更新环境

.idea/dataSources.xml

@@ -8,5 +8,12 @@
       <jdbc-url>jdbc:postgresql://81.71.3.24:6785/leopard_dev</jdbc-url>
       <working-dir>$ProjectFileDir$</working-dir>
     </data-source>
+    <data-source source="LOCAL" name="leopard.sqlite" uuid="c9e16f8e-81be-45cf-847c-47a6750eeee2">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:$USER_HOME$/Documents/leopard_data/leopard.sqlite</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
   </component>
 </project>

.idea/data_source_mapping.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DataSourcePerFileMappings">
+    <file url="file://$APPLICATION_CONFIG_DIR$/consoles/db/bd7b5f2a-eb99-4aad-81ec-1fec76b3d7fc/console.sql" value="bd7b5f2a-eb99-4aad-81ec-1fec76b3d7fc" />
+    <file url="file://$APPLICATION_CONFIG_DIR$/consoles/db/c9e16f8e-81be-45cf-847c-47a6750eeee2/console.sql" value="c9e16f8e-81be-45cf-847c-47a6750eeee2" />
+  </component>
+</project>

.idea/db-forest-config.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="db-tree-configuration">
+    <option name="data" value="" />
+  </component>
+</project>

.idea/sqldialects.xml

@@ -1,6 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="SqlDialectMappings">
+    <file url="file://$PROJECT_DIR$/sql/initial.sql" dialect="SQLite" />
     <file url="PROJECT" dialect="PostgreSQL" />
   </component>
 </project>

data.py

@@ -0,0 +1,136 @@
+from datetime import date, datetime, timedelta
+from time import sleep
+
+from sqlalchemy import Column, Double, Integer, String, create_engine
+from sqlalchemy.orm import DeclarativeBase, Session
+from tushare import pro_api
+
+TUSHARE_API_KEY = '64ebff4fa679167600b905ee45dd88e76f3963c0ff39157f3f085f0e'
+
+
+class Base(DeclarativeBase):
+    pass
+
+
+class Stock(Base):
+    __tablename__ = 'stock'
+
+    code = Column(String, primary_key=True, comment="代码")
+    name = Column(String, comment="名称")
+    fullname = Column(String, comment="全名")
+    market = Column(String, comment="市场")
+    exchange = Column(String, comment="交易所")
+    industry = Column(String, comment="行业")
+    list_date = Column(String, comment="上市日期")
+
+
+class Daily(Base):
+    __tablename__ = 'daily'
+
+    code = Column(String, primary_key=True)
+    trade_date = Column(String, primary_key=True)
+    open = Column(Double)
+    close = Column(Double)
+    high = Column(Double)
+    low = Column(Double)
+    previous_close = Column(Double)
+    turnover = Column(Double)
+    volume = Column(Integer)
+    price_change_amount = Column(Double)
+    factor = Column(Double)
+
+
+def main():
+    print("开始更新数据")
+
+    engine = create_engine(f"sqlite:////Users/lanyuanxiaoyao/Documents/leopard_data/leopard.sqlite")
+    try:
+        Stock.metadata.create_all(engine, checkfirst=True)
+        Daily.metadata.create_all(engine, checkfirst=True)
+        pro = pro_api(TUSHARE_API_KEY)
+        # with engine.connect() as connection:
+        #     stocks = pro.stock_basic(list_status="L", market="主板", fields="ts_code,name,fullname,market,exchange,industry,list_date")
+        #     for row in stocks.itertuples():
+        #         stmt = insert(Stock).values(
+        #             code=row.ts_code,
+        #             name=row.name,
+        #             fullname=row.fullname,
+        #             market=row.market,
+        #             exchange=row.exchange,
+        #             industry=row.industry,
+        #             list_date=row.list_date,
+        #         )
+        #         stmt = stmt.on_conflict_do_update(
+        #             index_elements=["code"],
+        #             set_={
+        #                 "name": stmt.excluded.name,
+        #                 "fullname": stmt.excluded.fullname,
+        #                 "market": stmt.excluded.market,
+        #                 "exchange": stmt.excluded.exchange,
+        #                 "industry": stmt.excluded.industry,
+        #                 "list_date": stmt.excluded.list_date,
+        #             },
+        #         )
+        #         print(stmt)
+        #         connection.execute(stmt)
+        #         connection.commit()
+        #
+        #     print("清理行情数据")
+        #     connection.execute(text("delete from daily where code not in (select distinct code from stock)"))
+        #     connection.commit()
+        #
+        #     print("清理财务数据")
+        #     connection.execute(text("delete from finance_indicator where code not in (select distinct code from stock)"))
+        #     connection.commit()
+
+        with Session(engine) as session:
+            stock_codes = [row[0] for row in session.query(Stock.code).all()]
+
+            latest_date = session.query(Daily.trade_date).order_by(Daily.trade_date.desc()).first()
+            if latest_date is None:
+                latest_date = '1990-12-19'
+            else:
+                latest_date = latest_date.trade_date
+            latest_date = datetime.strptime(latest_date, '%Y-%m-%d').date()
+            current_date = date.today() - timedelta(days=1)
+            delta = (current_date - latest_date).days
+            print(f"最新数据日期：{latest_date}，当前日期：{current_date}，待更新天数：{delta}")
+            if delta > 0:
+                update_dates = []
+                for i in range(delta):
+                    latest_date = latest_date + timedelta(days=1)
+                    update_dates.append(latest_date.strftime('%Y%m%d'))
+                for target_date in update_dates:
+                    print(f"正在采集：{target_date}")
+                    dailies = pro.daily(trade_date=target_date)
+                    dailies.set_index("ts_code", inplace=True)
+                    factors = pro.adj_factor(trade_date=target_date)
+                    factors.set_index("ts_code", inplace=True)
+                    results = dailies.join(factors, lsuffix="_daily", rsuffix="_factor", how="left")
+                    rows = []
+                    for row in results.itertuples():
+                        if row.Index in stock_codes:
+                            rows.append(
+                                Daily(
+                                    code=row.Index,
+                                    trade_date=datetime.strptime(target_date, '%Y%m%d').strftime("%Y-%m-%d"),
+                                    open=row.open,
+                                    close=row.close,
+                                    high=row.high,
+                                    low=row.low,
+                                    previous_close=row.pre_close,
+                                    turnover=row.amount,
+                                    volume=row.vol,
+                                    price_change_amount=row.pct_chg,
+                                    factor=row.adj_factor,
+                                )
+                            )
+                    session.add_all(rows)
+                    session.commit()
+                    sleep(1)
+    finally:
+        engine.dispose()
+
+
+if __name__ == '__main__':
+    main()

notebook/backtest.py

@@ -1,184 +0,0 @@
-import marimo
-
-__generated_with = "0.19.6"
-app = marimo.App(width="full", auto_download=["ipynb"], sql_output="pandas")
-
-
-@app.cell
-def _():
-    import marimo as mo
-    return (mo,)
-
-
-@app.cell
-def _():
-    import urllib
-    import sqlalchemy
-
-    host = "81.71.3.24"
-    port = 6785
-    username = "leopard"
-    password = urllib.parse.quote_plus("9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X")
-    database = "leopard_dev"
-    engine = sqlalchemy.create_engine(f"postgresql://{username}:{password}@{host}:{port}/{database}")
-    return (engine,)
-
-
-@app.cell
-def _(engine, mo):
-    dailies_df = mo.sql(
-        f"""
-        select trade_date,
-               open * factor         as Open,
-               close * factor        as Close,
-               high * factor         as High,
-               low * factor          as Low,
-               volume                as Volume,
-               coalesce(factor, 1.0) as factor
-        from leopard_daily daily
-                 left join leopard_stock stock on stock.id = daily.stock_id
-        where stock.code = '000001.SZ'
-          and daily.trade_date between '2024-01-01 00:00:00' and '2025-12-31 23:59:59'
-        order by daily.trade_date
-        """,
-        engine=engine
-    )
-    return (dailies_df,)
-
-
-@app.cell
-def _(dailies_df):
-    import pandas as pd
-
-    dailies_df.rename(
-        columns={'open': 'Open', 'close': 'Close', 'high': 'High', 'low': 'Low', 'volume': 'Volume'}, inplace=True
-    )
-    dailies_df['trade_date'] = pd.to_datetime(dailies_df['trade_date'], format='%Y-%m-%d')
-    dailies_df.set_index('trade_date', inplace=True)
-    dailies_df
-    return
-
-
-@app.cell
-def _(dailies_df):
-    import talib
-
-    dailies_df['sma10'] = talib.SMA(dailies_df['Close'], timeperiod=10)
-    dailies_df['sma30'] = talib.SMA(dailies_df['Close'], timeperiod=30)
-    dailies_df['sma60'] = talib.SMA(dailies_df['Close'], timeperiod=60)
-    dailies_df['sma120'] = talib.SMA(dailies_df['Close'], timeperiod=120)
-    dailies_df
-    return
-
-
-@app.cell
-def _(dailies_df):
-    # 指标计算完成后截取后面指标的完整的部份使用
-    target_dailies_df = dailies_df.loc['2025-01-01':'2025-12-31']
-    target_dailies_df
-    return (target_dailies_df,)
-
-
-@app.cell
-def _():
-    from backtesting import Strategy
-    from backtesting.lib import crossover
-
-    class SmaCross(Strategy):
-        def init(self):
-            self.sma10 = self.I(lambda x: x, self.data.sma10)
-            self.sma30 = self.I(lambda x: x, self.data.sma30)
-            self.sma60 = self.I(lambda x: x, self.data.sma60)
-            # self.sma120 = self.I(lambda x: x, self.data.sma120)
-
-        def next(self):
-            if self.sma60 > 0 and crossover(self.data.sma10, self.data.sma30):
-                self.buy()
-            elif self.position.size > 0 and crossover(self.data.sma30, self.data.sma10):
-                self.position.close()
-    return (SmaCross,)
-
-
-@app.function
-def stats_print(stats):
-    indicator_name_mapping = {
-        # 'Start': '回测开始时间',
-        # 'End': '回测结束时间',
-        # 'Duration': '回测持续时长',
-        # 'Exposure Time [%]': '持仓时间占比（%）',
-        'Equity Final [$]': '最终收益',
-        'Equity Peak [$]': '峰值收益',
-        'Return [%]': '总收益率（%）',
-        'Buy & Hold Return [%]': '买入并持有收益率（%）',
-        'Return (Ann.) [%]': '年化收益率（%）',
-        'Volatility (Ann.) [%]': '年化波动率（%）',
-        # 'CAGR [%]': '复合年均增长率（%）',
-        # 'Sharpe Ratio': '夏普比率',
-        'Sortino Ratio': '索提诺比率',
-        'Calmar Ratio': '卡尔玛比率',
-        # 'Alpha [%]': '阿尔法系数（%）',
-        # 'Beta': '贝塔系数',
-        'Max. Drawdown [%]': '最大回撤（%）',
-        'Avg. Drawdown [%]': '平均回撤（%）',
-        'Max. Drawdown Duration': '最大回撤持续时长',
-        'Avg. Drawdown Duration': '平均回撤持续时长',
-        '# Trades': '总交易次数',
-        'Win Rate [%]': '胜率（%）',
-        # 'Best Trade [%]': '最佳单笔交易收益率（%）',
-        # 'Worst Trade [%]': '最差单笔交易收益率（%）',
-        # 'Avg. Trade [%]': '平均单笔交易收益率（%）',
-        # 'Max. Trade Duration': '单笔交易最长持有时长',
-        # 'Avg. Trade Duration': '单笔交易平均持有时长',
-        # 'Profit Factor': '盈利因子',
-        # 'Expectancy [%]': '期望收益（%）',
-        'SQN': '系统质量数',
-        # 'Kelly Criterion': '凯利准则',
-    }
-    for k, v in stats.items():
-        if k in indicator_name_mapping:
-            cn_name = indicator_name_mapping.get(k, k)
-            if isinstance(v, (int, float)):
-                if "%" in cn_name or k in ['Sharpe Ratio', 'Sortino Ratio', 'Calmar Ratio', 'Profit Factor']:
-                    formatted_value = f"{v:.2f}"
-                elif "$" in cn_name:
-                    formatted_value = f"{v:.2f}"
-                elif "次数" in cn_name:
-                    formatted_value = f"{v:.0f}"
-                else:
-                    formatted_value = f"{v:.4f}"
-            else:
-                formatted_value = str(v)
-            print(f'{cn_name}: {formatted_value}')
-
-
-@app.cell
-def _(SmaCross, target_dailies_df):
-    from backtesting import Backtest
-
-    import backtesting._plotting as plotting
-    from bokeh.colors.named import tomato, lime
-
-    plotting.BULL_COLOR = tomato
-    plotting.BEAR_COLOR = lime
-
-    bt = Backtest(target_dailies_df, SmaCross, cash=100000, commission=.002, finalize_trades=True)
-    stats = bt.run()
-    stats_print(stats)
-    return bt, stats
-
-
-@app.cell
-def _(stats):
-    stats._trades
-    # stats._equity_curve
-    return
-
-
-@app.cell
-def _(bt):
-    bt.plot()
-    return
-
-
-if __name__ == "__main__":
-    app.run()

notebook/datasource/data_old.ipynb

@@ -0,0 +1,455 @@
+{
+ "cells": [
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-30T05:41:51.291397Z",
+     "start_time": "2026-01-30T04:34:22.917761Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import urllib.parse\n",
+    "\n",
+    "import pandas as pd\n",
+    "import sqlalchemy\n",
+    "from sqlalchemy import text\n",
+    "from sqlalchemy.orm import DeclarativeBase, Session\n",
+    "\n",
+    "postgresql_engin = sqlalchemy.create_engine(\n",
+    "    f\"postgresql://leopard:{urllib.parse.quote_plus(\"9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X\")}@81.71.3.24:6785/leopard\"\n",
+    ")\n",
+    "sqlite_engine = sqlalchemy.create_engine(f\"sqlite:////Users/lanyuanxiaoyao/Documents/leopard_data/leopard.sqlite\")\n",
+    "\n",
+    "\n",
+    "class Base(DeclarativeBase):\n",
+    "    pass\n",
+    "\n",
+    "\n",
+    "class Daily(Base):\n",
+    "    __tablename__ = 'daily'\n",
+    "\n",
+    "    code = sqlalchemy.Column(sqlalchemy.String, primary_key=True)\n",
+    "    trade_date = sqlalchemy.Column(sqlalchemy.Date, primary_key=True)\n",
+    "    open = sqlalchemy.Column(sqlalchemy.Double)\n",
+    "    close = sqlalchemy.Column(sqlalchemy.Double)\n",
+    "    high = sqlalchemy.Column(sqlalchemy.Double)\n",
+    "    low = sqlalchemy.Column(sqlalchemy.Double)\n",
+    "    previous_close = sqlalchemy.Column(sqlalchemy.Double)\n",
+    "    turnover = sqlalchemy.Column(sqlalchemy.Double)\n",
+    "    volume = sqlalchemy.Column(sqlalchemy.Integer)\n",
+    "    price_change_amount = sqlalchemy.Column(sqlalchemy.Double)\n",
+    "    factor = sqlalchemy.Column(sqlalchemy.Double)\n",
+    "\n",
+    "\n",
+    "try:\n",
+    "    with Session(postgresql_engin) as pg_session:\n",
+    "        results = pg_session.execute(text(\"select distinct trade_date from leopard_daily\")).fetchall()\n",
+    "        results = list(map(lambda x: x[0].strftime(\"%Y-%m-%d\"), results))\n",
+    "        dates = [results[i: i + 30] for i in range(0, len(results), 30)]\n",
+    "\n",
+    "        for index, date in enumerate(dates):\n",
+    "            print(date)\n",
+    "            daily_df = pd.read_sql(\n",
+    "                f\"\"\"\n",
+    "                   select code,\n",
+    "                          trade_date,\n",
+    "                          open,\n",
+    "                          close,\n",
+    "                          high,\n",
+    "                          low,\n",
+    "                          previous_close,\n",
+    "                          turnover,\n",
+    "                          volume,\n",
+    "                          price_change_amount,\n",
+    "                          factor\n",
+    "                   from leopard_daily d\n",
+    "                            left join leopard_stock s on d.stock_id = s.id\n",
+    "                   where d.trade_date in ('{\"','\".join(date)}')\n",
+    "               \"\"\",\n",
+    "                postgresql_engin\n",
+    "            )\n",
+    "            with Session(sqlite_engine) as session:\n",
+    "                rows = []\n",
+    "                for _, row in daily_df.iterrows():\n",
+    "                    rows.append(\n",
+    "                        Daily(\n",
+    "                            code=row[\"code\"],\n",
+    "                            trade_date=row[\"trade_date\"],\n",
+    "                            open=row[\"open\"],\n",
+    "                            close=row[\"close\"],\n",
+    "                            high=row[\"high\"],\n",
+    "                            low=row[\"low\"],\n",
+    "                            previous_close=row[\"previous_close\"],\n",
+    "                            turnover=row[\"turnover\"],\n",
+    "                            volume=row[\"volume\"],\n",
+    "                            price_change_amount=row[\"price_change_amount\"],\n",
+    "                            factor=row[\"factor\"]\n",
+    "                        )\n",
+    "                    )\n",
+    "                session.add_all(rows)\n",
+    "                session.commit()\n",
+    "finally:\n",
+    "    postgresql_engin.dispose()\n",
+    "    sqlite_engine.dispose()"
+   ],
+   "id": "48821306efc640a1",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['2025-12-25', '2025-12-26', '2025-12-29', '2025-12-30', '2025-12-31', '2026-01-05', '2026-01-06', '2026-01-07', '2026-01-08', '2026-01-09']\n"
+     ]
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-30T09:24:09.859231Z",
+     "start_time": "2026-01-30T09:24:09.746912Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import tushare as ts\n",
+    "\n",
+    "pro = ts.pro_api(\"64ebff4fa679167600b905ee45dd88e76f3963c0ff39157f3f085f0e\")\n",
+    "# stocks = pro.stock_basic(ts_code=\"600200.SH\", list_status=\"D\", fields=\"ts_code,name,fullname,market,exchange,industry,list_date,delist_date\")\n",
+    "# stocks"
+   ],
+   "id": "ed58a1faaf2cdb8e",
+   "outputs": [],
+   "execution_count": 34
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-30T07:14:29.897120Z",
+     "start_time": "2026-01-30T07:14:29.664124Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "# stocks.to_csv(\"dlist.csv\")",
+   "id": "3c8c0a38d6b2992e",
+   "outputs": [],
+   "execution_count": 24
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-30T09:46:34.808300Z",
+     "start_time": "2026-01-30T09:46:34.129412Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "daily_df = pro.daily(trade_date=\"20251231\")\n",
+    "daily_df.set_index(\"ts_code\", inplace=True)\n",
+    "factor_df = pro.adj_factor(trade_date=\"20251231\")\n",
+    "factor_df.set_index(\"ts_code\", inplace=True)"
+   ],
+   "id": "c052a945869aa329",
+   "outputs": [],
+   "execution_count": 50
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-30T09:46:36.697015Z",
+     "start_time": "2026-01-30T09:46:36.642975Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "result_df = daily_df.join(factor_df, lsuffix=\"_daily\", rsuffix=\"_factor\", how=\"left\")\n",
+    "result_df\n",
+    "# factor_df"
+   ],
+   "id": "d61ee80d2cd9f06b",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "          trade_date_daily    open    high     low   close  pre_close  change  \\\n",
+       "ts_code                                                                         \n",
+       "000001.SZ         20251231   11.48   11.49   11.40   11.41      11.48   -0.07   \n",
+       "000002.SZ         20251231    4.66    4.68    4.62    4.65       4.62    0.03   \n",
+       "000004.SZ         20251231   11.30   11.35   11.07   11.08      11.27   -0.19   \n",
+       "000006.SZ         20251231    9.95   10.03    9.69    9.95       9.86    0.09   \n",
+       "000007.SZ         20251231   11.72   11.75   11.28   11.44      11.62   -0.18   \n",
+       "...                    ...     ...     ...     ...     ...        ...     ...   \n",
+       "920978.BJ         20251231   37.64   38.39   36.88   36.90      37.78   -0.88   \n",
+       "920981.BJ         20251231   32.20   32.29   31.75   31.96      32.07   -0.11   \n",
+       "920982.BJ         20251231  233.00  238.49  232.10  233.70     234.80   -1.10   \n",
+       "920985.BJ         20251231    7.32    7.35    7.17    7.19       7.30   -0.11   \n",
+       "920992.BJ         20251231   17.33   17.60   17.29   17.39      17.38    0.01   \n",
+       "\n",
+       "           pct_chg         vol      amount trade_date_factor  adj_factor  \n",
+       "ts_code                                                                   \n",
+       "000001.SZ  -0.6098   590620.37  675457.357          20251231    134.5794  \n",
+       "000002.SZ   0.6494  1075561.25  499883.113          20251231    181.7040  \n",
+       "000004.SZ  -1.6859    18056.00   20248.567          20251231      4.0640  \n",
+       "000006.SZ   0.9128   270369.08  267758.676          20251231     39.7400  \n",
+       "000007.SZ  -1.5491    80556.00   92109.366          20251231      8.2840  \n",
+       "...            ...         ...         ...               ...         ...  \n",
+       "920978.BJ  -2.3293    33945.04  126954.937          20251231      1.2885  \n",
+       "920981.BJ  -0.3430     8237.16   26301.206          20251231      1.4343  \n",
+       "920982.BJ  -0.4685     5210.09  122452.646          20251231      4.2831  \n",
+       "920985.BJ  -1.5068    35174.30   25350.257          20251231      1.6280  \n",
+       "920992.BJ   0.0575     6991.87   12193.445          20251231      1.4932  \n",
+       "\n",
+       "[5458 rows x 12 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trade_date_daily</th>\n",
+       "      <th>open</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>close</th>\n",
+       "      <th>pre_close</th>\n",
+       "      <th>change</th>\n",
+       "      <th>pct_chg</th>\n",
+       "      <th>vol</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>trade_date_factor</th>\n",
+       "      <th>adj_factor</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ts_code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>000001.SZ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>11.48</td>\n",
+       "      <td>11.49</td>\n",
+       "      <td>11.40</td>\n",
+       "      <td>11.41</td>\n",
+       "      <td>11.48</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>-0.6098</td>\n",
+       "      <td>590620.37</td>\n",
+       "      <td>675457.357</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>134.5794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>000002.SZ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>4.66</td>\n",
+       "      <td>4.68</td>\n",
+       "      <td>4.62</td>\n",
+       "      <td>4.65</td>\n",
+       "      <td>4.62</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.6494</td>\n",
+       "      <td>1075561.25</td>\n",
+       "      <td>499883.113</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>181.7040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>000004.SZ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>11.30</td>\n",
+       "      <td>11.35</td>\n",
+       "      <td>11.07</td>\n",
+       "      <td>11.08</td>\n",
+       "      <td>11.27</td>\n",
+       "      <td>-0.19</td>\n",
+       "      <td>-1.6859</td>\n",
+       "      <td>18056.00</td>\n",
+       "      <td>20248.567</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>4.0640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>000006.SZ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>9.95</td>\n",
+       "      <td>10.03</td>\n",
+       "      <td>9.69</td>\n",
+       "      <td>9.95</td>\n",
+       "      <td>9.86</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.9128</td>\n",
+       "      <td>270369.08</td>\n",
+       "      <td>267758.676</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>39.7400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>000007.SZ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>11.72</td>\n",
+       "      <td>11.75</td>\n",
+       "      <td>11.28</td>\n",
+       "      <td>11.44</td>\n",
+       "      <td>11.62</td>\n",
+       "      <td>-0.18</td>\n",
+       "      <td>-1.5491</td>\n",
+       "      <td>80556.00</td>\n",
+       "      <td>92109.366</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>8.2840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>920978.BJ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>37.64</td>\n",
+       "      <td>38.39</td>\n",
+       "      <td>36.88</td>\n",
+       "      <td>36.90</td>\n",
+       "      <td>37.78</td>\n",
+       "      <td>-0.88</td>\n",
+       "      <td>-2.3293</td>\n",
+       "      <td>33945.04</td>\n",
+       "      <td>126954.937</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>1.2885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>920981.BJ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>32.20</td>\n",
+       "      <td>32.29</td>\n",
+       "      <td>31.75</td>\n",
+       "      <td>31.96</td>\n",
+       "      <td>32.07</td>\n",
+       "      <td>-0.11</td>\n",
+       "      <td>-0.3430</td>\n",
+       "      <td>8237.16</td>\n",
+       "      <td>26301.206</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>1.4343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>920982.BJ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>233.00</td>\n",
+       "      <td>238.49</td>\n",
+       "      <td>232.10</td>\n",
+       "      <td>233.70</td>\n",
+       "      <td>234.80</td>\n",
+       "      <td>-1.10</td>\n",
+       "      <td>-0.4685</td>\n",
+       "      <td>5210.09</td>\n",
+       "      <td>122452.646</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>4.2831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>920985.BJ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>7.19</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>-0.11</td>\n",
+       "      <td>-1.5068</td>\n",
+       "      <td>35174.30</td>\n",
+       "      <td>25350.257</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>1.6280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>920992.BJ</th>\n",
+       "      <td>20251231</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>17.60</td>\n",
+       "      <td>17.29</td>\n",
+       "      <td>17.39</td>\n",
+       "      <td>17.38</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.0575</td>\n",
+       "      <td>6991.87</td>\n",
+       "      <td>12193.445</td>\n",
+       "      <td>20251231</td>\n",
+       "      <td>1.4932</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5458 rows × 12 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 51
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebook/indicator.py

@@ -1,131 +0,0 @@
-import marimo
-
-__generated_with = "0.19.6"
-app = marimo.App(width="full", auto_download=["ipynb"], sql_output="pandas")
-
-
-@app.cell
-def _():
-    import marimo as mo
-    return (mo,)
-
-
-@app.cell
-def _():
-    import urllib
-    import sqlalchemy
-
-    host = "81.71.3.24"
-    port = 6785
-    username = "leopard"
-    password = urllib.parse.quote_plus("9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X")
-    database = "leopard_dev"
-    engine = sqlalchemy.create_engine(f"postgresql://{username}:{password}@{host}:{port}/{database}")
-    return (engine,)
-
-
-@app.cell
-def _(engine, mo):
-    dailies_df = mo.sql(
-        f"""
-        select trade_date, open * factor as "Open", close * factor as "Close", high * factor as "High", low * factor as "Low", volume as "Volume", coalesce(factor, 1.0) as factor
-        from leopard_daily daily
-                 left join leopard_stock stock on stock.id = daily.stock_id
-        where stock.code = '000001.SZ'
-          and daily.trade_date between '2024-01-01 00:00:00' and '2025-12-31 23:59:59'
-        order by daily.trade_date
-        """,
-        engine=engine
-    )
-    return (dailies_df,)
-
-
-@app.cell
-def _(dailies_df):
-    import pandas as pd
-
-    dailies_df["trade_date"] = pd.to_datetime(dailies_df["trade_date"], format='%Y-%m-%d')
-    dailies_df.set_index("trade_date", inplace=True)
-    dailies_df
-    return
-
-
-@app.cell
-def _(dailies_df):
-    import talib
-
-    dailies_df['sma30'] = talib.SMA(dailies_df['Close'], timeperiod=30)
-    dailies_df['sma60'] = talib.SMA(dailies_df['Close'], timeperiod=60)
-    dailies_df['sma120'] = talib.SMA(dailies_df['Close'], timeperiod=120)
-
-    macd, signal, hist = talib.MACD(dailies_df["Close"], fastperiod=10, slowperiod=20, signalperiod=9)
-    dailies_df['macd'] = macd
-    dailies_df['signal'] = signal
-    dailies_df['hist'] = hist
-
-    target_dailies_df = dailies_df.loc['2025-01-01':'2025-12-31']
-    target_dailies_df
-    return (target_dailies_df,)
-
-
-@app.cell
-def _(target_dailies_df):
-    target_dailies_df.reset_index(inplace=True)
-    target_dailies_df_inc = target_dailies_df[target_dailies_df["Close"] > target_dailies_df["Open"]]
-    target_dailies_df_dec = target_dailies_df[target_dailies_df["Close"] < target_dailies_df["Open"]]
-    return target_dailies_df_dec, target_dailies_df_inc
-
-
-@app.cell
-def daily_chart(
-        target_dailies_df,
-        target_dailies_df_dec,
-        target_dailies_df_inc,
-):
-    from bokeh.io import show, output_notebook
-    from bokeh.layouts import column
-    from bokeh.plotting import figure
-
-    output_notebook()
-
-    width = 1200
-    up_color = "black"
-    down_color = "grey"
-
-    price_figure = figure(width=width, height=400, tools='pan,wheel_zoom,box_zoom,reset')
-
-    price_figure.min_border = 0
-
-    x_padding = 1
-    y_padding = 10
-    price_figure.x_range.bounds = (min(target_dailies_df.index) - x_padding, max(target_dailies_df.index) + x_padding)
-    price_figure.y_range.bounds = (min(target_dailies_df['Low']) - y_padding, max(target_dailies_df['High']) + y_padding)
-    price_figure.xaxis.major_label_overrides = {i: date.strftime('%Y-%m-%d') for i, date in zip(target_dailies_df.index, target_dailies_df['trade_date'])}
-    price_figure.segment(target_dailies_df.index, target_dailies_df['High'], target_dailies_df.index, target_dailies_df['Low'], color='black')
-    price_figure.vbar(target_dailies_df_inc.index, 0.6, target_dailies_df_inc['Open'], target_dailies_df_inc['Close'], color=up_color)
-    price_figure.vbar(target_dailies_df_dec.index, 0.6, target_dailies_df_dec['Open'], target_dailies_df_dec['Close'], color=down_color)
-    price_figure.line(target_dailies_df.index, target_dailies_df['sma30'], color='orange')
-    price_figure.line(target_dailies_df.index, target_dailies_df['sma60'], color='red')
-    # 控制图表只能放大不能缩小
-    macd_figure = figure(width=width, height=200, tools='pan,wheel_zoom,box_zoom,reset')
-    macd_figure.x_range.bounds = (min(target_dailies_df.index) - x_padding, max(target_dailies_df.index) + x_padding)
-    macd_figure.y_range.bounds = (min(target_dailies_df['macd']) - y_padding, max(target_dailies_df['macd']) + y_padding)
-    macd_figure.line(target_dailies_df.index, target_dailies_df['macd'], color='orange')
-    macd_figure.line(target_dailies_df.index, target_dailies_df['signal'], color='red')
-    # Add MACD histogram bars for positive and negative values
-    macd_positive = target_dailies_df[target_dailies_df['macd'] > 0]
-    macd_negative = target_dailies_df[target_dailies_df['macd'] < 0]
-
-    macd_figure.vbar(macd_positive.index, 0.6, 0, macd_positive['macd'], color=up_color)
-    macd_figure.vbar(macd_negative.index, 0.6, 0, macd_negative['macd'], color=down_color)
-
-    # Add zero line
-    macd_figure.line(target_dailies_df.index, [0] * len(target_dailies_df), color='black', line_dash='dashed')
-    # show(price_figure)
-    # show(macd_figure)
-    show(column(price_figure, macd_figure))
-    return
-
-
-if __name__ == "__main__":
-    app.run()

notebook/sql2dataframe.ipynb

@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "host = '81.71.3.24'\n",
+    "port = 6785\n",
+    "database = 'leopard_dev'\n",
+    "username = 'leopard'\n",
+    "password = '9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X'"
+   ],
+   "id": "1e8d815ee9b8c936"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import psycopg2\n",
+    "\n",
+    "dailies_df = pd.DataFrame()\n",
+    "\n",
+    "with psycopg2.connect(host=host, port=port, database=database, user=username, password=password) as connection:\n",
+    "    with connection.cursor() as cursor:\n",
+    "        # language=PostgreSQL\n",
+    "        cursor.execute(\n",
+    "            \"\"\"select trade_date, open, close, high, low, factor\n",
+    "from leopard_daily daily\n",
+    "         left join leopard_stock stock on stock.id = daily.stock_id\n",
+    "where stock.code = '000001.SZ'\n",
+    "  and daily.trade_date between '2025-01-01 00:00:00' and '2025-12-31 23:59:59'\n",
+    "order by daily.trade_date\"\"\"\n",
+    "            )\n",
+    "        rows = cursor.fetchall()\n",
+    "\n",
+    "        dailies_df = pd.DataFrame.from_records(rows, columns=['trade_date', 'open', 'close', 'high', 'low', 'factor'])\n",
+    "\n",
+    "dailies_df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pyproject.toml

@@ -4,10 +4,13 @@ version = "0.1.0"
 description = "Stock analysis"
 requires-python = ">=3.14"
 dependencies = [
+    "adata>=2.9.5",
+    "akshare>=1.18.20",
     "backtesting~=0.6.5",
+    "baostock>=0.8.9",
+    "duckdb>=1.4.4",
     "jupyter~=1.1.1",
     "jupyter-bokeh>=4.0.5",
-    "marimo>=0.19.6",
     "matplotlib~=3.10.8",
     "mplfinance>=0.12.10b0",
     "pandas~=2.3.3",
@@ -18,13 +21,5 @@ dependencies = [
     "ta-lib>=0.6.8",
     "tabulate>=0.9.0",
     "tqdm>=4.67.1",
-]
-
-[dependency-groups]
-dev = [
-    "mcp>=1",
-    "openai>=2.16.0",
-    "pydantic>=2",
-    "pydantic-ai>=1.48.0",
-    "ruff>=0.14.14",
+    "tushare>=1.4.24",
 ]

sql/initial.sql

@@ -0,0 +1,94 @@
+CREATE TABLE stock
+(
+    code        varchar not null,
+    name        varchar not null,
+    fullname    varchar,
+    industry    varchar,
+    listed_date date,
+    market      varchar,
+    exchange    varchar,
+    primary key (code)
+);
+
+
+CREATE TABLE daily
+(
+    code                varchar not null,
+    trade_date          date    not null,
+    open                double,
+    close               double,
+    high                double,
+    low                 double,
+    previous_close      double,
+    turnover            double,
+    volume              integer,
+    price_change_amount double,
+    factor              double,
+    primary key (code, trade_date)
+);
+
+CREATE TABLE finance_indicator
+(
+    code                                             varchar not null,
+    year                                             integer not null,
+    accounts_payable                                 double,
+    accounts_payable_to_total_assets_ratio           double,
+    accounts_receivable                              double,
+    accounts_receivable_to_total_assets_ratio        double,
+    accounts_receivable_turnover                     double,
+    capital_surplus                                  double,
+    cash_and_cash_equivalents                        double,
+    cash_and_cash_equivalents_to_total_assets_ratio  double,
+    cash_flow_adequacy_ratio                         double,
+    cash_flow_from_financing_activities              double,
+    cash_flow_from_investing_activities              double,
+    cash_flow_from_operating_activities              double,
+    cash_flow_ratio                                  double,
+    cash_reinvestment_ratio                          double,
+    current_assets                                   double,
+    current_assets_to_total_assets_ratio             double,
+    current_liabilities                              double,
+    current_liabilities_to_total_assets_ratio        double,
+    current_liabilities_to_total_liabilities_ratio   double,
+    current_ratio                                    double,
+    days_accounts_receivable_turnover                double,
+    days_fixed_assets_turnover                       double,
+    days_inventory_turnover                          double,
+    days_total_assets_turnover                       double,
+    earnings_per_share                               double,
+    fixed_assets                                     double,
+    fixed_assets_to_total_assets_ratio               double,
+    fixed_assets_turnover                            double,
+    goodwill                                         double,
+    goodwill_to_total_assets_ratio                   double,
+    inventory                                        double,
+    inventory_to_total_assets_ratio                  double,
+    inventory_turnover                               double,
+    liabilities_to_total_assets_ratio                double,
+    long_term_funds_to_fixed_assets_ratio            double,
+    long_term_liabilities                            double,
+    long_term_liabilities_to_total_assets_ratio      double,
+    long_term_liabilities_to_total_liabilities_ratio double,
+    net_cash_flow_from_operating_activities          double,
+    net_profit                                       double,
+    net_profit_margin                                double,
+    operating_cost                                   double,
+    operating_expenses                               double,
+    operating_gross_profit_margin                    double,
+    operating_profit                                 double,
+    operating_profit_margin                          double,
+    operating_revenue                                double,
+    operating_safety_margin_ratio                    double,
+    quick_ratio                                      double,
+    return_on_assets                                 double,
+    return_on_equity                                 double,
+    shareholders_equity                              double,
+    shareholders_equity_to_total_assets_ratio        double,
+    surplus_reserve                                  double,
+    total_assets                                     double,
+    total_assets_turnover                            double,
+    total_liabilities                                double,
+    total_share_capital                              double,
+    undistributed_profit                             double,
+    primary key (code, year)
+)