TRADING SYSTEMS


FX


annualized return also stays positive most of the time – as showed in Figure 2 – with dips below the zero line being well controlled through the almost 30 year testing period.


Although this is far


it does show that long term historically profitable


from stellar results can


be obtained with machine learning systems in the Forex market, even with extremely simple machine learning


network based


algorithms. This neural system is also


Figure 1


fundamentally different from other types of strategies since the very mechanism used to predict the current day’s return changes and adapts as market conditions change. A constantly retraining neural network offers a level of flexibility above that of a trading system using a fixed logic set.


Of course many improvements to the above are possible and this just shows you the most basic machine learning


setup to give you a


practical start in the development of machine learning systems for Forex trading. Exploring different input and outputs structures, different machine learning algorithms, different neural network structures, functions and trading dynamics are all possible venues to explore in the search for even better machine learning strategies.


Daniel Fernández Figure 2


Founder of Asirikuy.com and Mechanicalforex.com


FX TRADER MAGAZINE April - June 2016 25


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