Extreme scalping using neural networks
During the past month I have been implementing and experimenting with a new Neural Network (NN) building approach that focuses on training neural networks with the use of trading charts (literally images) instead of inputs based on trading information (such as the OHLC). My first post about this technique described the general idea while my second post described how I made the first practical implementation for this and what possible improvements and experimentation routes I would be following. Today I want to share with you my first practical results for this technique, results which show that the Image based NN technique works and is able to achieve long term profitable results in simulation. These results show that the NN technique is able to find an edge through the use of images, constantly adapting against changes in market conditions (as matter of fact retraining the entire network on every day during the back-test). Through the following paragraphs you will be able to learn more about these first results and what new ideas and experiments I will be trying going forward.
My first experiments with this NN image processing technique were using candlestick pictures at a 50×50 pixel resolution – with auto-scaling – in order to train the neural network to predict the next day’s outcome (bullish or bearish day). These first efforts turned out to be unprofitable through the 25 year tests, because the information contained within the images didn’t seem to be enough for the neural network to generate predictive power. I tried several things in order to improve this results – such as the removal of automatic scaling to remove any normalization of trend magnitude – but this didn’t lead to any substantial improvement in the trading results. I then tried a variety of image sizes (larger and lower canvas) but all the 26 year profitable results I obtained were not reproducible because results were mainly achieved through random chance due to the initial NN weights and an insufficient number of training epochs. At this point it seemed clear that the problem was in the image input, it possibly contained too much irrelevant information that was not depicted in a way that was “clear” enough.
Source: Mechanical Forex
Photo Jigsaw Puzzle of Currency exchange at an airport from Science Photo Library
Toy (Media Storehouse)
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