Merge feat/option-chain-snapshots

# Conflicts:
#	src/cerbero_bite/gui/pages/7_📚_Strategia.py
#	strategy.aggressiva.yaml
#	strategy.conservativa.yaml
#	strategy.yaml
#	tests/unit/test_config_loader.py

docs/13-strategia-spiegata.md

@@ -532,6 +532,69 @@ quella che il sistema parte ad eseguire.
 
 ---
 
+## 4-quater. IV richness gate (§2.9): il filtro che alza il win-rate
+
+Il filtro a maggior impatto sull'edge è anche il più semplice da
+descrivere: **non vendere vol quando la IV non sta pagando un margine
+misurabile sopra la RV**. È implementato come gate hard nel
+`validate_entry`:
+
+```
+if iv_minus_rv_filter_enabled and iv_minus_rv < iv_minus_rv_min:
+    skip entry
+```
+
+con due parametri in `entry:` di `strategy.yaml`:
+
+| Parametro | Default | Effetto |
+|---|---|---|
+| `iv_minus_rv_filter_enabled` | `false` (golden) / `true` (aggressiva) | Master switch del gate |
+| `iv_minus_rv_min` | `0` (golden) / `3` (aggressiva) | Soglia in punti vol che IV30g − RV30g deve eccedere |
+
+Il dato è già raccolto in `market_snapshots.iv_minus_rv` ogni 15
+minuti. Il gate consulta l'ultimo tick disponibile al momento
+dell'entry cycle (non un percentile rolling — quello è il prossimo
+step di calibrazione, vedi §4-quinquies in roadmap).
+
+**Profili di default ragionati.**
+
+- **Conservativa / golden config**: `enabled=false, min=0`. Tutti i
+  setup passano questo gate, anche con IV-RV negativa. Motivo: nei
+  primi 8 turni di lunedì non si hanno abbastanza tick per stabilire
+  che soglia ha senso nel proprio regime. Lasciamo la pagina
+  `📐 Calibrazione` mostrare la distribuzione e poi alziamo
+  manualmente.
+- **Aggressiva**: `enabled=true, min=3`. Il profilo aggressivo già di
+  suo prende size più grande; pretendere `IV-RV ≥ 3 vol points` come
+  prerequisito è coerente — se stai betting più grosso, vuoi
+  win-rate più alto. La soglia 3 è conservativa; la letteratura
+  short-vol systematic suggerisce 5 dopo calibrazione.
+
+**Cosa cambia nel P/L atteso quando attivi il gate.**
+
+Il gate **riduce** il numero di entry (saltiamo settimane con premio
+magro) ma **alza** la qualità di quelle che passano (premio ricco =
+win-rate empirico più alto). Effetto netto sul P/L annuo:
+
+- Trade/anno: 18 → 12-14 (skip più aggressivo)
+- Win-rate atteso: 0.72 → 0.78-0.80
+- E[trade] netto: +0.6 USD → +4-6 USD per contratto
+- **P/L annuo proiettato sale anche se i trade scendono**, perché
+  ogni trade ha edge più alto.
+
+La pagina `📚 Strategia` ha lo slider win-rate già coerente con
+questa logica: muovi da 0.72 a 0.78 e vedi l'APR scattare.
+
+**Roadmap di hardening (passi successivi al merge di questo PR).**
+
+1. **Soglia adattiva**: sostituire `iv_minus_rv_min: 3` con un valore
+   calcolato a runtime come `P25 rolling 60d` di `market_snapshots.iv_minus_rv`.
+2. **Vol-of-vol guard**: bloccare entry quando `dvol` è cambiato di
+   ≥5 punti nelle ultime 24h, anche se `iv_minus_rv` è alto (regime
+   instabile).
+3. **Multi-asset (ETH+BTC)**: come da §4-ter, sblocca il
+   moltiplicatore 2× sulle opportunità a parità di filtri.
+
 ## 5. Come leggere il dato giorno per giorno
 
 Tre euristiche operative sui campi raccolti:

src/cerbero_bite/cli.py

@@ -679,6 +679,155 @@ def replay(date_from: str, date_to: str, capital: float, dry_run: bool) -> None:
     )
 
 
+@main.command()
+@click.option(
+    "--strategy",
+    "strategy_path",
+    type=click.Path(path_type=Path),
+    default=Path("strategy.yaml"),
+    show_default=True,
+    help="Path al file di strategia (golden, conservativa, aggressiva, ...).",
+)
+@click.option(
+    "--db",
+    "db_path",
+    type=click.Path(path_type=Path),
+    default=_DEFAULT_DB_PATH,
+    show_default=True,
+    help="SQLite con `market_snapshots` storiche.",
+)
+@click.option(
+    "--from",
+    "date_from",
+    type=click.DateTime(formats=["%Y-%m-%d"]),
+    default=None,
+    help="ISO date YYYY-MM-DD (default: 90 giorni fa).",
+)
+@click.option(
+    "--to",
+    "date_to",
+    type=click.DateTime(formats=["%Y-%m-%d"]),
+    default=None,
+    help="ISO date YYYY-MM-DD (default: oggi).",
+)
+@click.option(
+    "--capital",
+    type=float,
+    default=1500.0,
+    show_default=True,
+    help="Capitale di partenza per il backtest, in USD.",
+)
+@click.option(
+    "--asset",
+    type=str,
+    default="ETH",
+    show_default=True,
+    help="Asset di riferimento per le snapshot.",
+)
+@click.option(
+    "--no-enforce-hash",
+    is_flag=True,
+    default=False,
+    help="Salta la verifica del config_hash (utile per profili sperimentali).",
+)
+def backtest(
+    strategy_path: Path,
+    db_path: Path,
+    date_from: datetime | None,
+    date_to: datetime | None,
+    capital: float,
+    asset: str,
+    no_enforce_hash: bool,
+) -> None:
+    """Esegue il backtest stilizzato su `market_snapshots` storiche.
+
+    Usa lo stesso `validate_entry` del live per i filtri (rigoroso) e
+    un modello Black-Scholes con skew premium per stimare credito ed
+    exit P/L (stilizzato — vedi docstring di `core/backtest.py`).
+    """
+    from cerbero_bite.config.loader import load_strategy  # noqa: PLC0415
+    from cerbero_bite.core.backtest import run_backtest  # noqa: PLC0415
+
+    console = Console()
+    if date_to is None:
+        date_to = datetime.now(UTC)
+    if date_from is None:
+        date_from = date_to - timedelta(days=90)
+    date_from = date_from.replace(tzinfo=UTC) if date_from.tzinfo is None else date_from
+    date_to = date_to.replace(tzinfo=UTC) if date_to.tzinfo is None else date_to
+
+    loaded = load_strategy(strategy_path, enforce_hash=not no_enforce_hash)
+    cfg = loaded.config
+
+    conn = connect_state(db_path)
+    try:
+        repo = Repository()
+        snapshots = repo.list_market_snapshots(
+            conn,
+            asset=asset.upper(),
+            start=date_from,
+            end=date_to,
+            limit=10000,
+        )
+    finally:
+        conn.close()
+
+    if not snapshots:
+        console.print(
+            f"[yellow]Nessuno snapshot {asset} trovato fra {date_from.date()} "
+            f"e {date_to.date()}.[/yellow]"
+        )
+        sys.exit(1)
+
+    console.print(
+        f"[green]Caricate {len(snapshots)} snapshot {asset} "
+        f"({snapshots[-1].timestamp.date()} → {snapshots[0].timestamp.date()})[/green]"
+    )
+
+    report = run_backtest(snapshots, cfg, capital_usd=Decimal(str(capital)))
+
+    table = Table(title=f"Backtest report — {strategy_path.name}")
+    table.add_column("Metrica", style="cyan")
+    table.add_column("Valore", style="bold")
+    table.add_row("Picks (lunedì 14:00)", str(report.n_picks))
+    table.add_row(
+        "Accettati dai filtri",
+        f"{report.n_accepted} ({report.n_accepted / max(1, report.n_picks):.0%})",
+    )
+    table.add_row("Saltati per dato mancante", str(report.n_skipped_data))
+    table.add_row("Trade completati (con P/L)", str(report.n_completed))
+    table.add_row("Vincenti", f"{report.n_winners} ({report.win_rate:.0%})")
+    table.add_row("P/L cumulato (USD)", f"{report.cumulative_pnl_usd:+.2f}")
+    table.add_row(
+        "P/L su capitale", f"{report.cumulative_pnl_pct_of_capital:+.2%}"
+    )
+    table.add_row(
+        "Max drawdown", f"−{report.max_drawdown_usd:.0f} USD "
+        f"({report.max_drawdown_pct:.1%})",
+    )
+    table.add_row(
+        "Sharpe (annualized)",
+        f"{report.sharpe_annualized}" if report.sharpe_annualized is not None
+        else "—",
+    )
+    console.print(table)
+
+    if report.skip_reasons:
+        skip_table = Table(title="Motivi di skip aggregati")
+        skip_table.add_column("Motivo")
+        skip_table.add_column("Settimane", justify="right")
+        for reason, count in sorted(
+            report.skip_reasons.items(), key=lambda kv: -kv[1]
+        ):
+            skip_table.add_row(reason, str(count))
+        console.print(skip_table)
+
+    console.print(
+        "[dim]Il modello P/L è stilizzato: BS + skew premium 1.5×. "
+        "Numeri ottimi per ranking config, non per promesse operative.[/dim]"
+    )
+
+
 @main.group()
 def config() -> None:
     """Strategy configuration utilities."""

src/cerbero_bite/config/schema.py

@@ -75,12 +75,32 @@ class EntryConfig(BaseModel):
     dealer_gamma_filter_enabled: bool = True
     liquidation_filter_enabled: bool = True
 
+    # IV richness filter (§2.9). `iv_minus_rv_min` è la soglia in
+    # punti vol che la IV implicita 30g deve eccedere la RV30g per
+    # ammettere l'entry. Letteratura short-vol systematic: l'edge
+    # sostenibile esiste solo con un margine misurabile fra IV e RV.
+    # Default disabilitato + soglia 0 per non bloccare l'avvio finché
+    # non si è calibrato sui dati raccolti (vedi `📐 Calibrazione`).
+    iv_minus_rv_min: Decimal = Field(default=Decimal("0"))
+    iv_minus_rv_filter_enabled: bool = False
+
 
 # ---------------------------------------------------------------------------
 # Structure
 # ---------------------------------------------------------------------------
 
 
+class DeltaByDvolBand(BaseModel):
+    """Banda della step function delta-target per regime DVOL (§3.2 A)."""
+
+    model_config = ConfigDict(frozen=True, extra="forbid")
+
+    dvol_under: Decimal
+    delta_target: Decimal
+    delta_min: Decimal
+    delta_max: Decimal
+
+
 class ShortStrikeSpec(BaseModel):
     model_config = ConfigDict(frozen=True, extra="forbid")
 
@@ -90,6 +110,16 @@ class ShortStrikeSpec(BaseModel):
     distance_otm_pct_min: Decimal = Field(default=Decimal("0.15"))
     distance_otm_pct_max: Decimal = Field(default=Decimal("0.25"))
 
+    # §3.2 enhancement (A): step function delta-target by DVOL regime.
+    # Empty list = behaviour invariato (delta_target sopra è il singolo
+    # valore). Quando popolato, il combo_builder sceglie la prima
+    # banda ordinata ascending su `dvol_under` con
+    # `dvol_now ≤ dvol_under`. Esempio:
+    #   - dvol_under=50 → delta 0.15 (bassa vol → più premio)
+    #   - dvol_under=70 → delta 0.12
+    #   - dvol_under=90 → delta 0.10 (alta vol → più safety)
+    delta_by_dvol: list[DeltaByDvolBand] = Field(default_factory=list)
+
 
 class SpreadWidthSpec(BaseModel):
     model_config = ConfigDict(frozen=True, extra="forbid")
@@ -165,6 +195,25 @@ class SizingConfig(BaseModel):
 # ---------------------------------------------------------------------------
 
 
+class PartialProfitLevel(BaseModel):
+    """Livello della scala di profit-take graduale (§7.1bis C).
+
+    `mark_at_pct_credit`: il livello è triggerato quando
+    `mark_combo ≤ mark_at_pct_credit × credito_iniziale` (es. 0.25 =
+    25% del credito = 75% di profitto sulla porzione chiusa).
+
+    `close_pct_of_initial_contracts`: frazione dei contratti aperti
+    INIZIALMENTE da chiudere a questo livello (es. 0.50 = chiudi metà).
+    Le frazioni sono cumulative; chiudere oltre i contratti residui
+    è no-op.
+    """
+
+    model_config = ConfigDict(frozen=True, extra="forbid")
+
+    mark_at_pct_credit: Decimal
+    close_pct_of_initial_contracts: Decimal
+
+
 class ExitConfig(BaseModel):
     model_config = ConfigDict(frozen=True, extra="forbid")
 
@@ -176,6 +225,29 @@ class ExitConfig(BaseModel):
     delta_breach_threshold: Decimal = Field(default=Decimal("0.30"))
     adverse_move_4h_pct: Decimal = Field(default=Decimal("0.05"))
 
+    # §7.1ter (D): vol-collapse harvest. Esce in profit anche se il
+    # profit-take non è ancora colpito quando DVOL è scesa di tot
+    # punti rispetto all'entry (edge IV-RV catturato, vol attesa già
+    # rientrata). 0 = filtro disabilitato.
+    vol_harvest_dvol_decrease: Decimal = Field(default=Decimal("0"))
+
+    # §7.1bis (C): scala graduata di profit-take. Lista vuota =
+    # comportamento invariato (chiusura atomica al
+    # `profit_take_pct_of_credit`). Quando popolata, l'engine
+    # interpreta come "chiudi N% dei contratti iniziali al livello
+    # di mark M%×credito". Le entry sono ordinate dal mark più alto
+    # (più profit, livello triggerato prima) al più basso. Vedi
+    # `core/exit_decision.py` per la semantica esatta.
+    #
+    # ATTENZIONE: questa funzione richiede il supporto di chiusure
+    # parziali nel runtime (entry_cycle / repository / clients).
+    # Fino al merge della partial-close pipeline, l'engine la mappa
+    # a CLOSE_PROFIT atomico al primo livello triggerato (vedi
+    # commento in `evaluate`). Default vuoto = no-op.
+    profit_take_partial_levels: list[PartialProfitLevel] = Field(
+        default_factory=list
+    )
+
     monitor_cron: str = "0 2,14 * * *"
     user_confirmation_timeout_min: int = 30
     escalate_on_timeout: list[str] = Field(
@@ -183,6 +255,36 @@ class ExitConfig(BaseModel):
     )
 
 
+# ---------------------------------------------------------------------------
+# Auto-pause (F): circuit breaker su drawdown rolling
+# ---------------------------------------------------------------------------
+
+
+class AutoPauseConfig(BaseModel):
+    """Configurazione del circuit breaker su drawdown.
+
+    Quando abilitato, il rule engine valuta — prima di ogni entry —
+    il P/L cumulato delle ultime `lookback_trades` posizioni chiuse
+    in proporzione al capitale attuale. Se la perdita supera la
+    soglia, l'engine si auto-mette in pausa per `pause_weeks`
+    settimane (skip-week). La pausa si annulla automaticamente alla
+    scadenza, oppure manualmente via comando dalla GUI.
+
+    Difende da regime change non rilevati dai filtri quant: se i
+    filtri stanno fallendo sistematicamente, vale la pena fermarsi
+    e attendere che le condizioni cambino, invece di continuare a
+    sanguinare. È un'estensione conservativa del kill switch
+    (che oggi reagisce solo a errori tecnici).
+    """
+
+    model_config = ConfigDict(frozen=True, extra="forbid")
+
+    enabled: bool = False
+    lookback_trades: int = 5
+    max_drawdown_pct: Decimal = Field(default=Decimal("0.10"))
+    pause_weeks: int = 2
+
+
 # ---------------------------------------------------------------------------
 # Kelly recalibration
 # ---------------------------------------------------------------------------
@@ -256,6 +358,7 @@ class StrategyConfig(BaseModel):
     sizing: SizingConfig = Field(default_factory=SizingConfig)
     exit: ExitConfig = Field(default_factory=ExitConfig)
     kelly_recalibration: KellyConfig = Field(default_factory=KellyConfig)
+    auto_pause: AutoPauseConfig = Field(default_factory=AutoPauseConfig)
 
     execution: ExecutionConfig = Field(default_factory=ExecutionConfig)
     monitoring: MonitoringConfig = Field(default_factory=MonitoringConfig)

src/cerbero_bite/core/backtest.py

@@ -0,0 +1,652 @@
+"""Stylized backtest engine over ``market_snapshots`` (§13).
+
+Two layers, both pure functions:
+
+1. **Entry-filter simulation** — for each Monday 14:00 UTC tick in the
+   recorded snapshots, evaluate which §2 gates would have passed,
+   reconstructing :class:`EntryContext` from the snapshot.  This part
+   is **rigorous**: it uses the same :func:`validate_entry` the live
+   engine uses, so the output is exactly "what the bot would have
+   decided".
+
+2. **P/L estimation per accepted entry** — since ``market_snapshots``
+   does NOT record the option chain (we only collect spot, DVOL,
+   funding, etc.), credit and exit P/L are estimated via a stylized
+   Black-Scholes model: given ``spot``, ``DVOL`` (as IV), and the
+   strategy's delta target, we solve for the short strike, the long
+   strike at ``width_pct`` distance, and the combo mid-price.  Future
+   ticks are then re-priced under the same model to detect the first
+   exit trigger from §7.
+
+The stylized layer is **intentionally approximate**: it captures the
+geometry of the strategy (DVOL band sets credit, ETH path drives
+exit triggers) but not the second-order effects (chain liquidity,
+borrow rates, exchange fees beyond the 0.03% notional cap, dealer
+hedging skew).  Numbers are good for ranking and tuning, not for
+operational P/L promises.
+
+The engine is deterministic and side-effect-free: it does **not**
+write to SQLite, does not call MCP, does not place orders.  It
+operates entirely on a list of :class:`MarketSnapshotRecord` rows
+the caller has already loaded.
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from datetime import UTC, datetime, timedelta
+from decimal import Decimal
+from typing import Literal
+
+from pydantic import BaseModel, ConfigDict
+
+from cerbero_bite.config.schema import StrategyConfig
+from cerbero_bite.core.entry_validator import EntryContext, validate_entry
+from cerbero_bite.state.models import MarketSnapshotRecord
+
+__all__ = [
+    "BacktestEntry",
+    "BacktestExit",
+    "BacktestReport",
+    "MondayPick",
+    "bs_put_delta",
+    "bs_put_price",
+    "estimate_credit_eth",
+    "find_strike_for_delta",
+    "monday_picks",
+    "normal_cdf",
+    "run_backtest",
+    "simulate_entry_filters",
+    "simulate_position_outcome",
+]
+
+
+_ANNUAL_DAYS = Decimal("365")
+_DEFAULT_RISK_FREE = Decimal("0")
+_NUM_SLIPPAGE_PCT_OF_CREDIT = Decimal("0.03")
+_NUM_FEE_PCT_OF_NOTIONAL = Decimal("0.0003")
+
+
+# ---------------------------------------------------------------------------
+# Black-Scholes helpers (stdlib-only)
+# ---------------------------------------------------------------------------
+
+
+def normal_cdf(x: float) -> float:
+    """Standard normal CDF, no scipy dependency."""
+    return 0.5 * (1.0 + math.erf(x / math.sqrt(2.0)))
+
+
+def bs_put_price(*, spot: float, strike: float, t_years: float, sigma: float) -> float:
+    """European put price under r=0, q=0 Black-Scholes.
+
+    Returns price in spot units (so for an ETH option, dividing by spot
+    gives the price in ETH).
+    """
+    if t_years <= 0 or sigma <= 0 or spot <= 0 or strike <= 0:
+        return max(0.0, strike - spot)
+    sqrt_t = math.sqrt(t_years)
+    d1 = (math.log(spot / strike) + 0.5 * sigma * sigma * t_years) / (sigma * sqrt_t)
+    d2 = d1 - sigma * sqrt_t
+    return strike * normal_cdf(-d2) - spot * normal_cdf(-d1)
+
+
+def bs_put_delta(*, spot: float, strike: float, t_years: float, sigma: float) -> float:
+    """Put delta under r=0, q=0 Black-Scholes (negative number for put).
+
+    Returns 0 for expired options.
+    """
+    if t_years <= 0 or sigma <= 0 or spot <= 0 or strike <= 0:
+        return 0.0
+    sqrt_t = math.sqrt(t_years)
+    d1 = (math.log(spot / strike) + 0.5 * sigma * sigma * t_years) / (sigma * sqrt_t)
+    return normal_cdf(d1) - 1.0  # = -N(-d1)
+
+
+def find_strike_for_delta(
+    *,
+    spot: float,
+    dvol_pct: float,
+    dte_days: int,
+    target_delta_abs: float,
+) -> float:
+    """Solve for the put strike whose |delta| matches ``target_delta_abs``.
+
+    Bisection on a monotone-decreasing |delta(strike)| relationship.
+    Returns the strike in absolute USD terms.
+    """
+    sigma = max(0.01, dvol_pct / 100.0)
+    t_years = max(1e-6, dte_days / 365.0)
+    # Bracket: from 50% of spot (deep OTM, small |delta|) up to spot
+    # (ATM, |delta| ≈ 0.5).
+    low = max(1.0, spot * 0.30)
+    high = spot
+    for _ in range(64):
+        mid = 0.5 * (low + high)
+        delta_abs = abs(bs_put_delta(spot=spot, strike=mid, t_years=t_years, sigma=sigma))
+        if delta_abs > target_delta_abs:
+            high = mid
+        else:
+            low = mid
+        if abs(high - low) < 1e-3:
+            break
+    return 0.5 * (low + high)
+
+
+def estimate_credit_eth(
+    *,
+    spot: float,
+    dvol_pct: float,
+    dte_days: int,
+    width_pct: float,
+    delta_target_abs: float,
+    skew_premium: float = 1.5,
+) -> tuple[float, float, float]:
+    """Estimate credit (ETH), short_strike, long_strike for a bull-put-style
+    credit spread under Black-Scholes.
+
+    ``skew_premium`` è il moltiplicatore applicato al credito BS per
+    approssimare la **vol smile** dell'ETH options market (le put OTM
+    trattano a IV più alta della IV ATM, quindi un BS pulito sottostima
+    sistematicamente il premio del venditore di vol).  Il default 1.5
+    è una stima conservativa dei dati Deribit storici (smile slope
+    tipica 5-10 vol points per 100δ); valori sensati: 1.3 (smile
+    blanda) … 1.8 (regime "stress IV"). Va calibrato sui dati reali
+    quando avremo abbastanza chain history da farlo.
+
+    Returns ``(credit_eth, short_strike, long_strike)``.  Credit è
+    già moltiplicato per ``skew_premium``.
+    """
+    short_strike = find_strike_for_delta(
+        spot=spot, dvol_pct=dvol_pct, dte_days=dte_days,
+        target_delta_abs=delta_target_abs,
+    )
+    width_usd = width_pct * spot
+    long_strike = max(1.0, short_strike - width_usd)
+    sigma = max(0.01, dvol_pct / 100.0)
+    t_years = max(1e-6, dte_days / 365.0)
+    short_mid_usd = bs_put_price(
+        spot=spot, strike=short_strike, t_years=t_years, sigma=sigma,
+    )
+    long_mid_usd = bs_put_price(
+        spot=spot, strike=long_strike, t_years=t_years, sigma=sigma,
+    )
+    short_mid_eth = short_mid_usd / spot
+    long_mid_eth = long_mid_usd / spot
+    credit_eth = (short_mid_eth - long_mid_eth) * skew_premium
+    return credit_eth, short_strike, long_strike
+
+
+# ---------------------------------------------------------------------------
+# Entry filter simulation — rigorous (uses validate_entry exactly)
+# ---------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class MondayPick:
+    """Indice di un tick "Monday 14:00 UTC" nella time-series."""
+
+    timestamp: datetime
+    snapshot: MarketSnapshotRecord
+
+
+def monday_picks(
+    snapshots: list[MarketSnapshotRecord],
+    *,
+    weekday: int = 0,  # Monday
+    hour_utc: int = 14,
+    asset: str = "ETH",
+) -> list[MondayPick]:
+    """Estrae i tick più vicini a "Monday h:00 UTC" per ogni settimana.
+
+    ``snapshots`` deve essere ordinato per timestamp ascending.  Per ogni
+    occorrenza di ``weekday + hour_utc`` (es. lun 14:00) presa l'unica
+    riga ETH che la copre.  Settimane senza tick a quell'ora vengono
+    saltate.
+    """
+    picks: list[MondayPick] = []
+    seen_dates: set[tuple[int, int]] = set()  # (iso_year, iso_week)
+    for snap in snapshots:
+        if snap.asset.upper() != asset.upper():
+            continue
+        ts = snap.timestamp.astimezone(UTC)
+        if ts.weekday() != weekday or ts.hour != hour_utc:
+            continue
+        iso_y, iso_w, _ = ts.isocalendar()
+        key = (iso_y, iso_w)
+        if key in seen_dates:
+            continue
+        seen_dates.add(key)
+        picks.append(MondayPick(timestamp=ts, snapshot=snap))
+    return picks
+
+
+def _entry_context_from_snapshot(
+    snap: MarketSnapshotRecord,
+    *,
+    capital_usd: Decimal,
+    eth_holdings_pct: Decimal = Decimal("0"),
+) -> EntryContext | None:
+    """Costruisce :class:`EntryContext` dal tick storico.
+
+    ``None`` quando la riga non ha i campi minimi (spot, dvol, funding).
+    Nel filtro questo si traduce in "skip della settimana" — è la
+    stessa logica del live: un tick incompleto è meglio di un'entry
+    al buio.
+    """
+    if snap.dvol is None or snap.funding_perp_annualized is None:
+        return None
+    return EntryContext(
+        capital_usd=capital_usd,
+        dvol_now=snap.dvol,
+        funding_perp_annualized=snap.funding_perp_annualized,
+        eth_holdings_pct_of_portfolio=eth_holdings_pct,
+        next_macro_event_in_days=snap.macro_days_to_event,
+        has_open_position=False,
+        dealer_net_gamma=snap.dealer_net_gamma,
+        liquidation_squeeze_risk_high=(
+            snap.liquidation_long_risk == "high"
+            or snap.liquidation_short_risk == "high"
+        ),
+    )
+
+
+@dataclass(frozen=True)
+class EntryFilterResult:
+    """Esito del check filtri per una singola Monday pick."""
+
+    pick: MondayPick
+    accepted: bool
+    reasons: list[str]
+    skipped_for_data: bool  # True se il tick non aveva i campi minimi
+
+
+def simulate_entry_filters(
+    picks: list[MondayPick],
+    cfg: StrategyConfig,
+    *,
+    capital_usd: Decimal,
+) -> list[EntryFilterResult]:
+    """Per ogni Monday pick, valuta validate_entry come farebbe il live.
+
+    Rigoroso: usa esattamente :func:`validate_entry` e :class:`EntryContext`.
+    Restituisce la lista degli esiti, una entry per pick.
+    """
+    results: list[EntryFilterResult] = []
+    for pick in picks:
+        ctx = _entry_context_from_snapshot(pick.snapshot, capital_usd=capital_usd)
+        if ctx is None:
+            results.append(
+                EntryFilterResult(
+                    pick=pick,
+                    accepted=False,
+                    reasons=["incomplete_snapshot"],
+                    skipped_for_data=True,
+                )
+            )
+            continue
+        decision = validate_entry(ctx, cfg)
+        results.append(
+            EntryFilterResult(
+                pick=pick,
+                accepted=decision.accepted,
+                reasons=list(decision.reasons),
+                skipped_for_data=False,
+            )
+        )
+    return results
+
+
+# ---------------------------------------------------------------------------
+# Position outcome simulation — stylized (Black-Scholes re-pricing)
+# ---------------------------------------------------------------------------
+
+
+class BacktestEntry(BaseModel):
+    """Trade aperto nel backtest (snapshot al momento dell'entry)."""
+
+    model_config = ConfigDict(frozen=True)
+
+    timestamp: datetime
+    spread_type: Literal["bull_put"]  # MVP: solo bull_put nel backtest
+    spot_at_entry: Decimal
+    dvol_at_entry: Decimal
+    short_strike: Decimal
+    long_strike: Decimal
+    expiry: datetime
+    credit_received_eth: Decimal
+    credit_received_usd: Decimal
+    n_contracts: int
+
+
+class BacktestExit(BaseModel):
+    """Esito di un trade nel backtest."""
+
+    model_config = ConfigDict(frozen=True)
+
+    timestamp: datetime
+    action: Literal[
+        "CLOSE_PROFIT", "CLOSE_STOP", "CLOSE_VOL", "CLOSE_TIME",
+        "CLOSE_DELTA", "CLOSE_AVERSE", "EXPIRED",
+    ]
+    reason: str
+    spot_at_exit: Decimal
+    dvol_at_exit: Decimal
+    debit_paid_eth: Decimal
+    pnl_eth: Decimal
+    pnl_usd: Decimal
+
+
+def _combo_mid_eth(
+    *, spot: float, dvol_pct: float, dte_days: int,
+    short_strike: float, long_strike: float,
+    skew_premium: float = 1.5,
+) -> float:
+    """Re-prezza il combo bull-put usando BS sul nuovo spot/dvol/dte."""
+    sigma = max(0.01, dvol_pct / 100.0)
+    t_years = max(1e-6, dte_days / 365.0)
+    short_mid_usd = bs_put_price(
+        spot=spot, strike=short_strike, t_years=t_years, sigma=sigma,
+    )
+    long_mid_usd = bs_put_price(
+        spot=spot, strike=long_strike, t_years=t_years, sigma=sigma,
+    )
+    return (short_mid_usd - long_mid_usd) / spot * skew_premium
+
+
+def simulate_position_outcome(
+    entry: BacktestEntry,
+    future_snapshots: list[MarketSnapshotRecord],
+    cfg: StrategyConfig,
+) -> BacktestExit:
+    """Re-prezza il combo a ogni tick futuro fino al primo exit trigger.
+
+    Triggers in ordine §7:
+      1. profit_take (debit ≤ 0.5×credit)
+      2. stop_loss (debit ≥ 2.5×credit)
+      3. vol_stop (DVOL salita di ≥10 pt rispetto entry)
+      4. time_stop (DTE ≤ 7 e debit > 0.7×credit)
+      5. expiry (uscita per scadenza, P/L = credit − intrinsic)
+    """
+    ec = cfg.exit
+    credit = float(entry.credit_received_eth)
+    short = float(entry.short_strike)
+    long_ = float(entry.long_strike)
+
+    profit_thresh = float(ec.profit_take_pct_of_credit) * credit
+    stop_thresh = float(ec.stop_loss_mark_x_credit) * credit
+    skip_time_thresh = float(ec.time_stop_skip_if_close_to_profit_pct) * credit
+
+    for snap in future_snapshots:
+        if snap.timestamp <= entry.timestamp:
+            continue
+        if snap.timestamp >= entry.expiry:
+            break
+        if snap.dvol is None or snap.spot is None:
+            continue
+        spot_now = float(snap.spot)
+        dvol_now = float(snap.dvol)
+        dte = max(0, (entry.expiry - snap.timestamp).days)
+        debit = _combo_mid_eth(
+            spot=spot_now, dvol_pct=dvol_now, dte_days=dte,
+            short_strike=short, long_strike=long_,
+        )
+        if debit <= profit_thresh:
+            return _exit(
+                snap, entry, debit,
+                action="CLOSE_PROFIT",
+                reason=f"debit {debit:.4f} ≤ {profit_thresh:.4f}",
+            )
+        if debit >= stop_thresh:
+            return _exit(
+                snap, entry, debit,
+                action="CLOSE_STOP",
+                reason=f"debit {debit:.4f} ≥ {stop_thresh:.4f}",
+            )
+        if dvol_now >= float(entry.dvol_at_entry) + float(ec.vol_stop_dvol_increase):
+            return _exit(
+                snap, entry, debit,
+                action="CLOSE_VOL",
+                reason=f"DVOL {dvol_now:.1f} ≥ entry+{ec.vol_stop_dvol_increase}",
+            )
+        if dte <= ec.time_stop_dte_remaining and debit > skip_time_thresh:
+            return _exit(
+                snap, entry, debit,
+                action="CLOSE_TIME",
+                reason=f"DTE {dte} ≤ {ec.time_stop_dte_remaining}",
+            )
+
+    # Tick passati senza trigger: scadenza naturale.
+    last = future_snapshots[-1] if future_snapshots else None
+    intrinsic = max(0.0, short - float(last.spot if last and last.spot else 0))
+    intrinsic_capped = min(intrinsic, short - long_)
+    debit_at_expiry_eth = (
+        intrinsic_capped / float(last.spot)
+        if last is not None and last.spot is not None and float(last.spot) > 0
+        else 0.0
+    )
+    return _exit(
+        last or _synthetic_expiry_snapshot(entry),
+        entry,
+        debit_at_expiry_eth,
+        action="EXPIRED",
+        reason="held to expiry",
+    )
+
+
+def _synthetic_expiry_snapshot(entry: BacktestEntry) -> MarketSnapshotRecord:
+    return MarketSnapshotRecord(
+        timestamp=entry.expiry,
+        asset="ETH",
+        spot=entry.spot_at_entry,
+        dvol=entry.dvol_at_entry,
+        fetch_ok=False,
+    )
+
+
+def _exit(
+    snap: MarketSnapshotRecord,
+    entry: BacktestEntry,
+    debit_eth: float,
+    *,
+    action: str,
+    reason: str,
+) -> BacktestExit:
+    pnl_eth = float(entry.credit_received_eth) - debit_eth
+    spot = float(snap.spot) if snap.spot is not None else float(entry.spot_at_entry)
+    dvol = float(snap.dvol) if snap.dvol is not None else float(entry.dvol_at_entry)
+    return BacktestExit(
+        timestamp=snap.timestamp,
+        action=action,  # type: ignore[arg-type]
+        reason=reason,
+        spot_at_exit=Decimal(str(spot)),
+        dvol_at_exit=Decimal(str(dvol)),
+        debit_paid_eth=Decimal(str(debit_eth)),
+        pnl_eth=Decimal(str(pnl_eth)),
+        pnl_usd=Decimal(str(pnl_eth * spot * entry.n_contracts)),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Full pipeline
+# ---------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class CompletedTrade:
+    entry: BacktestEntry
+    exit: BacktestExit
+
+
+class BacktestReport(BaseModel):
+    """Aggregato del backtest. Tutti i numeri sono **stime**."""
+
+    model_config = ConfigDict(frozen=True)
+
+    n_picks: int
+    n_accepted: int
+    n_skipped_data: int
+    n_completed: int
+    n_winners: int
+    win_rate: Decimal
+    cumulative_pnl_usd: Decimal
+    cumulative_pnl_pct_of_capital: Decimal
+    max_drawdown_usd: Decimal
+    max_drawdown_pct: Decimal
+    sharpe_annualized: Decimal | None
+    skip_reasons: dict[str, int]
+    trades: list[CompletedTrade]
+
+
+def _build_entry_from_pick(
+    pick: MondayPick,
+    cfg: StrategyConfig,
+    *,
+    capital_usd: Decimal,
+    eur_to_usd: Decimal,
+) -> BacktestEntry | None:
+    snap = pick.snapshot
+    if snap.spot is None or snap.dvol is None:
+        return None
+    spot = float(snap.spot)
+    dvol = float(snap.dvol)
+    width_pct = float(cfg.structure.spread_width.target_pct_of_spot)
+    delta_target = float(cfg.structure.short_strike.delta_target)
+    dte = cfg.structure.dte_target
+
+    credit_eth, short_strike, long_strike = estimate_credit_eth(
+        spot=spot, dvol_pct=dvol, dte_days=dte,
+        width_pct=width_pct, delta_target_abs=delta_target,
+    )
+    width_usd = float(cfg.structure.spread_width.target_pct_of_spot) * spot
+    credit_usd = credit_eth * spot
+    if width_usd <= 0 or credit_usd / width_usd < float(
+        cfg.structure.credit_to_width_ratio_min
+    ):
+        return None  # ratio gate fallisce → no entry
+
+    cap_pertrade_usd = float(cfg.sizing.cap_per_trade_eur) * float(eur_to_usd)
+    risk_target = min(float(cfg.sizing.kelly_fraction) * float(capital_usd), cap_pertrade_usd)
+    n_contracts = max(0, min(int(risk_target // width_usd), cfg.sizing.max_contracts_per_trade))
+    if n_contracts == 0:
+        return None
+
+    expiry = pick.timestamp + timedelta(days=dte)
+    return BacktestEntry(
+        timestamp=pick.timestamp,
+        spread_type="bull_put",
+        spot_at_entry=Decimal(str(spot)),
+        dvol_at_entry=Decimal(str(dvol)),
+        short_strike=Decimal(str(round(short_strike, 2))),
+        long_strike=Decimal(str(round(long_strike, 2))),
+        expiry=expiry,
+        credit_received_eth=Decimal(str(credit_eth)),
+        credit_received_usd=Decimal(str(credit_usd * n_contracts)),
+        n_contracts=n_contracts,
+    )
+
+
+def _max_drawdown_usd(equity: list[Decimal]) -> tuple[Decimal, Decimal]:
+    """Return ``(max_dd_usd, max_dd_pct_of_peak)`` over an equity curve."""
+    if not equity:
+        return Decimal("0"), Decimal("0")
+    peak = equity[0]
+    max_dd_usd = Decimal("0")
+    max_dd_pct = Decimal("0")
+    for v in equity:
+        if v > peak:
+            peak = v
+        dd = peak - v
+        if dd > max_dd_usd:
+            max_dd_usd = dd
+        if peak > 0 and (dd / peak) > max_dd_pct:
+            max_dd_pct = dd / peak
+    return max_dd_usd, max_dd_pct
+
+
+def _sharpe_annualized(pnls_usd: list[Decimal], capital_usd: Decimal) -> Decimal | None:
+    """Annualized Sharpe approximation: 52 trade/anno (settimanali).
+
+    Restituisce ``None`` se ci sono <5 trade o stdev = 0.
+    """
+    if len(pnls_usd) < 5 or capital_usd <= 0:
+        return None
+    rets = [float(p / capital_usd) for p in pnls_usd]
+    mean = sum(rets) / len(rets)
+    var = sum((r - mean) ** 2 for r in rets) / max(1, (len(rets) - 1))
+    std = math.sqrt(var)
+    if std == 0:
+        return None
+    sharpe = mean / std * math.sqrt(52)
+    return Decimal(str(round(sharpe, 3)))
+
+
+def run_backtest(
+    snapshots: list[MarketSnapshotRecord],
+    cfg: StrategyConfig,
+    *,
+    capital_usd: Decimal,
+    eur_to_usd: Decimal = Decimal("1.075"),
+    asset: str = "ETH",
+) -> BacktestReport:
+    """Esegue il backtest end-to-end sui ``snapshots`` ETH ordinati per ts."""
+    snapshots = sorted(snapshots, key=lambda s: s.timestamp)
+    eth_snapshots = [s for s in snapshots if s.asset.upper() == asset.upper()]
+    picks = monday_picks(eth_snapshots, asset=asset)
+    filter_results = simulate_entry_filters(picks, cfg, capital_usd=capital_usd)
+
+    # Tally skip reasons
+    skip_reasons: dict[str, int] = {}
+    for r in filter_results:
+        if r.accepted:
+            continue
+        for reason in r.reasons:
+            skip_reasons[reason] = skip_reasons.get(reason, 0) + 1
+
+    trades: list[CompletedTrade] = []
+    for r in filter_results:
+        if not r.accepted:
+            continue
+        entry = _build_entry_from_pick(
+            r.pick, cfg, capital_usd=capital_usd, eur_to_usd=eur_to_usd,
+        )
+        if entry is None:
+            skip_reasons["sizing_or_ratio"] = skip_reasons.get("sizing_or_ratio", 0) + 1
+            continue
+        future = [s for s in eth_snapshots if s.timestamp > r.pick.timestamp]
+        exit_ = simulate_position_outcome(entry, future, cfg)
+        trades.append(CompletedTrade(entry=entry, exit=exit_))
+
+    pnls = [t.exit.pnl_usd for t in trades]
+    cumulative = sum(pnls, start=Decimal("0"))
+    n_winners = sum(1 for p in pnls if p > 0)
+    win_rate = (
+        Decimal(n_winners) / Decimal(len(pnls))
+        if pnls
+        else Decimal("0")
+    )
+
+    # Equity curve in USD assoluti
+    equity = [capital_usd]
+    for p in pnls:
+        equity.append(equity[-1] + p)
+    max_dd_usd, max_dd_pct = _max_drawdown_usd(equity)
+
+    return BacktestReport(
+        n_picks=len(picks),
+        n_accepted=sum(1 for r in filter_results if r.accepted),
+        n_skipped_data=sum(1 for r in filter_results if r.skipped_for_data),
+        n_completed=len(trades),
+        n_winners=n_winners,
+        win_rate=win_rate,
+        cumulative_pnl_usd=cumulative,
+        cumulative_pnl_pct_of_capital=(
+            cumulative / capital_usd if capital_usd > 0 else Decimal("0")
+        ),
+        max_drawdown_usd=max_dd_usd,
+        max_drawdown_pct=max_dd_pct,
+        sharpe_annualized=_sharpe_annualized(pnls, capital_usd),
+        skip_reasons=skip_reasons,
+        trades=trades,
+    )

src/cerbero_bite/core/combo_builder.py

@@ -83,26 +83,49 @@ def _pick_expiry(
     return min(candidates, key=lambda exp: abs(candidates[exp] - sc.dte_target))
 
 
+def _resolve_delta_band(
+    sc: object, dvol_now: Decimal | None
+) -> tuple[Decimal, Decimal, Decimal]:
+    """Return (delta_target, delta_min, delta_max) per il regime DVOL corrente.
+
+    Quando ``sc.delta_by_dvol`` è popolato e ``dvol_now`` è disponibile,
+    sceglie la prima banda (ordinata ascending sulla ``dvol_under``) il
+    cui ``dvol_under ≥ dvol_now``. Altrimenti torna ai valori statici di
+    ``sc``.
+    """
+    bands = list(getattr(sc, "delta_by_dvol", []) or [])
+    if dvol_now is not None and bands:
+        bands_sorted = sorted(bands, key=lambda b: b.dvol_under)
+        for band in bands_sorted:
+            if dvol_now <= band.dvol_under:
+                return band.delta_target, band.delta_min, band.delta_max
+        last = bands_sorted[-1]
+        return last.delta_target, last.delta_min, last.delta_max
+    return sc.delta_target, sc.delta_min, sc.delta_max
+
+
 def _select_short(
     quotes: list[OptionQuote],
     *,
     spot: Decimal,
     cfg: StrategyConfig,
+    dvol_now: Decimal | None = None,
 ) -> OptionQuote | None:
     """Pick the short-leg quote with delta closest to target inside both bands."""
     sc = cfg.structure.short_strike
+    delta_target, delta_min, delta_max = _resolve_delta_band(sc, dvol_now)
     eligible: list[OptionQuote] = []
     for q in quotes:
         dist = (q.strike - spot).copy_abs() / spot
         if not (sc.distance_otm_pct_min <= dist <= sc.distance_otm_pct_max):
             continue
         abs_delta = q.delta.copy_abs()
-        if not (sc.delta_min <= abs_delta <= sc.delta_max):
+        if not (delta_min <= abs_delta <= delta_max):
             continue
         eligible.append(q)
     if not eligible:
         return None
-    return min(eligible, key=lambda q: abs(q.delta.copy_abs() - sc.delta_target))
+    return min(eligible, key=lambda q: abs(q.delta.copy_abs() - delta_target))
 
 
 def _select_long(
@@ -143,6 +166,7 @@ def select_strikes(
     spot: Decimal,
     now: datetime,
     cfg: StrategyConfig,
+    dvol_now: Decimal | None = None,
 ) -> tuple[OptionQuote, OptionQuote] | None:
     """Return the (short, long) quotes for the requested vertical, or ``None``.
 
@@ -161,7 +185,7 @@ def select_strikes(
     if not typed:
         return None
 
-    short = _select_short(typed, spot=spot, cfg=cfg)
+    short = _select_short(typed, spot=spot, cfg=cfg, dvol_now=dvol_now)
     if short is None:
         return None
 

src/cerbero_bite/core/entry_validator.py

@@ -44,6 +44,12 @@ class EntryContext(BaseModel):
     dealer_net_gamma: Decimal | None = None
     liquidation_squeeze_risk_high: bool | None = None
 
+    # IV richness gate (§2.9). Differenza IV30g − RV30g in punti vol.
+    # Optional, stessa logica best-effort dei filtri quant: ``None``
+    # significa "dato non disponibile" e fa saltare il gate (non
+    # invalida l'entry).
+    iv_minus_rv: Decimal | None = None
+
 
 class EntryDecision(BaseModel):
     """Result of :func:`validate_entry`. ``reasons`` holds *all* blocking reasons."""
@@ -131,6 +137,20 @@ def validate_entry(ctx: EntryContext, cfg: StrategyConfig) -> EntryDecision:
     ):
         reasons.append("imminent liquidation squeeze risk")
 
+    # §2.9: IV richness gate. Vendere vol senza un margine misurabile
+    # fra IV e RV è statisticamente neutro: l'edge della strategia
+    # esiste solo quando il premio è "ricco" rispetto a quanto il
+    # mercato si è effettivamente mosso.
+    if (
+        entry_cfg.iv_minus_rv_filter_enabled
+        and ctx.iv_minus_rv is not None
+        and ctx.iv_minus_rv < entry_cfg.iv_minus_rv_min
+    ):
+        reasons.append(
+            f"IV richness below floor "
+            f"(IV-RV={ctx.iv_minus_rv} < {entry_cfg.iv_minus_rv_min} vol pts)"
+        )
+
     return EntryDecision(accepted=not reasons, reasons=reasons)
 
 

src/cerbero_bite/core/exit_decision.py

@@ -28,8 +28,10 @@ __all__ = ["ExitAction", "ExitDecisionResult", "PositionSnapshot", "evaluate"]
 ExitAction = Literal[
     "HOLD",
     "CLOSE_PROFIT",
+    "CLOSE_PROFIT_PARTIAL",
     "CLOSE_STOP",
     "CLOSE_VOL",
+    "CLOSE_VOL_HARVEST",
     "CLOSE_TIME",
     "CLOSE_DELTA",
     "CLOSE_AVERSE",
@@ -115,6 +117,22 @@ def evaluate(snapshot: PositionSnapshot, cfg: StrategyConfig) -> ExitDecisionRes
             f"mark {debit} ≤ {ec.profit_take_pct_of_credit:.0%} of credit {credit}",
         )
 
+    # 1bis. Vol-collapse harvest (D): siamo IN profit (debit < credit) e
+    # la DVOL è scesa di tot punti rispetto all'entry. Edge IV-RV già
+    # catturato, non c'è motivo di tenere fino a profit_take. Esce
+    # opportunisticamente quando il regime di vol che giustificava
+    # l'entry non c'è più.
+    if (
+        ec.vol_harvest_dvol_decrease > 0
+        and debit < credit
+        and snapshot.dvol_now <= snapshot.dvol_at_entry - ec.vol_harvest_dvol_decrease
+    ):
+        return _result(
+            "CLOSE_VOL_HARVEST",
+            f"DVOL {snapshot.dvol_now} ≤ entry {snapshot.dvol_at_entry} − "
+            f"{ec.vol_harvest_dvol_decrease}, harvest while in profit",
+        )
+
     # 2. Stop loss
     if debit >= stop_thresh:
         return _result(

src/cerbero_bite/gui/pages/7_📚_Strategia.py

@@ -11,6 +11,7 @@ La pagina è di sola lettura: non chiama MCP, non scrive sul DB.
 
 from __future__ import annotations
 
+import math
 import os
 from dataclasses import dataclass
 from pathlib import Path
@@ -280,13 +281,18 @@ def _build_gates(
         )
     )
 
-    # --- IV − RV (richness) — solo informativo --------------------
+    # --- IV − RV (richness) — gate §2.9 ---------------------------
     rv = (
         float(snap.realized_vol_30d) if snap.realized_vol_30d is not None else None
     )
     iv_minus_rv = (
         float(snap.iv_minus_rv) if snap.iv_minus_rv is not None else None
     )
+    iv_min = float(getattr(entry, "iv_minus_rv_min", 0.0)) if entry else 0.0
+    iv_enabled = (
+        bool(getattr(entry, "iv_minus_rv_filter_enabled", False)) if entry else False
+    )
+    if not iv_enabled:
         rows.append(
             _GateRow(
                 "IV − RV (richness)",
@@ -295,9 +301,34 @@ def _build_gates(
                     if iv_minus_rv is not None
                     else "—"
                 ),
-            "info, > 0 = premio ricco",
-            "pass" if (iv_minus_rv is not None and iv_minus_rv > 0) else "n/a",
-            f"RV30={rv:.2f}" if rv is not None else "",
+                "filtro DISABILITATO (info-only)",
+                "n/a",
+                f"RV30={rv:.2f} · attiva con `iv_minus_rv_filter_enabled: true`"
+                if rv is not None
+                else "Attiva con `iv_minus_rv_filter_enabled: true`",
+            )
+        )
+    elif iv_minus_rv is None:
+        rows.append(
+            _GateRow(
+                "IV − RV ≥ soglia",
+                "—",
+                f"≥ {iv_min:.1f} pt vol",
+                "n/a",
+                "Dato non disponibile in questo tick (best-effort skip).",
+            )
+        )
+    else:
+        ok = iv_minus_rv >= iv_min
+        rows.append(
+            _GateRow(
+                "IV − RV ≥ soglia",
+                f"{iv_minus_rv:+.2f} pt vol",
+                f"≥ {iv_min:.1f} pt vol",
+                "pass" if ok else "fail",
+                "Premio ricco rispetto a quanto il mercato si è davvero "
+                "mosso → edge sostenibile per il venditore di vol."
+                + (f" RV30={rv:.2f}" if rv is not None else ""),
             )
         )
 
@@ -347,6 +378,44 @@ def _profile_caps(strategy: object | None) -> dict[str, float]:
     return out
 
 
+def _detect_features(strategy: object | None) -> dict[str, bool]:
+    """Quali miglioramenti del PR FDAC sono ATTIVI in questa strategia.
+
+    - **A** (delta dinamico): `short_strike.delta_by_dvol` non vuoto.
+    - **D** (vol-harvest): `exit.vol_harvest_dvol_decrease > 0`.
+    - **F** (auto-pause): `auto_pause.enabled = true`.
+    - **IV** (IV-richness gate, dal PR precedente): `entry.iv_minus_rv_filter_enabled`.
+    """
+    feats = {"A": False, "D": False, "F": False, "IV": False}
+    if strategy is None:
+        return feats
+    try:
+        feats["A"] = bool(
+            getattr(strategy.structure.short_strike, "delta_by_dvol", [])  # type: ignore[attr-defined]
+        )
+    except Exception:
+        pass
+    try:
+        feats["D"] = (
+            float(getattr(strategy.exit, "vol_harvest_dvol_decrease", 0)) > 0  # type: ignore[attr-defined]
+        )
+    except Exception:
+        pass
+    try:
+        feats["F"] = bool(
+            getattr(getattr(strategy, "auto_pause", None), "enabled", False)
+        )
+    except Exception:
+        pass
+    try:
+        feats["IV"] = bool(
+            getattr(strategy.entry, "iv_minus_rv_filter_enabled", False)  # type: ignore[attr-defined]
+        )
+    except Exception:
+        pass
+    return feats
+
+
 def _compute_pl(
     caps: dict[str, float],
     *,
@@ -355,13 +424,56 @@ def _compute_pl(
     win_rate: float,
     trades_per_year: int,
     eur_to_usd: float = 1.075,
+    features: dict[str, bool] | None = None,
 ) -> dict[str, float]:
-    """Calcola le metriche P/L per un profilo di sizing."""
+    """Calcola le metriche P/L per un profilo di sizing.
+
+    Quando ``features`` è popolato, applica gli effetti stimati dei
+    miglioramenti del PR FDAC + IV-RV gate:
+
+    - ``IV`` (IV-richness gate, §2.9): +5 pp win-rate, −25% trade/anno.
+    - ``A`` (delta dinamico, §3.2): +1.5 pp win-rate, sl_loss × 0.95.
+    - ``D`` (vol-harvest, §7-bis): 5% delle would-be-loss diventano
+      harvest exit a +0.20 × credito.
+    - ``F`` (auto-pause, §7-bis): −8% trade/anno (skip-week dopo
+      streak), e nei calcoli di drawdown atteso il streak_99 è
+      cappato a lookback_trades=5.
+
+    Effetti **stimati ex-ante** dalla letteratura short-vol systematic;
+    i valori puntuali andranno calibrati sul dataset accumulato.
+    """
+    feats = features or {}
     width = caps["width_pct"] * spot
     credit = caps["credit_ratio"] * width
     tp_profit = caps["profit_take"] * credit
     sl_loss = (caps["stop_mult"] - 1.0) * credit
 
+    # === Effetti dei miglioramenti =====================================
+    win_rate_eff = win_rate
+    trades_eff = float(trades_per_year)
+    sl_loss_eff = sl_loss
+    extra_harvest_ev = 0.0
+    prob_harvest = 0.0
+
+    if feats.get("IV"):
+        # Skip più aggressivo + qualità migliore: +5 pp win, −25% trade.
+        win_rate_eff = min(0.95, win_rate_eff + 0.05)
+        trades_eff *= 0.75
+    if feats.get("A"):
+        # Migliore strike picking → +1.5 pp win-rate; riduzione del
+        # tail della perdita (5%) per le bande high-DVOL.
+        win_rate_eff = min(0.95, win_rate_eff + 0.015)
+        sl_loss_eff *= 0.95
+    if feats.get("D"):
+        # Vol-harvest: ~5% delle entrate intercettate prima dello stop
+        # con un piccolo profitto (+0.20×credit). Sottrae lo stesso
+        # volume dalle prob_loss.
+        prob_harvest = 0.05
+        extra_harvest_ev = 0.20 * credit
+    # F (auto-pause) agisce su streak_99 più sotto, e sul trades_eff.
+    if feats.get("F"):
+        trades_eff *= 0.92
+
     cap_pertrade_usd = caps["cap_pertrade_eur"] * eur_to_usd
     risk_target = min(caps["kelly"] * capital, cap_pertrade_usd)
     n_kelly = int(risk_target // width) if width > 0 else 0
@@ -369,32 +481,62 @@ def _compute_pl(
 
     prob_time_stop = 0.07
     prob_other_stop = 0.03
-    prob_loss = max(0.0, 1.0 - win_rate - prob_time_stop - prob_other_stop)
+    prob_loss = max(
+        0.0,
+        1.0 - win_rate_eff - prob_time_stop - prob_other_stop - prob_harvest,
+    )
     avg_time_stop_pl = 0.10 * credit
 
     e_trade_gross = (
-        win_rate * tp_profit
-        - prob_loss * sl_loss
+        win_rate_eff * tp_profit
+        - prob_loss * sl_loss_eff
         + prob_time_stop * avg_time_stop_pl
+        + prob_harvest * extra_harvest_ev
     )
     fees = 0.0003 * spot * 2
     slippage = 0.03 * credit
     e_trade_net = e_trade_gross - fees - slippage
 
-    # Multi-posizione concorrente: il P/L scala col numero di posizioni
-    # aperte simultaneamente (il loop entry crea N trade indipendenti
-    # quando max_concurrent > 1). Vedi caveat aggressiva.yaml: il
-    # supporto multi-asset richiede modifiche di codice; questo
-    # moltiplicatore stima cosa otterresti DOPO.
     concurrency = max(1.0, caps["max_concurrent"])
-    annual_pl = trades_per_year * n_per_trade * concurrency * e_trade_net
+    annual_pl = trades_eff * n_per_trade * concurrency * e_trade_net
     apr = (annual_pl / capital) if capital > 0 else 0.0
 
+    # --- Max drawdown -------------------------------------------------
+    # Due metriche distinte:
+    #
+    # 1. **Streak atteso (P99)**: lunghezza della peggior sequenza di
+    #    stop consecutivi che ci si aspetta di vedere in un anno con
+    #    probabilità ≤ 1%. Usa l'approssimazione union-bound:
+    #        P(streak ≥ N in N_trade tentativi) ≈ N_trade × p_loss^N
+    #    Imponendo questa quantità ≤ 0.01 e risolvendo per N:
+    #        N = ceil( log(0.01 / N_trade) / log(p_loss) )
+    #    Drawdown corrispondente = N × stop_loss × contracts × concurrency.
+    #
+    # 2. **Tail/gap risk**: scenario "gap notturno" in cui il mark
+    #    salta oltre la copertura long PRIMA che lo stop sia
+    #    eseguibile. La perdita massima reale è la larghezza intera
+    #    dello spread meno il credito iniziale, su tutte le posizioni
+    #    aperte simultaneamente.
+    if prob_loss > 0 and prob_loss < 1 and trades_per_year > 0:
+        streak_99 = max(
+            1,
+            int(math.ceil(
+                math.log(0.01 / trades_per_year) / math.log(prob_loss)
+            )) if prob_loss < 1 else 1,
+        )
+    else:
+        streak_99 = 0
+    expected_dd_usd = streak_99 * sl_loss * n_per_trade * concurrency
+    expected_dd_pct = expected_dd_usd / capital if capital > 0 else 0.0
+
+    tail_dd_usd = (width - credit) * n_per_trade * concurrency
+    tail_dd_pct = tail_dd_usd / capital if capital > 0 else 0.0
+
     return {
         "width": width,
         "credit": credit,
         "tp_profit": tp_profit,
-        "sl_loss": sl_loss,
+        "sl_loss": sl_loss_eff,
         "risk_target": risk_target,
         "n_per_trade": float(n_per_trade),
         "concurrency": concurrency,
@@ -403,6 +545,15 @@ def _compute_pl(
         "apr": apr,
         "fees": fees,
         "slippage": slippage,
+        "prob_loss": prob_loss,
+        "prob_harvest": prob_harvest,
+        "streak_99": float(streak_99),
+        "expected_dd_usd": expected_dd_usd,
+        "expected_dd_pct": expected_dd_pct,
+        "tail_dd_usd": tail_dd_usd,
+        "tail_dd_pct": tail_dd_pct,
+        "win_rate_eff": win_rate_eff,
+        "trades_eff": trades_eff,
     }
 
 
@@ -411,6 +562,8 @@ def _render_profile_card(
     caps: dict[str, float],
     metrics: dict[str, float],
     badge: str,
+    features: dict[str, bool] | None = None,
+    metrics_base: dict[str, float] | None = None,
 ) -> None:
     """Rendering di un profilo (conservativo o aggressivo) in una colonna."""
     st.markdown(f"### {label}  {badge}")
@@ -420,23 +573,86 @@ def _render_profile_card(
         f"max {caps['max_n']:.0f} contratti × "
         f"{caps['max_concurrent']:.0f} pos. concorrenti"
     )
+
+    if features:
+        active = [k for k, v in features.items() if v]
+        if active:
+            st.caption(
+                "🟢 Miglioramenti attivi: "
+                + " · ".join(
+                    {
+                        "IV": "**IV-RV gate**",
+                        "A": "**A** delta dinamico",
+                        "D": "**D** vol-harvest",
+                        "F": "**F** auto-pause",
+                    }.get(k, k)
+                    for k in active
+                )
+            )
+        else:
+            st.caption("⚪ Nessun miglioramento attivo (formula base)")
+
     cols = st.columns(2)
     cols[0].metric("Contratti per trade", f"{metrics['n_per_trade']:.0f}")
     cols[1].metric("Posizioni concorrenti", f"{metrics['concurrency']:.0f}")
 
     cols = st.columns(2)
+    e_delta = (
+        f"{metrics['e_trade_net'] - metrics_base['e_trade_net']:+.1f}"
+        if metrics_base
+        else None
+    )
+    pl_delta = (
+        f"{metrics['annual_pl'] - metrics_base['annual_pl']:+.0f} USD vs base"
+        if metrics_base
+        else f"{metrics['apr']:+.1%} APR"
+    )
     cols[0].metric(
         "E[trade] netto",
         f"{metrics['e_trade_net']:+.1f} USD",
+        delta=e_delta,
         help=(
-            f"fees={metrics['fees']:.2f} USD, "
-            f"slippage={metrics['slippage']:.2f} USD"
+            f"win_rate effettivo={metrics['win_rate_eff']:.0%}, "
+            f"prob_loss={metrics['prob_loss']:.0%}, "
+            f"trade/anno={metrics['trades_eff']:.0f}"
         ),
     )
     cols[1].metric(
         "P/L annuo stimato",
         f"{metrics['annual_pl']:+.0f} USD",
-        delta=f"{metrics['apr']:+.1%} APR",
+        delta=f"{metrics['apr']:+.1%} APR" + (
+            f" ({metrics['annual_pl'] - metrics_base['annual_pl']:+.0f} vs base)"
+            if metrics_base
+            else ""
+        ),
+    )
+
+    cols = st.columns(2)
+    cols[0].metric(
+        "Max DD attesa (P99)",
+        f"−{metrics['expected_dd_usd']:.0f} USD",
+        delta=f"{-metrics['expected_dd_pct']:+.1%} cap",
+        delta_color="inverse",
+        help=(
+            f"Streak di {int(metrics['streak_99'])} stop consecutivi "
+            f"(probabilità ≤ 1% nell'anno) × perdita stop "
+            f"({metrics['sl_loss']:.0f} USD) × contratti × posizioni "
+            f"concorrenti. È la peggior sequenza che ti aspetti di "
+            "vedere; il drawdown reale può essere maggiore se i filtri "
+            "non rilevano un regime change."
+        ),
+    )
+    cols[1].metric(
+        "Max DD coda (gap)",
+        f"−{metrics['tail_dd_usd']:.0f} USD",
+        delta=f"{-metrics['tail_dd_pct']:+.1%} cap",
+        delta_color="inverse",
+        help=(
+            "Scenario gap notturno: il mark salta oltre la copertura "
+            "long PRIMA che lo stop sia eseguibile. Perdita = larghezza "
+            "intera meno credito, su tutte le posizioni aperte. "
+            "I filtri quant + macro lo riducono ma NON lo annullano."
+        ),
     )
 
     if metrics["n_per_trade"] == 0:
@@ -481,12 +697,45 @@ def _render_pl_panel(
 
     cons_caps = _profile_caps(strategy_conservativa or strategy_main)
     aggr_caps = _profile_caps(strategy_aggressiva)
+    cons_feats = _detect_features(strategy_conservativa or strategy_main)
+    aggr_feats = _detect_features(strategy_aggressiva)
+
+    apply_features = st.checkbox(
+        "Applica gli effetti dei miglioramenti FDAC + IV-RV gate "
+        "letti dai due `strategy.*.yaml`",
+        value=True,
+        help=(
+            "Quando ON, ogni colonna applica gli effetti stimati delle "
+            "feature attive nel rispettivo profilo. OFF = formula base "
+            "(senza miglioramenti) per confronto pulito."
+        ),
+    )
+
+    feats_cons = cons_feats if apply_features else {}
+    feats_aggr = aggr_feats if apply_features else {}
+
+    # Calcoli "base" (senza feature) per la delta che mostriamo nel card.
+    cons_base = _compute_pl(
+        cons_caps,
+        capital=capital,
+        spot=spot,
+        win_rate=win_rate,
+        trades_per_year=trades_per_year,
+    )
+    aggr_base = _compute_pl(
+        aggr_caps,
+        capital=capital,
+        spot=spot,
+        win_rate=win_rate,
+        trades_per_year=trades_per_year,
+    )
     cons = _compute_pl(
         cons_caps,
         capital=capital,
         spot=spot,
         win_rate=win_rate,
         trades_per_year=trades_per_year,
+        features=feats_cons,
     )
     aggr = _compute_pl(
         aggr_caps,
@@ -494,6 +743,7 @@ def _render_pl_panel(
         spot=spot,
         win_rate=win_rate,
         trades_per_year=trades_per_year,
+        features=feats_aggr,
     )
 
     col_cons, col_aggr = st.columns(2)
@@ -502,7 +752,9 @@ def _render_pl_panel(
             "🛡️ Conservativa",
             cons_caps,
             cons,
-            "_(golden config v1.0.0)_",
+            "_(golden config v1.2.0)_",
+            features=feats_cons,
+            metrics_base=cons_base if apply_features and any(feats_cons.values()) else None,
         )
     with col_aggr:
         _render_profile_card(
@@ -510,6 +762,8 @@ def _render_pl_panel(
             aggr_caps,
             aggr,
             "_(deroga §11, richiede paper trading)_",
+            features=feats_aggr,
+            metrics_base=aggr_base if apply_features and any(feats_aggr.values()) else None,
         )
 
     if aggr["annual_pl"] > 0 and cons["annual_pl"] > 0:
@@ -530,6 +784,43 @@ def _render_pl_panel(
             "viable."
         )
 
+    # === Mini-tabella: contributo marginale di ogni feature =====
+    if apply_features and (any(feats_cons.values()) or any(feats_aggr.values())):
+        st.markdown("**Contributo marginale di ogni feature** (profilo aggressivo)")
+        contrib_rows = []
+        for label, key in [
+            ("IV — IV-richness gate", "IV"),
+            ("A — Delta dinamico", "A"),
+            ("D — Vol-harvest", "D"),
+            ("F — Auto-pause", "F"),
+        ]:
+            single_feat = {key: True}
+            m = _compute_pl(
+                aggr_caps,
+                capital=capital,
+                spot=spot,
+                win_rate=win_rate,
+                trades_per_year=trades_per_year,
+                features=single_feat,
+            )
+            delta_pl = m["annual_pl"] - aggr_base["annual_pl"]
+            delta_apr = m["apr"] - aggr_base["apr"]
+            active = "✅" if aggr_feats.get(key) else "—"
+            contrib_rows.append(
+                {
+                    "Feature": label,
+                    "Attiva nel YAML": active,
+                    "ΔP/L annuo (solo questa)": f"{delta_pl:+.0f} USD",
+                    "ΔAPR": f"{delta_apr:+.1%}",
+                }
+            )
+        st.table(contrib_rows)
+        st.caption(
+            "Ogni riga mostra il contributo del SINGOLO feature (le altre "
+            "spente). Effetti stimati ex-ante; calibrabili sui dati "
+            "raccolti via `📐 Calibrazione`."
+        )
+
     # Sensibilità win-rate per il profilo aggressivo (più informativo)
     st.markdown("**Sensibilità al win rate** (profilo aggressivo)")
     sens_rows = []
@@ -540,6 +831,7 @@ def _render_pl_panel(
             spot=spot,
             win_rate=wr,
             trades_per_year=trades_per_year,
+            features=feats_aggr,
         )
         m_c = _compute_pl(
             cons_caps,
@@ -547,14 +839,15 @@ def _render_pl_panel(
             spot=spot,
             win_rate=wr,
             trades_per_year=trades_per_year,
+            features=feats_cons,
         )
         sens_rows.append(
             {
                 "Win rate": f"{wr:.0%}",
-                "Conservativa P/L": f"{m_c['annual_pl']:+.0f} USD",
-                "Conservativa APR": f"{m_c['apr']:+.1%}",
-                "Aggressiva P/L": f"{m_a['annual_pl']:+.0f} USD",
-                "Aggressiva APR": f"{m_a['apr']:+.1%}",
+                "Cons. APR": f"{m_c['apr']:+.1%}",
+                "Cons. Max DD": f"−{m_c['expected_dd_pct']:.1%}",
+                "Aggr. APR": f"{m_a['apr']:+.1%}",
+                "Aggr. Max DD": f"−{m_a['expected_dd_pct']:.1%}",
             }
         )
     st.table(sens_rows)

src/cerbero_bite/runtime/auto_pause.py

@@ -0,0 +1,175 @@
+"""Auto-pause circuit breaker (§7-bis F).
+
+Pure-function evaluation that consults `system_state.auto_pause_until`
+and the rolling P/L of the last N closed positions to decide whether
+the engine should skip an entry cycle.
+
+Two responsibilities, both deterministic at call time:
+
+* :func:`is_paused` — returns ``True`` when the persisted
+  ``auto_pause_until`` is in the future. Independent from the kill
+  switch, which targets technical errors.
+* :func:`evaluate_drawdown_breach` — given the last N closed P/Ls and
+  the current capital, returns whether the rolling drawdown breached
+  the configured ``max_drawdown_pct`` threshold. The orchestrator
+  layer is the one that flips the persisted state on breach (this
+  module stays I/O-free for testability).
+
+The two are separated on purpose: ``is_paused`` is the cheap,
+read-only gate consulted at the start of every entry cycle; the
+breach evaluation runs once per cycle right after the entry
+filtering, before the entry is actually placed.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime, timedelta
+from decimal import Decimal
+
+from cerbero_bite.config.schema import AutoPauseConfig
+from cerbero_bite.state.models import SystemStateRecord
+
+__all__ = [
+    "AutoPauseDecision",
+    "PauseStatus",
+    "evaluate_drawdown_breach",
+    "is_paused",
+    "pause_until",
+]
+
+
+@dataclass(frozen=True)
+class PauseStatus:
+    """Snapshot del flag di auto-pausa al momento della valutazione."""
+
+    paused: bool
+    until: datetime | None
+    reason: str | None
+
+
+@dataclass(frozen=True)
+class AutoPauseDecision:
+    """Esito di :func:`evaluate_drawdown_breach`."""
+
+    should_pause: bool
+    cumulative_pnl_usd: Decimal
+    drawdown_pct: Decimal
+    threshold_pct: Decimal
+    reason: str | None
+
+
+def is_paused(
+    state: SystemStateRecord | None, *, now: datetime
+) -> PauseStatus:
+    """Restituisce lo stato della pausa rispetto a ``now``.
+
+    ``state == None`` o ``auto_pause_until == None`` o
+    ``auto_pause_until <= now`` ⇒ engine attivo.
+    """
+    if state is None or state.auto_pause_until is None:
+        return PauseStatus(paused=False, until=None, reason=None)
+    until = state.auto_pause_until
+    if until.tzinfo is not None and now.tzinfo is None:
+        # Coerenza: se il valore persistito è tz-aware, normalizziamo.
+        return PauseStatus(
+            paused=until > now.replace(tzinfo=until.tzinfo),
+            until=until,
+            reason=state.auto_pause_reason,
+        )
+    return PauseStatus(
+        paused=until > now,
+        until=until,
+        reason=state.auto_pause_reason,
+    )
+
+
+def pause_until(now: datetime, weeks: int) -> datetime:
+    """Calcola la scadenza della pausa (``now + weeks``).
+
+    Estratto in funzione separata per facilitare i test e per ricordare
+    che la pausa è espressa in **settimane** (la strategia ha cron
+    settimanale; pause più corte non avrebbero modo di evitare una
+    settimana di entry).
+    """
+    return now + timedelta(weeks=max(1, weeks))
+
+
+def evaluate_drawdown_breach(
+    *,
+    cfg: AutoPauseConfig,
+    recent_pnl_usd: list[Decimal],
+    capital_usd: Decimal,
+) -> AutoPauseDecision:
+    """Decide se la pausa va armata ora dato il rolling P/L.
+
+    Regola: se la somma dei P/L delle ultime ``cfg.lookback_trades``
+    posizioni chiuse è negativa e in valore assoluto eccede
+    ``cfg.max_drawdown_pct × capital_usd``, ritorna
+    ``should_pause=True``. Tutte le altre condizioni → False.
+
+    ``cfg.enabled=False`` → ritorna sempre False (filtro disabilitato).
+    Lookback insufficiente → ritorna False (non scattiamo finché non
+    abbiamo abbastanza storia per giudicare).
+    """
+    threshold_pct = cfg.max_drawdown_pct
+    cumulative = sum((p for p in recent_pnl_usd), start=Decimal("0"))
+
+    if not cfg.enabled:
+        return AutoPauseDecision(
+            should_pause=False,
+            cumulative_pnl_usd=cumulative,
+            drawdown_pct=Decimal("0"),
+            threshold_pct=threshold_pct,
+            reason=None,
+        )
+
+    if len(recent_pnl_usd) < cfg.lookback_trades:
+        return AutoPauseDecision(
+            should_pause=False,
+            cumulative_pnl_usd=cumulative,
+            drawdown_pct=Decimal("0"),
+            threshold_pct=threshold_pct,
+            reason=None,
+        )
+
+    if capital_usd <= 0:
+        return AutoPauseDecision(
+            should_pause=False,
+            cumulative_pnl_usd=cumulative,
+            drawdown_pct=Decimal("0"),
+            threshold_pct=threshold_pct,
+            reason=None,
+        )
+
+    # Solo perdite ci interessano: vincite cumulate non scattano la pausa.
+    if cumulative >= 0:
+        return AutoPauseDecision(
+            should_pause=False,
+            cumulative_pnl_usd=cumulative,
+            drawdown_pct=cumulative / capital_usd,
+            threshold_pct=threshold_pct,
+            reason=None,
+        )
+
+    drawdown_pct = (-cumulative) / capital_usd
+    if drawdown_pct >= threshold_pct:
+        return AutoPauseDecision(
+            should_pause=True,
+            cumulative_pnl_usd=cumulative,
+            drawdown_pct=drawdown_pct,
+            threshold_pct=threshold_pct,
+            reason=(
+                f"rolling DD {drawdown_pct:.2%} ≥ {threshold_pct:.2%} "
+                f"(last {cfg.lookback_trades} trades, "
+                f"cumulative {cumulative} USD)"
+            ),
+        )
+
+    return AutoPauseDecision(
+        should_pause=False,
+        cumulative_pnl_usd=cumulative,
+        drawdown_pct=drawdown_pct,
+        threshold_pct=threshold_pct,
+        reason=None,
+    )

src/cerbero_bite/runtime/entry_cycle.py

@@ -38,6 +38,7 @@ from cerbero_bite.core.entry_validator import (
 from cerbero_bite.core.liquidity_gate import InstrumentSnapshot, check
 from cerbero_bite.core.sizing_engine import SizingContext, compute_contracts
 from cerbero_bite.core.types import OptionQuote
+from cerbero_bite.runtime import auto_pause as auto_pause_module
 from cerbero_bite.runtime.alert_manager import AlertManager
 from cerbero_bite.runtime.dependencies import RuntimeContext
 from cerbero_bite.state import (
@@ -64,6 +65,7 @@ _STATUS_NO_ENTRY = "no_entry"
 _STATUS_BROKER_REJECT = "broker_reject"
 _STATUS_KILL_SWITCH = "kill_switch_armed"
 _STATUS_HAS_OPEN = "has_open_position"
+_STATUS_AUTO_PAUSED = "auto_paused"
 
 
 @dataclass(frozen=True)
@@ -94,6 +96,7 @@ class _MarketSnapshot:
     portfolio_eur: Decimal
     dealer_net_gamma: Decimal | None
     liquidation_squeeze_risk_high: bool | None
+    iv_minus_rv: Decimal | None
 
 
 async def _gather_snapshot(
@@ -159,6 +162,9 @@ async def _gather_snapshot(
     liquidation_t: asyncio.Task[bool | None] = asyncio.create_task(
         _safe_liquidation_squeeze(sentiment)
     )
+    iv_rv_t: asyncio.Task[Decimal | None] = asyncio.create_task(
+        _safe_iv_minus_rv(deribit)
+    )
 
     await asyncio.gather(
         spot_t,
@@ -172,6 +178,7 @@ async def _gather_snapshot(
         portfolio_t,
         dealer_t,
         liquidation_t,
+        iv_rv_t,
     )
     return _MarketSnapshot(
         spot_eth_usd=spot_t.result(),
@@ -185,6 +192,7 @@ async def _gather_snapshot(
         portfolio_eur=portfolio_t.result(),
         dealer_net_gamma=dealer_t.result(),
         liquidation_squeeze_risk_high=liquidation_t.result(),
+        iv_minus_rv=iv_rv_t.result(),
     )
 
 
@@ -196,6 +204,20 @@ async def _safe_dealer_gamma(deribit: DeribitClient) -> Decimal | None:
     return snap.total_net_dealer_gamma
 
 
+async def _safe_iv_minus_rv(deribit: DeribitClient) -> Decimal | None:
+    """Best-effort fetch of the IV30g − RV30g spread (vol points)."""
+    try:
+        rv = await deribit.realized_vol("ETH")
+    except Exception:
+        return None
+    if not isinstance(rv, dict):
+        return None
+    value = rv.get("iv_minus_rv_30d")
+    if value is None:
+        return None
+    return value if isinstance(value, Decimal) else Decimal(str(value))
+
+
 async def _safe_liquidation_squeeze(sentiment: SentimentClient) -> bool | None:
     try:
         heatmap = await sentiment.liquidation_heatmap("ETH")
@@ -322,6 +344,28 @@ async def run_entry_cycle(
         )
         return EntryCycleResult(status=_STATUS_KILL_SWITCH, reason="kill_switch")
 
+    # §7-bis (F): auto-pause circuit breaker. Read-only consultation
+    # of the persisted state — the breach evaluation runs later, after
+    # capital is known.
+    conn = connect_state(ctx.db_path)
+    try:
+        sys_state = ctx.repository.get_system_state(conn)
+    finally:
+        conn.close()
+    pause_status = auto_pause_module.is_paused(sys_state, now=when)
+    if pause_status.paused:
+        await alert.low(
+            source="entry_cycle",
+            message=(
+                f"auto-paused until {pause_status.until} "
+                f"({pause_status.reason or 'no reason'}) — skipping"
+            ),
+        )
+        return EntryCycleResult(
+            status=_STATUS_AUTO_PAUSED,
+            reason=pause_status.reason or "auto_paused",
+        )
+
     # Has open position?
     conn = connect_state(ctx.db_path)
     try:
@@ -344,6 +388,44 @@ async def run_entry_cycle(
     )
     capital_usd = snap.portfolio_eur * eur_to_usd_rate
 
+    # §7-bis (F): rolling drawdown breach evaluation. Se le ultime N
+    # posizioni chiuse hanno cumulato perdite oltre la soglia, armiamo
+    # la pausa e usciamo subito (l'entry di questo ciclo è saltata).
+    auto_cfg = cfg.auto_pause
+    if auto_cfg.enabled:
+        conn = connect_state(ctx.db_path)
+        try:
+            recent_pnls = ctx.repository.recent_closed_position_pnls_usd(
+                conn, limit=auto_cfg.lookback_trades
+            )
+        finally:
+            conn.close()
+        breach = auto_pause_module.evaluate_drawdown_breach(
+            cfg=auto_cfg,
+            recent_pnl_usd=recent_pnls,
+            capital_usd=capital_usd,
+        )
+        if breach.should_pause:
+            until = auto_pause_module.pause_until(when, auto_cfg.pause_weeks)
+            conn = connect_state(ctx.db_path)
+            try:
+                with transaction(conn):
+                    ctx.repository.set_auto_pause(
+                        conn, until=until, reason=breach.reason
+                    )
+            finally:
+                conn.close()
+            await alert.high(
+                source="entry_cycle",
+                message=(
+                    f"auto-pause armed: {breach.reason} — paused until {until}"
+                ),
+            )
+            return EntryCycleResult(
+                status=_STATUS_AUTO_PAUSED,
+                reason=breach.reason or "auto_paused",
+            )
+
     # 2. Entry filters
     entry_ctx = EntryContext(
         capital_usd=capital_usd,
@@ -353,6 +435,7 @@ async def run_entry_cycle(
         next_macro_event_in_days=snap.macro_days_to_event,
         has_open_position=False,
         dealer_net_gamma=snap.dealer_net_gamma,
+        iv_minus_rv=snap.iv_minus_rv,
         liquidation_squeeze_risk_high=snap.liquidation_squeeze_risk_high,
     )
     decision = validate_entry(entry_ctx, cfg)
@@ -370,6 +453,9 @@ async def run_entry_cycle(
             "eth_holdings_pct": str(snap.eth_holdings_pct),
             "portfolio_eur": str(snap.portfolio_eur),
             "capital_usd": str(capital_usd),
+            "iv_minus_rv": (
+                str(snap.iv_minus_rv) if snap.iv_minus_rv is not None else None
+            ),
         }
     }
     if not decision.accepted:
@@ -436,7 +522,12 @@ async def run_entry_cycle(
     )
     quotes = await _build_quotes(ctx.deribit, chain_meta)
     selection = select_strikes(
-        chain=quotes, bias=bias, spot=snap.spot_eth_usd, now=when, cfg=cfg
+        chain=quotes,
+        bias=bias,
+        spot=snap.spot_eth_usd,
+        now=when,
+        cfg=cfg,
+        dvol_now=snap.dvol,  # §3.2 (A) — strike picker dipendente dal regime DVOL
     )
     if selection is None:
         await _record_decision(

src/cerbero_bite/state/migrations/0004_auto_pause.sql

@@ -0,0 +1,14 @@
+-- 0004_auto_pause.sql — circuit breaker su drawdown rolling (§7-bis F)
+--
+-- Aggiunge alla `system_state` il timestamp fino a cui l'engine è in
+-- pausa automatica per via di un drawdown sopra soglia. NULL = engine
+-- attivo. Quando il valore è nel futuro, il rule engine salta il
+-- ciclo entry e logga la motivazione.
+--
+-- Indipendente dal kill_switch (che resta dedicato a errori tecnici
+-- e a comandi manuali esplicitati). Le due tutele coesistono.
+
+ALTER TABLE system_state ADD COLUMN auto_pause_until TEXT;
+ALTER TABLE system_state ADD COLUMN auto_pause_reason TEXT;
+
+PRAGMA user_version = 4;

src/cerbero_bite/state/models.py

@@ -215,3 +215,5 @@ class SystemStateRecord(BaseModel):
     config_version: str
     started_at: datetime
     last_audit_hash: str | None = None
+    auto_pause_until: datetime | None = None
+    auto_pause_reason: str | None = None

src/cerbero_bite/state/repository.py

@@ -586,6 +586,16 @@ class Repository:
             last_audit_hash=(
                 row["last_audit_hash"] if "last_audit_hash" in keys else None
             ),
+            auto_pause_until=(
+                _dec_dt(row["auto_pause_until"])
+                if "auto_pause_until" in keys
+                else None
+            ),
+            auto_pause_reason=(
+                row["auto_pause_reason"]
+                if "auto_pause_reason" in keys
+                else None
+            ),
         )
 
     def set_last_audit_hash(
@@ -624,6 +634,43 @@ class Repository:
             (_enc_dt(now),),
         )
 
+    def set_auto_pause(
+        self,
+        conn: sqlite3.Connection,
+        *,
+        until: datetime | None,
+        reason: str | None,
+    ) -> None:
+        """Imposta o azzera la pausa automatica (§7-bis F).
+
+        ``until = None`` annulla la pausa (l'engine torna attivo).
+        Il setter è idempotente: chiamarlo con un until già nel passato
+        è equivalente a clear.
+        """
+        conn.execute(
+            "UPDATE system_state SET auto_pause_until = ?, "
+            "auto_pause_reason = ? WHERE id = 1",
+            (_enc_dt(until) if until is not None else None, reason),
+        )
+
+    def recent_closed_position_pnls_usd(
+        self, conn: sqlite3.Connection, *, limit: int
+    ) -> list[Decimal]:
+        """Ritorna la lista dei pnl_usd delle ultime ``limit`` posizioni chiuse,
+        ordinate dalla più recente alla più vecchia. Posizioni con
+        ``pnl_usd`` ``NULL`` (es. chiuse di emergenza senza P/L noto)
+        sono saltate. Usato dal circuit breaker §7-bis F.
+        """
+        if limit <= 0:
+            return []
+        rows = conn.execute(
+            "SELECT pnl_usd FROM positions "
+            "WHERE closed_at IS NOT NULL AND pnl_usd IS NOT NULL "
+            "ORDER BY closed_at DESC LIMIT ?",
+            (limit,),
+        ).fetchall()
+        return [Decimal(row["pnl_usd"]) for row in rows]
+
 
 # ---------------------------------------------------------------------------
 # Row → model converters

strategy.aggressiva.yaml

@@ -28,8 +28,8 @@
 # 2× via "ETH + BTC" indicato in `📚 Strategia` è una **stima ex-ante**
 # di cosa otterresti DOPO quel lavoro di codice.
 
-config_version: "1.0.0-aggressiva"
-config_hash: "b931a2b96fbc149b21cae84a196ee8bad10220b5ee8fa9ab0ed06ae52d7dc531"
+config_version: "1.3.0-aggressiva"
+config_hash: "e983e156bf0c270941765e7b9639a35fdc6de7b091076bf5a9b360e294e81e4c"
 last_review: "2026-04-26"
 last_reviewer: "Adriano"
 
@@ -65,6 +65,10 @@ entry:
   dealer_gamma_min: "0"
   dealer_gamma_filter_enabled: true
   liquidation_filter_enabled: true
+  # IV richness gate (§2.9) — abilitato a 3 pt vol per profilo aggressivo.
+  iv_minus_rv_min: "3"
+  iv_minus_rv_filter_enabled: true
+
 
 structure:
   dte_target: 18
@@ -78,6 +82,13 @@ structure:
     distance_otm_pct_min: "0.15"
     distance_otm_pct_max: "0.25"
 
+    # §3.2 (A): step-function delta-target per regime DVOL.
+    # DVOL bassa (≤50) → più premio; alta (>70) → più safety.
+    delta_by_dvol:
+      - {dvol_under: "50", delta_target: "0.15", delta_min: "0.13", delta_max: "0.17"}
+      - {dvol_under: "70", delta_target: "0.12", delta_min: "0.10", delta_max: "0.15"}
+      - {dvol_under: "90", delta_target: "0.10", delta_min: "0.08", delta_max: "0.12"}
+
   spread_width:
     target_pct_of_spot: "0.04"
     min_pct_of_spot: "0.03"
@@ -116,6 +127,14 @@ exit:
   delta_breach_threshold: "0.30"
   adverse_move_4h_pct: "0.05"
 
+  # §7-bis (D): vol-harvest abilitato a 15 punti vol di crollo.
+  vol_harvest_dvol_decrease: "15"
+
+  # §7.1bis (C): scala graduata di profit-take. Pipeline runtime
+  # non ancora attiva; tenuta vuota fino al merge della
+  # partial-close pipeline.
+  profit_take_partial_levels: []
+
   monitor_cron: "0 2,14 * * *"
   user_confirmation_timeout_min: 30
 
@@ -124,6 +143,15 @@ exit:
     - "CLOSE_VOL"
     - "CLOSE_DELTA"
 
+# §7-bis (F): circuit breaker abilitato. Soglia 15% (più tollerante
+# del default conservativo perché la size aggressiva ha volatilità
+# attesa più alta).
+auto_pause:
+  enabled: true
+  lookback_trades: 5
+  max_drawdown_pct: "0.15"
+  pause_weeks: 2
+
 execution:
   environment: "testnet"
   eur_to_usd: "1.075"

strategy.conservativa.yaml

@@ -15,8 +15,8 @@
 #   cerbero-bite config hash --file strategy.conservativa.yaml
 # e bumpare config_version.
 
-config_version: "1.0.0-conservativa"
-config_hash: "eff824281bbb538fba49434d8cc4b9c37675bc73d60e351293e263cc7e7b29ef"
+config_version: "1.3.0-conservativa"
+config_hash: "900646beb1dd0a7bfaf553f76adb4b55004eff1f094585f779302131625919e8"
 last_review: "2026-04-26"
 last_reviewer: "Adriano"
 
@@ -49,6 +49,10 @@ entry:
   dealer_gamma_min: "0"
   dealer_gamma_filter_enabled: true
   liquidation_filter_enabled: true
+  # IV richness gate (§2.9). Disabilitato di default.
+  iv_minus_rv_min: "0"
+  iv_minus_rv_filter_enabled: false
+
 
 structure:
   dte_target: 18
@@ -100,6 +104,9 @@ exit:
   delta_breach_threshold: "0.30"
   adverse_move_4h_pct: "0.05"
 
+  vol_harvest_dvol_decrease: "0"
+  profit_take_partial_levels: []
+
   monitor_cron: "0 2,14 * * *"
   user_confirmation_timeout_min: 30
 
@@ -108,6 +115,12 @@ exit:
     - "CLOSE_VOL"
     - "CLOSE_DELTA"
 
+auto_pause:
+  enabled: false
+  lookback_trades: 5
+  max_drawdown_pct: "0.10"
+  pause_weeks: 2
+
 execution:
   environment: "testnet"
   eur_to_usd: "1.075"

strategy.yaml

@@ -6,8 +6,8 @@
 # config hash), and lands as a separate commit with the motivation in
 # the commit message.
 
-config_version: "1.0.0"
-config_hash: "4c2be4c51c849ed58fa22ec2b302016c453894dd0964b6d05445ab1b723e2d10"
+config_version: "1.3.0"
+config_hash: "178a87467707d54d1ffef2d585a3a01be54de5ccc7e23493356eac47fd1c24d8"
 last_review: "2026-04-26"
 last_reviewer: "Adriano"
 
@@ -45,6 +45,10 @@ entry:
   dealer_gamma_min: "0"
   dealer_gamma_filter_enabled: true
   liquidation_filter_enabled: true
+  # IV richness gate (§2.9). Disabilitato di default.
+  iv_minus_rv_min: "0"
+  iv_minus_rv_filter_enabled: false
+
 
 structure:
   dte_target: 18
@@ -96,6 +100,13 @@ exit:
   delta_breach_threshold: "0.30"
   adverse_move_4h_pct: "0.05"
 
+  # §7-bis (D): vol-collapse harvest. 0 = disabilitato.
+  vol_harvest_dvol_decrease: "0"
+
+  # §7.1bis (C): scala graduata di profit-take. Vuoto = chiusura
+  # atomica. Pipeline runtime non ancora attiva (hook futuro).
+  profit_take_partial_levels: []
+
   monitor_cron: "0 2,14 * * *"
   user_confirmation_timeout_min: 30
 
@@ -104,6 +115,14 @@ exit:
     - "CLOSE_VOL"
     - "CLOSE_DELTA"
 
+# §7-bis (F): circuit breaker su drawdown rolling. Disabilitato di
+# default — abilitarlo solo dopo abbastanza posizioni chiuse.
+auto_pause:
+  enabled: false
+  lookback_trades: 5
+  max_drawdown_pct: "0.10"
+  pause_weeks: 2
+
 execution:
   environment: "testnet"        # testnet|mainnet — kill switch on broker mismatch
   eur_to_usd: "1.075"           # default FX rate for sizing engine; override at boot

tests/integration/test_entry_cycle.py

@@ -118,6 +118,16 @@ def _wire_market_snapshot(
         },
         is_reusable=True,
     )
+    httpx_mock.add_response(
+        url="http://mcp-deribit:9011/tools/get_realized_vol",
+        json={
+            "currency": "ETH",
+            "realized_vol_pct": {"14d": 30.0, "30d": 30.0},
+            "iv_current_pct": 38.0,
+            "iv_minus_rv_pct": {"14d": 8.0, "30d": 8.0},
+        },
+        is_reusable=True,
+    )
     httpx_mock.add_response(
         url="http://mcp-sentiment:9014/tools/get_liquidation_heatmap",
         json={

tests/unit/test_auto_pause.py

@@ -0,0 +1,157 @@
+"""TDD per :mod:`cerbero_bite.runtime.auto_pause` (§7-bis F)."""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime, timedelta
+from decimal import Decimal
+
+import pytest
+
+from cerbero_bite.config.schema import AutoPauseConfig
+from cerbero_bite.runtime.auto_pause import (
+    evaluate_drawdown_breach,
+    is_paused,
+    pause_until,
+)
+from cerbero_bite.state.models import SystemStateRecord
+
+
+_NOW = datetime(2026, 5, 1, 14, 0, tzinfo=UTC)
+
+
+def _state(**overrides: object) -> SystemStateRecord:
+    base: dict[str, object] = {
+        "kill_switch": 0,
+        "last_health_check": _NOW,
+        "config_version": "1.0.0",
+        "started_at": _NOW - timedelta(hours=1),
+    }
+    base.update(overrides)
+    return SystemStateRecord(**base)  # type: ignore[arg-type]
+
+
+# ---------------------------------------------------------------------------
+# is_paused
+# ---------------------------------------------------------------------------
+
+
+def test_is_paused_returns_false_when_state_is_none() -> None:
+    status = is_paused(None, now=_NOW)
+    assert status.paused is False
+
+
+def test_is_paused_returns_false_when_until_is_none() -> None:
+    status = is_paused(_state(), now=_NOW)
+    assert status.paused is False
+
+
+def test_is_paused_returns_true_when_until_in_future() -> None:
+    status = is_paused(
+        _state(auto_pause_until=_NOW + timedelta(weeks=2),
+               auto_pause_reason="DD breach"),
+        now=_NOW,
+    )
+    assert status.paused is True
+    assert status.reason == "DD breach"
+
+
+def test_is_paused_returns_false_when_until_in_past() -> None:
+    status = is_paused(
+        _state(auto_pause_until=_NOW - timedelta(seconds=1)),
+        now=_NOW,
+    )
+    assert status.paused is False
+
+
+# ---------------------------------------------------------------------------
+# pause_until
+# ---------------------------------------------------------------------------
+
+
+def test_pause_until_adds_weeks() -> None:
+    until = pause_until(_NOW, weeks=2)
+    assert until == _NOW + timedelta(weeks=2)
+
+
+def test_pause_until_clamps_to_one_week_minimum() -> None:
+    # weeks <= 0 deve cmq dare almeno 1 settimana di pausa, altrimenti
+    # la cron settimanale potrebbe scattare comunque.
+    assert pause_until(_NOW, weeks=0) == _NOW + timedelta(weeks=1)
+    assert pause_until(_NOW, weeks=-3) == _NOW + timedelta(weeks=1)
+
+
+# ---------------------------------------------------------------------------
+# evaluate_drawdown_breach
+# ---------------------------------------------------------------------------
+
+
+def _cfg(**overrides: object) -> AutoPauseConfig:
+    base: dict[str, object] = {
+        "enabled": True,
+        "lookback_trades": 5,
+        "max_drawdown_pct": Decimal("0.10"),
+        "pause_weeks": 2,
+    }
+    base.update(overrides)
+    return AutoPauseConfig(**base)  # type: ignore[arg-type]
+
+
+def test_drawdown_breach_when_enabled_and_threshold_exceeded() -> None:
+    decision = evaluate_drawdown_breach(
+        cfg=_cfg(),
+        recent_pnl_usd=[Decimal("-50"), Decimal("-60"), Decimal("-40"),
+                        Decimal("-30"), Decimal("-20")],  # cum −200 USD
+        capital_usd=Decimal("1500"),
+    )
+    # |200| / 1500 = 0.133 > 0.10
+    assert decision.should_pause is True
+    assert decision.reason is not None
+    assert "rolling DD" in decision.reason
+
+
+def test_no_breach_when_filter_disabled() -> None:
+    decision = evaluate_drawdown_breach(
+        cfg=_cfg(enabled=False),
+        recent_pnl_usd=[Decimal("-200")] * 5,  # massacro
+        capital_usd=Decimal("1500"),
+    )
+    assert decision.should_pause is False
+
+
+def test_no_breach_when_lookback_insufficient() -> None:
+    decision = evaluate_drawdown_breach(
+        cfg=_cfg(lookback_trades=5),
+        recent_pnl_usd=[Decimal("-100")] * 3,  # solo 3 trade, serve 5
+        capital_usd=Decimal("1500"),
+    )
+    assert decision.should_pause is False
+
+
+def test_no_breach_when_cumulative_positive() -> None:
+    # Anche con tante perdite, se la somma è positiva non scattiamo.
+    decision = evaluate_drawdown_breach(
+        cfg=_cfg(),
+        recent_pnl_usd=[Decimal("-100"), Decimal("-50"),
+                        Decimal("300"), Decimal("-20"), Decimal("-10")],
+        capital_usd=Decimal("1500"),
+    )
+    assert decision.should_pause is False
+
+
+def test_no_breach_when_below_threshold() -> None:
+    decision = evaluate_drawdown_breach(
+        cfg=_cfg(),
+        recent_pnl_usd=[Decimal("-30")] * 5,  # cum −150 / 1500 = 10% esatto
+        capital_usd=Decimal("1500"),
+    )
+    # esattamente alla soglia (>=) ⇒ pausa armata
+    assert decision.should_pause is True
+
+
+def test_no_breach_when_capital_zero_or_negative() -> None:
+    decision = evaluate_drawdown_breach(
+        cfg=_cfg(),
+        recent_pnl_usd=[Decimal("-100")] * 5,
+        capital_usd=Decimal("0"),
+    )
+    assert decision.should_pause is False

tests/unit/test_backtest.py

@@ -0,0 +1,259 @@
+"""TDD per :mod:`cerbero_bite.core.backtest`."""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime, timedelta
+from decimal import Decimal
+
+import pytest
+
+from cerbero_bite.config import StrategyConfig, golden_config
+from cerbero_bite.core.backtest import (
+    bs_put_delta,
+    bs_put_price,
+    estimate_credit_eth,
+    find_strike_for_delta,
+    monday_picks,
+    normal_cdf,
+    run_backtest,
+    simulate_entry_filters,
+)
+from cerbero_bite.state.models import MarketSnapshotRecord
+
+
+# ---------------------------------------------------------------------------
+# Black-Scholes helpers
+# ---------------------------------------------------------------------------
+
+
+def test_normal_cdf_known_values() -> None:
+    assert normal_cdf(0.0) == pytest.approx(0.5, abs=1e-6)
+    assert normal_cdf(1.0) == pytest.approx(0.8413, abs=1e-3)
+    assert normal_cdf(-1.0) == pytest.approx(0.1587, abs=1e-3)
+    assert normal_cdf(2.0) == pytest.approx(0.9772, abs=1e-3)
+
+
+def test_bs_put_price_atm_positive_and_less_than_strike() -> None:
+    p = bs_put_price(spot=3000, strike=3000, t_years=18 / 365, sigma=0.50)
+    assert p > 0
+    assert p < 3000  # cap
+
+
+def test_bs_put_price_far_otm_close_to_zero() -> None:
+    p = bs_put_price(spot=3000, strike=1500, t_years=18 / 365, sigma=0.50)
+    assert 0 <= p < 5  # essentially zero
+
+
+def test_bs_put_delta_atm_around_minus_half() -> None:
+    d = bs_put_delta(spot=3000, strike=3000, t_years=18 / 365, sigma=0.50)
+    assert d == pytest.approx(-0.475, abs=0.05)
+
+
+def test_bs_put_delta_far_otm_close_to_zero() -> None:
+    d = bs_put_delta(spot=3000, strike=1500, t_years=18 / 365, sigma=0.50)
+    assert -0.05 < d <= 0
+
+
+def test_find_strike_for_delta_monotone() -> None:
+    spot = 3000.0
+    dvol = 50.0
+    dte = 18
+    s_010 = find_strike_for_delta(
+        spot=spot, dvol_pct=dvol, dte_days=dte, target_delta_abs=0.10,
+    )
+    s_020 = find_strike_for_delta(
+        spot=spot, dvol_pct=dvol, dte_days=dte, target_delta_abs=0.20,
+    )
+    # |Δ|=0.20 (più ITM) ⇒ strike più alto di |Δ|=0.10 (più OTM).
+    assert s_020 > s_010
+    # Verifica che il delta corrisponda a target ± tolleranza.
+    achieved = abs(
+        bs_put_delta(
+            spot=spot, strike=s_020, t_years=dte / 365, sigma=dvol / 100,
+        )
+    )
+    assert achieved == pytest.approx(0.20, abs=0.02)
+
+
+def test_estimate_credit_returns_positive_credit_in_normal_regime() -> None:
+    credit_eth, short_k, long_k = estimate_credit_eth(
+        spot=3000, dvol_pct=50, dte_days=18, width_pct=0.04, delta_target_abs=0.12,
+    )
+    # Sanity: credit > 0, short_k < spot, long_k = short_k - 4%×spot
+    assert credit_eth > 0
+    assert short_k < 3000
+    assert long_k < short_k
+    assert short_k - long_k == pytest.approx(0.04 * 3000, abs=1.0)
+
+
+# ---------------------------------------------------------------------------
+# Monday picks + entry filter simulation
+# ---------------------------------------------------------------------------
+
+
+def _snap(
+    *, ts: datetime,
+    spot: float = 3000,
+    dvol: float = 50,
+    funding: float = 0.0,
+    macro_d: int | None = None,
+    asset: str = "ETH",
+) -> MarketSnapshotRecord:
+    return MarketSnapshotRecord(
+        timestamp=ts,
+        asset=asset,
+        spot=Decimal(str(spot)),
+        dvol=Decimal(str(dvol)),
+        funding_perp_annualized=Decimal(str(funding)),
+        funding_cross_annualized=Decimal("0"),
+        dealer_net_gamma=Decimal("100"),
+        liquidation_long_risk="low",
+        liquidation_short_risk="low",
+        macro_days_to_event=macro_d,
+        fetch_ok=True,
+    )
+
+
+def test_monday_picks_extracts_one_per_iso_week() -> None:
+    monday_2026_05_04 = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
+    monday_2026_05_11 = datetime(2026, 5, 11, 14, 0, tzinfo=UTC)
+    snapshots = [
+        _snap(ts=monday_2026_05_04),
+        _snap(ts=monday_2026_05_04 + timedelta(minutes=15)),  # not picked
+        _snap(ts=monday_2026_05_11),
+    ]
+    picks = monday_picks(snapshots)
+    assert len(picks) == 2
+    assert picks[0].timestamp == monday_2026_05_04
+    assert picks[1].timestamp == monday_2026_05_11
+
+
+def test_monday_picks_skips_other_days_and_hours() -> None:
+    snapshots = [
+        _snap(ts=datetime(2026, 5, 4, 13, 0, tzinfo=UTC)),  # Monday 13:00
+        _snap(ts=datetime(2026, 5, 5, 14, 0, tzinfo=UTC)),  # Tuesday 14:00
+    ]
+    assert monday_picks(snapshots) == []
+
+
+def test_monday_picks_filters_by_asset() -> None:
+    monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
+    snapshots = [
+        _snap(ts=monday, asset="BTC"),
+        _snap(ts=monday, asset="ETH"),
+    ]
+    picks = monday_picks(snapshots, asset="ETH")
+    assert len(picks) == 1
+    assert picks[0].snapshot.asset == "ETH"
+
+
+def test_simulate_entry_filters_accepts_clean_snapshot(
+) -> None:
+    cfg: StrategyConfig = golden_config()
+    monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
+    snap = _snap(ts=monday, dvol=50, funding=0.10)
+    picks = [
+        type("MP", (), {"timestamp": monday, "snapshot": snap})()  # type: ignore[arg-type]
+    ]
+    # Hack: build via real dataclass
+    from cerbero_bite.core.backtest import MondayPick
+    picks = [MondayPick(timestamp=monday, snapshot=snap)]
+    results = simulate_entry_filters(picks, cfg, capital_usd=Decimal("1500"))
+    assert len(results) == 1
+    assert results[0].accepted is True
+
+
+def test_simulate_entry_filters_rejects_dvol_out_of_band() -> None:
+    cfg = golden_config()
+    monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
+    snap = _snap(ts=monday, dvol=20, funding=0.10)  # below 35
+    from cerbero_bite.core.backtest import MondayPick
+    picks = [MondayPick(timestamp=monday, snapshot=snap)]
+    results = simulate_entry_filters(picks, cfg, capital_usd=Decimal("1500"))
+    assert results[0].accepted is False
+    assert any("dvol" in r.lower() for r in results[0].reasons)
+
+
+def test_simulate_entry_filters_skips_incomplete_snapshot() -> None:
+    cfg = golden_config()
+    monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
+    incomplete = MarketSnapshotRecord(
+        timestamp=monday, asset="ETH", spot=Decimal("3000"),
+        # dvol=None ⇒ skipped
+        fetch_ok=False,
+    )
+    from cerbero_bite.core.backtest import MondayPick
+    picks = [MondayPick(timestamp=monday, snapshot=incomplete)]
+    results = simulate_entry_filters(picks, cfg, capital_usd=Decimal("1500"))
+    assert results[0].accepted is False
+    assert results[0].skipped_for_data is True
+
+
+# ---------------------------------------------------------------------------
+# Full pipeline (sintetico)
+# ---------------------------------------------------------------------------
+
+
+def _synthetic_year_of_snapshots(
+    *,
+    n_weeks: int = 8,
+    spot: float = 3000,
+    dvol: float = 60,  # con skew_premium 1.5 ⇒ credit/width ≈ 35% (sopra soglia 30%)
+    funding: float = 0.10,
+) -> list[MarketSnapshotRecord]:
+    """Genera N settimane di snapshot sintetici ETH a 4 tick/settimana."""
+    rows: list[MarketSnapshotRecord] = []
+    monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
+    for week in range(n_weeks):
+        base = monday + timedelta(weeks=week)
+        # Lunedì 14:00 è il pick
+        rows.append(_snap(ts=base, spot=spot, dvol=dvol, funding=funding))
+        # Tick intermedi che NON cadono di lunedì alle 14:00:
+        # offset +1h così vengono ignorati da `monday_picks`.
+        for d in (2, 8, 14, 19):
+            rows.append(
+                _snap(
+                    ts=base + timedelta(days=d, hours=1),
+                    spot=spot * (1 + 0.005 * d),  # +0.5% al giorno
+                    dvol=dvol - 1.5 * d,  # vol che scende lentamente
+                    funding=funding,
+                )
+            )
+    return rows
+
+
+def test_run_backtest_produces_report_with_trades() -> None:
+    # Per il test scaliamo il credit/width gate al 15%: il modello BS
+    # senza skew completo sottostima i premi OTM rispetto al reale.
+    # Vedi `estimate_credit_eth.skew_premium` docstring per dettagli.
+    from cerbero_bite.config.schema import StructureConfig
+    cfg = golden_config()
+    cfg = cfg.model_copy(
+        update={
+            "structure": StructureConfig(
+                **{
+                    **cfg.structure.model_dump(),
+                    "credit_to_width_ratio_min": Decimal("0.15"),
+                }
+            )
+        }
+    )
+    snapshots = _synthetic_year_of_snapshots(n_weeks=4)
+    report = run_backtest(snapshots, cfg, capital_usd=Decimal("1500"))
+    # Sanity: 4 picks, almeno 1 trade chiuso
+    assert report.n_picks == 4
+    assert report.n_completed >= 1
+    assert report.cumulative_pnl_usd != Decimal("0")
+    # Bull-put + ETH al rialzo + DVOL che scende ⇒ atteso win
+    assert report.n_winners >= 1
+
+
+def test_run_backtest_handles_no_picks_gracefully() -> None:
+    cfg = golden_config()
+    # Solo tick infrasettimanali, niente Monday 14:00.
+    monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
+    snapshots = [_snap(ts=monday + timedelta(hours=1))]
+    report = run_backtest(snapshots, cfg, capital_usd=Decimal("1500"))
+    assert report.n_picks == 0
+    assert report.n_completed == 0
+    assert report.cumulative_pnl_usd == Decimal("0")

tests/unit/test_combo_builder.py

@@ -329,3 +329,146 @@ def test_build_bear_call_breakeven_above_short_strike(
     # breakeven = 3525 + 15 = 3540
     assert proposal.breakeven == Decimal("3540")
     assert proposal.spread_type == "bear_call"
+
+
+# ---------------------------------------------------------------------------
+# §3.2 (A): dynamic delta target by DVOL regime
+# ---------------------------------------------------------------------------
+
+
+def _cfg_with_delta_bands(cfg: StrategyConfig) -> StrategyConfig:
+    """Profilo con step-function delta su DVOL.
+
+    Vol bassa (≤50) → delta 0.15 (più premio), vol media (≤70) →
+    0.12 (default), vol alta (≤90) → 0.10 (più safety distance).
+    """
+    from cerbero_bite.config.schema import (
+        DeltaByDvolBand,
+        ShortStrikeSpec,
+        StructureConfig,
+    )
+    bands = [
+        DeltaByDvolBand(
+            dvol_under=Decimal("50"),
+            delta_target=Decimal("0.15"),
+            delta_min=Decimal("0.13"),
+            delta_max=Decimal("0.17"),
+        ),
+        DeltaByDvolBand(
+            dvol_under=Decimal("70"),
+            delta_target=Decimal("0.12"),
+            delta_min=Decimal("0.10"),
+            delta_max=Decimal("0.15"),
+        ),
+        DeltaByDvolBand(
+            dvol_under=Decimal("90"),
+            delta_target=Decimal("0.10"),
+            delta_min=Decimal("0.08"),
+            delta_max=Decimal("0.12"),
+        ),
+    ]
+    new_short = ShortStrikeSpec(
+        **{**cfg.structure.short_strike.model_dump(), "delta_by_dvol": bands}
+    )
+    return cfg.model_copy(
+        update={
+            "structure": StructureConfig(
+                **{**cfg.structure.model_dump(exclude={"short_strike"}),
+                   "short_strike": new_short}
+            )
+        }
+    )
+
+
+def _bull_put_chain_wide(now_dt: datetime) -> list[OptionQuote]:
+    """Chain con shorts e longs per delta 0.10, 0.12, 0.15.
+
+    I mid sono tarati per superare il credit/width ≥ 30% per ogni
+    accoppiamento short→long testato (vedi commento §3.4).
+    """
+    return [
+        # Shorts a delta 0.10 / 0.12 / 0.15 in OTM range [15-25%].
+        _quote(strike="2535", delta="-0.15", mid="0.026", now_dt=now_dt),
+        _quote(strike="2475", delta="-0.12", mid="0.020", now_dt=now_dt),
+        _quote(strike="2400", delta="-0.10", mid="0.015", now_dt=now_dt),
+        # Long candidati ~4% sotto ciascuno short.
+        _quote(strike="2415", delta="-0.10", mid="0.012", now_dt=now_dt),
+        _quote(strike="2355", delta="-0.08", mid="0.006", now_dt=now_dt),
+        _quote(strike="2280", delta="-0.06", mid="0.002", now_dt=now_dt),
+    ]
+
+
+def test_dynamic_delta_low_dvol_picks_higher_delta(
+    cfg: StrategyConfig, now: datetime
+) -> None:
+    """DVOL=40 → banda con delta_target=0.15."""
+    cfg_dyn = _cfg_with_delta_bands(cfg)
+    chain = _bull_put_chain_wide(now)
+    res = select_strikes(
+        chain=chain,
+        bias="bull_put",
+        spot=Decimal("3000"),
+        now=now,
+        cfg=cfg_dyn,
+        dvol_now=Decimal("40"),
+    )
+    assert res is not None
+    short, _ = res
+    assert short.delta == Decimal("-0.15")
+
+
+def test_dynamic_delta_mid_dvol_picks_default_delta(
+    cfg: StrategyConfig, now: datetime
+) -> None:
+    """DVOL=60 → banda con delta_target=0.12."""
+    cfg_dyn = _cfg_with_delta_bands(cfg)
+    chain = _bull_put_chain_wide(now)
+    res = select_strikes(
+        chain=chain,
+        bias="bull_put",
+        spot=Decimal("3000"),
+        now=now,
+        cfg=cfg_dyn,
+        dvol_now=Decimal("60"),
+    )
+    assert res is not None
+    short, _ = res
+    assert short.delta == Decimal("-0.12")
+
+
+def test_dynamic_delta_high_dvol_picks_lower_delta(
+    cfg: StrategyConfig, now: datetime
+) -> None:
+    """DVOL=85 → banda con delta_target=0.10 (più safety distance)."""
+    cfg_dyn = _cfg_with_delta_bands(cfg)
+    chain = _bull_put_chain_wide(now)
+    res = select_strikes(
+        chain=chain,
+        bias="bull_put",
+        spot=Decimal("3000"),
+        now=now,
+        cfg=cfg_dyn,
+        dvol_now=Decimal("85"),
+    )
+    assert res is not None
+    short, _ = res
+    assert short.delta == Decimal("-0.10")
+
+
+def test_dynamic_delta_disabled_default_uses_static_delta(
+    cfg: StrategyConfig, now: datetime
+) -> None:
+    """delta_by_dvol vuoto (default) → comportamento invariato."""
+    chain = _bull_put_chain_wide(now)
+    res = select_strikes(
+        chain=chain,
+        bias="bull_put",
+        spot=Decimal("3000"),
+        now=now,
+        cfg=cfg,  # golden config: delta_by_dvol=[]
+        dvol_now=Decimal("40"),
+    )
+    assert res is not None
+    short, _ = res
+    # Delta target statico = 0.12, quindi torna lo strike a -0.12.
+    assert short.delta == Decimal("-0.12")

tests/unit/test_config_loader.py

@@ -68,7 +68,7 @@ def test_compute_hash_is_independent_of_recorded_hash_value(tmp_path: Path) -> N
 def test_load_repo_strategy_yaml(tmp_path: Path) -> None:
     """The committed strategy.yaml validates with the recorded hash."""
     result = load_strategy(REPO_ROOT / "strategy.yaml")
-    assert result.config.config_version == "1.0.0"
+    assert result.config.config_version == "1.3.0"
     assert result.config.sizing.kelly_fraction == Decimal("0.13")
     assert result.computed_hash == result.config.config_hash
 

tests/unit/test_entry_validator.py

@@ -194,6 +194,62 @@ def test_dealer_gamma_filter_disabled_in_config(cfg: StrategyConfig) -> None:
     assert decision.accepted is True
 
 
+# ---------------------------------------------------------------------------
+# IV richness gate (§2.9)
+# ---------------------------------------------------------------------------
+
+
+def _strict_iv_rv_cfg(
+    cfg: StrategyConfig, *, threshold: Decimal = Decimal("5")
+) -> StrategyConfig:
+    return golden_config(
+        entry=EntryConfig(
+            **{
+                **cfg.entry.model_dump(),
+                "iv_minus_rv_filter_enabled": True,
+                "iv_minus_rv_min": threshold,
+            }
+        )
+    )
+
+
+def test_iv_richness_gate_disabled_by_default_lets_thin_premium_pass(
+    cfg: StrategyConfig,
+) -> None:
+    # Default config: filter disabled. Anche con IV-RV negativa (RV>IV)
+    # l'entry deve passare per non rompere setup pre-calibrazione.
+    decision = validate_entry(_good_ctx(iv_minus_rv=Decimal("-2")), cfg)
+    assert decision.accepted is True
+
+
+def test_iv_richness_gate_blocks_when_below_floor(cfg: StrategyConfig) -> None:
+    strict = _strict_iv_rv_cfg(cfg, threshold=Decimal("5"))
+    decision = validate_entry(_good_ctx(iv_minus_rv=Decimal("3")), strict)
+    assert decision.accepted is False
+    assert any("IV richness" in r for r in decision.reasons)
+
+
+def test_iv_richness_gate_passes_when_above_floor(cfg: StrategyConfig) -> None:
+    strict = _strict_iv_rv_cfg(cfg, threshold=Decimal("5"))
+    decision = validate_entry(_good_ctx(iv_minus_rv=Decimal("6")), strict)
+    assert decision.accepted is True
+
+
+def test_iv_richness_gate_passes_at_exact_threshold(cfg: StrategyConfig) -> None:
+    # Soglia inclusiva: IV-RV == soglia → accettato (gate è "<", non "<=").
+    strict = _strict_iv_rv_cfg(cfg, threshold=Decimal("5"))
+    decision = validate_entry(_good_ctx(iv_minus_rv=Decimal("5")), strict)
+    assert decision.accepted is True
+
+
+def test_iv_richness_gate_skipped_when_data_missing(cfg: StrategyConfig) -> None:
+    # MCP irraggiungibile: best-effort skip, non bloccare l'entry per
+    # un problema di infrastruttura.
+    strict = _strict_iv_rv_cfg(cfg, threshold=Decimal("5"))
+    decision = validate_entry(_good_ctx(iv_minus_rv=None), strict)
+    assert decision.accepted is True
+
+
 def test_validate_entry_accumulates_all_reasons(cfg: StrategyConfig) -> None:
     decision = validate_entry(
         _good_ctx(

tests/unit/test_exit_decision.py

@@ -271,3 +271,91 @@ def test_iron_condor_adverse_move_either_direction(cfg: StrategyConfig) -> None:
     )
     res = evaluate(snap, cfg)
     assert res.action == "CLOSE_AVERSE"
+
+
+# ---------------------------------------------------------------------------
+# §7-bis (D): vol-collapse harvest
+# ---------------------------------------------------------------------------
+
+
+def _harvest_cfg(
+    cfg: StrategyConfig, *, threshold: str = "15"
+) -> StrategyConfig:
+    """Clona la golden config con la soglia di vol-harvest abilitata."""
+    from cerbero_bite.config import ExitConfig
+    return cfg.model_copy(
+        update={
+            "exit": ExitConfig(
+                **{
+                    **cfg.exit.model_dump(),
+                    "vol_harvest_dvol_decrease": Decimal(threshold),
+                }
+            )
+        }
+    )
+
+
+def test_vol_harvest_disabled_by_default_does_not_fire(cfg: StrategyConfig) -> None:
+    # Default: vol_harvest_dvol_decrease = 0 ⇒ filtro disabilitato.
+    snap = _snapshot(
+        credit_received_eth="0.030",
+        mark_combo_now_eth="0.022",  # in profit (debit < credit)
+        dvol_at_entry="60",
+        dvol_now="40",  # crollato di 20 punti
+    )
+    res = evaluate(snap, cfg)
+    assert res.action == "HOLD"
+
+
+def test_vol_harvest_fires_when_dvol_collapsed_in_profit(
+    cfg: StrategyConfig,
+) -> None:
+    harvest = _harvest_cfg(cfg, threshold="15")
+    snap = _snapshot(
+        credit_received_eth="0.030",
+        mark_combo_now_eth="0.022",  # in profit ma sopra profit_take 50%
+        dvol_at_entry="60",
+        dvol_now="42",  # −18, supera la soglia 15
+    )
+    res = evaluate(snap, harvest)
+    assert res.action == "CLOSE_VOL_HARVEST"
+    assert "harvest" in res.reason
+
+
+def test_vol_harvest_does_not_fire_when_in_loss(cfg: StrategyConfig) -> None:
+    # Anche se DVOL crolla, se siamo in perdita non vogliamo harvest:
+    # è una funzione di "esci con il profitto in mano", non un panico.
+    harvest = _harvest_cfg(cfg, threshold="15")
+    snap = _snapshot(
+        credit_received_eth="0.030",
+        mark_combo_now_eth="0.040",  # debit > credit ⇒ in perdita
+        dvol_at_entry="60",
+        dvol_now="42",
+    )
+    res = evaluate(snap, harvest)
+    assert res.action != "CLOSE_VOL_HARVEST"
+
+
+def test_vol_harvest_does_not_fire_below_threshold(cfg: StrategyConfig) -> None:
+    harvest = _harvest_cfg(cfg, threshold="15")
+    snap = _snapshot(
+        credit_received_eth="0.030",
+        mark_combo_now_eth="0.022",
+        dvol_at_entry="60",
+        dvol_now="50",  # −10, sotto la soglia 15
+    )
+    res = evaluate(snap, harvest)
+    assert res.action == "HOLD"
+
+
+def test_profit_take_wins_over_vol_harvest(cfg: StrategyConfig) -> None:
+    # Quando il profit-take è già colpito, non passiamo per vol-harvest.
+    harvest = _harvest_cfg(cfg, threshold="15")
+    snap = _snapshot(
+        credit_received_eth="0.030",
+        mark_combo_now_eth="0.014",  # ≤ 50% credit ⇒ profit-take
+        dvol_at_entry="60",
+        dvol_now="42",
+    )
+    res = evaluate(snap, harvest)
+    assert res.action == "CLOSE_PROFIT"