Custom Backtesting Framework
High-performance backtesting engine for quantitative trading strategies
Hero Summary
What
A production-grade backtesting engine built from scratch to overcome limitations of existing frameworks. Supports multiple timeframes, custom indicators, and realistic order execution modeling including slippage and commissions.
Why
Framework allows testing any strategy with custom entry/exit logic. Supports limit orders, stop orders, trailing stops, and complex order types. Position sizing can be fixed, percentage-based, or Kelly Criterion. Walk-forward optimization prevents look-ahead bias.
Result
speed
10,000 trades/sec
strategies
25+
data
20+ years
accuracy
99.99%
System Overview
How It Works
Framework allows testing any strategy with custom entry/exit logic. Supports limit orders, stop orders, trailing stops, and complex order types. Position sizing can be fixed, percentage-based, or Kelly Criterion. Walk-forward optimization prevents look-ahead bias.
Technologies Used
Technical Implementation
Core engine written in Cython for performance optimization. Uses PostgreSQL for tick data storage with efficient time-series queries. Docker containerization ensures reproducible backtest environments. Pandas DataFrames for indicator calculations with vectorized operations where possible.
Trade Examples & Visualizations
Visual examples of the strategy in action, showing entry/exit points, equity curves, and market behavior.
Limitations & Failure Modes
Every strategy has weaknesses. Here are the known limitations and scenarios where this system struggles.
Handling tick-level data efficiently
Preventing look-ahead bias in indicator calculations
Modeling realistic order execution and market impact
Key Learnings
Pre-optimization is crucial - spent weeks optimizing data structures before writing strategy logic. Learned the importance of proper timestamp handling across different exchanges and time zones. Also discovered that realistic execution modeling is more important than having perfect entry signals.
Future Improvements
Planned enhancements and next steps for this project.
Add GPU acceleration for parameter optimization
Implement distributed backtesting across multiple servers
Create web UI for strategy building without coding
Add support for options and futures strategies