Case study
Futures Backtesting Framework
A framework direction for testing futures strategies with clear assumptions, risk controls, and repeatable validation paths.
- Futures
- Backtesting
- Risk controls
Screenshot / demo TODO
Add project screenshots, architecture diagrams, backtest reports, or repository previews here.
Overview
A backtesting framework focused on futures strategy validation, scenario review, and the mechanics needed to avoid misleading results.
Problem
Backtests are easy to make impressive and hard to make trustworthy. Futures strategies need attention to contract behavior, risk limits, execution assumptions, and time-based validation.
What I Built
- Reusable structure for defining strategy inputs, market sessions, risk controls, and evaluation windows.
- Hooks for comparing in-sample and out-of-sample behavior.
- TODO: add exact implementation details once the repository or screenshots are ready.
Technologies Used
- Python
- pandas
- NumPy
- Futures market data
- Strategy validation
- TODO: add exact broker/platform integrations
Key Technical Challenges
- Preventing accidental lookahead bias and data leakage.
- Modeling assumptions clearly enough that results remain interpretable.
- Making risk controls first-class instead of an afterthought.
What It Demonstrates
- Quant engineering fundamentals
- Risk-aware system design
- Data pipeline thinking
- Backtest skepticism
TODO
- Add screenshots or diagrams.
- Add repository, demo, or write-up links when they are public.
- Add measured results only when they can be accurately sourced.