commit;
This commit is contained in:
parent
1070d41a00
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186
src/app.py
186
src/app.py
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@ -2816,6 +2816,12 @@ def bigscreen_page():
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"""渲染大屏展示页面"""
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return render_template('bigscreen.html')
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@app.route('/bigscreenv2')
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def bigscreen_page_v2():
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"""渲染大屏展示页面"""
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return render_template('bigscreen_v2.html')
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@app.route('/api/bigscreen_data', methods=['GET'])
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def bigscreen_data():
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"""聚合大屏所需的12张图数据，便于前端一次性加载"""
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@ -3001,6 +3007,186 @@ def run_batch_hk_stock_price_collection():
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logger.error(f"批量采集A股行情失败: {str(e)}")
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return jsonify({"status": "error", "message": str(e)})
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@app.route('/api/portfolio/industry_allocation', methods=['GET'])
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def get_portfolio_industry_allocation():
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"""获取行业持仓占比数据"""
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try:
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# 导入持仓分析器
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from src.valuation_analysis.portfolio_analyzer import PortfolioAnalyzer
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# 创建分析器实例
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analyzer = PortfolioAnalyzer()
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# 获取行业持仓分配数据
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result = analyzer.analyze_portfolio_allocation()
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if result.get("success"):
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return jsonify({
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"status": "success",
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"data": result["data"]
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})
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else:
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return jsonify({
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"status": "error",
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"message": result.get("message", "获取持仓数据失败")
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})
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except Exception as e:
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logger.error(f"获取行业持仓占比失败: {str(e)}")
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return jsonify({'status': 'error', 'message': str(e)})
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@app.route('/api/notice/list', methods=['GET'])
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def get_notice_list():
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"""获取重要提醒列表"""
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try:
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# 模拟数据 - 实际项目中应该从数据库或外部API获取
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mock_notices = [
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"上证指数突破3200点，市场情绪回暖",
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"北向资金今日净流入85.6亿元",
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"科技板块PE估值处于历史低位",
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"新能源概念股集体上涨，涨幅超3%",
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"医药板块回调，建议关注低吸机会",
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"融资融券余额连续三日增长",
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"消费板块资金流入明显",
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"市场恐贪指数回升至65",
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"机器人概念板块技术面突破",
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"先进封装概念获政策支持"
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]
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return jsonify({
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"status": "success",
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"data": mock_notices
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})
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except Exception as e:
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logger.error(f"获取提醒列表失败: {str(e)}")
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return jsonify({'status': 'error', 'message': str(e)})
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@app.route('/api/portfolio/summary', methods=['GET'])
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def get_portfolio_summary():
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"""获取持仓摘要信息"""
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try:
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# 导入持仓分析器
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from src.valuation_analysis.portfolio_analyzer import PortfolioAnalyzer
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# 创建分析器实例
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analyzer = PortfolioAnalyzer()
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# 获取持仓摘要数据
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result = analyzer.get_portfolio_summary()
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if result.get("success"):
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return jsonify({
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"status": "success",
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"data": result["data"]
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})
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else:
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return jsonify({
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"status": "error",
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"message": result.get("message", "获取持仓摘要失败")
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})
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except Exception as e:
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logger.error(f"获取持仓摘要失败: {str(e)}")
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return jsonify({'status': 'error', 'message': str(e)})
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@app.route('/api/portfolio/industry_holdings', methods=['GET'])
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def get_industry_holdings_detail():
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"""获取指定行业的详细持仓信息"""
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try:
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industry_name = request.args.get('industry_name')
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if not industry_name:
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return jsonify({'status': 'error', 'message': '缺少必要参数: industry_name'}), 400
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# 导入持仓分析器
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from src.valuation_analysis.portfolio_analyzer import PortfolioAnalyzer
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# 创建分析器实例
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analyzer = PortfolioAnalyzer()
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# 获取行业详细持仓数据
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result = analyzer.get_industry_holdings_detail(industry_name)
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if result.get("success"):
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return jsonify({
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"status": "success",
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"data": result["data"]
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})
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else:
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return jsonify({
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"status": "error",
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"message": result.get("message", "获取行业持仓详情失败")
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})
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except Exception as e:
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logger.error(f"获取行业持仓详情失败: {str(e)}")
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return jsonify({'status': 'error', 'message': str(e)})
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@app.route('/api/valuation/indicator', methods=['POST'])
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def analyze_valuation_indicator():
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"""分析股票应该使用PE还是PB估值
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POST参数:
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- stock_code: 股票代码 (例如: 000001)
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- stock_name: 股票名称 (例如: 平安银行)
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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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"recommended_indicator": "PB",
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"reasoning": "平安银行属于金融服务业，作为商业银行，其商业模式基于资产负债管理。金融机构的盈利受拨备、利率、市场波动影响而不够稳定。基于金融业的特殊性，PB是更合适的估值指标..."
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}
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}
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"""
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try:
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# 从POST表单参数获取
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stock_code = request.form.get('stock_code')
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stock_name = request.form.get('stock_name')
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if not stock_code or not stock_name:
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return jsonify({
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"status": "error",
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"message": "缺少必要参数: stock_code 或 stock_name"
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}), 400
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# 导入估值指标分析器
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try:
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from src.valuation_analysis.valuation_indicator_analyzer import ValuationIndicatorAnalyzer
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logger.info("成功导入估值指标分析器")
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except ImportError as e:
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logger.error(f"无法导入估值指标分析器: {str(e)}")
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return jsonify({
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"status": "error",
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"message": f"服务器配置错误: 估值指标分析器不可用，错误详情: {str(e)}"
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}), 500
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# 创建分析器实例
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analyzer = ValuationIndicatorAnalyzer()
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# 执行分析
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result = analyzer.analyze_valuation_indicator(stock_code, stock_name)
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if result.get("success"):
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return jsonify({
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"status": "success",
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"data": {
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"recommended_indicator": result.get("recommended_indicator"),
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"reasoning": result.get("reasoning", "")
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}
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})
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else:
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return jsonify({
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"status": "error",
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"message": result.get("error", "分析失败，无详细信息")
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}), 500
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except Exception as e:
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logger.error(f"估值指标分析失败: {str(e)}")
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return jsonify({
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"status": "error",
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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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if __name__ == '__main__':
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# 启动Web服务器
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@ -11,7 +11,7 @@ XUEQIU_HEADERS = {
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'Accept-Encoding': 'gzip, deflate, br, zstd',
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'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
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'Client-Version': 'v2.44.75',
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|
||||
'Cookie': 'cookiesu=811743062689927; device_id=33fa3c7fca4a65f8f4354e10ed6b7470; smidV2=20250327160437f244626e8b47ca2a7992f30f389e4e790074ae48656a22f10; HMACCOUNT=8B64A2E3C307C8C0; s=c611ttmqlj; xq_is_login=1; u=8493411634; bid=4065a77ca57a69c83405d6e591ab5449_m8r2nhs8; __utma=1.434320573.1747189698.1747189698.1747189698.1; __utmc=1; __utmz=1.1747189698.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); snbim_minify=true; _c_WBKFRo=dsWgHR8i8KGPbIyhFlN51PHOzVuuNytvUAFppfkD; _nb_ioWEgULi=; Hm_lvt_1db88642e346389874251b5a1eded6e3=1751936369; xq_a_token=ada154d4707b8d3f8aa521ff0c960aa7f81cbf9e; xqat=ada154d4707b8d3f8aa521ff0c960aa7f81cbf9e; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOjg0OTM0MTE2MzQsImlzcyI6InVjIiwiZXhwIjoxNzU2MDAyNjgyLCJjdG0iOjE3NTM0MTA2ODI0MTQsImNpZCI6ImQ5ZDBuNEFadXAifQ.AlnzQSY7oGKGABfaQcFLg0lAJsDdvBMiwUbgpCMCBlbx6VZPKhzERxWiylQb4dFIyyECvRRJ73SbO9cD46fAqgzOgTxArNHtTKD4lQapTnyb11diDADnpb_nzzaRr4k_BYQRKXWtcJxdUMzde2WLy-eAkSf76QkXmKrwS3kvRm5gfqhdye44whw5XMEGoZ_lXHzGLWGz_PludHZp6W3v-wwZc_0wLU6cTb_KdrwWUWT_8jw5JHXnJEmuZmQI8QWf60DtiHIYCYXarxv8XtyHK7lLKhIAa3C2QmGWw5wv2HGz4I5DPqm2uMPKumgkQxycfAk56-RWviLZ8LAPF-XcbA; xq_r_token=92527e51353f90ba14d5fd16581e5a7a2780baa2; acw_tc=0a27aa0f17542694317833912e006564153fcd1bb89f49a865e382d9953601; is_overseas=0; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1754269439; .thumbcache_f24b8bbe5a5934237bbc0eda20c1b6e7=HS+RscPvXRUz1ypZekks1pgGkAHHlHsHVuftTbDQCbUUaFqtm9BV4h7ghR2d5Nh+YD29otSyz2svRiKWvOJqgQ%3D%3D; ssxmod_itna=1-eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40Q0P6Dw1PtDCuq4wQWiYMrK4N4hGRtDl=YoDZDGFdDqx0Ei6Fi7HKzYhtBoqzWKjw_wv5YlCZMPO8//1P9PQCNzkOQ4hviDB3DbqDy/dePxYYjDBYD74G_DDeDixdDj4GmDGYtOeDFfCuNq6R5dxDwDB=DmMIbfeDEDG3D0fbeCLRYwDDBDGUFxtaDG4Gf0mDD0wDAo0jooDGWfnu4s6mkeFKN57G3x0tWDBL5QvG3x/lnoGWNVtlfkS2FkPGuDG6Ogl0kDqQO3i2AfP4KGGIm0iBPKY_5leOQDqQe4YwQGDpl0xliO7Gm0DOGDz0G4ixqYw1n0aSpwhixgPXieD1NZcX3ZXDK4rm0IlvYRGImxqnmmlG4eK40w4Am1BqGYeeGn5ixXWa3m2b/DDgi3YD; ssxmod_itna2=1-eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40Q0P6Dw1PtDCuq4wQWiYMrK4N4hGbYDiPbY44h7ie03dz7=3xDlouSdLRKl=Q_2YStYQ7OzOy_RBQ1oeziI2pkPsD8RSfPnSw5L7G4xcSPKKMxxoCD6zTiVCud28rNOm2tL7qASSMTjB2GcYPxzSRi94n0Kgjd6C6jKOMh5rMtOfkR2l8TGOPL277=81u9MRkBgIwRxDwx6iYEE4omE9FE1lonhzib3BUC6PD',
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'Referer': 'https://weibo.com/u/7735765253',
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'Sec-Ch-Ua': '"Chromium";v="122", "Not(A:Brand";v="24", "Google Chrome";v="122"',
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'Sec-Ch-Ua-Mobile': '?0',
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@ -0,0 +1,376 @@
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"""
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持仓分析模块
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提供持仓数据获取和行业分类统计功能，包括：
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1. 从外部API获取持仓数据
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2. 根据股票代码获取行业分类
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3. 计算各行业持仓金额和占比
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"""
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import requests
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import logging
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from typing import Dict, List, Optional
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from sqlalchemy import create_engine, text
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from .config import DB_URL, LOG_FILE
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# 配置日志
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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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handlers=[
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logging.FileHandler(LOG_FILE),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger("portfolio_analyzer")
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class PortfolioAnalyzer:
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"""持仓分析器类"""
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def __init__(self, db_url: str = DB_URL):
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"""
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初始化持仓分析器
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Args:
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db_url: 数据库连接URL
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"""
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self.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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self.api_url = ("https://to.bmbs.tech/aim/app/v1/derivativeTrading/getTradingRecordList?"
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"projectId=182AE38B8C254BC88C715D86C643A6DD,C9B533175D294648A2372CB2966BCC96")
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logger.info("持仓分析器初始化完成")
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def get_trading_records(self) -> Optional[Dict]:
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"""
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从外部API获取交易记录数据
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Returns:
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交易记录数据字典，如果请求失败返回None
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"""
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try:
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headers = {
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'authUserIdYh': '4028816c6759b6cb01675aacc98a00f6'
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}
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response = requests.get(self.api_url, headers=headers, timeout=10)
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response.raise_for_status()
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data = response.json()
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if data.get("success") and data.get("code") == 200:
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||||
logger.info("成功获取交易记录数据")
|
||||
return data.get("data", {})
|
||||
else:
|
||||
logger.error(f"API返回错误: {data.get('message', '未知错误')}")
|
||||
return None
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
logger.error(f"请求交易记录API失败: {e}")
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"处理交易记录数据失败: {e}")
|
||||
return None
|
||||
|
||||
def get_stock_industry(self, stock_code: str) -> List[str]:
|
||||
"""
|
||||
获取指定股票所属的行业列表
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码（格式如：603290.SH）
|
||||
|
||||
Returns:
|
||||
行业名称列表
|
||||
"""
|
||||
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_hybk
|
||||
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} 的行业数据")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取股票行业失败: {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 "603290.SH"
|
||||
|
||||
Returns:
|
||||
转换后的股票代码，格式如 "SH603290"
|
||||
"""
|
||||
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 calculate_margin_amount(self, notional_principal: float, margin_rate: float) -> float:
|
||||
"""
|
||||
计算保证金金额
|
||||
|
||||
Args:
|
||||
notional_principal: 名义本金
|
||||
margin_rate: 保证金率（百分比）
|
||||
|
||||
Returns:
|
||||
保证金金额
|
||||
"""
|
||||
return notional_principal * (margin_rate / 100)
|
||||
|
||||
def analyze_portfolio_allocation(self) -> Dict:
|
||||
"""
|
||||
分析持仓行业分配
|
||||
|
||||
Returns:
|
||||
包含行业持仓占比的字典
|
||||
"""
|
||||
try:
|
||||
# 1. 获取交易记录数据
|
||||
trading_data = self.get_trading_records()
|
||||
if not trading_data:
|
||||
return {"success": False, "message": "无法获取交易记录数据"}
|
||||
|
||||
data_list = trading_data.get("dataList", [])
|
||||
if not data_list:
|
||||
return {"success": False, "message": "交易记录数据为空"}
|
||||
|
||||
# 2. 处理持仓数据
|
||||
industry_amounts = {} # 行业金额统计
|
||||
total_amount = 0.0
|
||||
|
||||
for project in data_list:
|
||||
project_name = project.get("projectName", "")
|
||||
stock_code = project.get("stockCode", "")
|
||||
trading_records = project.get("tradingRecoderList", [])
|
||||
|
||||
# 只处理未清仓的记录
|
||||
for record in trading_records:
|
||||
if record.get("projectStatus") == "published":
|
||||
notional_principal = float(record.get("notionalPrincipal", 0))
|
||||
margin_rate = float(record.get("marginRate", 0))
|
||||
|
||||
# 计算保证金金额
|
||||
margin_amount = self.calculate_margin_amount(notional_principal, margin_rate)
|
||||
total_amount += margin_amount
|
||||
|
||||
# 获取股票所属行业
|
||||
industries = self.get_stock_industry(stock_code)
|
||||
|
||||
if industries:
|
||||
# 如果股票属于多个行业，按行业数量平均分配
|
||||
amount_per_industry = margin_amount / len(industries)
|
||||
for industry in industries:
|
||||
if industry in industry_amounts:
|
||||
industry_amounts[industry] += amount_per_industry
|
||||
else:
|
||||
industry_amounts[industry] = amount_per_industry
|
||||
else:
|
||||
# 如果无法获取行业信息，归类为"其他"
|
||||
other_industry = "其他"
|
||||
if other_industry in industry_amounts:
|
||||
industry_amounts[other_industry] += margin_amount
|
||||
else:
|
||||
industry_amounts[other_industry] = margin_amount
|
||||
|
||||
# 3. 生成返回数据
|
||||
if not industry_amounts:
|
||||
return {"success": False, "message": "未找到有效的持仓数据"}
|
||||
|
||||
# 按金额排序
|
||||
sorted_industries = sorted(industry_amounts.items(), key=lambda x: x[1], reverse=True)
|
||||
|
||||
# 定义颜色映射
|
||||
colors = [
|
||||
"#5470c6", "#91cc75", "#fac858", "#ee6666", "#73c0de",
|
||||
"#3ba272", "#fc8452", "#9a60b4", "#ea7ccc", "#ff9f7f"
|
||||
]
|
||||
|
||||
industries_data = []
|
||||
for i, (industry, amount) in enumerate(sorted_industries):
|
||||
color = colors[i % len(colors)]
|
||||
industries_data.append({
|
||||
"industry": industry,
|
||||
"amount": round(amount, 2),
|
||||
"color": color
|
||||
})
|
||||
|
||||
result = {
|
||||
"success": True,
|
||||
"data": {
|
||||
"total_amount": round(total_amount, 2),
|
||||
"industries": industries_data
|
||||
}
|
||||
}
|
||||
|
||||
logger.info(f"成功分析持仓行业分配，总金额: {total_amount:.2f}万元，共{len(industries_data)}个行业")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析持仓行业分配失败: {e}")
|
||||
return {"success": False, "message": f"分析持仓行业分配失败: {str(e)}"}
|
||||
|
||||
def get_portfolio_summary(self) -> Dict:
|
||||
"""
|
||||
获取持仓摘要信息
|
||||
|
||||
Returns:
|
||||
持仓摘要信息字典
|
||||
"""
|
||||
try:
|
||||
# 1. 获取交易记录数据
|
||||
trading_data = self.get_trading_records()
|
||||
if not trading_data:
|
||||
return {"success": False, "message": "无法获取交易记录数据"}
|
||||
|
||||
data_list = trading_data.get("dataList", [])
|
||||
if not data_list:
|
||||
return {"success": False, "message": "交易记录数据为空"}
|
||||
|
||||
# 2. 统计信息
|
||||
total_projects = len(data_list)
|
||||
active_projects = 0
|
||||
total_margin_amount = 0.0
|
||||
project_details = []
|
||||
|
||||
for project in data_list:
|
||||
project_name = project.get("projectName", "")
|
||||
stock_code = project.get("stockCode", "")
|
||||
trading_records = project.get("tradingRecoderList", [])
|
||||
|
||||
project_margin = 0.0
|
||||
has_active_position = False
|
||||
|
||||
for record in trading_records:
|
||||
if record.get("projectStatus") == "published":
|
||||
has_active_position = True
|
||||
notional_principal = float(record.get("notionalPrincipal", 0))
|
||||
margin_rate = float(record.get("marginRate", 0))
|
||||
margin_amount = self.calculate_margin_amount(notional_principal, margin_rate)
|
||||
project_margin += margin_amount
|
||||
|
||||
if has_active_position:
|
||||
active_projects += 1
|
||||
total_margin_amount += project_margin
|
||||
|
||||
# 获取行业信息
|
||||
industries = self.get_stock_industry(stock_code)
|
||||
industry_names = ", ".join(industries) if industries else "未知"
|
||||
|
||||
project_details.append({
|
||||
"project_name": project_name,
|
||||
"stock_code": stock_code,
|
||||
"industry": industry_names,
|
||||
"margin_amount": round(project_margin, 2)
|
||||
})
|
||||
|
||||
# 3. 生成摘要
|
||||
summary = {
|
||||
"success": True,
|
||||
"data": {
|
||||
"total_projects": total_projects,
|
||||
"active_projects": active_projects,
|
||||
"total_margin_amount": round(total_margin_amount, 2),
|
||||
"project_details": project_details
|
||||
}
|
||||
}
|
||||
|
||||
logger.info(f"成功获取持仓摘要，总项目数: {total_projects}，活跃项目数: {active_projects}")
|
||||
return summary
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取持仓摘要失败: {e}")
|
||||
return {"success": False, "message": f"获取持仓摘要失败: {str(e)}"}
|
||||
|
||||
def get_industry_holdings_detail(self, industry_name: str) -> Dict:
|
||||
"""
|
||||
获取指定行业的详细持仓信息
|
||||
|
||||
Args:
|
||||
industry_name: 行业名称
|
||||
|
||||
Returns:
|
||||
包含行业详细持仓信息的字典
|
||||
"""
|
||||
try:
|
||||
# 1. 获取交易记录数据
|
||||
trading_data = self.get_trading_records()
|
||||
if not trading_data:
|
||||
return {"success": False, "message": "无法获取交易记录数据"}
|
||||
|
||||
data_list = trading_data.get("dataList", [])
|
||||
if not data_list:
|
||||
return {"success": False, "message": "交易记录数据为空"}
|
||||
|
||||
# 2. 筛选该行业的持仓记录
|
||||
industry_holdings = []
|
||||
|
||||
for project in data_list:
|
||||
project_name = project.get("projectName", "")
|
||||
stock_code = project.get("stockCode", "")
|
||||
trading_records = project.get("tradingRecoderList", [])
|
||||
|
||||
# 获取股票所属行业
|
||||
industries = self.get_stock_industry(stock_code)
|
||||
|
||||
# 检查是否属于指定行业
|
||||
if industry_name in industries:
|
||||
for record in trading_records:
|
||||
if record.get("projectStatus") == "published":
|
||||
notional_principal = float(record.get("notionalPrincipal", 0))
|
||||
margin_rate = float(record.get("marginRate", 0))
|
||||
create_time = record.get("createTime", "")
|
||||
|
||||
# 计算保证金金额
|
||||
margin_amount = self.calculate_margin_amount(notional_principal, margin_rate)
|
||||
|
||||
industry_holdings.append({
|
||||
"project_name": project_name,
|
||||
"stock_code": stock_code,
|
||||
"notional_principal": notional_principal,
|
||||
"margin_rate": margin_rate,
|
||||
"margin_amount": margin_amount,
|
||||
"create_time": create_time
|
||||
})
|
||||
|
||||
# 3. 按保证金金额排序
|
||||
industry_holdings.sort(key=lambda x: x["margin_amount"], reverse=True)
|
||||
|
||||
# 4. 生成返回数据
|
||||
result = {
|
||||
"success": True,
|
||||
"data": {
|
||||
"industry_name": industry_name,
|
||||
"total_count": len(industry_holdings),
|
||||
"total_margin_amount": sum(item["margin_amount"] for item in industry_holdings),
|
||||
"holdings": industry_holdings
|
||||
}
|
||||
}
|
||||
|
||||
logger.info(f"成功获取行业 {industry_name} 的详细持仓信息，共 {len(industry_holdings)} 条记录")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取行业详细持仓信息失败: {e}")
|
||||
return {"success": False, "message": f"获取行业详细持仓信息失败: {str(e)}"}
|
||||
|
|
@ -0,0 +1,334 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
估值指标分析专用聊天机器人
|
||||
专门用于分析股票应该使用PE还是PB估值
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
import logging
|
||||
from typing import Dict, Any, Optional
|
||||
from datetime import datetime
|
||||
|
||||
# 添加项目根目录到 Python 路径
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
|
||||
from openai import OpenAI
|
||||
from src.scripts.config import get_random_api_key, get_model
|
||||
|
||||
# 设置日志
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class ValuationChatBot:
|
||||
"""估值指标分析专用聊天机器人"""
|
||||
|
||||
def __init__(self, model_type: str = "online_bot"):
|
||||
"""初始化估值分析聊天机器人
|
||||
|
||||
Args:
|
||||
model_type: 要使用的模型类型，默认为联网智能体
|
||||
"""
|
||||
try:
|
||||
# 从配置获取API密钥
|
||||
self.api_key = get_random_api_key()
|
||||
# 从配置获取模型ID
|
||||
self.model = get_model(model_type)
|
||||
|
||||
logger.info(f"初始化ValuationChatBot，使用模型: {self.model}")
|
||||
|
||||
# 初始化OpenAI客户端
|
||||
self.client = OpenAI(
|
||||
base_url="https://ark.cn-beijing.volces.com/api/v3/bots",
|
||||
api_key=self.api_key
|
||||
)
|
||||
|
||||
# 估值指标分析专用系统提示词
|
||||
self.system_message = {
|
||||
"role": "system",
|
||||
"content": """你是一名顶级的、注重第一性原理的基本面分析师。你的核心任务是深入剖析一家公司的内在价值驱动因素，并基于此判断“市盈盈率（PE）”和“市净率（PB）”哪个指标能更真实、更核心地反映其价值。
|
||||
|
||||
**你的分析必须超越简单的行业标签，聚焦于公司的个性化特征。** 即使是同一行业的公司，由于商业模式和财务状况的差异，也可能适用不同的估值指标。
|
||||
|
||||
**你的决策逻辑框架如下：**
|
||||
|
||||
1. **【盈利质量与可预测性分析】 - 这是判断PE有效性的基石**
|
||||
* **分析要点：** 公司的盈利是常态还是偶发？是内生增长还是外部输血？过去5年的盈利记录是否稳定且持续？是否存在大量非经常性损益扭曲了利润？公司的自由现金流状况如何，是否与净利润匹配？
|
||||
* **决策倾向：** 如果盈利质量高、可预测性强，则PE的权重增加。如果盈利波动巨大、不可持续或为负，则PE的权重降低甚至失效。
|
||||
|
||||
2. **【资产价值与商业模式分析】 - 这是判断PB有效性的基石**
|
||||
* **分析要点：** 公司的核心价值是沉淀在资产负债表上（如厂房、金融资产、土地），还是体现在资产负债表外（如品牌、技术、网络效应、客户关系）？公司的商业模式是“资产驱动型”还是“智力/品牌驱动型”？
|
||||
* **决策倾向：** 如果公司价值与净资产高度相关（如金融、重资产制造、资源型企业），则PB的权重增加。如果公司是典型的轻资产模式，则PB的权重降低。
|
||||
|
||||
3. **【周期性与成长性交叉验证】**
|
||||
* **分析要点：** 公司所处的行业周期性强弱如何？公司自身是否展现出超越行业的成长性或防御性？
|
||||
* **决策倾向：** 强周期性会削弱PE在特定时点的有效性，使PB成为更稳健的参照。而强成长性（尤其是有利可图的成长）会显著提升PE的适用性。
|
||||
|
||||
**最终决策原则：**
|
||||
|
||||
* **优先选择 PE 的核心理由：** 公司具备持续、稳定的盈利能力，并且其核心价值能通过利润得到体现。这是对股东回报最直接的衡量。
|
||||
* **优先选择 PB 的核心理由：** 公司的盈利能力不可靠（周期性/亏损），或者其商业模式的根本是基于净资产的规模和质量（如金融业）。PB此时是衡量价值的“锚”或“底线”。
|
||||
|
||||
**输出要求：**
|
||||
|
||||
1. **明确结论：** 首先明确推荐PE或PB作为主要估值指标。
|
||||
2. **深入的个股特质分析：**
|
||||
* **商业模式剖析：** 详细说明公司如何赚钱，其护城河是什么。
|
||||
* **财务特征分析：** 重点分析盈利的稳定性与质量、资产的轻重结构、现金流状况。
|
||||
* **行业背景补充：** 分析公司在行业中所处的生态位，有何不同于同行的特质。
|
||||
3. **提供决策依据：** 清晰地说明你是如何基于上述三层决策逻辑框架，最终做出选择的。
|
||||
4. **给出合理的估值区间建议：** 基于你选择的指标，并结合公司的历史估值水平和未来成长性，给出一个合理的估值区间。"""
|
||||
}
|
||||
|
||||
# 对话历史
|
||||
self.conversation_history = [self.system_message]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"初始化ValuationChatBot时出错: {str(e)}")
|
||||
raise
|
||||
|
||||
def chat(self, user_input: str, temperature: float = 0.3, top_p: float = 0.7, max_tokens: int = 2048, frequency_penalty: float = 0.0) -> Dict[str, Any]:
|
||||
"""与AI进行估值指标分析对话
|
||||
|
||||
Args:
|
||||
user_input: 用户输入的问题
|
||||
temperature: 控制输出的随机性，范围0-2，默认0.3（更确定性）
|
||||
top_p: 控制输出的多样性，范围0-1，默认0.7
|
||||
max_tokens: 控制输出的最大长度，默认2048
|
||||
frequency_penalty: 频率惩罚，范围-2.0到2.0，默认0.0
|
||||
|
||||
Returns:
|
||||
Dict包含对话结果
|
||||
"""
|
||||
try:
|
||||
# 添加用户消息到对话历史
|
||||
self.conversation_history.append({
|
||||
"role": "user",
|
||||
"content": user_input
|
||||
})
|
||||
|
||||
# 调用OpenAI API
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=self.conversation_history,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
max_tokens=max_tokens,
|
||||
frequency_penalty=frequency_penalty
|
||||
)
|
||||
|
||||
# 获取AI回复
|
||||
ai_response = response.choices[0].message.content
|
||||
|
||||
# 添加AI回复到对话历史
|
||||
self.conversation_history.append({
|
||||
"role": "assistant",
|
||||
"content": ai_response
|
||||
})
|
||||
|
||||
# 保持对话历史在合理长度内（避免token过多）
|
||||
if len(self.conversation_history) > 10:
|
||||
# 保留系统消息和最近的对话
|
||||
self.conversation_history = [self.system_message] + self.conversation_history[-8:]
|
||||
|
||||
logger.info(f"ValuationChatBot对话成功，回复长度: {len(ai_response)}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"response": ai_response,
|
||||
"model": self.model,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"ValuationChatBot对话失败: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"model": self.model,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
|
||||
def clear_history(self):
|
||||
"""清空对话历史"""
|
||||
self.conversation_history = [self.system_message]
|
||||
logger.info("ValuationChatBot对话历史已清空")
|
||||
|
||||
def get_conversation_history(self) -> list:
|
||||
"""获取对话历史"""
|
||||
return self.conversation_history.copy()
|
||||
|
||||
|
||||
class ValuationOfflineChatBot:
|
||||
"""估值指标分析专用离线聊天机器人"""
|
||||
|
||||
def __init__(self, model_type: str = "offline_bot"):
|
||||
"""初始化离线估值分析聊天机器人
|
||||
|
||||
Args:
|
||||
model_type: 要使用的模型类型，默认为离线模型
|
||||
"""
|
||||
try:
|
||||
# 尝试导入配置（参考chat_bot_with_offline.py的方式）
|
||||
try:
|
||||
from src.scripts.config import get_model_config
|
||||
config = get_model_config("tl_qw_private", "GLM")
|
||||
logger.info("成功从src.scripts.config导入配置")
|
||||
except ImportError:
|
||||
try:
|
||||
from scripts.config import get_model_config
|
||||
config = get_model_config("volc", "offline_model")
|
||||
logger.info("成功从scripts.config导入配置")
|
||||
except ImportError:
|
||||
logger.warning("无法导入配置模块，使用默认配置")
|
||||
# 使用默认配置
|
||||
config = {
|
||||
"base_url": "https://ark.cn-beijing.volces.com/api/v3/",
|
||||
"api_key": "28cfe71a-c6fa-4c5d-9b4e-d8474f0d3b93",
|
||||
"model": "ep-20250326090920-v7wns"
|
||||
}
|
||||
|
||||
# 保存配置信息
|
||||
self.api_key = config["api_key"]
|
||||
self.model = config["model"]
|
||||
self.base_url = config["base_url"]
|
||||
|
||||
logger.info(f"初始化ValuationOfflineChatBot，使用模型: {self.model}")
|
||||
|
||||
# 初始化OpenAI客户端
|
||||
self.client = OpenAI(
|
||||
base_url=self.base_url,
|
||||
api_key=self.api_key,
|
||||
timeout=600
|
||||
)
|
||||
|
||||
# 估值指标分析专用系统提示词（针对从分析报告中进行语义理解并提取最终结论）
|
||||
self.system_message = {
|
||||
"role": "system",
|
||||
"content": """你是一个专注于**语义理解和结论提取**的AI。你的唯一任务是阅读一段分析报告，理解其核心论点，并判断作者最终推荐的估值指标是 "PE" 还是 "PB"。
|
||||
|
||||
**你的核心工作流程：**
|
||||
|
||||
1. **通读全文**：完整地阅读用户提供的分析报告，理解其对公司业务模式、盈利能力和资产结构的整体评价。
|
||||
|
||||
2. **定位结论性语段**：重点关注报告的结尾部分或总结段落。寻找那些**承上启下、做出最终评判**的句子。这些句子不一定包含固定的关键词，但它们在语义上起到了总结和给出最终意见的作用。
|
||||
|
||||
3. **进行意图判断**：
|
||||
* **判断为 "PE" 的信号**：如果结论性语段的中心思想是强调“盈利的稳定性”、“高质量的增长”、“强大的品牌价值”、“轻资产模式的优势”，并最终将这些优势导向了某个估值方法，那么结论就是 "PE"。
|
||||
* *例子：* "考虑到该公司强大的品牌护城河和持续稳定的现金流创造能力，通过其盈利水平来评估价值显然是更为恰当的路径。" -> **应判断为 PE**
|
||||
|
||||
* **判断为 "PB" 的信号**：如果结论性语段的中心思想是强调“资产负债表的重要性”、“行业的周期性风险”、“盈利的不可靠性”，或者直接点明其“金融属性”，并基于这些论据做出最终选择，那么结论就是 "PB"。
|
||||
* *例子：* "尽管公司短期盈利尚可，但其重资产和强周期的本质意味着盈利波动是常态，因此，基于其净资产的估值方法提供了一个更稳固的价值锚点。" -> **应判断为 PB**
|
||||
|
||||
**你必须遵守的铁律：**
|
||||
|
||||
* **你的任务是理解和提取，不是再次分析**。你必须相信报告原文的逻辑是自洽的，你的工作只是找出它的最终论点。
|
||||
* **只输出最终结果**：你的输出**必须且只能是** "PE" 或 "PB"。不要添加任何解释、理由或多余的字符。
|
||||
* **处理歧义**：如果在极少数情况下，报告的结论确实模棱两可，无法从语义上明确判断，**请默认输出 "PE"**，以确保程序健壮性。
|
||||
"""
|
||||
}
|
||||
|
||||
# 对话历史
|
||||
self.conversation_history = [self.system_message]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"初始化ValuationOfflineChatBot时出错: {str(e)}")
|
||||
raise
|
||||
|
||||
def chat(self, user_input: str, temperature: float = 0.1, top_p: float = 0.7, max_tokens: int = 1024, frequency_penalty: float = 0.0) -> Dict[str, Any]:
|
||||
"""与离线AI进行估值指标分析对话
|
||||
|
||||
Args:
|
||||
user_input: 用户输入的问题
|
||||
temperature: 控制输出的随机性，范围0-2，默认0.1（更确定性）
|
||||
top_p: 控制输出的多样性，范围0-1，默认0.7
|
||||
max_tokens: 控制输出的最大长度，默认1024
|
||||
frequency_penalty: 频率惩罚，范围-2.0到2.0，默认0.0
|
||||
|
||||
Returns:
|
||||
Dict包含对话结果
|
||||
"""
|
||||
try:
|
||||
# 添加用户消息到对话历史
|
||||
self.conversation_history.append({
|
||||
"role": "user",
|
||||
"content": user_input
|
||||
})
|
||||
|
||||
# 调用本地GLM模型
|
||||
ai_response = self._call_local_model(user_input, temperature, top_p, max_tokens, frequency_penalty)
|
||||
|
||||
# 添加AI回复到对话历史
|
||||
self.conversation_history.append({
|
||||
"role": "assistant",
|
||||
"content": ai_response
|
||||
})
|
||||
|
||||
# 保持对话历史在合理长度内
|
||||
if len(self.conversation_history) > 6:
|
||||
self.conversation_history = [self.system_message] + self.conversation_history[-4:]
|
||||
|
||||
logger.info(f"ValuationOfflineChatBot对话成功，回复长度: {len(ai_response)}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"response": ai_response,
|
||||
"model": self.model,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"ValuationOfflineChatBot对话失败: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"model": self.model,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
|
||||
def _call_local_model(self, user_input: str, temperature: float = 0.1, top_p: float = 0.7, max_tokens: int = 1024, frequency_penalty: float = 0.0) -> str:
|
||||
"""调用本地GLM模型"""
|
||||
try:
|
||||
# 调用本地模型API（使用初始化时创建的客户端）
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=self.conversation_history,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
max_tokens=max_tokens,
|
||||
frequency_penalty=frequency_penalty,
|
||||
timeout=300
|
||||
)
|
||||
|
||||
# 获取AI回复
|
||||
ai_response = response.choices[0].message.content
|
||||
|
||||
# 清理回复内容，确保只返回PE或PB
|
||||
ai_response_clean = ai_response.strip().upper()
|
||||
if "PE" in ai_response_clean and "PB" not in ai_response_clean:
|
||||
return "PE"
|
||||
elif "PB" in ai_response_clean and "PE" not in ai_response_clean:
|
||||
return "PB"
|
||||
elif ai_response_clean == "PE" or ai_response_clean == "PB":
|
||||
return ai_response_clean
|
||||
else:
|
||||
# 如果回复不清晰，记录详细信息
|
||||
logger.warning(f"本地模型回复不清晰: {ai_response_clean}")
|
||||
return "PE" # 默认返回PE
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"调用本地模型失败: {str(e)}")
|
||||
return "PE" # 出错时默认返回PE
|
||||
|
||||
def clear_history(self):
|
||||
"""清空对话历史"""
|
||||
self.conversation_history = [self.system_message]
|
||||
logger.info("ValuationOfflineChatBot对话历史已清空")
|
||||
|
||||
def get_conversation_history(self) -> list:
|
||||
"""获取对话历史"""
|
||||
return self.conversation_history.copy()
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_valuation_chat_bot()
|
||||
|
|
@ -0,0 +1,397 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
估值指标分析器
|
||||
用于判断股票应该使用PE估值还是PB估值更合理
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
import logging
|
||||
from typing import Dict, Any, Optional, Tuple
|
||||
from datetime import datetime
|
||||
|
||||
# 添加项目根目录到 Python 路径
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
|
||||
from src.valuation_analysis.valuation_chat_bot import ValuationChatBot
|
||||
from src.scripts.config import get_random_api_key, get_model
|
||||
|
||||
# 设置日志
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class ValuationIndicatorAnalyzer:
|
||||
"""估值指标分析器"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化分析器"""
|
||||
try:
|
||||
# 初始化联网大模型（使用专用估值分析聊天机器人）
|
||||
self.online_chatbot = ValuationChatBot(model_type="online_bot")
|
||||
|
||||
# 初始化本地GLM模型（使用专用估值分析离线聊天机器人）
|
||||
try:
|
||||
from src.valuation_analysis.valuation_chat_bot import ValuationOfflineChatBot
|
||||
self.offline_chatbot = ValuationOfflineChatBot(model_type="offline_bot")
|
||||
self.has_offline_model = True
|
||||
except ImportError:
|
||||
logger.warning("无法导入离线模型，将只使用联网模型")
|
||||
self.offline_chatbot = None
|
||||
self.has_offline_model = False
|
||||
|
||||
logger.info("估值指标分析器初始化成功")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"初始化估值指标分析器失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def analyze_valuation_indicator(self, stock_code: str, stock_name: str) -> Dict[str, Any]:
|
||||
"""
|
||||
分析股票应该使用PE还是PB估值
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
|
||||
Returns:
|
||||
Dict包含分析结果
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始分析股票 {stock_name}({stock_code}) 的估值指标")
|
||||
|
||||
# 第一步：使用联网大模型进行初步分析
|
||||
online_result = self._analyze_with_online_model(stock_code, stock_name)
|
||||
|
||||
# 第二步：使用本地GLM模型进行格式化和验证
|
||||
if self.has_offline_model:
|
||||
offline_result = self._analyze_with_offline_model(stock_code, stock_name, online_result)
|
||||
else:
|
||||
offline_result = online_result
|
||||
|
||||
# 第三步：整合结果
|
||||
final_result = self._integrate_results(online_result, offline_result)
|
||||
|
||||
logger.info(f"完成股票 {stock_name}({stock_code}) 的估值指标分析")
|
||||
return final_result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析股票 {stock_name}({stock_code}) 估值指标时出错: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"recommended_indicator": None,
|
||||
"reasoning": None,
|
||||
"valuation_range": None
|
||||
}
|
||||
|
||||
def _analyze_with_online_model(self, stock_code: str, stock_name: str) -> Dict[str, Any]:
|
||||
"""
|
||||
使用联网大模型进行估值指标分析
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
|
||||
Returns:
|
||||
联网模型的分析结果
|
||||
"""
|
||||
try:
|
||||
# 构建用户提示词（让联网大模型专注于分析推理，不输出估值区间）
|
||||
user_prompt = f"""请深入分析股票代码为{stock_code}、名称为{stock_name}的公司，并判断其更适合使用PE还是PB进行估值。
|
||||
|
||||
你的分析应超越简单的行业标签，聚焦于该公司的个性化特征。请遵循以下分析框架，提供详细的、基于第一性原理的分析：
|
||||
|
||||
1. **盈利质量与可预测性分析**:
|
||||
* 这家公司的盈利是稳定、持续的，还是波动巨大、难以预测的？
|
||||
* 其净利润是否真实反映了业务的现金创造能力？
|
||||
* 是否存在大量非经常性损益影响了其盈利的真实性？
|
||||
|
||||
2. **资产价值与商业模式分析**:
|
||||
* 公司的核心价值更多体现在资产负债表内的有形资产（如设备、土地、金融资产），还是表外的无形资产（如品牌、技术、网络效应）？
|
||||
* 它的商业模式是“资产驱动型”还是“智力/品牌驱动型”？
|
||||
|
||||
3. **周期性与成长性分析**:
|
||||
* 公司所处行业的周期性强弱如何？它在周期中的位置是怎样的？
|
||||
* 公司自身的成长性如何，是高于还是低于行业平均水平？
|
||||
|
||||
4. **最终决策与依据**:
|
||||
* 综合以上分析，明确阐述你为什么认为PE或PB是更根本的估值指标。请详细说明你的决策逻辑，将公司的个性化特征与你的结论紧密联系起来。
|
||||
|
||||
请确保你的分析是客观、专业、有深度的。在本次分析中，请不要提供具体的估值区间，专注于提供选择估值指标的充分理由。"""
|
||||
|
||||
# 调用联网模型
|
||||
response = self.online_chatbot.chat(user_prompt, temperature=0.3, max_tokens=2048)
|
||||
|
||||
if response.get("success"):
|
||||
return {
|
||||
"success": True,
|
||||
"raw_response": response.get("response", ""),
|
||||
"model": "online"
|
||||
}
|
||||
else:
|
||||
logger.error(f"联网模型分析失败: {response.get('error', '未知错误')}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": response.get('error', '联网模型分析失败'),
|
||||
"model": "online"
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"使用联网模型分析时出错: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"model": "online"
|
||||
}
|
||||
|
||||
def _analyze_with_offline_model(self, stock_code: str, stock_name: str, online_result: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
使用本地GLM模型提取最终的PE/PB推荐
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
online_result: 联网模型的分析结果
|
||||
|
||||
Returns:
|
||||
本地模型的分析结果
|
||||
"""
|
||||
try:
|
||||
# 构建用户提示词（让本地GLM专注于提取最终结果）
|
||||
user_prompt = f"""你是一个专注于语义理解和结论提取的AI。你的唯一任务是阅读一段分析报告，理解其核心论点，并判断作者最终推荐的估值指标是 "PE" 还是 "PB"。
|
||||
|
||||
你的输出必须严格遵守以下规则：
|
||||
* 你的输出**必须且只能是** "PE" 或 "PB"。
|
||||
* 不要添加任何解释、理由或多余的字符。
|
||||
* 如果文本结论确实模棱两可，无法明确判断，**请默认输出 "PE"**。
|
||||
|
||||
分析内容如下：
|
||||
---
|
||||
{online_result.get('raw_response', '分析失败')}
|
||||
---
|
||||
"""
|
||||
|
||||
# 调用本地模型
|
||||
response = self.offline_chatbot.chat(user_prompt, temperature=0.1, max_tokens=10)
|
||||
|
||||
if response.get("success"):
|
||||
return {
|
||||
"success": True,
|
||||
"raw_response": response.get("response", "").strip(),
|
||||
"model": "offline"
|
||||
}
|
||||
else:
|
||||
logger.error(f"本地模型分析失败: {response.get('error', '未知错误')}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": response.get('error', '本地模型分析失败'),
|
||||
"model": "offline"
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"使用本地模型分析时出错: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"model": "offline"
|
||||
}
|
||||
|
||||
def _integrate_results(self, online_result: Dict[str, Any], offline_result: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
整合联网模型和本地模型的结果
|
||||
|
||||
Args:
|
||||
online_result: 联网模型结果
|
||||
offline_result: 本地模型结果
|
||||
|
||||
Returns:
|
||||
整合后的最终结果
|
||||
"""
|
||||
try:
|
||||
# 如果联网模型成功
|
||||
if online_result.get("success"):
|
||||
# 获取联网模型的分析内容
|
||||
online_analysis = online_result.get("raw_response", "")
|
||||
|
||||
# 如果本地模型也成功，使用本地模型提取的PE/PB推荐
|
||||
if offline_result.get("success"):
|
||||
recommended_indicator = offline_result.get("raw_response", "").strip()
|
||||
|
||||
# 验证推荐指标是否有效
|
||||
if recommended_indicator in ["PE", "PB"]:
|
||||
return {
|
||||
"success": True,
|
||||
"recommended_indicator": recommended_indicator,
|
||||
"reasoning": online_analysis
|
||||
}
|
||||
else:
|
||||
# 本地模型输出无效，使用联网模型的结果
|
||||
logger.warning(f"本地模型输出无效: {recommended_indicator}")
|
||||
return {
|
||||
"success": True,
|
||||
"recommended_indicator": self._extract_indicator_from_text(online_analysis),
|
||||
"reasoning": online_analysis
|
||||
}
|
||||
else:
|
||||
# 只有联网模型成功
|
||||
return {
|
||||
"success": True,
|
||||
"recommended_indicator": self._extract_indicator_from_text(online_analysis),
|
||||
"reasoning": online_analysis
|
||||
}
|
||||
else:
|
||||
# 联网模型失败
|
||||
return {
|
||||
"success": False,
|
||||
"error": online_result.get("error", "分析失败")
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"整合结果时出错: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def _extract_indicator_from_text(self, text: str) -> Optional[str]:
|
||||
"""从文本中提取推荐的估值指标"""
|
||||
try:
|
||||
import re
|
||||
|
||||
# 查找推荐估值指标
|
||||
patterns = [
|
||||
r'推荐估值指标[：:]\s*(PE|PB)',
|
||||
r'应该使用\s*(PE|PB)\s*估值',
|
||||
r'选择\s*(PE|PB)\s*作为',
|
||||
r'优先选择\s*(PE|PB)'
|
||||
]
|
||||
|
||||
for pattern in patterns:
|
||||
match = re.search(pattern, text, re.IGNORECASE)
|
||||
if match:
|
||||
return match.group(1).upper()
|
||||
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取估值指标时出错: {str(e)}")
|
||||
return None
|
||||
|
||||
def _extract_reasoning_from_text(self, text: str) -> Dict[str, str]:
|
||||
"""从文本中提取推理过程"""
|
||||
try:
|
||||
reasoning = {
|
||||
"industry_analysis": "",
|
||||
"business_model_analysis": "",
|
||||
"financial_analysis": "",
|
||||
"decision_basis": ""
|
||||
}
|
||||
|
||||
# 简单的文本提取逻辑
|
||||
lines = text.split('\n')
|
||||
current_section = None
|
||||
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
if '行业特征分析' in line or '行业分析' in line:
|
||||
current_section = 'industry_analysis'
|
||||
elif '商业模式分析' in line or '业务模式分析' in line:
|
||||
current_section = 'business_model_analysis'
|
||||
elif '财务特征分析' in line or '财务分析' in line:
|
||||
current_section = 'financial_analysis'
|
||||
elif '决策依据' in line or '选择依据' in line:
|
||||
current_section = 'decision_basis'
|
||||
elif current_section and line:
|
||||
reasoning[current_section] += line + " "
|
||||
|
||||
# 清理空白内容
|
||||
for key in reasoning:
|
||||
reasoning[key] = reasoning[key].strip()
|
||||
|
||||
return reasoning
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取推理过程时出错: {str(e)}")
|
||||
return {
|
||||
"industry_analysis": "",
|
||||
"business_model_analysis": "",
|
||||
"financial_analysis": "",
|
||||
"decision_basis": ""
|
||||
}
|
||||
|
||||
def _extract_valuation_range_from_text(self, text: str) -> Dict[str, Any]:
|
||||
"""从文本中提取估值区间"""
|
||||
try:
|
||||
import re
|
||||
|
||||
# 查找PE或PB的估值区间
|
||||
pe_pattern = r'PE.*?(\d+(?:\.\d+)?)[-~](\d+(?:\.\d+)?)'
|
||||
pb_pattern = r'PB.*?(\d+(?:\.\d+)?)[-~](\d+(?:\.\d+)?)'
|
||||
|
||||
pe_match = re.search(pe_pattern, text, re.IGNORECASE)
|
||||
pb_match = re.search(pb_pattern, text, re.IGNORECASE)
|
||||
|
||||
if pe_match:
|
||||
return {
|
||||
"type": "PE",
|
||||
"min_value": float(pe_match.group(1)),
|
||||
"max_value": float(pe_match.group(2)),
|
||||
"unit": "倍"
|
||||
}
|
||||
elif pb_match:
|
||||
return {
|
||||
"type": "PB",
|
||||
"min_value": float(pb_match.group(1)),
|
||||
"max_value": float(pb_match.group(2)),
|
||||
"unit": "倍"
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"type": None,
|
||||
"min_value": None,
|
||||
"max_value": None,
|
||||
"unit": "倍"
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取估值区间时出错: {str(e)}")
|
||||
return {
|
||||
"type": None,
|
||||
"min_value": None,
|
||||
"max_value": None,
|
||||
"unit": "倍"
|
||||
}
|
||||
|
||||
|
||||
def test_valuation_analyzer():
|
||||
"""测试估值指标分析器"""
|
||||
try:
|
||||
analyzer = ValuationIndicatorAnalyzer()
|
||||
|
||||
# 测试用例
|
||||
test_cases = [
|
||||
("000001", "平安银行"), # 金融业，应该推荐PB
|
||||
("000002", "万科A"), # 房地产，应该推荐PB
|
||||
("000858", "五粮液"), # 消费品，应该推荐PE
|
||||
("002415", "海康威视"), # 科技股，应该推荐PE
|
||||
]
|
||||
|
||||
for stock_code, stock_name in test_cases:
|
||||
print(f"\n测试股票: {stock_name}({stock_code})")
|
||||
result = analyzer.analyze_valuation_indicator(stock_code, stock_name)
|
||||
|
||||
if result.get("success"):
|
||||
print(f"推荐指标: {result.get('recommended_indicator')}")
|
||||
print(f"推理过程: {result.get('reasoning')}")
|
||||
print(f"估值区间: {result.get('valuation_range')}")
|
||||
else:
|
||||
print(f"分析失败: {result.get('error')}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"测试失败: {str(e)}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_valuation_analyzer()
|
||||
Loading…
Reference in New Issue