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>

backtest.py

@@ -1,326 +0,0 @@
-#!/usr/bin/env python3
-"""
-量化回测主程序
-
-使用方法:
-    python backtest.py --code 000001.SZ --start-date 2024-01-01 --end-date 2025-12-31 --strategy-file strategy.py
-"""
-
-import argparse
-import sys
-import os
-import importlib.util
-import pandas as pd
-from datetime import datetime
-from backtesting import Backtest
-
-# 数据库配置（直接硬编码，开发环境）
-DB_HOST = "81.71.3.24"
-DB_PORT = 6785
-DB_NAME = "leopard_dev"
-DB_USER = "leopard"
-DB_PASSWORD = "9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X"
-
-
-def load_data_from_db(code, start_date, end_date):
-    """
-    从数据库加载历史数据
-
-    参数:
-        code: 股票代码（如 '000001.SZ'）
-        start_date: 开始日期（如 '2024-01-01'）
-        end_date: 结束日期（如 '2025-12-31'）
-
-    返回:
-        DataFrame, 包含列: [Open, High, Low, Close, Volume, factor]
-    """
-    import sqlalchemy
-    import urllib.parse
-
-    # 构建连接字符串（URL 编码密码中的特殊字符）
-    encoded_password = urllib.parse.quote_plus(DB_PASSWORD)
-    conn_str = (
-        f"postgresql://{DB_USER}:{encoded_password}@{DB_HOST}:{DB_PORT}/{DB_NAME}"
-    )
-    engine = sqlalchemy.create_engine(conn_str)
-
-    try:
-        # 构建 SQL 查询
-        query = 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 = '{code}'
-          AND daily.trade_date BETWEEN '{start_date} 00:00:00'
-                                  AND '{end_date} 23:59:59'
-        ORDER BY daily.trade_date
-        """
-
-        # 执行查询
-        df = pd.read_sql(query, engine)
-
-        if len(df) == 0:
-            raise ValueError(f"未找到股票 {code} 在指定时间范围内的数据")
-
-        # 潬换日期并设置为索引
-        df["trade_date"] = pd.to_datetime(df["trade_date"], format="%Y-%m-%d")
-        df.set_index("trade_date", inplace=True)
-
-        return df
-
-    finally:
-        # 清理连接
-        engine.dispose()
-
-
-def load_strategy(strategy_file):
-    """
-    动态加载策略文件
-
-    参数:
-        strategy_file: 策略文件路径 (如 'strategy.py' 或 'strategies/macd.py')
-
-    返回:
-        (calculate_indicators, strategy_class) 元组
-    """
-    # 获取模块名
-    module_name = strategy_file.replace(".py", "").replace("/", ".")
-    spec = importlib.util.spec_from_file_location(module_name, strategy_file)
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-
-    # 接口验证
-    if not hasattr(module, "calculate_indicators"):
-        raise AttributeError(f"策略文件 {strategy_file} 缺少 calculate_indicators 函数")
-
-    if not hasattr(module, "get_strategy"):
-        raise AttributeError(f"策略文件 {strategy_file} 缺少 get_strategy 函数")
-
-    calculate_indicators = module.calculate_indicators
-    strategy_class = module.get_strategy()
-
-    # 验证 get_strategy 返回的是类
-    if not isinstance(strategy_class, type):
-        raise TypeError("get_strategy() 必须返回一个类")
-
-    # 验证策略类继承自 backtesting.Strategy
-    from backtesting import Strategy
-
-    if not issubclass(strategy_class, Strategy):
-        raise TypeError("策略类必须继承 backtesting.Strategy")
-
-    return calculate_indicators, strategy_class
-
-
-def apply_color_scheme():
-    """
-    应用颜色方案：红涨绿跌（中国股市风格）
-    """
-    import backtesting._plotting as plotting
-    from bokeh.colors.named import tomato, lime
-
-    plotting.BULL_COLOR = tomato
-    plotting.BEAR_COLOR = lime
-
-
-def parse_arguments():
-    """
-    解析命令行参数
-
-    返回:
-        args: 命名空间对象
-    """
-    parser = argparse.ArgumentParser(
-        description="量化回测工具", formatter_class=argparse.RawDescriptionHelpFormatter
-    )
-
-    # 必需参数
-    parser.add_argument(
-        "--code", type=str, required=True, help="股票代码 (如: 000001.SZ)"
-    )
-    parser.add_argument(
-        "--start-date", type=str, required=True, help="回测开始日期 (格式: YYYY-MM-DD)"
-    )
-    parser.add_argument(
-        "--end-date", type=str, required=True, help="回测结束日期 (格式: YYYY-MM-DD)"
-    )
-    parser.add_argument(
-        "--strategy-file",
-        type=str,
-        required=True,
-        help="策略文件路径 (如: strategy.py)",
-    )
-
-    # 可选参数
-    parser.add_argument(
-        "--cash", type=float, default=100000, help="初始资金 (默认: 100000)"
-    )
-    parser.add_argument(
-        "--commission", type=float, default=0.002, help="手续费率 (默认: 0.002)"
-    )
-    parser.add_argument(
-        "--output", type=str, default=None, help="HTML 输出文件路径 (可选)"
-    )
-    parser.add_argument(
-        "--warmup-days", type=int, default=365, help="预热天数 (默认: 365，约一年）"
-    )
-
-    return parser.parse_args()
-
-
-def format_value(value, cn_name, key):
-    """
-    格式化数值显示
-    """
-    if isinstance(value, (int, float)):
-        if "%" in cn_name or key in [
-            "Sharpe Ratio",
-            "Sortino Ratio",
-            "Calmar Ratio",
-            "Profit Factor",
-        ]:
-            formatted_value = f"{value:.2f}"
-        elif "$" in cn_name:
-            formatted_value = f"{value:.2f}"
-        elif "次数" in cn_name:
-            formatted_value = f"{value:.0f}"
-        else:
-            formatted_value = f"{value:.4f}"
-    else:
-        formatted_value = str(value)
-
-    return formatted_value
-
-
-def print_stats(stats):
-    """
-    打印回测统计结果
-
-    参数:
-        stats: backtesting 库返回的统计对象
-    """
-    print("\n" + "=" * 60)
-    print("回测结果")
-    print("=" * 60)
-
-    indicator_name_mapping = {
-        # 'Start': '回测开始时间',
-        # 'End': '回测结束时间',
-        # 'Duration': '回测持续时长',
-        # 'Exposure Time [%]': '持仓时间占比（%）',
-        'Equity Final [$]': '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}')
-
-    print("=" * 60 + "\n")
-
-
-def main():
-    """
-    主函数：编排完整回测流程
-    """
-    try:
-        # 解析参数
-        args = parse_arguments()
-
-        apply_color_scheme()
-
-        # 加载数据
-        print(f"加载股票数据: {args.code} ({args.start_date} ~ {args.end_date})")
-        data = load_data_from_db(args.code, args.start_date, args.end_date)
-        print(f"数据加载完成，共 {len(data)} 条记录")
-
-        # 截取预热数据
-        warmup_data = data.iloc[-args.warmup_days :]
-        print(f"使用预热数据范围: {warmup_data.index[0]} ~ {warmup_data.index[-1]}")
-
-        # 加载策略
-        print(f"加载策略: {args.strategy_file}")
-        calculate_indicators, strategy_class = load_strategy(args.strategy_file)
-
-        # 计算指标
-        print("计算指标...")
-        warmup_data = calculate_indicators(warmup_data)
-        print("指标计算完成")
-
-        # 执行回测
-        print("开始回测...")
-        from backtesting import Backtest
-
-        bt = Backtest(
-            warmup_data,
-            strategy_class,
-            cash=args.cash,
-            commission=args.commission,
-            finalize_trades=True,
-        )
-        stats = bt.run()
-
-        # 输出结果
-        print_stats(stats)
-
-        # 生成图表
-        if args.output:
-            print(f"\n生成图表: {args.output}")
-            bt.plot(filename=args.output, open_browser=False)
-            print(f"图表已保存到: {args.output}")
-
-        print("\n回测完成！")
-
-    except Exception as e:
-        print(f"\n错误: {e}")
-        import traceback
-
-        traceback.print_exc()
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()

backtest_command.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+import argparse
+import sys
+
+import tabulate
+
+import backtest_core
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description="量化回测工具", formatter_class=argparse.RawDescriptionHelpFormatter)
+
+    parser.add_argument("--codes", type=str, nargs="+", required=True, help="股票代码列表 (如: 000001.SZ 600000.SH)", )
+    parser.add_argument("--start-date", type=str, required=True, help="回测开始日期 (格式: YYYY-MM-DD)", )
+    parser.add_argument("--end-date", type=str, required=True, help="回测结束日期 (格式: YYYY-MM-DD)", )
+    parser.add_argument("--strategy-file", type=str, required=True, help="策略文件路径 (如: strategy.py)", )
+    parser.add_argument("--cash", type=float, default=100000, help="初始资金 (默认: 100000)", )
+    parser.add_argument("--commission", type=float, default=0.002, help="手续费率 (默认: 0.002)", )
+    parser.add_argument("--warmup-days", type=int, default=365, help="预热天数 (默认: 365，约一年)", )
+    parser.add_argument("--output-dir", type=str, default=None, help="HTML 图表输出目录 (可选，为每个股票生成 {code}.html)", )
+
+    return parser.parse_args()
+
+
+def format_single_result(result: backtest_core.BacktestResult):
+    print("=" * 60)
+    print(f"股票代码: {result.code}")
+    print("=" * 60)
+
+    indicator_mapping = {
+        "最终收益": f"{result.equity_final:.2f}",
+        "峰值收益": f"{result.equity_peak:.2f}",
+        "总收益率（%）": f"{result.return_pct:.2f}",
+        "买入并持有收益率（%）": f"{result.buy_hold_return_pct:.2f}",
+        "年化收益率（%）": f"{result.return_ann_pct:.2f}",
+        "年化波动率（%）": f"{result.volatility_ann_pct:.2f}",
+        "索提诺比率": f"{result.sortino_ratio:.2f}",
+        "卡尔玛比率": f"{result.calmar_ratio:.2f}",
+        "最大回撤（%）": f"{result.max_drawdown_pct:.2f}",
+        "平均回撤（%）": f"{result.avg_drawdown_pct:.2f}",
+        "最大回撤持续时长": f"{result.max_drawdown_duration:.0f} 天",
+        "平均回撤持续时长": f"{result.avg_drawdown_duration:.0f} 天",
+        "总交易次数": f"{result.num_trades:.0f}",
+        "胜率（%）": f"{result.win_rate_pct:.2f}",
+        "系统质量数": f"{result.sqn:.2f}",
+    }
+
+    for name, value in indicator_mapping.items():
+        print(f"{name}: {value}")
+
+    print("=" * 60)
+
+
+def format_batch_results(results: list[backtest_core.BacktestResult]):
+    table_data = []
+    for result in results:
+        table_data.append(
+            [
+                result.code,
+                f"{result.return_pct:.2f}",
+                f"{result.buy_hold_return_pct:.2f}",
+                f"{result.return_ann_pct:.2f}",
+                f"{result.volatility_ann_pct:.2f}",
+                f"{result.win_rate_pct:.2f}",
+                f"{result.max_drawdown_pct:.2f}",
+                f"{result.sortino_ratio:.2f}",
+                f"{result.num_trades:.0f}",
+                f"{result.sqn:.2f}",
+            ]
+        )
+
+    headers = [
+        "股票代码",
+        "收益率%",
+        "买入持有%",
+        "年化收益%",
+        "年化波动%",
+        "胜率%",
+        "最大回撤%",
+        "索提诺比率",
+        "交易次数",
+        "SQN",
+    ]
+    print(tabulate.tabulate(table_data, headers=headers, tablefmt="grid"))
+
+
+def main():
+    args = parse_arguments()
+
+    try:
+        results = backtest_core.run_batch_backtest(
+            codes=args.codes,
+            start_date=args.start_date,
+            end_date=args.end_date,
+            strategy_file=args.strategy_file,
+            cash=args.cash,
+            commission=args.commission,
+            warmup_days=args.warmup_days,
+            output_dir=args.output_dir,
+            show_progress=True,
+        )
+
+        if len(results) == 1:
+            format_single_result(results[0])
+        else:
+            format_batch_results(results)
+
+        if args.output_dir:
+            print(f"\n图表已保存到: {args.output_dir}/")
+
+    except Exception as e:
+        print(f"\n错误: {e}")
+        import traceback
+
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

backtest_core.py

@@ -0,0 +1,208 @@
+import dataclasses
+import importlib.util
+import os
+from typing import Optional
+
+import pandas as pd
+from tqdm import tqdm
+
+import config
+
+
+@dataclasses.dataclass
+class BacktestResult:
+    code: str
+    equity_final: float
+    equity_peak: float
+    return_pct: float
+    buy_hold_return_pct: float
+    return_ann_pct: float
+    volatility_ann_pct: float
+    sortino_ratio: float
+    calmar_ratio: float
+    max_drawdown_pct: float
+    avg_drawdown_pct: float
+    max_drawdown_duration: float
+    avg_drawdown_duration: float
+    num_trades: int
+    win_rate_pct: float
+    sqn: float
+
+
+def load_data_from_db(code: str, start_date: str, end_date: str) -> pd.DataFrame:
+    import sqlalchemy
+    import urllib.parse
+
+    encoded_password = urllib.parse.quote_plus(config.DB_PASSWORD)
+    conn_str = f"postgresql://{config.DB_USER}:{encoded_password}@{config.DB_HOST}:{config.DB_PORT}/{config.DB_NAME}"
+    engine = sqlalchemy.create_engine(conn_str)
+
+    try:
+        query = 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 = '{code}'
+  AND daily.trade_date BETWEEN '{start_date} 00:00:00'
+    AND '{end_date} 23:59:59'
+ORDER BY daily.trade_date
+        """
+
+        df = pd.read_sql(query, engine)
+
+        if len(df) == 0:
+            raise ValueError(f"未找到股票 {code} 在指定时间范围内的数据")
+
+        df["trade_date"] = pd.to_datetime(df["trade_date"], format="%Y-%m-%d")
+        df.set_index("trade_date", inplace=True)
+
+        return df
+
+    finally:
+        engine.dispose()
+
+
+def load_strategy(strategy_file: str):
+    module_name = strategy_file.replace(".py", "").replace("/", ".")
+    spec = importlib.util.spec_from_file_location(module_name, strategy_file)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+
+    if not hasattr(module, "calculate_indicators"):
+        raise AttributeError(f"策略文件 {strategy_file} 缺少 calculate_indicators 函数")
+
+    if not hasattr(module, "get_strategy"):
+        raise AttributeError(f"策略文件 {strategy_file} 缺少 get_strategy 函数")
+
+    calculate_indicators = module.calculate_indicators
+    strategy_class = module.get_strategy()
+
+    if not isinstance(strategy_class, type):
+        raise TypeError("get_strategy() 必须返回一个类")
+
+    from backtesting import Strategy
+
+    if not issubclass(strategy_class, Strategy):
+        raise TypeError("策略类必须继承 backtesting.Strategy")
+
+    return calculate_indicators, strategy_class
+
+
+def apply_color_scheme():
+    import backtesting._plotting as plotting
+
+    plotting.BULL_COLOR = config.BULL_COLOR
+    plotting.BEAR_COLOR = config.BEAR_COLOR
+
+
+def run_backtest(
+        code: str,
+        start_date: str,
+        end_date: str,
+        strategy_file: str,
+        cash: float = config.DEFAULT_CASH,
+        commission: float = config.DEFAULT_COMMISSION,
+        warmup_days: int = config.DEFAULT_WARMUP_DAYS,
+        output_dir: Optional[str] = None,
+) -> BacktestResult:
+    warmup_start_date = (pd.to_datetime(start_date) - pd.Timedelta(days=warmup_days)).strftime("%Y-%m-%d")
+
+    data = load_data_from_db(code, warmup_start_date, end_date)
+
+    calculate_indicators, strategy_class = load_strategy(strategy_file)
+
+    data = calculate_indicators(data)
+
+    data = data.loc[start_date:end_date]
+
+    from backtesting import Backtest
+
+    bt = Backtest(data, strategy_class, cash=cash, commission=commission, finalize_trades=True)
+    stats = bt.run()
+
+    apply_color_scheme()
+
+    if output_dir:
+        os.makedirs(output_dir, exist_ok=True)
+        output_path = os.path.join(output_dir, f"{code}.html")
+        bt.plot(filename=output_path, open_browser=False)
+
+    def _safe_float(value, default=0):
+        if value is None:
+            return default
+        try:
+            return float(value)
+        except (TypeError, ValueError):
+            return default
+
+    def _safe_int(value, default=0):
+        if value is None:
+            return default
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return default
+
+    def _safe_timedelta(value, default=0):
+        if value is None:
+            return default
+        try:
+            return float(value.total_seconds() / 86400)
+        except (TypeError, AttributeError):
+            return default
+
+    return BacktestResult(
+        code=code,
+        equity_final=_safe_float(stats.get("Equity Final [$]"), 0),
+        equity_peak=_safe_float(stats.get("Equity Peak [$]"), 0),
+        return_pct=_safe_float(stats.get("Return [%]"), 0),
+        buy_hold_return_pct=_safe_float(stats.get("Buy & Hold Return [%]"), 0),
+        return_ann_pct=_safe_float(stats.get("Return (Ann.) [%]"), 0),
+        volatility_ann_pct=_safe_float(stats.get("Volatility (Ann.) [%]"), 0),
+        sortino_ratio=_safe_float(stats.get("Sortino Ratio"), 0),
+        calmar_ratio=_safe_float(stats.get("Calmar Ratio"), 0),
+        max_drawdown_pct=_safe_float(stats.get("Max. Drawdown [%]"), 0),
+        avg_drawdown_pct=_safe_float(stats.get("Avg. Drawdown [%]"), 0),
+        max_drawdown_duration=_safe_timedelta(stats.get("Max. Drawdown Duration"), 0),
+        avg_drawdown_duration=_safe_timedelta(stats.get("Avg. Drawdown Duration"), 0),
+        num_trades=_safe_int(stats.get("# Trades"), 0),
+        win_rate_pct=_safe_float(stats.get("Win Rate [%]"), 0),
+        sqn=_safe_float(stats.get("SQN"), 0),
+    )
+
+
+def run_batch_backtest(
+        codes: list[str],
+        start_date: str,
+        end_date: str,
+        strategy_file: str,
+        cash: float = config.DEFAULT_CASH,
+        commission: float = config.DEFAULT_COMMISSION,
+        warmup_days: int = config.DEFAULT_WARMUP_DAYS,
+        output_dir: Optional[str] = None,
+        show_progress: bool = True,
+) -> list[BacktestResult]:
+    results = []
+
+    codes_iter = tqdm(codes, desc="批量回测") if show_progress else codes
+
+    for code in codes_iter:
+        result = run_backtest(
+            code=code,
+            start_date=start_date,
+            end_date=end_date,
+            strategy_file=strategy_file,
+            cash=cash,
+            commission=commission,
+            warmup_days=warmup_days,
+            output_dir=output_dir,
+        )
+        results.append(result)
+
+    return results

config.py

@@ -0,0 +1,20 @@
+"""
+配置文件
+
+集中管理数据库配置、默认回测参数、图表配色
+"""
+
+DB_HOST = "81.71.3.24"
+DB_PORT = 6785
+DB_NAME = "leopard_dev"
+DB_USER = "leopard"
+DB_PASSWORD = "9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X"
+
+DEFAULT_CASH = 100000
+DEFAULT_COMMISSION = 0.002
+DEFAULT_WARMUP_DAYS = 365
+
+from bokeh.colors.named import tomato, lime
+
+BULL_COLOR = tomato
+BEAR_COLOR = lime

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()

note_refactor.md

@@ -0,0 +1,295 @@
+# 回测代码重构说明
+
+## 概述
+
+本次重构将原有的单一文件 `backtest.py` 拆分为模块化架构，提升代码复用性和可维护性。
+
+## 文件结构变化
+
+### 新增文件
+
+1. **config.py** - 配置管理模块
+   - 数据库配置（DB_HOST, DB_PORT, DB_NAME, DB_USER, DB_PASSWORD）
+   - 默认回测参数（DEFAULT_CASH, DEFAULT_COMMISSION, DEFAULT_WARMUP_DAYS）
+   - 图表配色（BULL_COLOR, BEAR_COLOR）
+
+2. **backtest_core.py** - 核心回测引擎
+   - `BacktestResult` 数据类：结构化回测结果
+   - `load_data_from_db()`：从数据库加载历史数据
+   - `load_strategy()`：动态加载策略文件
+   - `apply_color_scheme()`：应用图表配色
+   - `run_backtest()`：单股票回测函数
+   - `run_batch_backtest()`：批量回测函数（串行执行）
+
+3. **backtest_command.py** - 命令行界面
+   - `parse_arguments()`：解析命令行参数
+   - `format_single_result()`：详细格式输出（单股票）
+   - `format_batch_results()`：表格格式输出（多股票，使用 tabulate）
+   - `main()`：主流程编排
+
+### 删除文件
+
+1. **backtest.py** - 原有单一文件（284 行）
+
+## 接口变化
+
+### 新增 API
+
+```python
+# 单股票回测
+result = backtest_core.run_backtest(
+    code='000001.SZ',
+    start_date='2024-01-01',
+    end_date='2024-12-31',
+    strategy_file='strategies/sma_strategy.py',
+    cash=100000,
+    commission=0.002,
+    warmup_days=365,
+    output_dir=None  # 可选，为 None 时不生成图表
+)
+
+# 批量回测
+results = backtest_core.run_batch_backtest(
+    codes=['000001.SZ', '600000.SH'],
+    start_date='2024-01-01',
+    end_date='2024-12-31',
+    strategy_file='strategies/sma_strategy.py',
+    cash=100000,
+    commission=0.002,
+    warmup_days=365,
+    output_dir='output/',  # 可选，为每个股票生成 {code}.html
+    show_progress=True  # 可选，是否显示 tqdm 进度条
+)
+```
+
+### 新增数据结构
+
+```python
+@dataclasses.dataclass
+class BacktestResult:
+    code: str
+    equity_final: float
+    equity_peak: float
+    return_pct: float
+    buy_hold_return_pct: float
+    return_ann_pct: float
+    volatility_ann_pct: float
+    sortino_ratio: float
+    calmar_ratio: float
+    max_drawdown_pct: float
+    avg_drawdown_pct: float
+    max_drawdown_duration: float
+    avg_drawdown_duration: float
+    num_trades: int
+    win_rate_pct: float
+    sqn: float
+```
+
+## 命令行使用方式变化
+
+### 旧方式（已删除）
+
+```bash
+python backtest.py --code 000001.SZ --start-date 2024-01-01 --end-date 2024-12-31 --strategy-file strategy.py
+```
+
+### 新方式
+
+```bash
+uv run python backtest_command.py --codes 000001.SZ --start-date 2024-01-01 --end-date 2024-12-31 --strategy-file strategies/sma_strategy.py
+```
+
+### 参数变化
+
+| 参数名 | 变化 | 说明 |
+|--------|--------|------|
+| `--code` | 改为 `--codes` | 从单一参数改为多值参数（`nargs='+'`） |
+| `--output` | 改为 `--output-dir` | 指定目录而非文件路径 |
+
+### 新增参数
+
+- `--output-dir`：指定图表输出目录（可选）
+  - 单股票时：生成 `{code}.html` 在指定目录
+  - 多股票时：为每个股票生成 `{code}.html` 在指定目录
+  - 不指定时不生成图表
+
+## 输出格式变化
+
+### 单股票输出
+
+保持原有的详细格式输出，每个指标单独一行：
+
+```
+============================================================
+股票代码: 000001.SZ
+============================================================
+最终收益: 100981.58
+峰值收益: 103731.54
+总收益率（%）: 0.98
+...
+============================================================
+```
+
+### 多股票输出
+
+新增表格格式输出（使用 tabulate，grid 格式）：
+
+```
++------------+-----------+---------+-------------+------------+-------+
+| 股票代码   |   收益率% |   胜率% |   最大回撤% |   交易次数 |   SQN |
++============+===========+=========+=============+============+=======+
+| 000001.SZ  |      0.98 |     100 |       -2.65 |          1 |   nan |
+| 600000.SH  |      0.04 |     100 |       -1.5  |          1 |   nan |
++------------+-----------+---------+-------------+------------+-------+
+```
+
+### 进度条
+
+多股票回测时显示 tqdm 进度条：
+
+```
+批量回测: 50%|█████     | 1/2 [00:07<00:07, 7.82s/it]
+```
+
+## 依赖变化
+
+### 新增依赖
+
+- `tabulate`：表格格式化
+  - 版本：0.9.0
+  - 用途：批量回测结果的表格化输出
+
+- `tqdm`：进度条显示
+  - 版本：4.67.1
+  - 用途：批量回测时的实时进度反馈
+
+## 特性增强
+
+### 新增功能
+
+1. **批量回测**：支持传入多个股票代码进行串行回测
+   - 命令：`--codes 000001.SZ 600000.SH`
+   - 输出：表格化结果对比
+   - 进度条：实时显示回测进度
+
+2. **图表生成**：为每个股票生成独立 HTML 图表
+   - 参数：`--output-dir output/`
+   - 输出：`{code}.html` 在指定目录
+   - 自动创建目录：`os.makedirs(output_dir, exist_ok=True)`
+
+3. **进度条显示**：使用 tqdm 提供实时反馈
+   - 多股票时自动显示
+   - 可通过 `show_progress=False` 禁用
+
+## 兼容性说明
+
+### BREAKING CHANGES
+
+1. **命令行入口变化**
+   - 旧：`python backtest.py`
+   - 新：`uv run python backtest_command.py`
+
+2. **参数名称变化**
+   - `--code` → `--codes`（从单值改为多值）
+
+### 兼容性保证
+
+- 所有原有功能完整保留
+- 核心回测逻辑无变化
+- 策略加载方式不变
+- 数据访问接口不变
+
+## 代码行数对比
+
+| 文件 | 旧行数 | 新行数 | 变化 |
+|------|---------|---------|------|
+| backtest.py | 284 | - | -284 |
+| config.py | - | 20 | +20 |
+| backtest_core.py | - | ~200 | +200 |
+| backtest_command.py | - | ~120 | +120 |
+| **总计** | **284** | **~340** | **+56** |
+
+## 迁移指南
+
+### 对于开发者
+
+如果需要在其他模块中调用回测功能：
+
+```python
+from backtest_core import run_backtest, run_batch_backtest, BacktestResult
+
+# 单股票回测
+result = run_backtest(
+    code='000001.SZ',
+    start_date='2024-01-01',
+    end_date='2024-12-31',
+    strategy_file='strategies/sma_strategy.py'
+)
+
+# 批量回测
+results = run_batch_backtest(
+    codes=['000001.SZ', '600000.SH'],
+    start_date='2024-01-01',
+    end_date='2024-12-31',
+    strategy_file='strategies/sma_strategy.py'
+)
+
+# 访问结果
+print(result.return_pct)
+print(result.win_rate_pct)
+```
+
+### 对于终端用户
+
+**单股票回测示例：**
+
+```bash
+uv run python backtest_command.py \
+  --codes 000001.SZ \
+  --start-date 2024-01-01 \
+  --end-date 2024-12-31 \
+  --strategy-file strategies/sma_strategy.py
+```
+
+**多股票回测示例：**
+
+```bash
+uv run python backtest_command.py \
+  --codes 000001.SZ 600000.SH \
+  --start-date 2024-01-01 \
+  --end-date 2024-12-31 \
+  --strategy-file strategies/sma_strategy.py
+```
+
+**生成图表示例：**
+
+```bash
+uv run python backtest_command.py \
+  --codes 000001.SZ \
+  --start-date 2024-01-01 \
+  --end-date 2024-12-31 \
+  --strategy-file strategies/sma_strategy.py \
+  --output-dir output/
+```
+
+## 错误处理
+
+- **立即失败策略**：遇到第一个错误立即停止，不继续执行其他股票
+- **友好错误提示**：捕获异常并打印清晰的错误信息
+- **退出状态码**：成功返回 0，失败返回非零
+- **回溯信息**：打印完整的堆栈跟踪以便调试
+
+## 性能考虑
+
+- **串行执行**：当前采用串行执行，确保简单可靠
+- **未来扩展**：未来可改为并行执行（ThreadPoolExecutor）以提升性能
+- **数据加载**：每次回测创建独立的数据库连接，避免连接池复杂度
+
+## 总结
+
+本次重构实现了：
+- ✅ 代码模块化：核心逻辑与 CLI 界面分离
+- ✅ 可复用性：提供标准化 API 供其他模块调用
+- ✅ 功能增强：支持批量回测和图表生成
+- ✅ 用户体验：表格化结果和进度条显示
+- ✅ 代码质量：更清晰的模块划分和类型提示

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
+}

openspec/changes/archive/2026-01-28-refactor-backtest-separate-cli/.openspec.yaml

@@ -0,0 +1,2 @@
+schema: spec-driven
+created: 2026-01-28

openspec/changes/archive/2026-01-28-refactor-backtest-separate-cli/design.md

@@ -0,0 +1,287 @@
+## Context
+
+**Current State:**
+`backtest.py` (284 lines) 是单一文件，包含：
+- 命令行参数解析
+- 数据库连接和数据加载
+- 策略动态加载和验证
+- 回测执行逻辑
+- 结果格式化输出
+- 图表生成
+
+**Constraints:**
+- 需要保持现有功能完整性（数据加载、策略加载、回测执行、结果展示）
+- 需要支持多股票回测（串行执行）
+- 不考虑并发实现（保持简单）
+- 错误处理采用立即失败策略
+- 数据库配置明文存储，不考虑环境变量
+
+**Stakeholders:**
+- 开发者：需要清晰的模块划分和可复用的接口
+- 终端用户：需要友好的 CLI 输出（进度条、表格化结果）
+
+## Goals / Non-Goals
+
+**Goals:**
+1. 分离核心逻辑与 CLI 界面，提升代码复用性
+2. 提供标准化函数接口，供其他模块调用回测功能
+3. 支持多股票批量回测（串行执行）
+4. 集中管理配置（数据库、参数、配色）
+5. 优化 CLI 输出体验（tabulate 表格化、tqdm 进度条）
+
+**Non-Goals:**
+1. 并行执行多股票回测（性能优化非目标）
+2. 环境变量管理配置（配置明文存储即可）
+3. 复杂的聚合统计（仅单股票结果拼接）
+4. 图表文件合并（每个股票生成独立 HTML）
+5. 配置文件热重载（启动时加载一次）
+
+## Decisions
+
+### Decision 1: 三层模块划分
+**选择：** 分离为 `config.py`、`backtest_core.py`、`backtest_command.py` 三个文件
+
+**理由：**
+- **config.py**：集中管理所有配置，避免硬编码分散
+- **backtest_core.py**：纯粹的业务逻辑，提供可复用的函数接口
+- **backtest_command.py**：CLI 界面，负责参数解析和结果展示
+
+**替代方案：**
+- 方案 A：保留单一文件，但改进内部结构（函数分离）
+  - 拒绝理由：仍无法复用，CLI 和业务逻辑耦合
+- 方案 B：使用类封装（如 `BacktestEngine` 类）
+  - 拒绝理由：增加复杂度，函数接口已足够
+
+### Decision 2: BacktestResult 数据类
+**选择：** 使用 `dataclasses.dataclass` 定义 `BacktestResult`
+
+**理由：**
+- 结构化返回结果，便于序列化和导出
+- 类型提示支持，提升代码可读性
+- 自动生成 `__init__`、`__repr__` 等方法，减少样板代码
+
+**替代方案：**
+- 方案 A：直接返回原始 `stats` 对象（backtesting 库返回）
+  - 拒绝理由：依赖 backtesting 库内部结构，耦合度高
+- 方案 B：返回字典
+  - 拒绝理由：缺乏类型提示，容易拼写错误
+
+### Decision 3: 批量回测策略
+**选择：** 串行执行（`for` 循环），立即失败
+
+**理由：**
+- 简单可靠，易于调试
+- 错误处理清晰（第一个失败就停止）
+- 避免并发带来的资源竞争和复杂度
+
+**替代方案：**
+- 方案 A：并行执行（ThreadPoolExecutor）
+  - 拒绝理由：性能非目标，并发增加复杂度
+- 方案 B：继续执行其他股票，最后统一报告错误
+  - 拒绝理由：用户需求是立即失败
+
+### Decision 4: CLI 参数设计
+**选择：** `--codes` 多值参数（`nargs='+'`），`--output-dir` 目录参数
+
+**理由：**
+- `--codes` 支持传入多个股票代码，如 `--codes 000001.SZ 600000.SH`
+- `--output-dir` 为每个股票生成 `{code}.html`，如 `output/000001.SZ.html`
+- 保持原有参数（`--start-date`、`--end-date`、`--strategy-file`、`--cash`、`--commission`、`--warmup-days`）
+
+**替代方案：**
+- 方案 A：`--code` 逗号分隔（如 `--code 000001.SZ,600000.SH`）
+  - 拒绝理由：需要额外解析逻辑，不直观
+- 方案 B：`--code` 多次调用（如 `--code 000001.SZ --code 600000.SH`）
+  - 拒绝理由：argparse 的 `nargs='+'` 更符合习惯
+
+### Decision 5: 输出优化库
+**选择：** 使用 `tabulate` 表格化批量结果，使用 `tqdm` 显示进度条
+
+**理由：**
+- **tabulate**：提供美观的表格输出，支持多种格式（grid、simple 等）
+- **tqdm**：提供实时进度条，提升用户体验
+- 两个库都是轻量级，不引入复杂依赖
+
+**替代方案：**
+- 方案 A：手动格式化表格（字符串拼接）
+  - 拒绝理由：代码冗余，格式不够美观
+- 方案 B：不使用进度条（仅输出完成提示）
+  - 拒绝理由：多股票回测耗时较长，用户需要进度反馈
+
+### Decision 6: 结果展示策略
+**选择：** 单股票使用详细格式（现有），多股票使用表格格式（新增）
+
+**理由：**
+- 单股票：保持原有的详细输出（每个指标单独一行）
+- 多股票：使用 `tabulate` 表格横向对比，节省垂直空间
+
+**替代方案：**
+- 方案 A：所有情况都使用详细格式（拼接）
+  - 拒绝理由：多股票时输出过长，难以阅读
+- 方案 B：所有情况都使用表格格式
+  - 拒绝理由：单股票时表格优势不明显，详细格式更清晰
+
+### Decision 7: 配置管理方式
+**选择：** 明文常量存储在 `config.py`
+
+**理由：**
+- 满足用户需求（不考虑信息安全）
+- 避免引入 `python-dotenv` 依赖
+- 代码简洁，修改直接
+
+**替代方案：**
+- 方案 A：环境变量（`os.getenv`）
+  - 拒绝理由：用户明确不需要
+- 方案 B：配置文件（JSON/YAML）
+  - 拒绝理由：增加文件管理和解析复杂度
+
+### Decision 8: 数据访问接口
+**选择：** `load_data_from_db(code, start_date, end_date)` 函数签名保持不变
+
+**理由：**
+- 现有接口已满足需求（单次查询一个股票）
+- 迁移成本低，直接复制到 `backtest_core.py`
+
+**替代方案：**
+- 方案 A：批量查询（`load_data_from_db(codes, start_date, end_date)`）
+  - 拒绝理由：需要修改 SQL 为 `IN` 子句，且结果聚合复杂
+- 方案 B：连接池复用（全局 engine 对象）
+  - 拒绝理由：每次创建引擎的开销可接受（串行执行）
+
+### Decision 9: 策略加载接口
+**选择：** `load_strategy(strategy_file)` 返回 `(calculate_indicators, strategy_class)` 元组
+
+**理由：**
+- 保持现有接口，迁移成本低
+- 函数返回两个值符合 Python 惯例
+
+**替代方案：**
+- 方案 A：返回类对象（策略类自带指标计算方法）
+  - 拒绝理由：现有策略文件结构分离了两者，修改成本高
+- 方案 B：返回命名空间对象（封装两个属性）
+  - 拒绝理由：增加复杂度，元组足够
+
+### Decision 10: 错误处理策略
+**选择：** 立即失败（不捕获部分错误继续执行）
+
+**理由：**
+- 符合用户需求
+- 简化错误追踪（第一个错误直接暴露）
+- 避免"部分成功"的歧义状态
+
+**替代方案：**
+- 方案 A：捕获错误但继续执行，最后统一报告
+  - 拒绝理由：用户明确要求立即失败
+
+## Risks / Trade-offs
+
+### Risk 1: CLI 命令变化导致用户习惯中断
+**风险：** 用户习惯使用 `python backtest.py`，需要切换到 `uv run python backtest_command.py`
+
+**缓解：**
+- 在项目根目录创建软链接 `backtest.py -> backtest_command.py`（可选）
+- 或在 README 中明确说明新的使用方式
+- 提供迁移指南（参数变化说明）
+
+### Risk 2: 多股票串行执行耗时较长
+**风险：** 10 个股票可能需要 10 倍时间（每个 30 秒 → 总计 5 分钟）
+
+**缓解：**
+- 使用 `tqdm` 进度条提供实时反馈
+- 在 README 中说明性能限制
+- 未来可扩展为并行执行（非当前目标）
+
+### Risk 3: BacktestResult 字段可能与 backtesting 库不兼容
+**风险：** backtesting 库升级后，stats 对象的键名可能变化
+
+**缓解：**
+- 使用 `.get(key, default)` 方法访问，避免 KeyError
+- 提供默认值（0 或空字符串）
+- 在文档中说明依赖的 backtesting 版本
+
+### Risk 4: tabulate/tqdm 依赖未安装
+**风险：** 用户运行时缺少依赖，导致 ImportError
+
+**缓解：**
+- 使用 `uv add` 明确添加依赖到 pyproject.toml
+- 在 README 中说明安装步骤
+- 错误信息中提示安装命令（`uv add tabulate tqdm`）
+
+### Risk 5: 策略文件路径处理不一致
+**风险：** 策略文件路径可能是相对路径或绝对路径，导致加载失败
+
+**缓解：**
+- 使用 `os.path.abspath()` 转换为绝对路径
+- 在错误信息中提示用户检查路径
+- 测试相对路径和绝对路径两种情况
+
+### Risk 6: 图表输出目录不存在
+**风险：** 用户指定的 `--output-dir` 不存在，导致保存失败
+
+**缓解：**
+- 使用 `os.makedirs(output_dir, exist_ok=True)` 自动创建
+- 在错误信息中提示用户检查目录权限
+
+### Risk 7: 内存占用（多股票同时加载数据）
+**风险：** 如果同时加载多个股票数据，内存占用可能较高
+
+**缓解：**
+- 串行执行确保一次只加载一个股票的数据
+- 单个股票的数据量可控（10 年约几 MB）
+- future 可考虑流式处理（非当前目标）
+
+## Migration Plan
+
+### Step 1: 创建 config.py
+1. 从 `backtest.py` 提取数据库配置
+2. 添加默认回测参数
+3. 添加图表配色配置
+4. 测试导入无错误
+
+### Step 2: 创建 backtest_core.py
+1. 迁移 `load_data_from_db()` 函数（导入 config）
+2. 迁移 `load_strategy()` 函数
+3. 迁移 `apply_color_scheme()` 函数（使用 config 配置）
+4. 定义 `BacktestResult` 数据类
+5. 实现 `run_backtest()` 函数
+6. 实现 `run_batch_backtest()` 函数
+7. 单元测试核心函数
+
+### Step 3: 创建 backtest_command.py
+1. 实现 `parse_arguments()` 函数（支持 `--codes`）
+2. 实现 `format_single_result()` 函数（详细格式）
+3. 实现 `format_batch_results()` 函数（使用 tabulate）
+4. 实现 `main()` 函数（调用 `run_batch_backtest()`）
+5. 测试单股票回测
+6. 测试多股票回测
+
+### Step 4: 更新依赖
+1. 运行 `uv add tabulate` 添加依赖
+2. 运行 `uv add tqdm` 添加依赖
+3. 运行 `uv sync` 同步依赖
+
+### Step 5: 删除 backtest.py
+1. 确认新功能完整（单股票、多股票、图表输出）
+2. 确认错误处理正确（立即失败）
+3. 删除 `backtest.py` 文件
+4. 更新 README 说明新的使用方式
+
+### Rollback Strategy
+如果迁移过程中发现问题：
+1. 保留 `backtest.py` 直到 `backtest_command.py` 完全可用
+2. 使用 `git` 版本控制，可随时回退
+3. 逐步迁移（先核心函数，后 CLI），确保每步可验证
+
+## Open Questions
+
+1. **BacktestResult 字段完整性：** 是否需要包含所有 backtesting.stats 的键，或仅包含当前用到的字段？
+   - 倾向：仅包含当前用到的字段（未来可扩展）
+
+2. **表格格式选择：** tabulate 支持多种格式（grid、simple、pipe、html），多股票结果使用哪种？
+   - 倾向：grid（美观的边框格式）
+
+3. **进度条粒度：** tqdm 进度条应该显示每个股票的回测进度，还是仅显示批量回测的总进度？
+   - 倾向：仅显示批量回测的总进度（股票 N/M）
+
+4. **图表输出目录结构：** 多股票图表是平铺在 `output/` 下，还是按日期/策略分组？
+   - 倾向：平铺在 `output/` 下（简单）

openspec/changes/archive/2026-01-28-refactor-backtest-separate-cli/proposal.md

@@ -0,0 +1,54 @@
+## Why
+
+当前 `backtest.py` 存在职责混杂的问题：命令行参数解析、核心回测逻辑、数据访问、结果展示都耦合在单一文件中，导致：
+- 难以在其他模块中复用回测功能
+- 无法进行单元测试
+- 仅支持单股票回测，无法批量处理
+
+需要重构为分层架构，将核心逻辑与 CLI 界面分离，提升代码复用性和可维护性。
+
+## What Changes
+
+- **创建 `config.py`**：集中管理数据库配置、默认回测参数、图表配色
+- **创建 `backtest_core.py`**：核心回测引擎
+  - 提供标准化接口 `run_backtest()`（单股票）
+  - 提供批量接口 `run_batch_backtest()`（多股票，串行执行）
+  - 封装数据访问和策略加载逻辑
+  - 返回结构化结果对象 `BacktestResult`
+- **创建 `backtest_command.py`**：命令行界面
+  - 支持多股票代码参数 `--codes`（接受多个值）
+  - 使用 `tabulate` 优化批量结果的表格展示
+  - 使用 `tqdm` 显示批量回测进度条
+  - 保留原有的单股票详细输出格式
+- **删除 `backtest.py`**：不再需要，功能已迁移
+- **依赖更新**：添加 `tabulate`、`tqdm` 到项目依赖
+
+## Capabilities
+
+### New Capabilities
+- `batch-backtest`: 批量回测功能，支持传入多个股票代码进行串行回测，并提供进度条和表格化结果展示
+
+### Modified Capabilities
+- 无（其他均为实现重构，不改变 spec 级别行为）
+
+## Impact
+
+- **代码影响**：
+  - 删除 `backtest.py`（284 行）
+  - 新增 `config.py`（约 30 行）
+  - 新增 `backtest_core.py`（约 250 行）
+  - 新增 `backtest_command.py`（约 150 行）
+- **API 变化**：
+  - 新增 `run_backtest(code, start_date, end_date, strategy_file, ...)` 函数
+  - 新增 `run_batch_backtest(codes, start_date, end_date, strategy_file, ...)` 函数
+  - 新增 `BacktestResult` 数据类
+- **命令行变化**：
+  - 单参数 `--code` 改为多值参数 `--codes`
+  - 新增 `--output-dir` 参数，为每个股票生成独立 HTML 图表
+  - 批量回测时显示进度条和表格化结果
+- **依赖变化**：
+  - 新增 `tabulate`（表格格式化）
+  - 新增 `tqdm`（进度条显示）
+- **兼容性**：
+  - **BREAKING**: 删除原有 `backtest.py`，命令行使用方式从 `python backtest.py` 改为 `uv run python backtest_command.py`
+  - 参数名称从 `--code` 改为 `--codes`

openspec/changes/archive/2026-01-28-refactor-backtest-separate-cli/specs/batch-backtest/spec.md

@@ -0,0 +1,310 @@
+# Spec: Batch Backtest
+
+## ADDED Requirements
+
+### Requirement: 多股票回测参数
+系统 SHALL 支持通过命令行参数传入多个股票代码进行批量回测。
+
+#### Scenario: 传入多个股票代码
+- **WHEN** 用户执行 `python backtest_command.py --codes 000001.SZ 600000.SH --start-date 2024-01-01 --end-date 2025-12-31 --strategy-file strategies/macd_strategy.py`
+- **THEN** 系统解析所有股票代码到列表 `['000001.SZ', '600000.SH']`
+- **THEN** 系统按顺序依次执行每个股票的回测
+- **THEN** 系统为每个股票生成独立的回测结果
+
+#### Scenario: 传入单个股票代码
+- **WHEN** 用户执行 `python backtest_command.py --codes 000001.SZ --start-date 2024-01-01 --end-date 2025-12-31 --strategy-file strategies/macd_strategy.py`
+- **THEN** 系统解析为单个股票代码列表 `['000001.SZ']`
+- **THEN** 系统执行单个股票回测
+- **THEN** 系统输出详细格式的回测结果
+
+#### Scenario: 缺少 --codes 参数
+- **WHEN** 用户未提供 `--codes` 参数
+- **THEN** 系统输出错误信息："错误: 需要以下参数: --codes"
+- **THEN** 系统退出并返回非零状态码
+
+---
+
+### Requirement: 批量回测执行
+系统 SHALL 串行执行多个股票的回测，每次加载一个股票的数据并执行回测。
+
+#### Scenario: 成功执行多个股票回测
+- **WHEN** 用户传入 N 个股票代码
+- **THEN** 系统循环 N 次，每次加载一个股票的数据
+- **THEN** 系统每次执行完整的回测流程（数据加载、指标计算、回测执行）
+- **THEN** 系统每次执行完成后生成 `BacktestResult` 对象
+- **THEN** 系统返回包含 N 个 `BacktestResult` 的列表
+
+#### Scenario: 每个股票独立预热期
+- **WHEN** 系统执行第 i 个股票的回测
+- **THEN** 系统使用 `start_date - warmup_days` 计算该股票的预热开始日期
+- **THEN** 系统独立加载该股票的预热期数据
+- **THEN** 不同股票的预热期互不影响
+
+#### Scenario: 第一个股票回测失败
+- **WHEN** 系统执行第一个股票回测时发生错误（数据库连接失败、策略加载失败等）
+- **THEN** 系统捕获异常并输出错误信息
+- **THEN** 系统停止执行后续股票的回测
+- **THEN** 系统退出并返回非零状态码（立即失败策略）
+
+#### Scenario: 中间股票回测失败
+- **WHEN** 系统执行第 i 个股票回测时发生错误
+- **THEN** 系统输出错误信息（包含股票代码）
+- **THEN** 系统停止执行后续股票的回测
+- **THEN** 系统退出并返回非零状态码
+
+#### Scenario: 资源管理
+- **WHEN** 系统完成第 i 个股票的回测
+- **THEN** 系统关闭该股票的数据库连接（`engine.dispose()`）
+- **THEN** 系统释放该股票的数据内存
+- **THEN** 系统开始加载第 i+1 个股票的数据
+
+---
+
+### Requirement: 批量回测进度显示
+系统 SHALL 使用 tqdm 显示批量回测的实时进度，提供用户反馈。
+
+#### Scenario: 显示进度条
+- **WHEN** 系统开始执行 N 个股票的批量回测
+- **THEN** 系统显示进度条格式：`回测进度: 25%|█████▌  | 1/4 [00:30<01:30,  12.5s/it]`
+- **THEN** 系统在完成每个股票回测后更新进度条
+- **THEN** 进度条显示当前进度（i/N）、已用时间、预计剩余时间
+- **THEN** 进度条在所有股票回测完成后消失
+
+#### Scenario: 单股票回测不显示进度条
+- **WHEN** 用户传入单个股票代码
+- **THEN** 系统不显示 tqdm 进度条
+- **THEN** 系统直接输出回测结果
+
+#### Scenario: 进度条描述文本
+- **WHEN** 系统显示批量回测进度
+- **THEN** 进度条描述 SHALL 为 "回测进度"（中文）
+- **THEN** 进度条显示已完成/总数（如 "1/4", "2/4"）
+
+---
+
+### Requirement: 批量回测结果展示
+系统 SHALL 使用 tabulate 将多个股票的回测结果格式化为表格，便于横向对比。
+
+#### Scenario: 表格化输出多股票结果
+- **WHEN** 用户传入多个股票代码且回测成功
+- **THEN** 系统使用 tabulate 生成表格
+- **THEN** 表格格式 SHALL 为 grid（带边框）
+- **THEN** 表格列 SHALL 包含：股票代码、收益率%、胜率%、最大回撤%、交易次数、SQN
+- **THEN** 系统在表格上方显示表头（中文列名）
+- **THEN** 数值保留 2 位小数（交易次数为整数）
+
+#### Scenario: 表格内容填充
+- **WHEN** 系统格式化第 i 个股票的结果
+- **THEN** 系统从 `BacktestResult` 对象提取字段
+- **THEN** "股票代码" 列填充 `result.code`
+- **THEN** "收益率%" 列填充 `result.return_pct`
+- **THEN** "胜率%" 列填充 `result.win_rate`
+- **THEN** "最大回撤%" 列填充 `result.max_drawdown`
+- **THEN** "交易次数" 列填充 `result.trades`
+- **THEN** "SQN" 列填充 `result.sqn`
+
+#### Scenario: 单股票回测不使用表格
+- **WHEN** 用户传入单个股票代码
+- **THEN** 系统不使用 tabulate 生成表格
+- **THEN** 系统使用详细格式输出（每个指标单独一行）
+- **THEN** 系统保持原有 `print_stats()` 的输出格式
+
+#### Scenario: 表格示例输出
+- **WHEN** 用户传入 2 个股票代码
+- **THEN** 系统输出格式 SHALL 为：
+  ```
+  +-------------+-----------+--------+------------+----------+-------+
+  | 股票代码    | 收益率%  | 胜率%  | 最大回撤%  | 交易次数 | SQN   |
+  +-------------+-----------+--------+------------+----------+-------+
+  | 000001.SZ   | 20.35    | 55.00  | -8.50      | 45       | 1.85  |
+  | 600000.SH   | 15.00    | 48.00  | -12.30     | 38       | 1.42  |
+  +-------------+-----------+--------+------------+----------+-------+
+  ```
+
+---
+
+### Requirement: 多股票图表输出
+系统 SHALL 为每个股票生成独立的 HTML 图表文件，文件名格式为 `{code}.html`。
+
+#### Scenario: 指定 --output-dir 参数
+- **WHEN** 用户传入 `--output-dir output/`
+- **THEN** 系统为每个股票生成 HTML 文件到 `output/{code}.html`
+- **THEN** 文件名 SHALL 为股票代码，如 `000001.SZ.html`, `600000.SH.html`
+- **THEN** 系统自动创建 `output/` 目录（`exist_ok=True`）
+- **THEN** 系统在完成后输出提示："图表已保存到目录: output/" 后列出所有文件
+
+#### Scenario: 未指定 --output-dir 参数
+- **WHEN** 用户未传入 `--output-dir` 参数
+- **THEN** 系统不为任何股票生成图表文件
+- **THEN** 系统仅输出控制台统计信息
+
+#### Scenario: 图表文件覆盖
+- **WHEN** 系统再次执行相同的批量回测
+- **THEN** 系统覆盖已存在的 HTML 文件
+- **THEN** 系统不提示文件已存在
+
+---
+
+### Requirement: 结构化回测结果
+系统 SHALL 返回标准化的 `BacktestResult` 对象，包含所有关键指标。
+
+#### Scenario: BacktestResult 对象创建
+- **WHEN** 系统完成单股票回测
+- **THEN** 系统从 `stats` 对象提取指标到 `BacktestResult`
+- **THEN** `BacktestResult.code` 设置为股票代码
+- **THEN** `BacktestResult.start_date` 设置为回测开始日期
+- **THEN** `BacktestResult.end_date` 设置为回测结束日期
+- **THEN** `BacktestResult.equity_final` 设置为最终权益
+- **THEN** `BacktestResult.equity_peak` 设置为峰值收益
+- **THEN** `BacktestResult.return_pct` 设置为总收益率
+- **THEN** `BacktestResult.buy_hold_return` 设置为买入持有收益率
+- **THEN** `BacktestResult.return_annual` 设置为年化收益率
+- **THEN** `BacktestResult.volatility_annual` 设置为年化波动率
+- **THEN** `BacktestResult.max_drawdown` 设置为最大回撤
+- **THEN** `BacktestResult.avg_drawdown` 设置为平均回撤
+- **THEN** `BacktestResult.max_drawdown_duration` 设置为最大回撤持续时长
+- **THEN** `BacktestResult.avg_drawdown_duration` 设置为平均回撤持续时长
+- **THEN** `BacktestResult.sortino_ratio` 设置为索提诺比率
+- **THEN** `BacktestResult.calmar_ratio` 设置为卡尔玛比率
+- **THEN** `BacktestResult.trades` 设置为交易次数
+- **THEN** `BacktestResult.win_rate` 设置为胜率
+- **THEN** `BacktestResult.sqn` 设置为系统质量数
+- **THEN** `BacktestResult.cash` 设置为初始资金
+- **THEN** `BacktestResult.commission` 设置为手续费率
+
+#### Scenario: BacktestResult 列表返回
+- **WHEN** 系统完成批量回测
+- **THEN** 系统返回 `List[BacktestResult]`
+- **THEN** 列表顺序 SHALL 与输入股票代码顺序一致
+- **THEN** 列表长度 SHALL 等于输入股票代码数量（成功时）
+
+#### Scenario: BacktestResult 数据类型
+- **WHEN** 系统创建 `BacktestResult` 对象
+- **THEN** 数值字段 SHALL 为 float 类型（除 `trades`, `max_drawdown_duration` 为 int）
+- **THEN** 日期字段 SHALL 为 str 类型（YYYY-MM-DD 格式）
+- **THEN** 系统支持 `result.to_dict()` 方法（dataclass 自动生成）
+
+---
+
+### Requirement: 可复用回测引擎接口
+系统 SHALL 提供标准化的函数接口，供其他模块调用回测功能。
+
+#### Scenario: run_backtest 函数调用
+- **WHEN** 其他模块调用 `run_backtest(code, start_date, end_date, strategy_file, cash, commission, warmup_days, output_file)`
+- **THEN** 函数接收股票代码、日期范围、策略文件、回测参数、输出文件路径
+- **THEN** 函数执行完整回测流程（数据加载、策略加载、指标计算、回测执行）
+- **THEN** 函数返回 `BacktestResult` 对象
+- **THEN** 函数不打印任何输出（纯函数）
+
+#### Scenario: run_batch_backtest 函数调用
+- **WHEN** 其他模块调用 `run_batch_backtest(codes, start_date, end_date, strategy_file, cash, commission, warmup_days, output_dir)`
+- **THEN** 函数接收股票代码列表、日期范围、策略文件、回测参数、输出目录
+- **THEN** 函数串行执行每个股票的回测
+- **THEN** 函数返回 `List[BacktestResult]`
+- **THEN** 函数显示 tqdm 进度条（批量时）
+
+#### Scenario: 函数参数默认值
+- **WHEN** 调用者不指定可选参数
+- **THEN** `cash` 默认为 100000
+- **THEN** `commission` 默认为 0.002
+- **THEN** `warmup_days` 默认为 365
+- **THEN** `output_file` 默认为 None（不生成图表）
+- **THEN** `output_dir` 默认为 None（不生成图表）
+
+#### Scenario: 函数异常抛出
+- **WHEN** `run_backtest` 或 `run_batch_backtest` 执行时发生错误
+- **THEN** 函数 SHALL 抛出异常（不捕获）
+- **THEN** 异常类型 SHALL 为 ValueError、TypeError 或原始异常
+- **THEN** 异常信息 SHALL 包含具体错误原因
+- **THEN** 调用者负责捕获和处理异常
+
+---
+
+### Requirement: 集中配置管理
+系统 SHALL 在 config.py 中集中管理数据库配置、默认回测参数、图表配色。
+
+#### Scenario: 数据库配置访问
+- **WHEN** backtest_core.py 需要数据库连接参数
+- **THEN** 模块从 config 导入 `DB_HOST`, `DB_PORT`, `DB_NAME`, `DB_USER`, `DB_PASSWORD`
+- **THEN** 模块使用这些常量构建连接字符串
+- **THEN** 模块不重复定义数据库配置
+
+#### Scenario: 默认参数访问
+- **WHEN** backtest_core.py 需要默认回测参数
+- **THEN** 模块从 config 导入 `DEFAULT_CASH`, `DEFAULT_COMMISSION`, `DEFAULT_WARMUP_DAYS`
+- **THEN** 模块使用这些常量作为函数默认值
+- **THEN** 模块不重复定义默认参数
+
+#### Scenario: 图表配色访问
+- **WHEN** backtest_core.py 需要设置图表配色
+- **THEN** 模块从 config 导入 `BULL_COLOR`, `BEAR_COLOR`
+- **THEN** 模块使用这些颜色设置 `plotting.BULL_COLOR` 和 `plotting.BEAR_COLOR`
+- **THEN** 模块不重复定义颜色配置
+
+#### Scenario: 配置文件内容
+- **WHEN** 查看 config.py 文件
+- **THEN** 文件包含数据库配置（DB_HOST, DB_PORT, DB_NAME, DB_USER, DB_PASSWORD）
+- **THEN** 文件包含默认回测参数（DEFAULT_CASH, DEFAULT_COMMISSION, DEFAULT_WARMUP_DAYS）
+- **THEN** 文件包含图表配色（BULL_COLOR, BEAR_COLOR）
+- **THEN** 所有配置使用明文常量（不使用环境变量）
+
+---
+
+### Requirement: 错误处理策略
+系统 SHALL 在批量回测失败时立即停止执行，不继续处理后续股票。
+
+#### Scenario: 数据加载失败
+- **WHEN** 系统加载第 i 个股票数据时失败（数据库错误、数据不存在）
+- **THEN** 系统捕获异常
+- **THEN** 系统输出错误信息："回测失败 [{code}]: {error}"
+- **THEN** 系统停止执行后续股票的回测
+- **THEN** 系统退出并返回非零状态码
+
+#### Scenario: 策略加载失败
+- **WHEN** 系统加载策略文件时失败（文件不存在、接口不完整）
+- **THEN** 系统捕获异常
+- **THEN** 系统输出错误信息："策略加载失败: {error}"
+- **THEN** 系统停止执行所有股票的回测
+- **THEN** 系统退出并返回非零状态码
+
+#### Scenario: 回测执行失败
+- **WHEN** 系统执行第 i 个股票回测时失败（策略逻辑错误）
+- **THEN** 系统捕获异常
+- **THEN** 系统输出错误信息和完整堆栈跟踪
+- **THEN** 系统停止执行后续股票的回测
+- **THEN** 系统退出并返回非零状态码
+
+#### Scenario: 图表生成失败
+- **WHEN** 系统生成第 i 个股票图表时失败
+- **THEN** 系统捕获异常
+- **THEN** 系统输出警告："图表生成失败 [{code}]: {error}，但回测已完成"
+- **THEN** 系统继续执行后续股票的回测
+- **THEN** 系统在返回的 `BacktestResult` 中设置 `error` 字段（如果设计支持）
+
+---
+
+### Requirement: 依赖管理
+系统 SHALL 在 pyproject.toml 中添加 tabulate 和 tqdm 依赖。
+
+#### Scenario: 添加 tabulate 依赖
+- **WHEN** 查看 pyproject.toml 文件
+- **THEN** 文件包含 `tabulate` 依赖
+- **THEN** 依赖版本 SHALL 为兼容当前 Python 版本的版本
+- **THEN** 系统可以导入 `import tabulate` 无错误
+
+#### Scenario: 添加 tqdm 依赖
+- **WHEN** 查看 pyproject.toml 文件
+- **THEN** 文件包含 `tqdm` 依赖
+- **THEN** 依赖版本 SHALL 为兼容当前 Python 版本的版本
+- **THEN** 系统可以导入 `from tqdm import tqdm` 无错误
+
+#### Scenario: 依赖安装
+- **WHEN** 用户运行 `uv sync` 或 `pip install -e .`
+- **THEN** 系统自动安装 tabulate 和 tqdm
+- **THEN** 系统显示依赖安装进度
+- **THEN** 系统完成安装后可以正常使用回测工具
+
+#### Scenario: 依赖缺失提示
+- **WHEN** 系统导入 tabulate 或 tqdm 时失败
+- **THEN** 系统输出友好错误信息："缺少依赖: {package_name}，请运行: uv add {package_name}"
+- **THEN** 系统退出并返回非零状态码

openspec/changes/archive/2026-01-28-refactor-backtest-separate-cli/tasks.md

@@ -0,0 +1,96 @@
+## 1. 依赖管理
+
+- [x] 1.1 运行 `uv add tabulate` 添加依赖
+- [x] 1.2 运行 `uv add tqdm` 添加依赖
+- [x] 1.3 运行 `uv sync` 同步依赖
+
+## 2. 配置管理模块
+
+- [x] 2.1 创建 config.py 文件
+- [x] 2.2 在 config.py 中定义数据库配置常量（DB_HOST, DB_PORT, DB_NAME, DB_USER, DB_PASSWORD）
+- [x] 2.3 在 config.py 中定义默认回测参数（DEFAULT_CASH, DEFAULT_COMMISSION, DEFAULT_WARMUP_DAYS）
+- [x] 2.4 在 config.py 中定义图表配色（BULL_COLOR, BEAR_COLOR）
+- [x] 2.5 测试 config.py 导入无错误
+
+## 3. 核心回测引擎
+
+- [x] 3.1 创建 backtest_core.py 文件
+- [x] 3.2 在 backtest_core.py 中导入必要模块和 config
+- [x] 3.3 定义 BacktestResult dataclass（包含所有回测指标字段）
+- [x] 3.4 迁移 load_data_from_db() 函数（使用 config 数据库配置）
+- [x] 3.5 迁移 load_strategy() 函数（保持原有逻辑）
+- [x] 3.6 迁移 apply_color_scheme() 函数（使用 config 配色）
+- [x] 3.7 实现 run_backtest() 函数（单股票回测）
+  - [x] 3.7.1 实现预热期日期计算逻辑
+  - [x] 3.7.2 实现数据加载和策略加载调用
+  - [x] 3.7.3 实现指标计算和数据截取
+  - [x] 3.7.4 实现 Backtest 执行
+  - [x] 3.7.5 实现图表生成（可选）
+  - [x] 3.7.6 实现 BacktestResult 对象构建和返回
+- [x] 3.8 实现 run_batch_backtest() 函数（批量回测，串行）
+  - [x] 3.8.1 实现循环遍历股票代码
+  - [x] 3.8.2 实现为每个股票调用 run_backtest()
+  - [x] 3.8.3 实现为每个股票生成独立 HTML 文件
+  - [x] 3.8.4 实现结果列表收集和返回
+  - [x] 3.8.5 实现 tqdm 进度条显示（批量时）
+- [x] 3.9 测试 run_backtest() 单股票回测
+- [x] 3.10 测试 run_batch_backtest() 多股票回测
+
+## 4. CLI 界面模块
+
+- [x] 4.1 创建 backtest_command.py 文件
+- [x] 4.2 在 backtest_command.py 中导入必要模块和 backtest_core
+- [x] 4.3 实现 parse_arguments() 函数
+  - [x] 4.3.1 定义 --codes 多值参数（nargs='+'）
+  - [x] 4.3.2 定义 --output-dir 目录参数
+  - [x] 4.3.3 保持原有参数（--start-date, --end-date, --strategy-file, --cash, --commission, --warmup-days）
+  - [x] 4.3.4 添加参数帮助文档和示例
+- [x] 4.4 实现 format_single_result() 函数（详细格式输出）
+  - [x] 4.4.1 实现每个指标单独一行的格式化
+  - [x] 4.4.2 保持原有 print_stats() 的输出格式
+- [x] 4.5 实现 format_batch_results() 函数（表格格式输出）
+  - [x] 4.5.1 实现使用 tabulate 生成表格
+  - [x] 4.5.2 定义表格列：股票代码、收益率%、胜率%、最大回撤%、交易次数、SQN
+  - [x] 4.5.3 实现表格数据填充（从 BacktestResult 对象提取）
+  - [x] 4.5.4 实现表格格式为 grid
+- [x] 4.6 实现 main() 函数
+  - [x] 4.6.1 调用 parse_arguments() 解析参数
+  - [x] 4.6.2 调用 run_batch_backtest() 执行批量回测
+  - [x] 4.6.3 根据结果数量调用 format_single_result() 或 format_batch_results()
+  - [x] 4.6.4 实现图表保存提示（指定 --output-dir 时）
+  - [x] 4.6.5 实现错误捕获和友好错误信息输出
+  - [x] 4.6.6 实现退出状态码设置（成功 0，失败非零）
+- [x] 4.7 添加 `if __name__ == "__main__": main()` 入口
+- [x] 4.8 测试单股票回测命令行调用 (`uv run python backtest_command.py`)
+- [x] 4.9 测试多股票回测命令行调用 (`uv run python backtest_command.py`)
+- [x] 4.10 测试错误处理（参数缺失、文件不存在等）
+
+## 5. 清理旧代码
+
+- [x] 5.1 确认新功能完整（单股票、多股票、图表输出）
+- [x] 5.2 确认错误处理正确（立即失败）
+- [x] 5.3 删除 backtest.py 文件
+- [x] 5.4 验证 git 状态（仅删除旧文件，无其他修改）
+
+## 6. 文档更新
+
+- [x] 6.1 更新 README.md（如果存在）
+  - [x] 6.1.1 说明新的命令行使用方式（`uv run python backtest_command.py`）
+  - [x] 6.1.2 说明参数变化（--code 改为 --codes）
+  - [x] 6.1.3 提供单股票和多股票示例
+  - [x] 6.1.4 说明 --output-dir 用法（多股票图表）
+- [x] 6.2 创建 note_refactor.md（可选，记录重构说明）
+  - [x] 6.2.1 说明文件结构变化
+  - [x] 6.2.2 说明接口变化
+  - [x] 6.2.3 提供迁移指南
+
+## 7. 集成测试
+
+- [x] 7.1 测试单个股票完整流程（000001.SZ）
+- [x] 7.2 测试多个股票完整流程（000001.SZ 600000.SH）
+- [x] 7.3 测试指定 --output-dir 生成图表
+- [x] 7.4 测试不指定 --output-dir（不生成图表）
+- [x] 7.5 测试错误情况（无效股票代码、不存在的策略文件等）
+- [x] 7.6 验证进度条显示（多股票时）
+- [x] 7.7 验证表格格式输出（多股票时）
+- [x] 7.8 验证详细格式输出（单股票时）

openspec/config.yaml

@@ -2,6 +2,6 @@ schema: spec-driven
 
 Example:
   context: |
-    使用 uv 工具进行 python 环境的管理和三方依赖的管理
-    严禁在主机环境直接运行 pip、pip3 安装依赖包，必须使用 uv 虚拟环境
+    使用 uv 工具进行 python 环境的管理和三方依赖的管理，运行python命令的时候使用uv run python xxx
+    严禁在主机环境直接运行 pip、pip3 安装依赖包，必须使用 uv add xxx命令安装
     项目面向中文开发者，文档输出、日志输出、agent 交流时都要使用中文

openspec/specs/batch-backtest/spec.md

@@ -0,0 +1,312 @@
+# batch-backtest Specification
+
+## Purpose
+TBD - created by archiving change refactor-backtest-separate-cli. Update Purpose after archive.
+## Requirements
+### Requirement: 多股票回测参数
+系统 SHALL 支持通过命令行参数传入多个股票代码进行批量回测。
+
+#### Scenario: 传入多个股票代码
+- **WHEN** 用户执行 `python backtest_command.py --codes 000001.SZ 600000.SH --start-date 2024-01-01 --end-date 2025-12-31 --strategy-file strategies/macd_strategy.py`
+- **THEN** 系统解析所有股票代码到列表 `['000001.SZ', '600000.SH']`
+- **THEN** 系统按顺序依次执行每个股票的回测
+- **THEN** 系统为每个股票生成独立的回测结果
+
+#### Scenario: 传入单个股票代码
+- **WHEN** 用户执行 `python backtest_command.py --codes 000001.SZ --start-date 2024-01-01 --end-date 2025-12-31 --strategy-file strategies/macd_strategy.py`
+- **THEN** 系统解析为单个股票代码列表 `['000001.SZ']`
+- **THEN** 系统执行单个股票回测
+- **THEN** 系统输出详细格式的回测结果
+
+#### Scenario: 缺少 --codes 参数
+- **WHEN** 用户未提供 `--codes` 参数
+- **THEN** 系统输出错误信息："错误: 需要以下参数: --codes"
+- **THEN** 系统退出并返回非零状态码
+
+---
+
+### Requirement: 批量回测执行
+系统 SHALL 串行执行多个股票的回测，每次加载一个股票的数据并执行回测。
+
+#### Scenario: 成功执行多个股票回测
+- **WHEN** 用户传入 N 个股票代码
+- **THEN** 系统循环 N 次，每次加载一个股票的数据
+- **THEN** 系统每次执行完整的回测流程（数据加载、指标计算、回测执行）
+- **THEN** 系统每次执行完成后生成 `BacktestResult` 对象
+- **THEN** 系统返回包含 N 个 `BacktestResult` 的列表
+
+#### Scenario: 每个股票独立预热期
+- **WHEN** 系统执行第 i 个股票的回测
+- **THEN** 系统使用 `start_date - warmup_days` 计算该股票的预热开始日期
+- **THEN** 系统独立加载该股票的预热期数据
+- **THEN** 不同股票的预热期互不影响
+
+#### Scenario: 第一个股票回测失败
+- **WHEN** 系统执行第一个股票回测时发生错误（数据库连接失败、策略加载失败等）
+- **THEN** 系统捕获异常并输出错误信息
+- **THEN** 系统停止执行后续股票的回测
+- **THEN** 系统退出并返回非零状态码（立即失败策略）
+
+#### Scenario: 中间股票回测失败
+- **WHEN** 系统执行第 i 个股票回测时发生错误
+- **THEN** 系统输出错误信息（包含股票代码）
+- **THEN** 系统停止执行后续股票的回测
+- **THEN** 系统退出并返回非零状态码
+
+#### Scenario: 资源管理
+- **WHEN** 系统完成第 i 个股票的回测
+- **THEN** 系统关闭该股票的数据库连接（`engine.dispose()`）
+- **THEN** 系统释放该股票的数据内存
+- **THEN** 系统开始加载第 i+1 个股票的数据
+
+---
+
+### Requirement: 批量回测进度显示
+系统 SHALL 使用 tqdm 显示批量回测的实时进度，提供用户反馈。
+
+#### Scenario: 显示进度条
+- **WHEN** 系统开始执行 N 个股票的批量回测
+- **THEN** 系统显示进度条格式：`回测进度: 25%|█████▌  | 1/4 [00:30<01:30,  12.5s/it]`
+- **THEN** 系统在完成每个股票回测后更新进度条
+- **THEN** 进度条显示当前进度（i/N）、已用时间、预计剩余时间
+- **THEN** 进度条在所有股票回测完成后消失
+
+#### Scenario: 单股票回测不显示进度条
+- **WHEN** 用户传入单个股票代码
+- **THEN** 系统不显示 tqdm 进度条
+- **THEN** 系统直接输出回测结果
+
+#### Scenario: 进度条描述文本
+- **WHEN** 系统显示批量回测进度
+- **THEN** 进度条描述 SHALL 为 "回测进度"（中文）
+- **THEN** 进度条显示已完成/总数（如 "1/4", "2/4"）
+
+---
+
+### Requirement: 批量回测结果展示
+系统 SHALL 使用 tabulate 将多个股票的回测结果格式化为表格，便于横向对比。
+
+#### Scenario: 表格化输出多股票结果
+- **WHEN** 用户传入多个股票代码且回测成功
+- **THEN** 系统使用 tabulate 生成表格
+- **THEN** 表格格式 SHALL 为 grid（带边框）
+- **THEN** 表格列 SHALL 包含：股票代码、收益率%、胜率%、最大回撤%、交易次数、SQN
+- **THEN** 系统在表格上方显示表头（中文列名）
+- **THEN** 数值保留 2 位小数（交易次数为整数）
+
+#### Scenario: 表格内容填充
+- **WHEN** 系统格式化第 i 个股票的结果
+- **THEN** 系统从 `BacktestResult` 对象提取字段
+- **THEN** "股票代码" 列填充 `result.code`
+- **THEN** "收益率%" 列填充 `result.return_pct`
+- **THEN** "胜率%" 列填充 `result.win_rate`
+- **THEN** "最大回撤%" 列填充 `result.max_drawdown`
+- **THEN** "交易次数" 列填充 `result.trades`
+- **THEN** "SQN" 列填充 `result.sqn`
+
+#### Scenario: 单股票回测不使用表格
+- **WHEN** 用户传入单个股票代码
+- **THEN** 系统不使用 tabulate 生成表格
+- **THEN** 系统使用详细格式输出（每个指标单独一行）
+- **THEN** 系统保持原有 `print_stats()` 的输出格式
+
+#### Scenario: 表格示例输出
+- **WHEN** 用户传入 2 个股票代码
+- **THEN** 系统输出格式 SHALL 为：
+  ```
+  +-------------+-----------+--------+------------+----------+-------+
+  | 股票代码    | 收益率%  | 胜率%  | 最大回撤%  | 交易次数 | SQN   |
+  +-------------+-----------+--------+------------+----------+-------+
+  | 000001.SZ   | 20.35    | 55.00  | -8.50      | 45       | 1.85  |
+  | 600000.SH   | 15.00    | 48.00  | -12.30     | 38       | 1.42  |
+  +-------------+-----------+--------+------------+----------+-------+
+  ```
+
+---
+
+### Requirement: 多股票图表输出
+系统 SHALL 为每个股票生成独立的 HTML 图表文件，文件名格式为 `{code}.html`。
+
+#### Scenario: 指定 --output-dir 参数
+- **WHEN** 用户传入 `--output-dir output/`
+- **THEN** 系统为每个股票生成 HTML 文件到 `output/{code}.html`
+- **THEN** 文件名 SHALL 为股票代码，如 `000001.SZ.html`, `600000.SH.html`
+- **THEN** 系统自动创建 `output/` 目录（`exist_ok=True`）
+- **THEN** 系统在完成后输出提示："图表已保存到目录: output/" 后列出所有文件
+
+#### Scenario: 未指定 --output-dir 参数
+- **WHEN** 用户未传入 `--output-dir` 参数
+- **THEN** 系统不为任何股票生成图表文件
+- **THEN** 系统仅输出控制台统计信息
+
+#### Scenario: 图表文件覆盖
+- **WHEN** 系统再次执行相同的批量回测
+- **THEN** 系统覆盖已存在的 HTML 文件
+- **THEN** 系统不提示文件已存在
+
+---
+
+### Requirement: 结构化回测结果
+系统 SHALL 返回标准化的 `BacktestResult` 对象，包含所有关键指标。
+
+#### Scenario: BacktestResult 对象创建
+- **WHEN** 系统完成单股票回测
+- **THEN** 系统从 `stats` 对象提取指标到 `BacktestResult`
+- **THEN** `BacktestResult.code` 设置为股票代码
+- **THEN** `BacktestResult.start_date` 设置为回测开始日期
+- **THEN** `BacktestResult.end_date` 设置为回测结束日期
+- **THEN** `BacktestResult.equity_final` 设置为最终权益
+- **THEN** `BacktestResult.equity_peak` 设置为峰值收益
+- **THEN** `BacktestResult.return_pct` 设置为总收益率
+- **THEN** `BacktestResult.buy_hold_return` 设置为买入持有收益率
+- **THEN** `BacktestResult.return_annual` 设置为年化收益率
+- **THEN** `BacktestResult.volatility_annual` 设置为年化波动率
+- **THEN** `BacktestResult.max_drawdown` 设置为最大回撤
+- **THEN** `BacktestResult.avg_drawdown` 设置为平均回撤
+- **THEN** `BacktestResult.max_drawdown_duration` 设置为最大回撤持续时长
+- **THEN** `BacktestResult.avg_drawdown_duration` 设置为平均回撤持续时长
+- **THEN** `BacktestResult.sortino_ratio` 设置为索提诺比率
+- **THEN** `BacktestResult.calmar_ratio` 设置为卡尔玛比率
+- **THEN** `BacktestResult.trades` 设置为交易次数
+- **THEN** `BacktestResult.win_rate` 设置为胜率
+- **THEN** `BacktestResult.sqn` 设置为系统质量数
+- **THEN** `BacktestResult.cash` 设置为初始资金
+- **THEN** `BacktestResult.commission` 设置为手续费率
+
+#### Scenario: BacktestResult 列表返回
+- **WHEN** 系统完成批量回测
+- **THEN** 系统返回 `List[BacktestResult]`
+- **THEN** 列表顺序 SHALL 与输入股票代码顺序一致
+- **THEN** 列表长度 SHALL 等于输入股票代码数量（成功时）
+
+#### Scenario: BacktestResult 数据类型
+- **WHEN** 系统创建 `BacktestResult` 对象
+- **THEN** 数值字段 SHALL 为 float 类型（除 `trades`, `max_drawdown_duration` 为 int）
+- **THEN** 日期字段 SHALL 为 str 类型（YYYY-MM-DD 格式）
+- **THEN** 系统支持 `result.to_dict()` 方法（dataclass 自动生成）
+
+---
+
+### Requirement: 可复用回测引擎接口
+系统 SHALL 提供标准化的函数接口，供其他模块调用回测功能。
+
+#### Scenario: run_backtest 函数调用
+- **WHEN** 其他模块调用 `run_backtest(code, start_date, end_date, strategy_file, cash, commission, warmup_days, output_file)`
+- **THEN** 函数接收股票代码、日期范围、策略文件、回测参数、输出文件路径
+- **THEN** 函数执行完整回测流程（数据加载、策略加载、指标计算、回测执行）
+- **THEN** 函数返回 `BacktestResult` 对象
+- **THEN** 函数不打印任何输出（纯函数）
+
+#### Scenario: run_batch_backtest 函数调用
+- **WHEN** 其他模块调用 `run_batch_backtest(codes, start_date, end_date, strategy_file, cash, commission, warmup_days, output_dir)`
+- **THEN** 函数接收股票代码列表、日期范围、策略文件、回测参数、输出目录
+- **THEN** 函数串行执行每个股票的回测
+- **THEN** 函数返回 `List[BacktestResult]`
+- **THEN** 函数显示 tqdm 进度条（批量时）
+
+#### Scenario: 函数参数默认值
+- **WHEN** 调用者不指定可选参数
+- **THEN** `cash` 默认为 100000
+- **THEN** `commission` 默认为 0.002
+- **THEN** `warmup_days` 默认为 365
+- **THEN** `output_file` 默认为 None（不生成图表）
+- **THEN** `output_dir` 默认为 None（不生成图表）
+
+#### Scenario: 函数异常抛出
+- **WHEN** `run_backtest` 或 `run_batch_backtest` 执行时发生错误
+- **THEN** 函数 SHALL 抛出异常（不捕获）
+- **THEN** 异常类型 SHALL 为 ValueError、TypeError 或原始异常
+- **THEN** 异常信息 SHALL 包含具体错误原因
+- **THEN** 调用者负责捕获和处理异常
+
+---
+
+### Requirement: 集中配置管理
+系统 SHALL 在 config.py 中集中管理数据库配置、默认回测参数、图表配色。
+
+#### Scenario: 数据库配置访问
+- **WHEN** backtest_core.py 需要数据库连接参数
+- **THEN** 模块从 config 导入 `DB_HOST`, `DB_PORT`, `DB_NAME`, `DB_USER`, `DB_PASSWORD`
+- **THEN** 模块使用这些常量构建连接字符串
+- **THEN** 模块不重复定义数据库配置
+
+#### Scenario: 默认参数访问
+- **WHEN** backtest_core.py 需要默认回测参数
+- **THEN** 模块从 config 导入 `DEFAULT_CASH`, `DEFAULT_COMMISSION`, `DEFAULT_WARMUP_DAYS`
+- **THEN** 模块使用这些常量作为函数默认值
+- **THEN** 模块不重复定义默认参数
+
+#### Scenario: 图表配色访问
+- **WHEN** backtest_core.py 需要设置图表配色
+- **THEN** 模块从 config 导入 `BULL_COLOR`, `BEAR_COLOR`
+- **THEN** 模块使用这些颜色设置 `plotting.BULL_COLOR` 和 `plotting.BEAR_COLOR`
+- **THEN** 模块不重复定义颜色配置
+
+#### Scenario: 配置文件内容
+- **WHEN** 查看 config.py 文件
+- **THEN** 文件包含数据库配置（DB_HOST, DB_PORT, DB_NAME, DB_USER, DB_PASSWORD）
+- **THEN** 文件包含默认回测参数（DEFAULT_CASH, DEFAULT_COMMISSION, DEFAULT_WARMUP_DAYS）
+- **THEN** 文件包含图表配色（BULL_COLOR, BEAR_COLOR）
+- **THEN** 所有配置使用明文常量（不使用环境变量）
+
+---
+
+### Requirement: 错误处理策略
+系统 SHALL 在批量回测失败时立即停止执行，不继续处理后续股票。
+
+#### Scenario: 数据加载失败
+- **WHEN** 系统加载第 i 个股票数据时失败（数据库错误、数据不存在）
+- **THEN** 系统捕获异常
+- **THEN** 系统输出错误信息："回测失败 [{code}]: {error}"
+- **THEN** 系统停止执行后续股票的回测
+- **THEN** 系统退出并返回非零状态码
+
+#### Scenario: 策略加载失败
+- **WHEN** 系统加载策略文件时失败（文件不存在、接口不完整）
+- **THEN** 系统捕获异常
+- **THEN** 系统输出错误信息："策略加载失败: {error}"
+- **THEN** 系统停止执行所有股票的回测
+- **THEN** 系统退出并返回非零状态码
+
+#### Scenario: 回测执行失败
+- **WHEN** 系统执行第 i 个股票回测时失败（策略逻辑错误）
+- **THEN** 系统捕获异常
+- **THEN** 系统输出错误信息和完整堆栈跟踪
+- **THEN** 系统停止执行后续股票的回测
+- **THEN** 系统退出并返回非零状态码
+
+#### Scenario: 图表生成失败
+- **WHEN** 系统生成第 i 个股票图表时失败
+- **THEN** 系统捕获异常
+- **THEN** 系统输出警告："图表生成失败 [{code}]: {error}，但回测已完成"
+- **THEN** 系统继续执行后续股票的回测
+- **THEN** 系统在返回的 `BacktestResult` 中设置 `error` 字段（如果设计支持）
+
+---
+
+### Requirement: 依赖管理
+系统 SHALL 在 pyproject.toml 中添加 tabulate 和 tqdm 依赖。
+
+#### Scenario: 添加 tabulate 依赖
+- **WHEN** 查看 pyproject.toml 文件
+- **THEN** 文件包含 `tabulate` 依赖
+- **THEN** 依赖版本 SHALL 为兼容当前 Python 版本的版本
+- **THEN** 系统可以导入 `import tabulate` 无错误
+
+#### Scenario: 添加 tqdm 依赖
+- **WHEN** 查看 pyproject.toml 文件
+- **THEN** 文件包含 `tqdm` 依赖
+- **THEN** 依赖版本 SHALL 为兼容当前 Python 版本的版本
+- **THEN** 系统可以导入 `from tqdm import tqdm` 无错误
+
+#### Scenario: 依赖安装
+- **WHEN** 用户运行 `uv sync` 或 `pip install -e .`
+- **THEN** 系统自动安装 tabulate 和 tqdm
+- **THEN** 系统显示依赖安装进度
+- **THEN** 系统完成安装后可以正常使用回测工具
+
+#### Scenario: 依赖缺失提示
+- **WHEN** 系统导入 tabulate 或 tqdm 时失败
+- **THEN** 系统输出友好错误信息："缺少依赖: {package_name}，请运行: uv add {package_name}"
+- **THEN** 系统退出并返回非零状态码
+

pyproject.toml

@@ -4,9 +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",
-    "duckdb>=1.4.3",
+    "baostock>=0.8.9",
+    "duckdb>=1.4.4",
     "jupyter~=1.1.1",
+    "jupyter-bokeh>=4.0.5",
     "matplotlib~=3.10.8",
     "mplfinance>=0.12.10b0",
     "pandas~=2.3.3",
@@ -15,4 +19,7 @@ dependencies = [
     "psycopg2-binary~=2.9.11",
     "sqlalchemy>=2.0.46",
     "ta-lib>=0.6.8",
+    "tabulate>=0.9.0",
+    "tqdm>=4.67.1",
+    "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)
+)

strategies/macd_strategy.py

@@ -2,28 +2,27 @@
 MACD 趋势跟踪策略
 
 策略逻辑:
-- 当 MACD 线上穿信号线时 (金叉)，且价格 > EMA200 时，买入
-- 当 MACD 线下穿信号线时 (死叉)，或价格 < EMA200 时，卖出
+- 当 MACD 线上穿信号线时 (金叉)，且价格 > EMA 时，买入
+- 当 MACD 线下穿信号线时 (死叉)，或价格 < EMA 时，卖出
 
 指标计算:
 - MACD(10, 20, 9): 快线 10 日，慢线 20 日，信号线 9 日
-- EMA200: 200 日指数移动平均线（趋势确认）
+- EMA: 200 日指数移动平均线（趋势确认）
 
 参数选择理由:
 - 快线 10: 比标准 12 更敏感，适应 A 股较高波动性
 - 慢线 20: 比标准 26 更快响应，同时保持趋势跟踪稳定性
 - 信号线 9: 保持标准，避免信号过于频繁
-- EMA200: 被广泛认可为牛熊分界线，避免逆势交易
+- EMA: 被广泛认可为牛熊分界线，避免逆势交易
 
 趋势过滤:
-- EMA200 上方: 确认为上升趋势，允许开多仓
-- EMA200 下方: 确认为下降趋势，不开多仓，强制平仓
+- EMA 上方: 确认为上升趋势，允许开多仓
+- EMA 下方: 确认为下降趋势，不开多仓，强制平仓
 
 Author: Sisyphus
 Date: 2025-01-27
 """
 
-import pandas as pd
 from backtesting import Strategy
 from backtesting.lib import crossover
 
@@ -32,32 +31,30 @@ def calculate_indicators(data):
     """
     计算策略所需的技术指标
 
-    使用 ta-lib 库计算 MACD 和 EMA200 指标
+    使用 ta-lib 库计算 MACD 和 EMA 指标
 
     参数:
         data: DataFrame, 包含 [Open, High, Low, Close, Volume, factor]
 
     返回:
         DataFrame, 添加了指标列:
-        - MACD_10_20_9: MACD 线 (DIF)
-        - MACDs_10_20_9: MACD 信号线 (DEA)
-        - MACDh_10_20_9: MACD 柱状图 (Histogram)
-        - EMA_200: 200 日指数移动平均线
+        - macd: MACD 线 (macd)
+        - signal: MACD 信号线 (DEA)
+        - hist: MACD 柱状图 (Histogram)
+        - ema: 日指数移动平均线
     """
     data = data.copy()
 
     # 计算 MACD 指标 (10, 20, 9)
     # talib.MACD 返回三个值: (macd, macdsignal, macdhist)
-    macd, macdsignal, macdhist = talib.MACD(
-        data["Close"], fastperiod=10, slowperiod=20, signalperiod=9
-    )
+    macd, macdsignal, macdhist = talib.MACD(data["Close"], fastperiod=10, slowperiod=20, signalperiod=9)
 
-    data["MACD_10_20_9"] = macd
-    data["MACDs_10_20_9"] = macdsignal
-    data["MACDh_10_20_9"] = macdhist
+    data["macd"] = macd
+    data["signal"] = macdsignal
+    data["hist"] = macdhist
 
-    # 计算 EMA200 趋势线
-    data["EMA_200"] = talib.EMA(data["Close"], timeperiod=200)
+    # 计算 EMA 趋势线
+    data["ema"] = talib.SMA(data["Close"], timeperiod=120)
 
     return data
 
@@ -76,7 +73,7 @@ class MacdTrendStrategy(Strategy):
     """
     MACD 趋势跟踪策略
 
-    结合 MACD 金叉/死叉信号和 EMA200 趋势过滤
+    结合 MACD 金叉/死叉信号和 EMA 趋势过滤
 
     参数:
         fast_period: MACD 快线周期 (默认: 10)
@@ -95,13 +92,13 @@ class MacdTrendStrategy(Strategy):
         注册指标到 backtesting 框架
         """
         # 注册 MACD 线
-        self.macd = self.I(lambda x: x, self.data.MACD_10_20_9)
+        self.macd = self.I(lambda x: x, self.data.macd)
 
         # 注册 MACD 信号线
-        self.macd_signal = self.I(lambda x: x, self.data.MACDs_10_20_9)
+        self.signal = self.I(lambda x: x, self.data.signal)
 
-        # 注册 EMA200 趋势线
-        self.ema200 = self.I(lambda x: x, self.data.EMA_200)
+        # 注册 EMA 趋势线
+        self.ema = self.I(lambda x: x, self.data.ema)
 
     def next(self):
         """
@@ -109,25 +106,18 @@ class MacdTrendStrategy(Strategy):
 
         买入条件:
         - MACD 金叉 (MACD 线上穿信号线)
-        - 价格 > EMA200 (确认上升趋势)
+        - 价格 > EMA (确认上升趋势)
 
         卖出条件:
         - MACD 死叉 (MACD 线下穿信号线)
-        - 或价格 < EMA200 (趋势转向，强制平仓)
+        - 或价格 < EMA (趋势转向，强制平仓)
         """
-        # 买入条件: MACD 金叉 AND 价格 > EMA200
-        if (
-            crossover(self.macd, self.macd_signal)
-            and self.data.Close[-1] > self.ema200[-1]
-        ):
-            self.position.close()  # 先平掉现有仓位
+        # 买入条件: MACD 金叉 AND 价格 > EMA
+        if crossover(self.macd, self.signal) and self.data.Close[-1] > self.ema[-1]:
             self.buy()  # 开多仓
 
-        # 卖出条件: MACD 死叉 OR 价格 < EMA200
-        elif (
-            crossover(self.macd_signal, self.macd)
-            or self.data.Close[-1] < self.ema200[-1]
-        ):
+        # 卖出条件: MACD 死叉 OR 价格 < EMA
+        elif self.position.size > 0 and (crossover(self.signal, self.macd) or self.data.Close[-1] < self.ema[-1]):
             self.position.close()  # 平掉多仓
 
 

strategies/sma_strategy.py

@@ -5,14 +5,13 @@ SMA 双均线交叉策略
 - 当短期均线上穿长期均线时 (金叉)，买入
 - 当短期均线下穿长期均线时 (死叉)，卖出
 
-指标计算:
+指标计算 (使用 ta-lib):
 - SMA10: 10 日简单移动平均线
 - SMA30: 30 日简单移动平均线
 - SMA60: 60 日简单移动平均线
 - SMA120: 120 日简单移动平均线
 """
 
-import pandas as pd
 from backtesting import Strategy
 from backtesting.lib import crossover
 
@@ -21,19 +20,25 @@ def calculate_indicators(data):
     """
     计算策略所需的技术指标
 
+    使用 ta-lib 库计算 SMA 指标
+
     参数:
         data: DataFrame, 包含 [Open, High, Low, Close, Volume, factor]
 
     返回:
-        DataFrame, 添加了指标列
+        DataFrame, 添加了指标列:
+        - sma10: 10 日简单移动平均线
+        - sma30: 30 日简单移动平均线
+        - sma60: 60 日简单移动平均线
+        - sma120: 120 日简单移动平均线
     """
     data = data.copy()
 
     # 计算不同周期的移动平均线
-    data["sma10"] = data["Close"].rolling(window=10).mean()
-    data["sma30"] = data["Close"].rolling(window=30).mean()
-    data["sma60"] = data["Close"].rolling(window=60).mean()
-    data["sma120"] = data["Close"].rolling(window=120).mean()
+    data["sma10"] = talib.SMA(data["Close"], timeperiod=10)
+    data["sma30"] = talib.SMA(data["Close"], timeperiod=30)
+    data["sma60"] = talib.SMA(data["Close"], timeperiod=60)
+    data["sma120"] = talib.SMA(data["Close"], timeperiod=120)
 
     return data
 
@@ -78,10 +83,12 @@ class SmaCross(Strategy):
         """
         # 金叉：短期均线上穿长期均线
         if crossover(self.data.sma10, self.data.sma30):
-            self.position.close()  # 先平掉现有仓位
             self.buy()  # 开多仓
 
         # 死叉：短期均线下穿长期均线
-        elif crossover(self.data.sma30, self.data.sma10):
-            self.position.close()  # 先平掉现有仓位
-            self.sell()  # 开空仓
+        elif self.position.size > 0 and crossover(self.data.sma30, self.data.sma10):
+            self.position.close()  # 开空仓
+
+
+# 导入 talib (必须在文件末尾，因为 calculate_indicators 函数中使用了 talib)
+import talib

uv.lock

@@ -2,6 +2,57 @@ version = 1
 revision = 3
 requires-python = ">=3.14"
 
+[[package]]
+name = "adata"
+version = "2.9.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "beautifulsoup4" },
+    { name = "pandas" },
+    { name = "py-mini-racer" },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0e/da/1eb2f05b14e4d41edcc017b9d6b428f30712d0d046f1b85cd54201b423a5/adata-2.9.5.tar.gz", hash = "sha256:b398fd885ee31baca41b8a141c586d3430ef0fec633f6088a830429437210cf6", size = 188823, upload-time = "2025-12-26T11:09:29.759Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1d/9f/51a65fb438febc0ab38f493a837c5aeb9135dfee2e2c1224920038bbc686/adata-2.9.5-py3-none-any.whl", hash = "sha256:f9dc5d276f8771cf5a5f11fb81c6d97a00d188e20cfcef67022f210c8b23cbf1", size = 229158, upload-time = "2025-12-26T11:09:23.007Z" },
+]
+
+[[package]]
+name = "akracer"
+version = "0.0.14"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1e/c6/f38feed5b961d73e1b4cb049fdb45338356e0f5b828b230c00d0e51f3137/akracer-0.0.14.tar.gz", hash = "sha256:e084c14bf6d9a02d5da375e3af1cba3d46f103aa1cf3a2010593b3e95bf1c29a", size = 10047643, upload-time = "2025-09-10T13:47:34.811Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/53/cb/1041355b14cd4b76ac082e8c676858f6eddb78f0ba37c59284adf36e5103/akracer-0.0.14-py3-none-any.whl", hash = "sha256:629eaccd0e1d18366804b797eb2692ed47bed0028f55b5a5af3cc277d521df04", size = 10076442, upload-time = "2025-09-10T13:47:29.061Z" },
+]
+
+[[package]]
+name = "akshare"
+version = "1.18.20"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "akracer", marker = "sys_platform == 'linux'" },
+    { name = "beautifulsoup4" },
+    { name = "curl-cffi" },
+    { name = "decorator" },
+    { name = "html5lib" },
+    { name = "jsonpath" },
+    { name = "lxml" },
+    { name = "mini-racer", marker = "sys_platform != 'linux'" },
+    { name = "openpyxl" },
+    { name = "pandas" },
+    { name = "py-mini-racer", marker = "sys_platform == 'linux'" },
+    { name = "requests" },
+    { name = "tabulate" },
+    { name = "tqdm" },
+    { name = "urllib3" },
+    { name = "xlrd" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/da/e0/48c0d7fc2527787b3179960454037dbe5b8d3409aa00eab23748a34317be/akshare-1.18.20.tar.gz", hash = "sha256:f3797d454fd2bc9e75f85e24abdd2af2c29989d4f89379b3385998bbf1464d16", size = 855384, upload-time = "2026-01-27T14:35:25.261Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/b4/2743787e5366eb281b966f8c3fcc85d6e3a8456cefbac27d30ca7baafedd/akshare-1.18.20-py3-none-any.whl", hash = "sha256:9ba6cb3a17ee4cf957cf81e01cec59d55962a3fd867ab669d151a213bb5a9fc3", size = 1074968, upload-time = "2026-01-27T14:35:23.937Z" },
+]
+
 [[package]]
 name = "anyio"
 version = "4.12.1"
@@ -129,6 +180,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b3/b6/cf57538b968c5caa60ee626ec8be1c31e420067d2a4cf710d81605356f8c/backtesting-0.6.5-py3-none-any.whl", hash = "sha256:8ac2fa500c8fd83dc783b72957b600653a72687986fe3ca86d6ef6c8b8d74363", size = 192105, upload-time = "2025-07-30T05:57:03.322Z" },
 ]
 
+[[package]]
+name = "baostock"
+version = "0.8.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pandas" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ee/d5/0fb2c61f392f89b1655490acb17a02861f1f1c38e973c9fc6aa049e54401/baostock-0.8.9.tar.gz", hash = "sha256:8169cdbed14fa442ace63c59549bef3f92b0c3dd1df9e5d9069f7bd04a76b0da", size = 21876, upload-time = "2024-05-31T02:56:54.546Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b2/37/bbabac2d33723d71bd8dbd5e819d9cbe5dc1e031b7dd12ed7de8fa040816/baostock-0.8.9-py3-none-any.whl", hash = "sha256:7a51fb30cd6b4325f5517198e350dc2fffaaab2923cd132b9f747b8b73ae7303", size = 45923, upload-time = "2024-05-31T02:56:53.161Z" },
+]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.14.3"
@@ -180,6 +243,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f6/a8/877f306720bc114c612579c5af36bcb359026b83d051226945499b306b1a/bokeh-3.8.2-py3-none-any.whl", hash = "sha256:5e2c0d84f75acb25d60efb9e4d2f434a791c4639b47d685534194c4e07bd0111", size = 7207131, upload-time = "2026-01-06T00:20:04.917Z" },
 ]
 
+[[package]]
+name = "bs4"
+version = "0.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "beautifulsoup4" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c9/aa/4acaf814ff901145da37332e05bb510452ebed97bc9602695059dd46ef39/bs4-0.0.2.tar.gz", hash = "sha256:a48685c58f50fe127722417bae83fe6badf500d54b55f7e39ffe43b798653925", size = 698, upload-time = "2024-01-17T18:15:47.371Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/51/bb/bf7aab772a159614954d84aa832c129624ba6c32faa559dfb200a534e50b/bs4-0.0.2-py2.py3-none-any.whl", hash = "sha256:abf8742c0805ef7f662dce4b51cca104cffe52b835238afc169142ab9b3fbccc", size = 1189, upload-time = "2024-01-17T18:15:48.613Z" },
+]
+
 [[package]]
 name = "build"
 version = "1.4.0"
@@ -312,6 +387,29 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831, upload-time = "2025-07-26T12:02:51.449Z" },
 ]
 
+[[package]]
+name = "curl-cffi"
+version = "0.14.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "cffi" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9b/c9/0067d9a25ed4592b022d4558157fcdb6e123516083700786d38091688767/curl_cffi-0.14.0.tar.gz", hash = "sha256:5ffbc82e59f05008ec08ea432f0e535418823cda44178ee518906a54f27a5f0f", size = 162633, upload-time = "2025-12-16T03:25:07.931Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/aa/f0/0f21e9688eaac85e705537b3a87a5588d0cefb2f09d83e83e0e8be93aa99/curl_cffi-0.14.0-cp39-abi3-macosx_14_0_arm64.whl", hash = "sha256:e35e89c6a69872f9749d6d5fda642ed4fc159619329e99d577d0104c9aad5893", size = 3087277, upload-time = "2025-12-16T03:24:49.607Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/a3/0419bd48fce5b145cb6a2344c6ac17efa588f5b0061f212c88e0723da026/curl_cffi-0.14.0-cp39-abi3-macosx_15_0_x86_64.whl", hash = "sha256:5945478cd28ad7dfb5c54473bcfb6743ee1d66554d57951fdf8fc0e7d8cf4e45", size = 5804650, upload-time = "2025-12-16T03:24:51.518Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/07/a238dd062b7841b8caa2fa8a359eb997147ff3161288f0dd46654d898b4d/curl_cffi-0.14.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c42e8fa3c667db9ccd2e696ee47adcd3cd5b0838d7282f3fc45f6c0ef3cfdfa7", size = 8231918, upload-time = "2025-12-16T03:24:52.862Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/d2/ce907c9b37b5caf76ac08db40cc4ce3d9f94c5500db68a195af3513eacbc/curl_cffi-0.14.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:060fe2c99c41d3cb7f894de318ddf4b0301b08dca70453d769bd4e74b36b8483", size = 8654624, upload-time = "2025-12-16T03:24:54.579Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/ae/6256995b18c75e6ef76b30753a5109e786813aa79088b27c8eabb1ef85c9/curl_cffi-0.14.0-cp39-abi3-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:b158c41a25388690dd0d40b5bc38d1e0f512135f17fdb8029868cbc1993d2e5b", size = 8010654, upload-time = "2025-12-16T03:24:56.507Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/10/ff64249e516b103cb762e0a9dca3ee0f04cf25e2a1d5d9838e0f1273d071/curl_cffi-0.14.0-cp39-abi3-manylinux_2_28_i686.whl", hash = "sha256:1439fbef3500fb723333c826adf0efb0e2e5065a703fb5eccce637a2250db34a", size = 7781969, upload-time = "2025-12-16T03:24:57.885Z" },
+    { url = "https://files.pythonhosted.org/packages/51/76/d6f7bb76c2d12811aa7ff16f5e17b678abdd1b357b9a8ac56310ceccabd5/curl_cffi-0.14.0-cp39-abi3-manylinux_2_34_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e7176f2c2d22b542e3cf261072a81deb018cfa7688930f95dddef215caddb469", size = 7969133, upload-time = "2025-12-16T03:24:59.261Z" },
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+    { url = "https://files.pythonhosted.org/packages/a2/77/78900e9b0833066d2274bda75cba426fdb4cef7fbf6a4f6a6ca447607bec/curl_cffi-0.14.0-cp39-abi3-win_amd64.whl", hash = "sha256:6e503f9a103f6ae7acfb3890c843b53ec030785a22ae7682a22cc43afb94123e", size = 1677416, upload-time = "2025-12-16T03:25:04.902Z" },
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+]
+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -354,17 +452,26 @@ wheels = [
 
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