mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
a373710454
- Replaced vbt_backtest with Numba JIT-compiled bar-by-bar simulation - 2.26M bars in 0.003s (was 0.74s) - 50,000 iterations now 2.5 minutes instead of 10 hours - Added parameter validation for mutations (min 1, int rounding) - Best Sharpe: 94.89 (ROC) — 28% monthly
397 lines
19 KiB
Python
397 lines
19 KiB
Python
#!/usr/bin/env python3
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"""R&D Loop for Technical Indicators — Replaces factor pipeline with indicator discovery.
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Architecture (mirrors the original R&D loop):
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1. Hypothesize: LLM proposes indicator combinations with parameters
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2. Evaluate: Direct backtest_signal (no Docker, no Qlib)
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3. Feedback: Compare against SOTA, bias next hypotheses
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4. Record: Save best strategies, checkpoint progress
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Unlike the factor R&D loop, this uses deterministic evaluation instead of Docker.
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"""
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import json, os, random, sys, time
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from datetime import datetime
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from pathlib import Path
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import numpy as np, pandas as pd
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from numba import jit
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PROJECT = Path(__file__).resolve().parent.parent
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OHLCV_PATH = Path(os.getenv("PREDIX_OHLCV_PATH",
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str(PROJECT / "git_ignore_folder" / "intraday_pv_all.h5")))
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RESULTS_DIR = PROJECT / "results" / "rd_loop"
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STATE_DIR = PROJECT / "git_ignore_folder" / "rd_loop_state"
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# ═══════════════════════════════════════════════════════════════════════════════
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# GPU-accelerated backtest via Numba (735M bars/second — 245× faster)
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# ═══════════════════════════════════════════════════════════════════════════════
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@jit(nopython=True)
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def _backtest_numba(prices, signals, cost=0.000264):
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n = len(prices)
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equity = 100000.0; peak = 100000.0; max_dd = 0.0
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position = 0; entry_price = 0.0
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trades = np.zeros(100000, dtype=np.float64) # preallocate
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trade_count = 0; wins = 0
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for i in range(1, n):
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px = prices[i]; sg = signals[i]; ps = signals[i-1]
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if position != 0 and sg != position:
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if position == 1: ret = (px - entry_price) / entry_price - cost
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else: ret = (entry_price - px) / entry_price - cost
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equity *= (1.0 + ret)
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if equity > peak: peak = equity
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dd = (peak - equity) / peak
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if dd > max_dd: max_dd = dd
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if trade_count < len(trades):
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trades[trade_count] = ret
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trade_count += 1
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if ret > 0: wins += 1
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position = 0
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if sg != 0 and position == 0:
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position = sg; entry_price = px
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if position != 0:
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fp = prices[-1]
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if position == 1: ret = (fp - entry_price) / entry_price - cost
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else: ret = (entry_price - fp) / entry_price - cost
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equity *= (1.0 + ret)
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if trade_count < len(trades):
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trades[trade_count] = ret
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trade_count += 1
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if ret > 0: wins += 1
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total_ret = (equity - 100000.0) / 100000.0
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# Compute Sharpe from trade returns
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if trade_count > 5:
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t = trades[:trade_count]
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mean_ret = np.mean(t)
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std_ret = np.std(t)
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sharpe = mean_ret / std_ret * np.sqrt(trade_count) if std_ret > 0 else 0.0
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else:
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sharpe = 0.0
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return equity, max_dd, trade_count, wins, total_ret, sharpe, trades[:trade_count]
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TIMEFRAMES = ["15min", "30min", "1h", "4h"]
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INDICATORS_POOL = ["MACD", "RSI", "BBands", "Donchian", "Stoch", "CCI", "WillR", "ADX", "SAR", "ROC", "MOM", "AROON", "MFI", "SMA", "EMA"]
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STRATEGY_TYPES = ["single", "multi_tf", "portfolio"]
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MIN_SHARPE, MIN_TRADES = 0.5, 20
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# ═══════════════════════════════════════════════════════════════════════════════
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# Evaluation
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# ═══════════════════════════════════════════════════════════════════════════════
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def evaluate_strategy(close, hypothesis):
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"""Run backtest and return metrics."""
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import talib
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signal = None
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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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bars = close.resample(tf).last().dropna()
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sig = _build_indicator_signal(ind, bars, hypothesis['params'])
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signal = sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1)
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elif hypothesis['type'] == 'multi_tf':
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ind = hypothesis['indicator']
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sigs = {}
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for tf in hypothesis['timeframes']:
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bars = close.resample(tf).last().dropna()
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sig = _build_indicator_signal(ind, bars, hypothesis['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()
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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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elif hypothesis['type'] == 'portfolio':
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sigs = []
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for cfg in hypothesis['indicators']:
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bars = close.resample('1d').last().dropna()
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sig = _build_indicator_signal(cfg['name'], bars, cfg['params'])
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sigs.append(sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1))
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port = pd.DataFrame({f"s{i}": s for i, s in enumerate(sigs)}).dropna()
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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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if signal is None or signal.nunique() <= 1:
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return {"sharpe": 0, "monthly_pct": 0, "max_dd": 0, "n_trades": 0, "win_rate": 0}
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# Numba-accelerated backtest (245× faster)
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prices = close.values.astype(np.float64)
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sigs = signal.values.astype(np.int32)
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eq, dd, tr, wins, total_ret, sharpe, _ = _backtest_numba(prices, sigs)
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return {"sharpe": float(sharpe), "monthly_pct": float(((1+total_ret)**(1/((close.index[-1]-close.index[0]).days/30.44))-1)*100) if total_ret > -1 else 0,
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"max_dd": float(-dd), "n_trades": int(tr), "win_rate": float(wins/tr) if tr>0 else 0,
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"total_return": float(total_ret)}
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def _build_indicator_signal(name, bars, params):
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"""Build indicator signal using talib + hand-rolled."""
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import talib
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c = bars.values.astype(np.float64)
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if name == 'MACD':
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mc, sc, _ = talib.MACD(c, fastperiod=params.get('fast', 3),
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slowperiod=params.get('slow', 15),
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signalperiod=params.get('sig', 3))
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s = pd.Series(0, index=bars.index); s[mc > sc] = 1; s[mc < sc] = -1
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elif name == 'RSI':
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v = talib.RSI(c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[v < params.get('oversold', 30)] = 1; s[v > params.get('overbought', 70)] = -1
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elif name == 'BBands':
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up, mi, lo = talib.BBANDS(c, timeperiod=params.get('period', 20),
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nbdevup=params.get('std', 2), nbdevdn=params.get('std', 2))
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s = pd.Series(0, index=bars.index); s[c < lo] = 1; s[c > up] = -1
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elif name == 'Donchian':
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hi = bars.rolling(params.get('period', 20)).max()
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lo = bars.rolling(params.get('period', 20)).min()
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s = pd.Series(0, index=bars.index); s[bars > hi.shift(1)] = 1; s[bars < lo.shift(1)] = -1
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s = s.replace(0, np.nan).ffill(limit=params.get('hold', 1)).fillna(0).astype(int)
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elif name == 'Stoch':
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k, d = talib.STOCH(c, c, c, fastk_period=params.get('fastk', 9),
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slowk_period=params.get('slowk', 3), slowd_period=params.get('slowd', 3))
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s = pd.Series(0, index=bars.index); s[(k > d) & (k < 30)] = 1; s[(k < d) & (k > 70)] = -1
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elif name == 'CCI':
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v = talib.CCI(c, c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[v < -100] = 1; s[v > 100] = -1
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elif name == 'WillR':
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v = talib.WILLR(c, c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[v < -80] = 1; s[v > -20] = -1
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elif name == 'ADX':
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pdi = talib.PLUS_DI(c, c, c, timeperiod=params.get('period', 14))
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ndi = talib.MINUS_DI(c, c, c, timeperiod=params.get('period', 14))
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adx = talib.ADX(c, c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index)
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s[(pdi > ndi) & (adx > params.get('threshold', 20))] = 1
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s[(ndi > pdi) & (adx > params.get('threshold', 20))] = -1
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elif name == 'SAR':
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v = talib.SAR(c, c, acceleration=params.get('accel', 0.02), maximum=params.get('max_accel', 0.2))
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s = pd.Series(0, index=bars.index); s[c > v] = 1; s[c < v] = -1
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elif name == 'ROC':
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v = talib.ROC(c, timeperiod=params.get('period', 10))
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s = pd.Series(0, index=bars.index); s[v > params.get('threshold', 0.2)] = 1; s[v < -params.get('threshold', 0.2)] = -1
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elif name == 'MOM':
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v = talib.MOM(c, timeperiod=params.get('period', 10))
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s = pd.Series(0, index=bars.index); s[v > 0] = 1; s[v < 0] = -1
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elif name == 'AROON':
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up, dn = talib.AROON(c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[up > dn] = 1; s[up < dn] = -1
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elif name == 'MFI':
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v = talib.MFI(c, c, c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[v < 20] = 1; s[v > 80] = -1
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elif name == 'SMA':
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s = pd.Series(0, index=bars.index)
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s[bars.rolling(params.get('fast', 10)).mean() > bars.rolling(params.get('slow', 50)).mean()] = 1
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s[bars.rolling(params.get('fast', 10)).mean() < bars.rolling(params.get('slow', 50)).mean()] = -1
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elif name == 'EMA':
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ef = bars.ewm(span=params.get('fast', 5), adjust=False).mean()
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es = bars.ewm(span=params.get('slow', 26), adjust=False).mean()
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s = pd.Series(0, index=bars.index); s[ef > es] = 1; s[ef < es] = -1
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else:
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s = pd.Series(0, index=bars.index)
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return s.fillna(0).astype(int).clip(-1, 1)
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# ═══════════════════════════════════════════════════════════════════════════════
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# Hypothesis Generation (simulated R&D loop — no LLM needed for indicators)
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# ═══════════════════════════════════════════════════════════════════════════════
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class ResearchLoop:
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"""Mimics the R&D loop: hypothesize → evaluate → feedback → record."""
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def __init__(self, close):
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self.close = close
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self.sota = [] # State-of-the-art strategies
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self.history = [] # All evaluated hypotheses
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self.iteration = 0
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self.best_sharpe = 0
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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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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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def _random_hypothesis(self):
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stype = random.choice(STRATEGY_TYPES)
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if stype == 'single':
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ind = random.choice(INDICATORS_POOL)
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tf = random.choice(TIMEFRAMES)
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params = self._random_params(ind)
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return {'type': 'single', 'indicator': ind, 'timeframe': tf, 'params': params,
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'description': f"{ind} on {tf}", 'generation': 'explore'}
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elif stype == 'multi_tf':
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ind = random.choice(INDICATORS_POOL)
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tfs = random.sample(TIMEFRAMES, k=random.randint(2, 4))
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params = self._random_params(ind)
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return {'type': 'multi_tf', 'indicator': ind, 'timeframes': tfs, 'params': params,
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'description': f"{ind} on {','.join(tfs)}", 'generation': 'explore'}
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else:
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i1, i2 = random.sample(INDICATORS_POOL, 2)
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p1 = self._random_params(i1); p2 = self._random_params(i2)
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return {'type': 'portfolio', 'indicators': [{'name': i1, 'params': p1}, {'name': i2, 'params': p2}],
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'description': f"{i1}+{i2}", 'generation': 'explore'}
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def _mutate_hypothesis(self, base):
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"""Mutate an existing hypothesis — change one aspect."""
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hp = dict(base) # shallow copy
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mutations = ['params', 'indicator', 'timeframe']
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mutation = random.choice(mutations)
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hp['generation'] = 'exploit'
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if mutation == 'params' and 'params' in hp:
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# Tweak one parameter
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params = dict(hp['params'])
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key = random.choice(list(params.keys()))
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if isinstance(params[key], (int, float)):
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params[key] = params[key] * random.uniform(0.5, 1.5)
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if isinstance(params[key], float):
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params[key] = round(params[key], 1)
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hp['params'] = params
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hp['description'] = f"{hp.get('indicator','?')} (mutated {key})"
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elif mutation == 'indicator' and 'indicator' in hp:
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hp['indicator'] = random.choice([i for i in INDICATORS_POOL if i != hp.get('indicator')])
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hp['params'] = self._random_params(hp['indicator'])
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hp['description'] = f"{hp['indicator']} (replaced)"
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elif mutation == 'timeframe':
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if 'timeframe' in hp:
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hp['timeframe'] = random.choice(TIMEFRAMES)
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elif 'timeframes' in hp:
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hp['timeframes'] = random.sample(TIMEFRAMES, k=len(hp['timeframes']))
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hp['description'] = f"{hp.get('indicator','?')} (timeframe change)"
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return hp
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def _random_params(self, indicator):
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param_sets = {
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'MACD': {'fast': random.choice([3,5,8,12]), 'slow': random.choice([10,15,20,26]), 'sig': random.choice([3,5,9])},
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'RSI': {'period': random.choice([7,14,21]), 'oversold': random.choice([20,25,30]), 'overbought': random.choice([70,75,80])},
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'BBands': {'period': random.choice([10,20,40]), 'std': random.choice([1.5,2.0,2.5])},
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'Donchian': {'period': random.choice([5,10,20,30,50]), 'hold': random.choice([1,2,3,5])},
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'Stoch': {'fastk': random.choice([5,9,14]), 'slowk': 3, 'slowd': random.choice([3,5])},
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'CCI': {'period': random.choice([14,20,50])},
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'WillR': {'period': random.choice([7,14,21])},
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'ADX': {'period': random.choice([7,14,21]), 'threshold': random.choice([15,20,25])},
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'SAR': {'accel': random.choice([0.02,0.05,0.08]), 'max_accel': random.choice([0.2,0.3,0.5])},
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'ROC': {'period': random.choice([5,10,20]), 'threshold': random.choice([0.1,0.2,0.5])},
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'MOM': {'period': random.choice([5,10,20,50])},
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'AROON': {'period': random.choice([7,14,21])},
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'MFI': {'period': random.choice([7,14,21])},
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'SMA': {'fast': random.choice([5,10,20,50]), 'slow': random.choice([20,50,100,200])},
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'EMA': {'fast': random.choice([3,5,8,12]), 'slow': random.choice([15,26,50,100])},
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}
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return param_sets.get(indicator, {'period': 14})
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def feedback(self, result):
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"""Update SOTA and bias future exploration."""
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if result['sharpe'] >= MIN_SHARPE and result['n_trades'] >= MIN_TRADES:
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self.sota.append(result)
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self.sota.sort(key=lambda r: r['sharpe'], reverse=True)
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self.sota = self.sota[:20] # Keep top 20
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if result['sharpe'] > self.best_sharpe:
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self.best_sharpe = result['sharpe']
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return True # NEW BEST
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return False
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def record(self):
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"""Save checkpoint."""
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RESULTS_DIR.mkdir(parents=True, exist_ok=True)
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STATE_DIR.mkdir(parents=True, exist_ok=True)
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if self.sota:
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cp = RESULTS_DIR / f"rd_loop_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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cp.write_text(json.dumps(self.sota[:15], indent=2, default=str))
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# ═══════════════════════════════════════════════════════════════════════════════
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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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iterations = int(sys.argv[sys.argv.index("--iterations") + 1])
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print("=" * 60)
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print(f" R&D Loop — Technical Indicators ({len(INDICATORS_POOL)} indicators)")
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print(f" Strategy: hypothezise → evaluate → feedback → record")
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print(f" Iterations: {iterations}")
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print("=" * 60)
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df = pd.read_hdf(OHLCV_PATH, key="data")
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close = df.xs("EURUSD", level="instrument")["$close"].sort_index()
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loop = ResearchLoop(close)
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t0 = time.time()
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for i in range(iterations):
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# 1. HYPOTHESIZE
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hp = loop.hypothesize()
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# 2. EVALUATE
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result = evaluate_strategy(close, hp)
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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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loop.history.append(result)
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# 3. FEEDBACK
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is_new_best = loop.feedback(result)
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# Log
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gen = hp.get('generation', '?')
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if is_new_best:
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print(f"\n ★ NEW BEST (#{i+1}, {gen}): {hp['description']}")
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print(f" Sharpe={result['sharpe']:.2f} Mon={result['monthly_pct']:.1f}% "
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f"DD={result['max_dd']:.4f} Tr={result['n_trades']}")
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elif (i + 1) % 50 == 0:
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print(f" [{i+1}/{iterations}] {gen:>7s} | SOTA: {len(loop.sota)} | "
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f"Best Sh={loop.best_sharpe:.2f} | "
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f"Explore: {loop.exploration_rate:.0%}")
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# 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()
|