- 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: 4.2 out of 5 stars See all reviews (24 customer reviews)
- Amazon Best Sellers Rank: #754,202 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
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Top Customer Reviews
This book gives implementation level detail of how to create and test predictive models for any class of market (equity, forex etc...). The implementation is done using the free software called TSSB. The use of TSSB requires learning its associated command line language, which is very very simple to learn yet extremely powerful. As a point of comparison, to program the same models in the R programming language would require perhaps 10 or 100 times more lines of code (I know this because I programmed similar models in R before trying TSSB).
Upon first use, it took me all of 1 hour to create my first model in TSSB and it was off to the races from there – having since computed over 900 predictive indicators and over 100 models. A great feature is the ability to combine models in ‘committees’ or ‘oracles’, which is common practice to achieve optimal results.
One of the best features is the ability to perform a multitude of tests (boostrap, monte carlo, and permutation) that will greatly aid in ones confidence that the proposed model will in fact generalize to unseen data. In particular, the permutation test separates and measures a models ‘skill’ in comparison to its measured bias as well as luck due to trend. It is my hypothesis that the majority of hedge funds do not do such tests and is the reason why so many fail so quickly.
Another great thing about the book being implementation detail is that much of the theory and best practices can be abstracted out of it. For example, it is one thing to read about ‘stationarity’ (which I have read extensively about through complex greek lettered equations and proofs in other books) yet it was still not completely clear until I actual saw its impact with my own eyes through performance of models I created. Sometimes (often, in my case), getting to the implementation level detail (developing and testing and reviewing results) is the best way to understand a concept. Additionally, I learned exactly how and why serial correlation could dupe someone into thinking a lucky model had any skill.
There also subtle hints given throughout the book (e.g. committees often perform better than individual models, regime specialization per model often performs better, models tend to perform better when specializing in long-only or short-only contexts – then combine to either a portfolio or committee, more esoteric indicators (e.g. non-trend type) are better used in more complex models (non-linear), use of more than 3 indicators often overfits a model (a big time saver too!).
Other great things about TSSB (and the book): Most of the indicators are scaled and normalized to an extent, which helps take care of those little things that must be done before getting to the actual modelling. There are also specific functions which help create one model for multiple markets (market regression, cross-market normalization, pooling variables, use of indexes etc...) – often such implementation complexities are overlooked in theoretical books.
Indicator selection is very clearly explained with the use of tests such as Chi-Sq, Non-Redundant Indicator scanning, visual examination of indicator-target relationships, find groups, exclusion groups, and model stepwise selection.
It is worth mentioning that currently TSSB is only a research a tool; the models created cannot be simply exported to a trading program (e.g. TradeStation) – but apparently this is planned for a future release. In the mean time, Perhaps this is my biggest gripe and fear – I will create an amazing system but be unable to implement it in real life. On the other hand, with some time and help, it should be possible to re-create a lot of functionality in R (and use a trading program that integrates with R) or directly re-create it in a trading program that supports predictive functions.
I initially found out about the book over at the CssAnalytics blog. Shortly after, I bought it. Here is why, the book fills a HUGE gap:
Over the last 6 months I have taken an interest as a hobby to learn predictive modelling and apply it to the Forex market. During that those months, I learned the basics of the data mining process, programming and how to implement predictive models in R, and how to apply it to Forex (outside of by day job) . It's been a lot of fun and have gotten somewhat successful results so far.
I learned from the following materials in order:
- "Data Science for Business" by Foster Provost & Tom Fawcett from my MBA alma matter NYU-Stern. This is a fantastic and clear book written which describes data mining process and various models from a conceptual and logical perspective.
- Online course at Stanford from Professor's Hastie and Tibshirani, pioneers in the field which blends theory with practice to show how to implement various classification and regression models using R.
- "Applied Predictive Modelling" by Max Kuhn - an amazing book on implementing predictive models in R - mostly using the R caret package, which is wrapper to over 100 predictive models in R that essentially automates re-sampling (bootstrapping, cross validation, data splitting) as well as model evaluation and comparison.
- White papers that outline some predictive models in forex by generating a bunch of technical indicators and then running predictive models on them (SVM, Random Forest etc..)
As you can see from the above, I have learned from some really smart people how to
1) Create and evaluate predictive models
2) Program in R
3) Apply to markets such as Forex
Of the above, I have gotten a fairly good grasp on #1 and #2. This book fills the gap on #3!
The following points in the blog interview that caught my initial interest in the book:
"It is possible for a model to have poor error reduction across the entire range of its forecasts while being profitable for trading because when its forecasts are extreme they carry useful information. It is more appropriate to use financial measures such as the profit factor which are all included as objective functions within TSSB."
- I literally just had that realization of the effective of EXTREME forecasts, just this last week - by plotting the residuals vs. prediction made this very clear. IF I read the book, I probably wouldn't have needed 6 months to discover that point! Though, the process of discovering it myself was fun too.
"Even the best conventional technical indicators have only small amount predictive information. The vast majority is noise. Thus the task is to model that tiny amount of useful information in each indicator"
Wow, is that true! I'm super interested in learning how to model just the useful information in each indicator. Cool concept.
"In my opinion, the way to differentiate or uncover real opportunities currently lie in the clever engineering of new features- such as better indicators."
I've been using R's TTR package as my sole source of indicators. While there a LOT of indicators in the TTR package, I'm very interested in the 100 or so you mentioned in the software
From an excited modeler. Cheers!
Originally I was a little skeptical about paying an amount in the $120 range for a book. But what got me over that were two things: first, I had bought David Aronson's earlier book, Evidence Based Technical Analysis, and I loved the scientific rigor and creative thinking that he evidenced there. And secondly, I figured that if I looked at this book and the TSSB software as a package (which they are obviously intended to be), $120 is dirt cheap for almost any kind of trading software, let alone for a program that claims to be able to do what this one does.
So I bit the bullet and ordered the book, it has turned out to be well worth the money, without even considering the software. The reason is the wealth of insights that Aronson and Masters supply throughout the book. If you are (or want to be) a successful systemic trader that uses sophisticated models to generate your trading signals, then I believe that this book is no less than required reading.
Some of the topics that you will find addressed in this text include the following:
- What are the reasons to use walk-forward testing over cross-validation in testing your model's performance?
- Why are low (or even negative) values of R-squared perfectly fine for trading models?
- Where is the best predictive information found in a predictive variable?
- How can you use synthetic (permuted) market data to validate your models?
- Is it better to have a strong model that uses weak predictive variables, or the reverse, and why?
- How do developers of modern trading systems select the most effective predictors from among a set of candidates?
- If I'm building models to trade at tomorrow's open and exit at tomorrow's close, how far back should my predictive data extend? 5 days? 20 days? 100 days?
- Why is visual examination of a time-series plot of every predictor mandatory?
- How important are committees in modern trading system design?
- Are you better off having a single model for long and short trades, or should you have specialized models for each?
- In addition to normal development of a model by partitioning the test data into out-of-sample and in-sample sets, what are five reasons why you sometimes might want to also try developing the model using all the data as in-sample, without saving any data for testing?
After reading this book, you will have the knowledge to determine if your trading system meets the following two conditions: a) the chances that it has good performance just due to being lucky are small, and b) you know what the expected level of future performance is, and it is good enough to make it worth trading. And honestly, if you have that knowledge, you are probably ahead of 99% of the traders in the market today. (that's just my opinion, not the authors')
As loaded with powerful information as this book is, I do have a few quibbles. The quality of the writing is very good, but I do think it could have used more editing, specially in getting the material organized a little better. And there are a couple of places where it is mentioned that there is the need for additional material, which will be included in a forthcoming edition of the book, which I found a little disconcerting.
But overall, if you are a serious system trader or developer (or both), I believe that you owe it to youself to thoroughly read and study this book. I think you will find it contains many important and useful insights that you won't find elsewhere. These are the kinds of things that can make a difference between a middling trading system and an outstanding one. Even more importantly, they can give you a high degree of comfort that you can rely on your trading system, which in my experience, is worth its weight in gold.
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