[ 01 ]
Stock Backtester
A high-performance backtesting engine for validating trading strategies against historical market data
Backtest Speed
100x faster than naive loop-based implementations
Cost
$0 (open source) vs $5K+/month platforms
Realism
Models commissions, slippage, and stock splits
Metrics
Sharpe ratio, max drawdown, win rate, profit factor
The Problem
The Challenge
I realized most retail traders were making decisions based on gut feeling or YouTube strategies without actually testing them. They'd lose money in real trading because they never validated their ideas first. The tools that existed were either insanely expensive (like $5000 a month) or so complex that nobody wanted to use them. There was a massive gap between having an idea for a trading strategy and actually knowing if it would work.
The Solution
How We Built It
I built a backtesting engine from scratch in Python that lets traders test their strategies against real historical data. The key was making it fast and accurate. I used NumPy and Pandas to vectorize the calculations so instead of looping through thousands of trades one by one, the engine processes everything in batches. That made it roughly 100 times faster than the naive approach. The engine handles all the messy real world stuff that people forget about like commissions eating into your profits, slippage when you actually try to execute trades, and stock splits that mess with historical data. I integrated it with multiple data sources so traders can pull data from Yahoo Finance, Alpaca, Interactive Brokers, wherever they want. And the results are beautiful. You get all the metrics that matter: Sharpe ratio, maximum drawdown, win rate, profit factor. It can backtest a full decade of data in under 2 seconds.
Key Features
Vectorized backtesting that runs 100x faster than traditional loops
Support for different timeframes from one minute to monthly data
Realistic modeling of real world costs like slippage and commissions
Portfolio rebalancing across multiple positions
Sharpe ratio and max drawdown calculations that actually matter
Walk forward and Monte Carlo analysis for robustness testing
Easy export to CSV and JSON for further analysis
Tech Stack
Results & Impact
What We Achieved
Building this revealed something uncomfortable: most strategies that look profitable in backtests fall apart once you account for real costs. People forget commissions and slippage, or they overfit to patterns that won't repeat. The backtester forces you to be honest about whether your edge is real or just luck, and that's exactly what it was built for. It's still evolving, and I keep refining it as I learn more.
Lessons Learned
1. Vectorization is literally everything in finance. Looping through 10,000 candles will kill your performance.
2. Small commission and slippage errors compound massively. A tiny 0.1% per trade becomes huge over thousands of trades.
3. Your results are only as good as your data. Garbage in really does mean garbage out.
4. Backtesting results feel good but they lie. You need to validate on data the model has never seen before.
5. Real trading always has surprises that your backtest didn't account for. You need proper risk management.
Timeline
3 months from research to launch with refinements
Role
Solo architect and builder handling everything
Status
In Development
Interested in working together?
Let's build something meaningful. I'm always excited to discuss new projects and collaborate with talented people.