Deep Learning Transformer Networks to Predict Future Price Direction from Time Series Forex Data

Below is an example of how you can implement a Transformer network in Python using TensorFlow to predict future Forex price directions from time series data. This code includes data preprocessing, model building, training, and evaluation.


```python

import numpy as np

import pandas as pd

import tensorflow as tf

from tensorflow.keras import layers, models

from sklearn.preprocessing import StandardScaler

from sklearn.model_selection import train_test_split


# Generate synthetic Forex data for demonstration (replace with your actual data)

def generate_synthetic_data(num_samples, num_features):

    np.random.seed(0)

    data = np.random.rand(num_samples, num_features)

    return data


# Generate synthetic labels (1 for up, 0 for down)

def generate_synthetic_labels(num_samples):

    np.random.seed(0)

    labels = np.random.randint(0, 2, num_samples)

    return labels


# Parameters

num_samples = 10000

num_features = 10

num_future = 10  # Predict next 10 candlesticks


# Generate data

data = generate_synthetic_data(num_samples, num_features)

labels = generate_synthetic_labels(num_samples)


# Split data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)


# Standardize the data

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)


# Reshape data for the Transformer model (batch_size, sequence_length, num_features)

sequence_length = 20

X_train = np.reshape(X_train, (-1, sequence_length, num_features))

X_test = np.reshape(X_test, (-1, sequence_length, num_features))

y_train = np.reshape(y_train, (-1, sequence_length))

y_test = np.reshape(y_test, (-1, sequence_length))


# Build Transformer model

def build_transformer_model(input_shape, num_heads, ff_dim, num_classes):

    inputs = layers.Input(shape=input_shape)

    x = layers.LayerNormalization(epsilon=1e-6)(inputs)

    x = layers.MultiHeadAttention(key_dim=ff_dim, num_heads=num_heads)(x, x)

    x = layers.Add()([x, inputs])

    x = layers.LayerNormalization(epsilon=1e-6)(x)

    x = layers.Dense(ff_dim, activation='relu')(x)

    x = layers.Dense(ff_dim, activation='relu')(x)

    x = layers.Add()([x, inputs])

    x = layers.GlobalAveragePooling1D()(x)

    x = layers.Dense(20, activation='relu')(x)

    outputs = layers.Dense(num_classes, activation='softmax')(x)

    model = models.Model(inputs, outputs)

    return model


# Define model parameters

num_heads = 4

ff_dim = 32

num_classes = 2  # Binary classification (up or down)


# Build and compile the model

model = build_transformer_model((sequence_length, num_features), num_heads, ff_dim, num_classes)

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])


# Train the model

history = model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test))


# Evaluate the model

loss, accuracy = model.evaluate(X_test, y_test)

print(f'Test Accuracy: {accuracy:.2f}')


# Predict future price direction

predictions = model.predict(X_test)

predicted_labels = np.argmax(predictions, axis=1)


# Example usage

print(predicted_labels[:10])

```


In this code:


1. **Data Preparation**: Synthetic data is generated for demonstration purposes. Replace it with your actual Forex time series data.

2. **Data Preprocessing**: The data is standardized and reshaped to fit the input shape required by the Transformer model.

3. **Model Building**: A simple Transformer model is built using TensorFlow/Keras, which includes multi-head attention and feed-forward layers.

4. **Model Training**: The model is trained on the training data and validated on the test data.

5. **Model Evaluation**: The model's performance is evaluated on the test data, and predictions are made.


You can further refine and customize this code based on your specific dataset and prediction requirements.

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