Statistically Sound Machine Learning For Algorithmic Trading Of Financial Instruments

In hindsight, normalizing each feature using a rolling 50-period normalization window very likely ensures that the model dynamically adapts to changing conditions, but I must admit that I stumbled upon this more by accident than by design. Cross validation is a very useful procedure for estimating the true out of sample performance while maximizing the utility of the training data. This sounds fantastic, and it is for most data sets, but time series data presents some unique challenges. Consider a data set with no temporal dimension and in which the observations are independent and identically distributed. In any predictive modelling task for such a data set, we can never have too much data .

Aronson and Masters caution against using too many variables due to the risk of overfitting the data, stating that two or three variables is usually the practical limit. In this analysis, I’ll take their advice and explore models with two or three features only. A representative portfolio that began in 1984 has earned a compounded annual return of 23.7%.

Carefully studying his sections on logic and psychology should lead to better market observations, which should lead to profitable systems. To clarify the knowledge conferred by the second-stage model, consider the knowledge possessed by the investor not using one. Each month all stocks in the investor’s universe are broken into deciles based on their PE ratio. A typical strategy would be to buy the stocks in the lowest PE decile . Assume that a historical back-test of the strategy has shown that stocks in the lowest PE decile produce an excess return versus the universe of 0.50% over the one month period following purchase. From this investor’s state of knowledge, all that can be said each time the low-PE strategy signals the purchase of a security is that it has an expected one month excess return of 0.50%.

For instance, a system with no basis in economic or financial reality has a profit expectancy of exactly zero, excluding transaction costs. However, due to the finite sample size of a backtest, sometimes such a system will show a backtested performance that can lead us to believe it is better than random. As the number of samples grow in live trading, the worthlessness of such a system becomes apparent. For this experiment, I’ll model the EUR/USD exchange rate using a gradient boosting machine, a neural network and a k-nearest neighbors algorithm using various window lengths in the cross-validation procedure. My hypothesis is that there exists an optimal amount of data that maximizes the performance of a model for this particular time series. I am choosing three different algorithms in order to test the sensitivity of the optimal window length to the choice of algorithm.

I simply just have not been able to surpass a MARS model or a custom nonparametric approach using a probabilistic programming library with any NN (and still haven’t after a few tests with deepnet BTW). However, this may have a great deal to do with my feature space and my choice of target. One observation I have made is that these R NN libraries have done a great job of setting some defaults on the myriad hyperparameters and providing some quick automated ways to iterate through them. Check out what happens with the directional accuracy of these predictions though.

Part I of Evidence-Based Technical Analysis is called, “Methodological, Psychological, Philosophical, and Statistical Foundations” and Aronson uses this title as an outline to define the processes which should underlie system development. Co-designer of TSSB a software platform for the automated david aronson trading development of statistically sound predictive model based trading systems. One particular architecture that I note is glaringly lacking is Recurrent Neural Networks. LSTMs, GRUs and other time series/sequence aware NN architectures have achieved the best performance for me wrt finance/trading.

  • Evidence-Based Technical Analysisexamines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals.
  • Evidence-Based Technical Analysis examines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals.
  • Throughout the book, expert David Aronson provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining.

They are actually good enough to give the model a decent cross-validated equity curve. For example, would a classification approach be better than the regression approach presented here?

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The subset of variables was constrained based on the feature selection process discussed in the last post. I’ve constrained the list of algorithms by attempting to maximize their diversity. For example, I’ve chosen a simple nearest neighbor algorithm, a bagging algorithm, boosting algorithms, tree-based models, neural networks and so on. Clearly, I’ve constrained my universe of models to only a fraction of what is possible. We could randomly choose various models in the hope of landing on something profitable, however since with today’s computing power we very much have the means to implement it, I much prefer the idea of a systematic, comprehensive assessment. He is author of “Evidence Based Technical Analysis” and his most recent book “Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments” is an in-depth look at developing predictive-model-based trading systems. EBTA rejects all subjective, interpretive methods of Technical Analysis as worse than wrong, because they are untestable.

I own both books and certainly consider EBTA more valuable than the CFA manual, worshipped by thousands. Don’t expect to download all of Aronson’s knowledge the first time – read it again. The chapters on statistical analysis are worth more than the price of the book in itself. Aronson presents a clear primer on statistics, and leaves the reader with all they need to understand how to design a statistically valid experiment. In what may very well be a publishing first, he presents clear, detailed and understandable descriptions of bootstrap and Monte Carlo randomization methods. Many of the dangers of data mining and curve fitting are grounded in psychology, and Aronson thoroughly explains many of the common problems that can contribute to inaccurate observations.

Popular Books

David Aronson got interested in technical analysis as early as the 1950s. He later went on to write technical memos for Merrill Lynch and later advised Tudor Investment Corporation in 1990. I have finished rereading David Aronson’s book Evidence- Based Technical Analysis, Applying the Scientific Method and Statistical Inference to Trading Signals, a book I bought back in November 2007. This is no easy read and a bit technical, but worthwhile its price tag. The book is very good for those who have no background in statistics. It’s a great book to get to know statistics without having to learn the mathematics behind the theory. I see that if you choose a specific time and data-window everything will be different.

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Asking questions that readers with an interest or involvement in the financial markets would love to pose to the financial superstars, Jack D. Schwager encourages these financial wizards to share their insights. Entertaining, informative, and invaluable, The New Market Wizards is destined to become another Schwager classic. Essential insights on the various aspects of financial derivatives If you want to understand derivatives without getting bogged down by the mathematics surrounding their pricing and valuation, Financial Derivatives is the book for you. Through in-depth insights gleaned from years of financial experience, Robert Kolb and James Overdahl clearly explain what derivatives are and how you can prudently use them within the context of your underlying business activities.

In 1990 AdvoCom advised Tudor Investment Corporation on their public multi-advisor fund. Victor Sperandeo , known as “Trader Vic”, is a US trader, index developer, and financial commentator based in Grapevine, Texas. This book provides a qualitative framework for designing trading systems, especially on how to make them more robust and deal with price shocks.

A Note On The Practicalities Of Trading Systems Research

These are techniques which help in deciding on stop losses and minimum targets based on past price behaviour of the stock, rather than charts – or some other arbitrary measure. This is a curated selection of several books about investing and trading, compiled by Deepak Mohoni. Check the list of global futures markets Wisdom Trading offer access to, from Maize in South Africa, Palm Oil in Malaysia to Korean Won, Brazilian Real or Japanese Kerosene to name a few, it is impressive and great to benefit from diversification. With hundreds of highly correlated rules but low datamining bias, versus hundreds of uncorrelated rules with high bias seems like the net edge would be zero. Of course if the rate at which the bias drops is non-linear relative the rate the correlation increases then there might be a “sweet spot”. The book moves on to concepts such as hypothesis testing, statistical significance and confidence interval, etc. and how they relate to rule testing. Professor Aronson’s book is a fascinating read for anyone frustrated with the current state of technical research and a must-read for those new to the field.

Thus classical chart patterns, Fibonacci based analysis, Elliott Waves and a host of other ill defined methods are rejected by EBTA. Yet there are numerous practitioners who believe strongly that these methods are david aronson trading not only real but effective. Here, EBTA relies on the findings of cognitive psychology to explain how erroneous beliefs arise and thrive despite the lack of valid evidence or even in the face of contrary evidence.

david aronson trading

This popular book is an autobiographical account of his forays into the market, and an explanation of his Box method. This book integrates statistical knowledge with the development and testing of trading systems, and is a very informative read for those looking for mathematical solutions, rather than assertions. Perry J. Kaufman is an American systematic trader, index developer, and quantitative financial theorist. He is considered a leading expert in the development of fully algorithmic trading programs.

Author of “Evidence Based Technical Analysis” published by John Wiley & Sons 2006. First popular book to deal with data mining bias and Monte Carlo Permutation Method for generating bias free p-values.

For the purpose of this exercise, I chose the six features from the previous post that I feel are most likely to convey predictive information about the target variable. There are numerous combinations of features that we could use to build individual models, for example, various combinations of 2, 3, etc variables. Assuming we only build models based on at least two variables, we have 57 possible unique combinations from a pool of six features.

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The rest of the chapter concentrate on methods to reduce/correct for the data mining bias and adapts the bootstrap method (using White’s reality check) and Monte Carlo permutation to be used in “data mining” mode . These two methods are the main take-away from the book, as they are valuable to identify the david aronson trading degree of randomness in a back-tested rule. This should probably be part of a standard trading system research methodology and I will cover these two methods in more detail in later posts. In The New Market Wizards, successful traders relate the financial strategies that have rocketed them to success.

However, many of the technical analysis disciplines cannot be defined. They may have some anecdotal importance, but true science is lacking. Well designed experiments in any scientific inquiry are based upon a verifiable hypothesis grounded in detailed observations. Popper contributed the concept of falsification to this framework, which readily lends itself to mechanical trading system design. As Aronson writes, “Popper’s central contention was that a scientific inquiry was unable to prove a hypothesis to be true. Rather, science was limited to identifying which hypotheses were false.”

In this example, I will train each model to maximize the return of trading the model’s predictions normalized to the recent volatility measured by the 100-period ATR. Simple enough, but how would we objectively assess the performance of each model against this metric? Ideally, we would measure the out-of-sample performance of each model, but of course we have a finite amount of data and we need to maximize its utility. There are plenty of great sources on the internet for detailed descriptions of cross validation, so I will only describe the procedure briefly.

The fourth of the Wizard books, Hedge Fund Market Wizards is more technical than its predecessors, and will appeal to those looking for finer detail in trading strategies. This is the newest of the Wizard books by Jack Schwager published in November 2020. It has another fascinating collection of superlative market success stories, but with a difference. These are the strategies of individual traders who have accumulated a fortune, and are generally not known to david aronson trading the world at large, while the earlier books featured mostly brilliant – but well-known – fund managers. Most of the traders featured in the new book have notched up extraordinary returns – such as that of a trader who averaged a 337% annual return over a 13-year period. Page 3 onward are several other informative books on markets, loosely ordered by subject and author. Page 1 lists fifteen books which influenced him the most in his understanding of markets.