#!/usr/bin/env python3 """ 6-month segmented run — processes one month at a time to avoid OOM. Carries equity forward between segments. Usage: python3 run_6month_segmented.py python3 run_6month_segmented.py --max-rows=2000000 # sample per month """ import sys, os, json, time, gc from datetime import datetime os.chdir(os.path.dirname(os.path.abspath(__file__))) sys.path.insert(0, ".") try: os.nice(10) except OSError: pass import settings from run_btc_backtest import load_ticks_to_candles from backtest import BacktestEngine from utils import setup_logging PROGRESS_FILE = "backtest_progress.json" RESULTS_DIR = "results" # Aug 2025 through Jan 2026 (6 months) MONTHS = ["202508", "202509", "202510", "202511", "202512", "202601"] def write_progress(data): tmp = PROGRESS_FILE + ".tmp" try: with open(tmp, "w") as f: json.dump(data, f, indent=2, default=str) os.replace(tmp, PROGRESS_FILE) except Exception: pass def load_month(month_code, max_rows=None): """Load a single monthly CSV.""" csv_path = f"data/monthly/BTCUSD_{month_code}.csv" if not os.path.exists(csv_path): print(f"WARNING: {csv_path} not found, skipping") return None return load_ticks_to_candles(csv_path, timeframe="5min", max_rows=max_rows) def run_month(data, starting_balance): """Run backtest on one month's data with given starting balance.""" settings.INITIAL_BALANCE = starting_balance engine = BacktestEngine( symbol=settings.SYMBOL, initial_balance=starting_balance, commission_percent=settings.COMMISSION_PERCENT, slippage_pips=settings.SLIPPAGE_PIPS, ) return engine.run(data, progress_callback=None) def main(): setup_logging(console=False, file=True) max_rows = None for arg in sys.argv[1:]: if arg.startswith("--max-rows="): max_rows = int(arg.split("=")[1]) print("=" * 70) print(" 6-MONTH SEGMENTED RUN — TIER 1 WINNER CONFIG") print(f" LOT={settings.LOT_SIZE} | ThreadTP=${settings.THREAD_PROFIT_TARGET}") print(f" RecTP=${settings.RECOVERY_PROFIT_TARGET} | MaxOrd={settings.MAX_INITIAL_ORDERS}") print(f" Months: {', '.join(MONTHS)}") print("=" * 70) equity = 20000.0 original_equity = equity total_trades = 0 total_commission = 0.0 total_gross_profit = 0.0 total_gross_loss = 0.0 worst_dd = 0.0 monthly_results = [] for i, month in enumerate(MONTHS): print(f"\n{'─' * 50}") print(f" SEGMENT {i+1}/{len(MONTHS)}: {month}") print(f" Starting equity: ${equity:,.2f}") print(f"{'─' * 50}") write_progress({ "status": "running", "segment": f"{i+1}/{len(MONTHS)}", "month": month, "equity": equity, "updated_at": datetime.now().isoformat(), }) t0 = time.time() data = load_month(month, max_rows) if data is None: print(f" SKIPPED (no data)") monthly_results.append({ "month": month, "start": equity, "end": equity, "return_pct": 0, "dd": 0, "trades": 0, "skipped": True }) continue print(f" Loaded {len(data)} candles in {time.time()-t0:.1f}s") t1 = time.time() result = run_month(data, equity) elapsed = time.time() - t1 end_equity = result.final_balance ret_pct = ((end_equity - equity) / equity) * 100 dd = result.max_drawdown_percent mr = { "month": month, "start": equity, "end": end_equity, "return_pct": ret_pct, "dd": dd, "trades": result.total_trades, "pf": result.gross_profit / result.gross_loss if result.gross_loss > 0 else 999, "commission": result.total_commission, "gross_profit": result.gross_profit, "gross_loss": result.gross_loss, "elapsed": elapsed, } monthly_results.append(mr) total_trades += result.total_trades total_commission += result.total_commission total_gross_profit += result.gross_profit total_gross_loss += result.gross_loss if dd > worst_dd: worst_dd = dd print(f" ${equity:,.0f} → ${end_equity:,.0f} ({ret_pct:+.1f}%)") print(f" DD: {dd:.2f}% | PF: {mr['pf']:.2f} | Trades: {result.total_trades:,}") print(f" Commission: ${result.total_commission:,.0f} | Time: {elapsed:.0f}s") equity = end_equity # Free memory del data, result gc.collect() time.sleep(1) # Final summary total_return_pct = ((equity - original_equity) / original_equity) * 100 overall_pf = total_gross_profit / total_gross_loss if total_gross_loss > 0 else 999 rar = total_return_pct / worst_dd if worst_dd > 0 else 0 print(f"\n\n{'═' * 70}") print(f" 6-MONTH FULL RESULTS — TIER 1 WINNER") print(f" LOT={settings.LOT_SIZE} | TTP=${settings.THREAD_PROFIT_TARGET} | RTP=${settings.RECOVERY_PROFIT_TARGET} | MO={settings.MAX_INITIAL_ORDERS}") print(f"{'═' * 70}") print(f" ${original_equity:,.0f} → ${equity:,.0f} ({total_return_pct:+,.1f}%)") print(f" Worst DD: {worst_dd:.2f}%") print(f" PF: {overall_pf:.2f}") print(f" RAR: {rar:.1f}") print(f" Trades: {total_trades:,}") print(f" Commission: ${total_commission:,.0f}") print(f" Gross Profit: ${total_gross_profit:,.0f}") print(f" Gross Loss: ${total_gross_loss:,.0f}") print(f"{'═' * 70}") print(f"\n Monthly Breakdown:") print(f" {'Month':<8} {'Start':>12} {'End':>12} {'Return':>8} {'DD':>6} {'PF':>6} {'Trades':>7}") print(f" {'-'*65}") for mr in monthly_results: if mr.get("skipped"): print(f" {mr['month']:<8} {'SKIPPED':>12}") else: print(f" {mr['month']:<8} ${mr['start']:>10,.0f} ${mr['end']:>10,.0f} {mr['return_pct']:>+7.1f}% {mr['dd']:>5.2f}% {mr['pf']:>5.2f} {mr['trades']:>7,}") # Save os.makedirs(RESULTS_DIR, exist_ok=True) result_path = os.path.join(RESULTS_DIR, f"6month_tier1_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json") save_data = { "config": { "LOT_SIZE": settings.LOT_SIZE, "THREAD_PROFIT_TARGET": settings.THREAD_PROFIT_TARGET, "RECOVERY_PROFIT_TARGET": settings.RECOVERY_PROFIT_TARGET, "MAX_INITIAL_ORDERS": settings.MAX_INITIAL_ORDERS, }, "summary": { "start_equity": original_equity, "end_equity": equity, "total_return_pct": total_return_pct, "worst_dd": worst_dd, "pf": overall_pf, "rar": rar, "total_trades": total_trades, "total_commission": total_commission, "gross_profit": total_gross_profit, "gross_loss": total_gross_loss, }, "monthly": monthly_results, "timestamp": datetime.now().isoformat(), } with open(result_path, "w") as f: json.dump(save_data, f, indent=2) print(f"\n Saved to {result_path}") write_progress({ "status": "completed", "final_equity": equity, "return_pct": total_return_pct, "worst_dd": worst_dd, "updated_at": datetime.now().isoformat(), }) if __name__ == "__main__": main()