#!/usr/bin/env python3 """ Validate optimizer winners — 6-month month-by-month with margin analysis. Usage: nohup python3 -u validate_winners.py > results/validation_log.txt 2>&1 & """ import os, sys, json, csv, subprocess, time, re, glob, signal from datetime import datetime os.chdir(os.path.dirname(os.path.abspath(__file__))) SETTINGS_FILE = "settings.py" RESULTS_DIR = "results" MONTHS = ["202508", "202509", "202510", "202511", "202512", "202601"] START_BALANCE = 20000.0 PROGRESS_FILE = os.path.join(RESULTS_DIR, "validation_progress.json") # Winners to validate WINNERS = [ {"label": "W1", "LOT_SIZE": 0.015, "THREAD_PROFIT_TARGET": 12, "MAX_INITIAL_ORDERS": 10, "MIN_ENTRY_SPACING": 0, "PROGRESSIVE_RLM_ENABLED": False, "desc": "0.015/TTP12/MO10"}, {"label": "W2", "LOT_SIZE": 0.015, "THREAD_PROFIT_TARGET": 15, "MAX_INITIAL_ORDERS": 10, "MIN_ENTRY_SPACING": 0, "PROGRESSIVE_RLM_ENABLED": False, "desc": "0.015/TTP15/MO10 (B5)"}, {"label": "W3", "LOT_SIZE": 0.010, "THREAD_PROFIT_TARGET": 12, "MAX_INITIAL_ORDERS": 10, "MIN_ENTRY_SPACING": 0, "PROGRESSIVE_RLM_ENABLED": False, "desc": "0.010/TTP12/MO10 (tiny)"}, {"label": "W4", "LOT_SIZE": 0.020, "THREAD_PROFIT_TARGET": 12, "MAX_INITIAL_ORDERS": 10, "MIN_ENTRY_SPACING": 50, "PROGRESSIVE_RLM_ENABLED": False, "desc": "0.020/TTP12/MO10/SP50"}, ] FIXED = { "RECOVERY_PROFIT_TARGET": 30.0, "RECOVERY_LOT_MULTIPLIER": 1.0, "ADAPTIVE_GRID": True, "REFERENCE_ATR": 132.1, "COMMISSION_PERCENT": 0.025, "PROGRESSIVE_RLM_START": 25, "PROGRESSIVE_RLM_RAMP": True, "PROGRESSIVE_RLM_MAX": 1.3, "MAX_LEVERAGE": 10.0, "MARGIN_CAP_ENABLED": False, "MARGIN_RESERVE_PCT": 0.20, "MARGIN_RELIEF_ENABLED": False, "MARGIN_RELIEF_LEVEL": 4, "MARGIN_RELIEF_CLOSE_SUBS": True, } SETTINGS_BACKUP = None def read_settings(): with open(SETTINGS_FILE) as f: return f.read() def write_setting(text, key, value): if isinstance(value, bool): val_str = "True" if value else "False" else: val_str = str(value) new_text = re.sub(rf'^{key}\s*=\s*.*$', f'{key} = {val_str}', text, flags=re.MULTILINE) return new_text def apply_settings(params, balance): text = read_settings() for k, v in FIXED.items(): text = write_setting(text, k, v) for k, v in params.items(): if k not in ('label', 'desc'): text = write_setting(text, k, v) text = write_setting(text, "INITIAL_BALANCE", balance) with open(SETTINGS_FILE, 'w') as f: f.write(text) def run_backtest(month, balance): apply_settings({}, balance) # just update balance cmd = [sys.executable, "bot_runner.py", f"--csv={month}", "--max-rows=0", "--tf=5min", "--throttle=0", "--timeout=300"] try: subprocess.run(cmd, capture_output=True, text=True, timeout=330) except subprocess.TimeoutExpired: return None result_files = sorted(glob.glob(os.path.join(RESULTS_DIR, "result_*.json")), reverse=True) if not result_files: return None with open(result_files[0]) as f: data = json.load(f) if data.get("status", "").lower() != "complete": return None return data def get_peak_notional(trades_file): """Calculate peak notional from trade CSV.""" if not os.path.exists(trades_file): return 0, 0 events = [] with open(trades_file) as f: for row in csv.DictReader(f): lots = float(row['lots']) price = float(row['entry_price']) n = lots * price events.append(('o', row['entry_time'], n, lots)) events.append(('c', row['exit_time'], -n, -lots)) events.sort(key=lambda x: x[1]) mx_n = mx_l = rn = rl = 0 for _, _, n, l in events: rn += n; rl += l if rn > mx_n: mx_n = rn; mx_l = rl return mx_n, mx_l def main(): global SETTINGS_BACKUP SETTINGS_BACKUP = read_settings() print("=" * 80) print(" 6-MONTH VALIDATION — OPTIMIZER WINNERS") print(f" {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print("=" * 80) all_results = {} for w in WINNERS: label = w['label'] print(f"\n{'='*80}") print(f" {label}: {w['desc']}") print(f"{'='*80}") # Apply winner settings text = read_settings() for k, v in FIXED.items(): text = write_setting(text, k, v) for k, v in w.items(): if k not in ('label', 'desc'): text = write_setting(text, k, v) with open(SETTINGS_FILE, 'w') as f: f.write(text) equity = START_BALANCE monthly = [] total_trades = 0 worst_dd = 0 peak_notional = 0 peak_lots = 0 for month in MONTHS: t0 = time.time() # Set balance and run text = read_settings() text = write_setting(text, "INITIAL_BALANCE", equity) with open(SETTINGS_FILE, 'w') as f: f.write(text) cmd = [sys.executable, "bot_runner.py", f"--csv={month}", "--max-rows=0", "--tf=5min", "--throttle=0", "--timeout=300"] try: subprocess.run(cmd, capture_output=True, text=True, timeout=330) except subprocess.TimeoutExpired: print(f" {month}: TIMEOUT") monthly.append({"month": month, "error": "timeout"}) continue result_files = sorted(glob.glob(os.path.join(RESULTS_DIR, "result_*.json")), reverse=True) if not result_files: print(f" {month}: NO RESULT FILE") continue with open(result_files[0]) as f: data = json.load(f) elapsed = time.time() - t0 if data.get("status", "").lower() != "complete": print(f" {month}: FAILED ({data.get('status')})") continue # Get peak notional from trades file trades_files = sorted(glob.glob(os.path.join(RESULTS_DIR, "trades_*.csv")), reverse=True) mn, ml = get_peak_notional(trades_files[0]) if trades_files else (0, 0) new_equity = equity + data["net_profit"] dd = data["max_dd_pct"] worst_dd = max(worst_dd, dd) total_trades += data["total_trades"] peak_notional = max(peak_notional, mn) peak_lots = max(peak_lots, ml) m = { "month": month, "start": equity, "end": new_equity, "net": data["net_profit"], "return_pct": data["return_pct"], "dd": dd, "pf": data["profit_factor"], "trades": data["total_trades"], "peak_notional": mn, "peak_lots": ml, } monthly.append(m) month_label = f"20{month[2:4]}-{month[4:6]}" print(f" {month_label}: ${equity:>12,.0f} → ${new_equity:>12,.0f} ({data['return_pct']:>+7.1f}%) " f"DD:{dd:.1f}% PF:{data['profit_factor']:.2f} " f"Peak:${mn:>10,.0f} [{elapsed:.0f}s]") equity = new_equity # Summary total_return = (equity / START_BALANCE - 1) * 100 # Margin analysis for acct in [15000, 20000, 25000, 30000, 40000, 50000]: margin_pct = peak_notional / (acct * 50) * 100 if margin_pct <= 85: min_acct = acct break else: min_acct = int(peak_notional / 50 / 0.85) print(f"\n SUMMARY {label}:") print(f" ${START_BALANCE:,.0f} → ${equity:,.0f} (+{total_return:,.0f}%)") print(f" Worst DD: {worst_dd:.1f}% | Trades: {total_trades:,}") print(f" Peak notional: ${peak_notional:,.0f} ({peak_lots:.1f} lots)") print(f" Min account @50x: ${min_acct:,} ({peak_notional/(min_acct*50)*100:.0f}%)") all_results[label] = { "desc": w['desc'], "final": equity, "return_pct": total_return, "worst_dd": worst_dd, "total_trades": total_trades, "peak_notional": peak_notional, "peak_lots": peak_lots, "min_account": min_acct, "monthly": monthly, "lot": w['LOT_SIZE'], "ttp": w['THREAD_PROFIT_TARGET'], "mo": w['MAX_INITIAL_ORDERS'], "sp": w['MIN_ENTRY_SPACING'], "rlm": w['PROGRESSIVE_RLM_ENABLED'], } with open(PROGRESS_FILE, 'w') as f: json.dump({"status": "RUNNING", "completed": label, "results": all_results, "timestamp": datetime.now().strftime('%Y-%m-%d %H:%M:%S')}, f, indent=2) # Restore settings with open(SETTINGS_FILE, 'w') as f: f.write(SETTINGS_BACKUP) print("\n✅ Settings restored") # Final comparison print("\n" + "=" * 100) print(" FINAL COMPARISON — ALL WINNERS") print("=" * 100) print(f"{'Label':<5} {'Config':<25} {'Final':>12} {'Return':>9} {'DD':>6} {'Trades':>7} {'Peak$':>12} {'MinAcct':>10}") print("-" * 100) for label, r in all_results.items(): print(f"{label:<5} {r['desc']:<25} ${r['final']:>11,.0f} {r['return_pct']:>+8,.0f}% {r['worst_dd']:>5.1f}% " f"{r['total_trades']:>7,} ${r['peak_notional']:>11,.0f} ${r['min_account']:>9,}") # Save final with open(PROGRESS_FILE, 'w') as f: json.dump({"status": "COMPLETE", "results": all_results, "timestamp": datetime.now().strftime('%Y-%m-%d %H:%M:%S')}, f, indent=2) print(f"\n✅ Done! Results in {PROGRESS_FILE}") if __name__ == "__main__": def cleanup(signum, frame): if SETTINGS_BACKUP: with open(SETTINGS_FILE, 'w') as f: f.write(SETTINGS_BACKUP) sys.exit(1) signal.signal(signal.SIGTERM, cleanup) signal.signal(signal.SIGINT, cleanup) main()