改用marimo编辑notebook方便跨平台使用

.idea/vcs.xml

@@ -2,6 +2,5 @@
 <project version="4">
   <component name="VcsDirectoryMappings">
     <mapping directory="$PROJECT_DIR$" vcs="Git" />
-    <mapping directory="$PROJECT_DIR$/backtestingpy" vcs="Git" />
   </component>
 </project>

notebook/backtest.py

@@ -0,0 +1,184 @@
+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/indicator.py

@@ -0,0 +1,131 @@
+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

@@ -1,72 +0,0 @@
-{
- "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

@@ -8,6 +8,7 @@ dependencies = [
     "duckdb>=1.4.3",
     "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",
@@ -19,3 +20,12 @@ dependencies = [
     "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",
+]