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 DescriptionThe
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 forIf 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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