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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...

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Jing Zhang Jan 31, 2021

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

wasif Mar 15, 2020

book has very good and quick contents but graphs miss colors which made them impossible to read

Mike Jan 06, 2020

Good book

Kindle Customer Nov 20, 2019

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.

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

What do you get with Print?

Hands-On Machine Learning for Algorithmic Trading

Market and Fundamental Data

How to work with market data

How to work with fundamental data

Efficient data storage with pandas

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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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