backtesting

name	backtesting
description	Backtest portfolio performance using historical data. Use when validating portfolio strategy with historical returns, calculating realized metrics, and stress testing.

Backtesting Skill

Backtest Process

Step 1: Run Backtest

from portfolio_optimizer import backtest_portfolio

# weights: dict of {ticker: weight}
# prices: DataFrame of historical prices
backtest_result = backtest_portfolio(weights, prices)

print(f"Cumulative Return: {backtest_result['cumulative_return']:.2%}")
print(f"Annualized Return: {backtest_result['annualized_return']:.2%}")
print(f"Volatility: {backtest_result['volatility']:.2%}")
print(f"Sharpe Ratio: {backtest_result['sharpe_ratio']:.3f}")
print(f"Max Drawdown: {backtest_result['max_drawdown']:.2%}")

Backtest Metrics Explained

Metric	What It Tells You
cumulative_return	Total return over the period
annualized_return	Yearly average return
volatility	Realized risk (annualized std dev)
sharpe_ratio	Realized risk-adjusted return
max_drawdown	Worst historical decline

Interpreting Results

Expected vs Realized Comparison

Comparison	Interpretation
Realized Sharpe > Expected	Strategy performed better than predicted
Realized Sharpe < Expected	Estimation error or regime change
Realized Vol > Expected	Higher risk than anticipated
Realized Vol < Expected	Lower risk (good if returns maintained)

Warning Signs

• Large gap between expected and realized: Model assumptions may be wrong
• Max drawdown > -30%: May be too risky for conservative investors
• Negative Sharpe ratio: Strategy lost money risk-adjusted

Best Practices

1. Use sufficient history: At least 3-5 years of data
2. Include market stress periods: 2008, 2020, 2022
3. Compare to benchmark: SPY for equity, AGG for bonds
4. Check for survivorship bias: Include delisted securities if possible

Code Example: Full Backtest Analysis

from portfolio_optimizer import (
    PortfolioConfig, optimize_portfolio,
    backtest_portfolio, download_market_data, get_universe
)

# Setup
tickers = get_universe("global_diversified")
prices = download_market_data(tickers, "2019-01-01", "2024-01-01")

# Optimize
config = PortfolioConfig(
    tickers=list(prices.columns),
    start_date="2019-01-01",
    end_date="2024-01-01",
    optimization_target="max_sharpe"
)
opt_result = optimize_portfolio(config, prices)

# Backtest
bt_result = backtest_portfolio(opt_result["weights"], prices)

# Compare
print(f"Expected Return: {opt_result['expected_return']:.2%}")
print(f"Realized Return: {bt_result['annualized_return']:.2%}")
print(f"Expected Sharpe: {opt_result['sharpe_ratio']:.3f}")
print(f"Realized Sharpe: {bt_result['sharpe_ratio']:.3f}")