457 lines
17 KiB
Python
457 lines
17 KiB
Python
"""
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布林带均线策略 - 全参数组合扫描 (0.5~1000, 0.5~1000)
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分层搜索：粗扫 → 精扫，在合理时间内覆盖全参数空间
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策略:
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- 阳线 + 先涨碰到均线(1m判断) → 开多
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- 持多: 碰上轨止盈
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- 阴线 + 先跌碰到均线(1m判断) → 平多开空
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- 持空: 碰下轨止盈
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配置: 200U | 1%权益/单 | 万五手续费 | 90%返佣次日8点 | 100x杠杆 | 全仓
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"""
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from __future__ import annotations
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import os
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import sys
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import tempfile
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import time
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from collections import defaultdict
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[0]))
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sys.stdout.reconfigure(line_buffering=True)
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import numpy as np
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import pandas as pd
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from strategy.bb_midline_backtest import BBMidlineConfig, run_bb_midline_backtest
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from strategy.data_loader import get_1m_touch_direction, load_klines
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from strategy.indicators import bollinger
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# ─── 全局变量 (多进程 worker 共享) ───
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G_DF: pd.DataFrame | None = None
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G_DF_1M: pd.DataFrame | None = None
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G_USE_1M: bool = True
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G_STEP_MIN: int = 5
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def _init_worker(df_path: str, df_1m_path: str | None, use_1m: bool, step_min: int):
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global G_DF, G_DF_1M, G_USE_1M, G_STEP_MIN
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G_DF = pd.read_pickle(df_path)
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G_DF_1M = pd.read_pickle(df_1m_path) if (use_1m and df_1m_path) else None
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G_USE_1M = bool(use_1m)
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G_STEP_MIN = int(step_min)
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def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
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"""评估一个 period 下的所有 std 组合"""
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period, std_list = args
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assert G_DF is not None
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# 对同一个 period，1m 触及方向只需计算一次
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arr_touch_dir = None
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if G_USE_1M and G_DF_1M is not None:
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close = G_DF["close"].astype(float)
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bb_mid, _, _, _ = bollinger(close, period, 1.0)
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arr_touch_dir = get_1m_touch_direction(
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G_DF, G_DF_1M, bb_mid.values, kline_step_min=G_STEP_MIN
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)
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rows: list[dict] = []
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for std in std_list:
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cfg = BBMidlineConfig(
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bb_period=period,
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bb_std=float(std),
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initial_capital=200.0,
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margin_pct=0.01,
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leverage=100.0,
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cross_margin=True,
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fee_rate=0.0005,
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rebate_pct=0.90,
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rebate_hour_utc=0,
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fill_at_close=True,
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use_1m_touch_filter=G_USE_1M,
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kline_step_min=G_STEP_MIN,
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)
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result = run_bb_midline_backtest(
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G_DF,
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cfg,
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df_1m=G_DF_1M if G_USE_1M else None,
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arr_touch_dir_override=arr_touch_dir,
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)
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eq = result.equity_curve["equity"].dropna()
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if len(eq) == 0:
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final_eq = 0.0
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ret_pct = -100.0
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dd_u = -200.0
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dd_pct = 100.0
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else:
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final_eq = float(eq.iloc[-1])
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ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100.0
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dd_u = float((eq.astype(float) - eq.astype(float).cummax()).min())
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dd_pct = abs(dd_u) / cfg.initial_capital * 100.0
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n_trades = len(result.trades)
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win_rate = (
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sum(1 for t in result.trades if t.net_pnl > 0) / n_trades * 100.0
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if n_trades > 0
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else 0.0
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)
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pnl = result.daily_stats["pnl"].astype(float)
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sharpe = (
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float(pnl.mean() / pnl.std()) * np.sqrt(365.0) if pnl.std() > 0 else 0.0
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)
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# 稳定性评分
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sparse_penalty = -5.0 if n_trades < 200 else 0.0
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score = ret_pct + sharpe * 12.0 - abs(dd_pct) * 0.8 + sparse_penalty
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rows.append({
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"period": period,
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"std": round(float(std), 2),
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"final_eq": round(final_eq, 2),
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"ret_pct": round(ret_pct, 2),
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"n_trades": n_trades,
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"win_rate": round(win_rate, 2),
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"sharpe": round(sharpe, 4),
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"max_dd_u": round(dd_u, 2),
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"max_dd_pct": round(dd_pct, 2),
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"stable_score": round(score, 2),
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})
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return rows
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def evaluate_grid(
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params: list[tuple[int, float]],
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*,
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workers: int,
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df_path: str,
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df_1m_path: str | None,
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use_1m: bool,
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step_min: int,
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label: str = "",
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) -> pd.DataFrame:
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"""多进程评估参数网格"""
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by_period: dict[int, set[float]] = defaultdict(set)
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for p, s in params:
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by_period[int(p)].add(round(float(s), 2))
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tasks = [(p, sorted(stds)) for p, stds in sorted(by_period.items())]
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total_periods = len(tasks)
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total_combos = sum(len(stds) for _, stds in tasks)
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print(f" [{label}] 评估 {total_combos:,} 组参数, {total_periods} 个 period, workers={workers}")
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start = time.time()
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rows: list[dict] = []
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done_periods = 0
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done_combos = 0
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with ProcessPoolExecutor(
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max_workers=workers,
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initializer=_init_worker,
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initargs=(df_path, df_1m_path, use_1m, step_min),
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) as ex:
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future_map = {ex.submit(_eval_period_task, task): task for task in tasks}
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for fut in as_completed(future_map):
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period, stds = future_map[fut]
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try:
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res = fut.result()
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rows.extend(res)
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except Exception as e:
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print(f" ⚠ period={period} 出错: {e}")
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done_periods += 1
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done_combos += len(stds)
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interval = max(1, total_periods // 20)
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if done_periods % interval == 0 or done_periods == total_periods:
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elapsed = time.time() - start
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speed = done_combos / elapsed if elapsed > 0 else 0
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eta = (total_combos - done_combos) / speed if speed > 0 else 0
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print(
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f" 进度 {done_combos:,}/{total_combos:,} "
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f"({done_combos/total_combos*100:.1f}%) "
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f"| {elapsed:.0f}s | ETA {eta:.0f}s"
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)
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df = pd.DataFrame(rows)
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print(f" [{label}] 完成, 用时 {time.time() - start:.1f}s")
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return df
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def build_grid(
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period_min: float, period_max: float, period_step: float,
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std_min: float, std_max: float, std_step: float,
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) -> list[tuple[int, float]]:
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"""生成 (period, std) 参数网格"""
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out = []
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p = period_min
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while p <= period_max + 1e-9:
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s = std_min
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while s <= std_max + 1e-9:
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out.append((max(1, int(round(p))), round(s, 2)))
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s += std_step
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p += period_step
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return sorted(set(out))
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def main():
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import argparse
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parser = argparse.ArgumentParser(description="布林带均线策略 - 全参数扫描 (分层搜索)")
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parser.add_argument("-p", "--kline-period", default="5m", choices=["5m", "15m", "30m"])
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parser.add_argument("-j", "--workers", type=int, default=max(1, (os.cpu_count() or 4) - 1))
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parser.add_argument("--no-1m", action="store_true", help="禁用 1m 触及方向过滤")
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parser.add_argument("--source", default="bitmart", choices=["bitmart", "binance"])
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parser.add_argument("--coarse-only", action="store_true", help="只做粗扫")
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parser.add_argument("--top-n", type=int, default=20, help="粗扫后取 top N 区域精扫")
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args = parser.parse_args()
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use_1m = not args.no_1m
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step_min = int(args.kline_period.replace("m", ""))
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out_dir = Path(__file__).resolve().parent / "strategy" / "results"
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out_dir.mkdir(parents=True, exist_ok=True)
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# ─── 加载数据 ───
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print("=" * 90)
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print("布林带均线策略 | 全参数扫描 | 2020-2025 | 200U | 1%/单 | 万五 | 90%返佣 | 100x全仓")
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print("=" * 90)
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print(f"\n加载 K 线数据 (2020-01-01 ~ 2026-01-01)...")
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t0 = time.time()
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try:
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df = load_klines(args.kline_period, "2020-01-01", "2026-01-01", source=args.source)
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df_1m = load_klines("1m", "2020-01-01", "2026-01-01", source=args.source) if use_1m else None
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except Exception as e:
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alt = "binance" if args.source == "bitmart" else "bitmart"
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print(f" {args.source} 加载失败 ({e}), 尝试 {alt}...")
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df = load_klines(args.kline_period, "2020-01-01", "2026-01-01", source=alt)
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df_1m = load_klines("1m", "2020-01-01", "2026-01-01", source=alt) if use_1m else None
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args.source = alt
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print(
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f" {args.kline_period}: {len(df):,} 条"
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+ (f", 1m: {len(df_1m):,} 条" if df_1m is not None else "")
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+ f" | 数据源: {args.source} ({time.time()-t0:.1f}s)\n"
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)
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# 序列化数据给子进程
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with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_df:
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df.to_pickle(f_df.name)
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df_path = f_df.name
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df_1m_path = None
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if df_1m is not None:
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with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_1m:
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df_1m.to_pickle(f_1m.name)
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df_1m_path = f_1m.name
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try:
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# ─── 第一阶段：粗扫 ───
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# period: 1~1000 步长50, std: 0.5~1000 步长50
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# 约 20 × 20 = 400 组
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print("=" * 60)
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print("第一阶段: 粗扫 (period 1~1000 step50, std 0.5~1000 step50)")
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print("=" * 60)
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coarse_grid = build_grid(1, 1000, 50, 0.5, 1000, 50)
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print(f" 参数组合数: {len(coarse_grid):,}")
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coarse_df = evaluate_grid(
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coarse_grid,
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workers=args.workers,
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df_path=df_path,
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df_1m_path=df_1m_path,
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use_1m=use_1m,
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step_min=step_min,
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label="粗扫",
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)
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stamp = time.strftime("%Y%m%d_%H%M%S")
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coarse_csv = out_dir / f"bb_sweep_coarse_{args.kline_period}_{stamp}.csv"
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coarse_df.to_csv(coarse_csv, index=False, encoding="utf-8-sig")
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print(f"\n 粗扫结果已保存: {coarse_csv}")
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# 显示粗扫 top 10
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if not coarse_df.empty:
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top10 = coarse_df.sort_values("stable_score", ascending=False).head(10)
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print("\n 粗扫 Top 10 (按稳定性评分):")
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print(" " + "-" * 85)
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print(f" {'排名':>4} {'period':>7} {'std':>7} {'最终权益':>10} {'收益%':>8} "
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f"{'交易数':>6} {'胜率%':>6} {'Sharpe':>8} {'回撤%':>7} {'评分':>8}")
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print(" " + "-" * 85)
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for rank, (_, row) in enumerate(top10.iterrows(), 1):
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print(
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f" {rank:>4} {int(row['period']):>7} {row['std']:>7.1f} "
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f"{row['final_eq']:>10.2f} {row['ret_pct']:>+8.1f} "
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f"{int(row['n_trades']):>6} {row['win_rate']:>6.1f} "
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f"{row['sharpe']:>8.4f} {row['max_dd_pct']:>7.1f} "
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f"{row['stable_score']:>8.2f}"
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)
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if args.coarse_only or coarse_df.empty:
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print("\n粗扫完成。")
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return
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# ─── 第二阶段：中扫 ───
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# 取粗扫 top N 的区域，在其周围 ±50 范围内用步长 10 精扫
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print(f"\n{'=' * 60}")
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print(f"第二阶段: 中扫 (粗扫 Top {args.top_n} 区域, 步长 10)")
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print("=" * 60)
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top_coarse = coarse_df.sort_values("stable_score", ascending=False).head(args.top_n)
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mid_params = set()
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for _, row in top_coarse.iterrows():
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p_center = int(row["period"])
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s_center = float(row["std"])
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for p in range(max(1, p_center - 50), min(1001, p_center + 51), 10):
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for s_val in np.arange(max(0.5, s_center - 50), min(1000.5, s_center + 51), 10):
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mid_params.add((max(1, int(round(p))), round(float(s_val), 2)))
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mid_grid = sorted(mid_params)
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print(f" 参数组合数: {len(mid_grid):,}")
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mid_df = evaluate_grid(
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mid_grid,
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workers=args.workers,
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df_path=df_path,
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df_1m_path=df_1m_path,
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use_1m=use_1m,
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step_min=step_min,
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label="中扫",
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)
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mid_csv = out_dir / f"bb_sweep_mid_{args.kline_period}_{stamp}.csv"
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mid_df.to_csv(mid_csv, index=False, encoding="utf-8-sig")
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print(f"\n 中扫结果已保存: {mid_csv}")
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# ─── 第三阶段：精扫 ───
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# 取中扫 top 10 区域，在其周围 ±10 范围内用步长 1 精扫
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print(f"\n{'=' * 60}")
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print("第三阶段: 精扫 (中扫 Top 10 区域, 步长 1)")
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print("=" * 60)
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all_mid = pd.concat([coarse_df, mid_df], ignore_index=True)
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top_mid = all_mid.sort_values("stable_score", ascending=False).head(10)
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fine_params = set()
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for _, row in top_mid.iterrows():
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p_center = int(row["period"])
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s_center = float(row["std"])
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for p in range(max(1, p_center - 10), min(1001, p_center + 11)):
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for s_val in np.arange(max(0.5, s_center - 10), min(1000.5, s_center + 11), 1.0):
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fine_params.add((max(1, int(round(p))), round(float(s_val), 2)))
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fine_grid = sorted(fine_params)
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print(f" 参数组合数: {len(fine_grid):,}")
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fine_df = evaluate_grid(
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fine_grid,
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workers=args.workers,
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df_path=df_path,
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df_1m_path=df_1m_path,
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use_1m=use_1m,
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step_min=step_min,
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label="精扫",
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)
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fine_csv = out_dir / f"bb_sweep_fine_{args.kline_period}_{stamp}.csv"
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fine_df.to_csv(fine_csv, index=False, encoding="utf-8-sig")
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print(f"\n 精扫结果已保存: {fine_csv}")
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# ─── 汇总 ───
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all_results = pd.concat([coarse_df, mid_df, fine_df], ignore_index=True)
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all_results = all_results.drop_duplicates(subset=["period", "std"], keep="last")
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all_results = all_results.sort_values("stable_score", ascending=False)
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all_csv = out_dir / f"bb_sweep_all_{args.kline_period}_{stamp}.csv"
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all_results.to_csv(all_csv, index=False, encoding="utf-8-sig")
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print(f"\n{'=' * 90}")
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print("全部扫描完成 | 汇总结果")
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print("=" * 90)
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print(f"总计评估: {len(all_results):,} 组参数")
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print(f"结果文件: {all_csv}\n")
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# Top 20
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top20 = all_results.head(20)
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print("Top 20 (按稳定性评分):")
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print("-" * 95)
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print(f"{'排名':>4} {'period':>7} {'std':>7} {'最终权益':>10} {'收益%':>8} "
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f"{'交易数':>6} {'胜率%':>6} {'Sharpe':>8} {'回撤%':>7} {'评分':>8}")
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print("-" * 95)
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for rank, (_, row) in enumerate(top20.iterrows(), 1):
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print(
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f"{rank:>4} {int(row['period']):>7} {row['std']:>7.1f} "
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f"{row['final_eq']:>10.2f} {row['ret_pct']:>+8.1f} "
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f"{int(row['n_trades']):>6} {row['win_rate']:>6.1f} "
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f"{row['sharpe']:>8.4f} {row['max_dd_pct']:>7.1f} "
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f"{row['stable_score']:>8.2f}"
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)
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# 最佳参数详细回测
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best = all_results.iloc[0]
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print(f"\n{'=' * 90}")
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print(f"最佳参数: BB({int(best['period'])}, {best['std']})")
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print(f"最终权益: {best['final_eq']:.2f} U | 收益: {best['ret_pct']:+.2f}%")
|
||
print(f"交易次数: {int(best['n_trades'])} | 胜率: {best['win_rate']:.1f}%")
|
||
print(f"Sharpe: {best['sharpe']:.4f} | 最大回撤: {best['max_dd_pct']:.1f}%")
|
||
print("=" * 90)
|
||
|
||
# 逐年权益
|
||
cfg = BBMidlineConfig(
|
||
bb_period=int(best["period"]),
|
||
bb_std=float(best["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=use_1m,
|
||
kline_step_min=step_min,
|
||
)
|
||
final_res = run_bb_midline_backtest(df, cfg, df_1m=df_1m if use_1m else None)
|
||
eq = final_res.equity_curve["equity"].dropna()
|
||
|
||
print("\n逐年权益 (年末):")
|
||
eq_ts = eq.copy()
|
||
eq_ts.index = pd.to_datetime(eq_ts.index)
|
||
prev = 200.0
|
||
for y in range(2020, 2026):
|
||
sub = eq_ts[eq_ts.index.year == y]
|
||
if len(sub) > 0:
|
||
ye = float(sub.iloc[-1])
|
||
ret = (ye - prev) / prev * 100.0 if prev > 0 else 0.0
|
||
print(f" {y}: {ye:.2f} U (当年收益 {ret:+.1f}%)")
|
||
prev = ye
|
||
|
||
print(f"\n总手续费: {final_res.total_fee:.2f} U")
|
||
print(f"总返佣: {final_res.total_rebate:.2f} U")
|
||
print(f"净手续费: {final_res.total_fee - final_res.total_rebate:.2f} U")
|
||
|
||
# 保存最佳参数交易明细
|
||
trade_path = out_dir / f"bb_sweep_best_trades_{args.kline_period}_{stamp}.csv"
|
||
trade_rows = []
|
||
for i, t in enumerate(final_res.trades, 1):
|
||
trade_rows.append({
|
||
"序号": i,
|
||
"方向": "做多" if t.side == "long" else "做空",
|
||
"开仓时间": t.entry_time,
|
||
"平仓时间": t.exit_time,
|
||
"开仓价": round(t.entry_price, 2),
|
||
"平仓价": round(t.exit_price, 2),
|
||
"净盈亏": round(t.net_pnl, 4),
|
||
"平仓原因": t.exit_reason,
|
||
})
|
||
pd.DataFrame(trade_rows).to_csv(trade_path, index=False, encoding="utf-8-sig")
|
||
print(f"\n最佳参数交易明细: {trade_path}")
|
||
|
||
finally:
|
||
Path(df_path).unlink(missing_ok=True)
|
||
if df_1m_path:
|
||
Path(df_1m_path).unlink(missing_ok=True)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|