#!/usr/bin/env python3 """ Regime-router on top of existing **MTM daily series**. Goal: improve 2022–2025 behavior by dynamically allocating between: - SPY Theta MTM book (lit4 + D6 + VRP) from `best_ideas_spy6_margin_daily.csv` - Dynamic VXX Regime Strategy Stack MTM from `vxx_regime_mtm_*_dynamic_vxx_regime_stack_daily_mtm.csv` Router signals (simple, transparent): - Trend: SPY close vs SMA50 - Realized-vol: 20-session realized vol percentile (computed over full history) Allocation rule (default): if (SPY < SMA50) or (rv20_pctile >= 0.75): shift toward VXX (risk-off) else: shift toward SPY Theta book (risk-on) Outputs: {out_prefix}_daily.csv {out_prefix}_meta.json {out_prefix}_yearly.csv Run: cd /Users/robzingale/trading_bot PYTHONUNBUFFERED=1 .venv/bin/python RenTech/strategy_stack/run_best_ideas_regime_router.py \ --start 2016-01-04 --end 2026-04-02 --capital 100000 \ --out-prefix RenTech/data/logs/best_ideas_router_spy6_vxx """ from __future__ import annotations import argparse import json import math import sys from pathlib import Path import numpy as np import pandas as pd _REPO = Path(__file__).resolve().parents[2] if str(_REPO) not in sys.path: sys.path.insert(0, str(_REPO)) from RenTech.strategy_stack.portfolio_vrp_plus_vxx import _metrics_block LOGS = _REPO / "RenTech" / "data" / "logs" DEFAULT_SPY6_MTM_DAILY = LOGS / "best_ideas_spy6_margin_daily.csv" DEFAULT_VXX_MTM_DAILY = LOGS / "vxx_regime_mtm_2016_2026_dynamic_vxx_regime_stack_daily_mtm.csv" def _yearly_table(dates: pd.Series, pnl: pd.Series, *, capital: float) -> pd.DataFrame: df = pd.DataFrame({"date": pd.to_datetime(dates).dt.normalize(), "pnl": pnl.astype(float)}) rows = [] eq = float(capital) for yr, g in df.groupby(df["date"].dt.year, sort=True): p = float(g["pnl"].sum()) end = eq + p ret = (end / eq - 1) * 100 if eq > 0 else 0.0 intra = eq + g["pnl"].cumsum() dd = float(((intra / intra.cummax()) - 1).min()) * 100 if len(intra) else 0.0 rows.append( { "year": int(yr), "start_equity": round(eq, 0), "end_equity": round(end, 0), "pnl_usd": round(p, 0), "return_pct": round(ret, 2), "max_dd_pct": round(dd, 2), "sessions": int(len(g)), } ) eq = end return pd.DataFrame(rows) def main() -> None: ap = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) ap.add_argument("--start", default="2016-01-04") ap.add_argument("--end", default="2026-04-02") ap.add_argument("--capital", type=float, default=100_000.0) ap.add_argument("--spy-mtm-daily", type=Path, default=DEFAULT_SPY6_MTM_DAILY) ap.add_argument("--vxx-mtm-daily", type=Path, default=DEFAULT_VXX_MTM_DAILY) ap.add_argument("--sma-window", type=int, default=50) ap.add_argument("--rv-window", type=int, default=20) ap.add_argument("--rv-riskoff-pctile", type=float, default=0.75) ap.add_argument("--w_spy_riskon", type=float, default=1.0) ap.add_argument("--w_vxx_riskon", type=float, default=0.5) ap.add_argument("--w_spy_riskoff", type=float, default=0.25) ap.add_argument("--w_vxx_riskoff", type=float, default=1.5) ap.add_argument("--out-prefix", type=Path, default=LOGS / "best_ideas_router_spy6_vxx") args = ap.parse_args() spy_path = args.spy_mtm_daily.expanduser().resolve() vxx_path = args.vxx_mtm_daily.expanduser().resolve() if not spy_path.is_file(): raise SystemExit(f"Missing {spy_path}") if not vxx_path.is_file(): raise SystemExit(f"Missing {vxx_path}") spy = pd.read_csv(spy_path, parse_dates=["date"]) spy["date"] = pd.to_datetime(spy["date"]).dt.normalize() spy = spy.set_index("date").sort_index() if "spy_close" not in spy.columns or "equity_mtm_usd" not in spy.columns: raise SystemExit(f"Expected columns spy_close, equity_mtm_usd in {spy_path}") vxx = pd.read_csv(vxx_path, parse_dates=["date"]) vxx["date"] = pd.to_datetime(vxx["date"]).dt.normalize() vxx = vxx.set_index("date").sort_index() if "pnl_stack" not in vxx.columns: raise SystemExit(f"Expected pnl_stack in {vxx_path}") idx = spy.index.intersection(vxx.index) idx = idx[(idx >= pd.Timestamp(args.start)) & (idx <= pd.Timestamp(args.end))] if len(idx) < 200: raise SystemExit(f"Too few aligned sessions ({len(idx)})") # Daily PnL series (MTM). pnl_spy = spy["equity_mtm_usd"].diff().fillna(0.0).reindex(idx).fillna(0.0) pnl_vxx = vxx["pnl_stack"].reindex(idx).fillna(0.0) # Signals from SPY close. close = spy["spy_close"].reindex(idx).astype(float) sma = close.rolling(int(args.sma_window)).mean() ret = close.pct_change() rv = ret.rolling(int(args.rv_window)).std() * math.sqrt(252.0) # Percentile computed over the whole sample (stable threshold across years). rv_valid = rv.dropna() if len(rv_valid) < 200: raise SystemExit("Not enough rv samples to compute percentile threshold") rv_thresh = float(rv_valid.quantile(float(args.rv_riskoff_pctile))) riskoff = (close < sma) | (rv >= rv_thresh) riskoff = riskoff.fillna(False) w_spy = np.where(riskoff.to_numpy(), float(args.w_spy_riskoff), float(args.w_spy_riskon)) w_vxx = np.where(riskoff.to_numpy(), float(args.w_vxx_riskoff), float(args.w_vxx_riskon)) pnl = pnl_spy.to_numpy() * w_spy + pnl_vxx.to_numpy() * w_vxx pnl = pd.Series(pnl, index=idx, dtype=float) eq = float(args.capital) + pnl.cumsum() out = pd.DataFrame( { "date": idx, "spy_close": close.values, "spy_sma": sma.values, "spy_rv20_ann": rv.values, "rv_thresh": rv_thresh, "is_riskoff": riskoff.values.astype(bool), "w_spy": w_spy, "w_vxx": w_vxx, "pnl_spy_theta_mtm": pnl_spy.values, "pnl_vxx_mtm": pnl_vxx.values, "pnl_router_mtm": pnl.values, "equity_mtm_usd": eq.values, "daily_return_mtm": eq.pct_change().fillna(0.0).values, } ) prefix = args.out_prefix.expanduser().resolve() prefix.parent.mkdir(parents=True, exist_ok=True) daily_path = Path(f"{prefix}_daily.csv") meta_path = Path(f"{prefix}_meta.json") yearly_path = Path(f"{prefix}_yearly.csv") out.to_csv(daily_path, index=False) m = _metrics_block(out.set_index("date")["equity_mtm_usd"], "Router") meta = { "name": "Best Ideas Regime Router (SPY6 MTM vs VXX MTM)", "capital_usd": float(args.capital), "start": str(idx[0].date()), "end": str(idx[-1].date()), "n_sessions": int(len(idx)), "inputs": { "spy_mtm_daily": str(spy_path), "vxx_mtm_daily": str(vxx_path), }, "signals": { "sma_window": int(args.sma_window), "rv_window": int(args.rv_window), "rv_riskoff_pctile": float(args.rv_riskoff_pctile), "rv_thresh": rv_thresh, }, "weights": { "w_spy_riskon": float(args.w_spy_riskon), "w_vxx_riskon": float(args.w_vxx_riskon), "w_spy_riskoff": float(args.w_spy_riskoff), "w_vxx_riskoff": float(args.w_vxx_riskoff), }, **m, } meta_path.write_text(json.dumps(meta, indent=2), encoding="utf-8") yearly = _yearly_table(out["date"], out["pnl_router_mtm"], capital=float(args.capital)) yearly.to_csv(yearly_path, index=False) print(json.dumps(meta, indent=2)[:4000], flush=True) print(f"\nWrote {daily_path}\nWrote {meta_path}\nWrote {yearly_path}", flush=True) if __name__ == "__main__": main()