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QuantLab
An algorithmic trading and backtesting platform for designing, testing and scoring stock-trading strategies on 16 years of market history.
- Year
- 2026
- Role
- Solo
- Stack
- Python, pandas, NumPy, SciPy
What it is
A quantitative trading platform for designing, testing and evaluating algorithmic stock-trading strategies on historical market data.
The backtesting framework spans 16 years of market history and measures every strategy against a passive S&P 500 buy-and-hold benchmark.
Each strategy is scored on returns, volatility, maximum drawdown and risk-adjusted metrics, so profitability is always weighed against risk.
Keeping it honest
Most backtests lie, usually by accident: they peek at future data or get tuned until they look good. QuantLab is built so it's hard to fool yourself.
Strategy families evolve on 2020–2023 data only; 2024 onward is held out for testing. Finalists have to pass statistical gates (probability of backtest overfitting and a deflated Sharpe ratio) before they're compared against simply holding VOO.
An audit script checks every model for lookahead bias and pricing errors, option pricing is calibrated against real quotes, and runs are pinned to be exactly reproducible. Each (starting cash, start date) pair is its own fresh simulation, since a small account can't take some trades at all.