- Paperback: 520 pages
- Publisher: CreateSpace Independent Publishing Platform (June 1, 2013)
- Language: English
- ISBN-10: 148950771X
- ISBN-13: 978-1489507716
- Product Dimensions: 7.4 x 1.2 x 9.7 inches
- Shipping Weight: 2.5 pounds (View shipping rates and policies)
- Average Customer Review: 26 customer reviews
- Amazon Best Sellers Rank: #385,181 in Books (See Top 100 in Books)
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Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB
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About the Author
David Aronson is a pioneer in machine learning and nonlinear trading system development and signal boosting/filtering. He has worked in this field since 1979 and has been a Chartered Market Technician certified by The Market Technicians Association since 1992. He was an adjunct professor of finance, and regularly taught to MBA and financial engineering students a graduate-level course in technical analysis, data mining and predictive analytics. His ground-breaking book “Evidence-Based Technical Analysis” was published by John Wiley & Sons 2006. --------- Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His current focus is methods for evaluating financial market trading systems. He has authored five books on prediction, classification, and practical applications of neural networks: Practical Neural Network Recipes in C++ (Academic Press, 1993) Signal and Image Processing with Neural Networks (Wiley, 1994) Advanced Algorithms for Neural Networks (Wiley, 1995) Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995) Assessing and Improving Prediction and Classification (CreateSpace, 2013) More information can be found on his website: TimothyMasters.info
Top customer reviews
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I am a data scientist outside finance, and this book helped me transfer some of my knowledge in machine learning to the world of finance. The authors also point out many rookie mistakes people make while preparing their data / trading system. This is a no-BS book that is worth 1000s of dollars. It's not written by marketers but by people who actually trade.
Do yourself a favor, and buy this book.
As a math and finance professional, I find it very difficult to just accept that because the price of a stock goes through a magic line it will go up. I need hard evidence, and this is what TSSB allows the user to do, and this manual is essential in order to be able to learn how to work with the software.
I have already discovered several statistically significant models for predicting price changes in stocks. Not just a model that randomly fits a small data set, a model that is incredibly likely to be successful into the future.
This book and accompanying software has changed how I look at the markets, and since I received my copy a few weeks ago I have not been able to put it (or TSSB) down.
Let me say, however, that this isn't a get rich quick scheme. It is a user guide for a statistically sound tool to build algorithmic based trading models using statistical inference. There is not a better tool, nor instruction manual on the market and it is an absolute must for anyone thinking of trading with algorithms.
I thank the authors for releasing this software and manual and not selling it to Bridgewater or Citadel for several million dollars.
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