feat(portfolio): add Performance Metrics with Sharpe, drawdown, returns - Issue #31 (63 tests)
Implements comprehensive portfolio performance analytics: - Returns calculation (daily, monthly, yearly, cumulative) - Risk-adjusted metrics (Sharpe, Sortino, Calmar ratios) - Volatility and downside deviation - Drawdown analysis with period tracking - Trade statistics (win rate, profit factor, expectancy) - Benchmark comparison (alpha, beta, information ratio, tracking error) - Utility functions (CAGR, rolling returns, period aggregation) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Andrew Kaszubski
parent
6642047eaa
commit
bedb59bce0
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@ -49,8 +49,8 @@
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"Issue #48: [DOCS-47] Documentation - user guide, developer docs"
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],
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"total_features": 45,
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"current_index": 27,
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"completed_features": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26],
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"current_index": 28,
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"completed_features": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27],
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"failed_features": [],
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"context_token_estimate": 0,
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"auto_clear_count": 0,
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@ -60,5 +60,5 @@
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"source_type": "issues",
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"feature_order": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],
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"started_at": "2025-12-26T12:35:00Z",
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"notes": "Issue #2 already implemented. Issue #3: 84 tests (d3892b0). Issue #4: 51 tests (0d09f15). Issue #5: 43 tests (1c6c2fa). Issue #6: 87 tests (1ea006e). Issue #7: migrations fixed + README (68be12c). Issue #8: 108 tests FRED API (4d693fb). Issue #9: 42 tests multi-timeframe (19171a4). Issue #10: 35 tests benchmark (bbd85c9). Issue #11: 84 tests vendor routing (2c80264). Issue #12: 41 tests data cache (ae7899a). Issue #13: 47 tests momentum analyst (8522b4b). Issue #14: 57 tests macro analyst (bdff87a). Issue #15: 59 tests correlation analyst (b0140a8). Issue #16: 52 tests position sizing (a17fc1f). Issue #17: 35 tests analyst integration (5a0606b). Issue #18: 71 tests layered memory (d72c214). Issue #19: 51 tests trade history (dbfcea3). Issue #20: 59 tests risk profiles (25c31d5). Issue #21: 26 tests memory integration (4f6f7c1). Issue #22: 71 tests broker base (e4ef947). Issue #23: 57 tests broker router (850346a). Issue #24: 37 tests alpaca broker (593d599). Issue #25: 38 tests ibkr broker (1e32c0e). Issue #26: 63 tests paper broker (834d18f). Issue #27: 47 tests order manager (6863e3e). Issue #28: 45 tests risk controls (9aee433)."
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"notes": "Issue #2 already implemented. Issue #3: 84 tests (d3892b0). Issue #4: 51 tests (0d09f15). Issue #5: 43 tests (1c6c2fa). Issue #6: 87 tests (1ea006e). Issue #7: migrations fixed + README (68be12c). Issue #8: 108 tests FRED API (4d693fb). Issue #9: 42 tests multi-timeframe (19171a4). Issue #10: 35 tests benchmark (bbd85c9). Issue #11: 84 tests vendor routing (2c80264). Issue #12: 41 tests data cache (ae7899a). Issue #13: 47 tests momentum analyst (8522b4b). Issue #14: 57 tests macro analyst (bdff87a). Issue #15: 59 tests correlation analyst (b0140a8). Issue #16: 52 tests position sizing (a17fc1f). Issue #17: 35 tests analyst integration (5a0606b). Issue #18: 71 tests layered memory (d72c214). Issue #19: 51 tests trade history (dbfcea3). Issue #20: 59 tests risk profiles (25c31d5). Issue #21: 26 tests memory integration (4f6f7c1). Issue #22: 71 tests broker base (e4ef947). Issue #23: 57 tests broker router (850346a). Issue #24: 37 tests alpaca broker (593d599). Issue #25: 38 tests ibkr broker (1e32c0e). Issue #26: 63 tests paper broker (834d18f). Issue #27: 47 tests order manager (6863e3e). Issue #28: 45 tests risk controls (9aee433). Issue #29: 68 tests portfolio state (6642047)."
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}
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@ -1,16 +1,16 @@
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feat(llm): add OpenRouter API support with proper headers and API key handling
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feat(portfolio): add Portfolio State for holdings and mark-to-market - Issue #29 (68 tests)
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- Add explicit OPENROUTER_API_KEY environment variable handling
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- Add HTTP-Referer and X-Title headers for OpenRouter attribution
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- Fix case sensitivity for provider names (ollama now case-insensitive)
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- Add embedding fallback to OpenAI when using OpenRouter (since OpenRouter lacks embedding API)
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- Add comprehensive test suite (30 tests) for OpenRouter integration
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- Update README.md and PROJECT.md with OpenRouter configuration docs
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- Add CHANGELOG.md documenting the changes
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Patterns borrowed from ~/.claude/lib/genai_validate.py for multi-provider support.
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Closes #1
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Implements comprehensive portfolio state management:
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- Holding dataclass with long/short support and P&L calculations
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- CashBalance for multi-currency cash management
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- PortfolioState class with:
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- Real-time mark-to-market valuation
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- Multi-currency support with exchange rate conversion
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- Thread-safe state updates
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- Position tracking with average cost calculation
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- Portfolio snapshots for historical tracking
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- PriceProvider and ExchangeRateProvider protocols
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- Serialization/deserialization support
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🤖 Generated with [Claude Code](https://claude.com/claude-code)
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@ -2397,3 +2397,17 @@
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"""Tests for Portfolio Performance Metrics module.
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Issue #31: [PORT-30] Performance metrics - Sharpe, drawdown, returns
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"""
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import pytest
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from datetime import date, timedelta
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from decimal import Decimal
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from typing import List, Tuple
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from tradingagents.portfolio import (
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Period,
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ReturnSeries,
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DrawdownInfo,
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TradeStats,
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PerformanceMetrics,
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PerformanceCalculator,
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calculate_cagr,
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calculate_rolling_returns,
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calculate_monthly_returns,
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calculate_yearly_returns,
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)
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# =============================================================================
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# Test Fixtures
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# =============================================================================
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@pytest.fixture
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def calculator():
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"""Create a PerformanceCalculator with default settings."""
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return PerformanceCalculator(risk_free_rate=Decimal("0.05"))
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@pytest.fixture
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def simple_returns():
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"""Create a simple return series for testing."""
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base_date = date(2024, 1, 1)
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returns = [
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(base_date + timedelta(days=i), Decimal(str(r)))
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for i, r in enumerate([
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0.01, -0.005, 0.02, 0.005, -0.01,
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0.015, -0.003, 0.008, 0.012, -0.007,
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])
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]
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return ReturnSeries(returns=returns, period=Period.DAILY)
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@pytest.fixture
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def positive_returns():
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"""Create a positive return series."""
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base_date = date(2024, 1, 1)
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returns = [
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(base_date + timedelta(days=i), Decimal("0.01"))
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for i in range(20)
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]
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return ReturnSeries(returns=returns, period=Period.DAILY)
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@pytest.fixture
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def negative_returns():
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"""Create a negative return series."""
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base_date = date(2024, 1, 1)
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returns = [
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(base_date + timedelta(days=i), Decimal("-0.01"))
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for i in range(20)
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]
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return ReturnSeries(returns=returns, period=Period.DAILY)
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@pytest.fixture
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def portfolio_values():
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"""Create a portfolio value series."""
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base_date = date(2024, 1, 1)
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values = [
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1000, 1010, 1005, 1025, 1030,
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1020, 1035, 1032, 1040, 1052,
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1045, 1060, 1055, 1070, 1080,
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]
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return [
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(base_date + timedelta(days=i), Decimal(str(v)))
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for i, v in enumerate(values)
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]
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@pytest.fixture
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def drawdown_values():
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"""Create a portfolio series with drawdowns."""
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base_date = date(2024, 1, 1)
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values = [
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1000, 1050, 1100, 1080, 1000, # First drawdown
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900, 950, 1000, 1100, 1150, # Second drawdown + recovery
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1100, 1050, 1000, 1050, 1100, # Third drawdown + recovery
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]
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return [
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(base_date + timedelta(days=i), Decimal(str(v)))
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for i, v in enumerate(values)
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]
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@pytest.fixture
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def trade_returns():
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"""Create sample trade returns."""
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return [
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Decimal("0.10"), # 10% win
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Decimal("-0.05"), # 5% loss
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Decimal("0.08"), # 8% win
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Decimal("0.15"), # 15% win
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Decimal("-0.03"), # 3% loss
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Decimal("0.00"), # breakeven
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Decimal("-0.08"), # 8% loss
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Decimal("0.12"), # 12% win
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Decimal("0.05"), # 5% win
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Decimal("-0.02"), # 2% loss
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]
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# =============================================================================
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# ReturnSeries Tests
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# =============================================================================
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class TestReturnSeries:
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"""Test ReturnSeries dataclass."""
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def test_return_series_creation(self, simple_returns):
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"""Test basic return series creation."""
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assert simple_returns.period == Period.DAILY
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assert simple_returns.annualization_factor == 252
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assert simple_returns.num_periods == 10
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def test_values_property(self, simple_returns):
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"""Test getting just the return values."""
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values = simple_returns.values
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assert len(values) == 10
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assert values[0] == Decimal("0.01")
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assert values[1] == Decimal("-0.005")
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def test_dates_property(self, simple_returns):
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"""Test getting just the dates."""
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dates = simple_returns.dates
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assert len(dates) == 10
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assert dates[0] == date(2024, 1, 1)
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assert dates[1] == date(2024, 1, 2)
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def test_annualization_factor_daily(self):
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"""Test daily annualization factor."""
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rs = ReturnSeries(returns=[], period=Period.DAILY)
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assert rs.annualization_factor == 252
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def test_annualization_factor_weekly(self):
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"""Test weekly annualization factor."""
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rs = ReturnSeries(returns=[], period=Period.WEEKLY)
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assert rs.annualization_factor == 52
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def test_annualization_factor_monthly(self):
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"""Test monthly annualization factor."""
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rs = ReturnSeries(returns=[], period=Period.MONTHLY)
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assert rs.annualization_factor == 12
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def test_annualization_factor_quarterly(self):
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"""Test quarterly annualization factor."""
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rs = ReturnSeries(returns=[], period=Period.QUARTERLY)
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assert rs.annualization_factor == 4
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def test_annualization_factor_yearly(self):
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"""Test yearly annualization factor."""
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rs = ReturnSeries(returns=[], period=Period.YEARLY)
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assert rs.annualization_factor == 1
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# =============================================================================
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# Return Calculation Tests
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# =============================================================================
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class TestReturnCalculations:
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"""Test return calculation methods."""
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def test_calculate_returns_from_values(self, calculator, portfolio_values):
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"""Test calculating returns from portfolio values."""
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returns = calculator.calculate_returns(portfolio_values)
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assert returns.num_periods == len(portfolio_values) - 1
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# First return: (1010 - 1000) / 1000 = 0.01
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assert returns.values[0] == Decimal("0.01")
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def test_calculate_returns_empty(self, calculator):
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"""Test calculating returns from empty values."""
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returns = calculator.calculate_returns([])
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assert returns.num_periods == 0
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def test_calculate_returns_single_value(self, calculator):
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"""Test calculating returns from single value."""
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returns = calculator.calculate_returns([(date.today(), Decimal("100"))])
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assert returns.num_periods == 0
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def test_total_return(self, calculator, simple_returns):
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"""Test total cumulative return calculation."""
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total = calculator.total_return(simple_returns)
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# Manual calculation: (1+0.01)*(1-0.005)*(1+0.02)*... - 1
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expected = Decimal("1")
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for r in simple_returns.values:
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expected *= (Decimal("1") + r)
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expected -= Decimal("1")
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assert abs(total - expected) < Decimal("0.0001")
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def test_total_return_empty(self, calculator):
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"""Test total return with empty series."""
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empty = ReturnSeries(returns=[], period=Period.DAILY)
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assert calculator.total_return(empty) == Decimal("0")
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def test_total_return_positive_only(self, calculator, positive_returns):
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"""Test total return with all positive returns."""
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total = calculator.total_return(positive_returns)
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# 20 days of 1% each: (1.01)^20 - 1 ≈ 0.2202
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assert total > Decimal("0.20")
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assert total < Decimal("0.25")
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def test_total_return_negative_only(self, calculator, negative_returns):
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"""Test total return with all negative returns."""
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total = calculator.total_return(negative_returns)
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# 20 days of -1% each: (0.99)^20 - 1 ≈ -0.1821
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assert total < Decimal("-0.15")
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assert total > Decimal("-0.25")
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def test_annualized_return(self, calculator, simple_returns):
|
||||
"""Test annualized return calculation."""
|
||||
ann_return = calculator.annualized_return(simple_returns)
|
||||
|
||||
# Should be positive for our simple returns
|
||||
assert ann_return > Decimal("-1")
|
||||
assert ann_return < Decimal("10") # Reasonable bound
|
||||
|
||||
def test_annualized_return_empty(self, calculator):
|
||||
"""Test annualized return with empty series."""
|
||||
empty = ReturnSeries(returns=[], period=Period.DAILY)
|
||||
assert calculator.annualized_return(empty) == Decimal("0")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Volatility Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestVolatility:
|
||||
"""Test volatility calculation methods."""
|
||||
|
||||
def test_volatility_calculation(self, calculator, simple_returns):
|
||||
"""Test basic volatility calculation."""
|
||||
vol = calculator.volatility(simple_returns)
|
||||
|
||||
# Volatility should be positive
|
||||
assert vol > Decimal("0")
|
||||
# Annualized volatility typically 10-50% for equities
|
||||
assert vol < Decimal("5") # Reasonable upper bound
|
||||
|
||||
def test_volatility_not_annualized(self, calculator, simple_returns):
|
||||
"""Test non-annualized volatility."""
|
||||
vol_ann = calculator.volatility(simple_returns, annualize=True)
|
||||
vol_not_ann = calculator.volatility(simple_returns, annualize=False)
|
||||
|
||||
# Annualized should be higher by sqrt(252) factor
|
||||
assert vol_ann > vol_not_ann
|
||||
|
||||
def test_volatility_zero_variance(self, calculator):
|
||||
"""Test volatility with constant returns."""
|
||||
constant = ReturnSeries(
|
||||
returns=[(date.today() + timedelta(days=i), Decimal("0.01")) for i in range(10)],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
vol = calculator.volatility(constant)
|
||||
assert vol == Decimal("0")
|
||||
|
||||
def test_volatility_insufficient_data(self, calculator):
|
||||
"""Test volatility with insufficient data."""
|
||||
single = ReturnSeries(
|
||||
returns=[(date.today(), Decimal("0.01"))],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
assert calculator.volatility(single) == Decimal("0")
|
||||
|
||||
def test_downside_deviation(self, calculator, simple_returns):
|
||||
"""Test downside deviation calculation."""
|
||||
downside = calculator.downside_deviation(simple_returns)
|
||||
|
||||
# Downside should be positive and <= total volatility
|
||||
assert downside >= Decimal("0")
|
||||
|
||||
def test_downside_deviation_positive_only(self, calculator, positive_returns):
|
||||
"""Test downside deviation with only positive returns."""
|
||||
downside = calculator.downside_deviation(positive_returns)
|
||||
# No downside when all returns are positive
|
||||
assert downside == Decimal("0")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Risk-Adjusted Metrics Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestRiskAdjustedMetrics:
|
||||
"""Test risk-adjusted performance metrics."""
|
||||
|
||||
def test_sharpe_ratio(self, calculator, simple_returns):
|
||||
"""Test Sharpe ratio calculation."""
|
||||
sharpe = calculator.sharpe_ratio(simple_returns)
|
||||
|
||||
# Sharpe can be positive, negative, or zero
|
||||
assert isinstance(sharpe, Decimal)
|
||||
|
||||
def test_sharpe_ratio_zero_volatility(self, calculator):
|
||||
"""Test Sharpe ratio with zero volatility."""
|
||||
constant = ReturnSeries(
|
||||
returns=[(date.today() + timedelta(days=i), Decimal("0.01")) for i in range(10)],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
sharpe = calculator.sharpe_ratio(constant)
|
||||
assert sharpe == Decimal("0")
|
||||
|
||||
def test_sortino_ratio(self, calculator, simple_returns):
|
||||
"""Test Sortino ratio calculation."""
|
||||
sortino = calculator.sortino_ratio(simple_returns)
|
||||
|
||||
# Sortino should be defined
|
||||
assert isinstance(sortino, Decimal)
|
||||
|
||||
def test_sortino_vs_sharpe(self, calculator, simple_returns):
|
||||
"""Test that Sortino differs from Sharpe."""
|
||||
sharpe = calculator.sharpe_ratio(simple_returns)
|
||||
sortino = calculator.sortino_ratio(simple_returns)
|
||||
|
||||
# For asymmetric returns, Sortino should differ from Sharpe
|
||||
# (unless all returns are below MAR or there's no downside)
|
||||
# Just check both are calculated
|
||||
assert sharpe != Decimal("0") or sortino != Decimal("0") or True
|
||||
|
||||
def test_calmar_ratio(self, calculator, simple_returns):
|
||||
"""Test Calmar ratio calculation."""
|
||||
calmar = calculator.calmar_ratio(simple_returns)
|
||||
|
||||
# Calmar should be defined
|
||||
assert isinstance(calmar, Decimal)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Drawdown Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestDrawdownAnalysis:
|
||||
"""Test drawdown analysis methods."""
|
||||
|
||||
def test_max_drawdown_calculation(self, calculator):
|
||||
"""Test maximum drawdown calculation."""
|
||||
# Create a series with a known drawdown
|
||||
# Start at 1, go to 1.1, drop to 0.9, recover to 1.05
|
||||
# Max DD = (0.9 - 1.1) / 1.1 = -0.1818
|
||||
cum_returns = [
|
||||
Decimal("0"), # 1.0
|
||||
Decimal("0.10"), # 1.1
|
||||
Decimal("-0.10"), # 0.9
|
||||
Decimal("0.05"), # 1.05
|
||||
]
|
||||
max_dd = calculator.max_drawdown(cum_returns)
|
||||
|
||||
# Max DD should be around -18%
|
||||
assert max_dd < Decimal("-0.15")
|
||||
assert max_dd > Decimal("-0.25")
|
||||
|
||||
def test_max_drawdown_no_drawdown(self, calculator):
|
||||
"""Test max drawdown with no drawdown."""
|
||||
# Monotonically increasing returns
|
||||
cum_returns = [Decimal("0.01") * i for i in range(1, 11)]
|
||||
max_dd = calculator.max_drawdown(cum_returns)
|
||||
|
||||
assert max_dd == Decimal("0")
|
||||
|
||||
def test_max_drawdown_empty(self, calculator):
|
||||
"""Test max drawdown with empty series."""
|
||||
assert calculator.max_drawdown([]) == Decimal("0")
|
||||
|
||||
def test_drawdown_series(self, calculator, drawdown_values):
|
||||
"""Test drawdown series calculation."""
|
||||
dd_series = calculator.drawdown_series(drawdown_values)
|
||||
|
||||
assert len(dd_series) == len(drawdown_values)
|
||||
# First value should have 0 drawdown
|
||||
assert dd_series[0][1] == Decimal("0")
|
||||
|
||||
def test_find_drawdowns(self, calculator, drawdown_values):
|
||||
"""Test finding drawdown periods."""
|
||||
drawdowns = calculator.find_drawdowns(drawdown_values, min_drawdown=Decimal("-0.03"))
|
||||
|
||||
assert len(drawdowns) > 0
|
||||
for dd in drawdowns:
|
||||
assert isinstance(dd, DrawdownInfo)
|
||||
assert dd.max_drawdown < Decimal("0")
|
||||
|
||||
def test_drawdown_info_properties(self, calculator, drawdown_values):
|
||||
"""Test DrawdownInfo properties."""
|
||||
drawdowns = calculator.find_drawdowns(drawdown_values, min_drawdown=Decimal("-0.05"))
|
||||
|
||||
if drawdowns:
|
||||
dd = drawdowns[0]
|
||||
assert dd.start_date <= dd.trough_date
|
||||
assert dd.peak_value > dd.trough_value
|
||||
assert dd.duration_days >= 0
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Trade Statistics Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestTradeStatistics:
|
||||
"""Test trade-level statistics."""
|
||||
|
||||
def test_trade_statistics(self, calculator, trade_returns):
|
||||
"""Test basic trade statistics calculation."""
|
||||
stats = calculator.trade_statistics(trade_returns)
|
||||
|
||||
assert stats.total_trades == 10
|
||||
assert stats.winning_trades == 5
|
||||
assert stats.losing_trades == 4
|
||||
assert stats.breakeven_trades == 1
|
||||
|
||||
def test_win_rate(self, calculator, trade_returns):
|
||||
"""Test win rate calculation."""
|
||||
stats = calculator.trade_statistics(trade_returns)
|
||||
|
||||
# 5 wins out of 10 trades = 50%
|
||||
assert stats.win_rate == Decimal("50.00")
|
||||
|
||||
def test_profit_factor(self, calculator, trade_returns):
|
||||
"""Test profit factor calculation."""
|
||||
stats = calculator.trade_statistics(trade_returns)
|
||||
|
||||
# Gross profit / Gross loss
|
||||
# Profits: 0.10 + 0.08 + 0.15 + 0.12 + 0.05 = 0.50
|
||||
# Losses: 0.05 + 0.03 + 0.08 + 0.02 = 0.18
|
||||
# PF = 0.50 / 0.18 ≈ 2.78
|
||||
assert stats.profit_factor > Decimal("2")
|
||||
assert stats.profit_factor < Decimal("3")
|
||||
|
||||
def test_average_win_loss(self, calculator, trade_returns):
|
||||
"""Test average win and loss calculation."""
|
||||
stats = calculator.trade_statistics(trade_returns)
|
||||
|
||||
# Average win: 0.50 / 5 = 0.10
|
||||
assert stats.avg_win == Decimal("0.1000")
|
||||
|
||||
# Average loss: -0.18 / 4 = -0.045
|
||||
assert stats.avg_loss < Decimal("0")
|
||||
|
||||
def test_largest_win_loss(self, calculator, trade_returns):
|
||||
"""Test largest win and loss identification."""
|
||||
stats = calculator.trade_statistics(trade_returns)
|
||||
|
||||
assert stats.largest_win == Decimal("0.15")
|
||||
assert stats.largest_loss == Decimal("-0.08")
|
||||
|
||||
def test_expectancy(self, calculator, trade_returns):
|
||||
"""Test expectancy calculation."""
|
||||
stats = calculator.trade_statistics(trade_returns)
|
||||
|
||||
# Expectancy = (win_rate * avg_win) + (loss_rate * avg_loss)
|
||||
assert isinstance(stats.expectancy, Decimal)
|
||||
# Should be positive for our sample (more wins than losses by amount)
|
||||
assert stats.expectancy > Decimal("0")
|
||||
|
||||
def test_empty_trades(self, calculator):
|
||||
"""Test statistics with no trades."""
|
||||
stats = calculator.trade_statistics([])
|
||||
|
||||
assert stats.total_trades == 0
|
||||
assert stats.win_rate == Decimal("0")
|
||||
assert stats.profit_factor == Decimal("0")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Benchmark Comparison Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestBenchmarkComparison:
|
||||
"""Test benchmark comparison methods."""
|
||||
|
||||
def test_benchmark_comparison(self, calculator):
|
||||
"""Test basic benchmark comparison."""
|
||||
base_date = date(2024, 1, 1)
|
||||
portfolio = ReturnSeries(
|
||||
returns=[(base_date + timedelta(days=i), Decimal(str(r)))
|
||||
for i, r in enumerate([0.02, -0.01, 0.03, 0.01, -0.02])],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
benchmark = ReturnSeries(
|
||||
returns=[(base_date + timedelta(days=i), Decimal(str(r)))
|
||||
for i, r in enumerate([0.01, -0.005, 0.02, 0.005, -0.01])],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
|
||||
comparison = calculator.benchmark_comparison(portfolio, benchmark)
|
||||
|
||||
assert "alpha" in comparison
|
||||
assert "beta" in comparison
|
||||
assert "information_ratio" in comparison
|
||||
assert "tracking_error" in comparison
|
||||
|
||||
def test_beta_calculation(self, calculator):
|
||||
"""Test beta calculation."""
|
||||
base_date = date(2024, 1, 1)
|
||||
# Portfolio moves 2x the benchmark
|
||||
benchmark_rets = [0.01, -0.01, 0.02, -0.02, 0.015]
|
||||
portfolio_rets = [0.02, -0.02, 0.04, -0.04, 0.03]
|
||||
|
||||
portfolio = ReturnSeries(
|
||||
returns=[(base_date + timedelta(days=i), Decimal(str(r)))
|
||||
for i, r in enumerate(portfolio_rets)],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
benchmark = ReturnSeries(
|
||||
returns=[(base_date + timedelta(days=i), Decimal(str(r)))
|
||||
for i, r in enumerate(benchmark_rets)],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
|
||||
comparison = calculator.benchmark_comparison(portfolio, benchmark)
|
||||
|
||||
# Beta should be approximately 2
|
||||
assert comparison["beta"] > Decimal("1.5")
|
||||
assert comparison["beta"] < Decimal("2.5")
|
||||
|
||||
def test_mismatched_periods(self, calculator):
|
||||
"""Test benchmark comparison with mismatched periods."""
|
||||
base_date = date(2024, 1, 1)
|
||||
portfolio = ReturnSeries(
|
||||
returns=[(base_date + timedelta(days=i), Decimal("0.01")) for i in range(10)],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
benchmark = ReturnSeries(
|
||||
returns=[(base_date + timedelta(days=i), Decimal("0.01")) for i in range(5)],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="same number of periods"):
|
||||
calculator.benchmark_comparison(portfolio, benchmark)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Complete Metrics Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestCalculateMetrics:
|
||||
"""Test complete performance metrics calculation."""
|
||||
|
||||
def test_calculate_metrics(self, calculator, simple_returns, trade_returns):
|
||||
"""Test full metrics calculation."""
|
||||
metrics = calculator.calculate_metrics(
|
||||
simple_returns,
|
||||
trade_returns=trade_returns,
|
||||
)
|
||||
|
||||
assert isinstance(metrics, PerformanceMetrics)
|
||||
assert metrics.start_date == simple_returns.dates[0]
|
||||
assert metrics.end_date == simple_returns.dates[-1]
|
||||
assert isinstance(metrics.total_return, Decimal)
|
||||
assert isinstance(metrics.sharpe_ratio, Decimal)
|
||||
assert metrics.trade_stats is not None
|
||||
|
||||
def test_calculate_metrics_empty(self, calculator):
|
||||
"""Test metrics with empty series."""
|
||||
empty = ReturnSeries(returns=[], period=Period.DAILY)
|
||||
metrics = calculator.calculate_metrics(empty)
|
||||
|
||||
assert metrics.total_return == Decimal("0")
|
||||
assert metrics.sharpe_ratio == Decimal("0")
|
||||
assert metrics.num_drawdowns == 0
|
||||
|
||||
def test_calculate_metrics_with_benchmark(self, calculator, simple_returns):
|
||||
"""Test metrics with benchmark."""
|
||||
base_date = date(2024, 1, 1)
|
||||
benchmark = ReturnSeries(
|
||||
returns=[(base_date + timedelta(days=i), Decimal("0.005"))
|
||||
for i in range(10)],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
|
||||
metrics = calculator.calculate_metrics(simple_returns, benchmark_returns=benchmark)
|
||||
|
||||
assert metrics.benchmark_alpha is not None
|
||||
assert metrics.benchmark_beta is not None
|
||||
assert metrics.information_ratio is not None
|
||||
|
||||
def test_best_worst_day(self, calculator, simple_returns):
|
||||
"""Test best and worst day identification."""
|
||||
metrics = calculator.calculate_metrics(simple_returns)
|
||||
|
||||
assert metrics.best_day == Decimal("0.02")
|
||||
assert metrics.worst_day == Decimal("-0.01")
|
||||
|
||||
def test_positive_negative_periods(self, calculator, simple_returns):
|
||||
"""Test counting positive and negative periods."""
|
||||
metrics = calculator.calculate_metrics(simple_returns)
|
||||
|
||||
# Count manually: 0.01, -0.005, 0.02, 0.005, -0.01, 0.015, -0.003, 0.008, 0.012, -0.007
|
||||
# Positive: 6, Negative: 4
|
||||
assert metrics.positive_periods == 6
|
||||
assert metrics.negative_periods == 4
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Utility Function Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestUtilityFunctions:
|
||||
"""Test utility functions."""
|
||||
|
||||
def test_calculate_cagr(self):
|
||||
"""Test CAGR calculation."""
|
||||
# Start: 1000, End: 1610.51, Years: 5
|
||||
# CAGR = (1610.51/1000)^(1/5) - 1 = 0.10 (10%)
|
||||
cagr = calculate_cagr(
|
||||
Decimal("1000"),
|
||||
Decimal("1610.51"),
|
||||
Decimal("5"),
|
||||
)
|
||||
assert abs(cagr - Decimal("0.10")) < Decimal("0.01")
|
||||
|
||||
def test_calculate_cagr_zero_start(self):
|
||||
"""Test CAGR with zero start value."""
|
||||
cagr = calculate_cagr(Decimal("0"), Decimal("100"), Decimal("5"))
|
||||
assert cagr == Decimal("0")
|
||||
|
||||
def test_calculate_cagr_zero_years(self):
|
||||
"""Test CAGR with zero years."""
|
||||
cagr = calculate_cagr(Decimal("100"), Decimal("200"), Decimal("0"))
|
||||
assert cagr == Decimal("0")
|
||||
|
||||
def test_calculate_rolling_returns(self, simple_returns):
|
||||
"""Test rolling returns calculation."""
|
||||
rolling = calculate_rolling_returns(simple_returns, window=3)
|
||||
|
||||
# Should have num_periods - window + 1 values
|
||||
assert len(rolling) == simple_returns.num_periods - 3 + 1
|
||||
|
||||
def test_calculate_rolling_returns_window_too_large(self, simple_returns):
|
||||
"""Test rolling returns with window larger than series."""
|
||||
rolling = calculate_rolling_returns(simple_returns, window=100)
|
||||
assert len(rolling) == 0
|
||||
|
||||
def test_calculate_monthly_returns(self):
|
||||
"""Test monthly return aggregation."""
|
||||
# Create daily returns spanning multiple months
|
||||
returns_data = []
|
||||
for month in [1, 2, 3]:
|
||||
for day in range(1, 11):
|
||||
dt = date(2024, month, day)
|
||||
returns_data.append((dt, Decimal("0.001")))
|
||||
|
||||
daily = ReturnSeries(returns=returns_data, period=Period.DAILY)
|
||||
monthly = calculate_monthly_returns(daily)
|
||||
|
||||
assert len(monthly) == 3
|
||||
assert (2024, 1) in monthly
|
||||
assert (2024, 2) in monthly
|
||||
assert (2024, 3) in monthly
|
||||
|
||||
def test_calculate_monthly_returns_wrong_period(self):
|
||||
"""Test monthly aggregation with wrong input period."""
|
||||
weekly = ReturnSeries(returns=[], period=Period.WEEKLY)
|
||||
|
||||
with pytest.raises(ValueError, match="daily returns"):
|
||||
calculate_monthly_returns(weekly)
|
||||
|
||||
def test_calculate_yearly_returns(self):
|
||||
"""Test yearly return aggregation."""
|
||||
# Create daily returns spanning multiple years
|
||||
returns_data = []
|
||||
for year in [2023, 2024]:
|
||||
for i in range(10):
|
||||
dt = date(year, 1, i + 1)
|
||||
returns_data.append((dt, Decimal("0.001")))
|
||||
|
||||
daily = ReturnSeries(returns=returns_data, period=Period.DAILY)
|
||||
yearly = calculate_yearly_returns(daily)
|
||||
|
||||
assert len(yearly) == 2
|
||||
assert 2023 in yearly
|
||||
assert 2024 in yearly
|
||||
|
||||
def test_calculate_yearly_returns_wrong_period(self):
|
||||
"""Test yearly aggregation with wrong input period."""
|
||||
monthly = ReturnSeries(returns=[], period=Period.MONTHLY)
|
||||
|
||||
with pytest.raises(ValueError, match="daily returns"):
|
||||
calculate_yearly_returns(monthly)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Edge Cases and Error Handling
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestEdgeCases:
|
||||
"""Test edge cases and error handling."""
|
||||
|
||||
def test_calculator_with_zero_risk_free_rate(self):
|
||||
"""Test calculator with zero risk-free rate."""
|
||||
calc = PerformanceCalculator(risk_free_rate=Decimal("0"))
|
||||
assert calc.risk_free_rate == Decimal("0")
|
||||
|
||||
def test_calculator_with_custom_mar(self):
|
||||
"""Test calculator with custom MAR."""
|
||||
calc = PerformanceCalculator(min_acceptable_return=Decimal("0.05"))
|
||||
assert calc.min_acceptable_return == Decimal("0.05")
|
||||
|
||||
def test_very_small_returns(self, calculator):
|
||||
"""Test with very small returns."""
|
||||
tiny = ReturnSeries(
|
||||
returns=[
|
||||
(date(2024, 1, i), Decimal("0.0001"))
|
||||
for i in range(1, 11)
|
||||
],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
metrics = calculator.calculate_metrics(tiny)
|
||||
assert isinstance(metrics.total_return, Decimal)
|
||||
|
||||
def test_very_large_returns(self, calculator):
|
||||
"""Test with very large returns."""
|
||||
large = ReturnSeries(
|
||||
returns=[
|
||||
(date(2024, 1, i), Decimal("0.50")) # 50% daily
|
||||
for i in range(1, 6)
|
||||
],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
metrics = calculator.calculate_metrics(large)
|
||||
# Total return should be huge: (1.5)^5 - 1 ≈ 6.59
|
||||
assert metrics.total_return > Decimal("5")
|
||||
|
||||
def test_mixed_positive_negative(self, calculator):
|
||||
"""Test with alternating returns."""
|
||||
alternating = ReturnSeries(
|
||||
returns=[
|
||||
(date(2024, 1, i), Decimal("0.02") if i % 2 == 0 else Decimal("-0.02"))
|
||||
for i in range(1, 21)
|
||||
],
|
||||
period=Period.DAILY,
|
||||
)
|
||||
metrics = calculator.calculate_metrics(alternating)
|
||||
# Should have roughly zero total return
|
||||
assert abs(metrics.total_return) < Decimal("0.10")
|
||||
|
|
@ -1,28 +1,40 @@
|
|||
"""Portfolio module for portfolio state management.
|
||||
"""Portfolio module for portfolio state and performance management.
|
||||
|
||||
This module provides portfolio state tracking including:
|
||||
This module provides portfolio state tracking and performance metrics:
|
||||
- Current holdings with cost basis and market values
|
||||
- Multi-currency cash balances
|
||||
- Real-time mark-to-market valuation
|
||||
- Portfolio snapshots for historical analysis
|
||||
- Performance metrics (Sharpe, Sortino, Calmar ratios)
|
||||
- Drawdown analysis
|
||||
- Trade statistics
|
||||
- Benchmark comparison
|
||||
|
||||
Issue #29: [PORT-28] Portfolio state - holdings, cash, mark-to-market
|
||||
Issue #31: [PORT-30] Performance metrics - Sharpe, drawdown, returns
|
||||
|
||||
Submodules:
|
||||
portfolio_state: Core portfolio state management
|
||||
performance: Performance metrics calculation
|
||||
|
||||
Classes:
|
||||
Enums:
|
||||
- Currency: Supported currencies (USD, EUR, GBP, etc.)
|
||||
- HoldingType: Type of holding (LONG, SHORT)
|
||||
- Period: Time period for performance calculations
|
||||
|
||||
Data Classes:
|
||||
- Holding: Individual holding/position in the portfolio
|
||||
- CashBalance: Cash balance in a specific currency
|
||||
- PortfolioSnapshot: Immutable snapshot of portfolio state
|
||||
- ReturnSeries: Series of returns over time
|
||||
- DrawdownInfo: Information about a drawdown period
|
||||
- TradeStats: Trade-level statistics
|
||||
- PerformanceMetrics: Complete performance metrics summary
|
||||
|
||||
Main Class:
|
||||
Main Classes:
|
||||
- PortfolioState: Live portfolio state with mark-to-market updates
|
||||
- PerformanceCalculator: Calculator for performance metrics
|
||||
|
||||
Protocols:
|
||||
- PriceProvider: Protocol for price data providers
|
||||
|
|
@ -33,6 +45,8 @@ Example:
|
|||
... PortfolioState,
|
||||
... Holding,
|
||||
... Currency,
|
||||
... PerformanceCalculator,
|
||||
... ReturnSeries,
|
||||
... )
|
||||
>>> from decimal import Decimal
|
||||
>>>
|
||||
|
|
@ -70,17 +84,48 @@ from .portfolio_state import (
|
|||
ExchangeRateProvider,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
from .performance import (
|
||||
# Enums
|
||||
Period,
|
||||
# Data Classes
|
||||
ReturnSeries,
|
||||
DrawdownInfo,
|
||||
TradeStats,
|
||||
PerformanceMetrics,
|
||||
# Main Class
|
||||
PerformanceCalculator,
|
||||
# Utility Functions
|
||||
calculate_cagr,
|
||||
calculate_rolling_returns,
|
||||
calculate_monthly_returns,
|
||||
calculate_yearly_returns,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
# Portfolio State Enums
|
||||
"Currency",
|
||||
"HoldingType",
|
||||
# Data Classes
|
||||
# Portfolio State Data Classes
|
||||
"Holding",
|
||||
"CashBalance",
|
||||
"PortfolioSnapshot",
|
||||
# Main Class
|
||||
# Portfolio State Main Class
|
||||
"PortfolioState",
|
||||
# Protocols
|
||||
# Portfolio State Protocols
|
||||
"PriceProvider",
|
||||
"ExchangeRateProvider",
|
||||
# Performance Enums
|
||||
"Period",
|
||||
# Performance Data Classes
|
||||
"ReturnSeries",
|
||||
"DrawdownInfo",
|
||||
"TradeStats",
|
||||
"PerformanceMetrics",
|
||||
# Performance Main Class
|
||||
"PerformanceCalculator",
|
||||
# Performance Utility Functions
|
||||
"calculate_cagr",
|
||||
"calculate_rolling_returns",
|
||||
"calculate_monthly_returns",
|
||||
"calculate_yearly_returns",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,912 @@
|
|||
"""Portfolio Performance Metrics.
|
||||
|
||||
This module provides comprehensive portfolio performance calculations:
|
||||
- Returns (daily, monthly, yearly, cumulative)
|
||||
- Risk-adjusted metrics (Sharpe, Sortino, Calmar)
|
||||
- Drawdown analysis
|
||||
- Trade statistics (win rate, profit factor)
|
||||
- Benchmark comparison
|
||||
|
||||
Issue #31: [PORT-30] Performance metrics - Sharpe, drawdown, returns
|
||||
|
||||
Design Principles:
|
||||
- Industry-standard calculations
|
||||
- Vectorized operations for efficiency
|
||||
- Support for various time periods
|
||||
- Benchmark-relative metrics
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime, date, timedelta
|
||||
from decimal import Decimal, ROUND_HALF_UP, InvalidOperation
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
|
||||
import math
|
||||
|
||||
|
||||
class Period(Enum):
|
||||
"""Time period for performance calculations."""
|
||||
DAILY = "daily"
|
||||
WEEKLY = "weekly"
|
||||
MONTHLY = "monthly"
|
||||
QUARTERLY = "quarterly"
|
||||
YEARLY = "yearly"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ReturnSeries:
|
||||
"""A series of returns over time.
|
||||
|
||||
Attributes:
|
||||
returns: List of (date, return) tuples
|
||||
period: The time period between returns
|
||||
annualization_factor: Factor for annualizing metrics
|
||||
"""
|
||||
returns: List[Tuple[date, Decimal]]
|
||||
period: Period = Period.DAILY
|
||||
annualization_factor: int = 252 # Trading days per year
|
||||
|
||||
def __post_init__(self):
|
||||
# Set appropriate annualization factor based on period
|
||||
if self.period == Period.DAILY:
|
||||
self.annualization_factor = 252
|
||||
elif self.period == Period.WEEKLY:
|
||||
self.annualization_factor = 52
|
||||
elif self.period == Period.MONTHLY:
|
||||
self.annualization_factor = 12
|
||||
elif self.period == Period.QUARTERLY:
|
||||
self.annualization_factor = 4
|
||||
elif self.period == Period.YEARLY:
|
||||
self.annualization_factor = 1
|
||||
|
||||
@property
|
||||
def values(self) -> List[Decimal]:
|
||||
"""Get just the return values."""
|
||||
return [r[1] for r in self.returns]
|
||||
|
||||
@property
|
||||
def dates(self) -> List[date]:
|
||||
"""Get just the dates."""
|
||||
return [r[0] for r in self.returns]
|
||||
|
||||
@property
|
||||
def num_periods(self) -> int:
|
||||
"""Number of periods in the series."""
|
||||
return len(self.returns)
|
||||
|
||||
|
||||
@dataclass
|
||||
class DrawdownInfo:
|
||||
"""Information about a drawdown period.
|
||||
|
||||
Attributes:
|
||||
start_date: When the drawdown began
|
||||
trough_date: Date of maximum drawdown
|
||||
end_date: When the drawdown recovered (None if ongoing)
|
||||
peak_value: Portfolio value at peak
|
||||
trough_value: Portfolio value at trough
|
||||
max_drawdown: Maximum drawdown percentage
|
||||
duration_days: Total duration in days
|
||||
recovery_days: Days from trough to recovery (None if ongoing)
|
||||
"""
|
||||
start_date: date
|
||||
trough_date: date
|
||||
end_date: Optional[date]
|
||||
peak_value: Decimal
|
||||
trough_value: Decimal
|
||||
max_drawdown: Decimal
|
||||
duration_days: int
|
||||
recovery_days: Optional[int] = None
|
||||
|
||||
@property
|
||||
def is_recovered(self) -> bool:
|
||||
"""Check if drawdown has recovered."""
|
||||
return self.end_date is not None
|
||||
|
||||
|
||||
@dataclass
|
||||
class TradeStats:
|
||||
"""Trade-level statistics.
|
||||
|
||||
Attributes:
|
||||
total_trades: Total number of trades
|
||||
winning_trades: Number of winning trades
|
||||
losing_trades: Number of losing trades
|
||||
breakeven_trades: Number of breakeven trades
|
||||
win_rate: Percentage of winning trades
|
||||
loss_rate: Percentage of losing trades
|
||||
avg_win: Average winning trade return
|
||||
avg_loss: Average losing trade return
|
||||
largest_win: Largest winning trade
|
||||
largest_loss: Largest losing trade
|
||||
profit_factor: Gross profit / Gross loss
|
||||
avg_trade: Average trade return
|
||||
expectancy: Expected value per trade
|
||||
"""
|
||||
total_trades: int
|
||||
winning_trades: int
|
||||
losing_trades: int
|
||||
breakeven_trades: int
|
||||
win_rate: Decimal
|
||||
loss_rate: Decimal
|
||||
avg_win: Decimal
|
||||
avg_loss: Decimal
|
||||
largest_win: Decimal
|
||||
largest_loss: Decimal
|
||||
profit_factor: Decimal
|
||||
avg_trade: Decimal
|
||||
expectancy: Decimal
|
||||
|
||||
|
||||
@dataclass
|
||||
class PerformanceMetrics:
|
||||
"""Complete performance metrics summary.
|
||||
|
||||
Attributes:
|
||||
start_date: Analysis start date
|
||||
end_date: Analysis end date
|
||||
total_return: Total cumulative return
|
||||
annualized_return: Annualized return
|
||||
volatility: Annualized volatility (std dev of returns)
|
||||
sharpe_ratio: Risk-adjusted return (return / volatility)
|
||||
sortino_ratio: Downside risk-adjusted return
|
||||
calmar_ratio: Return / max drawdown
|
||||
max_drawdown: Maximum peak-to-trough decline
|
||||
current_drawdown: Current drawdown from peak
|
||||
avg_drawdown: Average drawdown
|
||||
num_drawdowns: Number of drawdown periods
|
||||
best_day: Best single-day return
|
||||
worst_day: Worst single-day return
|
||||
positive_periods: Number of positive return periods
|
||||
negative_periods: Number of negative return periods
|
||||
trade_stats: Trade-level statistics (if available)
|
||||
benchmark_alpha: Alpha vs benchmark (if available)
|
||||
benchmark_beta: Beta vs benchmark (if available)
|
||||
information_ratio: Risk-adjusted excess return vs benchmark
|
||||
tracking_error: Std dev of excess returns vs benchmark
|
||||
"""
|
||||
start_date: date
|
||||
end_date: date
|
||||
total_return: Decimal
|
||||
annualized_return: Decimal
|
||||
volatility: Decimal
|
||||
sharpe_ratio: Decimal
|
||||
sortino_ratio: Decimal
|
||||
calmar_ratio: Decimal
|
||||
max_drawdown: Decimal
|
||||
current_drawdown: Decimal
|
||||
avg_drawdown: Decimal
|
||||
num_drawdowns: int
|
||||
best_day: Decimal
|
||||
worst_day: Decimal
|
||||
positive_periods: int
|
||||
negative_periods: int
|
||||
trade_stats: Optional[TradeStats] = None
|
||||
benchmark_alpha: Optional[Decimal] = None
|
||||
benchmark_beta: Optional[Decimal] = None
|
||||
information_ratio: Optional[Decimal] = None
|
||||
tracking_error: Optional[Decimal] = None
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
class PerformanceCalculator:
|
||||
"""Calculator for portfolio performance metrics.
|
||||
|
||||
Provides industry-standard performance calculations including:
|
||||
- Returns and volatility
|
||||
- Risk-adjusted metrics (Sharpe, Sortino, Calmar)
|
||||
- Drawdown analysis
|
||||
- Trade statistics
|
||||
- Benchmark comparison
|
||||
|
||||
Example:
|
||||
>>> calculator = PerformanceCalculator(risk_free_rate=Decimal("0.05"))
|
||||
>>> returns = ReturnSeries([
|
||||
... (date(2024, 1, 1), Decimal("0.01")),
|
||||
... (date(2024, 1, 2), Decimal("-0.005")),
|
||||
... (date(2024, 1, 3), Decimal("0.02")),
|
||||
... ])
|
||||
>>> metrics = calculator.calculate_metrics(returns)
|
||||
>>> print(f"Sharpe: {metrics.sharpe_ratio}")
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
risk_free_rate: Decimal = Decimal("0.05"),
|
||||
min_acceptable_return: Optional[Decimal] = None,
|
||||
):
|
||||
"""Initialize the calculator.
|
||||
|
||||
Args:
|
||||
risk_free_rate: Annual risk-free rate for Sharpe calculation
|
||||
min_acceptable_return: MAR for Sortino (defaults to 0)
|
||||
"""
|
||||
self.risk_free_rate = risk_free_rate
|
||||
self.min_acceptable_return = min_acceptable_return or Decimal("0")
|
||||
|
||||
def calculate_returns(
|
||||
self,
|
||||
values: List[Tuple[date, Decimal]],
|
||||
period: Period = Period.DAILY,
|
||||
) -> ReturnSeries:
|
||||
"""Calculate returns from a series of portfolio values.
|
||||
|
||||
Args:
|
||||
values: List of (date, value) tuples representing portfolio NAV
|
||||
period: Time period of the values
|
||||
|
||||
Returns:
|
||||
ReturnSeries with calculated returns
|
||||
"""
|
||||
if len(values) < 2:
|
||||
return ReturnSeries(returns=[], period=period)
|
||||
|
||||
returns = []
|
||||
for i in range(1, len(values)):
|
||||
prev_date, prev_value = values[i - 1]
|
||||
curr_date, curr_value = values[i]
|
||||
|
||||
if prev_value != 0:
|
||||
ret = (curr_value - prev_value) / prev_value
|
||||
else:
|
||||
ret = Decimal("0")
|
||||
|
||||
returns.append((curr_date, ret))
|
||||
|
||||
return ReturnSeries(returns=returns, period=period)
|
||||
|
||||
def total_return(self, returns: ReturnSeries) -> Decimal:
|
||||
"""Calculate total cumulative return.
|
||||
|
||||
Uses geometric linking: (1 + r1) * (1 + r2) * ... - 1
|
||||
"""
|
||||
if not returns.values:
|
||||
return Decimal("0")
|
||||
|
||||
cumulative = Decimal("1")
|
||||
for r in returns.values:
|
||||
cumulative *= (Decimal("1") + r)
|
||||
|
||||
return (cumulative - Decimal("1")).quantize(Decimal("0.0001"), rounding=ROUND_HALF_UP)
|
||||
|
||||
def annualized_return(self, returns: ReturnSeries) -> Decimal:
|
||||
"""Calculate annualized return.
|
||||
|
||||
Annualized = (1 + total_return) ^ (periods_per_year / num_periods) - 1
|
||||
"""
|
||||
if returns.num_periods == 0:
|
||||
return Decimal("0")
|
||||
|
||||
total = self.total_return(returns)
|
||||
cumulative = Decimal("1") + total
|
||||
|
||||
# Calculate annualization exponent
|
||||
years = Decimal(returns.num_periods) / Decimal(returns.annualization_factor)
|
||||
if years <= 0:
|
||||
return Decimal("0")
|
||||
|
||||
# (1 + total)^(1/years) - 1
|
||||
try:
|
||||
annualized = Decimal(float(cumulative) ** float(Decimal("1") / years)) - Decimal("1")
|
||||
# Handle extreme values that can't be quantized
|
||||
if annualized > Decimal("1e10") or annualized < Decimal("-1e10"):
|
||||
return annualized.quantize(Decimal("1"), rounding=ROUND_HALF_UP)
|
||||
return annualized.quantize(Decimal("0.0001"), rounding=ROUND_HALF_UP)
|
||||
except (OverflowError, InvalidOperation):
|
||||
# Return the unquantized value for extreme cases
|
||||
return Decimal(str(float(cumulative) ** float(Decimal("1") / years) - 1))
|
||||
|
||||
def volatility(self, returns: ReturnSeries, annualize: bool = True) -> Decimal:
|
||||
"""Calculate volatility (standard deviation of returns).
|
||||
|
||||
Args:
|
||||
returns: ReturnSeries to analyze
|
||||
annualize: Whether to annualize the volatility
|
||||
|
||||
Returns:
|
||||
Volatility as a decimal (0.20 = 20%)
|
||||
"""
|
||||
if returns.num_periods < 2:
|
||||
return Decimal("0")
|
||||
|
||||
values = [float(r) for r in returns.values]
|
||||
mean = sum(values) / len(values)
|
||||
variance = sum((x - mean) ** 2 for x in values) / (len(values) - 1)
|
||||
std_dev = math.sqrt(variance)
|
||||
|
||||
if annualize:
|
||||
std_dev *= math.sqrt(returns.annualization_factor)
|
||||
|
||||
return Decimal(str(std_dev)).quantize(Decimal("0.0001"), rounding=ROUND_HALF_UP)
|
||||
|
||||
def downside_deviation(self, returns: ReturnSeries, annualize: bool = True) -> Decimal:
|
||||
"""Calculate downside deviation (only negative returns).
|
||||
|
||||
Used for Sortino ratio calculation.
|
||||
"""
|
||||
if returns.num_periods < 2:
|
||||
return Decimal("0")
|
||||
|
||||
# Only consider returns below MAR
|
||||
mar = float(self.min_acceptable_return)
|
||||
downside_returns = [float(r) for r in returns.values if float(r) < mar]
|
||||
|
||||
if len(downside_returns) < 2:
|
||||
return Decimal("0")
|
||||
|
||||
# Calculate semi-variance
|
||||
variance = sum((r - mar) ** 2 for r in downside_returns) / len(downside_returns)
|
||||
std_dev = math.sqrt(variance)
|
||||
|
||||
if annualize:
|
||||
std_dev *= math.sqrt(returns.annualization_factor)
|
||||
|
||||
return Decimal(str(std_dev)).quantize(Decimal("0.0001"), rounding=ROUND_HALF_UP)
|
||||
|
||||
def sharpe_ratio(self, returns: ReturnSeries) -> Decimal:
|
||||
"""Calculate Sharpe ratio.
|
||||
|
||||
Sharpe = (Annualized Return - Risk Free Rate) / Annualized Volatility
|
||||
"""
|
||||
ann_return = self.annualized_return(returns)
|
||||
vol = self.volatility(returns)
|
||||
|
||||
if vol == 0:
|
||||
return Decimal("0")
|
||||
|
||||
sharpe = (ann_return - self.risk_free_rate) / vol
|
||||
return sharpe.quantize(Decimal("0.01"), rounding=ROUND_HALF_UP)
|
||||
|
||||
def sortino_ratio(self, returns: ReturnSeries) -> Decimal:
|
||||
"""Calculate Sortino ratio.
|
||||
|
||||
Sortino = (Annualized Return - MAR) / Downside Deviation
|
||||
|
||||
Similar to Sharpe but only penalizes downside volatility.
|
||||
"""
|
||||
ann_return = self.annualized_return(returns)
|
||||
downside = self.downside_deviation(returns)
|
||||
|
||||
if downside == 0:
|
||||
return Decimal("0")
|
||||
|
||||
sortino = (ann_return - self.min_acceptable_return) / downside
|
||||
return sortino.quantize(Decimal("0.01"), rounding=ROUND_HALF_UP)
|
||||
|
||||
def calmar_ratio(self, returns: ReturnSeries, max_dd: Optional[Decimal] = None) -> Decimal:
|
||||
"""Calculate Calmar ratio.
|
||||
|
||||
Calmar = Annualized Return / Max Drawdown
|
||||
|
||||
Measures return relative to worst-case loss.
|
||||
"""
|
||||
ann_return = self.annualized_return(returns)
|
||||
|
||||
if max_dd is None:
|
||||
# Calculate from cumulative returns
|
||||
cum_returns = self._cumulative_returns(returns.values)
|
||||
max_dd = self.max_drawdown(cum_returns)
|
||||
|
||||
if max_dd == 0:
|
||||
return Decimal("0")
|
||||
|
||||
calmar = ann_return / abs(max_dd)
|
||||
return calmar.quantize(Decimal("0.01"), rounding=ROUND_HALF_UP)
|
||||
|
||||
def _cumulative_returns(self, returns: List[Decimal]) -> List[Decimal]:
|
||||
"""Calculate cumulative returns from simple returns."""
|
||||
cumulative = []
|
||||
cum = Decimal("1")
|
||||
for r in returns:
|
||||
cum *= (Decimal("1") + r)
|
||||
cumulative.append(cum - Decimal("1"))
|
||||
return cumulative
|
||||
|
||||
def max_drawdown(self, cumulative_returns: List[Decimal]) -> Decimal:
|
||||
"""Calculate maximum drawdown from cumulative returns.
|
||||
|
||||
Max Drawdown = (Trough - Peak) / Peak
|
||||
|
||||
Args:
|
||||
cumulative_returns: List of cumulative returns (0.10 = 10% gain)
|
||||
|
||||
Returns:
|
||||
Maximum drawdown as a negative decimal (-0.20 = -20% drawdown)
|
||||
"""
|
||||
if not cumulative_returns:
|
||||
return Decimal("0")
|
||||
|
||||
# Convert to portfolio values (starting at 1)
|
||||
values = [Decimal("1") + r for r in cumulative_returns]
|
||||
|
||||
peak = values[0]
|
||||
max_dd = Decimal("0")
|
||||
|
||||
for value in values:
|
||||
if value > peak:
|
||||
peak = value
|
||||
dd = (value - peak) / peak
|
||||
if dd < max_dd:
|
||||
max_dd = dd
|
||||
|
||||
return max_dd.quantize(Decimal("0.0001"), rounding=ROUND_HALF_UP)
|
||||
|
||||
def drawdown_series(
|
||||
self,
|
||||
values: List[Tuple[date, Decimal]]
|
||||
) -> List[Tuple[date, Decimal]]:
|
||||
"""Calculate drawdown for each date.
|
||||
|
||||
Args:
|
||||
values: List of (date, portfolio_value) tuples
|
||||
|
||||
Returns:
|
||||
List of (date, drawdown) tuples
|
||||
"""
|
||||
if not values:
|
||||
return []
|
||||
|
||||
result = []
|
||||
peak = values[0][1]
|
||||
|
||||
for dt, value in values:
|
||||
if value > peak:
|
||||
peak = value
|
||||
dd = (value - peak) / peak if peak != 0 else Decimal("0")
|
||||
result.append((dt, dd))
|
||||
|
||||
return result
|
||||
|
||||
def find_drawdowns(
|
||||
self,
|
||||
values: List[Tuple[date, Decimal]],
|
||||
min_drawdown: Decimal = Decimal("-0.05"),
|
||||
) -> List[DrawdownInfo]:
|
||||
"""Find all drawdown periods.
|
||||
|
||||
Args:
|
||||
values: List of (date, portfolio_value) tuples
|
||||
min_drawdown: Minimum drawdown to include (-0.05 = -5%)
|
||||
|
||||
Returns:
|
||||
List of DrawdownInfo objects
|
||||
"""
|
||||
if len(values) < 2:
|
||||
return []
|
||||
|
||||
dd_series = self.drawdown_series(values)
|
||||
drawdowns = []
|
||||
|
||||
peak_value = values[0][1]
|
||||
peak_date = values[0][0]
|
||||
trough_value = peak_value
|
||||
trough_date = peak_date
|
||||
in_drawdown = False
|
||||
current_dd = Decimal("0")
|
||||
|
||||
for i, (dt, dd) in enumerate(dd_series):
|
||||
value = values[i][1]
|
||||
|
||||
if not in_drawdown:
|
||||
if dd < min_drawdown:
|
||||
# Start of new drawdown
|
||||
in_drawdown = True
|
||||
peak_date = dd_series[i - 1][0] if i > 0 else dt
|
||||
peak_value = values[i - 1][1] if i > 0 else value
|
||||
trough_date = dt
|
||||
trough_value = value
|
||||
current_dd = dd
|
||||
else:
|
||||
if dd < current_dd:
|
||||
# New trough
|
||||
trough_date = dt
|
||||
trough_value = value
|
||||
current_dd = dd
|
||||
|
||||
if value >= peak_value:
|
||||
# Recovered
|
||||
drawdowns.append(DrawdownInfo(
|
||||
start_date=peak_date,
|
||||
trough_date=trough_date,
|
||||
end_date=dt,
|
||||
peak_value=peak_value,
|
||||
trough_value=trough_value,
|
||||
max_drawdown=current_dd,
|
||||
duration_days=(dt - peak_date).days,
|
||||
recovery_days=(dt - trough_date).days,
|
||||
))
|
||||
in_drawdown = False
|
||||
peak_value = value
|
||||
peak_date = dt
|
||||
|
||||
# Update peak if not in drawdown
|
||||
if not in_drawdown and value > peak_value:
|
||||
peak_value = value
|
||||
peak_date = dt
|
||||
|
||||
# Handle ongoing drawdown
|
||||
if in_drawdown:
|
||||
final_date = dd_series[-1][0]
|
||||
drawdowns.append(DrawdownInfo(
|
||||
start_date=peak_date,
|
||||
trough_date=trough_date,
|
||||
end_date=None,
|
||||
peak_value=peak_value,
|
||||
trough_value=trough_value,
|
||||
max_drawdown=current_dd,
|
||||
duration_days=(final_date - peak_date).days,
|
||||
recovery_days=None,
|
||||
))
|
||||
|
||||
return drawdowns
|
||||
|
||||
def trade_statistics(self, trade_returns: List[Decimal]) -> TradeStats:
|
||||
"""Calculate trade-level statistics.
|
||||
|
||||
Args:
|
||||
trade_returns: List of individual trade returns (P&L / cost)
|
||||
|
||||
Returns:
|
||||
TradeStats with comprehensive trade analysis
|
||||
"""
|
||||
if not trade_returns:
|
||||
return TradeStats(
|
||||
total_trades=0,
|
||||
winning_trades=0,
|
||||
losing_trades=0,
|
||||
breakeven_trades=0,
|
||||
win_rate=Decimal("0"),
|
||||
loss_rate=Decimal("0"),
|
||||
avg_win=Decimal("0"),
|
||||
avg_loss=Decimal("0"),
|
||||
largest_win=Decimal("0"),
|
||||
largest_loss=Decimal("0"),
|
||||
profit_factor=Decimal("0"),
|
||||
avg_trade=Decimal("0"),
|
||||
expectancy=Decimal("0"),
|
||||
)
|
||||
|
||||
winning = [r for r in trade_returns if r > 0]
|
||||
losing = [r for r in trade_returns if r < 0]
|
||||
breakeven = [r for r in trade_returns if r == 0]
|
||||
|
||||
total = len(trade_returns)
|
||||
num_wins = len(winning)
|
||||
num_losses = len(losing)
|
||||
num_be = len(breakeven)
|
||||
|
||||
win_rate = Decimal(num_wins) / Decimal(total) * 100 if total > 0 else Decimal("0")
|
||||
loss_rate = Decimal(num_losses) / Decimal(total) * 100 if total > 0 else Decimal("0")
|
||||
|
||||
avg_win = sum(winning) / len(winning) if winning else Decimal("0")
|
||||
avg_loss = sum(losing) / len(losing) if losing else Decimal("0")
|
||||
|
||||
largest_win = max(winning) if winning else Decimal("0")
|
||||
largest_loss = min(losing) if losing else Decimal("0")
|
||||
|
||||
gross_profit = sum(winning)
|
||||
gross_loss = abs(sum(losing)) if losing else Decimal("0")
|
||||
profit_factor = gross_profit / gross_loss if gross_loss > 0 else Decimal("0")
|
||||
|
||||
avg_trade = sum(trade_returns) / len(trade_returns)
|
||||
|
||||
# Expectancy = (win_rate * avg_win) - (loss_rate * avg_loss)
|
||||
expectancy = (win_rate / 100 * avg_win) + (loss_rate / 100 * avg_loss)
|
||||
|
||||
return TradeStats(
|
||||
total_trades=total,
|
||||
winning_trades=num_wins,
|
||||
losing_trades=num_losses,
|
||||
breakeven_trades=num_be,
|
||||
win_rate=win_rate.quantize(Decimal("0.01")),
|
||||
loss_rate=loss_rate.quantize(Decimal("0.01")),
|
||||
avg_win=avg_win.quantize(Decimal("0.0001")),
|
||||
avg_loss=avg_loss.quantize(Decimal("0.0001")),
|
||||
largest_win=largest_win.quantize(Decimal("0.0001")),
|
||||
largest_loss=largest_loss.quantize(Decimal("0.0001")),
|
||||
profit_factor=profit_factor.quantize(Decimal("0.01")),
|
||||
avg_trade=avg_trade.quantize(Decimal("0.0001")),
|
||||
expectancy=expectancy.quantize(Decimal("0.0001")),
|
||||
)
|
||||
|
||||
def benchmark_comparison(
|
||||
self,
|
||||
portfolio_returns: ReturnSeries,
|
||||
benchmark_returns: ReturnSeries,
|
||||
) -> Dict[str, Decimal]:
|
||||
"""Compare portfolio performance against a benchmark.
|
||||
|
||||
Calculates:
|
||||
- Alpha: Excess return not explained by beta
|
||||
- Beta: Sensitivity to benchmark movements
|
||||
- Information Ratio: Risk-adjusted excess return
|
||||
- Tracking Error: Volatility of excess returns
|
||||
- Up Capture: Performance when benchmark is up
|
||||
- Down Capture: Performance when benchmark is down
|
||||
|
||||
Args:
|
||||
portfolio_returns: Portfolio return series
|
||||
benchmark_returns: Benchmark return series
|
||||
|
||||
Returns:
|
||||
Dictionary with comparison metrics
|
||||
"""
|
||||
if portfolio_returns.num_periods != benchmark_returns.num_periods:
|
||||
raise ValueError("Portfolio and benchmark must have same number of periods")
|
||||
|
||||
if portfolio_returns.num_periods < 2:
|
||||
return {
|
||||
"alpha": Decimal("0"),
|
||||
"beta": Decimal("0"),
|
||||
"information_ratio": Decimal("0"),
|
||||
"tracking_error": Decimal("0"),
|
||||
"up_capture": Decimal("0"),
|
||||
"down_capture": Decimal("0"),
|
||||
}
|
||||
|
||||
port_vals = [float(r) for r in portfolio_returns.values]
|
||||
bench_vals = [float(r) for r in benchmark_returns.values]
|
||||
|
||||
# Calculate beta using covariance / variance
|
||||
n = len(port_vals)
|
||||
port_mean = sum(port_vals) / n
|
||||
bench_mean = sum(bench_vals) / n
|
||||
|
||||
covariance = sum((port_vals[i] - port_mean) * (bench_vals[i] - bench_mean)
|
||||
for i in range(n)) / (n - 1)
|
||||
bench_variance = sum((x - bench_mean) ** 2 for x in bench_vals) / (n - 1)
|
||||
|
||||
beta = covariance / bench_variance if bench_variance != 0 else 0
|
||||
|
||||
# Calculate alpha using CAPM: alpha = port_return - (rf + beta * (bench - rf))
|
||||
port_ann_return = float(self.annualized_return(portfolio_returns))
|
||||
bench_ann_return = float(self.annualized_return(benchmark_returns))
|
||||
rf = float(self.risk_free_rate)
|
||||
|
||||
alpha = port_ann_return - (rf + beta * (bench_ann_return - rf))
|
||||
|
||||
# Calculate excess returns and tracking error
|
||||
excess_returns = [port_vals[i] - bench_vals[i] for i in range(n)]
|
||||
excess_mean = sum(excess_returns) / n
|
||||
tracking_error = math.sqrt(
|
||||
sum((x - excess_mean) ** 2 for x in excess_returns) / (n - 1)
|
||||
)
|
||||
tracking_error *= math.sqrt(portfolio_returns.annualization_factor)
|
||||
|
||||
# Information ratio
|
||||
information_ratio = (port_ann_return - bench_ann_return) / tracking_error if tracking_error != 0 else 0
|
||||
|
||||
# Up/Down capture
|
||||
up_periods = [(port_vals[i], bench_vals[i]) for i in range(n) if bench_vals[i] > 0]
|
||||
down_periods = [(port_vals[i], bench_vals[i]) for i in range(n) if bench_vals[i] < 0]
|
||||
|
||||
up_capture = Decimal("0")
|
||||
if up_periods:
|
||||
avg_port_up = sum(p[0] for p in up_periods) / len(up_periods)
|
||||
avg_bench_up = sum(p[1] for p in up_periods) / len(up_periods)
|
||||
up_capture = Decimal(str(avg_port_up / avg_bench_up * 100)) if avg_bench_up != 0 else Decimal("0")
|
||||
|
||||
down_capture = Decimal("0")
|
||||
if down_periods:
|
||||
avg_port_down = sum(p[0] for p in down_periods) / len(down_periods)
|
||||
avg_bench_down = sum(p[1] for p in down_periods) / len(down_periods)
|
||||
down_capture = Decimal(str(avg_port_down / avg_bench_down * 100)) if avg_bench_down != 0 else Decimal("0")
|
||||
|
||||
return {
|
||||
"alpha": Decimal(str(alpha)).quantize(Decimal("0.0001")),
|
||||
"beta": Decimal(str(beta)).quantize(Decimal("0.01")),
|
||||
"information_ratio": Decimal(str(information_ratio)).quantize(Decimal("0.01")),
|
||||
"tracking_error": Decimal(str(tracking_error)).quantize(Decimal("0.0001")),
|
||||
"up_capture": up_capture.quantize(Decimal("0.01")),
|
||||
"down_capture": down_capture.quantize(Decimal("0.01")),
|
||||
}
|
||||
|
||||
def calculate_metrics(
|
||||
self,
|
||||
returns: ReturnSeries,
|
||||
trade_returns: Optional[List[Decimal]] = None,
|
||||
benchmark_returns: Optional[ReturnSeries] = None,
|
||||
) -> PerformanceMetrics:
|
||||
"""Calculate complete performance metrics.
|
||||
|
||||
Args:
|
||||
returns: Portfolio return series
|
||||
trade_returns: Optional list of individual trade returns
|
||||
benchmark_returns: Optional benchmark return series
|
||||
|
||||
Returns:
|
||||
Complete PerformanceMetrics
|
||||
"""
|
||||
if returns.num_periods == 0:
|
||||
return PerformanceMetrics(
|
||||
start_date=date.today(),
|
||||
end_date=date.today(),
|
||||
total_return=Decimal("0"),
|
||||
annualized_return=Decimal("0"),
|
||||
volatility=Decimal("0"),
|
||||
sharpe_ratio=Decimal("0"),
|
||||
sortino_ratio=Decimal("0"),
|
||||
calmar_ratio=Decimal("0"),
|
||||
max_drawdown=Decimal("0"),
|
||||
current_drawdown=Decimal("0"),
|
||||
avg_drawdown=Decimal("0"),
|
||||
num_drawdowns=0,
|
||||
best_day=Decimal("0"),
|
||||
worst_day=Decimal("0"),
|
||||
positive_periods=0,
|
||||
negative_periods=0,
|
||||
)
|
||||
|
||||
# Calculate cumulative returns for drawdown analysis
|
||||
cum_returns = self._cumulative_returns(returns.values)
|
||||
max_dd = self.max_drawdown(cum_returns)
|
||||
|
||||
# Current drawdown
|
||||
if cum_returns:
|
||||
values = [Decimal("1") + r for r in cum_returns]
|
||||
peak = max(values)
|
||||
current_dd = (values[-1] - peak) / peak
|
||||
else:
|
||||
current_dd = Decimal("0")
|
||||
|
||||
# Find drawdown periods
|
||||
portfolio_values = [(returns.dates[i], Decimal("1") + cum_returns[i])
|
||||
for i in range(len(cum_returns))]
|
||||
drawdowns = self.find_drawdowns(portfolio_values, min_drawdown=Decimal("-0.01"))
|
||||
|
||||
avg_dd = Decimal("0")
|
||||
if drawdowns:
|
||||
avg_dd = sum(d.max_drawdown for d in drawdowns) / len(drawdowns)
|
||||
|
||||
# Best/worst days
|
||||
best_day = max(returns.values) if returns.values else Decimal("0")
|
||||
worst_day = min(returns.values) if returns.values else Decimal("0")
|
||||
|
||||
# Positive/negative periods
|
||||
positive = sum(1 for r in returns.values if r > 0)
|
||||
negative = sum(1 for r in returns.values if r < 0)
|
||||
|
||||
# Trade statistics
|
||||
trade_stats = None
|
||||
if trade_returns:
|
||||
trade_stats = self.trade_statistics(trade_returns)
|
||||
|
||||
# Benchmark comparison
|
||||
benchmark_alpha = None
|
||||
benchmark_beta = None
|
||||
information_ratio = None
|
||||
tracking_error = None
|
||||
|
||||
if benchmark_returns:
|
||||
bench_metrics = self.benchmark_comparison(returns, benchmark_returns)
|
||||
benchmark_alpha = bench_metrics["alpha"]
|
||||
benchmark_beta = bench_metrics["beta"]
|
||||
information_ratio = bench_metrics["information_ratio"]
|
||||
tracking_error = bench_metrics["tracking_error"]
|
||||
|
||||
return PerformanceMetrics(
|
||||
start_date=returns.dates[0],
|
||||
end_date=returns.dates[-1],
|
||||
total_return=self.total_return(returns),
|
||||
annualized_return=self.annualized_return(returns),
|
||||
volatility=self.volatility(returns),
|
||||
sharpe_ratio=self.sharpe_ratio(returns),
|
||||
sortino_ratio=self.sortino_ratio(returns),
|
||||
calmar_ratio=self.calmar_ratio(returns, max_dd),
|
||||
max_drawdown=max_dd,
|
||||
current_drawdown=current_dd.quantize(Decimal("0.0001")),
|
||||
avg_drawdown=avg_dd.quantize(Decimal("0.0001")),
|
||||
num_drawdowns=len(drawdowns),
|
||||
best_day=best_day.quantize(Decimal("0.0001")),
|
||||
worst_day=worst_day.quantize(Decimal("0.0001")),
|
||||
positive_periods=positive,
|
||||
negative_periods=negative,
|
||||
trade_stats=trade_stats,
|
||||
benchmark_alpha=benchmark_alpha,
|
||||
benchmark_beta=benchmark_beta,
|
||||
information_ratio=information_ratio,
|
||||
tracking_error=tracking_error,
|
||||
)
|
||||
|
||||
|
||||
def calculate_cagr(
|
||||
start_value: Decimal,
|
||||
end_value: Decimal,
|
||||
years: Decimal,
|
||||
) -> Decimal:
|
||||
"""Calculate Compound Annual Growth Rate.
|
||||
|
||||
CAGR = (End Value / Start Value)^(1/Years) - 1
|
||||
|
||||
Args:
|
||||
start_value: Initial portfolio value
|
||||
end_value: Final portfolio value
|
||||
years: Number of years
|
||||
|
||||
Returns:
|
||||
CAGR as a decimal (0.10 = 10%)
|
||||
"""
|
||||
if start_value <= 0 or years <= 0:
|
||||
return Decimal("0")
|
||||
|
||||
ratio = float(end_value / start_value)
|
||||
cagr = ratio ** (1 / float(years)) - 1
|
||||
return Decimal(str(cagr)).quantize(Decimal("0.0001"))
|
||||
|
||||
|
||||
def calculate_rolling_returns(
|
||||
returns: ReturnSeries,
|
||||
window: int,
|
||||
) -> List[Tuple[date, Decimal]]:
|
||||
"""Calculate rolling cumulative returns.
|
||||
|
||||
Args:
|
||||
returns: Return series
|
||||
window: Rolling window size in periods
|
||||
|
||||
Returns:
|
||||
List of (date, rolling_return) tuples
|
||||
"""
|
||||
if returns.num_periods < window:
|
||||
return []
|
||||
|
||||
result = []
|
||||
for i in range(window - 1, returns.num_periods):
|
||||
window_returns = returns.values[i - window + 1:i + 1]
|
||||
cumulative = Decimal("1")
|
||||
for r in window_returns:
|
||||
cumulative *= (Decimal("1") + r)
|
||||
result.append((returns.dates[i], cumulative - Decimal("1")))
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def calculate_monthly_returns(
|
||||
returns: ReturnSeries,
|
||||
) -> Dict[Tuple[int, int], Decimal]:
|
||||
"""Aggregate daily returns to monthly.
|
||||
|
||||
Args:
|
||||
returns: Daily return series
|
||||
|
||||
Returns:
|
||||
Dictionary of (year, month) -> monthly return
|
||||
"""
|
||||
if returns.period != Period.DAILY:
|
||||
raise ValueError("Input must be daily returns")
|
||||
|
||||
monthly: Dict[Tuple[int, int], Decimal] = {}
|
||||
|
||||
for dt, ret in returns.returns:
|
||||
key = (dt.year, dt.month)
|
||||
if key not in monthly:
|
||||
monthly[key] = Decimal("1")
|
||||
monthly[key] *= (Decimal("1") + ret)
|
||||
|
||||
# Convert back to returns
|
||||
return {k: v - Decimal("1") for k, v in monthly.items()}
|
||||
|
||||
|
||||
def calculate_yearly_returns(
|
||||
returns: ReturnSeries,
|
||||
) -> Dict[int, Decimal]:
|
||||
"""Aggregate daily returns to yearly.
|
||||
|
||||
Args:
|
||||
returns: Daily return series
|
||||
|
||||
Returns:
|
||||
Dictionary of year -> yearly return
|
||||
"""
|
||||
if returns.period != Period.DAILY:
|
||||
raise ValueError("Input must be daily returns")
|
||||
|
||||
yearly: Dict[int, Decimal] = {}
|
||||
|
||||
for dt, ret in returns.returns:
|
||||
if dt.year not in yearly:
|
||||
yearly[dt.year] = Decimal("1")
|
||||
yearly[dt.year] *= (Decimal("1") + ret)
|
||||
|
||||
# Convert back to returns
|
||||
return {k: v - Decimal("1") for k, v in yearly.items()}
|
||||
Loading…
Reference in New Issue