Explore effective trading strategies in real-world markets using
NumPy, spaCy, pandas, scikit-learn, and Keras Key Features
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 Book
DescriptionThe 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 workflow 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. 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 Who
this book is forHands-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.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
