
|
||||
|
||||
choice = input("\n请输入选项（1-10）: ").strip()
|
||||
|
||||
if choice == "1":
|
||||
stocks = screener.screen_high_growth_stocks()
|
||||
print("\n高成长潜力企业：")
|
||||
elif choice == "2":
|
||||
stocks = screener.screen_stable_leaders()
|
||||
print("\n稳定型龙头企业：")
|
||||
elif choice == "3":
|
||||
stocks = screener.screen_short_term_opportunities()
|
||||
print("\n短期投资机会：")
|
||||
elif choice == "4":
|
||||
stocks = screener.screen_value_investment_stocks()
|
||||
print("\n价值型投资标的：")
|
||||
elif choice == "5":
|
||||
stocks = screener.screen_turnaround_opportunities()
|
||||
print("\n困境反转机会：")
|
||||
elif choice == "6":
|
||||
stocks = screener.screen_risk_averse_stocks()
|
||||
print("\n风险规避标的：")
|
||||
elif choice == "7":
|
||||
stocks = screener.screen_innovation_driven_stocks()
|
||||
print("\n创新驱动型企业：")
|
||||
elif choice == "8":
|
||||
stocks = screener.screen_industry_integration_stocks()
|
||||
print("\n行业整合机会：")
|
||||
elif choice == "9":
|
||||
stocks = screener.screen_multi_term_investment_stocks()
|
||||
print("\n推荐投资企业：")
|
||||
elif choice == "10":
|
||||
print("程序退出")
|
||||
return
|
||||
else:
|
||||
print("无效的选项")
|
||||
return
|
||||
|
||||
# 输出结果
|
||||
if stocks:
|
||||
print(f"\n共找到 {len(stocks)} 只符合条件的股票：")
|
||||
for code, name in stocks:
|
||||
print(f"{name}({code})")
|
||||
else:
|
||||
print("\n未找到符合条件的股票")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,172 @@
|
|||
from sqlalchemy import create_engine, Column, Integer, String, Text, DateTime, JSON
|
||||
from sqlalchemy.ext.declarative import declarative_base
|
||||
from sqlalchemy.orm import sessionmaker, Session
|
||||
from datetime import datetime
|
||||
import os
|
||||
import logging
|
||||
from typing import Optional, List, Dict
|
||||
|
||||
# 设置日志记录
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 获取项目根目录的绝对路径
|
||||
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
# 数据库配置
|
||||
DB_CONFIG = {
|
||||
'host': '192.168.1.82',
|
||||
'port': 3306,
|
||||
'user': 'root',
|
||||
'password': 'Chlry$%.8',
|
||||
'database': 'db_gp_cj'
|
||||
}
|
||||
|
||||
# 创建数据库连接URL
|
||||
DATABASE_URL = f"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['database']}"
|
||||
|
||||
# 创建数据库引擎
|
||||
engine = create_engine(DATABASE_URL)
|
||||
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
|
||||
Base = declarative_base()
|
||||
|
||||
class GpFundamentalAnalysis(Base):
|
||||
"""基本面分析结果表"""
|
||||
__tablename__ = "fundamental_analysis"
|
||||
|
||||
id = Column(Integer, primary_key=True, index=True)
|
||||
stock_code = Column(String(10), nullable=False, comment="股票代码")
|
||||
stock_name = Column(String(50), nullable=False, comment="股票名称")
|
||||
dimension = Column(String(50), nullable=False, comment="分析维度")
|
||||
ai_response = Column(Text, nullable=False, comment="AI分析结果")
|
||||
reasoning_process = Column(Text, comment="推理过程")
|
||||
references = Column(JSON, comment="参考资料")
|
||||
update_time = Column(DateTime, default=datetime.now, onupdate=datetime.now, comment="更新时间")
|
||||
extra_info = Column(JSON, comment="扩展信息")
|
||||
|
||||
def __repr__(self):
|
||||
return f"<GpFundamentalAnalysis(stock_code={self.stock_code}, dimension={self.dimension})>"
|
||||
|
||||
def init_db():
|
||||
"""初始化数据库"""
|
||||
Base.metadata.create_all(bind=engine)
|
||||
|
||||
def get_db():
|
||||
"""获取数据库会话"""
|
||||
db = SessionLocal()
|
||||
try:
|
||||
yield db
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
# 定义基本面分析的维度
|
||||
ANALYSIS_DIMENSIONS = {
|
||||
"company_profile": "公司简介",
|
||||
"business_background": "业务背景",
|
||||
"core_advantages": "核心优势",
|
||||
"management_team": "管理团队",
|
||||
"financial_performance": "财务表现",
|
||||
"risk_factors": "风险因素",
|
||||
"development_prospects": "发展前景",
|
||||
"investment_suggestion": "投资建议"
|
||||
}
|
||||
|
||||
def save_analysis_result(
|
||||
db: Session,
|
||||
stock_code: str,
|
||||
stock_name: str,
|
||||
dimension: str,
|
||||
ai_response: str,
|
||||
reasoning_process: Optional[str] = None,
|
||||
references: Optional[List[Dict]] = None,
|
||||
extra_info: Optional[Dict] = None
|
||||
) -> bool:
|
||||
"""保存分析结果到数据库
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
dimension: 分析维度
|
||||
ai_response: AI分析结果
|
||||
reasoning_process: 推理过程
|
||||
references: 参考资料列表
|
||||
extra_info: 额外信息
|
||||
|
||||
Returns:
|
||||
bool: 是否保存成功
|
||||
"""
|
||||
try:
|
||||
# 检查是否已存在相同维度的分析结果
|
||||
existing_result = db.query(GpFundamentalAnalysis).filter(
|
||||
GpFundamentalAnalysis.stock_code == stock_code,
|
||||
GpFundamentalAnalysis.dimension == dimension
|
||||
).first()
|
||||
|
||||
if existing_result:
|
||||
# 如果存在，则更新
|
||||
existing_result.ai_response = ai_response
|
||||
existing_result.reasoning_process = reasoning_process
|
||||
existing_result.references = references
|
||||
existing_result.extra_info = extra_info
|
||||
existing_result.update_time = datetime.now()
|
||||
db.commit()
|
||||
return True
|
||||
|
||||
# 如果不存在，则创建新记录
|
||||
new_result = GpFundamentalAnalysis(
|
||||
stock_code=stock_code,
|
||||
stock_name=stock_name,
|
||||
dimension=dimension,
|
||||
ai_response=ai_response,
|
||||
reasoning_process=reasoning_process,
|
||||
references=references,
|
||||
extra_info=extra_info,
|
||||
update_time=datetime.now()
|
||||
)
|
||||
|
||||
db.add(new_result)
|
||||
db.commit()
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"保存分析结果失败: {str(e)}")
|
||||
db.rollback()
|
||||
return False
|
||||
|
||||
def get_analysis_result(db: Session, stock_code: str, dimension: str):
|
||||
"""获取特定股票特定维度的分析结果"""
|
||||
try:
|
||||
result = db.query(GpFundamentalAnalysis).filter(
|
||||
GpFundamentalAnalysis.stock_code == stock_code,
|
||||
GpFundamentalAnalysis.dimension == dimension
|
||||
).first()
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(f"获取分析结果失败: {str(e)}")
|
||||
return None
|
||||
|
||||
def update_analysis_result(
|
||||
db: Session,
|
||||
stock_code: str,
|
||||
dimension: str,
|
||||
ai_response: str,
|
||||
reasoning_process: Optional[str] = None,
|
||||
references: Optional[List[Dict]] = None,
|
||||
extra_info: Optional[Dict] = None
|
||||
) -> bool:
|
||||
"""更新分析结果"""
|
||||
try:
|
||||
result = get_analysis_result(db, stock_code, dimension)
|
||||
if result:
|
||||
result.ai_response = ai_response
|
||||
result.reasoning_process = reasoning_process
|
||||
result.references = references
|
||||
result.extra_info = extra_info
|
||||
result.update_time = datetime.now()
|
||||
db.commit()
|
||||
return True
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.error(f"更新分析结果失败: {str(e)}")
|
||||
db.rollback()
|
||||
return False
|
||||
|
|
@ -0,0 +1,203 @@
|
|||
import pandas as pd
|
||||
import os
|
||||
import sys
|
||||
from datetime import datetime
|
||||
from sqlalchemy import create_engine, Table, Column, String, DECIMAL, MetaData, text
|
||||
|
||||
# 数据库连接配置
|
||||
DB_CONFIG = {
|
||||
'host': '192.168.1.82',
|
||||
'port': 3306,
|
||||
'user': 'root',
|
||||
'password': 'Chlry$%.8',
|
||||
'database': 'db_gp_cj'
|
||||
}
|
||||
|
||||
# 获取项目根目录的绝对路径
|
||||
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
def connect_to_db():
|
||||
"""连接到数据库"""
|
||||
try:
|
||||
connection_string = f"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['database']}"
|
||||
engine = create_engine(connection_string)
|
||||
return engine
|
||||
except Exception as e:
|
||||
print(f"数据库连接失败: {str(e)}")
|
||||
sys.exit(1)
|
||||
|
||||
def create_table_if_not_exists(engine):
|
||||
"""如果不存在则创建表"""
|
||||
metadata = MetaData()
|
||||
|
||||
# 定义表结构
|
||||
main_business = Table(
|
||||
'gp_main_business',
|
||||
metadata,
|
||||
Column('stock_code', String(20), primary_key=True, comment='股票代码'),
|
||||
Column('stock_name', String(50), comment='股票简称'),
|
||||
Column('report_date', String(8), primary_key=True, comment='报告期(YYYYMMDD)'),
|
||||
Column('product_rank', String(1), primary_key=True, comment='项目排名(1-5)'),
|
||||
Column('product_name', String(100), comment='项目名称'),
|
||||
Column('revenue', DECIMAL(20, 2), comment='项目收入'),
|
||||
Column('cost', DECIMAL(20, 2), comment='项目成本'),
|
||||
Column('profit', DECIMAL(20, 2), comment='项目毛利'),
|
||||
Column('profit_margin', DECIMAL(10, 4), comment='项目毛利率')
|
||||
)
|
||||
|
||||
# 创建表
|
||||
try:
|
||||
metadata.create_all(engine)
|
||||
print("表创建成功或已存在")
|
||||
except Exception as e:
|
||||
print(f"创建表失败: {str(e)}")
|
||||
sys.exit(1)
|
||||
|
||||
return main_business
|
||||
|
||||
def clean_numeric_value(value):
|
||||
"""清理数字字符串，移除逗号并转换为浮点数"""
|
||||
if pd.isna(value) or value == "0" or value == "":
|
||||
return None
|
||||
try:
|
||||
# 移除逗号并转换为浮点数
|
||||
return float(str(value).replace(',', ''))
|
||||
except:
|
||||
return None
|
||||
|
||||
def import_csv_to_db(csv_file, engine, table, report_date=None):
|
||||
"""导入CSV文件到数据库"""
|
||||
try:
|
||||
# 确保使用绝对路径
|
||||
if not os.path.isabs(csv_file):
|
||||
csv_file = os.path.join(ROOT_DIR, csv_file)
|
||||
|
||||
# 检查文件是否存在
|
||||
if not os.path.exists(csv_file):
|
||||
print(f"文件不存在: {csv_file}")
|
||||
return
|
||||
print(f"正在处理文件: {csv_file}")
|
||||
# 读取CSV文件
|
||||
df = pd.read_csv(csv_file, encoding='gbk')
|
||||
# 如果没有指定报告期，从文件名或使用当前年份的半年报日期
|
||||
if report_date is None:
|
||||
filename = os.path.basename(csv_file)
|
||||
if len(filename.split('.')[0]) == 8 and filename.split('.')[0].isdigit():
|
||||
report_date = filename.split('.')[0]
|
||||
else:
|
||||
# 使用当前年份的半年报日期（20240630）
|
||||
current_year = datetime.now().year
|
||||
report_date = f"{current_year}0630"
|
||||
|
||||
print(f"处理报告期: {report_date}")
|
||||
|
||||
# 连接到数据库
|
||||
conn = engine.connect()
|
||||
|
||||
# 开始事务
|
||||
trans = conn.begin()
|
||||
|
||||
try:
|
||||
# 先删除该报告期的数据
|
||||
delete_query = text(f"DELETE FROM gp_main_business WHERE report_date = '{report_date}'")
|
||||
conn.execute(delete_query)
|
||||
|
||||
# 列名映射，方便访问列
|
||||
column_prefixes = [
|
||||
'主营构成(按产品)-第一名-',
|
||||
'主营构成(按产品)-第二名-',
|
||||
'主营构成(按产品)-第三名-',
|
||||
'主营构成(按产品)-第四名-',
|
||||
'主营构成(按产品)-第五名-'
|
||||
]
|
||||
|
||||
column_suffixes = [
|
||||
'项目名称', '项目收入', '项目成本', '项目毛利', '项目毛利率'
|
||||
]
|
||||
|
||||
# 插入新数据
|
||||
for _, row in df.iterrows():
|
||||
stock_code = row['代码']
|
||||
stock_name = row['简称']
|
||||
|
||||
# 处理前5个主营业务项目
|
||||
for i, prefix in enumerate(column_prefixes, 1):
|
||||
# 获取列名
|
||||
name_col = f"{prefix}{column_suffixes[0]}"
|
||||
revenue_col = f"{prefix}{column_suffixes[1]}"
|
||||
cost_col = f"{prefix}{column_suffixes[2]}"
|
||||
profit_col = f"{prefix}{column_suffixes[3]}"
|
||||
margin_col = f"{prefix}{column_suffixes[4]}"
|
||||
|
||||
# 获取项目名称
|
||||
product_name = row[name_col] if name_col in row else None
|
||||
|
||||
# 如果没有项目名称或项目名称为"0"，跳过
|
||||
if pd.isna(product_name) or product_name == "0" or product_name == "":
|
||||
continue
|
||||
|
||||
# 清理并转换数值
|
||||
revenue = clean_numeric_value(row[revenue_col] if revenue_col in row else None)
|
||||
cost = clean_numeric_value(row[cost_col] if cost_col in row else None)
|
||||
profit = clean_numeric_value(row[profit_col] if profit_col in row else None)
|
||||
margin = clean_numeric_value(row[margin_col] if margin_col in row else None)
|
||||
|
||||
# 准备插入的数据
|
||||
data = {
|
||||
'stock_code': stock_code,
|
||||
'stock_name': stock_name,
|
||||
'report_date': report_date,
|
||||
'product_rank': str(i),
|
||||
'product_name': product_name,
|
||||
'revenue': revenue,
|
||||
'cost': cost,
|
||||
'profit': profit,
|
||||
'profit_margin': margin
|
||||
}
|
||||
|
||||
# 插入数据
|
||||
insert_query = table.insert().values(**data)
|
||||
conn.execute(insert_query)
|
||||
|
||||
# 提交事务
|
||||
trans.commit()
|
||||
print(f"成功导入 {csv_file} 数据")
|
||||
|
||||
except Exception as e:
|
||||
# 回滚事务
|
||||
trans.rollback()
|
||||
print(f"导入数据失败: {str(e)}")
|
||||
finally:
|
||||
# 关闭连接
|
||||
conn.close()
|
||||
|
||||
except Exception as e:
|
||||
print(f"处理CSV文件 {csv_file} 失败: {str(e)}")
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
# 检查命令行参数
|
||||
if len(sys.argv) < 2:
|
||||
print("用法: python import_main_business.py <csv_file> [report_date]")
|
||||
print("例如: python import_main_business.py src/data/111.csv 20240630")
|
||||
print("如果不提供report_date，将使用文件名或当前年份的半年报日期")
|
||||
sys.exit(1)
|
||||
|
||||
csv_file = sys.argv[1]
|
||||
|
||||
# 如果提供了报告期参数
|
||||
report_date = sys.argv[2] if len(sys.argv) > 2 else None
|
||||
|
||||
# 连接数据库
|
||||
engine = connect_to_db()
|
||||
|
||||
# 创建表
|
||||
table = create_table_if_not_exists(engine)
|
||||
|
||||
# 导入数据
|
||||
import_csv_to_db(csv_file, engine, table, report_date)
|
||||
|
||||
print("数据导入完成")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,336 @@
|
|||
import logging
|
||||
from typing import Dict, List, Optional, Union
|
||||
from reportlab.lib import colors
|
||||
from reportlab.lib.pagesizes import A4
|
||||
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
|
||||
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
|
||||
from reportlab.pdfbase import pdfmetrics
|
||||
from reportlab.pdfbase.ttfonts import TTFont
|
||||
from reportlab.platypus.paragraph import Paragraph
|
||||
import markdown2
|
||||
from bs4 import BeautifulSoup
|
||||
import os
|
||||
from datetime import datetime
|
||||
from fpdf import FPDF
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
|
||||
# 修改导入路径，使用相对导入
|
||||
try:
|
||||
# 尝试相对导入
|
||||
from .chat_bot_with_offline import ChatBot
|
||||
from .fundamental_analysis_database import get_db, get_analysis_result
|
||||
except ImportError:
|
||||
# 如果相对导入失败，尝试绝对导入
|
||||
try:
|
||||
from src.fundamentals_llm.chat_bot_with_offline import ChatBot
|
||||
from src.fundamentals_llm.fundamental_analysis_database import get_db, get_analysis_result
|
||||
except ImportError:
|
||||
# 最后尝试直接导入
|
||||
from chat_bot_with_offline import ChatBot
|
||||
from fundamental_analysis_database import get_db, get_analysis_result
|
||||
|
||||
# 设置日志记录
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class PDFGenerator:
|
||||
"""PDF生成器类，用于将内容转换为PDF格式"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化PDF生成器"""
|
||||
# 注册中文字体
|
||||
try:
|
||||
# 尝试使用系统自带的中文字体
|
||||
if os.name == 'nt': # Windows
|
||||
font_path = "C:/Windows/Fonts/simhei.ttf" # 黑体
|
||||
else: # Linux/Mac
|
||||
font_path = "/usr/share/fonts/truetype/droid/DroidSansFallback.ttf"
|
||||
|
||||
if os.path.exists(font_path):
|
||||
pdfmetrics.registerFont(TTFont('SimHei', font_path))
|
||||
self.font_name = 'SimHei'
|
||||
else:
|
||||
# 如果找不到系统字体，尝试使用当前目录下的字体
|
||||
font_path = os.path.join(os.path.dirname(__file__), "fonts", "simhei.ttf")
|
||||
if os.path.exists(font_path):
|
||||
pdfmetrics.registerFont(TTFont('SimHei', font_path))
|
||||
self.font_name = 'SimHei'
|
||||
else:
|
||||
raise FileNotFoundError("找不到中文字体文件")
|
||||
except Exception as e:
|
||||
logger.error(f"注册中文字体失败: {str(e)}")
|
||||
raise
|
||||
|
||||
self.chat_bot = ChatBot()
|
||||
self.styles = getSampleStyleSheet()
|
||||
|
||||
# 创建自定义样式
|
||||
self.styles.add(ParagraphStyle(
|
||||
name='ReportTitle',
|
||||
parent=self.styles['Heading1'],
|
||||
fontSize=24,
|
||||
spaceAfter=30,
|
||||
alignment=1, # 居中对齐
|
||||
fontName=self.font_name
|
||||
))
|
||||
|
||||
self.styles.add(ParagraphStyle(
|
||||
name='ReportHeading',
|
||||
parent=self.styles['Heading2'],
|
||||
fontSize=16,
|
||||
spaceAfter=12,
|
||||
textColor=colors.HexColor('#2C3E50'),
|
||||
fontName=self.font_name
|
||||
))
|
||||
|
||||
self.styles.add(ParagraphStyle(
|
||||
name='ReportBody',
|
||||
parent=self.styles['Normal'],
|
||||
fontSize=12,
|
||||
leading=18,
|
||||
spaceAfter=12,
|
||||
fontName=self.font_name
|
||||
))
|
||||
|
||||
# 添加Markdown样式
|
||||
self.styles.add(ParagraphStyle(
|
||||
name='MarkdownHeading1',
|
||||
parent=self.styles['Heading1'],
|
||||
fontSize=20,
|
||||
spaceAfter=20,
|
||||
fontName=self.font_name
|
||||
))
|
||||
|
||||
self.styles.add(ParagraphStyle(
|
||||
name='MarkdownHeading2',
|
||||
parent=self.styles['Heading2'],
|
||||
fontSize=16,
|
||||
spaceAfter=16,
|
||||
fontName=self.font_name
|
||||
))
|
||||
|
||||
self.styles.add(ParagraphStyle(
|
||||
name='MarkdownHeading3',
|
||||
parent=self.styles['Heading3'],
|
||||
fontSize=14,
|
||||
spaceAfter=14,
|
||||
fontName=self.font_name
|
||||
))
|
||||
|
||||
self.styles.add(ParagraphStyle(
|
||||
name='MarkdownList',
|
||||
parent=self.styles['Normal'],
|
||||
fontSize=12,
|
||||
leading=18,
|
||||
spaceAfter=12,
|
||||
leftIndent=20,
|
||||
fontName=self.font_name
|
||||
))
|
||||
|
||||
self.styles.add(ParagraphStyle(
|
||||
name='MarkdownCode',
|
||||
parent=self.styles['Normal'],
|
||||
fontSize=10,
|
||||
leading=16,
|
||||
spaceAfter=12,
|
||||
leftIndent=20,
|
||||
fontName='Courier',
|
||||
textColor=colors.HexColor('#333333')
|
||||
))
|
||||
|
||||
def _convert_markdown_to_flowables(self, markdown_text: str) -> List:
|
||||
"""将Markdown文本转换为PDF流对象
|
||||
|
||||
Args:
|
||||
markdown_text: Markdown格式的文本
|
||||
|
||||
Returns:
|
||||
List: PDF流对象列表
|
||||
"""
|
||||
# 将Markdown转换为HTML
|
||||
html = markdown2.markdown(markdown_text)
|
||||
soup = BeautifulSoup(html, 'html.parser')
|
||||
|
||||
flowables = []
|
||||
for element in soup.children:
|
||||
if element.name is None: # 纯文本
|
||||
if element.strip():
|
||||
flowables.append(Paragraph(element.strip(), self.styles['ReportBody']))
|
||||
elif element.name in ['h1', 'h2', 'h3']: # 标题
|
||||
level = int(element.name[1])
|
||||
style_name = f'MarkdownHeading{level}'
|
||||
flowables.append(Paragraph(element.get_text(), self.styles[style_name]))
|
||||
elif element.name == 'p': # 段落
|
||||
flowables.append(Paragraph(element.get_text(), self.styles['ReportBody']))
|
||||
elif element.name == 'ul': # 无序列表
|
||||
for li in element.find_all('li'):
|
||||
flowables.append(Paragraph(f"• {li.get_text()}", self.styles['MarkdownList']))
|
||||
elif element.name == 'ol': # 有序列表
|
||||
for i, li in enumerate(element.find_all('li'), 1):
|
||||
flowables.append(Paragraph(f"{i}. {li.get_text()}", self.styles['MarkdownList']))
|
||||
elif element.name == 'code': # 代码块
|
||||
flowables.append(Paragraph(element.get_text(), self.styles['MarkdownCode']))
|
||||
elif element.name == 'blockquote': # 引用
|
||||
flowables.append(Paragraph(f"「{element.get_text()}」", self.styles['ReportBody']))
|
||||
elif element.name == 'hr': # 分隔线
|
||||
flowables.append(Spacer(1, 20))
|
||||
|
||||
return flowables
|
||||
|
||||
def _optimize_content(self, content: str) -> str:
|
||||
# 移除markdown代码块标记
|
||||
content = content.replace('```markdown', '').replace('```', '')
|
||||
"""优化内容格式并转换为Markdown
|
||||
|
||||
Args:
|
||||
content: 原始内容
|
||||
|
||||
Returns:
|
||||
str: 优化后的Markdown格式内容
|
||||
"""
|
||||
try:
|
||||
prompt = f"""请对以下内容进行优化和格式化。要求：
|
||||
1. 保持原文的专业性和准确性
|
||||
2. 将零散的内容整合成连贯的段落，并对不重要的内容精简
|
||||
3. 使用适当的标点符号和换行
|
||||
4. 使用Markdown格式进行排版
|
||||
5. 移除所有引用内容（包括"参考资料："等）
|
||||
6. 不要返回其他多余的内容
|
||||
7. 确保内容结构清晰，层次分明
|
||||
8. 将零散的内容整合成完整的段落，避免过于零散的表述
|
||||
9. 使用自然流畅的语言，避免过于机械的结构化表达
|
||||
|
||||
原始内容：
|
||||
{content}"""
|
||||
|
||||
result = self.chat_bot.chat(prompt)
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(f"优化内容失败: {str(e)}")
|
||||
return content
|
||||
|
||||
def generate_pdf(self,
|
||||
title: str,
|
||||
content_dict: Dict[str, str],
|
||||
output_dir: str = "reports",
|
||||
filename: Optional[str] = None) -> Optional[str]:
|
||||
"""生成PDF报告
|
||||
|
||||
Args:
|
||||
title: 报告标题
|
||||
content_dict: 包含各维度内容的字典，key为维度名称，value为内容
|
||||
output_dir: 输出目录
|
||||
filename: 文件名（可选），如果不提供则自动生成
|
||||
|
||||
Returns:
|
||||
Optional[str]: 生成的PDF文件路径，如果失败则返回None
|
||||
"""
|
||||
try:
|
||||
# 创建输出目录
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
# 生成文件名
|
||||
if not filename:
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
filename = f"report_{timestamp}.pdf"
|
||||
filepath = os.path.join(output_dir, filename)
|
||||
|
||||
# 创建PDF文档
|
||||
doc = SimpleDocTemplate(
|
||||
filepath,
|
||||
pagesize=A4,
|
||||
rightMargin=72,
|
||||
leftMargin=72,
|
||||
topMargin=72,
|
||||
bottomMargin=72
|
||||
)
|
||||
|
||||
# 准备内容
|
||||
story = []
|
||||
|
||||
# 添加标题
|
||||
title_para = Paragraph(title, self.styles['ReportTitle'])
|
||||
story.append(title_para)
|
||||
story.append(Spacer(1, 20))
|
||||
|
||||
# 添加生成时间
|
||||
time_str = datetime.now().strftime("%Y年%m月%d日 %H:%M:%S")
|
||||
time_para = Paragraph(f"生成时间：{time_str}", self.styles['ReportBody'])
|
||||
story.append(time_para)
|
||||
story.append(Spacer(1, 20))
|
||||
|
||||
# 添加各维度内容
|
||||
for section_title, content in content_dict.items():
|
||||
if content: # 只添加非空内容
|
||||
# 优化内容并转换为Markdown
|
||||
optimized_content = self._optimize_content(content)
|
||||
# 转换为PDF流对象
|
||||
# story.append(Paragraph(section_title, self.styles['ReportHeading']))
|
||||
story.extend(self._convert_markdown_to_flowables(optimized_content))
|
||||
story.append(Spacer(1, 20))
|
||||
|
||||
# 生成PDF
|
||||
doc.build(story)
|
||||
logger.info(f"PDF报告已生成: {filepath}")
|
||||
return filepath
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成PDF报告失败: {str(e)}")
|
||||
return None
|
||||
|
||||
# 使用示例
|
||||
if __name__ == "__main__":
|
||||
# 设置日志级别
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
# 创建PDF生成器实例
|
||||
generator = PDFGenerator()
|
||||
|
||||
# 示例内容
|
||||
test_content = {
|
||||
"公司简介": """# 公司简介
|
||||
这是一段公司简介的内容...
|
||||
|
||||
## 主营业务
|
||||
- 业务1
|
||||
- 业务2
|
||||
- 业务3
|
||||
|
||||
## 发展历程
|
||||
1. 2020年成立
|
||||
2. 2021年上市
|
||||
3. 2022年扩张""",
|
||||
"财务分析": """# 财务分析
|
||||
## 收入情况
|
||||
2023年收入达到100亿...
|
||||
|
||||
## 利润分析
|
||||
净利润同比增长20%...""",
|
||||
"行业分析": """# 行业分析
|
||||
## 市场格局
|
||||
目前市场呈现...""",
|
||||
"投资建议": """# 投资建议
|
||||
## 优势
|
||||
- 优势1
|
||||
- 优势2
|
||||
|
||||
## 风险
|
||||
- 风险1
|
||||
- 风险2"""
|
||||
}
|
||||
|
||||
# 生成PDF
|
||||
filepath = generator.generate_pdf(
|
||||
title="测试报告",
|
||||
content_dict=test_content,
|
||||
filename="test_report.pdf"
|
||||
)
|
||||
|
||||
if filepath:
|
||||
print(f"报告已生成: {filepath}")
|
||||
else:
|
||||
print("报告生成失败")
|
||||
|
|
@ -0,0 +1,330 @@
|
|||
import logging
|
||||
from typing import List, Dict, Optional
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine, text
|
||||
import os
|
||||
from openai import OpenAI
|
||||
|
||||
# 设置日志记录
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 获取项目根目录的绝对路径
|
||||
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
# API配置
|
||||
API_KEY = "28cfe71a-c6fa-4c5d-9b4e-d8474f0d3b93" # 请填入火山引擎的 API Key
|
||||
MODEL = "doubao-1-5-pro-32k-250115" # 请填入火山引擎的模型名称
|
||||
|
||||
# 数据库配置
|
||||
DB_CONFIG = {
|
||||
'host': '192.168.1.82',
|
||||
'port': 3306,
|
||||
'user': 'root',
|
||||
'password': 'Chlry$%.8',
|
||||
'database': 'db_gp_cj'
|
||||
}
|
||||
|
||||
# 初始化OpenAI客户端
|
||||
client = OpenAI(
|
||||
base_url="https://ark.cn-beijing.volces.com/api/v3/",
|
||||
api_key=API_KEY
|
||||
)
|
||||
|
||||
def connect_to_db():
|
||||
"""连接到数据库"""
|
||||
try:
|
||||
connection_string = f"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['database']}"
|
||||
engine = create_engine(connection_string)
|
||||
return engine
|
||||
except Exception as e:
|
||||
logger.error(f"数据库连接失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def load_category_list():
|
||||
"""加载类别列表"""
|
||||
try:
|
||||
# 使用项目根目录构建文件路径
|
||||
file_path = os.path.join(ROOT_DIR, 'data', '字典表.txt')
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
categories = [line.strip() for line in f if line.strip()]
|
||||
return categories
|
||||
except Exception as e:
|
||||
logger.error(f"加载类别列表失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def create_category_table(engine):
|
||||
"""创建产品类别表"""
|
||||
try:
|
||||
with engine.connect() as conn:
|
||||
conn.execute(text("""
|
||||
CREATE TABLE IF NOT EXISTS gp_product_category (
|
||||
id INT AUTO_INCREMENT PRIMARY KEY,
|
||||
category_name VARCHAR(100) NOT NULL,
|
||||
product_name VARCHAR(200) NOT NULL,
|
||||
stock_code VARCHAR(20) NOT NULL,
|
||||
stock_name VARCHAR(50) NOT NULL,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
INDEX idx_stock_code (stock_code),
|
||||
INDEX idx_product_name (product_name)
|
||||
)
|
||||
"""))
|
||||
conn.commit()
|
||||
except Exception as e:
|
||||
logger.error(f"创建表失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def get_company_categorization_prompt(stock_name: str, products: List[str], categories: List[str]) -> str:
|
||||
"""生成企业产品分类的提示词"""
|
||||
prompt = f"""你是一个专业的产品分类专家。请将以下企业的产品名称分类到最合适的类别中。
|
||||
|
||||
企业名称：{stock_name}
|
||||
|
||||
产品列表：
|
||||
{chr(10).join(f"- {product}" for product in products)}
|
||||
|
||||
可选的类别列表：
|
||||
{chr(10).join(f"- {category}" for category in categories)}
|
||||
|
||||
请为每个产品选择最合适的类别。如果产品名称与多个类别相关，选择最匹配的一个。
|
||||
如果确实无法确定类别，请返回"其他"。
|
||||
|
||||
请按照以下格式返回结果（每行一个产品，用制表符分隔产品名和类别）：
|
||||
产品名称1 类别1
|
||||
产品名称2 类别2
|
||||
...
|
||||
"""
|
||||
return prompt
|
||||
|
||||
def categorize_company_products(stock_name: str, products: List[str], categories: List[str]) -> Dict[str, str]:
|
||||
"""调用API对企业产品进行分类"""
|
||||
try:
|
||||
# 构建系统提示语
|
||||
system_message = {
|
||||
"role": "system",
|
||||
"content": """你是一个专业的产品分类专家。你的任务是准确地将产品名称分类到预定义的类别中。
|
||||
请仔细分析产品名称，考虑其功能、用途和行业特点，选择最匹配的类别。
|
||||
如果产品名称与多个类别相关，选择最匹配的一个。
|
||||
如果确实无法确定类别，请返回"其他"。
|
||||
请按照指定格式返回结果，每行一个产品，用制表符分隔产品名和类别。
|
||||
注意：请确保返回的类别名称完全匹配预定义类别列表中的名称。"""
|
||||
}
|
||||
|
||||
# 构建用户消息
|
||||
user_message = {
|
||||
"role": "user",
|
||||
"content": get_company_categorization_prompt(stock_name, products, categories)
|
||||
}
|
||||
|
||||
# 调用OpenAI API
|
||||
completion = client.chat.completions.create(
|
||||
model=MODEL,
|
||||
messages=[system_message, user_message],
|
||||
temperature=0.3
|
||||
)
|
||||
|
||||
content = completion.choices[0].message.content.strip()
|
||||
|
||||
# 打印原始返回内容以便调试
|
||||
print("\nAPI返回的原始内容：")
|
||||
print(content)
|
||||
|
||||
# 解析返回结果
|
||||
category_mapping = {}
|
||||
for line in content.split('\n'):
|
||||
line = line.strip()
|
||||
if not line: # 跳过空行
|
||||
continue
|
||||
|
||||
# 尝试不同的分隔符
|
||||
if '\t' in line:
|
||||
parts = line.split('\t')
|
||||
elif ' ' in line:
|
||||
# 使用第一个空格分割，保留产品名中的其他空格
|
||||
parts = line.split(' ', 1)
|
||||
else:
|
||||
print(f"警告：无法解析的行: {line}")
|
||||
continue
|
||||
|
||||
if len(parts) != 2:
|
||||
print(f"警告：格式不正确的行: {line}")
|
||||
continue
|
||||
|
||||
product, category = parts
|
||||
product = product.strip()
|
||||
category = category.strip()
|
||||
|
||||
# 验证类别是否在预定义类别中
|
||||
if category not in categories:
|
||||
print(f"警告：未知类别 '{category}'，将使用'其他'")
|
||||
category = "其他"
|
||||
|
||||
# 确保产品名称完整
|
||||
if product not in products:
|
||||
print(f"警告：产品名称不匹配 '{product}'，将使用原始名称")
|
||||
product = next((p for p in products if p in product or product in p), product)
|
||||
|
||||
category_mapping[product] = category
|
||||
|
||||
if not category_mapping:
|
||||
print("警告：没有成功解析任何分类结果")
|
||||
return {}
|
||||
|
||||
return category_mapping
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"产品分类失败: {str(e)}")
|
||||
return {}
|
||||
|
||||
def process_company_products():
|
||||
"""处理每个企业的产品并保存分类结果"""
|
||||
try:
|
||||
# 连接数据库
|
||||
engine = connect_to_db()
|
||||
|
||||
# 创建类别表
|
||||
create_category_table(engine)
|
||||
|
||||
# 加载类别列表
|
||||
categories = load_category_list()
|
||||
|
||||
while True:
|
||||
# 获取一个未处理企业的所有产品
|
||||
with engine.connect() as conn:
|
||||
query = """
|
||||
SELECT DISTINCT m.stock_code, m.stock_name, m.product_name
|
||||
FROM gp_main_business m
|
||||
LEFT JOIN gp_product_category c
|
||||
ON m.stock_code = c.stock_code
|
||||
AND m.product_name = c.product_name
|
||||
WHERE c.id IS NULL
|
||||
AND m.product_name IS NOT NULL
|
||||
AND m.product_name != ''
|
||||
GROUP BY m.stock_code, m.stock_name
|
||||
LIMIT 1
|
||||
"""
|
||||
company = pd.read_sql(query, conn)
|
||||
|
||||
if company.empty:
|
||||
print("所有产品都已处理完成！")
|
||||
break
|
||||
|
||||
stock_code = company['stock_code'].iloc[0]
|
||||
stock_name = company['stock_name'].iloc[0]
|
||||
|
||||
# 获取该企业的所有未处理产品
|
||||
query = f"""
|
||||
SELECT DISTINCT product_name
|
||||
FROM gp_main_business
|
||||
WHERE stock_code = '{stock_code}'
|
||||
AND product_name NOT IN (
|
||||
SELECT product_name
|
||||
FROM gp_product_category
|
||||
WHERE stock_code = '{stock_code}'
|
||||
)
|
||||
AND product_name IS NOT NULL
|
||||
AND product_name != ''
|
||||
"""
|
||||
products = pd.read_sql(query, conn)
|
||||
|
||||
if products.empty:
|
||||
continue
|
||||
|
||||
product_list = products['product_name'].tolist()
|
||||
|
||||
# 调用API进行分类
|
||||
print(f"\n处理企业：{stock_name} ({stock_code})")
|
||||
print(f"产品数量：{len(product_list)}")
|
||||
print("\n产品列表：")
|
||||
for product in product_list:
|
||||
print(f"- {product}")
|
||||
|
||||
category_mapping = categorize_company_products(stock_name, product_list, categories)
|
||||
|
||||
if not category_mapping:
|
||||
print("\n警告：未能获取分类结果，将使用'其他'类别")
|
||||
category_mapping = {product: "其他" for product in product_list}
|
||||
|
||||
# 显示分类结果
|
||||
print("\n分类结果：")
|
||||
for product, category in category_mapping.items():
|
||||
print(f"{product} -> {category}")
|
||||
|
||||
# 自动保存分类结果
|
||||
with engine.connect() as conn:
|
||||
for product, category in category_mapping.items():
|
||||
insert_query = """
|
||||
INSERT INTO gp_product_category
|
||||
(category_name, product_name, stock_code, stock_name)
|
||||
VALUES (:category_name, :product_name, :stock_code, :stock_name)
|
||||
"""
|
||||
conn.execute(text(insert_query),
|
||||
{"category_name": category,
|
||||
"product_name": product,
|
||||
"stock_code": stock_code,
|
||||
"stock_name": stock_name})
|
||||
conn.commit()
|
||||
print(f"已保存 {stock_name} 的分类结果")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理产品失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def update_product_categories_by_rules(category_rules: List[Dict[str, str]]):
|
||||
"""
|
||||
根据给定的规则批量更新产品分类
|
||||
|
||||
Args:
|
||||
category_rules: 分类规则列表，每个规则是一个字典，格式为：
|
||||
{
|
||||
"category": "类别名称",
|
||||
"products": "产品1,产品2,产品3,..."
|
||||
}
|
||||
"""
|
||||
try:
|
||||
# 连接数据库
|
||||
engine = connect_to_db()
|
||||
|
||||
with engine.connect() as conn:
|
||||
for rule in category_rules:
|
||||
category = rule["category"]
|
||||
products = [p.strip() for p in rule["products"].split(",")]
|
||||
|
||||
# 构建 IN 查询条件
|
||||
products_str = "','".join(products)
|
||||
update_query = f"""
|
||||
UPDATE gp_product_category
|
||||
SET category_name = '{category}'
|
||||
WHERE product_name IN ('{products_str}')
|
||||
"""
|
||||
|
||||
# 执行更新
|
||||
result = conn.execute(text(update_query))
|
||||
conn.commit()
|
||||
|
||||
print(f"已更新 {result.rowcount} 条记录到类别 '{category}'")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"批量更新产品分类失败: {str(e)}")
|
||||
raise
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 设置日志级别
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
# 示例：使用规则更新分类
|
||||
category_rules = [
|
||||
{
|
||||
"category": "其他汽车零部件",
|
||||
"num": 25,
|
||||
"products": "其他汽车零部件,汽车零部件制造,汽车检具,工装模具及汽车零部件,汽车应用类,缓速器,汽车件,汽车产品,汽车类零部件,汽车及工业产品,汽车部品,汽车、轨道交通构件,功能件,冲压业务,汽车零部件收入"
|
||||
}
|
||||
]
|
||||
|
||||
# 更新分类
|
||||
# update_product_categories_by_rules(category_rules)
|
||||
|
||||
# 处理产品
|
||||
process_company_products()
|
||||
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|
|
@ -0,0 +1,440 @@
|
|||
symbol,take_profit_pct,trades,profit
|
||||
SH600522,0.23999999999999994,6,-43674.7795507625
|
||||
SZ002340,0.23999999999999994,3,-245180.90374090202
|
||||
SZ000733,0.23999999999999994,3,-347623.76533204206
|
||||
SH601615,0.23999999999999994,7,-1615082.4259089434
|
||||
SH688005,0.23999999999999994,10,-834109.4481901171
|
||||
SZ002180,0.23999999999999994,4,-176132.27621242419
|
||||
SZ000723,0.23999999999999994,4,-752573.2644316294
|
||||
SZ002384,0.23999999999999994,8,1473846.791206649
|
||||
SH600885,0.23999999999999994,4,-539707.3907344582
|
||||
SZ000009,0.23999999999999994,9,-127072.46795410744
|
||||
SZ300285,0.23999999999999994,2,-436343.7380088445
|
||||
SH605358,0.23999999999999994,4,-2129677.3155513634
|
||||
SH601555,0.23999999999999994,2,7402.496042942948
|
||||
SH601117,0.23999999999999994,7,-1428770.9231015937
|
||||
SZ002385,0.23999999999999994,4,-1043581.0961502332
|
||||
SH688099,0.23999999999999994,5,-1818086.9533308106
|
||||
SH600884,0.23999999999999994,6,-1746854.5990375176
|
||||
SH600298,0.23999999999999994,2,133811.5536163634
|
||||
SH600399,0.23999999999999994,9,-648985.3287744978
|
||||
SZ002028,0.23999999999999994,8,916571.1877096295
|
||||
SH603613,0.23999999999999994,8,-2208393.878560411
|
||||
SZ300037,0.23999999999999994,6,-1159365.1137836596
|
||||
SH600765,0.23999999999999994,9,710357.9660585419
|
||||
SH600256,0.23999999999999994,1,-3968.097514121813
|
||||
SH600487,0.23999999999999994,12,1018112.6903361101
|
||||
SH600563,0.23999999999999994,7,-930007.7334416698
|
||||
SH600754,0.23999999999999994,11,-95179.30858576507
|
||||
SZ002185,0.23999999999999994,1,-174280.51580036187
|
||||
SZ300363,0.23999999999999994,4,-628917.4695524463
|
||||
SH601636,0.23999999999999994,1,-315662.8945801073
|
||||
SZ300751,0.23999999999999994,6,-92671.31444464304
|
||||
SZ300088,0.23999999999999994,5,-310461.5121726807
|
||||
SH603589,0.23999999999999994,6,153998.43266561982
|
||||
SZ000630,0.23999999999999994,5,-229648.1420479194
|
||||
SZ000988,0.23999999999999994,7,666193.2762578272
|
||||
SZ002268,0.23999999999999994,18,-940833.9327761073
|
||||
SZ002353,0.23999999999999994,5,-1261328.7973129307
|
||||
SH601689,0.23999999999999994,7,2012876.7123287672
|
||||
SH601168,0.23999999999999994,7,-261377.75598651802
|
||||
SZ300763,0.23999999999999994,11,-2106435.2415100574
|
||||
SH600141,0.23999999999999994,9,-1878681.1836915673
|
||||
SH600392,0.23999999999999994,6,-1829445.0393971722
|
||||
SZ000932,0.23999999999999994,2,-6414.174338192999
|
||||
SH603267,0.23999999999999994,5,-1418059.5726983906
|
||||
SZ300699,0.23999999999999994,7,-1457697.693979897
|
||||
SH600233,0.23999999999999994,1,-15445.359583114072
|
||||
SZ000728,0.23999999999999994,2,-224139.38823790126
|
||||
SH600516,0.23999999999999994,3,-272761.19589548017
|
||||
SZ300058,0.23999999999999994,8,1005779.3872667413
|
||||
SH600486,0.23999999999999994,3,-562409.3238461512
|
||||
SZ002797,0.23999999999999994,3,-148143.19422076663
|
||||
SH603456,0.23999999999999994,9,14617.140872640477
|
||||
SH603077,0.23999999999999994,6,-786827.0450873834
|
||||
SZ002138,0.23999999999999994,6,-147493.23408601043
|
||||
SH600862,0.23999999999999994,6,-341473.4067102635
|
||||
SZ300146,0.23999999999999994,2,-122617.02389449952
|
||||
SZ300223,0.23999999999999994,6,-2164086.3447719263
|
||||
SZ300136,0.23999999999999994,2,-661995.9748293064
|
||||
SH600188,0.23999999999999994,1,56099.89976612109
|
||||
SH600418,0.23999999999999994,13,1396486.0506442387
|
||||
SH600705,0.23999999999999994,4,-226699.74806433936
|
||||
SZ000960,0.23999999999999994,6,-1576698.2539201605
|
||||
SZ300308,0.23999999999999994,6,253170.8435795936
|
||||
SH688065,0.23999999999999994,5,-793622.7606358915
|
||||
SZ300253,0.23999999999999994,7,-1113143.3516747663
|
||||
SZ002080,0.23999999999999994,6,-1541985.6482786287
|
||||
SZ000998,0.23999999999999994,3,-108698.54774709925
|
||||
SH688536,0.23999999999999994,8,-2023634.1965992777
|
||||
SZ002092,0.23999999999999994,2,-200955.6268795189
|
||||
SH600875,0.23999999999999994,5,1133313.3754040324
|
||||
SZ000547,0.23999999999999994,5,-450690.60968294216
|
||||
SZ002409,0.23999999999999994,13,270988.3208274777
|
||||
SH600482,0.23999999999999994,3,111240.37890277478
|
||||
SH600521,0.23999999999999994,9,86256.12749127208
|
||||
SZ000807,0.23999999999999994,9,1055574.6051145988
|
||||
SH600733,0.23999999999999994,2,-634395.4619477554
|
||||
SH600988,0.23999999999999994,7,-433270.39680435037
|
||||
SZ000039,0.23999999999999994,2,-98696.25147860739
|
||||
SZ300296,0.23999999999999994,2,-152174.19326647877
|
||||
SZ002439,0.23999999999999994,6,234416.22751562507
|
||||
SZ002212,0.23999999999999994,1,-751249.3920035734
|
||||
SZ300418,0.23999999999999994,7,1267089.373918294
|
||||
SZ002078,0.23999999999999994,3,226101.00604063214
|
||||
SZ002223,0.23999999999999994,7,524930.0906560356
|
||||
SH603893,0.23999999999999994,7,-2988859.4851462715
|
||||
SZ002191,0.23999999999999994,5,-597532.4663337575
|
||||
SZ002030,0.23999999999999994,5,-1099278.1718986314
|
||||
SH600699,0.23999999999999994,7,-1128495.6012393427
|
||||
SZ002603,0.23999999999999994,3,1288123.2876712326
|
||||
SH600160,0.23999999999999994,3,220775.38101632416
|
||||
SZ002444,0.23999999999999994,8,-519355.2882119186
|
||||
SH601198,0.23999999999999994,3,135985.73668344688
|
||||
SZ300724,0.23999999999999994,12,-61472.84610050509
|
||||
SH600637,0.23999999999999994,1,11196.223620881161
|
||||
SZ300474,0.23999999999999994,11,661454.4890616687
|
||||
SZ002465,0.23999999999999994,1,1331.3315903262192
|
||||
SH603605,0.23999999999999994,3,-196393.47310201934
|
||||
SH600497,0.23999999999999994,6,-49359.44538289862
|
||||
SZ300383,0.23999999999999994,3,-1175522.8085433359
|
||||
SZ002507,0.23999999999999994,6,-1089443.7609141374
|
||||
SZ002557,0.23999999999999994,8,-1163436.976238242
|
||||
SH600549,0.23999999999999994,9,-645796.9774843798
|
||||
SZ000739,0.23999999999999994,6,-1437007.8107000138
|
||||
SH600529,0.23999999999999994,8,198301.5248332062
|
||||
SH600704,0.23999999999999994,1,-91194.22216194228
|
||||
SH603290,0.23999999999999994,10,-907704.3653794175
|
||||
SZ002508,0.23999999999999994,6,-195018.33632810984
|
||||
SH600038,0.23999999999999994,5,101680.88786086303
|
||||
SZ002739,0.23999999999999994,6,313580.80695160123
|
||||
SH603198,0.23999999999999994,9,1474984.254470205
|
||||
SZ002156,0.23999999999999994,9,-411596.6873152375
|
||||
SZ000830,0.23999999999999994,4,-112833.66581817532
|
||||
SH600201,0.23999999999999994,1,-356130.5664030538
|
||||
SH600153,0.23999999999999994,1,128031.92337187794
|
||||
SH600369,0.23999999999999994,2,-46748.998771027036
|
||||
SZ000983,0.23999999999999994,4,737839.4078219854
|
||||
SH600642,0.23999999999999994,3,104425.72831278076
|
||||
SZ002299,0.23999999999999994,3,-220903.85156744884
|
||||
SZ000629,0.23999999999999994,2,15828.143060004426
|
||||
SZ002131,0.23999999999999994,5,648355.9816976431
|
||||
SZ000738,0.23999999999999994,6,-613838.8905420647
|
||||
SH603127,0.23999999999999994,8,-1854538.8303613751
|
||||
SH601016,0.23999999999999994,2,-231764.87279666594
|
||||
SZ000825,0.23999999999999994,1,-299907.85894576914
|
||||
SZ000623,0.23999999999999994,1,13621.199038528765
|
||||
SH600879,0.23999999999999994,1,214575.34246575335
|
||||
SH600859,0.23999999999999994,9,602461.4726134406
|
||||
SZ000519,0.23999999999999994,8,-5882.184342348366
|
||||
SH600039,0.23999999999999994,1,135844.80806376415
|
||||
SZ300618,0.23999999999999994,2,-575673.9780720558
|
||||
SH688521,0.23999999999999994,9,167754.32084751024
|
||||
SZ002273,0.23999999999999994,6,-733838.4543750377
|
||||
SH603444,0.23999999999999994,4,473382.7845193192
|
||||
SH600166,0.23999999999999994,6,1132664.3830453216
|
||||
SZ002422,0.23999999999999994,2,439890.4109589041
|
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SH600299,0.23999999999999994,5,16935.144869784082
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SZ002948,0.23999999999999994,1,-130997.34930495129
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SZ000156,0.23999999999999994,3,98354.13973553944
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SZ002375,0.23999999999999994,5,-503704.1723574602
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SH600871,0.23999999999999994,1,212465.75342465754
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SZ300869,0.23999999999999994,1,-315929.3763211771
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SZ002302,0.23999999999999994,4,-345674.57053220057
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SH601228,0.23999999999999994,3,-204746.15346936608
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SH600639,0.23999999999999994,5,-874267.2713147431
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SH601298,0.23999999999999994,1,-5273.972602739588
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SH603379,0.23999999999999994,7,696080.2979047559
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SZ002945,0.23999999999999994,8,-208922.8332225199
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SZ002653,0.23999999999999994,8,1563206.2970911385
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SZ001872,0.23999999999999994,1,-181946.14910747032
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SH600823,0.23999999999999994,4,-1012634.7666783128
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SZ300741,0.23999999999999994,1,-175244.41893296523
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SH603056,0.23999999999999994,1,434712.32876712317
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SH601869,0.23999999999999994,5,155111.91156326624
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SH603786,0.23999999999999994,7,627636.9514816818
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SH603708,0.23999999999999994,2,137353.7448811492
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SZ000553,0.23999999999999994,5,-578522.740003638
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SH600350,0.23999999999999994,1,42420.46753100173
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SH600657,0.23999999999999994,2,280910.570705021
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SH603515,0.23999999999999994,2,-89726.10472652467
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SH603355,0.23999999999999994,6,55911.0146043969
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SH603719,0.23999999999999994,5,841904.7249719887
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SH601003,0.23999999999999994,3,-242322.08029099827
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SZ002946,0.23999999999999994,6,-166158.71718684668
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SH600917,0.23999999999999994,5,-55977.61636400802
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SZ002901,0.23999999999999994,7,211801.96585162956
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SH600903,0.23999999999999994,6,-434556.0797178237
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SH600956,0.23999999999999994,4,-1181595.5901998247
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SH601187,0.23999999999999994,1,-40634.25771244019
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SH603983,0.23999999999999994,3,-871427.2107680459
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||||
|
|
|
@ -0,0 +1,440 @@
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|||
symbol,take_profit_pct,trades,profit
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SH600522,0.24999999999999994,6,-34174.77955076238
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SZ002340,0.24999999999999994,3,-235680.90374090194
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SZ000733,0.24999999999999994,3,-347623.76533204206
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SH601615,0.24999999999999994,7,-1615082.4259089434
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SH688005,0.24999999999999994,10,-797164.2427106651
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SZ002180,0.24999999999999994,4,-166632.27621242424
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SZ000723,0.24999999999999994,4,-752573.2644316294
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SH600885,0.24999999999999994,4,-539707.3907344582
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SZ300285,0.24999999999999994,2,-436343.7380088445
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SH605358,0.24999999999999994,4,-2129677.3155513634
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SH601555,0.24999999999999994,2,7402.496042942948
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SH601117,0.24999999999999994,7,-1419270.9231015937
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SZ002385,0.24999999999999994,4,-1043581.0961502332
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SH688099,0.24999999999999994,5,-1818086.9533308106
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SH600884,0.24999999999999994,6,-1746854.5990375176
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SH600298,0.24999999999999994,2,142927.99197252787
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SH600399,0.24999999999999994,9,-610985.3287744983
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SZ002028,0.24999999999999994,7,953995.8452438759
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SH603613,0.24999999999999994,6,-2402518.661542465
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SZ300037,0.24999999999999994,6,-1150056.8946055775
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SH600765,0.24999999999999994,8,759494.9523599121
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SH600256,0.24999999999999994,1,-3968.097514121813
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SH600487,0.24999999999999994,12,1073962.0054046034
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SH600563,0.24999999999999994,7,-901507.7334416701
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SH600754,0.24999999999999994,11,-338478.9869903226
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SH601636,0.24999999999999994,1,-315662.8945801073
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SZ300751,0.24999999999999994,5,-300708.7505553808
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SH600392,0.24999999999999994,6,-1819945.0393971722
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SH603267,0.24999999999999994,5,-1418059.5726983906
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SH600233,0.24999999999999994,1,-15445.359583114072
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SZ000728,0.24999999999999994,2,-224139.38823790126
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SH600516,0.24999999999999994,3,-272761.19589548017
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SZ300058,0.24999999999999994,10,1071991.716033864
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SH600486,0.24999999999999994,3,-562409.3238461512
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SZ002797,0.24999999999999994,3,-148143.19422076663
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SH603456,0.24999999999999994,9,52329.46963976376
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SH603077,0.24999999999999994,6,-786827.0450873834
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SZ002138,0.24999999999999994,6,-137993.2340860105
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SH600862,0.24999999999999994,6,-341473.4067102635
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SZ300223,0.24999999999999994,6,-2154586.3447719263
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SZ300136,0.24999999999999994,2,-661995.9748293064
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SH600188,0.24999999999999994,1,56099.89976612109
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SH600418,0.24999999999999994,13,1500890.1602332797
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SH600705,0.24999999999999994,4,-217199.7480643392
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SZ300308,0.24999999999999994,6,272074.95316863485
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SH688065,0.24999999999999994,5,-793622.7606358915
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SZ300253,0.24999999999999994,8,-1066602.2557843556
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SZ000998,0.24999999999999994,3,-108698.54774709925
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SH688536,0.24999999999999994,8,-2015093.1007088665
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SZ002092,0.24999999999999994,2,-200955.6268795189
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SH600875,0.24999999999999994,5,1180813.3754040326
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SZ000547,0.24999999999999994,5,-431690.60968294204
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SZ002409,0.24999999999999994,12,355241.7454850115
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SH600482,0.24999999999999994,3,120740.37890277487
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SH600521,0.24999999999999994,8,45928.473614992196
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SZ000807,0.24999999999999994,9,1083786.9338817222
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SH600733,0.24999999999999994,2,-634395.4619477554
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SH600988,0.24999999999999994,7,-414270.39680435054
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SZ300296,0.24999999999999994,2,-152174.19326647877
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SZ002223,0.24999999999999994,7,572430.0906560353
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SH603893,0.24999999999999994,7,-2988859.4851462715
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SZ002030,0.24999999999999994,5,-1099278.1718986314
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SH600699,0.24999999999999994,7,-1109495.6012393425
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SZ002603,0.24999999999999994,4,1344931.506849315
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SH600160,0.24999999999999994,3,219727.43581084468
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SZ002444,0.24999999999999994,9,-500547.0690338364
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SH601198,0.24999999999999994,3,135985.73668344688
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SZ300724,0.24999999999999994,12,22780.57855702832
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SH600637,0.24999999999999994,1,11196.223620881161
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SZ300474,0.24999999999999994,12,752810.653445231
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SZ002465,0.24999999999999994,1,1331.3315903262192
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SH603605,0.24999999999999994,3,-196393.47310201934
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SH600497,0.24999999999999994,6,-40914.23990344644
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SZ300383,0.24999999999999994,3,-1166022.8085433359
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SZ002507,0.24999999999999994,6,-1089443.7609141374
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SZ002557,0.24999999999999994,8,-1163436.976238242
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SH600549,0.24999999999999994,9,-608468.210361092
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SZ000739,0.24999999999999994,6,-1437007.8107000138
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SH600529,0.24999999999999994,7,366691.8039537818
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SH600704,0.24999999999999994,1,-91194.22216194228
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SH603290,0.24999999999999994,10,-880834.5023657188
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SZ002508,0.24999999999999994,6,-694048.6864327432
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SH600038,0.24999999999999994,4,-28103.386213273792
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SH603198,0.24999999999999994,9,1541388.3640592464
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SH600153,0.24999999999999994,1,128031.92337187794
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SH600369,0.24999999999999994,2,-46748.998771027036
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SZ000983,0.24999999999999994,5,773825.7091918485
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SZ000629,0.24999999999999994,2,25328.143060004542
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SZ002131,0.24999999999999994,5,495469.87147145625
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SH603127,0.24999999999999994,9,-1835634.720772334
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SH601016,0.24999999999999994,2,-231764.87279666594
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SZ000825,0.24999999999999994,1,-299907.85894576914
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SZ000623,0.24999999999999994,1,13621.199038528765
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SH600879,0.24999999999999994,1,223212.3287671232
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SH600859,0.24999999999999994,9,659461.4726134406
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SH600166,0.24999999999999994,7,939046.4338108333
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SH600803,0.24999999999999994,7,-156930.6654421079
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SH600271,0.24999999999999994,2,-145996.14265563383
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SH600728,0.24999999999999994,5,-555760.1774863503
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SH601866,0.24999999999999994,2,-259384.2402137494
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SH603568,0.24999999999999994,6,-521884.09186301986
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SH603858,0.24999999999999994,10,18509.764629653917
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SH600970,0.24999999999999994,6,-7702.920531236377
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SH600155,0.24999999999999994,3,69311.26906736357
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SZ002128,0.24999999999999994,5,-333992.11703894834
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SZ000690,0.24999999999999994,4,677617.5468495472
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SH600060,0.24999999999999994,4,1015986.3013698631
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SH600282,0.24999999999999994,3,463386.1709067187
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||||
SH600517,0.24999999999999994,4,-467707.47385875665
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||||
SH600985,0.24999999999999994,8,122881.51089609615
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||||
SH600498,0.24999999999999994,2,-586332.6304971089
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||||
SZ300026,0.24999999999999994,12,297560.29389737145
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||||
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SZ000987,0.24999999999999994,2,-195615.5682460863
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SH600737,0.24999999999999994,2,291948.1988548218
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SH600908,0.24999999999999994,1,111991.45676830162
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SH603883,0.24999999999999994,7,-416010.0682682226
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SH603228,0.24999999999999994,6,-1239415.084637275
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SZ000883,0.24999999999999994,2,-257210.44089406607
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SH600811,0.24999999999999994,3,-600240.107391387
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SH601106,0.24999999999999994,1,-116778.11648598696
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SH600566,0.24999999999999994,1,236253.42465753425
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SH600158,0.24999999999999994,4,179161.94768058794
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SH600435,0.24999999999999994,2,119864.75585890887
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SH601000,0.24999999999999994,1,224458.90410958897
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SZ000877,0.24999999999999994,2,-179201.93804078037
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SH600906,0.24999999999999994,2,-99517.17386772847
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SZ002429,0.24999999999999994,2,458123.2876712327
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SH688088,0.24999999999999994,8,-351503.89567235403
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SH600398,0.24999999999999994,4,836344.9734077489
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SH601200,0.24999999999999994,1,-77619.81786755417
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SZ002081,0.24999999999999994,1,-66738.88043294422
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SH600373,0.24999999999999994,2,234201.10672548576
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SH600037,0.24999999999999994,4,-952485.6179281805
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SZ002867,0.24999999999999994,2,299338.1283725477
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SH600376,0.24999999999999994,11,-447637.4098378171
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SZ001914,0.24999999999999994,8,-558313.5825287102
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SZ002085,0.24999999999999994,7,280103.9815911473
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SH600167,0.24999999999999994,3,-220081.4814459467
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SZ000959,0.24999999999999994,3,-449324.7652787983
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SZ000528,0.24999999999999994,1,-22071.621331391136
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SZ002416,0.24999999999999994,7,4540.1250972535345
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SH600195,0.24999999999999994,4,-238255.6153599094
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SH600329,0.24999999999999994,5,1147609.589041096
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SH600259,0.24999999999999994,5,-1403866.8766314809
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SZ002434,0.24999999999999994,8,-178091.1886901378
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SH688321,0.24999999999999994,2,-632794.5804530663
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SH601718,0.24999999999999994,3,-156809.58588760858
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SH600026,0.24999999999999994,1,236157.5342465753
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SH603000,0.24999999999999994,9,3476390.410958904
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SZ002038,0.24999999999999994,3,-164654.91405582882
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SH600120,0.24999999999999994,3,-284947.83479280164
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SZ002242,0.24999999999999994,2,-180779.46358353007
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SZ001965,0.24999999999999994,2,134344.25907719668
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SH601608,0.24999999999999994,2,-241493.13988022093
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SZ300133,0.24999999999999994,8,1201786.6994961689
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SH600126,0.24999999999999994,1,-26813.670831338677
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SZ002705,0.24999999999999994,4,-265973.0959059878
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SZ000402,0.24999999999999994,3,-464993.35730929306
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SZ000158,0.24999999999999994,8,-1346804.5059729
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SZ002390,0.24999999999999994,7,-1748955.1016589077
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SZ002387,0.24999999999999994,7,-3666.93826962628
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SH601611,0.24999999999999994,2,-117538.91958813404
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SZ000758,0.24999999999999994,4,724588.5163101249
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SZ000415,0.24999999999999994,5,66417.40556438596
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SH600307,0.24999999999999994,1,-42153.20734215546
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SH601991,0.24999999999999994,6,854987.9923127802
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SH601598,0.24999999999999994,1,280627.5470204124
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SH603650,0.24999999999999994,7,-875495.9065298475
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SH600959,0.24999999999999994,1,-140231.6216793704
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SH688289,0.24999999999999994,1,-313049.8565376556
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SH600582,0.24999999999999994,3,281923.3856419214
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SH600643,0.24999999999999994,1,21366.661703394966
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SZ000563,0.24999999999999994,2,2317.887649553966
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SZ300257,0.24999999999999994,6,-848614.5105208516
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SH601958,0.24999999999999994,4,1602184.9315068491
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SH600266,0.24999999999999994,7,628144.555242081
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SZ000967,0.24999999999999994,1,-29095.75883393512
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SZ002399,0.24999999999999994,4,-825385.5926764316
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SH600056,0.24999999999999994,1,2151.1847995192393
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SZ002458,0.24999999999999994,4,1155184.9315068494
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SH601860,0.24999999999999994,1,-52849.00643384521
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SZ002563,0.24999999999999994,1,-38892.42318705994
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SZ003022,0.24999999999999994,14,-279228.91625846596
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SH601928,0.24999999999999994,3,1123253.424657534
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SH600928,0.24999999999999994,1,-108188.82970308381
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SH600729,0.24999999999999994,3,342042.77487486054
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SZ002423,0.24999999999999994,5,89300.1527161006
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SH600968,0.24999999999999994,3,396679.0905988618
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SZ000937,0.24999999999999994,2,469342.4657534247
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SH600556,0.24999999999999994,11,-696333.9431495302
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SZ003035,0.24999999999999994,4,-620737.3731457555
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SH600649,0.24999999999999994,2,-144447.9980746712
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SH600787,0.24999999999999994,3,389729.1990012821
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SZ000028,0.24999999999999994,4,918356.1643835617
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SH600095,0.24999999999999994,12,1375702.620969849
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SH600776,0.24999999999999994,6,-180806.48017921177
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SH600339,0.24999999999999994,4,1157006.8493150685
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SH603317,0.24999999999999994,7,538135.2379731609
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SH600648,0.24999999999999994,4,-320142.22412696923
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SH600623,0.24999999999999994,2,-315058.51019633736
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SZ000062,0.24999999999999994,5,210154.69743051793
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SH600835,0.24999999999999994,5,-694433.3623891072
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SZ002815,0.24999999999999994,3,-249404.85965067826
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SZ002424,0.24999999999999994,4,948092.9928223639
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SH600377,0.24999999999999994,1,55279.60882104716
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SH601139,0.24999999999999994,1,-85493.94819957169
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SH600299,0.24999999999999994,5,25763.911993071888
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SZ002948,0.24999999999999994,1,-130997.34930495129
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SZ000156,0.24999999999999994,3,107854.13973553933
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SZ002375,0.24999999999999994,5,-503704.1723574602
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SH600871,0.24999999999999994,1,221965.75342465757
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SZ300869,0.24999999999999994,1,-315929.3763211771
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SZ002302,0.24999999999999994,4,-345674.57053220057
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SH601228,0.24999999999999994,3,-204746.15346936608
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SH600639,0.24999999999999994,5,-874267.2713147431
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SH601298,0.24999999999999994,1,-5273.972602739588
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SH603379,0.24999999999999994,6,723333.72256229
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SZ002945,0.24999999999999994,8,-199422.83322251984
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SZ002653,0.24999999999999994,9,1499706.7853795919
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SZ001872,0.24999999999999994,1,-181946.14910747032
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SH600823,0.24999999999999994,4,-1012634.7666783128
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SZ300741,0.24999999999999994,1,-175244.41893296523
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SH603056,0.24999999999999994,1,453712.32876712317
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SH601869,0.24999999999999994,5,174111.91156326624
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SH603786,0.24999999999999994,7,674849.280248805
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SH603708,0.24999999999999994,2,137353.7448811492
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SZ000553,0.24999999999999994,5,-578522.740003638
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SH600350,0.24999999999999994,1,42420.46753100173
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SH600657,0.24999999999999994,2,290410.5707050209
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SH603515,0.24999999999999994,2,-89726.10472652467
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SH603355,0.24999999999999994,6,74911.01460439702
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SH603719,0.24999999999999994,5,898233.4920952762
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SH601003,0.24999999999999994,3,-242322.08029099827
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SZ002946,0.24999999999999994,6,-147542.2788306823
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SH600917,0.24999999999999994,5,-46477.61636400805
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SZ002901,0.24999999999999994,7,240301.9658516298
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SH600903,0.24999999999999994,6,-425056.07971782354
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SH600956,0.24999999999999994,4,-1181595.5901998247
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SH601187,0.24999999999999994,1,-40634.25771244019
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||||
SH603983,0.24999999999999994,3,-861927.2107680459
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||||
|
|
|
@ -0,0 +1,440 @@
|
|||
symbol,take_profit_pct,trades,profit
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SH600522,0.25999999999999995,6,-24674.779550762265
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SZ002340,0.25999999999999995,3,-226180.9037409021
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SZ300285,0.25999999999999995,2,-436343.7380088445
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SH605358,0.25999999999999995,4,-2129677.3155513634
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SH600884,0.25999999999999995,6,-1746854.5990375176
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SH600298,0.25999999999999995,2,152427.99197252796
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SH600399,0.25999999999999995,9,-750021.593759173
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SZ002028,0.25999999999999995,7,991995.8452438756
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SZ300037,0.25999999999999995,6,-1140556.8946055772
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SH600765,0.25999999999999995,9,816303.1715379943
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SH601689,0.25999999999999995,6,1925082.1917808221
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SH600141,0.25999999999999995,9,-1859777.074102526
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SH600392,0.25999999999999995,6,-1810445.0393971722
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SZ000932,0.25999999999999995,2,12202.264017971233
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SH603267,0.25999999999999995,5,-1418059.5726983906
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SH600233,0.25999999999999995,1,-15445.359583114072
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SZ000728,0.25999999999999995,2,-224139.38823790126
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SH600516,0.25999999999999995,3,-272761.19589548017
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SZ300058,0.25999999999999995,10,1137916.3735681106
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SH600486,0.25999999999999995,3,-562409.3238461512
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SH603456,0.25999999999999995,9,90137.6888178456
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SH603077,0.25999999999999995,6,-786827.0450873834
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SZ002138,0.25999999999999995,6,-151737.9149370744
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SH600862,0.25999999999999995,6,-341473.4067102635
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SZ300223,0.25999999999999995,6,-2145086.3447719263
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SZ300136,0.25999999999999995,2,-661995.9748293064
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SH600188,0.25999999999999995,1,56099.89976612109
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SH600418,0.25999999999999995,13,1605294.2698223214
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SH600705,0.25999999999999995,4,-207699.74806433925
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SZ000960,0.25999999999999995,6,-1557698.2539201607
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SZ002085,0.25999999999999995,7,96959.94957857783
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SH600167,0.25999999999999995,3,-220081.4814459467
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SZ000559,0.25999999999999995,3,-500042.5115762846
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SZ000959,0.25999999999999995,3,-449324.7652787983
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SZ000528,0.25999999999999995,1,-22071.621331391136
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SZ002416,0.25999999999999995,7,23540.125097253767
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SH600195,0.25999999999999995,4,-238255.6153599094
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SH600329,0.25999999999999995,5,1193767.1232876715
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SH600259,0.25999999999999995,5,-1403866.8766314809
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SZ002434,0.25999999999999995,8,-301596.337877783
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SH688321,0.25999999999999995,2,-632794.5804530663
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SH601718,0.25999999999999995,3,-156809.58588760858
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SH600026,0.25999999999999995,1,245561.6438356164
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SH603000,0.25999999999999995,8,3618602.739726027
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SZ002038,0.25999999999999995,3,-164654.91405582882
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SH600120,0.25999999999999995,3,-284947.83479280164
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SZ002242,0.25999999999999995,2,-180779.46358353007
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SZ001965,0.25999999999999995,2,134344.25907719668
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SH601608,0.25999999999999995,2,-241493.13988022093
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SZ300133,0.25999999999999995,8,1257731.904975621
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SH600126,0.25999999999999995,1,-26813.670831338677
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SZ002705,0.25999999999999995,4,-256568.98631694663
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SZ000402,0.25999999999999995,3,-464993.35730929306
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SZ000158,0.25999999999999995,8,-1364289.591764662
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SZ002390,0.25999999999999995,7,-1739455.1016589073
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SZ002387,0.25999999999999995,7,5833.061730373778
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SH601611,0.25999999999999995,2,-117538.91958813404
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SZ000758,0.25999999999999995,4,753088.5163101249
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SZ000415,0.25999999999999995,5,66417.40556438596
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SH600307,0.25999999999999995,1,-42153.20734215546
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SH601991,0.25999999999999995,6,883200.3210799034
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SH601598,0.25999999999999995,1,280627.5470204124
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SH603650,0.25999999999999995,7,-846995.9065298478
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SH600959,0.25999999999999995,1,-140231.6216793704
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SH688289,0.25999999999999995,1,-313049.8565376556
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SH600582,0.25999999999999995,3,281923.3856419214
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SH600643,0.25999999999999995,1,21366.661703394966
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SZ000563,0.25999999999999995,2,2317.887649553966
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SZ300257,0.25999999999999995,6,-848614.5105208516
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SH601958,0.25999999999999995,4,1668684.9315068491
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SH600266,0.25999999999999995,7,656644.555242081
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SZ000967,0.25999999999999995,1,-29095.75883393512
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SZ002399,0.25999999999999995,4,-825385.5926764316
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SH600056,0.25999999999999995,1,2151.1847995192393
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SZ002458,0.25999999999999995,4,1202684.9315068494
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SH601860,0.25999999999999995,1,-52849.00643384521
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||||
SZ002563,0.25999999999999995,1,-38892.42318705994
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SZ003022,0.25999999999999995,13,-194304.2587242192
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SH601928,0.25999999999999995,3,1169602.7397260275
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SH600928,0.25999999999999995,1,-108188.82970308381
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SH600729,0.25999999999999995,3,360946.8844639017
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SZ002423,0.25999999999999995,5,-125215.6089607437
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SH600968,0.25999999999999995,3,406179.0905988617
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SZ000937,0.25999999999999995,2,488150.68493150675
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SH600556,0.25999999999999995,10,-810341.9637847014
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SZ003035,0.25999999999999995,4,-620737.3731457555
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SH600649,0.25999999999999995,2,-144447.9980746712
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SH600787,0.25999999999999995,2,405852.48667251505
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SZ000028,0.25999999999999995,4,956356.1643835617
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SH600095,0.25999999999999995,11,1126670.567329784
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SH600776,0.25999999999999995,6,-152306.480179212
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SZ000031,0.25999999999999995,6,42142.77408537236
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SH600339,0.25999999999999995,4,1204506.8493150682
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SH600006,0.25999999999999995,7,-1592198.93277586
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SH603317,0.25999999999999995,7,576135.2379731614
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SH600648,0.25999999999999995,4,-320142.22412696923
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SH600623,0.25999999999999995,2,-315058.51019633736
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SZ000062,0.25999999999999995,5,77424.99648156941
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SH600835,0.25999999999999995,5,-684933.362389107
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SZ002815,0.25999999999999995,3,-239904.8596506782
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SZ002424,0.25999999999999995,4,986092.992822364
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SH600377,0.25999999999999995,1,55279.60882104716
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SH601139,0.25999999999999995,1,-85493.94819957169
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SH600299,0.25999999999999995,5,35263.911993071866
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SZ002948,0.25999999999999995,1,-130997.34930495129
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SZ000156,0.25999999999999995,3,117354.13973553921
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SZ002375,0.25999999999999995,5,-503704.1723574602
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SH600871,0.25999999999999995,1,74805.1630532484
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SZ300869,0.25999999999999995,1,-315929.3763211771
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SZ002302,0.25999999999999995,4,-345674.57053220057
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SH601228,0.25999999999999995,3,-204746.15346936608
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SH600639,0.25999999999999995,5,-874267.2713147431
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SH601298,0.25999999999999995,1,-5273.972602739588
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SH603379,0.25999999999999995,6,750011.8047540708
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SZ002945,0.25999999999999995,8,-190018.72363347892
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SZ002653,0.25999999999999995,9,1575227.333324797
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SZ001872,0.25999999999999995,1,-181946.14910747032
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SH600823,0.25999999999999995,4,-1012634.7666783128
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SZ300741,0.25999999999999995,1,-175244.41893296523
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SH603056,0.25999999999999995,1,472712.32876712317
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SH601869,0.25999999999999995,5,193111.91156326648
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SH603786,0.25999999999999995,7,722157.4994268874
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SH603708,0.25999999999999995,2,137353.7448811492
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SZ000553,0.25999999999999995,5,-578522.740003638
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||||
SH600350,0.25999999999999995,1,42420.46753100173
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SH600657,0.25999999999999995,2,299910.5707050211
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SH603515,0.25999999999999995,2,-89726.10472652467
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SH603355,0.25999999999999995,6,93048.00090576698
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SH603719,0.25999999999999995,5,688216.6074331223
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||||
SH601003,0.25999999999999995,3,-242322.08029099827
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SZ002946,0.25999999999999995,6,-128542.27883068207
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||||
SH600917,0.25999999999999995,5,-36977.61636400808
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||||
SZ002901,0.25999999999999995,7,268801.96585163
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||||
SH600903,0.25999999999999995,5,-492868.6188254448
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||||
SH600956,0.25999999999999995,4,-1181595.5901998247
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||||
SH601187,0.25999999999999995,1,-40634.25771244019
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||||
SH603983,0.25999999999999995,3,-852427.2107680459
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||||
|
|
|
@ -0,0 +1,440 @@
|
|||
symbol,take_profit_pct,trades,profit
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||||
SH600522,0.26999999999999996,6,-15174.779550762381
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SZ002340,0.26999999999999996,3,-216680.903740902
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SZ000733,0.26999999999999996,3,-347623.76533204206
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SH601615,0.26999999999999996,7,-1615082.4259089434
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SH688005,0.26999999999999996,10,-721260.133121624
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SZ002180,0.26999999999999996,4,-147632.27621242413
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SZ000723,0.26999999999999996,4,-752573.2644316294
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SZ002384,0.26999999999999996,10,1622670.3629800323
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SH600885,0.26999999999999996,4,-539707.3907344582
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SZ000009,0.26999999999999996,6,-236744.76840903255
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SZ300285,0.26999999999999996,2,-436343.7380088445
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SH605358,0.26999999999999996,4,-2129677.3155513634
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SH601555,0.26999999999999996,2,7402.496042942948
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SH601117,0.26999999999999996,7,-1401325.7176221414
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SZ002385,0.26999999999999996,4,-1043581.0961502332
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SH688099,0.26999999999999996,5,-1818086.9533308106
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SH600884,0.26999999999999996,6,-1746854.5990375176
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SH600298,0.26999999999999996,2,161927.99197252787
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SH600399,0.26999999999999996,9,-721521.593759173
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SZ002028,0.26999999999999996,7,1029995.8452438756
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SH603613,0.26999999999999996,6,-2383518.661542465
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SZ300037,0.26999999999999996,6,-1131056.8946055775
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SH600765,0.26999999999999996,10,385526.04748090304
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SH600256,0.26999999999999996,1,-3968.097514121813
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SH600487,0.26999999999999996,12,1187386.6629388505
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SH600563,0.26999999999999996,8,-844891.2950855055
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SH600754,0.26999999999999996,10,-488135.1508052357
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SZ002185,0.26999999999999996,1,-174280.51580036187
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SZ300363,0.26999999999999996,4,-1373681.0353223062
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SH601636,0.26999999999999996,1,-315662.8945801073
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SZ300751,0.26999999999999996,5,-263284.0930211342
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SZ300088,0.26999999999999996,5,-282057.4025836394
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SH603589,0.26999999999999996,6,238251.85732315417
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SZ000630,0.26999999999999996,5,-201723.48451367283
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SZ000988,0.26999999999999996,8,836809.7146139919
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SZ002268,0.26999999999999996,16,-926421.7440269169
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SZ002353,0.26999999999999996,5,-1261328.7973129307
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SH601689,0.26999999999999996,7,2000890.4109589043
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SH601168,0.26999999999999996,7,-204953.09845227157
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SZ300763,0.26999999999999996,11,-2021798.2552086874
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SH600141,0.26999999999999996,9,-1852194.8823217042
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SH600392,0.26999999999999996,6,-1800945.0393971722
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SZ000932,0.26999999999999996,2,21702.26401797135
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SH603267,0.26999999999999996,5,-1418059.5726983906
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SZ300699,0.26999999999999996,7,-1400697.6939798968
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SH600233,0.26999999999999996,1,-15445.359583114072
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SZ000728,0.26999999999999996,2,-224139.38823790126
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SH600516,0.26999999999999996,3,-272761.19589548017
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||||
SZ300058,0.26999999999999996,9,922576.5630557706
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SH600486,0.26999999999999996,3,-562409.3238461512
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SZ002797,0.26999999999999996,3,-148143.19422076663
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SH603456,0.26999999999999996,8,127082.89429729787
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SH603077,0.26999999999999996,6,-786827.0450873834
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SZ002138,0.26999999999999996,6,-151737.9149370744
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SH600862,0.26999999999999996,6,-341473.4067102635
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SZ300146,0.26999999999999996,2,-122617.02389449952
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SZ300223,0.26999999999999996,6,-2505360.9559267154
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SZ300136,0.26999999999999996,2,-661995.9748293064
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SH600188,0.26999999999999996,1,56099.89976612109
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SH600418,0.26999999999999996,11,1411872.628797789
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SH600705,0.26999999999999996,4,-198199.7480643393
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SZ000960,0.26999999999999996,6,-1548198.2539201607
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SZ300308,0.26999999999999996,6,309979.06275767577
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SH688065,0.26999999999999996,5,-793622.7606358915
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SZ300253,0.26999999999999996,8,-971698.1461953144
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SZ002080,0.26999999999999996,6,-1541985.6482786287
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SZ000998,0.26999999999999996,3,-108698.54774709925
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SH688536,0.26999999999999996,8,-1996093.1007088665
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SZ002092,0.26999999999999996,2,-200955.6268795189
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SH600875,0.26999999999999996,5,1274758.580883485
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SZ000547,0.26999999999999996,6,-867536.3097556427
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SZ002409,0.26999999999999996,12,167032.28565159324
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SH600482,0.26999999999999996,2,-146600.768733355
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SH600521,0.26999999999999996,9,-82904.9194073613
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SZ000807,0.26999999999999996,9,1140786.933881722
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SH600733,0.26999999999999996,2,-634395.4619477554
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SH600988,0.26999999999999996,7,-376270.39680435054
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SZ000039,0.26999999999999996,2,-98696.25147860739
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SZ300296,0.26999999999999996,2,-152174.19326647877
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SZ002439,0.26999999999999996,6,235306.78335692105
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SZ002212,0.26999999999999996,1,-751249.3920035734
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SZ300418,0.26999999999999996,7,1409397.5930963764
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SZ002078,0.26999999999999996,3,254025.66357487877
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SZ002223,0.26999999999999996,6,667238.3098341178
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SH603893,0.26999999999999996,7,-2988859.4851462715
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SZ002191,0.26999999999999996,5,-569128.3567447163
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SZ002030,0.26999999999999996,5,-1099278.1718986314
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SH600699,0.26999999999999996,6,-1071591.4916503015
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SZ002603,0.26999999999999996,4,1201376.7123287674
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SH600160,0.26999999999999996,3,238727.43581084482
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SZ002444,0.26999999999999996,8,-462738.84985575423
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SH601198,0.26999999999999996,3,135985.73668344688
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SZ300724,0.26999999999999996,11,-250324.18352604925
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SH600637,0.26999999999999996,1,11196.223620881161
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SZ300474,0.26999999999999996,11,656323.0547522397
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SZ002465,0.26999999999999996,1,1331.3315903262192
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SH603605,0.26999999999999996,3,-196393.47310201934
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SH600497,0.26999999999999996,6,-21914.23990344644
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SZ300383,0.26999999999999996,2,-1351168.4713010378
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||||
SZ002507,0.26999999999999996,6,-1089443.7609141374
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SZ002557,0.26999999999999996,8,-1163436.976238242
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SH600549,0.26999999999999996,9,-533523.0048816401
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SZ000739,0.26999999999999996,6,-1437007.8107000138
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SH600529,0.26999999999999996,7,214138.14016534237
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SH600704,0.26999999999999996,1,-91194.22216194228
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SH603290,0.26999999999999996,8,-1096216.7271700534
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SZ002508,0.26999999999999996,6,-694048.6864327432
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SH600038,0.26999999999999996,4,-28103.386213273792
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SZ002739,0.26999999999999996,6,356389.02612968354
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SH603198,0.26999999999999996,9,1489197.958438574
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SZ002156,0.26999999999999996,9,-485825.23095743614
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SZ000830,0.26999999999999996,4,-85388.46033872316
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SH600201,0.26999999999999996,1,-356130.5664030538
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SH600153,0.26999999999999996,1,128031.92337187794
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SH600369,0.26999999999999996,2,-46748.998771027036
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SZ000983,0.26999999999999996,4,847716.1201507527
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SZ000629,0.26999999999999996,2,44328.143060004484
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SZ002131,0.26999999999999996,5,318729.9846620153
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SZ000738,0.26999999999999996,6,-613838.8905420647
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SH603127,0.26999999999999996,8,-2118432.734340586
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SH601016,0.26999999999999996,2,-231764.87279666594
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SZ000825,0.26999999999999996,1,-299907.85894576914
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SZ000623,0.26999999999999996,1,13621.199038528765
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SH600879,0.26999999999999996,1,241445.2054794521
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SH600859,0.26999999999999996,9,337068.9003981262
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SZ000519,0.26999999999999996,8,384116.5202677256
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SH600039,0.26999999999999996,1,135844.80806376415
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SH688521,0.26999999999999996,10,338083.08797079825
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SH603444,0.26999999999999996,4,530095.1132864424
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SH600166,0.26999999999999996,8,582562.1948741323
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SZ002422,0.26999999999999996,2,496794.52054794517
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SH600732,0.26999999999999996,2,508972.60273972596
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SZ000750,0.26999999999999996,1,46073.27415968562
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SH600803,0.26999999999999996,7,-137930.66544210777
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SH600170,0.26999999999999996,1,-116717.77849339836
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SZ000050,0.26999999999999996,1,-615461.6288614838
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SZ000887,0.26999999999999996,9,-780765.4965863843
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SZ300357,0.26999999999999996,6,-231522.59334126447
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SH601699,0.26999999999999996,9,2043308.824904382
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SZ000656,0.26999999999999996,4,-1564916.565337401
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SH600118,0.26999999999999996,3,-132109.47018391412
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SZ300070,0.26999999999999996,2,196033.72372686397
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SZ002372,0.26999999999999996,4,908167.2827449532
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SZ000400,0.26999999999999996,8,473321.4228424609
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SH600271,0.26999999999999996,2,-145996.14265563383
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SH600258,0.26999999999999996,10,-134491.8603875283
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SZ300017,0.26999999999999996,5,607704.6115078077
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SZ002153,0.26999999999999996,9,806305.8347791502
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SZ002152,0.26999999999999996,3,155020.15483689838
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SH600909,0.26999999999999996,2,-25378.865880474004
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SZ002195,0.26999999999999996,3,745910.9589041097
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SZ300168,0.26999999999999996,8,-743008.1734490291
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SZ002572,0.26999999999999996,8,52808.959328933255
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SH600315,0.26999999999999996,2,-222532.41476629057
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SZ002065,0.26999999999999996,3,-919173.3883635276
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|
||||
SZ002946,0.26999999999999996,6,-109542.27883068207
|
||||
SH600917,0.26999999999999996,5,-27477.616364007874
|
||||
SZ002901,0.26999999999999996,7,297301.9658516297
|
||||
SH600903,0.26999999999999996,5,-492868.6188254448
|
||||
SH600956,0.26999999999999996,4,-1181595.5901998247
|
||||
SH601187,0.26999999999999996,1,-40634.25771244019
|
||||
SH603983,0.26999999999999996,3,-842927.210768046
|
||||
|
|
|
@ -0,0 +1,340 @@
|
|||
symbol,take_profit_pct,trades,profit
|
||||
SH600522,0.27999999999999997,3,-1513444.412158909
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||||
SZ000733,0.27999999999999997,2,-435244.91645000386
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||||
SH601615,0.27999999999999997,2,-823319.4230701359
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SH688005,0.27999999999999997,1,-244133.99111549443
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SZ002180,0.27999999999999997,1,-123694.66662118028
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SZ002384,0.27999999999999997,3,-25897.15789954079
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SH600885,0.27999999999999997,1,15045.655835719805
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SZ300285,0.27999999999999997,1,-50935.17863806413
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SH605358,0.27999999999999997,1,-828078.0799780886
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SH601117,0.27999999999999997,2,403504.4718668631
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SZ002385,0.27999999999999997,1,-868767.5923760945
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SH688099,0.27999999999999997,3,-40599.85048236279
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SH600399,0.27999999999999997,2,-821528.450223403
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||||
SH603613,0.27999999999999997,4,-1920724.8274091799
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||||
SZ300037,0.27999999999999997,1,-358519.0504778355
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||||
SH600765,0.27999999999999997,1,-244734.09263107023
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||||
SH600256,0.27999999999999997,2,-363514.4042124454
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||||
SH600487,0.27999999999999997,2,-882054.7201026274
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SH600563,0.27999999999999997,1,-813925.7052548798
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||||
SH600754,0.27999999999999997,2,-581104.6645359143
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SZ002185,0.27999999999999997,1,-84128.50294244921
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||||
SH601636,0.27999999999999997,1,-248015.44974821468
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||||
SZ300751,0.27999999999999997,4,-1258508.165734733
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||||
SZ300088,0.27999999999999997,1,-111603.75099279743
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SH603589,0.27999999999999997,2,-293261.02371649235
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SZ000630,0.27999999999999997,1,53263.52449500815
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SZ000988,0.27999999999999997,1,253917.80821917803
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SZ002268,0.27999999999999997,3,41642.6681708683
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SZ002353,0.27999999999999997,2,-359420.5714566336
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SH601689,0.27999999999999997,1,-1267314.6749274961
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SH601168,0.27999999999999997,3,486828.74406162096
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SZ300763,0.27999999999999997,6,-1826818.1331785503
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SH600141,0.27999999999999997,1,-401897.9913151059
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SH600392,0.27999999999999997,2,-212692.3833084136
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SZ000932,0.27999999999999997,1,13084.2682362079
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SZ300699,0.27999999999999997,1,-624779.0538711724
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SH600233,0.27999999999999997,1,-128919.90234786177
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SH600516,0.27999999999999997,1,-11806.650311141068
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SZ300058,0.27999999999999997,4,1574232.8767123292
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SH603456,0.27999999999999997,1,-100279.26348554276
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SZ002138,0.27999999999999997,1,-423561.8280298068
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SH600862,0.27999999999999997,1,-49604.07733259935
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SZ300146,0.27999999999999997,1,197057.5074153619
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SZ300223,0.27999999999999997,2,-453970.2580735048
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SZ300136,0.27999999999999997,1,-260512.04288398303
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||||
SH600188,0.27999999999999997,2,-791270.8444057853
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||||
SH600418,0.27999999999999997,4,-329611.4926197169
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||||
SH600705,0.27999999999999997,1,264753.42465753434
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||||
SZ000960,0.27999999999999997,2,37610.25502695062
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||||
SZ300308,0.27999999999999997,1,262835.61643835617
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||||
SH688065,0.27999999999999997,1,-131607.7732849428
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||||
SZ300253,0.27999999999999997,6,1708737.2571053442
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||||
SZ002080,0.27999999999999997,1,-67968.28526942959
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||||
SZ000998,0.27999999999999997,1,-155353.94328346726
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||||
SH688536,0.27999999999999997,3,-1224963.7279819294
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SZ002092,0.27999999999999997,1,132692.47743552108
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||||
SH600875,0.27999999999999997,1,-426999.23836363544
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SZ000547,0.27999999999999997,1,-137505.14378280676
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SZ002409,0.27999999999999997,2,407518.658818017
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SH600521,0.27999999999999997,2,-430087.5621215715
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SZ000807,0.27999999999999997,2,559657.534066806
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SH600988,0.27999999999999997,2,-348446.375789569
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SZ000039,0.27999999999999997,1,-32380.930268555046
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SZ300296,0.27999999999999997,1,-34205.89456205892
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SZ002439,0.27999999999999997,1,254780.82191780827
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SZ002212,0.27999999999999997,1,-360806.8703581906
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SZ300418,0.27999999999999997,2,526246.5753424658
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||||
SZ002078,0.27999999999999997,2,47659.029954266145
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SZ002223,0.27999999999999997,2,-480776.9775343193
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SH603893,0.27999999999999997,3,-121523.79109993391
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SZ002030,0.27999999999999997,1,-636236.0045284857
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||||
SH600699,0.27999999999999997,1,-24170.05322164726
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SH600160,0.27999999999999997,1,-266234.42815695767
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SZ002444,0.27999999999999997,1,-537604.0222512218
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SH601198,0.27999999999999997,1,81171.81332714178
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SZ300724,0.27999999999999997,1,-677780.009921089
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SH600637,0.27999999999999997,1,119002.52842217442
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SZ300474,0.27999999999999997,4,1467823.0402922535
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SZ002465,0.27999999999999997,1,-52937.71384977277
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SH688208,0.27999999999999997,6,-1056435.554969274
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SH603605,0.27999999999999997,1,-85250.13662380211
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SZ300383,0.27999999999999997,1,-501552.11704701756
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SZ002507,0.27999999999999997,2,139258.2442578951
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SZ002557,0.27999999999999997,1,-189788.26158249407
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SH600549,0.27999999999999997,1,-424205.94930489524
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SZ000739,0.27999999999999997,1,-509463.5833356554
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SH600529,0.27999999999999997,2,-328485.8275631084
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SH600704,0.27999999999999997,1,30733.62267733604
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SH603290,0.27999999999999997,1,-313747.8383818299
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SZ002508,0.27999999999999997,1,-450018.4305258162
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SZ002739,0.27999999999999997,1,239429.66496981378
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||||
SZ000636,0.27999999999999997,1,-223440.62372133648
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SH603198,0.27999999999999997,3,450997.1605846802
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SZ002156,0.27999999999999997,4,310690.1956103523
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||||
SZ000830,0.27999999999999997,2,155646.31349749063
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SH600201,0.27999999999999997,2,155332.87967099642
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SH600153,0.27999999999999997,2,-141299.12977052468
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SZ000983,0.27999999999999997,3,-597970.1907742193
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||||
SH600642,0.27999999999999997,1,248260.27397260285
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SZ002299,0.27999999999999997,1,-298496.8585956324
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SZ000629,0.27999999999999997,1,-453969.69837083487
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SZ002131,0.27999999999999997,2,552471.7725249963
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||||
SZ000738,0.27999999999999997,2,-366119.0996708082
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||||
SH603127,0.27999999999999997,2,-956553.9952791857
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SZ000623,0.27999999999999997,1,84973.97960374317
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||||
SH600859,0.27999999999999997,3,-235740.14264353516
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SZ000519,0.27999999999999997,1,-416957.88092634187
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||||
SH688521,0.27999999999999997,3,790424.6575342466
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SZ002273,0.27999999999999997,1,-200717.58507996763
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SH603444,0.27999999999999997,1,262068.49315068492
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SH600166,0.27999999999999997,2,253011.66920345012
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SZ002422,0.27999999999999997,1,250273.97260273984
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SH600732,0.27999999999999997,1,-74246.66904835813
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SZ002373,0.27999999999999997,2,382679.39592456573
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SH600803,0.27999999999999997,2,309292.9706360899
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SZ000050,0.27999999999999997,1,-271008.0285098457
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SZ000887,0.27999999999999997,1,-229827.11445533464
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SZ300357,0.27999999999999997,2,-45993.66252081178
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SH601699,0.27999999999999997,2,515027.3972602739
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SH600118,0.27999999999999997,1,-22487.197798630576
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SZ300070,0.27999999999999997,1,105982.91172927697
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SZ002372,0.27999999999999997,1,-186578.70969457328
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SZ000400,0.27999999999999997,1,8309.581773159014
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SH600271,0.27999999999999997,1,-32443.20193830951
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||||
SH600258,0.27999999999999997,2,-530386.3423364589
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||||
SZ300017,0.27999999999999997,1,265136.98630136973
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||||
SZ002153,0.27999999999999997,5,1485022.2248347043
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||||
SZ002152,0.27999999999999997,1,-7064.421846866502
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||||
SZ002195,0.27999999999999997,2,523178.0821917809
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||||
SZ300168,0.27999999999999997,2,534892.0367591758
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||||
SZ002572,0.27999999999999997,2,112798.04968992787
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||||
SH600315,0.27999999999999997,1,-44.37628252561626
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SZ002065,0.27999999999999997,1,-47862.65023413376
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SH600066,0.27999999999999997,1,261205.47945205486
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SZ000975,0.27999999999999997,1,179807.33324704343
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SH600027,0.27999999999999997,3,628294.472036212
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SZ000997,0.27999999999999997,1,121256.95978789235
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SH603218,0.27999999999999997,3,489552.52769568167
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SZ002203,0.27999999999999997,1,-35065.16884423813
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SZ002985,0.27999999999999997,1,-52346.75372453739
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SH600372,0.27999999999999997,2,-467381.62920061353
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SZ000513,0.27999999999999997,2,-125652.67299245916
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SZ300182,0.27999999999999997,2,823494.951094615
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SZ002506,0.27999999999999997,1,-36489.72602739728
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SH603156,0.27999999999999997,1,16759.01531841083
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SZ002408,0.27999999999999997,1,-162080.61778275188
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SZ002056,0.27999999999999997,1,-214483.52882205462
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SH600236,0.27999999999999997,1,-72654.4489695175
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SZ300001,0.27999999999999997,2,520638.96124459
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SZ300244,0.27999999999999997,1,-96758.57419513245
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SZ002250,0.27999999999999997,1,-319715.7034371034
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SZ002019,0.27999999999999997,1,85143.583447531
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SZ300024,0.27999999999999997,1,265041.09589041094
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SH601872,0.27999999999999997,3,-16693.082552861306
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SH600998,0.27999999999999997,1,162354.9682453791
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SZ300630,0.27999999999999997,1,-1007365.4423248414
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SZ002221,0.27999999999999997,1,29835.108502840547
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SZ000021,0.27999999999999997,1,263698.63013698626
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SH600348,0.27999999999999997,1,-163126.9860270784
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SZ300251,0.27999999999999997,3,1277747.1393800366
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SH600535,0.27999999999999997,2,606310.6538879281
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SZ000401,0.27999999999999997,1,94462.21829429975
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SZ000878,0.27999999999999997,1,-42475.16738730365
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SZ002831,0.27999999999999997,1,-210586.34659230086
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SZ300009,0.27999999999999997,2,115067.01216610166
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SH600728,0.27999999999999997,2,334182.3956353467
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SZ000686,0.27999999999999997,1,22844.439284885004
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SZ002430,0.27999999999999997,2,621562.6893486246
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SH600171,0.27999999999999997,1,-177214.17854564197
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SZ000537,0.27999999999999997,1,-154090.92829778875
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SZ300115,0.27999999999999997,1,-358638.2423844078
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SZ002683,0.27999999999999997,1,-77096.6918709695
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SZ002745,0.27999999999999997,1,-18533.19283456267
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SH600718,0.27999999999999997,1,-52359.65140299732
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SH603568,0.27999999999999997,1,-149165.163554819
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SZ002127,0.27999999999999997,1,-160562.04508296528
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SZ002532,0.27999999999999997,1,-287110.07818603644
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SH600390,0.27999999999999997,1,41204.474048007934
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SZ300482,0.27999999999999997,5,-1015844.5318751282
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SZ000970,0.27999999999999997,1,-152294.8948460112
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SH603858,0.27999999999999997,2,-7948.695167989645
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SH600970,0.27999999999999997,1,257561.64383561656
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SZ000690,0.27999999999999997,1,216739.5252398844
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SH600060,0.27999999999999997,1,255931.5068493152
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SH600282,0.27999999999999997,1,68721.70797037432
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SZ300463,0.27999999999999997,2,-543243.3943136034
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SH600498,0.27999999999999997,2,198965.9143900659
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SZ300026,0.27999999999999997,2,-684826.3565631679
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SH600380,0.27999999999999997,2,76291.94930422664
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SH600673,0.27999999999999997,1,-719279.0478975195
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SH603712,0.27999999999999997,1,-230341.23689260427
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SH600528,0.27999999999999997,1,86659.33090464931
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SZ300212,0.27999999999999997,4,2081876.7123287674
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SZ300072,0.27999999999999997,1,-130114.76699220028
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SZ000930,0.27999999999999997,1,28652.51016107185
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||||
SH600325,0.27999999999999997,1,263219.1780821918
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SH600667,0.27999999999999997,2,9572.485033621546
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SZ002382,0.27999999999999997,1,-130999.57684425372
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SZ000729,0.27999999999999997,1,258328.7671232876
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SH600895,0.27999999999999997,1,-80461.7952777588
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SH600511,0.27999999999999997,2,517616.43835616455
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SH603885,0.27999999999999997,2,584766.9934428362
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SH688006,0.27999999999999997,1,-336845.09424186486
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||||
SH601456,0.27999999999999997,1,-82496.13298131563
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SZ000581,0.27999999999999997,1,-111322.10502009522
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SH600500,0.27999999999999997,1,-95779.76145786009
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SH601717,0.27999999999999997,2,91388.1520476846
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SZ002505,0.27999999999999997,1,-301590.3755469239
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||||
SZ300315,0.27999999999999997,3,783808.2191780823
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SH600216,0.27999999999999997,1,-133293.33836612516
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||||
SZ002396,0.27999999999999997,1,-633432.4414896511
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SH603927,0.27999999999999997,1,252191.78082191793
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SH603638,0.27999999999999997,1,-145402.9629438503
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SH603225,0.27999999999999997,1,-333730.1995594551
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SZ002670,0.27999999999999997,1,-374249.24802401423
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SH600507,0.27999999999999997,1,-139833.76527212147
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SH603866,0.27999999999999997,1,-177187.2905346045
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SZ002004,0.27999999999999997,2,-98249.19623980546
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SZ002368,0.27999999999999997,2,522027.39726027404
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SZ300376,0.27999999999999997,1,-86290.84792891405
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SZ002048,0.27999999999999997,1,-139349.2905189767
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SH600446,0.27999999999999997,3,775369.8630136987
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SH601778,0.27999999999999997,1,-212928.6654808382
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SZ300271,0.27999999999999997,1,-244730.38432223932
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SH601577,0.27999999999999997,1,34506.15152961919
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SH688188,0.27999999999999997,1,-39543.25652740188
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SH600131,0.27999999999999997,3,500197.63592912594
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SZ002010,0.27999999999999997,1,-49868.78800909125
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SZ002925,0.27999999999999997,1,-280996.755190962
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SH600663,0.27999999999999997,1,-111377.21064052435
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||||
SZ300166,0.27999999999999997,2,135632.77767881542
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||||
SZ002124,0.27999999999999997,1,-45383.56164383554
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||||
SH600546,0.27999999999999997,2,114099.8145857684
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||||
SH600764,0.27999999999999997,1,42256.72142901252
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||||
SZ002690,0.27999999999999997,2,23868.223162499286
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SZ002174,0.27999999999999997,5,1573178.0821917807
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SH600597,0.27999999999999997,1,128936.09814842683
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SH600908,0.27999999999999997,1,111991.45676830162
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SH603883,0.27999999999999997,1,-373612.00157317956
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||||
SH603228,0.27999999999999997,1,-182012.17991804672
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||||
SZ002155,0.27999999999999997,1,257082.19178082203
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SH600566,0.27999999999999997,1,290129.54516193585
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SZ002281,0.27999999999999997,3,810357.8110957359
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SH600158,0.27999999999999997,1,-156914.9466925897
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SH600707,0.27999999999999997,1,-46697.984320517295
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SH600572,0.27999999999999997,2,508698.6301369864
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SZ002440,0.27999999999999997,2,-81300.12468645762
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SZ000961,0.27999999999999997,1,-336126.3460895936
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SH688029,0.27999999999999997,2,-279270.5189466817
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SZ000990,0.27999999999999997,1,-49743.61714003223
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SZ002183,0.27999999999999997,1,-76247.37698347209
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SH600435,0.27999999999999997,1,264369.8630136986
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SH601000,0.27999999999999997,1,252863.01369863018
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SZ000090,0.27999999999999997,1,42129.648806177494
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SH600906,0.27999999999999997,1,-57340.10804763426
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SZ002429,0.27999999999999997,1,-83813.95697613162
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SH688088,0.27999999999999997,3,270539.7822065453
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||||
SH600398,0.27999999999999997,2,412311.96787556866
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||||
SH600373,0.27999999999999997,1,264273.97260273976
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||||
SH600037,0.27999999999999997,1,93497.528872809
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SH600259,0.27999999999999997,1,-182437.11859343652
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SH601718,0.27999999999999997,1,17651.441024728676
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SH600026,0.27999999999999997,1,525287.6712328767
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SH603000,0.27999999999999997,2,1059205.479452055
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SZ002242,0.27999999999999997,1,-75026.78134114269
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SZ300133,0.27999999999999997,1,261205.47945205492
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SZ002705,0.27999999999999997,1,-192650.40977703256
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SZ000402,0.27999999999999997,1,-168408.8178462791
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SZ000158,0.27999999999999997,1,-50488.68629182845
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SZ002390,0.27999999999999997,1,-460004.06356281514
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SZ002387,0.27999999999999997,2,150480.09908860672
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SZ000415,0.27999999999999997,1,-84200.88522845072
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SH601991,0.27999999999999997,2,515274.037163403
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SH601975,0.27999999999999997,1,-57825.43812657848
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SH603650,0.27999999999999997,4,407375.36749334435
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SH600959,0.27999999999999997,1,85480.9527474218
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SH688289,0.27999999999999997,1,-234151.4408451484
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SH600339,0.27999999999999997,3,776328.7671232878
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SZ000062,0.27999999999999997,1,-11708.979037746254
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SH600835,0.27999999999999997,1,56151.14686845231
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SZ002815,0.27999999999999997,1,-252504.07705149206
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SZ300869,0.27999999999999997,3,-1077072.349230677
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SH601228,0.27999999999999997,1,100430.26357430262
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SH601969,0.27999999999999997,1,-234490.04055335387
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SH603379,0.27999999999999997,1,-260187.66938711266
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SZ002653,0.27999999999999997,2,509657.5342465753
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SH600823,0.27999999999999997,2,-632480.1781465504
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SZ300741,0.27999999999999997,1,-53309.09346133779
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SH603056,0.27999999999999997,2,217637.48189188482
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SH601869,0.27999999999999997,1,-535028.9303980002
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SH603786,0.27999999999999997,1,-386578.31189975026
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SZ000553,0.27999999999999997,1,-113668.25242161949
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SZ001203,0.27999999999999997,1,-945682.7211139722
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SH600657,0.27999999999999997,1,-637372.4789524117
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SH603355,0.27999999999999997,1,-144376.35722650908
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SH603719,0.27999999999999997,1,-240478.5410107494
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SH601003,0.27999999999999997,1,26329.158787201864
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SZ002946,0.27999999999999997,2,376201.16753402597
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SH603868,0.27999999999999997,1,-146359.49823391563
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SZ002901,0.27999999999999997,2,-528921.5500645036
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SH600903,0.27999999999999997,1,-44908.59216886615
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||||
|
|
|
@ -0,0 +1,341 @@
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|||
symbol,take_profit_pct,trades,profit
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SZ000733,0.29,2,-435244.91645000386
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SH601615,0.29,2,-823319.4230701359
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SH688099,0.29,3,-184267.35262485652
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SH603613,0.29,4,-1920724.8274091799
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SZ300037,0.29,1,-358519.0504778355
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SH600487,0.29,2,-882054.7201026274
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SH600563,0.29,1,-813925.7052548798
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SH600754,0.29,2,-581104.6645359143
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SH601636,0.29,1,-248015.44974821468
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SZ300751,0.29,4,-1258508.165734733
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SZ300088,0.29,1,-111603.75099279743
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SH603589,0.29,2,-283761.0237164923
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SZ000630,0.29,1,53263.52449500815
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SZ000988,0.29,1,263417.8082191781
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SZ002268,0.29,3,69759.10652703245
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SH600141,0.29,1,-401897.9913151059
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SH600233,0.29,1,-128919.90234786177
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SH600862,0.29,1,-49604.07733259935
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SZ300223,0.29,2,-444470.25807350466
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SZ300136,0.29,1,-260512.04288398303
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SH600188,0.29,2,-791270.8444057853
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SH600418,0.29,4,-310611.49261971685
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SH600705,0.29,1,274253.4246575343
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SZ300308,0.29,1,272335.6164383562
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SH688065,0.29,1,-131607.7732849428
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SZ300253,0.29,6,1775237.2571053447
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SZ002080,0.29,1,-67968.28526942959
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SZ000998,0.29,1,-155353.94328346726
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SH688536,0.29,3,-1224963.7279819294
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SZ002092,0.29,1,132692.47743552108
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SH600875,0.29,1,-426999.23836363544
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SZ000547,0.29,1,-137505.14378280676
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SZ002409,0.29,2,417018.6588180169
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SH600521,0.29,2,-411087.5621215716
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SZ000807,0.29,2,578657.534066806
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SZ002212,0.29,1,-360806.8703581906
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SZ002078,0.29,2,47659.029954266145
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SH603893,0.29,2,-331922.2377894429
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SH600160,0.29,1,-266234.42815695767
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SH601198,0.29,1,81171.81332714178
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SH688208,0.29,6,-1037435.554969274
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SH603605,0.29,1,-85250.13662380211
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SH600549,0.29,1,-424205.94930489524
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SZ000739,0.29,1,-509463.5833356554
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SH600529,0.29,2,-328485.8275631084
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SH600704,0.29,1,30733.62267733604
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SH603198,0.29,3,469997.1605846802
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SZ002156,0.29,4,348402.5243774756
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SZ000830,0.29,2,165050.42308653184
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SH600201,0.29,2,164832.8796709964
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SH600153,0.29,2,-131895.0201814836
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SH600642,0.29,1,257760.27397260274
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SZ000629,0.29,1,-453969.69837083487
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SH603127,0.29,2,-956553.9952791857
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SZ000623,0.29,1,84973.97960374317
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SZ000519,0.29,1,-416957.88092634187
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SH688521,0.29,2,818636.9863013698
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SH603444,0.29,1,271568.4931506849
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SH600166,0.29,2,262415.7787924912
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SH600732,0.29,1,-74246.66904835813
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SZ002373,0.29,2,392179.39592456573
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SH600803,0.29,2,318697.08022513083
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SH600118,0.29,1,-22487.197798630576
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SH603927,0.29,1,261691.78082191775
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SH603638,0.29,1,-145402.9629438503
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SH603225,0.29,1,-333730.1995594551
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SH600507,0.29,1,-139833.76527212147
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SH603866,0.29,1,-177187.2905346045
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SZ002004,0.29,2,-98249.19623980546
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SZ300376,0.29,1,-86290.84792891405
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SZ002048,0.29,1,-139349.2905189767
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SH600446,0.29,3,642447.926561848
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SZ300271,0.29,1,-244730.38432223932
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SH600131,0.29,3,519101.74551816704
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SZ002010,0.29,1,-49868.78800909125
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SZ300166,0.29,2,145132.77767881547
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SH600546,0.29,2,114099.8145857684
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SH600764,0.29,1,42256.72142901252
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SZ002690,0.29,2,33080.55192962257
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SZ002174,0.29,5,1630178.082191781
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SH600597,0.29,1,128936.09814842683
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SH600908,0.29,1,111991.45676830162
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SH603883,0.29,1,-373612.00157317956
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SH603228,0.29,1,-182012.17991804672
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SZ002155,0.29,1,266582.19178082206
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SH600566,0.29,1,290129.54516193585
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SH600707,0.29,1,-46697.984320517295
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SH600572,0.29,1,527602.7397260275
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SZ002440,0.29,2,-81300.12468645762
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SZ000961,0.29,1,-336126.3460895936
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SH688029,0.29,2,-279270.5189466817
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SZ000990,0.29,1,-49743.61714003223
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SZ002183,0.29,1,-76247.37698347209
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SZ002429,0.29,1,-83813.95697613162
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SH688088,0.29,3,20402.560474142083
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SH600373,0.29,1,273773.97260273964
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SH600037,0.29,1,93497.528872809
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SH600167,0.29,1,-3706.3234162753506
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SZ002416,0.29,1,-288404.60409325134
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SZ002244,0.29,1,272719.1780821919
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SH600329,0.29,2,536904.1095890413
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SH600259,0.29,1,-182437.11859343652
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SH688321,0.29,1,-341926.1377308939
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SH601718,0.29,1,17651.441024728676
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SH600026,0.29,1,543328.7671232878
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SH603000,0.29,1,1097109.5890410957
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SZ002038,0.29,1,26001.152585139454
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SZ002242,0.29,1,-75026.78134114269
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SZ300133,0.29,1,270705.47945205483
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SZ002705,0.29,1,-192650.40977703256
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SZ000402,0.29,1,-168408.8178462791
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SZ000158,0.29,1,-50488.68629182845
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SZ002390,0.29,1,-460004.06356281514
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SZ002387,0.29,2,159980.09908860654
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SZ000415,0.29,1,-84200.88522845072
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SH601991,0.29,2,515274.037163403
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SH601975,0.29,1,-57825.43812657848
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SH603650,0.29,4,435587.69626046764
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SH600959,0.29,1,85480.9527474218
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SH688289,0.29,1,-234151.4408451484
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SH600582,0.29,2,157908.24406621233
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SH600062,0.29,1,-310588.7763167048
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SZ300257,0.29,1,-100973.5523320915
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SH600266,0.29,2,21480.24740085198
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SZ002399,0.29,2,-409059.01035052026
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SZ003022,0.29,2,-743945.8168532269
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SH601928,0.29,3,791212.3287671232
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SH600729,0.29,2,498893.6319881526
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SZ002468,0.29,2,-47630.44991014914
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SH600556,0.29,2,-390069.14117424015
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SZ003035,0.29,1,-438044.38177176344
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SH600787,0.29,1,260445.20547945215
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SZ000028,0.29,2,799075.3424657533
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SH600095,0.29,2,-23573.837617082754
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SH600776,0.29,1,-6013.0195750683
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SZ000031,0.29,1,-77882.60936834523
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SH600339,0.29,3,804732.876712329
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SH600006,0.29,1,-249264.60456111893
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SH603317,0.29,1,-150409.15504277474
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SZ000062,0.29,1,-11708.979037746254
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SH600835,0.29,1,56151.14686845231
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SZ002815,0.29,1,-252504.07705149206
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SZ002424,0.29,4,878508.7738136043
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SH600299,0.29,1,-26473.269412054466
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SZ002948,0.29,1,-69899.45569697082
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SZ000156,0.29,1,129003.9245010847
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SZ002375,0.29,1,-28782.381111482235
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SH601828,0.29,2,-67347.87290165946
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SZ300869,0.29,3,-1077072.349230677
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SZ002302,0.29,1,-14126.419728664943
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SH601228,0.29,1,100430.26357430262
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SH601969,0.29,1,-234490.04055335387
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SH603379,0.29,1,-260187.66938711266
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SZ002945,0.29,1,-165933.4118780609
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SZ002653,0.29,2,528465.7534246575
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SH600823,0.29,2,-632480.1781465504
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SZ300741,0.29,1,-53309.09346133779
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SH603056,0.29,2,227137.48189188488
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SH601869,0.29,1,-535028.9303980002
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SH603786,0.29,1,-386578.31189975026
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SZ000553,0.29,1,-113668.25242161949
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SZ001203,0.29,1,-945682.7211139722
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SH600657,0.29,1,-637372.4789524117
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SH603355,0.29,1,-144376.35722650908
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SH603719,0.29,1,-240478.5410107494
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SH601003,0.29,1,26329.158787201864
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SZ002946,0.29,2,385701.1675340258
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SH603868,0.29,1,-146359.49823391563
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SZ002901,0.29,2,-528921.5500645036
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SH600903,0.29,1,-44908.59216886615
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||||
|
|
|
@ -0,0 +1,341 @@
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|||
symbol,take_profit_pct,trades,profit
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SH600522,0.3,3,-1513444.412158909
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SZ000733,0.3,2,-435244.91645000386
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SH601615,0.3,2,-823319.4230701359
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SH688005,0.3,1,-244133.99111549443
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SZ002180,0.3,1,-123694.66662118028
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SZ002384,0.3,3,12102.842100459384
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SH600885,0.3,1,15045.655835719805
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SZ300285,0.3,1,-50935.17863806413
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SH605358,0.3,1,-828078.0799780886
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SH601117,0.3,2,422504.4718668631
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SH688099,0.3,3,-165267.35262485652
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SH600399,0.3,2,-821528.450223403
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SH603613,0.3,4,-1920724.8274091799
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SZ300037,0.3,1,-358519.0504778355
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SH600765,0.3,1,-244734.09263107023
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SH600256,0.3,2,-363514.4042124454
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SH600487,0.3,2,-882054.7201026274
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SH600563,0.3,1,-813925.7052548798
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SH600754,0.3,2,-581104.6645359143
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SZ002185,0.3,1,-84128.50294244921
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SH601636,0.3,1,-248015.44974821468
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SZ300751,0.3,4,-1258508.165734733
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SZ300088,0.3,1,-111603.75099279743
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SH603589,0.3,1,-571209.5993851457
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SZ000630,0.3,1,53263.52449500815
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SZ000988,0.3,1,271383.56164383556
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SZ002268,0.3,3,-182450.77041014552
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SZ002353,0.3,2,-359420.5714566336
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SH601689,0.3,1,-1267314.6749274961
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SH601168,0.3,2,524732.8536506619
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SZ300763,0.3,6,-1826818.1331785503
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SH600141,0.3,1,-401897.9913151059
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SH600392,0.3,2,-212692.3833084136
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SH600233,0.3,1,-128919.90234786177
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SH600516,0.3,1,-11806.650311141068
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SH603456,0.3,1,-100279.26348554276
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SH600862,0.3,1,-49604.07733259935
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SZ300223,0.3,2,-434970.2580735047
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SZ300136,0.3,1,-260512.04288398303
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SH600188,0.3,2,-791270.8444057853
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SH600418,0.3,4,-291707.3830306759
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SH600705,0.3,1,283753.4246575342
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SZ300308,0.3,1,281739.7260273973
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SH688065,0.3,1,-131607.7732849428
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SZ300253,0.3,4,1230984.8746722974
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SZ002080,0.3,1,-67968.28526942959
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SZ000998,0.3,1,-155353.94328346726
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SH688536,0.3,3,-1224963.7279819294
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SZ002092,0.3,1,132692.47743552108
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SH600875,0.3,1,-426999.23836363544
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SZ000547,0.3,1,-137505.14378280676
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SZ002409,0.3,2,426518.658818017
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SH600521,0.3,2,-392087.5621215715
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SZ000807,0.3,2,597657.5340668058
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SH600988,0.3,2,-348446.375789569
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SZ000039,0.3,1,-32380.930268555046
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SZ002212,0.3,1,-360806.8703581906
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SH603893,0.3,2,-303422.2377894429
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SH600699,0.3,1,-24170.05322164726
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SH600160,0.3,1,-266234.42815695767
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SH601198,0.3,1,81171.81332714178
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SZ300474,0.3,5,1581727.1498812947
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SH688208,0.3,6,-1162579.3949928957
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SH603605,0.3,1,-85250.13662380211
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SH603444,0.3,1,281068.49315068504
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SH600166,0.3,2,271915.7787924911
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SH600732,0.3,1,-74246.66904835813
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SH600118,0.3,1,-22487.197798630576
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|
||||
SZ002430,0.3,2,640562.6893486246
|
||||
SH600171,0.3,1,-177214.17854564197
|
||||
SZ000537,0.3,1,-154090.92829778875
|
||||
SZ300115,0.3,1,-358638.2423844078
|
||||
SZ002683,0.3,1,-77096.6918709695
|
||||
SZ002745,0.3,1,-18533.19283456267
|
||||
SH600718,0.3,1,-52359.65140299732
|
||||
SH603568,0.3,1,-149165.163554819
|
||||
SZ002127,0.3,1,-160562.04508296528
|
||||
SZ002532,0.3,1,-287110.07818603644
|
||||
SH600390,0.3,1,41204.474048007934
|
||||
SZ300482,0.3,5,-977844.5318751279
|
||||
SZ000970,0.3,1,-152294.8948460112
|
||||
SH603858,0.3,2,10475.96236625698
|
||||
SH600970,0.3,1,276273.9726027398
|
||||
SZ000690,0.3,1,216739.5252398844
|
||||
SH600060,0.3,1,274643.8356164384
|
||||
SH600282,0.3,1,68721.70797037432
|
||||
SZ300463,0.3,2,-543243.3943136034
|
||||
SH600498,0.3,2,216719.3390476003
|
||||
SZ300026,0.3,2,-684826.3565631679
|
||||
SH600380,0.3,2,76291.94930422664
|
||||
SH600673,0.3,1,-719279.0478975195
|
||||
SH603712,0.3,1,-230341.23689260427
|
||||
SH600528,0.3,1,86659.33090464931
|
||||
SZ300212,0.3,5,2232150.684931507
|
||||
SZ300072,0.3,1,-130114.76699220028
|
||||
SZ000930,0.3,1,28652.51016107185
|
||||
SH600325,0.3,1,282123.287671233
|
||||
SH600667,0.3,2,28572.485033621604
|
||||
SZ002382,0.3,1,-130999.57684425372
|
||||
SZ000729,0.3,1,277232.8767123287
|
||||
SH600895,0.3,1,-80461.7952777588
|
||||
SH600511,0.3,2,459476.16670360795
|
||||
SH603885,0.3,3,622671.103031877
|
||||
SH688006,0.3,1,-336845.09424186486
|
||||
SH601456,0.3,1,-82496.13298131563
|
||||
SZ000581,0.3,1,-111322.10502009522
|
||||
SH600500,0.3,1,-95779.76145786009
|
||||
SH601717,0.3,2,91388.1520476846
|
||||
SZ002505,0.3,1,-301590.3755469239
|
||||
SZ300315,0.3,3,840808.2191780824
|
||||
SH600216,0.3,1,-133293.33836612516
|
||||
SZ002396,0.3,1,-633432.4414896511
|
||||
SH603927,0.3,1,271191.7808219178
|
||||
SH603638,0.3,1,-145402.9629438503
|
||||
SH603225,0.3,1,-333730.1995594551
|
||||
SZ002670,0.3,1,-374249.24802401423
|
||||
SH600507,0.3,1,-139833.76527212147
|
||||
SH603866,0.3,1,-177187.2905346045
|
||||
SZ002595,0.3,2,222642.5630169612
|
||||
SZ002004,0.3,2,-98249.19623980546
|
||||
SZ002368,0.3,2,559931.5068493153
|
||||
SZ300376,0.3,1,-86290.84792891405
|
||||
SZ002048,0.3,1,-139349.2905189767
|
||||
SH600446,0.3,3,660776.6936851358
|
||||
SH601778,0.3,1,-212928.6654808382
|
||||
SZ300271,0.3,1,-244730.38432223932
|
||||
SH601577,0.3,1,34506.15152961919
|
||||
SH688188,0.3,1,-39543.25652740188
|
||||
SH600131,0.3,2,354881.27746776666
|
||||
SZ002010,0.3,1,-49868.78800909125
|
||||
SZ002925,0.3,1,-280996.755190962
|
||||
SH600663,0.3,1,-111377.21064052435
|
||||
SZ300166,0.3,2,154632.77767881547
|
||||
SZ002124,0.3,1,-45383.56164383554
|
||||
SH600546,0.3,2,114099.8145857684
|
||||
SH600764,0.3,1,42256.72142901252
|
||||
SZ002690,0.3,2,42580.55192962263
|
||||
SZ002174,0.3,5,1687082.191780822
|
||||
SH600597,0.3,1,128936.09814842683
|
||||
SH600908,0.3,1,111991.45676830162
|
||||
SH603883,0.3,1,-373612.00157317956
|
||||
SH603228,0.3,1,-182012.17991804672
|
||||
SZ002155,0.3,1,276082.19178082206
|
||||
SH600566,0.3,1,290129.54516193585
|
||||
SZ002281,0.3,3,867357.8110957359
|
||||
SH600158,0.3,1,-156914.9466925897
|
||||
SH600707,0.3,1,-46697.984320517295
|
||||
SH600572,0.3,1,546602.7397260275
|
||||
SZ002440,0.3,2,-81300.12468645762
|
||||
SZ000961,0.3,1,-336126.3460895936
|
||||
SH688029,0.3,2,-279270.5189466817
|
||||
SZ000990,0.3,1,-49743.61714003223
|
||||
SZ002183,0.3,1,-76247.37698347209
|
||||
SH600435,0.3,1,24295.930701047648
|
||||
SH601000,0.3,1,271863.01369863027
|
||||
SZ000090,0.3,1,42129.648806177494
|
||||
SH600906,0.3,1,-57340.10804763426
|
||||
SZ002429,0.3,1,-83813.95697613162
|
||||
SH688088,0.3,3,39402.56047414185
|
||||
SH600398,0.3,2,431311.9678755688
|
||||
SH600373,0.3,1,283178.0821917809
|
||||
SH600037,0.3,1,93497.528872809
|
||||
SZ002867,0.3,2,532653.3334240331
|
||||
SH600376,0.3,3,-857128.8313875378
|
||||
SZ001914,0.3,1,-106114.87684669509
|
||||
SZ002085,0.3,1,-139543.91557772027
|
||||
SH600167,0.3,1,-3706.3234162753506
|
||||
SZ002416,0.3,1,-288404.60409325134
|
||||
SZ002244,0.3,1,-2270.8882957858586
|
||||
SH600329,0.3,2,553986.301369863
|
||||
SH600259,0.3,1,-182437.11859343652
|
||||
SH688321,0.3,1,-341926.1377308939
|
||||
SH601718,0.3,1,17651.441024728676
|
||||
SH600026,0.3,1,562136.9863013698
|
||||
SH603000,0.3,2,1134342.465753425
|
||||
SZ002038,0.3,1,26001.152585139454
|
||||
SZ002242,0.3,1,-75026.78134114269
|
||||
SZ300133,0.3,1,280205.4794520549
|
||||
SZ002705,0.3,1,-192650.40977703256
|
||||
SZ000402,0.3,1,-168408.8178462791
|
||||
SZ000158,0.3,1,-50488.68629182845
|
||||
SZ002390,0.3,1,-460004.06356281514
|
||||
SZ002387,0.3,2,169480.0990886066
|
||||
SZ000415,0.3,1,-84200.88522845072
|
||||
SH601991,0.3,2,515274.037163403
|
||||
SH601975,0.3,1,-57825.43812657848
|
||||
SH603650,0.3,4,463416.46338375553
|
||||
SH600959,0.3,1,85480.9527474218
|
||||
SH688289,0.3,1,-234151.4408451484
|
||||
SH600582,0.3,2,157908.24406621233
|
||||
SH600062,0.3,1,-310588.7763167048
|
||||
SZ300257,0.3,1,-100973.5523320915
|
||||
SH600266,0.3,2,30980.247400851862
|
||||
SZ002399,0.3,2,-409059.01035052026
|
||||
SH600056,0.3,3,1150818.1771470269
|
||||
SZ003022,0.3,2,-743945.8168532269
|
||||
SH601928,0.3,3,819712.3287671232
|
||||
SH600729,0.3,2,508297.7415771937
|
||||
SZ002423,0.3,1,412902.81213322
|
||||
SZ002468,0.3,2,-47630.44991014914
|
||||
SH600556,0.3,2,-380569.14117424015
|
||||
SZ003035,0.3,1,-438044.38177176344
|
||||
SH600787,0.3,1,269945.2054794521
|
||||
SZ000028,0.3,2,827575.3424657537
|
||||
SH600095,0.3,2,-4573.837617082871
|
||||
SH600776,0.3,1,-6013.0195750683
|
||||
SZ000031,0.3,1,-77882.60936834523
|
||||
SH600339,0.3,3,830835.6164383563
|
||||
SH600006,0.3,1,-249264.60456111893
|
||||
SH603317,0.3,1,-150409.15504277474
|
||||
SZ000062,0.3,1,-11708.979037746254
|
||||
SH600835,0.3,1,56151.14686845231
|
||||
SZ002815,0.3,1,-252504.07705149206
|
||||
SZ002424,0.3,4,907008.7738136043
|
||||
SH600299,0.3,1,-26473.269412054466
|
||||
SZ002948,0.3,1,-69899.45569697082
|
||||
SZ000156,0.3,1,129003.9245010847
|
||||
SZ002375,0.3,1,-28782.381111482235
|
||||
SH601828,0.3,2,-57847.872901659575
|
||||
SZ300869,0.3,3,-1077072.349230677
|
||||
SZ002302,0.3,1,-14126.419728664943
|
||||
SH601228,0.3,1,100430.26357430262
|
||||
SH601969,0.3,1,-234490.04055335387
|
||||
SH603379,0.3,1,-260187.66938711266
|
||||
SZ002945,0.3,1,-165933.4118780609
|
||||
SZ002653,0.3,2,547465.7534246575
|
||||
SH600823,0.3,2,-632480.1781465504
|
||||
SZ300741,0.3,1,-53309.09346133779
|
||||
SH603056,0.3,2,236637.48189188493
|
||||
SH601869,0.3,1,-535028.9303980002
|
||||
SH603786,0.3,1,-386578.31189975026
|
||||
SZ000553,0.3,1,-113668.25242161949
|
||||
SZ001203,0.3,1,-945682.7211139722
|
||||
SH600657,0.3,1,-637372.4789524117
|
||||
SH603355,0.3,1,-144376.35722650908
|
||||
SH603719,0.3,1,-240478.5410107494
|
||||
SH601003,0.3,1,26329.158787201864
|
||||
SZ002946,0.3,2,395201.16753402585
|
||||
SH603868,0.3,1,-146359.49823391563
|
||||
SZ002901,0.3,2,-528921.5500645036
|
||||
SH600903,0.3,1,-44908.59216886615
|
||||
|
|
|
@ -0,0 +1,62 @@
|
|||
symbol,reason,take_profit_pct
|
||||
SH600157,no_valid_signals,0.23999999999999994
|
||||
SH601077,no_valid_signals,0.23999999999999994
|
||||
SZ002013,empty_data,0.23999999999999994
|
||||
SZ002673,no_valid_signals,0.23999999999999994
|
||||
SH601128,no_valid_signals,0.23999999999999994
|
||||
SH600177,no_valid_signals,0.23999999999999994
|
||||
SH600739,no_valid_signals,0.23999999999999994
|
||||
SH688208,no_valid_signals,0.23999999999999994
|
||||
SH688002,no_valid_signals,0.23999999999999994
|
||||
SH601997,no_valid_signals,0.23999999999999994
|
||||
SZ000636,no_valid_signals,0.23999999999999994
|
||||
SZ002936,no_valid_signals,0.23999999999999994
|
||||
SH600867,no_valid_signals,0.23999999999999994
|
||||
SH600008,no_valid_signals,0.23999999999999994
|
||||
SZ000999,no_valid_signals,0.23999999999999994
|
||||
SZ002002,empty_data,0.23999999999999994
|
||||
SZ002500,no_valid_signals,0.23999999999999994
|
||||
SZ000401,no_valid_signals,0.23999999999999994
|
||||
SZ002511,no_valid_signals,0.23999999999999994
|
||||
SH601992,no_valid_signals,0.23999999999999994
|
||||
SZ002966,no_valid_signals,0.23999999999999994
|
||||
SZ300463,no_valid_signals,0.23999999999999994
|
||||
SZ002382,no_valid_signals,0.23999999999999994
|
||||
SZ000540,empty_data,0.23999999999999994
|
||||
SZ002958,no_valid_signals,0.23999999999999994
|
||||
SH600567,no_valid_signals,0.23999999999999994
|
||||
SH603638,no_valid_signals,0.23999999999999994
|
||||
SZ002075,no_valid_signals,0.23999999999999994
|
||||
SH601880,no_valid_signals,0.23999999999999994
|
||||
SH600598,no_valid_signals,0.23999999999999994
|
||||
SH600022,no_valid_signals,0.23999999999999994
|
||||
SH600546,no_valid_signals,0.23999999999999994
|
||||
SH600597,no_valid_signals,0.23999999999999994
|
||||
SZ000898,no_valid_signals,0.23999999999999994
|
||||
SH601118,no_valid_signals,0.23999999999999994
|
||||
SZ000488,no_valid_signals,0.23999999999999994
|
||||
SH688029,no_valid_signals,0.23999999999999994
|
||||
SH600064,no_valid_signals,0.23999999999999994
|
||||
SH601333,no_valid_signals,0.23999999999999994
|
||||
SZ000089,no_valid_signals,0.23999999999999994
|
||||
SZ002244,no_valid_signals,0.23999999999999994
|
||||
SZ000685,no_valid_signals,0.23999999999999994
|
||||
SZ000671,empty_data,0.23999999999999994
|
||||
SH601098,no_valid_signals,0.23999999999999994
|
||||
SH601975,no_valid_signals,0.23999999999999994
|
||||
SZ000718,no_valid_signals,0.23999999999999994
|
||||
SH600062,no_valid_signals,0.23999999999999994
|
||||
SH600901,no_valid_signals,0.23999999999999994
|
||||
SH600717,no_valid_signals,0.23999999999999994
|
||||
SZ002468,no_valid_signals,0.23999999999999994
|
||||
SZ000717,no_valid_signals,0.23999999999999994
|
||||
SZ000869,no_valid_signals,0.23999999999999994
|
||||
SH601828,no_valid_signals,0.23999999999999994
|
||||
SH601969,no_valid_signals,0.23999999999999994
|
||||
SZ000046,empty_data,0.23999999999999994
|
||||
SH601156,insufficient_history_217days,0.23999999999999994
|
||||
SZ001203,insufficient_history_239days,0.23999999999999994
|
||||
SH601568,no_valid_signals,0.23999999999999994
|
||||
SH600466,empty_data,0.23999999999999994
|
||||
SH601665,insufficient_history_211days,0.23999999999999994
|
||||
SH603868,no_valid_signals,0.23999999999999994
|
||||
|
|
|
@ -0,0 +1,62 @@
|
|||
symbol,reason,take_profit_pct
|
||||
SH600157,no_valid_signals,0.24999999999999994
|
||||
SH601077,no_valid_signals,0.24999999999999994
|
||||
SZ002013,empty_data,0.24999999999999994
|
||||
SZ002673,no_valid_signals,0.24999999999999994
|
||||
SH601128,no_valid_signals,0.24999999999999994
|
||||
SH600177,no_valid_signals,0.24999999999999994
|
||||
SH600739,no_valid_signals,0.24999999999999994
|
||||
SH688208,no_valid_signals,0.24999999999999994
|
||||
SH688002,no_valid_signals,0.24999999999999994
|
||||
SH601997,no_valid_signals,0.24999999999999994
|
||||
SZ000636,no_valid_signals,0.24999999999999994
|
||||
SZ002936,no_valid_signals,0.24999999999999994
|
||||
SH600867,no_valid_signals,0.24999999999999994
|
||||
SH600008,no_valid_signals,0.24999999999999994
|
||||
SZ000999,no_valid_signals,0.24999999999999994
|
||||
SZ002002,empty_data,0.24999999999999994
|
||||
SZ002500,no_valid_signals,0.24999999999999994
|
||||
SZ000401,no_valid_signals,0.24999999999999994
|
||||
SZ002511,no_valid_signals,0.24999999999999994
|
||||
SH601992,no_valid_signals,0.24999999999999994
|
||||
SZ002966,no_valid_signals,0.24999999999999994
|
||||
SZ300463,no_valid_signals,0.24999999999999994
|
||||
SZ002382,no_valid_signals,0.24999999999999994
|
||||
SZ000540,empty_data,0.24999999999999994
|
||||
SZ002958,no_valid_signals,0.24999999999999994
|
||||
SH600567,no_valid_signals,0.24999999999999994
|
||||
SH603638,no_valid_signals,0.24999999999999994
|
||||
SZ002075,no_valid_signals,0.24999999999999994
|
||||
SH601880,no_valid_signals,0.24999999999999994
|
||||
SH600598,no_valid_signals,0.24999999999999994
|
||||
SH600022,no_valid_signals,0.24999999999999994
|
||||
SH600546,no_valid_signals,0.24999999999999994
|
||||
SH600597,no_valid_signals,0.24999999999999994
|
||||
SZ000898,no_valid_signals,0.24999999999999994
|
||||
SH601118,no_valid_signals,0.24999999999999994
|
||||
SZ000488,no_valid_signals,0.24999999999999994
|
||||
SH688029,no_valid_signals,0.24999999999999994
|
||||
SH600064,no_valid_signals,0.24999999999999994
|
||||
SH601333,no_valid_signals,0.24999999999999994
|
||||
SZ000089,no_valid_signals,0.24999999999999994
|
||||
SZ002244,no_valid_signals,0.24999999999999994
|
||||
SZ000685,no_valid_signals,0.24999999999999994
|
||||
SZ000671,empty_data,0.24999999999999994
|
||||
SH601098,no_valid_signals,0.24999999999999994
|
||||
SH601975,no_valid_signals,0.24999999999999994
|
||||
SZ000718,no_valid_signals,0.24999999999999994
|
||||
SH600062,no_valid_signals,0.24999999999999994
|
||||
SH600901,no_valid_signals,0.24999999999999994
|
||||
SH600717,no_valid_signals,0.24999999999999994
|
||||
SZ002468,no_valid_signals,0.24999999999999994
|
||||
SZ000717,no_valid_signals,0.24999999999999994
|
||||
SZ000869,no_valid_signals,0.24999999999999994
|
||||
SH601828,no_valid_signals,0.24999999999999994
|
||||
SH601969,no_valid_signals,0.24999999999999994
|
||||
SZ000046,empty_data,0.24999999999999994
|
||||
SH601156,insufficient_history_217days,0.24999999999999994
|
||||
SZ001203,insufficient_history_239days,0.24999999999999994
|
||||
SH601568,no_valid_signals,0.24999999999999994
|
||||
SH600466,empty_data,0.24999999999999994
|
||||
SH601665,insufficient_history_211days,0.24999999999999994
|
||||
SH603868,no_valid_signals,0.24999999999999994
|
||||
|
|
|
@ -0,0 +1,62 @@
|
|||
symbol,reason,take_profit_pct
|
||||
SH600157,no_valid_signals,0.25999999999999995
|
||||
SH601077,no_valid_signals,0.25999999999999995
|
||||
SZ002013,empty_data,0.25999999999999995
|
||||
SZ002673,no_valid_signals,0.25999999999999995
|
||||
SH601128,no_valid_signals,0.25999999999999995
|
||||
SH600177,no_valid_signals,0.25999999999999995
|
||||
SH600739,no_valid_signals,0.25999999999999995
|
||||
SH688208,no_valid_signals,0.25999999999999995
|
||||
SH688002,no_valid_signals,0.25999999999999995
|
||||
SH601997,no_valid_signals,0.25999999999999995
|
||||
SZ000636,no_valid_signals,0.25999999999999995
|
||||
SZ002936,no_valid_signals,0.25999999999999995
|
||||
SH600867,no_valid_signals,0.25999999999999995
|
||||
SH600008,no_valid_signals,0.25999999999999995
|
||||
SZ000999,no_valid_signals,0.25999999999999995
|
||||
SZ002002,empty_data,0.25999999999999995
|
||||
SZ002500,no_valid_signals,0.25999999999999995
|
||||
SZ000401,no_valid_signals,0.25999999999999995
|
||||
SZ002511,no_valid_signals,0.25999999999999995
|
||||
SH601992,no_valid_signals,0.25999999999999995
|
||||
SZ002966,no_valid_signals,0.25999999999999995
|
||||
SZ300463,no_valid_signals,0.25999999999999995
|
||||
SZ002382,no_valid_signals,0.25999999999999995
|
||||
SZ000540,empty_data,0.25999999999999995
|
||||
SZ002958,no_valid_signals,0.25999999999999995
|
||||
SH600567,no_valid_signals,0.25999999999999995
|
||||
SH603638,no_valid_signals,0.25999999999999995
|
||||
SZ002075,no_valid_signals,0.25999999999999995
|
||||
SH601880,no_valid_signals,0.25999999999999995
|
||||
SH600598,no_valid_signals,0.25999999999999995
|
||||
SH600022,no_valid_signals,0.25999999999999995
|
||||
SH600546,no_valid_signals,0.25999999999999995
|
||||
SH600597,no_valid_signals,0.25999999999999995
|
||||
SZ000898,no_valid_signals,0.25999999999999995
|
||||
SH601118,no_valid_signals,0.25999999999999995
|
||||
SZ000488,no_valid_signals,0.25999999999999995
|
||||
SH688029,no_valid_signals,0.25999999999999995
|
||||
SH600064,no_valid_signals,0.25999999999999995
|
||||
SH601333,no_valid_signals,0.25999999999999995
|
||||
SZ000089,no_valid_signals,0.25999999999999995
|
||||
SZ002244,no_valid_signals,0.25999999999999995
|
||||
SZ000685,no_valid_signals,0.25999999999999995
|
||||
SZ000671,empty_data,0.25999999999999995
|
||||
SH601098,no_valid_signals,0.25999999999999995
|
||||
SH601975,no_valid_signals,0.25999999999999995
|
||||
SZ000718,no_valid_signals,0.25999999999999995
|
||||
SH600062,no_valid_signals,0.25999999999999995
|
||||
SH600901,no_valid_signals,0.25999999999999995
|
||||
SH600717,no_valid_signals,0.25999999999999995
|
||||
SZ002468,no_valid_signals,0.25999999999999995
|
||||
SZ000717,no_valid_signals,0.25999999999999995
|
||||
SZ000869,no_valid_signals,0.25999999999999995
|
||||
SH601828,no_valid_signals,0.25999999999999995
|
||||
SH601969,no_valid_signals,0.25999999999999995
|
||||
SZ000046,empty_data,0.25999999999999995
|
||||
SH601156,insufficient_history_217days,0.25999999999999995
|
||||
SZ001203,insufficient_history_239days,0.25999999999999995
|
||||
SH601568,no_valid_signals,0.25999999999999995
|
||||
SH600466,empty_data,0.25999999999999995
|
||||
SH601665,insufficient_history_211days,0.25999999999999995
|
||||
SH603868,no_valid_signals,0.25999999999999995
|
||||
|
|
|
@ -0,0 +1,62 @@
|
|||
symbol,reason,take_profit_pct
|
||||
SH600157,no_valid_signals,0.26999999999999996
|
||||
SH601077,no_valid_signals,0.26999999999999996
|
||||
SZ002013,empty_data,0.26999999999999996
|
||||
SZ002673,no_valid_signals,0.26999999999999996
|
||||
SH601128,no_valid_signals,0.26999999999999996
|
||||
SH600177,no_valid_signals,0.26999999999999996
|
||||
SH600739,no_valid_signals,0.26999999999999996
|
||||
SH688208,no_valid_signals,0.26999999999999996
|
||||
SH688002,no_valid_signals,0.26999999999999996
|
||||
SH601997,no_valid_signals,0.26999999999999996
|
||||
SZ000636,no_valid_signals,0.26999999999999996
|
||||
SZ002936,no_valid_signals,0.26999999999999996
|
||||
SH600867,no_valid_signals,0.26999999999999996
|
||||
SH600008,no_valid_signals,0.26999999999999996
|
||||
SZ000999,no_valid_signals,0.26999999999999996
|
||||
SZ002002,empty_data,0.26999999999999996
|
||||
SZ002500,no_valid_signals,0.26999999999999996
|
||||
SZ000401,no_valid_signals,0.26999999999999996
|
||||
SZ002511,no_valid_signals,0.26999999999999996
|
||||
SH601992,no_valid_signals,0.26999999999999996
|
||||
SZ002966,no_valid_signals,0.26999999999999996
|
||||
SZ300463,no_valid_signals,0.26999999999999996
|
||||
SZ002382,no_valid_signals,0.26999999999999996
|
||||
SZ000540,empty_data,0.26999999999999996
|
||||
SZ002958,no_valid_signals,0.26999999999999996
|
||||
SH600567,no_valid_signals,0.26999999999999996
|
||||
SH603638,no_valid_signals,0.26999999999999996
|
||||
SZ002075,no_valid_signals,0.26999999999999996
|
||||
SH601880,no_valid_signals,0.26999999999999996
|
||||
SH600598,no_valid_signals,0.26999999999999996
|
||||
SH600022,no_valid_signals,0.26999999999999996
|
||||
SH600546,no_valid_signals,0.26999999999999996
|
||||
SH600597,no_valid_signals,0.26999999999999996
|
||||
SZ000898,no_valid_signals,0.26999999999999996
|
||||
SH601118,no_valid_signals,0.26999999999999996
|
||||
SZ000488,no_valid_signals,0.26999999999999996
|
||||
SH688029,no_valid_signals,0.26999999999999996
|
||||
SH600064,no_valid_signals,0.26999999999999996
|
||||
SH601333,no_valid_signals,0.26999999999999996
|
||||
SZ000089,no_valid_signals,0.26999999999999996
|
||||
SZ002244,no_valid_signals,0.26999999999999996
|
||||
SZ000685,no_valid_signals,0.26999999999999996
|
||||
SZ000671,empty_data,0.26999999999999996
|
||||
SH601098,no_valid_signals,0.26999999999999996
|
||||
SH601975,no_valid_signals,0.26999999999999996
|
||||
SZ000718,no_valid_signals,0.26999999999999996
|
||||
SH600062,no_valid_signals,0.26999999999999996
|
||||
SH600901,no_valid_signals,0.26999999999999996
|
||||
SH600717,no_valid_signals,0.26999999999999996
|
||||
SZ002468,no_valid_signals,0.26999999999999996
|
||||
SZ000717,no_valid_signals,0.26999999999999996
|
||||
SZ000869,no_valid_signals,0.26999999999999996
|
||||
SH601828,no_valid_signals,0.26999999999999996
|
||||
SH601969,no_valid_signals,0.26999999999999996
|
||||
SZ000046,empty_data,0.26999999999999996
|
||||
SH601156,insufficient_history_217days,0.26999999999999996
|
||||
SZ001203,insufficient_history_239days,0.26999999999999996
|
||||
SH601568,no_valid_signals,0.26999999999999996
|
||||
SH600466,empty_data,0.26999999999999996
|
||||
SH601665,insufficient_history_211days,0.26999999999999996
|
||||
SH603868,no_valid_signals,0.26999999999999996
|
||||
|
|
|
@ -0,0 +1,162 @@
|
|||
symbol,reason,take_profit_pct
|
||||
SZ002340,no_valid_signals,0.27999999999999997
|
||||
SH600157,no_valid_signals,0.27999999999999997
|
||||
SZ000723,no_valid_signals,0.27999999999999997
|
||||
SZ000009,no_valid_signals,0.27999999999999997
|
||||
SH601555,no_valid_signals,0.27999999999999997
|
||||
SH600884,no_valid_signals,0.27999999999999997
|
||||
SH600298,no_valid_signals,0.27999999999999997
|
||||
SZ002028,no_valid_signals,0.27999999999999997
|
||||
SZ300363,no_valid_signals,0.27999999999999997
|
||||
SH601077,no_valid_signals,0.27999999999999997
|
||||
SZ002013,empty_data,0.27999999999999997
|
||||
SH603267,no_valid_signals,0.27999999999999997
|
||||
SZ000728,no_valid_signals,0.27999999999999997
|
||||
SH600486,no_valid_signals,0.27999999999999997
|
||||
SZ002797,no_valid_signals,0.27999999999999997
|
||||
SH603077,no_valid_signals,0.27999999999999997
|
||||
SZ002673,no_valid_signals,0.27999999999999997
|
||||
SH601128,no_valid_signals,0.27999999999999997
|
||||
SH600482,no_valid_signals,0.27999999999999997
|
||||
SH600733,no_valid_signals,0.27999999999999997
|
||||
SH600177,no_valid_signals,0.27999999999999997
|
||||
SH600739,no_valid_signals,0.27999999999999997
|
||||
SZ002191,no_valid_signals,0.27999999999999997
|
||||
SZ002603,no_valid_signals,0.27999999999999997
|
||||
SH688002,no_valid_signals,0.27999999999999997
|
||||
SH600497,no_valid_signals,0.27999999999999997
|
||||
SH601997,no_valid_signals,0.27999999999999997
|
||||
SH600038,no_valid_signals,0.27999999999999997
|
||||
SH600369,no_valid_signals,0.27999999999999997
|
||||
SZ002936,no_valid_signals,0.27999999999999997
|
||||
SH601016,no_valid_signals,0.27999999999999997
|
||||
SZ000825,no_valid_signals,0.27999999999999997
|
||||
SH600879,no_valid_signals,0.27999999999999997
|
||||
SH600039,no_valid_signals,0.27999999999999997
|
||||
SZ300618,no_valid_signals,0.27999999999999997
|
||||
SZ000750,no_valid_signals,0.27999999999999997
|
||||
SH600170,no_valid_signals,0.27999999999999997
|
||||
SH600867,no_valid_signals,0.27999999999999997
|
||||
SZ000656,no_valid_signals,0.27999999999999997
|
||||
SZ002926,no_valid_signals,0.27999999999999997
|
||||
SH600909,no_valid_signals,0.27999999999999997
|
||||
SH600008,no_valid_signals,0.27999999999999997
|
||||
SH600580,no_valid_signals,0.27999999999999997
|
||||
SZ000999,no_valid_signals,0.27999999999999997
|
||||
SH600536,no_valid_signals,0.27999999999999997
|
||||
SH601005,no_valid_signals,0.27999999999999997
|
||||
SH600316,no_valid_signals,0.27999999999999997
|
||||
SH603707,no_valid_signals,0.27999999999999997
|
||||
SZ000027,no_valid_signals,0.27999999999999997
|
||||
SZ002002,empty_data,0.27999999999999997
|
||||
SZ002266,no_valid_signals,0.27999999999999997
|
||||
SZ002500,no_valid_signals,0.27999999999999997
|
||||
SH600801,no_valid_signals,0.27999999999999997
|
||||
SH600208,no_valid_signals,0.27999999999999997
|
||||
SZ002249,no_valid_signals,0.27999999999999997
|
||||
SH600415,no_valid_signals,0.27999999999999997
|
||||
SZ000012,no_valid_signals,0.27999999999999997
|
||||
SH601866,no_valid_signals,0.27999999999999997
|
||||
SZ002511,no_valid_signals,0.27999999999999997
|
||||
SZ000060,no_valid_signals,0.27999999999999997
|
||||
SH601992,no_valid_signals,0.27999999999999997
|
||||
SH600839,no_valid_signals,0.27999999999999997
|
||||
SZ002966,no_valid_signals,0.27999999999999997
|
||||
SH600155,no_valid_signals,0.27999999999999997
|
||||
SZ002128,no_valid_signals,0.27999999999999997
|
||||
SH600517,no_valid_signals,0.27999999999999997
|
||||
SH600985,no_valid_signals,0.27999999999999997
|
||||
SZ000778,no_valid_signals,0.27999999999999997
|
||||
SH600782,no_valid_signals,0.27999999999999997
|
||||
SZ002939,no_valid_signals,0.27999999999999997
|
||||
SZ002294,no_valid_signals,0.27999999999999997
|
||||
SZ000540,empty_data,0.27999999999999997
|
||||
SH600863,no_valid_signals,0.27999999999999997
|
||||
SZ002958,no_valid_signals,0.27999999999999997
|
||||
SH601179,no_valid_signals,0.27999999999999997
|
||||
SH600755,no_valid_signals,0.27999999999999997
|
||||
SZ000598,no_valid_signals,0.27999999999999997
|
||||
SH600297,no_valid_signals,0.27999999999999997
|
||||
SZ002701,no_valid_signals,0.27999999999999997
|
||||
SH600820,no_valid_signals,0.27999999999999997
|
||||
SH600567,no_valid_signals,0.27999999999999997
|
||||
SZ000709,no_valid_signals,0.27999999999999997
|
||||
SZ002595,empty_data,0.27999999999999997
|
||||
SZ002075,no_valid_signals,0.27999999999999997
|
||||
SZ002146,no_valid_signals,0.27999999999999997
|
||||
SH600409,no_valid_signals,0.27999999999999997
|
||||
SH600021,no_valid_signals,0.27999999999999997
|
||||
SH600827,no_valid_signals,0.27999999999999997
|
||||
SH601880,no_valid_signals,0.27999999999999997
|
||||
SH600598,no_valid_signals,0.27999999999999997
|
||||
SH600808,no_valid_signals,0.27999999999999997
|
||||
SH600022,no_valid_signals,0.27999999999999997
|
||||
SZ000987,no_valid_signals,0.27999999999999997
|
||||
SH600737,no_valid_signals,0.27999999999999997
|
||||
SZ000883,no_valid_signals,0.27999999999999997
|
||||
SH600811,no_valid_signals,0.27999999999999997
|
||||
SH601106,no_valid_signals,0.27999999999999997
|
||||
SZ002233,no_valid_signals,0.27999999999999997
|
||||
SZ002110,no_valid_signals,0.27999999999999997
|
||||
SH600967,no_valid_signals,0.27999999999999997
|
||||
SZ000898,no_valid_signals,0.27999999999999997
|
||||
SH601118,no_valid_signals,0.27999999999999997
|
||||
SZ000488,no_valid_signals,0.27999999999999997
|
||||
SZ000877,no_valid_signals,0.27999999999999997
|
||||
SH600064,no_valid_signals,0.27999999999999997
|
||||
SH601333,no_valid_signals,0.27999999999999997
|
||||
SH601200,no_valid_signals,0.27999999999999997
|
||||
SZ002081,no_valid_signals,0.27999999999999997
|
||||
SZ000089,no_valid_signals,0.27999999999999997
|
||||
SZ000559,no_valid_signals,0.27999999999999997
|
||||
SZ000959,no_valid_signals,0.27999999999999997
|
||||
SZ000528,no_valid_signals,0.27999999999999997
|
||||
SH600195,no_valid_signals,0.27999999999999997
|
||||
SZ002434,no_valid_signals,0.27999999999999997
|
||||
SZ000685,no_valid_signals,0.27999999999999997
|
||||
SZ000671,empty_data,0.27999999999999997
|
||||
SH600120,no_valid_signals,0.27999999999999997
|
||||
SZ001965,no_valid_signals,0.27999999999999997
|
||||
SH601608,no_valid_signals,0.27999999999999997
|
||||
SH600126,no_valid_signals,0.27999999999999997
|
||||
SH601611,no_valid_signals,0.27999999999999997
|
||||
SZ000758,no_valid_signals,0.27999999999999997
|
||||
SH601098,no_valid_signals,0.27999999999999997
|
||||
SH600307,no_valid_signals,0.27999999999999997
|
||||
SH601598,no_valid_signals,0.27999999999999997
|
||||
SH600643,no_valid_signals,0.27999999999999997
|
||||
SZ000563,no_valid_signals,0.27999999999999997
|
||||
SZ000718,no_valid_signals,0.27999999999999997
|
||||
SH601958,no_valid_signals,0.27999999999999997
|
||||
SH600901,no_valid_signals,0.27999999999999997
|
||||
SH600717,no_valid_signals,0.27999999999999997
|
||||
SZ000967,no_valid_signals,0.27999999999999997
|
||||
SZ002458,no_valid_signals,0.27999999999999997
|
||||
SH601860,no_valid_signals,0.27999999999999997
|
||||
SZ002563,no_valid_signals,0.27999999999999997
|
||||
SH600928,no_valid_signals,0.27999999999999997
|
||||
SH600968,no_valid_signals,0.27999999999999997
|
||||
SZ000937,no_valid_signals,0.27999999999999997
|
||||
SZ000717,no_valid_signals,0.27999999999999997
|
||||
SH600649,no_valid_signals,0.27999999999999997
|
||||
SH600648,no_valid_signals,0.27999999999999997
|
||||
SH600623,no_valid_signals,0.27999999999999997
|
||||
SH600377,no_valid_signals,0.27999999999999997
|
||||
SH601139,no_valid_signals,0.27999999999999997
|
||||
SZ000869,no_valid_signals,0.27999999999999997
|
||||
SH600871,no_valid_signals,0.27999999999999997
|
||||
SH600639,no_valid_signals,0.27999999999999997
|
||||
SH601298,no_valid_signals,0.27999999999999997
|
||||
SZ000046,empty_data,0.27999999999999997
|
||||
SZ001872,no_valid_signals,0.27999999999999997
|
||||
SH601156,no_valid_signals,0.27999999999999997
|
||||
SH603708,no_valid_signals,0.27999999999999997
|
||||
SH600350,no_valid_signals,0.27999999999999997
|
||||
SH601568,no_valid_signals,0.27999999999999997
|
||||
SH600466,empty_data,0.27999999999999997
|
||||
SH603515,no_valid_signals,0.27999999999999997
|
||||
SH601665,no_valid_signals,0.27999999999999997
|
||||
SH600917,no_valid_signals,0.27999999999999997
|
||||
SH600956,no_valid_signals,0.27999999999999997
|
||||
SH601187,no_valid_signals,0.27999999999999997
|
||||
SH603983,no_valid_signals,0.27999999999999997
|
||||
|
|
|
@ -0,0 +1,161 @@
|
|||
symbol,reason,take_profit_pct
|
||||
SZ002340,no_valid_signals,0.29
|
||||
SH600157,no_valid_signals,0.29
|
||||
SZ000723,no_valid_signals,0.29
|
||||
SZ000009,no_valid_signals,0.29
|
||||
SH601555,no_valid_signals,0.29
|
||||
SH600884,no_valid_signals,0.29
|
||||
SH600298,no_valid_signals,0.29
|
||||
SZ002028,no_valid_signals,0.29
|
||||
SZ300363,no_valid_signals,0.29
|
||||
SH601077,no_valid_signals,0.29
|
||||
SZ002013,empty_data,0.29
|
||||
SH603267,no_valid_signals,0.29
|
||||
SZ000728,no_valid_signals,0.29
|
||||
SH600486,no_valid_signals,0.29
|
||||
SZ002797,no_valid_signals,0.29
|
||||
SH603077,no_valid_signals,0.29
|
||||
SZ002673,no_valid_signals,0.29
|
||||
SH601128,no_valid_signals,0.29
|
||||
SH600482,no_valid_signals,0.29
|
||||
SH600733,no_valid_signals,0.29
|
||||
SH600177,no_valid_signals,0.29
|
||||
SH600739,no_valid_signals,0.29
|
||||
SZ002191,no_valid_signals,0.29
|
||||
SZ002603,no_valid_signals,0.29
|
||||
SH688002,no_valid_signals,0.29
|
||||
SH600497,no_valid_signals,0.29
|
||||
SH601997,no_valid_signals,0.29
|
||||
SH600038,no_valid_signals,0.29
|
||||
SH600369,no_valid_signals,0.29
|
||||
SZ002936,no_valid_signals,0.29
|
||||
SH601016,no_valid_signals,0.29
|
||||
SZ000825,no_valid_signals,0.29
|
||||
SH600879,no_valid_signals,0.29
|
||||
SH600039,no_valid_signals,0.29
|
||||
SZ300618,no_valid_signals,0.29
|
||||
SZ000750,no_valid_signals,0.29
|
||||
SH600170,no_valid_signals,0.29
|
||||
SH600867,no_valid_signals,0.29
|
||||
SZ000656,no_valid_signals,0.29
|
||||
SZ002926,no_valid_signals,0.29
|
||||
SH600909,no_valid_signals,0.29
|
||||
SH600008,no_valid_signals,0.29
|
||||
SH600580,no_valid_signals,0.29
|
||||
SZ000999,no_valid_signals,0.29
|
||||
SH600536,no_valid_signals,0.29
|
||||
SH601005,no_valid_signals,0.29
|
||||
SH600316,no_valid_signals,0.29
|
||||
SH603707,no_valid_signals,0.29
|
||||
SZ000027,no_valid_signals,0.29
|
||||
SZ002002,empty_data,0.29
|
||||
SZ002266,no_valid_signals,0.29
|
||||
SZ002500,no_valid_signals,0.29
|
||||
SH600801,no_valid_signals,0.29
|
||||
SH600208,no_valid_signals,0.29
|
||||
SZ002249,no_valid_signals,0.29
|
||||
SH600415,no_valid_signals,0.29
|
||||
SZ000012,no_valid_signals,0.29
|
||||
SH601866,no_valid_signals,0.29
|
||||
SZ002511,no_valid_signals,0.29
|
||||
SZ000060,no_valid_signals,0.29
|
||||
SH601992,no_valid_signals,0.29
|
||||
SH600839,no_valid_signals,0.29
|
||||
SZ002966,no_valid_signals,0.29
|
||||
SH600155,no_valid_signals,0.29
|
||||
SZ002128,no_valid_signals,0.29
|
||||
SH600517,no_valid_signals,0.29
|
||||
SH600985,no_valid_signals,0.29
|
||||
SZ000778,no_valid_signals,0.29
|
||||
SH600782,no_valid_signals,0.29
|
||||
SZ002939,no_valid_signals,0.29
|
||||
SZ002294,no_valid_signals,0.29
|
||||
SZ000540,empty_data,0.29
|
||||
SH600863,no_valid_signals,0.29
|
||||
SZ002958,no_valid_signals,0.29
|
||||
SH601179,no_valid_signals,0.29
|
||||
SH600755,no_valid_signals,0.29
|
||||
SZ000598,no_valid_signals,0.29
|
||||
SH600297,no_valid_signals,0.29
|
||||
SZ002701,no_valid_signals,0.29
|
||||
SH600820,no_valid_signals,0.29
|
||||
SH600567,no_valid_signals,0.29
|
||||
SZ000709,no_valid_signals,0.29
|
||||
SZ002075,no_valid_signals,0.29
|
||||
SZ002146,no_valid_signals,0.29
|
||||
SH600409,no_valid_signals,0.29
|
||||
SH600021,no_valid_signals,0.29
|
||||
SH600827,no_valid_signals,0.29
|
||||
SH601880,no_valid_signals,0.29
|
||||
SH600598,no_valid_signals,0.29
|
||||
SH600808,no_valid_signals,0.29
|
||||
SH600022,no_valid_signals,0.29
|
||||
SZ000987,no_valid_signals,0.29
|
||||
SH600737,no_valid_signals,0.29
|
||||
SZ000883,no_valid_signals,0.29
|
||||
SH600811,no_valid_signals,0.29
|
||||
SH601106,no_valid_signals,0.29
|
||||
SZ002233,no_valid_signals,0.29
|
||||
SZ002110,no_valid_signals,0.29
|
||||
SH600967,no_valid_signals,0.29
|
||||
SZ000898,no_valid_signals,0.29
|
||||
SH601118,no_valid_signals,0.29
|
||||
SZ000488,no_valid_signals,0.29
|
||||
SZ000877,no_valid_signals,0.29
|
||||
SH600064,no_valid_signals,0.29
|
||||
SH601333,no_valid_signals,0.29
|
||||
SH601200,no_valid_signals,0.29
|
||||
SZ002081,no_valid_signals,0.29
|
||||
SZ000089,no_valid_signals,0.29
|
||||
SZ000559,no_valid_signals,0.29
|
||||
SZ000959,no_valid_signals,0.29
|
||||
SZ000528,no_valid_signals,0.29
|
||||
SH600195,no_valid_signals,0.29
|
||||
SZ002434,no_valid_signals,0.29
|
||||
SZ000685,no_valid_signals,0.29
|
||||
SZ000671,empty_data,0.29
|
||||
SH600120,no_valid_signals,0.29
|
||||
SZ001965,no_valid_signals,0.29
|
||||
SH601608,no_valid_signals,0.29
|
||||
SH600126,no_valid_signals,0.29
|
||||
SH601611,no_valid_signals,0.29
|
||||
SZ000758,no_valid_signals,0.29
|
||||
SH601098,no_valid_signals,0.29
|
||||
SH600307,no_valid_signals,0.29
|
||||
SH601598,no_valid_signals,0.29
|
||||
SH600643,no_valid_signals,0.29
|
||||
SZ000563,no_valid_signals,0.29
|
||||
SZ000718,no_valid_signals,0.29
|
||||
SH601958,no_valid_signals,0.29
|
||||
SH600901,no_valid_signals,0.29
|
||||
SH600717,no_valid_signals,0.29
|
||||
SZ000967,no_valid_signals,0.29
|
||||
SZ002458,no_valid_signals,0.29
|
||||
SH601860,no_valid_signals,0.29
|
||||
SZ002563,no_valid_signals,0.29
|
||||
SH600928,no_valid_signals,0.29
|
||||
SH600968,no_valid_signals,0.29
|
||||
SZ000937,no_valid_signals,0.29
|
||||
SZ000717,no_valid_signals,0.29
|
||||
SH600649,no_valid_signals,0.29
|
||||
SH600648,no_valid_signals,0.29
|
||||
SH600623,no_valid_signals,0.29
|
||||
SH600377,no_valid_signals,0.29
|
||||
SH601139,no_valid_signals,0.29
|
||||
SZ000869,no_valid_signals,0.29
|
||||
SH600871,no_valid_signals,0.29
|
||||
SH600639,no_valid_signals,0.29
|
||||
SH601298,no_valid_signals,0.29
|
||||
SZ000046,empty_data,0.29
|
||||
SZ001872,no_valid_signals,0.29
|
||||
SH601156,no_valid_signals,0.29
|
||||
SH603708,no_valid_signals,0.29
|
||||
SH600350,no_valid_signals,0.29
|
||||
SH601568,no_valid_signals,0.29
|
||||
SH600466,empty_data,0.29
|
||||
SH603515,no_valid_signals,0.29
|
||||
SH601665,no_valid_signals,0.29
|
||||
SH600917,no_valid_signals,0.29
|
||||
SH600956,no_valid_signals,0.29
|
||||
SH601187,no_valid_signals,0.29
|
||||
SH603983,no_valid_signals,0.29
|
||||
|
|
|
@ -0,0 +1,161 @@
|
|||
symbol,reason,take_profit_pct
|
||||
SZ002340,no_valid_signals,0.3
|
||||
SH600157,no_valid_signals,0.3
|
||||
SZ000723,no_valid_signals,0.3
|
||||
SZ000009,no_valid_signals,0.3
|
||||
SH601555,no_valid_signals,0.3
|
||||
SH600884,no_valid_signals,0.3
|
||||
SH600298,no_valid_signals,0.3
|
||||
SZ002028,no_valid_signals,0.3
|
||||
SZ300363,no_valid_signals,0.3
|
||||
SH601077,no_valid_signals,0.3
|
||||
SZ002013,empty_data,0.3
|
||||
SH603267,no_valid_signals,0.3
|
||||
SZ000728,no_valid_signals,0.3
|
||||
SH600486,no_valid_signals,0.3
|
||||
SZ002797,no_valid_signals,0.3
|
||||
SH603077,no_valid_signals,0.3
|
||||
SZ002673,no_valid_signals,0.3
|
||||
SH601128,no_valid_signals,0.3
|
||||
SH600482,no_valid_signals,0.3
|
||||
SH600733,no_valid_signals,0.3
|
||||
SH600177,no_valid_signals,0.3
|
||||
SH600739,no_valid_signals,0.3
|
||||
SZ002191,no_valid_signals,0.3
|
||||
SZ002603,no_valid_signals,0.3
|
||||
SH688002,no_valid_signals,0.3
|
||||
SH600497,no_valid_signals,0.3
|
||||
SH601997,no_valid_signals,0.3
|
||||
SH600038,no_valid_signals,0.3
|
||||
SH600369,no_valid_signals,0.3
|
||||
SZ002936,no_valid_signals,0.3
|
||||
SH601016,no_valid_signals,0.3
|
||||
SZ000825,no_valid_signals,0.3
|
||||
SH600879,no_valid_signals,0.3
|
||||
SH600039,no_valid_signals,0.3
|
||||
SZ300618,no_valid_signals,0.3
|
||||
SZ000750,no_valid_signals,0.3
|
||||
SH600170,no_valid_signals,0.3
|
||||
SH600867,no_valid_signals,0.3
|
||||
SZ000656,no_valid_signals,0.3
|
||||
SZ002926,no_valid_signals,0.3
|
||||
SH600909,no_valid_signals,0.3
|
||||
SH600008,no_valid_signals,0.3
|
||||
SH600580,no_valid_signals,0.3
|
||||
SZ000999,no_valid_signals,0.3
|
||||
SH600536,no_valid_signals,0.3
|
||||
SH601005,no_valid_signals,0.3
|
||||
SH600316,no_valid_signals,0.3
|
||||
SH603707,no_valid_signals,0.3
|
||||
SZ000027,no_valid_signals,0.3
|
||||
SZ002002,empty_data,0.3
|
||||
SZ002266,no_valid_signals,0.3
|
||||
SZ002500,no_valid_signals,0.3
|
||||
SH600801,no_valid_signals,0.3
|
||||
SH600208,no_valid_signals,0.3
|
||||
SZ002249,no_valid_signals,0.3
|
||||
SH600415,no_valid_signals,0.3
|
||||
SZ000012,no_valid_signals,0.3
|
||||
SH601866,no_valid_signals,0.3
|
||||
SZ002511,no_valid_signals,0.3
|
||||
SZ000060,no_valid_signals,0.3
|
||||
SH601992,no_valid_signals,0.3
|
||||
SH600839,no_valid_signals,0.3
|
||||
SZ002966,no_valid_signals,0.3
|
||||
SH600155,no_valid_signals,0.3
|
||||
SZ002128,no_valid_signals,0.3
|
||||
SH600517,no_valid_signals,0.3
|
||||
SH600985,no_valid_signals,0.3
|
||||
SZ000778,no_valid_signals,0.3
|
||||
SH600782,no_valid_signals,0.3
|
||||
SZ002939,no_valid_signals,0.3
|
||||
SZ002294,no_valid_signals,0.3
|
||||
SZ000540,empty_data,0.3
|
||||
SH600863,no_valid_signals,0.3
|
||||
SZ002958,no_valid_signals,0.3
|
||||
SH601179,no_valid_signals,0.3
|
||||
SH600755,no_valid_signals,0.3
|
||||
SZ000598,no_valid_signals,0.3
|
||||
SH600297,no_valid_signals,0.3
|
||||
SZ002701,no_valid_signals,0.3
|
||||
SH600820,no_valid_signals,0.3
|
||||
SH600567,no_valid_signals,0.3
|
||||
SZ000709,no_valid_signals,0.3
|
||||
SZ002075,no_valid_signals,0.3
|
||||
SZ002146,no_valid_signals,0.3
|
||||
SH600409,no_valid_signals,0.3
|
||||
SH600021,no_valid_signals,0.3
|
||||
SH600827,no_valid_signals,0.3
|
||||
SH601880,no_valid_signals,0.3
|
||||
SH600598,no_valid_signals,0.3
|
||||
SH600808,no_valid_signals,0.3
|
||||
SH600022,no_valid_signals,0.3
|
||||
SZ000987,no_valid_signals,0.3
|
||||
SH600737,no_valid_signals,0.3
|
||||
SZ000883,no_valid_signals,0.3
|
||||
SH600811,no_valid_signals,0.3
|
||||
SH601106,no_valid_signals,0.3
|
||||
SZ002233,no_valid_signals,0.3
|
||||
SZ002110,no_valid_signals,0.3
|
||||
SH600967,no_valid_signals,0.3
|
||||
SZ000898,no_valid_signals,0.3
|
||||
SH601118,no_valid_signals,0.3
|
||||
SZ000488,no_valid_signals,0.3
|
||||
SZ000877,no_valid_signals,0.3
|
||||
SH600064,no_valid_signals,0.3
|
||||
SH601333,no_valid_signals,0.3
|
||||
SH601200,no_valid_signals,0.3
|
||||
SZ002081,no_valid_signals,0.3
|
||||
SZ000089,no_valid_signals,0.3
|
||||
SZ000559,no_valid_signals,0.3
|
||||
SZ000959,no_valid_signals,0.3
|
||||
SZ000528,no_valid_signals,0.3
|
||||
SH600195,no_valid_signals,0.3
|
||||
SZ002434,no_valid_signals,0.3
|
||||
SZ000685,no_valid_signals,0.3
|
||||
SZ000671,empty_data,0.3
|
||||
SH600120,no_valid_signals,0.3
|
||||
SZ001965,no_valid_signals,0.3
|
||||
SH601608,no_valid_signals,0.3
|
||||
SH600126,no_valid_signals,0.3
|
||||
SH601611,no_valid_signals,0.3
|
||||
SZ000758,no_valid_signals,0.3
|
||||
SH601098,no_valid_signals,0.3
|
||||
SH600307,no_valid_signals,0.3
|
||||
SH601598,no_valid_signals,0.3
|
||||
SH600643,no_valid_signals,0.3
|
||||
SZ000563,no_valid_signals,0.3
|
||||
SZ000718,no_valid_signals,0.3
|
||||
SH601958,no_valid_signals,0.3
|
||||
SH600901,no_valid_signals,0.3
|
||||
SH600717,no_valid_signals,0.3
|
||||
SZ000967,no_valid_signals,0.3
|
||||
SZ002458,no_valid_signals,0.3
|
||||
SH601860,no_valid_signals,0.3
|
||||
SZ002563,no_valid_signals,0.3
|
||||
SH600928,no_valid_signals,0.3
|
||||
SH600968,no_valid_signals,0.3
|
||||
SZ000937,no_valid_signals,0.3
|
||||
SZ000717,no_valid_signals,0.3
|
||||
SH600649,no_valid_signals,0.3
|
||||
SH600648,no_valid_signals,0.3
|
||||
SH600623,no_valid_signals,0.3
|
||||
SH600377,no_valid_signals,0.3
|
||||
SH601139,no_valid_signals,0.3
|
||||
SZ000869,no_valid_signals,0.3
|
||||
SH600871,no_valid_signals,0.3
|
||||
SH600639,no_valid_signals,0.3
|
||||
SH601298,no_valid_signals,0.3
|
||||
SZ000046,empty_data,0.3
|
||||
SZ001872,no_valid_signals,0.3
|
||||
SH601156,no_valid_signals,0.3
|
||||
SH603708,no_valid_signals,0.3
|
||||
SH600350,no_valid_signals,0.3
|
||||
SH601568,no_valid_signals,0.3
|
||||
SH600466,empty_data,0.3
|
||||
SH603515,no_valid_signals,0.3
|
||||
SH601665,no_valid_signals,0.3
|
||||
SH600917,no_valid_signals,0.3
|
||||
SH600956,no_valid_signals,0.3
|
||||
SH601187,no_valid_signals,0.3
|
||||
SH603983,no_valid_signals,0.3
|
||||
|
|
|
@ -0,0 +1,2 @@
|
|||
take_profit_pct,total_profit,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count
|
||||
23.999999999999993,-51449903.2556674,-20176.432649281334,0,116.57411764705883,40.94117647058824,39.863325740318906,2550,439
|
||||
|
|
|
@ -0,0 +1,2 @@
|
|||
take_profit_pct,total_profit,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count
|
||||
24.999999999999993,-51842058.294730715,-20394.200745370068,0,118.2285601888277,40.440597954366645,39.40774487471526,2542,439
|
||||
|
|
|
@ -0,0 +1,2 @@
|
|||
take_profit_pct,total_profit,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count
|
||||
25.999999999999996,-52586377.81024381,-20851.06178042974,0,119.98215701823949,39.809674861221254,39.17995444191344,2522,439
|
||||
|
|
|
@ -0,0 +1,2 @@
|
|||
take_profit_pct,total_profit,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count
|
||||
26.999999999999996,-54650589.844712615,-21825.31543319194,0,121.754392971246,39.05750798722045,38.496583143507976,2504,439
|
||||
|
|
|
@ -0,0 +1,2 @@
|
|||
take_profit_pct,total_profit,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count
|
||||
27.999999999999996,-13478086.243568404,-13711.176239642324,0,78.75483214649033,37.945066124109864,39.823008849557525,983,339
|
||||
|
|
|
@ -0,0 +1,2 @@
|
|||
take_profit_pct,total_profit,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count
|
||||
28.999999999999996,-12628925.952513697,-12860.413393598468,0,79.27698574338086,37.67820773930753,40.294117647058826,982,340
|
||||
|
|
|
@ -0,0 +1,2 @@
|
|||
take_profit_pct,total_profit,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count
|
||||
30.0,-14121418.834878381,-14453.857558729153,0,80.489252814739,36.8474923234391,39.705882352941174,977,340
|
||||
|
|
|
@ -0,0 +1,102 @@
|
|||
#!/usr/bin/env python
|
||||
"""
|
||||
API服务启动脚本
|
||||
用于启动Flask API服务，确保所有必要的模块能被正确导入
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import logging
|
||||
import importlib.util
|
||||
|
||||
# 设置日志
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 获取项目根目录的绝对路径
|
||||
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
PROJECT_ROOT = os.path.dirname(ROOT_DIR)
|
||||
|
||||
# 添加项目根目录到 Python 路径
|
||||
sys.path.append(PROJECT_ROOT)
|
||||
sys.path.append(ROOT_DIR)
|
||||
|
||||
# 确保所有必要的包存在
|
||||
def ensure_package(package_name):
|
||||
"""确保包可以被导入"""
|
||||
logger.info(f"检查包 {package_name} 是否存在...")
|
||||
|
||||
# 检查包目录是否存在
|
||||
package_path = os.path.join(ROOT_DIR, package_name)
|
||||
if not os.path.isdir(package_path):
|
||||
logger.error(f"包目录 {package_path} 不存在!")
|
||||
return False
|
||||
|
||||
# 确保__init__.py文件存在
|
||||
init_file = os.path.join(package_path, "__init__.py")
|
||||
if not os.path.exists(init_file):
|
||||
logger.warning(f"创建缺失的 {init_file} 文件")
|
||||
with open(init_file, "w") as f:
|
||||
f.write('"""' + package_name + ' package"""')
|
||||
|
||||
return True
|
||||
|
||||
# 检查关键模块
|
||||
def check_modules():
|
||||
"""检查关键模块是否可以导入"""
|
||||
modules_to_check = [
|
||||
'fundamentals_llm.chat_bot',
|
||||
'fundamentals_llm.chat_bot_with_offline',
|
||||
'fundamentals_llm.fundamental_analysis',
|
||||
'fundamentals_llm.enterprise_screener'
|
||||
]
|
||||
|
||||
all_ok = True
|
||||
for module_name in modules_to_check:
|
||||
try:
|
||||
module = importlib.import_module(module_name)
|
||||
logger.info(f"成功导入模块 {module_name}")
|
||||
except ImportError as e:
|
||||
logger.error(f"导入模块 {module_name} 失败: {str(e)}")
|
||||
# 尝试从src导入
|
||||
try:
|
||||
module = importlib.import_module(f'src.{module_name}')
|
||||
logger.info(f"成功从src导入模块 {module_name}")
|
||||
except ImportError as e2:
|
||||
logger.error(f"导入模块 src.{module_name} 失败: {str(e2)}")
|
||||
all_ok = False
|
||||
|
||||
return all_ok
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
logger.info("启动API服务...")
|
||||
|
||||
# 确保必要的包存在
|
||||
if not ensure_package('fundamentals_llm'):
|
||||
logger.error("缺少必要的包，无法启动API服务")
|
||||
return False
|
||||
|
||||
if not ensure_package('scripts'):
|
||||
logger.warning("scripts包不存在或不完整，API服务可能无法正常工作")
|
||||
|
||||
# 检查关键模块
|
||||
if not check_modules():
|
||||
logger.warning("部分模块导入失败，API服务可能无法正常工作")
|
||||
|
||||
try:
|
||||
# 尝试导入app
|
||||
from app import app
|
||||
|
||||
# 启动Flask应用
|
||||
app.run(host='0.0.0.0', port=5000, debug=True)
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"启动API服务失败: {str(e)}")
|
||||
return False
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(0 if main() else 1)
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
"""
|
||||
Scripts package initialization
|
||||
用于存放各种脚本和工具的包
|
||||
"""
|
||||
|
||||
# 版本号
|
||||
__version__ = '1.0.0'
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,118 @@
|
|||
# coding:utf-8
|
||||
|
||||
"""
|
||||
通用配置文件，存储共享的配置项
|
||||
"""
|
||||
|
||||
import random
|
||||
|
||||
XUEQIU_HEADERS = {
|
||||
'Accept': 'application/json, text/plain, */*',
|
||||
'Accept-Encoding': 'gzip, deflate, br, zstd',
|
||||
'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
|
||||
'Client-Version': 'v2.44.75',
|
||||
'Cookie': 'bid=d298883c846926e46f61128493d71969_ltocon4a; device_id=99947aa416bde5e7418bd51327850c56; HMACCOUNT=2D95237D28EF1309; s=be11f4j7l4; cookiesu=651724318004914; Hm_lvt_1db88642e346389874251b5a1eded6e3=1739155871; xq_a_token=2b33ad6ac971d226d2d8e9e2487621e834ad3550; xqat=2b33ad6ac971d226d2d8e9e2487621e834ad3550; xq_r_token=f17217fe00776426e761885c67f799dac6d960f4; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOjg0OTM0MTE2MzQsImlzcyI6InVjIiwiZXhwIjoxNzQzNjQ1MjY2LCJjdG0iOjE3NDEwNTMyNjY3MjYsImNpZCI6ImQ5ZDBuNEFadXAifQ.UCe60fXpKIKGEHjA8fwHQ4m0WBCdpCllnuQrLQ_EbpSf9joPboNq4xanvs15SFl8EzEvWRFqgyaDb-TLUkb-YuSXb2vnDVQKeHW1CmQMwihpAS8bz9FKnspw3PeQZLJTfgwUFl7uTKRkjNu3x1lk1OJ3SmyjlGDAEEQgsvZlK-OZLcXvoTdOlM3B9ppERN0vicURiZkxVT68B_j4wEmegOWdsaEhTEXeBfhzMu1ObmztdcTolhBPB59vDFpMmVJz7YjWkkrESgq0Kjx6xpx_afwtMY2NDciuslSA3ji5zDG2objiX8tGzLFFqHdslZRg0ulK-MJeUvH6hpCbSQ7cEw; xq_is_login=1; u=8493411634; is_overseas=0; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1741144661; ssxmod_itna=YqIxnD0DyGehDkDhx+xmqAKQAQDOD2GSAD0x0dGM9/DILq7=GFKDCEKLumb2G+R0DeYrAG2mq1DBLAoDSxD67DK4GTI+OGe41i7G2vppqW7h+tK3FKoZUPo20do4gdOeIbed5=xB3DExGkeQ0re4iiWx0rD0eDPxDYDG4aD7PDoxDrF8YDjl7pOUgwoUxDKx0kDY5Dw62wDYPDWxDFNIGFNUAeDD5Dn1SUw4DWDWpmYDDzYjQ4wcY4DmRw7SHfkj8XeGENN4G1jD0H7Dt4NUkBwwGv7f6NivW+vz3DvxDkU8oGLbwmGfEhiueq0DoOGI7dqBe77oeAeFYxeD4KiGe04YDqK7iYODWGD7GeqOxk2QwlGIi7IDqSOdkowe3znMzN3sMuev7DdCDpEGox4mB=5rxq7Y70vmrGvE5VYYiAAMYpF04bWA7GiYiDD; ssxmod_itna2=YqIxnD0DyGehDkDhx+xmqAKQAQDOD2GSAD0x0dGM9/DILq7=GFKDCEKLumb2G+R0DeYrAG2mqTDGRW0wqQD7P4mGKeDsYGtidoeFEjpbF4WjtDqn+ao7pCSAt8ko=xEKjfG+96eKGNSNbhha7W5a2xKenxPKzaRC2BqW4hv+rf5eyoKauABacPT1TOpp+FIm2A7ssEHQA1Gai3fwGtMHG/fHC4vHiRvHctgGuDSCN7aAA=wWzgf4i=Fu3YduYqK8S1Cm=lFc20GFzq2FrS=xN5=RZWpyKlj6hKaSkvIWvFXNjoghz=iFf1j4mIygYfeADbPxzQq7QTOPyEPacND=IRWU2i4m=GgQN4Dozhjux=X1Irf6xAvHO2xAA1iRqp6I1DKxR77GSDhygvO7QwcQVEPwO25Wpj6RgD2574e7+IRnv8+xAqzBqGYeQDh1jn64aeYD',
|
||||
'Referer': 'https://weibo.com/u/7735765253',
|
||||
'Sec-Ch-Ua': '"Chromium";v="122", "Not(A:Brand";v="24", "Google Chrome";v="122"',
|
||||
'Sec-Ch-Ua-Mobile': '?0',
|
||||
'Sec-Ch-Ua-Platform': '"Windows"',
|
||||
'Sec-Fetch-Dest': 'empty',
|
||||
'Sec-Fetch-Mode': 'cors',
|
||||
'Sec-Fetch-Site': 'same-origin',
|
||||
'Server-Version': 'v2024.03.19.2',
|
||||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36',
|
||||
'X-Requested-With': 'XMLHttpRequest',
|
||||
'X-Xsrf-Token': 'spbnXpnWWKiHZFvOqW4rIejO'
|
||||
}
|
||||
|
||||
# 火山引擎API密钥列表
|
||||
VOLC_API_KEYS = [
|
||||
"28cfe71a-c6fa-4c5d-9b4e-d8474f0d3b93", # 测试密钥1
|
||||
"28cfe71a-c6fa-4c5d-9b4e-d8474f0d3b93", # 可添加更多密钥
|
||||
]
|
||||
|
||||
# 获取随机API密钥
|
||||
def get_random_api_key():
|
||||
return random.choice(VOLC_API_KEYS)
|
||||
|
||||
# 火山引擎模型配置
|
||||
VOLC_MODELS = {
|
||||
# 在线智能体模型
|
||||
"online_bot": "bot-20250325102825-h9kpq", # 联网智能体ID
|
||||
|
||||
# 离线模型
|
||||
"offline_model": "ep-20250326090920-v7wns", # 免费推理50W
|
||||
|
||||
"deepseek-private": "mlx-community/DeepSeek-R1-4bit",
|
||||
|
||||
# 其他可用模型
|
||||
"doubao": "doubao-1-5-pro-32k-250115", # 豆包模型
|
||||
"deepseek": "deepseek-r1-250120", # Deepseek模型
|
||||
}
|
||||
|
||||
# 获取指定类型的模型
|
||||
def get_model(model_type):
|
||||
return VOLC_MODELS.get(model_type, VOLC_MODELS["offline_model"])
|
||||
|
||||
# 模型配置字典
|
||||
MODEL_CONFIGS = {
|
||||
# 火山引擎配置
|
||||
"volc": {
|
||||
"base_url": "https://ark.cn-beijing.volces.com/api/v3/",
|
||||
"api_key": "28cfe71a-c6fa-4c5d-9b4e-d8474f0d3b93",
|
||||
"models": {
|
||||
"offline_model": "ep-20250326090920-v7wns",
|
||||
"online_bot": "bot-20250325102825-h9kpq",
|
||||
"doubao": "doubao-1-5-pro-32k-250115"
|
||||
}
|
||||
},
|
||||
# 天链苹果
|
||||
"tl_private": {
|
||||
"base_url": "http://192.168.32.118:1234/v1/",
|
||||
"api_key": "none",
|
||||
"models": {
|
||||
"ds-v1": "mlx-community/DeepSeek-R1-4bit"
|
||||
}
|
||||
},
|
||||
# 天链-千问
|
||||
"tl_qw_private": {
|
||||
"base_url": "http://192.168.16.178:11434/v1",
|
||||
"api_key": "sk-WaVRJKkyhrFlH4ZV35B9Aa61759b400c9cA002D00f3f1019",
|
||||
"models": {
|
||||
"qwq": "qwq:32b"
|
||||
}
|
||||
},
|
||||
# Deepseek配置
|
||||
"deepseek": {
|
||||
"base_url": "https://api.deepseek.com/v1/",
|
||||
"api_key": "your_deepseek_api_key",
|
||||
"models": {
|
||||
"deepseek": "deepseek-r1-250120",
|
||||
"deepseek-private": "mlx-community/DeepSeek-R1-4bit"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# 获取指定平台和模型的配置
|
||||
def get_model_config(platform: str, model_type: str) -> dict:
|
||||
"""获取指定平台和模型的配置
|
||||
|
||||
Args:
|
||||
platform: 平台名称（volc/doubao/deepseek）
|
||||
model_type: 模型类型
|
||||
|
||||
Returns:
|
||||
dict: 包含base_url、api_key和model的配置字典
|
||||
"""
|
||||
if platform not in MODEL_CONFIGS:
|
||||
raise ValueError(f"不支持的平台: {platform}")
|
||||
|
||||
platform_config = MODEL_CONFIGS[platform]
|
||||
if model_type not in platform_config["models"]:
|
||||
raise ValueError(f"平台 {platform} 不支持模型类型: {model_type}")
|
||||
|
||||
return {
|
||||
"base_url": platform_config["base_url"],
|
||||
"api_key": platform_config["api_key"],
|
||||
"model": platform_config["models"][model_type]
|
||||
}
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
# coding:utf-8
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine, text
|
||||
from datetime import datetime
|
||||
from config import XUEQIU_HEADERS
|
||||
|
||||
def ensure_convertible_bonds_table_exists(engine):
|
||||
"""确保可转债表存在"""
|
||||
try:
|
||||
create_table_query = text("""
|
||||
CREATE TABLE IF NOT EXISTS convertible_bonds (
|
||||
id INT AUTO_INCREMENT PRIMARY KEY,
|
||||
symbol VARCHAR(20),
|
||||
name VARCHAR(100),
|
||||
current FLOAT,
|
||||
percent FLOAT,
|
||||
chg FLOAT,
|
||||
underlying_symbol VARCHAR(20),
|
||||
underlying_name VARCHAR(100),
|
||||
underlying_current FLOAT,
|
||||
underlying_percent FLOAT,
|
||||
underlying_pb FLOAT,
|
||||
underlying_navps FLOAT,
|
||||
conversion_price FLOAT,
|
||||
conversion_value FLOAT,
|
||||
premium_rate FLOAT,
|
||||
convert_bond_ratio FLOAT,
|
||||
remain_year FLOAT,
|
||||
total_issue_scale FLOAT,
|
||||
benefit_before_tax FLOAT,
|
||||
benefit_after_tax FLOAT,
|
||||
putback_price FLOAT,
|
||||
maturity_time BIGINT,
|
||||
interest_memo TEXT,
|
||||
add_time DATETIME
|
||||
)
|
||||
""")
|
||||
with engine.connect() as conn:
|
||||
conn.execute(create_table_query)
|
||||
conn.commit()
|
||||
except Exception as e:
|
||||
print("创建可转债表时发生错误: {}".format(str(e)))
|
||||
|
||||
def fetch_convertible_bonds(db_url):
|
||||
"""获取可转债数据并保存到数据库"""
|
||||
base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
|
||||
headers = XUEQIU_HEADERS
|
||||
all_data = []
|
||||
page = 1
|
||||
|
||||
try:
|
||||
while True:
|
||||
params = {
|
||||
'page': page,
|
||||
'size': 90,
|
||||
'order': 'desc',
|
||||
'order_by': 'underlying_percent',
|
||||
'market': 'CN',
|
||||
'type': 'convert'
|
||||
}
|
||||
|
||||
response = requests.get(base_url, headers=headers, params=params)
|
||||
|
||||
if response.status_code != 200:
|
||||
print(f"响应内容: {response.text}")
|
||||
break
|
||||
|
||||
data = response.json()
|
||||
if data.get('error_code') != 0:
|
||||
break
|
||||
|
||||
bonds = data['data']['list']
|
||||
if not bonds: # 如果没有更多数据了
|
||||
print("没有更多数据了")
|
||||
break
|
||||
|
||||
all_data.extend(bonds)
|
||||
total_count = data['data'].get('count', 0)
|
||||
|
||||
# 如果已经获取了所有数据，就退出循环
|
||||
if len(all_data) >= total_count:
|
||||
break
|
||||
page += 1
|
||||
|
||||
if all_data:
|
||||
# 转换为DataFrame
|
||||
df = pd.DataFrame(all_data)
|
||||
|
||||
# 选择需要的列
|
||||
selected_columns = [
|
||||
'symbol', 'name', 'current', 'percent', 'chg',
|
||||
'underlying_symbol', 'underlying_name', 'underlying_current',
|
||||
'underlying_percent', 'underlying_pb', 'underlying_navps',
|
||||
'conversion_price', 'conversion_value', 'premium_rate',
|
||||
'convert_bond_ratio', 'remain_year', 'total_issue_scale',
|
||||
'benefit_before_tax', 'benefit_after_tax', 'putback_price',
|
||||
'maturity_time', 'interest_memo'
|
||||
]
|
||||
|
||||
df = df[selected_columns]
|
||||
|
||||
# 添加时间戳
|
||||
df['add_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
# 创建数据库连接
|
||||
engine = create_engine(db_url)
|
||||
|
||||
# 确保表存在
|
||||
ensure_convertible_bonds_table_exists(engine)
|
||||
|
||||
# 将数据写入数据库（使用replace模式）
|
||||
df.to_sql('convertible_bonds', con=engine, if_exists='replace', index=False)
|
||||
|
||||
else:
|
||||
print("未获取到可转债数据")
|
||||
|
||||
except Exception as e:
|
||||
print("获取可转债数据时发生错误:")
|
||||
print(f"错误类型: {type(e).__name__}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
fetch_convertible_bonds(db_url)
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
# coding:utf-8
|
||||
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine
|
||||
|
||||
def import_concept_sectors(file_path, db_url, table_name):
|
||||
"""
|
||||
从CSV文件导入概念板块数据到数据库。
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
file_path : str
|
||||
CSV文件的路径
|
||||
db_url : str
|
||||
数据库连接URL
|
||||
table_name : str, optional
|
||||
要导入的数据库表名，默认为'gp_gnbk'
|
||||
|
||||
Returns:
|
||||
--------
|
||||
bool
|
||||
导入成功返回True，失败返回False
|
||||
"""
|
||||
try:
|
||||
# 读取 CSV 文件
|
||||
df = pd.read_csv(file_path, dtype={'gp_code': str}, encoding='gbk')
|
||||
|
||||
# 创建数据库连接
|
||||
engine = create_engine(db_url)
|
||||
|
||||
# 将数据写入数据库表
|
||||
df.to_sql(table_name, con=engine, if_exists='replace', index=False)
|
||||
|
||||
print(f"数据已成功导入数据库表 '{table_name}'")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"导入数据时发生错误: {str(e)}")
|
||||
return False
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例调用
|
||||
file_path = "C:/Users/xy/Desktop/temp/概念板块.csv"
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
import_concept_sectors(file_path, db_url, "gp_gnbk")
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
/*
|
||||
Navicat Premium Data Transfer
|
||||
|
||||
Source Server : 测试用
|
||||
Source Server Type : MySQL
|
||||
Source Server Version : 80031
|
||||
Source Host : 192.168.1.82:3306
|
||||
Source Schema : db_gp_cj
|
||||
|
||||
Target Server Type : MySQL
|
||||
Target Server Version : 80031
|
||||
File Encoding : 65001
|
||||
|
||||
Date: 05/03/2025 13:40:26
|
||||
*/
|
||||
|
||||
SET NAMES utf8mb4;
|
||||
SET FOREIGN_KEY_CHECKS = 0;
|
||||
|
||||
-- ----------------------------
|
||||
-- Table structure for gp_code_all
|
||||
-- ----------------------------
|
||||
DROP TABLE IF EXISTS `gp_code_all`;
|
||||
CREATE TABLE `gp_code_all` (
|
||||
`id` int NOT NULL AUTO_INCREMENT,
|
||||
`gp_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL,
|
||||
`gp_code` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL,
|
||||
`gp_code_two` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL,
|
||||
`gp_code_three` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL,
|
||||
`market_cap` decimal(20, 2) NULL DEFAULT NULL,
|
||||
PRIMARY KEY (`id`) USING BTREE
|
||||
) ENGINE = InnoDB AUTO_INCREMENT = 6475 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin ROW_FORMAT = Dynamic;
|
||||
|
||||
-- ----------------------------
|
||||
-- Table structure for gp_day_data
|
||||
-- ----------------------------
|
||||
DROP TABLE IF EXISTS `gp_day_data`;
|
||||
CREATE TABLE `gp_day_data` (
|
||||
`id` bigint NOT NULL AUTO_INCREMENT COMMENT '主键',
|
||||
`symbol` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '个股代码',
|
||||
`timestamp` timestamp(0) NULL DEFAULT NULL COMMENT '时间戳',
|
||||
`volume` bigint NULL DEFAULT NULL COMMENT '数量',
|
||||
`open` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '开始价',
|
||||
`high` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '最高价',
|
||||
`low` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '最低价',
|
||||
`close` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '结束价',
|
||||
`chg` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '变化数值',
|
||||
`percent` decimal(10, 2) NULL DEFAULT NULL COMMENT '变化百分比',
|
||||
`turnoverrate` decimal(10, 2) NULL DEFAULT NULL COMMENT '换手率',
|
||||
`amount` bigint NULL DEFAULT NULL COMMENT '成交金额',
|
||||
PRIMARY KEY (`id`) USING BTREE,
|
||||
INDEX `idx_symbol`(`symbol`) USING BTREE,
|
||||
INDEX `idx_timestamp`(`timestamp`) USING BTREE
|
||||
) ENGINE = InnoDB AUTO_INCREMENT = 58520387 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin ROW_FORMAT = DYNAMIC;
|
||||
|
||||
SET FOREIGN_KEY_CHECKS = 1;
|
||||
|
|
@ -0,0 +1,113 @@
|
|||
# coding:utf-8
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine, text
|
||||
from datetime import datetime
|
||||
from config import XUEQIU_HEADERS
|
||||
|
||||
def ensure_hk_stocks_table_exists(engine):
|
||||
"""确保港股热门股票表存在"""
|
||||
try:
|
||||
create_table_query = text("""
|
||||
CREATE TABLE IF NOT EXISTS hk_hot_stocks (
|
||||
id INT AUTO_INCREMENT PRIMARY KEY,
|
||||
symbol VARCHAR(10),
|
||||
name VARCHAR(100),
|
||||
value INT,
|
||||
increment INT,
|
||||
rank_change INT,
|
||||
percent FLOAT,
|
||||
current FLOAT,
|
||||
chg FLOAT,
|
||||
exchange VARCHAR(10),
|
||||
stock_type INT,
|
||||
add_time DATETIME
|
||||
)
|
||||
""")
|
||||
with engine.connect() as conn:
|
||||
conn.execute(create_table_query)
|
||||
conn.commit()
|
||||
print("港股热门股票表检查/创建完成")
|
||||
except Exception as e:
|
||||
print("创建港股热门股票表时发生错误: {}".format(str(e)))
|
||||
|
||||
def fetch_hk_hot_stocks(db_url):
|
||||
"""获取港股热门股票数据并保存到数据库"""
|
||||
url = 'https://stock.xueqiu.com/v5/stock/hot_stock/list.json'
|
||||
params = {
|
||||
'page': 1,
|
||||
'size': 20,
|
||||
'_type': 13,
|
||||
'type': 13
|
||||
}
|
||||
headers = XUEQIU_HEADERS
|
||||
|
||||
try:
|
||||
# 发送请求获取数据
|
||||
response = requests.get(url, headers=headers, params=params)
|
||||
if response.status_code != 200:
|
||||
print(f"请求失败，状态码：{response.status_code}")
|
||||
return
|
||||
|
||||
data = response.json()
|
||||
if data.get('error_code') != 0:
|
||||
print(f"获取数据失败：{data.get('error_description')}")
|
||||
return
|
||||
|
||||
# 提取股票列表数据
|
||||
stocks = data['data']['items']
|
||||
|
||||
# 转换为DataFrame
|
||||
df = pd.DataFrame(stocks)
|
||||
|
||||
# 选择需要的列
|
||||
selected_columns = [
|
||||
'symbol', 'name', 'value', 'increment', 'rank_change',
|
||||
'percent', 'current', 'chg', 'exchange', 'stock_type'
|
||||
]
|
||||
df = df[selected_columns]
|
||||
|
||||
# 添加时间戳
|
||||
df['add_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
# 创建数据库连接
|
||||
engine = create_engine(db_url)
|
||||
|
||||
# 确保表存在
|
||||
ensure_hk_stocks_table_exists(engine)
|
||||
|
||||
# 将数据写入数据库
|
||||
df.to_sql('hk_hot_stocks', con=engine, if_exists='append', index=False)
|
||||
|
||||
print(f"成功保存{len(df)}条港股热门股票数据")
|
||||
|
||||
# 清理历史数据（只保留当天的数据）
|
||||
clean_historical_data(engine)
|
||||
|
||||
except Exception as e:
|
||||
print("获取港股热门股票数据时发生错误: {}".format(str(e)))
|
||||
|
||||
def clean_historical_data(engine):
|
||||
"""清理当天0点之前的历史数据"""
|
||||
try:
|
||||
today = datetime.now().strftime('%Y-%m-%d 00:00:00')
|
||||
|
||||
clean_query = text("""
|
||||
DELETE FROM hk_hot_stocks
|
||||
WHERE add_time < :today
|
||||
""")
|
||||
|
||||
with engine.connect() as conn:
|
||||
result = conn.execute(clean_query, {'today': today})
|
||||
conn.commit()
|
||||
|
||||
print("清理历史数据完成: 删除了{}条记录".format(result.rowcount))
|
||||
|
||||
except Exception as e:
|
||||
print("清理历史数据时发生错误: {}".format(str(e)))
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例调用
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
fetch_hk_hot_stocks(db_url)
|
||||
|
|
@ -0,0 +1,189 @@
|
|||
# coding:utf-8
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine
|
||||
from datetime import datetime
|
||||
from config import XUEQIU_HEADERS
|
||||
|
||||
def fetch_and_store_stock_data(db_url, table_name='limitup_analysis_stock_changes', page_size=100):
|
||||
"""获取雪球数据并保存到数据库，只获取前3页数据"""
|
||||
base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
|
||||
params = {
|
||||
'page': 1,
|
||||
'size': page_size,
|
||||
'order': 'desc',
|
||||
'order_by': 'percent',
|
||||
'market': 'CN',
|
||||
'type': 'sh_sz'
|
||||
}
|
||||
|
||||
all_data = []
|
||||
# 只获取前3页数据
|
||||
for page in range(1, 4):
|
||||
params['page'] = page
|
||||
try:
|
||||
response = requests.get(base_url, headers=XUEQIU_HEADERS, params=params)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
all_data.extend(data['data']['list'])
|
||||
else:
|
||||
print("请求第 {} 页失败，状态码：{}".format(page, response.status_code))
|
||||
except Exception as e:
|
||||
print("请求第 {} 页时发生错误: {}".format(page, str(e)))
|
||||
|
||||
if all_data:
|
||||
df = pd.DataFrame(all_data)
|
||||
df['id'] = range(1, len(df) + 1)
|
||||
engine = create_engine(db_url)
|
||||
df.to_sql(table_name, con=engine, if_exists='replace', index=False)
|
||||
return True
|
||||
return False
|
||||
|
||||
def analyze_limitup_stocks(db_url, sort_category):
|
||||
"""分析涨停股票的封单情况和概念重叠度"""
|
||||
engine = create_engine(db_url)
|
||||
|
||||
try:
|
||||
# 读取数据
|
||||
stock_changes_df = pd.read_sql_table('limitup_analysis_stock_changes', con=engine)
|
||||
gp_gnbk_df = pd.read_sql_table('gp_gnbk', con=engine)
|
||||
|
||||
# 筛选涨停股票
|
||||
limitup_stocks = stock_changes_df[stock_changes_df['limitup_days'] > 0]
|
||||
|
||||
results = []
|
||||
for _, row in limitup_stocks.iterrows():
|
||||
symbol = row['symbol']
|
||||
market_capital = row['float_market_capital']
|
||||
|
||||
# 获取封单数据
|
||||
pankou_url = 'https://stock.xueqiu.com/v5/stock/realtime/pankou.json?symbol={}'.format(symbol)
|
||||
response = requests.get(pankou_url, headers=XUEQIU_HEADERS)
|
||||
if response.status_code == 200:
|
||||
data = response.json().get('data', {})
|
||||
bp1 = data.get('bp1', 0)
|
||||
bc1 = data.get('bc1', 0)
|
||||
|
||||
if bp1 > 0 and bc1 > 0:
|
||||
sealing_amount = bp1 * bc1
|
||||
sealing_ratio = sealing_amount / market_capital
|
||||
results.append((symbol, row['name'], sealing_amount, sealing_ratio))
|
||||
else:
|
||||
print("请求 {} 封单数据失败，状态码：{}".format(symbol, response.status_code))
|
||||
|
||||
# 按指定方式排序并取前五
|
||||
results.sort(key=lambda x: x[2] if sort_category == 'sealed_amount' else x[3], reverse=True)
|
||||
top_stocks = results[:5]
|
||||
|
||||
final_results = []
|
||||
for index, (symbol, name, sealing_amount, sealing_ratio) in enumerate(top_stocks):
|
||||
# 获取相关概念
|
||||
symbol_trimmed = symbol[2:]
|
||||
concepts = gp_gnbk_df[gp_gnbk_df['gp_code'] == symbol_trimmed]['bk_name'].tolist()
|
||||
|
||||
# 找到概念重叠度最高的股票
|
||||
overlapping_stocks = gp_gnbk_df[
|
||||
gp_gnbk_df['bk_name'].isin(concepts) &
|
||||
(gp_gnbk_df['gp_code'] != symbol_trimmed)
|
||||
]
|
||||
overlap_counts = overlapping_stocks['gp_code'].value_counts()
|
||||
|
||||
if not overlap_counts.empty:
|
||||
top_related_stock = overlap_counts.idxmax()
|
||||
top_related_count = overlap_counts.max()
|
||||
stock_changes_df['symbol_trimmed'] = stock_changes_df['symbol'].str[2:]
|
||||
top_related_name = stock_changes_df[
|
||||
stock_changes_df['symbol_trimmed'] == top_related_stock
|
||||
]['name'].values[0] if top_related_stock in stock_changes_df['symbol_trimmed'].values else None
|
||||
else:
|
||||
top_related_stock = None
|
||||
top_related_count = 0
|
||||
top_related_name = None
|
||||
|
||||
final_results.append({
|
||||
'symbol': symbol_trimmed,
|
||||
'name': name,
|
||||
'sealing_amount': sealing_amount,
|
||||
'sealing_ratio': sealing_ratio,
|
||||
'top_related_stock': top_related_stock,
|
||||
'top_related_name': top_related_name,
|
||||
'overlap_count': top_related_count,
|
||||
'sequence': index + 1,
|
||||
'sort_categray': sort_category,
|
||||
})
|
||||
|
||||
return final_results
|
||||
except Exception as e:
|
||||
print("分析过程中发生错误: {}".format(str(e)))
|
||||
return []
|
||||
|
||||
def save_results_to_db(db_url, results):
|
||||
"""将分析结果保存到数据库"""
|
||||
if not results:
|
||||
print("没有数据需要保存")
|
||||
return
|
||||
|
||||
df = pd.DataFrame(results)
|
||||
df['add_date'] = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
engine = create_engine(db_url)
|
||||
|
||||
try:
|
||||
# 检查当天是否已有数据
|
||||
today = datetime.now().strftime('%Y-%m-%d')
|
||||
existing_data = pd.read_sql_query(
|
||||
"SELECT * FROM top_limitup_analysis WHERE add_date = '{}'".format(today),
|
||||
con=engine
|
||||
)
|
||||
|
||||
# 处理重复数据
|
||||
if not existing_data.empty:
|
||||
existing_pairs = set(zip(existing_data['symbol'], existing_data['add_date']))
|
||||
duplicate_flags = [
|
||||
(row['symbol'], row['add_date']) in existing_pairs
|
||||
for idx, row in df.iterrows()
|
||||
]
|
||||
df['is_duplicate'] = duplicate_flags
|
||||
new_records = df[df['is_duplicate'] == False].drop(columns=['is_duplicate'])
|
||||
else:
|
||||
new_records = df
|
||||
|
||||
# 保存新记录
|
||||
if not new_records.empty:
|
||||
new_records.to_sql('top_limitup_analysis', con=engine, if_exists='append', index=False)
|
||||
|
||||
# 打印结果
|
||||
for _, result in new_records.iterrows():
|
||||
print("股票代码: {}, 股票名称: {}, 封单金额: {:.2f}, 封单比例: {:.2%}".format(
|
||||
result['symbol'], result['name'], result['sealing_amount'], result['sealing_ratio']
|
||||
))
|
||||
print("概念重叠度最高的个股: {} ({}), 重叠度: {}, 涨停序列: {}".format(
|
||||
result['top_related_stock'], result['top_related_name'],
|
||||
result['overlap_count'], result['sequence']
|
||||
))
|
||||
|
||||
except Exception as e:
|
||||
print("保存数据时发生错误: {}".format(str(e)))
|
||||
|
||||
def analyze_limitup_stocks_main(db_url):
|
||||
"""主函数：分析涨停股票并保存结果"""
|
||||
print("\n开始分析 - {}".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
|
||||
|
||||
# 更新股票数据
|
||||
if not fetch_and_store_stock_data(db_url):
|
||||
print("更新股票数据失败")
|
||||
return
|
||||
|
||||
print("\n按封单比例分析...")
|
||||
results_ratio = analyze_limitup_stocks(db_url, 'sealing_ratio')
|
||||
save_results_to_db(db_url, results_ratio)
|
||||
|
||||
print("\n按封单金额分析...")
|
||||
results_amount = analyze_limitup_stocks(db_url, 'sealed_amount')
|
||||
save_results_to_db(db_url, results_amount)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例调用
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
analyze_limitup_stocks_main(db_url)
|
||||
|
|
@ -0,0 +1,145 @@
|
|||
# coding:utf-8
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine, text
|
||||
from datetime import datetime
|
||||
from tqdm import tqdm
|
||||
from config import XUEQIU_HEADERS
|
||||
|
||||
class StockDailyDataCollector:
|
||||
"""股票日线数据采集器类"""
|
||||
|
||||
def __init__(self, db_url):
|
||||
"""
|
||||
初始化采集器
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
db_url : str
|
||||
数据库连接URL
|
||||
"""
|
||||
self.engine = create_engine(db_url)
|
||||
self.headers = XUEQIU_HEADERS
|
||||
|
||||
def fetch_all_stock_codes(self):
|
||||
"""从数据库获取所有股票代码"""
|
||||
query = "SELECT gp_code FROM gp_code_all_copy"
|
||||
df = pd.read_sql(query, self.engine)
|
||||
return df['gp_code'].tolist()
|
||||
|
||||
def fetch_daily_stock_data(self, symbol, begin):
|
||||
"""
|
||||
获取股票日线数据
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
symbol : str
|
||||
股票代码
|
||||
begin : int
|
||||
开始时间戳（毫秒）
|
||||
"""
|
||||
url = f"https://stock.xueqiu.com/v5/stock/chart/kline.json?symbol={symbol}&begin={begin}&period=day&type=before&count=-1500&indicator=kline,pe,pb,ps,pcf,market_capital,agt,ggt,balance"
|
||||
response = requests.get(url, headers=self.headers)
|
||||
return response.json()
|
||||
|
||||
def save_daily_data_to_database(self, data, symbol):
|
||||
"""
|
||||
保存日线数据到数据库
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
data : dict
|
||||
API返回的数据
|
||||
symbol : str
|
||||
股票代码
|
||||
"""
|
||||
try:
|
||||
items = data['data']['item']
|
||||
columns = data['data']['column']
|
||||
except KeyError as e:
|
||||
print(f"KeyError for {symbol}: {e}")
|
||||
return
|
||||
|
||||
df = pd.DataFrame(items, columns=columns)
|
||||
df['symbol'] = symbol
|
||||
|
||||
# 数据库中有的字段
|
||||
required_columns = ['timestamp', 'volume', 'open', 'high', 'low', 'close',
|
||||
'chg', 'percent', 'turnoverrate', 'amount', 'symbol']
|
||||
|
||||
# 检查并保留实际存在的字段
|
||||
existing_columns = [col for col in required_columns if col in df.columns]
|
||||
df = df[existing_columns]
|
||||
|
||||
# 数据类型转换
|
||||
if 'timestamp' in df.columns:
|
||||
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True).dt.tz_convert('Asia/Shanghai')
|
||||
|
||||
df.to_sql('gp_day_data', self.engine, if_exists='append', index=False)
|
||||
|
||||
def fetch_data_for_date(self, date=None):
|
||||
"""
|
||||
获取指定日期或当天的日线数据并保存到数据库
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
date : str, optional
|
||||
日期字符串，格式为'YYYY-MM-DD'，如果为None则获取当天数据
|
||||
"""
|
||||
if date is None:
|
||||
# 如果没有指定日期，使用当天日期
|
||||
start_date = datetime.now()
|
||||
date_str = start_date.strftime('%Y-%m-%d')
|
||||
else:
|
||||
start_date = datetime.strptime(date, '%Y-%m-%d')
|
||||
date_str = date
|
||||
|
||||
# 在插入数据之前执行删除操作
|
||||
delete_query = text(f"DELETE FROM gp_day_data WHERE `timestamp` LIKE :date_str")
|
||||
with self.engine.connect() as conn:
|
||||
conn.execute(delete_query, {"date_str": f"{date_str}%"})
|
||||
|
||||
# 获取所有股票代码
|
||||
stock_codes = self.fetch_all_stock_codes()
|
||||
|
||||
# 循环请求每只股票的数据并保存，使用进度条显示进度
|
||||
for symbol in tqdm(stock_codes, desc=f"Fetching and saving daily stock data for {date_str}"):
|
||||
begin = int(start_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000)
|
||||
|
||||
data = self.fetch_daily_stock_data(symbol, begin)
|
||||
if data['error_code'] == 0:
|
||||
self.save_daily_data_to_database(data, symbol)
|
||||
else:
|
||||
print(f"Error fetching data for {symbol} on {date_str}: {data['error_description']}")
|
||||
|
||||
print(f"Daily data fetching and saving completed for {date_str}.")
|
||||
|
||||
def collect_stock_daily_data(db_url, date=None):
|
||||
"""
|
||||
快捷方法：收集股票日线数据
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
db_url : str
|
||||
数据库连接URL
|
||||
date : str, optional
|
||||
日期字符串，格式为'YYYY-MM-DD'，如果为None则获取当天数据
|
||||
"""
|
||||
collector = StockDailyDataCollector(db_url)
|
||||
collector.fetch_data_for_date(date)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例调用
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
|
||||
# 方法1：使用快捷函数获取当天数据
|
||||
collect_stock_daily_data(db_url)
|
||||
|
||||
# 方法2：使用快捷函数获取指定日期数据
|
||||
# collect_stock_daily_data(db_url, '2020-01-01')
|
||||
|
||||
# 方法3：使用完整的类
|
||||
# collector = StockDailyDataCollector(db_url)
|
||||
# collector.fetch_data_for_date() # 获取当天数据
|
||||
# collector.fetch_data_for_date('2024-09-11') # 获取指定日期数据
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
2025-03-24 11:14:46,306 - stock_data_import - ERROR - 创建表失败: Not an executable object: "\n CREATE TABLE IF NOT EXISTS stock_semi_annual_report (\n id INT AUTO_INCREMENT PRIMARY KEY,\n stock_code VARCHAR(20) NOT NULL COMMENT '股票代码',\n stock_name VARCHAR(50) NOT NULL COMMENT '股票名称',\n report_date DATE NOT NULL COMMENT '报告日期',\n \n product_1_name VARCHAR(100) COMMENT '产品1名称',\n product_1_revenue DECIMAL(20,4) COMMENT '产品1营业收入',\n product_1_cost DECIMAL(20,4) COMMENT '产品1成本',\n product_1_gross_profit DECIMAL(20,4) COMMENT '产品1毛利',\n product_1_gross_margin DECIMAL(10,4) COMMENT '产品1毛利率',\n \n product_2_name VARCHAR(100) COMMENT '产品2名称',\n product_2_revenue DECIMAL(20,4) COMMENT '产品2营业收入',\n product_2_cost DECIMAL(20,4) COMMENT '产品2成本',\n product_2_gross_profit DECIMAL(20,4) COMMENT '产品2毛利',\n product_2_gross_margin DECIMAL(10,4) COMMENT '产品2毛利率',\n \n product_3_name VARCHAR(100) COMMENT '产品3名称',\n product_3_revenue DECIMAL(20,4) COMMENT '产品3营业收入',\n product_3_cost DECIMAL(20,4) COMMENT '产品3成本',\n product_3_gross_profit DECIMAL(20,4) COMMENT '产品3毛利',\n product_3_gross_margin DECIMAL(10,4) COMMENT '产品3毛利率',\n \n product_4_name VARCHAR(100) COMMENT '产品4名称',\n product_4_revenue DECIMAL(20,4) COMMENT '产品4营业收入',\n product_4_cost DECIMAL(20,4) COMMENT '产品4成本',\n product_4_gross_profit DECIMAL(20,4) COMMENT '产品4毛利',\n product_4_gross_margin DECIMAL(10,4) COMMENT '产品4毛利率',\n \n product_5_name VARCHAR(100) COMMENT '产品5名称',\n product_5_revenue DECIMAL(20,4) COMMENT '产品5营业收入',\n product_5_cost DECIMAL(20,4) COMMENT '产品5成本',\n product_5_gross_profit DECIMAL(20,4) COMMENT '产品5毛利',\n product_5_gross_margin DECIMAL(10,4) COMMENT '产品5毛利率',\n \n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,\n \n UNIQUE KEY idx_stock_report (stock_code, report_date)\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='股票半年报主营业务数据';\n "
|
||||
2025-03-24 11:14:46,308 - stock_data_import - ERROR - 表创建失败，程序退出
|
||||
2025-03-24 11:16:57,259 - stock_data_import - ERROR - 创建表失败: Not an executable object: "\n CREATE TABLE IF NOT EXISTS stock_semi_annual_report (\n id INT AUTO_INCREMENT PRIMARY KEY,\n stock_code VARCHAR(20) NOT NULL COMMENT '股票代码',\n stock_name VARCHAR(50) NOT NULL COMMENT '股票名称',\n report_date DATE NOT NULL COMMENT '报告日期',\n \n product_1_name VARCHAR(100) COMMENT '产品1名称',\n product_1_revenue DECIMAL(20,4) COMMENT '产品1营业收入',\n product_1_cost DECIMAL(20,4) COMMENT '产品1成本',\n product_1_gross_profit DECIMAL(20,4) COMMENT '产品1毛利',\n product_1_gross_margin DECIMAL(10,4) COMMENT '产品1毛利率',\n \n product_2_name VARCHAR(100) COMMENT '产品2名称',\n product_2_revenue DECIMAL(20,4) COMMENT '产品2营业收入',\n product_2_cost DECIMAL(20,4) COMMENT '产品2成本',\n product_2_gross_profit DECIMAL(20,4) COMMENT '产品2毛利',\n product_2_gross_margin DECIMAL(10,4) COMMENT '产品2毛利率',\n \n product_3_name VARCHAR(100) COMMENT '产品3名称',\n product_3_revenue DECIMAL(20,4) COMMENT '产品3营业收入',\n product_3_cost DECIMAL(20,4) COMMENT '产品3成本',\n product_3_gross_profit DECIMAL(20,4) COMMENT '产品3毛利',\n product_3_gross_margin DECIMAL(10,4) COMMENT '产品3毛利率',\n \n product_4_name VARCHAR(100) COMMENT '产品4名称',\n product_4_revenue DECIMAL(20,4) COMMENT '产品4营业收入',\n product_4_cost DECIMAL(20,4) COMMENT '产品4成本',\n product_4_gross_profit DECIMAL(20,4) COMMENT '产品4毛利',\n product_4_gross_margin DECIMAL(10,4) COMMENT '产品4毛利率',\n \n product_5_name VARCHAR(100) COMMENT '产品5名称',\n product_5_revenue DECIMAL(20,4) COMMENT '产品5营业收入',\n product_5_cost DECIMAL(20,4) COMMENT '产品5成本',\n product_5_gross_profit DECIMAL(20,4) COMMENT '产品5毛利',\n product_5_gross_margin DECIMAL(10,4) COMMENT '产品5毛利率',\n \n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,\n \n UNIQUE KEY idx_stock_report (stock_code, report_date)\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='股票半年报主营业务数据';\n "
|
||||
2025-03-24 11:16:57,259 - stock_data_import - ERROR - 表创建失败，程序退出
|
||||
2025-03-24 11:18:55,299 - stock_data_import - INFO - 表创建成功或已存在
|
||||
2025-03-24 11:18:55,301 - stock_data_import - INFO - 使用 utf-8 编码成功读取CSV
|
||||
2025-03-24 11:18:55,432 - stock_data_import - ERROR - 导入数据失败: Not an executable object: "\n SELECT COUNT(*) FROM stock_semi_annual_report \n WHERE stock_code = '688981.SH' AND report_date = '2024-06-30'\n "
|
||||
2025-03-24 11:18:55,432 - stock_data_import - ERROR - 数据导入失败
|
||||
2025-03-24 11:21:49,621 - stock_data_import - INFO - 表创建成功或已存在
|
||||
2025-03-24 11:21:49,623 - stock_data_import - INFO - 使用 utf-8 编码成功读取CSV
|
||||
2025-03-24 11:21:49,759 - stock_data_import - INFO - 插入股票 688981.SH 的数据
|
||||
2025-03-24 11:21:49,768 - stock_data_import - INFO - 成功导入 1 条记录到数据库
|
||||
2025-03-24 11:21:49,769 - stock_data_import - INFO - 数据导入成功
|
||||
2025-03-24 11:22:10,329 - stock_data_import - INFO - 表创建成功或已存在
|
||||
2025-03-24 11:22:10,330 - stock_data_import - INFO - 使用 utf-8 编码成功读取CSV
|
||||
2025-03-24 11:22:10,467 - stock_data_import - INFO - 插入股票 688981.SH 的数据
|
||||
2025-03-24 11:22:10,476 - stock_data_import - INFO - 成功导入 1 条记录到数据库
|
||||
2025-03-24 11:22:10,476 - stock_data_import - INFO - 数据导入成功
|
||||
|
|
@ -0,0 +1,142 @@
|
|||
# coding:utf-8
|
||||
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine
|
||||
from datetime import datetime
|
||||
|
||||
class StockFilter:
|
||||
"""股票筛选器类，用于根据特定条件筛选股票"""
|
||||
|
||||
def __init__(self, db_url):
|
||||
"""
|
||||
初始化股票筛选器
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
db_url : str
|
||||
数据库连接URL
|
||||
"""
|
||||
self.engine = create_engine(db_url)
|
||||
|
||||
def get_high_amplitude_stocks(self, date, amplitude_threshold=0.07):
|
||||
"""
|
||||
获取指定日期振幅超过阈值且收盘价低于开盘价的股票
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
date : str
|
||||
日期字符串，格式为'YYYY-MM-DD'
|
||||
amplitude_threshold : float
|
||||
振幅阈值，默认为0.07（7%）
|
||||
|
||||
Returns:
|
||||
--------
|
||||
pandas.DataFrame
|
||||
符合条件的股票数据，包含以下列：
|
||||
- symbol: 股票代码
|
||||
- high: 最高价
|
||||
- low: 最低价
|
||||
- open: 开盘价
|
||||
- close: 收盘价
|
||||
"""
|
||||
query = f"""
|
||||
WITH ranked_data AS (
|
||||
SELECT symbol, timestamp, high, low, open, close,
|
||||
ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY timestamp) AS row_num,
|
||||
ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY timestamp DESC) AS rev_row_num
|
||||
FROM gp_min_data
|
||||
WHERE DATE(timestamp) = '{date}'
|
||||
),
|
||||
daily_data AS (
|
||||
SELECT symbol,
|
||||
MAX(high) AS high,
|
||||
MIN(low) AS low,
|
||||
MAX(CASE WHEN row_num = 1 THEN open ELSE NULL END) AS open,
|
||||
MAX(CASE WHEN rev_row_num = 1 THEN close ELSE NULL END) AS close
|
||||
FROM ranked_data
|
||||
GROUP BY symbol
|
||||
)
|
||||
SELECT symbol, high, low, open, close
|
||||
FROM daily_data
|
||||
WHERE (high - low) / open > {amplitude_threshold}
|
||||
AND close < open
|
||||
"""
|
||||
try:
|
||||
df = pd.read_sql(query, self.engine)
|
||||
return df
|
||||
except Exception as e:
|
||||
print(f"查询数据时发生错误: {str(e)}")
|
||||
return pd.DataFrame()
|
||||
|
||||
# def save_filtered_stocks(self, date, table_name='filtered_stocks'):
|
||||
# """
|
||||
# 将筛选结果保存到数据库
|
||||
|
||||
# Parameters:
|
||||
# -----------
|
||||
# date : str
|
||||
# 日期字符串，格式为'YYYY-MM-DD'
|
||||
# table_name : str
|
||||
# 保存结果的表名，默认为'filtered_stocks'
|
||||
|
||||
# Returns:
|
||||
# --------
|
||||
# bool
|
||||
# 保存成功返回True，失败返回False
|
||||
# """
|
||||
# try:
|
||||
# # 获取筛选结果
|
||||
# df = self.get_high_amplitude_stocks(date)
|
||||
|
||||
# # 添加筛选日期列
|
||||
# df['filter_date'] = date
|
||||
|
||||
# # 保存到数据库
|
||||
# df.to_sql(table_name, self.engine, if_exists='append', index=False)
|
||||
|
||||
# print(f"数据已成功保存到表 '{table_name}'")
|
||||
# return True
|
||||
# except Exception as e:
|
||||
# print(f"保存数据时发生错误: {str(e)}")
|
||||
# return False
|
||||
|
||||
def get_todays_filtered_stocks(db_url):
|
||||
"""
|
||||
获取今日符合条件的股票的快捷方法
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
db_url : str
|
||||
数据库连接URL
|
||||
|
||||
Returns:
|
||||
--------
|
||||
pandas.DataFrame
|
||||
今日符合条件的股票数据
|
||||
"""
|
||||
# 获取今日日期
|
||||
today = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
# 创建筛选器实例
|
||||
filter = StockFilter(db_url)
|
||||
|
||||
# 获取并返回结果
|
||||
return filter.get_high_amplitude_stocks(today)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例调用
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
|
||||
# 方法1：使用快捷函数获取今日数据
|
||||
# today_stocks = get_todays_filtered_stocks(db_url)
|
||||
# print("今日符合条件的股票：")
|
||||
# print(today_stocks)
|
||||
|
||||
# 方法2：使用完整的类，指定日期
|
||||
filter = StockFilter(db_url)
|
||||
stocks = filter.get_high_amplitude_stocks('2024-06-19')
|
||||
print("\n指定日期符合条件的股票：")
|
||||
print(stocks)
|
||||
|
||||
# 保存结果到数据库 这个是AI自动加的，暂时不需要保存数据库
|
||||
# filter.save_filtered_stocks('2024-06-19')
|
||||
|
|
@ -0,0 +1,138 @@
|
|||
# coding:utf-8
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine, text
|
||||
from datetime import datetime, timedelta
|
||||
from tqdm import tqdm
|
||||
from config import XUEQIU_HEADERS
|
||||
|
||||
class StockMinuteDataCollector:
|
||||
"""股票分时数据采集器类"""
|
||||
|
||||
def __init__(self, db_url):
|
||||
"""
|
||||
初始化采集器
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
db_url : str
|
||||
数据库连接URL
|
||||
"""
|
||||
self.engine = create_engine(db_url)
|
||||
self.headers = XUEQIU_HEADERS
|
||||
|
||||
def fetch_all_stock_codes(self):
|
||||
"""从数据库获取所有股票代码"""
|
||||
query = "SELECT gp_code FROM gp_code_all_copy"
|
||||
df = pd.read_sql(query, self.engine)
|
||||
return df['gp_code'].tolist()
|
||||
|
||||
def update_market_cap(self, symbol, market_cap):
|
||||
"""更新数据库中的市值信息"""
|
||||
query = text("UPDATE gp_code_all_copy SET market_cap = :market_cap WHERE gp_code = :symbol")
|
||||
with self.engine.connect() as conn:
|
||||
conn.execute(query, {'market_cap': market_cap, 'symbol': symbol})
|
||||
|
||||
def fetch_market_cap(self, symbol):
|
||||
"""获取股票市值信息"""
|
||||
url = f"https://stock.xueqiu.com/v5/stock/realtime/quotec.json?symbol={symbol}"
|
||||
response = requests.get(url, headers=self.headers)
|
||||
data = response.json()
|
||||
if data['error_code'] == 0:
|
||||
return data['data'][0]['market_capital']
|
||||
else:
|
||||
print(f"Error fetching market cap for {symbol}: {data['error_description']}")
|
||||
return None
|
||||
|
||||
def fetch_stock_data(self, symbol, begin, end):
|
||||
"""获取股票分时数据"""
|
||||
url = f"https://stock.xueqiu.com/v5/stock/chart/kline.json?symbol={symbol}&begin={begin}&end={end}&period=1m&type=before&count=-284&indicator=kline,pe,pb,ps,pcf,market_capital,agt,ggt,balance"
|
||||
response = requests.get(url, headers=self.headers)
|
||||
return response.json()
|
||||
|
||||
def save_to_database(self, data, symbol):
|
||||
"""保存数据到数据库"""
|
||||
try:
|
||||
items = data['data']['item']
|
||||
columns = data['data']['column']
|
||||
except KeyError as e:
|
||||
print(f"KeyError for {symbol}: {e}")
|
||||
return
|
||||
|
||||
df = pd.DataFrame(items, columns=columns)
|
||||
df['symbol'] = symbol
|
||||
|
||||
# 数据库中有的字段
|
||||
required_columns = ['timestamp', 'volume', 'open', 'high', 'low', 'close', 'chg', 'percent', 'turnoverrate', 'amount', 'symbol']
|
||||
|
||||
# 检查并保留实际存在的字段
|
||||
existing_columns = [col for col in required_columns if col in df.columns]
|
||||
df = df[existing_columns]
|
||||
|
||||
# 数据类型转换
|
||||
if 'timestamp' in df.columns:
|
||||
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True).dt.tz_convert('Asia/Shanghai')
|
||||
|
||||
df.to_sql('gp_min_data', self.engine, if_exists='append', index=False)
|
||||
|
||||
def fetch_data_for_date(self, date=None):
|
||||
"""
|
||||
获取指定日期或当天的数据
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
date : str, optional
|
||||
日期字符串，格式为'YYYY-MM-DD'，如果为None则获取当天数据
|
||||
"""
|
||||
if date is None:
|
||||
# 如果没有指定日期，使用当天日期
|
||||
date = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
start_date = datetime.strptime(date, '%Y-%m-%d')
|
||||
end_date = start_date + timedelta(days=1)
|
||||
|
||||
# 获取所有股票代码
|
||||
stock_codes = self.fetch_all_stock_codes()
|
||||
|
||||
# 循环请求每只股票的数据并保存，使用进度条显示进度
|
||||
for symbol in tqdm(stock_codes, desc=f"Fetching and saving stock data for {date}"):
|
||||
begin = int(start_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000)
|
||||
end = int(end_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000)
|
||||
|
||||
data = self.fetch_stock_data(symbol, begin, end)
|
||||
if data['error_code'] == 0:
|
||||
self.save_to_database(data, symbol)
|
||||
else:
|
||||
print(f"Error fetching data for {symbol} on {date}: {data['error_description']}")
|
||||
|
||||
print(f"Data fetching and saving completed for {date}.")
|
||||
|
||||
def collect_stock_minute_data(db_url, date=None):
|
||||
"""
|
||||
快捷方法：收集股票分时数据
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
db_url : str
|
||||
数据库连接URL
|
||||
date : str, optional
|
||||
日期字符串，格式为'YYYY-MM-DD'，如果为None则获取当天数据
|
||||
"""
|
||||
collector = StockMinuteDataCollector(db_url)
|
||||
collector.fetch_data_for_date(date)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例调用
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
|
||||
# 方法1：使用快捷函数获取当天数据
|
||||
collect_stock_minute_data(db_url)
|
||||
|
||||
# 方法2：使用快捷函数获取指定日期数据
|
||||
# collect_stock_minute_data(db_url, '2024-07-29')
|
||||
|
||||
# 方法3：使用完整的类
|
||||
# collector = StockMinuteDataCollector(db_url)
|
||||
# collector.fetch_data_for_date() # 获取当天数据
|
||||
# collector.fetch_data_for_date('2024-07-29') # 获取指定日期数据
|
||||
|
|
@ -0,0 +1,280 @@
|
|||
# coding:utf-8
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine, text
|
||||
from datetime import datetime
|
||||
import time
|
||||
from config import XUEQIU_HEADERS
|
||||
|
||||
def ensure_stock_price_changes_table_exists(engine):
|
||||
"""确保股价变化表存在"""
|
||||
try:
|
||||
create_table_query = text("""
|
||||
CREATE TABLE IF NOT EXISTS stock_price_changes (
|
||||
id INT AUTO_INCREMENT PRIMARY KEY,
|
||||
symbol VARCHAR(10),
|
||||
name VARCHAR(100),
|
||||
current FLOAT,
|
||||
percent FLOAT,
|
||||
time_mark VARCHAR(20), -- '9:24:40' 或 '9:25:00'
|
||||
add_time DATETIME,
|
||||
UNIQUE KEY unique_stock_time (symbol, time_mark, add_time)
|
||||
)
|
||||
""")
|
||||
|
||||
create_analysis_table_query = text("""
|
||||
CREATE TABLE IF NOT EXISTS rapid_price_changes (
|
||||
id INT AUTO_INCREMENT PRIMARY KEY,
|
||||
symbol VARCHAR(10),
|
||||
name VARCHAR(100),
|
||||
price_change_ratio FLOAT, -- 两个时间点之间的涨幅
|
||||
sector_count INT, -- 所属板块数量
|
||||
sectors TEXT, -- 所属板块名称（逗号分隔）
|
||||
add_time DATETIME
|
||||
)
|
||||
""")
|
||||
|
||||
with engine.connect() as conn:
|
||||
conn.execute(create_table_query)
|
||||
conn.execute(create_analysis_table_query)
|
||||
conn.commit()
|
||||
print("股价变化表检查/创建完成")
|
||||
except Exception as e:
|
||||
print("创建股价变化表时发生错误: {}".format(str(e)))
|
||||
|
||||
def fetch_stock_data(db_url, time_mark):
|
||||
"""获取雪球数据并保存到数据库"""
|
||||
base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
|
||||
types = ['sha', 'sza', 'kcb']
|
||||
headers = XUEQIU_HEADERS
|
||||
|
||||
all_data = []
|
||||
|
||||
for stock_type in types:
|
||||
# 先获取总数据量
|
||||
params = {
|
||||
'page': 1,
|
||||
'size': 100,
|
||||
'order': 'desc',
|
||||
'order_by': 'percent',
|
||||
'market': 'CN',
|
||||
'type': stock_type
|
||||
}
|
||||
|
||||
# 初次请求以获取总页数
|
||||
response = requests.get(base_url, headers=headers, params=params)
|
||||
if response.status_code != 200:
|
||||
print(f"请求 {stock_type} 数据失败，状态码：{response.status_code}")
|
||||
continue
|
||||
|
||||
data = response.json()
|
||||
total_count = data['data']['count']
|
||||
total_pages = (total_count // params['size']) + 1
|
||||
|
||||
# 获取所有页面的数据
|
||||
for page in range(1, total_pages + 1):
|
||||
params['page'] = page
|
||||
response = requests.get(base_url, headers=headers, params=params)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
all_data.extend(data['data']['list'])
|
||||
else:
|
||||
print(f"请求 {stock_type} 数据第 {page} 页失败，状态码：{response.status_code}")
|
||||
|
||||
if all_data:
|
||||
df = pd.DataFrame(all_data)
|
||||
selected_columns = ['symbol', 'name', 'current', 'percent']
|
||||
df = df[selected_columns]
|
||||
df['symbol'] = df['symbol'].str[2:] # 去掉前缀
|
||||
df['time_mark'] = time_mark
|
||||
df['add_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
engine = create_engine(db_url)
|
||||
ensure_stock_price_changes_table_exists(engine)
|
||||
|
||||
# 先删除同一时间标记的旧数据
|
||||
with engine.connect() as conn:
|
||||
delete_query = text("""
|
||||
DELETE FROM stock_price_changes
|
||||
WHERE time_mark = :time_mark
|
||||
AND DATE(add_time) = CURDATE()
|
||||
""")
|
||||
conn.execute(delete_query, {'time_mark': time_mark})
|
||||
conn.commit()
|
||||
|
||||
# 保存新数据
|
||||
df.to_sql('stock_price_changes', con=engine, if_exists='append', index=False)
|
||||
print(f"成功保存{len(df)}条{time_mark}的股价数据")
|
||||
return True
|
||||
return False
|
||||
|
||||
def analyze_price_changes(db_url):
|
||||
"""分析两个时间点之间的板块涨跌情况"""
|
||||
engine = create_engine(db_url)
|
||||
|
||||
try:
|
||||
# 获取所有股票的涨跌幅数据
|
||||
query = text("""
|
||||
SELECT
|
||||
t1.symbol,
|
||||
t1.name,
|
||||
((t2.current - t1.current) / t1.current * 100) as price_change_ratio
|
||||
FROM
|
||||
stock_price_changes t1
|
||||
JOIN stock_price_changes t2 ON t1.symbol = t2.symbol
|
||||
WHERE
|
||||
t1.time_mark = '9:24:00'
|
||||
AND t2.time_mark = '9:25:10'
|
||||
AND DATE(t1.add_time) = CURDATE()
|
||||
AND DATE(t2.add_time) = CURDATE()
|
||||
""")
|
||||
|
||||
price_changes_df = pd.read_sql_query(query, engine)
|
||||
|
||||
if not price_changes_df.empty:
|
||||
# 获取板块信息
|
||||
gp_gnbk_df = pd.read_sql_table('gp_gnbk', con=engine)
|
||||
|
||||
# 合并获取板块信息
|
||||
merged_df = price_changes_df.merge(gp_gnbk_df, left_on='symbol', right_on='gp_code')
|
||||
|
||||
# 按板块统计涨幅超过1%的股票数量和总数
|
||||
sector_stats = []
|
||||
for bk_name in merged_df['bk_name'].unique():
|
||||
sector_stocks = merged_df[merged_df['bk_name'] == bk_name]
|
||||
total_count = len(sector_stocks)
|
||||
up_count = len(sector_stocks[sector_stocks['price_change_ratio'] > 0.7])
|
||||
ratio = up_count / total_count if total_count > 0 else 0
|
||||
|
||||
sector_stats.append({
|
||||
'sector_name': bk_name,
|
||||
'up_count': up_count,
|
||||
'total_count': total_count,
|
||||
'ratio': ratio,
|
||||
'add_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
})
|
||||
|
||||
# 转换为DataFrame并按比例排序
|
||||
result_df = pd.DataFrame(sector_stats)
|
||||
result_df = result_df.sort_values('ratio', ascending=False)
|
||||
|
||||
# 打印分析结果
|
||||
print("\n=== 板块涨幅分析结果 ===")
|
||||
print(f"分析时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
print("\n{:<15} {:<8} {:<8} {:<10}".format(
|
||||
"板块名称", "上涨家数", "总家数", "占比"
|
||||
))
|
||||
print("-" * 45)
|
||||
|
||||
# 打印所有板块数据
|
||||
for _, row in result_df.iterrows():
|
||||
print("{:<15} {:<8d} {:<8d} {:<10.2f}%".format(
|
||||
row['sector_name'],
|
||||
row['up_count'],
|
||||
row['total_count'],
|
||||
row['ratio'] * 100
|
||||
))
|
||||
|
||||
print("\n总计分析板块数量:", len(result_df))
|
||||
|
||||
else:
|
||||
print("未获取到股价变化数据")
|
||||
|
||||
except Exception as e:
|
||||
print("分析数据时发生错误: {}".format(str(e)))
|
||||
|
||||
def get_previous_sector_ranks(engine):
|
||||
"""获取上一次的板块排名"""
|
||||
try:
|
||||
query = text("""
|
||||
SELECT sector_name, rank_num
|
||||
FROM sector_performance
|
||||
WHERE DATE(add_time) = CURDATE()
|
||||
ORDER BY add_time DESC
|
||||
LIMIT 1
|
||||
""")
|
||||
|
||||
result = pd.read_sql_query(query, engine)
|
||||
if result.empty:
|
||||
return {}
|
||||
|
||||
return dict(zip(result['sector_name'], result['rank_num']))
|
||||
except Exception as e:
|
||||
print("获取上一次排名数据时发生错误: {}".format(str(e)))
|
||||
return {}
|
||||
|
||||
def calculate_rank_change(row, previous_ranks):
|
||||
"""计算排名变化"""
|
||||
previous_rank = previous_ranks.get(row['sector_name'])
|
||||
if previous_rank is None:
|
||||
return 0
|
||||
return previous_rank - row['rank_num']
|
||||
|
||||
def get_cache_mark():
|
||||
"""获取当前时间对应的缓存标记"""
|
||||
current_minute = datetime.now().minute
|
||||
mark = (current_minute % 10) // 2 * 2
|
||||
return "{}m".format(mark)
|
||||
|
||||
def save_sector_cache(engine, df_result, cache_mark):
|
||||
"""保存板块数据到缓存表"""
|
||||
try:
|
||||
df_cache = df_result.copy()
|
||||
df_cache['cache_mark'] = cache_mark
|
||||
|
||||
with engine.connect() as conn:
|
||||
delete_query = text("DELETE FROM sector_performance_cache WHERE cache_mark = :cache_mark")
|
||||
conn.execute(delete_query, {'cache_mark': cache_mark})
|
||||
conn.commit()
|
||||
|
||||
df_cache.to_sql('sector_performance_cache', con=engine, if_exists='append', index=False)
|
||||
print(f"缓存数据已保存，标记: {cache_mark}")
|
||||
except Exception as e:
|
||||
print("保存缓存数据时发生错误: {}".format(str(e)))
|
||||
|
||||
def main(db_url):
|
||||
"""主函数"""
|
||||
engine = create_engine(db_url)
|
||||
|
||||
target_time_1 = datetime.now().replace(hour=9, minute=24, second=0, microsecond=0)
|
||||
target_time_2 = datetime.now().replace(hour=9, minute=25, second=10, microsecond=0)
|
||||
current_time = datetime.now()
|
||||
|
||||
# 如果当前时间已经超过了第二个时间点，直接进行分析
|
||||
if current_time > target_time_2:
|
||||
print("当前时间已超过9:24:00，直接进行数据分析...")
|
||||
analyze_price_changes(db_url)
|
||||
return
|
||||
|
||||
# 如果还没到第一个时间点，等待
|
||||
if current_time < target_time_1:
|
||||
wait_seconds = (target_time_1 - current_time).total_seconds()
|
||||
print(f"等待{wait_seconds}9:24:00...")
|
||||
time.sleep(wait_seconds)
|
||||
|
||||
# 获取第一次数据
|
||||
success_1 = fetch_stock_data(db_url, '9:24:00')
|
||||
|
||||
if success_1:
|
||||
# 如果还没到第二个时间点，等待
|
||||
current_time = datetime.now()
|
||||
if current_time < target_time_2:
|
||||
wait_seconds = (target_time_2 - current_time).total_seconds()
|
||||
print(f"等待{wait_seconds}9:25:10...")
|
||||
time.sleep(wait_seconds)
|
||||
|
||||
# 获取第二次数据
|
||||
success_2 = fetch_stock_data(db_url, '9:25:10')
|
||||
|
||||
if success_2:
|
||||
# 分析数据
|
||||
analyze_price_changes(db_url)
|
||||
else:
|
||||
print("9:25:10数据失败")
|
||||
else:
|
||||
print("9:24:00数据失败")
|
||||
|
||||
if __name__ == "__main__":
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
main(db_url)
|
||||
|
|
@ -0,0 +1,336 @@
|
|||
# coding:utf-8
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine, text
|
||||
from datetime import datetime
|
||||
from config import XUEQIU_HEADERS
|
||||
|
||||
def fetch_and_store_stock_data(db_url, table_name='stock_changes', page_size=90):
|
||||
"""获取雪球数据并保存到数据库"""
|
||||
base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
|
||||
types = ['sha', 'sza', 'kcb'] # 数据类型
|
||||
headers = XUEQIU_HEADERS
|
||||
|
||||
all_data = []
|
||||
|
||||
for stock_type in types:
|
||||
params = {
|
||||
'page': 1,
|
||||
'size': page_size,
|
||||
'order': 'desc',
|
||||
'order_by': 'percent',
|
||||
'market': 'CN',
|
||||
'type': stock_type
|
||||
}
|
||||
|
||||
# 初次请求以获取总页数
|
||||
response = requests.get(base_url, headers=headers, params=params)
|
||||
if response.status_code != 200:
|
||||
print(f"请求 {stock_type} 数据失败，状态码：{response.status_code}")
|
||||
continue
|
||||
|
||||
data = response.json()
|
||||
total_count = data['data']['count']
|
||||
total_pages = (total_count // page_size) + 1
|
||||
|
||||
for page in range(1, total_pages + 1):
|
||||
params['page'] = page
|
||||
response = requests.get(base_url, headers=headers, params=params)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
all_data.extend(data['data']['list'])
|
||||
else:
|
||||
print(f"请求 {stock_type} 数据第 {page} 页失败，状态码：{response.status_code}")
|
||||
# 转换为 DataFrame
|
||||
df = pd.DataFrame(all_data)
|
||||
|
||||
if not df.empty:
|
||||
# 添加 id 列
|
||||
df['id'] = range(1, len(df) + 1)
|
||||
|
||||
# 创建数据库连接
|
||||
engine = create_engine(db_url)
|
||||
|
||||
# 将数据写入数据库表
|
||||
df.to_sql(table_name, con=engine, if_exists='replace', index=False)
|
||||
print(f"成功将数据写入数据库表 {table_name}。")
|
||||
else:
|
||||
print("未获取到任何数据。")
|
||||
|
||||
def get_cache_mark():
|
||||
"""获取当前时间对应的缓存标记（0m, 2m, 4m, 6m, 8m）"""
|
||||
current_minute = datetime.now().minute
|
||||
mark = (current_minute % 10) // 2 * 2 # 将分钟数转换为0,2,4,6,8
|
||||
return "{}m".format(mark)
|
||||
|
||||
def save_sector_cache(engine, df_result, cache_mark):
|
||||
"""保存板块数据到缓存表"""
|
||||
try:
|
||||
# 复制数据并添加缓存标记
|
||||
df_cache = df_result.copy()
|
||||
df_cache['cache_mark'] = cache_mark
|
||||
|
||||
# 删除旧的缓存数据
|
||||
delete_query = text("DELETE FROM sector_performance_cache WHERE cache_mark = :cache_mark")
|
||||
with engine.connect() as conn: # 使用 connect() 创建连接
|
||||
conn.execute(delete_query, {'cache_mark': cache_mark})
|
||||
conn.commit() # 提交事务
|
||||
|
||||
# 保存新的缓存数据
|
||||
df_cache.to_sql('sector_performance_cache', con=engine, if_exists='append', index=False)
|
||||
print("缓存数据已保存，标记: {}".format(cache_mark))
|
||||
except Exception as e:
|
||||
print("保存缓存数据时发生错误: {}".format(str(e)))
|
||||
|
||||
def ensure_cache_table_exists(engine):
|
||||
"""确保缓存表存在"""
|
||||
try:
|
||||
create_table_query = text("""
|
||||
CREATE TABLE IF NOT EXISTS sector_performance_cache (
|
||||
id INT AUTO_INCREMENT PRIMARY KEY,
|
||||
sector_name VARCHAR(100),
|
||||
up_count INT,
|
||||
total_count INT,
|
||||
ratio FLOAT,
|
||||
rank_num INT,
|
||||
rank_change INT,
|
||||
add_time DATETIME,
|
||||
cache_mark VARCHAR(10)
|
||||
)
|
||||
""")
|
||||
with engine.connect() as conn: # 使用 connect() 创建连接
|
||||
conn.execute(create_table_query)
|
||||
conn.commit() # 提交事务
|
||||
print("缓存表检查/创建完成")
|
||||
except Exception as e:
|
||||
print("创建缓存表时发生错误: {}".format(str(e)))
|
||||
|
||||
def get_previous_cache_marks():
|
||||
"""获取缓存标记的对应关系"""
|
||||
return {
|
||||
0: 4, # 0m 对比 4m的数据
|
||||
2: 6, # 2m 对比 6m的数据
|
||||
4: 8, # 4m 对比 8m的数据
|
||||
6: 0, # 6m 对比 0m的数据
|
||||
8: 2 # 8m 对比 2m的数据
|
||||
}
|
||||
|
||||
def get_previous_cache_ranks(engine):
|
||||
"""获取上一个时间点的缓存数据"""
|
||||
try:
|
||||
# 获取当前缓存标记
|
||||
current_mark = int(get_cache_mark().replace('m', ''))
|
||||
|
||||
# 获取应该对比的缓存标记
|
||||
cache_marks = get_previous_cache_marks()
|
||||
previous_mark = cache_marks.get(current_mark)
|
||||
|
||||
if previous_mark is None:
|
||||
print("无法确定对比的缓存标记")
|
||||
return {}
|
||||
|
||||
previous_cache_mark = "{}m".format(previous_mark)
|
||||
print("当前时间标记: {}m, 对比的缓存标记: {}".format(current_mark, previous_cache_mark))
|
||||
|
||||
query = text("""
|
||||
SELECT sector_name, rank_num
|
||||
FROM sector_performance_cache
|
||||
WHERE cache_mark = :cache_mark
|
||||
""")
|
||||
|
||||
result = pd.read_sql_query(query, engine, params={'cache_mark': previous_cache_mark})
|
||||
if result.empty:
|
||||
print("未找到缓存标记 {} 的数据".format(previous_cache_mark))
|
||||
return {}
|
||||
|
||||
return dict(zip(result['sector_name'], result['rank_num']))
|
||||
except Exception as e:
|
||||
print("获取缓存数据时发生错误: {}".format(str(e)))
|
||||
return {}
|
||||
|
||||
def calculate_rank_change(row, previous_ranks):
|
||||
"""计算排名变化"""
|
||||
previous_rank = previous_ranks.get(row['sector_name'])
|
||||
if previous_rank is None:
|
||||
return 0
|
||||
return previous_rank - row['rank_num']
|
||||
|
||||
def get_high_performance_stocks(db_url):
|
||||
"""获取涨幅超过1.5%的股票及其板块分布情况"""
|
||||
engine = create_engine(db_url)
|
||||
|
||||
try:
|
||||
# 确保缓存表存在
|
||||
ensure_cache_table_exists(engine)
|
||||
|
||||
# 先获取最新数据
|
||||
fetch_and_store_stock_data(db_url)
|
||||
|
||||
# 读取数据
|
||||
stock_changes_df = pd.read_sql_table('stock_changes', con=engine)
|
||||
gp_gnbk_df = pd.read_sql_table('gp_gnbk', con=engine)
|
||||
|
||||
# 去掉 symbol 字段的前两个字符
|
||||
stock_changes_df['symbol'] = stock_changes_df['symbol'].str[2:]
|
||||
|
||||
# 筛选涨幅超过 1.5% 的股票
|
||||
high_performance_stocks = stock_changes_df[stock_changes_df['percent'] > 1.5]
|
||||
|
||||
# 关联两个表，获取 bk_name
|
||||
merged_df = high_performance_stocks.merge(gp_gnbk_df, left_on='symbol', right_on='gp_code')
|
||||
|
||||
# 统计每个 bk_name 的数量
|
||||
total_counts = gp_gnbk_df['bk_name'].value_counts()
|
||||
high_performance_counts = merged_df['bk_name'].value_counts()
|
||||
|
||||
# 计算每个板块的上涨家数及占比
|
||||
result = []
|
||||
for bk_name, count in high_performance_counts.items():
|
||||
total_count = total_counts[bk_name]
|
||||
ratio = count / total_count
|
||||
result.append((bk_name, count, total_count, ratio))
|
||||
|
||||
# 按比重排序
|
||||
result.sort(key=lambda x: x[3], reverse=True)
|
||||
|
||||
# 创建DataFrame并添加排序字段
|
||||
df_result = pd.DataFrame(result, columns=['sector_name', 'up_count', 'total_count', 'ratio'])
|
||||
df_result['rank_num'] = range(1, len(df_result) + 1) # 添加排序字段，从1开始
|
||||
current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
df_result['add_time'] = current_time # 添加时间字段
|
||||
|
||||
# 获取上一个时间点的缓存数据进行对比
|
||||
previous_ranks = get_previous_cache_ranks(engine)
|
||||
# 计算排名变化
|
||||
df_result['rank_change'] = df_result.apply(
|
||||
lambda row: calculate_rank_change(row, previous_ranks), axis=1
|
||||
)
|
||||
|
||||
# 保存到数据库（追加模式）
|
||||
df_result.to_sql('sector_performance', con=engine, if_exists='append', index=False)
|
||||
|
||||
# 保存到缓存表
|
||||
cache_mark = get_cache_mark()
|
||||
save_sector_cache(engine, df_result, cache_mark)
|
||||
|
||||
# 输出结果
|
||||
for _, row in df_result.iterrows():
|
||||
print("板块名称: {}, 上涨家数: {}, 总数: {}, 比重: {:.2%}, 排名: {}, 排名变化: {}".format(
|
||||
row['sector_name'], row['up_count'], row['total_count'],
|
||||
row['ratio'], row['rank_num'], row['rank_change']
|
||||
))
|
||||
|
||||
return result
|
||||
except Exception as e:
|
||||
print("分析数据时发生错误: {}".format(str(e)))
|
||||
return []
|
||||
|
||||
def get_top_industries_and_stocks(db_url, top_start=1, top_end=10):
|
||||
"""
|
||||
分析在多个热门板块中出现的股票
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
db_url : str
|
||||
数据库连接URL
|
||||
top_start : int
|
||||
起始排名
|
||||
top_end : int
|
||||
结束排名
|
||||
"""
|
||||
try:
|
||||
engine = create_engine(db_url)
|
||||
|
||||
# 获取高表现股票及其板块信息
|
||||
top_industries = get_high_performance_stocks(db_url)
|
||||
if not top_industries:
|
||||
print("无法获取行业数据")
|
||||
return
|
||||
|
||||
# 提取指定范围的行名称
|
||||
top_industry_names = [industry[0] for industry in top_industries[top_start-1:top_end]]
|
||||
|
||||
# 读取数据
|
||||
stock_changes_df = pd.read_sql_table('stock_changes', con=engine)
|
||||
gp_gnbk_df = pd.read_sql_table('gp_gnbk', con=engine)
|
||||
|
||||
# 去掉 symbol 字段的前两个字符
|
||||
stock_changes_df['symbol'] = stock_changes_df['symbol'].str[2:]
|
||||
|
||||
# 关联两个表，获取 bk_name 和 gp_name
|
||||
merged_df = stock_changes_df.merge(gp_gnbk_df, left_on='symbol', right_on='gp_code')
|
||||
|
||||
# 筛选指定范围的行业的股票
|
||||
filtered_df = merged_df[merged_df['bk_name'].isin(top_industry_names)]
|
||||
|
||||
# 统计每只股票命中行业的数量
|
||||
stock_industry_counts = filtered_df.groupby(['symbol', 'gp_name'])['bk_name'].nunique().sort_values(ascending=False).head(10)
|
||||
|
||||
# 获取每只股票的命中行业数量
|
||||
stock_industry_list = stock_industry_counts.reset_index()
|
||||
stock_industry_list.columns = ['symbol', 'stock_name', 'sector_count']
|
||||
current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
stock_industry_list['add_time'] = current_time # 添加时间字段
|
||||
|
||||
# 保存结果到数据库（改为追加模式）
|
||||
stock_industry_list.to_sql('top_sector_stocks', con=engine, if_exists='append', index=False)
|
||||
|
||||
# 输出结果
|
||||
for index, row in stock_industry_list.iterrows():
|
||||
print("股票代码: {}, 股票名称: {}, 命中行业数量: {}".format(
|
||||
row['symbol'], row['stock_name'], row['sector_count']
|
||||
))
|
||||
|
||||
except Exception as e:
|
||||
print("分析行业数据时发生错误: {}".format(str(e)))
|
||||
|
||||
def clean_historical_data(engine):
|
||||
"""清理当天0点之前的历史数据"""
|
||||
try:
|
||||
today = datetime.now().strftime('%Y-%m-%d 00:00:00')
|
||||
|
||||
# 清理sector_performance表
|
||||
clean_performance_query = text("""
|
||||
DELETE FROM sector_performance
|
||||
WHERE add_time < :today
|
||||
""")
|
||||
|
||||
# 清理sector_performance_cache表
|
||||
clean_cache_query = text("""
|
||||
DELETE FROM sector_performance_cache
|
||||
WHERE add_time < :today
|
||||
""")
|
||||
|
||||
# 清理top_sector_stocks表
|
||||
clean_stocks_query = text("""
|
||||
DELETE FROM top_sector_stocks
|
||||
WHERE add_time < :today
|
||||
""")
|
||||
|
||||
with engine.connect() as conn: # 使用 connect() 创建连接
|
||||
result1 = conn.execute(clean_performance_query, {'today': today})
|
||||
result2 = conn.execute(clean_cache_query, {'today': today})
|
||||
result3 = conn.execute(clean_stocks_query, {'today': today})
|
||||
conn.commit() # 提交事务
|
||||
|
||||
print("清理历史数据完成:")
|
||||
print("- sector_performance表删除了{}条记录".format(result1.rowcount))
|
||||
print("- sector_performance_cache表删除了{}条记录".format(result2.rowcount))
|
||||
print("- top_sector_stocks表删除了{}条记录".format(result3.rowcount))
|
||||
|
||||
except Exception as e:
|
||||
print("清理历史数据时发生错误: {}".format(str(e)))
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例调用
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
|
||||
# 创建数据库连接
|
||||
engine = create_engine(db_url)
|
||||
|
||||
# 清理历史数据
|
||||
clean_historical_data(engine)
|
||||
|
||||
# 执行主要分析
|
||||
get_top_industries_and_stocks(db_url, 1, 10)
|
||||
|
|
@ -0,0 +1,157 @@
|
|||
# coding:utf-8
|
||||
|
||||
from easy_spider_tool import cookie_to_dic, format_json
|
||||
from easy_twitter_crawler import set_proxy, set_cookie, search_crawler, TwitterFilter
|
||||
|
||||
class TwitterKeywordCrawler:
|
||||
"""Twitter关键词爬虫类，用于获取特定关键词的Twitter数据"""
|
||||
|
||||
def __init__(self, proxy_host="127.0.0.1", proxy_port="10808"):
|
||||
"""
|
||||
初始化Twitter爬虫
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
proxy_host : str
|
||||
代理服务器地址
|
||||
proxy_port : str
|
||||
代理服务器端口
|
||||
"""
|
||||
self.proxy = {
|
||||
'http': f'http://{proxy_host}:{proxy_port}',
|
||||
'https': f'http://{proxy_host}:{proxy_port}'
|
||||
}
|
||||
self.cookie = None
|
||||
self.twitter_filter = None
|
||||
|
||||
def set_authentication(self, cookie):
|
||||
"""
|
||||
设置Twitter认证信息
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
cookie : str
|
||||
Twitter的cookie字符串
|
||||
"""
|
||||
self.cookie = cookie
|
||||
set_proxy(self.proxy)
|
||||
set_cookie(cookie_to_dic(cookie))
|
||||
|
||||
def configure_filter(self, key_word, lang='zh', from_user='', to_user='', at_user='',
|
||||
only_replies=None, only_links=None, exclude_replies=None,
|
||||
exclude_links=None, min_replies='', min_faves='',
|
||||
min_retweets='', since='', until=''):
|
||||
"""
|
||||
配置Twitter搜索过滤器
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
key_word : str
|
||||
搜索关键词
|
||||
lang : str
|
||||
语言设置
|
||||
from_user : str
|
||||
来源用户
|
||||
to_user : str
|
||||
发送给指定用户
|
||||
at_user : str
|
||||
提及用户
|
||||
only_replies : bool
|
||||
是否只包含回复
|
||||
only_links : bool
|
||||
是否只包含链接
|
||||
exclude_replies : bool
|
||||
是否排除回复
|
||||
exclude_links : bool
|
||||
是否排除链接
|
||||
min_replies : str
|
||||
最小回复数
|
||||
min_faves : str
|
||||
最小点赞数
|
||||
min_retweets : str
|
||||
最小转发数
|
||||
since : str
|
||||
开始日期
|
||||
until : str
|
||||
结束日期
|
||||
"""
|
||||
self.twitter_filter = TwitterFilter(key_word)
|
||||
self.twitter_filter.word_category(lang=lang)
|
||||
self.twitter_filter.account_category(filter_from=from_user, to=to_user, at=at_user)
|
||||
self.twitter_filter.filter_category(
|
||||
only_replies=only_replies,
|
||||
only_links=only_links,
|
||||
exclude_replies=exclude_replies,
|
||||
exclude_links=exclude_links
|
||||
)
|
||||
self.twitter_filter.interact_category(
|
||||
min_replies=min_replies,
|
||||
min_faves=min_faves,
|
||||
min_retweets=min_retweets
|
||||
)
|
||||
self.twitter_filter.date_category(since=since, until=until)
|
||||
|
||||
def crawl(self, data_type='Latest', count=20):
|
||||
"""
|
||||
执行爬取操作
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
data_type : str
|
||||
数据类型，默认为'Latest'
|
||||
count : int
|
||||
爬取数量
|
||||
|
||||
Returns:
|
||||
--------
|
||||
list
|
||||
爬取到的数据列表
|
||||
"""
|
||||
if not self.twitter_filter or not self.cookie:
|
||||
raise ValueError("请先设置认证信息和过滤器")
|
||||
|
||||
key_word = self.twitter_filter.filter_join()
|
||||
results = []
|
||||
|
||||
for info in search_crawler(key_word, data_type=data_type, count=count):
|
||||
# 每次请求前重新设置代理和cookie
|
||||
set_proxy(self.proxy)
|
||||
set_cookie(cookie_to_dic(self.cookie))
|
||||
results.append(format_json(info))
|
||||
|
||||
return results
|
||||
|
||||
def get_bloomberg_news(count=20):
|
||||
"""
|
||||
获取彭博社新闻数据的快速方法
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
count : int
|
||||
获取的新闻数量
|
||||
|
||||
Returns:
|
||||
--------
|
||||
list
|
||||
彭博社新闻数据列表
|
||||
"""
|
||||
# Twitter认证信息
|
||||
cookie = 'auth_token=ca95aaf4a95bdc3514d9ba26d7941089e5368ab7; ct0=d5f1bb1b10435f8b5e700c679fae30b40945952649f92bd0bd201fee750e6ca0828aad6ef01094beb22c6cc5363284c993ed7c660bab327f12f483b1ecb3627ff1194f5f0bf3b01fe51548075ab986ae'
|
||||
|
||||
# 创建爬虫实例
|
||||
crawler = TwitterKeywordCrawler()
|
||||
|
||||
# 设置认证信息
|
||||
crawler.set_authentication(cookie)
|
||||
|
||||
# 配置过滤器
|
||||
crawler.configure_filter(key_word='彭博社', lang='zh')
|
||||
|
||||
# 执行爬取
|
||||
return crawler.crawl(count=count)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例：获取彭博社新闻
|
||||
news_list = get_bloomberg_news(20)
|
||||
for news in news_list:
|
||||
print(news)
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
# coding:utf-8
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine
|
||||
from config import XUEQIU_HEADERS
|
||||
|
||||
def fetch_and_store_stock_data(db_url, table_name, page_size=100):
|
||||
"""
|
||||
Fetches stock data from the given API and stores it into a specified database table.
|
||||
Supports fetching data for multiple types: 'sha', 'sza', 'kcb'.
|
||||
"""
|
||||
import requests
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine
|
||||
|
||||
base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
|
||||
types = ['sha', 'sza', 'kcb'] # 数据类型
|
||||
headers = XUEQIU_HEADERS
|
||||
|
||||
all_data = []
|
||||
|
||||
for stock_type in types:
|
||||
params = {
|
||||
'page': 1,
|
||||
'size': page_size,
|
||||
'order': 'desc',
|
||||
'order_by': 'percent',
|
||||
'market': 'CN',
|
||||
'type': stock_type
|
||||
}
|
||||
|
||||
# 初次请求以获取总页数
|
||||
response = requests.get(base_url, headers=headers, params=params)
|
||||
if response.status_code != 200:
|
||||
print(f"请求 {stock_type} 数据失败，状态码：{response.status_code}")
|
||||
continue
|
||||
|
||||
data = response.json()
|
||||
total_count = data['data']['count']
|
||||
total_pages = (total_count // page_size) + 1
|
||||
|
||||
for page in range(1, total_pages + 1):
|
||||
params['page'] = page
|
||||
response = requests.get(base_url, headers=headers, params=params)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
all_data.extend(data['data']['list'])
|
||||
else:
|
||||
print(f"请求 {stock_type} 数据第 {page} 页失败，状态码：{response.status_code}")
|
||||
|
||||
# 转换为 DataFrame
|
||||
df = pd.DataFrame(all_data)
|
||||
|
||||
if not df.empty:
|
||||
# 添加 id 列
|
||||
df['id'] = range(1, len(df) + 1)
|
||||
|
||||
# 创建数据库连接
|
||||
engine = create_engine(db_url)
|
||||
|
||||
# 将数据写入数据库表
|
||||
df.to_sql(table_name, con=engine, if_exists='replace', index=False)
|
||||
print(f"成功将数据写入数据库表 {table_name}。")
|
||||
else:
|
||||
print("未获取到任何数据。")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 示例调用
|
||||
db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
|
||||
table_name = 'stock_changes'
|
||||
fetch_and_store_stock_data(db_url, table_name)
|
||||
Binary file not shown.
|
|
@ -0,0 +1,25 @@
|
|||
2025-03-05 14:51:02,426 - INFO - === 股票分析程序开始运行 ===
|
||||
2025-03-05 14:51:02,427 - INFO - 初始化 StockAnalyzer...
|
||||
2025-03-05 14:51:02,453 - INFO - StockAnalyzer 初始化完成
|
||||
2025-03-05 14:51:02,453 - INFO - 分析参数: 开始日期=2024-01-01, 结束日期=2024-12-31
|
||||
2025-03-05 14:51:02,453 - INFO - 止盈=10.0%, 止损=-10.0%, 回调建仓=-5.0%
|
||||
2025-03-05 14:51:02,453 - INFO - 开始分析所有股票...
|
||||
2025-03-05 14:51:02,453 - INFO - 开始获取股票列表...
|
||||
2025-03-05 14:51:02,597 - INFO - 获取到 1 支股票
|
||||
2025-03-05 14:51:02,600 - INFO - 开始分析股票 SZ300750
|
||||
2025-03-05 14:51:02,685 - INFO - 股票 SZ300750 分析完成，总收益: -141823874.93
|
||||
2025-03-05 14:51:02,686 - INFO - 分析完成，共分析 1/1 支股票
|
||||
2025-03-05 14:51:02,686 - INFO - 开始保存 1 支股票的分析结果
|
||||
2025-03-05 14:51:02,687 - ERROR - 程序运行出错: [Errno 13] Permission denied: 'results/SZ300750_analysis.csv'
|
||||
Traceback (most recent call last):
|
||||
File "D:\python_project\gpfx\src\stock_analysis.py", line 254, in main
|
||||
result.to_csv(csv_path, index=False)
|
||||
File "C:\Users\xy\.conda\envs\feapder_project\lib\site-packages\pandas\core\generic.py", line 3902, in to_csv
|
||||
return DataFrameRenderer(formatter).to_csv(
|
||||
File "C:\Users\xy\.conda\envs\feapder_project\lib\site-packages\pandas\io\formats\format.py", line 1152, in to_csv
|
||||
csv_formatter.save()
|
||||
File "C:\Users\xy\.conda\envs\feapder_project\lib\site-packages\pandas\io\formats\csvs.py", line 247, in save
|
||||
with get_handle(
|
||||
File "C:\Users\xy\.conda\envs\feapder_project\lib\site-packages\pandas\io\common.py", line 863, in get_handle
|
||||
handle = open(
|
||||
PermissionError: [Errno 13] Permission denied: 'results/SZ300750_analysis.csv'
|
||||
|
|
@ -0,0 +1,838 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
from sqlalchemy import create_engine
|
||||
from datetime import datetime, timedelta
|
||||
from tqdm import tqdm
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
|
||||
# 添加中文字体支持
|
||||
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
|
||||
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
|
||||
|
||||
class StockAnalyzer:
|
||||
def __init__(self, db_connection_string):
|
||||
"""
|
||||
Initialize the stock analyzer
|
||||
:param db_connection_string: Database connection string
|
||||
"""
|
||||
self.engine = create_engine(db_connection_string)
|
||||
self.initial_capital = 300000 # 30万本金
|
||||
self.total_position = 1000000 # 100万建仓规模
|
||||
self.borrowed_capital = 700000 # 70万借入资金
|
||||
self.annual_interest_rate = 0.05 # 年化5%利息
|
||||
self.daily_interest_rate = self.annual_interest_rate / 365
|
||||
self.commission_rate = 0.05 # 5%手续费
|
||||
self.min_holding_days = 7 # 最少持仓天数
|
||||
|
||||
def get_stock_list(self):
|
||||
"""获取所有股票列表"""
|
||||
query = "SELECT DISTINCT gp_code as symbol FROM gp_code_all where mark1 = '1'"
|
||||
return pd.read_sql(query, self.engine)['symbol'].tolist()
|
||||
|
||||
def get_stock_data(self, symbol, start_date, end_date, include_history=False):
|
||||
"""获取指定股票在时间范围内的数据"""
|
||||
if include_history:
|
||||
# 如果需要历史数据，获取start_date之前240天的数据
|
||||
query = f"""
|
||||
WITH history_data AS (
|
||||
SELECT
|
||||
symbol,
|
||||
timestamp,
|
||||
CAST(close as DECIMAL(10,2)) as close
|
||||
FROM gp_day_data
|
||||
WHERE symbol = '{symbol}'
|
||||
AND timestamp < '{start_date}'
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT 240
|
||||
)
|
||||
SELECT
|
||||
symbol,
|
||||
timestamp,
|
||||
CAST(open as DECIMAL(10,2)) as open,
|
||||
CAST(high as DECIMAL(10,2)) as high,
|
||||
CAST(low as DECIMAL(10,2)) as low,
|
||||
CAST(close as DECIMAL(10,2)) as close,
|
||||
CAST(volume as DECIMAL(20,0)) as volume
|
||||
FROM gp_day_data
|
||||
WHERE symbol = '{symbol}'
|
||||
AND timestamp BETWEEN '{start_date}' AND '{end_date}'
|
||||
UNION ALL
|
||||
SELECT
|
||||
symbol,
|
||||
timestamp,
|
||||
NULL as open,
|
||||
NULL as high,
|
||||
NULL as low,
|
||||
close,
|
||||
NULL as volume
|
||||
FROM history_data
|
||||
ORDER BY timestamp
|
||||
"""
|
||||
else:
|
||||
query = f"""
|
||||
SELECT
|
||||
symbol,
|
||||
timestamp,
|
||||
CAST(open as DECIMAL(10,2)) as open,
|
||||
CAST(high as DECIMAL(10,2)) as high,
|
||||
CAST(low as DECIMAL(10,2)) as low,
|
||||
CAST(close as DECIMAL(10,2)) as close,
|
||||
CAST(volume as DECIMAL(20,0)) as volume
|
||||
FROM gp_day_data
|
||||
WHERE symbol = '{symbol}'
|
||||
AND timestamp BETWEEN '{start_date}' AND '{end_date}'
|
||||
ORDER BY timestamp
|
||||
"""
|
||||
df = pd.read_sql(query, self.engine)
|
||||
|
||||
# 确保数值列的类型正确
|
||||
numeric_columns = ['open', 'high', 'low', 'close', 'volume']
|
||||
for col in numeric_columns:
|
||||
if col in df.columns: # 只处理存在的列
|
||||
df[col] = pd.to_numeric(df[col], errors='coerce')
|
||||
|
||||
# 删除任何包含 NaN 的行，但只考虑分析期间需要的列
|
||||
if include_history:
|
||||
# 历史数据只需要close列不为空
|
||||
historical_mask = (df['timestamp'] < start_date) & df['close'].notna()
|
||||
analysis_mask = (df['timestamp'] >= start_date) & df[['open', 'high', 'low', 'close', 'volume']].notna().all(axis=1)
|
||||
df = df[historical_mask | analysis_mask]
|
||||
else:
|
||||
df = df.dropna()
|
||||
|
||||
return df
|
||||
|
||||
def calculate_trade_signals(self, df, take_profit_pct, stop_loss_pct, pullback_entry_pct, start_date):
|
||||
"""计算交易信号和收益"""
|
||||
# 检查历史数据是否足够
|
||||
historical_data = df[df['timestamp'] < start_date]
|
||||
if len(historical_data) < 240:
|
||||
print(f"历史数据不足240天，跳过分析")
|
||||
return pd.DataFrame()
|
||||
|
||||
# 计算240日均线基准价格（使用历史数据）
|
||||
ma240_base_price = historical_data['close'].tail(240).mean()
|
||||
print(f"240日均线基准价格: {ma240_base_price:.2f}")
|
||||
|
||||
# 只使用分析期间的数据进行交易信号计算
|
||||
analysis_df = df[df['timestamp'] >= start_date].copy()
|
||||
if len(analysis_df) < 10: # 确保分析期间有足够的数据
|
||||
print(f"分析期间数据不足10天，跳过分析")
|
||||
return pd.DataFrame()
|
||||
|
||||
results = []
|
||||
positions = [] # 记录所有持仓，每个元素是一个字典，包含入场价格和日期
|
||||
max_positions = 5 # 最大建仓次数
|
||||
cumulative_profit = 0 # 总累计收益
|
||||
trades_count = 0
|
||||
max_holding_period = timedelta(days=180) # 最长持仓6个月
|
||||
max_loss_amount = -300000 # 最大亏损金额为30万
|
||||
|
||||
# 添加统计变量
|
||||
total_holding_days = 0 # 总持仓天数
|
||||
max_capital_usage = 0 # 最大资金占用
|
||||
total_trades = 0 # 总交易次数
|
||||
profitable_trades = 0 # 盈利交易次数
|
||||
loss_trades = 0 # 亏损交易次数
|
||||
|
||||
print(f"\n{'='*50}")
|
||||
print(f"开始分析股票: {df.iloc[0]['symbol']}")
|
||||
print(f"{'='*50}")
|
||||
|
||||
# 记录前一天的收盘价
|
||||
prev_close = None
|
||||
|
||||
for index, row in analysis_df.iterrows():
|
||||
current_price = (float(row['high']) + float(row['low'])) / 2
|
||||
day_result = {
|
||||
'timestamp': row['timestamp'],
|
||||
'profit': cumulative_profit, # 记录当前的累计收益
|
||||
'position': False,
|
||||
'action': 'hold'
|
||||
}
|
||||
|
||||
# 更新所有持仓的收益
|
||||
positions_to_remove = []
|
||||
|
||||
for pos in positions:
|
||||
pos_days_held = (row['timestamp'] - pos['entry_date']).days
|
||||
pos_size = self.total_position / pos['entry_price']
|
||||
|
||||
# 检查是否需要平仓
|
||||
should_close = False
|
||||
close_type = None
|
||||
close_price = current_price
|
||||
|
||||
if pos_days_held >= self.min_holding_days:
|
||||
# 检查止盈
|
||||
if float(row['high']) >= pos['entry_price'] * (1 + take_profit_pct):
|
||||
should_close = True
|
||||
close_type = 'sell_profit'
|
||||
close_price = pos['entry_price'] * (1 + take_profit_pct)
|
||||
# 检查最大亏损
|
||||
elif (close_price - pos['entry_price']) * pos_size <= max_loss_amount:
|
||||
should_close = True
|
||||
close_type = 'sell_loss'
|
||||
# 检查持仓时间
|
||||
elif (row['timestamp'] - pos['entry_date']) >= max_holding_period:
|
||||
should_close = True
|
||||
close_type = 'time_limit'
|
||||
|
||||
if should_close:
|
||||
profit = (close_price - pos['entry_price']) * pos_size
|
||||
commission = profit * self.commission_rate if profit > 0 else 0
|
||||
interest_cost = self.borrowed_capital * self.daily_interest_rate * pos_days_held
|
||||
net_profit = profit - commission - interest_cost
|
||||
|
||||
# 更新统计数据
|
||||
total_holding_days += pos_days_held
|
||||
total_trades += 1
|
||||
if net_profit > 0:
|
||||
profitable_trades += 1
|
||||
else:
|
||||
loss_trades += 1
|
||||
|
||||
cumulative_profit += net_profit
|
||||
positions_to_remove.append(pos)
|
||||
day_result['action'] = close_type
|
||||
day_result['profit'] = cumulative_profit
|
||||
|
||||
print(f"\n>>> {'止盈平仓' if close_type == 'sell_profit' else '止损平仓' if close_type == 'sell_loss' else '到期平仓'}")
|
||||
print(f"持仓天数: {pos_days_held}")
|
||||
print(f"持仓数量: {pos_size:.0f}股")
|
||||
print(f"平仓价格: {close_price:.2f}")
|
||||
print(f"{'盈利' if profit > 0 else '亏损'}: {profit:,.2f}")
|
||||
print(f"手续费: {commission:,.2f}")
|
||||
print(f"利息成本: {interest_cost:,.2f}")
|
||||
print(f"净{'盈利' if net_profit > 0 else '亏损'}: {net_profit:,.2f}")
|
||||
|
||||
# 移除已平仓的持仓
|
||||
for pos in positions_to_remove:
|
||||
positions.remove(pos)
|
||||
|
||||
# 检查是否可以建仓
|
||||
if len(positions) < max_positions and prev_close is not None:
|
||||
# 计算当日跌幅
|
||||
daily_drop = (prev_close - float(row['low'])) / prev_close
|
||||
|
||||
# 检查价格是否在均线区间内
|
||||
price_in_range = (
|
||||
current_price >= ma240_base_price * 0.7 and
|
||||
current_price <= ma240_base_price * 1.3
|
||||
)
|
||||
|
||||
# 如果跌幅超过5%且价格在均线区间内
|
||||
if daily_drop >= 0.05 and price_in_range:
|
||||
positions.append({
|
||||
'entry_price': current_price,
|
||||
'entry_date': row['timestamp']
|
||||
})
|
||||
trades_count += 1
|
||||
print(f"\n>>> 建仓信号 #{trades_count}")
|
||||
print(f"日期: {row['timestamp'].strftime('%Y-%m-%d')}")
|
||||
print(f"建仓价格: {current_price:.2f}")
|
||||
print(f"建仓数量: {self.total_position/current_price:.0f}股")
|
||||
print(f"当日跌幅: {daily_drop*100:.2f}%")
|
||||
print(f"距离均线: {((current_price/ma240_base_price)-1)*100:.2f}%")
|
||||
|
||||
# 更新前一天收盘价
|
||||
prev_close = float(row['close'])
|
||||
|
||||
# 更新日结果
|
||||
day_result['position'] = len(positions) > 0
|
||||
results.append(day_result)
|
||||
|
||||
# 计算当前资金占用
|
||||
current_capital_usage = sum(self.total_position for pos in positions)
|
||||
max_capital_usage = max(max_capital_usage, current_capital_usage)
|
||||
|
||||
# 在最后一天强制平仓所有持仓
|
||||
if positions:
|
||||
final_price = (float(analysis_df.iloc[-1]['high']) + float(analysis_df.iloc[-1]['low'])) / 2
|
||||
final_total_profit = 0
|
||||
|
||||
for pos in positions:
|
||||
position_size = self.total_position / pos['entry_price']
|
||||
days_held = (analysis_df.iloc[-1]['timestamp'] - pos['entry_date']).days
|
||||
final_profit = (final_price - pos['entry_price']) * position_size
|
||||
commission = final_profit * self.commission_rate if final_profit > 0 else 0
|
||||
interest_cost = self.borrowed_capital * self.daily_interest_rate * days_held
|
||||
net_profit = final_profit - commission - interest_cost
|
||||
|
||||
# 更新统计数据
|
||||
total_holding_days += days_held
|
||||
total_trades += 1
|
||||
if net_profit > 0:
|
||||
profitable_trades += 1
|
||||
else:
|
||||
loss_trades += 1
|
||||
|
||||
final_total_profit += net_profit
|
||||
|
||||
print(f"\n>>> 到期强制平仓")
|
||||
print(f"持仓天数: {days_held}")
|
||||
print(f"持仓数量: {position_size:.0f}股")
|
||||
print(f"平仓价格: {final_price:.2f}")
|
||||
print(f"毛利润: {final_profit:,.2f}")
|
||||
print(f"手续费: {commission:,.2f}")
|
||||
print(f"利息成本: {interest_cost:,.2f}")
|
||||
print(f"净利润: {net_profit:,.2f}")
|
||||
|
||||
results[-1]['action'] = 'final_sell'
|
||||
cumulative_profit += final_total_profit # 更新最终的累计收益
|
||||
results[-1]['profit'] = cumulative_profit # 更新最后一天的累计收益
|
||||
|
||||
# 计算统计数据
|
||||
avg_holding_days = total_holding_days / total_trades if total_trades > 0 else 0
|
||||
win_rate = profitable_trades / total_trades * 100 if total_trades > 0 else 0
|
||||
|
||||
print(f"\n{'='*50}")
|
||||
print(f"交易统计")
|
||||
print(f"总交易次数: {trades_count}")
|
||||
print(f"累计收益: {cumulative_profit:,.2f}")
|
||||
print(f"最大资金占用: {max_capital_usage:,.2f}")
|
||||
print(f"平均持仓天数: {avg_holding_days:.1f}")
|
||||
print(f"胜率: {win_rate:.1f}%")
|
||||
print(f"盈利交易: {profitable_trades}次")
|
||||
print(f"亏损交易: {loss_trades}次")
|
||||
print(f"{'='*50}\n")
|
||||
|
||||
# 将统计数据添加到结果中
|
||||
results[-1]['stats'] = {
|
||||
'total_trades': total_trades,
|
||||
'profitable_trades': profitable_trades,
|
||||
'loss_trades': loss_trades,
|
||||
'win_rate': win_rate,
|
||||
'avg_holding_days': avg_holding_days,
|
||||
'max_capital_usage': max_capital_usage,
|
||||
'final_profit': cumulative_profit
|
||||
}
|
||||
|
||||
return pd.DataFrame(results)
|
||||
|
||||
def analyze_stock(self, symbol, start_date, end_date, take_profit_pct, stop_loss_pct, pullback_entry_pct):
|
||||
"""分析单个股票"""
|
||||
df = self.get_stock_data(symbol, start_date, end_date, include_history=True)
|
||||
|
||||
if len(df) < 240: # 确保总数据量足够
|
||||
print(f"股票 {symbol} 总数据不足240天，跳过分析")
|
||||
return None
|
||||
|
||||
return self.calculate_trade_signals(
|
||||
df,
|
||||
take_profit_pct=take_profit_pct,
|
||||
stop_loss_pct=stop_loss_pct,
|
||||
pullback_entry_pct=pullback_entry_pct,
|
||||
start_date=start_date
|
||||
)
|
||||
|
||||
def analyze_all_stocks(self, start_date, end_date, take_profit_pct, stop_loss_pct, pullback_entry_pct):
|
||||
"""分析所有股票"""
|
||||
stocks = self.get_stock_list()
|
||||
all_results = {}
|
||||
|
||||
print(f"\n开始分析，共 {len(stocks)} 支股票")
|
||||
print(f"分析参数:")
|
||||
print(f"- 开始日期: {start_date}")
|
||||
print(f"- 结束日期: {end_date}")
|
||||
print(f"- 止盈: {take_profit_pct*100}%")
|
||||
print(f"- 止损: {stop_loss_pct*100}%")
|
||||
print(f"- 回调建仓: {pullback_entry_pct*100}%\n")
|
||||
|
||||
for symbol in tqdm(stocks, desc="Analyzing stocks"):
|
||||
result = self.analyze_stock(
|
||||
symbol,
|
||||
start_date,
|
||||
end_date,
|
||||
take_profit_pct,
|
||||
stop_loss_pct,
|
||||
pullback_entry_pct
|
||||
)
|
||||
if result is not None:
|
||||
all_results[symbol] = result
|
||||
|
||||
return all_results
|
||||
|
||||
def plot_results(self, results, symbol):
|
||||
"""绘制单个股票的收益走势图"""
|
||||
plt.figure(figsize=(12, 6))
|
||||
plt.plot(results['timestamp'], results['profit'].cumsum(), label='Cumulative Profit')
|
||||
plt.title(f'Stock {symbol} Trading Results')
|
||||
plt.xlabel('Date')
|
||||
plt.ylabel('Profit (CNY)')
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
plt.xticks(rotation=45)
|
||||
plt.tight_layout()
|
||||
return plt
|
||||
|
||||
def plot_all_stocks(self, all_results):
|
||||
"""绘制所有股票的收益走势图"""
|
||||
plt.figure(figsize=(15, 8))
|
||||
|
||||
# 记录所有股票的累计收益
|
||||
total_profits = {}
|
||||
max_profit = float('-inf')
|
||||
min_profit = float('inf')
|
||||
|
||||
# 绘制每支股票的曲线
|
||||
for symbol, results in all_results.items():
|
||||
# 直接使用已经计算好的累计收益
|
||||
profits = results['profit']
|
||||
plt.plot(results['timestamp'], profits, label=symbol, alpha=0.5, linewidth=1)
|
||||
|
||||
# 更新最大最小收益
|
||||
max_profit = max(max_profit, profits.max())
|
||||
min_profit = min(min_profit, profits.min())
|
||||
|
||||
# 记录总收益（使用最后一个累计收益值）
|
||||
total_profits[symbol] = profits.iloc[-1]
|
||||
|
||||
# 计算所有股票的中位数收益曲线
|
||||
# 首先找到共同的日期范围
|
||||
all_dates = set()
|
||||
for results in all_results.values():
|
||||
all_dates.update(results['timestamp'])
|
||||
all_dates = sorted(list(all_dates))
|
||||
|
||||
# 创建一个包含所有日期的DataFrame
|
||||
profits_df = pd.DataFrame(index=all_dates)
|
||||
|
||||
# 对每支股票，填充所有日期的收益
|
||||
for symbol, results in all_results.items():
|
||||
daily_profits = pd.Series(index=all_dates, data=np.nan)
|
||||
# 直接使用已经计算好的累计收益
|
||||
for date, profit in zip(results['timestamp'], results['profit']):
|
||||
daily_profits[date] = profit
|
||||
# 对于没有交易的日期，使用最后一个累计收益填充
|
||||
daily_profits.fillna(method='ffill', inplace=True)
|
||||
daily_profits.fillna(0, inplace=True) # 对开始之前的日期填充0
|
||||
profits_df[symbol] = daily_profits
|
||||
|
||||
# 计算并绘制中位数收益曲线
|
||||
median_line = profits_df.median(axis=1)
|
||||
plt.plot(all_dates, median_line, 'r-', label='Median', linewidth=2)
|
||||
|
||||
plt.title('All Stocks Trading Results')
|
||||
plt.xlabel('Date')
|
||||
plt.ylabel('Profit (CNY)')
|
||||
plt.grid(True)
|
||||
plt.xticks(rotation=45)
|
||||
|
||||
# 添加利润区间标注
|
||||
plt.text(0.02, 0.98,
|
||||
f'Profit Range:\nMax: {max_profit:,.0f}\nMin: {min_profit:,.0f}\nMedian: {median_line.iloc[-1]:,.0f}',
|
||||
transform=plt.gca().transAxes,
|
||||
bbox=dict(facecolor='white', alpha=0.8))
|
||||
|
||||
# 计算所有股票的统计数据
|
||||
total_stats = {
|
||||
'total_trades': 0,
|
||||
'profitable_trades': 0,
|
||||
'loss_trades': 0,
|
||||
'total_holding_days': 0,
|
||||
'max_capital_usage': 0,
|
||||
'total_profit': 0,
|
||||
'profitable_stocks': 0,
|
||||
'loss_stocks': 0,
|
||||
'total_capital_usage': 0, # 添加总资金占用
|
||||
'total_win_rate': 0, # 添加总胜率
|
||||
}
|
||||
|
||||
# 获取前5名和后5名股票
|
||||
top_5 = sorted(total_profits.items(), key=lambda x: x[1], reverse=True)[:5]
|
||||
bottom_5 = sorted(total_profits.items(), key=lambda x: x[1])[:5]
|
||||
|
||||
# 添加排名信息
|
||||
rank_text = "Top 5 Stocks:\n"
|
||||
for symbol, profit in top_5:
|
||||
rank_text += f"{symbol}: {profit:,.0f}\n"
|
||||
rank_text += "\nBottom 5 Stocks:\n"
|
||||
for symbol, profit in bottom_5:
|
||||
rank_text += f"{symbol}: {profit:,.0f}\n"
|
||||
|
||||
plt.text(1.02, 0.98, rank_text, transform=plt.gca().transAxes,
|
||||
bbox=dict(facecolor='white', alpha=0.8), verticalalignment='top')
|
||||
|
||||
# 记录每支股票的统计数据用于计算平均值
|
||||
stock_stats = []
|
||||
|
||||
for symbol, results in all_results.items():
|
||||
if 'stats' in results.iloc[-1]:
|
||||
stats = results.iloc[-1]['stats']
|
||||
stock_stats.append(stats) # 记录每支股票的统计数据
|
||||
|
||||
total_stats['total_trades'] += stats['total_trades']
|
||||
total_stats['profitable_trades'] += stats['profitable_trades']
|
||||
total_stats['loss_trades'] += stats['loss_trades']
|
||||
total_stats['total_holding_days'] += stats['avg_holding_days'] * stats['total_trades']
|
||||
total_stats['max_capital_usage'] = max(total_stats['max_capital_usage'], stats['max_capital_usage'])
|
||||
total_stats['total_capital_usage'] += stats['max_capital_usage']
|
||||
total_stats['total_profit'] += stats['final_profit']
|
||||
total_stats['total_win_rate'] += stats['win_rate']
|
||||
if stats['final_profit'] > 0:
|
||||
total_stats['profitable_stocks'] += 1
|
||||
else:
|
||||
total_stats['loss_stocks'] += 1
|
||||
|
||||
# 计算总体统计
|
||||
total_stocks = total_stats['profitable_stocks'] + total_stats['loss_stocks']
|
||||
avg_holding_days = total_stats['total_holding_days'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
|
||||
win_rate = total_stats['profitable_trades'] / total_stats['total_trades'] * 100 if total_stats['total_trades'] > 0 else 0
|
||||
stock_win_rate = total_stats['profitable_stocks'] / total_stocks * 100 if total_stocks > 0 else 0
|
||||
|
||||
# 计算平均值统计
|
||||
avg_trades_per_stock = total_stats['total_trades'] / total_stocks if total_stocks > 0 else 0
|
||||
avg_profit_per_stock = total_stats['total_profit'] / total_stocks if total_stocks > 0 else 0
|
||||
avg_capital_usage = total_stats['total_capital_usage'] / total_stocks if total_stocks > 0 else 0
|
||||
avg_win_rate = total_stats['total_win_rate'] / total_stocks if total_stocks > 0 else 0
|
||||
|
||||
# 添加总体统计信息
|
||||
stats_text = (
|
||||
f"总体统计:\n"
|
||||
f"总交易次数: {total_stats['total_trades']}\n"
|
||||
f"总盈利: {total_stats['total_profit']:,.0f}\n"
|
||||
# f"最大资金占用: {total_stats['max_capital_usage']:,.0f}\n"
|
||||
f"平均持仓天数: {avg_holding_days:.1f}\n"
|
||||
f"交易胜率: {win_rate:.1f}%\n"
|
||||
f"股票胜率: {stock_win_rate:.1f}%\n"
|
||||
f"盈利股票数: {total_stats['profitable_stocks']}\n"
|
||||
f"亏损股票数: {total_stats['loss_stocks']}\n"
|
||||
f"\n平均值统计:\n"
|
||||
f"平均交易次数: {avg_trades_per_stock:.1f}\n"
|
||||
f"平均收益: {avg_profit_per_stock:,.0f}\n"
|
||||
f"平均资金占用: {avg_capital_usage:,.0f}\n"
|
||||
f"平均持仓天数: {avg_holding_days:.1f}\n"
|
||||
f"平均胜率: {avg_win_rate:.1f}%"
|
||||
)
|
||||
|
||||
plt.text(0.02, 0.6, stats_text,
|
||||
transform=plt.gca().transAxes,
|
||||
bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'),
|
||||
verticalalignment='top',
|
||||
fontsize=10)
|
||||
|
||||
# 获取前5名和后5名股票
|
||||
top_5 = sorted(total_profits.items(), key=lambda x: x[1], reverse=True)[:5]
|
||||
bottom_5 = sorted(total_profits.items(), key=lambda x: x[1])[:5]
|
||||
|
||||
# 添加排名信息
|
||||
rank_text = "Top 5 Stocks:\n"
|
||||
for symbol, profit in top_5:
|
||||
rank_text += f"{symbol}: {profit:,.0f}\n"
|
||||
rank_text += "\nBottom 5 Stocks:\n"
|
||||
for symbol, profit in bottom_5:
|
||||
rank_text += f"{symbol}: {profit:,.0f}\n"
|
||||
|
||||
plt.text(1.02, 0.98, rank_text, transform=plt.gca().transAxes,
|
||||
bbox=dict(facecolor='white', alpha=0.8), verticalalignment='top')
|
||||
|
||||
plt.tight_layout()
|
||||
return plt, total_profits
|
||||
|
||||
def plot_profit_distribution(self, total_profits):
|
||||
"""绘制所有股票的收益分布小提琴图"""
|
||||
plt.figure(figsize=(12, 8))
|
||||
|
||||
# 将收益数据转换为数组，确保使用最终累计收益
|
||||
profits = np.array([profit for profit in total_profits.values()])
|
||||
|
||||
# 计算统计数据
|
||||
mean_profit = np.mean(profits)
|
||||
median_profit = np.median(profits)
|
||||
max_profit = np.max(profits)
|
||||
min_profit = np.min(profits)
|
||||
std_profit = np.std(profits)
|
||||
|
||||
# 绘制小提琴图
|
||||
violin_parts = plt.violinplot(profits, positions=[0], showmeans=True, showmedians=True)
|
||||
|
||||
# 设置小提琴图的颜色
|
||||
violin_parts['bodies'][0].set_facecolor('lightblue')
|
||||
violin_parts['bodies'][0].set_alpha(0.7)
|
||||
violin_parts['cmeans'].set_color('red')
|
||||
violin_parts['cmedians'].set_color('blue')
|
||||
|
||||
# 添加统计信息
|
||||
stats_text = (
|
||||
f"统计信息:\n"
|
||||
f"平均收益: {mean_profit:,.0f}\n"
|
||||
f"中位数收益: {median_profit:,.0f}\n"
|
||||
f"最大收益: {max_profit:,.0f}\n"
|
||||
f"最小收益: {min_profit:,.0f}\n"
|
||||
f"标准差: {std_profit:,.0f}"
|
||||
)
|
||||
|
||||
plt.text(0.65, 0.95, stats_text,
|
||||
transform=plt.gca().transAxes,
|
||||
bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'),
|
||||
verticalalignment='top',
|
||||
fontsize=10)
|
||||
|
||||
# 设置图表样式
|
||||
plt.title('股票收益分布图', fontsize=14, pad=20)
|
||||
plt.ylabel('收益 (元)', fontsize=12)
|
||||
plt.xticks([0], ['所有股票'], fontsize=10)
|
||||
plt.grid(True, axis='y', alpha=0.3)
|
||||
|
||||
# 添加零线
|
||||
plt.axhline(y=0, color='r', linestyle='--', alpha=0.3)
|
||||
|
||||
# 计算盈利和亏损的股票数量
|
||||
profit_count = np.sum(profits > 0)
|
||||
loss_count = np.sum(profits < 0)
|
||||
total_count = len(profits)
|
||||
|
||||
# 添加盈亏比例信息
|
||||
ratio_text = (
|
||||
f"盈亏比例:\n"
|
||||
f"盈利: {profit_count}支 ({profit_count/total_count*100:.1f}%)\n"
|
||||
f"亏损: {loss_count}支 ({loss_count/total_count*100:.1f}%)\n"
|
||||
f"总计: {total_count}支"
|
||||
)
|
||||
|
||||
plt.text(0.65, 0.6, ratio_text,
|
||||
transform=plt.gca().transAxes,
|
||||
bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'),
|
||||
verticalalignment='top',
|
||||
fontsize=10)
|
||||
|
||||
plt.tight_layout()
|
||||
return plt
|
||||
|
||||
def plot_profit_matrix(self, df):
|
||||
"""绘制止盈比例分析矩阵的结果图"""
|
||||
plt.figure(figsize=(15, 10))
|
||||
|
||||
# 创建子图
|
||||
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))
|
||||
|
||||
# 1. 平均收益曲线
|
||||
ax1.plot(df.index, df['avg_profit'], 'b-', linewidth=2)
|
||||
ax1.set_title('平均收益 vs 止盈比例')
|
||||
ax1.set_xlabel('止盈比例 (%)')
|
||||
ax1.set_ylabel('平均收益 (元)')
|
||||
ax1.grid(True)
|
||||
|
||||
# 标记最优止盈比例点
|
||||
best_profit_pct = df['avg_profit'].idxmax()
|
||||
best_profit = df['avg_profit'].max()
|
||||
ax1.plot(best_profit_pct, best_profit, 'ro')
|
||||
ax1.annotate(f'最优: {best_profit_pct:.1f}%\n{best_profit:,.0f}元',
|
||||
(best_profit_pct, best_profit),
|
||||
xytext=(10, 10), textcoords='offset points')
|
||||
|
||||
# 2. 平均胜率曲线
|
||||
ax2.plot(df.index, df['avg_win_rate'], 'g-', linewidth=2)
|
||||
ax2.set_title('平均胜率 vs 止盈比例')
|
||||
ax2.set_xlabel('止盈比例 (%)')
|
||||
ax2.set_ylabel('胜率 (%)')
|
||||
ax2.grid(True)
|
||||
|
||||
# 3. 平均持仓天数曲线
|
||||
ax3.plot(df.index, df['avg_holding_days'], 'r-', linewidth=2)
|
||||
ax3.set_title('平均持仓天数 vs 止盈比例')
|
||||
ax3.set_xlabel('止盈比例 (%)')
|
||||
ax3.set_ylabel('持仓天数')
|
||||
ax3.grid(True)
|
||||
|
||||
# 4. 平均交易次数和资金占用
|
||||
ax4.plot(df.index, df['avg_trades'], 'c-', linewidth=2, label='平均交易次数')
|
||||
ax4_twin = ax4.twinx()
|
||||
ax4_twin.plot(df.index, df['avg_capital_usage'], 'm-', linewidth=2, label='平均资金占用')
|
||||
ax4.set_title('平均交易次数和资金占用 vs 止盈比例')
|
||||
ax4.set_xlabel('止盈比例 (%)')
|
||||
ax4.set_ylabel('交易次数')
|
||||
ax4_twin.set_ylabel('资金占用 (元)')
|
||||
|
||||
# 合并两个y轴的图例
|
||||
lines1, labels1 = ax4.get_legend_handles_labels()
|
||||
lines2, labels2 = ax4_twin.get_legend_handles_labels()
|
||||
ax4_twin.legend(lines1 + lines2, labels1 + labels2, loc='upper right')
|
||||
ax4.grid(True)
|
||||
|
||||
# 调整布局
|
||||
plt.tight_layout()
|
||||
return plt
|
||||
|
||||
def analyze_profit_matrix(self, start_date, end_date, stop_loss_pct, pullback_entry_pct):
|
||||
"""分析不同止盈比例的表现矩阵"""
|
||||
# 创建结果列表
|
||||
matrix_results = []
|
||||
|
||||
# 从30%到5%，每次减少1%
|
||||
for take_profit_pct in np.arange(0.30, 0.04, -0.01):
|
||||
print(f"\n分析止盈比例: {take_profit_pct*100:.1f}%")
|
||||
|
||||
# 运行分析
|
||||
results = self.analyze_all_stocks(
|
||||
start_date,
|
||||
end_date,
|
||||
take_profit_pct,
|
||||
stop_loss_pct,
|
||||
pullback_entry_pct
|
||||
)
|
||||
|
||||
# 过滤掉空结果
|
||||
valid_results = {symbol: result for symbol, result in results.items() if result is not None and not result.empty}
|
||||
|
||||
if valid_results:
|
||||
# 计算统计数据
|
||||
total_stats = {
|
||||
'total_trades': 0,
|
||||
'profitable_trades': 0,
|
||||
'loss_trades': 0,
|
||||
'total_holding_days': 0,
|
||||
'total_profit': 0,
|
||||
'total_capital_usage': 0,
|
||||
'total_win_rate': 0,
|
||||
'stock_count': len(valid_results)
|
||||
}
|
||||
|
||||
# 收集每支股票的统计数据
|
||||
for symbol, result in valid_results.items():
|
||||
if 'stats' in result.iloc[-1]:
|
||||
stats = result.iloc[-1]['stats']
|
||||
total_stats['total_trades'] += stats['total_trades']
|
||||
total_stats['profitable_trades'] += stats['profitable_trades']
|
||||
total_stats['loss_trades'] += stats['loss_trades']
|
||||
total_stats['total_holding_days'] += stats['avg_holding_days'] * stats['total_trades']
|
||||
total_stats['total_profit'] += stats['final_profit']
|
||||
total_stats['total_capital_usage'] += stats['max_capital_usage']
|
||||
total_stats['total_win_rate'] += stats['win_rate']
|
||||
|
||||
# 计算平均值
|
||||
avg_trades = total_stats['total_trades'] / total_stats['stock_count']
|
||||
avg_holding_days = total_stats['total_holding_days'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
|
||||
avg_profit = total_stats['total_profit'] / total_stats['stock_count']
|
||||
avg_capital_usage = total_stats['total_capital_usage'] / total_stats['stock_count']
|
||||
avg_win_rate = total_stats['total_win_rate'] / total_stats['stock_count']
|
||||
|
||||
# 添加到结果矩阵
|
||||
matrix_results.append({
|
||||
'take_profit_pct': take_profit_pct * 100,
|
||||
'avg_trades': avg_trades,
|
||||
'avg_holding_days': avg_holding_days,
|
||||
'avg_profit': avg_profit,
|
||||
'avg_capital_usage': avg_capital_usage,
|
||||
'avg_win_rate': avg_win_rate,
|
||||
'total_trades': total_stats['total_trades'],
|
||||
'stock_count': total_stats['stock_count']
|
||||
})
|
||||
|
||||
# 创建DataFrame并格式化
|
||||
df = pd.DataFrame(matrix_results)
|
||||
df = df.round({
|
||||
'take_profit_pct': 1,
|
||||
'avg_trades': 1,
|
||||
'avg_holding_days': 1,
|
||||
'avg_profit': 0,
|
||||
'avg_capital_usage': 0,
|
||||
'avg_win_rate': 1,
|
||||
'total_trades': 0,
|
||||
'stock_count': 0
|
||||
})
|
||||
|
||||
# 设置止盈比例为索引
|
||||
df.set_index('take_profit_pct', inplace=True)
|
||||
|
||||
# 保存到CSV文件
|
||||
df.to_csv('results/profit_matrix_analysis.csv')
|
||||
|
||||
# 打印结果
|
||||
print("\n止盈比例分析矩阵:")
|
||||
print("=" * 120)
|
||||
print(df.to_string())
|
||||
print("=" * 120)
|
||||
|
||||
# 绘制并保存矩阵分析图表
|
||||
plt = self.plot_profit_matrix(df)
|
||||
plt.savefig('results/profit_matrix_analysis.png', bbox_inches='tight', dpi=300)
|
||||
plt.close()
|
||||
|
||||
return df
|
||||
|
||||
def main():
|
||||
# 数据库连接配置
|
||||
db_config = {
|
||||
'host': '192.168.1.82',
|
||||
'port': 3306,
|
||||
'user': 'root',
|
||||
'password': 'Chlry$%.8',
|
||||
'database': 'db_gp_cj'
|
||||
}
|
||||
|
||||
try:
|
||||
connection_string = f"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}"
|
||||
|
||||
# 创建results目录
|
||||
if not os.path.exists('results'):
|
||||
os.makedirs('results')
|
||||
|
||||
# 创建分析器实例
|
||||
analyzer = StockAnalyzer(connection_string)
|
||||
|
||||
# 设置分析参数
|
||||
start_date = '2024-01-01'
|
||||
end_date = '2024-12-31'
|
||||
take_profit_pct = 0.25 # 30%止盈
|
||||
stop_loss_pct = -0.99 # -99%止损
|
||||
pullback_entry_pct = -0.05 # -5%回调建仓
|
||||
|
||||
# 控制是否运行止盈比例分析的标志
|
||||
run_profit_matrix = False # 设置为 True 时运行止盈比例分析
|
||||
|
||||
if run_profit_matrix:
|
||||
# 运行止盈比例分析
|
||||
profit_matrix = analyzer.analyze_profit_matrix(
|
||||
start_date,
|
||||
end_date,
|
||||
stop_loss_pct,
|
||||
pullback_entry_pct
|
||||
)
|
||||
# 使用最优止盈比例
|
||||
take_profit_pct = profit_matrix['avg_profit'].idxmax() / 100
|
||||
print(f"\n使用最优止盈比例 {take_profit_pct*100:.1f}% 运行详细分析")
|
||||
|
||||
# 运行分析
|
||||
results = analyzer.analyze_all_stocks(
|
||||
start_date,
|
||||
end_date,
|
||||
take_profit_pct,
|
||||
stop_loss_pct,
|
||||
pullback_entry_pct
|
||||
)
|
||||
|
||||
# 保存结果
|
||||
for symbol, result in results.items():
|
||||
if result is not None and not result.empty:
|
||||
result.to_csv(f'results/{symbol}_analysis.csv', index=False)
|
||||
plt = analyzer.plot_results(result, symbol)
|
||||
plt.savefig(f'results/{symbol}_chart.png')
|
||||
plt.close()
|
||||
else:
|
||||
print(f"跳过空结果: {symbol}")
|
||||
|
||||
# 过滤掉空结果
|
||||
valid_results = {symbol: result for symbol, result in results.items() if result is not None and not result.empty}
|
||||
|
||||
if valid_results:
|
||||
# 绘制所有股票的收益曲线
|
||||
plt, total_profits = analyzer.plot_all_stocks(valid_results)
|
||||
plt.savefig('results/all_stocks_analysis.png', bbox_inches='tight', dpi=300)
|
||||
plt.close()
|
||||
|
||||
# 绘制收益分布小提琴图
|
||||
plt = analyzer.plot_profit_distribution(total_profits)
|
||||
plt.savefig('results/profit_distribution.png', bbox_inches='tight', dpi=300)
|
||||
plt.close()
|
||||
else:
|
||||
print("没有有效的分析结果可供绘图")
|
||||
|
||||
except Exception as e:
|
||||
print(f"程序运行出错: {str(e)}")
|
||||
raise
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,444 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
from sqlalchemy import create_engine
|
||||
from datetime import datetime, timedelta
|
||||
from tqdm import tqdm
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
# 止盈10%，不止损，较上一次止盈或者止损价回调5%之后建仓
|
||||
# 添加中文字体支持
|
||||
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
|
||||
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
|
||||
|
||||
class StockAnalyzer:
|
||||
def __init__(self, db_connection_string):
|
||||
"""
|
||||
Initialize the stock analyzer
|
||||
:param db_connection_string: Database connection string
|
||||
"""
|
||||
self.engine = create_engine(db_connection_string)
|
||||
self.initial_capital = 300000 # 30万本金
|
||||
self.total_position = 1000000 # 100万建仓规模
|
||||
self.borrowed_capital = 700000 # 70万借入资金
|
||||
self.annual_interest_rate = 0.05 # 年化5%利息
|
||||
self.daily_interest_rate = self.annual_interest_rate / 365
|
||||
self.commission_rate = 0.05 # 5%手续费
|
||||
self.min_holding_days = 7 # 最少持仓天数
|
||||
|
||||
def get_stock_list(self):
|
||||
"""获取所有股票列表"""
|
||||
query = "SELECT DISTINCT gp_code as symbol FROM gp_code_all"
|
||||
return pd.read_sql(query, self.engine)['symbol'].tolist()
|
||||
|
||||
def get_stock_data(self, symbol, start_date, end_date):
|
||||
"""获取指定股票在时间范围内的数据"""
|
||||
query = f"""
|
||||
SELECT * FROM gp_day_data
|
||||
WHERE symbol = '{symbol}'
|
||||
AND timestamp BETWEEN '{start_date}' AND '{end_date}'
|
||||
ORDER BY timestamp
|
||||
"""
|
||||
return pd.read_sql(query, self.engine)
|
||||
|
||||
def calculate_trade_signals(self, df, take_profit_pct, stop_loss_pct, pullback_entry_pct):
|
||||
"""计算交易信号和收益"""
|
||||
results = []
|
||||
position = False
|
||||
entry_price = 0
|
||||
entry_date = None
|
||||
last_exit_price = None
|
||||
cumulative_profit = 0
|
||||
trades_count = 0
|
||||
max_holding_period = timedelta(days=180) # 最长持仓6个月
|
||||
max_loss_amount = -300000 # 最大亏损金额为30万
|
||||
|
||||
print(f"\n{'='*50}")
|
||||
print(f"开始分析股票: {df.iloc[0]['symbol']}")
|
||||
print(f"{'='*50}")
|
||||
|
||||
for index, row in df.iterrows():
|
||||
current_price = (float(row['high']) + float(row['low'])) / 2
|
||||
day_result = {
|
||||
'date': row['timestamp'],
|
||||
'profit': 0,
|
||||
'position': False,
|
||||
'action': 'hold'
|
||||
}
|
||||
|
||||
if not position:
|
||||
# 检查是否可以建仓
|
||||
if last_exit_price is None or (
|
||||
(current_price - last_exit_price) / last_exit_price <= pullback_entry_pct
|
||||
):
|
||||
position = True
|
||||
entry_price = current_price
|
||||
entry_date = row['timestamp']
|
||||
day_result['action'] = 'buy'
|
||||
day_result['position'] = True
|
||||
trades_count += 1
|
||||
print(f"\n>>> 建仓信号 #{trades_count}")
|
||||
print(f"日期: {entry_date.strftime('%Y-%m-%d')}")
|
||||
print(f"建仓价格: {entry_price:.2f}")
|
||||
print(f"建仓数量: {self.total_position/entry_price:.0f}股")
|
||||
if last_exit_price is not None:
|
||||
print(f"距离上次平仓价格变化: {((current_price - last_exit_price) / last_exit_price * 100):.2f}%")
|
||||
else:
|
||||
# 计算当前持仓收益
|
||||
current_profit = (current_price - entry_price) / entry_price
|
||||
days_held = (row['timestamp'] - entry_date).days
|
||||
holding_period = row['timestamp'] - entry_date
|
||||
|
||||
# 计算当天的未实现收益
|
||||
position_size = self.total_position / entry_price # 计算持仓数量
|
||||
day_profit = (current_price - entry_price) * position_size # 用持仓数量计算收益
|
||||
day_result['profit'] = day_profit
|
||||
day_result['position'] = True
|
||||
|
||||
# 计算当前亏损（包括利息成本）
|
||||
interest_cost = self.borrowed_capital * self.daily_interest_rate * days_held
|
||||
current_net_profit = day_profit - interest_cost
|
||||
|
||||
# 打印每日收益情况
|
||||
print(f"\n当日交易 - {row['timestamp'].strftime('%Y-%m-%d')}")
|
||||
print(f"当前价格: {current_price:.2f}")
|
||||
print(f"持仓天数: {days_held}")
|
||||
print(f"当日收益: {day_profit:,.2f} ({current_profit*100:.2f}%)")
|
||||
|
||||
# 检查是否满足平仓条件
|
||||
if days_held >= self.min_holding_days:
|
||||
should_close = False
|
||||
close_type = None
|
||||
close_price = current_price
|
||||
|
||||
# 检查止盈
|
||||
if float(row['high']) >= entry_price * (1 + take_profit_pct):
|
||||
should_close = True
|
||||
close_type = 'sell_profit'
|
||||
close_price = entry_price * (1 + take_profit_pct)
|
||||
# 检查最大亏损
|
||||
elif current_net_profit <= max_loss_amount:
|
||||
should_close = True
|
||||
close_type = 'sell_loss'
|
||||
close_price = current_price
|
||||
# 检查持仓时间
|
||||
elif holding_period >= max_holding_period:
|
||||
should_close = True
|
||||
close_type = 'time_limit'
|
||||
close_price = current_price
|
||||
|
||||
if should_close:
|
||||
position_size = self.total_position / entry_price
|
||||
profit = (close_price - entry_price) * position_size
|
||||
commission = profit * self.commission_rate if profit > 0 else 0
|
||||
interest_cost = self.borrowed_capital * self.daily_interest_rate * days_held
|
||||
net_profit = profit - commission - interest_cost
|
||||
|
||||
cumulative_profit += net_profit
|
||||
position = False
|
||||
last_exit_price = close_price
|
||||
day_result['action'] = close_type
|
||||
day_result['profit'] = net_profit
|
||||
|
||||
print(f"\n>>> {'止盈平仓' if close_type == 'sell_profit' else '止损平仓' if close_type == 'sell_loss' else '到期平仓'}")
|
||||
print(f"持仓天数: {days_held}")
|
||||
print(f"持仓数量: {position_size:.0f}股")
|
||||
print(f"平仓价格: {close_price:.2f}")
|
||||
print(f"{'盈利' if profit > 0 else '亏损'}: {profit:,.2f}")
|
||||
print(f"手续费: {commission:,.2f}")
|
||||
print(f"利息成本: {interest_cost:,.2f}")
|
||||
print(f"净{'盈利' if net_profit > 0 else '亏损'}: {net_profit:,.2f}")
|
||||
|
||||
results.append(day_result)
|
||||
|
||||
# 在最后一天强制平仓
|
||||
if position:
|
||||
final_price = (float(df.iloc[-1]['high']) + float(df.iloc[-1]['low'])) / 2
|
||||
position_size = self.total_position / entry_price
|
||||
final_profit = (final_price - entry_price) * position_size
|
||||
days_held = (df.iloc[-1]['timestamp'] - entry_date).days
|
||||
commission = final_profit * self.commission_rate if final_profit > 0 else 0
|
||||
interest_cost = self.borrowed_capital * self.daily_interest_rate * days_held
|
||||
net_profit = final_profit - commission - interest_cost
|
||||
|
||||
results[-1]['action'] = 'final_sell'
|
||||
results[-1]['profit'] = net_profit
|
||||
cumulative_profit += net_profit
|
||||
|
||||
print(f"\n>>> 到期强制平仓")
|
||||
print(f"持仓天数: {days_held}")
|
||||
print(f"持仓数量: {position_size:.0f}股")
|
||||
print(f"平仓价格: {final_price:.2f}")
|
||||
print(f"毛利润: {final_profit:,.2f}")
|
||||
print(f"手续费: {commission:,.2f}")
|
||||
print(f"利息成本: {interest_cost:,.2f}")
|
||||
print(f"净利润: {net_profit:,.2f}")
|
||||
|
||||
print(f"\n{'='*50}")
|
||||
print(f"交易统计")
|
||||
print(f"总交易次数: {trades_count}")
|
||||
print(f"累计收益: {cumulative_profit:,.2f}")
|
||||
print(f"{'='*50}\n")
|
||||
|
||||
return pd.DataFrame(results)
|
||||
|
||||
def analyze_stock(self, symbol, start_date, end_date, take_profit_pct, stop_loss_pct, pullback_entry_pct):
|
||||
"""分析单个股票"""
|
||||
df = self.get_stock_data(symbol, start_date, end_date)
|
||||
|
||||
if len(df) < 10: # 跳过数据少于10条的股票
|
||||
print(f"股票 {symbol} 数据不足10条，跳过分析")
|
||||
return None
|
||||
|
||||
return self.calculate_trade_signals(
|
||||
df,
|
||||
take_profit_pct=take_profit_pct,
|
||||
stop_loss_pct=stop_loss_pct,
|
||||
pullback_entry_pct=pullback_entry_pct
|
||||
)
|
||||
|
||||
def analyze_all_stocks(self, start_date, end_date, take_profit_pct, stop_loss_pct, pullback_entry_pct):
|
||||
"""分析所有股票"""
|
||||
stocks = self.get_stock_list()
|
||||
all_results = {}
|
||||
|
||||
print(f"\n开始分析，共 {len(stocks)} 支股票")
|
||||
print(f"分析参数:")
|
||||
print(f"- 开始日期: {start_date}")
|
||||
print(f"- 结束日期: {end_date}")
|
||||
print(f"- 止盈: {take_profit_pct*100}%")
|
||||
print(f"- 止损: {stop_loss_pct*100}%")
|
||||
print(f"- 回调建仓: {pullback_entry_pct*100}%\n")
|
||||
|
||||
for symbol in tqdm(stocks, desc="Analyzing stocks"):
|
||||
result = self.analyze_stock(
|
||||
symbol,
|
||||
start_date,
|
||||
end_date,
|
||||
take_profit_pct,
|
||||
stop_loss_pct,
|
||||
pullback_entry_pct
|
||||
)
|
||||
if result is not None:
|
||||
all_results[symbol] = result
|
||||
|
||||
return all_results
|
||||
|
||||
def plot_results(self, results, symbol):
|
||||
"""绘制单个股票的收益走势图"""
|
||||
plt.figure(figsize=(12, 6))
|
||||
plt.plot(results['date'], results['profit'].cumsum(), label='Cumulative Profit')
|
||||
plt.title(f'Stock {symbol} Trading Results')
|
||||
plt.xlabel('Date')
|
||||
plt.ylabel('Profit (CNY)')
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
plt.xticks(rotation=45)
|
||||
plt.tight_layout()
|
||||
return plt
|
||||
|
||||
def plot_all_stocks(self, all_results):
|
||||
"""绘制所有股票的收益走势图"""
|
||||
plt.figure(figsize=(15, 8))
|
||||
|
||||
# 记录所有股票的累计收益
|
||||
total_profits = {}
|
||||
max_profit = float('-inf')
|
||||
min_profit = float('inf')
|
||||
|
||||
# 绘制每支股票的曲线
|
||||
for symbol, results in all_results.items():
|
||||
cumulative_profits = results['profit'].cumsum()
|
||||
plt.plot(results['date'], cumulative_profits, label=symbol, alpha=0.5, linewidth=1)
|
||||
|
||||
# 更新最大最小收益
|
||||
max_profit = max(max_profit, cumulative_profits.max())
|
||||
min_profit = min(min_profit, cumulative_profits.min())
|
||||
|
||||
# 记录最终收益
|
||||
total_profits[symbol] = cumulative_profits.iloc[-1]
|
||||
|
||||
# 计算所有股票的平均收益曲线
|
||||
# 首先找到共同的日期范围
|
||||
all_dates = set()
|
||||
for results in all_results.values():
|
||||
all_dates.update(results['date'])
|
||||
all_dates = sorted(list(all_dates))
|
||||
|
||||
# 创建一个包含所有日期的DataFrame
|
||||
avg_profits = pd.DataFrame(index=all_dates)
|
||||
|
||||
# 对每支股票，填充所有日期的收益
|
||||
for symbol, results in all_results.items():
|
||||
daily_profits = pd.Series(index=all_dates, data=0.0)
|
||||
cumulative = 0
|
||||
last_cumulative = 0
|
||||
for date, profit in zip(results['date'], results['profit']):
|
||||
cumulative += profit
|
||||
daily_profits[date] = cumulative
|
||||
last_cumulative = cumulative
|
||||
# 对于没有交易的日期，使用最后一个累计收益填充
|
||||
daily_profits.fillna(method='ffill', inplace=True)
|
||||
daily_profits.fillna(0, inplace=True) # 对开始之前的日期填充0
|
||||
avg_profits[symbol] = daily_profits
|
||||
|
||||
# 计算并绘制平均收益曲线
|
||||
avg_line = avg_profits.mean(axis=1)
|
||||
plt.plot(all_dates, avg_line, 'r-', label='Average', linewidth=2)
|
||||
|
||||
plt.title('All Stocks Trading Results')
|
||||
plt.xlabel('Date')
|
||||
plt.ylabel('Profit (CNY)')
|
||||
plt.grid(True)
|
||||
plt.xticks(rotation=45)
|
||||
|
||||
# 添加利润区间标注
|
||||
plt.text(0.02, 0.98, f'Profit Range:\nMax: {max_profit:,.0f}\nMin: {min_profit:,.0f}\nAvg: {avg_line.iloc[-1]:,.0f}',
|
||||
transform=plt.gca().transAxes, bbox=dict(facecolor='white', alpha=0.8))
|
||||
|
||||
# 获取前5名和后5名股票
|
||||
top_5 = sorted(total_profits.items(), key=lambda x: x[1], reverse=True)[:5]
|
||||
bottom_5 = sorted(total_profits.items(), key=lambda x: x[1])[:5]
|
||||
|
||||
# 添加排名信息
|
||||
rank_text = "Top 5 Stocks:\n"
|
||||
for symbol, profit in top_5:
|
||||
rank_text += f"{symbol}: {profit:,.0f}\n"
|
||||
rank_text += "\nBottom 5 Stocks:\n"
|
||||
for symbol, profit in bottom_5:
|
||||
rank_text += f"{symbol}: {profit:,.0f}\n"
|
||||
|
||||
plt.text(1.02, 0.98, rank_text, transform=plt.gca().transAxes,
|
||||
bbox=dict(facecolor='white', alpha=0.8), verticalalignment='top')
|
||||
|
||||
plt.tight_layout()
|
||||
return plt, total_profits
|
||||
|
||||
def plot_profit_distribution(self, total_profits):
|
||||
"""绘制所有股票的收益分布小提琴图"""
|
||||
plt.figure(figsize=(12, 8))
|
||||
|
||||
# 将收益数据转换为数组
|
||||
profits = np.array(list(total_profits.values()))
|
||||
|
||||
# 计算统计数据
|
||||
mean_profit = np.mean(profits)
|
||||
median_profit = np.median(profits)
|
||||
max_profit = np.max(profits)
|
||||
min_profit = np.min(profits)
|
||||
std_profit = np.std(profits)
|
||||
|
||||
# 绘制小提琴图
|
||||
violin_parts = plt.violinplot(profits, positions=[0], showmeans=True, showmedians=True)
|
||||
|
||||
# 设置小提琴图的颜色
|
||||
violin_parts['bodies'][0].set_facecolor('lightblue')
|
||||
violin_parts['bodies'][0].set_alpha(0.7)
|
||||
violin_parts['cmeans'].set_color('red')
|
||||
violin_parts['cmedians'].set_color('blue')
|
||||
|
||||
# 添加统计信息
|
||||
stats_text = (
|
||||
f"统计信息:\n"
|
||||
f"平均值: {mean_profit:,.0f}\n"
|
||||
f"中位数: {median_profit:,.0f}\n"
|
||||
f"最大值: {max_profit:,.0f}\n"
|
||||
f"最小值: {min_profit:,.0f}\n"
|
||||
f"标准差: {std_profit:,.0f}"
|
||||
)
|
||||
|
||||
plt.text(0.65, 0.95, stats_text,
|
||||
transform=plt.gca().transAxes,
|
||||
bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'),
|
||||
verticalalignment='top',
|
||||
fontsize=10)
|
||||
|
||||
# 设置图表样式
|
||||
plt.title('股票收益分布图', fontsize=14, pad=20)
|
||||
plt.ylabel('收益 (元)', fontsize=12)
|
||||
plt.xticks([0], ['所有股票'], fontsize=10)
|
||||
plt.grid(True, axis='y', alpha=0.3)
|
||||
|
||||
# 添加零线
|
||||
plt.axhline(y=0, color='r', linestyle='--', alpha=0.3)
|
||||
|
||||
# 计算盈利和亏损的股票数量
|
||||
profit_count = np.sum(profits > 0)
|
||||
loss_count = np.sum(profits < 0)
|
||||
total_count = len(profits)
|
||||
|
||||
# 添加盈亏比例信息
|
||||
ratio_text = (
|
||||
f"盈亏比例:\n"
|
||||
f"盈利: {profit_count}支 ({profit_count/total_count*100:.1f}%)\n"
|
||||
f"亏损: {loss_count}支 ({loss_count/total_count*100:.1f}%)\n"
|
||||
f"总计: {total_count}支"
|
||||
)
|
||||
|
||||
plt.text(0.65, 0.6, ratio_text,
|
||||
transform=plt.gca().transAxes,
|
||||
bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'),
|
||||
verticalalignment='top',
|
||||
fontsize=10)
|
||||
|
||||
plt.tight_layout()
|
||||
return plt
|
||||
|
||||
def main():
|
||||
# 数据库连接配置
|
||||
db_config = {
|
||||
'host': '192.168.1.82',
|
||||
'port': 3306,
|
||||
'user': 'root',
|
||||
'password': 'Chlry$%.8',
|
||||
'database': 'db_gp_cj'
|
||||
}
|
||||
|
||||
try:
|
||||
connection_string = f"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}"
|
||||
|
||||
# 创建results目录
|
||||
if not os.path.exists('results'):
|
||||
os.makedirs('results')
|
||||
|
||||
# 创建分析器实例
|
||||
analyzer = StockAnalyzer(connection_string)
|
||||
|
||||
# 设置分析参数
|
||||
start_date = '2024-01-01'
|
||||
end_date = '2024-12-31'
|
||||
take_profit_pct = 0.10 # 10%止盈
|
||||
stop_loss_pct = -0.99 # -10%止损
|
||||
pullback_entry_pct = -0.05 # -5%回调建仓
|
||||
|
||||
# 运行分析
|
||||
results = analyzer.analyze_all_stocks(
|
||||
start_date,
|
||||
end_date,
|
||||
take_profit_pct,
|
||||
stop_loss_pct,
|
||||
pullback_entry_pct
|
||||
)
|
||||
|
||||
# 保存结果
|
||||
for symbol, result in results.items():
|
||||
result.to_csv(f'results/{symbol}_analysis.csv', index=False)
|
||||
plt = analyzer.plot_results(result, symbol)
|
||||
plt.savefig(f'results/{symbol}_chart.png')
|
||||
plt.close()
|
||||
|
||||
# 绘制所有股票的收益曲线
|
||||
plt, total_profits = analyzer.plot_all_stocks(results)
|
||||
plt.savefig('results/all_stocks_analysis.png', bbox_inches='tight', dpi=300)
|
||||
plt.close()
|
||||
|
||||
# 绘制收益分布小提琴图
|
||||
plt = analyzer.plot_profit_distribution(total_profits)
|
||||
plt.savefig('results/profit_distribution.png', bbox_inches='tight', dpi=300)
|
||||
plt.close()
|
||||
|
||||
except Exception as e:
|
||||
print(f"程序运行出错: {str(e)}")
|
||||
raise
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,118 @@
|
|||
20230508_20240508下跌后快速反弹走势,,,,,,,,
|
||||
take_profit_pct,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count,
|
||||
30,-4712,0,100.8,49.8,53.1,1815,405,
|
||||
29,-5206,0,100,49.9,53.3,1818,405,
|
||||
28,-6335,0,99.2,50,53.6,1822,405,
|
||||
27,-6590,0,98,50.2,53.6,1827,405,
|
||||
26,-6865,0,96.7,50.5,54.1,1833,405,
|
||||
25,-6648,0,95.4,51,54.3,1836,405,
|
||||
24,-6931,0,93.9,51.4,54.1,1835,403,
|
||||
23,-5709,0,91.9,52.2,54.5,1837,404,
|
||||
22,-6556,0,90.7,52.4,54.6,1852,405,
|
||||
21,-5242,0,88.3,53.1,55.3,1858,405,
|
||||
20,-5913,0,86.6,53.7,55.3,1866,405,
|
||||
19,-5848,0,84.1,54.5,55.6,1876,405,
|
||||
18,-4698,0,81.5,55.6,56.3,1882,405,
|
||||
17,-5508,0,79.3,56.4,56.3,1888,405,
|
||||
16,-4942,0,76.6,57.5,57,1897,405,
|
||||
15,-5598,0,74.1,58.6,58.5,1903,405,
|
||||
14,-6530,0,71,59.6,58.5,1910,405,
|
||||
13,-6346,0,68.3,61,58.8,1919,405,
|
||||
12,-6840,0,65.3,62.6,58.8,1926,405,
|
||||
11,-7236,0,62.5,64.4,58.5,1931,405,
|
||||
10,-7611,0,59.3,66.1,58.8,1939,405,
|
||||
9,-9419,0,55.9,67.5,59,1949,405,
|
||||
8,-9855,0,52.7,69.4,59,1958,405,
|
||||
7,-10832,0,49.2,71.4,58.8,1969,405,
|
||||
6,-11762,0,45.3,73.7,59.8,1973,405,
|
||||
5,-13031,0,41.6,75.9,60.5,1978,405,
|
||||
,,,,,,,,
|
||||
,,,,,,,,
|
||||
20230508_20240205下跌走势,,,,,,,,
|
||||
take_profit_pct,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count,
|
||||
30,-120990,0,75.6,10.9,8.8,1465,396,
|
||||
29,-120762,0,75.4,10.9,8.6,1466,396,
|
||||
28,-120539,0,75.1,11.1,8.3,1468,396,
|
||||
27,-119956,0,74.8,11.4,9.1,1469,396,
|
||||
26,-118942,0,74.3,11.6,9.6,1471,396,
|
||||
25,-117868,0,73.8,11.9,9.3,1471,396,
|
||||
24,-117173,0,73.3,12.3,9.3,1471,396,
|
||||
23,-114536,0,72.2,13,9.6,1474,396,
|
||||
22,-113484,0,71.5,13.3,9.8,1479,396,
|
||||
21,-109829,0,69.9,14.4,10.9,1482,396,
|
||||
20,-108867,0,69,15,10.9,1486,396,
|
||||
19,-106925,0,67.9,15.8,11.4,1492,396,
|
||||
18,-104300,0,66.7,16.8,11.4,1496,396,
|
||||
17,-102194,0,65.5,17.7,12.6,1499,396,
|
||||
16,-99208,0,64,18.9,12.9,1506,396,
|
||||
15,-97766,0,62.6,19.8,13.1,1511,396,
|
||||
14,-96754,0,61.2,20.6,13.1,1515,396,
|
||||
13,-93449,0,59.5,22.3,13.9,1522,396,
|
||||
12,-91252,0,57.7,23.9,14.6,1526,396,
|
||||
11,-88804,0,56.1,25.3,15.2,1527,396,
|
||||
10,-84415,0,53.6,27.9,17.4,1535,396,
|
||||
9,-80819,0,50.9,30.7,19.7,1543,396,
|
||||
8,-77402,0,48.2,33.2,21.5,1547,396,
|
||||
7,-72609,0,45.1,36.2,23.5,1556,396,
|
||||
6,-67130,0,41.5,40.1,27,1559,396,
|
||||
5,-63089,0,38.3,43.3,27,1563,396,
|
||||
,,,,,,,,
|
||||
,,,,,,,,
|
||||
20221028_20230508上涨走势,,,,,,,,
|
||||
take_profit_pct,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count,
|
||||
30,-26094,0,84.9,35.4,39.4,1022,343,
|
||||
29,-24312,0,83.6,36.2,40.2,1027,343,
|
||||
28,-24443,0,82.7,36.7,40.5,1031,343,
|
||||
27,-23021,0,81.2,37.2,41.1,1034,343,
|
||||
26,-22763,0,80,37.7,41.4,1036,343,
|
||||
25,-21882,0,78,38.5,42,1040,343,
|
||||
24,-22570,0,77.1,38.9,41.4,1042,343,
|
||||
23,-22085,0,75.5,39.6,42,1048,343,
|
||||
22,-21214,0,74.1,40.4,43.4,1053,343,
|
||||
21,-20730,0,72.7,41.3,44,1059,343,
|
||||
20,-18191,0,70.6,42.5,44.6,1064,343,
|
||||
19,-17678,0,68.8,43.4,43.7,1068,343,
|
||||
18,-18481,0,67.2,44,43.1,1072,343,
|
||||
17,-16782,0,64.9,45.4,44.9,1076,343,
|
||||
16,-16490,0,62.9,46.4,45.8,1081,343,
|
||||
15,-15946,0,60.4,47.7,46.4,1089,343,
|
||||
14,-16037,0,58.4,48.9,47.2,1095,343,
|
||||
13,-16701,0,56.4,49.8,48.1,1100,343,
|
||||
12,-16062,0,54,51.3,49,1106,343,
|
||||
11,-16000,0,51.4,52.9,49.6,1110,343,
|
||||
10,-15453,0,49.1,54.4,49.6,1114,343,
|
||||
9,-14841,0,46.2,56.2,49.9,1121,343,
|
||||
8,-14211,0,43,58.3,51.3,1132,343,
|
||||
7,-14199,0,39.9,60.5,53.9,1138,343,
|
||||
6,-15366,0,37.3,62.4,52.8,1144,343,
|
||||
5,-15711,0,34.1,64.9,50.7,1152,343,
|
||||
,,,,,,,,
|
||||
,,,,,,,,
|
||||
20210106_20220224震荡走势,,,,,,,,
|
||||
take_profit_pct,avg_profit_per_trade,median_profit_per_trade,avg_holding_days,trade_win_rate,stock_win_rate,total_trades,stock_count,
|
||||
30,12047,0,115.6,44.5,48.8,2508,424,
|
||||
29,12857,0,113.5,45.1,48.5,2525,425,
|
||||
28,13349,0,112,45.7,48.9,2538,425,
|
||||
27,12644,0,110.5,46.3,49.4,2554,425,
|
||||
26,12878,0,108.4,47.1,48.7,2571,425,
|
||||
25,12319,0,106.9,47.7,48.5,2589,425,
|
||||
24,12744,0,104.5,48.6,50.4,2608,425,
|
||||
23,11949,0,102.3,49.4,51.3,2626,425,
|
||||
22,11246,0,100.6,50.3,51.3,2637,425,
|
||||
21,10399,0,98.7,51.1,51.1,2644,425,
|
||||
20,9908,0,96.3,52,51.1,2666,425,
|
||||
19,9327,0,93.8,53,52.5,2681,425,
|
||||
18,7509,0,92.1,53.6,52.6,2686,424,
|
||||
17,7333,0,89.4,55.1,53.9,2713,425,
|
||||
16,6404,0,86.6,56.2,54.8,2745,425,
|
||||
15,5296,0,83.7,57.3,55.3,2763,425,
|
||||
14,5382,0,80.5,59,54.6,2788,425,
|
||||
13,4527,0,77.7,60.4,56.6,2800,424,
|
||||
12,3650,0,74.3,62,57.2,2826,425,
|
||||
11,1583,0,71.1,63.3,57.2,2849,425,
|
||||
10,-741,0,68.1,64.7,56.9,2859,425,
|
||||
9,-2581,0,64.5,66.3,56.9,2879,425,
|
||||
8,-3833,0,60.7,68.3,56.7,2902,425,
|
||||
7,-4992,0,56.7,70.5,57.9,2924,425,
|
||||
6,-4815,0,52.1,73.6,60.5,2948,425,
|
||||
5,-7635,0,47.9,75.6,58.8,2965,425,
|
||||
|
|
|
@ -0,0 +1,102 @@
|
|||
#!/usr/bin/env python
|
||||
"""
|
||||
测试脚本: 验证导入路径是否正常工作
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
import logging
|
||||
|
||||
# 设置日志
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 显示当前工作目录和Python路径
|
||||
logger.info(f"当前工作目录: {os.getcwd()}")
|
||||
logger.info(f"Python路径: {sys.path}")
|
||||
|
||||
def test_imports():
|
||||
"""测试导入各个模块"""
|
||||
success = True
|
||||
|
||||
# 测试导入 chat_bot
|
||||
try:
|
||||
logger.info("尝试导入 chat_bot 模块...")
|
||||
from fundamentals_llm.chat_bot import ChatBot
|
||||
logger.info("成功导入 chat_bot 模块")
|
||||
except ImportError as e:
|
||||
logger.error(f"导入 chat_bot 模块失败: {str(e)}")
|
||||
success = False
|
||||
|
||||
# 尝试替代路径
|
||||
try:
|
||||
from src.fundamentals_llm.chat_bot import ChatBot
|
||||
logger.info("成功从替代路径导入 chat_bot 模块")
|
||||
success = True
|
||||
except ImportError as e2:
|
||||
logger.error(f"从替代路径导入 chat_bot 模块失败: {str(e2)}")
|
||||
|
||||
# 测试导入 chat_bot_with_offline
|
||||
try:
|
||||
logger.info("尝试导入 chat_bot_with_offline 模块...")
|
||||
from fundamentals_llm.chat_bot_with_offline import ChatBot
|
||||
logger.info("成功导入 chat_bot_with_offline 模块")
|
||||
except ImportError as e:
|
||||
logger.error(f"导入 chat_bot_with_offline 模块失败: {str(e)}")
|
||||
success = False
|
||||
|
||||
# 尝试替代路径
|
||||
try:
|
||||
from src.fundamentals_llm.chat_bot_with_offline import ChatBot
|
||||
logger.info("成功从替代路径导入 chat_bot_with_offline 模块")
|
||||
success = True
|
||||
except ImportError as e2:
|
||||
logger.error(f"从替代路径导入 chat_bot_with_offline 模块失败: {str(e2)}")
|
||||
|
||||
# 测试导入 fundamental_analysis
|
||||
try:
|
||||
logger.info("尝试导入 fundamental_analysis 模块...")
|
||||
from fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
|
||||
logger.info("成功导入 fundamental_analysis 模块")
|
||||
except ImportError as e:
|
||||
logger.error(f"导入 fundamental_analysis 模块失败: {str(e)}")
|
||||
success = False
|
||||
|
||||
# 尝试替代路径
|
||||
try:
|
||||
from src.fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
|
||||
logger.info("成功从替代路径导入 fundamental_analysis 模块")
|
||||
success = True
|
||||
except ImportError as e2:
|
||||
logger.error(f"从替代路径导入 fundamental_analysis 模块失败: {str(e2)}")
|
||||
|
||||
# 测试导入 pdf_generator
|
||||
try:
|
||||
logger.info("尝试导入 pdf_generator 模块...")
|
||||
from fundamentals_llm.pdf_generator import generate_investment_report
|
||||
logger.info("成功导入 pdf_generator 模块")
|
||||
except ImportError as e:
|
||||
logger.error(f"导入 pdf_generator 模块失败: {str(e)}")
|
||||
success = False
|
||||
|
||||
# 尝试替代路径
|
||||
try:
|
||||
from src.fundamentals_llm.pdf_generator import generate_investment_report
|
||||
logger.info("成功从替代路径导入 pdf_generator 模块")
|
||||
success = True
|
||||
except ImportError as e2:
|
||||
logger.error(f"从替代路径导入 pdf_generator 模块失败: {str(e2)}")
|
||||
|
||||
return success
|
||||
|
||||
if __name__ == "__main__":
|
||||
logger.info("开始测试导入...")
|
||||
if test_imports():
|
||||
logger.info("所有导入测试通过")
|
||||
sys.exit(0)
|
||||
else:
|
||||
logger.error("导入测试失败")
|
||||
sys.exit(1)
|
||||
|
|
@ -0,0 +1,124 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
from sqlalchemy import create_engine, text
|
||||
import logging
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class VolatilityCalculator:
|
||||
def __init__(self, db_connection_string):
|
||||
"""
|
||||
初始化波动率计算器
|
||||
:param db_connection_string: 数据库连接字符串，例如: 'mysql+pymysql://user:password@localhost:3306/db_gp_cj'
|
||||
"""
|
||||
self.engine = create_engine(db_connection_string)
|
||||
|
||||
def create_volatility_table(self):
|
||||
"""创建存储波动率的表"""
|
||||
create_table_sql = """
|
||||
CREATE TABLE IF NOT EXISTS gp_volatility (
|
||||
id INT NOT NULL AUTO_INCREMENT,
|
||||
gp_code VARCHAR(255),
|
||||
calculation_date DATE,
|
||||
volatility_yearly DECIMAL(10, 4),
|
||||
update_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
PRIMARY KEY (id),
|
||||
INDEX idx_gp_code (gp_code),
|
||||
INDEX idx_calc_date (calculation_date)
|
||||
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin;
|
||||
"""
|
||||
with self.engine.connect() as conn:
|
||||
conn.execute(text(create_table_sql))
|
||||
conn.commit()
|
||||
|
||||
def calculate_volatility(self, end_date=None):
|
||||
"""
|
||||
计算所有股票的年化波动率
|
||||
:param end_date: 计算截止日期，默认为最新日期
|
||||
"""
|
||||
if end_date is None:
|
||||
end_date = datetime.now().date()
|
||||
start_date = end_date - timedelta(days=365)
|
||||
|
||||
# 获取所有股票代码
|
||||
with self.engine.connect() as conn:
|
||||
stocks = pd.read_sql("SELECT DISTINCT gp_code FROM gp_code_all", conn)
|
||||
|
||||
results = []
|
||||
for stock_code in stocks['gp_code']:
|
||||
try:
|
||||
# 获取股票的日线数据
|
||||
query = f"""
|
||||
SELECT timestamp, close
|
||||
FROM gp_day_data
|
||||
WHERE symbol = '{stock_code}'
|
||||
AND timestamp BETWEEN '{start_date}' AND '{end_date}'
|
||||
ORDER BY timestamp
|
||||
"""
|
||||
|
||||
with self.engine.connect() as conn:
|
||||
df = pd.read_sql(query, conn)
|
||||
|
||||
if len(df) < 30: # 数据太少，跳过
|
||||
continue
|
||||
|
||||
# 计算日收益率
|
||||
df['close'] = df['close'].astype(float)
|
||||
df['daily_return'] = df['close'].pct_change()
|
||||
|
||||
# 计算年化波动率 (标准差 * sqrt(交易日数量))
|
||||
volatility = df['daily_return'].std() * np.sqrt(252) # 252为一年的交易日数
|
||||
|
||||
results.append({
|
||||
'gp_code': stock_code,
|
||||
'calculation_date': end_date,
|
||||
'volatility_yearly': float(volatility)
|
||||
})
|
||||
|
||||
logger.info(f"Calculated volatility for {stock_code}: {volatility:.4f}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating volatility for {stock_code}: {str(e)}")
|
||||
continue
|
||||
|
||||
# 批量插入结果
|
||||
if results:
|
||||
insert_sql = """
|
||||
INSERT INTO gp_volatility (gp_code, calculation_date, volatility_yearly)
|
||||
VALUES (:gp_code, :calculation_date, :volatility_yearly)
|
||||
"""
|
||||
with self.engine.connect() as conn:
|
||||
conn.execute(text(insert_sql), results)
|
||||
conn.commit()
|
||||
|
||||
def get_top_volatile_stocks(self, top_n=10, calculation_date=None):
|
||||
"""
|
||||
获取波动率最高的股票
|
||||
:param top_n: 返回前N个股票
|
||||
:param calculation_date: 特定日期的数据，默认为最新日期
|
||||
:return: DataFrame包含股票代码和波动率
|
||||
"""
|
||||
query = """
|
||||
SELECT v.gp_code, c.gp_name, v.volatility_yearly, v.calculation_date
|
||||
FROM gp_volatility v
|
||||
LEFT JOIN gp_code_all c ON v.gp_code = c.gp_code
|
||||
WHERE v.calculation_date = (
|
||||
SELECT MAX(calculation_date) FROM gp_volatility
|
||||
WHERE calculation_date = COALESCE(:calc_date, calculation_date)
|
||||
)
|
||||
ORDER BY v.volatility_yearly DESC
|
||||
LIMIT :top_n
|
||||
"""
|
||||
with self.engine.connect() as conn:
|
||||
return pd.read_sql(text(query), conn, params={'calc_date': calculation_date, 'top_n': top_n})
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 使用示例
|
||||
# calculator = VolatilityCalculator('mysql+pymysql://user:password@localhost:3306/db_gp_cj')
|
||||
# calculator.create_volatility_table()
|
||||
# calculator.calculate_volatility()
|
||||
# top_volatile = calculator.get_top_volatile_stocks(10)
|
||||
# print(top_volatile)
|
||||
pass
|
||||
Loading…
Reference in New Issue