Systems
Independent build
Evolutionary Trading Lab
A research and backtesting lab that evolves trading strategies with a hand-written genetic algorithm and tests them hard against overfitting. Simulation only: it never places an order.
Stack: Python, FastAPI, numpy, pandas, ccxt, React, TypeScript, WebSockets
Problem
A strategy that looks great on past data is usually overfit. The lab is built to say so: every result is judged on data the strategy never saw.
What I built
- Genetic algorithm (genome, operators, fitness, loop) written by hand for readability, with penalties for drawdown, overtrading and complexity.
- Strict time-ordered train, validation and test split, plus walk-forward analysis; no look-ahead, since a signal at bar t fills at the t+1 open; fees and slippage included.
- PSR and deflated Sharpe on every single-split run, and a ROBUST or OVERFIT verdict with in-sample against out-of-sample equity curves.
- FastAPI with a WebSocket stream of per-generation progress, checkpoint resume, a batch runner, paper trading and alert-only signals.
Stack
- Python
- FastAPI
- numpy
- pandas
- ccxt
- React
- TypeScript
- WebSockets
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