/AI-for-Trading

Udacity nanodegree: AI for Trading

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AI for Trading

This repo contains my work to Udacity nanodegree AI for Trading.

Table of Contents

1. Trading with Momentum. Project

2. Breakout Strategy. Project

  • Learn about the overall quant workflow, including alpha signal generation, alpha combination, portfolio optimization, and trading.

  • Learn the importance of outliers and how to detect them. Learn about methods designed to handle outliers.

  • Learn about regression, and related statistical tools that pre-process data before regression analysis. Learn commonly-used time series models.

  • Learn about stock volatility, and how the GARCH model analysis volatility. See how volatility is used in equity trading.

  • Learn about pair trading, and study the tools used in identifying stock pairs and making trading decision.

3. Smart beta and portfolio optimization. Project

Quiz: funds_etfs_portfolio_optimization: cumsum_and_cumprod, cov, cvxpy_basis, cvxpy_adv

4. Alpha Research and Factor Modeling. Project

5. Intro to NLP. Project

NLP pipeline consists of text processing, feature extraction, and modeling.

6. Sentiment Analysis with Neural Networks. Project

7. Combining Signals for Enhanced Alpha. Project

  • Decision Tree: Learn how to branching decision tree using entropy and information gain. Implement decision tree using sklearn for Titanic Survival Exploration and visualize the decision tree using graphviz.

  • Model Testing and Evaluation: Learn Type 1 and Type 2 errors, Precision vs. Recall, Cross validation for time series, and using learning curve to determine underfitting and overfitting.

  • Random Forest: Learn the ensemble random forest method and implement it in sklearn.

  • Feature Engineering: Certain alphas perform better or worse depending on market conditions. Feature engineering creates additional inputs to give models more contexts about the current market condition so that the model can adjust its prediction accordingly.

  • Overlapping Labels: Mitigate the problem when features are dependent on each other (non-IID).

  • Feature Importance: Company would prefer simple interpretable models to black-box complex models. interpretability opens the door for complex models to be readily acceptable. One way to interpret a model is to measure how much each feature contributed to the model prediction called feature importance. Learn how sklearn computes features importance for tree-based method. Learn how to calculate shap for feature importance of a single sample.

8. Backtesting. Project

Additions

1D Kalman filter

Dataframe indexing and selection

Hypothesis testing