"""OPT03 — Calendar Spread (DVOL term proxy). IDEA: A calendar spread (sell short-dated, buy long-dated option) profits when: - Term structure is in contango (short vol < long vol) -> theta decay favors the short leg - The vol term slope is MEAN-REVERTING: extreme backwardation -> enter long calendar MODELED APPROACH (since we lack real term surface): - Short-dated vol proxy: EMA(DVOL, span_short) — reacts quickly to spot moves - Long-dated vol proxy: EMA(DVOL, span_long) — slower, represents long-term expectation - Term slope = short_proxy - long_proxy (positive = backwardation, negative = contango) - Position: go long calendar when slope is high (extreme backwardation -> mean-revert to flat) go short calendar when slope is very negative (extreme contango -> normalize) Signal: zscore of (short_ema - long_ema) over rolling window. Direction: mean-reversion -> go LONG when z is high (backwardation = short vol elevated) because short vol will eventually fall back to long vol. Vol-target the position (20%, cap 2x). GRID: 4 configs (short_span x long_span) - (7d, 30d): short-term vs monthly - (7d, 60d): short-term vs 2-month - (14d, 60d): 2-week vs 2-month - (14d, 90d): 2-week vs 3-month CAVEAT: premiums are MODELED using DVOL (no real term surface available). This is a lead/research indicator only, not deployable as-is. Data starts 2021-03 (DVOL history constraint). """ import sys sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt") import altlib as al import numpy as np # DVOL is daily -> span parameters in DAYS CONFIGS = [ {"short_days": 7, "long_days": 30, "zscore_win": 60}, {"short_days": 7, "long_days": 60, "zscore_win": 90}, {"short_days": 14, "long_days": 60, "zscore_win": 90}, {"short_days": 14, "long_days": 90, "zscore_win": 120}, ] def make_target(short_days: int, long_days: int, zscore_win: int): """Return target_fn(df) -> position array.""" def target_fn(df): n = len(df) bpd = al.bars_per_day(df) # DVOL aligned causally to df bars dv = al.dvol(df, "BTC") # NOTE: asset-specific handled below via closure # Mask where DVOL is available valid = np.isfinite(dv) # Compute EMAs of DVOL as short/long term structure proxies # spans in days -> convert to bars short_span = max(2, int(short_days * bpd)) long_span = max(4, int(long_days * bpd)) import pandas as pd dv_s = pd.Series(dv) # EMA on valid-filled series (forward-fill to avoid NaN inside EMA) dv_ffilled = dv_s.ffill() ema_short = dv_ffilled.ewm(span=short_span, adjust=False).mean().values ema_long = dv_ffilled.ewm(span=long_span, adjust=False).mean().values # Term slope: positive = backwardation (short > long) slope = ema_short - ema_long # Z-score of slope over rolling window zscore_win_bars = max(10, int(zscore_win * bpd)) z = al.zscore(slope, zscore_win_bars) # Mean-reversion signal: when backwardation is extreme (high z), # short vol is elevated -> will mean-revert down -> calendar spread gains # Position: +1 when z > 0 (backwardation -> long calendar) # -1 when z < 0 (contango -> short calendar / flat) # Use continuous sizing based on z-score, clipped to [-1, 1] direction = np.clip(z, -1.0, 1.0) # NaN where DVOL not available (pre-2021-03) direction = np.where(valid & np.isfinite(z), direction, 0.0) # Vol-target tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0) return tgt return target_fn def make_target_asset(short_days: int, long_days: int, zscore_win: int, asset: str): """Per-asset version that uses the correct DVOL.""" def target_fn(df): n = len(df) bpd = al.bars_per_day(df) dv = al.dvol(df, asset) valid = np.isfinite(dv) short_span = max(2, int(short_days * bpd)) long_span = max(4, int(long_days * bpd)) import pandas as pd dv_s = pd.Series(dv) dv_ffilled = dv_s.ffill() ema_short = dv_ffilled.ewm(span=short_span, adjust=False).mean().values ema_long = dv_ffilled.ewm(span=long_span, adjust=False).mean().values slope = ema_short - ema_long zscore_win_bars = max(10, int(zscore_win * bpd)) z = al.zscore(slope, zscore_win_bars) direction = np.clip(z, -1.0, 1.0) direction = np.where(valid & np.isfinite(z), direction, 0.0) tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0) return tgt return target_fn def run_config(cfg: dict, tfs=("1d", "12h")) -> dict: """Run one config across assets+tfs.""" sd, ld, zw = cfg["short_days"], cfg["long_days"], cfg["zscore_win"] name = f"OPT03-CAL-s{sd}d-l{ld}d-z{zw}d" # Build per-asset closures btc_fn = make_target_asset(sd, ld, zw, "BTC") eth_fn = make_target_asset(sd, ld, zw, "ETH") cells = [] for tf in tfs: per_asset = {} fee_ok_all = True for a, fn in [("BTC", btc_fn), ("ETH", eth_fn)]: df = al.get(a, tf) tgt = fn(df) base = al.eval_weights(df, tgt, fee_side=al.FEE_SIDE) sweep = {f"{2*f*100:.2f}%RT": al.eval_weights(df, tgt, fee_side=f)["full"]["sharpe"] for f in al.FEE_SWEEP} fee_ok = sweep.get("0.20%RT", -9) > 0 fee_ok_all = fee_ok_all and fee_ok per_asset[a] = dict( full=base["full"], holdout=base["holdout"], tim=base["time_in_market"], turnover=base["turnover_per_year"], fee_sweep=sweep, yearly=base["yearly"] ) min_full = min(per_asset[a]["full"]["sharpe"] for a in ("BTC", "ETH")) min_hold = min(per_asset[a]["holdout"].get("sharpe", 0.0) for a in ("BTC", "ETH")) cells.append(dict( tf=tf, per_asset=per_asset, min_asset_full_sharpe=round(min_full, 3), min_asset_holdout_sharpe=round(min_hold, 3), full_sharpe=round(np.mean([per_asset[a]["full"]["sharpe"] for a in ("BTC", "ETH")]), 3), fee_survives=fee_ok_all )) return dict(name=name, kind="weights", cells=cells, verdict=al._verdict(cells), config=cfg) if __name__ == "__main__": print("OPT03 — Calendar Spread via DVOL term proxy") print("CAVEAT: premiums are MODELED (DVOL only, no real term surface) — lead/research only") print("DVOL history starts 2021-03 -> effective backtest from ~2021-Q3") print() # Run all 4 configs on 1d only (DVOL is daily; 12h would repeat same info) # We test 1d and 12h to see if intraday resolution helps, but expect 1d to be canonical results = [] for cfg in CONFIGS: print(f"Running config: short={cfg['short_days']}d long={cfg['long_days']}d zscore={cfg['zscore_win']}d ...") rep = run_config(cfg, tfs=("1d",)) results.append(rep) print(al.fmt(rep)) print() # Pick best config by min_asset_holdout_sharpe best = max(results, key=lambda r: max( (c["min_asset_holdout_sharpe"] for c in r["cells"]), default=-9)) print("=" * 60) print("BEST CONFIG:", best["name"]) print(al.fmt(best)) print() print("JSON:", al.as_json(best))