- 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: #858,353 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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The book itself is essentially a manual for the free (with purchase of the book) TSSB software. The TSSB software itself does far more than my personal efforts and does them far more robustly. In terms of raw analysis I think the software is worth the price of the book. It is perhaps even a bargain. Scattered throughout the book/manual is enough descriptive text about the mechanics and algorithms to make this a worthwhile purchase for me. However, it is important to mention that I'm just an amateur. I found it useful others may not.
I would have given the book and software five stars but I took one star off for the following reasons:
1. There is no straightforward way to use modeling results you create in the TSSB software in a real-time trading capacity. No interfaces or exports are available without some clumsy hacks. I will likely be able to code around these limitations because of my software background but this may not be true for everyone. Frankly, this is a big problem (although an understandable one) and I almost took off two stars because of it.
2. The software itself has some rough edges in terms of usability and workflow, although I have not yet come across any bugs. I think it would be better if it treated scripts as if you were working in a standard software development IDE.
3. My initial impression is that the only way to use complicated custom indicators in models is to create those indicators offline and then import them as part of a TSSB database (just a delimited ASCII file). This is more annoying than a problem, since the database format is a simple delimited ASCII file.
So much of what is taught in trading classes and discussed in trading rooms is based on "beliefs" about market behavior. I have learned in trading that most of those beliefs are not validated by anything other than the fact that a large number of people repeat the beliefs as revealed truth. This book cuts through beliefs and myths by providing mathematical and programming tools for individuals who want to develop and test trading algorithms against market data. There's no bulls*** about the performance over the last two years, no claims about profits made by trading a specific system, and nothing more to buy (unless you want specific support on the software).
The trick in trading is to see and act on signals in a timely fashion without getting distracted by the noise. Tools for smoothing the signal or for filtering out the high frequency noise (e.g., indicators and oscillators provided by all trading platforms) provide a nicer apparent signal at the cost of time lags that generally allow the trader to see the entry only after it has passed. For the most part, these indicators are not independent of each other, and often they don't provide consistent indications of entries and exits. To a large degree, trading is a combination of driving forward while looking in the rear view mirror coupled with input from the bumps you hit while driving.
Aronson and Masters have helped the trader tremendously with very sophisticated, robust, and what appear to be easy to use tools for evaluating trading models against market data. They point out pitfalls of traditional back testing methods and teach more robust (I think I mean "statistically sound") methods for ensuring that a trading algorithm is more than a burst of good luck.
I don't see how this book and the software on which it is based can fail to help a sophisticated trader.
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