#!/usr/bin/env python3 """R&D Loop V2 — Multi-Instrument + Correlation Score + Session/Vola Filter + OOS. Changes from V1: 1. Multi-Instrument: Evaluate on EUR/USD + GBP/USD + BTC/USD 2. Correlation Score: Reward uncorrelated strategies (Sharpe × (1−corr)) 3. Session Filter: Only trade London session (07:00-16:00 UTC) 4. Volatility Filter: No trades when ATR < threshold 5. OOS Split: Report IS/OOS separately (80/20) """ import json, os, random, sys, time from datetime import datetime from pathlib import Path import numpy as np, pandas as pd from numba import jit PROJECT = Path(__file__).resolve().parent.parent OHLCV_PATH = Path(os.getenv("PREDIX_OHLCV_PATH", str(PROJECT / "git_ignore_folder" / "intraday_pv_all.h5"))) RESULTS_DIR = PROJECT / "results" / "rd_loop" STATE_DIR = PROJECT / "git_ignore_folder" / "rd_loop_state" INSTRUMENTS = ["EURUSD", "GBPUSD", "BTCUSD"] INSTRUMENT_ALIASES = {"GBPUSDT": "GBPUSD"} # Merge aliases under canonical name TIMEFRAMES = ["5min", "15min", "30min", "1h", "4h"] INDICATORS_POOL = ["MACD", "RSI", "BBands", "Donchian", "Stoch", "CCI", "WillR", "ADX", "SAR", "ROC", "MOM", "AROON", "MFI", "SMA", "EMA"] STRATEGY_TYPES = ["single", "multi_tf", "multi_role"] TREND_TFS = ["30min", "1h", "4h"] ENTRY_TFS = ["5min", "15min", "30min"] MIN_SHARPE, MIN_TRADES = 0.3, 10 EXPLORATION_RATE = 0.40 OOS_SPLIT = 0.2 # ═══════════════════════════════════════════════════════════════════════════════ # Numba-accelerated backtest # ═══════════════════════════════════════════════════════════════════════════════ @jit(nopython=True) def _backtest_numba(prices, signals, cost=0.000264): n = len(prices) equity = np.zeros(n, dtype=np.float64) equity[0] = 100000.0; peak = 100000.0; max_dd = 0.0 trade_returns = np.zeros(100000, dtype=np.float64) position = 0; entry_price = 0.0; trade_count = 0; wins = 0 for i in range(1, n): px = prices[i]; sg = signals[i]; ps = signals[i-1] # Close position on signal reversal or flatten if position != 0 and (sg != position or sg == 0 and position != 0): if position == 1: ret = (px - entry_price) / entry_price - cost else: ret = (entry_price - px) / entry_price - cost equity[i] = equity[i-1] * (1.0 + ret) if equity[i] > peak: peak = equity[i] dd = (peak - equity[i]) / peak if dd > max_dd: max_dd = dd if trade_count < len(trade_returns): trade_returns[trade_count] = ret trade_count += 1 if ret > 0: wins += 1 position = 0 else: equity[i] = equity[i-1] # Open new position if position == 0 and sg != 0: position = sg; entry_price = px # Close final position if position != 0: fp = prices[-1] if position == 1: ret = (fp - entry_price) / entry_price - cost else: ret = (entry_price - fp) / entry_price - cost equity[-1] = equity[-2] * (1.0 + ret) if trade_count < len(trade_returns): trade_returns[trade_count] = ret trade_count += 1 if ret > 0: wins += 1 # Ensure monotonic equity (carry forward zeros) for i in range(1, n): if equity[i] == 0: equity[i] = equity[i-1] total_ret = (equity[-1] - 100000.0) / 100000.0 if trade_count > 5: t = trade_returns[:trade_count] mean_ret = np.mean(t); std_ret = np.std(t) sharpe = mean_ret / std_ret * np.sqrt(trade_count) if std_ret > 0 else 0.0 else: sharpe = 0.0 return equity, max_dd, trade_count, wins, total_ret, sharpe, trade_returns[:trade_count] # ═══════════════════════════════════════════════════════════════════════════════ # Signal Construction # ═══════════════════════════════════════════════════════════════════════════════ def build_signal(close, hypothesis): """Build trading signal from hypothesis. Returns (-1,0,1) Series.""" import talib signal = None if hypothesis['type'] == 'single': ind = hypothesis['indicator']; tf = hypothesis['timeframe'] bars = close.resample(tf).last().dropna() sig = _build_indicator_signal(ind, bars, hypothesis['params']) signal = sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1) elif hypothesis['type'] == 'multi_tf': ind = hypothesis['indicator'] sigs = {} for tf in hypothesis['timeframes']: bars = close.resample(tf).last().dropna() sig = _build_indicator_signal(ind, bars, hypothesis['params']) sigs[tf] = sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1) port = pd.DataFrame(sigs).dropna() vote = port.mean(axis=1) signal = pd.Series(0, index=close.index) signal[vote > 0.25] = 1; signal[vote < -0.25] = -1 elif hypothesis['type'] == 'multi_role': trend_ind = hypothesis['trend_ind']; entry_ind = hypothesis['entry_ind'] trend_tf = hypothesis['trend_tf']; entry_tf = hypothesis['entry_tf'] trend_bars = close.resample(trend_tf).last().dropna() trend_sig = _build_indicator_signal(trend_ind, trend_bars, hypothesis['trend_params']) trend_sig = trend_sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1) entry_bars = close.resample(entry_tf).last().dropna() entry_sig = _build_indicator_signal(entry_ind, entry_bars, hypothesis['entry_params']) entry_sig = entry_sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1) signal = pd.Series(0, index=close.index) signal[(trend_sig == 1) & (entry_sig == 1)] = 1 signal[(trend_sig == -1) & (entry_sig == -1)] = -1 return signal if signal is not None and signal.nunique() > 1 else None def _apply_session_filter(signal, index): """Only trade London session (07:00-16:00 UTC Mon-Fri).""" hours = index.hour days = index.dayofweek in_session = (days < 5) & (hours >= 7) & (hours < 16) if hasattr(in_session, 'values'): in_session = in_session.values return (signal * in_session.astype(int)).astype(int).clip(-1, 1) def _apply_vola_filter(signal, close, atr_period=14, min_atr_pct=0.0003): """Don't trade when ATR is too low (flat/quiet markets).""" tr = pd.DataFrame({ 'hl': close.diff().abs(), 'hc': (close - close.shift(1)).abs(), 'lc': (close.shift(1) - close).abs(), }).max(axis=1) atr = tr.rolling(atr_period).mean() atr_pct = atr / close too_quiet = atr_pct < min_atr_pct return (signal * (~too_quiet).astype(int)).fillna(0).astype(int).clip(-1, 1) def _build_indicator_signal(name, bars, params): """Build indicator signal using talib + hand-rolled.""" import talib c = bars.values.astype(np.float64) if name == 'MACD': mc, sc, _ = talib.MACD(c, fastperiod=params.get('fast', 3), slowperiod=params.get('slow', 15), signalperiod=params.get('sig', 3)) s = pd.Series(0, index=bars.index); s[mc > sc] = 1; s[mc < sc] = -1 elif name == 'RSI': v = talib.RSI(c, timeperiod=params.get('period', 14)) s = pd.Series(0, index=bars.index); s[v < params.get('oversold', 30)] = 1; s[v > params.get('overbought', 70)] = -1 elif name == 'BBands': up, mi, lo = talib.BBANDS(c, timeperiod=params.get('period', 20), nbdevup=params.get('std', 2), nbdevdn=params.get('std', 2)) s = pd.Series(0, index=bars.index); s[c < lo] = 1; s[c > up] = -1 elif name == 'Donchian': hi = bars.rolling(params.get('period', 20)).max() lo = bars.rolling(params.get('period', 20)).min() s = pd.Series(0, index=bars.index); s[bars > hi.shift(1)] = 1; s[bars < lo.shift(1)] = -1 s = s.replace(0, np.nan).ffill(limit=params.get('hold', 1)).fillna(0).astype(int) elif name == 'Stoch': k, d = talib.STOCH(c, c, c, fastk_period=params.get('fastk', 9), slowk_period=params.get('slowk', 3), slowd_period=params.get('slowd', 3)) s = pd.Series(0, index=bars.index); s[(k > d) & (k < 30)] = 1; s[(k < d) & (k > 70)] = -1 elif name == 'CCI': v = talib.CCI(c, c, c, timeperiod=params.get('period', 14)) s = pd.Series(0, index=bars.index); s[v < -100] = 1; s[v > 100] = -1 elif name == 'WillR': v = talib.WILLR(c, c, c, timeperiod=params.get('period', 14)) s = pd.Series(0, index=bars.index); s[v < -80] = 1; s[v > -20] = -1 elif name == 'ADX': pdi = talib.PLUS_DI(c, c, c, timeperiod=params.get('period', 14)) ndi = talib.MINUS_DI(c, c, c, timeperiod=params.get('period', 14)) adx = talib.ADX(c, c, c, timeperiod=params.get('period', 14)) s = pd.Series(0, index=bars.index) s[(pdi > ndi) & (adx > params.get('threshold', 20))] = 1 s[(ndi > pdi) & (adx > params.get('threshold', 20))] = -1 elif name == 'SAR': v = talib.SAR(c, c, acceleration=params.get('accel', 0.02), maximum=params.get('max_accel', 0.2)) s = pd.Series(0, index=bars.index); s[c > v] = 1; s[c < v] = -1 elif name == 'ROC': v = talib.ROC(c, timeperiod=params.get('period', 10)) s = pd.Series(0, index=bars.index); s[v > params.get('threshold', 0.2)] = 1; s[v < -params.get('threshold', 0.2)] = -1 elif name == 'MOM': v = talib.MOM(c, timeperiod=params.get('period', 10)) s = pd.Series(0, index=bars.index); s[v > 0] = 1; s[v < 0] = -1 elif name == 'AROON': up, dn = talib.AROON(c, c, timeperiod=params.get('period', 14)) s = pd.Series(0, index=bars.index); s[up > dn] = 1; s[up < dn] = -1 elif name == 'MFI': v = talib.MFI(c, c, c, c, timeperiod=params.get('period', 14)) s = pd.Series(0, index=bars.index); s[v < 20] = 1; s[v > 80] = -1 elif name == 'SMA': s = pd.Series(0, index=bars.index) s[bars.rolling(params.get('fast', 10)).mean() > bars.rolling(params.get('slow', 50)).mean()] = 1 s[bars.rolling(params.get('fast', 10)).mean() < bars.rolling(params.get('slow', 50)).mean()] = -1 elif name == 'EMA': ef = bars.ewm(span=params.get('fast', 5), adjust=False).mean() es = bars.ewm(span=params.get('slow', 26), adjust=False).mean() s = pd.Series(0, index=bars.index); s[ef > es] = 1; s[ef < es] = -1 else: s = pd.Series(0, index=bars.index) return s.fillna(0).astype(int).clip(-1, 1) # ═══════════════════════════════════════════════════════════════════════════════ # Multi-Instrument Evaluation # ═══════════════════════════════════════════════════════════════════════════════ def evaluate_multi(closes, hypothesis, use_session=True, use_vola=True): """Evaluate strategy on all instruments, return combined metrics + per-instrument.""" results = {} equity_curves = {} for inst, close in closes.items(): signal = build_signal(close, hypothesis) if signal is None: results[inst] = {"sharpe": 0, "monthly_pct": 0, "n_trades": 0} continue # Apply filters if use_session: signal = _apply_session_filter(signal, close.index) if use_vola: signal = _apply_vola_filter(signal, close) if signal.nunique() <= 1: results[inst] = {"sharpe": 0, "monthly_pct": 0, "n_trades": 0} continue # OOS split n = len(close) is_n = int(n * (1 - OOS_SPLIT)) close_is = close.iloc[:is_n]; signal_is = signal.iloc[:is_n] close_oos = close.iloc[is_n:]; signal_oos = signal.iloc[is_n:] # IS backtest prices_is = close_is.values.astype(np.float64); sigs_is = signal_is.values.astype(np.int32) eq_is, dd_is, tr_is, wins_is, ret_is, sh_is, _ = _backtest_numba(prices_is, sigs_is) # OOS backtest prices_oos = close_oos.values.astype(np.float64); sigs_oos = signal_oos.values.astype(np.int32) eq_oos, dd_oos, tr_oos, wins_oos, ret_oos, sh_oos, _ = _backtest_numba(prices_oos, sigs_oos) # Full backtest (for equity curve) prices_full = close.values.astype(np.float64); sigs_full = signal.values.astype(np.int32) eq_full, dd_full, tr_full, wins_full, ret_full, sh_full, trades_full = _backtest_numba(prices_full, sigs_full) n_days = (close.index[-1] - close.index[0]).days mon = ((1+ret_full)**(1/(n_days/30.44))-1)*100 if ret_full > -1 else 0 mon_oos = ((1+ret_oos)**(1/((close_oos.index[-1] - close_oos.index[0]).days/30.44))-1)*100 if ret_oos > -1 else 0 results[inst] = { "sharpe": float(sh_full), "sharpe_is": float(sh_is), "sharpe_oos": float(sh_oos), "monthly_pct": float(mon), "monthly_oos": float(mon_oos), "n_trades": int(tr_full), "n_trades_oos": int(tr_oos), "win_rate": float(wins_full/tr_full) if tr_full>0 else 0, "max_dd": float(-dd_full), "total_return": float(ret_full), } equity_curves[inst] = eq_full.copy() # Combined metrics (harmonic mean — only good if ALL instruments good) valid = [r for r in results.values() if r['sharpe'] > 0] if not valid: combined = {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0, "n_trades_oos": 0} else: combined = { "sharpe": float(np.mean([r['sharpe'] for r in valid])), "monthly_pct": float(np.mean([r['monthly_pct'] for r in valid])), "monthly_oos": float(np.mean([r['monthly_oos'] for r in valid])), "n_trades": int(np.sum([r['n_trades'] for r in valid])), "n_trades_oos": int(np.sum([r['n_trades_oos'] for r in valid])), } combined['per_instrument'] = results combined['equity_curves'] = equity_curves return combined def correlation_penalty(result, sota_equity_curves): """Compute avg correlation of this strategy's returns with SOTA returns.""" if not sota_equity_curves: return 0.0 my_returns = [] for eq in result.get('equity_curves', {}).values(): if len(eq) > 1: my_returns.append(np.diff(eq) / eq[:-1]) if not my_returns: return 0.5 # Use longest equity curve for this strategy my_ret = max(my_returns, key=len) correlations = [] for sota_eq_dict in sota_equity_curves: for eq in sota_eq_dict.values(): if len(eq) > 1: sota_ret = np.diff(eq) / eq[:-1] # Align to shorter length min_len = min(len(my_ret), len(sota_ret)) if min_len > 10: corr = np.corrcoef(my_ret[:min_len], sota_ret[:min_len])[0, 1] if not np.isnan(corr): correlations.append(corr) return np.mean(correlations) if correlations else 0.0 def composite_score(result, sota_equity_curves): """Composite score = sharpe × (1 - correlation) → rewards uncorrelated profit.""" sh = result.get('sharpe', 0) if sh <= 0: return 0 corr = abs(correlation_penalty(result, sota_equity_curves)) # Bonus for OOS consistency oos_ratio = min(result.get('monthly_oos', 0) / max(result.get('monthly_pct', 1), 0.01), 1.0) oos_ratio = max(oos_ratio, 0) return sh * (1 - 0.5 * corr) * (0.3 + 0.7 * oos_ratio) # ═══════════════════════════════════════════════════════════════════════════════ # Hypothesis Generation # ═══════════════════════════════════════════════════════════════════════════════ class ResearchLoop: """Multi-instrument R&D loop with correlation-aware feedback.""" def __init__(self, closes): self.closes = closes # {instrument: close_series} self.sota = [] # State-of-the-art strategies (sorted by composite score) self.sota_equity = [] # Equity curves for correlation calc self.history = [] self.iteration = 0 self.best_score = 0 self.best_sharpe = 0 self.exploration_rate = EXPLORATION_RATE def hypothesize(self): self.iteration += 1 # Every 2000: ML (higher priority, runs before Optuna) if self.iteration % 2000 == 0 and len(self.sota) >= 5: return {'type': 'ml', 'generation': 'ml', 'description': f"ML: LightGBM on {len(self.sota)} strategies", 'sota': self.sota[:5]} # Every 500: Optuna optimize best strategy if self.iteration % 500 == 0 and self.sota: hp = dict(self.sota[0]['hypothesis']) hp['generation'] = 'optuna' hp['description'] = f"Optuna: {hp.get('description','?')}" return hp # Every 100: force non-dominant indicator if self.iteration % 100 == 0 and len(self.sota) >= 5: top = self._top_indicator() hp = self._random_hypothesis() hp = self._force_different_indicator(hp, top) hp['generation'] = 'explore' return hp # Adaptive exploration rate effective_rate = self.exploration_rate if len(self.sota) >= 10: top = self._top_indicator() dominated = sum(1 for r in self.sota if r['hypothesis'].get('trend_ind', r['hypothesis'].get('indicator')) == top) if dominated > len(self.sota) * 0.8: effective_rate += 0.25 if random.random() < effective_rate or not self.sota: return self._random_hypothesis() else: base = random.choice(self.sota[:5]) return self._mutate_hypothesis(base['hypothesis']) def _top_indicator(self): if not self.sota: return 'MACD' return self.sota[0]['hypothesis'].get('trend_ind', self.sota[0]['hypothesis'].get('indicator', 'MACD')) def _force_different_indicator(self, hp, top_ind): if hp.get('type') == 'multi_role': if hp['trend_ind'] == top_ind and hp['entry_ind'] == top_ind: if random.random() < 0.5: hp['trend_ind'] = random.choice([i for i in INDICATORS_POOL if i != top_ind]) hp['trend_params'] = self._random_params(hp['trend_ind']) else: hp['entry_ind'] = random.choice([i for i in INDICATORS_POOL if i != top_ind]) hp['entry_params'] = self._random_params(hp['entry_ind']) elif hp.get('indicator') == top_ind: hp['indicator'] = random.choice([i for i in INDICATORS_POOL if i != top_ind]) hp['params'] = self._random_params(hp['indicator']) hp['description'] = self._make_desc(hp) return hp def _make_desc(self, hp): t = hp.get('type', '?') if t == 'multi_role': return f"{hp['trend_ind']}({hp['trend_tf']})→{hp['entry_ind']}({hp['entry_tf']})" elif t == 'multi_tf': return f"{hp.get('indicator','?')} on {','.join(hp.get('timeframes',[])[:2])}" else: return f"{hp.get('indicator','?')} on {hp.get('timeframe','?')}" def _random_hypothesis(self): stype = random.choice(STRATEGY_TYPES) if stype == 'single': ind = random.choice(INDICATORS_POOL); tf = random.choice(TIMEFRAMES) return {'type': 'single', 'indicator': ind, 'timeframe': tf, 'params': self._random_params(ind), 'description': f"{ind} on {tf}", 'generation': 'explore'} elif stype == 'multi_tf': ind = random.choice(INDICATORS_POOL) tfs = random.sample(TIMEFRAMES, k=random.randint(2, 4)) return {'type': 'multi_tf', 'indicator': ind, 'timeframes': tfs, 'params': self._random_params(ind), 'description': f"{ind} on {','.join(tfs)}", 'generation': 'explore'} else: # multi_role trend_ind = random.choice(INDICATORS_POOL) entry_ind = random.choice(INDICATORS_POOL) trend_tf = random.choice(TREND_TFS) entry_tf = random.choice([t for t in ENTRY_TFS if t < trend_tf]) return {'type': 'multi_role', 'trend_ind': trend_ind, 'trend_params': self._random_params(trend_ind), 'trend_tf': trend_tf, 'entry_ind': entry_ind, 'entry_params': self._random_params(entry_ind), 'entry_tf': entry_tf, 'description': f"{trend_ind}({trend_tf})→{entry_ind}({entry_tf})", 'generation': 'explore'} def _mutate_hypothesis(self, base): hp = dict(base); hp['generation'] = 'exploit' if hp.get('type') == 'multi_role': mut = random.choice(['trend_ind', 'entry_ind', 'trend_tf', 'entry_tf', 'trend_params', 'entry_params']) if mut == 'trend_ind': hp['trend_ind'] = random.choice([i for i in INDICATORS_POOL if i != hp['trend_ind']]) hp['trend_params'] = self._random_params(hp['trend_ind']) elif mut == 'entry_ind': hp['entry_ind'] = random.choice([i for i in INDICATORS_POOL if i != hp['entry_ind']]) hp['entry_params'] = self._random_params(hp['entry_ind']) elif mut == 'trend_tf': hp['trend_tf'] = random.choice(TREND_TFS) if hp['trend_tf'] <= hp['entry_tf']: hp['entry_tf'] = random.choice([t for t in ENTRY_TFS if t < hp['trend_tf']]) elif mut == 'entry_tf': hp['entry_tf'] = random.choice([t for t in ENTRY_TFS if t < hp['trend_tf']]) elif mut == 'trend_params': p = dict(hp['trend_params']); k = random.choice(list(p.keys())) if isinstance(p[k], (int, float)): p[k] = p[k] * random.uniform(0.5, 1.5) hp['trend_params'] = p elif mut == 'entry_params': p = dict(hp['entry_params']); k = random.choice(list(p.keys())) if isinstance(p[k], (int, float)): p[k] = p[k] * random.uniform(0.5, 1.5) hp['entry_params'] = p hp['description'] = f"{hp['trend_ind']}({hp['trend_tf']})→{hp['entry_ind']}({hp['entry_tf']})" return hp mutations = ['params', 'indicator', 'timeframe'] mutation = random.choice(mutations) if mutation == 'params' and 'params' in hp: params = dict(hp['params']); key = random.choice(list(params.keys())) if isinstance(params[key], (int, float)): params[key] = params[key] * random.uniform(0.5, 1.5) if isinstance(params[key], float): params[key] = round(params[key], 1) hp['params'] = params hp['description'] = f"{hp.get('indicator','?')} (mutated {key})" elif mutation == 'indicator' and 'indicator' in hp: hp['indicator'] = random.choice([i for i in INDICATORS_POOL if i != hp.get('indicator')]) hp['params'] = self._random_params(hp['indicator']) hp['description'] = f"{hp['indicator']} (replaced)" elif mutation == 'timeframe': if 'timeframe' in hp: hp['timeframe'] = random.choice(TIMEFRAMES) elif 'timeframes' in hp: hp['timeframes'] = random.sample(TIMEFRAMES, k=len(hp['timeframes'])) hp['description'] = f"{hp.get('indicator','?')} (timeframe change)" return hp def _random_params(self, indicator): param_sets = { 'MACD': {'fast': random.choice([3,5,8,12]), 'slow': random.choice([10,15,20,26]), 'sig': random.choice([3,5,9])}, 'RSI': {'period': random.choice([7,14,21]), 'oversold': random.choice([20,25,30]), 'overbought': random.choice([70,75,80])}, 'BBands': {'period': random.choice([10,20,40]), 'std': random.choice([1.5,2.0,2.5])}, 'Donchian': {'period': random.choice([5,10,20,30,50]), 'hold': random.choice([1,2,3,5])}, 'Stoch': {'fastk': random.choice([5,9,14]), 'slowk': 3, 'slowd': random.choice([3,5])}, 'CCI': {'period': random.choice([14,20,50])}, 'WillR': {'period': random.choice([7,14,21])}, 'ADX': {'period': random.choice([7,14,21]), 'threshold': random.choice([15,20,25])}, 'SAR': {'accel': random.choice([0.02,0.05,0.08]), 'max_accel': random.choice([0.2,0.3,0.5])}, 'ROC': {'period': random.choice([5,10,20]), 'threshold': random.choice([0.1,0.2,0.5])}, 'MOM': {'period': random.choice([5,10,20,50])}, 'AROON': {'period': random.choice([7,14,21])}, 'MFI': {'period': random.choice([7,14,21])}, 'SMA': {'fast': random.choice([5,10,20,50]), 'slow': random.choice([20,50,100,200])}, 'EMA': {'fast': random.choice([3,5,8,12]), 'slow': random.choice([15,26,50,100])}, } return param_sets.get(indicator, {'period': 14}) def feedback(self, result): """Update SOTA sorted by COMPOSITE score (not just Sharpe).""" if result['sharpe'] <= MIN_SHARPE or result['n_trades'] < MIN_TRADES: return False score = composite_score(result, self.sota_equity) result['composite_score'] = float(score) # Check if this strategy is diverse enough to add is_diverse = True if self.sota: # Skip if very similar to existing (same indicators, TF, type) for existing in self.sota[:3]: if self._similar(result, existing): is_diverse = False break if is_diverse: self.sota.append(result) self.sota.sort(key=lambda r: r.get('composite_score', 0), reverse=True) self.sota = self.sota[:30] # Keep top 30 self.sota_equity = [s['equity_curves'] for s in self.sota] if score > self.best_score: self.best_score = score return True # NEW BEST if result['sharpe'] > self.best_sharpe: self.best_sharpe = result['sharpe'] return False def _similar(self, a, b): """Check if two strategies are too similar (same indicator combo, type, TFs).""" ha = a['hypothesis']; hb = b['hypothesis'] if ha.get('type') != hb.get('type'): return False if ha.get('type') == 'multi_role': return (ha.get('trend_ind') == hb.get('trend_ind') and ha.get('entry_ind') == hb.get('entry_ind') and ha.get('trend_tf') == hb.get('trend_tf') and ha.get('entry_tf') == hb.get('entry_tf')) return ha.get('indicator') == hb.get('indicator') def record(self): """Save checkpoint.""" RESULTS_DIR.mkdir(parents=True, exist_ok=True) STATE_DIR.mkdir(parents=True, exist_ok=True) if self.sota: cp = RESULTS_DIR / f"rd_loop_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" # Strip equity_curves (too large) from saved results stripped = [] for r in self.sota[:30]: s = {k: v for k, v in r.items() if k != 'equity_curves'} stripped.append(s) cp.write_text(json.dumps(stripped, indent=2, default=str)) def _run_optuna(closes, hypothesis): """Optuna optimization on the primary instrument.""" import optuna optuna.logging.set_verbosity(optuna.logging.WARNING) hp = hypothesis close = list(closes.values())[0] # Use first instrument for Optuna ind = hp.get('indicator', hp.get('trend_ind', 'MACD')) base_params = hp.get('params', hp.get('trend_params', {})) param_ranges = { 'MACD': {'fast': (2,15), 'slow': (5,40), 'sig': (2,15)}, 'RSI': {'period': (5,30), 'oversold': (10,40), 'overbought': (60,90)}, 'Donchian': {'period': (3,100), 'hold': (1,10)}, 'SAR': {'accel': (0.01, 0.2), 'max_accel': (0.1, 1.0)}, 'ADX': {'period': (5,30), 'threshold': (10,40)}, } ranges = param_ranges.get(ind, {}) def objective(trial): params = {} for k, (lo, hi) in ranges.items(): if isinstance(base_params.get(k, 1), int): params[k] = trial.suggest_int(k, int(lo), int(hi)) else: params[k] = trial.suggest_float(k, lo, hi) if 'fast' in params and 'slow' in params: params['fast'] = min(params['fast'], params['slow']-2) result = evaluate_multi(closes, hp, use_session=True, use_vola=True) return float(result.get('sharpe', 0)) if result.get('sharpe', 0) > 0 else -999.0 try: study = optuna.create_study(direction='maximize') study.optimize(objective, n_trials=15, show_progress_bar=False) best = study.best_params if 'params' in hp: hp['params'] = {k: int(v) if v == int(v) else v for k, v in best.items()} elif 'trend_params' in hp: hp['trend_params'] = {k: int(v) if v == int(v) else v for k, v in best.items()} hp['generation'] = 'optuna' result = evaluate_multi(closes, hp, use_session=True, use_vola=True) print(f" Optuna best: {best} → Sh={result['sharpe']:.1f} " f"Mon={result['monthly_pct']:.1f}% OOS={result['monthly_oos']:.1f}% ({study.best_value:.1f})") return result except Exception: return {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0} def _train_ml(closes, hypothesis): """Train LightGBM on SOTA indicator signals.""" try: from lightgbm import LGBMClassifier except ImportError: return {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0} sota = hypothesis.get('sota', []) if not sota: return {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0} close = list(closes.values())[0] daily = close.resample('1h').last().dropna() features = pd.DataFrame(index=daily.index) for s in sota[:5]: hp_s = s['hypothesis'] # Generate signal from each SOTA strategy as a feature from nexquant_rd_loop import build_signal, _build_indicator_signal sig = build_signal(close, hp_s) if sig is not None: sig = sig.reindex(daily.index, method='ffill') name = hp_s.get('description', f"strat_{id(s)}")[:30] features[name] = sig.fillna(0) features = features.iloc[100:] # Skip warmup if len(features) < 200: return {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0} target = (daily.pct_change().shift(-1) > 0).astype(int) target = target.reindex(features.index).fillna(0) split = int(len(features) * 0.8) X_train, X_test = features.iloc[:split], features.iloc[split:] y_train, y_test = target.iloc[:split], target.iloc[split:] model = LGBMClassifier(n_estimators=100, max_depth=5, verbosity=-1) model.fit(X_train, y_train) preds = model.predict(X_test) acc = float((preds == y_test).mean()) ml_signal = pd.Series(0, index=X_test.index) ml_signal[preds == 1] = 1; ml_signal[preds == 0] = -1 ml_signal = ml_signal.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1) ml_signal = _apply_session_filter(ml_signal, close.index) prices = close.values.astype(np.float64); sigs = ml_signal.values.astype(np.int32) eq, dd, tr, wins, ret, sh, _ = _backtest_numba(prices, sigs) n_days = (close.index[-1] - close.index[0]).days mon = ((1+ret)**(1/(n_days/30.44))-1)*100 if ret > -1 else 0 print(f" ML LightGBM: Test acc={acc:.1%} → Sh={sh:.1f} Mon={mon:.1f}% Tr={tr}") return {"sharpe": float(sh), "monthly_pct": float(mon), "monthly_oos": 0, "n_trades": int(tr), "win_rate": float(wins/tr) if tr>0 else 0, "ml_accuracy": float(acc), "ml_model": "LightGBM"} def load_data(): """Load OHLCV data for all instruments from one or multiple HDF5 files.""" closes = {} data_dir = OHLCV_PATH.parent # Try main file first if OHLCV_PATH.exists(): df = pd.read_hdf(OHLCV_PATH, key="data") for inst in INSTRUMENTS: try: close = df.xs(inst, level="instrument")["$close"].sort_index() closes[inst] = close except KeyError: pass # Load from individual files if not found instrument_files = { "EURUSD": OHLCV_PATH, "GBPUSD": data_dir / "gbpusdt_1min.h5", "BTCUSD": data_dir / "btc_1min.h5", } for inst, path in instrument_files.items(): if inst in closes: continue if not path.exists(): print(f" {inst}: file not found — skipping") continue try: df = pd.read_hdf(path, key="data") if isinstance(df.index, pd.MultiIndex): try: close = df.xs(inst, level="instrument")["$close"].sort_index() except KeyError: # Try with T suffix for crypto pairs alt = inst + "T" if not inst.endswith("T") else inst.rstrip("T") try: close = df.xs(alt, level="instrument")["$close"].sort_index() except KeyError: inst_vals = df.index.get_level_values("instrument").unique() for iv in inst_vals: if inst[:3] in str(iv)[:3]: close = df.xs(iv, level="instrument")["$close"].sort_index() break else: raise KeyError(f"No instrument matching {inst}") elif "$close" in df.columns: close = df["$close"].sort_index() close.index = pd.to_datetime(close.index) elif "close" in df.columns: close = df["close"].sort_index() close.index = pd.to_datetime(close.index) else: close = df.iloc[:, 3].sort_index() close.index = pd.to_datetime(close.index) closes[inst] = close except Exception as e: print(f" {inst}: load error {e} — skipping") for inst, close in closes.items(): print(f" {inst}: {len(close):,} bars, {close.index[0]} → {close.index[-1]}") return closes def main(): iterations = 200 if "--iterations" in sys.argv: iterations = int(sys.argv[sys.argv.index("--iterations") + 1]) print("=" * 60) print(f" R&D Loop V2 — Multi-Instrument + Correlation Score") print(f" Instruments: {', '.join(INSTRUMENTS)}") print(f" Indicators: {len(INDICATORS_POOL)} | Strategy types: {len(STRATEGY_TYPES)}") print(f" Features: Session Filter + Volatility Filter + OOS Split") print(f" Iterations: {iterations}") print("=" * 60) print(" Loading data...") closes = load_data() if not closes: print(" ERROR: No instruments loaded!"); return loop = ResearchLoop(closes) t0 = time.time() for i in range(iterations): hp = loop.hypothesize() # Evaluate try: result = evaluate_multi(closes, hp, use_session=True, use_vola=True) except Exception: continue result['hypothesis'] = hp result['iteration'] = i + 1 result['timestamp'] = datetime.now().isoformat() loop.history.append(result) # Feedback is_new_best = loop.feedback(result) best_inst_metrics = [f"{inst}: {m['sharpe']:.1f}" for inst, m in result.get('per_instrument', {}).items() if m.get('sharpe', 0) != 0] gen = hp.get('generation', '?') if is_new_best: print(f"\n ★ NEW BEST (#{i+1}, {gen}): {hp['description']}") print(f" Score={result['composite_score']:.1f} Sh={result['sharpe']:.1f} " f"Mon={result['monthly_pct']:.1f}% OOS={result['monthly_oos']:.1f}% " f"Tr={result['n_trades']} [{', '.join(best_inst_metrics[:3])}]") elif (i + 1) % 50 == 0: top_indicators = set() for r in loop.sota[:5]: top_indicators.add(r['hypothesis'].get('trend_ind', r['hypothesis'].get('indicator', '?'))) print(f" [{i+1}/{iterations}] {gen:>7s} | SOTA: {len(loop.sota)} | " f"Best Sh={loop.best_sharpe:.1f} Score={loop.best_score:.1f} | " f"Explore: {loop.exploration_rate:.0%} | Inds: {','.join(sorted(top_indicators)[:4])}") if (i + 1) % 100 == 0: loop.record() if len(loop.sota) > 10: loop.exploration_rate = max(0.15, EXPLORATION_RATE - len(loop.sota) * 0.003) elapsed = time.time() - t0 print(f"\n{'=' * 60}") print(f" R&D Loop V2 Complete: {iterations} iterations in {elapsed:.0f}s") print(f" SOTA Strategies: {len(loop.sota)} | Best Score: {loop.best_score:.1f}") print(f"{'=' * 60}") if loop.sota: print(f"\n TOP DISCOVERIES (by composite score):") for i, r in enumerate(loop.sota[:15], 1): hp = r['hypothesis'] per_inst = r.get('per_instrument', {}) insts = ' '.join([f"{k}:{v['sharpe']:.0f}" for k, v in per_inst.items() if v['sharpe'] != 0]) print(f" {i:>2d}. {hp['description'][:45]:45s} " f"Sc={r['composite_score']:.1f} Sh={r['sharpe']:+.1f} " f"Mo={r['monthly_pct']:+.1f}% OOS={r['monthly_oos']:+.1f}% " f"[{insts}]") final = RESULTS_DIR / f"rd_loop_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" stripped = [{k: v for k, v in r.items() if k != 'equity_curves'} for r in loop.sota] final.write_text(json.dumps(stripped, indent=2, default=str)) print(f"\n Saved: {final}") exploit_best = [r for r in loop.sota if r['hypothesis'].get('generation') == 'exploit'] explore_best = [r for r in loop.sota if r['hypothesis'].get('generation') == 'explore'] optuna_best = [r for r in loop.sota if r['hypothesis'].get('generation') == 'optuna'] ml_best = [r for r in loop.sota if r['hypothesis'].get('generation') == 'ml'] print(f" Exploit: {len(exploit_best)} | Explore: {len(explore_best)} | " f"Optuna: {len(optuna_best)} | ML: {len(ml_best)}") if __name__ == "__main__": main()