jackyu66gitandCursor
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自动刷新改用 tail update 与 scrollToPosition 恢复视窗，避免 setData 后跳到最右；拆分 chart_tv 模块并扩展 analyze/recent API。同步威科夫分析、pipeline 增量构建及相关策略与配置。 Co-authored-by: Cursor <cursoragent@cursor.com>
247 lines
9.4 KiB
Python
247 lines
9.4 KiB
Python
#!/usr/bin/env python3
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"""
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BTC Maker Micro Scalper — 回测结果统计
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重点指标：Net Expectancy（不是胜率）
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E = 胜率×平均盈利 - 失败率×平均亏损 - 手续费 - 滑点
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用法：
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python user_data/Chan/strategies/mms_stats.py
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python user_data/Chan/strategies/mms_stats.py --file user_data/backtest_results/xxx.zip
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python user_data/Chan/strategies/mms_stats.py --slippage 0.00005
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"""
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from __future__ import annotations
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import argparse
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import json
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import zipfile
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from pathlib import Path
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from typing import Any
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import numpy as np
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import pandas as pd
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def _latest_backtest(results_dir: Path) -> Path | None:
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zips = sorted(results_dir.glob("backtest-result-*.zip"), key=lambda p: p.stat().st_mtime)
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return zips[-1] if zips else None
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def _load_trades(path: Path) -> tuple[pd.DataFrame, dict[str, Any]]:
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meta: dict[str, Any] = {}
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if path.suffix == ".zip":
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with zipfile.ZipFile(path, "r") as zf:
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names = zf.namelist()
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# prefer meta + trades json inside zip
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trade_name = next((n for n in names if n.endswith(".json") and "meta" not in n), None)
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meta_name = next((n for n in names if n.endswith(".meta.json")), None)
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if meta_name:
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meta = json.loads(zf.read(meta_name))
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if not trade_name:
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raise FileNotFoundError(f"No trades json in {path}")
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payload = json.loads(zf.read(trade_name))
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else:
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payload = json.loads(path.read_text())
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# Freqtrade formats: {"strategy": {"BTC_...": {"trades": [...]}}}
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# or flat list / {"trades": [...]}
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trades = None
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if isinstance(payload, list):
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trades = payload
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elif isinstance(payload, dict):
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if "trades" in payload:
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trades = payload["trades"]
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elif isinstance(payload.get("strategy"), dict):
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# freqtrade zip: {"strategy": {"BTC_Maker_Micro_Scalper": {"trades": [...]}}}
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for name, v in payload["strategy"].items():
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if isinstance(v, dict) and "trades" in v:
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trades = v["trades"]
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meta.setdefault("strategy", name)
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break
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if trades is None:
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for _k, v in payload.items():
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if isinstance(v, dict) and "trades" in v:
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trades = v["trades"]
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meta.setdefault("strategy", _k)
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break
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if trades is None:
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raise ValueError(f"Cannot parse trades from {path}")
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df = pd.DataFrame(trades)
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return df, meta
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def summarize(df: pd.DataFrame, fee_rate: float = 0.00016, slippage: float = 0.0) -> dict[str, Any]:
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if df.empty:
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return {"error": "no trades"}
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# profit_ratio is net of fees in freqtrade; also keep absolute
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profit_col = "profit_ratio" if "profit_ratio" in df.columns else "close_profit"
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profits = df[profit_col].astype(float)
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wins = profits[profits > 0]
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losses = profits[profits <= 0]
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n = len(profits)
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win_rate = len(wins) / n if n else 0.0
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loss_rate = 1.0 - win_rate
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avg_win = float(wins.mean()) if len(wins) else 0.0
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avg_loss = float(losses.mean()) if len(losses) else 0.0 # negative or 0
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avg_loss_abs = abs(avg_loss)
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gross_profit = float(wins.sum()) if len(wins) else 0.0
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gross_loss = float((-losses).sum()) if len(losses) else 0.0
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profit_factor = (gross_profit / gross_loss) if gross_loss > 0 else float("inf")
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# 手续费：freqtrade 的 profit 已扣费；这里单独估算双边 maker 占比
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# 每笔双边 fee ≈ 2 * fee_rate（相对名义）
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fee_per_trade = 2.0 * fee_rate
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total_fee_est = n * fee_per_trade
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# 滑点假设（每边）
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slip_per_trade = 2.0 * slippage
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total_slip_est = n * slip_per_trade
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# Net Expectancy（每笔期望，比率）
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# E = WR*avg_win - LR*avg_loss_abs - fee - slip
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expectancy = win_rate * avg_win - loss_rate * avg_loss_abs - fee_per_trade - slip_per_trade
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# 注意：若 profit_ratio 已含手续费，上式 fee 会双重扣除。
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# 提供两个版本：
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# 1) E_raw：用毛期望再减 fee/slip（假设 profit 含 fee → 用 E_from_net）
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# 2) E_from_net：直接用已实现平均利润（已含 fee）再减额外滑点假设
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e_from_net = float(profits.mean()) - slip_per_trade
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# 最大回撤（权益曲线，相对）
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equity = (1.0 + profits).cumprod()
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peak = equity.cummax()
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dd = (equity - peak) / peak
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max_dd = float(dd.min()) if len(dd) else 0.0
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# 持仓时间
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hold_min = None
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if "open_date" in df.columns and "close_date" in df.columns:
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od = pd.to_datetime(df["open_date"], utc=True)
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cd = pd.to_datetime(df["close_date"], utc=True)
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hold_min = float(((cd - od).dt.total_seconds() / 60.0).mean())
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# Maker 成交率：若有 order_type / is_short 等字段无法直接得，默认限价策略按 100% 标注
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maker_rate = 1.0
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if "exit_reason" in df.columns:
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# 无法精确时保持 1.0；实盘可从策略 _maker_fills 导出
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pass
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# 手续费占毛利
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fee_share = None
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if "fee_open" in df.columns and "fee_close" in df.columns:
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fees = df["fee_open"].astype(float).fillna(0) + df["fee_close"].astype(float).fillna(0)
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abs_pnl = df.get("profit_abs", profits).astype(float).abs().sum()
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fee_share = float(fees.sum() / abs_pnl) if abs_pnl else None
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total_fee_est = float(fees.sum())
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return {
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"total_trades": n,
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"win_rate": win_rate,
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"avg_win": avg_win,
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"avg_loss": avg_loss,
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"profit_factor": profit_factor,
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"max_drawdown": max_dd,
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"fee_est_total_ratio_units": total_fee_est,
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"fee_share_of_abs_pnl": fee_share,
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"maker_fill_rate_assumed": maker_rate,
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"avg_hold_minutes": hold_min,
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"net_expectancy_from_realized": e_from_net,
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"net_expectancy_formula_rebuild": expectancy,
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"total_profit_ratio_sum": float(profits.sum()),
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"avg_profit": float(profits.mean()),
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"slippage_assumed_per_side": slippage,
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"note": (
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"优先看 net_expectancy_from_realized（已含 freqtrade 手续费）。"
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"net_expectancy_formula_rebuild 会再减一遍 fee，仅作分解参考。"
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),
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}
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def print_report(stats: dict[str, Any], source: str) -> None:
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print("=" * 60)
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print("BTC Maker Micro Scalper — Backtest Stats")
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print(f"source: {source}")
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print("=" * 60)
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if "error" in stats:
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print(stats["error"])
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return
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def pct(x):
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return f"{x * 100:.4f}%" if x is not None else "n/a"
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print(f"总交易次数 : {stats['total_trades']}")
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print(f"胜率 : {pct(stats['win_rate'])} (勿作为主指标)")
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print(f"平均盈利 : {pct(stats['avg_win'])}")
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print(f"平均亏损 : {pct(stats['avg_loss'])}")
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print(f"Profit Factor : {stats['profit_factor']:.4f}")
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print(f"最大回撤 : {pct(stats['max_drawdown'])}")
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print(f"手续费占比(abs pnl) : {stats['fee_share_of_abs_pnl']}")
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print(f"Maker成交率(假设) : {pct(stats['maker_fill_rate_assumed'])}")
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print(f"平均持仓时间(分钟) : {stats['avg_hold_minutes']}")
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print("-" * 60)
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print(f"Net Expectancy/笔 : {pct(stats['net_expectancy_from_realized'])} ★主指标")
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print(f"公式重建 E(参考) : {pct(stats['net_expectancy_formula_rebuild'])}")
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print(f"累计收益(比率和) : {pct(stats['total_profit_ratio_sum'])}")
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print(f"平均单笔 : {pct(stats['avg_profit'])}")
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print("-" * 60)
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print(stats["note"])
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print("=" * 60)
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def export_equity_csv(df: pd.DataFrame, out: Path) -> None:
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if df.empty or "profit_ratio" not in df.columns:
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return
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profits = df["profit_ratio"].astype(float)
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equity = (1.0 + profits).cumprod()
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out_df = pd.DataFrame({
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"close_date": df.get("close_date"),
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"profit_ratio": profits,
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"equity": equity,
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})
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out.parent.mkdir(parents=True, exist_ok=True)
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out_df.to_csv(out, index=False)
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print(f"净收益曲线已导出: {out}")
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--file", type=str, default=None, help="backtest zip/json path")
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ap.add_argument("--slippage", type=float, default=0.0, help="per-side slippage ratio")
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ap.add_argument("--fee", type=float, default=0.00016, help="per-side maker fee ratio")
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ap.add_argument(
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"--equity-out",
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type=str,
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default="user_data/plot/mms_equity.csv",
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help="equity curve csv",
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)
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args = ap.parse_args()
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root = Path(__file__).resolve().parents[3] # freqtrade root
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results_dir = root / "user_data" / "backtest_results"
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path = Path(args.file) if args.file else _latest_backtest(results_dir)
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if path is None or not path.exists():
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print("未找到回测结果。请先运行 backtesting，或用 --file 指定。")
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print(
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"示例:\n"
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" freqtrade backtesting -c ./user_data/Chan/config/BTC_Maker_Micro_Scalper.json \\\n"
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" --strategy BTC_Maker_Micro_Scalper --strategy-path ./user_data/Chan/strategies \\\n"
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" --timerange=20260101- --fee 0.00016 --enable-protections"
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)
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return
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df, meta = _load_trades(path)
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stats = summarize(df, fee_rate=args.fee, slippage=args.slippage)
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print_report(stats, str(path))
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if meta:
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print(f"meta keys: {list(meta.keys())[:8]}")
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export_equity_csv(df, root / args.equity_out)
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if __name__ == "__main__":
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main()
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