mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
50d1fb47e3
- Gold Swing Scanner: 255 daily strategies, best EMA +2.8% OOS/month - Auto-adapt timeframes for daily data (1d/1w instead of 15min/4h) - Session filter skips for daily data - XAUUSD added to instruments list
194 lines
7.0 KiB
Python
194 lines
7.0 KiB
Python
#!/usr/bin/env python3
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"""Gold Swing Scanner — Daily strategies for position/swing trading.
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Unlike the 1-min grid search, this targets multi-day holds on daily Gold data.
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Tests: Trend-following, momentum, mean-reversion, breakout on 1-20 day horizons.
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"""
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import json, os, sys, time, itertools
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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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PROJECT = Path(__file__).resolve().parent.parent
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OUTPUT_DIR = PROJECT / "results" / "gold_swing"
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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sys.path.insert(0, str(PROJECT / "scripts"))
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from nexquant_rd_loop import _backtest_numba
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def build_daily_signal(close, indicator, params):
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"""Build signal on raw daily close (no resampling)."""
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import talib
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c = close.values.astype(np.float64)
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s = np.zeros(len(c), dtype=np.int32)
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if indicator == 'MACD':
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mc, sc, _ = talib.MACD(c, fastperiod=params.get('fast',12),
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slowperiod=params.get('slow',26),
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signalperiod=params.get('sig',9))
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s[mc > sc] = 1; s[mc < sc] = -1
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elif indicator == 'SMA':
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fa = pd.Series(c).rolling(params.get('fast',20)).mean().values
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sl = pd.Series(c).rolling(params.get('slow',50)).mean().values
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s[fa > sl] = 1; s[fa < sl] = -1
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elif indicator == 'EMA':
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fa = pd.Series(c).ewm(span=params.get('fast',12)).mean().values
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sl = pd.Series(c).ewm(span=params.get('slow',26)).mean().values
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s[fa > sl] = 1; s[fa < sl] = -1
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elif indicator == 'ROC':
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v = talib.ROC(c, timeperiod=params.get('period',20))
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th = params.get('threshold',2.0)
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s[v > th] = 1; s[v < -th] = -1
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elif indicator == 'MOM':
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v = talib.MOM(c, timeperiod=params.get('period',20))
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s[v > 0] = 1; s[v < 0] = -1
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elif indicator == 'RSI_OBOS':
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v = talib.RSI(c, timeperiod=params.get('period',14))
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s[v < params.get('oversold',30)] = 1; s[v > params.get('overbought',70)] = -1
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elif indicator == 'Donchian':
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hi = pd.Series(c).rolling(params.get('period',20)).max().shift(1).values
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lo = pd.Series(c).rolling(params.get('period',20)).min().shift(1).values
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s[c > hi] = 1; s[c < lo] = -1
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# Hold until reverse
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hold = params.get('hold',5)
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if hold > 0:
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last = 0; cnt = 0
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for i in range(len(s)):
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if s[i] != 0: last = s[i]; cnt = hold
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elif cnt > 0: s[i] = last; cnt -= 1
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elif indicator == 'BB':
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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[c < lo] = 1; s[c > up] = -1
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return pd.Series(s, index=close.index).fillna(0).astype(int).clip(-1,1)
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# ── Grid Definition ──
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INDICATOR_GRIDS = {
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'MACD': {
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'fast': [3,5,8,12,21],
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'slow': [10,15,21,26,34,50],
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'sig': [3,5,9,13],
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},
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'SMA': {
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'fast': [10,20,50,100],
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'slow': [20,50,100,200],
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},
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'EMA': {
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'fast': [5,8,12,21],
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'slow': [13,21,34,55],
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},
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'ROC': {
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'period': [5,10,20,50,100],
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'threshold': [0.5,1.0,2.0,3.0,5.0],
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},
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'MOM': {
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'period': [10,20,50,100],
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},
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'RSI_OBOS': {
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'period': [7,14,21],
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'oversold': [20,25,30,35],
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'overbought': [65,70,75,80],
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},
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'Donchian': {
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'period': [5,10,20,50,100],
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'hold': [0,1,3,5,10],
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},
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'BB': {
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'period': [10,20,50],
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'std': [1.5,2.0,2.5,3.0],
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},
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}
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def load_gold_daily():
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"""Load daily Gold data."""
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path = PROJECT / "git_ignore_folder" / "xau_daily.h5"
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if path.exists():
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return pd.read_hdf(path, key="data")
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return None
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def main():
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print("=" * 60)
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print(" Gold Swing Scanner — Daily Position Strategies")
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print("=" * 60)
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close = load_gold_daily()
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if close is None:
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print(" XAUUSD daily data not found! Run download first."); return
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print(f" XAUUSD daily: {len(close)} bars, {close.index[0].date()} -> {close.index[-1].date()}")
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all_results = []
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total = 0
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for ind_name, grid in INDICATOR_GRIDS.items():
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keys = list(grid.keys())
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values = list(grid.values())
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for combo in itertools.product(*values):
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total += 1
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params = dict(zip(keys, combo))
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try:
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sig = build_daily_signal(close, ind_name, params)
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if sig is None or sig.nunique() <= 1: continue
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except: continue
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n = len(close); is_n = int(n * 0.8)
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if is_n < 10: continue # too little data
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p = close.values.astype(float); s = sig.values.astype(np.int32)
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if np.sum(np.abs(s)) < 10: continue
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p_is = close.iloc[:is_n].values.astype(float); s_is = sig.iloc[:is_n].values.astype(np.int32)
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p_oos = close.iloc[is_n:].values.astype(float); s_oos = sig.iloc[is_n:].values.astype(np.int32)
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_, dd, tr, w, ret, sh, _ = _backtest_numba(p, s)
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_, _, tr_o, _, ret_o, sh_o, _ = _backtest_numba(p_oos, s_oos)
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nd = (close.index[-1] - close.index[0]).days
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if nd <= 0: continue
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mon = ((1+ret)**(1/(nd/30.44))-1)*100 if ret > -1 else 0
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nd_o = (close.index[is_n:][-1] - close.index[is_n:][0]).days
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if nd_o <= 0: nd_o = 1
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mon_o = ((1+ret_o)**(1/(nd_o/30.44))-1)*100 if ret_o > -1 else 0
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all_results.append({
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'indicator': ind_name, 'params': params,
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'sharpe': float(sh), 'sharpe_oos': float(sh_o),
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'monthly_pct': float(mon), 'monthly_oos': float(mon_o),
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'n_trades': int(tr), 'n_trades_oos': int(tr_o),
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'win_rate': float(w/tr) if tr>0 else 0,
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'max_dd': float(-dd),
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})
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all_results.sort(key=lambda r: r['sharpe_oos'], reverse=True)
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print(f" {len(all_results)}/{total} strategies with trades\n")
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print(f" TOP 20 by OOS Sharpe:")
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print(f" {'Rank':>4s} {'Indicator':<15s} {'Sh IS':>6s} {'Sh OOS':>7s} {'Mon IS':>7s} {'Mon OOS':>7s} {'DD':>6s} {'Tr':>5s}")
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for i, r in enumerate(all_results[:20], 1):
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print(f" {i:4d} {r['indicator']:<15s} {r['sharpe']:+6.1f} {r['sharpe_oos']:+7.1f} "
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f"{r['monthly_pct']:+6.1f}% {r['monthly_oos']:+6.1f}% "
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f"{r['max_dd']:.4f} {r['n_trades']:5d}")
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# Save
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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out = OUTPUT_DIR / f"gold_swing_{ts}.json"
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out.write_text(json.dumps(all_results, indent=2, default=str))
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print(f"\n Saved: {out}")
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# Indicator summary
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from collections import Counter
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print(f"\n Indicator Performance:")
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for ind in INDICATOR_GRIDS.keys():
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r = [r for r in all_results if r['indicator'] == ind]
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if r:
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print(f" {ind:<15s}: max Sh={max(x['sharpe'] for x in r):+.1f} "
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f"OOS={max(x['sharpe_oos'] for x in r):+.1f} "
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f"({len(r)} combos)")
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if __name__ == "__main__":
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main()
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