Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization.

The Program 

1. Basic quantitative trading.
2. Advanced quantitative trading.
3. Stocks, Indices, and ETFs.
4. Factor investing and alpha research.
5. Sentiment analysis with natural language processing.
6. Advanced natural language processing with deep learning.
7. Combining multiple signals.
8. Simulating trades with historical data.

What will you learn 

• Learn about market mechanics and how to generate signals with stock data. Work on developing a momentum-trading strategy in your first project.
• Learn the quant workflow for signal generation, and apply advanced quantitative methods commonly used in trading.
• Learn about portfolio optimization, and financial securities formed by stocks, including market indices, vanilla ETFs, and Smart Beta ETFs.
• Learn about alpha and risk factors, and construct a portfolio with advanced optimization techniques.
• Learn the fundamentals of text processing, and analyze corporate filings to generate sentiment-based trading signals.
• Learn to apply deep learning in quantitative analysis and use recurrent neural networks and long short-term memory to generate trading signals.
• Learn advanced techniques to select and combine the factors you’ve generated from both traditional and alternative data.
• Learn to refine trading signals by running rigorous back tests. Track your P&L while your algorithm buys and sells.