Steps to use convolutional neural networks in trading with Python
We will now see a simple model with the CNN architecture for the image with the candlestick patterns. The model will be trained for 10 epochs. Here, one Epoch is equivalent to one cycle for training a machine learning model.
The number of epochs keeps increasing until the validation error reduces.
The Conv2D layers define the convolutional layers with ReLU activation, while MaxPooling2D is used for regularisation. Also, the Dense layers are used for classification.
Hence, the final outcome will help you find out the performance of the model.
Step 1: Importing necessary libraries
We will first of all import TensorFlow and will use tf.keras.
# Importing libraries import numpy as np import tensorflow as tf
Step 2: Generate random train and test data for demonstration
# We create image data for train and test purposes with the following inputs num_train_samples = 1000 num_test_samples = 200 image_width = 128 image_height = 128 channels = 1 # Grayscale image (single channel) num_classes = 2 # Binary classification (two classes) # Generate random training and test data for demonstration X_train = np.random.random((num_train_samples, image_width, image_height, channels)) y_train = np.random.randint(low=0, high=num_classes, size=num_train_samples) X_test = np.random.random((num_test_samples, image_width, image_height, channels))
Step 3: Define the CNN model
Now, we will define the CNN model that will help with prediction in trading.
# Define the CNN model model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(image_width, image_height, channels)), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(num_classes, activation='softmax') ])
The model is defined using the Sequential API, and the layers are added sequentially. The architecture consists of several Conv2D layers with ReLU activation, followed by MaxPooling2D layers to reduce spatial dimensions. The final layers include a Flatten layer to flatten the output, fully connected Dense layers, and an output layer with softmax activation for classification.
Step 4: Normalise the training and test data
# Normalise the training and test images if necessary X_train = X_train / 255.0 X_test = X_test / 255.0
Step 5: Compile and train the model
Finally, the model is compiled, trained and made to make predictions on the new images.
# Compile and train the model model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(X_train, y_train, epochs=10, batch_size=32) # Use the trained model to make predictions on new images predictions = model.predict(X_test) model.fit(X_train, y_train, epochs=10, batch_size=32) # Use the trained model to make predictions on new images predictions = model.predict(X_test)
The model is compiled with the Adam optimizer, sparse categorical cross-entropy loss function, and accuracy as the evaluation metric.
Epoch 1/10 32/32 [==============================] – 8s 223ms/step – loss: 2.3030 – accuracy: 0.0990
Epoch 2/10 32/32 [==============================] – 10s 330ms/step – loss: 2.2998 – accuracy: 0.1200
Epoch 3/10 32/32 [==============================] – 5s 172ms/step – loss: 2.3015 – accuracy: 0.1200
Epoch 4/10 32/32 [==============================] – 6s 201ms/step – loss: 2.2994 – accuracy: 0.1200
Epoch 5/10 32/32 [==============================] – 6s 183ms/step – loss: 2.2996 – accuracy: 0.1200
Epoch 6/10 32/32 [==============================] – 5s 170ms/step – loss: 2.2981 – accuracy: 0.1200
Epoch 7/10 32/32 [==============================] – 7s 210ms/step – loss: 2.2987 – accuracy: 0.1200
Epoch 8/10 32/32 [==============================] – 5s 168ms/step – loss: 2.2981 – accuracy: 0.1200
Epoch 9/10 32/32 [==============================] – 7s 216ms/step – loss: 2.2993 – accuracy: 0.1200 Epoch 10/10 32/32 [==============================] – 5s 167ms/step – loss: 2.2975 – accuracy: 0.1200 7/7 [==============================] – 0s 43ms/step
The above output shows the final loss and accuracy values on the test set.
In this specific output, the model did not achieve a very high accuracy on both the training and test sets. Hence, the output is not indicating a good performance.
Also, the final outcome shows that the loss values are not decreasing over the epochs, indicating that the model is not learning and improving its predictions.
For making the loss values decrease over the epochs and to make the model achieve a high accuracy rate, you need to input the model with more number of epochs and you can change the parameters accordingly.
In the similar manner, you can fetch the image data (candlestick pattern, line chart) for a stock (for example, AAPL, TSLA, GOOGL etc.) and train the model on a certain number of epochs.
Python codes for trading with CNN
For trading, you will need the following lines of code below to give you the result. In this case, also the result will be the computation of final loss and accuracy.
# Import libraries import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense # Load and preprocess the data data = pd.read_csv('trading_data.csv') # Perform data preprocessing steps as per your requirements # Split the data into training and testing sets train_data = data.loc[data['date'] < date_to_split] test_data = data.loc[data['date'] >= date_to_split] # Define the input and output variables x_train = train_data[['feature1', 'feature2', 'feature3']].values y_train = train_data['target'].values x_test = test_data[['feature1', 'feature2', 'feature3']].values y_test = test_data['target'].values
And, we reach the end of this blog! You can now use the convolutional neural networks on your own for training the CNN model.
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