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feat: Grid Search — systematic parameter scanning for 10 indicators
- Tests ALL parameter/TF combinations (603 total, vs random sampling) - Guarantees global optimum discovery (random search converges to local) - 10 indicators: MACD, Donchian, SAR, ADX, RSI, BBands, ROC, MOM, Stoch, CCI - Multi-instrument: EUR/USD, GBP/USD, BTC/USD - Session filter only (no vola — vola killed forex in V3)
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TPTBusiness
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#!/usr/bin/env python3
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"""Strategy Grid Search — Systematic parameter scanning for optimal strategies.
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Unlike the random R&D loop, this tests ALL parameter/TF combinations
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for the best indicators, guaranteeing global optimum discovery.
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Output: Ranked list of strategies with per-instrument + combined metrics.
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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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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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OUTPUT_DIR = PROJECT / "results" / "grid_search"
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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 (
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evaluate_multi, build_signal, _apply_session_filter, _apply_news_filter,
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_apply_vola_filter, _apply_cross_confirm, LEADER_MAP, load_data,
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)
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# ── Grid Definition ──
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INDICATOR_GRIDS = {
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"MACD": {
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"type": "multi_tf",
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"params": {
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"fast": [3, 5, 8, 12],
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"slow": [10, 15, 20, 26, 40],
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"sig": [3, 5, 9],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["15min", "30min", "1h"],
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["30min", "1h", "4h"],
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["15min", "1h", "4h"],
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],
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},
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"Donchian": {
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"type": "multi_tf",
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"params": {
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"period": [5, 10, 20, 30, 50, 80, 100],
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"hold": [1, 2, 3, 5, 10],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["30min", "1h", "4h"],
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["15min", "1h", "4h"],
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],
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},
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"SAR": {
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"type": "multi_tf",
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"params": {
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"accel": [0.02, 0.05, 0.08, 0.1, 0.15],
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"max_accel": [0.1, 0.2, 0.3, 0.5],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["30min", "1h", "4h"],
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["15min", "1h", "4h"],
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],
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},
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"ADX": {
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"type": "multi_tf",
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"params": {
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"period": [7, 10, 14, 21, 30],
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"threshold": [15, 20, 25, 30],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["30min", "1h", "4h"],
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],
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},
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"RSI": {
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"type": "multi_tf",
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"params": {
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"period": [7, 10, 14, 21],
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"oversold": [20, 25, 30],
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"overbought": [70, 75, 80],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["30min", "1h", "4h"],
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],
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},
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"BBands": {
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"type": "multi_tf",
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"params": {
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"period": [10, 20, 40],
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"std": [1.5, 2.0, 2.5],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["30min", "1h", "4h"],
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],
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},
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"ROC": {
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"type": "multi_tf",
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"params": {
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"period": [5, 10, 20, 50],
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"threshold": [0.1, 0.2, 0.5, 1.0],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["30min", "1h", "4h"],
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],
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},
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"MOM": {
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"type": "multi_tf",
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"params": {
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"period": [5, 10, 20, 50, 100],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["30min", "1h", "4h"],
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],
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},
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"Stoch": {
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"type": "multi_tf",
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"params": {
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"fastk": [5, 9, 14],
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"slowd": [3, 5, 9],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["30min", "1h", "4h"],
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],
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},
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"CCI": {
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"type": "multi_tf",
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"params": {
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"period": [10, 14, 20, 50],
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},
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"tfs": [
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["15min", "30min", "1h", "4h"],
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["30min", "1h", "4h"],
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],
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},
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}
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def expand_grid(indicator_name):
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"""Expand a grid definition into all parameter+TF combinations."""
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grid = INDICATOR_GRIDS[indicator_name]
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param_keys = list(grid["params"].keys())
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param_values = [grid["params"][k] for k in param_keys]
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hypotheses = []
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for tf_list in grid["tfs"]:
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for param_combo in itertools.product(*param_values):
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params = dict(zip(param_keys, param_combo))
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hypotheses.append({
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"type": grid["type"],
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"indicator": indicator_name,
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"timeframes": tf_list,
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"params": params,
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"description": f"{indicator_name}({'-'.join(str(v) for v in param_combo)}) on {','.join(tf_list[:2])}",
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"generation": "grid",
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})
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return hypotheses
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def main():
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import argparse
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ap = argparse.ArgumentParser()
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ap.add_argument("--indicators", nargs="*", default=None,
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help="Indicators to grid-search (default: all)")
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ap.add_argument("--top", type=int, default=20,
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help="Number of top results to show")
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args = ap.parse_args()
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indicators = args.indicators or list(INDICATOR_GRIDS.keys())
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if isinstance(indicators, str):
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indicators = [indicators]
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print("=" * 60)
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print(" Strategy Grid Search")
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print(f" Indicators: {', '.join(indicators)}")
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print("=" * 60)
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# Load data
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print(" Loading data...")
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closes = load_data()
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if not closes:
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print(" No instruments found!"); return
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# Generate all hypotheses
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all_hypotheses = []
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for ind in indicators:
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hyps = expand_grid(ind)
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all_hypotheses.extend(hyps)
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print(f" Total combinations to test: {len(all_hypotheses)}")
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print()
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# Evaluate all
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results = []
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t0 = time.time()
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for i, hp in enumerate(all_hypotheses):
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try:
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r = evaluate_multi(closes, hp, use_session=True, use_vola=False)
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r["hypothesis"] = hp
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r["rank"] = i + 1
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results.append(r)
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except Exception:
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continue
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elapsed = time.time() - t0
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rate = (i + 1) / elapsed if elapsed > 0 else 0
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eta = (len(all_hypotheses) - i - 1) / rate if rate > 0 else 0
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if (i + 1) % 50 == 0:
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best_so_far = max(results, key=lambda x: x["sharpe"]) if results else {"sharpe": 0}
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print(f" [{i+1}/{len(all_hypotheses)}] "
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f"Best Sh={best_so_far['sharpe']:.1f} "
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f"Mon={best_so_far['monthly_pct']:.1f}% "
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f"OOS={best_so_far['monthly_oos']:.1f}% | "
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f"{rate:.0f}/s | ETA {eta/60:.0f}min")
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# Sort by OOS Sharpe (most important metric)
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results.sort(key=lambda r: r.get("sharpe", 0), reverse=True)
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elapsed = time.time() - t0
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print(f"\n{'=' * 60}")
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print(f" Grid Search Complete: {len(results)}/{len(all_hypotheses)} valid")
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print(f" Time: {elapsed:.0f}s ({elapsed/60:.1f}min)")
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print(f"{'=' * 60}")
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# Save all results
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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out_file = OUTPUT_DIR / f"grid_results_{ts}.json"
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stripped = [{k: v for k, v in r.items() if k != "equity_curves"} for r in results]
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out_file.write_text(json.dumps(stripped, indent=2, default=str))
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print(f" Saved: {out_file}")
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# Show top results
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top_n = min(args.top, len(results))
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print(f"\n TOP {top_n} (by OOS Sharpe):")
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print(f" {'Rank':>4s} {'Strategy':<45s} {'Sh_IS':>6s} {'Sh_OOS':>6s} {'Mon%':>7s} {'OOS%':>7s} {'DD':>6s} {'Tr':>5s} {'BTC':>5s}")
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for i, r in enumerate(results[:top_n], 1):
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hp = r["hypothesis"]
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per = r.get("per_instrument", {})
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btc_sh = per.get("BTCUSD", {}).get("sharpe_oos", 0)
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print(f" {i:4d} {hp['description'][:45]:45s} "
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f"{r.get('sharpe_is', 0):+6.1f} {r.get('sharpe_oos', 0):+6.1f} "
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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} {btc_sh:+5.0f}")
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# Indicator performance summary
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print(f"\n Indicator Performance (avg OOS Sharpe):")
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for ind in indicators:
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ind_results = [r for r in results if r["hypothesis"].get("indicator") == ind]
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if ind_results:
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avg_sh = np.mean([r["sharpe"] for r in ind_results])
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best = ind_results[0]
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print(f" {ind:12s}: avg Sh={avg_sh:+.1f} best={best['sharpe']:+.1f} "
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f"({best['monthly_pct']:+.1f}%/{best['monthly_oos']:+.1f}% OOS)")
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if __name__ == "__main__":
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main()
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