research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer
Ondata di ricerca onesta a largo spettro su BTC/ETH+DVOL certificati: 104 ipotesi distinte (11 famiglie), un agente-finder per ipotesi, verifica avversariale a 3 scettici sui promettenti, sintesi (153 agenti totali). Esito: NIENTE di nuovo regge -> conferma del soffitto strutturale ~1.3 BTC/ETH-direzionale; lo stack TP01+XS01+VRP01 resta imbattuto. - altlib.py: harness condiviso vettoriale leak-free (eval_weights/study_weights, fee-sweep, both-asset + hold-out 2025+). Riproduce i numeri canonici di TP01. - MARGINAL SCORER (study_marginal/marginal_vs_tp01): Sharpe INCREMENTALE vs baseline TP01 (corr, blend uplift OOS, alpha residua) + jackknife OOS (clean-year + drop-best-month). earns_slot = abs!=FAIL & ADDS & robust_oos. Smaschera gli overlay su TSMOM con PASS assoluti fasulli (CMB04, VOL11, ...) e il falso positivo KAMA (ADDS ma muore al jackknife). - runs/*.py (104) script riproducibili per ipotesi; wf_altstrat.js workflow. - Verdetto: 0 candidati deployabili; 2 LEAD fragili (VOL08, STA05_LS) da forward-monitor. - test_marginal_scorer.py blocca baseline + invarianti. Suite: 32 verde. Diario: docs/diary/2026-06-20-alt-strategies-100agent-sweep.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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"""OPT03 — Calendar Spread (DVOL term proxy).
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IDEA: A calendar spread (sell short-dated, buy long-dated option) profits when:
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- Term structure is in contango (short vol < long vol) -> theta decay favors the short leg
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- The vol term slope is MEAN-REVERTING: extreme backwardation -> enter long calendar
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MODELED APPROACH (since we lack real term surface):
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- Short-dated vol proxy: EMA(DVOL, span_short) — reacts quickly to spot moves
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- Long-dated vol proxy: EMA(DVOL, span_long) — slower, represents long-term expectation
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- Term slope = short_proxy - long_proxy (positive = backwardation, negative = contango)
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- Position: go long calendar when slope is high (extreme backwardation -> mean-revert to flat)
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go short calendar when slope is very negative (extreme contango -> normalize)
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Signal: zscore of (short_ema - long_ema) over rolling window.
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Direction: mean-reversion -> go LONG when z is high (backwardation = short vol elevated)
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because short vol will eventually fall back to long vol.
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Vol-target the position (20%, cap 2x).
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GRID: 4 configs (short_span x long_span)
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- (7d, 30d): short-term vs monthly
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- (7d, 60d): short-term vs 2-month
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- (14d, 60d): 2-week vs 2-month
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- (14d, 90d): 2-week vs 3-month
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CAVEAT: premiums are MODELED using DVOL (no real term surface available).
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This is a lead/research indicator only, not deployable as-is.
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Data starts 2021-03 (DVOL history constraint).
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"""
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
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import altlib as al
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import numpy as np
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# DVOL is daily -> span parameters in DAYS
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CONFIGS = [
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{"short_days": 7, "long_days": 30, "zscore_win": 60},
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{"short_days": 7, "long_days": 60, "zscore_win": 90},
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{"short_days": 14, "long_days": 60, "zscore_win": 90},
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{"short_days": 14, "long_days": 90, "zscore_win": 120},
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]
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def make_target(short_days: int, long_days: int, zscore_win: int):
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"""Return target_fn(df) -> position array."""
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def target_fn(df):
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n = len(df)
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bpd = al.bars_per_day(df)
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# DVOL aligned causally to df bars
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dv = al.dvol(df, "BTC") # NOTE: asset-specific handled below via closure
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# Mask where DVOL is available
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valid = np.isfinite(dv)
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# Compute EMAs of DVOL as short/long term structure proxies
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# spans in days -> convert to bars
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short_span = max(2, int(short_days * bpd))
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long_span = max(4, int(long_days * bpd))
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import pandas as pd
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dv_s = pd.Series(dv)
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# EMA on valid-filled series (forward-fill to avoid NaN inside EMA)
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dv_ffilled = dv_s.ffill()
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ema_short = dv_ffilled.ewm(span=short_span, adjust=False).mean().values
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ema_long = dv_ffilled.ewm(span=long_span, adjust=False).mean().values
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# Term slope: positive = backwardation (short > long)
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slope = ema_short - ema_long
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# Z-score of slope over rolling window
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zscore_win_bars = max(10, int(zscore_win * bpd))
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z = al.zscore(slope, zscore_win_bars)
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# Mean-reversion signal: when backwardation is extreme (high z),
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# short vol is elevated -> will mean-revert down -> calendar spread gains
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# Position: +1 when z > 0 (backwardation -> long calendar)
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# -1 when z < 0 (contango -> short calendar / flat)
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# Use continuous sizing based on z-score, clipped to [-1, 1]
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direction = np.clip(z, -1.0, 1.0)
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# NaN where DVOL not available (pre-2021-03)
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direction = np.where(valid & np.isfinite(z), direction, 0.0)
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# Vol-target
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tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return tgt
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return target_fn
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def make_target_asset(short_days: int, long_days: int, zscore_win: int, asset: str):
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"""Per-asset version that uses the correct DVOL."""
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def target_fn(df):
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n = len(df)
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bpd = al.bars_per_day(df)
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dv = al.dvol(df, asset)
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valid = np.isfinite(dv)
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short_span = max(2, int(short_days * bpd))
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long_span = max(4, int(long_days * bpd))
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import pandas as pd
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dv_s = pd.Series(dv)
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dv_ffilled = dv_s.ffill()
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ema_short = dv_ffilled.ewm(span=short_span, adjust=False).mean().values
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ema_long = dv_ffilled.ewm(span=long_span, adjust=False).mean().values
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slope = ema_short - ema_long
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zscore_win_bars = max(10, int(zscore_win * bpd))
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z = al.zscore(slope, zscore_win_bars)
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direction = np.clip(z, -1.0, 1.0)
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direction = np.where(valid & np.isfinite(z), direction, 0.0)
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tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return tgt
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return target_fn
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def run_config(cfg: dict, tfs=("1d", "12h")) -> dict:
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"""Run one config across assets+tfs."""
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sd, ld, zw = cfg["short_days"], cfg["long_days"], cfg["zscore_win"]
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name = f"OPT03-CAL-s{sd}d-l{ld}d-z{zw}d"
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# Build per-asset closures
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btc_fn = make_target_asset(sd, ld, zw, "BTC")
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eth_fn = make_target_asset(sd, ld, zw, "ETH")
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cells = []
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for tf in tfs:
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per_asset = {}
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fee_ok_all = True
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for a, fn in [("BTC", btc_fn), ("ETH", eth_fn)]:
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df = al.get(a, tf)
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tgt = fn(df)
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base = al.eval_weights(df, tgt, fee_side=al.FEE_SIDE)
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sweep = {f"{2*f*100:.2f}%RT": al.eval_weights(df, tgt, fee_side=f)["full"]["sharpe"]
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for f in al.FEE_SWEEP}
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fee_ok = sweep.get("0.20%RT", -9) > 0
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fee_ok_all = fee_ok_all and fee_ok
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per_asset[a] = dict(
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full=base["full"], holdout=base["holdout"],
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tim=base["time_in_market"], turnover=base["turnover_per_year"],
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fee_sweep=sweep, yearly=base["yearly"]
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)
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min_full = min(per_asset[a]["full"]["sharpe"] for a in ("BTC", "ETH"))
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min_hold = min(per_asset[a]["holdout"].get("sharpe", 0.0) for a in ("BTC", "ETH"))
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cells.append(dict(
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tf=tf, per_asset=per_asset,
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min_asset_full_sharpe=round(min_full, 3),
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min_asset_holdout_sharpe=round(min_hold, 3),
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full_sharpe=round(np.mean([per_asset[a]["full"]["sharpe"] for a in ("BTC", "ETH")]), 3),
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fee_survives=fee_ok_all
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))
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return dict(name=name, kind="weights", cells=cells,
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verdict=al._verdict(cells), config=cfg)
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if __name__ == "__main__":
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print("OPT03 — Calendar Spread via DVOL term proxy")
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print("CAVEAT: premiums are MODELED (DVOL only, no real term surface) — lead/research only")
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print("DVOL history starts 2021-03 -> effective backtest from ~2021-Q3")
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print()
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# Run all 4 configs on 1d only (DVOL is daily; 12h would repeat same info)
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# We test 1d and 12h to see if intraday resolution helps, but expect 1d to be canonical
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results = []
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for cfg in CONFIGS:
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print(f"Running config: short={cfg['short_days']}d long={cfg['long_days']}d zscore={cfg['zscore_win']}d ...")
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rep = run_config(cfg, tfs=("1d",))
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results.append(rep)
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print(al.fmt(rep))
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print()
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# Pick best config by min_asset_holdout_sharpe
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best = max(results, key=lambda r: max(
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(c["min_asset_holdout_sharpe"] for c in r["cells"]), default=-9))
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print("=" * 60)
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print("BEST CONFIG:", best["name"])
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print(al.fmt(best))
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print()
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print("JSON:", al.as_json(best))
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