第一版策略

run_bb_full_grid_search.py

@@ -0,0 +1,427 @@
+"""
+布林带中轨策略 - 全参数网格搜索 (2020-2025)
+
+策略逻辑:
+- 阳线 + 碰到布林带均线(先涨碰到) → 开多，碰上轨止盈
+- 阴线 + 碰到布林带均线(先跌碰到) → 平多开空，碰下轨止盈
+- 使用 1m 线判断当前K线是先跌碰均线还是先涨碰均线
+- 每根K线只能操作一次
+
+参数范围: period 1~1000, std 0.5~1000，按 (0.5,0.5),(0.5,1)...(0.5,1000),(1,0.5),(1,1)...(1000,1000) 顺序遍历
+
+回测设置: 200U本金 | 全仓 | 1%权益/单 | 万五手续费 | 90%返佣次日8点到账 | 100x杠杆
+
+数据来源: 抓取多周期K线.py 抓取并存入 models/database.db 的 bitmart_eth_5m / bitmart_eth_1m
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import os
+import tempfile
+import time
+from collections import defaultdict
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from strategy.bb_midline_backtest import BBMidlineConfig, run_bb_midline_backtest
+from strategy.data_loader import get_1m_touch_direction, load_klines
+from strategy.indicators import bollinger
+
+
+def frange(start: float, end: float, step: float) -> list[float]:
+    out: list[float] = []
+    x = float(start)
+    while x <= end + 1e-9:
+        out.append(round(x, 6))
+        x += step
+    return out
+
+
+def build_full_grid(
+    p_start: float = 1,
+    p_end: float = 1000,
+    p_step: float = 1,
+    s_start: float = 0.5,
+    s_end: float = 1000,
+    s_step: float = 0.5,
+) -> list[tuple[int, float]]:
+    """构建完整参数网格，按 (0.5,0.5),(0.5,1)...(0.5,1000),(1,0.5)... 顺序"""
+    periods = sorted({max(1, min(1000, int(round(v)))) for v in frange(p_start, p_end, p_step)})
+    stds = sorted({round(max(0.5, min(1000.0, v)), 2) for v in frange(s_start, s_end, s_step)})
+    out = [(p, s) for p in periods for s in stds]
+    return sorted(set(out))
+
+
+def score_stable(ret_pct: float, sharpe: float, dd_pct: float, n_trades: int) -> float:
+    """收益稳定性评分：收益+夏普加分，回撤惩罚，交易过少惩罚"""
+    sparse_penalty = -5.0 if n_trades < 200 else 0.0
+    return ret_pct + sharpe * 12.0 - dd_pct * 0.8 + sparse_penalty
+
+
+def _checkpoint_meta(
+    period: str,
+    start: str,
+    end: str,
+    p_step: float,
+    s_step: float,
+    sample: bool,
+    focus: bool,
+    fine: bool,
+) -> dict:
+    return {
+        "period": period,
+        "start": start,
+        "end": end,
+        "p_step": p_step,
+        "s_step": s_step,
+        "sample": sample,
+        "focus": focus,
+        "fine": fine,
+    }
+
+
+def _checkpoint_path(out_dir: Path, meta: dict) -> tuple[Path, Path]:
+    """返回 checkpoint 数据文件和 meta 文件路径"""
+    h = hashlib.md5(json.dumps(meta, sort_keys=True).encode()).hexdigest()[:12]
+    return (
+        out_dir / f"bb_full_grid_{meta['period']}_resume_{h}.csv",
+        out_dir / f"bb_full_grid_{meta['period']}_resume_{h}.meta.json",
+    )
+
+
+def load_checkpoint(
+    ckpt_path: Path,
+    meta_path: Path,
+    meta: dict,
+) -> tuple[pd.DataFrame, set[tuple[int, float]]]:
+    """
+    加载断点数据。若文件存在且 meta 一致，返回 (已完成结果df, 已完成的(period,std)集合)。
+    否则返回 (空df, 空集合)。
+    """
+    if not ckpt_path.exists() or not meta_path.exists():
+        return pd.DataFrame(), set()
+
+    try:
+        with open(meta_path, "r", encoding="utf-8") as f:
+            saved = json.load(f)
+        if saved != meta:
+            return pd.DataFrame(), set()
+    except (json.JSONDecodeError, OSError):
+        return pd.DataFrame(), set()
+
+    try:
+        df = pd.read_csv(ckpt_path)
+        if "period" not in df.columns or "std" not in df.columns:
+            return pd.DataFrame(), set()
+        done = {(int(r["period"]), round(float(r["std"]), 2)) for _, r in df.iterrows()}
+        return df, done
+    except Exception:
+        return pd.DataFrame(), set()
+
+
+def save_checkpoint(ckpt_path: Path, meta_path: Path, meta: dict, rows: list[dict]) -> None:
+    """追加/覆盖保存断点"""
+    ckpt_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(meta_path, "w", encoding="utf-8") as f:
+        json.dump(meta, f, indent=2)
+    df = pd.DataFrame(rows)
+    df.to_csv(ckpt_path, index=False)
+
+
+def _init_worker(df_path: str, df_1m_path: str | None, use_1m: bool, step_min: int):
+    global G_DF, G_DF_1M, G_USE_1M, G_STEP_MIN
+    G_DF = pd.read_pickle(df_path)
+    G_DF_1M = pd.read_pickle(df_1m_path) if (use_1m and df_1m_path) else None
+    G_USE_1M = bool(use_1m)
+    G_STEP_MIN = int(step_min)
+
+
+def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
+    period, std_list = args
+    assert G_DF is not None
+
+    arr_touch_dir = None
+    if G_USE_1M and G_DF_1M is not None:
+        close = G_DF["close"].astype(float)
+        bb_mid, _, _, _ = bollinger(close, period, 1.0)
+        arr_touch_dir = get_1m_touch_direction(G_DF, G_DF_1M, bb_mid.values, kline_step_min=G_STEP_MIN)
+
+    rows: list[dict] = []
+    for std in std_list:
+        cfg = BBMidlineConfig(
+            bb_period=period,
+            bb_std=float(std),
+            initial_capital=200.0,
+            margin_pct=0.01,
+            leverage=100.0,
+            cross_margin=True,
+            fee_rate=0.0005,
+            rebate_pct=0.90,
+            rebate_hour_utc=0,
+            fill_at_close=True,
+            use_1m_touch_filter=G_USE_1M,
+            kline_step_min=G_STEP_MIN,
+        )
+        result = run_bb_midline_backtest(
+            G_DF,
+            cfg,
+            df_1m=G_DF_1M if G_USE_1M else None,
+            arr_touch_dir_override=arr_touch_dir,
+        )
+
+        eq = result.equity_curve["equity"].dropna()
+        if len(eq) == 0:
+            final_eq = 0.0
+            ret_pct = -100.0
+            dd_u = -200.0
+            dd_pct = 100.0
+        else:
+            final_eq = float(eq.iloc[-1])
+            ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100.0
+            dd_u = float((eq.astype(float) - eq.astype(float).cummax()).min())
+            dd_pct = abs(dd_u) / cfg.initial_capital * 100.0
+
+        n_trades = len(result.trades)
+        win_rate = (
+            sum(1 for t in result.trades if t.net_pnl > 0) / n_trades * 100.0
+            if n_trades > 0
+            else 0.0
+        )
+        pnl = result.daily_stats["pnl"].astype(float)
+        sharpe = float(pnl.mean() / pnl.std()) * math.sqrt(365.0) if pnl.std() > 0 else 0.0
+        stable_score = score_stable(ret_pct, sharpe, dd_pct, n_trades)
+
+        rows.append({
+            "period": period,
+            "std": round(float(std), 2),
+            "final_eq": final_eq,
+            "ret_pct": ret_pct,
+            "n_trades": n_trades,
+            "win_rate": win_rate,
+            "sharpe": sharpe,
+            "max_dd_u": dd_u,
+            "max_dd_pct": dd_pct,
+            "stable_score": stable_score,
+        })
+    return rows
+
+
+def run_grid_search(
+    params: list[tuple[int, float]],
+    *,
+    workers: int,
+    df_path: str,
+    df_1m_path: str | None,
+    use_1m: bool,
+    step_min: int,
+    existing_rows: list[dict] | None = None,
+    ckpt_path: Path | None = None,
+    meta_path: Path | None = None,
+    meta: dict | None = None,
+    checkpoint_interval: int = 5,
+) -> pd.DataFrame:
+    by_period: dict[int, set[float]] = defaultdict(set)
+    for p, s in params:
+        by_period[int(p)].add(round(float(s), 2))
+
+    tasks = [(p, sorted(stds)) for p, stds in sorted(by_period.items())]
+    total_periods = len(tasks)
+    total_combos = sum(len(stds) for _, stds in tasks)
+    rows: list[dict] = list(existing_rows) if existing_rows else []
+
+    print(f"待运行: {total_combos} 组合 ({total_periods} period组), workers={workers}" + (
+        f", 断点续跑 (已有 {len(rows)} 条)" if rows else ""
+    ))
+    start = time.time()
+    last_save = 0
+
+    def maybe_save(n_done_periods: int):
+        nonlocal last_save
+        if ckpt_path and meta_path and meta and n_done_periods > 0:
+            if n_done_periods - last_save >= checkpoint_interval:
+                save_checkpoint(ckpt_path, meta_path, meta, rows)
+                last_save = n_done_periods
+
+    if workers <= 1:
+        _init_worker(df_path, df_1m_path, use_1m, step_min)
+        done_periods = 0
+        done_combos = 0
+        for task in tasks:
+            res = _eval_period_task(task)
+            rows.extend(res)
+            done_periods += 1
+            done_combos += len(task[1])
+            maybe_save(done_periods)
+            if done_periods % max(1, total_periods // 20) == 0 or done_periods == total_periods:
+                elapsed = time.time() - start
+                print(f"进度 {done_combos}/{total_combos} ({done_periods}/{total_periods} periods), 用时 {elapsed:.0f}s")
+    else:
+        done_periods = 0
+        done_combos = 0
+        try:
+            with ProcessPoolExecutor(
+                max_workers=workers,
+                initializer=_init_worker,
+                initargs=(df_path, df_1m_path, use_1m, step_min),
+            ) as ex:
+                future_map = {ex.submit(_eval_period_task, task): task for task in tasks}
+                for fut in as_completed(future_map):
+                    period, stds = future_map[fut]
+                    res = fut.result()
+                    rows.extend(res)
+                    done_periods += 1
+                    done_combos += len(stds)
+                    maybe_save(done_periods)
+                    if done_periods % max(1, total_periods // 20) == 0 or done_periods == total_periods:
+                        elapsed = time.time() - start
+                        print(f"进度 {done_combos}/{total_combos} ({done_periods}/{total_periods} periods), 用时 {elapsed:.0f}s")
+        except (PermissionError, OSError) as e:
+            print(f"多进程不可用 ({e})，改用单进程...")
+            _init_worker(df_path, df_1m_path, use_1m, step_min)
+            done_periods = 0
+            done_combos = 0
+            for task in tasks:
+                res = _eval_period_task(task)
+                rows.extend(res)
+                done_periods += 1
+                done_combos += len(task[1])
+                maybe_save(done_periods)
+                if done_periods % max(1, total_periods // 20) == 0 or done_periods == total_periods:
+                    elapsed = time.time() - start
+                    print(f"进度 {done_combos}/{total_combos} ({done_periods}/{total_periods} periods), 用时 {elapsed:.0f}s")
+
+    if ckpt_path and meta_path and meta and rows:
+        save_checkpoint(ckpt_path, meta_path, meta, rows)
+
+    df = pd.DataFrame(rows)
+    print(f"完成, 总用时 {time.time() - start:.1f}s")
+    return df
+
+
+def main():
+    parser = argparse.ArgumentParser(description="布林带全参数网格搜索 (1-1000, 0.5-1000)")
+    parser.add_argument("-p", "--period", default="5m", choices=["5m", "15m", "30m"])
+    parser.add_argument("--start", default="2020-01-01")
+    parser.add_argument("--end", default="2026-01-01")
+    parser.add_argument("-j", "--workers", type=int, default=max(1, (os.cpu_count() or 4) - 1))
+    parser.add_argument("--no-1m", action="store_true", help="禁用 1m 触及方向过滤")
+    parser.add_argument("--p-step", type=float, default=5, help="period 步长 (默认5, 全量用1)")
+    parser.add_argument("--s-step", type=float, default=5, help="std 步长 (默认5, 全量用0.5或1)")
+    parser.add_argument("--quick", action="store_true", help="快速模式: p-step=20, s-step=20")
+    parser.add_argument("--sample", action="store_true", help="采样模式: 仅用2022-2024两年加速")
+    parser.add_argument("--focus", action="store_true", help="聚焦模式: 仅在period 50-400, std 100-800 细搜")
+    parser.add_argument("--fine", action="store_true", help="精细模式: 在period 280-310, std 450-550 细搜")
+    parser.add_argument("--no-resume", action="store_true", help="禁用断点续跑，重新开始")
+    parser.add_argument("--checkpoint-interval", type=int, default=10,
+                        help="每完成 N 个 period 组保存一次断点 (默认 10)")
+    args = parser.parse_args()
+
+    use_1m = not args.no_1m
+    step_min = int(args.period.replace("m", ""))
+    if args.sample:
+        args.start, args.end = "2022-01-01", "2024-01-01"
+        print("采样模式: 使用 2022-2024 数据加速")
+
+    if args.quick:
+        p_step, s_step = 20.0, 20.0
+    else:
+        p_step, s_step = args.p_step, args.s_step
+
+    if args.fine:
+        params = build_full_grid(p_start=280, p_end=300, p_step=2, s_start=480, s_end=510, s_step=2)
+        print("精细模式: period 280-300 step=2, std 480-510 step=2 (~176组合)")
+    elif args.focus:
+        params = build_full_grid(p_start=50, p_end=400, p_step=25, s_start=100, s_end=800, s_step=50)
+        print("聚焦模式: period 50-400 step=25, std 100-800 step=50 (~225组合)")
+    else:
+        params = build_full_grid(p_step=p_step, s_step=s_step)
+    print(f"网格参数: period 1-1000 step={p_step}, std 0.5-1000 step={s_step} → {len(params)} 组合")
+
+    out_dir = Path(__file__).resolve().parent / "strategy" / "results"
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    meta = _checkpoint_meta(
+        args.period, args.start, args.end, p_step, s_step,
+        args.sample, args.focus, args.fine,
+    )
+    ckpt_path, meta_path = _checkpoint_path(out_dir, meta)
+    existing_rows: list[dict] = []
+    params_to_run = params
+
+    if not args.no_resume:
+        ckpt_df, done_set = load_checkpoint(ckpt_path, meta_path, meta)
+        if len(done_set) > 0:
+            existing_rows = ckpt_df.to_dict("records")
+            params_to_run = [(p, s) for p, s in params if (int(p), round(float(s), 2)) not in done_set]
+            print(f"断点续跑: 已完成 {len(done_set)} 组合，剩余 {len(params_to_run)} 组合")
+
+    if not params_to_run:
+        print("无待运行组合，直接使用断点结果")
+        all_df = pd.DataFrame(existing_rows)
+    else:
+        print(f"\n加载数据: {args.period} + 1m, {args.start} ~ {args.end}")
+        t0 = time.time()
+        df = load_klines(args.period, args.start, args.end)
+        df_1m = load_klines("1m", args.start, args.end) if use_1m else None
+        print(f"  {args.period}: {len(df):,} 条" + (f", 1m: {len(df_1m):,} 条" if df_1m is not None else "") + f", {time.time()-t0:.1f}s\n")
+
+        with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_df:
+            df.to_pickle(f_df.name)
+            df_path = f_df.name
+        df_1m_path = None
+        if df_1m is not None:
+            with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_1m:
+                df_1m.to_pickle(f_1m.name)
+                df_1m_path = f_1m.name
+
+        try:
+            all_df = run_grid_search(
+                params_to_run,
+                workers=args.workers,
+                df_path=df_path,
+                df_1m_path=df_1m_path,
+                use_1m=use_1m,
+                step_min=step_min,
+                existing_rows=existing_rows,
+                ckpt_path=ckpt_path,
+                meta_path=meta_path,
+                meta=meta,
+                checkpoint_interval=args.checkpoint_interval,
+            )
+        finally:
+            Path(df_path).unlink(missing_ok=True)
+            if df_1m_path:
+                Path(df_1m_path).unlink(missing_ok=True)
+
+    all_df = all_df.drop_duplicates(subset=["period", "std"], keep="last")
+
+    best_stable = all_df.sort_values("stable_score", ascending=False).iloc[0]
+    best_return = all_df.sort_values("ret_pct", ascending=False).iloc[0]
+
+    stamp = time.strftime("%Y%m%d_%H%M%S")
+    out_path = out_dir / f"bb_full_grid_{args.period}_{stamp}.csv"
+    all_df.sort_values("stable_score", ascending=False).to_csv(out_path, index=False)
+
+    print("\n" + "=" * 80)
+    print("全参数网格搜索完成")
+    print(f"最佳稳定参数: period={int(best_stable['period'])}, std={float(best_stable['std']):.2f}")
+    print(f"  最终权益: {best_stable['final_eq']:.2f} U | 收益: {best_stable['ret_pct']:+.2f}%")
+    print(f"  最大回撤: {best_stable['max_dd_pct']:.2f}% | 夏普: {best_stable['sharpe']:.3f} | 交易数: {int(best_stable['n_trades'])}")
+    print()
+    print(f"最高收益参数: period={int(best_return['period'])}, std={float(best_return['std']):.2f}")
+    print(f"  最终权益: {best_return['final_eq']:.2f} U | 收益: {best_return['ret_pct']:+.2f}%")
+    print(f"  最大回撤: {best_return['max_dd_pct']:.2f}% | 夏普: {best_return['sharpe']:.3f} | 交易数: {int(best_return['n_trades'])}")
+    print(f"\n结果已保存: {out_path}")
+    print(f"断点文件: {ckpt_path.name} (可用 --no-resume 重新开始)")
+    print("=" * 80)
+
+
+if __name__ == "__main__":
+    main()

strategy/results/bb_full_grid_5m_20260226_214834.csv

@@ -0,0 +1,26 @@
+period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
+801,0.5,10.34628311811287,-94.82685844094357,11410,31.0692375109553,-0.8073996538916506,-208.98636227084955,104.49318113542478,-188.1101991959832
+401,400.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
+401,800.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
+401,600.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
+401,200.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
+401,0.5,3.0098224997841756,-98.49508875010791,17114,34.983054808928365,-1.24562923011871,-197.42490102080006,98.71245051040003,-192.41259991985248
+201,200.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
+201,400.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
+201,600.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
+201,800.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
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