增加更多的历史记录，修正部份API数据获取错误，增加备用API (#5)
* 增加更多的历史记录，修正部份数据获取错误 * 增加更多的历史记录，修正部份API数据获取错误，增加备用API --------- Co-authored-by: bathfire <>
This commit is contained in:
Eikwang
+58
-52
@@ -10,6 +10,7 @@ from longhubang_scoring import LonghubangScoring
|
||||
from typing import Dict, Any, List
|
||||
from datetime import datetime, timedelta
|
||||
import time
|
||||
import logging
|
||||
|
||||
|
||||
class LonghubangEngine:
|
||||
@@ -27,7 +28,11 @@ class LonghubangEngine:
|
||||
self.database = LonghubangDatabase(db_path)
|
||||
self.agents = LonghubangAgents(model=model)
|
||||
self.scoring = LonghubangScoring()
|
||||
print(f"[智瞰龙虎] 分析引擎初始化完成")
|
||||
# 初始化日志
|
||||
self.logger = logging.getLogger(__name__)
|
||||
if not self.logger.handlers:
|
||||
logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s %(name)s: %(message)s')
|
||||
self.logger.info("[智瞰龙虎] 分析引擎初始化完成")
|
||||
|
||||
def run_comprehensive_analysis(self, date=None, days=1) -> Dict[str, Any]:
|
||||
"""
|
||||
@@ -40,9 +45,9 @@ class LonghubangEngine:
|
||||
Returns:
|
||||
完整的分析结果
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print("🚀 智瞰龙虎综合分析系统启动")
|
||||
print("=" * 60)
|
||||
self.logger.info("=" * 60)
|
||||
self.logger.info("🚀 智瞰龙虎综合分析系统启动")
|
||||
self.logger.info("=" * 60)
|
||||
|
||||
results = {
|
||||
"success": False,
|
||||
@@ -55,8 +60,8 @@ class LonghubangEngine:
|
||||
|
||||
try:
|
||||
# 阶段1: 获取龙虎榜数据
|
||||
print("\n[阶段1] 获取龙虎榜数据...")
|
||||
print("-" * 60)
|
||||
self.logger.info("[阶段1] 获取龙虎榜数据...")
|
||||
self.logger.info("-" * 60)
|
||||
|
||||
if date:
|
||||
data_list = [self.data_fetcher.get_longhubang_data(date)]
|
||||
@@ -65,21 +70,21 @@ class LonghubangEngine:
|
||||
data_list = self.data_fetcher.get_recent_days_data(days)
|
||||
|
||||
if not data_list:
|
||||
print("✗ 未获取到龙虎榜数据")
|
||||
self.logger.error("未获取到龙虎榜数据")
|
||||
results["error"] = "未获取到龙虎榜数据"
|
||||
return results
|
||||
|
||||
print(f"✓ 成功获取 {len(data_list)} 条龙虎榜记录")
|
||||
|
||||
self.logger.info(f"成功获取 {len(data_list)} 条龙虎榜记录")
|
||||
|
||||
# 阶段2: 保存数据到数据库
|
||||
print("\n[阶段2] 保存数据到数据库...")
|
||||
print("-" * 60)
|
||||
self.logger.info("[阶段2] 保存数据到数据库...")
|
||||
self.logger.info("-" * 60)
|
||||
saved_count = self.database.save_longhubang_data(data_list)
|
||||
print(f"✓ 保存 {saved_count} 条记录")
|
||||
self.logger.info(f"保存 {saved_count} 条记录")
|
||||
|
||||
# 阶段3: 数据分析和统计
|
||||
print("\n[阶段3] 数据分析和统计...")
|
||||
print("-" * 60)
|
||||
self.logger.info("[阶段3] 数据分析和统计...")
|
||||
self.logger.info("-" * 60)
|
||||
summary = self.data_fetcher.analyze_data_summary(data_list)
|
||||
formatted_data = self.data_fetcher.format_data_for_ai(data_list, summary)
|
||||
|
||||
@@ -89,83 +94,86 @@ class LonghubangEngine:
|
||||
"total_youzi": summary.get('total_youzi', 0),
|
||||
"summary": summary
|
||||
}
|
||||
print(f"✓ 数据统计完成")
|
||||
self.logger.info("数据统计完成")
|
||||
|
||||
# 阶段3.5: AI智能评分排名
|
||||
print("\n[阶段3.5] AI智能评分排名...")
|
||||
print("-" * 60)
|
||||
self.logger.info("[阶段3.5] AI智能评分排名...")
|
||||
self.logger.info("-" * 60)
|
||||
scoring_df = self.scoring.score_all_stocks(data_list)
|
||||
results["scoring_ranking"] = scoring_df
|
||||
print(f"✓ 完成 {len(scoring_df)} 只股票的智能评分排名")
|
||||
# 转换为可序列化格式以避免UI/存储类型问题
|
||||
scoring_ranking_data: List[Dict[str, Any]] = []
|
||||
try:
|
||||
if scoring_df is not None and hasattr(scoring_df, 'to_dict'):
|
||||
scoring_ranking_data = scoring_df.to_dict('records')
|
||||
self.logger.info(f"完成 {len(scoring_ranking_data)} 只股票的智能评分排名")
|
||||
else:
|
||||
self.logger.warning("评分结果为空或格式不支持转换")
|
||||
except Exception as e:
|
||||
self.logger.exception(f"评分排名数据转换失败: {e}", exc_info=True)
|
||||
scoring_ranking_data = []
|
||||
results["scoring_ranking"] = scoring_ranking_data
|
||||
|
||||
# 阶段4: AI分析师团队分析
|
||||
print("\n[阶段4] AI分析师团队工作中...")
|
||||
print("-" * 60)
|
||||
self.logger.info("[阶段4] AI分析师团队工作中...")
|
||||
self.logger.info("-" * 60)
|
||||
|
||||
agents_results = {}
|
||||
|
||||
# 1. 游资行为分析师
|
||||
print("1/5 游资行为分析师...")
|
||||
self.logger.info("1/5 游资行为分析师...")
|
||||
youzi_result = self.agents.youzi_behavior_analyst(formatted_data, summary)
|
||||
agents_results["youzi"] = youzi_result
|
||||
|
||||
# 2. 个股潜力分析师
|
||||
print("2/5 个股潜力分析师...")
|
||||
self.logger.info("2/5 个股潜力分析师...")
|
||||
stock_result = self.agents.stock_potential_analyst(formatted_data, summary)
|
||||
agents_results["stock"] = stock_result
|
||||
|
||||
# 3. 题材追踪分析师
|
||||
print("3/5 题材追踪分析师...")
|
||||
self.logger.info("3/5 题材追踪分析师...")
|
||||
theme_result = self.agents.theme_tracker_analyst(formatted_data, summary)
|
||||
agents_results["theme"] = theme_result
|
||||
|
||||
# 4. 风险控制专家
|
||||
print("4/5 风险控制专家...")
|
||||
self.logger.info("4/5 风险控制专家...")
|
||||
risk_result = self.agents.risk_control_specialist(formatted_data, summary)
|
||||
agents_results["risk"] = risk_result
|
||||
|
||||
# 5. 首席策略师综合
|
||||
print("5/5 首席策略师综合分析...")
|
||||
self.logger.info("5/5 首席策略师综合分析...")
|
||||
all_analyses = [youzi_result, stock_result, theme_result, risk_result]
|
||||
chief_result = self.agents.chief_strategist(all_analyses)
|
||||
agents_results["chief"] = chief_result
|
||||
|
||||
results["agents_analysis"] = agents_results
|
||||
print("\n✓ 所有AI分析师分析完成")
|
||||
self.logger.info("所有AI分析师分析完成")
|
||||
|
||||
# 阶段5: 提取推荐股票
|
||||
print("\n[阶段5] 提取推荐股票...")
|
||||
print("-" * 60)
|
||||
self.logger.info("[阶段5] 提取推荐股票...")
|
||||
self.logger.info("-" * 60)
|
||||
recommended_stocks = self._extract_recommended_stocks(
|
||||
chief_result.get('analysis', ''),
|
||||
stock_result.get('analysis', ''),
|
||||
summary
|
||||
)
|
||||
results["recommended_stocks"] = recommended_stocks
|
||||
print(f"✓ 提取 {len(recommended_stocks)} 只推荐股票")
|
||||
self.logger.info(f"提取 {len(recommended_stocks)} 只推荐股票")
|
||||
|
||||
# 阶段6: 生成最终报告
|
||||
print("\n[阶段6] 生成最终报告...")
|
||||
print("-" * 60)
|
||||
self.logger.info("[阶段6] 生成最终报告...")
|
||||
self.logger.info("-" * 60)
|
||||
final_report = self._generate_final_report(agents_results, summary, recommended_stocks)
|
||||
results["final_report"] = final_report
|
||||
print("✓ 最终报告生成完成")
|
||||
self.logger.info("最终报告生成完成")
|
||||
|
||||
# 阶段7: 保存完整分析报告到数据库
|
||||
print("\n[阶段7] 保存完整分析报告...")
|
||||
print("-" * 60)
|
||||
self.logger.info("[阶段7] 保存完整分析报告...")
|
||||
self.logger.info("-" * 60)
|
||||
data_date_range = self._get_date_range(data_list)
|
||||
|
||||
# 转换评分排名数据为可序列化格式
|
||||
scoring_ranking_data = []
|
||||
if scoring_df is not None and hasattr(scoring_df, 'to_dict'):
|
||||
try:
|
||||
# 转换DataFrame为字典列表，确保所有数据都被序列化
|
||||
scoring_ranking_data = scoring_df.to_dict('records')
|
||||
print(f"✓ 评分排名数据已转换: {len(scoring_ranking_data)} 条记录")
|
||||
except Exception as e:
|
||||
print(f"⚠ 评分排名数据转换失败: {e}")
|
||||
scoring_ranking_data = []
|
||||
# 复用前面转换的评分数据
|
||||
# 若前面转换失败，此处不再重复转换，避免错误
|
||||
|
||||
# 构建完整的分析内容（结构化）
|
||||
full_analysis_content = {
|
||||
@@ -184,20 +192,18 @@ class LonghubangEngine:
|
||||
full_result=results # 传入完整结果
|
||||
)
|
||||
results["report_id"] = report_id
|
||||
print(f"✓ 完整报告已保存 (ID: {report_id})")
|
||||
self.logger.info(f"完整报告已保存 (ID: {report_id})")
|
||||
|
||||
results["success"] = True
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("✓ 智瞰龙虎综合分析完成！")
|
||||
print("=" * 60)
|
||||
self.logger.info("=" * 60)
|
||||
self.logger.info("✓ 智瞰龙虎综合分析完成！")
|
||||
self.logger.info("=" * 60)
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n✗ 分析过程出错: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
self.logger.exception(f"分析过程出错: {e}", exc_info=True)
|
||||
results["error"] = str(e)
|
||||
|
||||
|
||||
return results
|
||||
|
||||
def _extract_recommended_stocks(self, chief_analysis: str, stock_analysis: str, summary: Dict) -> List[Dict]:
|
||||
|
||||
Reference in New Issue
Block a user