root
f664ea1a15
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>
260 lines
9.0 KiB
Python
260 lines
9.0 KiB
Python
"""TDD per :mod:`cerbero_bite.core.backtest`."""
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from __future__ import annotations
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from datetime import UTC, datetime, timedelta
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from decimal import Decimal
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import pytest
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from cerbero_bite.config import StrategyConfig, golden_config
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from cerbero_bite.core.backtest import (
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bs_put_delta,
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bs_put_price,
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estimate_credit_eth,
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find_strike_for_delta,
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monday_picks,
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normal_cdf,
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run_backtest,
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simulate_entry_filters,
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)
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from cerbero_bite.state.models import MarketSnapshotRecord
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# ---------------------------------------------------------------------------
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# Black-Scholes helpers
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# ---------------------------------------------------------------------------
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def test_normal_cdf_known_values() -> None:
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assert normal_cdf(0.0) == pytest.approx(0.5, abs=1e-6)
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assert normal_cdf(1.0) == pytest.approx(0.8413, abs=1e-3)
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assert normal_cdf(-1.0) == pytest.approx(0.1587, abs=1e-3)
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assert normal_cdf(2.0) == pytest.approx(0.9772, abs=1e-3)
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def test_bs_put_price_atm_positive_and_less_than_strike() -> None:
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p = bs_put_price(spot=3000, strike=3000, t_years=18 / 365, sigma=0.50)
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assert p > 0
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assert p < 3000 # cap
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def test_bs_put_price_far_otm_close_to_zero() -> None:
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p = bs_put_price(spot=3000, strike=1500, t_years=18 / 365, sigma=0.50)
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assert 0 <= p < 5 # essentially zero
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def test_bs_put_delta_atm_around_minus_half() -> None:
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d = bs_put_delta(spot=3000, strike=3000, t_years=18 / 365, sigma=0.50)
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assert d == pytest.approx(-0.475, abs=0.05)
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def test_bs_put_delta_far_otm_close_to_zero() -> None:
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d = bs_put_delta(spot=3000, strike=1500, t_years=18 / 365, sigma=0.50)
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assert -0.05 < d <= 0
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def test_find_strike_for_delta_monotone() -> None:
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spot = 3000.0
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dvol = 50.0
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dte = 18
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s_010 = find_strike_for_delta(
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spot=spot, dvol_pct=dvol, dte_days=dte, target_delta_abs=0.10,
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)
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s_020 = find_strike_for_delta(
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spot=spot, dvol_pct=dvol, dte_days=dte, target_delta_abs=0.20,
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)
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# |Δ|=0.20 (più ITM) ⇒ strike più alto di |Δ|=0.10 (più OTM).
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assert s_020 > s_010
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# Verifica che il delta corrisponda a target ± tolleranza.
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achieved = abs(
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bs_put_delta(
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spot=spot, strike=s_020, t_years=dte / 365, sigma=dvol / 100,
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)
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)
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assert achieved == pytest.approx(0.20, abs=0.02)
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def test_estimate_credit_returns_positive_credit_in_normal_regime() -> None:
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credit_eth, short_k, long_k = estimate_credit_eth(
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spot=3000, dvol_pct=50, dte_days=18, width_pct=0.04, delta_target_abs=0.12,
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)
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# Sanity: credit > 0, short_k < spot, long_k = short_k - 4%×spot
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assert credit_eth > 0
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assert short_k < 3000
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assert long_k < short_k
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assert short_k - long_k == pytest.approx(0.04 * 3000, abs=1.0)
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# ---------------------------------------------------------------------------
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# Monday picks + entry filter simulation
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# ---------------------------------------------------------------------------
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def _snap(
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*, ts: datetime,
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spot: float = 3000,
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dvol: float = 50,
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funding: float = 0.0,
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macro_d: int | None = None,
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asset: str = "ETH",
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) -> MarketSnapshotRecord:
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return MarketSnapshotRecord(
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timestamp=ts,
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asset=asset,
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spot=Decimal(str(spot)),
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dvol=Decimal(str(dvol)),
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funding_perp_annualized=Decimal(str(funding)),
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funding_cross_annualized=Decimal("0"),
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dealer_net_gamma=Decimal("100"),
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liquidation_long_risk="low",
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liquidation_short_risk="low",
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macro_days_to_event=macro_d,
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fetch_ok=True,
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)
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def test_monday_picks_extracts_one_per_iso_week() -> None:
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monday_2026_05_04 = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
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monday_2026_05_11 = datetime(2026, 5, 11, 14, 0, tzinfo=UTC)
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snapshots = [
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_snap(ts=monday_2026_05_04),
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_snap(ts=monday_2026_05_04 + timedelta(minutes=15)), # not picked
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_snap(ts=monday_2026_05_11),
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]
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picks = monday_picks(snapshots)
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assert len(picks) == 2
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assert picks[0].timestamp == monday_2026_05_04
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assert picks[1].timestamp == monday_2026_05_11
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def test_monday_picks_skips_other_days_and_hours() -> None:
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snapshots = [
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_snap(ts=datetime(2026, 5, 4, 13, 0, tzinfo=UTC)), # Monday 13:00
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_snap(ts=datetime(2026, 5, 5, 14, 0, tzinfo=UTC)), # Tuesday 14:00
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]
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assert monday_picks(snapshots) == []
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def test_monday_picks_filters_by_asset() -> None:
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monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
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snapshots = [
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_snap(ts=monday, asset="BTC"),
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_snap(ts=monday, asset="ETH"),
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]
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picks = monday_picks(snapshots, asset="ETH")
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assert len(picks) == 1
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assert picks[0].snapshot.asset == "ETH"
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def test_simulate_entry_filters_accepts_clean_snapshot(
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) -> None:
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cfg: StrategyConfig = golden_config()
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monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
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snap = _snap(ts=monday, dvol=50, funding=0.10)
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picks = [
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type("MP", (), {"timestamp": monday, "snapshot": snap})() # type: ignore[arg-type]
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]
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# Hack: build via real dataclass
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from cerbero_bite.core.backtest import MondayPick
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picks = [MondayPick(timestamp=monday, snapshot=snap)]
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results = simulate_entry_filters(picks, cfg, capital_usd=Decimal("1500"))
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assert len(results) == 1
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assert results[0].accepted is True
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def test_simulate_entry_filters_rejects_dvol_out_of_band() -> None:
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cfg = golden_config()
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monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
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snap = _snap(ts=monday, dvol=20, funding=0.10) # below 35
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from cerbero_bite.core.backtest import MondayPick
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picks = [MondayPick(timestamp=monday, snapshot=snap)]
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results = simulate_entry_filters(picks, cfg, capital_usd=Decimal("1500"))
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assert results[0].accepted is False
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assert any("dvol" in r.lower() for r in results[0].reasons)
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def test_simulate_entry_filters_skips_incomplete_snapshot() -> None:
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cfg = golden_config()
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monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
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incomplete = MarketSnapshotRecord(
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timestamp=monday, asset="ETH", spot=Decimal("3000"),
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# dvol=None ⇒ skipped
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fetch_ok=False,
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)
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from cerbero_bite.core.backtest import MondayPick
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picks = [MondayPick(timestamp=monday, snapshot=incomplete)]
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results = simulate_entry_filters(picks, cfg, capital_usd=Decimal("1500"))
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assert results[0].accepted is False
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assert results[0].skipped_for_data is True
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# ---------------------------------------------------------------------------
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# Full pipeline (sintetico)
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# ---------------------------------------------------------------------------
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def _synthetic_year_of_snapshots(
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*,
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n_weeks: int = 8,
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spot: float = 3000,
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dvol: float = 60, # con skew_premium 1.5 ⇒ credit/width ≈ 35% (sopra soglia 30%)
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funding: float = 0.10,
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) -> list[MarketSnapshotRecord]:
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"""Genera N settimane di snapshot sintetici ETH a 4 tick/settimana."""
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rows: list[MarketSnapshotRecord] = []
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monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
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for week in range(n_weeks):
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base = monday + timedelta(weeks=week)
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# Lunedì 14:00 è il pick
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rows.append(_snap(ts=base, spot=spot, dvol=dvol, funding=funding))
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# Tick intermedi che NON cadono di lunedì alle 14:00:
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# offset +1h così vengono ignorati da `monday_picks`.
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for d in (2, 8, 14, 19):
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rows.append(
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_snap(
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ts=base + timedelta(days=d, hours=1),
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spot=spot * (1 + 0.005 * d), # +0.5% al giorno
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dvol=dvol - 1.5 * d, # vol che scende lentamente
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funding=funding,
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)
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)
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return rows
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def test_run_backtest_produces_report_with_trades() -> None:
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# Per il test scaliamo il credit/width gate al 15%: il modello BS
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# senza skew completo sottostima i premi OTM rispetto al reale.
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# Vedi `estimate_credit_eth.skew_premium` docstring per dettagli.
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from cerbero_bite.config.schema import StructureConfig
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cfg = golden_config()
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cfg = cfg.model_copy(
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update={
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"structure": StructureConfig(
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**{
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**cfg.structure.model_dump(),
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"credit_to_width_ratio_min": Decimal("0.15"),
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}
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)
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}
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)
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snapshots = _synthetic_year_of_snapshots(n_weeks=4)
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report = run_backtest(snapshots, cfg, capital_usd=Decimal("1500"))
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# Sanity: 4 picks, almeno 1 trade chiuso
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assert report.n_picks == 4
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assert report.n_completed >= 1
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assert report.cumulative_pnl_usd != Decimal("0")
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# Bull-put + ETH al rialzo + DVOL che scende ⇒ atteso win
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assert report.n_winners >= 1
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def test_run_backtest_handles_no_picks_gracefully() -> None:
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cfg = golden_config()
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# Solo tick infrasettimanali, niente Monday 14:00.
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monday = datetime(2026, 5, 4, 14, 0, tzinfo=UTC)
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snapshots = [_snap(ts=monday + timedelta(hours=1))]
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report = run_backtest(snapshots, cfg, capital_usd=Decimal("1500"))
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assert report.n_picks == 0
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assert report.n_completed == 0
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assert report.cumulative_pnl_usd == Decimal("0")
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