Backpropagation is an advanced algorithm which enables us to update all the weights in the neural network simultaneously. This drastically reduces the complexity of the process to adjust weights. If we were not using this algorithm, we would have to adjust each weight individually by figuring out what impact that particular weight has on the error in the prediction. Let us look at the steps involved in training the neural network with Stochastic Gradient Descent:. We have covered a lot in this neural network tutorial and this leads us to apply these concepts in practice.

Thus, we will now learn how to develop our own Artificial Neural Network to predict the movement of a stock price. You will understand how to code a strategy using the predictions from a neural network that we will build from scratch.

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You will also learn how to code the Artificial Neural Network in Python, making use of powerful libraries for building a robust trading model using the power of Neural Networks. We will start by importing a few libraries , the others will be imported as and when they are used in the program at different stages. For now, we will import the libraries which will help us in importing and preparing the dataset for training and testing the model.

Numpy is a fundamental package for scientific computing, we will be using this library for computations on our dataset. The library is imported using the alias np. Pandas will help us in using the powerful dataframe object, which will be used throughout the code for building the artificial neural network in Python. These will be used as features for training our artificial neural network.

We could add more features using this library. Random will be used to initialize the seed to a fixed number so that every time we run the code we start with the same seed.

We then import our dataset, which is stored in the. This is done using the pandas library, and the data is stored in a dataframe named dataset. We then drop the missing values in the dataset using the dropna function. We will be building our input features by using only the OHLC values. We then prepare the various input features which will be used by the artificial neural network learning for making the predictions. We define the following input features:. We then define the output value as price rise, which is a binary variable storing 1 when the closing price of tomorrow is greater than the closing price of today.

We then create two data frames storing the input and the output variables. The last column will be stored in the dataframe y, which is the value we want to predict, i. In this part of the code, we will split our input and output variables to create the test and train datasets. This is done by creating a variable called split, which is defined to be the integer value of 0. We then slice the X and y variables into four separate data frames: Xtrain, Xtest, ytrain and ytest.

This is an essential part of any machine learning algorithm, the training data is used by the model to arrive at the weights of the model. The test dataset is used to see how the model will perform on new data which would be fed into the model. The test dataset also has the actual value for the output, which helps us in understanding how efficient the model is. We will look at the confusion matrix later in the code, which essentially is a measure of how accurate the predictions made by the model are.

Another important step in data preprocessing is to standardize the dataset. This process makes the mean of all the input features equal to zero and also converts their variance to 1.

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This ensures that there is no bias while training the model due to the different scales of all input features. If this is not done the neural network might get confused and give a higher weight to those features which have a higher average value than others. We implement this step by importing the StandardScaler method from the sklearn. We instantiate the variable sc with the StandardScaler function.

After which we use the fittransform function for implementing these changes on the Xtrain and Xtest datasets. Now that the datasets are ready, we may proceed with building the Artificial Neural Network using the Keras library. Now we will import the functions which will be used to build the artificial neural network. We import the Sequential method from the keras.

This will be used to sequentially build the layers of the neural networks learning.

The next method that we import will be the Dense function from the keras. We instantiate the Sequential function into the variable classifier. This variable will then be used to build the layers of the artificial neural network learning in python.

To add layers into our Classifier, we make use of the add function. The argument of the add function is the Dense function, which in turn has the following arguments:. We are only building two hidden layers in this neural network.

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The next layer that we build will be the output layer, from which we require a single output. Therefore, the units passed are 1, and the activation function is chosen to be the Sigmoid function because we would want the prediction to be a probability of market moving upwards.

Now we need to fit the neural network that we have created to our train datasets. This is done by passing Xtrain, ytrain, batch size and the number of epochs in the fit function. The batch size refers to the number of data points that the model uses to compute the error before backpropagating the errors and making modifications to the weights.

The number of epochs represents the number of times the training of the model will be performed on the train dataset. With this, our artificial neural network in Python has been compiled and is ready to make predictions. Now that the neural network has been compiled, we can use the predict method for making the prediction. We pass Xtest as its argument and store the result in a variable named ypred. We then store the values of ypred into this new column, starting from the rows of the test dataset.

This is done by slicing the dataframe using the iloc method as shown in the code above. Now that we have the predicted values of the stock movement. We can compute the returns of the strategy. We will be taking a long position when the predicted value of y is true and will take a short position when the predicted signal is False. We first compute the returns that the strategy will earn if a long position is taken at the end of today, and squared off at the end of the next day. We use the decimal notation to indicate that floating point values will be stored in this new column.

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