Aritificial Intelligence for Trading

Udacity Inc. and WorldQuant LLC

Certificate

My Solutions & Projects - Artificial Intelligence for Trading

Content

• Brief Description
• Projects & Solutions

Brief Description

This is an extremely interesting Nanodegree if you want to apply Artificial Intelligence to track patterns in the Financial Markets. The topics and projects include:

Advanced Portifolio Optimization

• Basic 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.

• Stocks and common terminology used for analysis.

• Modern Market functions: How trades are executed, analyse price and volume data to identify potential trading signals.

• Data Processing: How to adjust market data for corporate actions, include fundamental information in your analysis and compute technical indicators.

• Stock Returns: How to calculate stock returns, and log returns in particular. Learn why log returns are used to analyze financial data.

• Momentum Trading: Alpha signals, and how they can be applied to a long/short trading strategy. Learn about momentum, a common alpha signal used in trading strategies.

• Quant Workflow: Overall quant workflow, including alpha signal generation, alpha combination, portfolio optimization, and trading.

• Outliers and Filtering: Importance of outliers and how to detect them. Learn about methods designed to handle outliers.

• Regression: Regression, and related statistical tools that pre-process data before regression analysis. See how regression relates to trading and other more advanced methods.

• Time Series Modeling: Advanced methods for time series analysis, including ARMA, ARIMA, Kalman Filters, Particle Filters, and recurrent neural networks.

• Volatility: Stock volatility, and how the GARCH model analysis volatility. See how volatility is used in equity trading.

• Pairs Trading and Mean Reversion: Pairs trading, and study the tools used in identifying stock pairs and making trading decisions.

• Stocks, Indices, Funds: Gain an overview of stocks, indices and funds. Also learn how to construct an index.

• ETFs: Learn about Exchanged Traded Funds (ETFs) and how they are used by investors and fund managers.

• Portfolio Risk and Return: Fundamentals of portfolio theory, which are key to designing portfolios for mutual funds, hedge funds and ETFs.

• Portfolio Optimization: Optimize portfolios to meet certain criteria and constraints. Get hands on experience in optimizing a portfolio with the cvxpy Python library.

• Factors: Factor investing and alpha research. ps. Project designed by Jonathan Larkin, equities trader and quant investor.

• Factor Models and Types of Factors: Theory of factor models, distinguish between alpha and risk factors, and get an overview of types of factors.

• Risk Factor Models: Model portfolio risk using factors.

• Time Series and Cross Sectional Risk Models: Time series and cross-sectional risk models.

• Risk Factor Models with PCA: Principle Component Analysis and how it's used to build risk factor models.

• Alpha Factors: Alpha generation and evaluation from a practitioner's perspective.

• Alpha Factor Research Methods: Alpha research from a practitioner's perspective.

• Advanced Portfolio Optimization: Portfolio optimization using alpha factors and risk factor models.

Natural Language Processing using Deep Learning

• Natural Language Processing: How to build a Natural Language Processing pipeline.

• Text Processing: Prepare text obtained from different sources for further processing, by cleaning, normalizing and splitting it into individual words or tokens.

• Feature Extraction: Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.

• Financial Statements: How to scrape data from financial documents using Regular Expressions and BeautifulSoup

• NLP Analysis: How to apply to NLP to financial statements

• Neural Networks: Implement gradient descent and backpropagation in Python, use several techniques to improve their training, use PyTorch for building deep learning models.

• RNN and LSTM: Recurrent neural networks to learn from sequential data such as text. Build a network that can generate realistic text one letter at a time.

• Embeddings & Word2Vec: Embeddings in neural networks by implementing the Word2Vec model.

• Sentiment Prediction RNN: Implement a sentiment prediction RNN for predicting whether a movie review is positive or negative!

Sentiment Analysis and Alphas with Random Forest, PCA, and Deep Neural Networks

• Machine Learning & Decision Trees: Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.

• Model Testing and Evaluation: Metrics to evaluate models and about how to avoid over- and underfitting.

• Random Forests: Random forest models and how to use them to combine alpha factors.

• Feature Engineering: Engineer features such as market dispersion, market volatility, sector and date parts. Also learn to engineer targets (labels) that are robust to market changes over time.

• Overlapping Labels: Non-independent labels that comes up during alpha combination with machine learning models.

• Feature Importance: Decide relevant each feature is to a machine learning model's predictions. Two methods for calculating feature importance.

• Backtesting: Backtesting helps you determine whether or not your strategies can be generalizable to future unseen data.

• Optimization with Transaction Costs: How to make the portfolio optimization in a backtest more realistic, and also more computationally efficient.

• Attribution: Use performance attribution to determine how each factor contributed to the portfolio's results.

Projects

My Solutions

• Trading with Momentum

    Implement a trading strategy on your own and test to see if it has the potential to be 
    profitable.
  

• Breakout Strategy

    Implement the breakout strategy, find and remove outliers, and test to see if it can be a 
    profitable strategy.
  

• Smart Beta and Portfolio Optimization

    Build a smart beta portfolio against an index and optimize a portfolio using quadratic 
    programming.
  

• Alpha Research and Factor Modeling

    Research and implement alpha factors, build a risk factor model. Use alpha factors and risk 
    factors to optimize a portfolio.
  

• Natural Language Processing on Financial Statements

    NLP Analysis on 10-k financial statements to generate an alpha factor.
  

• Sentiment Analysis with Neural Networks (Pytorch, NLTK & Stocktwits)

    Build a deep learning model to classify the sentiment of messages.
  

• Combining Signals for Enhanced Alpha (Random Forest for Alpha)

    Build a random forest to generate better alpha.
  

• Backtesting

    Build a backtester using Barra data.