390 lines
13 KiB
Python
390 lines
13 KiB
Python
"""
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布林带均线策略 - 全参数组合扫描 (1-1000, 1-1000)
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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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参数遍历: (0.5,0.5)(0.5,1)...(0.5,std_max), (1,0.5)(1,1)...(1,std_max), ...
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直至 (period_max, std_max)
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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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def build_full_param_grid(
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period_min: float = 1.0,
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period_max: float = 1000.0,
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period_step: float = 1.0,
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std_min: float = 1.0,
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std_max: float = 1000.0,
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std_step: float = 1.0,
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) -> list[tuple[int, float]]:
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"""生成全量 (period, std) 组合，period 取整"""
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out = []
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p = period_min
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while p <= period_max:
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s = std_min
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while s <= std_max:
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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 stable_score(ret_pct: float, sharpe: float, dd_pct: float, n_trades: int) -> float:
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"""收益稳定性评分"""
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sparse_penalty = -5.0 if n_trades < 200 else 0.0
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return ret_pct + sharpe * 12.0 - abs(dd_pct) * 0.8 + sparse_penalty
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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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period, std_list = args
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assert G_DF is not None
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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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score = stable_score(ret_pct, sharpe, dd_pct, n_trades)
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rows.append(
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{
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"period": period,
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"std": round(float(std), 2),
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"final_eq": final_eq,
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"ret_pct": ret_pct,
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"n_trades": n_trades,
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"win_rate": win_rate,
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"sharpe": sharpe,
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"max_dd_u": dd_u,
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"max_dd_pct": dd_pct,
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"stable_score": score,
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}
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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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) -> pd.DataFrame:
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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" 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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res = fut.result()
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rows.extend(res)
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done_periods += 1
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done_combos += len(stds)
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if done_periods % max(1, total_periods // 20) == 0 or done_periods == total_periods:
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elapsed = time.time() - start
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print(f" 进度 {done_combos:,}/{total_combos:,} ({elapsed:.0f}s)")
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df = pd.DataFrame(rows)
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print(f" 完成, 用时 {time.time() - start:.1f}s")
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return df
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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 (1-1000, 1-1000)")
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parser.add_argument(
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"--period-min", type=float, default=1.0, help="period 下限"
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)
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parser.add_argument(
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"--period-max", type=float, default=1000.0, help="period 上限"
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)
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parser.add_argument(
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"--period-step", type=float, default=10.0, help="period 步长 (建议10以缩短时间)"
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)
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parser.add_argument("--std-min", type=float, default=0.5, help="std 下限")
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parser.add_argument("--std-max", type=float, default=1000.0, help="std 上限")
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parser.add_argument(
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"--std-step", type=float, default=1.0, help="std 步长"
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)
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parser.add_argument(
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"-p", "--kline-period", default="5m", choices=["5m", "15m", "30m"]
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)
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parser.add_argument(
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"-j", "--workers", type=int, default=max(1, (os.cpu_count() or 4) - 1)
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)
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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(
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"--source",
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default="bitmart",
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choices=["bitmart", "binance"],
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help="数据源",
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)
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parser.add_argument(
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"--quick",
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action="store_true",
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help="快速模式: period 1-200 step20, std 1-20 step2",
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)
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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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if args.quick:
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args.period_min = 1.0
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args.period_max = 200.0
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args.period_step = 20.0
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args.std_min = 0.5
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args.std_max = 20.0
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args.std_step = 1.0
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print(" 快速模式: period 1-200 step20, std 1-20 step2")
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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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print("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 = (
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load_klines("1m", "2020-01-01", "2026-01-01", source=args.source)
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if use_1m
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else None
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)
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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 = (
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load_klines("1m", "2020-01-01", "2026-01-01", source=alt)
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if use_1m
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else None
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)
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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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grid = build_full_param_grid(
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period_min=args.period_min,
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period_max=args.period_max,
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period_step=args.period_step,
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std_min=args.std_min,
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std_max=args.std_max,
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std_step=args.std_step,
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)
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print(f"参数网格: {len(grid):,} 组")
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print(
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f" period: {args.period_min}~{args.period_max} step{args.period_step}, "
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f"std: {args.std_min}~{args.std_max} step{args.std_step}"
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)
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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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result_df = evaluate_grid(
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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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)
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finally:
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Path(df_path).unlink(missing_ok=True)
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if df_1m_path:
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Path(df_1m_path).unlink(missing_ok=True)
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if result_df.empty:
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print("无有效结果")
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return
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best_stable = result_df.sort_values("stable_score", ascending=False).iloc[0]
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best_return = result_df.sort_values("ret_pct", ascending=False).iloc[0]
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stamp = time.strftime("%Y%m%d_%H%M%S")
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csv_path = out_dir / f"bb_midline_full_grid_{args.kline_period}_{stamp}.csv"
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result_df.to_csv(csv_path, index=False)
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print(f"\n扫描结果已保存: {csv_path}")
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print("\n" + "=" * 90)
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print("布林带均线策略 | 2020-2025 | 200U | 1%权益/单 | 万五 | 90%返佣次日8点 | 100x全仓")
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print("=" * 90)
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print(
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f"最佳稳定参数: BB({int(best_stable['period'])},{best_stable['std']}) | "
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f"权益={best_stable['final_eq']:.1f}U | 收益={best_stable['ret_pct']:+.1f}% | "
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f"回撤={best_stable['max_dd_pct']:.1f}% | Sharpe={best_stable['sharpe']:.2f} | "
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f"交易={int(best_stable['n_trades'])}"
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)
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print(
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f"最高收益参数: BB({int(best_return['period'])},{best_return['std']}) | "
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f"权益={best_return['final_eq']:.1f}U | 收益={best_return['ret_pct']:+.1f}% | "
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f"回撤={best_return['max_dd_pct']:.1f}% | Sharpe={best_return['sharpe']:.2f} | "
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f"交易={int(best_return['n_trades'])}"
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)
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print("=" * 90)
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cfg = BBMidlineConfig(
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bb_period=int(best_stable["period"]),
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bb_std=float(best_stable["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=use_1m,
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kline_step_min=step_min,
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)
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final_res = run_bb_midline_backtest(
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df, cfg, df_1m=df_1m if use_1m else None
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)
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eq = final_res.equity_curve["equity"].dropna()
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print("\n逐年权益 (年末):")
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eq_ts = eq.copy()
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eq_ts.index = pd.to_datetime(eq_ts.index)
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prev = 200.0
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for y in range(2020, 2026):
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sub = eq_ts[eq_ts.index.year == y]
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if len(sub) > 0:
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ye = float(sub.iloc[-1])
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ret = (ye - prev) / prev * 100.0 if prev > 0 else 0.0
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print(f" {y}: {ye:.1f} U (当年收益 {ret:+.1f}%)")
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prev = ye
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trade_path = out_dir / f"bb_midline_best_trades_{args.kline_period}_{stamp}.csv"
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rows = []
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for i, t in enumerate(final_res.trades, 1):
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rows.append({
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"序号": i,
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"方向": "做多" if t.side == "long" else "做空",
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"开仓时间": t.entry_time,
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"平仓时间": t.exit_time,
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"开仓价": round(t.entry_price, 2),
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"平仓价": round(t.exit_price, 2),
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"净盈亏": round(t.net_pnl, 2),
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"平仓原因": t.exit_reason,
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})
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pd.DataFrame(rows).to_csv(trade_path, index=False, encoding="utf-8-sig")
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print(f"\n最佳参数交易明细: {trade_path}")
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if __name__ == "__main__":
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main()
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