From 6b3d134a5eb782bb1503c6667e523c12dd95c0c3 Mon Sep 17 00:00:00 2001 From: liao Date: Wed, 9 Jul 2025 11:41:41 +0800 Subject: [PATCH] commit; --- src/app.py | 38 +- .../average_distance_factor.py | 4 +- ...undamental_factor_growth_20250701_1230.csv | 741 +++++++++++ ...undamental_factor_mature_20250701_1230.csv | 1078 +++++++++++++++++ src/scripts/ProxyIP.py | 2 +- src/scripts/config.py | 2 +- src/scripts/stock_daily_data_collector.py | 187 ++- src/scripts/stock_daily_data_collector_v2.py | 5 +- src/valuation_analysis/industry_analysis.py | 36 +- 9 files changed, 2039 insertions(+), 54 deletions(-) create mode 100644 src/quantitative_analysis/data/tech_fundamental_factor_growth_20250701_1230.csv create mode 100644 src/quantitative_analysis/data/tech_fundamental_factor_mature_20250701_1230.csv diff --git a/src/app.py b/src/app.py index a8bfac9..3b3ce38 100644 --- a/src/app.py +++ b/src/app.py @@ -41,7 +41,7 @@ from src.valuation_analysis.fear_greed_index import FearGreedIndexManager from src.valuation_analysis.index_analyzer import IndexAnalyzer # 导入股票日线数据采集器 -from src.scripts.stock_daily_data_collector import collect_stock_daily_data +from src.scripts.stock_daily_data_collector import collect_stock_daily_data, collect_stock_daily_data_optimized from src.scripts.stock_daily_data_collector_v2 import collect_stock_daily_data_v2 from valuation_analysis.financial_analysis import FinancialAnalyzer @@ -229,7 +229,7 @@ def run_stock_daily_collection2(): # 定义数据库连接地址 db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' - collect_stock_daily_data(db_url, today) + collect_stock_daily_data_optimized(db_url, today) except Exception as e: logger.error(f"启动股票日线数据采集任务失败: {str(e)}") return jsonify({ @@ -249,40 +249,6 @@ def run_rzrq_initial_collection1(): "status": "success" }), 200 - -@app.route('/scheduler/industry/crowding1/bak', methods=['GET']) -def precalculate_industry_crowding1(): - """预计算部分行业和概念板块的拥挤度指标 晚上10点开始""" - try: - from src.valuation_analysis.industry_analysis import IndustryAnalyzer - - analyzer = IndustryAnalyzer() - # 固定行业和概念板块 - industries = ["IT设备", "消费电子", "半导体", "军工电子", "专用设备", "乘用车", "产业互联网", "元器件", "光学光电", "医疗器械", "医疗服务", "汽车零部件", "航天装备", "自动化设备"] - concepts = ["先进封装", "芯片", "消费电子概念", "机器人概念"] - - # 计算行业拥挤度 - for industry in industries: - try: - analyzer.get_industry_crowding_index(industry, use_cache=False) - except Exception as e: - logger.error(f"预计算行业 {industry} 的拥挤度指标时出错: {str(e)}") - continue - # 计算概念板块拥挤度 - for concept in concepts: - try: - analyzer.get_industry_crowding_index(concept, use_cache=False, is_concept=True) - except Exception as e: - logger.error(f"预计算概念板块 {concept} 的拥挤度指标时出错: {str(e)}") - continue - logger.info("指定行业和概念板块的拥挤度指标预计算完成") - except Exception as e: - logger.error(f"预计算行业拥挤度指标失败: {str(e)}") - return jsonify({ - "status": "success" - }), 200 - - @app.route('/scheduler/financial/analysis', methods=['GET']) def scheduler_financial_analysis(): """预计算所有股票的财务分析数据 早晚各一次""" diff --git a/src/quantitative_analysis/average_distance_factor.py b/src/quantitative_analysis/average_distance_factor.py index 3394523..8a12630 100644 --- a/src/quantitative_analysis/average_distance_factor.py +++ b/src/quantitative_analysis/average_distance_factor.py @@ -289,10 +289,10 @@ def main(): analyzer = AverageDistanceFactor(db_url) # 示例1: 分析特定行业 - result = analyzer.analyze_industry(industry_name="证券") + # result = analyzer.analyze_industry(industry_name="证券") # 示例2: 分析特定概念 - # result = analyzer.analyze_industry(concept_name="3D打印") + result = analyzer.analyze_industry(concept_name="飞行汽车") # 示例3: 查看可用的行业列表 # industries = analyzer.get_available_industries() diff --git a/src/quantitative_analysis/data/tech_fundamental_factor_growth_20250701_1230.csv b/src/quantitative_analysis/data/tech_fundamental_factor_growth_20250701_1230.csv new file mode 100644 index 0000000..a62ab62 --- /dev/null +++ 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Chrome";v="122"', 'Sec-Ch-Ua-Mobile': '?0', diff --git a/src/scripts/stock_daily_data_collector.py b/src/scripts/stock_daily_data_collector.py index f87a00f..44ee070 100644 --- a/src/scripts/stock_daily_data_collector.py +++ b/src/scripts/stock_daily_data_collector.py @@ -40,8 +40,8 @@ class StockDailyDataCollector: codes_hk = df_hk['gp_code'].tolist() # 合并去重 - all_codes = list(set(codes_hk)) # all_codes = list(set(codes_all + codes_zs + codes_hk)) + all_codes = list(set(codes_zs + codes_hk)) print(f"获取到股票代码: {len(codes_all)} 个来自gp_code_all, {len(codes_zs)}个来自gp_code_zs, {len(codes_hk)}个来自gp_code_hk, 去重后共{len(all_codes)}个") return all_codes @@ -283,18 +283,199 @@ class StockDailyDataCollector: print(f"获取 {symbol} 数据失败: {data.get('error_description')}") return False + def fetch_and_check_ex_rights_optimized(self, date=None): + """ + 优化版：一次遍历完成数据采集和除权检查 + 获取最近2天数据，检查除权，决定是更新当天数据还是重新获取历史数据 + """ + if date is None: + start_date = datetime.now() + date_str = start_date.strftime('%Y-%m-%d') + else: + start_date = datetime.strptime(date, '%Y-%m-%d') + date_str = date + + print(f"开始优化版数据采集和除权检查 - {date_str}") + + # 删除今天的旧数据 + # delete_query = text("DELETE FROM gp_day_data WHERE `timestamp` LIKE :date_str") + # with self.engine.begin() as conn: + # conn.execute(delete_query, {"date_str": f"{date_str}%"}) + # print(f"已删除今日 {date_str} 的旧数据") + + stock_codes = self.fetch_all_stock_codes() + begin = int(start_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000) + + # 统计信息 + normal_update_count = 0 + ex_rights_count = 0 + error_count = 0 + skipped_count = 0 # 跳过的股票数量（停牌等原因） + ex_rights_log_data = [] + normal_batch_data = [] + + for idx, symbol in enumerate(tqdm(stock_codes, desc=f"采集和检查除权 {date_str}")): + try: + # 获取最近2天的数据 + data = self.fetch_daily_stock_data(symbol, begin, count=-2) + + if data.get('error_code') != 0: + print(f"获取 {symbol} 数据失败: {data.get('error_description')}") + error_count += 1 + continue + + df = self.transform_data(data, symbol) + if df is None or df.empty: + print(f"转换 {symbol} 数据失败") + error_count += 1 + continue + + # 检查是否有足够的数据进行除权判断 + if len(df) < 2: + # 只有一天数据，检查是否为今天的数据 + if len(df) == 1: + latest_date = df.iloc[0]['timestamp'].strftime('%Y-%m-%d') + if latest_date == date_str: + # 是今天的数据，保存 + normal_batch_data.append(df) + normal_update_count += 1 + else: + # 不是今天的数据，可能停牌，跳过 + print(f"股票 {symbol} 最新数据日期 {latest_date} 不是今天 {date_str}，跳过") + skipped_count += 1 + continue + + # 按时间排序，确保最新的数据在最后 + df_sorted = df.sort_values('timestamp') + + # 获取昨天和今天的数据 + latest_row = df_sorted.iloc[-1] # 最新一天（今天） + previous_row = df_sorted.iloc[-2] # 前一天（昨天） + + # 检查最新一天是否为今天的数据 + latest_date = latest_row['timestamp'].strftime('%Y-%m-%d') + if latest_date != date_str: + # 最新数据不是今天的，可能停牌，跳过 + print(f"股票 {symbol} 最新数据日期 {latest_date} 不是今天 {date_str}，跳过") + skipped_count += 1 + continue + + current_close = latest_row['close'] + previous_close = previous_row['close'] + + # 查询数据库中该股票昨天的收盘价 + yesterday_date = previous_row['timestamp'].strftime('%Y-%m-%d') + query = text(""" + SELECT `close` FROM gp_day_data + WHERE symbol = :symbol AND DATE(`timestamp`) = :date + LIMIT 1 + """) + + with self.engine.connect() as conn: + result = conn.execute(query, {"symbol": symbol, "date": yesterday_date}).fetchone() + + # 判断是否除权 + is_ex_rights = False + if result: + db_previous_close = float(result[0]) + # 比较API返回的昨日收盘价与数据库中的收盘价 + if abs(db_previous_close - previous_close) > 0.001: + is_ex_rights = True + print(f"发现除权股票: {symbol}, 数据库昨收: {db_previous_close}, API昨收: {previous_close}") + + # 记录除权日志 + ex_rights_log_data.append({ + 'symbol': symbol, + 'date': date_str, + 'db_price': db_previous_close, + 'api_price': previous_close, + 'log_time': datetime.now() + }) + + if is_ex_rights: + # 除权处理：删除历史数据，重新获取1800天数据 + delete_all_query = text("DELETE FROM gp_day_data WHERE symbol = :symbol") + with self.engine.begin() as conn: + conn.execute(delete_all_query, {"symbol": symbol}) + + # 重新获取1800天历史数据 + success = self.fetch_single_stock_history(symbol, 1800) + if success: + ex_rights_count += 1 + print(f"除权股票 {symbol} 历史数据重新获取成功") + else: + error_count += 1 + print(f"除权股票 {symbol} 历史数据重新获取失败") + else: + # 正常更新：只保存今天的数据 + today_data = df_sorted.tail(1) # 只取最新一天的数据 + normal_batch_data.append(today_data) + normal_update_count += 1 + + # 批量保存正常更新的数据 + if len(normal_batch_data) >= 100: + for batch_df in normal_batch_data: + batch_df.to_sql('gp_day_data', self.engine, if_exists='append', index=False) + normal_batch_data.clear() + gc.collect() + + except Exception as e: + print(f"处理股票 {symbol} 时发生错误: {e}") + error_count += 1 + continue + + # 保存剩余的正常更新数据 + if normal_batch_data: + for batch_df in normal_batch_data: + batch_df.to_sql('gp_day_data', self.engine, if_exists='append', index=False) + gc.collect() + + # 保存除权日志 + if ex_rights_log_data: + self.save_ex_rights_log(ex_rights_log_data) + + # 输出统计信息 + total_processed = normal_update_count + ex_rights_count + error_count + skipped_count + print(f"\n=== 采集完成统计 ===") + print(f"总处理股票数: {total_processed}") + print(f"正常更新: {normal_update_count}") + print(f"除权处理: {ex_rights_count}") + print(f"跳过股票: {skipped_count} (停牌等原因)") + print(f"错误处理: {error_count}") + print(f"除权日志: {len(ex_rights_log_data)} 条") + + self.engine.dispose() + print(f"优化版数据采集和除权检查完成 - {date_str}") + def collect_stock_daily_data(db_url, date=None): + """ + 原始版本：分两步执行（先采集，后检查除权） + """ collector = StockDailyDataCollector(db_url) collector.fetch_data_for_date(date) collector.check_and_fix_ex_rights_data() +def collect_stock_daily_data_optimized(db_url, date=None): + """ + 优化版本：一次遍历完成数据采集和除权检查 + """ + collector = StockDailyDataCollector(db_url) + collector.fetch_and_check_ex_rights_optimized(date) + if __name__ == "__main__": db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' # --- 使用方式 --- - # 1. 日常采集当天数据 + # 1. 【推荐】优化版：一次遍历完成数据采集和除权检查 + collect_stock_daily_data_optimized(db_url) + + # 2. 原始版本：分两步执行（先采集，后检查除权） # collect_stock_daily_data(db_url) - # 2. 手动执行除权检查和数据修复 + # 3. 手动执行除权检查和数据修复 # collector = StockDailyDataCollector(db_url) # collector.check_and_fix_ex_rights_data() + + # 4. 单独获取某只股票的历史数据 + # collector = StockDailyDataCollector(db_url) + # collector.fetch_single_stock_history('SH600000', 1800) diff --git a/src/scripts/stock_daily_data_collector_v2.py b/src/scripts/stock_daily_data_collector_v2.py index 79f426b..104241e 100644 --- a/src/scripts/stock_daily_data_collector_v2.py +++ b/src/scripts/stock_daily_data_collector_v2.py @@ -95,7 +95,9 @@ class StockDailyDataCollectorV2: # 映射字段 gp_day_df['symbol'] = merged_df['symbol'] - gp_day_df['timestamp'] = pd.to_datetime(merged_df['fetch_time']) + # 将timestamp设置为当天的00:00:00格式 + today = datetime.now().date() + gp_day_df['timestamp'] = pd.to_datetime(today) gp_day_df['volume'] = merged_df['volume'] gp_day_df['open'] = merged_df['open_price'] gp_day_df['high'] = merged_df['high_price'] @@ -201,7 +203,6 @@ class StockDailyDataCollectorV2: 'api_price': current_pre_close, 'log_time': datetime.now() }) - except Exception as e: print(f"查询 {symbol} 历史数据失败: {e}") continue diff --git a/src/valuation_analysis/industry_analysis.py b/src/valuation_analysis/industry_analysis.py index fc65389..895242a 100644 --- a/src/valuation_analysis/industry_analysis.py +++ b/src/valuation_analysis/industry_analysis.py @@ -385,8 +385,14 @@ class IndustryAnalyzer: # 计算行业/概念板块成交额占比 df['industry_amount_ratio'] = (df['sector_amount'] / df['total_market_amount']) * 100 - # 在Python中计算百分位 - df['percentile'] = df['industry_amount_ratio'].rank(pct=True) * 100 + # 在Python中计算百分位 - 使用与SQL PERCENT_RANK()一致的计算方式 + # PERCENT_RANK() = (rank - 1) / (total_rows - 1) + ranks = df['industry_amount_ratio'].rank(method='min') - 1 # rank从0开始 + total_rows = len(df) - 1 + if total_rows > 0: + df['percentile'] = (ranks / total_rows) * 100 + else: + df['percentile'] = 0 # 添加拥挤度评级 df['crowding_level'] = pd.cut( @@ -842,13 +848,13 @@ class IndustryAnalyzer: # 2. 一次性获取全市场所有股票的交易数据 query = text(""" SELECT - symbol, - `timestamp` AS trade_date, - amount + a.symbol, + a.`timestamp` AS trade_date, + a.amount FROM - gp_day_data + gp_day_data a left join gp_code_all b on a.symbol = b.gp_code WHERE - `timestamp` BETWEEN :start_date AND :end_date + a.`timestamp` BETWEEN :start_date AND :end_date and b.id is not null """) with self.engine.connect() as conn: @@ -884,7 +890,13 @@ class IndustryAnalyzer: # 计算指标 df['industry_amount_ratio'] = (df['sector_amount'] / df['total_market_amount']) * 100 - df['percentile'] = df['industry_amount_ratio'].rank(pct=True) * 100 + # 使用与SQL PERCENT_RANK()一致的计算方式 + ranks = df['industry_amount_ratio'].rank(method='min') - 1 + total_rows = len(df) - 1 + if total_rows > 0: + df['percentile'] = (ranks / total_rows) * 100 + else: + df['percentile'] = 0 df['crowding_level'] = pd.cut( df['percentile'], bins=[0, 20, 40, 60, 80, 100], @@ -917,7 +929,13 @@ class IndustryAnalyzer: # 计算指标 df['industry_amount_ratio'] = (df['sector_amount'] / df['total_market_amount']) * 100 - df['percentile'] = df['industry_amount_ratio'].rank(pct=True) * 100 + # 使用与SQL PERCENT_RANK()一致的计算方式 + ranks = df['industry_amount_ratio'].rank(method='min') - 1 + total_rows = len(df) - 1 + if total_rows > 0: + df['percentile'] = (ranks / total_rows) * 100 + else: + df['percentile'] = 0 df['crowding_level'] = pd.cut( df['percentile'], bins=[0, 20, 40, 60, 80, 100],