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Quantitative Portfolio Optimization & Rolling Backtest

Overview

This project implements a full quantitative portfolio research pipeline including:

Data download
Return calculation
Covariance & correlation analysis
Monte Carlo portfolio optimization
Efficient Frontier visualization
Rolling out-of-sample backtest
Risk metrics (Sharpe, Sortino, VaR, CVaR)
Benchmark comparison

Methodology

Optimization Objective: Maximize Sharpe Ratio
Rolling Window: 5-year training, 1-year test
Rebalancing: Annual
Monte Carlo Simulations: 2000 per rebalance

Key Results

Rolling CAGR: 25%+
Rolling Sharpe: ~1.0
Sortino Ratio: > 1
Outperformance vs SPY benchmark

Structure

src/ → Research code
data/raw/ → Raw downloaded data
data/processed/ → Processed returns
data/outputs/ → Charts & results

How To Run

pip install -r requirements.txt
python src/rolling_backtest.py
python src/risk_metrics.py

Author: Indrajith