Advanced Algorithmic Trading

Machine Learning Applied To Real World Quant Strategies

Finally...implement advanced trading strategies using time series analysis, machine learning and Bayesian statistics with the open source R and Python programming languages, for direct, actionable results on your strategy profitability.

No doubt you've noticed the oversaturation of beginner Python tutorials and stats/machine learning references available on the internet.

Few tutorials actually tell you how to apply them to your algorithmic trading strategies in an end-to-end fashion.

There are hundreds of textbooks, research papers, blogs and forum posts on time series analysis, econometrics, machine learning and Bayesian statistics.

Nearly all of them concentrate on the theory.

What about practical implementation? How do you use that method for your strategy? How do you actually program up that formula in software?

We've written Advanced Algorithmic Trading to solve these problems.

It provides real world application of time series analysis, statistical machine learning and Bayesian statistics, to directly produce profitable trading strategies with freely available open source software.

  • 500+ pages of professional quantitative trading and risk management techniques
  • Advanced quant methods implemented in easy-to-read R and Python code
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You're Happy With Basic Programming But Want To Apply Your Skills To More Advanced Quant Trading

If you've read our previous book, Successful Algorithmic Trading, you will have had a chance to learn some basic Python skills and apply them to simple trading strategies.

However, you've grown beyond simple strategies and want to start improving your profitability and introducing some robust, professional risk management techniques to your portfolio.

In Advanced Algorithmic Trading we take a detailed look at some of the most popular quant finance libraries for both Python and R, including pandas, scikit-learn, statsmodels, QSTrader, timeseries, rugarch and forecast among many others.

We will use these libraries to look at a wealth of methods in the fields of Bayesian statistics, time series analysis and machine learning, using these methods directly in trading strategy research.

We apply these tools in an end-to-end backtesting and risk management scenario, using both R and the QSTrader libraries, allowing you to easily "slot them in" to your current trading infrastructure.

No Need For Expensive Off-The-Shelf Quant Software

You may have spent a lot of money purchasing some sophisticated backtesting tools in the past and ultimately found them hard to use and not relevant to your style of quant trading.

Advanced Algorithmic Trading makes use of completely free open source software, including Python and R libraries, that have knowledgeable, welcoming communities behind them.

More importantly, we apply these libraries directly to real world quant trading problems such as alpha generation and portfolio risk management.


"But I Don't Have A PhD In Statistics."

While machine learning, time series analysis and Bayesian statistics are quantitative topics, they also contain a wealth of intuitive methods, many of which can be explained without recourse to advanced mathematics.

In Advanced Algorithmic Trading we've provided not only the theory to help you understand what you're implementing (and improve upon it yourself!), but also detailed step-by-step coding tutorials that take the equations and directly apply them to real strategies.

Thus if you're much more comfortable coding than with mathematics, you can easily follow the snippets and start working to improve your strategy profitability.

What Topics Are Included In The Book?

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 and Kalman Filters

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.


What Technical Skills Will You Learn?

R: Time Series Analysis

You will be introduced to R, which is one of the most widely used research environments in quantitative hedge funds and asset managers. We will make use of many libraries including timeseries, rugarch and forecast.

Strategy Decay

We will use R and Python to estimate our strategy performance over time allowing us to produce strategy decay curves. This will help determine whether a strategy needs to be retired or is still viable and profitable.

Python: Scikit-Learn

We will dig deeper into the advanced features of scikit-learn, Python's ML library, including parameter optimisation, cross-validation, parallelisation, and produce sophisticated predictive models.

Robust Backtesting

How to create efficient vectorised and event-driven backtests for preliminary research, with realistic transaction cost assumptions and position handling, using R and the popular QSTrader library.

Python: PyMC3

We will introduce PyMC3, the flexible Bayesian modelling, or "Probabilistic Programming" toolkit and Markov Chain Monte Carlo sampler to help us carry out effective Bayesian inference on financial time series data.

Risk Management

We will continue our risk management discussion from previous books and look at regime detection and stochastic volatility as a means of determining our current risk level and portfolio allocation.


What Trading and Risk Management Strategies Will You Implement?

Monthly Rebalance Portfolios

We will introduce our backtesting framework with long-term monthly-rebalanced ETF portfolios, across multiple financial markets, comparing our results to a benchmark.

ARIMA+GARCH

We will look at a linear time series technique based on the ARIMA+GARCH model on a range of equity stock indexes and see how the strategy performance changes over time.

Kalman Filters for Pairs Trading

We will apply the Bayesian Kalman Filter to cointegrated time series to dynamically estimate the hedging ratio between asset pairs, improving a static estimate of a traditional hedge ratio.

Regime Detection

We will use Hidden Markov Models to produce a volatility regime detection model. This will be used to veto orders in a short-term trend following strategy to increase profitability.

Asset Returns Forecasting using Machine Learning

We will use numerous machine learning techniques such as Random Forests to forecast asset direction and level by regressing against other transformed features.

Sentiment Analysis

We will use sentiment analysis vendor data to generate a sentiment-based trading signal generator, applying it to a set of S&P500 stocks across various market sectors.

Questions?

Where can you learn more about us?

We have written over 200 posts on QuantStart.com covering quant trading, quant careers, quant development, data science and machine learning. You can read through the archives to learn more about our trading methodology and strategies.

What if you're not happy with the book?

While we think you will find Advanced Algorithmic Trading very useful in your quantitative trading education, we also believe that if you are not 100% satisfied with the book for any reason you can return it no questions asked for a full refund.

Will you get a hardcopy of the book?

No. At this stage the book is only available in Adobe PDF format, while the code itself is provided as a zip file of fully functional R and Python scripts, if you purchase the "Book + Software" option.

Which package should you buy?

This mostly depends on your budget. The book with full extra source code is the best if you want to dig into the code immediately, but the book itself contains a huge amount of code snippets that will aid your quant trading process.

Can we be contacted?

Of course! If you still have questions after reading this page please get in touch and we will do our best to provide you with a necessary answer. However, please take a look at the articles list, which may also help you.

Will you need a degree in mathematics?

The majority of the book requires an understanding of calculus, linear algebra and probability. However, many of the methods are intuitive and the code can be followed without recourse to advanced mathematics.

Select Your Preferred Package

THE BOOK FOR $49

  • The book in PDF format

THE BOOK + SOFTWARE FOR $99

  • 500+ pages of advanced algorithmic trading techniques
  • The book in PDF format
  • Full R and Python source code