### 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.

### State-Space Models

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.

### Machine Learning

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.

### Linear Regression

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.

### Tree-Based Methods

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.

### Kernel Methods

We'll discuss the family of Support Vector Classifiers, including the Support Vector Machine, and how we can apply it to financial data series.

### Unsupervised Methods

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.

### Bayesian Statistics

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.