Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python - Image 1

£31.31

Packt Publishing Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Name: Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
Brand: Packt Publishing
Price: 31.31 GBP
Availability: InStock

AI & Machine Learning

This is the usual price. Wait for it to drop, or tell us your number.

£31 today · usual range £0–£0 · best ever £25

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 617 days • 617 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 617 days • 5 price ranges

Days at Price

Current Price

Price Analysis

Most common range: £44-49 (568 days, 92.1%)

Price range: £25 - £49

Price levels: 5 price ranges over 617 days

Description

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 Description: 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 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 for: 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.

Product Specifications

Brand: Packt Publishing
Format: paperback
ASIN: 178934641X
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 31 December 2018
Listed Since: 19 October 2018

Barcode

No barcode data available

Packt Publishing Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

This is the usual price. Wait for it to drop, or tell us your number.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Machine Learning for Algorithmic Trading - Second Edition: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python

Introduction To Machine Learning In Quantitative Finance, An: 0 (Advanced Textbooks In Mathematics)

Day Trade With AI: Theoretical foundations for discretionary, algorithmic, and diversified trading with AI

Machine Learning for Factor Investing: Python Version (Chapman and Hall/CRC Financial Mathematics Series)

Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

Online Portfolio Selection: Principles and Algorithms

Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques

Machine Learning in Finance: From Theory to Practice

Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices

Artificial Intelligence in Finance: A Python-Based Guide

Machine Learning in Finance: From Theory to Practice

Algorithmic Approaches to Financial Technology: Forecasting, Trading, and Optimization

Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python

Machine Learning and Big Data with kdb+/q (Wiley Finance)

Building Winning Algorithmic Trading Systems + Website: A Trader's Journey From Data Mining to MonteCarlo Simulation to Live Trading (Wiley Trading)

Machine Learning for Factor Investing: R Version (Chapman and Hall/CRC Financial Mathematics Series)

Cambridge Deep Learning in Quantitative Trading Book

Data Analytics for Finance Using Python (Advances in Digital Technologies for Smart Applications)

Applications of Computational Intelligence in Data-Driven Trading

Quantitative Trading Strategies Using Python: Technical Analysis, Statistical Testing, and Machine Learning

Algorithmic Trading Hands-On Approach Using Python

AI in the Financial Markets: New Algorithms and Solutions (Computational Social Sciences)