Book Image

Hands-On Machine Learning for Algorithmic Trading

By : Stefan Jansen
Book Image

Hands-On Machine Learning for Algorithmic Trading

By: Stefan Jansen

Overview of this book

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.
Table of Contents (23 chapters)

Strategy Evaluation

Alpha factors drive an algorithmic strategy that translates into trades that, in turn, produce a portfolio. The returns and risk of the resulting portfolio determine the success of the strategy. Testing a strategy requires simulating the portfolios generated by an algorithm to verify its performance under market conditions. Strategy evaluation includes backtesting against historical data to optimize the strategy's parameters, and forward-testing to validate the in-sample performance against new, out-of-sample data and avoid false discoveries from tailoring a strategy to specific past circumstances.

In a portfolio context, positive asset returns can offset negative price movements in a non-linear way so that the overall variation of portfolio returns is less than the weighted average of the variation of the portfolio positions unless their returns are perfectly...