Aritificial Intelligence for Trading.
Udacity and WorldQuant Asset Management
My Solutions & Projects – Artificial Intelligence for Trading
“Never underestimate the power of AI …”
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.
Implement a trading strategy on your own and test to see if it has the potential to be profitable.
Implement the breakout strategy, find and remove outliers, and test to see if it can be a profitable strategy.
Build a smart beta portfolio against an index and optimize a portfolio using quadratic programming.
Research and implement alpha factors, build a risk factor model. Use alpha factors and risk factors to optimize a portfolio.
NLP Analysis on 10-k financial statements to generate an alpha factor.
Build a deep learning model to classify the sentiment of messages.
Build a random forest to generate better alpha.
Build a backtester using Barra data.