chore(config): 添加环境变量示例及.gitignore更新

- 新增 .env.example，包含 Tushare API、钉钉机器人和PostgreSQL数据库配置模板
- 更新.gitignore，忽略本地配置文件如 .env.local 和 config_local.py
- 添加对报表文件命名规则的支持，保留示例文件不忽略
- 删除废弃的 chart.py 及相关图表模块代码
- 新增 config/settings.py，实现从环境变量读取配置的统一接口
- 设置数据目录及缓存目录，确保目录存在，提高配置管理规范性

test.py

@@ -0,0 +1,100 @@
+import pandas as pd  
+import numpy as np  
+import vectorbt as vbt  
+from numba import njit  
+import vectorbt as vbt
+import pandas as pd
+import numpy as np
+
+import pandas as pd
+from loguru import logger
+from chart import resample_data, QuantChart
+from db_config import DatabaseManager, DatabaseConfig
+from vectorbt.base.reshape_fns import to_2d_array  
+
+
+def get_kline(code: str) -> list:
+    """
+    获取所有指数代码
+    :return:
+    """
+    db_config = DatabaseConfig()
+    logger.info(f"数据库连接: {db_config.connection_string}")
+
+    db_manager = DatabaseManager(db_config)
+    sql = f"SELECT date as time, open, high, low, close, volume FROM public.index_kline where code='{code}' order by date;"
+    res = db_manager.execute_query(sql)
+    data_list = [dict(item) for item in res]
+    df = pd.DataFrame(data_list)
+    df["time"] = pd.to_datetime(df["time"])
+    num_cols = ["open", "high", "low", "close", "volume"]
+    for col in num_cols:
+        if col in df.columns:
+            df[col] = pd.to_numeric(df[col], errors="coerce").astype(float)
+    return df
+
+symbol = "399998"
+timeframe = "1D"
+
+df = get_kline(code=symbol)
+df = resample_data(df, timeframe)
+df.rename(columns={'time': 'date'}, inplace=True)
+print(df.head())
+if 'date' in df.columns:  
+    df = df.set_index('date')  
+price = df['close']  
+  
+# 2. 计算90天滚动波动率(年化)  
+returns = price.pct_change()  
+volatility_90d = returns.rolling(window=90, min_periods=90).std() * np.sqrt(365)  
+  
+# 3. 计算波动率倒数作为权重  
+inv_vol = 1 / volatility_90d  
+# 标准化权重(可选,使其更易解释)  
+inv_vol_normalized = inv_vol / inv_vol.rolling(window=252).mean()  
+  
+# 4. 创建每周重新平衡的信号  
+# 获取每周最后一个交易日  
+weekly_rebalance = pd.Series(False, index=price.index)  
+weekly_last_days = price.resample('W').last().index  
+for date in weekly_last_days:  
+    # 找到最接近的交易日  
+    idx = price.index.get_indexer([date], method='ffill')[0]  
+    if idx >= 0:  
+        weekly_rebalance.iloc[idx] = True  
+  
+# 5. 定义订单函数  
+@njit  
+def order_func_nb(c, inv_vol_arr, rebalance_arr):  
+    # 获取当前的波动率倒数权重  
+    inv_vol_now = vbt.nb.flex_select_auto_nb(inv_vol_arr, c.i, c.col, False)  
+    rebalance_now = vbt.nb.flex_select_auto_nb(rebalance_arr, c.i, c.col, False)  
+      
+    # 只在重新平衡日调整仓位  
+    if not rebalance_now or np.isnan(inv_vol_now):  
+        return vbt.nb.order_nothing_nb()  
+      
+    # 目标仓位 = 总价值 * 波动率倒数权重  
+    # 这里使用 TargetPercent 类型,权重越高仓位越大  
+    target_percent = min(inv_vol_now, 1.0)  # 限制最大100%仓位  
+      
+    return vbt.nb.order_nb(  
+        size=target_percent,  
+        size_type=vbt.SizeType.TargetPercent,  
+        direction=vbt.Direction.LongOnly  
+    )  
+  
+# 6. 运行回测  
+pf = vbt.Portfolio.from_order_func(  
+    price,  
+    order_func_nb,  
+    to_2d_array(inv_vol_normalized),  
+    to_2d_array(weekly_rebalance),  
+    init_cash=100,  
+    freq='1D'  
+)  
+  
+# 7. 查看结果  
+print(pf.stats())  
+print(f"\n波动率倒数策略 vs 买入持有:")  
+print(f"总收益率: {pf.total_return():.2%}")