NexQuant/scripts/nexquant_rd_loop.py

#!/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()