num_pages: 552
description: Explore effective trading strategies in real-world markets using NumPy spaCy pandas scikit-learn and Keras 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. Youll practice the ML workow 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. 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.
language: English
title: Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
publisher: Packt Publishing