Phase 1: core algorithms

Implementa i sette algoritmi puri di docs/03-algorithms.md con disciplina TDD: 112 test, copertura statement+branch al 100% su core/ e config/, mypy --strict pulito, ruff pulito. Moduli: - config/schema.py: StrategyConfig Pydantic v2 con validatori di consistenza (kelly, delta, OTM, spread width, profit/stop). - core/types.py: OptionQuote e OptionLeg condivisi. - core/entry_validator.py: validate_entry (accumula motivi) e compute_bias (bull_put/bear_call/iron_condor/None). - core/liquidity_gate.py: check OI/volume/spread/depth + slippage stimato in % del credito. - core/sizing_engine.py: Quarter Kelly con cap 200/1000 EUR e bande DVOL. - core/combo_builder.py: select_strikes (DTE/OTM/delta/width/credit) e build (ComboProposal con credit/max_loss/breakeven). - core/greeks_aggregator.py: somma firmata BUY/SELL, theta in USD. - core/exit_decision.py: 6 trigger ordinati con eccezione skip-time vicino a profit (mark in (50%,70%] credito). - core/kelly_recalibration.py: full/quarter Kelly, confidence per sample size, blend medio in fascia 30-99 trade. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-27 10:14:06 +02:00
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+"""TDD for :mod:`cerbero_bite.core.kelly_recalibration`.
+
+Spec: ``docs/03-algorithms.md §7``.
+"""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime, timedelta
+from decimal import Decimal
+from uuid import uuid4
+
+import pytest
+
+from cerbero_bite.config import StrategyConfig, golden_config
+from cerbero_bite.core.kelly_recalibration import TradeRecord, recalibrate
+
+
+@pytest.fixture
+def cfg() -> StrategyConfig:
+    return golden_config()
+
+
+@pytest.fixture
+def now() -> datetime:
+    return datetime(2026, 4, 27, 14, 0, tzinfo=UTC)
+
+
+def _trade(
+    *,
+    pnl_usd: str,
+    risk_usd: str = "100",
+    days_ago: int = 30,
+    outcome: str = "CLOSE_PROFIT",
+    now_dt: datetime = datetime(2026, 4, 27, 14, 0, tzinfo=UTC),
+) -> TradeRecord:
+    return TradeRecord(
+        proposal_id=uuid4(),
+        pnl_usd=Decimal(pnl_usd),
+        risk_usd=Decimal(risk_usd),
+        closed_at=now_dt - timedelta(days=days_ago),
+        outcome=outcome,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Confidence bands and recommended fraction
+# ---------------------------------------------------------------------------
+
+
+def test_empty_history_returns_low_confidence_with_cfg_kelly(
+    cfg: StrategyConfig, now: datetime
+) -> None:
+    res = recalibrate(trades=[], now=now, cfg=cfg)
+    assert res.confidence == "low"
+    assert res.sample_size == 0
+    assert res.recommended_fraction == cfg.sizing.kelly_fraction
+    assert res.win_rate == Decimal("0")
+    assert res.full_kelly_pct == Decimal("0")
+    assert res.quarter_kelly_pct == Decimal("0")
+
+
+def test_low_sample_keeps_cfg_kelly_fraction(cfg: StrategyConfig, now: datetime) -> None:
+    trades = [_trade(pnl_usd="10", now_dt=now) for _ in range(20)]
+    trades += [_trade(pnl_usd="-15", now_dt=now) for _ in range(5)]
+    res = recalibrate(trades=trades, now=now, cfg=cfg)
+    assert res.sample_size == 25
+    assert res.confidence == "low"
+    assert res.recommended_fraction == cfg.sizing.kelly_fraction
+
+
+def test_medium_sample_blends_quarter_kelly_with_cfg(
+    cfg: StrategyConfig, now: datetime
+) -> None:
+    # 60 trades, 70% wins (avg_win=10/100=0.10), 30% losses (avg_loss=15/100=0.15)
+    # b = 0.10 / 0.15 = 0.667
+    # full_kelly = (0.7 × 0.667 - 0.3) / 0.667 = (0.467 - 0.3) / 0.667 = 0.25
+    # quarter_kelly = 0.0625
+    # blended = 0.5 × 0.0625 + 0.5 × 0.13 = 0.09625
+    wins = [_trade(pnl_usd="10", now_dt=now) for _ in range(42)]
+    losses = [_trade(pnl_usd="-15", now_dt=now) for _ in range(18)]
+    res = recalibrate(trades=wins + losses, now=now, cfg=cfg)
+    assert res.sample_size == 60
+    assert res.confidence == "medium"
+    assert res.win_rate == Decimal("0.7")
+    expected = Decimal("0.5") * res.quarter_kelly_pct + Decimal("0.5") * cfg.sizing.kelly_fraction
+    assert res.recommended_fraction == expected
+
+
+def test_high_sample_adopts_quarter_kelly(cfg: StrategyConfig, now: datetime) -> None:
+    wins = [_trade(pnl_usd="10", now_dt=now) for _ in range(120)]
+    losses = [_trade(pnl_usd="-15", now_dt=now) for _ in range(40)]
+    res = recalibrate(trades=wins + losses, now=now, cfg=cfg)
+    assert res.sample_size == 160
+    assert res.confidence == "high"
+    assert res.recommended_fraction == res.quarter_kelly_pct
+
+
+# ---------------------------------------------------------------------------
+# Lookback filter
+# ---------------------------------------------------------------------------
+
+
+def test_trades_older_than_lookback_are_ignored(
+    cfg: StrategyConfig, now: datetime
+) -> None:
+    fresh = [_trade(pnl_usd="10", days_ago=30, now_dt=now) for _ in range(40)]
+    stale = [_trade(pnl_usd="-50", days_ago=400, now_dt=now) for _ in range(60)]
+    res = recalibrate(trades=fresh + stale, now=now, cfg=cfg)
+    # Only 40 trades in window; all wins → win_rate = 1.0
+    assert res.sample_size == 40
+    assert res.win_rate == Decimal("1")
+
+
+# ---------------------------------------------------------------------------
+# Edge cases for full_kelly arithmetic
+# ---------------------------------------------------------------------------
+
+
+def test_all_losses_clip_full_kelly_to_zero(cfg: StrategyConfig, now: datetime) -> None:
+    trades = [_trade(pnl_usd="-20", now_dt=now) for _ in range(40)]
+    res = recalibrate(trades=trades, now=now, cfg=cfg)
+    assert res.win_rate == Decimal("0")
+    assert res.full_kelly_pct == Decimal("0")
+    assert res.quarter_kelly_pct == Decimal("0")
+
+
+def test_all_wins_yield_full_kelly_equal_to_win_rate(
+    cfg: StrategyConfig, now: datetime
+) -> None:
+    # No losses → division by zero. Convention: full_kelly = win_rate (= 1.0).
+    trades = [_trade(pnl_usd="20", now_dt=now) for _ in range(40)]
+    res = recalibrate(trades=trades, now=now, cfg=cfg)
+    assert res.win_rate == Decimal("1")
+    assert res.avg_loss_pct_risk == Decimal("0")
+    assert res.full_kelly_pct == Decimal("1")
+    assert res.quarter_kelly_pct == Decimal("0.25")
+
+
+def test_avg_loss_uses_absolute_value(cfg: StrategyConfig, now: datetime) -> None:
+    wins = [_trade(pnl_usd="10", now_dt=now) for _ in range(30)]
+    losses = [_trade(pnl_usd="-20", now_dt=now) for _ in range(30)]
+    res = recalibrate(trades=wins + losses, now=now, cfg=cfg)
+    # avg_win_pct_risk = 10/100 = 0.10; avg_loss_pct_risk = 20/100 = 0.20 (positive)
+    assert res.avg_win_pct_risk == Decimal("0.1")
+    assert res.avg_loss_pct_risk == Decimal("0.2")
+
+
+def test_zero_pnl_counts_as_loss_for_winrate(cfg: StrategyConfig, now: datetime) -> None:
+    # Spec: "trade con pnl > 0" → wins. 0 is not a win.
+    trades = [
+        *[_trade(pnl_usd="10", now_dt=now) for _ in range(20)],
+        *[_trade(pnl_usd="0", now_dt=now) for _ in range(20)],
+    ]
+    res = recalibrate(trades=trades, now=now, cfg=cfg)
+    assert res.win_rate == Decimal("0.5")