816 lines
35 KiB
Python
816 lines
35 KiB
Python
import sys
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import os
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from src.fundamentals_llm.fundamental_analysis_database import get_analysis_result, get_db
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# 添加项目根目录到 Python 路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from flask import Flask, jsonify, request
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from flask_cors import CORS
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import logging
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# 导入企业筛选器
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from src.fundamentals_llm.enterprise_screener import EnterpriseScreener
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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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# 创建 Flask 应用
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app = Flask(__name__)
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CORS(app) # 启用跨域请求支持
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# 创建企业筛选器实例
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screener = EnterpriseScreener()
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# 获取数据库连接
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db = next(get_db())
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@app.route('/api/health', methods=['GET'])
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def health_check():
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"""健康检查接口"""
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return jsonify({"status": "ok", "message": "Service is running"})
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@app.route('/api/stock_profiles', methods=['GET'])
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def get_stock_profiles():
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"""获取企业画像筛选结果列表"""
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return jsonify({
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"profiles": [
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{"id": 1, "name": "高成长潜力企业", "method": "high_growth"},
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{"id": 2, "name": "稳定型龙头企业", "method": "stable_leaders"},
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{"id": 3, "name": "短期投资机会", "method": "short_term"},
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{"id": 4, "name": "价值型投资标的", "method": "value_investment"},
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{"id": 5, "name": "困境反转机会", "method": "turnaround"},
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{"id": 6, "name": "风险规避标的", "method": "risk_averse"},
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{"id": 7, "name": "创新驱动型企业", "method": "innovation_driven"},
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{"id": 8, "name": "行业整合机会", "method": "industry_integration"},
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{"id": 9, "name": "推荐投资企业", "method": "recommended"}
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]
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})
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@app.route('/api/screen/<profile_type>', methods=['GET'])
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def screen_stocks(profile_type):
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"""根据企业画像类型筛选股票
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Args:
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profile_type: 企业画像类型 (method 字段值)
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"""
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try:
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# 解析limit参数
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limit = request.args.get('limit', None, type=int)
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# 处理别名
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if profile_type == 'innovation_driven':
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profile_type = 'innovation'
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elif profile_type == 'industry_integration':
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profile_type = 'integration'
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# 映射画像类型到筛选方法
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method_map = {
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'high_growth': screener.screen_high_growth_stocks,
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'stable_leaders': screener.screen_stable_leaders,
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'short_term': screener.screen_short_term_opportunities,
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'value_investment': screener.screen_value_investment_stocks,
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'turnaround': screener.screen_turnaround_opportunities,
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'risk_averse': screener.screen_risk_averse_stocks,
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'innovation': screener.screen_innovation_driven_stocks,
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'integration': screener.screen_industry_integration_stocks,
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'recommended': screener.screen_multi_term_investment_stocks
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}
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# 检查请求的画像类型是否有效
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if profile_type not in method_map:
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return jsonify({
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"status": "error",
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"message": f"Invalid profile type: {profile_type}"
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}), 400
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# 调用相应的筛选方法
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stocks = method_map[profile_type](limit=limit)
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# 转换结果格式
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result = []
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for code, name in stocks:
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result.append({
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"code": code,
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"name": name
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})
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return jsonify({
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"status": "success",
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"profile_type": profile_type,
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"count": len(result),
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"stocks": result
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})
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except Exception as e:
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logger.error(f"筛选股票失败: {str(e)}")
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return jsonify({
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"status": "error",
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"message": f"Failed to screen stocks: {str(e)}"
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}), 500
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# 条件筛选接口，接受自定义的筛选条件
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@app.route('/api/screen/custom', methods=['POST'])
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def screen_custom():
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"""使用自定义条件筛选股票"""
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try:
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# 从请求体获取筛选条件
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data = request.get_json()
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if not data or 'conditions' not in data:
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return jsonify({
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"status": "error",
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"message": "Missing conditions in request body"
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}), 400
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# 调用通用筛选方法
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stocks = screener._screen_stocks_by_conditions(data['conditions'])
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# 转换结果格式
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result = []
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for code, name in stocks:
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result.append({
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"code": code,
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"name": name
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})
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return jsonify({
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"status": "success",
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"count": len(result),
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"stocks": result
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})
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except Exception as e:
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logger.error(f"自定义筛选股票失败: {str(e)}")
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return jsonify({
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"status": "error",
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"message": f"Failed to screen stocks with custom conditions: {str(e)}"
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}), 500
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@app.route('/api/generate_reports', methods=['POST'])
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def generate_reports():
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"""为指定的股票列表生成PDF投资报告
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请求体格式:
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{
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"stocks": [
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["603690", "至纯科技"],
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["688053", "思科瑞"],
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["300750", "宁德时代"]
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]
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}
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"""
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try:
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# 从请求体获取股票列表
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data = request.get_json()
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if not data or 'stocks' not in data or not isinstance(data['stocks'], list):
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return jsonify({
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"status": "error",
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"message": "请求格式错误: 需要提供股票列表"
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}), 400
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# 检查股票列表格式
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stocks = data['stocks']
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if not all(isinstance(item, list) and len(item) == 2 for item in stocks):
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return jsonify({
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"status": "error",
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"message": "股票列表格式错误: 每个股票应该是 [代码, 名称] 格式"
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}), 400
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# 导入 PDF 生成器模块
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try:
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from src.fundamentals_llm.pdf_generator import generate_investment_report
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except ImportError:
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try:
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from fundamentals_llm.pdf_generator import generate_investment_report
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except ImportError as e:
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logger.error(f"无法导入 PDF 生成器模块: {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: PDF 生成器模块不可用, 错误详情: {str(e)}"
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}), 500
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# 生成报告
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generated_reports = []
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for stock_code, stock_name in stocks:
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try:
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# 调用 PDF 生成器
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report_path = generate_investment_report(stock_code, stock_name)
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generated_reports.append({
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"code": stock_code,
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"name": stock_name,
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"report_path": report_path,
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"status": "success"
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})
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logger.info(f"成功生成 {stock_name}({stock_code}) 的投资报告: {report_path}")
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except Exception as e:
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logger.error(f"生成 {stock_name}({stock_code}) 的投资报告失败: {str(e)}")
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generated_reports.append({
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"code": stock_code,
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"name": stock_name,
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"status": "error",
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"error": str(e)
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})
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# 返回结果
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return jsonify({
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"status": "success",
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"message": f"处理了 {len(stocks)} 个股票的报告生成请求",
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"reports": generated_reports
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})
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except Exception as e:
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logger.error(f"处理报告生成请求失败: {str(e)}")
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return jsonify({
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"status": "error",
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"message": f"Failed to process report generation request: {str(e)}"
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}), 500
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@app.route('/api/recommended_stocks', methods=['GET'])
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def recommended_stocks():
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"""获取系统推荐的投资股票
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该接口会返回系统基于基本面分析推荐的股票列表，具有较高投资价值
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可以通过以下参数进行过滤:
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- profile_type: 企业画像类型，例如 'high_growth', 'stable_leaders' 等
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- limit: 限制返回的股票数量
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"""
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try:
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# 解析请求参数
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profile_type = request.args.get('profile_type', 'high_growth')
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limit = request.args.get('limit', 10, type=int)
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# 导入筛选器模块
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try:
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from src.fundamentals_llm.enterprise_screener import EnterpriseScreener
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except ImportError:
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try:
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from fundamentals_llm.enterprise_screener import EnterpriseScreener
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except ImportError as e:
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logger.error(f"无法导入企业筛选器模块: {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: 企业筛选器模块不可用, 错误详情: {str(e)}"
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}), 500
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# 创建筛选器实例
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screener = EnterpriseScreener()
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# 根据类型获取推荐股票
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try:
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if profile_type == 'recommended':
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stocks = screener.screen_multi_term_investment_stocks(limit=limit)
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elif profile_type == 'high_growth':
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stocks = screener.screen_high_growth_stocks(limit=limit)
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elif profile_type == 'stable_leaders':
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stocks = screener.screen_stable_leaders(limit=limit)
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elif profile_type == 'short_term':
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stocks = screener.screen_short_term_opportunities(limit=limit)
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elif profile_type == 'value_investment':
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stocks = screener.screen_value_investment_stocks(limit=limit)
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elif profile_type == 'turnaround':
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stocks = screener.screen_turnaround_opportunities(limit=limit)
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elif profile_type == 'risk_averse':
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stocks = screener.screen_risk_averse_stocks(limit=limit)
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elif profile_type == 'innovation' or profile_type == 'innovation_driven':
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stocks = screener.screen_innovation_driven_stocks(limit=limit)
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elif profile_type == 'integration' or profile_type == 'industry_integration':
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stocks = screener.screen_industry_integration_stocks(limit=limit)
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elif profile_type == 'custom' and 'conditions' in request.args:
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# 解析自定义条件
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try:
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import json
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conditions = json.loads(request.args.get('conditions'))
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stocks = screener.screen_by_custom_conditions(conditions, limit=limit)
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except Exception as e:
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return jsonify({
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"status": "error",
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"message": f"解析自定义条件失败: {str(e)}"
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}), 400
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else:
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return jsonify({
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"status": "error",
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"message": f"不支持的企业画像类型: {profile_type}"
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}), 400
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except Exception as e:
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logger.error(f"筛选股票时出错: {str(e)}")
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return jsonify({
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"status": "error",
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"message": f"筛选股票时出错: {str(e)}"
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}), 500
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# 格式化返回结果
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formatted_stocks = []
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for stock_code, stock_name in stocks:
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formatted_stocks.append({
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"code": stock_code,
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"name": stock_name
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})
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# 返回结果
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return jsonify({
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"status": "success",
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"profile_type": profile_type,
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"count": len(formatted_stocks),
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"stocks": formatted_stocks
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})
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except Exception as e:
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logger.error(f"获取推荐股票失败: {str(e)}")
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return jsonify({
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"status": "error",
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"message": f"获取推荐股票失败: {str(e)}"
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}), 500
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@app.route('/api/analyze_and_recommend', methods=['POST'])
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def analyze_and_recommend():
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"""分析指定企业列表，生成投资建议，并筛选推荐投资的企业
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请求体格式:
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{
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"stocks": [
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["603690", "至纯科技"],
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["688053", "思科瑞"],
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["300750", "宁德时代"]
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],
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"limit": 10 // 可选，限制返回推荐股票的数量
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}
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"""
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try:
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# 从请求体获取股票列表
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data = request.get_json()
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if not data or 'stocks' not in data or not isinstance(data['stocks'], list):
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return jsonify({
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"status": "error",
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"message": "请求格式错误: 需要提供股票列表"
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}), 400
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# 检查股票列表格式
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stocks = data['stocks']
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if not all(isinstance(item, list) and len(item) == 2 for item in stocks):
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return jsonify({
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"status": "error",
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"message": "股票列表格式错误: 每个股票应该是 [代码, 名称] 格式"
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}), 400
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# 解析limit参数
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limit = data.get('limit', 10)
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if not isinstance(limit, int) or limit <= 0:
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limit = 10
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# 导入必要的聊天机器人模块
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try:
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# 首先尝试导入聊天机器人模块
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try:
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from src.fundamentals_llm.chat_bot import ChatBot as OnlineChatBot
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logger.info("成功从 src.fundamentals_llm.chat_bot 导入 ChatBot")
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except ImportError as e1:
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try:
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from fundamentals_llm.chat_bot import ChatBot as OnlineChatBot
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logger.info("成功从 fundamentals_llm.chat_bot 导入 ChatBot")
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except ImportError as e2:
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logger.error(f"无法导入在线聊天机器人模块: {str(e1)}, {str(e2)}")
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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: 聊天机器人模块不可用，错误详情: {str(e2)}"
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}), 500
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# 然后尝试导入离线聊天机器人模块
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try:
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from src.fundamentals_llm.chat_bot_with_offline import ChatBot as OfflineChatBot
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logger.info("成功从 src.fundamentals_llm.chat_bot_with_offline 导入 ChatBot")
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except ImportError as e1:
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try:
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from fundamentals_llm.chat_bot_with_offline import ChatBot as OfflineChatBot
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logger.info("成功从 fundamentals_llm.chat_bot_with_offline 导入 ChatBot")
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except ImportError as e2:
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logger.warning(f"无法导入离线聊天机器人模块: {str(e1)}, {str(e2)}")
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# 这里可以继续执行，因为某些功能可能不需要离线模型
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# 最后导入基本面分析器
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try:
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from src.fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
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logger.info("成功从 src.fundamentals_llm.fundamental_analysis 导入 FundamentalAnalyzer")
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except ImportError as e1:
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try:
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from fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
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logger.info("成功从 fundamentals_llm.fundamental_analysis 导入 FundamentalAnalyzer")
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except ImportError as e2:
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logger.error(f"无法导入基本面分析模块: {str(e1)}, {str(e2)}")
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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: 基本面分析模块不可用，错误详情: {str(e2)}"
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}), 500
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except Exception as e:
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logger.error(f"导入必要模块时出错: {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: 导入必要模块时出错，错误详情: {str(e)}"
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}), 500
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# 创建基本面分析器实例
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analyzer = FundamentalAnalyzer()
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# 为每个股票生成投资建议
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investment_advices = []
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for stock_code, stock_name in stocks:
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try:
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# 生成投资建议
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success, advice, reasoning, references = analyzer.query_analysis(
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stock_code, stock_name, "investment_advice"
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)
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if success:
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investment_advices.append({
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"code": stock_code,
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"name": stock_name,
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"advice": advice,
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"reasoning": reasoning,
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"references": references,
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"status": "success"
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})
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logger.info(f"成功生成 {stock_name}({stock_code}) 的投资建议")
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else:
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investment_advices.append({
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"code": stock_code,
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"name": stock_name,
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"status": "error",
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"error": advice
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})
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logger.error(f"生成 {stock_name}({stock_code}) 的投资建议失败: {advice}")
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except Exception as e:
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logger.error(f"处理 {stock_name}({stock_code}) 时出错: {str(e)}")
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investment_advices.append({
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"code": stock_code,
|
||
"name": stock_name,
|
||
"status": "error",
|
||
"error": str(e)
|
||
})
|
||
|
||
# 导入企业筛选器
|
||
try:
|
||
from src.fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||
except ImportError:
|
||
try:
|
||
from fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||
except ImportError as e:
|
||
logger.error(f"无法导入企业筛选器模块: {str(e)}")
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": f"服务器配置错误: 企业筛选器模块不可用，错误详情: {str(e)}"
|
||
}), 500
|
||
|
||
# 创建筛选器实例
|
||
screener = EnterpriseScreener()
|
||
|
||
# 筛选符合推荐投资条件的股票
|
||
# 获取传入的所有股票代码
|
||
input_stock_codes = set(code for code, _ in stocks)
|
||
|
||
# 获取所有符合推荐投资条件的股票
|
||
recommended_stocks = screener.screen_multi_term_investment_stocks(limit=limit)
|
||
|
||
# 筛选出传入列表中符合推荐投资条件的股票
|
||
recommended_input_stocks = []
|
||
for code, name in recommended_stocks:
|
||
if code in input_stock_codes:
|
||
recommended_input_stocks.append({
|
||
"code": code,
|
||
"name": name
|
||
})
|
||
|
||
# 返回结果
|
||
return jsonify({
|
||
"status": "success",
|
||
"total_input_stocks": len(stocks),
|
||
"investment_advices": investment_advices,
|
||
"recommended_stocks": {
|
||
"count": len(recommended_input_stocks),
|
||
"stocks": recommended_input_stocks
|
||
}
|
||
})
|
||
|
||
except Exception as e:
|
||
logger.error(f"分析和推荐股票失败: {str(e)}")
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": f"分析和推荐股票失败: {str(e)}"
|
||
}), 500
|
||
|
||
@app.route('/api/comprehensive_analysis', methods=['POST'])
|
||
def comprehensive_analysis():
|
||
"""综合分析接口 - 组合多种功能和参数
|
||
|
||
请求体格式:
|
||
{
|
||
"stocks": [
|
||
["603690", "至纯科技"],
|
||
["688053", "思科瑞"],
|
||
["300750", "宁德时代"]
|
||
],
|
||
"generate_pdf": true,
|
||
"limit": 10, // 可选，限制返回推荐股票的数量
|
||
"profile_filter": {
|
||
"type": "custom", // 可选值: 预定义的画像类型或 "custom"
|
||
"profile_name": "high_growth", // 当 type = 预定义画像类型时使用
|
||
"conditions": [ // 当 type = "custom" 时使用
|
||
{
|
||
"dimension": "financial_report",
|
||
"field": "financial_report_level",
|
||
"operator": ">=",
|
||
"value": 1
|
||
},
|
||
// 其他条件...
|
||
]
|
||
}
|
||
}
|
||
"""
|
||
try:
|
||
# 从请求体获取参数
|
||
data = request.get_json()
|
||
|
||
if not data or 'stocks' not in data or not isinstance(data['stocks'], list):
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": "请求格式错误: 需要提供股票列表"
|
||
}), 400
|
||
|
||
# 检查股票列表格式
|
||
stocks = data['stocks']
|
||
if not all(isinstance(item, list) and len(item) == 2 for item in stocks):
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": "股票列表格式错误: 每个股票应该是 [代码, 名称] 格式"
|
||
}), 400
|
||
|
||
# 解析其他参数
|
||
generate_pdf = data.get('generate_pdf', False)
|
||
profile_filter = data.get('profile_filter', None)
|
||
limit = data.get('limit', 10)
|
||
if not isinstance(limit, int) or limit <= 0:
|
||
limit = 10
|
||
|
||
# 导入必要的聊天机器人模块
|
||
try:
|
||
# 首先尝试导入聊天机器人模块
|
||
try:
|
||
from src.fundamentals_llm.chat_bot import ChatBot as OnlineChatBot
|
||
logger.info("成功从 src.fundamentals_llm.chat_bot 导入 ChatBot")
|
||
except ImportError as e1:
|
||
try:
|
||
from fundamentals_llm.chat_bot import ChatBot as OnlineChatBot
|
||
logger.info("成功从 fundamentals_llm.chat_bot 导入 ChatBot")
|
||
except ImportError as e2:
|
||
logger.error(f"无法导入在线聊天机器人模块: {str(e1)}, {str(e2)}")
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": f"服务器配置错误: 聊天机器人模块不可用，错误详情: {str(e2)}"
|
||
}), 500
|
||
|
||
# 然后尝试导入离线聊天机器人模块
|
||
try:
|
||
from src.fundamentals_llm.chat_bot_with_offline import ChatBot as OfflineChatBot
|
||
logger.info("成功从 src.fundamentals_llm.chat_bot_with_offline 导入 ChatBot")
|
||
except ImportError as e1:
|
||
try:
|
||
from fundamentals_llm.chat_bot_with_offline import ChatBot as OfflineChatBot
|
||
logger.info("成功从 fundamentals_llm.chat_bot_with_offline 导入 ChatBot")
|
||
except ImportError as e2:
|
||
logger.warning(f"无法导入离线聊天机器人模块: {str(e1)}, {str(e2)}")
|
||
# 这里可以继续执行，因为某些功能可能不需要离线模型
|
||
|
||
# 最后导入基本面分析器
|
||
try:
|
||
from src.fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
|
||
logger.info("成功从 src.fundamentals_llm.fundamental_analysis 导入 FundamentalAnalyzer")
|
||
except ImportError as e1:
|
||
try:
|
||
from fundamentals_llm.fundamental_analysis import FundamentalAnalyzer
|
||
logger.info("成功从 fundamentals_llm.fundamental_analysis 导入 FundamentalAnalyzer")
|
||
except ImportError as e2:
|
||
logger.error(f"无法导入基本面分析模块: {str(e1)}, {str(e2)}")
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": f"服务器配置错误: 基本面分析模块不可用，错误详情: {str(e2)}"
|
||
}), 500
|
||
except Exception as e:
|
||
logger.error(f"导入必要模块时出错: {str(e)}")
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": f"服务器配置错误: 导入必要模块时出错，错误详情: {str(e)}"
|
||
}), 500
|
||
|
||
# 创建基本面分析器实例
|
||
analyzer = FundamentalAnalyzer()
|
||
|
||
# 为每个股票生成投资建议
|
||
investment_advices = []
|
||
for stock_code, stock_name in stocks:
|
||
try:
|
||
# 生成投资建议
|
||
success, advice, reasoning, references = analyzer.query_analysis(
|
||
stock_code, stock_name, "investment_advice"
|
||
)
|
||
|
||
if success:
|
||
investment_advices.append({
|
||
"code": stock_code,
|
||
"name": stock_name,
|
||
"advice": advice,
|
||
"reasoning": reasoning,
|
||
"references": references,
|
||
"status": "success"
|
||
})
|
||
logger.info(f"成功生成 {stock_name}({stock_code}) 的投资建议")
|
||
else:
|
||
investment_advices.append({
|
||
"code": stock_code,
|
||
"name": stock_name,
|
||
"status": "error",
|
||
"error": advice
|
||
})
|
||
logger.error(f"生成 {stock_name}({stock_code}) 的投资建议失败: {advice}")
|
||
except Exception as e:
|
||
logger.error(f"处理 {stock_name}({stock_code}) 时出错: {str(e)}")
|
||
investment_advices.append({
|
||
"code": stock_code,
|
||
"name": stock_name,
|
||
"status": "error",
|
||
"error": str(e)
|
||
})
|
||
|
||
# 生成PDF报告（如果需要）
|
||
pdf_results = []
|
||
if generate_pdf:
|
||
try:
|
||
# 导入PDF生成器模块
|
||
try:
|
||
from src.fundamentals_llm.pdf_generator import generate_investment_report
|
||
except ImportError:
|
||
try:
|
||
from fundamentals_llm.pdf_generator import generate_investment_report
|
||
except ImportError as e:
|
||
logger.error(f"无法导入 PDF 生成器模块: {str(e)}")
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": f"服务器配置错误: PDF 生成器模块不可用, 错误详情: {str(e)}"
|
||
}), 500
|
||
|
||
# 生成报告
|
||
for stock_code, stock_name in stocks:
|
||
try:
|
||
# 调用 PDF 生成器
|
||
report_path = generate_investment_report(stock_code, stock_name)
|
||
pdf_results.append({
|
||
"code": stock_code,
|
||
"name": stock_name,
|
||
"report_path": report_path,
|
||
"status": "success"
|
||
})
|
||
logger.info(f"成功生成 {stock_name}({stock_code}) 的投资报告: {report_path}")
|
||
except Exception as e:
|
||
logger.error(f"生成 {stock_name}({stock_code}) 的投资报告失败: {str(e)}")
|
||
pdf_results.append({
|
||
"code": stock_code,
|
||
"name": stock_name,
|
||
"status": "error",
|
||
"error": str(e)
|
||
})
|
||
except Exception as e:
|
||
logger.error(f"处理PDF生成请求失败: {str(e)}")
|
||
pdf_results = []
|
||
|
||
# 应用企业画像筛选
|
||
filtered_stocks = []
|
||
if profile_filter:
|
||
try:
|
||
# 导入企业筛选器
|
||
try:
|
||
from src.fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||
except ImportError:
|
||
try:
|
||
from fundamentals_llm.enterprise_screener import EnterpriseScreener
|
||
except ImportError as e:
|
||
logger.error(f"无法导入企业筛选器模块: {str(e)}")
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": f"服务器配置错误: 企业筛选器模块不可用，错误详情: {str(e)}"
|
||
}), 500
|
||
|
||
# 创建筛选器实例
|
||
screener = EnterpriseScreener()
|
||
|
||
# 根据筛选类型获取推荐股票
|
||
filter_type = profile_filter.get('type', 'custom')
|
||
|
||
# 如果是自定义条件筛选
|
||
if filter_type == 'custom':
|
||
conditions = profile_filter.get('conditions', [])
|
||
if conditions:
|
||
print(f"使用自定义条件筛选: {len(conditions)} 个条件")
|
||
all_stocks = screener.screen_by_custom_conditions(conditions, limit=limit)
|
||
else:
|
||
# 如果没有提供条件，使用默认的high_growth类型
|
||
print("未提供自定义条件，使用默认的high_growth类型")
|
||
all_stocks = screener.screen_high_growth_stocks(limit=limit)
|
||
# 如果是预定义类型
|
||
else:
|
||
# 直接使用type作为画像类型，如果用户同时提供了profile_name也兼容处理
|
||
profile_name = profile_filter.get('profile_name', filter_type)
|
||
|
||
print(f"使用预定义画像类型: {profile_name}")
|
||
|
||
if profile_name == 'high_growth':
|
||
all_stocks = screener.screen_high_growth_stocks(limit=limit)
|
||
elif profile_name == 'stable_leaders':
|
||
all_stocks = screener.screen_stable_leaders(limit=limit)
|
||
elif profile_name == 'short_term':
|
||
all_stocks = screener.screen_short_term_opportunities(limit=limit)
|
||
elif profile_name == 'value_investment':
|
||
all_stocks = screener.screen_value_investment_stocks(limit=limit)
|
||
elif profile_name == 'turnaround':
|
||
all_stocks = screener.screen_turnaround_opportunities(limit=limit)
|
||
elif profile_name == 'risk_averse':
|
||
all_stocks = screener.screen_risk_averse_stocks(limit=limit)
|
||
elif profile_name == 'innovation' or profile_name == 'innovation_driven':
|
||
all_stocks = screener.screen_innovation_driven_stocks(limit=limit)
|
||
elif profile_name == 'integration' or profile_name == 'industry_integration':
|
||
all_stocks = screener.screen_industry_integration_stocks(limit=limit)
|
||
elif profile_name == 'recommended':
|
||
all_stocks = screener.screen_multi_term_investment_stocks(limit=limit)
|
||
else:
|
||
# 如果类型无效，使用默认的high_growth类型
|
||
logger.warning(f"无效的画像类型: {profile_name}，使用默认的high_growth类型")
|
||
all_stocks = screener.screen_high_growth_stocks(limit=limit)
|
||
|
||
# 获取传入的所有股票代码
|
||
input_stock_codes = set(code for code, _ in stocks)
|
||
|
||
# 筛选出传入列表中符合条件的股票
|
||
for code, name in all_stocks:
|
||
if code in input_stock_codes:
|
||
# 获取各个维度的分析结果
|
||
investment_advice_result = get_analysis_result(db, code, "investment_advice")
|
||
industry_competition_result = get_analysis_result(db, code, "industry_competition")
|
||
financial_report_result = get_analysis_result(db, code, "financial_report")
|
||
valuation_level_result = get_analysis_result(db, code, "valuation_level")
|
||
|
||
# 从ai_response和extra_info中提取所需的值
|
||
investment_advice = investment_advice_result.ai_response if investment_advice_result else None
|
||
industry_space = industry_competition_result.extra_info.get("industry_space") if industry_competition_result else 0
|
||
financial_report_level = financial_report_result.extra_info.get("financial_report_level") if financial_report_result else 0
|
||
pe_industry = valuation_level_result.extra_info.get("pe_industry") if valuation_level_result else 0
|
||
|
||
filtered_stocks.append({
|
||
"code": code,
|
||
"name": name,
|
||
"investment_advice": investment_advice, # 投资建议（从ai_response获取）
|
||
"industry_space": industry_space, # 行业发展空间（2:高速增长, 1:稳定经营, 0:不确定性大, -1:不利经营）
|
||
"financial_report_level": financial_report_level, # 经营质量（2:优秀, 1:较好, 0:一般, -1:存在隐患，-2：较大隐患）
|
||
"pe_industry": pe_industry # 个股在行业的PE水平（-1:高于行业, 0:接近行业, 1:低于行业）
|
||
})
|
||
|
||
logger.info(f"筛选出 {len(filtered_stocks)} 个符合条件的股票")
|
||
|
||
except Exception as e:
|
||
logger.error(f"应用企业画像筛选失败: {str(e)}")
|
||
filtered_stocks = []
|
||
|
||
# 返回结果
|
||
response = {
|
||
"status": "success",
|
||
"total_input_stocks": len(stocks),
|
||
"investment_advices": investment_advices
|
||
}
|
||
|
||
if profile_filter:
|
||
response["filtered_stocks"] = {
|
||
"count": len(filtered_stocks),
|
||
"stocks": filtered_stocks
|
||
}
|
||
|
||
if generate_pdf:
|
||
response["pdf_results"] = {
|
||
"count": len(pdf_results),
|
||
"reports": pdf_results
|
||
}
|
||
|
||
return jsonify(response)
|
||
|
||
except Exception as e:
|
||
logger.error(f"综合分析失败: {str(e)}")
|
||
return jsonify({
|
||
"status": "error",
|
||
"message": f"综合分析失败: {str(e)}"
|
||
}), 500
|
||
|
||
if __name__ == '__main__':
|
||
app.run(host='0.0.0.0', port=5000, debug=True) |