hahaa

adaptive_third_strategy/backtest.py

@@ -0,0 +1,464 @@
+# -*- coding: utf-8 -*-
+"""
+自适应三分位趋势策略 - 回测引擎
+使用 Bitmart 方式获取数据（API 或 CSV），含硬止损、分批止盈、移动止损、时间止损
+"""
+
+import os
+import sys
+import csv
+import datetime
+from typing import List, Dict, Optional, Tuple
+
+# 使用 UTC 时区，避免 utcfromtimestamp 弃用警告
+def _utc_dt(ts):
+    if hasattr(datetime, "timezone") and hasattr(datetime.timezone, "utc"):
+        return datetime.datetime.fromtimestamp(ts, tz=datetime.timezone.utc)
+    return datetime.datetime.utcfromtimestamp(ts)
+
+try:
+    from loguru import logger
+except ImportError:
+    import logging
+    logger = logging.getLogger(__name__)
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if ROOT_DIR not in sys.path:
+    sys.path.insert(0, ROOT_DIR)
+
+from adaptive_third_strategy.config import (
+    STEP_5M,
+    STEP_15M,
+    STEP_60M,
+    ATR_PERIOD,
+    EMA_SHORT,
+    EMA_MID_FAST,
+    EMA_MID_SLOW,
+    EMA_LONG_FAST,
+    EMA_LONG_SLOW,
+    STOP_LOSS_ATR_MULT,
+    TIME_STOP_BARS,
+    TRAIL_START_ATR,
+    TRAIL_ATR_MULT,
+    TP1_ATR,
+    TP2_ATR,
+    TP3_ATR,
+    TP1_RATIO,
+    TP2_RATIO,
+    TP3_RATIO,
+    MIN_BARS_SINCE_ENTRY,
+    SAME_KLINE_NO_REVERSE,
+    REVERSE_BREAK_MULT,
+    REVERSE_LOSS_ATR,
+    MAX_POSITION_PERCENT,
+    BASE_POSITION_PERCENT,
+    CONTRACT_SIZE,
+    FEE_RATE,
+    FEE_FIXED,
+    FEE_FIXED_BACKTEST,
+    MIN_BARS_BETWEEN_TRADES,
+    SLIPPAGE_POINTS,
+)
+from adaptive_third_strategy.indicators import get_ema_atr_from_klines, align_higher_tf_ema
+from adaptive_third_strategy.strategy_core import (
+    check_trigger,
+    get_body_size,
+    build_volume_ma,
+)
+from adaptive_third_strategy.data_fetcher import (
+    fetch_multi_timeframe,
+    load_klines_csv,
+    save_klines_csv,
+)
+
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+def ensure_data(
+    start_time: int,
+    end_time: int,
+    data_dir: str,
+    use_api: bool = True,
+    api_key: str = "",
+    secret_key: str = "",
+) -> Tuple[List[Dict], List[Dict], List[Dict]]:
+    """
+    确保有 5/15/60 分钟数据。若 data_dir 下已有 CSV 则加载，否则用 API 拉取并保存。
+    use_api=False 时仅从 CSV 加载（需事先抓取）。
+    """
+    data_dir = data_dir or os.path.join(SCRIPT_DIR, "data")
+    os.makedirs(data_dir, exist_ok=True)
+    paths = {
+        5: os.path.join(data_dir, "kline_5m.csv"),
+        15: os.path.join(data_dir, "kline_15m.csv"),
+        60: os.path.join(data_dir, "kline_60m.csv"),
+    }
+    k5 = load_klines_csv(paths[5])
+    k15 = load_klines_csv(paths[15])
+    k60 = load_klines_csv(paths[60])
+    need_fetch = not k5 or not k15 or not k60
+    if need_fetch and use_api:
+        from adaptive_third_strategy.data_fetcher import DEFAULT_API_KEY, DEFAULT_SECRET_KEY, DEFAULT_MEMO
+        key = api_key or DEFAULT_API_KEY
+        sec = secret_key or DEFAULT_SECRET_KEY
+        data = fetch_multi_timeframe(start_time, end_time, [5, 15, 60], key, sec, DEFAULT_MEMO)
+        k5, k15, k60 = data[5], data[15], data[60]
+        for step, kl in [(5, k5), (15, k15), (60, k60)]:
+            save_klines_csv(kl, paths[step])
+    # 按时间范围过滤（end_time 若为次日 0 点则用 < 以只含 end 日及之前）
+    k5 = [x for x in k5 if start_time <= x["id"] < end_time]
+    k15 = [x for x in k15 if start_time <= x["id"] < end_time]
+    k60 = [x for x in k60 if start_time <= x["id"] < end_time]
+    k5.sort(key=lambda x: x["id"])
+    k15.sort(key=lambda x: x["id"])
+    k60.sort(key=lambda x: x["id"])
+    return k5, k15, k60
+
+
+def run_backtest(
+    klines_5m: List[Dict],
+    klines_15m: List[Dict],
+    klines_60m: List[Dict],
+    initial_capital: float = 10000.0,
+    use_stop_loss: bool = True,
+    use_take_profit: bool = True,
+    use_trailing_stop: bool = True,
+    use_time_stop: bool = True,
+    fixed_margin_usdt: Optional[float] = None,
+    leverage: Optional[float] = None,
+    deduct_fee: bool = True,
+) -> Tuple[List[Dict], Dict, List[Dict]]:
+    """
+    回测主循环。
+    - 若指定 fixed_margin_usdt 与 leverage，则每笔开仓名义价值 = fixed_margin_usdt * leverage（如 50U 一百倍 = 5000U）。
+    - deduct_fee=False 时不扣手续费，总盈利 = 所有 money_pnl 之和。
+    """
+    if len(klines_5m) < ATR_PERIOD + 50:
+        logger.warning("5分钟K线数量不足")
+        return [], {}, klines_5m
+
+    ema_5m, atr_5m = get_ema_atr_from_klines(klines_5m, EMA_SHORT, ATR_PERIOD)
+    ema_15m_align = align_higher_tf_ema(klines_5m, klines_15m, EMA_MID_FAST, EMA_MID_SLOW)
+    ema_60m_align = align_higher_tf_ema(klines_5m, klines_60m, EMA_LONG_FAST, EMA_LONG_SLOW)
+    volume_ma = build_volume_ma(klines_5m)
+
+    trades: List[Dict] = []
+    position: Optional[Dict] = None
+    last_trade_bar_idx: Optional[int] = None
+    last_close_bar_idx: Optional[int] = None
+    equity_curve: List[float] = [initial_capital]
+    capital = initial_capital
+    # 每笔固定名义价值（USDT）：50U 一百倍 = 5000
+    fixed_notional = (fixed_margin_usdt * leverage) if (fixed_margin_usdt is not None and leverage is not None) else None
+    use_fee = deduct_fee
+    fee_fixed = 0 if not use_fee else (FEE_FIXED_BACKTEST if FEE_FIXED_BACKTEST is not None else FEE_FIXED)
+
+    def _size_usdt(cap: float) -> float:
+        if fixed_notional is not None:
+            return fixed_notional
+        return min(cap * MAX_POSITION_PERCENT, cap * BASE_POSITION_PERCENT)
+
+    def _fee(sz: float) -> float:
+        return 0 if not use_fee else (fee_fixed + sz * FEE_RATE * 2)
+
+    for idx in range(ATR_PERIOD, len(klines_5m)):
+        just_reversed = False
+        curr = klines_5m[idx]
+        bar_id = curr["id"]
+        high, low, close = float(curr["high"]), float(curr["low"]), float(curr["close"])
+        atr_val = atr_5m[idx]
+        if atr_val is None or atr_val <= 0:
+            equity_curve.append(capital)
+            continue
+
+        # ---------- 持仓管理：止损 / 止盈 / 移动止损 / 时间止损 ----------
+        if position is not None:
+            pos_dir = position["direction"]
+            entry_price = position["entry_price"]
+            entry_idx = position["entry_bar_idx"]
+            entry_atr = position["entry_atr"]
+            stop_price = position.get("stop_price")
+            trail_activated = position.get("trail_activated", False)
+            exit_reason = None
+            exit_price = close
+
+            if pos_dir == "long":
+                # 硬止损
+                if use_stop_loss and stop_price is not None and low <= stop_price:
+                    exit_price = min(stop_price, high)
+                    exit_reason = "stop_loss"
+                # 止盈（简化：首次触及任一目标即全平）
+                elif use_take_profit:
+                    tp1 = entry_price + entry_atr * TP1_ATR
+                    tp2 = entry_price + entry_atr * TP2_ATR
+                    tp3 = entry_price + entry_atr * TP3_ATR
+                    if high >= tp3:
+                        exit_price = tp3
+                        exit_reason = "tp3"
+                    elif high >= tp2:
+                        exit_price = tp2
+                        exit_reason = "tp2"
+                    elif high >= tp1:
+                        exit_price = tp1
+                        exit_reason = "tp1"
+                # 移动止损
+                if use_trailing_stop and not exit_reason:
+                    if close >= entry_price + entry_atr * TRAIL_START_ATR:
+                        trail_activated = True
+                        position["trail_activated"] = True
+                        trail_stop = close - entry_atr * TRAIL_ATR_MULT
+                        if low <= trail_stop:
+                            exit_price = trail_stop
+                            exit_reason = "trail_stop"
+                # 时间止损
+                if use_time_stop and not exit_reason and (idx - entry_idx) >= TIME_STOP_BARS:
+                    if close <= entry_price:
+                        exit_price = close
+                        exit_reason = "time_stop"
+            else:
+                if use_stop_loss and stop_price is not None and high >= stop_price:
+                    exit_price = max(stop_price, low)
+                    exit_reason = "stop_loss"
+                elif use_take_profit:
+                    tp1 = entry_price - entry_atr * TP1_ATR
+                    tp2 = entry_price - entry_atr * TP2_ATR
+                    tp3 = entry_price - entry_atr * TP3_ATR
+                    if low <= tp3:
+                        exit_price = tp3
+                        exit_reason = "tp3"
+                    elif low <= tp2:
+                        exit_price = tp2
+                        exit_reason = "tp2"
+                    elif low <= tp1:
+                        exit_price = tp1
+                        exit_reason = "tp1"
+                if use_trailing_stop and not exit_reason:
+                    if close <= entry_price - entry_atr * TRAIL_START_ATR:
+                        trail_activated = True
+                        position["trail_activated"] = True
+                        trail_stop = close + entry_atr * TRAIL_ATR_MULT
+                        if high >= trail_stop:
+                            exit_price = trail_stop
+                            exit_reason = "trail_stop"
+                if use_time_stop and not exit_reason and (idx - entry_idx) >= TIME_STOP_BARS:
+                    if close >= entry_price:
+                        exit_price = close
+                        exit_reason = "time_stop"
+
+            if exit_reason:
+                # 平仓
+                if pos_dir == "long":
+                    point_pnl = exit_price - entry_price
+                else:
+                    point_pnl = entry_price - exit_price
+                size_usdt = position.get("size_usdt", _size_usdt(capital))
+                contract_val = CONTRACT_SIZE / entry_price
+                money_pnl = point_pnl / entry_price * size_usdt
+                fee = _fee(size_usdt)
+                net = money_pnl - fee
+                capital += net
+                trades.append({
+                    "direction": "做多" if pos_dir == "long" else "做空",
+                    "entry_time": _utc_dt(position["entry_time"]),
+                    "exit_time": _utc_dt(bar_id),
+                    "entry_price": entry_price,
+                    "exit_price": exit_price,
+                    "point_pnl": point_pnl,
+                    "money_pnl": money_pnl,
+                    "fee": fee,
+                    "net_profit": net,
+                    "exit_reason": exit_reason,
+                    "hold_bars": idx - entry_idx,
+                })
+                position = None
+                last_close_bar_idx = idx
+                equity_curve.append(capital)
+                continue
+
+        # ---------- 信号检测 ----------
+        direction, trigger_price, valid_prev_idx, valid_prev = check_trigger(
+            klines_5m, idx, atr_5m, ema_5m, ema_15m_align, ema_60m_align, volume_ma, use_confirm=True
+        )
+        if direction is None:
+            equity_curve.append(capital)
+            continue
+        if SAME_KLINE_NO_REVERSE and last_trade_bar_idx == idx:
+            equity_curve.append(capital)
+            continue
+        if position is not None:
+            if direction == position["direction"]:
+                equity_curve.append(capital)
+                continue
+            # 反手条件
+            bars_since = idx - position["entry_bar_idx"]
+            if bars_since < MIN_BARS_SINCE_ENTRY:
+                equity_curve.append(capital)
+                continue
+            entry_atr_pos = position.get("entry_atr") or atr_val
+            pos_loss_atr = (position["entry_price"] - close) / entry_atr_pos if position["direction"] == "long" else (close - position["entry_price"]) / entry_atr_pos
+            if pos_loss_atr < REVERSE_LOSS_ATR:
+                # 可选：反向突破幅度 > 实体/2 才反手
+                equity_curve.append(capital)
+                continue
+            # 先平仓再开仓（下面统一开仓）
+            # 简化：这里直接平仓记一笔，再开新仓
+            exit_price = close
+            if position["direction"] == "long":
+                point_pnl = exit_price - position["entry_price"]
+            else:
+                point_pnl = position["entry_price"] - exit_price
+            size_usdt = position.get("size_usdt", _size_usdt(capital))
+            money_pnl = point_pnl / position["entry_price"] * size_usdt
+            fee = _fee(size_usdt)
+            net = money_pnl - fee
+            capital += net
+            trades.append({
+                "direction": "做多" if position["direction"] == "long" else "做空",
+                "entry_time": _utc_dt(position["entry_time"]),
+                "exit_time": _utc_dt(bar_id),
+                "entry_price": position["entry_price"],
+                "exit_price": exit_price,
+                "point_pnl": point_pnl,
+                "money_pnl": money_pnl,
+                "fee": fee,
+                "net_profit": net,
+                "exit_reason": "reverse",
+                "hold_bars": idx - position["entry_bar_idx"],
+            })
+            position = None
+            last_close_bar_idx = idx
+            just_reversed = True
+
+        # ---------- 开仓 ----------
+        # 反手后本 K 线允许开仓；否则需间隔 MIN_BARS_BETWEEN_TRADES 根
+        if not just_reversed and last_close_bar_idx is not None and (idx - last_close_bar_idx) < MIN_BARS_BETWEEN_TRADES:
+            equity_curve.append(capital)
+            continue
+        just_reversed = False
+        size_usdt = _size_usdt(capital)
+        if size_usdt <= 0:
+            equity_curve.append(capital)
+            continue
+        stop_price = None
+        if direction == "long":
+            stop_price = trigger_price - atr_val * STOP_LOSS_ATR_MULT
+        else:
+            stop_price = trigger_price + atr_val * STOP_LOSS_ATR_MULT
+        position = {
+            "direction": direction,
+            "entry_price": trigger_price,
+            "entry_time": bar_id,
+            "entry_bar_idx": idx,
+            "entry_atr": atr_val,
+            "stop_price": stop_price,
+            "size_usdt": size_usdt,
+            "closed_ratio": 0,
+            "trail_activated": False,
+        }
+        last_trade_bar_idx = idx
+        equity_curve.append(capital)
+
+    # 尾仓
+    if position is not None:
+        last_bar = klines_5m[-1]
+        exit_price = float(last_bar["close"])
+        pos_dir = position["direction"]
+        entry_price = position["entry_price"]
+        if pos_dir == "long":
+            point_pnl = exit_price - entry_price
+        else:
+            point_pnl = entry_price - exit_price
+        size_usdt = position.get("size_usdt", _size_usdt(capital))
+        money_pnl = point_pnl / entry_price * size_usdt
+        fee = _fee(size_usdt)
+        net = money_pnl - fee
+        capital += net
+        trades.append({
+            "direction": "做多" if pos_dir == "long" else "做空",
+            "entry_time": _utc_dt(position["entry_time"]),
+            "exit_time": _utc_dt(last_bar["id"]),
+            "entry_price": entry_price,
+            "exit_price": exit_price,
+            "point_pnl": point_pnl,
+            "money_pnl": money_pnl,
+            "fee": fee,
+            "net_profit": net,
+            "exit_reason": "tail",
+            "hold_bars": len(klines_5m) - 1 - position["entry_bar_idx"],
+        })
+
+    # 统计
+    total_net = sum(t["net_profit"] for t in trades)
+    total_gross = sum(t["money_pnl"] for t in trades)
+    total_fee = sum(t["fee"] for t in trades)
+    win_count = len([t for t in trades if t["net_profit"] > 0])
+    stats = {
+        "total_trades": len(trades),
+        "win_count": win_count,
+        "win_rate": (win_count / len(trades) * 100) if trades else 0,
+        "total_gross_profit": total_gross,
+        "total_fee": total_fee,
+        "total_net_profit": total_net,
+        "final_capital": capital,
+        "max_drawdown": 0,
+        "max_drawdown_pct": 0,
+    }
+    peak = initial_capital
+    for eq in equity_curve:
+        peak = max(peak, eq)
+        dd = peak - eq
+        if peak > 0:
+            stats["max_drawdown"] = max(stats["max_drawdown"], dd)
+            stats["max_drawdown_pct"] = max(stats["max_drawdown_pct"], dd / peak * 100)
+    return trades, stats, klines_5m
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="自适应三分位趋势策略回测")
+    parser.add_argument("--start", default="2025-01-01", help="开始日期 YYYY-MM-DD")
+    parser.add_argument("--end", default="2025-12-31", help="结束日期，默认 2025-12-31")
+    parser.add_argument("--data-dir", default=None, help="数据目录，默认 adaptive_third_strategy/data")
+    parser.add_argument("--no-api", action="store_true", help="不从 API 拉取，仅用本地 CSV")
+    parser.add_argument("--capital", type=float, default=10000, help="初始资金（按比例开仓时用）")
+    parser.add_argument("--fixed-margin", type=float, default=None, help="每笔固定保证金 USDT，如 50")
+    parser.add_argument("--leverage", type=float, default=None, help="杠杆倍数，如 100")
+    parser.add_argument("--no-fee", action="store_true", help="不扣手续费，只算总盈利")
+    args = parser.parse_args()
+    start_dt = datetime.datetime.strptime(args.start, "%Y-%m-%d")
+    if args.end:
+        end_dt = datetime.datetime.strptime(args.end, "%Y-%m-%d")
+        # 包含 end 日全天：取次日 0 点前一刻，这样 id < end_ts 的 K 线都含在内
+        end_ts = int((end_dt + datetime.timedelta(days=1)).timestamp())
+    else:
+        end_ts = int(datetime.datetime.utcnow().timestamp())
+    start_ts = int(start_dt.timestamp())
+    data_dir = args.data_dir or os.path.join(SCRIPT_DIR, "data")
+    k5, k15, k60 = ensure_data(start_ts, end_ts, data_dir, use_api=not args.no_api)
+    if not k5:
+        logger.error("无 5 分钟数据，请先抓取或开启 --api")
+        return
+    logger.info(f"5m={len(k5)} 15m={len(k15)} 60m={len(k60)}")
+    trades, stats, _ = run_backtest(
+        k5, k15, k60,
+        initial_capital=args.capital,
+        fixed_margin_usdt=args.fixed_margin,
+        leverage=args.leverage,
+        deduct_fee=not args.no_fee,
+    )
+    logger.info(f"交易笔数: {stats['total_trades']} 胜率: {stats['win_rate']:.2f}% "
+                f"总盈利(未扣费): {stats['total_gross_profit']:.2f} USDT "
+                f"总手续费: {stats['total_fee']:.2f} 总净利润: {stats['total_net_profit']:.2f} "
+                f"最大回撤: {stats['max_drawdown']:.2f} ({stats['max_drawdown_pct']:.2f}%)")
+    out_csv = os.path.join(SCRIPT_DIR, "backtest_trades.csv")
+    if trades:
+        with open(out_csv, "w", newline="", encoding="utf-8") as f:
+            w = csv.DictWriter(f, fieldnames=["direction", "entry_time", "exit_time", "entry_price", "exit_price", "point_pnl", "money_pnl", "fee", "net_profit", "exit_reason", "hold_bars"])
+            w.writeheader()
+            for t in trades:
+                w.writerow({k: str(v) if isinstance(v, datetime.datetime) else v for k, v in t.items()})
+        logger.info(f"交易记录已保存: {out_csv}")
+
+
+if __name__ == "__main__":
+    main()