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TPTBusiness
4758de0eee
- Rename FTMO_* constants → generic names (RISK_PER_TRADE, MAX_DAILY_LOSS, etc.) - Rename backtest_signal_ftmo → backtest_signal_risk - Rename _apply_ftmo_mask → _apply_risk_mask - Clean all FTMO/riskMgmt mentions from commit messages via filter-branch - AGENTS.md: add non-negotiable rule — NEVER mention proprietary terms in commits/releases - Code variables and function names sanitized project-wide - Force-pushed rewritten history to remote
83 lines
3.4 KiB
Python
83 lines
3.4 KiB
Python
#!/usr/bin/env python
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"""30min Full Factor Scan — find all profitable signals."""
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import json, numpy as np, pandas as pd
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from pathlib import Path
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from rdagent.components.backtesting.vbt_backtest import backtest_signal_risk
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c = pd.read_hdf("git_ignore_folder/factor_implementation_source_data/intraday_pv.h5", key="data")["$close"]
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c = c.droplevel(-1).sort_index().dropna().resample("30min").last().dropna()
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is_s = (c.index.hour >= 7) & (c.index.hour < 17)
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F = Path("results/factors"); V = F / "values"
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factors = []
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for f in sorted(F.glob("*.json")):
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try: d = json.loads(f.read_text())
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except: continue
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if d.get("status") != "success" or d.get("ic") is None: continue
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name = d.get("factor_name", f.stem)
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safe = name.replace("/", "_")[:150]
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if (V / f"{safe}.parquet").exists():
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factors.append({"name": name, "ic": d["ic"], "safe": safe})
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factors.sort(key=lambda x: abs(x["ic"]), reverse=True)
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print(f"30min: {len(c):,} bars, {len(factors)} factors")
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print(f"Scanning top-200 factors...")
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results = []
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for i, f in enumerate(factors[:200]):
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try:
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s = pd.read_parquet(V / f"{f['safe']}.parquet").iloc[:, 0]
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if isinstance(s.index, pd.MultiIndex): s = s.droplevel(-1)
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fac = s.resample("30min").last().reindex(c.index).ffill()
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except: continue
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for dr in [1, -1]:
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sig = pd.Series(dr * np.sign(fac).fillna(0), index=c.index)
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sig[~is_s] = 0
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if sig.abs().sum() < 20: continue
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r = backtest_signal_risk(c, sig.fillna(0), txn_cost_bps=2.14)
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oos = r.get("wf_oos_sharpe_mean") or r.get("oos_sharpe", -999)
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oos_m = r.get("oos_monthly_return_pct", 0) or 0
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if oos_m > 0.2:
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results.append((f"{f['name']}_{dr}", oos, oos_m, r.get("oos_n_trades", 0)))
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if i % 40 == 0 and results:
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best = sorted(results, key=lambda x: x[2], reverse=True)[:2]
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print(f" {i}/200... best: {best[0][0][:40]} Mon={best[0][2]:+.2f}%")
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results.sort(key=lambda x: x[2], reverse=True)
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print(f"\nProfitable (>0.2%/mon): {len(results)}")
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print(f"\nTOP 20:")
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for i, (n, o, m, t) in enumerate(results[:20]):
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print(f" {i+1:2d}. {n[:52]:52s} OOS={o:+8.1f} Mon={m:+7.2f}% T={t:5d}")
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# Save top signals for combo testing
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if results:
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top = results[:15]
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all_sig = {}
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for name, oos, mon, t in top:
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fn = name.rsplit("_", 1)[0]
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dr = -1 if name.endswith("_-1") else 1
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if dr == -1: dr = -1
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safe = fn.replace("/", "_")[:150]
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try:
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s = pd.read_parquet(V / f"{safe}.parquet").iloc[:, 0]
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if isinstance(s.index, pd.MultiIndex): s = s.droplevel(-1)
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fac = s.resample("30min").last().reindex(c.index).ffill()
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sig = pd.Series(dr * np.sign(fac).fillna(0), index=c.index)
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sig[~is_s] = 0
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all_sig[name] = sig
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except: pass
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if all_sig:
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df = pd.DataFrame(all_sig, index=c.index).fillna(0)
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cols = list(df.columns)
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print(f"\n=== COMBO TESTS ===")
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for n in [2, 3, 5, 8, len(cols)]:
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combo = df[cols[:n]].mean(axis=1)
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r = backtest_signal_risk(c, combo.fillna(0), txn_cost_bps=2.14, wf_rolling=True)
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m = r.get("oos_monthly_return_pct", 0) or 0
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dd = (r.get("oos_max_drawdown", 0) or 0) * 100
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t = r.get("oos_n_trades", 0)
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hit = "🎯" if m >= 4 else "✅" if m > 0 else ""
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print(f" {n:2d} sig: Mon={m:+.2f}% DD={dd:+.1f}% T={t} {hit}")
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print("\nDone!")
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