第一版策略

strategy/data_loader.py

@@ -0,0 +1,69 @@
+"""
+数据加载模块 - 从 SQLite 加载多周期K线数据为 DataFrame
+"""
+import pandas as pd
+from peewee import SqliteDatabase
+from pathlib import Path
+
+DB_PATH = Path(__file__).parent.parent / 'models' / 'database.db'
+
+# 周期 -> 表名
+PERIOD_MAP = {
+    '1m': 'bitmart_eth_1m',
+    '3m': 'bitmart_eth_3m',
+    '5m': 'bitmart_eth_5m',
+    '15m': 'bitmart_eth_15m',
+    '30m': 'bitmart_eth_30m',
+    '1h': 'bitmart_eth_1h',
+}
+
+
+def load_klines(period: str, start_date: str, end_date: str) -> pd.DataFrame:
+    """
+    加载指定周期、指定日期范围的K线数据
+    :param period: '1m','3m','5m','15m','30m','1h'
+    :param start_date: 'YYYY-MM-DD'
+    :param end_date: 'YYYY-MM-DD' (不包含该日)
+    :return: DataFrame with columns: datetime, open, high, low, close
+    """
+    table = PERIOD_MAP.get(period)
+    if not table:
+        raise ValueError(f"不支持的周期: {period}, 可选: {list(PERIOD_MAP.keys())}")
+
+    start_ts = int(pd.Timestamp(start_date).timestamp() * 1000)
+    end_ts = int(pd.Timestamp(end_date).timestamp() * 1000)
+
+    db = SqliteDatabase(str(DB_PATH))
+    db.connect()
+    cursor = db.execute_sql(
+        f'SELECT id, open, high, low, close FROM [{table}] '
+        f'WHERE id >= ? AND id < ? ORDER BY id',
+        (start_ts, end_ts)
+    )
+    rows = cursor.fetchall()
+    db.close()
+
+    df = pd.DataFrame(rows, columns=['timestamp_ms', 'open', 'high', 'low', 'close'])
+    df['datetime'] = pd.to_datetime(df['timestamp_ms'], unit='ms')
+    df.set_index('datetime', inplace=True)
+    df.drop(columns=['timestamp_ms'], inplace=True)
+    df = df.astype(float)
+    return df
+
+
+def load_multi_period(periods: list, start_date: str, end_date: str) -> dict:
+    """
+    加载多个周期的数据
+    :return: {period: DataFrame}
+    """
+    result = {}
+    for p in periods:
+        result[p] = load_klines(p, start_date, end_date)
+        print(f"  加载 {p}: {len(result[p])} 条 ({start_date} ~ {end_date})")
+    return result
+
+
+if __name__ == '__main__':
+    data = load_multi_period(['5m', '15m', '1h'], '2020-01-01', '2024-01-01')
+    for k, v in data.items():
+        print(f"{k}: {v.shape}, {v.index[0]} ~ {v.index[-1]}")