#!/usr/bin/env python3 """Price-Action R&D Loop — TA-Lib powered. 17 indicators, deterministic. Uses TA-Lib (161 indicators) for standardized technical analysis. Generates random strategy hypotheses and evaluates via backtest_signal. """ import json, os, random, sys, time from datetime import datetime from pathlib import Path import numpy as np, pandas as pd import talib PROJECT = Path(__file__).resolve().parent.parent OHLCV_PATH = Path(os.getenv("PREDIX_OHLCV_PATH", str(PROJECT / "git_ignore_folder" / "intraday_pv_all.h5"))) RESULTS_DIR = PROJECT / "results" / "strategies_new" TIMEFRAMES = ["15min", "30min", "1h", "4h", "1d"] VOTE_THRESHOLD = 0.25 MIN_SHARPE, MIN_TRADES, TOP_N = 1.0, 20, 20 # ═══════════════════════════════════════════════════════════════════════════════ # Indicator functions — all use (close, high, low, volume, **params) signature # ═══════════════════════════════════════════════════════════════════════════════ def _macd(c, h, l, v, fast, slow, sig): mc, sc, _ = talib.MACD(c.values.astype(np.float64), fastperiod=fast, slowperiod=slow, signalperiod=sig) s = pd.Series(0, index=c.index); s[mc > sc] = 1; s[mc < sc] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _rsi(c, h, l, v, period, oversold, overbought): vv = talib.RSI(c.values.astype(np.float64), timeperiod=period) s = pd.Series(0, index=c.index); s[vv < oversold] = 1; s[vv > overbought] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _bbands(c, h, l, v, period, std): up, mi, lo = talib.BBANDS(c.values.astype(np.float64), timeperiod=period, nbdevup=std, nbdevdn=std) s = pd.Series(0, index=c.index); s[c.values < lo] = 1; s[c.values > up] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _stoch(c, h, l, v, fastk, slowk, slowd): k, d = talib.STOCH(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), fastk_period=fastk, slowk_period=slowk, slowd_period=slowd) s = pd.Series(0, index=c.index); s[(k > d) & (k < 30)] = 1; s[(k < d) & (k > 70)] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _cci(c, h, l, v, period): vv = talib.CCI(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period) s = pd.Series(0, index=c.index); s[vv < -100] = 1; s[vv > 100] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _willr(c, h, l, v, period): vv = talib.WILLR(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period) s = pd.Series(0, index=c.index); s[vv < -80] = 1; s[vv > -20] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _adx(c, h, l, v, period, threshold): pdi = talib.PLUS_DI(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period) ndi = talib.MINUS_DI(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period) adx = talib.ADX(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period) s = pd.Series(0, index=c.index); s[(pdi > ndi) & (adx > threshold)] = 1; s[(ndi > pdi) & (adx > threshold)] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _sar(c, h, l, v, accel, max_accel): vv = talib.SAR(h.values.astype(np.float64), l.values.astype(np.float64), acceleration=accel, maximum=max_accel) s = pd.Series(0, index=c.index); s[c.values > vv] = 1; s[c.values < vv] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _roc(c, h, l, v, period, threshold): vv = talib.ROC(c.values.astype(np.float64), timeperiod=period) s = pd.Series(0, index=c.index); s[vv > threshold] = 1; s[vv < -threshold] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _mom(c, h, l, v, period): vv = talib.MOM(c.values.astype(np.float64), timeperiod=period) s = pd.Series(0, index=c.index); s[vv > 0] = 1; s[vv < 0] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _aroon(c, h, l, v, period): up, dn = talib.AROON(h.values.astype(np.float64), l.values.astype(np.float64), timeperiod=period) s = pd.Series(0, index=c.index); s[up > dn] = 1; s[up < dn] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _mfi(c, h, l, v, period): 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) s = pd.Series(0, index=c.index); s[vv < 20] = 1; s[vv > 80] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _ultosc(c, h, l, v, p1, p2, p3): vv = talib.ULTOSC(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod1=p1, timeperiod2=p2, timeperiod3=p3) s = pd.Series(0, index=c.index); s[vv < 30] = 1; s[vv > 70] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _natr(c, h, l, v, period): vv = talib.NATR(h.values.astype(np.float64), l.values.astype(np.float64), c.values.astype(np.float64), timeperiod=period) m, s = vv[-200:].mean(), vv[-200:].std() s = pd.Series(0, index=c.index); s[c.values > m+s] = 1; s[c.values < m-s] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _donchian(c, h, l, v, period, hold): hi, lo = c.rolling(period).max(), c.rolling(period).min() s = pd.Series(0, index=c.index); s[c > hi.shift(1)] = 1; s[c < lo.shift(1)] = -1 return s.replace(0, np.nan).ffill(limit=hold).fillna(0).astype(int).clip(-1, 1) def _sma(c, h, l, v, fast, slow): s = pd.Series(0, index=c.index) s[c.rolling(fast).mean() > c.rolling(slow).mean()] = 1 s[c.rolling(fast).mean() < c.rolling(slow).mean()] = -1 return s.fillna(0).astype(int).clip(-1, 1) def _ema(c, h, l, v, fast, slow): ef, es = c.ewm(span=fast, adjust=False).mean(), c.ewm(span=slow, adjust=False).mean() s = pd.Series(0, index=c.index); s[ef > es] = 1; s[ef < es] = -1 return s.fillna(0).astype(int).clip(-1, 1) # ═══════════════════════════════════════════════════════════════════════════════ INDICATORS = { "MACD": ({"fast":[3,5,8,12], "slow":[10,15,20,26,35], "sig":[3,5,9]}, _macd, "MACD({fast},{slow},{sig})"), "RSI": ({"period":[7,14,21], "oversold":[20,25,30,35], "overbought":[65,70,75,80]}, _rsi, "RSI({period})[{oversold}/{overbought}]"), "BBands": ({"period":[10,20,40], "std":[1.5,2.0,2.5]}, _bbands, "BB({period},{std}s)"), "Stoch": ({"fastk":[5,9,14], "slowk":[3], "slowd":[3,5]}, _stoch, "Stoch({fastk},{slowk},{slowd})"), "CCI": ({"period":[14,20,50]}, _cci, "CCI({period})"), "WillR": ({"period":[7,14,21]}, _willr, "WR({period})"), "ADX": ({"period":[7,14,21], "threshold":[15,20,25]}, _adx, "ADX({period}>{threshold})"), "SAR": ({"accel":[0.02,0.05,0.08], "max_accel":[0.2,0.3,0.5]}, _sar, "SAR({accel},{max_accel})"), "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})"), } # ═══════════════════════════════════════════════════════════════════════════════ # Strategy generation # ═══════════════════════════════════════════════════════════════════════════════ 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 def random_hypothesis(): stype = random.choice(["single", "multi_tf", "portfolio"]) if stype == "single": ind_name = random.choice(list(INDICATORS.keys())) 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) 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())) 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)) 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) 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, hypothesis): hp = hypothesis if hp["type"] == "single": _, 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": _, fn, _ = INDICATORS[hp["indicator"]] sigs = {} for tf in hp["timeframes"]: 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 return result else: sigs = [] daily, dh, dl, dv = _resample_ohlc(close_1min, "1d") for cfg in hp["indicators"]: _, 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 return result 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": 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 # ═══════════════════════════════════════════════════════════════════════════════ 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(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, best_sh, total, iteration = [], 0, 0, 0 while True: iteration += 1 if not continuous and iteration > iterations: break hp = random_hypothesis() result = evaluate(hp, close) result["iteration"] = iteration result["timestamp"] = datetime.now().isoformat() total += 1 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] 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}] Evals: {total} | Top: {len(top)} | Best Sh={best_sh:.2f}") if iteration % 50 == 0 and top: RESULTS_DIR.mkdir(parents=True, exist_ok=True) 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 print(f"\n{'=' * 60}") 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__": main()