第一版策略

strategy/bb_midline_backtest.py

@@ -0,0 +1,294 @@
+"""布林带均线策略回测（优化版）
+
+策略逻辑:
+  - 阳线 + 碰到布林带均线 → 开多（可选 1m 线过滤：先涨碰到才开）
+  - 持多: 碰到上轨 → 止盈（无下轨止损）
+  - 阴线 + 碰到布林带均线 → 平多开空（可选 1m：先跌碰到才开）
+  - 持空: 碰到下轨 → 止盈（无上轨止损）
+
+全仓模式 | 200U | 1% 权益/单 | 万五手续费 | 90%返佣次日8点到账 | 100x杠杆
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import List, Optional
+
+import numpy as np
+import pandas as pd
+
+from .indicators import bollinger
+
+
+@dataclass
+class BBMidlineConfig:
+    bb_period: int = 20
+    bb_std: float = 2.0
+    initial_capital: float = 200.0
+    margin_pct: float = 0.01
+    leverage: float = 100.0
+    cross_margin: bool = True
+    fee_rate: float = 0.0005
+    rebate_pct: float = 0.90
+    rebate_hour_utc: int = 0
+    slippage_pct: float = 0.0
+    fill_at_close: bool = True
+    # 是否用 1m 线判断「先涨碰到」/「先跌碰到」均线
+    use_1m_touch_filter: bool = True
+    # 主K线周期(分钟)，用于 1m 触及方向时的桶对齐，5/15/30
+    kline_step_min: int = 5
+
+
+@dataclass
+class BBTrade:
+    side: str
+    entry_price: float
+    exit_price: float
+    entry_time: object
+    exit_time: object
+    margin: float
+    leverage: float
+    qty: float
+    gross_pnl: float
+    fee: float
+    net_pnl: float
+    exit_reason: str
+
+
+@dataclass
+class BBMidlineResult:
+    equity_curve: pd.DataFrame
+    trades: List[BBTrade]
+    daily_stats: pd.DataFrame
+    total_fee: float
+    total_rebate: float
+    config: BBMidlineConfig
+
+
+def run_bb_midline_backtest(
+    df: pd.DataFrame,
+    cfg: BBMidlineConfig,
+    df_1m: Optional[pd.DataFrame] = None,
+    arr_touch_dir_override: Optional[np.ndarray] = None,
+) -> BBMidlineResult:
+    close = df["close"].astype(float)
+    high = df["high"].astype(float)
+    low = df["low"].astype(float)
+    open_ = df["open"].astype(float)
+    n = len(df)
+
+    bb_mid, bb_upper, bb_lower, _ = bollinger(close, cfg.bb_period, cfg.bb_std)
+    arr_mid = bb_mid.values
+
+    # 1m 触及方向：1=先涨碰到, -1=先跌碰到, 0=未碰到
+    arr_touch_dir = None
+    if arr_touch_dir_override is not None:
+        arr_touch_dir = np.asarray(arr_touch_dir_override, dtype=np.int32)
+        if len(arr_touch_dir) != n:
+            raise ValueError(f"arr_touch_dir_override 长度不匹配: {len(arr_touch_dir)} != {n}")
+    elif cfg.use_1m_touch_filter and df_1m is not None and len(df_1m) > 0:
+        from .data_loader import get_1m_touch_direction
+        arr_touch_dir = get_1m_touch_direction(df, df_1m, arr_mid, kline_step_min=cfg.kline_step_min)
+
+    arr_close = close.values
+    arr_high = high.values
+    arr_low = low.values
+    arr_open = open_.values
+    arr_upper = bb_upper.values
+    arr_lower = bb_lower.values
+    ts_index = df.index
+
+    balance = cfg.initial_capital
+    position = 0
+    entry_price = 0.0
+    entry_time = None
+    entry_margin = 0.0
+    entry_qty = 0.0
+
+    trades: List[BBTrade] = []
+    total_fee = 0.0
+    total_rebate = 0.0
+
+    day_pnl = 0.0
+    current_day = None
+    today_fees = 0.0
+    pending_rebate = 0.0
+    rebate_applied_today = False
+
+    out_equity = np.full(n, np.nan)
+    out_balance = np.full(n, np.nan)
+    out_position = np.zeros(n)
+
+    def unrealised(price):
+        if position == 0:
+            return 0.0
+        if position == 1:
+            return entry_qty * (price - entry_price)
+        return entry_qty * (entry_price - price)
+
+    def close_position(exit_price, exit_idx, reason: str):
+        nonlocal balance, position, entry_price, entry_time, entry_margin, entry_qty
+        nonlocal total_fee, total_rebate, day_pnl, today_fees
+
+        if position == 0:
+            return
+
+        if position == 1:
+            exit_price = exit_price * (1 - cfg.slippage_pct)
+        else:
+            exit_price = exit_price * (1 + cfg.slippage_pct)
+
+        if position == 1:
+            gross = entry_qty * (exit_price - entry_price)
+        else:
+            gross = entry_qty * (entry_price - exit_price)
+
+        exit_notional = entry_qty * exit_price
+        fee = exit_notional * cfg.fee_rate
+        net = gross - fee
+
+        trades.append(BBTrade(
+            side="long" if position == 1 else "short",
+            entry_price=entry_price,
+            exit_price=exit_price,
+            entry_time=entry_time,
+            exit_time=ts_index[exit_idx],
+            margin=entry_margin,
+            leverage=cfg.leverage,
+            qty=entry_qty,
+            gross_pnl=gross,
+            fee=fee,
+            net_pnl=net,
+            exit_reason=reason,
+        ))
+
+        balance += net
+        total_fee += fee
+        today_fees += fee
+        day_pnl += net
+        position = 0
+        entry_price = 0.0
+        entry_time = None
+        entry_margin = 0.0
+        entry_qty = 0.0
+
+    def open_position(side, price, idx):
+        nonlocal position, entry_price, entry_time, entry_margin, entry_qty
+        nonlocal balance, total_fee, day_pnl, today_fees
+
+        if side == "long":
+            price = price * (1 + cfg.slippage_pct)
+        else:
+            price = price * (1 - cfg.slippage_pct)
+
+        equity = balance + unrealised(price) if position != 0 else balance
+        margin = equity * cfg.margin_pct
+        margin = min(margin, balance * 0.95)
+        if margin <= 0:
+            return False
+
+        notional = margin * cfg.leverage
+        qty = notional / price
+        fee = notional * cfg.fee_rate
+
+        balance -= fee
+        total_fee += fee
+        today_fees += fee
+        day_pnl -= fee
+
+        position = 1 if side == "long" else -1
+        entry_price = price
+        entry_time = ts_index[idx]
+        entry_margin = margin
+        entry_qty = qty
+        return True
+
+    for i in range(n):
+        bar_day = ts_index[i].date() if hasattr(ts_index[i], 'date') else None
+        bar_hour = ts_index[i].hour if hasattr(ts_index[i], 'hour') else 0
+
+        if bar_day is not None and bar_day != current_day:
+            pending_rebate += today_fees * cfg.rebate_pct
+            today_fees = 0.0
+            rebate_applied_today = False
+            day_pnl = 0.0
+            current_day = bar_day
+
+        if cfg.rebate_pct > 0 and not rebate_applied_today and bar_hour >= cfg.rebate_hour_utc and pending_rebate > 0:
+            balance += pending_rebate
+            total_rebate += pending_rebate
+            pending_rebate = 0.0
+            rebate_applied_today = True
+
+        if np.isnan(arr_upper[i]) or np.isnan(arr_lower[i]) or np.isnan(arr_mid[i]):
+            out_equity[i] = balance + unrealised(arr_close[i])
+            out_balance[i] = balance
+            out_position[i] = position
+            continue
+
+        fill_price = arr_close[i] if cfg.fill_at_close else None
+        bullish = arr_close[i] > arr_open[i]
+        bearish = arr_close[i] < arr_open[i]
+        # 碰到均线：K 线贯穿或触及 mid
+        touched_mid = arr_low[i] <= arr_mid[i] <= arr_high[i]
+        touched_upper = arr_high[i] >= arr_upper[i]
+        touched_lower = arr_low[i] <= arr_lower[i]
+
+        exec_upper = fill_price if fill_price is not None else arr_upper[i]
+        exec_lower = fill_price if fill_price is not None else arr_lower[i]
+
+        # 1m 过滤：开多需先涨碰到，开空需先跌碰到
+        touch_up_ok = True if arr_touch_dir is None else (arr_touch_dir[i] == 1)
+        touch_down_ok = True if arr_touch_dir is None else (arr_touch_dir[i] == -1)
+
+        # 单根 K 线只允许一次操作
+        if position == 1 and touched_upper:
+            # 持多止盈
+            close_position(exec_upper, i, "tp_upper")
+        elif position == -1 and touched_lower:
+            # 持空止盈
+            close_position(exec_lower, i, "tp_lower")
+        elif position == 1 and bearish and touched_mid and touch_down_ok:
+            # 阴线触中轨: 平多并反手开空
+            close_position(arr_close[i], i, "flip_to_short")
+            if balance > 0:
+                open_position("short", arr_close[i], i)
+        elif position == -1 and bullish and touched_mid and touch_up_ok:
+            # 阳线触中轨: 平空并反手开多
+            close_position(arr_close[i], i, "flip_to_long")
+            if balance > 0:
+                open_position("long", arr_close[i], i)
+        elif position == 0 and bullish and touched_mid and touch_up_ok:
+            # 空仓开多
+            open_position("long", arr_close[i], i)
+        elif position == 0 and bearish and touched_mid and touch_down_ok:
+            # 空仓开空
+            open_position("short", arr_close[i], i)
+
+        out_equity[i] = balance + unrealised(arr_close[i])
+        out_balance[i] = balance
+        out_position[i] = position
+
+    if position != 0:
+        close_position(arr_close[n - 1], n - 1, "end")
+        out_equity[n - 1] = balance
+        out_balance[n - 1] = balance
+        out_position[n - 1] = 0
+
+    eq_df = pd.DataFrame({
+        "equity": out_equity,
+        "balance": out_balance,
+        "price": arr_close,
+        "position": out_position,
+    }, index=ts_index)
+
+    daily_eq = eq_df["equity"].resample("1D").last().dropna().to_frame("equity")
+    daily_eq["pnl"] = daily_eq["equity"].diff().fillna(0.0)
+
+    return BBMidlineResult(
+        equity_curve=eq_df,
+        trades=trades,
+        daily_stats=daily_eq,
+        total_fee=total_fee,
+        total_rebate=total_rebate,
+        config=cfg,
+    )

strategy/data_loader.py

@@ -1,14 +1,16 @@
 """
 数据加载模块 - 从 SQLite 加载多周期K线数据为 DataFrame
 """
+import numpy as np
 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
+# 周期 -> 表名 (bitmart / binance)
 PERIOD_MAP = {
+    '1s': 'bitmart_eth_1s',
     '1m': 'bitmart_eth_1m',
     '3m': 'bitmart_eth_3m',
     '5m': 'bitmart_eth_5m',
@@ -17,17 +19,29 @@ PERIOD_MAP = {
     '1h': 'bitmart_eth_1h',
 }
 
+BINANCE_PERIOD_MAP = {
+    '1s': 'binance_eth_1s',
+    '1m': 'binance_eth_1m',
+    '3m': 'binance_eth_3m',
+    '5m': 'binance_eth_5m',
+    '15m': 'binance_eth_15m',
+    '30m': 'binance_eth_30m',
+    '1h': 'binance_eth_1h',
+}
 
-def load_klines(period: str, start_date: str, end_date: str, tz: str | None = None) -> pd.DataFrame:
+
+def load_klines(period: str, start_date: str, end_date: str, tz: str | None = None,
+                source: str = "bitmart") -> 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 period: '1s','1m','3m','5m','15m','30m','1h'
     :param start_date: 'YYYY-MM-DD'
     :param end_date: 'YYYY-MM-DD' (不包含该日)
     :param tz: 日期解释的时区，如 'Asia/Shanghai' 表示按北京时间；None 则用本地时区
     :return: DataFrame with columns: datetime, open, high, low, close
     """
-    table = PERIOD_MAP.get(period)
+    period_map = BINANCE_PERIOD_MAP if source == "binance" else PERIOD_MAP
+    table = period_map.get(period)
     if not table:
         raise ValueError(f"不支持的周期: {period}, 可选: {list(PERIOD_MAP.keys())}")
 
@@ -56,18 +70,55 @@ def load_klines(period: str, start_date: str, end_date: str, tz: str | None = No
     return df
 
 
-def load_multi_period(periods: list, start_date: str, end_date: str) -> dict:
+def load_multi_period(periods: list, start_date: str, end_date: str, source: str = "bitmart") -> 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)
+        result[p] = load_klines(p, start_date, end_date, source=source)
         print(f"  加载 {p}: {len(result[p])} 条 ({start_date} ~ {end_date})")
     return result
 
 
+def get_1m_touch_direction(df_5m: pd.DataFrame, df_1m: pd.DataFrame,
+                           arr_mid: np.ndarray, kline_step_min: int = 5) -> np.ndarray:
+    """
+    根据 1 分钟线判断每根 5m K 线「先涨碰到均线」还是「先跌碰到均线」。
+    返回: 1=先涨碰到(可开多), -1=先跌碰到(可开空), 0=未碰到或无法判断
+    """
+    df_1m = df_1m.copy()
+    df_1m["_bucket"] = df_1m.index.floor(f"{kline_step_min}min")
+
+    # 5m 索引与 mid 对齐
+    mid_sr = pd.Series(arr_mid, index=df_5m.index)
+
+    touch_map: dict[pd.Timestamp, int] = {}
+    for bucket, grp in df_1m.groupby("_bucket", sort=True):
+        mid = mid_sr.get(bucket, np.nan)
+        if pd.isna(mid):
+            touch_map[bucket] = 0
+            continue
+
+        o = grp["open"].to_numpy(dtype=float)
+        h = grp["high"].to_numpy(dtype=float)
+        l_ = grp["low"].to_numpy(dtype=float)
+
+        touch = 0
+        for j in range(len(grp)):
+            if l_[j] <= mid <= h[j]:
+                touch = 1 if o[j] < mid else -1
+                break
+        touch_map[bucket] = touch
+
+    # 对齐到主周期 index
+    out = np.zeros(len(df_5m), dtype=np.int32)
+    for i, t in enumerate(df_5m.index):
+        out[i] = touch_map.get(t, 0)
+    return out
+
+
 if __name__ == '__main__':
     data = load_multi_period(['5m', '15m', '1h'], '2020-01-01', '2024-01-01')
     for k, v in data.items():

strategy/results/bb_midline_15m_param_sweep.csv

@@ -0,0 +1,85 @@
+period,std,period_step,std_step,final_eq,ret_pct,n_trades,win_rate,sharpe,dd
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