Adriano
fbb7753cc6
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>
156 lines
5.7 KiB
Python
156 lines
5.7 KiB
Python
"""TDD for :mod:`cerbero_bite.core.kelly_recalibration`.
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Spec: ``docs/03-algorithms.md §7``.
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"""
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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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from uuid import uuid4
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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.kelly_recalibration import TradeRecord, recalibrate
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@pytest.fixture
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def cfg() -> StrategyConfig:
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return golden_config()
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@pytest.fixture
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def now() -> datetime:
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return datetime(2026, 4, 27, 14, 0, tzinfo=UTC)
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def _trade(
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*,
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pnl_usd: str,
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risk_usd: str = "100",
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days_ago: int = 30,
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outcome: str = "CLOSE_PROFIT",
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now_dt: datetime = datetime(2026, 4, 27, 14, 0, tzinfo=UTC),
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) -> TradeRecord:
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return TradeRecord(
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proposal_id=uuid4(),
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pnl_usd=Decimal(pnl_usd),
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risk_usd=Decimal(risk_usd),
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closed_at=now_dt - timedelta(days=days_ago),
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outcome=outcome,
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)
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# ---------------------------------------------------------------------------
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# Confidence bands and recommended fraction
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# ---------------------------------------------------------------------------
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def test_empty_history_returns_low_confidence_with_cfg_kelly(
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cfg: StrategyConfig, now: datetime
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) -> None:
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res = recalibrate(trades=[], now=now, cfg=cfg)
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assert res.confidence == "low"
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assert res.sample_size == 0
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assert res.recommended_fraction == cfg.sizing.kelly_fraction
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assert res.win_rate == Decimal("0")
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assert res.full_kelly_pct == Decimal("0")
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assert res.quarter_kelly_pct == Decimal("0")
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def test_low_sample_keeps_cfg_kelly_fraction(cfg: StrategyConfig, now: datetime) -> None:
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trades = [_trade(pnl_usd="10", now_dt=now) for _ in range(20)]
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trades += [_trade(pnl_usd="-15", now_dt=now) for _ in range(5)]
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res = recalibrate(trades=trades, now=now, cfg=cfg)
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assert res.sample_size == 25
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assert res.confidence == "low"
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assert res.recommended_fraction == cfg.sizing.kelly_fraction
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def test_medium_sample_blends_quarter_kelly_with_cfg(
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cfg: StrategyConfig, now: datetime
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) -> None:
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# 60 trades, 70% wins (avg_win=10/100=0.10), 30% losses (avg_loss=15/100=0.15)
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# b = 0.10 / 0.15 = 0.667
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# full_kelly = (0.7 × 0.667 - 0.3) / 0.667 = (0.467 - 0.3) / 0.667 = 0.25
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# quarter_kelly = 0.0625
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# blended = 0.5 × 0.0625 + 0.5 × 0.13 = 0.09625
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wins = [_trade(pnl_usd="10", now_dt=now) for _ in range(42)]
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losses = [_trade(pnl_usd="-15", now_dt=now) for _ in range(18)]
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res = recalibrate(trades=wins + losses, now=now, cfg=cfg)
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assert res.sample_size == 60
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assert res.confidence == "medium"
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assert res.win_rate == Decimal("0.7")
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expected = Decimal("0.5") * res.quarter_kelly_pct + Decimal("0.5") * cfg.sizing.kelly_fraction
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assert res.recommended_fraction == expected
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def test_high_sample_adopts_quarter_kelly(cfg: StrategyConfig, now: datetime) -> None:
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wins = [_trade(pnl_usd="10", now_dt=now) for _ in range(120)]
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losses = [_trade(pnl_usd="-15", now_dt=now) for _ in range(40)]
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res = recalibrate(trades=wins + losses, now=now, cfg=cfg)
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assert res.sample_size == 160
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assert res.confidence == "high"
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assert res.recommended_fraction == res.quarter_kelly_pct
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# ---------------------------------------------------------------------------
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# Lookback filter
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# ---------------------------------------------------------------------------
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def test_trades_older_than_lookback_are_ignored(
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cfg: StrategyConfig, now: datetime
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) -> None:
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fresh = [_trade(pnl_usd="10", days_ago=30, now_dt=now) for _ in range(40)]
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stale = [_trade(pnl_usd="-50", days_ago=400, now_dt=now) for _ in range(60)]
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res = recalibrate(trades=fresh + stale, now=now, cfg=cfg)
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# Only 40 trades in window; all wins → win_rate = 1.0
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assert res.sample_size == 40
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assert res.win_rate == Decimal("1")
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# ---------------------------------------------------------------------------
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# Edge cases for full_kelly arithmetic
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# ---------------------------------------------------------------------------
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def test_all_losses_clip_full_kelly_to_zero(cfg: StrategyConfig, now: datetime) -> None:
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trades = [_trade(pnl_usd="-20", now_dt=now) for _ in range(40)]
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res = recalibrate(trades=trades, now=now, cfg=cfg)
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assert res.win_rate == Decimal("0")
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assert res.full_kelly_pct == Decimal("0")
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assert res.quarter_kelly_pct == Decimal("0")
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def test_all_wins_yield_full_kelly_equal_to_win_rate(
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cfg: StrategyConfig, now: datetime
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) -> None:
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# No losses → division by zero. Convention: full_kelly = win_rate (= 1.0).
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trades = [_trade(pnl_usd="20", now_dt=now) for _ in range(40)]
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res = recalibrate(trades=trades, now=now, cfg=cfg)
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assert res.win_rate == Decimal("1")
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assert res.avg_loss_pct_risk == Decimal("0")
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assert res.full_kelly_pct == Decimal("1")
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assert res.quarter_kelly_pct == Decimal("0.25")
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def test_avg_loss_uses_absolute_value(cfg: StrategyConfig, now: datetime) -> None:
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wins = [_trade(pnl_usd="10", now_dt=now) for _ in range(30)]
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losses = [_trade(pnl_usd="-20", now_dt=now) for _ in range(30)]
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res = recalibrate(trades=wins + losses, now=now, cfg=cfg)
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# avg_win_pct_risk = 10/100 = 0.10; avg_loss_pct_risk = 20/100 = 0.20 (positive)
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assert res.avg_win_pct_risk == Decimal("0.1")
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assert res.avg_loss_pct_risk == Decimal("0.2")
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def test_zero_pnl_counts_as_loss_for_winrate(cfg: StrategyConfig, now: datetime) -> None:
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# Spec: "trade con pnl > 0" → wins. 0 is not a win.
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trades = [
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*[_trade(pnl_usd="10", now_dt=now) for _ in range(20)],
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*[_trade(pnl_usd="0", now_dt=now) for _ in range(20)],
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]
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res = recalibrate(trades=trades, now=now, cfg=cfg)
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assert res.win_rate == Decimal("0.5")
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