Machine Learning for Algorithmic Trading
eBook - ePub

Machine Learning for Algorithmic Trading

Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

Stefan Jansen

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  1. 820 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Machine Learning for Algorithmic Trading

Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

Stefan Jansen

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About This Book

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio.

Purchase of the print or Kindle book includes a free eBook in the PDF format.

Key Features

  • Design, train, and evaluate machine learning algorithms that underpin automated trading strategies
  • Create a research and strategy development process to apply predictive modeling to trading decisions
  • Leverage NLP and deep learning to extract tradeable signals from market and alternative data

Book Description

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.

This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.

This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.

By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.

What you will learn

  • Leverage market, fundamental, and alternative text and image data
  • Research and evaluate alpha factors using statistics, Alphalens, and SHAP values
  • Implement machine learning techniques to solve investment and trading problems
  • Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader
  • Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio
  • Create a pairs trading strategy based on cointegration for US equities and ETFs
  • Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data

Who this book is for

If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies.

Some understanding of Python and machine learning techniques is required.

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Yes, you can access Machine Learning for Algorithmic Trading by Stefan Jansen in PDF and/or ePUB format, as well as other popular books in Business & Financial Engineering. We have over one million books available in our catalogue for you to explore.




1/N portfolio 132
(first), high, low, and closing (last) price and volume (OHLCV) 35
LSTM architecture 599, 600
Accession Number (adsh) 55
AdaBoost algorithm 367
advantages 369
disadvantages 369
used, for predicting monthly price moves 369, 371
AdaGrad 528
adaptive boosting 366, 367
adaptive learning rates
about 527
AdaGrad 528
adaptive moment derivation (Adam) 528
RMSProp 528
adaptive moment derivation (Adam) 528
agglomerative clustering 435
aggressive strategies 4
Akaike information criterion (AIC) 183
AlexNet 564
AlexNet performance
comparing 566, 567
finding, for task 149
Algorithm API 243
algorithmic trading libraries
Alpha Trading Labs
Interactive Brokers
Python Algorithmic Trading Library (PyAlgoTrade)
Trading with Python
AlgoSeek 41
AlgoSeek intraday data
processing 43, 44, 45
AlgoSeek NASDAQ 100 dataset
all or none orders 23
alpha 124
alpha factor
from market data 110, 111, 112
alpha factor research 14
execution phase 15
research phase 15
alpha factors 82, 83
denoising, with Kalman filter 102, 103
engineering 94
engineering, NumPy used 95
engineering, pandas used 95
factor evaluation 114
pyfolio input, obtaining from 142
use, for backtesting long-short trading strategy
Alphalens analysis
information coefficient 394
quantile spread 394
Alpha Trading Labs
alternative betas 127
alternative data
data providers 71
email receipt data 74
geolocation data 73
market 70, 71
satellite data 72
social sentiment data 71
URL 71
use cases 71
working with 74
alternative data revolution 60
alternative data revolution, technical aspects
format 70
latency 69
alternative datasets
evaluating, based on quality of data 67
evaluating, based on quality of signal content 65
evaluating, criteria 65
alternative datasets, sources
about 62
business processes data 63
individuals data 62, 63
sensors data 63
alternative RNN architectures 595
attention mechanism 596
bidirectional RNNs 595
encoder-decoder architectures 596
output recurrence 595
teacher forcing 595
transformer architecture 596
alternative trading system (ATS) 3
alternative trading systems (ATSs) 24
American Depositary Receipts (ADR) 24
Amihud Illiquidity
analytical tools
for diagnostics and feature extraction 256, 257
Apache HBASE
Apache Hive
Apache MXNet 546
Apache Pig
Apache Spark
API access
to market data 45
Applied Quantitative Research (AQR) 8, 10
appraisal risk 124

Table of contents