Advanced Algorithmic Trading – Michael L. Halls-Moore
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.
I’m sure 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?
I’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.
You’re Happy With Basic Programming But Want To Apply Your Skills To More Advanced Quant Trading
If you’ve read my 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, 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 libraries in an end-to-end vectorised backtesting and risk management scenario, 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.
About the Author
So who’s behind this?
Hi! My name is Mike Halls-Moore and I’m the guy behind QuantStart and the ‘Advanced Algorithmic Trading’ package.
Since working as a quantitative trading developer in a hedge fund I have been passionate about quantitative trading research and implementation.
I started the QuantStart community and wrote ‘Advanced Algorithmic Trading’ to expose practising retail quants to the methods used in quantitative hedge funds and asset management firms.
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
I’ll 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 ETFs strategies.
State-Space Models
You’ll find an in-depth discussion on state-space models such as the Kalman Filter and the Hidden Markov Model, as applied to quantitative trading.
High Frequency Data
You’ll get an introduction to trading at higher frequencies and an in-depth look at market microstructure in the equities and forex markets.
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.
The Bias-Variance Tradeoff
I’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
I’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.
Natural Language Processing
We’ll discuss sentiment analysis and how we can build trading strategies of natural language data using clustering and cosine similarity.
Unsupervised Methods
I’ll explain how you can apply unsupervised learning techniques such as PCA, K-Means Clustering and NMF to large datasets in order to make them easier to analyse.
Bayesian Statistics
I’ll provide a full introduction to Bayesian inference in probability and why it will give us a huge advantage when implementing more advanced models.
Markov-Chain Monte Carlo
You’ll learn about MCMC, including Gibbs Sampling and Metropolis-Hastings, the main algorithm for sampling in Bayesian statistics, using the PyMC3 software.
Bayesian Networks
We’ll define and discuss Bayesian Networks, a type of graphical probabilistic model. We’ll apply Bayes Nets to our portfolio.
Bayesian Econometrics
I’ll provide an introduction to this new, but exciting, area of statistics and trading where we apply Bayesian methods to econometrics data.
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.
Vectorised Backtesting
How to create efficient vectorised backtests for preliminary research, with realistic transaction costs assumptions, using R and pandas, without the need to implement a full event-driven system.
Python: PyMC3
We will introduce PyMC3, the flexible Bayesian modelling toolkit and Markov Chain Monte Carlo sampler to help us carry out effective Bayesian inference for our risk management infrastructure and trading strategies.
Risk Management
We will continue our risk management discussion from previous books and look at regime detection and stochastic volatility as a means to help us determine our risk level and portfolio allocation.
What Trading and Risk Management Strategies Will You Implement?
ARIMA+GARCH
We will look at a linear time series model 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 two pairs, improving a static estimate of a traditional hedge ratio.
HFT Bid-Ask Spread Prediction
We will use advanced time series and machine learning methods to forecast the bid-ask spread in high frequency forex data in order to determine the best periods to execute trades.
Volatility Forecasting
We will use stochastic volatility models to forecast volatility in order to produce a regime detection model, that will help us identify periods of higher and lower risk.
Asset Returns Forecasting using ML
We will use numerous machine learning techniques to forecast asset direction and level, on both the equities and forex markets, by regressing against other factors.
Sentiment Analysis
We will use SVMs and other ML methods to build a sentiment analysis signal generator based on social media data and blog data, applying it to liquid equities and ETFs.
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