research(cerbero-bite): validazione edge credit-spread su prezzi reali

Backtest dell'edge credit-spread ETH di cerbero-bite con la chain reale
(data/options/). Esito: NON edge robusto su ciclo completo. Entry cw reale
0.106 (short 9.4% OTM, max-loss/credito 8.4x); hold-to-expiry EV -1.0 cr/trade
7/9 anni negativi; managed (skew) EV -0.02 cr win-rate 37%. Il "+0.48%/mese"
era artefatto di finestra calma; coda concentrata col fade ETH. TODO aperto:
calibrazione esatta credito (bid/ask + griglia) per EV managed definitivo.
Script riprendibile + diario.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/analysis/cerbero_bite_credit_spread.py

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+"""Validazione dell'edge del credit-spread di cerbero-bite sui PREZZI REALI.
+
+cerbero-bite (container accanto) vende credit spread su ETH (bull-put primario,
+short delta ~0.18, DTE 18, PT 50% / stop 2.5x credito / delta-breach 0.30 / vol-stop
++10 DVOL / time-stop 7 DTE). Domanda: l'edge regge su un CICLO ETH completo, o e'
+profittevole solo nei campioni calmi?
+
+Tre analisi (riprendibili):
+  1) entry_economics()  -> economia d'ingresso REALE dalla chain (data/options/eth_chain.parquet):
+     credit/width effettivo a delta 0.18 dai bid/ask veri, eleggibilita' sotto i gate liquidita'.
+  2) tail_model_free()  -> esito terminale dai prezzi ETH reali (2018-2026), cw reale 0.106,
+     NESSUN modello opzioni (niente errore BS): win-rate, EV, frequenza max-loss.
+  3) managed_backtest() -> lifecycle CON management; mark con skew calibrato sulle IV reali.
+
+ESITO (2026-06-09):
+  - cw reale a delta 0.18 = 0.106 (short ~9.4% OTM, NON 18%), max-loss/credito = 8.4x, eleggibilita' 65%.
+  - hold-to-expiry @0.106: EV -1.0 crediti/trade, 7/9 anni NEGATIVI, max-loss 17.8% delle volte.
+  - managed (skew): EV -0.02 cr/trade, win-rate 37% (delta-breach esce sul 62% dei trade a piccola perdita).
+  - VERDETTO: NON edge robusto su ciclo completo. Il "+0.48%/mese" era artefatto di finestra calma
+    (mag-giu 2026, no crash). Premium-selling a skew negativo: vince nei campioni calmi, restituisce
+    tutto (o piu') nei crash. Tune "Profilo B" (vendere a 9.4% OTM) PEGGIORA la frequenza di max-loss.
+    Coda CONCENTRATA col fade ETH di PythagorasGoal (stesso crash colpisce entrambi).
+
+TODO APERTO (per nail-are l'EV managed esatto): la calibrazione non e' ancora perfetta
+(mark mid+skew da cw 0.228 vs 0.106 reale -> sovrastima il credito ~2x). Manca: modellare
+bid/ask reale incrociato sulle 2 gambe + griglia strike reale (entrambi nella chain) cosi'
+l'entry cw scende a 0.106 e l'EV managed diventa esatto. Allora chiudere il sì/no definitivo.
+
+    uv run python scripts/analysis/cerbero_bite_credit_spread.py
+"""
+from __future__ import annotations
+import sys, math, collections
+from pathlib import Path
+import numpy as np, pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+from scripts.analysis.options_chain import OptionChain, load_market
+from scripts.analysis.explore_lab import get_df
+from scripts.analysis.option_overlay_lab import bs_put, _ncdf, dvol_for
+
+SHORT_OTM, LONG_OTM, DTE = 0.094, 0.134, 17        # da chain reale (delta 0.18, width 4%)
+CW_REAL = 0.106
+
+
+def entry_economics():
+    oc = OptionChain("ETH"); ch = oc.df
+    mk = load_market("ETH")[["ts_ms", "spot"]].dropna().sort_values("ts_ms")
+    p = ch[ch["option_type"] == "P"].copy()
+    p = pd.merge_asof(p.sort_values("ts_ms"), mk, on="ts_ms", direction="backward")
+    cand = p[(p["tenor_d"] >= 14) & (p["tenor_d"] <= 21)].dropna(subset=["delta", "bid", "ask", "strike", "spot"])
+    rows = []
+    for (ts, exp), g in cand.groupby(["timestamp", "expiry"]):
+        spot = g["spot"].iloc[0]
+        sc = g[(g["delta"] <= -0.12) & (g["delta"] >= -0.22)]
+        if sc.empty: continue
+        short = sc.iloc[(sc["delta"] + 0.18).abs().argmin()]
+        Ks = short["strike"]; longc = g[g["strike"] < Ks]
+        if longc.empty: continue
+        longp = longc.iloc[(longc["strike"] - (Ks - spot * 0.04)).abs().argmin()]
+        W = Ks - longp["strike"]
+        if W <= 0: continue
+        credit = short["bid"] - longp["ask"]
+        def ok(o):
+            sp = (o["ask"] - o["bid"]) / ((o["ask"] + o["bid"]) / 2) if (o["ask"] + o["bid"]) > 0 else 9
+            return (o["open_interest"] or 0) >= 100 and sp <= 0.15 and o["bid"] > 0
+        cw = credit / (W / spot)
+        rows.append(dict(cw=cw, credit=credit, elig=ok(short) and ok(longp) and cw >= 0.08 and credit > 0,
+                         short_otm=(spot - Ks) / spot, delta=short["delta"]))
+    r = pd.DataFrame(rows)
+    print(f"[ENTRY] {len(r)} spread | eleggibili {r['elig'].mean()*100:.0f}% | cw mediano {r['cw'].median():.3f} "
+          f"| short OTM {r['short_otm'].median()*100:.1f}% | max-loss/credito {((1-r['cw'].median())/r['cw'].median()):.1f}x")
+
+
+def tail_model_free():
+    df = get_df("ETH", "1h"); c = df["close"].values; n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True); H = DTE * 24
+    res = []
+    for i in range(200, n - H - 1, 24 * 2):
+        S0 = c[i]; Ks = S0 * (1 - SHORT_OTM); Kl = S0 * (1 - LONG_OTM); W = Ks - Kl
+        Sx = c[i + H]; intr = min(max(Ks - Sx, 0.0), W); credit = CW_REAL * W
+        res.append((ts.iloc[i].year, 1 - intr / credit, Sx < Kl))
+    R = pd.DataFrame(res, columns=["y", "pnl", "maxloss"]); P = R["pnl"].values
+    print(f"[TAIL model-free @cw0.106] win {(P>0).mean()*100:.0f}% | EV {P.mean():+.2f}cr | max-loss {R['maxloss'].mean()*100:.0f}% "
+          f"| anni neg {(R.groupby('y')['pnl'].mean()<0).sum()}/{R['y'].nunique()}")
+
+
+def _skew_fit():
+    oc = OptionChain("ETH"); ch = oc.df
+    mk = load_market("ETH")[["ts_ms", "spot"]].dropna().sort_values("ts_ms")
+    p = ch[ch["option_type"] == "P"].copy()
+    p = pd.merge_asof(p.sort_values("ts_ms"), mk, on="ts_ms", direction="backward")
+    p = p.dropna(subset=["iv", "strike", "spot", "delta", "tenor_d"])
+    p = p[(p["tenor_d"] >= 7) & (p["tenor_d"] <= 35) & (p["iv"] > 0)]
+    p["dd"] = (p["delta"] + 0.5).abs()
+    atm = p.sort_values("dd").groupby("timestamp")["iv"].first()
+    p["atm_iv"] = p["timestamp"].map(atm); p = p.dropna(subset=["atm_iv"])
+    p["k"] = np.log(p["strike"] / p["spot"]); p["ratio"] = p["iv"] / p["atm_iv"]
+    p = p[(p["k"] > -0.35) & (p["k"] < 0.15) & (p["ratio"] > 0.5) & (p["ratio"] < 3)]
+    coef, *_ = np.linalg.lstsq(np.c_[p["k"], p["k"]**2], p["ratio"] - 1.0, rcond=None)
+    return coef  # a, b
+
+
+def managed_backtest():
+    a, b = _skew_fit()
+    def ivol(S, K, atm):
+        k = math.log(K / S); return max(atm * (1 + a * k + b * k * k), 0.05)
+    def put_delta(S, K, T, sig):
+        if T <= 0 or sig <= 0: return -1.0 if S < K else 0.0
+        return _ncdf((math.log(S / K) + 0.5 * sig * sig * T) / (sig * math.sqrt(T))) - 1.0
+    def mark(S, Ks, Kl, T, atm):
+        return bs_put(S, Ks, T, ivol(S, Ks, atm)) - bs_put(S, Kl, T, ivol(S, Kl, atm))
+    df = get_df("ETH", "1h"); c = df["close"].values; n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True); dvol = dvol_for(df, "ETH")
+    H = DTE * 24; STEP = 6; cw = []; tr = []
+    for i in range(200, n - H - 1, 24 * 2):
+        S0 = c[i]; atm0 = dvol[i] if not np.isnan(dvol[i]) else 0.6
+        Ks = S0 * (1 - SHORT_OTM); Kl = S0 * (1 - LONG_OTM); W = Ks - Kl
+        credit = mark(S0, Ks, Kl, DTE / 365.0, atm0)
+        if credit <= 0: continue
+        cw.append(credit / W); pnl = why = None
+        for k in range(STEP, H + 1, STEP):
+            j = i + k; Trem = max((H - k) / (24 * 365.0), 1e-6); Sj = c[j]
+            atmj = dvol[j] if not np.isnan(dvol[j]) else atm0; mk = mark(Sj, Ks, Kl, Trem, atmj)
+            if mk <= 0.5 * credit: pnl, why = 1 - mk / credit, "PT"; break
+            if mk >= 2.5 * credit: pnl, why = 1 - mk / credit, "stop"; break
+            if put_delta(Sj, Ks, Trem, ivol(Sj, Ks, atmj)) <= -0.30: pnl, why = 1 - mk / credit, "delta"; break
+            if atmj - atm0 >= 0.10: pnl, why = 1 - mk / credit, "vol"; break
+            if k >= (DTE - 7) * 24: pnl, why = 1 - mk / credit, "time"; break
+        if pnl is None:
+            Sx = c[i + H]; intr = min(max(Ks - Sx, 0), W); pnl, why = 1 - intr / credit, "expiry"
+        tr.append((ts.iloc[i].year, pnl, why))
+    P = np.array([t[1] for t in tr])
+    print(f"[MANAGED skew] cw@entry {np.median(cw):.3f} (vs 0.106 reale: sovrastima ~2x, EV vero <=) | "
+          f"win {(P>0).mean()*100:.0f}% | EV {P.mean():+.3f}cr | worst {P.min():.1f} | "
+          f"uscite {dict(collections.Counter(t[2] for t in tr))}")
+    R = pd.DataFrame({"y": [t[0] for t in tr], "p": P})
+    print(f"           2021+: EV {R[R.y>=2021]['p'].mean():+.3f}cr/trade")
+
+
+if __name__ == "__main__":
+    entry_economics()
+    tail_model_free()
+    managed_backtest()