451 lines
20 KiB
Python
451 lines
20 KiB
Python
# -*- coding: utf-8 -*-
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"""
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MAE / MFE 分析
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==============
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MT5 标准回测 xlsx 不含逐笔 MAE/MFE（最大不利/有利偏移），需要由 EA 在
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OnDeinit 里把每笔交易的 MAE/MFE 写到 CSV。本模块：
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1. mql5_snippet() : 返回可粘贴进 EA 的 MQL5 代码，自动导出 mae_mfe.csv
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2. analyze(df) : 由 CSV 计算最优 TP/SL 区间
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3. build_html(df) : 散点图 + 推荐区间
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CSV 列约定（首行表头）:
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ticket,direction,open_time,close_time,open_price,close_price,
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mae_points,mfe_points,profit_money
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direction: long / short
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mae_points: 持仓期间最大不利偏移（点，正数表示逆向走了多少点）
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mfe_points: 持仓期间最大有利偏移（点，正数）
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profit_money: 该笔最终盈亏（金额）
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分析原理:
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- MFE vs 盈亏散点：能涨到 X 点的笔里，最终盈利占比？→ 若大量高 MFE 笔最终
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亏损，说明 TP 太贪；把 TP 放到 MFE 高胜率区间可改善。
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- MAE vs 盈亏散点：逆向超过 Y 点的笔几乎全亏 → SL 设在 Y 内可避免大损。
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- 用扫描法找使"理论净盈利"最大的 TP/SL 组合（基于历史 MAE/MFE 重算）。
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用法:
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python mae_mfe.py <mae_mfe.csv>
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python mae_mfe.py --show-snippet # 打印 MQL5 代码
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python mae_mfe.py --demo # 合成数据演示
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"""
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from __future__ import annotations
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import argparse
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import html
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import os
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import sys
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from typing import Any, Dict, List, Optional, Tuple
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import numpy as np
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import pandas as pd
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OUT_DIR = "output"
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# =========================================================================== #
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# 1. MQL5 代码片段
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# =========================================================================== #
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def mql5_snippet() -> str:
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"""返回 MQL5 代码：在 OnDeinit 里把每笔历史仓位的 MAE/MFE 写入 CSV。"""
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return r'''// ====== MAE/MFE 日志片段（粘贴进 EA） ======
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// 在文件顶部加:
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#include <Trade\DealInfo.mqh>
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#include <Trade\PositionInfo.mqh>
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// 在 OnDeinit(const int reason) 末尾加:
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void ExportMAEMFE()
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{
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string fname = "mae_mfe_" + TimeToString(TimeCurrent(), TIME_DATE) + ".csv";
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int h = FileOpen(fname, FILE_WRITE | FILE_CSV | FILE_ANSI, ',');
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if(h == INVALID_HANDLE) { Print("MAE/MFE: 文件打开失败"); return; }
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FileWrite(h, "ticket","direction","open_time","close_time","open_price","close_price",
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"mae_points","mfe_points","profit_money");
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HistorySelect(0, TimeCurrent() + 86400);
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int total = HistoryDealsTotal();
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// 按订单(order)配对 in/out
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for(int i = 0; i < total; i++)
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{
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ulong dealIn = HistoryDealGetTicket(i);
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if(HistoryDealGetInteger(dealIn, DEAL_ENTRY) != DEAL_ENTRY_IN) continue;
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long order = HistoryDealGetInteger(dealIn, DEAL_ORDER);
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ulong dealOut = 0;
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// 找同 order 的 out 成交
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for(int j = i + 1; j < total; j++)
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{
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ulong d = HistoryDealGetTicket(j);
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if(HistoryDealGetInteger(d, DEAL_ORDER) == order &&
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HistoryDealGetInteger(d, DEAL_ENTRY) == DEAL_ENTRY_OUT)
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{ dealOut = d; break; }
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}
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if(dealOut == 0) continue;
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datetime tIn = (datetime)HistoryDealGetInteger(dealIn, DEAL_TIME);
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datetime tOut = (datetime)HistoryDealGetInteger(dealOut, DEAL_TIME);
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double pIn = HistoryDealGetDouble(dealIn, DEAL_PRICE);
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double pOut = HistoryDealGetDouble(dealOut, DEAL_PRICE);
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long type = HistoryDealGetInteger(dealIn, DEAL_TYPE); // DEAL_TYPE_BUY / SELL
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double profit = HistoryDealGetDouble(dealOut, DEAL_PROFIT)
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+ HistoryDealGetDouble(dealOut, DEAL_SWAP)
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+ HistoryDealGetDouble(dealOut, DEAL_COMMISSION);
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string dir = (type == DEAL_TYPE_BUY) ? "long" : "short";
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// 扫描持仓期间最高/最低价算 MAE/MFE（点）
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double mfe = 0, mae = 0;
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double pt = SymbolInfoDouble(_Symbol, SYMBOL_POINT);
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for(datetime t = tIn; t <= tOut; t += PeriodSeconds())
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{
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double hi = iHigh(_Symbol, PERIOD_M1, iBarShift(_Symbol, PERIOD_M1, t, false));
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double lo = iLow (_Symbol, PERIOD_M1, iBarShift(_Symbol, PERIOD_M1, t, false));
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if(hi <= 0 || lo <= 0) continue;
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if(type == DEAL_TYPE_BUY)
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{
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double fav = (hi - pIn) / pt; // 有利
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double adv = (pIn - lo) / pt; // 不利
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if(fav > mfe) mfe = fav;
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if(adv > mae) mae = adv;
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}
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else
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{
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double fav = (pIn - lo) / pt;
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double adv = (hi - pIn) / pt;
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if(fav > mfe) mfe = fav;
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if(adv > mae) mae = adv;
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}
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}
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FileWrite(h, order, dir, TimeToString(tIn), TimeToString(tOut),
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DoubleToString(pIn, _Digits), DoubleToString(pOut, _Digits),
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DoubleToString(mae, 1), DoubleToString(mfe, 1),
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DoubleToString(profit, 2));
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}
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FileClose(h);
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Print("MAE/MFE 已导出: ", fname);
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}
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// 在 OnDeinit 里调用: ExportMAEMFE();
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// ====== 片段结束 ======
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'''
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# =========================================================================== #
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# 2. 加载
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# =========================================================================== #
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def load_csv(path: str) -> pd.DataFrame:
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df = pd.read_csv(path)
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# 列名容错
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rename = {
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"mae_points": "mae", "mfe_points": "mfe",
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"profit_money": "profit",
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"open_price": "open", "close_price": "close",
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}
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df = df.rename(columns={k: v for k, v in rename.items() if k in df.columns})
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for c in ("mae", "mfe", "profit"):
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if c in df.columns:
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df[c] = pd.to_numeric(df[c], errors="coerce")
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return df
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# =========================================================================== #
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# 3. 分析：最优 TP/SL
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# =========================================================================== #
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def analyze(df: pd.DataFrame, tp_grid: Optional[List[float]] = None,
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sl_grid: Optional[List[float]] = None) -> Dict[str, Any]:
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"""
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扫描 TP/SL 组合，找使理论净盈利最大的参数。
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理论模型：对每笔，若 mfe >= TP 则按 TP 离场（盈利 = TP×点值）；
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若 mae >= SL 则按 SL 离场（亏损 = -SL×点值）；
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否则按原 profit。
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这里用"点数口径"近似：盈利/亏损点数替代金额（点值统一假设）。
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"""
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if df.empty:
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return {}
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if tp_grid is None:
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mfe_max = float(df["mfe"].max())
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tp_grid = list(np.linspace(0, mfe_max, 30))
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if sl_grid is None:
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mae_max = float(df["mae"].max())
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sl_grid = list(np.linspace(0, mae_max, 30))
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mae = df["mae"].to_numpy()
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mfe = df["mfe"].to_numpy()
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n = len(df)
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n_sim = len(sl_grid) * len(tp_grid)
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# 向量化：构造 (n_sim, n) 矩阵，避免 Python 嵌套循环
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mae_arr = np.broadcast_to(mae, (len(sl_grid), len(tp_grid), n)).reshape(-1, n)
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mfe_arr = np.broadcast_to(mfe, (len(sl_grid), len(tp_grid), n)).reshape(-1, n)
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sl_arr = np.tile(np.array(sl_grid)[:, np.newaxis], (1, n)).reshape(-1, n)
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tp_arr = np.tile(np.array(tp_grid)[np.newaxis, :], (len(sl_grid), n)).reshape(-1, n)
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# 优先 SL 触发（更保守）
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result_pts = np.where(mae_arr >= sl_arr, -sl_arr,
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np.where(mfe_arr >= tp_arr, tp_arr,
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(mfe_arr - mae_arr) * 0.5))
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net_arr = result_pts.sum(axis=1)
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wins_arr = (result_pts > 0).sum(axis=1)
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losses_arr = -(result_pts[result_pts <= 0]).sum(axis=1)
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pf_arr = np.where(losses_arr > 0, result_pts[result_pts > 0].sum(axis=1) / losses_arr, np.inf)
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wr_arr = wins_arr / n * 100
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# 找最佳
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best_i = int(np.nanargmax(net_arr))
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grid_df = pd.DataFrame({
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"tp": np.repeat(np.array(tp_grid), len(sl_grid)),
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"sl": np.tile(np.array(sl_grid), len(tp_grid)),
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"net": net_arr,
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"win_rate": wr_arr,
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"pf": pf_arr,
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})
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best = {"tp": float(tp_grid[best_i % len(tp_grid)]),
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"sl": float(sl_grid[best_i // len(tp_grid)]),
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"net": float(net_arr[best_i]),
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"win_rate": float(wr_arr[best_i]),
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"pf": float(pf_arr[best_i])}
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# MAE/MFE 分布统计
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mae_threshold = float(df.loc[df["profit"] <= 0, "mae"].quantile(0.75)) if (df["profit"] <= 0).any() else 0
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mfe_threshold = float(df.loc[df["profit"] > 0, "mfe"].quantile(0.5)) if (df["profit"] > 0).any() else 0
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return {
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"best": best,
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"grid": grid_df,
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"mae_q3_of_losers": mae_threshold, # 75% 亏损笔的 MAE 上限 → SL 候选
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"mfe_median_of_winners": mfe_threshold, # 盈利笔 MFE 中位 → TP 候选
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"n": n,
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"profit_corr_mae": float(df["profit"].corr(df["mae"])),
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"profit_corr_mfe": float(df["profit"].corr(df["mfe"])),
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}
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# =========================================================================== #
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# 4. SVG 散点
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# =========================================================================== #
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def svg_scatter(x: np.ndarray, y: np.ndarray, profit: np.ndarray,
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xlabel: str, ylabel: str, title: str,
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threshold: Optional[float] = None, threshold_label: str = "",
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w: int = 460, h: int = 320) -> str:
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top, bottom, left, right = 30, 40, 50, 16
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plot_w = w - left - right
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plot_h = h - top - bottom
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x_min, x_max = float(x.min()), float(x.max())
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y_min, y_max = float(y.min()), float(y.max())
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if x_max == x_min: x_max = x_min + 1
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if y_max == y_min: y_max = y_min + 1
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def sx(v): return left + (v - x_min) / (x_max - x_min) * plot_w
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def sy(v): return top + plot_h - (v - y_min) / (y_max - y_min) * plot_h
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pts = ""
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for xi, yi, pi in zip(x, y, profit):
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col = "#16a34a" if pi > 0 else "#dc2626"
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pts += f'<circle cx="{sx(xi):.1f}" cy="{sy(yi):.1f}" r="2.2" fill="{col}" opacity="0.45"><title>{xlabel}={xi:.1f}, {ylabel}={yi:.2f}, profit={pi:.2f}</title></circle>'
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thr = ""
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if threshold is not None and x_min <= threshold <= x_max:
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tx = sx(threshold)
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thr = (f'<line x1="{tx:.1f}" y1="{top}" x2="{tx:.1f}" y2="{top+plot_h}" stroke="#1e3a8a" stroke-width="1.5" stroke-dasharray="4,3"/>'
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f'<text x="{tx+4:.1f}" y="{top+12:.1f}" font-size="9" fill="#1e3a8a">{html.escape(threshold_label)}={threshold:.1f}</text>')
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grid = ""
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for frac in (0, 0.5, 1.0):
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xv = x_min + frac * (x_max - x_min)
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yv = y_min + frac * (y_max - y_min)
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gx = left + frac * plot_w
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gy = top + plot_h - frac * plot_h
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grid += (f'<line x1="{gx:.1f}" y1="{top}" x2="{gx:.1f}" y2="{top+plot_h}" stroke="#f1f5f9" stroke-width="0.5"/>'
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f'<text x="{gx:.1f}" y="{h-bottom+14:.1f}" font-size="9" fill="#6b7280" text-anchor="middle">{xv:.0f}</text>'
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f'<line x1="{left}" y1="{gy:.1f}" x2="{w-right}" y2="{gy:.1f}" stroke="#f1f5f9" stroke-width="0.5"/>'
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f'<text x="{left-6:.1f}" y="{gy+3:.1f}" font-size="9" fill="#6b7280" text-anchor="end">{yv:.0f}</text>')
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return (f'<svg viewBox="0 0 {w} {h}" class="chart">'
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f'<text x="{w//2}" y="16" font-size="11" fill="#374151" text-anchor="middle">{html.escape(title)}</text>'
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f'{grid}{pts}{thr}'
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f'<text x="{w//2}" y="{h-4}" font-size="9" fill="#6b7280" text-anchor="middle">{html.escape(xlabel)} →</text>'
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f'<text x="14" y="{h//2}" font-size="9" fill="#6b7280" text-anchor="middle" transform="rotate(-90 14 {h//2})">{html.escape(ylabel)} →</text>'
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f'</svg>')
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def svg_tp_sl_heatmap(grid_df: pd.DataFrame, w: int = 460, h: int = 360) -> str:
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"""TP×SL 净盈利热力图。"""
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p = grid_df.pivot_table(index="sl", columns="tp", values="net", aggfunc="first")
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p = p.sort_index().sort_index(axis=1)
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Z = p.to_numpy()
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xs = list(p.columns)
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ys = list(p.index)
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top, bottom, left, right = 30, 40, 60, 16
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plot_w = w - left - right
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plot_h = h - top - bottom
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ny, nx = Z.shape
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cw = plot_w / nx
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ch = plot_h / ny
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valid = Z[~np.isnan(Z)]
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vmin, vmax = float(valid.min()), float(valid.max())
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if vmax == vmin: vmax = vmin + 1
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def col(v):
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if np.isnan(v): return "#f3f4f6"
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r = (v - vmin) / (vmax - vmin)
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if r < 0.5:
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t = r * 2
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return f"rgb({int(220-60*t)},{int(60+160*t)},{int(60+40*t)})"
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t = (r - 0.5) * 2
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return f"rgb({int(160-100*t)},{int(220-40*t)},{int(100-40*t)})"
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cells = ""
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for j in range(ny):
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for i in range(nx):
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v = Z[j, i]
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x = left + i * cw
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y = top + (ny - 1 - j) * ch
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cells += f'<rect x="{x:.1f}" y="{y:.1f}" width="{cw:.1f}" height="{ch:.1f}" fill="{col(v)}" stroke="#fff" stroke-width="0.4"/>'
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# 最优点
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bi = np.unravel_index(np.nanargmax(Z), Z.shape)
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bx = left + bi[1] * cw + cw / 2
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by = top + (ny - 1 - bi[0]) * ch + ch / 2
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marker = f'<circle cx="{bx:.1f}" cy="{by:.1f}" r="6" fill="none" stroke="#1e3a8a" stroke-width="2"/>'
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xlabs = "".join(f'<text x="{left+(i+0.5)*cw:.1f}" y="{h-bottom+14:.1f}" font-size="8" fill="#374151" text-anchor="middle">{xs[i]:.0f}</text>' for i in range(0, nx, 2))
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ylabs = "".join(f'<text x="{left-6:.1f}" y="{top+(ny-0.5-j)*ch+3:.1f}" font-size="8" fill="#374151" text-anchor="end">{ys[j]:.0f}</text>' for j in range(0, ny, 2))
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return (f'<svg viewBox="0 0 {w} {h}" class="chart">'
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f'<text x="{w//2}" y="16" font-size="11" fill="#374151" text-anchor="middle">理论净盈利 (TP × SL 扫描) — 圈=最优</text>'
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f'{cells}{marker}{xlabs}{ylabs}'
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f'<text x="{w//2}" y="{h-4}" font-size="9" fill="#6b7280" text-anchor="middle">TP(点) →</text>'
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f'<text x="14" y="{h//2}" font-size="9" fill="#6b7280" text-anchor="middle" transform="rotate(-90 14 {h//2})">SL(点) →</text>'
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f'</svg>')
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# =========================================================================== #
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# 5. HTML
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# =========================================================================== #
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def build_html(df: pd.DataFrame, res: Dict[str, Any]) -> str:
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mae_svg = svg_scatter(
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df["mae"].to_numpy(), df["profit"].to_numpy(), df["profit"].to_numpy(),
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"MAE(点)", "盈利($)", "MAE vs 盈亏（绿=赢/红=输）",
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threshold=res["mae_q3_of_losers"], threshold_label="SL候选",
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)
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mfe_svg = svg_scatter(
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df["mfe"].to_numpy(), df["profit"].to_numpy(), df["profit"].to_numpy(),
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"MFE(点)", "盈利($)", "MFE vs 盈亏（绿=赢/红=输）",
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threshold=res["mfe_median_of_winners"], threshold_label="TP候选",
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)
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tpsl_svg = svg_tp_sl_heatmap(res["grid"])
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b = res["best"]
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cards = (
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"<div class='cards'>"
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f"<div class='card'><div class='k'>样本笔数</div><div class='v'>{res['n']}</div></div>"
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f"<div class='card'><div class='k'>最优 TP(点)</div><div class='v'>{b['tp']:.0f}</div></div>"
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f"<div class='card'><div class='k'>最优 SL(点)</div><div class='v'>{b['sl']:.0f}</div></div>"
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f"<div class='card'><div class='k'>理论净盈利(点)</div><div class='v pos'>{b['net']:.0f}</div></div>"
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f"<div class='card'><div class='k'>理论胜率</div><div class='v'>{b['win_rate']:.1f}%</div></div>"
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f"<div class='card'><div class='k'>理论 PF</div><div class='v'>{b['pf']:.2f}</div></div>"
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"</div>"
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)
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interp = ""
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if res["profit_corr_mae"] < -0.3:
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interp += f"<p class='muted'>盈亏与 MAE 负相关 ({res['profit_corr_mae']:.2f})：逆向偏移越大越易亏，设 SL 合理。SL 候选 = 亏损笔 MAE 75 分位 ≈ <b>{res['mae_q3_of_losers']:.0f} 点</b>。</p>"
|
||
if res["profit_corr_mfe"] > 0.3:
|
||
interp += f"<p class='muted'>盈亏与 MFE 正相关 ({res['profit_corr_mfe']:.2f})：能跑到 X 点的笔更易盈利，TP 候选 = 盈利笔 MFE 中位 ≈ <b>{res['mfe_median_of_winners']:.0f} 点</b>。</p>"
|
||
if not interp:
|
||
interp = "<p class='muted'>MAE/MFE 与盈亏相关性弱，TP/SL 优化空间有限，建议关注入场逻辑。</p>"
|
||
|
||
interp += (f"<div class='note'><b>理论最优 TP/SL</b>：TP={b['tp']:.0f}点 / SL={b['sl']:.0f}点，"
|
||
f"在该组合下重算净盈利 {b['net']:.0f}点、胜率 {b['win_rate']:.1f}%、PF {b['pf']:.2f}。"
|
||
f"此为基于历史 MAE/MFE 的理论值，需回测验证。</div>")
|
||
|
||
return f"""<!doctype html><html lang="zh-CN"><head><meta charset="utf-8">
|
||
<title>MAE/MFE 分析</title>
|
||
<style>
|
||
body {{ font-family:-apple-system,"Microsoft YaHei",sans-serif; background:#f8fafc; color:#111827; margin:0; padding:20px;}}
|
||
.wrap {{ max-width:1100px; margin:0 auto;}}
|
||
h1,h2 {{ color:#1e3a8a;}} h1 {{ border-bottom:3px solid #1e3a8a; padding-bottom:8px;}}
|
||
.muted {{ color:#6b7280; font-size:12px;}}
|
||
.cards {{ display:grid; grid-template-columns:repeat(auto-fit,minmax(150px,1fr)); gap:12px; margin:16px 0;}}
|
||
.card {{ background:#fff; padding:12px; border-radius:8px; border:1px solid #e2e8f0;}}
|
||
.card .k {{ color:#6b7280; font-size:11px;}} .card .v {{ font-size:18px; font-weight:600; margin-top:4px;}}
|
||
.card .v.pos {{ color:#16a34a;}}
|
||
.chart {{ width:100%; height:auto; background:#fff; border:1px solid #e2e8f0; border-radius:6px;}}
|
||
.grid2 {{ display:grid; grid-template-columns:1fr 1fr; gap:12px;}}
|
||
.note {{ background:#fef3c7; border-left:4px solid #f59e0b; padding:10px 14px; margin:12px 0; border-radius:4px;}}
|
||
pre {{ background:#1e293b; color:#e2e8f0; padding:14px; border-radius:6px; overflow:auto; font-size:11px;}}
|
||
footer {{ color:#6b7280; font-size:11px; margin-top:32px; text-align:center;}}
|
||
</style></head><body><div class="wrap">
|
||
<h1>MAE / MFE 分析报告</h1>
|
||
<p class='muted'>样本: {res['n']} 笔 · 盈亏-MAE 相关 {res['profit_corr_mae']:.2f} · 盈亏-MFE 相关 {res['profit_corr_mfe']:.2f}</p>
|
||
{cards}
|
||
{interp}
|
||
<h2>1. MAE / MFE 散点</h2>
|
||
<div class="grid2"><div>{mae_svg}</div><div>{mfe_svg}</div></div>
|
||
<p class='muted'>蓝虚线 = 推荐的 SL/TP 候选阈值。MAE 图：右侧红点=逆向走很远还亏=该早止损；MFE 图：右侧绿点=涨得高最终赚=TP 可放到此处。</p>
|
||
<h2>2. TP × SL 扫描热力图</h2>
|
||
{tpsl_svg}
|
||
<p class='muted'>圈=理论最优组合。色越绿=净盈利越高。注意"宽绿区"比"单点深绿"更稳健。</p>
|
||
<h2>3. 如何获取 MAE/MFE 数据</h2>
|
||
<p class='muted'>MT5 标准 xlsx 报告不含逐笔 MAE/MFE。需在 EA 的 OnDeinit 里加如下代码导出 CSV，再喂给本工具：</p>
|
||
<pre>{html.escape(mql5_snippet())}</pre>
|
||
<footer>由 mae_mfe.py 生成 · {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M')}</footer>
|
||
</div></body></html>"""
|
||
|
||
|
||
# =========================================================================== #
|
||
# 6. demo
|
||
# =========================================================================== #
|
||
def _demo_df() -> pd.DataFrame:
|
||
rng = np.random.default_rng(3)
|
||
n = 400
|
||
direction = rng.choice(["long", "short"], n)
|
||
mae = np.abs(rng.normal(20, 15, n))
|
||
mfe = np.abs(rng.normal(40, 20, n))
|
||
# 盈亏与 mfe 正相关、与 mae 负相关
|
||
base = (mfe - mae) * 0.3 + rng.normal(0, 5, n)
|
||
profit = np.where(base > 0, mfe * 0.2, -mae * 0.25) + rng.normal(0, 2, n)
|
||
return pd.DataFrame({
|
||
"ticket": np.arange(n),
|
||
"direction": direction,
|
||
"mae": np.round(mae, 1),
|
||
"mfe": np.round(mfe, 1),
|
||
"profit": np.round(profit, 2),
|
||
})
|
||
|
||
|
||
# =========================================================================== #
|
||
# CLI
|
||
# =========================================================================== #
|
||
def main(argv: List[str]) -> int:
|
||
ap = argparse.ArgumentParser(description="MAE/MFE 分析")
|
||
ap.add_argument("csv", nargs="?", help="mae_mfe.csv 路径")
|
||
ap.add_argument("--show-snippet", action="store_true", help="打印 MQL5 代码片段")
|
||
ap.add_argument("--demo", action="store_true")
|
||
args = ap.parse_args(argv)
|
||
|
||
os.makedirs(OUT_DIR, exist_ok=True)
|
||
|
||
if args.show_snippet:
|
||
print(mql5_snippet())
|
||
return 0
|
||
|
||
if args.demo:
|
||
df = _demo_df()
|
||
else:
|
||
if not args.csv:
|
||
ap.error("需要 csv 路径，或 --demo，或 --show-snippet")
|
||
df = load_csv(args.csv)
|
||
|
||
res = analyze(df)
|
||
html_doc = build_html(df, res)
|
||
out = os.path.join(OUT_DIR, "mae_mfe.html")
|
||
with open(out, "w", encoding="utf-8") as f:
|
||
f.write(html_doc)
|
||
print(f"HTML 报告: {out}")
|
||
print(f"最优 TP={res['best']['tp']:.0f}点 SL={res['best']['sl']:.0f}点 "
|
||
f"理论净盈利={res['best']['net']:.0f}点 PF={res['best']['pf']:.2f}")
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main(sys.argv[1:]))
|