commit;
This commit is contained in:
parent
4e4f4c8e4a
commit
8203b5dd74
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@ -11,7 +11,8 @@ show_help() {
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echo " start [实例ID] 启动指定实例或所有实例"
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echo " stop [实例ID] 停止指定实例或所有实例"
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echo " restart [实例ID] 重启指定实例或所有实例"
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echo " logs [实例ID] 实时查看指定实例的日志 (Ctrl+C 退出)"
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echo " logs [实例ID] 查看最新10条日志并实时跟踪新日志"
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echo " logs-follow [实例ID] 实时查看指定实例的日志 (Ctrl+C 退出)"
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echo " status 显示实例状态概览"
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echo " remove [实例ID] 删除指定实例或所有实例"
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echo " rebuild [数量] 重新构建镜像并部署指定数量的实例"
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@ -21,7 +22,8 @@ show_help() {
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echo " $0 list 列出所有实例"
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echo " $0 start 2 启动实例2"
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echo " $0 stop all 停止所有实例"
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echo " $0 logs 1 查看实例1的日志"
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echo " $0 logs 1 查看实例1最新10条日志并实时跟踪"
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echo " $0 logs-follow 1 实时跟踪实例1的日志"
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echo " $0 rebuild 2 重新构建并部署2个实例"
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echo " $0 update 热更新所有实例的代码"
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exit 1
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@ -68,6 +70,13 @@ restart_instance() {
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# 函数：查看实例日志
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view_logs() {
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echo "显示实例 $1 的最新10条日志，然后实时跟踪新日志 (按 Ctrl+C 退出):"
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echo "----------------------------------------"
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docker logs --tail 10 -f stock-app-$1
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}
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# 函数：实时查看实例日志
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view_logs_follow() {
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echo "正在实时显示实例 $1 的日志 (按 Ctrl+C 退出):"
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echo "----------------------------------------"
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docker logs -f stock-app-$1
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@ -187,6 +196,13 @@ case "$1" in
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fi
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view_logs $2
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;;
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logs-follow)
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if [ "$#" -lt 2 ]; then
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echo "错误: 缺少实例ID参数"
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show_help
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fi
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view_logs_follow $2
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;;
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status)
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show_status
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;;
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@ -245,7 +245,7 @@ def create_buy_strategy_callback(xt_trader, acc, buy_amount, logger):
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# 检查是否有在途订单（Redis）
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if is_stock_pending_order(stock_code):
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logger.info(f"{stock_code} 有在途订单，跳过买入")
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# logger.info(f"{stock_code} 有在途订单，跳过买入")
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continue
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# 集合竞价时段：只观察，不下单
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@ -339,7 +339,7 @@ def create_sell_strategy_callback(xt_trader, acc, logger):
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# 检查是否有在途订单
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if is_stock_pending_order(stock_code):
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logger.info(f"{stock_code} 有在途订单，跳过卖出")
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# logger.info(f"{stock_code} 有在途订单，跳过卖出")
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continue
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# 集合竞价时段：只观察，不下单
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68
src/app.py
68
src/app.py
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@ -3404,6 +3404,74 @@ def analyze_valuation_indicator():
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"message": f"估值指标分析失败: {str(e)}"
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}), 500
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@app.route('/api/overlap/analysis', methods=['GET'])
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def analyze_stock_overlap():
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"""分析股票重叠度和滞涨情况
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GET参数:
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- stock_code: 股票代码 (例如: 300661.SZ)
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- days: 分析天数，默认3个交易日
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返回格式:
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{
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"status": "success",
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"data": {
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"target_stock": "300661.SZ",
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"target_stock_name": "圣邦股份",
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"analysis_date": "2025-09-28 15:30:42",
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"analysis_period_days": 3,
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"industries": [...],
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"concepts": [...],
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"overlap_threshold": 5,
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"top5_stocks": [...],
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"lag_analysis": {...}
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}
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}
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"""
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try:
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# 获取参数
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stock_code = request.args.get('stock_code')
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days = int(request.args.get('days', 3))
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if not stock_code:
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return jsonify({
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"status": "error",
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"message": "缺少必需参数: stock_code"
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}), 400
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# 导入重叠度分析器
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from src.quantitative_analysis.overlap_analyzer import OverlapAnalyzer
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# 创建分析器实例
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analyzer = OverlapAnalyzer()
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try:
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# 执行分析
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result = analyzer.analyze_stock_overlap(stock_code, days)
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if 'error' in result:
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return jsonify({
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"status": "error",
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"message": result['error']
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}), 500
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return jsonify({
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"status": "success",
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"data": result
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})
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finally:
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# 关闭数据库连接
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analyzer.close_connection()
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except Exception as e:
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logger.error(f"重叠度分析失败: {str(e)}")
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return jsonify({
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"status": "error",
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"message": f"重叠度分析失败: {str(e)}"
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}), 500
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if __name__ == '__main__':
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# 启动Web服务器
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Binary file not shown.
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@ -289,10 +289,10 @@ def main():
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analyzer = AverageDistanceFactor(db_url)
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# 示例1: 分析特定行业
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# result = analyzer.analyze_industry(industry_name="旅游")
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result = analyzer.analyze_industry(industry_name="军工电子")
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# 示例2: 分析特定概念
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result = analyzer.analyze_industry(concept_name="人形机器人")
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# result = analyzer.analyze_industry(concept_name="固态电池")
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# 示例3: 查看可用的行业列表
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# industries = analyzer.get_available_industries()
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@ -0,0 +1,668 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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行业重叠度和概念重叠度分析工具
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用于分析某股票与相似股票的重叠度，识别滞涨投资机会
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"""
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import sys
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import os
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import logging
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from typing import Dict, List, Optional, Tuple
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from pathlib import Path
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from sqlalchemy import create_engine, text
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import pandas as pd
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from datetime import datetime, timedelta
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# 添加项目根路径到Python路径
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project_root = Path(__file__).parent.parent.parent
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sys.path.append(str(project_root))
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# 导入配置
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try:
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from src.valuation_analysis.config import DB_URL
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except ImportError:
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# 如果上面的导入失败，尝试直接导入
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import importlib.util
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config_path = os.path.join(project_root, 'src', 'valuation_analysis', 'config.py')
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spec = importlib.util.spec_from_file_location("config", config_path)
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config_module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(config_module)
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DB_URL = config_module.DB_URL
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# 导入股票代码格式转换工具
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try:
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from tools.stock_code_formatter import StockCodeFormatter
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except ImportError:
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# 如果上面的导入失败，尝试直接导入
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import importlib.util
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formatter_path = os.path.join(project_root, 'tools', 'stock_code_formatter.py')
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spec = importlib.util.spec_from_file_location("stock_code_formatter", formatter_path)
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formatter_module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(formatter_module)
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StockCodeFormatter = formatter_module.StockCodeFormatter
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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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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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class OverlapAnalyzer:
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"""行业重叠度和概念重叠度分析器"""
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def __init__(self):
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"""初始化"""
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# MySQL连接
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self.mysql_engine = None
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# 股票代码格式转换器
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self.code_formatter = StockCodeFormatter()
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# 重叠度评分权重
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self.INDUSTRY_WEIGHT = 3 # 行业重叠权重
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self.CONCEPT_WEIGHT = 1 # 概念重叠权重
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self.MIN_OVERLAP_SCORE = 5 # 最小重叠分数阈值
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self.connect_mysql()
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def connect_mysql(self):
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"""连接MySQL数据库"""
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try:
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self.mysql_engine = create_engine(
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DB_URL,
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pool_size=5,
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max_overflow=10,
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pool_recycle=3600
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)
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# 测试连接
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with self.mysql_engine.connect() as conn:
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conn.execute(text("SELECT 1"))
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logger.info("MySQL数据库连接成功")
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except Exception as e:
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logger.error(f"MySQL数据库连接失败: {str(e)}")
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raise
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def normalize_stock_code(self, stock_code: str) -> str:
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"""
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标准化股票代码格式，转换为数据库中使用的格式
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Args:
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stock_code: 输入的股票代码，支持多种格式
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Returns:
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str: 标准化后的股票代码
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"""
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return self.code_formatter.to_prefix_format(stock_code)
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def get_stock_industries(self, stock_code: str) -> List[Tuple[str, str]]:
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"""
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获取股票所属的行业板块
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Args:
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stock_code: 股票代码
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Returns:
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List[Tuple[str, str]]: [(板块代码, 板块名称)] 列表
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"""
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try:
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normalized_code = self.normalize_stock_code(stock_code)
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query = text("""
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SELECT DISTINCT bk_code, bk_name
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FROM gp_hybk
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WHERE gp_code = :stock_code
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AND bk_code IS NOT NULL
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AND bk_name IS NOT NULL
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""")
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with self.mysql_engine.connect() as conn:
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result = conn.execute(query, {"stock_code": normalized_code}).fetchall()
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industries = [(str(row[0]), str(row[1])) for row in result]
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logger.info(f"股票 {stock_code} 找到 {len(industries)} 个行业板块")
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return industries
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except Exception as e:
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logger.error(f"获取股票 {stock_code} 行业板块失败: {str(e)}")
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return []
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def get_stock_concepts(self, stock_code: str) -> List[Tuple[str, str]]:
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"""
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获取股票所属的概念板块
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Args:
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stock_code: 股票代码
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Returns:
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List[Tuple[str, str]]: [(板块代码, 板块名称)] 列表
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"""
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try:
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normalized_code = self.normalize_stock_code(stock_code)
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query = text("""
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SELECT DISTINCT bk_code, bk_name
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FROM gp_gnbk
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WHERE gp_code = :stock_code
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AND bk_code IS NOT NULL
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AND bk_name IS NOT NULL
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""")
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with self.mysql_engine.connect() as conn:
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result = conn.execute(query, {"stock_code": normalized_code}).fetchall()
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concepts = [(str(row[0]), str(row[1])) for row in result]
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logger.info(f"股票 {stock_code} 找到 {len(concepts)} 个概念板块")
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return concepts
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except Exception as e:
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logger.error(f"获取股票 {stock_code} 概念板块失败: {str(e)}")
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return []
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def get_similar_stocks_by_industry(self, industries: List[Tuple[str, str]]) -> Dict[str, List[str]]:
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"""
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根据行业板块获取相似股票
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Args:
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industries: 行业板块列表 [(板块代码, 板块名称)]
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Returns:
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Dict[str, List[str]]: {板块代码: [股票代码列表]}
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"""
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try:
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similar_stocks = {}
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for bk_code, bk_name in industries:
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query = text("""
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SELECT DISTINCT gp_code
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FROM gp_hybk
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WHERE bk_code = :bk_code
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AND gp_code IS NOT NULL
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""")
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with self.mysql_engine.connect() as conn:
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result = conn.execute(query, {"bk_code": bk_code}).fetchall()
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stock_codes = [row[0] for row in result if row[0]]
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similar_stocks[bk_code] = stock_codes
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logger.info(f"行业板块 {bk_name}({bk_code}) 包含 {len(stock_codes)} 只股票")
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return similar_stocks
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except Exception as e:
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logger.error(f"获取行业相似股票失败: {str(e)}")
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return {}
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def get_similar_stocks_by_concept(self, concepts: List[Tuple[str, str]]) -> Dict[str, List[str]]:
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"""
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根据概念板块获取相似股票
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Args:
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concepts: 概念板块列表 [(板块代码, 板块名称)]
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Returns:
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Dict[str, List[str]]: {板块代码: [股票代码列表]}
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"""
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try:
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similar_stocks = {}
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for bk_code, bk_name in concepts:
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query = text("""
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SELECT DISTINCT gp_code
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FROM gp_gnbk
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WHERE bk_code = :bk_code
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AND gp_code IS NOT NULL
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""")
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with self.mysql_engine.connect() as conn:
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result = conn.execute(query, {"bk_code": bk_code}).fetchall()
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stock_codes = [row[0] for row in result if row[0]]
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similar_stocks[bk_code] = stock_codes
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logger.info(f"概念板块 {bk_name}({bk_code}) 包含 {len(stock_codes)} 只股票")
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return similar_stocks
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except Exception as e:
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logger.error(f"获取概念相似股票失败: {str(e)}")
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return {}
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def calculate_overlap_scores(self, target_stock: str, industry_stocks: Dict[str, List[str]], concept_stocks: Dict[str, List[str]]) -> Dict[str, float]:
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"""
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计算重叠度分数
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Args:
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target_stock: 目标股票代码
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industry_stocks: 行业相似股票字典 {板块代码: [股票代码列表]}
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concept_stocks: 概念相似股票字典 {板块代码: [股票代码列表]}
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Returns:
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Dict[str, float]: {股票代码: 重叠分数}
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"""
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try:
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overlap_scores = {}
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target_stock_normalized = self.normalize_stock_code(target_stock)
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# 计算行业重叠分数（每个行业+3分）
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for bk_code, stock_list in industry_stocks.items():
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for stock_code in stock_list:
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if stock_code == target_stock_normalized:
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continue # 跳过目标股票本身
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if stock_code not in overlap_scores:
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overlap_scores[stock_code] = 0
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overlap_scores[stock_code] += self.INDUSTRY_WEIGHT
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# 计算概念重叠分数（每个概念+1分）
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for bk_code, stock_list in concept_stocks.items():
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for stock_code in stock_list:
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if stock_code == target_stock_normalized:
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continue # 跳过目标股票本身
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||||
if stock_code not in overlap_scores:
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overlap_scores[stock_code] = 0
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overlap_scores[stock_code] += self.CONCEPT_WEIGHT
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logger.info(f"计算出 {len(overlap_scores)} 只股票的重叠分数")
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return overlap_scores
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||||
except Exception as e:
|
||||
logger.error(f"计算重叠分数失败: {str(e)}")
|
||||
return {}
|
||||
|
||||
def filter_high_overlap_stocks(self, overlap_scores: Dict[str, float]) -> List[Tuple[str, float]]:
|
||||
"""
|
||||
筛选高重叠度股票
|
||||
|
||||
Args:
|
||||
overlap_scores: 重叠分数字典
|
||||
|
||||
Returns:
|
||||
List[Tuple[str, float]]: [(股票代码, 分数)] 按分数降序排列
|
||||
"""
|
||||
try:
|
||||
# 筛选分数大于阈值的股票
|
||||
high_overlap_stocks = [
|
||||
(stock_code, score) for stock_code, score in overlap_scores.items()
|
||||
if score > self.MIN_OVERLAP_SCORE
|
||||
]
|
||||
|
||||
# 按分数降序排列
|
||||
high_overlap_stocks.sort(key=lambda x: x[1], reverse=True)
|
||||
|
||||
logger.info(f"筛选出 {len(high_overlap_stocks)} 只高重叠度股票（分数>{self.MIN_OVERLAP_SCORE}）")
|
||||
return high_overlap_stocks
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"筛选高重叠度股票失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def get_recent_price_changes(self, stock_codes: List[str], days: int = 3) -> Dict[str, Dict]:
|
||||
"""
|
||||
获取股票的近期涨跌幅（默认近3个交易日）
|
||||
|
||||
Args:
|
||||
stock_codes: 股票代码列表
|
||||
days: 统计天数，默认3个交易日
|
||||
|
||||
Returns:
|
||||
Dict[str, Dict]: {股票代码: {price_change, change_pct, latest_price}}
|
||||
"""
|
||||
try:
|
||||
price_changes = {}
|
||||
|
||||
for stock_code in stock_codes:
|
||||
# 获取最近N个交易日的价格数据
|
||||
query = text("""
|
||||
SELECT close, percent, timestamp
|
||||
FROM gp_day_data
|
||||
WHERE symbol = :stock_code
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT :days
|
||||
""")
|
||||
|
||||
with self.mysql_engine.connect() as conn:
|
||||
result = conn.execute(query, {"stock_code": stock_code, "days": days}).fetchall()
|
||||
|
||||
if len(result) >= 2: # 至少需要2个交易日的数据
|
||||
# 最新价格（第一个）
|
||||
latest_price = float(result[0][0]) if result[0][0] else None
|
||||
latest_change_pct = float(result[0][1]) if result[0][1] else None
|
||||
|
||||
# N天前价格（最后一个）
|
||||
historical_price = float(result[-1][0]) if result[-1][0] else None
|
||||
|
||||
if latest_price and historical_price:
|
||||
price_change = latest_price - historical_price
|
||||
change_pct = (price_change / historical_price) * 100
|
||||
|
||||
price_changes[stock_code] = {
|
||||
'latest_price': latest_price,
|
||||
'price_change': price_change,
|
||||
'change_pct': change_pct,
|
||||
'latest_change_pct': latest_change_pct,
|
||||
'trading_days': len(result)
|
||||
}
|
||||
|
||||
logger.info(f"获取到 {len(price_changes)} 只股票的近{days}个交易日涨跌幅数据")
|
||||
return price_changes
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取近期涨跌幅失败: {str(e)}")
|
||||
return {}
|
||||
|
||||
def analyze_lag_performance(self, target_change: Dict, top5_stocks: List[Tuple[str, float]], similar_changes: Dict[str, Dict]) -> Dict:
|
||||
"""
|
||||
分析目标股票的滞涨情况（基于近3个交易日）
|
||||
|
||||
Args:
|
||||
target_change: 目标股票涨跌幅数据
|
||||
top5_stocks: 重叠度最高的5只股票 [(股票代码, 重叠分数)]
|
||||
similar_changes: 相似股票涨跌幅数据
|
||||
|
||||
Returns:
|
||||
Dict: 滞涨分析结果
|
||||
"""
|
||||
try:
|
||||
if not target_change or 'change_pct' not in target_change:
|
||||
logger.warning("目标股票涨跌幅数据不完整，无法进行滞涨分析")
|
||||
return {
|
||||
'target_lag_vs_top5': None,
|
||||
'target_lag_vs_top1': None,
|
||||
'top5_avg_change': None,
|
||||
'top1_change': None
|
||||
}
|
||||
|
||||
target_change_pct = target_change['change_pct']
|
||||
|
||||
# 获取前5只股票的有效涨跌幅数据
|
||||
top5_changes = []
|
||||
for stock_code, _ in top5_stocks:
|
||||
if stock_code in similar_changes and 'change_pct' in similar_changes[stock_code]:
|
||||
change_pct = similar_changes[stock_code]['change_pct']
|
||||
if change_pct is not None:
|
||||
top5_changes.append(change_pct)
|
||||
|
||||
if len(top5_changes) < 1:
|
||||
logger.warning("没有有效的前5只股票涨跌幅数据")
|
||||
return {
|
||||
'target_lag_vs_top5': None,
|
||||
'target_lag_vs_top1': None,
|
||||
'top5_avg_change': None,
|
||||
'top1_change': None
|
||||
}
|
||||
|
||||
# 计算前5只股票的平均涨幅
|
||||
top5_avg_change = sum(top5_changes) / len(top5_changes)
|
||||
|
||||
# 获取重叠度最高的1只股票涨幅
|
||||
top1_change = top5_changes[0] if top5_changes else None
|
||||
|
||||
# 计算滞涨分数
|
||||
lag_vs_top5 = target_change_pct - top5_avg_change if top5_avg_change is not None else None
|
||||
lag_vs_top1 = target_change_pct - top1_change if top1_change is not None else None
|
||||
|
||||
# 滞涨等级判断
|
||||
def get_lag_level(lag_score):
|
||||
if lag_score is None:
|
||||
return "无数据"
|
||||
elif lag_score < -8:
|
||||
return "严重滞涨"
|
||||
elif lag_score < -4:
|
||||
return "明显滞涨"
|
||||
elif lag_score < -1.5:
|
||||
return "轻微滞涨"
|
||||
elif lag_score > 4:
|
||||
return "跑赢同行"
|
||||
elif lag_score > 1.5:
|
||||
return "略胜同行"
|
||||
else:
|
||||
return "正常水平"
|
||||
result = {
|
||||
'target_lag_vs_top5': {
|
||||
'lag_score': lag_vs_top5,
|
||||
'lag_level': get_lag_level(lag_vs_top5),
|
||||
'target_change_pct': target_change_pct,
|
||||
'top5_avg_change': top5_avg_change
|
||||
},
|
||||
'target_lag_vs_top1': {
|
||||
'lag_score': lag_vs_top1,
|
||||
'lag_level': get_lag_level(lag_vs_top1),
|
||||
'target_change_pct': target_change_pct,
|
||||
'top1_change': top1_change
|
||||
},
|
||||
'top5_avg_change': top5_avg_change,
|
||||
'top1_change': top1_change
|
||||
}
|
||||
|
||||
logger.info(f"滞涨分析完成 - 目标股票: {target_change_pct:.2f}%, 前5平均: {top5_avg_change:.2f}%, 最高1只: {top1_change:.2f}%")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"滞涨分析失败: {str(e)}")
|
||||
return {
|
||||
'target_lag_vs_top5': None,
|
||||
'target_lag_vs_top1': None,
|
||||
'top5_avg_change': None,
|
||||
'top1_change': None
|
||||
}
|
||||
|
||||
def get_stock_name(self, stock_code: str) -> Optional[str]:
|
||||
"""
|
||||
获取股票名称
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
str: 股票名称
|
||||
"""
|
||||
try:
|
||||
normalized_code = self.normalize_stock_code(stock_code)
|
||||
|
||||
query = text("""
|
||||
SELECT gp_name
|
||||
FROM gp_code_all
|
||||
WHERE gp_code = :stock_code
|
||||
LIMIT 1
|
||||
""")
|
||||
|
||||
with self.mysql_engine.connect() as conn:
|
||||
result = conn.execute(query, {"stock_code": normalized_code}).fetchone()
|
||||
|
||||
if result and result[0]:
|
||||
return result[0]
|
||||
else:
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取股票名称失败 {stock_code}: {str(e)}")
|
||||
return None
|
||||
|
||||
def analyze_stock_overlap(self, stock_code: str, days: int = 20) -> Dict:
|
||||
"""
|
||||
分析股票重叠度的主函数
|
||||
|
||||
Args:
|
||||
stock_code: 目标股票代码
|
||||
days: 统计涨跌幅的天数
|
||||
|
||||
Returns:
|
||||
Dict: 分析结果
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始分析股票 {stock_code} 的重叠度")
|
||||
|
||||
# 1. 获取目标股票的行业和概念板块
|
||||
industries = self.get_stock_industries(stock_code)
|
||||
concepts = self.get_stock_concepts(stock_code)
|
||||
|
||||
if not industries and not concepts:
|
||||
logger.warning(f"股票 {stock_code} 没有找到行业或概念板块数据")
|
||||
return {
|
||||
'target_stock': stock_code,
|
||||
'error': '未找到行业或概念板块数据'
|
||||
}
|
||||
|
||||
# 2. 获取相似股票
|
||||
industry_stocks = self.get_similar_stocks_by_industry(industries)
|
||||
concept_stocks = self.get_similar_stocks_by_concept(concepts)
|
||||
|
||||
# 3. 计算重叠分数
|
||||
overlap_scores = self.calculate_overlap_scores(stock_code, industry_stocks, concept_stocks)
|
||||
|
||||
# 4. 筛选高重叠度股票
|
||||
high_overlap_stocks = self.filter_high_overlap_stocks(overlap_scores)
|
||||
|
||||
if not high_overlap_stocks:
|
||||
logger.info(f"股票 {stock_code} 没有找到高重叠度股票（分数>{self.MIN_OVERLAP_SCORE}）")
|
||||
return {
|
||||
'target_stock': stock_code,
|
||||
'similar_stocks_count': 0,
|
||||
'message': f'没有找到重叠度超过{self.MIN_OVERLAP_SCORE}分的股票'
|
||||
}
|
||||
|
||||
# 5. 获取前5只重叠度最高的股票
|
||||
top5_stocks = high_overlap_stocks[:5] # 取前5只
|
||||
top5_stock_codes = [stock[0] for stock in top5_stocks]
|
||||
all_stock_codes = [self.normalize_stock_code(stock_code)] + top5_stock_codes
|
||||
|
||||
price_changes = self.get_recent_price_changes(all_stock_codes, days)
|
||||
|
||||
# 6. 滞涨分析
|
||||
target_change = price_changes.get(self.normalize_stock_code(stock_code))
|
||||
similar_changes = {code: data for code, data in price_changes.items()
|
||||
if code != self.normalize_stock_code(stock_code)}
|
||||
|
||||
lag_analysis = self.analyze_lag_performance(target_change, top5_stocks, similar_changes)
|
||||
|
||||
# 7. 组装前5只股票结果
|
||||
top5_results = []
|
||||
for similar_stock, overlap_score in top5_stocks:
|
||||
stock_name = self.get_stock_name(similar_stock)
|
||||
price_data = price_changes.get(similar_stock, {})
|
||||
|
||||
top5_results.append({
|
||||
'stock_code': similar_stock,
|
||||
'stock_name': stock_name,
|
||||
'overlap_score': overlap_score,
|
||||
'latest_price': price_data.get('latest_price'),
|
||||
'price_change': price_data.get('price_change'),
|
||||
'change_pct': price_data.get('change_pct')
|
||||
})
|
||||
|
||||
return {
|
||||
'target_stock': stock_code,
|
||||
'target_stock_name': self.get_stock_name(stock_code),
|
||||
'target_price_data': target_change,
|
||||
'analysis_date': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||||
'analysis_period_days': days,
|
||||
'industries': [{'code': code, 'name': name} for code, name in industries],
|
||||
'concepts': [{'code': code, 'name': name} for code, name in concepts],
|
||||
'overlap_threshold': self.MIN_OVERLAP_SCORE,
|
||||
'top5_stocks': top5_results,
|
||||
'lag_analysis': lag_analysis
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析股票重叠度失败 {stock_code}: {str(e)}")
|
||||
return {
|
||||
'target_stock': stock_code,
|
||||
'error': str(e)
|
||||
}
|
||||
|
||||
def close_connection(self):
|
||||
"""关闭数据库连接"""
|
||||
try:
|
||||
if self.mysql_engine:
|
||||
self.mysql_engine.dispose()
|
||||
logger.info("MySQL连接已关闭")
|
||||
except Exception as e:
|
||||
logger.error(f"关闭MySQL连接失败: {str(e)}")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数 - 示例用法"""
|
||||
analyzer = None
|
||||
try:
|
||||
# 创建分析器实例
|
||||
analyzer = OverlapAnalyzer()
|
||||
|
||||
# 示例：分析某只股票的重叠度
|
||||
stock_code = "300661.SZ" # 圣邦股份
|
||||
|
||||
print(f"=== 股票重叠度分析：{stock_code} ===")
|
||||
|
||||
# 执行分析
|
||||
result = analyzer.analyze_stock_overlap(stock_code, days=3)
|
||||
|
||||
if 'error' in result:
|
||||
print(f"分析失败: {result['error']}")
|
||||
return
|
||||
|
||||
# 输出结果
|
||||
print(f"\n目标股票: {result['target_stock_name']} ({result['target_stock']})")
|
||||
print(f"分析日期: {result['analysis_date']}")
|
||||
print(f"统计周期: {result['analysis_period_days']}天")
|
||||
|
||||
if 'target_price_data' in result and result['target_price_data']:
|
||||
target_data = result['target_price_data']
|
||||
print(f"目标股票涨跌幅: {target_data.get('change_pct', 0):.2f}%")
|
||||
|
||||
print(f"\n行业板块: {len(result['industries'])}个")
|
||||
for industry in result['industries'][:3]: # 显示前3个
|
||||
print(f" - {industry['name']} ({industry['code']})")
|
||||
|
||||
print(f"\n概念板块: {len(result['concepts'])}个")
|
||||
for concept in result['concepts'][:3]: # 显示前3个
|
||||
print(f" - {concept['name']} ({concept['code']})")
|
||||
|
||||
print(f"\n重叠度最高的5只股票:")
|
||||
print("-" * 80)
|
||||
print(f"{'股票代码':<12} {'股票名称':<15} {'重叠分数':<8} {'最新价格':<10} {'涨跌幅':<10}")
|
||||
print("-" * 80)
|
||||
|
||||
for stock in result['top5_stocks']:
|
||||
print(f"{stock['stock_code']:<12} "
|
||||
f"{stock['stock_name'] or '未知':<15} "
|
||||
f"{stock['overlap_score']:<8.1f} "
|
||||
f"{stock['latest_price'] or 0:<10.2f} "
|
||||
f"{stock['change_pct'] or 0:<10.2f}%")
|
||||
|
||||
# 显示滞涨分析结果
|
||||
lag_analysis = result.get('lag_analysis', {})
|
||||
if lag_analysis:
|
||||
print(f"\n滞涨分析结果:")
|
||||
print("-" * 50)
|
||||
|
||||
# vs 前5平均
|
||||
vs_top5 = lag_analysis.get('target_lag_vs_top5', {})
|
||||
if vs_top5:
|
||||
print(f"vs 前5平均: {vs_top5.get('lag_score', 0):.2f}% ({vs_top5.get('lag_level', '未知')})")
|
||||
print(f" 目标股票: {vs_top5.get('target_change_pct', 0):.2f}%")
|
||||
print(f" 前5平均: {vs_top5.get('top5_avg_change', 0):.2f}%")
|
||||
|
||||
# vs 最高1只
|
||||
vs_top1 = lag_analysis.get('target_lag_vs_top1', {})
|
||||
if vs_top1:
|
||||
print(f"vs 最高1只: {vs_top1.get('lag_score', 0):.2f}% ({vs_top1.get('lag_level', '未知')})")
|
||||
print(f" 目标股票: {vs_top1.get('target_change_pct', 0):.2f}%")
|
||||
print(f" 最高1只: {vs_top1.get('top1_change', 0):.2f}%")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"程序执行失败: {str(e)}")
|
||||
finally:
|
||||
if analyzer:
|
||||
analyzer.close_connection()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -713,6 +713,28 @@ def main():
|
|||
print(f" 最高总分: {df['total_score'].max():.2f}")
|
||||
print(f" 最低总分: {df['total_score'].min():.2f}")
|
||||
|
||||
# 生成成长+成熟合并的简表（仅三列：股票代码、总分、排名）
|
||||
combined_parts = []
|
||||
for stage, df in results.items():
|
||||
if isinstance(df, pd.DataFrame) and not df.empty:
|
||||
if 'stock_code' in df.columns and 'total_score' in df.columns:
|
||||
combined_parts.append(df[['stock_code', 'total_score']].copy())
|
||||
|
||||
if combined_parts:
|
||||
combined_df = pd.concat(combined_parts, ignore_index=True)
|
||||
# 去除总分为空的数据
|
||||
combined_df = combined_df.dropna(subset=['total_score'])
|
||||
# 按总分降序并重新排名
|
||||
combined_df = combined_df.sort_values('total_score', ascending=False).reset_index(drop=True)
|
||||
combined_df['rank'] = range(1, len(combined_df) + 1)
|
||||
# 保存文件
|
||||
combined_file = f"tech_fundamental_factor_all_{datetime.now().strftime('%Y%m%d_%H%M')}.csv"
|
||||
combined_df.to_csv(combined_file, index=False, encoding='utf-8-sig')
|
||||
print(f"\n=== 合并结果（成长+成熟） ===")
|
||||
print(f"总股票数量: {len(combined_df)}")
|
||||
print(combined_df.head(10).to_string(index=False))
|
||||
print(f"\n合并简表已保存到: {combined_file}")
|
||||
|
||||
print(f"\n=== 策略运行完成 ===")
|
||||
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,757 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
是固态电池、人形机器人、通信设备、算力租赁四个赛道的筛选。
|
||||
结合个股历史基本面，以及个股波动情况，这里波动指标是一个波动越大，分数越高的指标
|
||||
v2并不是科技主题选股！！！！！！--这里就是入口--请执行这个文件！
|
||||
整合企业生命周期、财务指标和平均距离因子分析
|
||||
"""
|
||||
|
||||
import sys
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import logging
|
||||
from typing import Dict, List, Tuple
|
||||
from pathlib import Path
|
||||
from sqlalchemy import create_engine, text
|
||||
from datetime import datetime
|
||||
import math
|
||||
|
||||
# 添加项目根路径到Python路径
|
||||
project_root = Path(__file__).parent.parent.parent
|
||||
sys.path.append(str(project_root))
|
||||
|
||||
# 导入依赖的工具类
|
||||
from src.quantitative_analysis.company_lifecycle_factor import CompanyLifecycleFactor
|
||||
from src.quantitative_analysis.financial_indicator_analyzer import FinancialIndicatorAnalyzer
|
||||
from src.quantitative_analysis.average_distance_factor import AverageDistanceFactor
|
||||
from src.valuation_analysis.config import MONGO_CONFIG2, DB_URL
|
||||
|
||||
# 设置日志
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TechFundamentalFactorStrategy:
|
||||
"""科技主题基本面因子选股策略"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化策略"""
|
||||
self.lifecycle_calculator = CompanyLifecycleFactor()
|
||||
self.financial_analyzer = FinancialIndicatorAnalyzer()
|
||||
self.distance_calculator = AverageDistanceFactor(DB_URL)
|
||||
|
||||
# MySQL连接
|
||||
self.mysql_engine = create_engine(
|
||||
DB_URL,
|
||||
pool_size=5,
|
||||
max_overflow=10,
|
||||
pool_recycle=3600
|
||||
)
|
||||
|
||||
# 科技概念板块列表
|
||||
self.tech_concepts = [
|
||||
"固态电池", "人形机器人", "通信设备", "算力租赁"
|
||||
]
|
||||
logger.info("科技主题基本面因子选股策略初始化完成")
|
||||
|
||||
def get_tech_stocks(self) -> pd.DataFrame:
|
||||
"""
|
||||
获取科技概念板块的股票列表
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 包含股票代码和名称的DataFrame
|
||||
"""
|
||||
try:
|
||||
# 构建查询条件
|
||||
concepts_str = "', '".join(self.tech_concepts)
|
||||
query = text(f"""
|
||||
SELECT DISTINCT gp_code as stock_code, gp_name as stock_name, bk_name as concept_name
|
||||
FROM gp_gnbk
|
||||
WHERE bk_name IN ('{concepts_str}')
|
||||
UNION
|
||||
SELECT DISTINCT gp_code as stock_code, gp_name as stock_name, bk_name as concept_name
|
||||
FROM gp_hybk
|
||||
WHERE bk_name = '通信设备'
|
||||
ORDER BY stock_code
|
||||
""")
|
||||
|
||||
with self.mysql_engine.connect() as conn:
|
||||
df = pd.read_sql(query, conn)
|
||||
|
||||
logger.info(f"获取到 {len(df)} 只科技概念股票")
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取科技概念股票失败: {str(e)}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def filter_by_lifecycle(self, stock_codes: List[str], year: int = 2024) -> Dict[str, List[str]]:
|
||||
"""
|
||||
根据企业生命周期筛选股票
|
||||
|
||||
Args:
|
||||
stock_codes: 股票代码列表
|
||||
year: 分析年份
|
||||
|
||||
Returns:
|
||||
Dict: 包含成长期和成熟期股票的字典
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始分析 {len(stock_codes)} 只股票的企业生命周期")
|
||||
|
||||
# 批量计算生命周期
|
||||
lifecycle_df = self.lifecycle_calculator.batch_calculate_lifecycle_factors(stock_codes, year)
|
||||
|
||||
# 筛选目标阶段的股票
|
||||
# 引入期(1)和成长期(2)合并为成长期，成熟期(3)保持不变
|
||||
growth_stage_stocks = lifecycle_df[
|
||||
lifecycle_df['stage_id'].isin([1, 2])
|
||||
]['stock_code'].tolist()
|
||||
|
||||
mature_stage_stocks = lifecycle_df[
|
||||
lifecycle_df['stage_id'] == 3
|
||||
]['stock_code'].tolist()
|
||||
|
||||
result = {
|
||||
'growth': growth_stage_stocks,
|
||||
'mature': mature_stage_stocks
|
||||
}
|
||||
|
||||
logger.info(f"成长期股票: {len(growth_stage_stocks)} 只")
|
||||
logger.info(f"成熟期股票: {len(mature_stage_stocks)} 只")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生命周期筛选失败: {str(e)}")
|
||||
return {'growth': [], 'mature': []}
|
||||
|
||||
def calculate_distance_factors(self, growth_stocks: List[str], mature_stocks: List[str]) -> Tuple[pd.DataFrame, pd.DataFrame]:
|
||||
"""
|
||||
分别计算成长期和成熟期股票的平均距离因子
|
||||
|
||||
Args:
|
||||
growth_stocks: 成长期股票列表
|
||||
mature_stocks: 成熟期股票列表
|
||||
|
||||
Returns:
|
||||
Tuple: (成长期距离因子DataFrame, 成熟期距离因子DataFrame)
|
||||
"""
|
||||
try:
|
||||
growth_distance_df = pd.DataFrame()
|
||||
mature_distance_df = pd.DataFrame()
|
||||
|
||||
# 计算成长期股票距离因子
|
||||
if growth_stocks:
|
||||
logger.info(f"计算 {len(growth_stocks)} 只成长期股票的距离因子")
|
||||
growth_data = self.distance_calculator.get_stock_data(growth_stocks)
|
||||
if not growth_data.empty:
|
||||
growth_indicators = self.distance_calculator.calculate_technical_indicators(growth_data)
|
||||
growth_distance_df = self.distance_calculator.calculate_distance_factor(growth_indicators)
|
||||
|
||||
# 计算成熟期股票距离因子
|
||||
if mature_stocks:
|
||||
logger.info(f"计算 {len(mature_stocks)} 只成熟期股票的距离因子")
|
||||
mature_data = self.distance_calculator.get_stock_data(mature_stocks)
|
||||
if not mature_data.empty:
|
||||
mature_indicators = self.distance_calculator.calculate_technical_indicators(mature_data)
|
||||
mature_distance_df = self.distance_calculator.calculate_distance_factor(mature_indicators)
|
||||
|
||||
return growth_distance_df, mature_distance_df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算距离因子失败: {str(e)}")
|
||||
return pd.DataFrame(), pd.DataFrame()
|
||||
|
||||
def calculate_common_factors(self, stock_codes: List[str]) -> pd.DataFrame:
|
||||
"""
|
||||
计算通用因子
|
||||
|
||||
Args:
|
||||
stock_codes: 股票代码列表
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 包含通用因子的DataFrame
|
||||
"""
|
||||
try:
|
||||
logger.info(f"计算 {len(stock_codes)} 只股票的通用因子")
|
||||
|
||||
results = []
|
||||
latest_date = "2025-06-30" # 最新季度数据
|
||||
annual_date = "2024-12-31" # 年报数据
|
||||
|
||||
for stock_code in stock_codes:
|
||||
try:
|
||||
|
||||
factor_data = {'stock_code': stock_code}
|
||||
|
||||
# 1. 毛利率（使用最新数据）
|
||||
gross_margin = self.financial_analyzer.analyze_gross_profit_margin(stock_code, latest_date)
|
||||
factor_data['gross_profit_margin'] = gross_margin
|
||||
|
||||
# 2. 成长能力指标
|
||||
growth_capability = self.financial_analyzer.analyze_growth_capability(stock_code)
|
||||
if growth_capability is not None:
|
||||
# 成长能力越高越好，使用sigmoid函数映射到0-1
|
||||
growth_score = 1 / (1 + math.exp(-growth_capability))
|
||||
else:
|
||||
growth_score = 0.5 # 默认中性评分
|
||||
factor_data['growth_score'] = growth_score
|
||||
|
||||
# 3. 前五大供应商占比（使用年报数据）
|
||||
supplier_conc = self.financial_analyzer.analyze_supplier_concentration(stock_code, annual_date)
|
||||
factor_data['supplier_concentration'] = supplier_conc
|
||||
|
||||
# 4. 前五大客户占比（使用年报数据）
|
||||
customer_conc = self.financial_analyzer.analyze_customer_concentration(stock_code, annual_date)
|
||||
factor_data['customer_concentration'] = customer_conc
|
||||
|
||||
results.append(factor_data)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"计算股票 {stock_code} 通用因子失败: {str(e)}")
|
||||
continue
|
||||
|
||||
df = pd.DataFrame(results)
|
||||
logger.info(f"成功计算 {len(df)} 只股票的通用因子")
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算通用因子失败: {str(e)}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def calculate_growth_specific_factors(self, stock_codes: List[str]) -> pd.DataFrame:
|
||||
"""
|
||||
计算成长期特色因子
|
||||
|
||||
Args:
|
||||
stock_codes: 成长期股票代码列表
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 包含成长期特色因子的DataFrame
|
||||
"""
|
||||
try:
|
||||
logger.info(f"计算 {len(stock_codes)} 只成长期股票的特色因子")
|
||||
|
||||
results = []
|
||||
latest_date = "2025-06-30" # 使用最新数据
|
||||
annual_date = "2024-12-31" # 使用年度数据
|
||||
|
||||
for stock_code in stock_codes:
|
||||
try:
|
||||
|
||||
factor_data = {'stock_code': stock_code}
|
||||
|
||||
# 1. 管理费用率（使用最新数据）
|
||||
admin_ratio = self.financial_analyzer.analyze_admin_expense_ratio(stock_code, latest_date)
|
||||
factor_data['admin_expense_ratio'] = admin_ratio
|
||||
|
||||
# 2. 研发费用折旧摊销占比（使用年度数据）
|
||||
# financial_data = self.financial_analyzer.get_financial_data(stock_code, latest_date)
|
||||
financial_data = self.financial_analyzer.get_financial_data(stock_code, annual_date)
|
||||
if financial_data:
|
||||
intangible_amortize = financial_data.get('cash_flow_statement', {}).get('IA_AMORTIZE', 0)
|
||||
rd_expense = financial_data.get('profit_statement', {}).get('RESEARCH_EXPENSE', 0)
|
||||
|
||||
if rd_expense and rd_expense != 0:
|
||||
rd_amortize_ratio = intangible_amortize / rd_expense if intangible_amortize else 0
|
||||
else:
|
||||
rd_amortize_ratio = None # 使用None而不是0，避免这些股票获得最高分
|
||||
|
||||
factor_data['rd_amortize_ratio'] = rd_amortize_ratio
|
||||
else:
|
||||
factor_data['rd_amortize_ratio'] = None
|
||||
|
||||
# 3. 资产负债率（使用最新数据）
|
||||
asset_liability_ratio = self.financial_analyzer.analyze_asset_liability_ratio(stock_code, latest_date)
|
||||
factor_data['asset_liability_ratio'] = asset_liability_ratio
|
||||
|
||||
results.append(factor_data)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"计算股票 {stock_code} 成长期特色因子失败: {str(e)}")
|
||||
continue
|
||||
|
||||
df = pd.DataFrame(results)
|
||||
logger.info(f"成功计算 {len(df)} 只成长期股票的特色因子")
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算成长期特色因子失败: {str(e)}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def calculate_mature_specific_factors(self, stock_codes: List[str]) -> pd.DataFrame:
|
||||
"""
|
||||
计算成熟期特色因子
|
||||
|
||||
Args:
|
||||
stock_codes: 成熟期股票代码列表
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 包含成熟期特色因子的DataFrame
|
||||
"""
|
||||
try:
|
||||
logger.info(f"计算 {len(stock_codes)} 只成熟期股票的特色因子")
|
||||
|
||||
latest_date = "2025-06-30" # 使用最新数据
|
||||
|
||||
# 在循环外获取全A股PB和ROE数据，避免重复查询
|
||||
logger.info("获取全A股PB数据...")
|
||||
all_pb_data = self.financial_analyzer.get_all_stocks_pb_data()
|
||||
|
||||
logger.info("获取全A股ROE数据...")
|
||||
all_roe_data = self.financial_analyzer.get_all_stocks_roe_data(latest_date)
|
||||
|
||||
results = []
|
||||
|
||||
for stock_code in stock_codes:
|
||||
try:
|
||||
factor_data = {'stock_code': stock_code}
|
||||
|
||||
# 1. 应收账款周转率（使用最新数据）
|
||||
formatted_stock_code = self.financial_analyzer.code_formatter.to_dot_format(stock_code)
|
||||
financial_data = self.financial_analyzer.get_financial_data(formatted_stock_code, latest_date)
|
||||
if financial_data:
|
||||
revenue = financial_data.get('profit_statement', {}).get('OPERATE_INCOME', 0)
|
||||
accounts_rece = financial_data.get('balance_sheet', {}).get('ACCOUNTS_RECE', 0)
|
||||
|
||||
if accounts_rece and accounts_rece != 0:
|
||||
turnover_ratio = revenue / accounts_rece if revenue else 0
|
||||
else:
|
||||
turnover_ratio = None # 使用None而不是0
|
||||
|
||||
factor_data['accounts_receivable_turnover'] = turnover_ratio
|
||||
else:
|
||||
factor_data['accounts_receivable_turnover'] = None
|
||||
|
||||
# 2. 研发强度（使用最新数据）
|
||||
rd_intensity = self.financial_analyzer.analyze_rd_expense_ratio(stock_code, latest_date)
|
||||
factor_data['rd_intensity'] = rd_intensity
|
||||
|
||||
# 3. PB-ROE排名因子：使用预获取的全A股数据
|
||||
if all_pb_data and all_roe_data:
|
||||
pb_roe_rank_factor = self.financial_analyzer.calculate_pb_roe_rank_factor(
|
||||
stock_code, all_pb_data, all_roe_data
|
||||
)
|
||||
factor_data['pb_roe_rank_factor'] = pb_roe_rank_factor
|
||||
else:
|
||||
factor_data['pb_roe_rank_factor'] = None
|
||||
|
||||
results.append(factor_data)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"计算股票 {stock_code} 成熟期特色因子失败: {str(e)}")
|
||||
continue
|
||||
|
||||
df = pd.DataFrame(results)
|
||||
logger.info(f"成功计算 {len(df)} 只成熟期股票的特色因子")
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算成熟期特色因子失败: {str(e)}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def run_strategy(self, year: int = 2024) -> Dict[str, pd.DataFrame]:
|
||||
"""
|
||||
运行完整的选股策略
|
||||
|
||||
Args:
|
||||
year: 分析年份
|
||||
|
||||
Returns:
|
||||
Dict: 包含成长期和成熟期股票分析结果的字典
|
||||
"""
|
||||
try:
|
||||
logger.info("开始运行科技主题基本面因子选股策略")
|
||||
|
||||
# 1. 获取科技概念股票
|
||||
tech_stocks_df = self.get_tech_stocks()
|
||||
if tech_stocks_df.empty:
|
||||
logger.error("未获取到科技概念股票")
|
||||
return {}
|
||||
|
||||
stock_codes = tech_stocks_df['stock_code'].unique().tolist()
|
||||
logger.info(f"共获取到 {len(stock_codes)} 只科技概念股票")
|
||||
|
||||
# 2. 按企业生命周期筛选
|
||||
lifecycle_result = self.filter_by_lifecycle(stock_codes, year)
|
||||
growth_stocks = lifecycle_result['growth']
|
||||
mature_stocks = lifecycle_result['mature']
|
||||
|
||||
if not growth_stocks and not mature_stocks:
|
||||
logger.warning("未找到符合条件的成长期或成熟期股票")
|
||||
return {}
|
||||
|
||||
# 3. 计算平均距离因子
|
||||
growth_distance_df, mature_distance_df = self.calculate_distance_factors(growth_stocks, mature_stocks)
|
||||
|
||||
# 4. 计算通用因子
|
||||
all_qualified_stocks = growth_stocks + mature_stocks
|
||||
common_factors_df = self.calculate_common_factors(all_qualified_stocks)
|
||||
|
||||
# 5. 计算特色因子
|
||||
growth_specific_df = self.calculate_growth_specific_factors(growth_stocks) if growth_stocks else pd.DataFrame()
|
||||
mature_specific_df = self.calculate_mature_specific_factors(mature_stocks) if mature_stocks else pd.DataFrame()
|
||||
|
||||
# 6. 合并结果并计算分数
|
||||
result = {}
|
||||
|
||||
# 处理成长期股票
|
||||
if not growth_specific_df.empty:
|
||||
# 成长期结果合并
|
||||
growth_result = growth_specific_df.copy()
|
||||
|
||||
# 合并距离因子
|
||||
if not growth_distance_df.empty:
|
||||
growth_result = growth_result.merge(
|
||||
growth_distance_df[['symbol', 'avg_distance_factor']],
|
||||
left_on='stock_code', right_on='symbol', how='left'
|
||||
).drop('symbol', axis=1)
|
||||
|
||||
# 合并通用因子
|
||||
if not common_factors_df.empty:
|
||||
growth_result = growth_result.merge(
|
||||
common_factors_df, on='stock_code', how='left'
|
||||
)
|
||||
|
||||
# 计算因子分数
|
||||
growth_result = self.calculate_factor_scores(growth_result, 'growth')
|
||||
|
||||
# 计算总分并排序
|
||||
growth_result = self.calculate_total_score(growth_result, 'growth')
|
||||
|
||||
result['growth'] = growth_result
|
||||
logger.info(f"成长期结果: {len(growth_result)} 只股票")
|
||||
|
||||
# 处理成熟期股票
|
||||
if not mature_specific_df.empty:
|
||||
# 成熟期结果合并
|
||||
mature_result = mature_specific_df.copy()
|
||||
|
||||
# 合并距离因子
|
||||
if not mature_distance_df.empty:
|
||||
mature_result = mature_result.merge(
|
||||
mature_distance_df[['symbol', 'avg_distance_factor']],
|
||||
left_on='stock_code', right_on='symbol', how='left'
|
||||
).drop('symbol', axis=1)
|
||||
|
||||
# 合并通用因子
|
||||
if not common_factors_df.empty:
|
||||
mature_result = mature_result.merge(
|
||||
common_factors_df, on='stock_code', how='left'
|
||||
)
|
||||
|
||||
# 计算因子分数
|
||||
mature_result = self.calculate_factor_scores(mature_result, 'mature')
|
||||
|
||||
# 计算总分并排序
|
||||
mature_result = self.calculate_total_score(mature_result, 'mature')
|
||||
|
||||
result['mature'] = mature_result
|
||||
logger.info(f"成熟期结果: {len(mature_result)} 只股票")
|
||||
|
||||
logger.info("科技主题基本面因子选股策略运行完成")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"策略运行失败: {str(e)}")
|
||||
return {}
|
||||
|
||||
def calculate_factor_scores(self, df: pd.DataFrame, stage: str) -> pd.DataFrame:
|
||||
"""
|
||||
计算单因子打分（0-100分位数）
|
||||
|
||||
Args:
|
||||
df: 包含因子数据的DataFrame
|
||||
stage: 阶段类型 ('growth' 或 'mature')
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 包含因子分数的DataFrame
|
||||
"""
|
||||
try:
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df_scored = df.copy()
|
||||
|
||||
# 定义因子方向（正向为True，负向为False）
|
||||
factor_directions = {
|
||||
# 通用因子
|
||||
'gross_profit_margin': True, # 毛利率_环比增量 - 正向
|
||||
'growth_score': True, # 成长能力 - 正向
|
||||
'supplier_concentration': False, # 前5大供应商金额占比合计 - 负向
|
||||
'customer_concentration': False, # 前5大客户收入金额占比合计 - 负向
|
||||
'avg_distance_factor': True, # 平均距离因子 - 负向
|
||||
|
||||
# 成长期特色因子
|
||||
'admin_expense_ratio': False, # 管理费用/营业总收入_环比增量 - 负向
|
||||
'rd_amortize_ratio': False, # 研发费用折旧摊销占比_环比增量 - 负向
|
||||
'asset_liability_ratio': True, # 资产负债率 - 正向
|
||||
|
||||
# 成熟期特色因子
|
||||
'accounts_receivable_turnover': True, # 应收账款周转率 - 正向
|
||||
'rd_intensity': True, # 研发费用直接投入占比_环比增量 - 正向
|
||||
'pb_roe_rank_factor': False # PB-ROE排名因子 - 负向（越小越好）
|
||||
}
|
||||
|
||||
# 为每个因子计算分位数分数
|
||||
for column in df.columns:
|
||||
if column == 'stock_code':
|
||||
continue
|
||||
|
||||
# 只对有效值进行排名计算
|
||||
values = df_scored[column].dropna()
|
||||
if len(values) <= 1:
|
||||
# 如果只有一个值或没有值，所有股票都得50分或0分
|
||||
if len(values) == 1:
|
||||
df_scored[f'{column}_score'] = df_scored[column].apply(lambda x: 50 if pd.notna(x) else 0)
|
||||
else:
|
||||
df_scored[f'{column}_score'] = 0
|
||||
continue
|
||||
|
||||
# 根据因子方向确定排序方式
|
||||
is_positive = factor_directions.get(column, True)
|
||||
|
||||
# 计算排名分数
|
||||
if is_positive:
|
||||
# 正向因子：值越大分数越高
|
||||
ranked_values = values.rank(pct=True) * 100
|
||||
else:
|
||||
# 负向因子：值越小分数越高
|
||||
ranked_values = (1 - values.rank(pct=True)) * 100
|
||||
|
||||
# 初始化分数列
|
||||
df_scored[f'{column}_score'] = 0.0
|
||||
|
||||
# 将分数赋值给对应的行
|
||||
for idx in ranked_values.index:
|
||||
df_scored.loc[idx, f'{column}_score'] = ranked_values[idx]
|
||||
|
||||
logger.info(f"完成 {stage} 阶段 {len(df_scored)} 只股票的因子打分")
|
||||
return df_scored
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算因子分数失败: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return df
|
||||
|
||||
def calculate_total_score(self, df: pd.DataFrame, stage: str) -> pd.DataFrame:
|
||||
"""
|
||||
计算总分
|
||||
使用公式：总分 = 1/8 * Mean(Si) + Mean(Si)/Std(Si)
|
||||
|
||||
Args:
|
||||
df: 包含因子分数的DataFrame
|
||||
stage: 阶段类型 ('growth' 或 'mature')
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 包含总分的DataFrame
|
||||
"""
|
||||
try:
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df_result = df.copy()
|
||||
|
||||
# 定义因子权重（注意：这里是factor_score而不是factor）
|
||||
if stage == 'growth':
|
||||
factor_weights = {
|
||||
# 通用因子
|
||||
'gross_profit_margin_score': 1/8,
|
||||
'growth_score_score': 1/8, # 注意这里是growth_score_score
|
||||
'supplier_concentration_score': 1/8,
|
||||
'customer_concentration_score': 1/8,
|
||||
'avg_distance_factor_score': 2/8,
|
||||
|
||||
# 成长期特色因子
|
||||
'admin_expense_ratio_score': 1/8,
|
||||
'rd_amortize_ratio_score': 1/8,
|
||||
'asset_liability_ratio_score': 1/8
|
||||
}
|
||||
else: # mature
|
||||
factor_weights = {
|
||||
# 通用因子
|
||||
'gross_profit_margin_score': 1/8,
|
||||
'growth_score_score': 1/8, # 注意这里是growth_score_score
|
||||
'supplier_concentration_score': 1/8,
|
||||
'customer_concentration_score': 1/8,
|
||||
'avg_distance_factor_score': 2/8,
|
||||
|
||||
# 成熟期特色因子
|
||||
'accounts_receivable_turnover_score': 1/8,
|
||||
'rd_intensity_score': 1/8,
|
||||
'pb_roe_rank_factor_score': 1/8
|
||||
}
|
||||
|
||||
# 计算每只股票的总分
|
||||
total_scores = []
|
||||
|
||||
for index, row in df_result.iterrows():
|
||||
# 获取该股票的所有因子分数
|
||||
factor_scores = []
|
||||
valid_weights = []
|
||||
|
||||
for factor, weight in factor_weights.items():
|
||||
if factor in row and pd.notna(row[factor]) and row[factor] > 0:
|
||||
factor_scores.append(row[factor])
|
||||
valid_weights.append(weight)
|
||||
|
||||
if len(factor_scores) == 0:
|
||||
total_scores.append(0)
|
||||
continue
|
||||
|
||||
factor_scores = np.array(factor_scores)
|
||||
valid_weights = np.array(valid_weights)
|
||||
|
||||
# 重新标准化权重
|
||||
valid_weights = valid_weights / valid_weights.sum()
|
||||
|
||||
# 计算加权平均分数
|
||||
mean_score = np.average(factor_scores, weights=valid_weights)
|
||||
|
||||
# 计算调整项 Mean(Si)/Std(Si)
|
||||
if len(factor_scores) > 1 and np.std(factor_scores) > 0:
|
||||
adjustment = np.mean(factor_scores) / np.std(factor_scores)
|
||||
else:
|
||||
adjustment = 0
|
||||
|
||||
# 计算总分：1/8 * Mean(Si) + Mean(Si)/Std(Si)
|
||||
total_score = (1/8) * mean_score + adjustment
|
||||
total_scores.append(total_score)
|
||||
|
||||
df_result['total_score'] = total_scores
|
||||
|
||||
# 按总分降序排列
|
||||
df_result = df_result.sort_values('total_score', ascending=False).reset_index(drop=True)
|
||||
df_result['rank'] = range(1, len(df_result) + 1)
|
||||
|
||||
logger.info(f"完成 {stage} 阶段 {len(df_result)} 只股票的总分计算")
|
||||
return df_result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算总分失败: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return df
|
||||
|
||||
def close_connections(self):
|
||||
"""关闭所有数据库连接"""
|
||||
try:
|
||||
if hasattr(self, 'lifecycle_calculator'):
|
||||
del self.lifecycle_calculator
|
||||
if hasattr(self, 'financial_analyzer'):
|
||||
self.financial_analyzer.close_connection()
|
||||
if hasattr(self, 'distance_calculator'):
|
||||
del self.distance_calculator
|
||||
if hasattr(self, 'mysql_engine'):
|
||||
self.mysql_engine.dispose()
|
||||
logger.info("数据库连接已关闭")
|
||||
except Exception as e:
|
||||
logger.error(f"关闭连接失败: {str(e)}")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数 - 科技主题基本面因子选股策略"""
|
||||
strategy = None
|
||||
try:
|
||||
print("=== 科技主题基本面因子选股策略 ===")
|
||||
print("数据说明:")
|
||||
print("- 毛利率、净利润增长率等：使用最新数据 (2025-06-30)")
|
||||
print("- 供应商客户集中度、折旧摊销、研发费用：使用年报数据 (2024-12-31)")
|
||||
print()
|
||||
|
||||
# 创建策略实例
|
||||
strategy = TechFundamentalFactorStrategy()
|
||||
logger.info("策略实例创建成功")
|
||||
|
||||
# 运行策略
|
||||
results = strategy.run_strategy(year=2024)
|
||||
|
||||
# 输出结果
|
||||
if not results:
|
||||
print("未获得分析结果")
|
||||
return
|
||||
|
||||
for stage, df in results.items():
|
||||
print(f"\n=== {stage.upper()} 阶段股票分析结果 ===")
|
||||
print(f"股票数量: {len(df)}")
|
||||
|
||||
if not df.empty:
|
||||
# 调试：显示所有列名
|
||||
print(f"数据列: {list(df.columns)}")
|
||||
# 显示前5只股票的关键指标
|
||||
print("\n前5只股票:")
|
||||
display_columns = [
|
||||
'stock_code', 'gross_profit_margin', 'growth_score',
|
||||
'supplier_concentration', 'customer_concentration',
|
||||
'total_score', 'rank'
|
||||
]
|
||||
available_columns = [col for col in display_columns if col in df.columns]
|
||||
print(df[available_columns].head(5).to_string(index=False))
|
||||
|
||||
# 保存完整结果
|
||||
output_file = f"tech_fundamental_factor_{stage}_{datetime.now().strftime('%Y%m%d_%H%M')}.csv"
|
||||
df.to_csv(output_file, index=False, encoding='utf-8-sig')
|
||||
print(f"\n完整结果已保存到: {output_file}")
|
||||
|
||||
# 显示统计信息
|
||||
print(f"\n统计信息:")
|
||||
print(f" 平均总分: {df['total_score'].mean():.2f}")
|
||||
print(f" 最高总分: {df['total_score'].max():.2f}")
|
||||
print(f" 最低总分: {df['total_score'].min():.2f}")
|
||||
|
||||
# 生成成长+成熟合并的简表（仅三列：股票代码、总分、排名）
|
||||
combined_parts = []
|
||||
for stage, df in results.items():
|
||||
if isinstance(df, pd.DataFrame) and not df.empty:
|
||||
if 'stock_code' in df.columns and 'total_score' in df.columns:
|
||||
combined_parts.append(df[['stock_code', 'total_score']].copy())
|
||||
|
||||
if combined_parts:
|
||||
combined_df = pd.concat(combined_parts, ignore_index=True)
|
||||
# 去除总分为空的数据
|
||||
combined_df = combined_df.dropna(subset=['total_score'])
|
||||
# 按总分降序并重新排名
|
||||
combined_df = combined_df.sort_values('total_score', ascending=False).reset_index(drop=True)
|
||||
combined_df['rank'] = range(1, len(combined_df) + 1)
|
||||
# 追加概念列：获取每个股票所属概念（若多概念则以逗号分隔）
|
||||
try:
|
||||
concept_df = strategy.get_tech_stocks()
|
||||
if not concept_df.empty and 'stock_code' in concept_df.columns and 'concept_name' in concept_df.columns:
|
||||
concept_map = (
|
||||
concept_df.groupby('stock_code')['concept_name']
|
||||
.apply(lambda s: ','.join(sorted(set([str(x) for x in s if pd.notna(x)]))))
|
||||
.to_dict()
|
||||
)
|
||||
combined_df['concepts'] = combined_df['stock_code'].map(concept_map)
|
||||
else:
|
||||
combined_df['concepts'] = None
|
||||
except Exception:
|
||||
combined_df['concepts'] = None
|
||||
# 保存文件
|
||||
combined_file = f"tech_fundamental_factor_all_{datetime.now().strftime('%Y%m%d_%H%M')}.csv"
|
||||
combined_df.to_csv(combined_file, index=False, encoding='utf-8-sig')
|
||||
print(f"\n=== 合并结果（成长+成熟） ===")
|
||||
print(f"总股票数量: {len(combined_df)}")
|
||||
print(combined_df.head(10).to_string(index=False))
|
||||
print(f"\n合并简表已保存到: {combined_file}")
|
||||
|
||||
print(f"\n=== 策略运行完成 ===")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"程序执行失败: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
finally:
|
||||
if strategy:
|
||||
strategy.close_connections()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,3 +1,5 @@
|
|||
#美股行业板块导出，从通达信里面打开美股首页，然后栏目里面有个细分行业。点击导出所有栏目即可
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
|
|
@ -291,3 +293,5 @@ if __name__ == '__main__':
|
|||
fetch_and_store_us_stock_data_optimized(use_proxy=False) # 默认不使用代理
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ XUEQIU_HEADERS = {
|
|||
'Accept-Encoding': 'gzip, deflate, br, zstd',
|
||||
'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
|
||||
'Client-Version': 'v2.44.75',
|
||||
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||||
'Referer': 'https://weibo.com/u/7735765253',
|
||||
'Sec-Ch-Ua': '"Chromium";v="122", "Not(A:Brand";v="24", "Google Chrome";v="122"',
|
||||
'Sec-Ch-Ua-Mobile': '?0',
|
||||
|
|
|
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Loading…
Reference in New Issue