#!/usr/bin/env python3 """R&D Loop for Technical Indicators — Replaces factor pipeline with indicator discovery. Architecture (mirrors the original R&D loop): 1. Hypothesize: LLM proposes indicator combinations with parameters 2. Evaluate: Direct backtest_signal (no Docker, no Qlib) 3. Feedback: Compare against SOTA, bias next hypotheses 4. Record: Save best strategies, checkpoint progress Unlike the factor R&D loop, this uses deterministic evaluation instead of Docker. """ import json, os, random, sys, time from datetime import datetime from pathlib import Path import numpy as np, pandas as pd 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" TIMEFRAMES = ["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", "portfolio"] MIN_SHARPE, MIN_TRADES = 0.5, 20 # ═══════════════════════════════════════════════════════════════════════════════ # Evaluation # ═══════════════════════════════════════════════════════════════════════════════ def evaluate_strategy(close, hypothesis): """Run backtest and return metrics.""" 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=vote.index) signal[vote > 0.25] = 1; signal[vote < -0.25] = -1 elif hypothesis['type'] == 'portfolio': sigs = [] for cfg in hypothesis['indicators']: bars = close.resample('1d').last().dropna() sig = _build_indicator_signal(cfg['name'], bars, cfg['params']) sigs.append(sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1)) port = pd.DataFrame({f"s{i}": s for i, s in enumerate(sigs)}).dropna() vote = port.mean(axis=1) signal = pd.Series(0, index=vote.index) signal[vote > 0.25] = 1; signal[vote < -0.25] = -1 if signal is None or signal.nunique() <= 1: return {"sharpe": 0, "monthly_pct": 0, "max_dd": 0, "n_trades": 0, "win_rate": 0} from rdagent.components.backtesting.vbt_backtest import backtest_signal bt = backtest_signal(close=close, signal=signal) return {"sharpe": bt.get("sharpe", 0) or 0, "monthly_pct": bt.get("monthly_return_pct", 0) or 0, "max_dd": bt.get("max_drawdown", 0) or 0, "n_trades": bt.get("n_trades", 0) or 0, "win_rate": bt.get("win_rate", 0) or 0} 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) # ═══════════════════════════════════════════════════════════════════════════════ # Hypothesis Generation (simulated R&D loop — no LLM needed for indicators) # ═══════════════════════════════════════════════════════════════════════════════ class ResearchLoop: """Mimics the R&D loop: hypothesize → evaluate → feedback → record.""" def __init__(self, close): self.close = close self.sota = [] # State-of-the-art strategies self.history = [] # All evaluated hypotheses self.iteration = 0 self.best_sharpe = 0 self.exploration_rate = 0.3 # % of time we explore randomly vs exploit SOTA def hypothesize(self): """Generate a new strategy hypothesis. Uses a bandit-inspired approach: - 70%: Exploit — mutate the best-known strategy - 30%: Explore — random new strategy """ self.iteration += 1 if random.random() < self.exploration_rate or not self.sota: # EXPLORE: random new strategy return self._random_hypothesis() else: # EXPLOIT: mutate the best strategy base = random.choice(self.sota[:5]) return self._mutate_hypothesis(base['hypothesis']) def _random_hypothesis(self): stype = random.choice(STRATEGY_TYPES) if stype == 'single': ind = random.choice(INDICATORS_POOL) tf = random.choice(TIMEFRAMES) params = self._random_params(ind) return {'type': 'single', 'indicator': ind, 'timeframe': tf, 'params': params, '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)) params = self._random_params(ind) return {'type': 'multi_tf', 'indicator': ind, 'timeframes': tfs, 'params': params, 'description': f"{ind} on {','.join(tfs)}", 'generation': 'explore'} else: i1, i2 = random.sample(INDICATORS_POOL, 2) p1 = self._random_params(i1); p2 = self._random_params(i2) return {'type': 'portfolio', 'indicators': [{'name': i1, 'params': p1}, {'name': i2, 'params': p2}], 'description': f"{i1}+{i2}", 'generation': 'explore'} def _mutate_hypothesis(self, base): """Mutate an existing hypothesis — change one aspect.""" hp = dict(base) # shallow copy mutations = ['params', 'indicator', 'timeframe'] mutation = random.choice(mutations) hp['generation'] = 'exploit' if mutation == 'params' and 'params' in hp: # Tweak one parameter 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 and bias future exploration.""" if result['sharpe'] >= MIN_SHARPE and result['n_trades'] >= MIN_TRADES: self.sota.append(result) self.sota.sort(key=lambda r: r['sharpe'], reverse=True) self.sota = self.sota[:20] # Keep top 20 if result['sharpe'] > self.best_sharpe: self.best_sharpe = result['sharpe'] return True # NEW BEST return False 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" cp.write_text(json.dumps(self.sota[:15], indent=2, default=str)) # ═══════════════════════════════════════════════════════════════════════════════ 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 — Technical Indicators ({len(INDICATORS_POOL)} indicators)") print(f" Strategy: hypothezise → evaluate → feedback → record") print(f" Iterations: {iterations}") print("=" * 60) df = pd.read_hdf(OHLCV_PATH, key="data") close = df.xs("EURUSD", level="instrument")["$close"].sort_index() loop = ResearchLoop(close) t0 = time.time() for i in range(iterations): # 1. HYPOTHESIZE hp = loop.hypothesize() # 2. EVALUATE result = evaluate_strategy(close, hp) result['hypothesis'] = hp result['iteration'] = i + 1 result['timestamp'] = datetime.now().isoformat() loop.history.append(result) # 3. FEEDBACK is_new_best = loop.feedback(result) # Log gen = hp.get('generation', '?') if is_new_best: print(f"\n ★ NEW BEST (#{i+1}, {gen}): {hp['description']}") print(f" Sharpe={result['sharpe']:.2f} Mon={result['monthly_pct']:.1f}% " f"DD={result['max_dd']:.4f} Tr={result['n_trades']}") elif (i + 1) % 50 == 0: print(f" [{i+1}/{iterations}] {gen:>7s} | SOTA: {len(loop.sota)} | " f"Best Sh={loop.best_sharpe:.2f} | " f"Explore: {loop.exploration_rate:.0%}") # 4. RECORD checkpoint if (i + 1) % 100 == 0: loop.record() # Adaptive exploration: reduce over time as SOTA grows if len(loop.sota) > 10: loop.exploration_rate = max(0.1, 0.3 - len(loop.sota) * 0.01) # Final elapsed = time.time() - t0 print(f"\n{'=' * 60}") print(f" R&D Loop Complete: {iterations} iterations in {elapsed:.0f}s") print(f" SOTA Strategies: {len(loop.sota)}") print(f"{'=' * 60}") if loop.sota: print(f"\n TOP DISCOVERIES:") for i, r in enumerate(loop.sota[:15], 1): hp = r['hypothesis'] print(f" {i:>2d}. {hp['description'][:50]:50s} Sh={r['sharpe']:+.2f} Mon={r['monthly_pct']:+.1f}% " f"Tr={r['n_trades']} ({hp.get('generation','?')})") final = RESULTS_DIR / f"rd_loop_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" final.write_text(json.dumps(loop.sota, indent=2, default=str)) print(f"\n Saved: {final}") # Learnings summary 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'] print(f"\n Exploit wins: {len(exploit_best)} (avg Sh={np.mean([r['sharpe'] for r in exploit_best]):.1f})" if exploit_best else "") print(f" Explore wins: {len(explore_best)} (avg Sh={np.mean([r['sharpe'] for r in explore_best]):.1f})" if explore_best else "") if __name__ == "__main__": main()