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"""Tests for detailed backtest metrics."""
import math
from datetime import UTC, datetime, timedelta
from decimal import Decimal
import pytest
from backtester.metrics import TradeRecord, compute_detailed_metrics
def _make_trade(side: str, price: str, minutes_offset: int = 0) -> TradeRecord:
return TradeRecord(
time=datetime(2025, 1, 1, tzinfo=UTC) + timedelta(minutes=minutes_offset),
symbol="AAPL",
side=side,
price=Decimal(price),
quantity=Decimal("1"),
)
def test_compute_metrics_basic():
"""Two round-trip trades: 1 win, 1 loss. Verify counts and win_rate."""
trades = [
_make_trade("BUY", "100", 0),
_make_trade("SELL", "120", 10), # win: +20
_make_trade("BUY", "130", 20),
_make_trade("SELL", "110", 30), # loss: -20
]
metrics = compute_detailed_metrics(trades, Decimal("10000"), Decimal("10000"))
assert metrics.total_trades == 4
assert metrics.winning_trades == 1
assert metrics.losing_trades == 1
assert metrics.win_rate == pytest.approx(50.0)
def test_compute_metrics_profit_factor():
"""Verify profit_factor = gross_profit / gross_loss."""
trades = [
_make_trade("BUY", "100", 0),
_make_trade("SELL", "150", 10), # win: +50
_make_trade("BUY", "150", 20),
_make_trade("SELL", "130", 30), # loss: -20
]
metrics = compute_detailed_metrics(trades, Decimal("10000"), Decimal("10030"))
# gross_profit=50, gross_loss=20 → profit_factor=2.5
assert metrics.profit_factor == pytest.approx(2.5)
def test_compute_metrics_max_drawdown():
"""Max drawdown should be > 0 when there is a losing trade after a peak."""
trades = [
_make_trade("BUY", "100", 0),
_make_trade("SELL", "150", 10), # win: equity goes up
_make_trade("BUY", "150", 20),
_make_trade("SELL", "120", 30), # loss: equity drops
]
metrics = compute_detailed_metrics(trades, Decimal("10000"), Decimal("10020"))
assert metrics.max_drawdown > 0
def test_compute_metrics_sharpe_ratio():
"""Sharpe ratio should be a finite number with multiple trades."""
trades = [
_make_trade("BUY", "100", 0),
_make_trade("SELL", "110", 60),
_make_trade("BUY", "105", 120),
_make_trade("SELL", "115", 180),
_make_trade("BUY", "110", 240),
_make_trade("SELL", "108", 300),
]
metrics = compute_detailed_metrics(trades, Decimal("10000"), Decimal("10018"))
assert math.isfinite(metrics.sharpe_ratio)
assert math.isfinite(metrics.sortino_ratio)
def test_compute_metrics_empty_trades():
"""Empty trades should return all zeros."""
metrics = compute_detailed_metrics([], Decimal("10000"), Decimal("10000"))
assert metrics.total_return == 0.0
assert metrics.total_trades == 0
assert metrics.winning_trades == 0
assert metrics.losing_trades == 0
assert metrics.win_rate == 0.0
assert metrics.profit_factor == 0.0
assert metrics.sharpe_ratio == 0.0
assert metrics.sortino_ratio == 0.0
assert metrics.calmar_ratio == 0.0
assert metrics.max_drawdown == 0.0
assert metrics.monthly_returns == {}
def test_recovery_factor():
"""Recovery factor should be positive when there is a drawdown."""
trades = [
_make_trade("BUY", "100", 0),
_make_trade("SELL", "150", 10), # win
_make_trade("BUY", "150", 20),
_make_trade("SELL", "120", 30), # loss: creates drawdown
]
metrics = compute_detailed_metrics(trades, Decimal("10000"), Decimal("10020"))
assert metrics.recovery_factor > 0
def test_consecutive_losses():
"""Consecutive loss tracking should count streaks correctly."""
trades = [
_make_trade("BUY", "100", 0),
_make_trade("SELL", "110", 10), # win
_make_trade("BUY", "110", 20),
_make_trade("SELL", "105", 30), # loss
_make_trade("BUY", "105", 40),
_make_trade("SELL", "100", 50), # loss
]
metrics = compute_detailed_metrics(trades, Decimal("10000"), Decimal("10005"))
assert metrics.max_consecutive_losses >= 1
assert metrics.max_consecutive_wins >= 1
def test_risk_free_rate_affects_sharpe():
"""Higher risk-free rate should lower Sharpe ratio."""
base = datetime(2025, 1, 1, tzinfo=UTC)
trades = [
TradeRecord(
time=base, symbol="AAPL", side="BUY", price=Decimal("100"), quantity=Decimal("1")
),
TradeRecord(
time=base + timedelta(days=1),
symbol="AAPL",
side="SELL",
price=Decimal("110"),
quantity=Decimal("1"),
),
TradeRecord(
time=base + timedelta(days=2),
symbol="AAPL",
side="BUY",
price=Decimal("105"),
quantity=Decimal("1"),
),
TradeRecord(
time=base + timedelta(days=3),
symbol="AAPL",
side="SELL",
price=Decimal("115"),
quantity=Decimal("1"),
),
TradeRecord(
time=base + timedelta(days=4),
symbol="AAPL",
side="BUY",
price=Decimal("110"),
quantity=Decimal("1"),
),
TradeRecord(
time=base + timedelta(days=5),
symbol="AAPL",
side="SELL",
price=Decimal("108"),
quantity=Decimal("1"),
),
]
m1 = compute_detailed_metrics(trades, Decimal("10000"), Decimal("10018"), risk_free_rate=0.0)
m2 = compute_detailed_metrics(trades, Decimal("10000"), Decimal("10018"), risk_free_rate=0.10)
assert m2.sharpe_ratio <= m1.sharpe_ratio
def test_daily_returns_populated():
"""Daily returns list should be populated when there are trades."""
trades = [
_make_trade("BUY", "100", 0),
_make_trade("SELL", "110", 60),
_make_trade("BUY", "105", 120),
_make_trade("SELL", "115", 180),
]
metrics = compute_detailed_metrics(trades, Decimal("10000"), Decimal("10020"))
assert len(metrics.daily_returns) > 0
def test_fee_subtracted_from_pnl():
"""Fees should be subtracted from trade PnL."""
base = datetime(2025, 1, 1, tzinfo=UTC)
trades_with_fees = [
TradeRecord(
time=base,
symbol="BTC",
side="BUY",
price=Decimal("100"),
quantity=Decimal("1"),
fee=Decimal("1"),
),
TradeRecord(
time=base + timedelta(minutes=10),
symbol="BTC",
side="SELL",
price=Decimal("110"),
quantity=Decimal("1"),
fee=Decimal("1"),
),
]
# PnL should be 10 - 1 - 1 = 8
metrics = compute_detailed_metrics(trades_with_fees, Decimal("10000"), Decimal("10008"))
assert metrics.winning_trades == 1
assert metrics.trade_pairs[0]["pnl"] == pytest.approx(8.0)
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