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https://github.com/NicolasBohn/NexQuant.git
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feat: R&D loop — Optuna optimization + LightGBM ML training
- Optuna: every 500 iterations, 20-trial hyperparameter optimization - ML: every 2000 iterations, LightGBM classifier on SOTA indicator signals - Numba backtest: 245× faster (735M bars/s) - All 3 discovery methods: explore → exploit → optuna → ml
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TPTBusiness
+150
-8
@@ -86,6 +86,14 @@ def evaluate_strategy(close, hypothesis):
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import talib
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signal = None
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# Optuna optimization branch
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if hypothesis.get('generation') == 'optuna':
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return _run_optuna(close, hypothesis)
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# ML training branch
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if hypothesis.get('type') == 'ml':
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return _train_ml(close, hypothesis)
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if hypothesis['type'] == 'single':
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ind = hypothesis['indicator']
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tf = hypothesis['timeframe']
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@@ -210,19 +218,25 @@ class ResearchLoop:
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self.exploration_rate = 0.3 # % of time we explore randomly vs exploit SOTA
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def hypothesize(self):
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"""Generate a new strategy hypothesis.
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Uses a bandit-inspired approach:
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- 70%: Exploit — mutate the best-known strategy
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- 30%: Explore — random new strategy
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"""
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self.iteration += 1
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# Every 500 iterations: Optuna-optimize best strategy
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if self.iteration % 500 == 0 and self.sota:
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best = self.sota[0]
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hp = dict(best['hypothesis'])
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hp['generation'] = 'optuna'
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hp['description'] = f"Optuna: {hp.get('description','?')}"
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return hp
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# Every 2000 iterations: train ML model
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if self.iteration % 2000 == 0 and len(self.sota) >= 5:
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return {'type': 'ml', 'generation': 'ml',
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'description': f"ML: LightGBM on {len(self.sota)} strategies",
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'sota': self.sota[:5]}
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if random.random() < self.exploration_rate or not self.sota:
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# EXPLORE: random new strategy
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return self._random_hypothesis()
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else:
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# EXPLOIT: mutate the best strategy
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base = random.choice(self.sota[:5])
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return self._mutate_hypothesis(base['hypothesis'])
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@@ -317,6 +331,131 @@ class ResearchLoop:
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# ═══════════════════════════════════════════════════════════════════════════════
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# ═══════════════════════════════════════════════════════════════════════════════
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# Optuna Optimization
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# ═══════════════════════════════════════════════════════════════════════════════
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def _run_optuna(close, hypothesis):
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"""Run Optuna hyperparameter optimization on a strategy."""
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import optuna
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optuna.logging.set_verbosity(optuna.logging.WARNING)
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hp = hypothesis
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ind = hp.get('indicator', 'MACD')
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tfs = hp.get('timeframes', ['15min','30min','1h','4h'])
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base_params = hp.get('params', {})
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param_ranges = {
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'MACD': {'fast': (2,15), 'slow': (5,40), 'sig': (2,15)},
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'RSI': {'period': (5,30), 'oversold': (10,40), 'overbought': (60,90)},
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'Donchian': {'period': (3,100), 'hold': (1,10)},
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'SAR': {'accel': (0.01, 0.2), 'max_accel': (0.1, 1.0)},
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'ADX': {'period': (5,30), 'threshold': (10,40)},
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}
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ranges = param_ranges.get(ind, {})
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def objective(trial):
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params = {}
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for k, (lo, hi) in ranges.items():
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if isinstance(base_params.get(k, 1), int):
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params[k] = trial.suggest_int(k, int(lo), int(hi))
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else:
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params[k] = trial.suggest_float(k, lo, hi)
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params['fast'] = min(params.get('fast',99), params.get('slow',99)-2)
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sigs = {}
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for tf in tfs:
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bars = close.resample(tf).last().dropna()
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sig = _build_indicator_signal(ind, bars, params)
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sigs[tf] = sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1,1)
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port = pd.DataFrame(sigs).dropna(); vote = port.mean(axis=1)
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signal = pd.Series(0, index=vote.index)
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signal[vote > 0.25] = 1; signal[vote < -0.25] = -1
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prices = close.values.astype(np.float64); sigs_arr = signal.values.astype(np.int32)
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_, dd, tr, wins, total_ret, sharpe, _ = _backtest_numba(prices, sigs_arr)
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return float(sharpe) if sharpe > 0 else -999.0
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try:
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study = optuna.create_study(direction='maximize')
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study.optimize(objective, n_trials=20, show_progress_bar=False)
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best = study.best_params
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hp['params'] = {k: int(v) if v == int(v) else v for k, v in best.items()}
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hp['description'] = f"Optuna: {ind} on {','.join(tfs[:2])}"
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hp['generation'] = 'optuna'
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# Re-evaluate with best params
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sigs = {}
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for tf in tfs:
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bars = close.resample(tf).last().dropna()
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sig = _build_indicator_signal(ind, bars, hp['params'])
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sigs[tf] = sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1,1)
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port = pd.DataFrame(sigs).dropna(); vote = port.mean(axis=1)
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signal = pd.Series(0, index=vote.index)
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signal[vote > 0.25] = 1; signal[vote < -0.25] = -1
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prices = close.values.astype(np.float64); sigs_arr = signal.values.astype(np.int32)
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eq, dd, tr, wins, ret, sh, _ = _backtest_numba(prices, sigs_arr)
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n_days = (close.index[-1] - close.index[0]).days
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mon = ((1+ret)**(1/(n_days/30.44))-1)*100 if ret > -1 else 0
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print(f" Optuna best: {best} → Sh={sh:.1f} Mon={mon:.1f}% ({study.best_value:.1f})")
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return {"sharpe": float(sh), "monthly_pct": float(mon), "max_dd": float(-dd),
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"n_trades": int(tr), "win_rate": float(wins/tr) if tr>0 else 0,
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"optuna_best": best, "optuna_value": float(study.best_value)}
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except Exception as e:
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return {"sharpe": 0, "monthly_pct": 0, "max_dd": 0, "n_trades": 0, "win_rate": 0}
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def _train_ml(close, hypothesis):
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"""Train LightGBM classifier on indicator signals to predict direction."""
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try:
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from lightgbm import LGBMClassifier
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except ImportError:
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return {"sharpe": 0, "monthly_pct": 0, "max_dd": 0, "n_trades": 0, "win_rate": 0}
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sota = hypothesis.get('sota', [])
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if not sota: return {"sharpe": 0, "monthly_pct": 0, "max_dd": 0, "n_trades": 0, "win_rate": 0}
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# Generate features from all SOTA strategies
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daily = close.resample('1h').last().dropna()
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features = pd.DataFrame(index=daily.index)
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for s in sota[:5]:
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hp_s = s['hypothesis']
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ind = hp_s.get('indicator', 'MACD')
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sig = _build_indicator_signal(ind, daily, hp_s.get('params', {}))
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features[f"{ind}_{hp_s.get('generation','?')}"] = sig
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features = features.fillna(0)
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# Target: next bar direction (1=up, 0=down)
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target = (daily.pct_change().shift(-1) > 0).astype(int)
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target = target.reindex(features.index).fillna(0)
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# Train/test split (80/20)
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split = int(len(features) * 0.8)
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X_train, X_test = features.iloc[:split], features.iloc[split:]
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y_train, y_test = target.iloc[:split], target.iloc[split:]
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if len(X_train) < 100: return {"sharpe": 0, "monthly_pct": 0, "max_dd": 0, "n_trades": 0, "win_rate": 0}
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model = LGBMClassifier(n_estimators=100, max_depth=5, verbosity=-1)
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model.fit(X_train, y_train)
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preds = model.predict(X_test)
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# Convert predictions to trading signal
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ml_signal = pd.Series(0, index=X_test.index)
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ml_signal[preds == 1] = 1; ml_signal[preds == 0] = -1
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ml_signal = ml_signal.reindex(close.index).ffill().fillna(0).astype(int).clip(-1,1)
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prices = close.values.astype(np.float64); sigs = ml_signal.values.astype(np.int32)
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eq, dd, tr, wins, ret, sh, _ = _backtest_numba(prices, sigs)
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n_days = (close.index[-1] - close.index[0]).days
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mon = ((1+ret)**(1/(n_days/30.44))-1)*100 if ret > -1 else 0
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acc = (preds == y_test).mean()
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print(f" ML LightGBM: Test acc={acc:.1%} → Sh={sh:.1f} Mon={mon:.1f}% Tr={tr}")
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return {"sharpe": float(sh), "monthly_pct": float(mon), "max_dd": float(-dd),
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"n_trades": int(tr), "win_rate": float(wins/tr) if tr>0 else 0,
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"ml_accuracy": float(acc), "ml_model": "LightGBM"}
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def main():
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iterations = 200
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if "--iterations" in sys.argv:
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@@ -339,7 +478,10 @@ def main():
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hp = loop.hypothesize()
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# 2. EVALUATE
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try:
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result = evaluate_strategy(close, hp)
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except Exception:
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continue # skip bad parameters
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result['hypothesis'] = hp
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result['iteration'] = i + 1
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result['timestamp'] = datetime.now().isoformat()
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