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feat: migrate R&D loop to TA-Lib (17 indicators, 161 available)
- 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
This commit is contained in:
TPTBusiness
@@ -1,409 +1,285 @@
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#!/usr/bin/env python3
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"""Price-Action R&D Loop — Generates, evaluates, and optimizes technical strategies.
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"""Price-Action R&D Loop — TA-Lib powered. 17 indicators, deterministic.
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Unlike the factor-based R&D loop (CoSTEER → Docker → Qlib), this loop uses
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deterministic technical indicators evaluated via backtest_signal.
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Loop steps:
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1. Hypothesize: Randomly sample indicator + parameter combination
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2. Evaluate: Run backtest_signal on 1-min data
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3. Feedback: Compare against best-so-far, adjust search space
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4. Record: Save top-N strategies to results/
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Usage:
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python scripts/nexquant_priceaction_loop.py --iterations 100
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python scripts/nexquant_priceaction_loop.py --live # Continuously optimize
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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
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import os
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import random
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import time
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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
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import pandas as pd
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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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# ── Indicator Library ────────────────────────────────────────────────────────
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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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def _macd_signal(c, fast, slow, sig):
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ema_f = c.ewm(span=fast, adjust=False).mean()
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ema_s = c.ewm(span=slow, adjust=False).mean()
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ml = ema_f - ema_s
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sl = ml.ewm(span=sig, adjust=False).mean()
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s = pd.Series(0, index=c.index)
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s[ml > sl] = 1
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s[ml < sl] = -1
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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 _donchian_signal(c, period, hold):
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s = pd.Series(0, index=c.index)
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s[c > c.rolling(period).max().shift(1)] = 1
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s[c < c.rolling(period).min().shift(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 _rsi_signal(c, period, oversold, overbought):
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d = c.diff()
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g = d.clip(lower=0).rolling(period).mean()
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l = (-d.clip(upper=0)).rolling(period).mean()
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rsi = 100 - 100 / (1 + g / l.replace(0, 1e-8))
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s = pd.Series(0, index=c.index)
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s[rsi < oversold] = 1
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s[rsi > overbought] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _sma_signal(c, fast, slow):
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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 _bb_signal(c, period, std):
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ma = c.rolling(period).mean()
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st = c.rolling(period).std()
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s = pd.Series(0, index=c.index)
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s[c < ma - std * st] = 1
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s[c > ma + std * st] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _atr_signal(c, period, mult):
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atr = (c.diff().abs()).rolling(period).mean()
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ma = c.rolling(period).mean()
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s = pd.Series(0, index=c.index)
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s[c > ma + mult * atr] = 1
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s[c < ma - mult * atr] = -1
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return s.replace(0, np.nan).ffill(limit=2).fillna(0).astype(int).clip(-1, 1)
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def _ma_env_signal(c, period, pct):
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ma = c.rolling(period).mean()
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s = pd.Series(0, index=c.index)
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s[c < ma * (1 - pct)] = 1
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s[c > ma * (1 + pct)] = -1
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return s.replace(0, np.nan).ffill(limit=3).fillna(0).astype(int).clip(-1, 1)
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def _stoch_signal(c, period, smooth):
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"""Stochastic Oscillator — oversold/overbought crossover."""
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lo = c.rolling(period).min()
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hi = c.rolling(period).max()
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k = 100 * (c - lo) / (hi - lo + 1e-8)
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d = k.rolling(smooth).mean()
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s = pd.Series(0, index=c.index)
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s[(k > d) & (k < 30)] = 1
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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_signal(c, h, l, period):
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"""Commodity Channel Index."""
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tp = (h + l + c) / 3
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ma = tp.rolling(period).mean()
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md = (tp - ma).abs().rolling(period).mean()
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cci = (tp - ma) / (0.015 * md + 1e-8)
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s = pd.Series(0, index=c.index)
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s[cci < -100] = 1
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s[cci > 100] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _williams_r(c, h, l, period):
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"""Williams %R — overbought/oversold."""
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hi = h.rolling(period).max()
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lo = l.rolling(period).min()
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wr = -100 * (hi - c) / (hi - lo + 1e-8)
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s = pd.Series(0, index=c.index)
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s[wr < -80] = 1
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s[wr > -20] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _roc_signal(c, period, threshold):
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"""Rate of Change — momentum threshold."""
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roc = c.pct_change(period) * 100
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s = pd.Series(0, index=c.index)
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s[roc > threshold] = 1
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s[roc < -threshold] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _ema_cross(c, fast, slow):
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"""EMA Crossover (separate from SMA)."""
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ef = c.ewm(span=fast, adjust=False).mean()
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es = c.ewm(span=slow, adjust=False).mean()
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s = pd.Series(0, index=c.index)
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s[ef > es] = 1
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s[ef < es] = -1
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return s.fillna(0).astype(int).clip(-1, 1)
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def _keltner(c, h, l, period, mult):
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"""Keltner Channel breakout."""
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ma = c.rolling(period).mean()
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atr = ((h - l).abs()).rolling(period).mean()
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s = pd.Series(0, index=c.index)
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s[c > ma + mult * atr] = 1
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s[c < ma - mult * atr] = -1
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return s.replace(0, np.nan).ffill(limit=2).fillna(0).astype(int).clip(-1, 1)
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def _adx_filter(c, h, l, period, threshold):
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"""ADX trend-strength filter — only trade when ADX > threshold."""
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tr = pd.concat([h - l, (h - c.shift()).abs(), (l - c.shift()).abs()], axis=1).max(axis=1)
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atr = tr.rolling(period).mean()
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up = h - h.shift()
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dn = l.shift() - l
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pdi = 100 * (up.clip(lower=0).rolling(period).mean() / (atr + 1e-8))
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ndi = 100 * (dn.clip(lower=0).rolling(period).mean() / (atr + 1e-8))
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dx = 100 * (pdi - ndi).abs() / (pdi + ndi + 1e-8)
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adx = dx.rolling(period).mean()
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s = pd.Series(0, index=c.index)
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s[(pdi > ndi) & (adx > threshold)] = 1
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s[(ndi > pdi) & (adx > threshold)] = -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": {"params": {"fast": [3,5,8,12], "slow": [10,15,20,26,35], "sig": [3,5,9]},
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"build": _macd_signal, "desc": "MACD({fast},{slow},{sig})"},
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"Donchian": {"params": {"period": [5,10,20,30,50,100], "hold": [1,2,3,5]},
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"build": _donchian_signal, "desc": "Donchian({period},{hold})"},
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"RSI": {"params": {"period": [7,14,21], "oversold": [20,25,30,35], "overbought": [65,70,75,80]},
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"build": _rsi_signal, "desc": "RSI({period})[{oversold}/{overbought}]"},
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"SMA_Cross": {"params": {"fast": [5,10,20,50], "slow": [20,50,100,200]},
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"build": _sma_signal, "desc": "SMA({fast},{slow})"},
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"EMA_Cross": {"params": {"fast": [3,5,8,12], "slow": [15,26,50,100]},
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"build": _ema_cross, "desc": "EMA({fast},{slow})"},
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"Bollinger": {"params": {"period": [10,20,40], "std": [1.5,2.0,2.5]},
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"build": _bb_signal, "desc": "BB({period},{std}s)"},
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"Keltner": {"params": {"period": [10,20,40], "mult": [1.0,1.5,2.0,2.5]},
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"build": lambda c, period, mult: _keltner(c, c, c, period, mult), "desc": "Keltner({period},{mult})"},
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"ATR_Channel":{"params": {"period": [10,20,40], "mult": [1.0,1.5,2.0,2.5]},
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"build": _atr_signal, "desc": "ATR({period},{mult})"},
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"MA_Envelope":{"params": {"period": [20,50,100], "pct": [0.01,0.02,0.03,0.05]},
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"build": _ma_env_signal, "desc": "MA_Env({period},{pct})"},
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"Stochastic": {"params": {"period": [5,9,14], "smooth": [3,5]},
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"build": lambda c, period, smooth: _stoch_signal(c, period, smooth), "desc": "Stoch({period},{smooth})"},
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"CCI": {"params": {"period": [14,20,50]},
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"build": lambda c, period: _cci_signal(c, c, c, period), "desc": "CCI({period})"},
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"WilliamsR": {"params": {"period": [7,14,21]},
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"build": lambda c, period: _williams_r(c, c, c, period), "desc": "WR({period})"},
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"ROC_Momentum":{"params": {"period": [5,10,20], "threshold": [0.1,0.2,0.5,1.0]},
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"build": _roc_signal, "desc": "ROC({period},{threshold}%)"},
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"ADX": {"params": {"period": [7,14,21], "threshold": [15,20,25]},
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"build": lambda c, period, threshold: _adx_filter(c, c, c, period, threshold), "desc": "ADX({period}>{threshold})"},
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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}%)"),
|
||||
"MOM": ({"period":[5,10,20,50]}, _mom, "MOM({period})"),
|
||||
"AROON": ({"period":[7,14,21]}, _aroon, "AROON({period})"),
|
||||
"MFI": ({"period":[7,14,21]}, _mfi, "MFI({period})"),
|
||||
"UltOsc": ({"p1":[7], "p2":[14], "p3":[28]}, _ultosc, "UltOsc(7,14,28)"),
|
||||
"NATR": ({"period":[7,14,21]}, _natr, "NATR({period})"),
|
||||
"Donchian":({"period":[5,10,20,30,50,100], "hold":[1,2,3,5]}, _donchian, "Donchian({period},{hold})"),
|
||||
"SMA": ({"fast":[5,10,20,50], "slow":[20,50,100,200]}, _sma, "SMA({fast},{slow})"),
|
||||
"EMA": ({"fast":[3,5,8,12], "slow":[15,26,50,100]}, _ema, "EMA({fast},{slow})"),
|
||||
}
|
||||
|
||||
TIMEFRAMES = ["15min", "30min", "1h", "4h", "1d"]
|
||||
VOTE_THRESHOLD = 0.25
|
||||
MIN_SHARPE = 1.0
|
||||
MIN_TRADES = 20
|
||||
TOP_N = 20
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
# Strategy Generation & Evaluation
|
||||
# Strategy generation
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
|
||||
def random_hypothesis() -> dict:
|
||||
"""Generate a random strategy hypothesis."""
|
||||
strategy_type = random.choice(["single", "multi_tf", "portfolio"])
|
||||
def _resample_ohlc(close_1min, tf):
|
||||
"""Resample to timeframe, producing OHLCV bars."""
|
||||
bars = close_1min.resample(tf).ohlc()
|
||||
# Flatten MultiIndex columns
|
||||
o = bars['close']['close'] if isinstance(bars.columns, pd.MultiIndex) else bars['close']
|
||||
h = bars['high']['high'] if isinstance(bars.columns, pd.MultiIndex) else bars['high']
|
||||
l = bars['low']['low'] if isinstance(bars.columns, pd.MultiIndex) else bars['low']
|
||||
c = bars['close']['close'] if isinstance(bars.columns, pd.MultiIndex) else bars['close']
|
||||
v = pd.Series(1000, index=c.index) # dummy volume
|
||||
return c, h, l, v
|
||||
|
||||
if strategy_type == "single":
|
||||
# One indicator on one timeframe
|
||||
tf = random.choice(TIMEFRAMES)
|
||||
def random_hypothesis():
|
||||
stype = random.choice(["single", "multi_tf", "portfolio"])
|
||||
if stype == "single":
|
||||
ind_name = random.choice(list(INDICATORS.keys()))
|
||||
ind = INDICATORS[ind_name]
|
||||
params = {k: random.choice(v) for k, v in ind["params"].items()}
|
||||
# Filter invalid combos
|
||||
if ind_name == "SMA_Cross" and params["fast"] >= params["slow"]:
|
||||
params_def, _, desc_tpl = INDICATORS[ind_name]
|
||||
params = {k: random.choice(v) for k, v in params_def.items()}
|
||||
if ind_name == "SMA" and params["fast"] >= params["slow"]:
|
||||
params["fast"] = min(params["fast"], params["slow"] // 2)
|
||||
if ind_name == "RSI" and params["oversold"] >= params["overbought"]:
|
||||
params["oversold"], params["overbought"] = 30, 70
|
||||
return {
|
||||
"type": "single",
|
||||
"indicator": ind_name,
|
||||
"timeframe": tf,
|
||||
"params": params,
|
||||
"description": ind['desc'].format(**params) + f" on {tf}",
|
||||
}
|
||||
|
||||
elif strategy_type == "multi_tf":
|
||||
# Same indicator on multiple timeframes, majority vote
|
||||
return {"type": "single", "indicator": ind_name, "timeframe": random.choice(TIMEFRAMES),
|
||||
"params": params, "description": desc_tpl.format(**params)}
|
||||
elif stype == "multi_tf":
|
||||
ind_name = random.choice(list(INDICATORS.keys()))
|
||||
ind = INDICATORS[ind_name]
|
||||
params_def, _, desc_tpl = INDICATORS[ind_name]
|
||||
params = {k: random.choice(v) for k, v in params_def.items()}
|
||||
tfs = random.sample(TIMEFRAMES, k=random.randint(2, 4))
|
||||
params = {k: random.choice(v) for k, v in ind["params"].items()}
|
||||
if ind_name == "SMA_Cross" and params["fast"] >= params["slow"]:
|
||||
params["fast"] = min(params["fast"], params["slow"] // 2)
|
||||
return {
|
||||
"type": "multi_tf",
|
||||
"indicator": ind_name,
|
||||
"timeframes": tfs,
|
||||
"params": params,
|
||||
"description": f"{ind_name} on {','.join(tfs)} majority-vote",
|
||||
}
|
||||
|
||||
else: # portfolio
|
||||
# Two different indicators, daily timeframe, majority vote
|
||||
return {"type": "multi_tf", "indicator": ind_name, "timeframes": tfs,
|
||||
"params": params, "description": f"{ind_name} on {','.join(tfs)} maj-vote"}
|
||||
else:
|
||||
i1, i2 = random.sample(list(INDICATORS.keys()), 2)
|
||||
ind1 = INDICATORS[i1]
|
||||
ind2 = INDICATORS[i2]
|
||||
p1 = {k: random.choice(v) for k, v in ind1["params"].items()}
|
||||
p2 = {k: random.choice(v) for k, v in ind2["params"].items()}
|
||||
if i1 == "SMA_Cross" and p1["fast"] >= p1["slow"]:
|
||||
p1["fast"] = min(p1["fast"], p1["slow"] // 2)
|
||||
if i2 == "SMA_Cross" and p2["fast"] >= p2["slow"]:
|
||||
p2["fast"] = min(p2["fast"], p2["slow"] // 2)
|
||||
return {
|
||||
"type": "portfolio",
|
||||
"indicators": [{"name": i1, "params": p1}, {"name": i2, "params": p2}],
|
||||
"timeframe": "1d",
|
||||
"description": f"{i1} + {i2} portfolio on daily",
|
||||
}
|
||||
p1_def, _, _ = INDICATORS[i1]; p2_def, _, _ = INDICATORS[i2]
|
||||
p1 = {k: random.choice(v) for k, v in p1_def.items()}
|
||||
p2 = {k: random.choice(v) for k, v in p2_def.items()}
|
||||
return {"type": "portfolio", "indicators": [{"name": i1, "params": p1}, {"name": i2, "params": p2}],
|
||||
"timeframe": "1d", "description": f"{i1} + {i2} portfolio daily"}
|
||||
|
||||
|
||||
def build_signal(close_1min: pd.Series, hypothesis: dict) -> pd.Series:
|
||||
"""Build a 1-min signal from a hypothesis."""
|
||||
def build_signal(close_1min, hypothesis):
|
||||
hp = hypothesis
|
||||
|
||||
if hp["type"] == "single":
|
||||
tf = hp["timeframe"]
|
||||
bars = close_1min.resample(tf).last().dropna()
|
||||
ind = INDICATORS[hp["indicator"]]
|
||||
s = ind["build"](bars, **hp["params"])
|
||||
_, fn, _ = INDICATORS[hp["indicator"]]
|
||||
c, h, l, v = _resample_ohlc(close_1min, hp["timeframe"])
|
||||
s = fn(c, h, l, v, **hp["params"])
|
||||
return s.reindex(close_1min.index).ffill().fillna(0).astype(int).clip(-1, 1)
|
||||
|
||||
elif hp["type"] == "multi_tf":
|
||||
signals = {}
|
||||
ind = INDICATORS[hp["indicator"]]
|
||||
_, fn, _ = INDICATORS[hp["indicator"]]
|
||||
sigs = {}
|
||||
for tf in hp["timeframes"]:
|
||||
bars = close_1min.resample(tf).last().dropna()
|
||||
signals[tf] = ind["build"](bars, **hp["params"]).reindex(
|
||||
close_1min.index).ffill().fillna(0).astype(int).clip(-1, 1)
|
||||
port = pd.DataFrame(signals).dropna()
|
||||
vote = port.mean(axis=1)
|
||||
c, h, l, v = _resample_ohlc(close_1min, tf)
|
||||
sigs[tf] = fn(c, h, l, v, **hp["params"]).reindex(close_1min.index).ffill().fillna(0).astype(int).clip(-1, 1)
|
||||
port_df = pd.DataFrame(sigs).dropna()
|
||||
vote = port_df.mean(axis=1)
|
||||
result = pd.Series(0, index=vote.index)
|
||||
result[vote > VOTE_THRESHOLD] = 1
|
||||
result[vote < -VOTE_THRESHOLD] = -1
|
||||
result[vote > VOTE_THRESHOLD] = 1; result[vote < -VOTE_THRESHOLD] = -1
|
||||
return result
|
||||
|
||||
else: # portfolio
|
||||
signals = []
|
||||
daily = close_1min.resample("1d").last().dropna()
|
||||
else:
|
||||
sigs = []
|
||||
daily, dh, dl, dv = _resample_ohlc(close_1min, "1d")
|
||||
for cfg in hp["indicators"]:
|
||||
ind = INDICATORS[cfg["name"]]
|
||||
s = ind["build"](daily, **cfg["params"])
|
||||
signals.append(s.reindex(close_1min.index).ffill().fillna(0).astype(int).clip(-1, 1))
|
||||
port = pd.DataFrame({f"s{i}": s for i, s in enumerate(signals)}).dropna()
|
||||
vote = port.mean(axis=1)
|
||||
_, fn, _ = INDICATORS[cfg["name"]]
|
||||
s = fn(daily, dh, dl, dv, **cfg["params"]).reindex(close_1min.index).ffill().fillna(0).astype(int).clip(-1, 1)
|
||||
sigs.append(s)
|
||||
port_df = pd.DataFrame({f"s{i}": s for i, s in enumerate(sigs)}).dropna()
|
||||
vote = port_df.mean(axis=1)
|
||||
result = pd.Series(0, index=vote.index)
|
||||
result[vote > VOTE_THRESHOLD] = 1
|
||||
result[vote < -VOTE_THRESHOLD] = -1
|
||||
result[vote > VOTE_THRESHOLD] = 1; result[vote < -VOTE_THRESHOLD] = -1
|
||||
return result
|
||||
|
||||
|
||||
def evaluate_strategy(close: pd.Series, hypothesis: dict) -> dict:
|
||||
"""Evaluate a strategy via backtest_signal."""
|
||||
signal = build_signal(close, hypothesis)
|
||||
def evaluate(hp, close):
|
||||
signal = build_signal(close, hp)
|
||||
from rdagent.components.backtesting.vbt_backtest import backtest_signal
|
||||
|
||||
bt = backtest_signal(close=close, signal=signal)
|
||||
return {
|
||||
"hypothesis": hypothesis,
|
||||
"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,
|
||||
"total_return": bt.get("total_return", 0) or 0,
|
||||
}
|
||||
|
||||
return {"hypothesis": hp, "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}
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
# Main Loop
|
||||
# Main loop
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
|
||||
def run_loop(iterations: int = 100, continuous: bool = False):
|
||||
def main():
|
||||
iterations = 100; continuous = False
|
||||
if "--iterations" in sys.argv:
|
||||
iterations = int(sys.argv[sys.argv.index("--iterations") + 1])
|
||||
if "--live" in sys.argv: continuous = True
|
||||
|
||||
print("=" * 60)
|
||||
print(" Price-Action R&D Loop")
|
||||
print(" Indicators:", ", ".join(INDICATORS.keys()))
|
||||
print(" Iterations:", iterations if not continuous else "continuous")
|
||||
print(f" Price-Action R&D Loop — TA-Lib ({len(INDICATORS)} indicators)")
|
||||
print(f" Iterations: {'continuous' if continuous else iterations}")
|
||||
print("=" * 60)
|
||||
|
||||
df = pd.read_hdf(OHLCV_PATH, key="data")
|
||||
close = df.xs("EURUSD", level="instrument")["$close"].sort_index()
|
||||
|
||||
top_strategies = []
|
||||
best_sharpe = 0
|
||||
total_evaluated = 0
|
||||
iteration = 0
|
||||
top, best_sh, total, iteration = [], 0, 0, 0
|
||||
|
||||
while True:
|
||||
iteration += 1
|
||||
if not continuous and iteration > iterations:
|
||||
break
|
||||
if not continuous and iteration > iterations: break
|
||||
|
||||
# Generate hypothesis
|
||||
hp = random_hypothesis()
|
||||
total_evaluated += 1
|
||||
|
||||
# Evaluate
|
||||
result = evaluate_strategy(close, hp)
|
||||
result = evaluate(hp, close)
|
||||
result["iteration"] = iteration
|
||||
result["timestamp"] = datetime.now().isoformat()
|
||||
total += 1
|
||||
|
||||
# Track top strategies
|
||||
if (result["sharpe"] >= MIN_SHARPE and result["n_trades"] >= MIN_TRADES
|
||||
and result["monthly_pct"] > 0):
|
||||
top_strategies.append(result)
|
||||
top_strategies.sort(key=lambda r: r["sharpe"], reverse=True)
|
||||
top_strategies = top_strategies[:TOP_N]
|
||||
if result["sharpe"] >= MIN_SHARPE and result["n_trades"] >= MIN_TRADES and result["monthly_pct"] > 0:
|
||||
top.append(result)
|
||||
top.sort(key=lambda r: r["sharpe"], reverse=True)
|
||||
top = top[:TOP_N]
|
||||
|
||||
# Progress
|
||||
if iteration % 10 == 0 or result["sharpe"] > best_sharpe:
|
||||
if result["sharpe"] > best_sharpe:
|
||||
best_sharpe = result["sharpe"]
|
||||
print(f"\n ★ NEW BEST (#{iteration}): {hp['description']}")
|
||||
if iteration % 10 == 0 or result["sharpe"] > best_sh:
|
||||
if result["sharpe"] > best_sh:
|
||||
best_sh = result["sharpe"]
|
||||
print(f"\n * NEW BEST (#{iteration}): {hp['description']}")
|
||||
print(f" Sharpe={result['sharpe']:.2f} Mon={result['monthly_pct']:.2f}% "
|
||||
f"DD={result['max_dd']:.4f} Tr={result['n_trades']} WR={result['win_rate']:.1%}")
|
||||
else:
|
||||
print(f" [{iteration}/{iterations}] Evaluated: {total_evaluated} | "
|
||||
f"Top: {len(top_strategies)} | Best Sh={best_sharpe:.2f}")
|
||||
print(f" [{iteration}/{iterations}] Evals: {total} | Top: {len(top)} | Best Sh={best_sh:.2f}")
|
||||
|
||||
# Save checkpoint every 50 iterations
|
||||
if iteration % 50 == 0 and top_strategies:
|
||||
if iteration % 50 == 0 and top:
|
||||
RESULTS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
cp_path = RESULTS_DIR / f"pal_loop_checkpoint_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
||||
cp_path.write_text(json.dumps(top_strategies[:10], indent=2, default=str))
|
||||
print(f" Checkpoint saved: {cp_path.name}")
|
||||
cp = RESULTS_DIR / f"pal_talib_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
||||
cp.write_text(json.dumps(top[:10], indent=2, default=str))
|
||||
print(f" Checkpoint: {cp.name}")
|
||||
|
||||
# Final results
|
||||
# Final
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f" Loop Complete: {total_evaluated} strategies evaluated")
|
||||
print(f" Top strategies: {len(top_strategies)} meeting criteria")
|
||||
print(f"{'=' * 60}")
|
||||
|
||||
if top_strategies:
|
||||
print(f"\n{'#':>3s} {'Description':<50s} {'Sharpe':>7s} {'Mon%':>7s} {'DD':>7s} {'Tr':>5s} {'WR':>6s}")
|
||||
print("-" * 90)
|
||||
for i, r in enumerate(top_strategies[:15], 1):
|
||||
hp = r["hypothesis"]
|
||||
print(f"{i:>3d} {hp['description'][:50]:<50s} {r['sharpe']:>+7.2f} "
|
||||
f"{r['monthly_pct']:>+6.2f}% {r['max_dd']:>+6.4f} "
|
||||
f"{r['n_trades']:>5d} {r['win_rate']:>6.1%}")
|
||||
|
||||
final_path = RESULTS_DIR / f"pal_loop_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
||||
final_path.write_text(json.dumps(top_strategies, indent=2, default=str))
|
||||
print(f"\n Final results saved: {final_path}")
|
||||
print(f" Done: {total} evaluated, {len(top)} strategies")
|
||||
if top:
|
||||
print(f"\n{'#':>3s} {'Strategy':<50s} {'Sharpe':>7s} {'Mon%':>7s} {'DD':>7s} {'Tr':>5s}")
|
||||
print("-" * 80)
|
||||
for i, r in enumerate(top[:15], 1):
|
||||
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}")
|
||||
final = RESULTS_DIR / f"pal_talib_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
||||
final.write_text(json.dumps(top, indent=2, default=str))
|
||||
print(f"\n Saved: {final}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
iterations = 100
|
||||
continuous = False
|
||||
if "--iterations" in sys.argv:
|
||||
idx = sys.argv.index("--iterations")
|
||||
iterations = int(sys.argv[idx + 1])
|
||||
if "--live" in sys.argv:
|
||||
continuous = True
|
||||
run_loop(iterations=iterations, continuous=continuous)
|
||||
main()
|
||||
|
||||
Reference in New Issue
Block a user