commit;
This commit is contained in:
parent
8f73099e18
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29
src/app.py
29
src/app.py
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@ -236,12 +236,12 @@ def initialize_stock_price_schedule():
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collector.update_latest_data()
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except Exception as e:
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logger.error(f"更新实时股价数据失败: {e}")
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# 添加定时任务
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scheduler.add_job(
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func=update_stock_price,
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trigger='interval',
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minutes=5,
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minutes=60,
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id='stock_price_update',
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name='实时股价数据采集',
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replace_existing=True
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@ -249,7 +249,7 @@ def initialize_stock_price_schedule():
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# 启动调度器
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scheduler.start()
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logger.info("实时股价数据采集定时任务已初始化，将在交易时间内每5分钟执行一次")
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logger.info("实时股价数据采集定时任务已初始化，将在交易时间内每60分钟执行一次")
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return scheduler
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except Exception as e:
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@ -1452,6 +1452,22 @@ def comprehensive_analysis():
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if 'db_session2' in locals() and db_session2 is not None: # 确保 db_session 已定义
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db_session2.close() # <--- 关闭会话
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# 获取企业市值信息
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market_value_results = {}
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try:
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from src.valuation_analysis.stock_price_collector import StockPriceCollector
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price_collector = StockPriceCollector()
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for stock_code in input_stock_codes:
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price_data = price_collector.get_stock_price_data(stock_code)
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if price_data and 'total_market_value' in price_data:
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market_value_results[stock_code] = price_data['total_market_value']
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else:
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market_value_results[stock_code] = None
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except Exception as e:
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logger.error(f"获取企业市值信息失败: {str(e)}")
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market_value_results = {}
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db_session = next(get_db())
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# 筛选出传入列表中符合条件的股票
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for code, name in all_stocks:
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@ -1475,7 +1491,8 @@ def comprehensive_analysis():
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"industry_space": industry_space, # 行业发展空间（2:高速增长, 1:稳定经营, 0:不确定性大, -1:不利经营）
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"financial_report_level": financial_report_level, # 经营质量（2:优秀, 1:较好, 0:一般, -1:存在隐患，-2：较大隐患）
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"pe_industry": pe_industry, # 个股在行业的PE水平（-1:高于行业, 0:接近行业, 1:低于行业）
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"tracks": track_results.get(code, []) # 添加赛道信息
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"tracks": track_results.get(code, []), # 添加赛道信息
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"market_value": market_value_results.get(code) # 添加企业市值信息
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})
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logger.info(f"筛选出 {len(filtered_stocks)} 个符合条件的股票")
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@ -2675,7 +2692,7 @@ def precalculate_industry_crowding():
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except Exception as e:
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logger.error(f"预计算行业 {industry} 的拥挤度指标时出错: {str(e)}")
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continue
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logger.info("所有行业的拥挤度指标预计算完成")
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except Exception as e:
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logger.error(f"预计算行业拥挤度指标失败: {str(e)}")
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@ -2819,6 +2836,6 @@ if __name__ == '__main__':
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# 初始化实时股价数据采集定时任务
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initialize_stock_price_schedule()
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# 启动Web服务器
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app.run(host='0.0.0.0', port=5000, debug=True)
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@ -1477,6 +1477,13 @@ class FundamentalAnalyzer:
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- 长期持有：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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- 不建议投资：存在明显风险因素、基本面恶化、估值过高、行业前景不佳或者存在退市风险
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请注意：
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1. 请完全基于提供的分析结果中的最新数据进行分析，不要使用任何历史数据或过时信息
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2. 如果分析结果中包含2024年或2025年的数据，请优先使用这些最新数据
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3. 避免使用"2023年"等历史时间点的数据，除非分析结果中明确提供了这些数据
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4. 重点关注公司最新的业务发展、财务表现和市场定位
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5. 在分析行业环境时，请使用最新的行业数据和竞争格局信息
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请提供专业、客观的分析，突出关键信息，避免冗长描述。重点关注投资价值和风险。在输出投资建议时，请明确指出是短期持有、中期持有、长期持有还是不建议投资。
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各维度分析结果：
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@ -11,7 +11,7 @@ XUEQIU_HEADERS = {
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'Accept-Encoding': 'gzip, deflate, br, zstd',
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'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
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'Client-Version': 'v2.44.75',
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'Cookie': 'cookiesu=811743062689927; device_id=33fa3c7fca4a65f8f4354e10ed6b7470; HMACCOUNT=8B64A2E3C307C8C0; s=c611ttmqlj; xq_is_login=1; u=8493411634; bid=4065a77ca57a69c83405d6e591ab5449_m8r2nhs8; Hm_lvt_1db88642e346389874251b5a1eded6e3=1746410725; xq_a_token=660fb18cf1d15162da76deedc46b649370124dca; xqat=660fb18cf1d15162da76deedc46b649370124dca; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOjg0OTM0MTE2MzQsImlzcyI6InVjIiwiZXhwIjoxNzQ5ODYxNjY5LCJjdG0iOjE3NDcyNjk2Njk0NDgsImNpZCI6ImQ5ZDBuNEFadXAifQ.jc_E9qvguLwBDASn1Z-KjGtU89pNJRwJq_hIaiR3r2re7-_xiXH8qhuhC3Se8rlfKGZ8sHsb3rSND_vnF7yMp90QQHdK_brSmlgd6_ltHmJfWSFNJvMk7F3s0yPjcpeMqeUTPFnZwKmoWwZVKEwdVBN8f25z6e9M2JjtSTZ2huADH_FdEn1rb9IU-H35z_MLWW1M7vB5xc2rh57yFIBnQoxu9OLfeETpeIpASP1UBeZXoQZ_v1gIWiFYItwuudIz0tPYzB-o2duRe31G0S_hNvEGl3HH4M5FjTyaPAq2PRuiZCyRF-25gHXBZnLcxyavZ1VAURfHng_377_IJNSXsw; xq_r_token=8a5dec9c93caf88d0e1f98f1d23ea1bb60eb6225; is_overseas=0; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1747356850; ssxmod_itna=eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40QuHhqDyGGdVmpmghQehYtDDsqze4GzDiLPGhDBWAFdYCdqt4NKWooqCWKCwdUme9Ill25QAClcymm=0Iil4OAe8oGLDY=DCTKK420iDYAEDBYD74G+DDeDiO3Dj4GmDGY=aeDFIQutVCRKdxDwDB=DmqG23ObDm4DfDDLorBD4Il2YDDtDAkaGNPDADA3doDDlYD84edb4DYpogQ0FdgahphuXIeDMixGXzAlzx9CnoiWtV/LfNf2aHPGuDG=OcC0Hh2bmRT3f8hGxYBY5QeOhx+BxorKq0DW7HRYqexx=CD=WKK7oQ7YBGxPG4KiKy7hAQd5dpOodYYrcqsMkbZMshieygdyhxogYO2deGd46DAQ5MA5VBxiT5/h4WB++l=Eet4D; ssxmod_itna2=eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40QuHhqDyGGdVmpmghQehY4Dfie4pCoTp35CT5NsKziGGtvkoYD',
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'Cookie': 'cookiesu=811743062689927; device_id=33fa3c7fca4a65f8f4354e10ed6b7470; HMACCOUNT=8B64A2E3C307C8C0; s=c611ttmqlj; xq_is_login=1; u=8493411634; bid=4065a77ca57a69c83405d6e591ab5449_m8r2nhs8; Hm_lvt_1db88642e346389874251b5a1eded6e3=1746410725; xq_a_token=660fb18cf1d15162da76deedc46b649370124dca; xqat=660fb18cf1d15162da76deedc46b649370124dca; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOjg0OTM0MTE2MzQsImlzcyI6InVjIiwiZXhwIjoxNzQ5ODYxNjY5LCJjdG0iOjE3NDcyNjk2Njk0NDgsImNpZCI6ImQ5ZDBuNEFadXAifQ.jc_E9qvguLwBDASn1Z-KjGtU89pNJRwJq_hIaiR3r2re7-_xiXH8qhuhC3Se8rlfKGZ8sHsb3rSND_vnF7yMp90QQHdK_brSmlgd6_ltHmJfWSFNJvMk7F3s0yPjcpeMqeUTPFnZwKmoWwZVKEwdVBN8f25z6e9M2JjtSTZ2huADH_FdEn1rb9IU-H35z_MLWW1M7vB5xc2rh57yFIBnQoxu9OLfeETpeIpASP1UBeZXoQZ_v1gIWiFYItwuudIz0tPYzB-o2duRe31G0S_hNvEGl3HH4M5FjTyaPAq2PRuiZCyRF-25gHXBZnLcxyavZ1VAURfHng_377_IJNSXsw; xq_r_token=8a5dec9c93caf88d0e1f98f1d23ea1bb60eb6225; snbim_minify=true; is_overseas=0; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1747905410; ssxmod_itna=eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40QuHhqDyGGTrlGiGbtOh01qDsqze4GzDiLPGhDBWAFdYCdqtsqfmmxXxyB+doh6odserKO5sg=EiqfqztqpiexCPGnD0=O77N4xYAEDBYD74G+DDeDiO3Dj4GmDGYd=eDFzjRQyl2edxDwDB=DmqG23grDm4DfDDL5xRD4zC2YDDtDAMWz5PDADA3ooDDlYGO44Lr4DYp52nXWdOaspxTXzeDMixGXzYlCgaCRo0TQy9LAN32TNPGuDG=H6e0ahrbicn0AP4KGGwQ0imPKY+5meOQDqixGYwQGGiGGetGe3qqjeKYw10G4ixqim2mpbK+h1iaIPeQAieNS1X5pXZP4rQ04Iv4zmQWvplG40P4Gw4CqRjwzlwGjPwlD3iho+qKlD4hi3YD; ssxmod_itna2=eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40QuHhqDyGGTrlGiGbtOh0P4DWhYebouIdHtBItz/DboqtwisfWD',
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'Referer': 'https://weibo.com/u/7735765253',
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'Sec-Ch-Ua': '"Chromium";v="122", "Not(A:Brand";v="24", "Google Chrome";v="122"',
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'Sec-Ch-Ua-Mobile': '?0',
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@ -11,6 +11,7 @@ from pymongo import MongoClient
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import logging
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from typing import Dict, List, Optional, Union, Tuple
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import json
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import requests
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from .config import DB_URL, MONGO_CONFIG, LOG_FILE
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from .stock_price_collector import StockPriceCollector
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@ -394,6 +395,69 @@ class FinancialAnalyzer:
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'message': f'查询失败: {str(e)}'
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}
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def get_momentum_indicators(self, stock_code: str, industry_codes: List[str]) -> Dict:
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"""
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获取动量指标数据
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Args:
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stock_code: 目标股票代码
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industry_codes: 行业股票代码列表
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Returns:
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动量指标数据字典
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"""
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try:
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url = "http://192.168.18.42:5000/api/dify/getStockMomentumIndex"
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payload = {
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"code_list": industry_codes,
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"target_code": stock_code
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}
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response = requests.post(url, json=payload)
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if response.status_code != 200:
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return {
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'success': False,
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'message': f'获取动量指标失败: HTTP {response.status_code}'
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}
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data = response.json()
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# 计算OBV和NATR的rank_score
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obv_rank_score = round((1 - data['obv_rank'] / len(industry_codes)) * 10, 3)
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natr_rank_score = round((1 - data['natr_rank'] / len(industry_codes)) * 10, 3)
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return {
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'success': True,
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'indicators': [
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{
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'key': 'obv',
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'desc': '能量',
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'value': round(data['OBV'], 3),
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'rank_score': obv_rank_score
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},
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{
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'key': 'form',
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'desc': f'技术形态-{data["form"]}',
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'value': round(data['form_probability'], 3),
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'rank_score': round(data['form_probability'] * 10, 3) # 将概率转换为0-10的分数
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},
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{
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'key': 'natr',
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'desc': '阶段位置',
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'value': round(data['NATR'], 3),
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'rank_score': natr_rank_score
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}
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],
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'avg_score': round((obv_rank_score + natr_rank_score + round(data['form_probability'] * 10, 3)) / 3, 3)
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}
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except Exception as e:
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logger.error(f"获取动量指标失败: {str(e)}")
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return {
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'success': False,
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'message': f'获取动量指标失败: {str(e)}'
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}
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def analyze_financial_data(self, stock_code: str) -> Dict:
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"""
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分析财务数据
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@ -413,6 +477,12 @@ class FinancialAnalyzer:
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industry_analyzer = IndustryAnalyzer()
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concepts = industry_analyzer.get_stock_concepts(stock_code)
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# 获取同行业股票列表
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industry_stocks = self.get_industry_stocks(stock_code)
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# 获取动量指标数据
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momentum_result = self.get_momentum_indicators(stock_code, industry_stocks)
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# 获取基础财务指标
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base_result = self.extract_financial_indicators(stock_code)
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if not base_result.get('success'):
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@ -578,6 +648,7 @@ class FinancialAnalyzer:
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'growth': growth_data,
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'value_rating': process_indicators(value_rating_indicators),
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'liquidity': process_indicators(liquidity_indicators),
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'momentum': momentum_result.get('indicators', []), # 添加动量指标数据
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'concepts': concepts, # 添加概念板块数据
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'price_data': price_data # 添加实时股价数据
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}
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@ -299,25 +299,25 @@ class IndustryAnalyzer:
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# 检查缓存
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if use_cache:
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cache_key = f"industry_crowding:{industry_name}"
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cached_data = redis_client.get(cache_key)
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if cached_data:
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try:
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# 尝试解析缓存的JSON数据
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cached_df_dict = json.loads(cached_data)
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logger.info(f"从缓存获取行业 {industry_name} 的拥挤度数据")
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# 将缓存的字典转换回DataFrame
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df = pd.DataFrame(cached_df_dict)
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# 确保trade_date列是日期类型
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df['trade_date'] = pd.to_datetime(df['trade_date'])
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return df
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except Exception as cache_error:
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logger.warning(f"解析缓存的拥挤度数据失败，将重新查询: {cache_error}")
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cache_key = f"industry_crowding:{industry_name}"
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cached_data = redis_client.get(cache_key)
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if cached_data:
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try:
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# 尝试解析缓存的JSON数据
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cached_df_dict = json.loads(cached_data)
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logger.info(f"从缓存获取行业 {industry_name} 的拥挤度数据")
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# 将缓存的字典转换回DataFrame
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df = pd.DataFrame(cached_df_dict)
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# 确保trade_date列是日期类型
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df['trade_date'] = pd.to_datetime(df['trade_date'])
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return df
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except Exception as cache_error:
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logger.warning(f"解析缓存的拥挤度数据失败，将重新查询: {cache_error}")
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# 获取行业所有股票
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stock_codes = self.get_industry_stocks(industry_name)
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if not stock_codes:
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@ -400,15 +400,15 @@ class IndustryAnalyzer:
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# 缓存结果，有效期1天（86400秒）
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if use_cache:
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try:
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redis_client.set(
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cache_key,
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json.dumps(df_dict, default=str), # 使用default=str处理日期等特殊类型
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ex=86400 # 1天的秒数
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)
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logger.info(f"已缓存行业 {industry_name} 的拥挤度数据，有效期为1天")
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except Exception as cache_error:
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logger.warning(f"缓存行业拥挤度数据失败: {cache_error}")
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try:
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redis_client.set(
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cache_key,
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json.dumps(df_dict, default=str), # 使用default=str处理日期等特殊类型
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ex=86400 # 1天的秒数
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)
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logger.info(f"已缓存行业 {industry_name} 的拥挤度数据，有效期为1天")
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except Exception as cache_error:
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logger.warning(f"缓存行业拥挤度数据失败: {cache_error}")
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logger.info(f"成功计算行业 {industry_name} 的拥挤度指标，共 {len(df)} 条记录")
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return df
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@ -512,7 +512,7 @@ class IndustryAnalyzer:
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except Exception as e:
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logger.error(f"获取行业综合分析失败: {e}")
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return {"success": False, "message": f"获取行业综合分析失败: {e}"}
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return {"success": False, "message": f"获取行业综合分析失败: {e}"}
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def get_stock_concepts(self, stock_code: str) -> List[str]:
|
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"""
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|
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Reference in New Issue