of market information. # import the relevant Keras modules from dels import Sequential from yers import Activation, Dense from yers import lstm from yers import Dropout def build_model(inputs, output_size, neurons, activ_func "linear dropout.25, loss"mae optimizer"adam model Sequential d(lstm(neurons, input_shape(ape1, ape2) d(Dropout(dropout) d(Dense(unitsoutput_size) d(Activation(activ_func) mpile(lossloss, optimizeroptimizer) return model. Like the random walk model, lstm models can be sensitive to the choice of random seed (the model weights are initially randomly assigned). Daily returns of S P 500 index Classification problem. . The volatility columns are simply the difference between high and low price divided by the opening price. Get more and/or better data : If past prices alone are sufficient to decently forecast future prices, we need to include other features that provide comparable predictive power.
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Conclusions We can see, that treating financial time series prediction as regression problem is better approach, it can learn the trend and prices close to the actual. Here is example of loading, splitting into training samples and preprocessing of raw input data: Regression problem. . Ill opt for Keras, as I find it the most intuitive for non-experts. Furthermore, the model seems to be systemically overestimating the future value of Ether (join the club, right? Important update: Ive made a mistake in this post while preprocessing data for regression problem check this issue m/Rachnog/Deep-, trading /issues/1 to fix.
We need to normalise the data, so that our inputs are somewhat consistent. It causes to worse results, which can be partly improved by better hyperparameter search, using whole ohlc data and training for 50 epochs. As Ive stated earlier, single point predictions can be deceptive. Follow me also in Facebook for AI articles that are too short for Medium, Instagram for personal stuff and Linkedin! You can reproduce results and get better using code from repository.