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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)

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* 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
2025-10-29 16:22:18 +08:00
committed by GitHub
parent 16071f81e8
commit 91d32c6ffa
39 changed files with 2377 additions and 824 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.gitignore
+1
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@@ -9,3 +9,4 @@
.env
/.cursor
/openspec
TradEnv/
.streamlit/config.toml
+6
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@@ -0,0 +1,6 @@
[theme]
base = "light"
[server]
port = 8503
address = "127.0.0.1"
ai_agents.py
+3 -3
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@@ -55,10 +55,10 @@ class StockAnalysisAgents:
print("💰 资金面分析师正在分析中...")
# 如果有资金流向数据，显示数据来源
if fund_flow_data and fund_flow_data.get('query_success'):
print(" ✓ 已获取问财资金流向数据")
if fund_flow_data and fund_flow_data.get('data_success'):
print(" ✓ 已获取资金流向数据（akshare数据源）")
else:
print(" ⚠ 未获取到问财资金流向数据，将基于技术指标分析")
print(" ⚠ 未获取到资金流向数据，将基于技术指标分析")
time.sleep(1)
app.py
+7 -7
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@@ -302,7 +302,7 @@ def main():
st.markdown("---")
# 🎯 选股板块
with st.expander("🎯 选股板块", expanded=False):
with st.expander("🎯 选股板块", expanded=True):
st.markdown("**根据不同策略筛选优质股票**")
if st.button("💰 主力选股", width='stretch', key="nav_main_force", help="基于主力资金流向的选股策略"):
@@ -313,7 +313,7 @@ def main():
del st.session_state[key]
# 📊 策略分析
with st.expander("📊 策略分析", expanded=False):
with st.expander("📊 策略分析", expanded=True):
st.markdown("**AI驱动的板块和龙虎榜策略**")
if st.button("🎯 智策板块", width='stretch', key="nav_sector_strategy", help="AI板块策略分析"):
@@ -331,7 +331,7 @@ def main():
del st.session_state[key]
# 💼 投资管理
with st.expander("💼 投资管理", expanded=False):
with st.expander("💼 投资管理", expanded=True):
st.markdown("**持仓跟踪与实时监测**")
if st.button("📊 持仓分析", width='stretch', key="nav_portfolio", help="投资组合分析与定时跟踪"):
@@ -571,9 +571,9 @@ def main():
help="负责风险识别、风险评估、风险控制策略制定")
with col3:
enable_sentiment = st.checkbox("📈 市场情绪分析师", value=False,
enable_sentiment = st.checkbox("📈 市场情绪分析师", value=True,
help="负责市场情绪研究、ARBR指标分析（仅A股）")
enable_news = st.checkbox("📰 新闻分析师", value=False,
enable_news = st.checkbox("📰 新闻分析师", value=True,
help="负责新闻事件分析、舆情研究（仅A股，qstock数据源）")
# 显示已选择的分析师
@@ -1427,7 +1427,7 @@ def display_stock_chart(stock_data, stock_info):
# 生成唯一的key
chart_key = f"main_stock_chart_{stock_info.get('symbol', 'unknown')}_{int(time.time())}"
st.plotly_chart(fig, width='stretch', key=chart_key)
st.plotly_chart(fig, use_container_width=True, config={'responsive': True}, key=chart_key)
# 成交量图
if 'Volume' in stock_data.columns:
@@ -1448,7 +1448,7 @@ def display_stock_chart(stock_data, stock_info):
# 生成唯一的key
volume_key = f"volume_chart_{stock_info.get('symbol', 'unknown')}_{int(time.time())}"
st.plotly_chart(fig_volume, width='stretch', key=volume_key)
st.plotly_chart(fig_volume, use_container_width=True, config={'responsive': True}, key=volume_key)
def display_agents_analysis(agents_results):
"""显示各分析师报告"""
docs/toshare说明.md
+100
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@@ -0,0 +1,100 @@
Python SDK
下载SDK
下载并安装最新版tushare SDK 【安装和升级方法】
导入tushare
import tushare as ts
这里注意， tushare版本需大于1.2.10
设置token
ts.set_token('your token here')
以上方法只需要在第一次或者token失效后调用，完成调取tushare数据凭证的设置，正常情况下不需要重复设置。也可以忽略此步骤，直接用pro_api('your token')完成初始化
初始化pro接口
pro = ts.pro_api()
如果上一步骤ts.set_token('your token')无效或不想保存token到本地，也可以在初始化接口里直接设置token:
pro = ts.pro_api('your token')
数据调取
以获取交易日历信息为例：
df = pro.trade_cal(exchange='', start_date='20180901', end_date='20181001', fields='exchange,cal_date,is_open,pretrade_date', is_open='0')
或者
df = pro.query('trade_cal', exchange='', start_date='20180901', end_date='20181001', fields='exchange,cal_date,is_open,pretrade_date', is_open='0')
调取结果：
exchange cal_date is_open pretrade_date
0 SSE 20180901 0 20180831
1 SSE 20180902 0 20180831
2 SSE 20180908 0 20180907
3 SSE 20180909 0 20180907
4 SSE 20180915 0 20180914
5 SSE 20180916 0 20180914
6 SSE 20180922 0 20180921
7 SSE 20180923 0 20180921
8 SSE 20180924 0 20180921
9 SSE 20180929 0 20180928
10 SSE 20180930 0 20180928
11 SSE 20181001 0 20180928
沪深港通资金流向
接口：moneyflow_hsgt，可以通过数据工具调试和查看数据。
描述：获取沪股通、深股通、港股通每日资金流向数据，每次最多返回300条记录，总量不限制。每天18~20点之间完成当日更新
积分要求：2000积分起，5000积分每分钟可提取500次
输入参数
名称 类型 必选 描述
trade_date str N 交易日期 (二选一)
start_date str N 开始日期 (二选一)
end_date str N 结束日期
输出参数
名称 类型 描述
trade_date str 交易日期
ggt_ss float 港股通（上海）
ggt_sz float 港股通（深圳）
hgt float 沪股通（百万元）
sgt float 深股通（百万元）
north_money float 北向资金（百万元）
south_money float 南向资金（百万元）
接口用法
pro = ts.pro_api()
pro.moneyflow_hsgt(start_date='20180125', end_date='20180808')
或者
pro.query('moneyflow_hsgt', trade_date='20180725')
数据样例
trade_date ggt_ss ggt_sz hgt sgt north_money south_money
0 20180808 -476.0 -188.0 962.68 799.94 1762.62 -664.0
1 20180807 -261.0 177.0 2140.85 1079.82 3220.67 -84.0
2 20180803 667.0 -32.0 -436.99 1088.07 651.08 635.0
3 20180802 -1651.0 -366.0 874.97 -216.65 658.32 -2017.0
4 20180801 -1443.0 -443.0 544.36 542.79 1087.15 -1886.0
5 20180731 -299.0 -21.0 1923.72 1345.48 3269.20 -320.0
6 20180730 -588.0 611.0 2536.54 146.24 2682.78 23.0
7 20180727 -13.0 363.0 2182.84 533.06 2715.90 350.0
8 20180726 -566.0 -339.0 1113.28 -567.47 545.81 -905.0
9 20180725 319.0 370.0 1470.29 311.27 1781.56 689.0
10 20180724 924.0 2312.0 1748.88 1053.52 2802.40 3236.0
11 20180723 1628.0 1172.0 -279.96 334.82 54.86 2800.0
12 20180720 2233.0 1773.0 606.33 1711.77 2318.10 4006.0
13 20180719 456.0 206.0 1831.41 874.40 2705.81 662.0
14 20180718 -181.0 261.0 126.80 -111.83 14.97 80.0
15 20180717 -390.0 187.0 -90.32 -404.24 -494.56 -203.0
16 20180716 -539.0 52.0 -457.00 487.60 30.60 -487.0
17 20180713 -297.0 751.0 599.38 658.07 1257.45 454.0
18 20180712 2635.0 1699.0 1695.62 269.56 1965.18 4334.0
19 20180711 19.0 646.0 261.96 -339.20 -77.24 665.0
20 20180710 668.0 889.0 514.05 262.33 776.38 1557.0
fund_flow_akshare.py
+2 -2
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@@ -36,7 +36,7 @@ class FundFlowAkshareDataFetcher:
"""资金流向数据获取类（使用akshare数据源）"""
def __init__(self):
self.days = 20 # 获取最近20个交易日
self.days = 30 # 获取最近30个交易日
self.available = True
print("[OK] 资金流向数据获取器初始化成功（akshare数据源）")
@@ -259,7 +259,7 @@ class FundFlowAkshareDataFetcher:
# 添加统计汇总
text_parts.append("""
═══════════════════════════════════════
[统计汇总 - 最近20个交易日]
[统计汇总 - 最近30个交易日]
═══════════════════════════════════════
""")
longhubang.db
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longhubang_db.py
+55 -8
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@@ -7,6 +7,7 @@ import sqlite3
from datetime import datetime
import json
import pandas as pd
import logging
class LonghubangDatabase:
@@ -20,6 +21,10 @@ class LonghubangDatabase:
db_path: 数据库文件路径
"""
self.db_path = db_path
# 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.init_database()
def get_connection(self):
@@ -100,7 +105,7 @@ class LonghubangDatabase:
conn.commit()
conn.close()
print("[智瞰龙虎] 数据库初始化完成")
self.logger.info("[智瞰龙虎] 数据库初始化完成")
def save_longhubang_data(self, data_list):
"""
@@ -141,13 +146,13 @@ class LonghubangDatabase:
))
saved_count += 1
except Exception as e:
print(f"保存记录失败: {e}")
self.logger.exception(f"保存记录失败: {e}", exc_info=True)
continue
conn.commit()
conn.close()
print(f"[智瞰龙虎] 成功保存 {saved_count} 条龙虎榜记录")
self.logger.info(f"[智瞰龙虎] 成功保存 {saved_count} 条龙虎榜记录")
return saved_count
def get_longhubang_data(self, start_date=None, end_date=None, stock_code=None):
@@ -321,7 +326,7 @@ class LonghubangDatabase:
conn.commit()
conn.close()
print(f"[智瞰龙虎] 分析报告已保存 (ID: {report_id})")
self.logger.info(f"[智瞰龙虎] 分析报告已保存 (ID: {report_id})")
return report_id
def get_analysis_reports(self, limit=10):
@@ -365,31 +370,73 @@ class LonghubangDatabase:
''', (report_id,))
row = cursor.fetchone()
# 在关闭连接之前获取列名，避免关闭后访问游标属性报错
columns = [desc[0] for desc in cursor.description] if cursor.description else []
conn.close()
if row:
columns = [desc[0] for desc in cursor.description]
report = dict(zip(columns, row))
# 解析JSON字段
if report.get('recommended_stocks'):
try:
report['recommended_stocks'] = json.loads(report['recommended_stocks'])
except:
pass
except Exception as e:
self.logger.warning(f"推荐股票JSON解析失败: {e}")
# 解析analysis_content字段
if report.get('analysis_content'):
try:
report['analysis_content_parsed'] = json.loads(report['analysis_content'])
except:
except json.JSONDecodeError as e:
# 如果不是JSON格式，保持原样
report['analysis_content_parsed'] = None
self.logger.debug(f"analysis_content字段不是有效JSON格式，将保持原始文本格式: {str(e)[:100]}")
except Exception as e:
report['analysis_content_parsed'] = None
self.logger.warning(f"analysis_content字段解析时发生未知错误: {str(e)[:100]}")
return report
return None
def delete_analysis_report(self, report_id):
"""
删除分析报告
Args:
report_id: 报告ID
Returns:
bool: 删除是否成功
"""
conn = self.get_connection()
cursor = conn.cursor()
try:
# 先删除相关的股票追踪记录
cursor.execute('DELETE FROM stock_tracking WHERE analysis_id = ?', (report_id,))
# 删除分析报告
cursor.execute('DELETE FROM longhubang_analysis WHERE id = ?', (report_id,))
deleted_count = cursor.rowcount
conn.commit()
if deleted_count > 0:
self.logger.info(f"[智瞰龙虎] 成功删除分析报告 (ID: {report_id})")
return True
else:
self.logger.warning(f"[智瞰龙虎] 未找到要删除的分析报告 (ID: {report_id})")
return False
except Exception as e:
self.logger.error(f"[智瞰龙虎] 删除分析报告失败: {e}")
conn.rollback()
return False
finally:
conn.close()
def update_stock_tracking(self, analysis_id, stock_code, current_price, status, notes=None):
"""
更新股票追踪信息
longhubang_engine.py
+56 -50
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@@ -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 = {
@@ -184,18 +192,16 @@ 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
longhubang_scoring.py
+6 -4
View File
@@ -445,6 +445,7 @@ class LonghubangScoring:
results.append({
'排名': 0, # 稍后填充
'排名_display': '', # 用于显示奖牌
'股票名称': stock_info['name'],
'股票代码': code,
'综合评分': round(total_score, 1),
@@ -467,13 +468,14 @@ class LonghubangScoring:
df = df.sort_values('综合评分', ascending=False).reset_index(drop=True)
df['排名'] = range(1, len(df) + 1)
# 添加奖牌
# 添加奖牌显示
df['排名_display'] = df['排名'].astype(str)
if len(df) >= 1:
df.loc[0, '排名'] = '🥇 1'
df.loc[0, '排名_display'] = '🥇 1'
if len(df) >= 2:
df.loc[1, '排名'] = '🥈 2'
df.loc[1, '排名_display'] = '🥈 2'
if len(df) >= 3:
df.loc[2, '排名'] = '🥉 3'
df.loc[2, '排名_display'] = '🥉 3'
return df
longhubang_ui.py
+148 -47
View File
@@ -146,10 +146,10 @@ def display_analysis_tab():
col1, col2, col3 = st.columns([2, 2, 2])
with col1:
analyze_button = st.button("🚀 开始分析", type="primary", use_container_width=True)
analyze_button = st.button("🚀 开始分析", type="primary", width='stretch')
with col2:
if st.button("🔄 清除结果", use_container_width=True):
if st.button("🔄 清除结果", width='stretch'):
if 'longhubang_result' in st.session_state:
del st.session_state.longhubang_result
st.success("已清除分析结果")
@@ -335,13 +335,29 @@ def display_scoring_ranking(result):
# 显示TOP10评分表格
st.markdown("### 🥇 TOP10 综合评分排名")
# 兼容历史数据与类型统一，避免 Arrow 序列化错误
if isinstance(scoring_df, list):
scoring_df = pd.DataFrame(scoring_df)
numeric_cols = ['排名','综合评分','资金含金量','净买入额','卖出压力','机构共振','加分项','顶级游资','买方数','净流入']
for col in numeric_cols:
if col in scoring_df.columns:
scoring_df[col] = pd.to_numeric(scoring_df[col], errors='coerce')
text_cols = ['股票名称','股票代码','机构参与']
for col in text_cols:
if col in scoring_df.columns:
scoring_df[col] = scoring_df[col].astype(str)
top10_df = scoring_df.head(10).copy()
if '排名' in top10_df.columns:
top10_df['排名'] = pd.to_numeric(top10_df['排名'], errors='coerce').fillna(0).astype(int)
# 格式化显示
st.dataframe(
top10_df,
column_config={
"排名": st.column_config.TextColumn("排名", width="small"),
"排名": st.column_config.NumberColumn("排名", format="%d", width="small"),
"股票名称": st.column_config.TextColumn("股票名称", width="medium"),
"股票代码": st.column_config.TextColumn("代码", width="small"),
"综合评分": st.column_config.NumberColumn(
@@ -385,7 +401,7 @@ def display_scoring_ranking(result):
"净流入": st.column_config.NumberColumn("净流入(元)", format="%.2f")
},
hide_index=True,
use_container_width=True
width='stretch'
)
# 一键批量分析功能
@@ -401,12 +417,15 @@ def display_scoring_ranking(result):
"分析数量",
options=[3, 5, 10],
index=0,
help="选择分析前N只股票"
help="选择分析前N只股票",
key="batch_count_selector"
)
# 同步更新session_state中的batch_count
st.session_state.batch_count = batch_count
with col_batch3:
st.write("") # 占位
if st.button("🚀 开始批量分析", type="primary", use_container_width=True):
if st.button("🚀 开始批量分析", type="primary", width='stretch'):
# 提取股票代码
stock_codes = top10_df.head(batch_count)['股票代码'].tolist()
@@ -439,25 +458,35 @@ def display_scoring_ranking(result):
showlegend=False,
height=400
)
st.plotly_chart(fig1, use_container_width=True)
st.plotly_chart(fig1, config={'displayModeBar': False}, use_container_width=True)
with col2:
# 五维评分雷达图（显示第一名）
# 五维评分雷达图（显示批量分析数量的股票）
if len(top10_df) > 0:
first_place = top10_df.iloc[0]
display_count = min(5, len(top10_df))
fig2 = go.Figure(data=go.Scatterpolar(
r=[
first_place['资金含金量'] / 30 * 100,
first_place['净买入额'] / 25 * 100,
first_place['卖出压力'] / 20 * 100,
first_place['机构共振'] / 15 * 100,
first_place['加分项'] / 10 * 100
],
theta=['资金含金量', '净买入额', '卖出压力', '机构共振', '加分项'],
fill='toself',
name=first_place['股票名称']
))
fig2 = go.Figure()
# 为每只股票添加雷达图
colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7']
for i in range(display_count):
stock = top10_df.iloc[i]
fig2.add_trace(go.Scatterpolar(
r=[
stock['资金含金量'] / 30 * 100,
stock['净买入额'] / 25 * 100,
stock['卖出压力'] / 20 * 100,
stock['机构共振'] / 15 * 100,
stock['加分项'] / 10 * 100
],
theta=['资金含金量', '净买入额', '卖出压力', '机构共振', '加分项'],
fill='toself',
name=f"{stock['股票名称']}",
line_color=colors[i % len(colors)],
fillcolor=colors[i % len(colors)],
opacity=0.6
))
fig2.update_layout(
polar=dict(
@@ -467,10 +496,17 @@ def display_scoring_ranking(result):
)
),
showlegend=True,
title=f"🥇 {first_place['股票名称']} 五维评分",
height=400
title=f"🏆 TOP{display_count} 五维评分对比",
height=400,
legend=dict(
orientation="h",
yanchor="auto",
y=-0.2,
xanchor="center",
x=0.5
)
)
st.plotly_chart(fig2, use_container_width=True)
st.plotly_chart(fig2, config={'displayModeBar': False}, use_container_width=True)
st.markdown("---")
@@ -480,7 +516,7 @@ def display_scoring_ranking(result):
st.dataframe(
scoring_df,
column_config={
"排名": st.column_config.TextColumn("排名", width="small"),
"排名": st.column_config.NumberColumn("排名", format="%d", width="small"),
"股票名称": st.column_config.TextColumn("股票名称"),
"股票代码": st.column_config.TextColumn("代码"),
"综合评分": st.column_config.NumberColumn("综合评分", format="%.1f"),
@@ -490,7 +526,7 @@ def display_scoring_ranking(result):
"净流入": st.column_config.NumberColumn("净流入(元)", format="%.2f")
},
hide_index=True,
use_container_width=True
width='stretch'
)
@@ -523,7 +559,7 @@ def display_recommended_stocks(result):
"reason": st.column_config.TextColumn("推荐理由")
},
hide_index=True,
use_container_width=True
width='stretch'
)
# 详细推荐理由
@@ -599,7 +635,7 @@ def display_data_details(result):
"净流入金额": st.column_config.NumberColumn("净流入金额(元)", format="%.2f")
},
hide_index=True,
use_container_width=True
width='stretch'
)
# TOP股票
@@ -616,7 +652,7 @@ def display_data_details(result):
"net_inflow": st.column_config.NumberColumn("净流入金额(元)", format="%.2f")
},
hide_index=True,
use_container_width=True
width='stretch'
)
# 热门概念
@@ -637,7 +673,7 @@ def display_data_details(result):
"出现次数": st.column_config.NumberColumn("出现次数", format="%d")
},
hide_index=True,
use_container_width=True
width='stretch'
)
@@ -664,7 +700,7 @@ def display_visualizations(result):
labels={'name': '股票名称', 'net_inflow': '净流入金额(元)'}
)
fig.update_layout(xaxis_tickangle=-45)
st.plotly_chart(fig, use_container_width=True)
st.plotly_chart(fig, config={'displayModeBar': False}, use_container_width=True)
# 热门概念图表
if summary.get('hot_concepts'):
@@ -679,7 +715,7 @@ def display_visualizations(result):
names='概念',
title='热门概念出现次数分布'
)
st.plotly_chart(fig, use_container_width=True)
st.plotly_chart(fig, config={'displayModeBar': False}, use_container_width=True)
def display_pdf_export_section(result):
@@ -693,7 +729,7 @@ def display_pdf_export_section(result):
st.info("💡 点击按钮生成并下载专业的PDF分析报告")
with col2:
if st.button("📥 生成PDF", type="primary", use_container_width=True):
if st.button("📥 生成PDF", type="primary", width='stretch'):
with st.spinner("正在生成PDF报告..."):
try:
generator = LonghubangPDFGenerator()
@@ -709,7 +745,7 @@ def display_pdf_export_section(result):
data=pdf_bytes,
file_name=f"智瞰龙虎报告_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf",
mime="application/pdf",
use_container_width=True
width='stretch'
)
st.success("✅ PDF报告生成成功！")
@@ -780,7 +816,7 @@ def display_history_tab():
"hold_period": st.column_config.TextColumn("持有周期")
},
hide_index=True,
use_container_width=True
width='stretch'
)
st.markdown("---")
@@ -818,6 +854,17 @@ def display_history_tab():
st.markdown("#### 🏆 AI智能评分排名 (TOP10)")
df_scoring = pd.DataFrame(scoring_ranking[:10])
# 类型统一，避免Arrow序列化错误
numeric_cols = ['排名','综合评分','资金含金量','净买入额','卖出压力','机构共振','加分项','顶级游资','买方数','净流入']
for col in numeric_cols:
if col in df_scoring.columns:
df_scoring[col] = pd.to_numeric(df_scoring[col], errors='coerce')
text_cols = ['股票名称','股票代码','机构参与']
for col in text_cols:
if col in df_scoring.columns:
df_scoring[col] = df_scoring[col].astype(str)
if '排名' in df_scoring.columns:
df_scoring['排名'] = pd.to_numeric(df_scoring['排名'], errors='coerce').fillna(0).astype(int)
# 显示完整的评分表格
st.dataframe(
@@ -867,7 +914,7 @@ def display_history_tab():
"净流入": st.column_config.NumberColumn("净流入(元)", format="%.2f")
},
hide_index=True,
use_container_width=True
width='stretch'
)
# 显示评分说明
@@ -903,34 +950,88 @@ def display_history_tab():
st.markdown("#### 📄 原始分析内容")
analysis_content = report_detail.get('analysis_content', '')
if analysis_content:
st.text_area("", value=analysis_content[:2000], height=200, disabled=True)
st.text_area("原始分析内容", value=analysis_content[:2000], height=200, disabled=True)
if len(analysis_content) > 2000:
st.caption("(内容过长，仅显示前2000字符)")
# 导出按钮
# 操作按钮
st.markdown("---")
col_export1, col_export2 = st.columns(2)
col_export1, col_export2, col_export3 = st.columns(3)
with col_export1:
if st.button(f"📥 导出为PDF", key=f"export_pdf_{report_id}"):
st.info("PDF导出功能开发中...")
with col_export2:
if st.button(f"📋 加载到分析页", key=f"load_report_{report_id}"):
# 使用session_state来管理按钮状态，避免需要点击两次的问题
load_key = f"load_report_{report_id}"
if st.button(f"📋 加载到分析页", key=load_key):
# 将历史报告加载到当前分析结果中
if analysis_content_parsed:
# 重建完整的result结构
scoring_data = analysis_content_parsed.get('scoring_ranking', [])
if scoring_data:
df_scoring = pd.DataFrame(scoring_data)
# 类型统一，避免Arrow序列化错误
numeric_cols = ['排名','综合评分','资金含金量','净买入额','卖出压力','机构共振','加分项','顶级游资','买方数','净流入']
for col in numeric_cols:
if col in df_scoring.columns:
df_scoring[col] = pd.to_numeric(df_scoring[col], errors='coerce')
text_cols = ['股票名称','股票代码','机构参与']
for col in text_cols:
if col in df_scoring.columns:
df_scoring[col] = df_scoring[col].astype(str)
if '排名' in df_scoring.columns:
df_scoring['排名'] = pd.to_numeric(df_scoring['排名'], errors='coerce').fillna(0).astype(int)
else:
df_scoring = None
loaded_result = {
"success": True,
"timestamp": report_detail.get('analysis_date', ''),
"data_info": analysis_content_parsed.get('data_info', {}),
"agents_analysis": analysis_content_parsed.get('agents_analysis', {}),
"scoring_ranking": pd.DataFrame(analysis_content_parsed.get('scoring_ranking', [])) if analysis_content_parsed.get('scoring_ranking') else None,
"scoring_ranking": df_scoring,
"final_report": analysis_content_parsed.get('final_report', {}),
"recommended_stocks": report_detail.get('recommended_stocks', [])
}
st.session_state.longhubang_result = loaded_result
# 使用rerun来立即刷新页面状态
st.success('✅ 报告已加载到分析页面，请切换到"龙虎榜分析"标签查看')
st.rerun()
with col_export3:
# 删除按钮
delete_key = f"delete_report_{report_id}"
if st.button(f"🗑️ 删除报告", key=delete_key, type="secondary"):
# 使用session_state来管理删除确认状态
st.session_state[f"confirm_delete_{report_id}"] = True
st.rerun()
# 删除确认对话框
if st.session_state.get(f"confirm_delete_{report_id}", False):
st.warning(f"⚠️ 确认删除报告 #{report_id}？此操作不可撤销！")
col_confirm1, col_confirm2 = st.columns(2)
with col_confirm1:
if st.button(f"✅ 确认删除", key=f"confirm_delete_yes_{report_id}", type="primary"):
try:
# 调用数据库删除方法 - 修复属性名
engine.database.delete_analysis_report(report_id)
st.success(f"✅ 报告 #{report_id} 已成功删除")
# 清除确认状态并刷新页面
if f"confirm_delete_{report_id}" in st.session_state:
del st.session_state[f"confirm_delete_{report_id}"]
st.rerun()
except Exception as e:
st.error(f"❌ 删除失败: {str(e)}")
with col_confirm2:
if st.button(f"❌ 取消", key=f"confirm_delete_no_{report_id}"):
# 清除确认状态
if f"confirm_delete_{report_id}" in st.session_state:
del st.session_state[f"confirm_delete_{report_id}"]
st.rerun()
except Exception as e:
st.error(f"❌ 加载历史报告失败: {str(e)}")
@@ -986,7 +1087,7 @@ def display_statistics_tab():
"total_net_inflow": st.column_config.NumberColumn("总净流入(元)", format="%.2f")
},
hide_index=True,
use_container_width=True
width='stretch'
)
st.markdown("---")
@@ -1006,7 +1107,7 @@ def display_statistics_tab():
"total_net_inflow": st.column_config.NumberColumn("总净流入(元)", format="%.2f")
},
hide_index=True,
use_container_width=True
width='stretch'
)
except Exception as e:
@@ -1026,7 +1127,7 @@ def run_longhubang_batch_analysis():
# 返回按钮
col_back, col_clear = st.columns(2)
with col_back:
if st.button("🔙 返回龙虎榜分析", use_container_width=True):
if st.button("🔙 返回龙虎榜分析", width='stretch'):
# 清除所有批量分析相关状态
if 'longhubang_batch_trigger' in st.session_state:
del st.session_state.longhubang_batch_trigger
@@ -1037,7 +1138,7 @@ def run_longhubang_batch_analysis():
st.rerun()
with col_clear:
if st.button("🔄 重新分析", use_container_width=True):
if st.button("🔄 重新分析", width='stretch'):
# 清除结果，保留触发标志和代码
if 'longhubang_batch_results' in st.session_state:
del st.session_state.longhubang_batch_results
@@ -1098,11 +1199,11 @@ def run_longhubang_batch_analysis():
start_analysis = False
with col_confirm:
if st.button("🚀 确认开始分析", type="primary", use_container_width=True):
if st.button("🚀 确认开始分析", type="primary", width='stretch'):
start_analysis = True
with col_cancel:
if st.button("❌ 取消", type="secondary", use_container_width=True):
if st.button("❌ 取消", type="secondary", width='stretch'):
# 清除所有批量分析相关状态
if 'longhubang_batch_trigger' in st.session_state:
del st.session_state.longhubang_batch_trigger
main_force_analysis.py
+23 -9
View File
@@ -26,15 +26,19 @@ class MainForceAnalyzer:
self.raw_stocks = None
self.final_recommendations = []
def run_full_analysis(self, start_date: str = None, days_ago: int = 90,
final_n: int = 5) -> Dict:
def run_full_analysis(self, start_date: str = None, days_ago: int = None,
final_n: int = None, max_range_change: float = None,
min_market_cap: float = None, max_market_cap: float = None) -> Dict:
"""
运行完整的主力选股分析流程 - 整体批量分析
Args:
start_date: 开始日期，格式如"2025年10月1日"
days_ago: 距今多少天，默认90天
final_n: 最终精选N只，默认5只
days_ago: 距今多少天
final_n: 最终精选N只
max_range_change: 最大涨跌幅限制
min_market_cap: 最小市值限制
max_market_cap: 最大市值限制
Returns:
分析结果字典
@@ -44,7 +48,15 @@ class MainForceAnalyzer:
'total_stocks': 0,
'filtered_stocks': 0,
'final_recommendations': [],
'error': None
'error': None,
'params': {
'start_date': start_date,
'days_ago': days_ago,
'final_n': final_n,
'max_range_change': max_range_change,
'min_market_cap': min_market_cap,
'max_market_cap': max_market_cap
}
}
try:
@@ -55,7 +67,9 @@ class MainForceAnalyzer:
# 步骤1: 获取主力资金净流入前100名股票
success, raw_data, message = self.selector.get_main_force_stocks(
start_date=start_date,
days_ago=days_ago
days_ago=days_ago,
min_market_cap=min_market_cap,
max_market_cap=max_market_cap
)
if not success:
@@ -67,9 +81,9 @@ class MainForceAnalyzer:
# 步骤2: 智能筛选（涨幅、市值等）
filtered_data = self.selector.filter_stocks(
raw_data,
max_range_change=30.0,
min_market_cap=50.0,
max_market_cap=1300.0
max_range_change=max_range_change,
min_market_cap=min_market_cap,
max_market_cap=max_market_cap
)
result['filtered_stocks'] = len(filtered_data)
main_force_batch.db
BIN
View File
Binary file not shown.
main_force_history_ui.py
+15 -3
View File
@@ -108,14 +108,26 @@ def display_batch_history():
'代码': r.get('symbol', 'N/A'),
'名称': stock_info.get('name', stock_info.get('股票名称', 'N/A')),
'评级': final_decision.get('rating', final_decision.get('investment_rating', 'N/A')),
'信心度': f"{final_decision.get('confidence_level', 0)}%",
'信心度': final_decision.get('confidence_level', 'N/A'),
'进场区间': final_decision.get('entry_range', 'N/A'),
'止盈位': final_decision.get('take_profit', 'N/A'),
'止损位': final_decision.get('stop_loss', 'N/A')
})
df = pd.DataFrame(table_data)
st.dataframe(df, use_container_width=True)
# 类型统一，避免Arrow序列化错误
numeric_cols = ['信心度', '止盈位', '止损位']
for col in numeric_cols:
if col in df.columns:
df[col] = pd.to_numeric(df[col], errors='coerce')
text_cols = ['代码', '名称', '评级', '进场区间']
for col in text_cols:
if col in df.columns:
df[col] = df[col].astype(str)
st.dataframe(df, width='content')
# 显示详细分析（可展开）
with st.expander("📊 查看详细分析报告"):
@@ -147,7 +159,7 @@ def display_batch_history():
})
df_fail = pd.DataFrame(fail_data)
st.dataframe(df_fail, use_container_width=True)
st.dataframe(df_fail, width='content')
# 操作按钮
col_del, col_reload = st.columns([1, 1])
main_force_pdf_generator.py
+1 -1
View File
@@ -451,6 +451,6 @@ def display_report_download_section(analyzer, result):
data=csv,
file_name=csv_filename,
mime="text/csv",
use_container_width=True
width='content'
)
main_force_selector.py
+22 -18
View File
@@ -5,6 +5,7 @@
使用pywencai获取主力资金净流入前100名股票，并进行智能筛选
"""
from numpy.ma import minimum_fill_value
import pandas as pd
import pywencai
from datetime import datetime, timedelta
@@ -18,13 +19,16 @@ class MainForceStockSelector:
self.raw_data = None
self.filtered_stocks = None
def get_main_force_stocks(self, start_date: str = None, days_ago: int = 90) -> Tuple[bool, pd.DataFrame, str]:
def get_main_force_stocks(self, start_date: str = None, days_ago: int = None,
min_market_cap: float = None, max_market_cap: float = None) -> Tuple[bool, pd.DataFrame, str]:
"""
获取主力资金净流入前100名股票
Args:
start_date: 开始日期，格式如"2025年10月1日"，如果不提供则使用days_ago
days_ago: 距今多少天，默认90天（约3个月）
days_ago: 距今多少天
min_market_cap: 最小市值限制
max_market_cap: 最大市值限制
Returns:
(success, dataframe, message)
@@ -44,21 +48,21 @@ class MainForceStockSelector:
# 构建查询语句 - 使用多个备选方案，所有方案都要求计算区间涨跌幅
queries = [
# 方案1: 完整查询（最优）
f"{start_date}以来主力资金净流入排名，并计算区间涨跌幅，市值50-5000亿之间，非科创非st，"
f"{start_date}以来主力资金净流入排名，并计算区间涨跌幅，市值{min_market_cap}-{max_market_cap}亿之间，非科创非st，"
f"所属同花顺行业，总市值，净利润，营收，市盈率，市净率，"
f"盈利能力评分，成长能力评分，营运能力评分，偿债能力评分，"
f"现金流评分，资产质量评分，流动性评分，资本充足性评分",
# 方案2: 简化查询
f"{start_date}以来主力资金净流入，并计算区间涨跌幅，市值50-5000亿，非科创非st，"
f"{start_date}以来主力资金净流入，并计算区间涨跌幅，市值{min_market_cap}-{max_market_cap}亿，非科创非st，"
f"所属同花顺行业，总市值，净利润，营收，市盈率，市净率",
# 方案3: 基础查询
f"{start_date}以来主力资金净流入排名，并计算区间涨跌幅，市值50-5000亿，非科创非st，"
f"{start_date}以来主力资金净流入排名，并计算区间涨跌幅，市值{min_market_cap}-{max_market_cap}亿，非科创非st，"
f"所属行业，总市值",
# 方案4: 最简查询
f"{start_date}以来主力资金净流入前100名，并计算区间涨跌幅，市值50-5000亿，非st非科创板，所属行业，总市值",
f"{start_date}以来主力资金净流入前100名，并计算区间涨跌幅，市值{min_market_cap}-{max_market_cap}亿，非st非科创板，所属行业，总市值",
]
# 尝试不同的查询方案
@@ -132,17 +136,17 @@ class MainForceStockSelector:
return None
def filter_stocks(self, df: pd.DataFrame,
max_range_change: float = 30.0,
min_market_cap: float = 50.0,
max_market_cap: float = 1300.0) -> pd.DataFrame:
max_range_change: float = None,
min_market_cap: float = None,
max_market_cap: float = None) -> pd.DataFrame:
"""
智能筛选股票
智能筛选股票 - 基于涨跌幅和市值
Args:
df: 原始数据
max_range_change: 区间涨跌幅上限（%），默认30%
min_market_cap: 最小市值（亿），默认50亿
max_market_cap: 最大市值（亿），默认1300亿
df: 原始股票数据DataFrame
max_range_change: 最大涨跌幅限制
min_market_cap: 最小市值限制
max_market_cap: 最大市值限制
Returns:
筛选后的DataFrame
@@ -233,13 +237,13 @@ class MainForceStockSelector:
self.filtered_stocks = filtered_df
return filtered_df
def get_top_stocks(self, df: pd.DataFrame, top_n: int = 20) -> pd.DataFrame:
def get_top_stocks(self, df: pd.DataFrame, top_n: int = None) -> pd.DataFrame:
"""
获取主力资金净流入最多的前N只股票
获取主力资金净流入前N名股票
Args:
df: 筛选后的数据
top_n: 取前N名，默认20
df: 筛选后的股票数据
top_n: 返回前N名
Returns:
前N名股票DataFrame
main_force_ui.py
+29 -14
View File
@@ -30,7 +30,7 @@ def display_main_force_selector():
st.markdown("## 🎯 主力选股 - 智能筛选优质标的")
with col_history:
st.write("") # 占位
if st.button("📚 批量分析历史", use_container_width=True):
if st.button("📚 批量分析历史", width='content'):
st.session_state.main_force_view_history = True
st.rerun()
@@ -102,8 +102,8 @@ def display_main_force_selector():
with col1:
max_change = st.number_input(
"最大涨跌幅(%)",
min_value=10.0,
max_value=100.0,
min_value=5.0,
max_value=200.0,
value=30.0,
step=5.0,
help="过滤掉涨幅过高的股票，避免追高"
@@ -121,7 +121,7 @@ def display_main_force_selector():
with col3:
max_cap = st.number_input(
"最大市值(亿)",
min_value=100.0,
min_value=50.0,
max_value=50000.0,
value=5000.0,
step=100.0
@@ -137,7 +137,7 @@ def display_main_force_selector():
st.markdown("---")
# 开始分析按钮
if st.button("🚀 开始主力选股", type="primary", use_container_width=True):
if st.button("🚀 开始主力选股", type="primary", width='content'):
with st.spinner("正在获取数据并分析，这可能需要几分钟..."):
@@ -148,7 +148,10 @@ def display_main_force_selector():
result = analyzer.run_full_analysis(
start_date=start_date,
days_ago=days_ago,
final_n=final_n
final_n=final_n,
max_range_change=max_change,
min_market_cap=min_cap,
max_market_cap=max_cap
)
# 保存结果到session_state
@@ -277,7 +280,7 @@ def display_analysis_results(result: dict, analyzer):
# 显示DataFrame
display_df = analyzer.raw_stocks[final_cols].copy()
st.dataframe(display_df, use_container_width=True, height=400)
st.dataframe(display_df, width='content', height=400)
# 显示统计
st.caption(f"共 {len(display_df)} 只候选股票，显示 {len(final_cols)} 个字段")
@@ -309,7 +312,7 @@ def display_analysis_results(result: dict, analyzer):
with col_batch3:
st.write("") # 占位
if st.button("🚀 开始批量分析", type="primary", use_container_width=True):
if st.button("🚀 开始批量分析", type="primary", width='content'):
# 准备数据：按主力资金净流入排序
df_sorted = analyzer.raw_stocks.copy()
@@ -497,7 +500,7 @@ def run_main_force_batch_analysis():
# 返回按钮
col_back, col_clear = st.columns(2)
with col_back:
if st.button("🔙 返回主力选股", use_container_width=True):
if st.button("🔙 返回主力选股", width='content'):
# 清除所有批量分析相关状态
if 'main_force_batch_trigger' in st.session_state:
del st.session_state.main_force_batch_trigger
@@ -508,7 +511,7 @@ def run_main_force_batch_analysis():
st.rerun()
with col_clear:
if st.button("🔄 重新分析", use_container_width=True):
if st.button("🔄 重新分析", width='content'):
# 清除结果，保留触发标志和代码
if 'main_force_batch_results' in st.session_state:
del st.session_state.main_force_batch_results
@@ -569,11 +572,11 @@ def run_main_force_batch_analysis():
start_analysis = False
with col_confirm:
if st.button("🚀 确认开始分析", type="primary", use_container_width=True):
if st.button("🚀 确认开始分析", type="primary", width='content'):
start_analysis = True
with col_cancel:
if st.button("❌ 取消", type="secondary", use_container_width=True):
if st.button("❌ 取消", type="secondary", width='content'):
# 清除所有批量分析相关状态
if 'main_force_batch_trigger' in st.session_state:
del st.session_state.main_force_batch_trigger
@@ -858,7 +861,19 @@ def display_main_force_batch_results(batch_results):
})
df_display = pd.DataFrame(display_data)
st.dataframe(df_display, use_container_width=True, height=400)
# 类型统一，避免Arrow序列化错误
numeric_cols = ['信心度', '止盈位', '止损位', '目标价']
for col in numeric_cols:
if col in df_display.columns:
df_display[col] = pd.to_numeric(df_display[col], errors='coerce')
text_cols = ['股票代码', '股票名称', '评级', '进场区间']
for col in text_cols:
if col in df_display.columns:
df_display[col] = df_display[col].astype(str)
st.dataframe(df_display, width='content', height=400)
# 详细分析结果（可展开）
st.markdown("---")
@@ -977,5 +992,5 @@ def display_main_force_batch_results(batch_results):
})
df_failed = pd.DataFrame(failed_data)
st.dataframe(df_failed, use_container_width=True)
st.dataframe(df_failed, width='content')
monitor_manager.py
+14 -14
View File
@@ -145,7 +145,7 @@ def display_add_stock_section():
auto_take_profit = st.checkbox("自动止盈", value=True)
# 添加按钮
if st.button("✅ 添加监测", type="primary", use_container_width=True):
if st.button("✅ 添加监测", type="primary", width='stretch'):
if symbol and entry_min > 0 and entry_max > 0 and entry_max > entry_min:
try:
# 准备数据
@@ -408,10 +408,10 @@ def display_edit_dialog(stock_id: int):
col1, col2, col3 = st.columns(3)
with col1:
submit = st.form_submit_button("✅ 保存修改", type="primary", use_container_width=True)
submit = st.form_submit_button("✅ 保存修改", type="primary", width='stretch')
with col2:
cancel = st.form_submit_button("❌ 取消", use_container_width=True)
cancel = st.form_submit_button("❌ 取消", width='stretch')
if submit:
if entry_min > 0 and entry_max > 0 and entry_max > entry_min:
@@ -482,7 +482,7 @@ def display_delete_confirm_dialog(stock_id: int):
col1, col2, col3 = st.columns([1, 1, 1])
with col1:
if st.button("🗑️ 确认删除", type="primary", use_container_width=True, key=f"confirm_delete_{stock_id}"):
if st.button("🗑️ 确认删除", type="primary", width='stretch', key=f"confirm_delete_{stock_id}"):
try:
result = monitor_db.remove_monitored_stock(stock_id)
if result:
@@ -508,7 +508,7 @@ def display_delete_confirm_dialog(stock_id: int):
st.rerun()
with col2:
if st.button("❌ 取消", use_container_width=True, key=f"cancel_delete_{stock_id}"):
if st.button("❌ 取消", width='stretch', key=f"cancel_delete_{stock_id}"):
del st.session_state.deleting_stock_id
st.rerun()
@@ -568,7 +568,7 @@ def display_notification_management():
# 测试邮件按钮
if email_config['configured']:
if st.button("📧 发送测试邮件", type="primary", use_container_width=True):
if st.button("📧 发送测试邮件", type="primary", width='stretch'):
with st.spinner("正在发送测试邮件..."):
success, message = notification_service.send_test_email()
if success:
@@ -577,7 +577,7 @@ def display_notification_management():
else:
st.error(f"❌ {message}")
else:
st.button("📧 发送测试邮件", type="primary", use_container_width=True, disabled=True)
st.button("📧 发送测试邮件", type="primary", width='stretch', disabled=True)
st.caption("请先在.env文件中配置邮件参数")
with col2:
@@ -688,7 +688,7 @@ def display_miniqmt_status():
# 连接按钮
if qmt_status['enabled'] and not qmt_status['connected']:
if st.button("🔗 连接MiniQMT", type="primary", use_container_width=True):
if st.button("🔗 连接MiniQMT", type="primary", width='stretch'):
success, msg = miniqmt.connect()
if success:
st.success(f"✅ {msg}")
@@ -696,7 +696,7 @@ def display_miniqmt_status():
st.error(f"❌ {msg}")
st.rerun()
elif qmt_status['connected']:
if st.button("🔌 断开连接", use_container_width=True):
if st.button("🔌 断开连接", width='stretch'):
if miniqmt.disconnect():
st.info("⏸️ 已断开MiniQMT连接")
st.rerun()
@@ -820,7 +820,7 @@ def display_scheduler_section():
col1, col2, col3 = st.columns([1, 1, 1])
with col1:
if st.button("💾 保存设置", type="primary", use_container_width=True):
if st.button("💾 保存设置", type="primary", width='stretch'):
try:
# 更新配置
scheduler.update_config(
@@ -841,24 +841,24 @@ def display_scheduler_section():
with col2:
if status['scheduler_running']:
if st.button("⏹️ 停止调度器", use_container_width=True):
if st.button("⏹️ 停止调度器", width='stretch'):
scheduler.stop_scheduler()
st.info("⏸️ 调度器已停止")
time.sleep(0.5)
st.rerun()
else:
if enabled:
if st.button("▶️ 启动调度器", type="secondary", use_container_width=True):
if st.button("▶️ 启动调度器", type="secondary", width='stretch'):
scheduler.start_scheduler()
st.success("✅ 调度器已启动")
time.sleep(0.5)
st.rerun()
else:
st.button("▶️ 启动调度器", use_container_width=True, disabled=True)
st.button("▶️ 启动调度器", width='stretch', disabled=True)
st.caption("请先启用定时调度")
with col3:
if st.button("🔄 刷新状态", use_container_width=True):
if st.button("🔄 刷新状态", width='stretch'):
st.rerun()
def get_monitor_summary():
pdf_generator.py
+1 -1
View File
@@ -270,7 +270,7 @@ def display_pdf_export_section(stock_info, agents_results, discussion_result, fi
with col2:
# 生成PDF报告按钮（使用股票代码作为key的一部分，确保唯一性）
button_key = f"pdf_btn_{stock_info.get('symbol', 'unknown')}"
if st.button("📄 生成并下载PDF报告", type="primary", use_container_width=True, key=button_key):
if st.button("📄 生成并下载PDF报告", type="primary", width='content', key=button_key):
with st.spinner("正在生成PDF报告..."):
try:
# 生成PDF内容
pdf_generator_fixed.py
+1 -1
View File
@@ -291,7 +291,7 @@ def display_pdf_export_section(stock_info, agents_results, discussion_result, fi
import uuid
import time
button_key = f"generate_report_btn_{int(time.time())}_{uuid.uuid4().hex[:8]}"
if st.button("📊 生成并下载报告", type="primary", use_container_width=True, key=button_key):
if st.button("📊 生成并下载报告", type="primary", width='content', key=button_key):
with st.spinner("正在生成报告..."):
try:
# 生成Markdown内容
pdf_generator_pandoc.py
+1 -1
View File
@@ -267,7 +267,7 @@ def display_pdf_export_section(stock_info, agents_results, discussion_result, fi
col1, col2, col3 = st.columns([1, 2, 1])
with col2:
if st.button("📊 生成并下载报告", type="primary", use_container_width=True, key="generate_report_btn"):
if st.button("📊 生成并下载报告", type="primary", width='content', key="generate_report_btn"):
st.session_state.show_download_links = True
with st.spinner("正在生成报告..."):
success = generate_pdf_report(stock_info, agents_results, discussion_result, final_decision)
portfolio_manager.py
+12 -1
View File
@@ -418,7 +418,18 @@ class PortfolioManager:
# 使用正确的字段名
rating = final_decision.get("rating", "持有")
confidence = final_decision.get("confidence_level", 5.0)
# 确保信心度为float类型，避免Arrow序列化错误
confidence_raw = final_decision.get("confidence_level", 5.0)
try:
confidence = float(confidence_raw)
# 确保信心度在合理范围内
if confidence < 0:
confidence = 0.0
elif confidence > 10:
confidence = 10.0
except (ValueError, TypeError):
# 如果转换失败，使用默认值
confidence = 5.0
current_price = stock_info.get("current_price", 0.0)
target_price_str = final_decision.get("target_price", "")
entry_range = final_decision.get("entry_range", "")
portfolio_ui.py
+6 -6
View File
@@ -286,7 +286,7 @@ def display_batch_analysis():
)
# 立即分析按钮
if st.button("🚀 立即开始分析", type="primary", use_container_width=True):
if st.button("🚀 立即开始分析", type="primary", width='content'):
with st.spinner("正在批量分析持仓股票..."):
# 显示进度
progress_bar = st.progress(0)
@@ -551,7 +551,7 @@ def display_scheduler_management():
with col_add:
st.write("") # 占位，对齐按钮
st.write("")
if st.button("➕ 添加", type="primary", use_container_width=True):
if st.button("➕ 添加", type="primary", width='content'):
time_str = new_time.strftime("%H:%M")
if portfolio_scheduler.add_schedule_time(time_str):
st.success(f"已添加 {time_str}")
@@ -631,20 +631,20 @@ def display_scheduler_management():
with col_btn1:
if is_running:
if st.button("⏹️ 停止调度器", type="secondary", use_container_width=True):
if st.button("⏹️ 停止调度器", type="secondary", width='content'):
portfolio_scheduler.stop_scheduler()
st.success("调度器已停止")
time.sleep(0.5)
st.rerun()
else:
if st.button("▶️ 启动调度器", type="primary", use_container_width=True):
if st.button("▶️ 启动调度器", type="primary", width='content'):
portfolio_scheduler.start_scheduler()
st.success("调度器已启动")
time.sleep(0.5)
st.rerun()
with col_btn2:
if st.button("🚀 立即执行一次", type="primary", use_container_width=True):
if st.button("🚀 立即执行一次", type="primary", width='content'):
with st.spinner("正在执行持仓分析..."):
try:
portfolio_scheduler.run_analysis_now()
@@ -653,7 +653,7 @@ def display_scheduler_management():
st.error(f"执行失败: {str(e)}")
with col_btn3:
if st.button("🔄 刷新状态", use_container_width=True):
if st.button("🔄 刷新状态", width='content'):
st.rerun()
quarterly_report_data.py
+36 -17
View File
@@ -228,35 +228,54 @@ class QuarterlyReportDataFetcher:
def _get_financial_indicators(self, symbol):
"""获取财务指标数据"""
try:
# stock_financial_analysis_indicator - 财务指标
df = ak.stock_financial_analysis_indicator(symbol=symbol)
# 使用stock_financial_abstract替代已失效的stock_financial_analysis_indicator
df = ak.stock_financial_abstract(symbol=symbol)
if df is None or df.empty:
print(f" 未找到财务指标数据")
return None
# 获取最近8期
df = df.head(self.periods)
df = df.head(self.periods * 2) # 取更多数据以确保有足够的季度数据
# 转换为字典列表
# 提取关键财务指标
key_indicators = [
'净资产收益率(ROE)', '总资产报酬率(ROA)', '销售净利率', '销售毛利率',
'资产负债率', '流动比率', '速动比率', '应收账款周转率', '存货周转率',
'总资产周转率', '基本每股收益', '每股净资产', '每股现金流'
]
# 筛选出包含关键指标的行
indicator_rows = df[df['指标'].isin(key_indicators)]
if indicator_rows.empty:
print(f" 未找到关键财务指标数据")
return None
# 获取日期列（排除'选项'和'指标'列）
date_columns = [col for col in df.columns if col not in ['选项', '指标']]
# 转换为字典列表，每个字典代表一个时期的财务指标
data_list = []
for idx, row in df.iterrows():
item = {}
for col in df.columns:
value = row.get(col)
if value is None or (isinstance(value, float) and pd.isna(value)):
continue
try:
item[col] = str(value)
except:
item[col] = "N/A"
if item:
data_list.append(item)
for date_col in date_columns[:self.periods]: # 只取最近的periods期
item = {'报告期': date_col}
for _, row in indicator_rows.iterrows():
indicator_name = row['指标']
value = row.get(date_col)
if value is not None and not (isinstance(value, float) and pd.isna(value)):
try:
# 尝试转换为字符串
item[indicator_name] = str(value)
except:
item[indicator_name] = "N/A"
else:
item[indicator_name] = "N/A"
data_list.append(item)
return {
"data": data_list,
"periods": len(data_list),
"columns": df.columns.tolist(),
"columns": ['报告期'] + key_indicators,
"query_time": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
run.py
+3 -3
View File
@@ -52,15 +52,15 @@ def main():
# 启动Streamlit应用
print("🌐 正在启动Web界面...")
print("📝 访问地址: http://localhost:8501")
print("📝 访问地址: http://localhost:8503")
print("⏹️ 按 Ctrl+C 停止服务")
print("=" * 50)
try:
subprocess.run([
sys.executable, "-m", "streamlit", "run", "app.py",
"--server.port", "8501",
"--server.address", "0.0.0.0"
"--server.port", "8503",
"--server.address", "127.0.0.1"
])
except KeyboardInterrupt:
print("\n👋 感谢使用AI股票分析系统！")
sector_strategy.db
BIN
View File
Binary file not shown.
sector_strategy_data.py
+323 -25
View File
@@ -8,6 +8,13 @@ import pandas as pd
from datetime import datetime, timedelta
import warnings
import time
import logging
import os
from dotenv import load_dotenv
from sector_strategy_db import SectorStrategyDatabase
# 加载环境变量
load_dotenv()
warnings.filterwarnings('ignore')
@@ -21,6 +28,18 @@ class SectorStrategyDataFetcher:
self.retry_delay = 2 # 重试延迟（秒）
self.request_delay = 1 # 请求间隔（秒）
# 初始化数据库和日志
self.database = SectorStrategyDatabase()
self.logger = logging.getLogger(__name__)
# 配置日志
if not self.logger.handlers:
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
self.logger.addHandler(handler)
self.logger.setLevel(logging.INFO)
def _safe_request(self, func, *args, **kwargs):
"""安全的请求函数，包含重试机制"""
for attempt in range(self.max_retries):
@@ -102,6 +121,9 @@ class SectorStrategyDataFetcher:
data["success"] = True
print("[智策] ✓ 板块数据获取完成！")
# 保存原始数据到数据库
self._save_raw_data_to_db(data)
except Exception as e:
print(f"[智策] ✗ 数据获取出错: {e}")
data["error"] = str(e)
@@ -276,39 +298,111 @@ class SectorStrategyDataFetcher:
return {}
def _get_north_money_flow(self):
"""获取北向资金流向"""
"""获取北向资金流向（优先使用Tushare，失败时使用Akshare）"""
# 优先使用Tushare获取沪深港通资金流向
self.ts_pro = None
tushare_token = os.getenv('TUSHARE_TOKEN', '')
try:
# 获取沪深港通资金流向（使用重试机制）
# 初始化Tushare（如果尚未初始化）
if not hasattr(self, '_tushare_api'):
TUSHARE_TOKEN = os.getenv('TUSHARE_TOKEN', '')
if TUSHARE_TOKEN:
try:
import tushare as ts
ts.set_token(tushare_token)
self.ts_pro = ts.pro_api()
print(" [Tushare] ✅ 初始化成功")
except Exception as e:
print(f" [Tushare] 初始化失败: {e}")
self._tushare_api = None
else:
print(" [Tushare] 未配置Token")
self._tushare_api = None
# 如果Tushare可用，获取数据
if hasattr(self, '_tushare_api') and self._tushare_api:
print(" [Tushare] 正在获取沪深港通资金流向...")
# 获取最近30天的数据
end_date = datetime.now()
start_date = end_date - timedelta(days=20)
df = self._tushare_api.moneyflow_hsgt(
start_date=start_date.strftime('%Y%m%d'),
end_date=end_date.strftime('%Y%m%d')
)
if df is not None and not df.empty:
print(" [Tushare] ✅ 成功获取数据")
# 按日期降序排列，获取最新数据
df = df.sort_values('trade_date', ascending=False)
latest = df.iloc[0]
# 转换数据格式以匹配原有结构
north_flow = {
"date": str(latest['trade_date']),
"north_net_inflow": float(latest['north_money']),
"hgt_net_inflow": float(latest['hgt']),
"sgt_net_inflow": float(latest['sgt']),
"north_total_amount": float(latest['north_money']) # Tushare没有总成交金额，使用净流入作为近似值
}
# 获取历史趋势（最近20天）
history = []
for idx, row in df.head(20).iterrows():
history.append({
"date": str(row['trade_date']),
"net_inflow": float(row['north_money'])
})
north_flow["history"] = history
return north_flow
else:
print(" [Tushare] ❌ 未获取到数据")
else:
print(" [Tushare] 不可用")
except Exception as e:
print(f" [Tushare] 获取北向资金失败: {e}")
# Tushare失败，尝试使用Akshare
try:
print(" [Akshare] 正在获取沪深港通资金流向（备用数据源）...")
df = self._safe_request(ak.stock_hsgt_fund_flow_summary_em)
if df is None or df.empty:
return {}
if df is not None and not df.empty:
print(" [Akshare] ✅ 成功获取数据")
# 获取最新数据
latest = df.iloc[0]
# 获取最新数据
latest = df.iloc[0]
north_flow = {
"date": str(latest.get('日期', '')),
"north_net_inflow": latest.get('北向资金-成交净买额', 0),
"hgt_net_inflow": latest.get('沪股通-成交净买额', 0),
"sgt_net_inflow": latest.get('深股通-成交净买额', 0),
"north_total_amount": latest.get('北向资金-成交金额', 0)
}
north_flow = {
"date": str(latest.get('日期', '')),
"north_net_inflow": latest.get('北向资金-成交净买额', 0),
"hgt_net_inflow": latest.get('沪股通-成交净买额', 0),
"sgt_net_inflow": latest.get('深股通-成交净买额', 0),
"north_total_amount": latest.get('北向资金-成交金额', 0)
}
# 获取历史趋势（最近10天）
history = []
for idx, row in df.head(10).iterrows():
history.append({
"date": str(row.get('日期', '')),
"net_inflow": row.get('北向资金-成交净买额', 0)
})
north_flow["history"] = history
return north_flow
# 获取历史趋势（最近20天）
history = []
for idx, row in df.head(20).iterrows():
history.append({
"date": str(row.get('日期', '')),
"net_inflow": row.get('北向资金-成交净买额', 0)
})
north_flow["history"] = history
return north_flow
else:
print(" [Akshare] ❌ 未获取到数据")
except Exception as e:
print(f" 获取北向资金失败: {e}")
return {}
print(f" [Akshare] 获取北向资金失败: {e}")
# 所有数据源都失败
print(" ❌ 所有数据源均获取失败")
return {}
def _get_financial_news(self):
"""获取财经新闻"""
@@ -439,6 +533,210 @@ class SectorStrategyDataFetcher:
return "\n".join(text_parts)
def _save_raw_data_to_db(self, data):
"""保存原始数据到数据库"""
try:
if not data.get("success"):
self.logger.warning("[智策数据] 数据获取失败，跳过保存")
return
# 保存板块数据
if data.get("sectors"):
# 将字典转换为DataFrame并映射必要列
sectors_df = pd.DataFrame([
{
'板块名称': v.get('name', k),
'涨跌幅': v.get('change_pct', 0),
'成交额': 0,
'总市值': v.get('total_market_cap', 0),
'市盈率': v.get('pe_ratio', 0),
'市净率': v.get('pb_ratio', 0),
'最新价': 0,
'成交量': 0,
'turnover': v.get('turnover', 0) # 兼容保存方法中的fallback
}
for k, v in data["sectors"].items()
])
self.database.save_sector_raw_data(
data_date=datetime.now().strftime('%Y-%m-%d'),
data_type="industry",
data_df=sectors_df
)
self.logger.info(f"[智策数据] 保存行业板块数据: {len(data['sectors'])} 个板块")
# 保存概念板块数据
if data.get("concepts"):
concepts_df = pd.DataFrame([
{
'板块名称': v.get('name', k),
'涨跌幅': v.get('change_pct', 0),
'成交额': 0,
'总市值': v.get('total_market_cap', 0),
'市盈率': v.get('pe_ratio', 0),
'市净率': v.get('pb_ratio', 0),
'最新价': 0,
'成交量': 0,
'turnover': v.get('turnover', 0)
}
for k, v in data["concepts"].items()
])
self.database.save_sector_raw_data(
data_date=datetime.now().strftime('%Y-%m-%d'),
data_type="concept",
data_df=concepts_df
)
self.logger.info(f"[智策数据] 保存概念板块数据: {len(data['concepts'])} 个概念")
# 保存资金流向数据
if data.get("sector_fund_flow"):
flow_today = data["sector_fund_flow"].get("today", [])
fund_df = pd.DataFrame([
{
'行业': item.get('sector', ''),
'主力净流入-净额': item.get('main_net_inflow', 0),
'主力净流入-净占比': item.get('main_net_inflow_pct', 0),
'超大单净流入-净额': item.get('super_large_net_inflow', 0),
'超大单净流入-净占比': item.get('super_large_net_inflow_pct', 0),
'大单净流入-净额': item.get('large_net_inflow', 0),
'大单净流入-净占比': item.get('large_net_inflow_pct', 0)
}
for item in flow_today
])
if not fund_df.empty:
self.database.save_sector_raw_data(
data_date=datetime.now().strftime('%Y-%m-%d'),
data_type="fund_flow",
data_df=fund_df
)
self.logger.info("[智策数据] 保存资金流向数据")
# 保存市场概况数据
if data.get("market_overview"):
market = data["market_overview"]
mo_df = pd.DataFrame([
{'名称': '上证指数', '最新价': market.get('sh_index', {}).get('close', 0), '涨跌幅': market.get('sh_index', {}).get('change_pct', 0), '成交量': market.get('sh_index', {}).get('volume', 0), '成交额': market.get('sh_index', {}).get('turnover', 0)},
{'名称': '深证成指', '最新价': market.get('sz_index', {}).get('close', 0), '涨跌幅': market.get('sz_index', {}).get('change_pct', 0), '成交量': market.get('sz_index', {}).get('volume', 0), '成交额': market.get('sz_index', {}).get('turnover', 0)},
{'名称': '创业板指', '最新价': market.get('cyb_index', {}).get('close', 0), '涨跌幅': market.get('cyb_index', {}).get('change_pct', 0), '成交量': market.get('cyb_index', {}).get('volume', 0), '成交额': market.get('cyb_index', {}).get('turnover', 0)}
])
self.database.save_sector_raw_data(
data_date=datetime.now().strftime('%Y-%m-%d'),
data_type="market_overview",
data_df=mo_df
)
self.logger.info("[智策数据] 保存市场概况数据")
# 保存北向资金数据
# 注：north_flow结构与原始表不一致，此处暂不保存以避免歧义
# 保存新闻数据
if data.get("news"):
self.database.save_news_data(
news_list=data["news"],
news_date=datetime.now().strftime('%Y-%m-%d'),
source="akshare"
)
self.logger.info(f"[智策数据] 保存财经新闻: {len(data['news'])} 条")
except Exception as e:
self.logger.error(f"[智策数据] 保存原始数据失败: {e}")
def get_cached_data_with_fallback(self):
"""获取缓存数据，支持回退机制"""
try:
# 首先尝试获取最新数据
print("[智策] 尝试获取最新数据...")
fresh_data = self.get_all_sector_data()
if fresh_data.get("success"):
return fresh_data
# 如果获取失败，回退到缓存数据
print("[智策] 获取最新数据失败，尝试加载缓存数据...")
cached_data = self._load_cached_data()
if cached_data:
print("[智策] ✓ 成功加载缓存数据")
cached_data["from_cache"] = True
cached_data["cache_warning"] = "当前显示为缓存数据（24小时内），可能不是最新信息"
return cached_data
else:
print("[智策] ✗ 无可用缓存数据")
return {
"success": False,
"error": "无法获取数据且无可用缓存",
"timestamp": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
except Exception as e:
self.logger.error(f"[智策数据] 获取数据失败: {e}")
return {
"success": False,
"error": str(e),
"timestamp": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
def _load_cached_data(self):
"""加载缓存数据"""
try:
# 获取最近的各类数据
cached_data = {
"success": True,
"timestamp": datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
"sectors": {},
"concepts": {},
"sector_fund_flow": {},
"market_overview": {},
"north_flow": {},
"news": []
}
# 加载板块数据
sectors_data = self.database.get_latest_raw_data("sectors")
if sectors_data:
cached_data["sectors"] = sectors_data.get("data_content", {})
# 加载概念数据
concepts_data = self.database.get_latest_raw_data("concepts")
if concepts_data:
cached_data["concepts"] = concepts_data.get("data_content", {})
# 加载资金流向数据
fund_flow_data = self.database.get_latest_raw_data("fund_flow")
if fund_flow_data:
cached_data["sector_fund_flow"] = fund_flow_data.get("data_content", {})
# 加载市场概况数据
market_data = self.database.get_latest_raw_data("market_overview")
if market_data:
cached_data["market_overview"] = market_data.get("data_content", {})
# 加载北向资金数据
north_data = self.database.get_latest_raw_data("north_flow")
if north_data:
cached_data["north_flow"] = north_data.get("data_content", {})
# 加载新闻数据
news_data = self.database.get_latest_news_data()
if news_data:
# 仅传递内容列表给下游分析，避免结构不一致
cached_data["news"] = news_data.get("data_content", [])
# 检查是否有有效数据
has_data = any([
cached_data["sectors"],
cached_data["concepts"],
cached_data["sector_fund_flow"],
cached_data["market_overview"],
cached_data["north_flow"],
cached_data["news"]
])
return cached_data if has_data else None
except Exception as e:
self.logger.error(f"[智策数据] 加载缓存数据失败: {e}")
return None
# 测试函数
if __name__ == "__main__":
sector_strategy_db.py
+934
View File
@@ -0,0 +1,934 @@
"""
智策板块数据库模块
用于存储板块策略历史数据和分析报告
"""
import sqlite3
from datetime import datetime
import json
import pandas as pd
import logging
class SectorStrategyDatabase:
"""智策板块数据库管理类"""
def __init__(self, db_path='sector_strategy.db'):
"""
初始化数据库
Args:
db_path: 数据库文件路径
"""
self.db_path = db_path
# 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.init_database()
def get_connection(self):
"""获取数据库连接"""
return sqlite3.connect(self.db_path)
def init_database(self):
"""初始化数据库表"""
conn = self.get_connection()
cursor = conn.cursor()
# 板块原始数据表
cursor.execute('''
CREATE TABLE IF NOT EXISTS sector_raw_data (
id INTEGER PRIMARY KEY AUTOINCREMENT,
data_date TEXT NOT NULL,
sector_code TEXT NOT NULL,
sector_name TEXT,
price REAL,
change_pct REAL,
volume REAL,
turnover REAL,
market_cap REAL,
pe_ratio REAL,
pb_ratio REAL,
data_type TEXT,
data_version INTEGER DEFAULT 1,
created_at TEXT DEFAULT CURRENT_TIMESTAMP,
UNIQUE(data_date, sector_code, data_type)
)
''')
# 创建索引
cursor.execute('''
CREATE INDEX IF NOT EXISTS idx_sector_data_date ON sector_raw_data(data_date)
''')
cursor.execute('''
CREATE INDEX IF NOT EXISTS idx_sector_code ON sector_raw_data(sector_code)
''')
cursor.execute('''
CREATE INDEX IF NOT EXISTS idx_data_type ON sector_raw_data(data_type)
''')
cursor.execute('''
CREATE INDEX IF NOT EXISTS idx_data_version ON sector_raw_data(data_version)
''')
# 板块新闻数据表
cursor.execute('''
CREATE TABLE IF NOT EXISTS sector_news_data (
id INTEGER PRIMARY KEY AUTOINCREMENT,
news_date TEXT NOT NULL,
title TEXT,
content TEXT,
source TEXT,
url TEXT,
related_sectors TEXT,
sentiment_score REAL,
importance_score REAL,
data_version INTEGER DEFAULT 1,
created_at TEXT DEFAULT CURRENT_TIMESTAMP
)
''')
# AI分析报告表
cursor.execute('''
CREATE TABLE IF NOT EXISTS sector_analysis_reports (
id INTEGER PRIMARY KEY AUTOINCREMENT,
analysis_date TEXT NOT NULL,
data_date_range TEXT,
analysis_content TEXT,
recommended_sectors TEXT,
summary TEXT,
confidence_score REAL,
risk_level TEXT,
investment_horizon TEXT,
market_outlook TEXT,
created_at TEXT DEFAULT CURRENT_TIMESTAMP
)
''')
# 板块追踪表（记录推荐板块的后续表现）
cursor.execute('''
CREATE TABLE IF NOT EXISTS sector_tracking (
id INTEGER PRIMARY KEY AUTOINCREMENT,
analysis_id INTEGER,
sector_code TEXT NOT NULL,
sector_name TEXT,
recommended_date TEXT,
recommended_price REAL,
target_price REAL,
stop_loss_price REAL,
current_price REAL,
profit_loss_pct REAL,
status TEXT,
notes TEXT,
updated_at TEXT DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (analysis_id) REFERENCES sector_analysis_reports (id)
)
''')
# 数据版本管理表
cursor.execute('''
CREATE TABLE IF NOT EXISTS data_versions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
data_type TEXT NOT NULL,
data_date TEXT NOT NULL,
version INTEGER NOT NULL,
status TEXT DEFAULT 'active',
fetch_success BOOLEAN DEFAULT 1,
error_message TEXT,
record_count INTEGER DEFAULT 0,
created_at TEXT DEFAULT CURRENT_TIMESTAMP,
UNIQUE(data_type, data_date, version)
)
''')
conn.commit()
conn.close()
self.logger.info("[智策板块] 数据库初始化完成")
def save_raw_data(self, data_date, data_type, data_df, version=None):
"""
保存原始数据
Args:
data_date: 数据日期
data_type: 数据类型 (sector_data, news_data等)
data_df: 数据DataFrame
version: 数据版本号，如果为None则自动生成
Returns:
int: 数据版本号
"""
conn = self.get_connection()
cursor = conn.cursor()
try:
# 获取或生成版本号
if version is None:
cursor.execute('''
SELECT COALESCE(MAX(version), 0) + 1
FROM data_versions
WHERE data_type = ? AND data_date = ?
''', (data_type, data_date))
version = cursor.fetchone()[0]
# 保存数据
if data_type == 'sector_data':
self._save_sector_data(cursor, data_date, data_df, version)
elif data_type == 'news_data':
self._save_news_data(cursor, data_date, data_df, version)
# 记录版本信息
cursor.execute('''
INSERT OR REPLACE INTO data_versions
(data_type, data_date, version, status, fetch_success, record_count)
VALUES (?, ?, ?, 'active', 1, ?)
''', (data_type, data_date, version, len(data_df)))
conn.commit()
self.logger.info(f"[智策板块] 保存{data_type}数据成功 (日期: {data_date}, 版本: {version}, 记录数: {len(data_df)})")
return version
except Exception as e:
conn.rollback()
# 记录失败版本
cursor.execute('''
INSERT OR REPLACE INTO data_versions
(data_type, data_date, version, status, fetch_success, error_message, record_count)
VALUES (?, ?, ?, 'failed', 0, ?, 0)
''', (data_type, data_date, version or 1, str(e)))
conn.commit()
self.logger.error(f"[智策板块] 保存{data_type}数据失败: {e}")
raise
finally:
conn.close()
def _save_sector_data(self, cursor, data_date, data_df, version):
"""保存板块数据"""
for _, row in data_df.iterrows():
cursor.execute('''
INSERT OR REPLACE INTO sector_raw_data
(data_date, sector_code, sector_name, price, change_pct, volume,
turnover, market_cap, pe_ratio, pb_ratio, data_type, data_version)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'sector_data', ?)
''', (
data_date,
row.get('sector_code', ''),
row.get('sector_name', ''),
row.get('price', 0),
row.get('change_pct', 0),
row.get('volume', 0),
row.get('turnover', 0),
row.get('market_cap', 0),
row.get('pe_ratio', 0),
row.get('pb_ratio', 0),
version
))
def _save_news_data(self, cursor, data_date, data_df, version):
"""保存新闻数据"""
for _, row in data_df.iterrows():
cursor.execute('''
INSERT OR REPLACE INTO sector_news_data
(news_date, title, content, source, url, related_sectors,
sentiment_score, importance_score, data_version)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
''', (
data_date,
row.get('title', ''),
row.get('content', ''),
row.get('source', ''),
row.get('url', ''),
json.dumps(row.get('related_sectors', []), ensure_ascii=False),
row.get('sentiment_score', 0),
row.get('importance_score', 0),
version
))
def get_latest_data(self, data_type, data_date=None):
"""
获取最新的成功数据
Args:
data_type: 数据类型
data_date: 指定日期，如果为None则获取最新日期的数据
Returns:
pd.DataFrame: 数据DataFrame
"""
conn = self.get_connection()
try:
# 获取最新成功的数据版本
if data_date:
query = '''
SELECT version FROM data_versions
WHERE data_type = ? AND data_date = ? AND fetch_success = 1
ORDER BY version DESC LIMIT 1
'''
params = [data_type, data_date]
else:
query = '''
SELECT data_date, version FROM data_versions
WHERE data_type = ? AND fetch_success = 1
ORDER BY data_date DESC, version DESC LIMIT 1
'''
params = [data_type]
version_df = pd.read_sql_query(query, conn, params=params)
if version_df.empty:
self.logger.warning(f"[智策板块] 未找到{data_type}的成功数据")
return pd.DataFrame()
if data_date is None:
data_date = version_df.iloc[0]['data_date']
version = version_df.iloc[0]['version']
# 获取具体数据
if data_type == 'sector_data':
data_query = '''
SELECT * FROM sector_raw_data
WHERE data_date = ? AND data_version = ?
ORDER BY sector_code
'''
elif data_type == 'news_data':
data_query = '''
SELECT * FROM sector_news_data
WHERE news_date = ? AND data_version = ?
ORDER BY importance_score DESC
'''
else:
return pd.DataFrame()
data_df = pd.read_sql_query(data_query, conn, params=[data_date, version])
self.logger.info(f"[智策板块] 获取{data_type}数据成功 (日期: {data_date}, 版本: {version}, 记录数: {len(data_df)})")
return data_df
except Exception as e:
self.logger.error(f"[智策板块] 获取{data_type}数据失败: {e}")
return pd.DataFrame()
finally:
conn.close()
def save_analysis_report(self, data_date_range, analysis_content,
recommended_sectors, summary, confidence_score=None,
risk_level=None, investment_horizon=None, market_outlook=None):
"""
保存AI分析报告
Args:
data_date_range: 数据日期范围
analysis_content: 分析内容（JSON字符串或字典）
recommended_sectors: 推荐板块列表
summary: 摘要
confidence_score: 置信度分数
risk_level: 风险等级
investment_horizon: 投资周期
market_outlook: 市场展望
Returns:
int: 报告ID
"""
conn = self.get_connection()
cursor = conn.cursor()
# 如果传入的是字典，转换为JSON字符串
if isinstance(analysis_content, dict):
analysis_content = json.dumps(analysis_content, ensure_ascii=False, indent=2)
cursor.execute('''
INSERT INTO sector_analysis_reports
(analysis_date, data_date_range, analysis_content, recommended_sectors,
summary, confidence_score, risk_level, investment_horizon, market_outlook)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
''', (
datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
data_date_range,
analysis_content,
json.dumps(recommended_sectors, ensure_ascii=False),
summary,
confidence_score,
risk_level,
investment_horizon,
market_outlook
))
report_id = cursor.lastrowid
conn.commit()
conn.close()
self.logger.info(f"[智策板块] 分析报告已保存 (ID: {report_id})")
return report_id
def get_analysis_reports(self, limit=10):
"""
获取历史分析报告
Args:
limit: 返回数量
Returns:
pd.DataFrame: 报告列表
"""
conn = self.get_connection()
query = '''
SELECT * FROM sector_analysis_reports
ORDER BY created_at DESC
LIMIT ?
'''
df = pd.read_sql_query(query, conn, params=[limit])
conn.close()
return df
def get_analysis_report(self, report_id):
"""
获取单个分析报告详情
Args:
report_id: 报告ID
Returns:
dict: 报告详情
"""
conn = self.get_connection()
cursor = conn.cursor()
cursor.execute('''
SELECT * FROM sector_analysis_reports WHERE id = ?
''', (report_id,))
row = cursor.fetchone()
columns = [desc[0] for desc in cursor.description] if cursor.description else []
conn.close()
if row:
report = dict(zip(columns, row))
# 解析JSON字段
try:
if report.get('analysis_content'):
report['analysis_content_parsed'] = json.loads(report['analysis_content'])
if report.get('recommended_sectors'):
report['recommended_sectors_parsed'] = json.loads(report['recommended_sectors'])
except json.JSONDecodeError as e:
self.logger.warning(f"[智策板块] JSON解析失败: {e}")
return report
return None
def delete_analysis_report(self, report_id):
"""
删除分析报告
Args:
report_id: 报告ID
Returns:
bool: 删除是否成功
"""
conn = self.get_connection()
cursor = conn.cursor()
try:
# 删除相关的追踪记录
cursor.execute('DELETE FROM sector_tracking WHERE analysis_id = ?', (report_id,))
# 删除报告
cursor.execute('DELETE FROM sector_analysis_reports WHERE id = ?', (report_id,))
deleted_count = cursor.rowcount
conn.commit()
if deleted_count > 0:
self.logger.info(f"[智策板块] 报告删除成功 (ID: {report_id})")
return True
else:
self.logger.warning(f"[智策板块] 未找到要删除的报告 (ID: {report_id})")
return False
except Exception as e:
conn.rollback()
self.logger.error(f"[智策板块] 删除报告失败: {e}")
return False
finally:
conn.close()
def get_data_versions(self, data_type, limit=10):
"""
获取数据版本历史
Args:
data_type: 数据类型
limit: 返回数量
Returns:
pd.DataFrame: 版本历史
"""
conn = self.get_connection()
query = '''
SELECT * FROM data_versions
WHERE data_type = ?
ORDER BY data_date DESC, version DESC
LIMIT ?
'''
df = pd.read_sql_query(query, conn, params=[data_type, limit])
conn.close()
return df
def save_sector_raw_data(self, data_date, data_type, data_df):
"""
保存板块原始数据
Args:
data_date: 数据日期
data_type: 数据类型 ('industry', 'concept', 'fund_flow', 'market_overview', 'north_fund', 'news')
data_df: 数据DataFrame
"""
# 兼容不同数据结构的空值判断
is_empty = False
if data_df is None:
is_empty = True
elif hasattr(data_df, 'empty'):
is_empty = data_df.empty
elif isinstance(data_df, (list, tuple, set, dict)):
is_empty = len(data_df) == 0
if is_empty:
self.logger.warning(f"[智策板块] {data_type}数据为空，跳过保存")
return
conn = self.get_connection()
cursor = conn.cursor()
try:
# 获取下一个版本号
version = self._get_next_version(data_date, data_type)
# 根据数据类型保存数据
if data_type in ['industry', 'concept']:
self._save_sector_data_raw(cursor, data_date, data_df, data_type, version)
elif data_type == 'fund_flow':
self._save_fund_flow_data(cursor, data_date, data_df, version)
elif data_type == 'market_overview':
self._save_market_overview_data(cursor, data_date, data_df, version)
elif data_type == 'north_fund':
self._save_north_fund_data(cursor, data_date, data_df, version)
elif data_type == 'news':
self._save_news_data_raw(cursor, data_date, data_df, version)
# 记录版本信息
cursor.execute('''
INSERT OR REPLACE INTO data_versions
(data_date, data_type, version, fetch_success, record_count)
VALUES (?, ?, ?, 1, ?)
''', (data_date, data_type, version, len(data_df)))
conn.commit()
self.logger.info(f"[智策板块] {data_type}数据保存成功 (日期: {data_date}, 版本: {version}, 记录数: {len(data_df)})")
except Exception as e:
conn.rollback()
self.logger.error(f"[智策板块] 保存{data_type}数据失败: {e}")
raise
finally:
conn.close()
def _save_sector_data_raw(self, cursor, data_date, data_df, data_type, version):
"""保存板块原始数据"""
for _, row in data_df.iterrows():
cursor.execute('''
INSERT OR REPLACE INTO sector_raw_data
(data_date, sector_code, sector_name, price, change_pct, volume,
turnover, market_cap, pe_ratio, pb_ratio, data_type, data_version)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
''', (
data_date,
str(row.get('板块代码', row.get('sector_code', ''))),
str(row.get('板块名称', row.get('sector_name', ''))),
float(row.get('最新价', row.get('price', 0))) if pd.notna(row.get('最新价', row.get('price', 0))) else 0,
float(row.get('涨跌幅', row.get('change_pct', 0))) if pd.notna(row.get('涨跌幅', row.get('change_pct', 0))) else 0,
float(row.get('成交量', row.get('volume', 0))) if pd.notna(row.get('成交量', row.get('volume', 0))) else 0,
float(row.get('成交额', row.get('turnover', 0))) if pd.notna(row.get('成交额', row.get('turnover', 0))) else 0,
float(row.get('总市值', row.get('market_cap', 0))) if pd.notna(row.get('总市值', row.get('market_cap', 0))) else 0,
float(row.get('市盈率', row.get('pe_ratio', 0))) if pd.notna(row.get('市盈率', row.get('pe_ratio', 0))) else 0,
float(row.get('市净率', row.get('pb_ratio', 0))) if pd.notna(row.get('市净率', row.get('pb_ratio', 0))) else 0,
data_type,
version
))
def _save_fund_flow_data(self, cursor, data_date, data_df, version):
"""保存资金流向数据"""
for _, row in data_df.iterrows():
cursor.execute('''
INSERT OR REPLACE INTO sector_raw_data
(data_date, sector_code, sector_name, price, change_pct, volume,
turnover, market_cap, pe_ratio, pb_ratio, data_type, data_version)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'fund_flow', ?)
''', (
data_date,
str(row.get('行业', '')),
str(row.get('行业', '')),
float(row.get('主力净流入-净额', 0)) if pd.notna(row.get('主力净流入-净额', 0)) else 0,
float(row.get('主力净流入-净占比', 0)) if pd.notna(row.get('主力净流入-净占比', 0)) else 0,
float(row.get('超大单净流入-净额', 0)) if pd.notna(row.get('超大单净流入-净额', 0)) else 0,
float(row.get('超大单净流入-净占比', 0)) if pd.notna(row.get('超大单净流入-净占比', 0)) else 0,
float(row.get('大单净流入-净额', 0)) if pd.notna(row.get('大单净流入-净额', 0)) else 0,
float(row.get('大单净流入-净占比', 0)) if pd.notna(row.get('大单净流入-净占比', 0)) else 0,
0,
version
))
def _save_market_overview_data(self, cursor, data_date, data_df, version):
"""保存市场概况数据"""
for _, row in data_df.iterrows():
cursor.execute('''
INSERT OR REPLACE INTO sector_raw_data
(data_date, sector_code, sector_name, price, change_pct, volume,
turnover, market_cap, pe_ratio, pb_ratio, data_type, data_version)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'market_overview', ?)
''', (
data_date,
str(row.get('名称', '')),
str(row.get('名称', '')),
float(row.get('最新价', 0)) if pd.notna(row.get('最新价', 0)) else 0,
float(row.get('涨跌幅', 0)) if pd.notna(row.get('涨跌幅', 0)) else 0,
float(row.get('成交量', 0)) if pd.notna(row.get('成交量', 0)) else 0,
float(row.get('成交额', 0)) if pd.notna(row.get('成交额', 0)) else 0,
0, 0, 0,
version
))
def _save_north_fund_data(self, cursor, data_date, data_df, version):
"""保存北向资金数据"""
for _, row in data_df.iterrows():
cursor.execute('''
INSERT OR REPLACE INTO sector_raw_data
(data_date, sector_code, sector_name, price, change_pct, volume,
turnover, market_cap, pe_ratio, pb_ratio, data_type, data_version)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'north_fund', ?)
''', (
data_date,
str(row.get('代码', '')),
str(row.get('名称', '')),
float(row.get('收盘价', 0)) if pd.notna(row.get('收盘价', 0)) else 0,
float(row.get('涨跌幅', 0)) if pd.notna(row.get('涨跌幅', 0)) else 0,
float(row.get('持股数量', 0)) if pd.notna(row.get('持股数量', 0)) else 0,
float(row.get('持股市值', 0)) if pd.notna(row.get('持股市值', 0)) else 0,
float(row.get('持股变化', 0)) if pd.notna(row.get('持股变化', 0)) else 0,
0, 0,
version
))
def _save_news_data_raw(self, cursor, data_date, data_df, version):
"""保存新闻数据"""
for _, row in data_df.iterrows():
cursor.execute('''
INSERT OR REPLACE INTO sector_news_data
(news_date, title, content, source, url, related_sectors,
sentiment_score, importance_score, data_version)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
''', (
data_date,
str(row.get('新闻标题', row.get('title', ''))),
str(row.get('新闻内容', row.get('content', ''))),
str(row.get('新闻来源', row.get('source', ''))),
str(row.get('新闻链接', row.get('url', ''))),
json.dumps([], ensure_ascii=False), # 暂时为空
0, # 暂时为0
0, # 暂时为0
version
))
def cleanup_old_data(self, data_type, keep_days=30):
"""
清理旧数据，保留指定天数的数据
Args:
data_type: 数据类型
keep_days: 保留天数
Returns:
int: 删除的记录数
"""
conn = self.get_connection()
cursor = conn.cursor()
try:
cutoff_date = (datetime.now() - pd.Timedelta(days=keep_days)).strftime('%Y-%m-%d')
if data_type == 'sector_data':
cursor.execute('''
DELETE FROM sector_raw_data
WHERE data_date < ?
''', (cutoff_date,))
elif data_type == 'news_data':
cursor.execute('''
DELETE FROM sector_news_data
WHERE news_date < ?
''', (cutoff_date,))
deleted_count = cursor.rowcount
# 同时清理版本记录
cursor.execute('''
DELETE FROM data_versions
WHERE data_type = ? AND data_date < ?
''', (data_type, cutoff_date))
conn.commit()
self.logger.info(f"[智策板块] 清理{data_type}旧数据完成，删除{deleted_count}条记录")
return deleted_count
except Exception as e:
conn.rollback()
self.logger.error(f"[智策板块] 清理{data_type}旧数据失败: {e}")
return 0
finally:
conn.close()
# =====================
# 缓存与最近数据读取接口
# =====================
def save_news_data(self, news_list, news_date, source="akshare"):
"""
保存新闻列表（字典列表）到数据库，用于非DataFrame场景
Args:
news_list: [{title, content, url, related_sectors, sentiment_score, importance_score}]
news_date: 新闻日期字符串
source: 新闻来源
"""
if not news_list:
self.logger.warning("[智策板块] 新闻列表为空，跳过保存")
return 0
conn = self.get_connection()
cursor = conn.cursor()
try:
# 版本号按日期累加
version = self._get_next_version(news_date, 'news')
inserted = 0
for item in news_list:
cursor.execute('''
INSERT OR REPLACE INTO sector_news_data
(news_date, title, content, source, url, related_sectors,
sentiment_score, importance_score, data_version)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
''', (
str(news_date),
str(item.get('title', '')),
str(item.get('content', '')),
str(item.get('source', source)),
str(item.get('url', '')),
json.dumps(item.get('related_sectors', []), ensure_ascii=False),
float(item.get('sentiment_score', 0) or 0),
float(item.get('importance_score', 0) or 0),
version
))
inserted += 1
# 记录版本信息
cursor.execute('''
INSERT OR REPLACE INTO data_versions
(data_date, data_type, version, fetch_success, record_count)
VALUES (?, ?, ?, 1, ?)
''', (str(news_date), 'news', version, inserted))
conn.commit()
self.logger.info(f"[智策板块] 保存新闻数据成功 (日期: {news_date}, 版本: {version}, 记录数: {inserted})")
return inserted
except Exception as e:
conn.rollback()
self.logger.error(f"[智策板块] 保存新闻数据失败: {e}")
return 0
finally:
conn.close()
def _get_next_version(self, data_date: str, data_type: str) -> int:
"""获取指定日期与类型的下一个版本号"""
conn = self.get_connection()
cursor = conn.cursor()
try:
cursor.execute('''
SELECT COALESCE(MAX(version), 0) + 1 FROM data_versions
WHERE data_type = ? AND data_date = ?
''', (data_type, data_date))
next_version = cursor.fetchone()[0] or 1
return int(next_version)
finally:
conn.close()
def get_latest_raw_data(self, key: str, within_hours: int = 24):
"""
获取最近within_hours小时内的原始数据并组装为分析所需结构
Args:
key: 'sectors' | 'concepts' | 'fund_flow' | 'market_overview' | 'north_flow'
within_hours: 有效缓存时长（小时）
Returns:
dict 或 None
"""
# 将key映射到内部data_type
key_map = {
'sectors': 'industry',
'concepts': 'concept',
'fund_flow': 'fund_flow',
'market_overview': 'market_overview',
'north_flow': 'north_fund'
}
data_type = key_map.get(key)
if not data_type:
return None
conn = self.get_connection()
try:
cutoff = (pd.Timestamp.now() - pd.Timedelta(hours=within_hours)).strftime('%Y-%m-%d %H:%M:%S')
# 选取最近版本的数据（同一天可能有多版本）
# 先查最近有效版本记录
version_df = pd.read_sql_query('''
SELECT data_date, version FROM data_versions
WHERE data_type = ? AND fetch_success = 1
AND datetime(created_at) >= datetime(?)
ORDER BY data_date DESC, version DESC LIMIT 1
''', conn, params=[data_type, cutoff])
if version_df.empty:
return None
data_date = version_df.iloc[0]['data_date']
version = int(version_df.iloc[0]['version'])
# 读取具体行
raw_df = pd.read_sql_query('''
SELECT * FROM sector_raw_data
WHERE data_type = ? AND data_date = ? AND data_version = ?
''', conn, params=[data_type, data_date, version])
if raw_df.empty:
return None
# 组装成预期结构
if key in ['sectors', 'concepts']:
result = {}
for _, row in raw_df.iterrows():
name = str(row.get('sector_name', ''))
result[name] = {
'name': name,
'change_pct': float(row.get('change_pct', 0) or 0),
'price': float(row.get('price', 0) or 0),
'volume': float(row.get('volume', 0) or 0),
'turnover': float(row.get('turnover', 0) or 0),
'market_cap': float(row.get('market_cap', 0) or 0),
'pe_ratio': float(row.get('pe_ratio', 0) or 0),
'pb_ratio': float(row.get('pb_ratio', 0) or 0),
}
return {
'data_date': data_date,
'data_content': result
}
if key == 'fund_flow':
today = []
for _, row in raw_df.iterrows():
name = str(row.get('sector_name', ''))
today.append({
'sector': name,
'main_net_inflow': float(row.get('price', 0) or 0), # 映射自主力净额
'main_net_inflow_pct': float(row.get('change_pct', 0) or 0),
'super_large_net_inflow': float(row.get('volume', 0) or 0),
'super_large_net_inflow_pct': float(row.get('turnover', 0) or 0),
'large_net_inflow': float(row.get('market_cap', 0) or 0),
'large_net_inflow_pct': float(row.get('pe_ratio', 0) or 0),
'medium_net_inflow': 0,
'small_net_inflow': 0
})
return {
'data_date': data_date,
'data_content': {
'today': today
}
}
if key == 'market_overview':
overview = {}
for _, row in raw_df.iterrows():
name = str(row.get('sector_name', ''))
entry = {
'price': float(row.get('price', 0) or 0),
'change_pct': float(row.get('change_pct', 0) or 0),
'turnover': float(row.get('turnover', 0) or 0),
'volume': float(row.get('volume', 0) or 0)
}
# 简单映射：名称包含上证/深证/创业板
if '上证' in name or '沪指' in name or 'SH' in name:
overview['sh_index'] = entry
elif '深证' in name or 'SZ' in name:
overview['sz_index'] = entry
elif '创业' in name or 'CYB' in name:
overview['cyb_index'] = entry
return {
'data_date': data_date,
'data_content': overview
}
if key == 'north_flow':
# 北向资金结构差异较大，返回最简结构用于提示
total_value = float(raw_df['turnover'].sum()) if not raw_df.empty else 0
return {
'data_date': data_date,
'data_content': {
'north_total_amount': total_value,
'history': []
}
}
return None
except Exception as e:
self.logger.error(f"[智策板块] 获取最近原始数据失败: {e}")
return None
finally:
conn.close()
def get_latest_news_data(self, within_hours: int = 24):
"""获取最近within_hours小时的新闻列表"""
conn = self.get_connection()
try:
cutoff = (pd.Timestamp.now() - pd.Timedelta(hours=within_hours)).strftime('%Y-%m-%d %H:%M:%S')
df = pd.read_sql_query('''
SELECT * FROM sector_news_data
WHERE datetime(created_at) >= datetime(?)
ORDER BY importance_score DESC, created_at DESC
''', conn, params=[cutoff])
if df.empty:
return None
news = []
for _, row in df.iterrows():
try:
related = json.loads(row.get('related_sectors', '[]'))
except Exception:
related = []
news.append({
'title': row.get('title', ''),
'content': row.get('content', ''),
'source': row.get('source', ''),
'url': row.get('url', ''),
'related_sectors': related,
'sentiment_score': float(row.get('sentiment_score', 0) or 0),
'importance_score': float(row.get('importance_score', 0) or 0),
'news_date': row.get('news_date', '')
})
return {
'data_date': df.iloc[0]['news_date'] if not df.empty else None,
'data_content': news
}
except Exception as e:
self.logger.error(f"[智策板块] 获取最近新闻数据失败: {e}")
return None
finally:
conn.close()
sector_strategy_engine.py
+243
View File
@@ -4,10 +4,13 @@
"""
from sector_strategy_agents import SectorStrategyAgents
from sector_strategy_db import SectorStrategyDatabase
from deepseek_client import DeepSeekClient
from typing import Dict, Any
import time
import json
import pandas as pd
import logging
class SectorStrategyEngine:
@@ -17,8 +20,81 @@ class SectorStrategyEngine:
self.model = model
self.agents = SectorStrategyAgents(model=model)
self.deepseek_client = DeepSeekClient(model=model)
self.database = SectorStrategyDatabase()
self.logger = logging.getLogger(__name__)
if not self.logger.handlers:
logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s %(name)s: %(message)s')
print(f"[智策引擎] 初始化完成 (模型: {model})")
def save_raw_data_with_fallback(self, data_type, data_df, data_date=None):
"""
保存原始数据，支持失败回退机制
Args:
data_type: 数据类型
data_df: 数据DataFrame
data_date: 数据日期，默认为今天
Returns:
tuple: (success, version, message)
"""
if data_date is None:
data_date = time.strftime("%Y-%m-%d")
try:
is_empty = False
if data_df is None:
is_empty = True
elif hasattr(data_df, 'empty'):
is_empty = data_df.empty
elif isinstance(data_df, (list, tuple, set, dict)):
is_empty = len(data_df) == 0
if is_empty:
self.logger.warning(f"[智策引擎] {data_type}数据为空，跳过保存")
return False, None, "数据为空"
version = self.database.save_raw_data(data_date, data_type, data_df)
return True, version, f"保存成功，版本: {version}"
except Exception as e:
self.logger.error(f"[智策引擎] 保存{data_type}数据失败: {e}")
return False, None, str(e)
def get_data_with_fallback(self, data_type, data_date=None):
"""
获取数据，支持失败时回退到历史数据
Args:
data_type: 数据类型
data_date: 数据日期，默认为今天
Returns:
tuple: (data_df, is_fallback, message)
"""
if data_date is None:
data_date = time.strftime("%Y-%m-%d")
try:
# 尝试获取指定日期的数据
data_df = self.database.get_latest_data(data_type, data_date)
if not data_df.empty:
return data_df, False, f"获取{data_date}数据成功"
# 如果指定日期没有数据，获取最新的历史数据
self.logger.warning(f"[智策引擎] {data_date}的{data_type}数据不存在，尝试获取历史数据")
data_df = self.database.get_latest_data(data_type)
if not data_df.empty:
fallback_date = data_df.iloc[0].get('data_date', '未知日期')
return data_df, True, f"回退到{fallback_date}的历史数据"
else:
return pd.DataFrame(), True, "无可用的历史数据"
except Exception as e:
self.logger.error(f"[智策引擎] 获取{data_type}数据失败: {e}")
return pd.DataFrame(), True, str(e)
def run_comprehensive_analysis(self, data: Dict) -> Dict[str, Any]:
"""
运行综合分析流程
@@ -102,6 +178,24 @@ class SectorStrategyEngine:
results["success"] = True
# 4. 保存分析报告
print("\n[阶段4] 保存分析报告...")
print("-" * 60)
try:
report_id = self.save_analysis_report(results, data)
results["report_id"] = report_id
print(f"✓ 分析报告已保存 (ID: {report_id})")
# 保存后读取报告详情并回传到结果，用于主页面动态渲染
try:
saved_report = self.database.get_analysis_report(report_id)
if saved_report:
results["saved_report"] = saved_report
except Exception as fetch_e:
self.logger.warning(f"[智策引擎] 获取保存报告详情失败: {fetch_e}")
except Exception as e:
print(f"⚠ 保存分析报告失败: {e}")
self.logger.error(f"[智策引擎] 保存分析报告失败: {e}")
print("\n" + "=" * 60)
print("✓ 智策综合分析完成！")
print("=" * 60)
@@ -321,6 +415,155 @@ class SectorStrategyEngine:
print(f" ⚠ JSON解析失败: {e}，返回文本格式")
return {"prediction_text": response}
def save_analysis_report(self, results: Dict, original_data: Dict) -> int:
"""
保存分析报告到数据库
Args:
results: 分析结果
original_data: 原始数据
Returns:
int: 报告ID
"""
try:
# 提取数据日期范围
data_date_range = f"{time.strftime('%Y-%m-%d')} 数据分析"
# 提取推荐板块
recommended_sectors = []
predictions = results.get("final_predictions", {})
if isinstance(predictions, dict):
# 从预测结果中提取推荐板块
hot_sectors = predictions.get("hot_sectors", [])
rotation_sectors = predictions.get("rotation_opportunities", [])
for sector in hot_sectors[:5]: # 取前5个热门板块
if isinstance(sector, dict):
recommended_sectors.append({
"sector_name": sector.get("name", ""),
"reason": sector.get("reason", ""),
"confidence": sector.get("confidence", ""),
"type": "热门板块"
})
for sector in rotation_sectors[:3]: # 取前3个轮动机会
if isinstance(sector, dict):
recommended_sectors.append({
"sector_name": sector.get("name", ""),
"reason": sector.get("reason", ""),
"confidence": sector.get("confidence", ""),
"type": "轮动机会"
})
# 生成摘要
summary = self._generate_report_summary(results)
# 提取其他信息
confidence_score = self._extract_confidence_score(results)
risk_level = self._extract_risk_level(results)
investment_horizon = self._extract_investment_horizon(results)
market_outlook = self._extract_market_outlook(results)
# 保存到数据库
report_id = self.database.save_analysis_report(
data_date_range=data_date_range,
analysis_content=results,
recommended_sectors=recommended_sectors,
summary=summary,
confidence_score=confidence_score,
risk_level=risk_level,
investment_horizon=investment_horizon,
market_outlook=market_outlook
)
return report_id
except Exception as e:
self.logger.error(f"[智策引擎] 保存分析报告失败: {e}")
raise
def _generate_report_summary(self, results: Dict) -> str:
"""生成报告摘要"""
try:
predictions = results.get("final_predictions", {})
if isinstance(predictions, dict):
# 从summary中提取市场趋势信息
summary_info = predictions.get("summary", {})
market_trend = summary_info.get("market_view", "") if isinstance(summary_info, dict) else ""
# 从long_short.bullish中计算热门板块数量
long_short_info = predictions.get("long_short", {})
bullish_sectors = long_short_info.get("bullish", []) if isinstance(long_short_info, dict) else []
hot_sectors_count = len(bullish_sectors)
# 如果有看多板块信息，则添加到摘要中
if bullish_sectors and isinstance(bullish_sectors, list):
# 提取前3个看多板块名称
bullish_names = [sector.get("sector", "") for sector in bullish_sectors[:3] if isinstance(sector, dict)]
if bullish_names:
bullish_text = "，".join(bullish_names)
return f"市场趋势: {market_trend}，识别{hot_sectors_count}个热门板块机会，看多板块: {bullish_text}"
return f"市场趋势: {market_trend}，识别{hot_sectors_count}个热门板块机会"
else:
return "智策板块分析报告"
except:
return "智策板块分析报告"
def _extract_confidence_score(self, results: Dict) -> float:
"""提取置信度分数"""
try:
predictions = results.get("final_predictions", {})
if isinstance(predictions, dict):
return predictions.get("confidence_score", 0.75)
return 0.75
except:
return 0.75
def _extract_risk_level(self, results: Dict) -> str:
"""提取风险等级"""
try:
predictions = results.get("final_predictions", {})
if isinstance(predictions, dict):
return predictions.get("risk_level", "中等")
return "中等"
except:
return "中等"
def _extract_investment_horizon(self, results: Dict) -> str:
"""提取投资周期"""
try:
predictions = results.get("final_predictions", {})
if isinstance(predictions, dict):
return predictions.get("investment_horizon", "短期")
return "短期"
except:
return "短期"
def _extract_market_outlook(self, results: Dict) -> str:
"""提取市场展望"""
try:
predictions = results.get("final_predictions", {})
if isinstance(predictions, dict):
return predictions.get("market_outlook", "谨慎乐观")
return "谨慎乐观"
except:
return "谨慎乐观"
def get_historical_reports(self, limit=10):
"""获取历史报告"""
return self.database.get_analysis_reports(limit)
def get_report_detail(self, report_id):
"""获取报告详情"""
return self.database.get_analysis_report(report_id)
def delete_report(self, report_id):
"""删除报告"""
return self.database.delete_analysis_report(report_id)
# 测试函数
if __name__ == "__main__":
sector_strategy_ui.py
+222 -17
View File
@@ -4,19 +4,39 @@
"""
import streamlit as st
import time
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
from datetime import datetime, time as dt_time
import time
import base64
import json
from sector_strategy_data import SectorStrategyDataFetcher
from sector_strategy_engine import SectorStrategyEngine
from sector_strategy_pdf import SectorStrategyPDFGenerator
from sector_strategy_db import SectorStrategyDatabase
from sector_strategy_scheduler import sector_strategy_scheduler
def _parse_json_field(value, default):
"""将可能的JSON字符串安全转换为Python对象"""
try:
if isinstance(value, (dict, list)):
return value
if value is None:
return default
if isinstance(value, str):
v = value.strip()
if not v:
return default
return json.loads(v)
return default
except Exception:
return default
def display_sector_strategy():
"""显示智策板块分析主界面"""
@@ -29,6 +49,19 @@ def display_sector_strategy():
st.markdown("---")
# 创建标签页
tab1, tab2 = st.tabs(["📊 智策分析", "📋 历史报告"])
with tab1:
display_analysis_tab()
with tab2:
display_history_tab()
def display_analysis_tab():
"""显示分析标签页"""
# 定时任务设置区域
display_scheduler_settings()
@@ -92,12 +125,12 @@ def display_sector_strategy():
with col2:
st.write("")
st.write("")
analyze_button = st.button("🚀 开始智策分析", type="primary", use_container_width=True)
analyze_button = st.button("🚀 开始智策分析", type="primary", width='content')
with col3:
st.write("")
st.write("")
if st.button("🔄 清除结果", use_container_width=True):
if st.button("🔄 清除结果", width='content'):
if 'sector_strategy_result' in st.session_state:
del st.session_state.sector_strategy_result
st.success("已清除分析结果")
@@ -123,6 +156,108 @@ def display_sector_strategy():
st.error(f"❌ 分析失败: {result.get('error', '未知错误')}")
def display_history_tab():
"""显示历史报告标签页"""
st.markdown("### 📋 智策历史报告")
st.markdown("查看和管理历史分析报告")
try:
# 初始化引擎以获取历史报告
engine = SectorStrategyEngine()
# 获取历史报告
reports = engine.get_historical_reports(limit=20)
if reports.empty:
st.info("📝 暂无历史报告")
st.markdown("""
**提示**:
- 运行智策分析后，报告将自动保存到历史记录中
- 您可以在此查看和管理所有历史分析报告
""")
return
st.success(f"📊 共找到 {len(reports)} 份历史报告")
# 报告列表（精简摘要展示）
for i, report in reports.iterrows():
report_id = report['id'] if 'id' in report else None
created_at = report['created_at'] if 'created_at' in report else ''
data_date_range = report['data_date_range'] if 'data_date_range' in report else ''
summary = report['summary'] if 'summary' in report else '智策板块分析报告'
confidence_score = report['confidence_score'] if 'confidence_score' in report else 0
risk_level = report['risk_level'] if 'risk_level' in report else '中等'
market_outlook = report['market_outlook'] if 'market_outlook' in report else '谨慎乐观'
with st.container():
st.markdown(f"**📊 报告 #{report_id}**")
st.caption(f"生成时间: {created_at} | 数据区间: {data_date_range}")
col1, col2, col3 = st.columns([1, 1, 1])
with col1:
st.metric("置信度", f"{confidence_score:.1%}")
with col2:
st.metric("风险等级", risk_level)
with col3:
st.metric("市场展望", market_outlook)
# 操作区：加载到分析视图 / 删除
op1, op2 = st.columns([1, 1])
with op1:
if st.button("📥 加载到分析视图", key=f"load_{report_id}"):
# 获取报告详情并写入session以展示到分析视图
detail = engine.get_report_detail(report_id)
if detail and isinstance(detail.get('analysis_content_parsed'), dict):
st.session_state.sector_strategy_result = detail['analysis_content_parsed']
st.session_state.sector_strategy_result_source = 'from_history'
st.session_state.loaded_report_id = report_id
st.success("✅ 已加载到分析视图，请切换到‘智策分析’标签查看")
time.sleep(0.5)
st.rerun()
else:
st.error("❌ 加载失败：报告内容缺失")
with op2:
if st.button(f"🗑️ 删除", key=f"delete_{report_id}"):
if engine.delete_report(report_id):
st.success("报告已删除")
st.rerun()
else:
st.error("删除失败")
# 改进的摘要展示逻辑，突出看多板块信息
st.markdown("**📝 报告摘要**")
summary_text = summary or "智策板块分析报告"
# 解析摘要中的看多板块信息
if "看多板块:" in summary_text:
parts = summary_text.split("，看多板块:")
main_summary = parts[0]
bullish_info = parts[1] if len(parts) > 1 else ""
# 显示主要摘要信息
st.markdown(f"🔹 {main_summary}")
# 特别突出显示看多板块
if bullish_info:
st.markdown(f"📈 **看多板块**: :green[{bullish_info}]")
else:
# 原有的简单展示方式
short = summary_text if len(summary_text) <= 120 else (summary_text[:120] + "...")
with st.expander(f"{short}", expanded=False):
st.write(summary_text)
st.markdown("-")
except Exception as e:
st.error(f"❌ 加载历史报告失败: {e}")
def display_report_detail(report_id):
"""详细报告页面已移除：保留占位以避免旧调用报错"""
st.info("当前版本仅提供报告摘要，详细页面已移除。")
def run_sector_strategy_analysis(model="deepseek-chat"):
"""运行智策分析"""
@@ -136,7 +271,8 @@ def run_sector_strategy_analysis(model="deepseek-chat"):
progress_bar.progress(10)
fetcher = SectorStrategyDataFetcher()
data = fetcher.get_all_sector_data()
# 使用带缓存回退的获取逻辑
data = fetcher.get_cached_data_with_fallback()
if not data.get("success"):
st.error("❌ 数据获取失败")
@@ -145,7 +281,7 @@ def run_sector_strategy_analysis(model="deepseek-chat"):
progress_bar.progress(30)
status_text.text("✓ 数据获取完成")
# 显示数据摘要
# 显示数据摘要（含缓存提示）
display_data_summary(data)
# 2. 运行AI分析
@@ -154,6 +290,13 @@ def run_sector_strategy_analysis(model="deepseek-chat"):
engine = SectorStrategyEngine(model=model)
result = engine.run_comprehensive_analysis(data)
# 传递缓存元信息到结果以便页面提示
if data.get("from_cache") or data.get("cache_warning"):
result["cache_meta"] = {
"from_cache": bool(data.get("from_cache")),
"cache_warning": data.get("cache_warning", ""),
"data_timestamp": data.get("timestamp")
}
progress_bar.progress(90)
@@ -185,6 +328,9 @@ def run_sector_strategy_analysis(model="deepseek-chat"):
def display_data_summary(data):
"""显示数据摘要"""
st.subheader("📊 市场数据概览")
# 缓存提示横幅
if data.get("from_cache") or data.get("cache_warning"):
st.warning(data.get("cache_warning", "当前数据来自缓存，可能不是最新信息"))
col1, col2, col3, col4 = st.columns(4)
@@ -216,11 +362,70 @@ def display_data_summary(data):
st.metric("概念板块", concepts_count)
def display_saved_report_summary(saved_report: dict):
"""在主页面显示保存的报告摘要（标题、时间、关键指标）"""
st.subheader("📝 报告摘要")
summary = saved_report.get('summary', '智策板块分析报告')
created_at = saved_report.get('created_at', '')
data_date_range = saved_report.get('data_date_range', '')
confidence_score = saved_report.get('confidence_score', 0)
risk_level = saved_report.get('risk_level', '中等')
market_outlook = saved_report.get('market_outlook', '谨慎乐观')
st.caption(f"生成时间: {created_at} | 数据区间: {data_date_range}")
# 使用改进的摘要展示逻辑，突出看多板块信息
summary_text = summary or "智策板块分析报告"
# 解析摘要中的看多板块信息
if "看多板块:" in summary_text:
parts = summary_text.split("，看多板块:")
main_summary = parts[0]
bullish_info = parts[1] if len(parts) > 1 else ""
# 显示主要摘要信息
st.markdown(f"🔹 {main_summary}")
# 特别突出显示看多板块
if bullish_info:
st.markdown(f"📈 **看多板块**: :green[{bullish_info}]")
else:
# 原有的简单展示方式
st.info(summary_text)
col1, col2, col3 = st.columns(3)
with col1:
st.metric("置信度", f"{confidence_score:.1%}")
with col2:
st.metric("风险等级", risk_level)
with col3:
st.metric("市场展望", market_outlook)
def display_analysis_results(result):
"""显示分析结果"""
st.success("✅ 智策分析完成！")
st.info(f"📅 分析时间: {result.get('timestamp', 'N/A')}")
# 显示缓存提示（如果本次分析使用了缓存数据）
cache_meta = result.get("cache_meta")
if cache_meta and (cache_meta.get("from_cache") or cache_meta.get("cache_warning")):
st.warning(cache_meta.get("cache_warning", "当前分析基于缓存数据，可能不是最新信息"))
# 如果内容源自历史报告，给出返回入口
if st.session_state.get('sector_strategy_result_source') == 'from_history':
loaded_id = st.session_state.get('loaded_report_id')
st.info(f"🗂️ 当前展示为历史报告内容（ID: {loaded_id}）")
if st.button("↩️ 返回历史报告列表"):
# 清除已加载的历史报告并返回
for key in ['sector_strategy_result', 'sector_strategy_result_source', 'loaded_report_id']:
if key in st.session_state:
del st.session_state[key]
st.rerun()
# 显示引擎回传的保存报告摘要（用于主页面动态更新）
saved_report = result.get("saved_report")
if saved_report:
display_saved_report_summary(saved_report)
# PDF导出功能
display_pdf_export_section(result)
@@ -525,7 +730,7 @@ def display_visualizations(predictions):
title='板块多空信心度对比')
fig.update_layout(height=400)
st.plotly_chart(fig, use_container_width=True, key="sector_confidence")
st.plotly_chart(fig, use_container_width=True, config={'responsive': True}, key="sector_confidence")
st.markdown("---")
@@ -562,7 +767,7 @@ def display_visualizations(predictions):
title='板块热度分布图')
fig.update_layout(height=400)
st.plotly_chart(fig, use_container_width=True, key="sector_heat")
st.plotly_chart(fig, use_container_width=True, config={'responsive': True}, key="sector_heat")
def display_pdf_export_section(result):
@@ -575,7 +780,7 @@ def display_pdf_export_section(result):
st.write("将分析报告导出为PDF文件，方便保存和分享")
with col2:
if st.button("📥 生成PDF报告", type="primary", use_container_width=True):
if st.button("📥 生成PDF报告", type="primary", width='content'):
with st.spinner("正在生成PDF报告..."):
try:
# 生成PDF
@@ -600,12 +805,12 @@ def display_pdf_export_section(result):
# 如果已经生成了PDF，显示下载按钮
if 'sector_pdf_data' in st.session_state:
st.download_button(
label="💾 下载PDF",
data=st.session_state.sector_pdf_data,
file_name=st.session_state.sector_pdf_filename,
mime="application/pdf",
use_container_width=True
)
label="💾 下载PDF",
data=st.session_state.sector_pdf_data,
file_name=st.session_state.sector_pdf_filename,
mime="application/pdf",
width='content'
)
def display_scheduler_settings():
@@ -653,7 +858,7 @@ def display_scheduler_settings():
with col_a:
if not status['running']:
if st.button("▶️ 启动", use_container_width=True, type="primary"):
if st.button("▶️ 启动", width='content', type="primary"):
if sector_strategy_scheduler.start(schedule_time_str):
st.success(f"✅ 定时任务已启动！每天 {schedule_time_str} 运行")
time.sleep(1)
@@ -661,7 +866,7 @@ def display_scheduler_settings():
else:
st.error("❌ 启动失败")
else:
if st.button("⏹️ 停止", use_container_width=True):
if st.button("⏹️ 停止", width='content'):
if sector_strategy_scheduler.stop():
st.success("✅ 定时任务已停止")
time.sleep(1)
@@ -670,13 +875,13 @@ def display_scheduler_settings():
st.error("❌ 停止失败")
with col_b:
if st.button("🔄 立即运行", use_container_width=True):
if st.button("🔄 立即运行", width='content'):
with st.spinner("正在运行分析..."):
sector_strategy_scheduler.manual_run()
st.success("✅ 手动分析完成！")
with col_c:
if st.button("📧 测试邮件", use_container_width=True):
if st.button("📧 测试邮件", width='content'):
test_email_notification()
# 邮件配置检查
sector_strategy_ui_clean.py
View File
smart_monitor_ui.py
+4 -4
View File
@@ -354,7 +354,7 @@ def render_monitor_tasks():
notify_email = st.text_input("通知邮箱（可选）")
# 添加任务按钮（表单提交按钮）
submitted = st.form_submit_button("➕ 添加任务", type="primary", use_container_width=True)
submitted = st.form_submit_button("➕ 添加任务", type="primary", width='stretch')
if submitted:
# 验证必填项（form中直接使用局部变量）
@@ -564,7 +564,7 @@ def render_position_management():
"profit_loss_pct": "盈亏%"
},
hide_index=True,
use_container_width=True
width='stretch'
)
# 单只股票操作
@@ -652,7 +652,7 @@ def render_history():
"profit_loss": "盈亏"
},
hide_index=True,
use_container_width=True
width='stretch'
)
# 通知记录
@@ -801,7 +801,7 @@ def _render_task_kline_and_decisions(task: Dict, db: SmartMonitorDB, engine):
)
# 显示图表
st.plotly_chart(fig, use_container_width=True)
st.plotly_chart(fig, use_container_width=True, config={'responsive': True})
st.caption(f"📅 数据时间范围：{kline_data['日期'].min()} ~ {kline_data['日期'].max()}")
else:
stock_analysis.db
BIN
View File
Binary file not shown.
stock_data.py
+91 -76
View File
@@ -125,7 +125,7 @@ class StockDataFetcher:
print(f"[Akshare] 获取个股详细信息失败: {e}")
# 如果akshare失败，尝试从tushare获取
if self.data_source_manager.tushare_available and info['name'] == '未知':
print(f"[Tushare] 尝试获取基本信息（备用数据源）...")
print(f"[Tushare] 尝试获取基本信息（tushare）...")
try:
ts_code = self.data_source_manager._convert_to_ts_code(symbol)
df = self.data_source_manager.tushare_api.daily_basic(
@@ -141,65 +141,65 @@ class StockDataFetcher:
except Exception as te:
print(f"[Tushare] ❌ 获取失败: {te}")
# 方法2: 尝试获取实时价格和涨跌幅（如果网络允许）
# 方法2: 尝试获取历史价格和涨跌幅（如果网络允许）
# try:
# # 使用更简单的接口获取实时价格
# real_time_data = ak.stock_zh_a_spot_em()
# if real_time_data is not None and not real_time_data.empty:
# stock_real_time = real_time_data[real_time_data['代码'] == symbol]
# if not stock_real_time.empty:
# row = stock_real_time.iloc[0]
# info['current_price'] = row.get('最新价', 'N/A')
# info['change_percent'] = row.get('涨跌幅', 'N/A')
# if info['name'] == '未知':
# info['name'] = row.get('名称', '未知')
# # 如果实时数据中有市盈率和市净率，优先使用
# if '市盈率-动态' in row and info['pe_ratio'] == 'N/A':
# try:
# pe_val = row['市盈率-动态']
# if pe_val and pe_val != '-':
# pe_val = float(pe_val)
# if 0 < pe_val <= 1000:
# info['pe_ratio'] = pe_val
# except:
# pass
# if '市净率' in row and info['pb_ratio'] == 'N/A':
# try:
# pb_val = row['市净率']
# if pb_val and pb_val != '-':
# pb_val = float(pb_val)
# if 0 < pb_val <= 100:
# info['pb_ratio'] = pb_val
# except:
# pass
# except Exception as e:
# print(f"[Akshare] 获取实时数据失败: {e}")
# # 如果实时数据获取失败，尝试使用数据源管理器获取历史数据（支持tushare备用）
try:
# 使用更简单的接口获取实时价格
real_time_data = ak.stock_zh_a_spot_em()
if real_time_data is not None and not real_time_data.empty:
stock_real_time = real_time_data[real_time_data['代码'] == symbol]
if not stock_real_time.empty:
row = stock_real_time.iloc[0]
info['current_price'] = row.get('最新价', 'N/A')
info['change_percent'] = row.get('涨跌幅', 'N/A')
if info['name'] == '未知':
info['name'] = row.get('名称', '未知')
print(f"[数据源管理器] 尝试获取最近交易数据...")
hist_data = self.data_source_manager.get_stock_hist_data(
symbol=symbol,
start_date=(datetime.now() - timedelta(days=30)).strftime('%Y%m%d'),
end_date=datetime.now().strftime('%Y%m%d'),
adjust='qfq'
)
# 如果实时数据中有市盈率和市净率，优先使用
if '市盈率-动态' in row and info['pe_ratio'] == 'N/A':
try:
pe_val = row['市盈率-动态']
if pe_val and pe_val != '-':
pe_val = float(pe_val)
if 0 < pe_val <= 1000:
info['pe_ratio'] = pe_val
except:
pass
if '市净率' in row and info['pb_ratio'] == 'N/A':
try:
pb_val = row['市净率']
if pb_val and pb_val != '-':
pb_val = float(pb_val)
if 0 < pb_val <= 100:
info['pb_ratio'] = pb_val
except:
pass
except Exception as e:
print(f"[Akshare] 获取实时数据失败: {e}")
# 如果实时数据获取失败，尝试使用数据源管理器获取历史数据（支持tushare备用）
try:
print(f"[数据源管理器] 尝试获取最近交易数据...")
hist_data = self.data_source_manager.get_stock_hist_data(
symbol=symbol,
start_date=(datetime.now() - timedelta(days=5)).strftime('%Y%m%d'),
end_date=datetime.now().strftime('%Y%m%d'),
adjust='qfq'
)
if hist_data is not None and not hist_data.empty:
# 标准化列名
if 'close' in hist_data.columns:
latest = hist_data.iloc[-1]
info['current_price'] = latest['close']
# 计算涨跌幅
if len(hist_data) > 1:
prev_close = hist_data.iloc[-2]['close']
change_pct = ((latest['close'] - prev_close) / prev_close) * 100
info['change_percent'] = round(change_pct, 2)
print(f"[数据源管理器] ✅ 成功获取价格数据")
except Exception as e2:
print(f"获取历史数据也失败: {e2}")
if hist_data is not None and not hist_data.empty:
# 标准化列名
if 'close' in hist_data.columns:
latest = hist_data.iloc[-1]
info['current_price'] = latest['close']
# 计算涨跌幅
if len(hist_data) > 1:
prev_close = hist_data.iloc[-2]['close']
change_pct = ((latest['close'] - prev_close) / prev_close) * 100
info['change_percent'] = round(change_pct, 2)
print(f"[数据源管理器] ✅ 成功获取价格数据")
except Exception as e2:
print(f"获取历史数据也失败: {e2}")
# 方法3: 使用百度估值数据获取市盈率和市净率
if info['pe_ratio'] == 'N/A':
@@ -638,25 +638,40 @@ class StockDataFetcher:
# 4. 获取主要财务指标
try:
financial_indicators = ak.stock_financial_analysis_indicator(symbol=symbol)
if financial_indicators is not None and not financial_indicators.empty:
latest_data = financial_indicators.iloc[0]
financial_abstract = ak.stock_financial_abstract(symbol=symbol)
if financial_abstract is not None and not financial_abstract.empty:
# 提取关键财务指标
key_indicators = [
'净资产收益率(ROE)', '总资产报酬率(ROA)', '销售毛利率', '销售净利率',
'资产负债率', '流动比率', '速动比率', '存货周转率', '应收账款周转率',
'总资产周转率', '营业收入同比增长', '净利润同比增长'
]
financial_data["financial_ratios"] = {
"报告期": latest_data.get('报告期', 'N/A'),
"净资产收益率ROE": latest_data.get('净资产收益率', 'N/A'),
"总资产收益率ROA": latest_data.get('总资产收益率', 'N/A'),
"销售毛利率": latest_data.get('销售毛利率', 'N/A'),
"销售净利率": latest_data.get('销售净利率', 'N/A'),
"资产负债率": latest_data.get('资产负债率', 'N/A'),
"流动比率": latest_data.get('流动比率', 'N/A'),
"速动比率": latest_data.get('速动比率', 'N/A'),
"存货周转率": latest_data.get('存货周转率', 'N/A'),
"应收账款周转率": latest_data.get('应收账款周转率', 'N/A'),
"总资产周转率": latest_data.get('总资产周转率', 'N/A'),
"营业收入同比增长": latest_data.get('营业收入同比增长', 'N/A'),
"净利润同比增长": latest_data.get('净利润同比增长', 'N/A'),
}
# 筛选出包含关键指标的行
indicator_rows = financial_abstract[financial_abstract['指标'].isin(key_indicators)]
if not indicator_rows.empty:
# 获取最新的报告期数据（第一列日期）
date_columns = [col for col in financial_abstract.columns if col not in ['选项', '指标']]
if date_columns:
latest_date = date_columns[0] # 最新日期列
# 构建财务比率字典
financial_ratios = {"报告期": latest_date}
# 提取每个指标的最新值
for _, row in indicator_rows.iterrows():
indicator_name = row['指标']
value = row.get(latest_date, 'N/A')
if value is not None and not (isinstance(value, float) and pd.isna(value)):
try:
financial_ratios[indicator_name] = str(value)
except:
financial_ratios[indicator_name] = "N/A"
else:
financial_ratios[indicator_name] = "N/A"
financial_data["financial_ratios"] = financial_ratios
except Exception as e:
print(f"获取财务指标失败: {e}")
stock_monitor.db
BIN
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Binary file not shown.
test_main_force_batch_db.py
-137
View File
@@ -1,137 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
主力选股批量分析数据库功能测试
"""
from main_force_batch_db import batch_db
import json
def test_database():
"""测试数据库功能"""
print("=" * 60)
print("主力选股批量分析数据库功能测试")
print("=" * 60)
# 测试1: 保存批量分析结果
print("\n📝 测试1: 保存批量分析结果")
test_results = [
{
"symbol": "000001",
"success": True,
"stock_info": {"股票名称": "平安银行"},
"final_decision": {
"investment_rating": "买入",
"confidence_level": 85,
"entry_range": "10.0-10.5",
"take_profit": "12.0",
"stop_loss": "9.5"
}
},
{
"symbol": "600036",
"success": True,
"stock_info": {"股票名称": "招商银行"},
"final_decision": {
"investment_rating": "持有",
"confidence_level": 75,
"entry_range": "35.0-36.0",
"take_profit": "40.0",
"stop_loss": "33.0"
}
},
{
"symbol": "600519",
"success": False,
"error": "数据获取失败"
}
]
try:
record_id = batch_db.save_batch_analysis(
batch_count=3,
analysis_mode="sequential",
success_count=2,
failed_count=1,
total_time=180.5,
results=test_results
)
print(f"✅ 保存成功，记录ID: {record_id}")
except Exception as e:
print(f"❌ 保存失败: {str(e)}")
return
# 测试2: 获取统计信息
print("\n📊 测试2: 获取统计信息")
try:
stats = batch_db.get_statistics()
print(f"✅ 统计信息:")
print(f" 总记录数: {stats['total_records']}")
print(f" 分析股票总数: {stats['total_stocks_analyzed']}")
print(f" 成功数: {stats['total_success']}")
print(f" 失败数: {stats['total_failed']}")
print(f" 成功率: {stats['success_rate']}%")
print(f" 平均耗时: {stats['average_time']}秒")
except Exception as e:
print(f"❌ 获取统计信息失败: {str(e)}")
# 测试3: 获取历史记录列表
print("\n📚 测试3: 获取历史记录列表")
try:
history = batch_db.get_all_history(limit=5)
print(f"✅ 获取到 {len(history)} 条记录")
for idx, record in enumerate(history[:3], 1):
print(f"\n 记录{idx}:")
print(f" - ID: {record['id']}")
print(f" - 时间: {record['analysis_date']}")
print(f" - 数量: {record['batch_count']}")
print(f" - 成功: {record['success_count']}")
print(f" - 失败: {record['failed_count']}")
print(f" - 耗时: {record['total_time']}秒")
except Exception as e:
print(f"❌ 获取历史记录失败: {str(e)}")
# 测试4: 获取单条记录
print(f"\n🔍 测试4: 获取单条记录 (ID: {record_id})")
try:
record = batch_db.get_record_by_id(record_id)
if record:
print(f"✅ 获取成功")
print(f" 分析时间: {record['analysis_date']}")
print(f" 结果数量: {len(record['results'])}")
print(f" 成功股票: {[r['symbol'] for r in record['results'] if r.get('success')]}")
print(f" 失败股票: {[r['symbol'] for r in record['results'] if not r.get('success')]}")
else:
print(f"❌ 记录不存在")
except Exception as e:
print(f"❌ 获取记录失败: {str(e)}")
# 测试5: 删除记录
print(f"\n🗑️ 测试5: 删除记录 (ID: {record_id})")
confirm = input(" 是否删除测试记录? (y/n): ")
if confirm.lower() == 'y':
try:
success = batch_db.delete_record(record_id)
if success:
print(f"✅ 删除成功")
else:
print(f"❌ 删除失败")
except Exception as e:
print(f"❌ 删除失败: {str(e)}")
else:
print(" 跳过删除")
print("\n" + "=" * 60)
print("测试完成！")
print("=" * 60)
print("\n💡 提示:")
print(" - 数据库文件: main_force_batch.db")
print(" - 可使用 SQLite 工具查看数据库内容")
print(" - 在Streamlit应用中点击'📚 批量分析历史'查看UI界面")
print("=" * 60)
if __name__ == "__main__":
test_database()
test_smart_monitor_data.py
-103
View File
@@ -1,103 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
智能盯盘 - 数据获取功能测试脚本
用于验证实时行情数据获取是否正常工作
"""
import logging
import sys
# 配置日志
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
def test_data_fetcher():
"""测试数据获取功能"""
print("\n" + "="*70)
print("智能盯盘 - 数据获取功能测试")
print("="*70 + "\n")
try:
from smart_monitor_data import SmartMonitorDataFetcher
# 创建数据获取器
fetcher = SmartMonitorDataFetcher()
print("✅ 数据获取器初始化成功\n")
# 测试股票列表（可以自行修改）
test_stocks = [
('600519', '贵州茅台'),
('000001', '平安银行'),
('002167', '东方锆业')
]
for stock_code, stock_name in test_stocks:
print(f"\n{'─'*70}")
print(f"测试股票: {stock_code} ({stock_name})")
print(f"{'─'*70}")
# 获取实时行情
quote = fetcher.get_realtime_quote(stock_code)
if quote:
print(f"\n✅ 数据获取成功:")
print(f" 📌 股票代码: {quote['code']}")
print(f" 📌 股票名称: {quote['name']}")
print(f" 💰 当前价格: ¥{quote['current_price']:.2f}")
print(f" 📊 涨跌幅: {quote['change_pct']:+.2f}%")
print(f" 💵 涨跌额: ¥{quote['change_amount']:+.2f}")
print(f" 📦 成交量: {quote['volume']:.0f}手")
print(f" 💸 成交额: ¥{quote['amount']/10000:.2f}万")
print(f" 📈 最高: ¥{quote['high']:.2f}")
print(f" 📉 最低: ¥{quote['low']:.2f}")
print(f" 🔓 今开: ¥{quote['open']:.2f}")
print(f" 🔒 昨收: ¥{quote['pre_close']:.2f}")
print(f" 🔄 换手率: {quote['turnover_rate']:.2f}%")
print(f" ⏰ 更新时间: {quote['update_time']}")
print(f" 🌐 数据源: {quote['data_source']}")
# 验证数据是否有效（不全为0）
if quote['current_price'] > 0:
print(f"\n ✅ 数据有效性检查: 通过")
else:
print(f"\n ⚠️ 数据有效性检查: 价格为0，可能是非交易时间")
else:
print(f"\n❌ 获取 {stock_code} 的数据失败")
return False
print("\n" + "="*70)
print("✅ 所有测试通过！数据获取功能正常工作")
print("="*70 + "\n")
return True
except ImportError as e:
print(f"❌ 导入模块失败: {e}")
print("请确保 smart_monitor_data.py 文件存在")
return False
except Exception as e:
print(f"❌ 测试过程中出现错误: {e}")
import traceback
traceback.print_exc()
return False
if __name__ == '__main__':
print("\n💡 提示：")
print(" - 此脚本用于测试智能盯盘的数据获取功能")
print(" - 需要网络连接以访问AKShare API")
print(" - 如果所有数据都正常显示，说明修复成功")
print(" - 如果仍然显示0，请检查网络或查看日志\n")
success = test_data_fetcher()
if success:
print("🎉 恭喜！数据获取功能测试通过，可以正常使用智能盯盘了！\n")
sys.exit(0)
else:
print("⚠️ 测试失败，请查看上方错误信息并联系技术支持\n")
sys.exit(1)
test_tushare_monitoring.py
-247
View File
@@ -1,247 +0,0 @@
"""
测试Tushare数据源是否能满足AI盯盘监控要求
测试内容：
1. 实时行情数据获取
2. 技术指标计算
3. K线图数据获取
"""
import logging
import os
from dotenv import load_dotenv
# 设置日志
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
# 加载环境变量
load_dotenv()
def test_tushare_token():
"""测试Tushare Token是否配置"""
token = os.getenv('TUSHARE_TOKEN', '')
if token:
print(f"✅ Tushare Token已配置: {token[:10]}...")
return True
else:
print("❌ Tushare Token未配置")
return False
def test_realtime_quote(stock_code='000063'):
"""测试实时行情获取（Tushare降级）"""
print(f"\n{'='*60}")
print(f"测试1: 实时行情数据 - {stock_code}")
print(f"{'='*60}")
from smart_monitor_data import SmartMonitorDataFetcher
fetcher = SmartMonitorDataFetcher()
# 强制使用Tushare（模拟AKShare失败）
print("正在通过Tushare获取实时行情...")
quote = fetcher._get_realtime_quote_from_tushare(stock_code)
if quote:
print("✅ 实时行情获取成功！")
print(f" 股票名称: {quote.get('stock_name', 'N/A')}")
print(f" 当前价格: ¥{quote.get('current_price', 0):.2f}")
print(f" 涨跌幅: {quote.get('change_pct', 0):+.2f}%")
print(f" 成交量: {quote.get('volume', 0):,}手")
print(f" 换手率: {quote.get('turnover_rate', 0):.2f}%")
print(f" 数据来源: {quote.get('data_source', 'N/A')}")
return True
else:
print("❌ 实时行情获取失败")
return False
def test_technical_indicators(stock_code='000063'):
"""测试技术指标计算（Tushare降级）"""
print(f"\n{'='*60}")
print(f"测试2: 技术指标计算 - {stock_code}")
print(f"{'='*60}")
from smart_monitor_data import SmartMonitorDataFetcher
fetcher = SmartMonitorDataFetcher()
# 强制使用Tushare
print("正在通过Tushare获取历史数据并计算技术指标...")
indicators = fetcher._get_technical_indicators_from_tushare(stock_code)
if indicators:
print("✅ 技术指标计算成功！")
print(f"\n均线系统:")
print(f" MA5: {indicators.get('ma5', 0):.2f}")
print(f" MA20: {indicators.get('ma20', 0):.2f}")
print(f" MA60: {indicators.get('ma60', 0):.2f}")
print(f" 趋势: {indicators.get('trend', 'N/A')}")
print(f"\nMACD指标:")
print(f" DIF: {indicators.get('macd_dif', 0):.4f}")
print(f" DEA: {indicators.get('macd_dea', 0):.4f}")
print(f" MACD: {indicators.get('macd', 0):.4f}")
print(f"\nRSI指标:")
print(f" RSI6: {indicators.get('rsi6', 0):.2f}")
print(f" RSI12: {indicators.get('rsi12', 0):.2f}")
print(f" RSI24: {indicators.get('rsi24', 0):.2f}")
print(f"\nKDJ指标:")
print(f" K: {indicators.get('kdj_k', 0):.2f}")
print(f" D: {indicators.get('kdj_d', 0):.2f}")
print(f" J: {indicators.get('kdj_j', 0):.2f}")
print(f"\n布林带:")
print(f" 上轨: {indicators.get('boll_upper', 0):.2f}")
print(f" 中轨: {indicators.get('boll_mid', 0):.2f}")
print(f" 下轨: {indicators.get('boll_lower', 0):.2f}")
print(f" 位置: {indicators.get('boll_position', 'N/A')}")
return True
else:
print("❌ 技术指标计算失败")
return False
def test_kline_data(stock_code='000063'):
"""测试K线图数据获取（Tushare降级）"""
print(f"\n{'='*60}")
print(f"测试3: K线图数据 - {stock_code}")
print(f"{'='*60}")
from smart_monitor_kline import SmartMonitorKline
from smart_monitor_data import SmartMonitorDataFetcher
kline = SmartMonitorKline()
fetcher = SmartMonitorDataFetcher()
# 使用Tushare获取K线数据
print("正在通过Tushare获取K线数据（60天）...")
df = kline._get_kline_from_tushare(stock_code, days=60, ts_pro=fetcher.ts_pro)
if df is not None and not df.empty:
print(f"✅ K线数据获取成功！")
print(f" 数据条数: {len(df)}条")
print(f" 日期范围: {df['日期'].min()} ~ {df['日期'].max()}")
print(f"\n数据列:")
for col in df.columns:
print(f" - {col}")
print(f"\n最近5条数据预览:")
print(df.tail(5)[['日期', '开盘', '最高', '最低', '收盘', '成交量']].to_string())
return True
else:
print("❌ K线数据获取失败")
return False
def test_full_monitoring_flow(stock_code='000063'):
"""测试完整的监控流程（使用Tushare）"""
print(f"\n{'='*60}")
print(f"测试4: 完整监控流程 - {stock_code}")
print(f"{'='*60}")
from smart_monitor_data import SmartMonitorDataFetcher
fetcher = SmartMonitorDataFetcher()
# 1. 获取实时行情
print("\n步骤1: 获取实时行情...")
quote = fetcher.get_realtime_quote(stock_code, retry=1)
if not quote:
print(" ❌ 实时行情获取失败")
return False
print(f" ✅ 当前价: ¥{quote.get('current_price', 0):.2f}")
# 2. 计算技术指标
print("\n步骤2: 计算技术指标...")
indicators = fetcher.get_technical_indicators(stock_code, retry=1)
if not indicators:
print(" ❌ 技术指标计算失败")
return False
print(f" ✅ MA5: {indicators.get('ma5', 0):.2f}, 趋势: {indicators.get('trend', 'N/A')}")
# 3. 综合数据
print("\n步骤3: 获取综合数据...")
comprehensive_data = fetcher.get_comprehensive_data(stock_code)
if not comprehensive_data:
print(" ❌ 综合数据获取失败")
return False
print(" ✅ 综合数据包含:")
print(f" - 实时行情: {comprehensive_data.get('realtime_quote') is not None}")
print(f" - 技术指标: {comprehensive_data.get('technical_indicators') is not None}")
print("\n✅ 完整监控流程测试通过！")
print(" Tushare可以满足AI盯盘的监控要求")
return True
def main():
"""主测试函数"""
print("="*60)
print("Tushare数据源监控能力测试")
print("="*60)
# 检查Token
if not test_tushare_token():
print("\n❌ 请在.env文件中配置TUSHARE_TOKEN")
return
# 测试股票代码
test_stock = '000063' # 中兴通讯
results = []
# 测试1: 实时行情
results.append(("实时行情获取", test_realtime_quote(test_stock)))
# 测试2: 技术指标
results.append(("技术指标计算", test_technical_indicators(test_stock)))
# 测试3: K线数据
results.append(("K线图数据", test_kline_data(test_stock)))
# 测试4: 完整流程
results.append(("完整监控流程", test_full_monitoring_flow(test_stock)))
# 汇总结果
print(f"\n{'='*60}")
print("测试结果汇总")
print(f"{'='*60}")
for test_name, result in results:
status = "✅ 通过" if result else "❌ 失败"
print(f"{test_name:20} {status}")
all_passed = all(result for _, result in results)
if all_passed:
print(f"\n{'='*60}")
print("🎉 所有测试通过！")
print(f"{'='*60}")
print("✅ Tushare完全可以满足AI盯盘的监控要求")
print("✅ 数据源降级策略工作正常")
print("✅ 可以在AKShare IP被封时使用Tushare作为备用")
print("\n建议:")
print("1. 保持Tushare Token配置在.env文件中")
print("2. AKShare重试次数已设置为1次，减少IP封禁风险")
print("3. Tushare 10000积分可以支持日常监控需求")
else:
print(f"\n{'='*60}")
print("⚠️ 部分测试失败")
print(f"{'='*60}")
print("请检查:")
print("1. Tushare Token是否有效")
print("2. Tushare积分是否足够")
print("3. 网络连接是否正常")
if __name__ == '__main__':
main()
启动系统.bat
+7
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@echo off
set VENV_PATH=.\TradEnv
set PYTHON_EXE="%VENV_PATH%\python.exe"
set STREAMLIT_MODULE="streamlit.cli"
cd /d ..\AIagentsStock
%PYTHON_EXE% -m streamlit run app.py
pause