QuantFinance-Trading

This project deals with Quantative Trading, building a trading strategy by generating alpha, optimizing a portfolio .

This project is divided into 5 sections :

1. Trading with Momentum Strategy :
   • Generated a trading signal based on Momentum Indicator.
   • Tested the strategy to see if it has potential to be profitable.
2. Break-Out Strategy :
   • Implemented Break-Out Strategy i.e when stocks break out of range, due to, e.g., a significant news release or from market pressure from a large investor.
3. Smart-Beta Portfolio Optimization:
   • Built a portfolio using Quadratic programming to optimize the weights.
   • The portfolio formed was compared to a benchmark index by calculatiing a tracking error against the index.
4. Multi-Factor Model using PCA:
   • Created a statistical risk model using Principal Component Analysis.
   • Created factors, then evaluated them using factor-weighted returns, quantile analysis, sharpe ratio, and turnover analysis.
   • Optimized the portfolio using the risk model and factors using multiple optimization formulations.
5. Backtesting:
   • Built a fairly realistic backtester that uses the Barra data. The backtester will perform portfolio optimization that includes transaction costs.
   • Implemented it with computational efficiency in mind, to allow for a reasonably fast backtest.
   • Performanced attribution to identify the major drivers of your portfolio's profit-and-loss (PnL).

Dataset

Since the project was under Udacity's AI for Trading Nanodegree the dataset was provided by thier partners Quotemedia, Barra and Sharadar.

Installation

pip install -r requirements.txt

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT