commit;
This commit is contained in:
parent
5b9ae03000
commit
8f73099e18
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@ -16,4 +16,5 @@ markdown2>=2.5.3
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google-genai
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redis==5.2.1
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pandas==2.2.3
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apscheduler==3.11.0
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apscheduler==3.11.0
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pymongo==4.13.0
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244
src/app.py
244
src/app.py
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@ -1,6 +1,6 @@
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import sys
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import os
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from datetime import datetime, timedelta
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from datetime import datetime, timedelta, time
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import pandas as pd
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import uuid
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import json
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@ -45,6 +45,9 @@ from src.scripts.stock_daily_data_collector import collect_stock_daily_data
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from utils.distributed_lock import DistributedLock
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from valuation_analysis.industry_analysis import redis_client
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from valuation_analysis.financial_analysis import FinancialAnalyzer
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from src.valuation_analysis.stock_price_collector import StockPriceCollector
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# 设置日志
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logging.basicConfig(
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level=logging.INFO,
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@ -183,6 +186,77 @@ def run_backtest_task(task_id, stocks_buy_dates, end_date):
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backtest_tasks[task_id]['error'] = str(e)
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logger.error(f"回测任务 {task_id} 失败：{str(e)}")
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def initialize_stock_price_schedule():
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"""
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初始化实时股价数据采集定时任务
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"""
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# 创建分布式锁
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price_lock = DistributedLock(redis_client, "stock_price_collector", expire_time=3600) # 1小时过期
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# 尝试获取锁
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if not price_lock.acquire():
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logger.info("其他服务器正在运行实时股价数据采集任务，本服务器跳过")
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return None
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try:
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from apscheduler.schedulers.background import BackgroundScheduler
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from apscheduler.triggers.cron import CronTrigger
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# 创建定时任务调度器
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scheduler = BackgroundScheduler()
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def is_trading_time():
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"""判断当前是否为交易时间"""
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now = datetime.now()
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current_time = now.time()
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# 定义交易时间段
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morning_start = time(9, 25) # 上午开盘前5分钟
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morning_end = time(11, 30) # 上午收盘
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afternoon_start = time(13, 0) # 下午开盘
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afternoon_end = time(15, 0) # 下午收盘
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# 判断是否为工作日
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if now.weekday() >= 5: # 5是周六，6是周日
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return False
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# 判断是否在交易时间段内
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is_morning = morning_start <= current_time <= morning_end
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is_afternoon = afternoon_start <= current_time <= afternoon_end
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return is_morning or is_afternoon
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def update_stock_price():
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"""更新实时股价数据"""
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if not is_trading_time():
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return
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try:
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collector = StockPriceCollector()
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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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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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)
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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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return scheduler
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except Exception as e:
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logger.error(f"初始化实时股价数据采集定时任务失败: {str(e)}")
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price_lock.release()
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return None
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def initialize_rzrq_collector_schedule():
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"""初始化融资融券数据采集定时任务"""
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# 创建分布式锁
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@ -203,7 +277,7 @@ def initialize_rzrq_collector_schedule():
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# 添加每天下午5点执行的任务
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scheduler.add_job(
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func=run_rzrq_initial_collection,
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trigger=CronTrigger(hour=18, minute=0),
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trigger=CronTrigger(hour=18, minute=40),
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id='rzrq_daily_update',
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name='每日更新融资融券数据',
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replace_existing=True
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@ -284,9 +358,9 @@ def run_stock_daily_collection():
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return False
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def run_rzrq_initial_collection():
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"""执行融资融券数据初始全量采集"""
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"""执行融资融券数据更新采集"""
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try:
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logger.info("开始执行融资融券数据初始全量采集")
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logger.info("开始执行融资融券数据更新采集")
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# 生成任务ID
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task_id = f"rzrq-{uuid.uuid4().hex[:16]}"
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@ -296,7 +370,7 @@ def run_rzrq_initial_collection():
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'status': 'running',
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'created_at': datetime.now().isoformat(),
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'type': 'initial_collection',
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'message': '开始执行融资融券数据初始全量采集'
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'message': '开始执行融资融券数据更新采集'
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}
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# 在新线程中执行采集任务
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@ -307,16 +381,16 @@ def run_rzrq_initial_collection():
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if result:
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rzrq_tasks[task_id]['status'] = 'completed'
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rzrq_tasks[task_id]['message'] = '融资融券数据初始全量采集完成'
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logger.info(f"融资融券数据初始全量采集任务 {task_id} 完成")
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rzrq_tasks[task_id]['message'] = '融资融券数据更新完成'
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logger.info(f"融资融券数据更新任务 {task_id} 完成")
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else:
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rzrq_tasks[task_id]['status'] = 'failed'
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rzrq_tasks[task_id]['message'] = '融资融券数据初始全量采集失败'
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logger.error(f"融资融券数据初始全量采集任务 {task_id} 失败")
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rzrq_tasks[task_id]['message'] = '融资融券数据更新失败'
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logger.error(f"融资融券数据更新任务 {task_id} 失败")
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except Exception as e:
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rzrq_tasks[task_id]['status'] = 'failed'
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rzrq_tasks[task_id]['message'] = f'执行失败: {str(e)}'
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logger.error(f"执行融资融券数据初始全量采集线程中出错: {str(e)}")
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logger.error(f"执行融资融券数据更新线程中出错: {str(e)}")
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# 创建并启动线程
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thread = Thread(target=collection_task)
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return task_id
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except Exception as e:
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logger.error(f"启动融资融券数据初始全量采集任务失败: {str(e)}")
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logger.error(f"启动融资融券数据更新任务失败: {str(e)}")
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if 'task_id' in locals():
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rzrq_tasks[task_id]['status'] = 'failed'
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rzrq_tasks[task_id]['message'] = f'启动失败: {str(e)}'
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@ -2567,7 +2641,7 @@ def initialize_industry_crowding_schedule():
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# 添加每天晚上10点执行的任务
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scheduler.add_job(
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func=precalculate_industry_crowding,
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trigger=CronTrigger(hour=22, minute=0),
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trigger=CronTrigger(hour=20, minute=30),
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id='industry_crowding_precalc',
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name='预计算行业拥挤度指标',
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replace_existing=True
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@ -2575,7 +2649,7 @@ def initialize_industry_crowding_schedule():
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# 启动调度器
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scheduler.start()
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logger.info("行业拥挤度指标预计算定时任务已初始化，将在每天22:00执行")
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logger.info("行业拥挤度指标预计算定时任务已初始化，将在每天20:30执行")
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return scheduler
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except Exception as e:
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logger.error(f"初始化行业拥挤度指标预计算定时任务失败: {str(e)}")
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def precalculate_industry_crowding():
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"""预计算所有行业的拥挤度指标"""
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try:
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logger.info("开始预计算所有行业的拥挤度指标")
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from .valuation_analysis.industry_analysis import IndustryAnalyzer
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# 获取所有行业列表
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industries = industry_analyzer.get_industry_list()
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if not industries:
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logger.error("获取行业列表失败")
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return
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# 记录成功和失败的数量
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success_count = 0
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fail_count = 0
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analyzer = IndustryAnalyzer()
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industries = analyzer.get_all_industries()
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# 遍历所有行业
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for industry in industries:
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try:
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industry_name = industry['name']
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logger.info(f"正在计算行业 {industry_name} 的拥挤度指标")
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# 调用拥挤度计算方法
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df = industry_analyzer.get_industry_crowding_index(industry_name)
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# 调用时设置 use_cache=False，强制重新计算
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df = analyzer.get_industry_crowding_index(industry, use_cache=False)
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if not df.empty:
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success_count += 1
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logger.info(f"成功计算行业 {industry_name} 的拥挤度指标")
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logger.info(f"成功预计算行业 {industry} 的拥挤度指标")
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else:
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fail_count += 1
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logger.warning(f"计算行业 {industry_name} 的拥挤度指标失败")
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logger.warning(f"行业 {industry} 的拥挤度指标计算失败")
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except Exception as e:
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fail_count += 1
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logger.error(f"计算行业 {industry_name} 的拥挤度指标时出错: {str(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(f"行业拥挤度指标预计算完成，成功: {success_count}，失败: {fail_count}")
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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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finally:
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# 释放分布式锁
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industry_crowding_lock = DistributedLock(redis_client, "industry_crowding_calculator")
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industry_crowding_lock.release()
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@app.route('/api/financial/analysis', methods=['GET'])
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def financial_analysis():
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"""
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财务分析接口
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请求参数:
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stock_code: 股票代码
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返回:
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分析结果JSON
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"""
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try:
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stock_code = request.args.get('stock_code')
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if not stock_code:
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return jsonify({
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'success': False,
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'message': '缺少必要参数：stock_code'
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}), 400
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analyzer = FinancialAnalyzer()
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result = analyzer.analyze_financial_data(stock_code)
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return jsonify(result)
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except Exception as e:
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logger.error(f"财务分析失败: {str(e)}")
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return jsonify({
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'success': False,
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'message': f'财务分析失败: {str(e)}'
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}), 500
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@app.route('/api/financial/indicators', methods=['GET'])
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def get_financial_indicators():
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"""
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获取财务指标接口
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请求参数:
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stock_code: 股票代码
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返回:
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JSON格式的财务指标数据
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"""
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try:
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# 获取股票代码
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stock_code = request.args.get('stock_code')
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if not stock_code:
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return jsonify({
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'success': False,
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'message': '缺少必要参数: stock_code'
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}), 400
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# 创建分析器实例
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analyzer = FinancialAnalyzer()
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# 获取财务指标
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result = analyzer.extract_financial_indicators(stock_code)
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return jsonify(result)
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except Exception as e:
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logger.error(f"获取财务指标失败: {str(e)}")
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return jsonify({
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'success': False,
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'message': f'获取财务指标失败: {str(e)}'
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}), 500
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@app.route('/api/financial/test_structure', methods=['GET'])
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def test_mongo_structure():
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"""
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测试MongoDB集合结构接口
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请求参数:
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stock_code: 股票代码（可选）
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返回:
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JSON格式的集合结构信息
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"""
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try:
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# 获取股票代码（可选）
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stock_code = request.args.get('stock_code')
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# 创建分析器实例
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analyzer = FinancialAnalyzer()
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# 获取集合结构
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result = analyzer.test_mongo_structure(stock_code)
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return jsonify(result)
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except Exception as e:
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logger.error(f"测试MongoDB结构失败: {str(e)}")
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return jsonify({
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'success': False,
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'message': f'测试MongoDB结构失败: {str(e)}'
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}), 500
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if __name__ == '__main__':
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"""
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@ -2644,9 +2801,9 @@ if __name__ == '__main__':
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else:
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print("股票日线采集器锁释放失败或不存在")
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if industry_crowding_lock.release():
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print("成功释放股票日线采集器锁")
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print("成功释放行业拥挤度锁")
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else:
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print("股票日线采集器锁释放失败或不存在")
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print("行业拥挤度锁释放失败或不存在")
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print("锁释放操作完成")
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"""
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@ -2660,5 +2817,8 @@ if __name__ == '__main__':
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# 初始化行业拥挤度指标预计算定时任务
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industry_crowding_scheduler = initialize_industry_crowding_schedule()
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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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@ -17,6 +17,16 @@ DB_CONFIG = {
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# 创建数据库连接URL
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DB_URL = f"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['database']}"
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# MongoDB配置
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MONGO_CONFIG = {
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'host': '192.168.18.75',
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'port': 27017,
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'db': 'judge',
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'username': 'root',
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'password': 'wlkj2018',
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'collection': 'wind_financial_analysis'
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}
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# 项目根目录
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ROOT_DIR = Path(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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@ -284,7 +284,6 @@ class EastmoneyRzrqCollector:
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# 确保数据表存在
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if not self._ensure_table_exists():
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return False
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# 将nan值转换为None（在SQL中会变成NULL）
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data = data.replace({pd.NA: None, pd.NaT: None})
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data = data.where(pd.notnull(data), None)
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@ -422,18 +421,18 @@ class EastmoneyRzrqCollector:
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def initial_data_collection(self) -> bool:
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"""
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首次全量采集融资融券数据
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每日更新任务采集融资融券数据
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Returns:
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是否成功采集所有数据
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"""
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try:
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logger.info("开始获取最新融资融券数据...")
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df = collector.fetch_data(page=1)
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df = self.fetch_data(page=1)
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if not df.empty:
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# 保存数据到数据库
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if collector.save_to_database(df):
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if self.save_to_database(df):
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logger.info(f"成功更新最新数据，日期：{df.iloc[0]['trade_date']}")
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else:
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logger.error("更新最新数据失败")
|
||||
|
|
@ -443,7 +442,7 @@ class EastmoneyRzrqCollector:
|
|||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"首次全量采集失败: {e}")
|
||||
logger.error(f"每日更新任务采集失败: {e}")
|
||||
return False
|
||||
|
||||
def get_chart_data(self, limit_days: int = 30) -> dict:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,869 @@
|
|||
"""
|
||||
财务分析模块
|
||||
|
||||
提供从MySQL和MongoDB获取财务数据并进行分析的功能
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sqlalchemy import create_engine
|
||||
from pymongo import MongoClient
|
||||
import logging
|
||||
from typing import Dict, List, Optional, Union, Tuple
|
||||
import json
|
||||
|
||||
from .config import DB_URL, MONGO_CONFIG, LOG_FILE
|
||||
from .stock_price_collector import StockPriceCollector
|
||||
from .industry_analysis import IndustryAnalyzer
|
||||
|
||||
# 配置日志
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
handlers=[
|
||||
logging.FileHandler(LOG_FILE),
|
||||
logging.StreamHandler()
|
||||
]
|
||||
)
|
||||
logger = logging.getLogger("financial_analysis")
|
||||
|
||||
class FinancialAnalyzer:
|
||||
"""财务分析器类"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化财务分析器"""
|
||||
# 初始化MySQL连接
|
||||
self.mysql_engine = create_engine(
|
||||
DB_URL,
|
||||
pool_size=5,
|
||||
max_overflow=10,
|
||||
pool_recycle=3600
|
||||
)
|
||||
|
||||
# 初始化MongoDB连接
|
||||
self.mongo_client = MongoClient(
|
||||
host=MONGO_CONFIG['host'],
|
||||
port=MONGO_CONFIG['port'],
|
||||
username=MONGO_CONFIG['username'],
|
||||
password=MONGO_CONFIG['password']
|
||||
)
|
||||
self.mongo_db = self.mongo_client[MONGO_CONFIG['db']]
|
||||
self.mongo_collection = self.mongo_db[MONGO_CONFIG['collection']]
|
||||
self.wacc_collection = self.mongo_db['wind_stock_wacc_roic']
|
||||
|
||||
logger.info("财务分析器初始化完成")
|
||||
|
||||
def _get_growth_indicators(self, wind_data: Dict) -> Dict[str, float]:
|
||||
"""
|
||||
从wind_data中提取增长指标
|
||||
|
||||
Args:
|
||||
wind_data: wind_data字典
|
||||
|
||||
Returns:
|
||||
包含增长指标的字典
|
||||
"""
|
||||
indicators = {
|
||||
'rd_expense_growth': self._find_indicator(wind_data, 'grows', '研发费用同比增长'),
|
||||
'roe_growth': self._find_indicator(wind_data, 'grows', '净资产收益率(摊薄)(同比增长)'),
|
||||
'diluted_eps_growth': self._find_indicator(wind_data, 'grows', '稀释每股收益(同比增长率)'),
|
||||
'operating_cash_flow_per_share_growth': self._find_indicator(wind_data, 'grows', '每股经营活动产生的现金流量净额(同比增长率)'),
|
||||
'revenue_growth': self._find_indicator(wind_data, 'grows', '营业收入(同比增长率)'),
|
||||
'operating_profit_growth': self._find_indicator(wind_data, 'grows', '营业利润(同比增长率)'),
|
||||
'net_profit_growth_excl_nonrecurring': self._find_indicator(wind_data, 'grows', '归属母公司股东的净利润-扣除非经常性损益(同比增长率)'),
|
||||
'operating_cash_flow_growth': self._find_indicator(wind_data, 'grows', '经营活动产生的现金流量净额(同比增长率)')
|
||||
}
|
||||
return indicators
|
||||
|
||||
def _calculate_growth_change(self, current_value: float, previous_value: float) -> Optional[float]:
|
||||
"""
|
||||
计算增长率的增长变化
|
||||
|
||||
Args:
|
||||
current_value: 当前值
|
||||
previous_value: 上一期值
|
||||
|
||||
Returns:
|
||||
增长变化率，如果无法计算则返回None
|
||||
"""
|
||||
try:
|
||||
if current_value is None or previous_value is None:
|
||||
return None
|
||||
if previous_value == 0:
|
||||
return None
|
||||
return (current_value - previous_value) / abs(previous_value)
|
||||
except Exception as e:
|
||||
logger.error(f"计算增长变化率失败: {str(e)}")
|
||||
return None
|
||||
|
||||
def get_growth_change_indicators(self, stock_code: str) -> Dict:
|
||||
"""
|
||||
获取增长指标的变化率
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
包含增长指标变化率的字典
|
||||
"""
|
||||
try:
|
||||
# 查询MongoDB
|
||||
record = self.mongo_collection.find_one({'code': stock_code})
|
||||
if not record or 'wind_data' not in record:
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'未找到股票 {stock_code} 的财务数据'
|
||||
}
|
||||
|
||||
# 获取2023-12-31和2024-12-31的数据
|
||||
wind_data_2023 = None
|
||||
wind_data_2024 = None
|
||||
|
||||
for data in record['wind_data']:
|
||||
if data['time'] == '2023-12-31':
|
||||
wind_data_2023 = data
|
||||
elif data['time'] == '2024-12-31':
|
||||
wind_data_2024 = data
|
||||
|
||||
if not wind_data_2023 or not wind_data_2024:
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'未找到股票 {stock_code} 的2023或2024年财务数据'
|
||||
}
|
||||
|
||||
# 获取两个时间点的指标
|
||||
indicators_2023 = self._get_growth_indicators(wind_data_2023)
|
||||
indicators_2024 = self._get_growth_indicators(wind_data_2024)
|
||||
|
||||
# 计算变化率
|
||||
growth_changes = {}
|
||||
for key in indicators_2023.keys():
|
||||
current_value = indicators_2024.get(key)
|
||||
previous_value = indicators_2023.get(key)
|
||||
growth_changes[key] = self._calculate_growth_change(current_value, previous_value)
|
||||
|
||||
return {
|
||||
'success': True,
|
||||
'stock_code': stock_code,
|
||||
'data': {
|
||||
'indicators_2023': indicators_2023,
|
||||
'indicators_2024': indicators_2024,
|
||||
'growth_changes': growth_changes
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取增长指标变化率失败: {str(e)}")
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'获取增长指标变化率失败: {str(e)}'
|
||||
}
|
||||
|
||||
def get_wacc_data(self, stock_code: str) -> Optional[float]:
|
||||
"""
|
||||
获取股票的WACC数据
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
WACC值，如果未找到则返回None
|
||||
"""
|
||||
try:
|
||||
# 查询MongoDB
|
||||
record = self.wacc_collection.find_one({
|
||||
'code': stock_code,
|
||||
'endTime': '20241231'
|
||||
})
|
||||
|
||||
if not record:
|
||||
logger.warning(f"未找到股票 {stock_code} 的WACC数据")
|
||||
return None
|
||||
|
||||
return record['wacc']
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取WACC数据失败: {str(e)}")
|
||||
return None
|
||||
|
||||
def _calculate_profit_years(self, wind_data_list: List[Dict]) -> int:
|
||||
"""
|
||||
计算近五年的盈利年数
|
||||
|
||||
Args:
|
||||
wind_data_list: 按时间排序的wind_data列表
|
||||
|
||||
Returns:
|
||||
盈利年数
|
||||
"""
|
||||
try:
|
||||
profit_years = 0
|
||||
# 获取最近5年的数据
|
||||
recent_years = sorted(wind_data_list, key=lambda x: x['time'], reverse=True)[:5]
|
||||
|
||||
for year_data in recent_years:
|
||||
# 获取销售净利率
|
||||
net_profit_ratio = self._find_indicator(year_data, 'profitability', '净利润/营业总收入')
|
||||
if net_profit_ratio is not None and net_profit_ratio > 0:
|
||||
profit_years += 1
|
||||
|
||||
return profit_years
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算盈利年数失败: {str(e)}")
|
||||
return 0
|
||||
|
||||
def extract_financial_indicators(self, stock_code: str) -> Dict:
|
||||
"""
|
||||
从MongoDB中提取指定的财务指标
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
包含财务指标的字典
|
||||
"""
|
||||
try:
|
||||
# 查询MongoDB
|
||||
record = self.mongo_collection.find_one({'code': stock_code})
|
||||
if not record or 'wind_data' not in record:
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'未找到股票 {stock_code} 的财务数据'
|
||||
}
|
||||
|
||||
# 获取最新的财务数据（按时间排序）
|
||||
wind_data = sorted(record['wind_data'], key=lambda x: x['time'], reverse=True)[0]
|
||||
|
||||
# 获取WACC数据
|
||||
wacc = self.get_wacc_data(stock_code)
|
||||
|
||||
# 计算近五年盈利年数
|
||||
profit_years = self._calculate_profit_years(record['wind_data'])
|
||||
|
||||
|
||||
# 定义指标映射
|
||||
indicators = {
|
||||
# 偿债能力指标
|
||||
'debt_equity_ratio': self._find_indicator(wind_data, 'solvency', '产权比率'),
|
||||
'debt_ebitda_ratio': self._find_indicator(wind_data, 'solvency', '全部债务/EBITDA'),
|
||||
'interest_coverage_ratio': self._find_indicator(wind_data, 'solvency', '已获利息倍数(EBIT/利息费用)'),
|
||||
'current_ratio': self._find_indicator(wind_data, 'solvency', '流动比率'),
|
||||
'quick_ratio': self._find_indicator(wind_data, 'solvency', '速动比率'),
|
||||
'cash_ratio': self._find_indicator(wind_data, 'solvency', '现金比率'),
|
||||
'cash_to_debt_ratio': self._find_indicator(wind_data, 'solvency', '经营活动产生的现金流量净额/负债合计'),
|
||||
|
||||
# 资本结构指标
|
||||
'equity_ratio': self._find_indicator(wind_data, 'capitalStructure', '股东权益比'),
|
||||
|
||||
# 盈利能力指标
|
||||
'profit_years': profit_years, # 添加近五年盈利年数
|
||||
|
||||
# 成长能力指标
|
||||
'diluted_eps_growth': self._find_indicator(wind_data, 'grows', '稀释每股收益(同比增长率)'),
|
||||
'operating_cash_flow_per_share_growth': self._find_indicator(wind_data, 'grows', '每股经营活动产生的现金流量净额(同比增长率)'),
|
||||
'revenue_growth': self._find_indicator(wind_data, 'grows', '营业收入(同比增长率)'),
|
||||
'operating_profit_growth': self._find_indicator(wind_data, 'grows', '营业利润(同比增长率)'),
|
||||
'net_profit_growth_excl_nonrecurring': self._find_indicator(wind_data, 'grows', '归属母公司股东的净利润-扣除非经常性损益(同比增长率)'),
|
||||
'operating_cash_flow_growth': self._find_indicator(wind_data, 'grows', '经营活动产生的现金流量净额(同比增长率)'),
|
||||
'rd_expense_growth': self._find_indicator(wind_data, 'grows', '研发费用同比增长'),
|
||||
'roe_growth': self._find_indicator(wind_data, 'grows', '净资产收益率(摊薄)(同比增长)'),
|
||||
|
||||
# Z值相关指标
|
||||
'working_capital_to_assets': self._find_indicator(wind_data, 'ZValue', '营运资本/总资产'),
|
||||
'retained_earnings_to_assets': self._find_indicator(wind_data, 'ZValue', '留存收益/总资产'),
|
||||
'ebit_to_assets': self._find_indicator(wind_data, 'ZValue', '息税前利润(TTM)/总资产'),
|
||||
'market_value_to_liabilities': self._find_indicator(wind_data, 'ZValue', '当日总市值/负债总计'),
|
||||
'equity_to_liabilities': self._find_indicator(wind_data, 'ZValue', '股东权益合计(含少数)/负债总计'),
|
||||
'revenue_to_assets': self._find_indicator(wind_data, 'ZValue', '营业收入/总资产'),
|
||||
'z_score': self._find_indicator(wind_data, 'ZValue', 'Z值'),
|
||||
|
||||
# 营运能力指标
|
||||
'inventory_turnover_days': self._find_indicator(wind_data, 'operatingCapacity', '存货周转天数'),
|
||||
'receivables_turnover_days': self._find_indicator(wind_data, 'operatingCapacity', '应收账款周转天数'),
|
||||
'payables_turnover_days': self._find_indicator(wind_data, 'operatingCapacity', '应付账款周转天数'),
|
||||
|
||||
# 盈利能力指标
|
||||
'gross_profit_margin': self._find_indicator(wind_data, 'profitability', '销售毛利率'),
|
||||
'operating_profit_margin': self._find_indicator(wind_data, 'profitability', '营业利润/营业总收入'),
|
||||
'net_profit_margin': self._find_indicator(wind_data, 'profitability', '销售净利率'),
|
||||
'roe': self._find_indicator(wind_data, 'profitability', '净资产收益率ROE(平均)'),
|
||||
'roa': self._find_indicator(wind_data, 'profitability', '总资产净利率ROA'),
|
||||
'roic': self._find_indicator(wind_data, 'profitability', '投入资本回报率ROIC'),
|
||||
|
||||
# WACC数据
|
||||
'wacc': wacc
|
||||
}
|
||||
|
||||
# 对数值进行四舍五入处理
|
||||
for key, value in indicators.items():
|
||||
if isinstance(value, (int, float)) and value is not None:
|
||||
indicators[key] = round(value, 3)
|
||||
|
||||
# 添加数据时间
|
||||
indicators['data_time'] = wind_data['time']
|
||||
|
||||
|
||||
return {
|
||||
'success': True,
|
||||
'stock_code': stock_code,
|
||||
'indicators': indicators
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取财务指标失败: {str(e)}")
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'提取财务指标失败: {str(e)}'
|
||||
}
|
||||
|
||||
def _find_indicator(self, data: Dict, category: str, meaning: str) -> Optional[float]:
|
||||
"""
|
||||
在指定类别中查找指标值
|
||||
|
||||
Args:
|
||||
data: 财务数据字典
|
||||
category: 指标类别
|
||||
meaning: 指标含义
|
||||
|
||||
Returns:
|
||||
指标值，如果未找到则返回None
|
||||
"""
|
||||
try:
|
||||
if category not in data:
|
||||
return None
|
||||
|
||||
for item in data[category]['list']:
|
||||
if item['meaning'] == meaning:
|
||||
return item['data']
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"查找指标 {category}.{meaning} 失败: {str(e)}")
|
||||
return None
|
||||
|
||||
def test_mongo_structure(self, stock_code: str = None) -> Dict:
|
||||
"""
|
||||
测试方法：查看MongoDB集合的字段结构
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码，如果为None则返回第一条记录
|
||||
|
||||
Returns:
|
||||
包含字段结构的字典
|
||||
"""
|
||||
try:
|
||||
# 构建查询条件
|
||||
query = {}
|
||||
if stock_code:
|
||||
query['code'] = stock_code
|
||||
|
||||
# 获取一条记录
|
||||
record = self.mongo_collection.find_one(query)
|
||||
|
||||
if not record:
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'未找到股票 {stock_code} 的记录' if stock_code else '集合为空'
|
||||
}
|
||||
|
||||
# 移除MongoDB的_id字段
|
||||
if '_id' in record:
|
||||
record.pop('_id')
|
||||
|
||||
# 获取所有字段名
|
||||
fields = list(record.keys())
|
||||
|
||||
# 格式化输出
|
||||
result = {
|
||||
'success': True,
|
||||
'fields': fields,
|
||||
'sample_data': record
|
||||
}
|
||||
|
||||
# 打印字段信息
|
||||
logger.info(f"集合字段列表: {json.dumps(fields, ensure_ascii=False, indent=2)}")
|
||||
logger.info(f"示例数据: {json.dumps(record, ensure_ascii=False, indent=2)}")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"查询MongoDB结构失败: {str(e)}")
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'查询失败: {str(e)}'
|
||||
}
|
||||
|
||||
def analyze_financial_data(self, stock_code: str) -> Dict:
|
||||
"""
|
||||
分析财务数据
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
分析结果字典，包含所有财务指标及其排名得分
|
||||
"""
|
||||
try:
|
||||
# 获取股票价格数据
|
||||
price_collector = StockPriceCollector()
|
||||
price_data = price_collector.get_stock_price_data(stock_code)
|
||||
|
||||
# 获取概念板块数据
|
||||
industry_analyzer = IndustryAnalyzer()
|
||||
concepts = industry_analyzer.get_stock_concepts(stock_code)
|
||||
|
||||
# 获取基础财务指标
|
||||
base_result = self.extract_financial_indicators(stock_code)
|
||||
if not base_result.get('success'):
|
||||
return base_result
|
||||
|
||||
# 获取增长指标变化
|
||||
growth_result = self.get_growth_change_indicators(stock_code)
|
||||
if not growth_result.get('success'):
|
||||
return growth_result
|
||||
|
||||
# 获取行业排名
|
||||
rank_result = self.calculate_industry_rankings(stock_code)
|
||||
if not rank_result.get('success'):
|
||||
return rank_result
|
||||
|
||||
# 定义指标说明映射
|
||||
indicator_descriptions = {
|
||||
# 财务实力指标
|
||||
'debt_equity_ratio': '债务股本比率',
|
||||
'debt_ebitda_ratio': '债务／税息折旧及摊销前利润',
|
||||
'interest_coverage_ratio': '利息保障倍数',
|
||||
'cash_to_debt_ratio': '现金负债率',
|
||||
'equity_ratio': '股东权益比',
|
||||
'wacc': '加权平均资本成本',
|
||||
'roic': '资本回报率',
|
||||
|
||||
# 盈利能力指标
|
||||
'profit_years': '过去五年盈利年数',
|
||||
'gross_profit_margin': '毛利率',
|
||||
'operating_profit_margin': '营业利润率',
|
||||
'net_profit_margin': '净利率',
|
||||
'roe': '股本回报率ROE',
|
||||
'roa': '资产收益率ROA',
|
||||
|
||||
# 成长能力指标
|
||||
'diluted_eps_growth': '稀释每股收益增长率',
|
||||
'operating_cash_flow_per_share_growth': '每股经营活动产生的现金流量净额增长率',
|
||||
'revenue_growth': '营业收入增长率',
|
||||
'operating_profit_growth': '营业利润增长率',
|
||||
'net_profit_growth_excl_nonrecurring': '扣非净利润增长率',
|
||||
'operating_cash_flow_growth': '经营活动产生的现金流量净额增长率',
|
||||
'rd_expense_growth': '研发费用增长率',
|
||||
'roe_growth': '净资产收益率增长率',
|
||||
|
||||
# 价值评级指标
|
||||
'z_score': 'Z值',
|
||||
'working_capital_to_assets': '营运资本/总资产',
|
||||
'retained_earnings_to_assets': '留存收益/总资产',
|
||||
'ebit_to_assets': '息税前利润/总资产',
|
||||
'market_value_to_liabilities': '总市值/负债总计',
|
||||
'equity_to_liabilities': '股东权益/负债总计',
|
||||
'revenue_to_assets': '营业收入/总资产',
|
||||
|
||||
# 流动性指标
|
||||
'inventory_turnover_days': '存货周转天数',
|
||||
'receivables_turnover_days': '应收账款周转天数',
|
||||
'payables_turnover_days': '应付账款周转天数',
|
||||
'current_ratio': '流动比率',
|
||||
'quick_ratio': '速动比率',
|
||||
'cash_ratio': '现金比率'
|
||||
}
|
||||
|
||||
# 构建基础指标数据
|
||||
base_indicators = base_result.get('indicators', {})
|
||||
rankings = rank_result.get('rankings', {})
|
||||
|
||||
# 定义各板块的指标列表
|
||||
financial_strength_indicators = [
|
||||
'debt_equity_ratio', 'debt_ebitda_ratio', 'interest_coverage_ratio',
|
||||
'cash_to_debt_ratio', 'equity_ratio', 'wacc', 'roic'
|
||||
]
|
||||
|
||||
profitability_indicators = [
|
||||
'gross_profit_margin', 'operating_profit_margin',
|
||||
'net_profit_margin', 'roe', 'roa', 'roic', 'profit_years'
|
||||
]
|
||||
|
||||
growth_indicators = [
|
||||
'diluted_eps_growth', 'operating_cash_flow_per_share_growth',
|
||||
'revenue_growth', 'operating_profit_growth',
|
||||
'net_profit_growth_excl_nonrecurring', 'operating_cash_flow_growth',
|
||||
'rd_expense_growth', 'roe_growth'
|
||||
]
|
||||
|
||||
value_rating_indicators = [
|
||||
'z_score', 'working_capital_to_assets', 'retained_earnings_to_assets',
|
||||
'ebit_to_assets', 'market_value_to_liabilities',
|
||||
'equity_to_liabilities', 'revenue_to_assets'
|
||||
]
|
||||
|
||||
liquidity_indicators = [
|
||||
'inventory_turnover_days', 'receivables_turnover_days',
|
||||
'payables_turnover_days', 'current_ratio', 'quick_ratio', 'cash_ratio'
|
||||
]
|
||||
|
||||
# 处理各板块指标
|
||||
def process_indicators(indicator_list):
|
||||
result = []
|
||||
total_score = 0
|
||||
valid_scores = 0
|
||||
|
||||
for key in indicator_list:
|
||||
if key in base_indicators:
|
||||
rank_score = rankings.get(key, 0)
|
||||
if rank_score is not None:
|
||||
total_score += rank_score
|
||||
valid_scores += 1
|
||||
|
||||
result.append({
|
||||
'key': key,
|
||||
'desc': indicator_descriptions.get(key, key),
|
||||
'value': base_indicators[key],
|
||||
'rank_score': rank_score
|
||||
})
|
||||
|
||||
# 计算平均得分
|
||||
avg_score = round(total_score / valid_scores, 2) if valid_scores > 0 else 0
|
||||
|
||||
return {
|
||||
'indicators': result,
|
||||
'avg_score': avg_score
|
||||
}
|
||||
|
||||
# 处理增长指标变化
|
||||
growth_changes = growth_result.get('data', {}).get('growth_changes', {})
|
||||
growth_changes_list = []
|
||||
total_growth_score = 0
|
||||
valid_growth_scores = 0
|
||||
|
||||
for key in growth_indicators:
|
||||
if key in growth_changes:
|
||||
value = growth_changes[key]
|
||||
if isinstance(value, (int, float)) and value is not None:
|
||||
value = round(value, 3)
|
||||
|
||||
rank_score = rankings.get(f'{key}_change', 0)
|
||||
if rank_score is not None:
|
||||
total_growth_score += rank_score
|
||||
valid_growth_scores += 1
|
||||
|
||||
growth_changes_list.append({
|
||||
'key': f'{key}_change',
|
||||
'desc': f'{indicator_descriptions.get(key, key)}增长变化率',
|
||||
'value': value,
|
||||
'rank_score': rank_score
|
||||
})
|
||||
|
||||
# 计算增长指标的平均得分
|
||||
growth_avg_score = round(total_growth_score / valid_growth_scores, 2) if valid_growth_scores > 0 else 0
|
||||
|
||||
# 构建增长指标数据
|
||||
growth_data = {
|
||||
'indicators': growth_changes_list,
|
||||
'avg_score': growth_avg_score
|
||||
}
|
||||
|
||||
return {
|
||||
'success': True,
|
||||
'stock_code': stock_code,
|
||||
'data_time': base_indicators.get('data_time'),
|
||||
'financial_strength': process_indicators(financial_strength_indicators),
|
||||
'profitability': process_indicators(profitability_indicators),
|
||||
'growth': growth_data,
|
||||
'value_rating': process_indicators(value_rating_indicators),
|
||||
'liquidity': process_indicators(liquidity_indicators),
|
||||
'concepts': concepts, # 添加概念板块数据
|
||||
'price_data': price_data # 添加实时股价数据
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析财务数据失败: {str(e)}")
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'分析财务数据失败: {str(e)}'
|
||||
}
|
||||
|
||||
def __del__(self):
|
||||
"""析构函数，关闭数据库连接"""
|
||||
if hasattr(self, 'mongo_client'):
|
||||
self.mongo_client.close()
|
||||
logger.info("数据库连接已关闭")
|
||||
|
||||
def _convert_stock_code_format(self, stock_code: str) -> str:
|
||||
"""
|
||||
转换股票代码格式
|
||||
|
||||
Args:
|
||||
stock_code: 原始股票代码，格式如 "603507.SH"
|
||||
|
||||
Returns:
|
||||
转换后的股票代码，格式如 "SH603507"
|
||||
"""
|
||||
try:
|
||||
code, market = stock_code.split('.')
|
||||
return f"{market}{code}"
|
||||
except Exception as e:
|
||||
logger.error(f"转换股票代码格式失败: {str(e)}")
|
||||
return stock_code
|
||||
|
||||
def get_industry_stocks(self, stock_code: str) -> List:
|
||||
"""
|
||||
获取同行业股票列表
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码，格式如 "603507.SH"
|
||||
|
||||
Returns:
|
||||
包含同行业股票列表的字典
|
||||
"""
|
||||
try:
|
||||
# 转换股票代码格式
|
||||
formatted_code = self._convert_stock_code_format(stock_code)
|
||||
|
||||
# 查询行业板块
|
||||
query = """
|
||||
SELECT bk_name
|
||||
FROM gp_hybk
|
||||
WHERE gp_code = %s
|
||||
"""
|
||||
result = pd.read_sql(query, self.mysql_engine, params=(formatted_code,))
|
||||
|
||||
if result.empty:
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'未找到股票 {stock_code} 的行业信息'
|
||||
}
|
||||
|
||||
bk_name = result.iloc[0]['bk_name']
|
||||
|
||||
# 查询同行业股票
|
||||
query = """
|
||||
SELECT gp_code
|
||||
FROM gp_hybk
|
||||
WHERE bk_name = %s
|
||||
"""
|
||||
stocks = pd.read_sql(query, self.mysql_engine, params=(bk_name,))
|
||||
|
||||
# 转换回原始格式
|
||||
stock_list = []
|
||||
for code in stocks['gp_code']:
|
||||
if code.startswith('SH'):
|
||||
stock_list.append(f"{code[2:]}.SH")
|
||||
elif code.startswith('SZ'):
|
||||
stock_list.append(f"{code[2:]}.SZ")
|
||||
|
||||
return stock_list
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取同行业股票列表失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def _calculate_industry_rank_score(self, value: float, values: List[float], is_higher_better: bool = True) -> float:
|
||||
"""
|
||||
计算行业排名得分
|
||||
|
||||
Args:
|
||||
value: 当前值
|
||||
values: 行业所有值列表
|
||||
is_higher_better: 是否越高越好，默认为True
|
||||
|
||||
Returns:
|
||||
0-10的得分，10表示第一名，0表示最后一名
|
||||
"""
|
||||
try:
|
||||
if value is None or not values:
|
||||
return 0
|
||||
|
||||
# 过滤掉None值
|
||||
valid_values = [v for v in values if v is not None]
|
||||
if not valid_values:
|
||||
return 0
|
||||
|
||||
# 计算排名
|
||||
if is_higher_better:
|
||||
rank = sum(1 for x in valid_values if x > value) + 1
|
||||
else:
|
||||
rank = sum(1 for x in valid_values if x < value) + 1
|
||||
|
||||
# 计算得分 (10 * (1 - (rank - 1) / (n - 1)))
|
||||
n = len(valid_values)
|
||||
if n == 1:
|
||||
return 10
|
||||
score = 10 * (1 - (rank - 1) / (n - 1))
|
||||
return round(score, 2)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算行业排名得分失败: {str(e)}")
|
||||
return 0
|
||||
|
||||
def _get_industry_indicators(self, stock_list: List[str]) -> Dict[str, List[float]]:
|
||||
"""
|
||||
获取行业所有公司的指标值
|
||||
|
||||
Args:
|
||||
stock_list: 股票代码列表
|
||||
|
||||
Returns:
|
||||
包含所有指标值的字典，key为指标名，value为该指标的所有公司值列表
|
||||
"""
|
||||
try:
|
||||
industry_indicators = {}
|
||||
|
||||
# 遍历所有股票获取指标
|
||||
for stock_code in stock_list:
|
||||
result = self.extract_financial_indicators(stock_code)
|
||||
if not result.get('success'):
|
||||
continue
|
||||
|
||||
indicators = result.get('indicators', {})
|
||||
for key, value in indicators.items():
|
||||
if key != 'data_time':
|
||||
if key not in industry_indicators:
|
||||
industry_indicators[key] = []
|
||||
industry_indicators[key].append(value)
|
||||
|
||||
return industry_indicators
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取行业指标失败: {str(e)}")
|
||||
return {}
|
||||
|
||||
def calculate_industry_rankings(self, stock_code: str) -> Dict:
|
||||
"""
|
||||
计算公司在行业中的排名得分
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
包含所有指标排名得分的字典
|
||||
"""
|
||||
try:
|
||||
# 获取同行业股票列表
|
||||
stock_list = self.get_industry_stocks(stock_code)
|
||||
if not stock_list:
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'未找到股票 {stock_code} 的同行业公司'
|
||||
}
|
||||
|
||||
# 获取当前公司的指标
|
||||
current_result = self.extract_financial_indicators(stock_code)
|
||||
if not current_result.get('success'):
|
||||
return current_result
|
||||
|
||||
# 获取当前公司的增长指标变化
|
||||
current_growth_result = self.get_growth_change_indicators(stock_code)
|
||||
if not current_growth_result.get('success'):
|
||||
return current_growth_result
|
||||
|
||||
# 获取行业所有公司的指标
|
||||
industry_indicators = self._get_industry_indicators(stock_list)
|
||||
|
||||
# 获取行业所有公司的增长指标变化
|
||||
industry_growth_indicators = {}
|
||||
for stock in stock_list:
|
||||
growth_result = self.get_growth_change_indicators(stock)
|
||||
if growth_result.get('success'):
|
||||
growth_changes = growth_result.get('data', {}).get('growth_changes', {})
|
||||
for key, value in growth_changes.items():
|
||||
if key not in industry_growth_indicators:
|
||||
industry_growth_indicators[key] = []
|
||||
industry_growth_indicators[key].append(value)
|
||||
|
||||
# 定义指标是否越高越好
|
||||
higher_better_indicators = {
|
||||
# 偿债能力指标
|
||||
'current_ratio': True,
|
||||
'quick_ratio': True,
|
||||
'cash_ratio': True,
|
||||
'interest_coverage_ratio': True,
|
||||
|
||||
# 资本结构指标
|
||||
'equity_ratio': True,
|
||||
|
||||
# 盈利能力指标
|
||||
'profit_years': True, # 盈利年数越高越好
|
||||
|
||||
# 成长能力指标
|
||||
'diluted_eps_growth': True,
|
||||
'operating_cash_flow_per_share_growth': True,
|
||||
'revenue_growth': True,
|
||||
'operating_profit_growth': True,
|
||||
'net_profit_growth_excl_nonrecurring': True,
|
||||
'operating_cash_flow_growth': True,
|
||||
'rd_expense_growth': True,
|
||||
'roe_growth': True,
|
||||
|
||||
# Z值相关指标
|
||||
'working_capital_to_assets': True,
|
||||
'retained_earnings_to_assets': True,
|
||||
'ebit_to_assets': True,
|
||||
'market_value_to_liabilities': True,
|
||||
'equity_to_liabilities': True,
|
||||
'revenue_to_assets': True,
|
||||
'z_score': True,
|
||||
|
||||
# 营运能力指标
|
||||
'inventory_turnover_days': False,
|
||||
'receivables_turnover_days': False,
|
||||
'payables_turnover_days': False,
|
||||
|
||||
# 盈利能力指标
|
||||
'gross_profit_margin': True,
|
||||
'operating_profit_margin': True,
|
||||
'net_profit_margin': True,
|
||||
'roe': True,
|
||||
'roa': True,
|
||||
'roic': True,
|
||||
|
||||
# WACC数据
|
||||
'wacc': False
|
||||
}
|
||||
|
||||
# 计算每个指标的排名得分
|
||||
current_indicators = current_result.get('indicators', {})
|
||||
rankings = {}
|
||||
|
||||
# 计算基础指标的排名
|
||||
for key, value in current_indicators.items():
|
||||
if key != 'data_time' and key in industry_indicators:
|
||||
is_higher_better = higher_better_indicators.get(key, True)
|
||||
score = self._calculate_industry_rank_score(
|
||||
value,
|
||||
industry_indicators[key],
|
||||
is_higher_better
|
||||
)
|
||||
rankings[key] = score
|
||||
|
||||
# 计算增长指标的排名
|
||||
current_growth_changes = current_growth_result.get('data', {}).get('growth_changes', {})
|
||||
for key, value in current_growth_changes.items():
|
||||
if key in industry_growth_indicators:
|
||||
is_higher_better = higher_better_indicators.get(key, True)
|
||||
score = self._calculate_industry_rank_score(
|
||||
value,
|
||||
industry_growth_indicators[key],
|
||||
is_higher_better
|
||||
)
|
||||
rankings[f'{key}_change'] = score
|
||||
|
||||
return {
|
||||
'success': True,
|
||||
'stock_code': stock_code,
|
||||
'data_time': current_indicators.get('data_time'),
|
||||
'rankings': rankings
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算行业排名失败: {str(e)}")
|
||||
return {
|
||||
'success': False,
|
||||
'message': f'计算行业排名失败: {str(e)}'
|
||||
}
|
||||
|
|
@ -274,7 +274,7 @@ class IndustryAnalyzer:
|
|||
logger.info(f"计算行业 {metric} 分位数完成: 当前{metric}={result['current']:.2f}, 百分位={result['percentile']:.2f}%")
|
||||
return result
|
||||
|
||||
def get_industry_crowding_index(self, industry_name: str, start_date: str = None, end_date: str = None) -> pd.DataFrame:
|
||||
def get_industry_crowding_index(self, industry_name: str, start_date: str = None, end_date: str = None, use_cache: bool = True) -> pd.DataFrame:
|
||||
"""
|
||||
计算行业交易拥挤度指标，并使用Redis缓存结果
|
||||
|
||||
|
|
@ -285,6 +285,7 @@ class IndustryAnalyzer:
|
|||
industry_name: 行业名称
|
||||
start_date: 不再使用此参数，保留是为了兼容性
|
||||
end_date: 结束日期（默认为当前日期）
|
||||
use_cache: 是否使用缓存，默认为True
|
||||
|
||||
Returns:
|
||||
包含行业拥挤度指标的DataFrame
|
||||
|
|
@ -297,25 +298,26 @@ class IndustryAnalyzer:
|
|||
end_date = datetime.datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
# 检查缓存
|
||||
cache_key = f"industry_crowding:{industry_name}"
|
||||
cached_data = redis_client.get(cache_key)
|
||||
|
||||
if cached_data:
|
||||
try:
|
||||
# 尝试解析缓存的JSON数据
|
||||
cached_df_dict = json.loads(cached_data)
|
||||
logger.info(f"从缓存获取行业 {industry_name} 的拥挤度数据")
|
||||
|
||||
# 将缓存的字典转换回DataFrame
|
||||
df = pd.DataFrame(cached_df_dict)
|
||||
|
||||
# 确保trade_date列是日期类型
|
||||
df['trade_date'] = pd.to_datetime(df['trade_date'])
|
||||
|
||||
return df
|
||||
except Exception as cache_error:
|
||||
logger.warning(f"解析缓存的拥挤度数据失败，将重新查询: {cache_error}")
|
||||
if use_cache:
|
||||
cache_key = f"industry_crowding:{industry_name}"
|
||||
cached_data = redis_client.get(cache_key)
|
||||
|
||||
if cached_data:
|
||||
try:
|
||||
# 尝试解析缓存的JSON数据
|
||||
cached_df_dict = json.loads(cached_data)
|
||||
logger.info(f"从缓存获取行业 {industry_name} 的拥挤度数据")
|
||||
|
||||
# 将缓存的字典转换回DataFrame
|
||||
df = pd.DataFrame(cached_df_dict)
|
||||
|
||||
# 确保trade_date列是日期类型
|
||||
df['trade_date'] = pd.to_datetime(df['trade_date'])
|
||||
|
||||
return df
|
||||
except Exception as cache_error:
|
||||
logger.warning(f"解析缓存的拥挤度数据失败，将重新查询: {cache_error}")
|
||||
|
||||
# 获取行业所有股票
|
||||
stock_codes = self.get_industry_stocks(industry_name)
|
||||
if not stock_codes:
|
||||
|
|
@ -397,15 +399,16 @@ class IndustryAnalyzer:
|
|||
df_dict = df.to_dict(orient='records')
|
||||
|
||||
# 缓存结果，有效期1天（86400秒）
|
||||
try:
|
||||
redis_client.set(
|
||||
cache_key,
|
||||
json.dumps(df_dict, default=str), # 使用default=str处理日期等特殊类型
|
||||
ex=86400 # 1天的秒数
|
||||
)
|
||||
logger.info(f"已缓存行业 {industry_name} 的拥挤度数据，有效期为1天")
|
||||
except Exception as cache_error:
|
||||
logger.warning(f"缓存行业拥挤度数据失败: {cache_error}")
|
||||
if use_cache:
|
||||
try:
|
||||
redis_client.set(
|
||||
cache_key,
|
||||
json.dumps(df_dict, default=str), # 使用default=str处理日期等特殊类型
|
||||
ex=86400 # 1天的秒数
|
||||
)
|
||||
logger.info(f"已缓存行业 {industry_name} 的拥挤度数据，有效期为1天")
|
||||
except Exception as cache_error:
|
||||
logger.warning(f"缓存行业拥挤度数据失败: {cache_error}")
|
||||
|
||||
logger.info(f"成功计算行业 {industry_name} 的拥挤度指标，共 {len(df)} 条记录")
|
||||
return df
|
||||
|
|
@ -509,4 +512,54 @@ class IndustryAnalyzer:
|
|||
|
||||
except Exception as e:
|
||||
logger.error(f"获取行业综合分析失败: {e}")
|
||||
return {"success": False, "message": f"获取行业综合分析失败: {e}"}
|
||||
return {"success": False, "message": f"获取行业综合分析失败: {e}"}
|
||||
|
||||
def get_stock_concepts(self, stock_code: str) -> List[str]:
|
||||
"""
|
||||
获取指定股票所属的概念板块列表
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
概念板块名称列表
|
||||
"""
|
||||
try:
|
||||
# 转换股票代码格式
|
||||
formatted_code = self._convert_stock_code_format(stock_code)
|
||||
|
||||
query = text("""
|
||||
SELECT DISTINCT bk_name
|
||||
FROM gp_gnbk
|
||||
WHERE gp_code = :stock_code
|
||||
""")
|
||||
|
||||
with self.engine.connect() as conn:
|
||||
result = conn.execute(query, {"stock_code": formatted_code}).fetchall()
|
||||
|
||||
if result:
|
||||
return [row[0] for row in result]
|
||||
else:
|
||||
logger.warning(f"未找到股票 {stock_code} 的概念板块数据")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取股票概念板块失败: {e}")
|
||||
return []
|
||||
|
||||
def _convert_stock_code_format(self, stock_code: str) -> str:
|
||||
"""
|
||||
转换股票代码格式
|
||||
|
||||
Args:
|
||||
stock_code: 原始股票代码，格式如 "600519.SH"
|
||||
|
||||
Returns:
|
||||
转换后的股票代码，格式如 "SH600519"
|
||||
"""
|
||||
try:
|
||||
code, market = stock_code.split('.')
|
||||
return f"{market}{code}"
|
||||
except Exception as e:
|
||||
logger.error(f"转换股票代码格式失败: {str(e)}")
|
||||
return stock_code
|
||||
|
|
@ -0,0 +1,463 @@
|
|||
"""
|
||||
东方财富实时股价数据采集模块
|
||||
提供从东方财富网站采集实时股价数据并存储到数据库的功能
|
||||
功能包括：
|
||||
1. 采集实时股价数据
|
||||
2. 存储数据到数据库
|
||||
3. 定时自动更新数据
|
||||
"""
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
import datetime
|
||||
import logging
|
||||
import time
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from sqlalchemy import create_engine, text
|
||||
from typing import Dict
|
||||
|
||||
# 添加项目根目录到Python路径
|
||||
current_file = Path(__file__)
|
||||
project_root = current_file.parent.parent.parent
|
||||
sys.path.append(str(project_root))
|
||||
|
||||
from src.valuation_analysis.config import DB_URL, LOG_FILE
|
||||
|
||||
# 获取项目根目录
|
||||
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
# 确保日志目录存在
|
||||
os.makedirs(os.path.dirname(LOG_FILE), exist_ok=True)
|
||||
|
||||
# 配置日志
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
handlers=[
|
||||
logging.FileHandler(LOG_FILE),
|
||||
logging.StreamHandler()
|
||||
]
|
||||
)
|
||||
logger = logging.getLogger("stock_price_collector")
|
||||
|
||||
|
||||
def get_create_table_sql() -> str:
|
||||
"""
|
||||
获取创建实时股价数据表的SQL语句
|
||||
|
||||
Returns:
|
||||
创建表的SQL语句
|
||||
"""
|
||||
return """
|
||||
CREATE TABLE IF NOT EXISTS stock_price_data (
|
||||
stock_code VARCHAR(10) PRIMARY KEY COMMENT '股票代码',
|
||||
stock_name VARCHAR(50) COMMENT '股票名称',
|
||||
latest_price DECIMAL(10,2) COMMENT '最新价',
|
||||
change_percent DECIMAL(10,2) COMMENT '涨跌幅',
|
||||
change_amount DECIMAL(10,2) COMMENT '涨跌额',
|
||||
volume BIGINT COMMENT '成交量(手)',
|
||||
amount DECIMAL(20,2) COMMENT '成交额',
|
||||
amplitude DECIMAL(10,2) COMMENT '振幅',
|
||||
turnover_rate DECIMAL(10,2) COMMENT '换手率',
|
||||
pe_ratio DECIMAL(10,2) COMMENT '市盈率',
|
||||
high_price DECIMAL(10,2) COMMENT '最高价',
|
||||
low_price DECIMAL(10,2) COMMENT '最低价',
|
||||
open_price DECIMAL(10,2) COMMENT '开盘价',
|
||||
pre_close DECIMAL(10,2) COMMENT '昨收价',
|
||||
total_market_value DECIMAL(20,2) COMMENT '总市值',
|
||||
float_market_value DECIMAL(20,2) COMMENT '流通市值',
|
||||
pb_ratio DECIMAL(10,2) COMMENT '市净率',
|
||||
list_date DATE COMMENT '上市日期',
|
||||
update_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间',
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间'
|
||||
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='实时股价数据表';
|
||||
"""
|
||||
|
||||
|
||||
class StockPriceCollector:
|
||||
"""东方财富实时股价数据采集器类"""
|
||||
|
||||
def __init__(self, db_url: str = DB_URL):
|
||||
"""
|
||||
初始化东方财富实时股价数据采集器
|
||||
|
||||
Args:
|
||||
db_url: 数据库连接URL
|
||||
"""
|
||||
self.engine = create_engine(
|
||||
db_url,
|
||||
pool_size=5,
|
||||
max_overflow=10,
|
||||
pool_recycle=3600
|
||||
)
|
||||
self.base_url = "https://push2.eastmoney.com/api/qt/clist/get"
|
||||
self.headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
|
||||
"Accept": "application/json, text/plain, */*",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Origin": "https://quote.eastmoney.com",
|
||||
"Referer": "https://quote.eastmoney.com/",
|
||||
}
|
||||
logger.info("东方财富实时股价数据采集器初始化完成")
|
||||
|
||||
def _ensure_table_exists(self) -> bool:
|
||||
"""
|
||||
确保数据表存在，如果不存在则创建
|
||||
|
||||
Returns:
|
||||
是否成功确保表存在
|
||||
"""
|
||||
try:
|
||||
create_table_query = text(get_create_table_sql())
|
||||
|
||||
with self.engine.connect() as conn:
|
||||
conn.execute(create_table_query)
|
||||
conn.commit()
|
||||
|
||||
logger.info("实时股价数据表创建成功")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"确保数据表存在失败: {e}")
|
||||
return False
|
||||
|
||||
def _convert_stock_code(self, code: str) -> str:
|
||||
"""
|
||||
转换股票代码格式
|
||||
|
||||
Args:
|
||||
code: 原始股票代码
|
||||
|
||||
Returns:
|
||||
转换后的股票代码
|
||||
"""
|
||||
if code.startswith(('0', '3')):
|
||||
return f"{code}.SZ"
|
||||
else:
|
||||
return f"{code}.SH"
|
||||
|
||||
def _parse_list_date(self, date_str: str) -> datetime.date:
|
||||
"""
|
||||
解析上市日期
|
||||
|
||||
Args:
|
||||
date_str: 日期字符串
|
||||
|
||||
Returns:
|
||||
日期对象
|
||||
"""
|
||||
if not date_str or date_str == '-':
|
||||
return None
|
||||
try:
|
||||
# 如果输入是整数，先转换为字符串
|
||||
if isinstance(date_str, int):
|
||||
date_str = str(date_str)
|
||||
return datetime.datetime.strptime(date_str, "%Y%m%d").date()
|
||||
except ValueError:
|
||||
logger.warning(f"无法解析日期: {date_str}")
|
||||
return None
|
||||
|
||||
def fetch_data(self, page: int = 1) -> pd.DataFrame:
|
||||
"""
|
||||
获取指定页码的实时股价数据
|
||||
|
||||
Args:
|
||||
page: 页码
|
||||
|
||||
Returns:
|
||||
包含实时股价数据的DataFrame
|
||||
"""
|
||||
try:
|
||||
params = {
|
||||
"np": 1,
|
||||
"fltt": 2,
|
||||
"invt": 2,
|
||||
"fs": "m:0+t:6,m:0+t:80,m:1+t:2,m:1+t:23,m:0+t:81+s:2048",
|
||||
"fid": "f12",
|
||||
"pn": page,
|
||||
"pz": 100,
|
||||
"po": 0,
|
||||
"dect": 1
|
||||
}
|
||||
|
||||
logger.info(f"开始获取第 {page} 页数据")
|
||||
|
||||
response = requests.get(self.base_url, params=params, headers=self.headers)
|
||||
if response.status_code != 200:
|
||||
logger.error(f"获取第 {page} 页数据失败: HTTP {response.status_code}")
|
||||
return pd.DataFrame()
|
||||
|
||||
data = response.json()
|
||||
if not data.get("rc") == 0:
|
||||
logger.error(f"获取数据失败: {data.get('message', '未知错误')}")
|
||||
return pd.DataFrame()
|
||||
|
||||
# 提取数据列表
|
||||
items = data.get("data", {}).get("diff", [])
|
||||
if not items:
|
||||
logger.warning(f"第 {page} 页未找到有效数据")
|
||||
return pd.DataFrame()
|
||||
|
||||
# 转换为DataFrame
|
||||
df = pd.DataFrame(items)
|
||||
|
||||
# 重命名列
|
||||
column_mapping = {
|
||||
"f12": "stock_code",
|
||||
"f14": "stock_name",
|
||||
"f2": "latest_price",
|
||||
"f3": "change_percent",
|
||||
"f4": "change_amount",
|
||||
"f5": "volume",
|
||||
"f6": "amount",
|
||||
"f7": "amplitude",
|
||||
"f8": "turnover_rate",
|
||||
"f9": "pe_ratio",
|
||||
"f15": "high_price",
|
||||
"f16": "low_price",
|
||||
"f17": "open_price",
|
||||
"f18": "pre_close",
|
||||
"f20": "total_market_value",
|
||||
"f21": "float_market_value",
|
||||
"f23": "pb_ratio",
|
||||
"f26": "list_date"
|
||||
}
|
||||
|
||||
df = df.rename(columns=column_mapping)
|
||||
|
||||
# 转换股票代码格式
|
||||
df['stock_code'] = df['stock_code'].apply(self._convert_stock_code)
|
||||
|
||||
# 转换上市日期
|
||||
df['list_date'] = df['list_date'].apply(self._parse_list_date)
|
||||
|
||||
logger.info(f"第 {page} 页数据获取成功，包含 {len(df)} 条记录")
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取第 {page} 页数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def fetch_all_data(self) -> pd.DataFrame:
|
||||
"""
|
||||
获取所有页的实时股价数据
|
||||
|
||||
Returns:
|
||||
包含所有实时股价数据的DataFrame
|
||||
"""
|
||||
all_data = []
|
||||
page = 1
|
||||
|
||||
while True:
|
||||
page_data = self.fetch_data(page)
|
||||
if page_data.empty:
|
||||
logger.info(f"第 {page} 页数据为空，停止采集")
|
||||
break
|
||||
|
||||
all_data.append(page_data)
|
||||
|
||||
# 如果返回的数据少于100条，说明是最后一页
|
||||
if len(page_data) < 100:
|
||||
break
|
||||
|
||||
page += 1
|
||||
# 添加延迟，避免请求过于频繁
|
||||
time.sleep(1)
|
||||
|
||||
if all_data:
|
||||
combined_df = pd.concat(all_data, ignore_index=True)
|
||||
logger.info(f"数据采集完成，共采集 {len(combined_df)} 条记录")
|
||||
return combined_df
|
||||
else:
|
||||
logger.warning("未获取到任何有效数据")
|
||||
return pd.DataFrame()
|
||||
|
||||
def save_to_database(self, data: pd.DataFrame) -> bool:
|
||||
"""
|
||||
将数据保存到数据库
|
||||
|
||||
Args:
|
||||
data: 要保存的数据DataFrame
|
||||
|
||||
Returns:
|
||||
是否成功保存数据
|
||||
"""
|
||||
if data.empty:
|
||||
logger.warning("没有数据需要保存")
|
||||
return False
|
||||
|
||||
try:
|
||||
# 确保数据表存在
|
||||
if not self._ensure_table_exists():
|
||||
return False
|
||||
data = data.replace('-', None)
|
||||
# 将nan值转换为None（在SQL中会变成NULL）
|
||||
data = data.replace({pd.NA: None, pd.NaT: None})
|
||||
data = data.where(pd.notnull(data), None)
|
||||
|
||||
# 添加数据或更新已有数据
|
||||
inserted_count = 0
|
||||
updated_count = 0
|
||||
|
||||
with self.engine.connect() as conn:
|
||||
for _, row in data.iterrows():
|
||||
# 将Series转换为dict，并处理nan值
|
||||
row_dict = {k: (None if pd.isna(v) else v) for k, v in row.items()}
|
||||
|
||||
# 检查该股票的数据是否已存在
|
||||
check_query = text("""
|
||||
SELECT COUNT(*) FROM stock_price_data WHERE stock_code = :stock_code
|
||||
""")
|
||||
result = conn.execute(check_query, {"stock_code": row_dict['stock_code']}).scalar()
|
||||
|
||||
if result > 0: # 数据已存在，执行更新
|
||||
update_query = text("""
|
||||
UPDATE stock_price_data SET
|
||||
stock_name = :stock_name,
|
||||
latest_price = :latest_price,
|
||||
change_percent = :change_percent,
|
||||
change_amount = :change_amount,
|
||||
volume = :volume,
|
||||
amount = :amount,
|
||||
amplitude = :amplitude,
|
||||
turnover_rate = :turnover_rate,
|
||||
pe_ratio = :pe_ratio,
|
||||
high_price = :high_price,
|
||||
low_price = :low_price,
|
||||
open_price = :open_price,
|
||||
pre_close = :pre_close,
|
||||
total_market_value = :total_market_value,
|
||||
float_market_value = :float_market_value,
|
||||
pb_ratio = :pb_ratio,
|
||||
list_date = :list_date
|
||||
WHERE stock_code = :stock_code
|
||||
""")
|
||||
conn.execute(update_query, row_dict)
|
||||
updated_count += 1
|
||||
else: # 数据不存在，执行插入
|
||||
insert_query = text("""
|
||||
INSERT INTO stock_price_data (
|
||||
stock_code, stock_name, latest_price, change_percent,
|
||||
change_amount, volume, amount, amplitude, turnover_rate,
|
||||
pe_ratio, high_price, low_price, open_price, pre_close,
|
||||
total_market_value, float_market_value, pb_ratio, list_date
|
||||
) VALUES (
|
||||
:stock_code, :stock_name, :latest_price, :change_percent,
|
||||
:change_amount, :volume, :amount, :amplitude, :turnover_rate,
|
||||
:pe_ratio, :high_price, :low_price, :open_price, :pre_close,
|
||||
:total_market_value, :float_market_value, :pb_ratio, :list_date
|
||||
)
|
||||
""")
|
||||
conn.execute(insert_query, row_dict)
|
||||
inserted_count += 1
|
||||
|
||||
conn.commit()
|
||||
|
||||
logger.info(f"数据保存成功：新增 {inserted_count} 条记录，更新 {updated_count} 条记录")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"保存数据到数据库失败: {e}")
|
||||
return False
|
||||
|
||||
def update_latest_data(self) -> bool:
|
||||
"""
|
||||
更新最新实时股价数据
|
||||
|
||||
Returns:
|
||||
是否成功更新最新数据
|
||||
"""
|
||||
try:
|
||||
logger.info("开始更新最新实时股价数据")
|
||||
|
||||
# 获取所有数据
|
||||
df = self.fetch_all_data()
|
||||
if df.empty:
|
||||
logger.warning("未获取到最新数据")
|
||||
return False
|
||||
|
||||
# 保存数据到数据库
|
||||
result = self.save_to_database(df)
|
||||
|
||||
if result:
|
||||
logger.info(f"最新数据更新成功，共更新 {len(df)} 条记录")
|
||||
else:
|
||||
logger.warning("最新数据更新失败")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"更新最新数据失败: {e}")
|
||||
return False
|
||||
|
||||
def get_stock_price_data(self, stock_code: str, convert_code: bool = False) -> Dict:
|
||||
"""
|
||||
获取指定股票的最新价格数据
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
convert_code: 是否需要转换股票代码格式，默认为False
|
||||
|
||||
Returns:
|
||||
包含股票价格数据的字典
|
||||
"""
|
||||
try:
|
||||
# 转换股票代码格式
|
||||
formatted_code = self._convert_stock_code(stock_code) if convert_code else stock_code
|
||||
|
||||
query = text("""
|
||||
SELECT
|
||||
stock_code,
|
||||
stock_name,
|
||||
latest_price,
|
||||
change_percent,
|
||||
change_amount,
|
||||
volume,
|
||||
amount,
|
||||
amplitude,
|
||||
turnover_rate,
|
||||
pe_ratio,
|
||||
high_price,
|
||||
low_price,
|
||||
total_market_value,
|
||||
float_market_value,
|
||||
pb_ratio,
|
||||
list_date,
|
||||
update_time
|
||||
FROM
|
||||
stock_price_data
|
||||
WHERE
|
||||
stock_code = :stock_code
|
||||
""")
|
||||
|
||||
with self.engine.connect() as conn:
|
||||
result = conn.execute(query, {"stock_code": formatted_code}).fetchone()
|
||||
|
||||
if result:
|
||||
# 将结果转换为字典
|
||||
data = dict(result._mapping)
|
||||
# 处理日期类型
|
||||
if data['list_date']:
|
||||
data['list_date'] = data['list_date'].strftime('%Y-%m-%d')
|
||||
if data['update_time']:
|
||||
data['update_time'] = data['update_time'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
return data
|
||||
else:
|
||||
logger.warning(f"未找到股票 {stock_code} 的价格数据")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取股票价格数据失败: {e}")
|
||||
return None
|
||||
|
||||
|
||||
# 示例使用方式
|
||||
if __name__ == "__main__":
|
||||
# 创建实时股价数据采集器
|
||||
collector = StockPriceCollector()
|
||||
|
||||
# 更新最新数据
|
||||
logger.info("开始更新最新实时股价数据...")
|
||||
collector.update_latest_data()
|
||||
Loading…
Reference in New Issue