A portfolio-oriented quantitative research project focused on building a disciplined backtesting workflow for trading strategy research.
Status: early-stage portfolio project. This repository uses synthetic data for demonstration. It is not financial advice and is not intended for live trading.
Many simple trading backtests are misleading because they ignore costs, slippage, drawdowns, overfitting, and regime changes. This project is intended to explore a more careful research workflow for testing strategies before any live deployment.
The repository now includes a minimal reproducible synthetic backtest:
pip install -r requirements.txt
python examples/minimal_backtest.pyThe example:
- generates synthetic daily price data
- runs a basic moving-average crossover strategy
- calculates a synthetic equity curve
- reports total return, annualized volatility, Sharpe ratio, and max drawdown
- Multi-strategy backtesting structure
- Realistic transaction costs and slippage assumptions
- Walk-forward testing concepts
- Monte Carlo robustness checks
- Drawdown and risk metrics
- Strategy comparison framework
- Clear research documentation
This project is designed to show practical skills relevant to data and analytics roles:
- Python-based analytical workflows
- Time-series thinking
- Risk and performance metrics
- Data cleaning and validation
- Experimental design
- Structured documentation
- Translating messy data into decision-ready summaries
examples/
└── minimal_backtest.py
requirements.txt
README.md
This project is for educational and portfolio purposes only. It does not provide investment advice, trading signals, or live execution recommendations.
- Add transaction cost and slippage assumptions
- Add walk-forward validation example
- Add Monte Carlo simulation
- Add visual charts
- Add strategy comparison table