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>
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Cursor
Cursor
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"""
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RSIScalpingAPPL — bar backtest mirroring main.mq5 inputs.
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Outputs in this folder:
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backtest_report.json, trades.csv, report.png,
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equity_curve.png, drawdown.png, monthly_returns.png,
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pnl_distribution.png, exit_reasons.png
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Usage:
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python run_backtest.py
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python run_backtest.py --start 2021-01-01 --end 2026-01-01
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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 sys
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from dataclasses import asdict, dataclass
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from datetime import datetime
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import MetaTrader5 as mt5
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import numpy as np
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import pandas as pd
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ROOT = Path(__file__).resolve().parents[3]
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sys.path.insert(0, str(ROOT / "backtesting" / "MT5"))
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from cluster_audit.backtest_core import ( # noqa: E402
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BacktestReport,
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CostModel,
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load_bars,
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resolve_symbol,
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run_single_position,
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)
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from indicator_utils import calculate_adx, calculate_atr, calculate_dmi, calculate_ema, calculate_rsi # noqa: E402
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STRATEGY_ID = "RSIScalpingAPPL"
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def save_reports(report: BacktestReport, out_dir: Path) -> None:
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rows = [
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{
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"side": t.side,
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"open_time": t.open_time,
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"close_time": t.close_time,
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"open_price": t.open_price,
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"close_price": t.close_price,
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"volume": t.volume,
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"profit": t.profit,
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"bars_held": t.bars_held,
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"exit_reason": t.exit_reason,
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}
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for t in report.trades_list
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]
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pd.DataFrame(rows).to_csv(out_dir / "trades.csv", index=False)
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with open(out_dir / "backtest_report.json", "w", encoding="utf-8") as f:
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json.dump(report.to_dict(), f, indent=2, ensure_ascii=False)
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trades = report.trades_list
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if not trades:
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fig, ax = plt.subplots(figsize=(10, 4))
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ax.text(0.5, 0.5, "No trades in backtest window", ha="center", va="center", fontsize=14)
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ax.axis("off")
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fig.savefig(out_dir / "report.png", dpi=200, bbox_inches="tight")
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plt.close(fig)
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return
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df = pd.DataFrame(rows)
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df["close_time"] = pd.to_datetime(df["close_time"])
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df = df.sort_values("close_time")
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bal0 = report.params.get("initial_balance", 10_000.0)
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if report.equity_curve is not None and len(report.equity_curve) > 1:
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eq = report.equity_curve
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else:
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eq = pd.Series(bal0 + df["profit"].cumsum().values, index=df["close_time"])
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equity_times = eq.index
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equity = eq
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fig = plt.figure(figsize=(14, 10))
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gs = fig.add_gridspec(3, 2, height_ratios=[2, 1.2, 1.2])
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ax1 = fig.add_subplot(gs[0, :])
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ax1.plot(equity_times, equity, lw=1.8)
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ax1.axhline(bal0, color="gray", ls="--")
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ax1.set_title("Equity Curve")
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ax1.grid(alpha=0.3)
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ax2 = fig.add_subplot(gs[1, 0])
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dd = (equity - equity.cummax()) / equity.cummax() * 100
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ax2.fill_between(equity_times, dd, 0, color="#d62728", alpha=0.35)
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ax2.set_title("Drawdown %")
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ax2.grid(alpha=0.3)
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ax3 = fig.add_subplot(gs[1, 1])
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df["month"] = df["close_time"].dt.to_period("M")
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monthly = df.groupby("month")["profit"].sum()
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ax3.bar(range(len(monthly)), monthly.values, color=["#2ca02c" if v >= 0 else "#d62728" for v in monthly])
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ax3.set_title("Monthly PnL")
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ax3.axhline(0, color="black", lw=0.6)
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ax4 = fig.add_subplot(gs[2, 0])
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ax4.hist(df["profit"], bins=30, color="#9467bd", alpha=0.85)
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ax4.axvline(0, color="black")
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ax4.set_title("Trade PnL Distribution")
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ax5 = fig.add_subplot(gs[2, 1])
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rc = df["exit_reason"].value_counts()
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ax5.bar(rc.index.astype(str), rc.values, color="#ff7f0e")
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ax5.set_title("Exit Reasons")
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fig.suptitle(
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f"{STRATEGY_ID} — Net ${report.net_profit:,.2f} | Trades {report.total_trades} | "
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f"WR {report.win_rate:.1f}% | PF {report.profit_factor:.2f} | MaxDD {report.max_drawdown_pct:.2f}%",
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fontsize=11,
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)
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fig.tight_layout(rect=[0, 0, 1, 0.96])
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fig.savefig(out_dir / "report.png", dpi=200, bbox_inches="tight")
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plt.close(fig)
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plt.figure(figsize=(12, 5))
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plt.plot(equity_times, equity, lw=2)
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plt.title("Equity Curve")
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plt.grid(alpha=0.3)
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plt.tight_layout()
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plt.savefig(out_dir / "equity_curve.png", dpi=200, bbox_inches="tight")
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plt.close()
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plt.figure(figsize=(12, 5))
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plt.fill_between(equity_times, dd, 0, color="red", alpha=0.3)
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plt.plot(equity_times, dd, color="darkred")
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plt.title("Drawdown %")
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plt.grid(alpha=0.3)
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plt.tight_layout()
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plt.savefig(out_dir / "drawdown.png", dpi=200, bbox_inches="tight")
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plt.close()
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plt.figure(figsize=(12, 5))
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plt.bar(range(len(monthly)), monthly.values, color=["green" if v >= 0 else "red" for v in monthly], alpha=0.75)
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plt.title("Monthly PnL")
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plt.axhline(0, color="black")
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plt.grid(alpha=0.3, axis="y")
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plt.tight_layout()
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plt.savefig(out_dir / "monthly_returns.png", dpi=200, bbox_inches="tight")
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plt.close()
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plt.figure(figsize=(10, 5))
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plt.hist(df["profit"], bins=40, color="#6a5acd", alpha=0.85)
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plt.axvline(0, color="black")
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plt.title("Per-Trade PnL Distribution")
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plt.tight_layout()
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plt.savefig(out_dir / "pnl_distribution.png", dpi=200, bbox_inches="tight")
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plt.close()
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if report.exit_reason_breakdown:
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labels = list(report.exit_reason_breakdown.keys())
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counts = [report.exit_reason_breakdown[k]["count"] for k in labels]
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plt.figure(figsize=(8, 5))
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plt.bar(labels, counts, color="#e377c2")
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plt.title("Exit Reason Counts")
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plt.tight_layout()
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plt.savefig(out_dir / "exit_reasons.png", dpi=200, bbox_inches="tight")
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plt.close()
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@dataclass
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class StrategyParams:
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rsi_period: int = 14
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rsi_overbought: float = 80
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rsi_oversold: float = 78
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rsi_target_buy: float = 94
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rsi_target_sell: float = 44
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bars_to_wait: int = 7
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lot_size: float = 25
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use_reversal_escape: bool = False
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reversal_atr_period: int = 14
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reversal_adverse_atr_mult: float = 1.5
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reversal_signs_required: int = 2
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reversal_rsi_velocity: float = 8.0
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initial_balance: float = 10_000.0
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def to_dict(self) -> dict:
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return asdict(self)
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def make_params(balance: float) -> StrategyParams:
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return StrategyParams(initial_balance=balance)
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def run_backtest(df, symbol, params: StrategyParams, costs, period_label):
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info = mt5.symbol_info(symbol)
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point = float(info.point) if info else 0.01
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rsi = calculate_rsi(df["close"], params.rsi_period).to_numpy()
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atr = calculate_atr(df, params.reversal_atr_period).to_numpy()
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p = params.to_dict()
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def on_bar(i, st, open_pos, close):
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if i < 3 or np.isnan(rsi[i - 1]):
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return
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sig, prev, two = rsi[i - 1], rsi[i - 2], rsi[i - 3]
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mid = float(df["open"].iloc[i])
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hi, lo = float(df["high"].iloc[i]), float(df["low"].iloc[i])
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if st.side and params.use_reversal_escape:
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a = float(atr[i - 1]) if not np.isnan(atr[i - 1]) else 0.0
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if a > 0:
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signs = 0
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if st.side == "BUY":
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if st.entry - lo >= params.reversal_adverse_atr_mult * a:
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signs += 1
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if sig - prev >= params.reversal_rsi_velocity:
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signs += 1
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else:
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if hi - st.entry >= params.reversal_adverse_atr_mult * a:
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signs += 1
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if prev - sig >= params.reversal_rsi_velocity:
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signs += 1
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if signs >= params.reversal_signs_required:
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close(i, mid, "reversal_escape")
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return
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if st.side == "BUY":
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if sig < params.rsi_oversold:
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st.bars_against = st.bars_against + 1 if st.rsi_against else 1
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st.rsi_against = True
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if st.bars_against >= params.bars_to_wait:
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close(i, mid, "rsi_against")
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else:
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st.rsi_against = False
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st.bars_against = 0
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if sig >= params.rsi_target_buy:
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close(i, mid, "target")
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elif st.side == "SELL":
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if sig > params.rsi_overbought:
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st.bars_against = st.bars_against + 1 if st.rsi_against else 1
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st.rsi_against = True
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if st.bars_against >= params.bars_to_wait:
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close(i, mid, "rsi_against")
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else:
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st.rsi_against = False
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st.bars_against = 0
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if sig <= params.rsi_target_sell:
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close(i, mid, "target")
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else:
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if two <= params.rsi_oversold and prev > params.rsi_oversold:
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open_pos(i, "BUY", mid)
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elif two >= params.rsi_overbought and prev < params.rsi_overbought:
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open_pos(i, "SELL", mid)
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return run_single_position(
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df, symbol, point, costs, params.lot_size, STRATEGY_ID, "M10", period_label, p, params.initial_balance, on_bar
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)
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def parse_args() -> argparse.Namespace:
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p = argparse.ArgumentParser(description=f"{STRATEGY_ID} Python backtest")
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p.add_argument("--symbol", default="AAPL")
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p.add_argument("--start", default="2021-01-01")
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p.add_argument("--end", default="2026-01-01")
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p.add_argument("--balance", type=float, default=10_000.0)
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return p.parse_args()
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def main() -> None:
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args = parse_args()
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out_dir = Path(__file__).resolve().parent
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params = make_params(args.balance)
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if not mt5.initialize():
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raise SystemExit("MetaTrader5 initialize() failed")
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try:
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symbol = resolve_symbol(args.symbol)
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start = datetime.fromisoformat(args.start)
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end = datetime.fromisoformat(args.end)
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period_label = f"{args.start}_{args.end}"
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print(f"Loading {symbol} bars ...")
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df = load_bars(symbol, mt5.TIMEFRAME_M10, start, end)
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costs = CostModel.for_symbol(symbol)
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report = run_backtest(df, symbol, params, costs, period_label)
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save_reports(report, out_dir)
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print(f"Net: ${report.net_profit:,.2f} | Trades: {report.total_trades} | WR: {report.win_rate:.1f}% | PF: {report.profit_factor:.2f}")
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print(f"Saved to {out_dir}")
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finally:
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mt5.shutdown()
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if __name__ == "__main__":
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main()
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