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Although many papers published do seem to show promising results, it is often the case that these papers fall into a variety of different statistical bias problems that make the real market success of their machine learning strategies highly improbable. Most pitfalls in machine learning strategy design when doing Forex trading are inevitably inherited from the world of deterministic learning problems.
When building a machine learning algorithm for something like face recognition or letter recognition there is a well defined problem that does not change, which is generally tackled by building a machine learning model on a subset of the data a training set and then testing if the model was able to correctly solve the problem by using the reminder of the data a testing set.
This is why you have some famous and well established data-sets that can be used to establish the quality of newly developed machine learning techniques. The key point here however, is that the problems initially tackled by machine learning were mostly deterministic and time independent. The mere act of attempting to select training and testing sets introduces a significant amount of bias a data selection bias that creates a problem.
If the selection is repeated to improve results in the testing set — which you must assume happens in at least some cases — then the problem also adds a great amount of data-mining bias.
If an algorithm is trained with data and was cross validated with data there is no reason to believe that the same success will happen if trained in data and then live traded from to , the data sets are very different in nature. Measuring algorithm success is also a very relevant problem here. Correct predictions do not necessarily equal profitable trading as you can easily see when building binary classifiers. This does not mean that this methodology is completely problem free however, it is still subject to the classical problems relevant to all strategy building exercises, including curve-fitting bias and data-mining bias.
My friend AlgoTraderJo — who also happens to be a member of my trading community — is currently growing a thread at ForexFactory following this same type of philosophy for machine learning development, as we work on some new machine learning algorithms for my trading community.
You can refer to his thread or past posts on my blog for several examples of machine learning algorithms developed in this manner. A question I have, is it normal for say an EA to do exceedingly well in a certain pair and do terrible in all others? That question is interesting ;o. Provided you take care of bias sources such as data-mining bias and curve-fitting bias there is no reason why this will not work.
Now, if you have a system that works across many symbols then data-mining bias will be exponentially lower for an equal system that only works on one symbol and curve-fitting bias will also be lower due to the use of more data. I am so glad that you said it does not have to make a profit across all pairs!
Also curve fitting, how does one know the limit of tweaking allowed before it becomes fitted? I wondered what you make of the results! You need to make a distinction between curve-fitting bias and data-mining bias or at least these two different types of bias, however you may want to call them.
Curve-fitting bias is a bias created by finding an inefficiency across a set of data, it answers the question: Data-mining bias answers the question: By increasing parameter spaces and degrees of freedom you are increasing data-mining bias you are more likely to find a system just by chance, instead of a system that trades a real historical inefficiency.
Doing this type of test is fundamental to reliable strategy design. Read more about this distinction between biases here: Also read this paper on the subject: Before dwelling into the complexities of trading system design and finding strategies for trading I strongly advice getting a solid formation in statistics coursera statistics courses are an excellent free start. Statistics will give you the power to analyse your own results and methodically address questions like these ;o.
Why many academics are doing it all wrong [Mechanical Forex] Building machine learning strategies that can obtain decent results under live market conditions has always been an important challenge in algorithmic trading.
Despite the great amount of interest and the incredible potential rewards, there are still no academic publications that are able to show good machine learning models that can successfully tackle the trading problem in the real m […]. Mail will not be published required. Mechanical Forex Trading in the FX market using mechanical trading strategies.
Machine Learning in Forex Trading: Why many academics are doing it all wrong May 12th, 5 Comments. Posted in Articles Tags: The drivers behind top trader selection. Some algorithmic trading systems from May 12, at May 12, at 5: May 12, at 6: May 12, at 8: May 13, at 5: Leave a Reply Click here to cancel reply.