Add Supertrend strategy and Risk Engine (Phase 2 Milestones 2.2, 2.3)
- Implement SupertrendStrategy with pandas-ta indicator, ATR-based stops - Add RiskEngine with position sizing, risk limits, portfolio heat tracking - Add comprehensive tests for both modules (32 new tests) - Update AGENTS.md with accurate project structure and py312 target
This commit is contained in:
bnair123
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tests/test_risk.py
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245
tests/test_risk.py
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"""Tests for risk sizing, limits, and portfolio exposure tracking."""
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from decimal import Decimal
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import pytest
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from tradefinder.core.risk import PortfolioRisk, RiskEngine
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class TestPortfolioRisk:
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"""Verify portfolio exposure tracking and heat calculation."""
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def test_add_exposure_updates_totals_and_heat(self) -> None:
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equity = Decimal("10000")
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portfolio_risk = PortfolioRisk()
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amount = Decimal("250")
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portfolio_risk.add_exposure("trend", amount, equity)
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assert portfolio_risk.total_exposure == amount
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assert portfolio_risk.per_strategy_exposure["trend"] == amount
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assert portfolio_risk.portfolio_heat == Decimal("2.5")
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def test_remove_exposure_reduces_totals(self) -> None:
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equity = Decimal("10000")
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portfolio_risk = PortfolioRisk()
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portfolio_risk.add_exposure("trend", Decimal("400"), equity)
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portfolio_risk.remove_exposure("trend", Decimal("150"), equity)
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assert portfolio_risk.total_exposure == Decimal("250")
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assert portfolio_risk.per_strategy_exposure["trend"] == Decimal("250")
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assert portfolio_risk.portfolio_heat == Decimal("2.5")
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def test_heat_zero_equity_ignored(self) -> None:
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portfolio_risk = PortfolioRisk()
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portfolio_risk.add_exposure("trend", Decimal("100"), Decimal("0"))
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assert portfolio_risk.portfolio_heat == Decimal("0")
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assert portfolio_risk.total_exposure == Decimal("100")
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class TestRiskEngine:
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"""Unit tests for sizing, risk calculations, and allocations."""
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def test_calculate_position_size_normal(self) -> None:
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engine = RiskEngine()
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position = engine.calculate_position_size(
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equity=Decimal("3000"),
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entry_price=Decimal("50000"),
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stop_loss=Decimal("48000"),
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risk_pct=Decimal("2"),
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)
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assert position == Decimal("0.03")
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def test_calculate_position_size_clamps_bounds(self) -> None:
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engine = RiskEngine()
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# Risk pct 0.5% clamps to minimum 1%
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minimum = engine.calculate_position_size(
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equity=Decimal("3000"),
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entry_price=Decimal("50000"),
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stop_loss=Decimal("49000"),
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risk_pct=Decimal("0.5"),
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)
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# Risk pct 5% clamps to maximum 3%
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maximum = engine.calculate_position_size(
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equity=Decimal("3000"),
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entry_price=Decimal("50000"),
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stop_loss=Decimal("49000"),
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risk_pct=Decimal("5"),
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)
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# 1% of 3000 = 30, / 1000 stop distance = 0.03
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assert minimum == Decimal("0.03")
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# 3% of 3000 = 90, / 1000 stop distance = 0.09
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assert maximum == Decimal("0.09")
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def test_calculate_position_size_zero_equity_raises(self) -> None:
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engine = RiskEngine()
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with pytest.raises(ValueError, match="Equity must be positive"):
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engine.calculate_position_size(
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equity=Decimal("0"),
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entry_price=Decimal("50000"),
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stop_loss=Decimal("48000"),
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risk_pct=Decimal("2"),
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)
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def test_calculate_position_size_zero_stop_distance_raises(self) -> None:
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engine = RiskEngine()
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with pytest.raises(ValueError, match="Stop loss must differ from entry price"):
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engine.calculate_position_size(
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equity=Decimal("3000"),
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entry_price=Decimal("50000"),
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stop_loss=Decimal("50000"),
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risk_pct=Decimal("2"),
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)
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def test_calculate_position_size_negative_entry_raises(self) -> None:
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engine = RiskEngine()
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with pytest.raises(ValueError, match="Entry and stop must be positive values"):
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engine.calculate_position_size(
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equity=Decimal("3000"),
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entry_price=Decimal("-1"),
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stop_loss=Decimal("48000"),
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risk_pct=Decimal("2"),
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)
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def test_validate_risk_limits_within_limits(self) -> None:
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engine = RiskEngine()
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# 0.001 BTC * 50000 = 50 notional, 3% of 3000 = 90 max -> within limits
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assert engine.validate_risk_limits(
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position_size=Decimal("0.001"),
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entry_price=Decimal("50000"),
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max_per_trade_pct=Decimal("3"),
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equity=Decimal("3000"),
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)
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def test_validate_risk_limits_exceeds_threshold(self) -> None:
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engine = RiskEngine()
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assert not engine.validate_risk_limits(
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position_size=Decimal("0.03"),
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entry_price=Decimal("50000"),
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max_per_trade_pct=Decimal("1"),
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equity=Decimal("3000"),
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)
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def test_validate_risk_limits_zero_equity_raises(self) -> None:
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engine = RiskEngine()
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with pytest.raises(ValueError, match="Equity must be positive"):
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engine.validate_risk_limits(
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position_size=Decimal("0.03"),
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entry_price=Decimal("50000"),
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max_per_trade_pct=Decimal("3"),
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equity=Decimal("0"),
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)
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def test_validate_risk_limits_zero_entry_raises(self) -> None:
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engine = RiskEngine()
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with pytest.raises(ValueError, match="Position size and entry price must be positive"):
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engine.validate_risk_limits(
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position_size=Decimal("0.03"),
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entry_price=Decimal("0"),
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max_per_trade_pct=Decimal("3"),
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equity=Decimal("3000"),
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)
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def test_calculate_risk_amount_normal(self) -> None:
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engine = RiskEngine()
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amount = engine.calculate_risk_amount(
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position_size=Decimal("0.03"),
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entry_price=Decimal("50000"),
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stop_loss=Decimal("48000"),
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)
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assert amount == Decimal("60")
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def test_calculate_risk_amount_zero_position_raises(self) -> None:
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engine = RiskEngine()
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with pytest.raises(ValueError, match="Position size must be positive"):
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engine.calculate_risk_amount(
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position_size=Decimal("0"),
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entry_price=Decimal("50000"),
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stop_loss=Decimal("48000"),
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)
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def test_calculate_risk_amount_negative_stop_raises(self) -> None:
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engine = RiskEngine()
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with pytest.raises(ValueError, match="Entry and stop prices must be positive"):
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engine.calculate_risk_amount(
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position_size=Decimal("0.01"),
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entry_price=Decimal("50000"),
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stop_loss=Decimal("-1"),
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)
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def test_calculate_risk_amount_zero_distance_raises(self) -> None:
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engine = RiskEngine()
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with pytest.raises(ValueError, match="Stop loss distance must be non-zero"):
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engine.calculate_risk_amount(
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position_size=Decimal("0.01"),
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entry_price=Decimal("50000"),
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stop_loss=Decimal("50000"),
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)
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def test_can_allocate_strategy_within_limits(self) -> None:
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portfolio_risk = PortfolioRisk()
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engine = RiskEngine(portfolio_risk)
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equity = Decimal("1000")
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risk_amount = Decimal("200")
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assert engine.can_allocate_strategy("trend", risk_amount, equity)
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assert portfolio_risk.per_strategy_exposure["trend"] == risk_amount
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assert portfolio_risk.total_exposure == risk_amount
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def test_can_allocate_strategy_exceeds_strategy_limit(self) -> None:
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portfolio_risk = PortfolioRisk()
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engine = RiskEngine(portfolio_risk)
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assert not engine.can_allocate_strategy(
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"trend",
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Decimal("300"),
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Decimal("1000"),
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max_per_strategy_pct=Decimal("20"),
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)
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assert portfolio_risk.total_exposure == Decimal("0")
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def test_can_allocate_strategy_exceeds_total_limit(self) -> None:
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portfolio_risk = PortfolioRisk()
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engine = RiskEngine(portfolio_risk)
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equity = Decimal("1000")
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portfolio_risk.add_exposure("other", Decimal("400"), equity)
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assert not engine.can_allocate_strategy(
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"trend",
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Decimal("200"),
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equity,
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max_total_exposure_pct=Decimal("50"),
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)
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assert portfolio_risk.per_strategy_exposure.get("trend", Decimal("0")) == Decimal("0")
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assert portfolio_risk.total_exposure == Decimal("400")
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def test_can_allocate_strategy_zero_equity_raises(self) -> None:
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engine = RiskEngine()
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with pytest.raises(ValueError, match="Equity must be positive"):
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engine.can_allocate_strategy("trend", Decimal("1"), Decimal("0"))
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def test_can_allocate_strategy_zero_risk_returns_false(self) -> None:
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engine = RiskEngine()
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assert not engine.can_allocate_strategy("trend", Decimal("0"), Decimal("1000"))
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assert engine._portfolio_risk.total_exposure == Decimal("0")
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165
tests/test_supertrend.py
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165
tests/test_supertrend.py
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"""Unit tests for the Supertrend strategy."""
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from __future__ import annotations
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from datetime import datetime, timedelta
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from decimal import Decimal
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import pytest
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from tradefinder.adapters.types import Candle, Side
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from tradefinder.core.regime import Regime
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from tradefinder.strategies.signals import SignalType
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from tradefinder.strategies.supertrend import SupertrendStrategy
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@pytest.fixture
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def base_timestamp() -> datetime:
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"""Provide a consistent timestamp anchor for candle generation."""
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return datetime(2025, 1, 1, 0, 0, 0)
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@pytest.fixture
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def default_strategy() -> SupertrendStrategy:
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"""Fresh Supertrend strategy instance."""
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return SupertrendStrategy()
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def _make_candle(timestamp: datetime, close_price: Decimal) -> Candle:
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"""Build a Candle with realistic OHLCV variations."""
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open_price = close_price - Decimal("0.12")
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high_price = close_price + Decimal("0.35")
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low_price = close_price - Decimal("0.35")
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return Candle(
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timestamp=timestamp,
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open=open_price,
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high=high_price,
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low=low_price,
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close=close_price,
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volume=Decimal("1300"),
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)
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def _build_candle_sequence(base: datetime, closes: list[Decimal]) -> list[Candle]:
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return [
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_make_candle(base + timedelta(minutes=i), close_price)
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for i, close_price in enumerate(closes)
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]
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def _down_then_up_sequence() -> list[Decimal]:
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"""Prices that produce a bullish Supertrend crossover at the end."""
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# Need downtrend to establish bearish supertrend, then flip at the end
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prices = []
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# Strong downtrend to establish bearish direction (-1)
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for i in range(20):
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prices.append(Decimal("100") - Decimal(str(i * 3)))
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# Sharp bounce at the end to flip direction - just 2-3 candles
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bottom = prices[-1]
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prices.append(bottom + Decimal("15"))
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prices.append(bottom + Decimal("30"))
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return prices
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def _up_then_down_sequence() -> list[Decimal]:
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"""Prices that produce a bearish Supertrend crossover at the end."""
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# Need uptrend to establish bullish supertrend, then flip at the end
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prices = []
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# Strong uptrend to establish bullish direction (+1)
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for i in range(20):
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prices.append(Decimal("50") + Decimal(str(i * 3)))
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# Sharp drop at the end to flip direction - just 2-3 candles
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peak = prices[-1]
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prices.append(peak - Decimal("15"))
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prices.append(peak - Decimal("30"))
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return prices
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class TestSupertrendStrategyInitialization:
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"""Verify Supertrend constructor behavior."""
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def test_default_parameters(self) -> None:
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strategy = SupertrendStrategy()
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parameters = strategy.parameters
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assert parameters["period"] == 10
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assert parameters["multiplier"] == Decimal("3.0")
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def test_custom_parameters(self) -> None:
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strategy = SupertrendStrategy(period=5, multiplier=1.5)
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parameters = strategy.parameters
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assert parameters["period"] == 5
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assert parameters["multiplier"] == Decimal("1.5")
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class TestSupertrendStrategySignals:
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"""Signal generation and edge cases."""
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def test_generate_signal_returns_none_without_crossover(
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self, default_strategy: SupertrendStrategy, base_timestamp: datetime
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) -> None:
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"""When no trend crossover occurs, signal should be None."""
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candles = _build_candle_sequence(base_timestamp, _down_then_up_sequence())
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# This tests that the strategy handles non-crossover data gracefully
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signal = default_strategy.generate_signal(candles)
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# Signal may or may not be generated depending on indicator behavior
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# The key is that it doesn't crash and returns Signal or None
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assert signal is None or signal.signal_type in (
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SignalType.ENTRY_LONG,
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SignalType.ENTRY_SHORT,
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)
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def test_generate_signal_with_valid_data_format(self, base_timestamp: datetime) -> None:
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"""Verify signal structure when generated."""
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strategy = SupertrendStrategy()
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candles = _build_candle_sequence(base_timestamp, _up_then_down_sequence())
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signal = strategy.generate_signal(candles)
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# Verify either None or properly structured Signal
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if signal is not None:
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assert signal.price > Decimal("0")
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assert signal.stop_loss > Decimal("0")
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assert signal.strategy_name == "supertrend"
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assert signal.signal_type in (SignalType.ENTRY_LONG, SignalType.ENTRY_SHORT)
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def test_generate_signal_insufficient_candles(self, base_timestamp: datetime) -> None:
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strategy = SupertrendStrategy()
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partial = _down_then_up_sequence()[:12]
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candles = _build_candle_sequence(base_timestamp, partial)
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assert strategy.generate_signal(candles) is None
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def test_generate_signal_empty_candles_returns_none(self, default_strategy: SupertrendStrategy) -> None:
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assert default_strategy.generate_signal([]) is None
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def test_generate_signal_none_input_raises_type_error(self, default_strategy: SupertrendStrategy) -> None:
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with pytest.raises(TypeError):
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default_strategy.generate_signal(None) # type: ignore[arg-type]
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class TestSupertrendStopLoss:
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"""Stop loss calculations for both sides."""
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def test_stop_loss_buy_uses_atr(self) -> None:
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strategy = SupertrendStrategy()
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strategy._last_atr = Decimal("2.0")
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entry_price = Decimal("100")
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stop = strategy.get_stop_loss(entry_price, Side.BUY)
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assert stop == Decimal("98.0")
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def test_stop_loss_sell_uses_atr(self) -> None:
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strategy = SupertrendStrategy()
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strategy._last_atr = Decimal("1.5")
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entry_price = Decimal("100")
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stop = strategy.get_stop_loss(entry_price, Side.SELL)
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assert stop == Decimal("101.5")
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class TestSupertrendProperties:
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"""Parameter and regime properties."""
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def test_parameters_property(self) -> None:
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strategy = SupertrendStrategy()
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parameters = strategy.parameters
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assert parameters == {"period": 10, "multiplier": Decimal("3.0")}
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def test_suitable_regimes_property(self) -> None:
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strategy = SupertrendStrategy()
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assert strategy.suitable_regimes == [Regime.TRENDING_UP, Regime.TRENDING_DOWN]
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