mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
4b6dff1710
- Replaced 7 hand-rolled indicators with TA-Lib equivalents - Added 10 new TA-Lib indicators: Stoch, CCI, WillR, ADX, SAR, ROC, MOM, AROON, MFI, UltOsc, NATR - Indicator functions now accept (close, high, low, volume, **params) for full OHLCV access - quantstats integration for professional HTML reports - Riskfolio-Lib installed for future portfolio optimization
286 lines
15 KiB
Python
286 lines
15 KiB
Python
#!/usr/bin/env python3
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"""Price-Action R&D Loop — TA-Lib powered. 17 indicators, deterministic.
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Uses TA-Lib (161 indicators) for standardized technical analysis.
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Generates random strategy hypotheses and evaluates via backtest_signal.
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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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import talib
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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" / "strategies_new"
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TIMEFRAMES = ["15min", "30min", "1h", "4h", "1d"]
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VOTE_THRESHOLD = 0.25
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MIN_SHARPE, MIN_TRADES, TOP_N = 1.0, 20, 20
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# ═══════════════════════════════════════════════════════════════════════════════
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# Indicator functions — all use (close, high, low, volume, **params) signature
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# ═══════════════════════════════════════════════════════════════════════════════
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def _macd(c, h, l, v, fast, slow, sig):
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mc, sc, _ = talib.MACD(c.values.astype(np.float64), fastperiod=fast, slowperiod=slow, signalperiod=sig)
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s = pd.Series(0, index=c.index); s[mc > sc] = 1; s[mc < sc] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _rsi(c, h, l, v, period, oversold, overbought):
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vv = talib.RSI(c.values.astype(np.float64), timeperiod=period)
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s = pd.Series(0, index=c.index); s[vv < oversold] = 1; s[vv > overbought] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _bbands(c, h, l, v, period, std):
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up, mi, lo = talib.BBANDS(c.values.astype(np.float64), timeperiod=period, nbdevup=std, nbdevdn=std)
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s = pd.Series(0, index=c.index); s[c.values < lo] = 1; s[c.values > up] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _stoch(c, h, l, v, fastk, slowk, slowd):
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k, d = talib.STOCH(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64),
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fastk_period=fastk, slowk_period=slowk, slowd_period=slowd)
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s = pd.Series(0, index=c.index); s[(k > d) & (k < 30)] = 1; s[(k < d) & (k > 70)] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _cci(c, h, l, v, period):
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vv = talib.CCI(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period)
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s = pd.Series(0, index=c.index); s[vv < -100] = 1; s[vv > 100] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _willr(c, h, l, v, period):
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vv = talib.WILLR(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period)
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s = pd.Series(0, index=c.index); s[vv < -80] = 1; s[vv > -20] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _adx(c, h, l, v, period, threshold):
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pdi = talib.PLUS_DI(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period)
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ndi = talib.MINUS_DI(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period)
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adx = talib.ADX(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period)
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s = pd.Series(0, index=c.index); s[(pdi > ndi) & (adx > threshold)] = 1; s[(ndi > pdi) & (adx > threshold)] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _sar(c, h, l, v, accel, max_accel):
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vv = talib.SAR(h.values.astype(np.float64), l.values.astype(np.float64), acceleration=accel, maximum=max_accel)
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s = pd.Series(0, index=c.index); s[c.values > vv] = 1; s[c.values < vv] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _roc(c, h, l, v, period, threshold):
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vv = talib.ROC(c.values.astype(np.float64), timeperiod=period)
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s = pd.Series(0, index=c.index); s[vv > threshold] = 1; s[vv < -threshold] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _mom(c, h, l, v, period):
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vv = talib.MOM(c.values.astype(np.float64), timeperiod=period)
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s = pd.Series(0, index=c.index); s[vv > 0] = 1; s[vv < 0] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _aroon(c, h, l, v, period):
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up, dn = talib.AROON(h.values.astype(np.float64), l.values.astype(np.float64), timeperiod=period)
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s = pd.Series(0, index=c.index); s[up > dn] = 1; s[up < dn] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _mfi(c, h, l, v, period):
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vv = talib.MFI(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), v.values.astype(np.float64), timeperiod=period)
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s = pd.Series(0, index=c.index); s[vv < 20] = 1; s[vv > 80] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _ultosc(c, h, l, v, p1, p2, p3):
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vv = talib.ULTOSC(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod1=p1, timeperiod2=p2, timeperiod3=p3)
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s = pd.Series(0, index=c.index); s[vv < 30] = 1; s[vv > 70] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _natr(c, h, l, v, period):
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vv = talib.NATR(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period)
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m, s = vv[-200:].mean(), vv[-200:].std()
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s = pd.Series(0, index=c.index); s[c.values > m+s] = 1; s[c.values < m-s] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _donchian(c, h, l, v, period, hold):
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hi, lo = c.rolling(period).max(), c.rolling(period).min()
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s = pd.Series(0, index=c.index); s[c > hi.shift(1)] = 1; s[c < lo.shift(1)] = -1
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return s.replace(0, np.nan).ffill(limit=hold).fillna(0).astype(int).clip(-1, 1)
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def _sma(c, h, l, v, fast, slow):
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s = pd.Series(0, index=c.index)
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s[c.rolling(fast).mean() > c.rolling(slow).mean()] = 1
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s[c.rolling(fast).mean() < c.rolling(slow).mean()] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _ema(c, h, l, v, fast, slow):
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ef, es = c.ewm(span=fast, adjust=False).mean(), c.ewm(span=slow, adjust=False).mean()
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s = pd.Series(0, index=c.index); s[ef > es] = 1; s[ef < es] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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# ═══════════════════════════════════════════════════════════════════════════════
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INDICATORS = {
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"MACD": ({"fast":[3,5,8,12], "slow":[10,15,20,26,35], "sig":[3,5,9]}, _macd, "MACD({fast},{slow},{sig})"),
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"RSI": ({"period":[7,14,21], "oversold":[20,25,30,35], "overbought":[65,70,75,80]}, _rsi, "RSI({period})[{oversold}/{overbought}]"),
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"BBands": ({"period":[10,20,40], "std":[1.5,2.0,2.5]}, _bbands, "BB({period},{std}s)"),
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"Stoch": ({"fastk":[5,9,14], "slowk":[3], "slowd":[3,5]}, _stoch, "Stoch({fastk},{slowk},{slowd})"),
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"CCI": ({"period":[14,20,50]}, _cci, "CCI({period})"),
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"WillR": ({"period":[7,14,21]}, _willr, "WR({period})"),
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"ADX": ({"period":[7,14,21], "threshold":[15,20,25]}, _adx, "ADX({period}>{threshold})"),
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"SAR": ({"accel":[0.02,0.05,0.08], "max_accel":[0.2,0.3,0.5]}, _sar, "SAR({accel},{max_accel})"),
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"ROC": ({"period":[5,10,20], "threshold":[0.1,0.2,0.5]}, _roc, "ROC({period},{threshold}%)"),
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"MOM": ({"period":[5,10,20,50]}, _mom, "MOM({period})"),
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"AROON": ({"period":[7,14,21]}, _aroon, "AROON({period})"),
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"MFI": ({"period":[7,14,21]}, _mfi, "MFI({period})"),
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"UltOsc": ({"p1":[7], "p2":[14], "p3":[28]}, _ultosc, "UltOsc(7,14,28)"),
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"NATR": ({"period":[7,14,21]}, _natr, "NATR({period})"),
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"Donchian":({"period":[5,10,20,30,50,100], "hold":[1,2,3,5]}, _donchian, "Donchian({period},{hold})"),
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"SMA": ({"fast":[5,10,20,50], "slow":[20,50,100,200]}, _sma, "SMA({fast},{slow})"),
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"EMA": ({"fast":[3,5,8,12], "slow":[15,26,50,100]}, _ema, "EMA({fast},{slow})"),
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}
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# ═══════════════════════════════════════════════════════════════════════════════
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# Strategy generation
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# ═══════════════════════════════════════════════════════════════════════════════
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def _resample_ohlc(close_1min, tf):
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"""Resample to timeframe, producing OHLCV bars."""
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bars = close_1min.resample(tf).ohlc()
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# Flatten MultiIndex columns
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o = bars['close']['close'] if isinstance(bars.columns, pd.MultiIndex) else bars['close']
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h = bars['high']['high'] if isinstance(bars.columns, pd.MultiIndex) else bars['high']
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l = bars['low']['low'] if isinstance(bars.columns, pd.MultiIndex) else bars['low']
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c = bars['close']['close'] if isinstance(bars.columns, pd.MultiIndex) else bars['close']
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v = pd.Series(1000, index=c.index) # dummy volume
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return c, h, l, v
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def random_hypothesis():
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stype = random.choice(["single", "multi_tf", "portfolio"])
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if stype == "single":
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ind_name = random.choice(list(INDICATORS.keys()))
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params_def, _, desc_tpl = INDICATORS[ind_name]
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params = {k: random.choice(v) for k, v in params_def.items()}
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if ind_name == "SMA" and params["fast"] >= params["slow"]:
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params["fast"] = min(params["fast"], params["slow"] // 2)
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return {"type": "single", "indicator": ind_name, "timeframe": random.choice(TIMEFRAMES),
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"params": params, "description": desc_tpl.format(**params)}
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elif stype == "multi_tf":
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ind_name = random.choice(list(INDICATORS.keys()))
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params_def, _, desc_tpl = INDICATORS[ind_name]
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params = {k: random.choice(v) for k, v in params_def.items()}
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tfs = random.sample(TIMEFRAMES, k=random.randint(2, 4))
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return {"type": "multi_tf", "indicator": ind_name, "timeframes": tfs,
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"params": params, "description": f"{ind_name} on {','.join(tfs)} maj-vote"}
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else:
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i1, i2 = random.sample(list(INDICATORS.keys()), 2)
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p1_def, _, _ = INDICATORS[i1]; p2_def, _, _ = INDICATORS[i2]
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p1 = {k: random.choice(v) for k, v in p1_def.items()}
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p2 = {k: random.choice(v) for k, v in p2_def.items()}
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return {"type": "portfolio", "indicators": [{"name": i1, "params": p1}, {"name": i2, "params": p2}],
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"timeframe": "1d", "description": f"{i1} + {i2} portfolio daily"}
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def build_signal(close_1min, hypothesis):
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hp = hypothesis
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if hp["type"] == "single":
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_, fn, _ = INDICATORS[hp["indicator"]]
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c, h, l, v = _resample_ohlc(close_1min, hp["timeframe"])
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s = fn(c, h, l, v, **hp["params"])
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return s.reindex(close_1min.index).ffill().fillna(0).astype(int).clip(-1, 1)
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elif hp["type"] == "multi_tf":
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_, fn, _ = INDICATORS[hp["indicator"]]
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sigs = {}
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for tf in hp["timeframes"]:
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c, h, l, v = _resample_ohlc(close_1min, tf)
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sigs[tf] = fn(c, h, l, v, **hp["params"]).reindex(close_1min.index).ffill().fillna(0).astype(int).clip(-1, 1)
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port_df = pd.DataFrame(sigs).dropna()
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vote = port_df.mean(axis=1)
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result = pd.Series(0, index=vote.index)
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result[vote > VOTE_THRESHOLD] = 1; result[vote < -VOTE_THRESHOLD] = -1
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return result
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else:
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sigs = []
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daily, dh, dl, dv = _resample_ohlc(close_1min, "1d")
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for cfg in hp["indicators"]:
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_, fn, _ = INDICATORS[cfg["name"]]
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s = fn(daily, dh, dl, dv, **cfg["params"]).reindex(close_1min.index).ffill().fillna(0).astype(int).clip(-1, 1)
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sigs.append(s)
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port_df = pd.DataFrame({f"s{i}": s for i, s in enumerate(sigs)}).dropna()
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vote = port_df.mean(axis=1)
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result = pd.Series(0, index=vote.index)
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result[vote > VOTE_THRESHOLD] = 1; result[vote < -VOTE_THRESHOLD] = -1
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return result
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def evaluate(hp, close):
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signal = build_signal(close, hp)
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from rdagent.components.backtesting.vbt_backtest import backtest_signal
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bt = backtest_signal(close=close, signal=signal)
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return {"hypothesis": hp, "sharpe": bt.get("sharpe", 0) or 0,
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"monthly_pct": bt.get("monthly_return_pct", 0) or 0,
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"max_dd": bt.get("max_drawdown", 0) or 0, "n_trades": bt.get("n_trades", 0) or 0,
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"win_rate": bt.get("win_rate", 0) or 0}
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# ═══════════════════════════════════════════════════════════════════════════════
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# Main loop
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# ═══════════════════════════════════════════════════════════════════════════════
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def main():
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iterations = 100; continuous = False
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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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if "--live" in sys.argv: continuous = True
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print("=" * 60)
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print(f" Price-Action R&D Loop — TA-Lib ({len(INDICATORS)} indicators)")
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print(f" Iterations: {'continuous' if continuous else 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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top, best_sh, total, iteration = [], 0, 0, 0
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while True:
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iteration += 1
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if not continuous and iteration > iterations: break
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hp = random_hypothesis()
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result = evaluate(hp, close)
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result["iteration"] = iteration
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result["timestamp"] = datetime.now().isoformat()
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total += 1
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if result["sharpe"] >= MIN_SHARPE and result["n_trades"] >= MIN_TRADES and result["monthly_pct"] > 0:
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top.append(result)
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top.sort(key=lambda r: r["sharpe"], reverse=True)
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top = top[:TOP_N]
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if iteration % 10 == 0 or result["sharpe"] > best_sh:
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if result["sharpe"] > best_sh:
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best_sh = result["sharpe"]
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print(f"\n * NEW BEST (#{iteration}): {hp['description']}")
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print(f" Sharpe={result['sharpe']:.2f} Mon={result['monthly_pct']:.2f}% "
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f"DD={result['max_dd']:.4f} Tr={result['n_trades']} WR={result['win_rate']:.1%}")
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else:
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print(f" [{iteration}/{iterations}] Evals: {total} | Top: {len(top)} | Best Sh={best_sh:.2f}")
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if iteration % 50 == 0 and top:
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RESULTS_DIR.mkdir(parents=True, exist_ok=True)
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cp = RESULTS_DIR / f"pal_talib_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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cp.write_text(json.dumps(top[:10], indent=2, default=str))
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print(f" Checkpoint: {cp.name}")
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# Final
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print(f"\n{'=' * 60}")
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print(f" Done: {total} evaluated, {len(top)} strategies")
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if top:
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print(f"\n{'#':>3s} {'Strategy':<50s} {'Sharpe':>7s} {'Mon%':>7s} {'DD':>7s} {'Tr':>5s}")
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print("-" * 80)
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for i, r in enumerate(top[:15], 1):
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print(f"{i:>3d} {r['hypothesis']['description'][:50]:<50s} {r['sharpe']:>+7.2f} {r['monthly_pct']:>+6.2f}% {r['max_dd']:>+6.4f} {r['n_trades']:>5d}")
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final = RESULTS_DIR / f"pal_talib_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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final.write_text(json.dumps(top, indent=2, default=str))
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print(f"\n Saved: {final}")
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if __name__ == "__main__":
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main()
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