Strict NDA DisclaimerThis case study showcases a Non-Disclosure Agreement (NDA) signed project. Due to strict legal confidentiality agreements, actual proprietary trading data, client identities, exact performance figures, and live interface images are not real and cannot be publicly shown by us as the development team. All metrics and visuals shown are representative architectural simulations.
Quant Research & Intelligence
Institutional Backtesting Engine
High-performance Python backtesting engine for equities, forex, and crypto. Supports large datasets, custom strategies, walk-forward analysis, portfolio simulations, and performance metrics including Sharpe, drawdown, and expectancy.
Primary TechPython / Pandas
Data FormatHDF5 & Binary
Target PlatformTick Engine
01The Problem
Quantitative analysts required a robust system to backtest multi-year data with realistic slippage and commissions.
02Our Solution
We created a custom Python backtesting engine using Pandas and HDF5 that can process millions of ticks per second.
Core Features Developed
- Large-Scale Historical Datasets
- Walk-Forward Adaptability
- Portfolio Risk Simulations
- Expectancy & Sharpe Metric Reports
Technology Stack
PythonPandasNumPyHDF5