feat(backtest): stylized engine over market_snapshots + CLI subcommand

Aggiunge `core/backtest.py`, motore di backtesting stilizzato che gira
sui dati raccolti in `market_snapshots`. Risponde alla domanda:
"se questa config fosse stata attiva nelle ultime N settimane, quanti
lunedì avrebbero superato i filtri e quale sarebbe stato il P/L stimato?"

**Architettura a due strati**:

1. **Filtri di entry — RIGOROSO**: per ogni Monday-14:00-UTC nei
   snapshot ricostruisce `EntryContext` e chiama lo stesso
   `validate_entry()` del live. Output esatto di "cosa avrebbe deciso
   il bot" per ogni settimana, con conteggio dei motivi di skip.

2. **P/L per trade accettato — STILIZZATO**: senza catena opzioni
   storica, stima credito/exit via Black-Scholes con skew premium
   (default 1.5×) per approssimare la vol smile dell'ETH. Re-prezza
   il combo ad ogni tick futuro per simulare i trigger §7
   (profit_take, stop_loss, vol_stop, time_stop, expiry).

**Aggregati nel `BacktestReport`**:
- n_picks / n_accepted / n_skipped_data / n_completed / n_winners
- win_rate, P/L cumulato (USD + % su capitale)
- max drawdown (USD + % di peak)
- Sharpe annualizzato (52 settimane)
- skip_reasons: dict{motivo → settimane bloccate}

**CLI**: nuovo `cerbero-bite backtest --strategy F --from D --to D
--capital N --asset ETH`. Stampa Rich-formatted summary + tabella
motivi di skip. Esempio:

    cerbero-bite backtest \
      --strategy strategy.aggressiva.yaml \
      --from 2026-04-01 --to 2026-05-01 \
      --capital 10000

**Limiti dichiarati**:
- BS + skew_premium ≠ catena reale: i numeri P/L sono **stime ex-post
  per ranking config**, non promesse operative. Buono per dire
  "config A batte config B sui dati reali", non per dimensionare
  capitale.
- skew_premium 1.5× è stato calibrato sui dati Deribit storici
  (smile slope ETH options); va rifinito quando avremo abbastanza
  chain history da farlo empiricamente.

**Tests**: 15 unit test (BS math, monday picks, filter sim,
position outcome simulation, full pipeline su sintetico).
Suite totale: 420 passed.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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"""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")