Skip to content

ISSUE_694_IMPLEMENTATION.md

Latest commit

 

History

History
445 lines (379 loc) · 12.2 KB

ISSUE_694_IMPLEMENTATION.md

File metadata and controls

445 lines (379 loc) · 12.2 KB

ISSUE #694: Portfolio Performance Attribution & Risk Analytics - Implementation Guide

Overview

Complete implementation of advanced portfolio analytics engine featuring performance attribution, comprehensive risk metrics (volatility, Sharpe ratio, max drawdown, beta, VaR), benchmark comparisons against major indices, and historical trend analysis.

Problem Solved

Users previously lacked visibility into:

  • Which assets/sectors drive portfolio returns
  • Risk metrics (volatility, Sharpe ratio, drawdown)
  • Performance comparison against benchmarks (S&P 500, MSCI World, etc.)
  • Historical performance trends and patterns

Solution Implemented

1. Extended Performance Attribution Service

File: backend/services/performanceAttributionService.js

New Methods Added:

calculateRiskMetrics(userId, periodStart, periodEnd, vaultId)

Calculates comprehensive risk metrics:

  • Volatility: Annualized standard deviation of returns
  • Sharpe Ratio: Risk-adjusted return metric
  • Sortino Ratio: Downside risk-adjusted return
  • Calmar Ratio: Return to max drawdown ratio
  • Maximum Drawdown: Largest peak-to-trough decline
  • Beta: Sensitivity to market movements (vs S&P 500)
  • VaR (95%): Value at Risk at 95% confidence
  • CVaR (95%): Conditional VaR (expected shortfall)

Returns:

{
  success: true,
  period: { start, end },
  volatility: "15.23",               // Annualized %
  sharpeRatio: "1.234",
  sortinoRatio: "1.567",
  calmarRatio: "2.345",
  maxDrawdown: "12.45",              // %
  beta: "0.987",
  var95: "-2.34",                    // %
  cvar95: "-3.12",                   // %
  avgDailyReturn: "0.0543",
  annualizedReturn: "14.23",
  sampleSize: 252
}
compareToBenchmarks(userId, periodStart, periodEnd, vaultId)

Compares portfolio against major market benchmarks:

  • S&P 500 (SPY)
  • NASDAQ 100 (QQQ)
  • MSCI World (ACWI)
  • MSCI EAFE (EFA)
  • US Aggregate Bonds (AGG)
  • Gold (GLD)
  • Real Estate/REITs (VNQ)
  • Bitcoin (BTC-USD)

Calculates:

  • Portfolio vs benchmark returns
  • Alpha (excess return)
  • Relative performance
  • Outperformance indicators

Returns:

{
  success: true,
  period: { start, end },
  portfolioReturn: "18.45",
  comparisons: [
    {
      benchmark: "S&P 500",
      symbol: "SPY",
      portfolioReturn: "18.45",
      benchmarkReturn: "15.20",
      alpha: "3.25",                  // Excess return
      outperforming: true,
      relativePerformance: "21.38"   // % better
    },
    // ... more benchmarks
  ]
}
analyzePerformanceTrends(userId, periodStart, periodEnd, vaultId)

Analyzes historical performance patterns:

  • 30-day and 90-day trend analysis
  • Momentum calculation (acceleration/deceleration)
  • Volatility trend (increasing/decreasing/stable)
  • Market regime identification (bull/bear/sideways)
  • Rolling returns and volatility metrics

Returns:

{
  success: true,
  period: { start, end },
  trend: "upward",
  trend30Day: "0.0125",
  trend90Day: "0.0098",
  momentum: "0.0027",
  momentumSignal: "accelerating",
  volatilityTrend: "decreasing",
  volatility30Day: "1.234",
  volatility90Day: "1.543",
  regime: "bull_market",
  rollingMetrics: {
    returns: [...],
    volatility: [...]
  }
}

Supporting Methods:

  • calculateStdDev(values) - Standard deviation calculation
  • calculateMaxDrawdown(snapshots) - Peak-to-trough analysis
  • calculateSortinoRatio(returns, riskFreeRate) - Downside risk metric
  • calculateBeta(userId, returns, periodStart, periodEnd) - Market sensitivity
  • getBenchmarkReturn(symbol, startDate, endDate) - Fetch/cache benchmark data
  • getBenchmarkReturns(symbol, startDate, endDate) - Time series benchmark data
  • calculateReturnsFromPrices(prices) - Convert prices to returns
  • getPortfolioTimeSeries(userId, startDate, endDate, vaultId) - Fetch snapshots
  • calculateLinearTrend(data) - Least squares trend line
  • calculateRollingReturns(returns, window) - Rolling period analysis
  • calculateRollingVolatility(returns, window) - Rolling volatility
  • identifyMarketRegime(returns) - Bull/bear/sideways classification

2. API Endpoints

File: backend/routes/analytics.js

New Routes:

GET /api/analytics/portfolio/performance-attribution

Query Parameters:

  • startDate (required) - Analysis start date
  • endDate (required) - Analysis end date
  • vaultId (optional) - Specific vault/portfolio

Response: Complete attribution breakdown by asset class, sector, holding, and geography

GET /api/analytics/portfolio/risk-metrics

Query Parameters:

  • startDate (required)
  • endDate (required)
  • vaultId (optional)

Response: All risk metrics (volatility, Sharpe, drawdown, beta, VaR, etc.)

GET /api/analytics/portfolio/benchmark-comparison

Query Parameters:

  • startDate (required)
  • endDate (required)
  • vaultId (optional)

Response: Comparison against 8 major benchmarks with alpha calculations

GET /api/analytics/portfolio/performance-trends

Query Parameters:

  • startDate (required)
  • endDate (required)
  • vaultId (optional)

Response: Trend analysis with momentum and regime identification

GET /api/analytics/portfolio/comprehensive

Query Parameters:

  • startDate (required)
  • endDate (required)
  • vaultId (optional)

Response: ALL analytics in one call (attribution + risk + benchmarks + trends)

  • Optimized with Promise.all() for parallel execution
  • Single API call for dashboard views

3. Database Integration

Uses existing schema tables:

  • portfolioSnapshots - Time series portfolio values
  • benchmarkPrices - Cached benchmark historical data
  • benchmarkComparisons - Stored comparison results
  • performanceAttributions - Attribution analysis logs

Usage Examples

Calculate Risk Metrics

GET /api/analytics/portfolio/risk-metrics?startDate=2025-01-01&endDate=2026-03-03

Response:
{
  "success": true,
  "data": {
    "volatility": "15.23",
    "sharpeRatio": "1.234",
    "maxDrawdown": "12.45",
    "beta": "0.987",
    "var95": "-2.34",
    "cvar95": "-3.12"
  }
}

Compare to Benchmarks

GET /api/analytics/portfolio/benchmark-comparison?startDate=2025-01-01&endDate=2026-03-03

Response:
{
  "success": true,
  "data": {
    "portfolioReturn": "18.45",
    "comparisons": [
      {
        "benchmark": "S&P 500",
        "portfolioReturn": "18.45",
        "benchmarkReturn": "15.20",
        "alpha": "3.25",
        "outperforming": true
      },
      {
        "benchmark": "NASDAQ 100",
        "portfolioReturn": "18.45",
        "benchmarkReturn": "22.10",
        "alpha": "-3.65",
        "outperforming": false
      }
    ]
  }
}

Analyze Trends

GET /api/analytics/portfolio/performance-trends?startDate=2025-01-01&endDate=2026-03-03

Response:
{
  "success": true,
  "data": {
    "trend": "upward",
    "momentum": "0.0027",
    "momentumSignal": "accelerating",
    "volatilityTrend": "decreasing",
    "regime": "bull_market"
  }
}

Get Everything at Once

GET /api/analytics/portfolio/comprehensive?startDate=2025-01-01&endDate=2026-03-03

Response:
{
  "success": true,
  "data": {
    "attribution": { /* full attribution */ },
    "riskMetrics": { /* all risk metrics */ },
    "benchmarkComparison": { /* all benchmarks */ },
    "trends": { /* trend analysis */ },
    "generatedAt": "2026-03-03T10:30:00Z"
  }
}

Key Features

✅ Performance Attribution

  • Asset-level contribution to returns
  • Sector-level aggregation
  • Weight and return calculations
  • Contribution decomposition

✅ Risk Metrics

  • Volatility (annualized)
  • Sharpe Ratio
  • Sortino Ratio (downside risk)
  • Calmar Ratio
  • Maximum Drawdown
  • Beta (market correlation)
  • Value at Risk (VaR)
  • Conditional VaR (CVaR)

✅ Benchmark Comparison

  • 8 major market indices
  • Alpha calculation
  • Relative performance
  • Automatic data caching
  • Historical price storage

✅ Trend Analysis

  • Multiple timeframe trends (30/90 day)
  • Momentum indicators
  • Volatility trend tracking
  • Market regime classification
  • Rolling metrics

✅ Optimization

  • Parallel API execution
  • Benchmark data caching
  • Efficient time-series queries
  • Minimal database hits

Technical Highlights

Benchmark Data Caching

// Fetches from market data service only if not cached
// Stores in benchmarkPrices table for future use
const benchmarkReturn = await this.getBenchmarkReturn('SPY', startDate, endDate);

Risk-Free Rate Assumption

const riskFreeRate = 0.02; // 2% assumption
// Can be made configurable for different markets

Annualization Factors

const annualizedVolatility = dailyVolatility * Math.sqrt(252); // 252 trading days
const annualizedReturn = Math.pow(1 + avgDailyReturn, 252) - 1;

Market Beta Calculation

// Covariance(portfolio, market) / Variance(market)
// Uses S&P 500 (SPY) as market proxy
beta = covariance / marketVariance;

Error Handling

All endpoints return structured error responses:

{
  "success": false,
  "message": "Insufficient data for risk calculation"
}

Common error cases handled:

  • Insufficient historical data (< 2 snapshots)
  • Missing benchmark data
  • Invalid date ranges
  • Network failures on market data fetch

Testing Recommendations

Unit Tests

  • Risk metric calculations with known data
  • Benchmark return calculations
  • Trend analysis with synthetic data
  • Edge cases (zero volatility, negative returns)

Integration Tests

  • API endpoint responses
  • Database interactions
  • Caching behavior
  • Error scenarios

Performance Tests

  • Large portfolios (1000+ assets)
  • Long time periods (10+ years)
  • Multiple concurrent requests
  • Benchmark data fetching

Security Considerations

  • ✅ All endpoints protected with protect middleware
  • ✅ User isolation (can only access own portfolio)
  • ✅ Vault-level permissions respected
  • ✅ No sensitive data in error messages

Future Enhancements

Potential Additions

  1. Configurable Risk-Free Rate - Per-market adjustment
  2. Custom Benchmarks - User-defined comparison indices
  3. Factor Attribution - Fama-French multi-factor analysis
  4. Monte Carlo Simulations - Probabilistic forecasting
  5. Correlation Matrix - Asset correlation heatmaps
  6. Attribution Charts - Visual decomposition
  7. Stress Testing - Scenario analysis
  8. Information Ratio - Risk-adjusted alpha
  9. Tracking Error - Benchmark deviation measurement
  10. Real-time Updates - WebSocket streaming metrics

Dependencies

  • drizzle-orm - Database queries
  • marketData service - Historical price data
  • Existing portfolioSnapshots infrastructure
  • Existing benchmarkPrices and benchmarkComparisons tables

Deployment Checklist

  • Service methods implemented
  • API routes created
  • Database schema verified
  • Error handling added
  • Unit tests written
  • Integration tests added
  • API documentation updated
  • Frontend integration points defined
  • Performance benchmarking completed
  • Monitoring/logging configured

API Documentation

All endpoints include Swagger annotations for automatic API documentation generation.

Migration Notes

  • No database migrations required (uses existing tables)
  • No breaking changes to existing endpoints
  • Backward compatible with current analytics
  • Can be deployed incrementally

Success Metrics

Users now have complete visibility into: ✅ Which assets drive portfolio performance
✅ Portfolio risk profile (volatility, max loss potential)
✅ Performance vs major market benchmarks
✅ Historical trends and momentum
✅ Risk-adjusted returns (Sharpe, Sortino)
✅ Market correlation (beta)

Related Issues

  • Closes #694
  • Enhances #653 (Portfolio Analytics)
  • Complements existing asset tracking

Files Modified

  1. backend/services/performanceAttributionService.js - Extended with 500+ lines
  2. backend/routes/analytics.js - Added 5 new endpoints

Lines of Code

  • Service additions: ~600 lines
  • Route additions: ~100 lines
  • Total: ~700 lines of production code

Implementation Status: ✅ COMPLETE

Date: March 3, 2026
Issue: #694
Feature: Portfolio Performance Attribution & Risk Analytics Engine