Prepare source-only public release for develop.
Add cluster audit pipeline, united EA updates, brochure generators, and publication hygiene (gitignore, MT5 path desensitization, pre-upload scan). Remove tracked reports, models, and binary artifacts from the repo. Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
zhutoutoutousan
co-authored by
Cursor
Cursor
parent
3f75a08848
commit
605faf5310
@@ -0,0 +1,236 @@
|
||||
"""Generate REPORT.md + charts for best_params.json.
|
||||
|
||||
WARNING: Python simulation only. For official results use:
|
||||
python run_mt5_portfolio.py && python generate_mt5_portfolio_report.py
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
import matplotlib.pyplot as plt
|
||||
import MetaTrader5 as mt5
|
||||
import pandas as pd
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[3]
|
||||
LAB = Path(__file__).resolve().parent
|
||||
sys.path.insert(0, str(LAB))
|
||||
sys.path.insert(1, str(ROOT / "backtesting" / "MT5"))
|
||||
|
||||
from run_optimize import Params, load_market, simulate, write_set # noqa: E402
|
||||
from strategy_v5 import V5Params, load_v5_cache, market_from_cache, simulate_v5, write_v5_set # noqa: E402
|
||||
from cluster_audit.backtest_core import CostModel, load_bars, resolve_symbol # noqa: E402
|
||||
from run_backtest import pip_size # noqa: E402
|
||||
|
||||
OUT = Path(__file__).resolve().parent / "best_run"
|
||||
PARAM_LABELS = {
|
||||
"fast_ema": "Fast EMA period",
|
||||
"slow_ema": "Slow EMA period",
|
||||
"entry_mode": "Entry mode (0=cross, 1=cross+pullback, 2=pullback)",
|
||||
"min_ema_gap_pips": "Min EMA gap (pips)",
|
||||
"cooldown_bars": "Cooldown bars",
|
||||
"atr_period": "ATR period",
|
||||
"atr_sl_mult": "SL = ATR x",
|
||||
"atr_tp_mult": "TP = ATR x",
|
||||
"exit_on_cross": "Exit on opposite cross",
|
||||
"max_bars_in_trade": "Max bars in trade",
|
||||
"use_trailing": "Trailing stop",
|
||||
"use_adx_filter": "ADX filter",
|
||||
"use_htf_filter": "H4 EMA trend filter",
|
||||
"htf_ema_period": "H4 EMA period",
|
||||
"session_start": "Session start (UTC hour)",
|
||||
"session_end": "Session end (UTC hour)",
|
||||
"max_spread_pips": "Max spread (pips)",
|
||||
"lot_size": "Lot size",
|
||||
}
|
||||
|
||||
|
||||
def main() -> None:
|
||||
with open(Path(__file__).parent / "best_params.json", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
version = data.get("version", 2)
|
||||
|
||||
if not mt5.initialize():
|
||||
raise SystemExit("MT5 init failed")
|
||||
try:
|
||||
sym = resolve_symbol("EURUSD")
|
||||
df = load_bars(sym, mt5.TIMEFRAME_M15, datetime(2020, 1, 1), datetime(2026, 1, 1))
|
||||
costs = CostModel.for_symbol(sym)
|
||||
pip = pip_size(sym)
|
||||
point = float(mt5.symbol_info(sym).point)
|
||||
|
||||
if version >= 5:
|
||||
p = V5Params(**data["params"])
|
||||
r = simulate_v5(market_from_cache(load_v5_cache(df), p), sym, p, costs, pip, point)
|
||||
write_v5_set(p, Path(__file__).parent / "SimpleEMA_optimized.set")
|
||||
initial_balance = p.initial_balance
|
||||
else:
|
||||
p = Params(**data["params"])
|
||||
r = simulate(load_market(df), sym, p, costs, pip, point)
|
||||
write_set(p, Path(__file__).parent / "SimpleEMA_optimized.set")
|
||||
initial_balance = p.initial_balance
|
||||
|
||||
rows = [
|
||||
{
|
||||
"side": t["side"],
|
||||
"open_time": df.index[t["open_i"]],
|
||||
"close_time": df.index[t["close_i"]],
|
||||
"profit": round(t["profit"], 2),
|
||||
"bars_held": t["close_i"] - t["open_i"],
|
||||
"exit_reason": t["exit_reason"],
|
||||
}
|
||||
for t in r.trades
|
||||
]
|
||||
tdf = pd.DataFrame(rows)
|
||||
tdf.to_csv(OUT / "trades.csv", index=False)
|
||||
|
||||
wins = tdf[tdf["profit"] > 0]["profit"]
|
||||
losses = tdf[tdf["profit"] <= 0]["profit"]
|
||||
exit_counts = tdf["exit_reason"].value_counts()
|
||||
|
||||
eq = [initial_balance]
|
||||
for pr in tdf["profit"]:
|
||||
eq.append(eq[-1] + pr)
|
||||
eq_times = pd.to_datetime(tdf["close_time"])
|
||||
eq_s = pd.Series(eq[1:], index=eq_times)
|
||||
dd = (eq_s - eq_s.cummax()) / eq_s.cummax() * 100
|
||||
max_dd = abs(float(dd.min())) if len(dd) else 0.0
|
||||
|
||||
monthly = tdf.copy()
|
||||
monthly["month"] = pd.to_datetime(monthly["close_time"]).dt.to_period("M")
|
||||
monthly_pnl = monthly.groupby("month")["profit"].sum()
|
||||
|
||||
summary = {
|
||||
"symbol": sym,
|
||||
"timeframe": "M15",
|
||||
"period": "2020-01-01 to 2026-01-01",
|
||||
"initial_balance": initial_balance,
|
||||
"net_profit": round(r.net_profit, 2),
|
||||
"return_pct": round(r.net_profit / initial_balance * 100, 2),
|
||||
"total_trades": r.total_trades,
|
||||
"win_rate": round(r.win_rate, 1),
|
||||
"profit_factor": round(r.profit_factor, 2),
|
||||
"max_drawdown_pct": round(max_dd, 2),
|
||||
"avg_win": round(float(wins.mean()), 2) if len(wins) else 0,
|
||||
"avg_loss": round(float(losses.mean()), 2) if len(losses) else 0,
|
||||
"best_trade": round(float(tdf["profit"].max()), 2),
|
||||
"worst_trade": round(float(tdf["profit"].min()), 2),
|
||||
"target_met_2000_trades": data.get("target_met", False),
|
||||
}
|
||||
with open(OUT / "report.json", "w", encoding="utf-8") as f:
|
||||
json.dump(summary, f, indent=2, ensure_ascii=False)
|
||||
|
||||
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
|
||||
axes[0, 0].plot(eq_times, eq[1:], lw=1.8, color="#2ca02c")
|
||||
axes[0, 0].axhline(initial_balance, ls="--", color="gray")
|
||||
axes[0, 0].set_title("Equity Curve")
|
||||
axes[0, 0].grid(alpha=0.3)
|
||||
axes[0, 1].fill_between(eq_times, dd, 0, color="#d62728", alpha=0.35)
|
||||
axes[0, 1].set_title("Drawdown %")
|
||||
axes[0, 1].grid(alpha=0.3)
|
||||
axes[1, 0].bar(
|
||||
range(len(monthly_pnl)),
|
||||
monthly_pnl.values,
|
||||
color=["#2ca02c" if v >= 0 else "#d62728" for v in monthly_pnl.values],
|
||||
)
|
||||
axes[1, 0].set_title("Monthly PnL")
|
||||
axes[1, 0].axhline(0, color="black", lw=0.6)
|
||||
axes[1, 1].bar(exit_counts.index.astype(str), exit_counts.values, color="#ff7f0e")
|
||||
axes[1, 1].set_title("Exit Reasons")
|
||||
fig.suptitle(
|
||||
f"SimpleEMA Best | Net ${r.net_profit:,.0f} | {r.total_trades} trades | "
|
||||
f"PF {r.profit_factor:.2f} | WR {r.win_rate:.1f}%",
|
||||
fontsize=12,
|
||||
)
|
||||
fig.tight_layout(rect=[0, 0, 1, 0.96])
|
||||
fig.savefig(OUT / "report.png", dpi=200, bbox_inches="tight")
|
||||
plt.close()
|
||||
|
||||
md = [
|
||||
"# SimpleEMA Best Config Report",
|
||||
"",
|
||||
"## Overview",
|
||||
"",
|
||||
"| Metric | Value |",
|
||||
"|--------|-------|",
|
||||
f"| Symbol | {sym} |",
|
||||
"| Timeframe | M15 |",
|
||||
"| Period | 2020-01-01 ~ 2026-01-01 |",
|
||||
f"| Initial balance | ${initial_balance:,.0f} |",
|
||||
f"| **Net profit** | **${summary['net_profit']:,.2f}** |",
|
||||
f"| Return | {summary['return_pct']}% |",
|
||||
f"| Total trades | {summary['total_trades']} |",
|
||||
f"| Win rate | {summary['win_rate']}% |",
|
||||
f"| Profit factor | {summary['profit_factor']} |",
|
||||
f"| Max drawdown | {summary['max_drawdown_pct']}% |",
|
||||
f"| Avg win | ${summary['avg_win']} |",
|
||||
f"| Avg loss | ${summary['avg_loss']} |",
|
||||
f"| Best trade | ${summary['best_trade']} |",
|
||||
f"| Worst trade | ${summary['worst_trade']} |",
|
||||
"",
|
||||
"> v5 trend-leg engine: cross entries + selective pullbacks (ADX/gap filtered). "
|
||||
"Does **not** meet 2000-3000 trades with profit on EURUSD M15, but improves on v2 (~81 trades) "
|
||||
f"to **{summary['total_trades']} trades** with positive expectancy.",
|
||||
"",
|
||||
"## Best parameters",
|
||||
"",
|
||||
"| Parameter | Value |",
|
||||
"|-----------|-------|",
|
||||
]
|
||||
for k, v in data["params"].items():
|
||||
label = PARAM_LABELS.get(k, k.replace("_", " ").title())
|
||||
md.append(f"| {label} | {v} |")
|
||||
|
||||
md += ["", "## Exit reasons", ""]
|
||||
for reason, cnt in exit_counts.items():
|
||||
md.append(f"- **{reason}**: {cnt} ({cnt / r.total_trades * 100:.1f}%)")
|
||||
|
||||
if version >= 5:
|
||||
logic = [
|
||||
"",
|
||||
"## Strategy logic (v5)",
|
||||
"",
|
||||
"1. **Cross entry**: fast/slow EMA cross + H4 trend + session/spread filters",
|
||||
"2. **Pullback entry**: only inside active trend leg; touch fast EMA; ADX >= pullback min; gap filter",
|
||||
"3. **Leg cap**: max 1 pullback per trend leg to avoid chop re-entries",
|
||||
"4. **Exit**: ATR SL/TP + max bars in trade",
|
||||
]
|
||||
else:
|
||||
logic = [
|
||||
"",
|
||||
"## Strategy logic",
|
||||
"",
|
||||
"1. **Entry**: EMA cross only (fast 10 / slow 46)",
|
||||
"2. **Filters**: H4 EMA(200) trend alignment; UTC 08:00-22:00; spread <= 6 pips",
|
||||
"3. **Stops**: SL = ATR(20) x 2.71, TP = ATR(20) x 6.36",
|
||||
"4. **Exit**: TP / SL / max 64 M15 bars (~16h); no trailing; no cross exit",
|
||||
"5. **Cooldown**: 8 bars between entries",
|
||||
]
|
||||
md += logic + [
|
||||
"## Artifacts",
|
||||
"",
|
||||
"- `best_run/trades.csv` — per-trade review",
|
||||
"- `best_run/report.png` — equity / drawdown / monthly chart",
|
||||
"- `SimpleEMA_optimized.set` — load in MT5 Strategy Tester",
|
||||
"",
|
||||
"## MT5 validation",
|
||||
"",
|
||||
"```powershell",
|
||||
"cd lab/EAs/SimpleEMA",
|
||||
"python run_mt5_tester.py backtest --period M15 --from 2020.01.01 --to 2026.01.01 --set SimpleEMA_optimized.set",
|
||||
"```",
|
||||
]
|
||||
(OUT / "REPORT.md").write_text("\n".join(md), encoding="utf-8")
|
||||
print(f"Report saved to {OUT}")
|
||||
print(json.dumps(summary, indent=2))
|
||||
finally:
|
||||
mt5.shutdown()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user