Initial commit: Vault Dashboard for options hedging
- FastAPI + NiceGUI web application - QuantLib-based Black-Scholes pricing with Greeks - Protective put, laddered, and LEAPS strategies - Real-time WebSocket updates - TradingView-style charts via Lightweight-Charts - Docker containerization - GitLab CI/CD pipeline for VPS deployment - VPN-only access configuration
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tests/test_strategies.py
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94
tests/test_strategies.py
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from __future__ import annotations
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import pytest
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import app.core.pricing.black_scholes as black_scholes
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from app.strategies.base import StrategyConfig
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from app.strategies.laddered_put import LadderSpec, LadderedPutStrategy
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from app.strategies.protective_put import ProtectivePutSpec, ProtectivePutStrategy
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def _force_analytic_pricing(monkeypatch: pytest.MonkeyPatch) -> None:
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"""Use deterministic analytical pricing for stable expected values."""
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monkeypatch.setattr(black_scholes, "ql", None)
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def test_protective_put_costs(
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monkeypatch: pytest.MonkeyPatch,
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sample_strategy_config: StrategyConfig,
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) -> None:
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_force_analytic_pricing(monkeypatch)
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strategy = ProtectivePutStrategy(sample_strategy_config, ProtectivePutSpec(label="ATM", strike_pct=1.0, months=12))
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cost = strategy.calculate_cost()
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assert cost["strategy"] == "protective_put_atm"
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assert cost["label"] == "ATM"
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assert cost["strike"] == 460.0
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assert cost["premium_per_share"] == pytest.approx(19.6894, abs=1e-4)
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assert cost["total_cost"] == pytest.approx(42803.14, abs=1e-2)
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assert cost["cost_pct_of_portfolio"] == pytest.approx(0.042803, abs=1e-6)
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assert cost["annualized_cost"] == pytest.approx(42803.14, abs=1e-2)
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assert cost["annualized_cost_pct"] == pytest.approx(0.042803, abs=1e-6)
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def test_laddered_strategy(sample_strategy_config: StrategyConfig, monkeypatch: pytest.MonkeyPatch) -> None:
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_force_analytic_pricing(monkeypatch)
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strategy = LadderedPutStrategy(
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sample_strategy_config,
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LadderSpec(label="50_50_ATM_OTM95", weights=(0.5, 0.5), strike_pcts=(1.0, 0.95), months=12),
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)
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cost = strategy.calculate_cost()
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protection = strategy.calculate_protection()
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assert cost["strategy"] == "laddered_put_50_50_atm_otm95"
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assert len(cost["legs"]) == 2
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assert cost["legs"][0]["weight"] == 0.5
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assert cost["legs"][0]["strike"] == 460.0
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assert cost["legs"][1]["strike"] == 437.0
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assert cost["blended_cost"] == pytest.approx(34200.72, abs=1e-2)
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assert cost["cost_pct_of_portfolio"] == pytest.approx(0.034201, abs=1e-6)
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assert protection["portfolio_floor_value"] == pytest.approx(975000.0, rel=1e-12)
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assert protection["payoff_at_threshold"] == pytest.approx(175000.0, abs=1e-2)
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assert protection["hedged_ltv_at_threshold"] == pytest.approx(0.615385, rel=1e-6)
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assert protection["maintains_margin_call_buffer"] is True
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def test_scenario_analysis(
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monkeypatch: pytest.MonkeyPatch,
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sample_strategy_config: StrategyConfig,
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) -> None:
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_force_analytic_pricing(monkeypatch)
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protective = ProtectivePutStrategy(
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sample_strategy_config,
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ProtectivePutSpec(label="ATM", strike_pct=1.0, months=12),
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)
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ladder = LadderedPutStrategy(
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sample_strategy_config,
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LadderSpec(label="50_50_ATM_OTM95", weights=(0.5, 0.5), strike_pcts=(1.0, 0.95), months=12),
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)
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protective_scenarios = protective.get_scenarios()
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ladder_scenarios = ladder.get_scenarios()
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assert len(protective_scenarios) == 12
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assert len(ladder_scenarios) == 12
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first_protective = protective_scenarios[0]
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assert first_protective["price_change_pct"] == -0.6
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assert first_protective["gld_price"] == 184.0
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assert first_protective["option_payoff"] == pytest.approx(600000.0, abs=1e-2)
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assert first_protective["hedge_cost"] == pytest.approx(42803.14, abs=1e-2)
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assert first_protective["hedged_ltv"] == pytest.approx(0.6, rel=1e-12)
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assert first_protective["margin_call_with_hedge"] is False
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first_ladder = ladder_scenarios[0]
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assert first_ladder["gld_price"] == 184.0
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assert first_ladder["option_payoff"] == pytest.approx(575000.0, abs=1e-2)
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assert first_ladder["hedge_cost"] == pytest.approx(34200.72, abs=1e-2)
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assert first_ladder["hedged_ltv"] == pytest.approx(0.615385, rel=1e-6)
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worst_ladder = ladder_scenarios[-1]
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assert worst_ladder["gld_price"] == 690.0
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assert worst_ladder["hedged_ltv"] == pytest.approx(0.4, rel=1e-12)
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assert worst_ladder["margin_call_with_hedge"] is False
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