Initial strategy32 research and live runtime
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164
scripts/run_exhaustive_combo_analysis.py
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164
scripts/run_exhaustive_combo_analysis.py
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from __future__ import annotations
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import copy
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import itertools
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import json
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import sys
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from pathlib import Path
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import pandas as pd
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PACKAGE_PARENT = Path(__file__).resolve().parents[2]
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if str(PACKAGE_PARENT) not in sys.path:
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sys.path.insert(0, str(PACKAGE_PARENT))
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from strategy29.backtest.window_analysis import evaluate_window_result, slice_bundle
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from strategy32.backtest.simulator import Strategy32Backtester
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from strategy32.config import PROFILE_V5_BASELINE, build_strategy32_config
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from strategy32.data import build_strategy32_market_bundle
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WINDOWS = [(30, "1m"), (365, "1y"), (1095, "3y"), (1825, "5y")]
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FEATURES: list[tuple[str, str, bool]] = [
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("no_sideways", "enable_sideways_engine", False),
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("strong_kill_switch", "enable_strong_kill_switch", True),
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("daily_trend_filter", "enable_daily_trend_filter", True),
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("expanded_hedge", "enable_expanded_hedge", True),
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("max_holding_exit", "enable_max_holding_exit", True),
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]
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def variant_name(enabled: list[str]) -> str:
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return "baseline_v5" if not enabled else "+".join(enabled)
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def balanced_score(results: dict[str, dict[str, float | int | str]]) -> float:
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score = 0.0
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for label, weight in (("1y", 1.0), ("3y", 1.0), ("5y", 1.2)):
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annualized = float(results[label]["annualized_return"])
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drawdown = abs(float(results[label]["max_drawdown"]))
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score += weight * (annualized / max(drawdown, 0.01))
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score += 0.25 * float(results["1m"]["total_return"])
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return score
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def build_variants() -> list[tuple[str, dict[str, bool], list[str]]]:
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variants: list[tuple[str, dict[str, bool], list[str]]] = [("baseline_v5", {}, [])]
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feature_names = [feature[0] for feature in FEATURES]
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for r in range(1, len(FEATURES) + 1):
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for combo in itertools.combinations(range(len(FEATURES)), r):
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overrides: dict[str, bool] = {}
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enabled: list[str] = []
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for idx in combo:
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label, attr, value = FEATURES[idx]
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overrides[attr] = value
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enabled.append(label)
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variants.append((variant_name(enabled), overrides, enabled))
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return variants
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def main() -> None:
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base = build_strategy32_config(PROFILE_V5_BASELINE)
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end = pd.Timestamp("2026-03-15 00:00:00", tz="UTC")
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start = end - pd.Timedelta(days=max(days for days, _ in WINDOWS) + base.warmup_days + 14)
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print("fetching bundle...")
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bundle, latest_completed_bar, accepted_symbols, rejected_symbols, quote_by_symbol = build_strategy32_market_bundle(
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symbols=base.symbols,
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auto_discover_symbols=base.auto_discover_symbols,
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quote_assets=base.quote_assets,
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excluded_base_assets=base.excluded_base_assets,
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min_quote_volume_24h=base.discovery_min_quote_volume_24h,
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start=start,
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end=end,
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timeframe=base.timeframe,
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max_staleness_days=base.max_symbol_staleness_days,
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)
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print("latest", latest_completed_bar)
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results: dict[str, dict[str, dict[str, float | int | str]]] = {}
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summary_rows: list[dict[str, float | int | str | list[str]]] = []
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for idx, (name, overrides, enabled) in enumerate(build_variants(), start=1):
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cfg = copy.deepcopy(base)
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for attr, value in overrides.items():
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setattr(cfg, attr, value)
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variant_results = {}
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print(f"\n[{idx:02d}/32] {name}")
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for days, label in WINDOWS:
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eval_end = latest_completed_bar
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eval_start = eval_end - pd.Timedelta(days=days)
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raw_start = eval_start - pd.Timedelta(days=cfg.warmup_days)
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sliced = slice_bundle(bundle, raw_start, eval_end)
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backtester = Strategy32Backtester(cfg, sliced, trade_start=eval_start)
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backtester.engine_config.initial_capital = 1000.0
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result = backtester.run()
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metrics = evaluate_window_result(result, eval_start=eval_start, bars_per_day=backtester.engine_config.bars_per_day)
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metrics["engine_pnl"] = result.engine_pnl
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metrics["total_trades"] = result.total_trades
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variant_results[label] = metrics
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print(
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label,
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"ret",
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round(float(metrics["total_return"]) * 100, 2),
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"mdd",
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round(float(metrics["max_drawdown"]) * 100, 2),
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"sharpe",
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round(float(metrics["sharpe"]), 2),
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"trades",
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metrics["trade_count"],
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)
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score = balanced_score(variant_results)
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results[name] = variant_results
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summary_rows.append(
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{
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"name": name,
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"enabled": enabled,
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"balanced_score": score,
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"ret_1m": float(variant_results["1m"]["total_return"]),
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"ret_1y": float(variant_results["1y"]["total_return"]),
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"ret_3y": float(variant_results["3y"]["total_return"]),
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"ret_5y": float(variant_results["5y"]["total_return"]),
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"mdd_1y": float(variant_results["1y"]["max_drawdown"]),
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"mdd_3y": float(variant_results["3y"]["max_drawdown"]),
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"mdd_5y": float(variant_results["5y"]["max_drawdown"]),
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}
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)
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summary_rows.sort(key=lambda row: float(row["balanced_score"]), reverse=True)
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payload = {
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"strategy": "strategy32",
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"analysis": "v6_exhaustive_combo",
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"initial_capital": 1000.0,
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"auto_discover_symbols": base.auto_discover_symbols,
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"latest_completed_bar": str(latest_completed_bar),
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"requested_symbols": [] if base.auto_discover_symbols else base.symbols,
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"accepted_symbols": accepted_symbols,
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"rejected_symbols": rejected_symbols,
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"quote_by_symbol": quote_by_symbol,
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"timeframe": base.timeframe,
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"results": results,
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"summary": summary_rows,
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}
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out = Path("/tmp/strategy32_v6_exhaustive_combos.json")
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out.write_text(json.dumps(payload, indent=2), encoding="utf-8")
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print("\nTop 10 by balanced score")
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for row in summary_rows[:10]:
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print(
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row["name"],
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"score",
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round(float(row["balanced_score"]), 3),
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"1y",
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round(float(row["ret_1y"]) * 100, 2),
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"3y",
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round(float(row["ret_3y"]) * 100, 2),
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"5y",
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round(float(row["ret_5y"]) * 100, 2),
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"mdd5y",
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round(float(row["mdd_5y"]) * 100, 2),
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)
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print("\nwrote", out)
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if __name__ == "__main__":
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main()
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