- 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: #246,345 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!
First, as others have said, if you view this book combined with the software as a single purchase then personally I think it's hard to imagine a much better deal. As someone who's primarily a Linux Open Source user I'm used to getting 'free' software. I can tell you much of it comes with no support, no documentation and can chew up loads of time trying to figure it out. While the TSSB software doesn't cost any money to run on your system (or multiple systems) if you're someone with my sort of background you'll decide very quickly that valuing your own time even minimally the book isn't really that expensive even if you never end up using the software for anything other than a learning experience. However if you invest the time to learn what's here then I suspect you will ending up using TSSB. I believe I will. Beyond that the authors offer paid-for support for things you want to tackle with their help.
OK, yes, much of the book is just a software manual documenting all the command you can use in TSSB scripts. However, there's a LOT of additional writing in the book that happily is quite readable. The authors are to be commended for taking the time to make the software approachable to traders that don't necessarily have their level of statistical expertise. (I.e. - ME!) There's a lot of information in here I've run into before, but there's probably more info that I haven't gotten into but have been able to generally understand using Wikipedia and other free sources. As with all of these things YMMV.
As best I can tell so far the way the book approaches trading system development is well thought out and, coming from a background of TradeStation/EasyLanguage, R & more recently MatLab, TSSB itself has a lot to offer that aren't easy or inexpensive to do in those tools. First, trading systems are developed across many markets with the means to trade only a percentage of the markets at any given time. TSSB facilitates finding indicators that work well, or at least similar, in multiple markets. It supports developing more types of trading models than I'm likely to use in the next year or two. It has built in walk-forward testing at the day, month and yearly level and there are likely ways to use other periods. Best of all so far the software seems quite stable and reasonably fast. I've just completed a 13 year monthly walk-forward on 88 markets using CPU-based processing in about an hour. TSSB supports GPU-processing but I haven't tested it yet.
Lest I be accused of gushing let me also be clear about what are likely to be issues for some. First, TSSB is not a trading platform and actually there isn't a direct path to get TSSB models into EasyLanguage or R without considerable work on the user's part. (Read the TSSB website for more on that) Getting TradeStation data into TSSB wasn't hard but did require that I write my own EL indicator to get it formatted correctly. I want to thank the authors for their support getting past that hurdle. Depending on where you get your data this may be an issue for you.
Next, as you'll learn if you dig into TSSB, the indicators in TSSB have considerable intellectual property in them which isn't made available to the user. (At least not as code that you could use anywhere else.) What this means to me is that for now my models in TSSB must remain in TSSB which doesn't easily support automated trading. However TSSB does support reading in and studying indicators I have written in EasyLanguage and, if they work well, would begin to reveal a path toward automated trading down the road. Not direct, and not easy, but that wasn't a purchase requirement for me at all. Again, YMMV.
As there is no officially supported 'user's group' of any kind I've created a Google Group where other people interested in TSSB might join me for conversations. This is completely unofficial and no one should look for support from TSSB's authors in this forum. Search it out on Google Groups if you're interested.
If you're considering purchasing this book I'd recommend going through the free Kindle preview in depth. It as some scripting code that will give you a good feel for whether these tools are for you. Run the code - it works - and study the results. If you purchase the book I'd recommend and your an organized individual then you may want to jump to the end of the book to the section entitled 'Developing A Stand-Alone System' and working through what is presented there. This will lead you to read about commands you need them but will also start to reveal more of what TSSB can do.
For now TSSB is, for me, a way to study what indicators to use, what markets to use them on and when to potentially trade or not trade. I'm happy with that but you may or may not be. That's up to you.
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