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Hands-On Machine Learning for Algorithmic Trading
Hands-On Machine Learning for Algorithmic Trading

Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

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Profile Icon Yau Profile Icon Stefan Jansen
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$31.99 $45.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (20 Ratings)
eBook Dec 2018 684 pages 1st Edition
eBook
$31.99 $45.99
Paperback
$65.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Yau Profile Icon Stefan Jansen
Arrow right icon
$31.99 $45.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (20 Ratings)
eBook Dec 2018 684 pages 1st Edition
eBook
$31.99 $45.99
Paperback
$65.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$31.99 $45.99
Paperback
$65.99
Subscription
Free Trial
Renews at $19.99p/m

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Hands-On Machine Learning for Algorithmic Trading

Market and Fundamental Data

Data has always been an essential driver of trading, and traders have long made efforts to gain an advantage by having access to superior information. These efforts date back at least to the rumors that the House Rothschild benefited handsomely from bond purchases upon advance news about the British victory at Waterloo carried by pigeons across the channel.

Today, investments in faster data access take the shape of the Go West consortium of leading high-frequency trading (HFT) firms that connects the Chicago Mercantile Exchange (CME) with Tokyo. The round-trip latency between the CME and the BATS exchange in New York has dropped to close to the theoretical limit of eight milliseconds as traders compete to exploit arbitrage opportunities.

Traditionally, investment strategies mostly relied on publicly available data, with limited efforts to create or...

How to work with market data

Market data results from the placement and processing of buy and sell orders in the course of the trading of financial instruments on the many marketplaces. The data reflects the institutional environment of trading venues, including the rules and regulations that govern orders, trade execution, and price formation.

Algorithmic traders use ML algorithms to analyze the flow of buy and sell orders and the resulting volume and price statistics to extract trade signals or features that capture insights into, for example, demand-supply dynamics or the behavior of certain market participants.

We will first review institutional features that impact the simulation of a trading strategy during a backtest. Then, we will take a look at how tick data can be reconstructed from the order book source. Next, we will highlight several methods that regularize tick data...

How to work with fundamental data

Fundamental data pertains to the economic drivers that determine the value of securities. The nature of the data depends on the asset class:

  • For equities and corporate credit, it includes corporate financials as well as industry and economy-wide data.
  • For government bonds, it includes international macro-data and foreign exchange.
  • For commodities, it includes asset-specific supply-and-demand determinants, such as weather data for crops.

We will focus on equity fundamentals for the US, where data is easier to access. There are some 13,000+ public companies worldwide that generate 2 million pages of annual reports and 30,000+ hours of earnings calls. In algorithmic trading, fundamental data and features engineered from this data may be used to derive trading signals directly, for example as value indicators, and are an essential input for predictive...

Efficient data storage with pandas

We'll be using many different data sets in this book, and it's worth comparing the main formats for efficiency and performance. In particular, we compare the following:

  • CSV: Comma-separated, standard flat text file format.
  • HDF5: Hierarchical data format, developed initially at the National Center for Supercomputing, is a fast and scalable storage format for numerical data, available in pandas using the PyTables library.
  • Parquet: A binary, columnar storage format, part of the Apache Hadoop ecosystem, that provides efficient data compression and encoding and has been developed by Cloudera and Twitter. It is available for pandas through the pyarrow library, led by Wes McKinney, the original author of pandas.

The storage_benchmark.ipynb notebook compares the performance of the preceding libraries using a test DataFrame that can be configured...

Summary

This chapter introduced the market and fundamental data sources that form the backbone of most trading strategies. You learned about numerous ways to access this data, and how to preprocess the raw information so that you can begin extracting trading signals using the machine learning techniques that we will be introducing shortly.

Before we move onto the design and evaluation of trading strategies and the use of ML models, we need to cover alternative datasets that have emerged in recent years and have been a significant driver of the popularity of ML for algorithmic trading.

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Key benefits

  • Implement machine learning algorithms to build, train, and validate algorithmic models
  • Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions
  • Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics

Description

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.

Who is this book for?

Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.

What you will learn

  • Implement machine learning techniques to solve investment and trading problems
  • Leverage market, fundamental, and alternative data to research alpha factors
  • Design and fine-tune supervised, unsupervised, and reinforcement learning models
  • Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn
  • Integrate machine learning models into a live trading strategy on Quantopian
  • Evaluate strategies using reliable backtesting methodologies for time series
  • Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow
  • Work with reinforcement learning for trading strategies in the OpenAI Gym

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 31, 2018
Length: 684 pages
Edition : 1st
Language : English
ISBN-13 : 9781789342710
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Product Details

Publication date : Dec 31, 2018
Length: 684 pages
Edition : 1st
Language : English
ISBN-13 : 9781789342710
Category :
Languages :
Tools :

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Table of Contents

22 Chapters
Machine Learning for Trading Chevron down icon Chevron up icon
Market and Fundamental Data Chevron down icon Chevron up icon
Alternative Data for Finance Chevron down icon Chevron up icon
Alpha Factor Research Chevron down icon Chevron up icon
Strategy Evaluation Chevron down icon Chevron up icon
The Machine Learning Process Chevron down icon Chevron up icon
Linear Models Chevron down icon Chevron up icon
Time Series Models Chevron down icon Chevron up icon
Bayesian Machine Learning Chevron down icon Chevron up icon
Decision Trees and Random Forests Chevron down icon Chevron up icon
Gradient Boosting Machines Chevron down icon Chevron up icon
Unsupervised Learning Chevron down icon Chevron up icon
Working with Text Data Chevron down icon Chevron up icon
Topic Modeling Chevron down icon Chevron up icon
Word Embeddings Chevron down icon Chevron up icon
Deep Learning Chevron down icon Chevron up icon
Convolutional Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks Chevron down icon Chevron up icon
Autoencoders and Generative Adversarial Nets Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon
Next Steps Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Jing Zhang Jan 31, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I love all Stefan's books, which are all well written and logically organized - very easy to follow. They not only provide detailed information on the theories behind, but also provide many practical examples and even Jupyter Notebooks that can be used in real life situations. They also covered almost all areas of Machine Learning in trading. They are just like THE bibles of Machine Learning in trading to me. Highly recommended!Got both "Hands-On Machine Learning for Algorithmic Trading" and "Machine Learning for Algorithmic Trading", if you want to master Machine Learning in trading!
Amazon Verified review Amazon
Jing Zhang Jan 31, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I love all Stefan's books, which are all well written and logically organized - very easy to follow. They not only provide detailed information on the theories behind, but also provide many practical examples and even Jupyter Notebooks that can be used in real life situations. They also covered almost all areas of Machine Learning in trading. They are just like THE bibles of Machine Learning in trading to me. Highly recommended!Got both "Hands-On Machine Learning for Algorithmic Trading" and "Machine Learning for Algorithmic Trading", if you want to master Machine Learning in trading!
Amazon Verified review Amazon
wasif Mar 15, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
book has very good and quick contents but graphs miss colors which made them impossible to read
Amazon Verified review Amazon
Mike Jan 06, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book
Amazon Verified review Amazon
Kindle Customer Nov 20, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is an excellent book on algorithmic trading! I read the book from cover to cover and found it very serious and interesting. My suggestion to get the best of the book, is to read it first without trying to run a program. The best approach is:1.Read the theory, understand it. Even go to online references to understand a definition.2.Understand the mathematics underlying trading and machine learning trading. The book expresses this very well.3.Then only, once you have read the book, take the programs for what they are: examples. If you have the theory and the math, you can easily find alternatives on GitHub, run the programs of the book, tweak them and more.As an AI author myself, I take time to read many other AI books to see how my fellow experts see things. It widens my horizons and bibliography. I really enjoyed this one.
Amazon Verified review Amazon
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