Time Series Analysis
You'll receive a complete beginner's guide to time series analysis, including asset returns characteristics, serial correlation, the white noise and random walk models.
Time Series Models
We provide a thorough discussion of Autoregressive Moving Average (ARMA) and Autoregressive Conditional Heteroskedastic (ARCH) models using the R statistical environment.
Cointegrated Time Series
We will continue the discussion on cointegrated time series from Successful Algorithmic Trading and consider the Johansen test, applying it to ETF strategies.
You'll find an in-depth discussion on how the Kalman Filter can be used to create dynamic hedging ratios between pairs of ETF assets, using freely-available Python tools.
Hidden Markov Models
You'll get an introduction to Hidden Markov Models and how they can be applied to financial data for the purposes of regime detection.
We'll discover exactly what "statistical machine learning" is, including supervised and unsupervised learning, and how they can help us produce profitable systematic trading strategies.
We will initially use the familiar technique of linear regression, in both a Bayesian and classical sense, as a means of teaching more advanced machine learning concepts.
The Bias-Variance Tradeoff
We'll talk about one of the most important concepts in machine learning, namely the bias-variance trade-off and how we can minimise its effects using cross-validation.
We'll discuss one of the most versatile ML model familes, namely the Decision Tree, Random Forest and Boosted Tree models, and how we can apply them to predict asset returns.
We'll discuss the family of Support Vector Classifiers, including the Support Vector Machine, and how we can apply it to financial data series.
We'll explain how you can apply unsupervised learning techniques such as K-Means Clustering to financial OHLCV bar data in order to cluster "candles" into regimes.
Natural Language Processing
We'll discuss how to apply machine learning methods to a large natural language document corpus and predict categories on unseen test data, as a precursor to sentiment-based models.
We'll provide a full introduction to Bayesian probability models, including a detailed look at inference, which forms the basis for more complex models throughout the book.
Markov-Chain Monte Carlo
You'll learn about MCMC, in particular the Metropolis-Hastings algorithm, which is one of the main techniques for sampling in Bayesian statistics, using the PyMC3 software.
Bayesian Stochastic Volatility
We'll look at stochastic volatility models under a Bayesian framework, using these to identify periods of large market volatility for risk management.