Machine Learning for Algorithmic Trading Book [PDF] Download

Download the fantastic book titled Machine Learning for Algorithmic Trading written by Stefan Jansen, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Machine Learning for Algorithmic Trading", which was released on 31 July 2020. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Business & Economics genre.

Summary of Machine Learning for Algorithmic Trading by Stefan Jansen PDF

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 FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The 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 learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If 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.


Detail About Machine Learning for Algorithmic Trading PDF

  • Author : Stefan Jansen
  • Publisher : Packt Publishing Ltd
  • Genre : Business & Economics
  • Total Pages : 822 pages
  • ISBN : 1839216786
  • PDF File Size : 54,7 Mb
  • Language : English
  • Rating : 5/5 from 1 reviews

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Machine Learning for Algorithmic Trading

Machine Learning for Algorithmic Trading
  • Publisher : Packt Publishing Ltd
  • File Size : 55,6 Mb
  • Release Date : 31 July 2020
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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

Hands-On Machine Learning for Algorithmic Trading

Hands-On Machine Learning for Algorithmic Trading
  • Publisher : Packt Publishing Ltd
  • File Size : 51,9 Mb
  • Release Date : 31 December 2018
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Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key FeaturesImplement machine learning algorithms to build, train, and validate algorithmic modelsCreate your own algorithmic design

The Science of Algorithmic Trading and Portfolio Management

The Science of Algorithmic Trading and Portfolio Management
  • Publisher : Academic Press
  • File Size : 49,5 Mb
  • Release Date : 01 October 2013
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The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first

Algorithmic Trading

Algorithmic Trading
  • Publisher : John Wiley & Sons
  • File Size : 34,6 Mb
  • Release Date : 28 May 2013
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Praise for Algorithmic TRADING “Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is

Machine Learning

Machine Learning
  • Publisher : CRC Press
  • File Size : 21,8 Mb
  • Release Date : 23 March 2011
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Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how

Python for Algorithmic Trading

Python for Algorithmic Trading
  • Publisher : O'Reilly Media
  • File Size : 48,6 Mb
  • Release Date : 12 November 2020
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Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is

Quantitative Trading

Quantitative Trading
  • Publisher : Unknown Publisher
  • File Size : 25,7 Mb
  • Release Date : 26 May 2024
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"While institutional traders continue to implement quantitative (or algorithmic) trading, many independent traders have wondered if they can still challenge powerful industry professionals at their own game? The answer is "

High-Frequency Trading

High-Frequency Trading
  • Publisher : John Wiley and Sons
  • File Size : 28,7 Mb
  • Release Date : 22 December 2009
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A hands-on guide to the fast and ever-changing world of high-frequency, algorithmic trading Financial markets are undergoing rapid innovation due to the continuing proliferation of computer power and algorithms. These

Advances in Financial Machine Learning

Advances in Financial Machine Learning
  • Publisher : John Wiley & Sons
  • File Size : 34,7 Mb
  • Release Date : 23 January 2018
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Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this