457 lines
18 KiB
Python
457 lines
18 KiB
Python
"""
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布林带中轨策略 - 全参数网格搜索 (2020-2025)
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策略逻辑:
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- 阳线 + 碰到布林带均线(先涨碰到) → 开多，碰上轨止盈
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- 阴线 + 碰到布林带均线(先跌碰到) → 平多开空，碰下轨止盈
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- 使用 1m 线判断当前K线是先跌碰均线还是先涨碰均线
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- 每根K线只能操作一次
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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
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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
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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
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import io
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import json
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import math
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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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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 frange(start: float, end: float, step: float) -> list[float]:
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out: list[float] = []
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x = float(start)
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while x <= end + 1e-9:
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out.append(round(x, 6))
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x += step
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return out
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def build_full_grid(
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p_start: float = 1,
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p_end: float = 1000,
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p_step: float = 1,
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s_start: float = 0.5,
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s_end: float = 1000,
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s_step: float = 0.5,
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) -> list[tuple[int, float]]:
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"""构建完整参数网格，按 (0.5,0.5),(0.5,1)...(0.5,1000),(1,0.5)... 顺序"""
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periods = sorted({max(1, min(1000, int(round(v)))) for v in frange(p_start, p_end, p_step)})
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stds = sorted({round(max(0.5, min(1000.0, v)), 2) for v in frange(s_start, s_end, s_step)})
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out = [(p, s) for p in periods for s in stds]
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return sorted(set(out))
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def score_stable(ret_pct: float, sharpe: float, dd_pct: float, n_trades: int) -> float:
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"""收益稳定性评分：收益+夏普加分，回撤惩罚，交易过少惩罚"""
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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 - dd_pct * 0.8 + sparse_penalty
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def _checkpoint_meta(
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period: str,
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start: str,
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end: str,
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p_step: float,
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s_step: float,
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sample: bool,
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focus: bool,
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fine: bool,
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) -> dict:
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return {
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"period": period,
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"start": start,
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"end": end,
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"p_step": p_step,
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"s_step": s_step,
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"sample": sample,
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"focus": focus,
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"fine": fine,
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}
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def _checkpoint_path(out_dir: Path, meta: dict) -> tuple[Path, Path]:
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"""返回 checkpoint 数据文件和 meta 文件路径"""
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h = hashlib.md5(json.dumps(meta, sort_keys=True).encode()).hexdigest()[:12]
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return (
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out_dir / f"bb_full_grid_{meta['period']}_resume_{h}.csv",
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out_dir / f"bb_full_grid_{meta['period']}_resume_{h}.meta.json",
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)
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def load_checkpoint(
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ckpt_path: Path,
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meta_path: Path,
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meta: dict,
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) -> tuple[pd.DataFrame, set[tuple[int, float]]]:
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"""
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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)。
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否则返回 (空df, 空集合)。
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"""
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if not ckpt_path.exists() or not meta_path.exists():
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return pd.DataFrame(), set()
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try:
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with open(meta_path, "r", encoding="utf-8") as f:
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saved = json.load(f)
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if saved != meta:
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return pd.DataFrame(), set()
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except (json.JSONDecodeError, OSError):
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return pd.DataFrame(), set()
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try:
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df = pd.read_csv(ckpt_path)
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if "period" not in df.columns or "std" not in df.columns:
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return pd.DataFrame(), set()
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done = {(int(r["period"]), round(float(r["std"]), 2)) for _, r in df.iterrows()}
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return df, done
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except Exception:
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return pd.DataFrame(), set()
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def save_checkpoint(ckpt_path: Path, meta_path: Path, meta: dict, rows: list[dict]) -> None:
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"""追加/覆盖保存断点"""
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ckpt_path.parent.mkdir(parents=True, exist_ok=True)
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with open(meta_path, "w", encoding="utf-8") as f:
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json.dump(meta, f, indent=2)
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df = pd.DataFrame(rows)
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df.to_csv(ckpt_path, index=False)
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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(G_DF, G_DF_1M, bb_mid.values, kline_step_min=G_STEP_MIN)
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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 = float(pnl.mean() / pnl.std()) * math.sqrt(365.0) if pnl.std() > 0 else 0.0
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stable_score = score_stable(ret_pct, sharpe, dd_pct, n_trades)
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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": 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": stable_score,
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})
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return rows
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def run_grid_search(
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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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existing_rows: list[dict] | None = None,
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ckpt_path: Path | None = None,
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meta_path: Path | None = None,
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meta: dict | None = None,
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checkpoint_interval: int = 5,
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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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rows: list[dict] = list(existing_rows) if existing_rows else []
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print(f"待运行: {total_combos} 组合 ({total_periods} period组), workers={workers}" + (
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f", 断点续跑 (已有 {len(rows)} 条)" if rows else ""
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))
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start = time.time()
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last_save = 0
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def maybe_save(n_done_periods: int):
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nonlocal last_save
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if ckpt_path and meta_path and meta and n_done_periods > 0:
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if n_done_periods - last_save >= checkpoint_interval:
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save_checkpoint(ckpt_path, meta_path, meta, rows)
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last_save = n_done_periods
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if workers <= 1:
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_init_worker(df_path, df_1m_path, use_1m, step_min)
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done_periods = 0
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done_combos = 0
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for task in tasks:
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res = _eval_period_task(task)
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period = task[0]
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# 打印该period的所有结果
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for row in res:
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print(f"✓ period={int(row['period']):4d}, std={float(row['std']):7.2f} | "
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f"收益: {row['ret_pct']:+7.2f}% | 回撤: {row['max_dd_pct']:6.2f}% | "
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f"夏普: {row['sharpe']:7.3f} | 交易: {int(row['n_trades']):6d} | "
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f"评分: {row['stable_score']:7.1f}", flush=True)
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rows.extend(res)
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done_periods += 1
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done_combos += len(task[1])
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maybe_save(done_periods)
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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} ({done_periods}/{total_periods} periods), 用时 {elapsed:.0f}s", flush=True)
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else:
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done_periods = 0
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done_combos = 0
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try:
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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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# 打印该period的所有结果
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for row in res:
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print(f"✓ period={int(row['period']):4d}, std={float(row['std']):7.2f} | "
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f"收益: {row['ret_pct']:+7.2f}% | 回撤: {row['max_dd_pct']:6.2f}% | "
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f"夏普: {row['sharpe']:7.3f} | 交易: {int(row['n_trades']):6d} | "
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f"评分: {row['stable_score']:7.1f}", flush=True)
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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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maybe_save(done_periods)
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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} ({done_periods}/{total_periods} periods), 用时 {elapsed:.0f}s", flush=True)
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except (PermissionError, OSError) as e:
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print(f"多进程不可用 ({e})，改用单进程...")
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_init_worker(df_path, df_1m_path, use_1m, step_min)
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done_periods = 0
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done_combos = 0
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for task in tasks:
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res = _eval_period_task(task)
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# 打印该period的所有结果
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for row in res:
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print(f"✓ period={int(row['period']):4d}, std={float(row['std']):7.2f} | "
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f"收益: {row['ret_pct']:+7.2f}% | 回撤: {row['max_dd_pct']:6.2f}% | "
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f"夏普: {row['sharpe']:7.3f} | 交易: {int(row['n_trades']):6d} | "
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f"评分: {row['stable_score']:7.1f}", flush=True)
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rows.extend(res)
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done_periods += 1
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done_combos += len(task[1])
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maybe_save(done_periods)
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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} ({done_periods}/{total_periods} periods), 用时 {elapsed:.0f}s", flush=True)
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if ckpt_path and meta_path and meta and rows:
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save_checkpoint(ckpt_path, meta_path, meta, rows)
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df = pd.DataFrame(rows)
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print(f"完成, 总用时 {time.time() - start:.1f}s")
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return df
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def main():
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# Windows 兼容的无缓冲设置
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if os.name == 'nt': # Windows
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# Windows 上通过重定向来禁用缓冲
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import io
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', line_buffering=True)
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else: # Linux/macOS
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sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 1, encoding='utf-8')
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parser = argparse.ArgumentParser(description="布林带全参数网格搜索 (1-1000, 0.5-1000)")
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parser.add_argument("-p", "--period", default="5m", choices=["5m", "15m", "30m"])
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parser.add_argument("--start", default="2020-01-01")
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parser.add_argument("--end", default="2026-01-01")
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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("--p-step", type=float, default=5, help="period 步长 (默认5, 全量用1)")
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parser.add_argument("--s-step", type=float, default=5, help="std 步长 (默认5, 全量用0.5或1)")
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parser.add_argument("--quick", action="store_true", help="快速模式: p-step=20, s-step=20")
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parser.add_argument("--sample", action="store_true", help="采样模式: 仅用2022-2024两年加速")
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parser.add_argument("--focus", action="store_true", help="聚焦模式: 仅在period 50-400, std 100-800 细搜")
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parser.add_argument("--fine", action="store_true", help="精细模式: 在period 280-310, std 450-550 细搜")
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parser.add_argument("--no-resume", action="store_true", help="禁用断点续跑，重新开始")
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parser.add_argument("--checkpoint-interval", type=int, default=10,
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help="每完成 N 个 period 组保存一次断点 (默认 10)")
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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.period.replace("m", ""))
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if args.sample:
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args.start, args.end = "2022-01-01", "2024-01-01"
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print("采样模式: 使用 2022-2024 数据加速")
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if args.quick:
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p_step, s_step = 20.0, 20.0
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else:
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p_step, s_step = args.p_step, args.s_step
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if args.fine:
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params = build_full_grid(p_start=280, p_end=300, p_step=2, s_start=480, s_end=510, s_step=2)
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print("精细模式: period 280-300 step=2, std 480-510 step=2 (~176组合)")
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elif args.focus:
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params = build_full_grid(p_start=50, p_end=400, p_step=25, s_start=100, s_end=800, s_step=50)
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print("聚焦模式: period 50-400 step=25, std 100-800 step=50 (~225组合)")
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else:
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params = build_full_grid(p_step=p_step, s_step=s_step)
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print(f"网格参数: period 1-1000 step={p_step}, std 0.5-1000 step={s_step} → {len(params)} 组合")
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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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meta = _checkpoint_meta(
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args.period, args.start, args.end, p_step, s_step,
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args.sample, args.focus, args.fine,
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)
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ckpt_path, meta_path = _checkpoint_path(out_dir, meta)
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existing_rows: list[dict] = []
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params_to_run = params
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if not args.no_resume:
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ckpt_df, done_set = load_checkpoint(ckpt_path, meta_path, meta)
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if len(done_set) > 0:
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existing_rows = ckpt_df.to_dict("records")
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params_to_run = [(p, s) for p, s in params if (int(p), round(float(s), 2)) not in done_set]
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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()
|