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For deeper networks the obsession with image classification tasks seems to have also caused tutorials to appear on the more complex convolutional neural networks. I am far more interested in data with timeframes. The full code for this forex neural network prediction start trading in binary can be found on the topics GitHub page. I made an excel spreadsheet to make a sin wave with amplitude and frequency of 1 giving an angular frequency of 6.

The full sin wave dataset visualized: Now that we have the data, what are we actually trying to achieve? The way Keras LSTM layers work is by taking in a numpy array of 3 dimensions N, W, F where N is the number of training sequences, W is the sequence length and F is the number of features of each sequence.

I chose to go with a sequence length read window size of 50 which allows for the network so get glimpses of the shape of the sin wave at each sequence and hence will hopefully teach itself to build up forex neural network prediction start trading in binary pattern of the sequences based on the forex neural network prediction start trading in binary window received.

The sequences themselves are sliding windows and hence shift by 1 each time, causing a constant overlap with the prior windows. An example of a sequence of length Next up we need to actually build the network itself. This is the simple part! I used a network structure of [1, 50,1] where we have 1 input layer consisting of a sequence of size 50 which feeds into an LSTM layer with 50 neurons, that in turn feeds into another LSTM layer with neurons which then feeds into a fully connected normal layer of 1 neuron with a linear activation function which will be used to give the prediction of the next time step.

I used only 1 training epoch with this LSTM, which unlike traditional networks where you need lots of epochs for the network to be trained on lots of training examples, with this 1 epoch an LSTM will cycle through all the sequence windows in the training set once. If this data had less structure to it, a large number of epochs would be required, but as this is a sin wave with a predictable pattern that maps onto a simple function 1 training epoch will be good enough to get a very good approximation of the full sin function.

However what we need to watch out for here is what we actually want to achieve in the prediction of the time series. If we were to use the test set as it is, we would be running each window full of the true data to predict the next time step. You can see below the graph of using this approach to predict only one time step ahead at each step in time:. If however we want to do real magic and predict many time steps ahead we only use the first window from the testing data as an initiation window.

At each time step we then pop the oldest entry out of the rear of the window and append the prediction for the next time step to the front of the window, in essence shifting the window along so it slowly builds itself with predictions, until the window is full of only predicted values in our case, as our window is of size 50 this would occur after 50 time steps. We then keep this up indefinitely, predicting the next time step on the predictions of the previous future time steps, to hopefully see an emerging trend.

Overlaid with the true data we can see that with just 1 epoch and a reasonably small training set of data the LSTM has already done a forex neural network prediction start trading in binary damn good job of predicting the sin function.

You can see that as we predict more and more into the future the error margin increases as errors in forex neural network prediction start trading in binary prior predictions are amplified more and more when they are used for future predictions. However as the sin function is a very easy oscillating function with zero noise it can predict it to a good degree. I mean after all, what is the real world when we can make real data for a sin wave and predict on it We predicted a several hundred time steps of a sin wave on an accurate point-by-point basis.

So we can now just do the forex neural network prediction start trading in binary on a stock market time series and make a shit load of money right? A stock time series is unfortunately not a function that can be mapped. It can best described more as a random walk, which makes the whole prediction thing considerably harder.

But what about the Forex neural network prediction start trading in binary identifying any underlying hidden trends? There is one slight change we need to make to our data however, because a sin wave is already a nicely normalized repeating pattern it works well running the raw data points through the network. However running the adjusted returns of a stock index through a network would make the optimization process shit itself and not converge to any sort of optimums for such large numbers.

This now normalised the windows as mentioned above and hence we can now run our stock data through our LSTM network. Running the data on a single point-by-point prediction forex neural network prediction start trading in binary mentioned above gives something that matches the returns pretty closely. But this is deceptive! Well if you look more closely, the prediction line is made up of singular prediction points that have had the whole prior true history window behind them.

So even if it gets the prediction for the point wrong, the next prediction will then factor in the true history and disregard the incorrect prediction, yet again allowing for an error to be made. So what would we look at if we wanted to see whether there truly was some underlying pattern discernable in just the price movements? Well we would do the same as for the sin wave problem and let the network predict a sequence of points rather than just the next one.

Doing that we can now see that unlike the sin wave which carried on as a sin wave sequence that was almost identical to the true data, our stock data predictions converge very quickly into some sort of equilibrium.

This wild difference seems to be orthogonal to what you might expect; usually a higher epoch would mean a more accurate model, however in this case it almost looks as if the single epoch model is tending towards some sort of reversion that generally follows the short time price movement. I was expecting to be able to demonstrate that it would be a fools game to try to predict future price movements from purely historical price movements on a stock index due to the fact that there are so many underlying factors that influence daily price fluctuations; from fundamental factors of the underlying companies, macro events, investor sentiment and market noise… however checking the predictions of the very limited test above we can see that for a lot of movements, especially the large ones, there seems to be quite the consensus of the model predictions and the subsequent price movement.

Sampling errors, pure luck in a small sample size… nothing in this graph should be taken at face value and blindly followed into a money sucking pit without some thorough and extensive series of backtests which are out of scope for this article. In fact when we take a look at the graph above of the same run but with the epochs increased to which should make the model mode accurate we see that actually it now just tries is predict an upwards momentum for almost every time period!

However, with that I hope all you eager young chaps have learnt the basics of what makes LSTM networks tick and how they can be used to predict and map a time series, as well as the potential pitfalls of doing so! LSTM uses are currently rich in the world of text prediction, AI chat apps, self-driving cars…and many other areas.

For completeness, below is the full project code which you can also find on the GitHub page:. About Articles CV Contact. We put all this run code into a seperate run. You can see below the graph of using this approach to predict only one time step ahead at each step in time: A Not-So-Simple Stock Market We predicted a several hundred time steps of a forex neural network prediction start trading in binary wave on an accurate point-by-point basis.

For completeness, below is the full project code which you can also find on the GitHub page:

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