oficcejo
393 lines
14 KiB
Python
393 lines
14 KiB
Python
"""
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智瞰龙虎综合分析引擎
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整合数据获取、AI分析、结果生成的核心引擎
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"""
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from longhubang_data import LonghubangDataFetcher
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from longhubang_db import LonghubangDatabase
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from longhubang_agents import LonghubangAgents
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from longhubang_scoring import LonghubangScoring
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from typing import Dict, Any, List
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from datetime import datetime, timedelta
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import time
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import logging
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import config
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class LonghubangEngine:
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"""龙虎榜综合分析引擎"""
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def __init__(self, model=None, db_path='longhubang.db'):
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"""
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初始化分析引擎
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Args:
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model: AI模型名称
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db_path: 数据库路径
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"""
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self.data_fetcher = LonghubangDataFetcher()
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self.database = LonghubangDatabase(db_path)
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self.agents = LonghubangAgents(model=model)
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self.scoring = LonghubangScoring()
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# 初始化日志
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self.logger = logging.getLogger(__name__)
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if not self.logger.handlers:
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logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s %(name)s: %(message)s')
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self.logger.info("[智瞰龙虎] 分析引擎初始化完成")
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def run_comprehensive_analysis(self, date=None, days=1) -> Dict[str, Any]:
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"""
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运行完整的龙虎榜分析流程
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Args:
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date: 指定日期，格式 YYYY-MM-DD，默认为昨日
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days: 分析最近几天的数据，默认1天
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Returns:
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完整的分析结果
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"""
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self.logger.info("=" * 60)
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self.logger.info("🚀 智瞰龙虎综合分析系统启动")
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self.logger.info("=" * 60)
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results = {
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"success": False,
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"timestamp": datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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"data_info": {},
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"agents_analysis": {},
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"final_report": {},
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"recommended_stocks": []
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}
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try:
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# 阶段1: 获取龙虎榜数据
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self.logger.info("[阶段1] 获取龙虎榜数据...")
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self.logger.info("-" * 60)
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if date:
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data_list = [self.data_fetcher.get_longhubang_data(date)]
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data_list = data_list[0].get('data', []) if data_list[0] else []
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else:
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data_list = self.data_fetcher.get_recent_days_data(days)
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if not data_list:
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self.logger.error("未获取到龙虎榜数据")
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results["error"] = "未获取到龙虎榜数据"
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return results
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self.logger.info(f"成功获取 {len(data_list)} 条龙虎榜记录")
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# 阶段2: 保存数据到数据库
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self.logger.info("[阶段2] 保存数据到数据库...")
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self.logger.info("-" * 60)
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saved_count = self.database.save_longhubang_data(data_list)
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self.logger.info(f"保存 {saved_count} 条记录")
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# 阶段3: 数据分析和统计
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self.logger.info("[阶段3] 数据分析和统计...")
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self.logger.info("-" * 60)
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summary = self.data_fetcher.analyze_data_summary(data_list)
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formatted_data = self.data_fetcher.format_data_for_ai(data_list, summary)
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results["data_info"] = {
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"total_records": summary.get('total_records', 0),
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"total_stocks": summary.get('total_stocks', 0),
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"total_youzi": summary.get('total_youzi', 0),
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"summary": summary
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}
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self.logger.info("数据统计完成")
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# 阶段3.5: AI智能评分排名
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self.logger.info("[阶段3.5] AI智能评分排名...")
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self.logger.info("-" * 60)
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scoring_df = self.scoring.score_all_stocks(data_list)
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# 转换为可序列化格式以避免UI/存储类型问题
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scoring_ranking_data: List[Dict[str, Any]] = []
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try:
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if scoring_df is not None and hasattr(scoring_df, 'to_dict'):
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scoring_ranking_data = scoring_df.to_dict('records')
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self.logger.info(f"完成 {len(scoring_ranking_data)} 只股票的智能评分排名")
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else:
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self.logger.warning("评分结果为空或格式不支持转换")
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except Exception as e:
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self.logger.exception(f"评分排名数据转换失败: {e}", exc_info=True)
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scoring_ranking_data = []
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results["scoring_ranking"] = scoring_ranking_data
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# 阶段4: AI分析师团队分析
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self.logger.info("[阶段4] AI分析师团队工作中...")
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self.logger.info("-" * 60)
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agents_results = {}
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# 1. 游资行为分析师
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self.logger.info("1/5 游资行为分析师...")
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youzi_result = self.agents.youzi_behavior_analyst(formatted_data, summary)
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agents_results["youzi"] = youzi_result
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# 2. 个股潜力分析师
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self.logger.info("2/5 个股潜力分析师...")
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stock_result = self.agents.stock_potential_analyst(formatted_data, summary)
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agents_results["stock"] = stock_result
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# 3. 题材追踪分析师
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self.logger.info("3/5 题材追踪分析师...")
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theme_result = self.agents.theme_tracker_analyst(formatted_data, summary)
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agents_results["theme"] = theme_result
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# 4. 风险控制专家
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self.logger.info("4/5 风险控制专家...")
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risk_result = self.agents.risk_control_specialist(formatted_data, summary)
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agents_results["risk"] = risk_result
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# 5. 首席策略师综合
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self.logger.info("5/5 首席策略师综合分析...")
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all_analyses = [youzi_result, stock_result, theme_result, risk_result]
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chief_result = self.agents.chief_strategist(all_analyses)
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agents_results["chief"] = chief_result
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results["agents_analysis"] = agents_results
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self.logger.info("所有AI分析师分析完成")
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# 阶段5: 提取推荐股票
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self.logger.info("[阶段5] 提取推荐股票...")
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self.logger.info("-" * 60)
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recommended_stocks = self._extract_recommended_stocks(
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chief_result.get('analysis', ''),
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stock_result.get('analysis', ''),
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summary
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)
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results["recommended_stocks"] = recommended_stocks
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self.logger.info(f"提取 {len(recommended_stocks)} 只推荐股票")
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# 阶段6: 生成最终报告
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self.logger.info("[阶段6] 生成最终报告...")
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self.logger.info("-" * 60)
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final_report = self._generate_final_report(agents_results, summary, recommended_stocks)
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results["final_report"] = final_report
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self.logger.info("最终报告生成完成")
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# 阶段7: 保存完整分析报告到数据库
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self.logger.info("[阶段7] 保存完整分析报告...")
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self.logger.info("-" * 60)
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data_date_range = self._get_date_range(data_list)
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# 转换评分排名数据为可序列化格式
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# 复用前面转换的评分数据
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# 若前面转换失败，此处不再重复转换，避免错误
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# 构建完整的分析内容（结构化）
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full_analysis_content = {
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"agents_analysis": agents_results,
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"data_info": results["data_info"],
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"scoring_ranking": scoring_ranking_data,
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"final_report": final_report,
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"timestamp": results["timestamp"]
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}
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report_id = self.database.save_analysis_report(
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data_date_range=data_date_range,
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analysis_content=full_analysis_content, # 保存完整的结构化数据
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recommended_stocks=recommended_stocks,
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summary=final_report.get('summary', ''),
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full_result=results # 传入完整结果
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)
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results["report_id"] = report_id
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self.logger.info(f"完整报告已保存 (ID: {report_id})")
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results["success"] = True
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self.logger.info("=" * 60)
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self.logger.info("✓ 智瞰龙虎综合分析完成！")
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self.logger.info("=" * 60)
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except Exception as e:
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self.logger.exception(f"分析过程出错: {e}", exc_info=True)
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results["error"] = str(e)
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return results
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def _extract_recommended_stocks(self, chief_analysis: str, stock_analysis: str, summary: Dict) -> List[Dict]:
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"""
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从AI分析中提取推荐股票
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Args:
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chief_analysis: 首席策略师分析
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stock_analysis: 个股潜力分析师分析
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summary: 数据摘要
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Returns:
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推荐股票列表
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"""
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recommended = []
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# 从摘要中获取TOP股票作为基础
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if summary.get('top_stocks'):
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for idx, stock in enumerate(summary['top_stocks'][:10], 1):
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recommended.append({
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'rank': idx,
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'code': stock['code'],
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'name': stock['name'],
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'net_inflow': stock['net_inflow'],
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'reason': f"资金净流入 {stock['net_inflow']:,.2f} 元",
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'confidence': '中',
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'buy_price': '待定',
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'target_price': '待定',
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'stop_loss': '待定',
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'hold_period': '短线'
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})
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return recommended
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def _generate_final_report(self, agents_results: Dict, summary: Dict,
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recommended_stocks: List[Dict]) -> Dict:
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"""
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生成最终报告
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Args:
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agents_results: 所有分析师的分析结果
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summary: 数据摘要
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recommended_stocks: 推荐股票列表
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Returns:
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最终报告字典
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"""
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report = {
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'title': '智瞰龙虎榜综合分析报告',
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'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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'summary': '',
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'data_overview': {
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'total_records': summary.get('total_records', 0),
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'total_stocks': summary.get('total_stocks', 0),
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'total_youzi': summary.get('total_youzi', 0),
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'total_net_inflow': summary.get('total_net_inflow', 0)
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},
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'recommended_stocks_count': len(recommended_stocks),
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'agents_count': len(agents_results)
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}
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# 生成摘要
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summary_parts = []
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summary_parts.append(f"本次分析共涵盖 {summary.get('total_records', 0)} 条龙虎榜记录")
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summary_parts.append(f"涉及 {summary.get('total_stocks', 0)} 只股票")
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summary_parts.append(f"涉及 {summary.get('total_youzi', 0)} 个游资席位")
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summary_parts.append(f"共推荐 {len(recommended_stocks)} 只潜力股票")
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report['summary'] = "，".join(summary_parts) + "。"
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return report
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def _get_date_range(self, data_list: List[Dict]) -> str:
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"""
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获取数据的日期范围
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Args:
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data_list: 数据列表
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Returns:
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日期范围字符串
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"""
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if not data_list:
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return "未知"
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dates = []
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for record in data_list:
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date = record.get('rq') or record.get('日期')
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if date:
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dates.append(date)
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if not dates:
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return "未知"
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dates = sorted(set(dates))
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if len(dates) == 1:
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return dates[0]
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else:
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return f"{dates[0]} 至 {dates[-1]}"
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def get_historical_reports(self, limit=10):
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"""
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获取历史分析报告
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Args:
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limit: 返回数量
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Returns:
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报告列表
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"""
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return self.database.get_analysis_reports(limit)
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def get_report_detail(self, report_id):
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"""
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获取报告详情
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Args:
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report_id: 报告ID
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Returns:
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报告详情
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"""
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return self.database.get_analysis_report(report_id)
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def get_statistics(self):
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"""
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获取数据库统计信息
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Returns:
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统计信息
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"""
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return self.database.get_statistics()
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def get_top_youzi(self, start_date=None, end_date=None, limit=20):
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"""
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获取活跃游资排名
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Args:
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start_date: 开始日期
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end_date: 结束日期
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limit: 返回数量
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Returns:
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游资排名
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"""
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return self.database.get_top_youzi(start_date, end_date, limit)
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def get_top_stocks(self, start_date=None, end_date=None, limit=20):
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"""
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获取热门股票排名
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Args:
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start_date: 开始日期
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end_date: 结束日期
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limit: 返回数量
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Returns:
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股票排名
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"""
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return self.database.get_top_stocks(start_date, end_date, limit)
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# 测试函数
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if __name__ == "__main__":
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print("=" * 60)
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print("测试智瞰龙虎分析引擎")
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print("=" * 60)
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# 创建引擎实例
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engine = LonghubangEngine()
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# 运行综合分析（分析昨天的数据）
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yesterday = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')
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results = engine.run_comprehensive_analysis(date=yesterday)
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if results.get('success'):
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print("\n" + "=" * 60)
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print("分析成功！")
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print("=" * 60)
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print(f"数据记录: {results['data_info']['total_records']}")
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print(f"涉及股票: {results['data_info']['total_stocks']}")
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print(f"推荐股票: {len(results['recommended_stocks'])}")
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else:
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print(f"\n分析失败: {results.get('error', '未知错误')}")
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