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An artificial neural network is made up of several layers of interconnected “neurons”, each layer having a specific role in the data processing process. The three main types of layers are:

Input Layer: This layer receives the raw data as input. In the context of trading, this can be historical data such as open prices, close prices, trading volumes, etc.

Hidden Layers: These layers are responsible for extracting and transforming features from the data. Each neuron in a hidden layer performs a linear combination of the inputs and then passes them through a nonlinear activation function. This allows the network to capture complex, nonlinear relationships between variables.

Output Layer: This layer generates the final results of the network. In the context of trading, neurons in the output layer can represent predictions of price movements, buy/sell signals, or other relevant information.

Depending on the specific trading tasks, different types of neural networks can be used:

Deep Neural Networks (DNN): Also known as Multilayer Neural Networks, DNNs are composed of multiple hidden layers. They excel at capturing complex patterns from financial data, making them popular candidates for price prediction, anomaly detection, and other advanced tasks.

Recurrent Neural Networks (RNNs): RNNs are designed to work with sequential data, such as time series of market prices. They are suitable for modeling time dependencies and are often used for short-term forecasting of price movements.

Machine learning capability: Neural networks can learn from large amounts of historical data, allowing them to spot complex patterns and use them for future predictions.

Non-linear data processing: Financial markets are influenced by multiple interconnected factors. Neural networks are able to model nonlinear relationships between these factors.

Adaptability: Neural networks can adapt to new data and market changes, making them relevant to changing financial environments.

Now let’s look at some simple examples on the technical side, for example with Python for creating neural networks.

Three possibilities:

1/ Let’s use TensorFlow to create a sequential neural network. The model has three layers: an input layer with 10 neurons (as we have 10 features in the example data), a hidden layer with 64 neurons and a ReLU activation function, another hidden layer with 32 neurons and a ReLU activation function, and finally an output layer with 1 neuron and a sigmoid activation function for binary classification.

The model is then compiled with the Adam optimizer and binary_crossentropy loss function, and we train it using the training data.

pip install tensorflow
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Génération de données d'exemple
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Générer des données d'exemple
X, y = make_classification(n_samples=1000, n_features=10, random_state=42)

# Diviser les données en ensembles d'entraînement et de test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Création du modèle du réseau de neurones
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=10))
model.add(Dense(units=32, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

# Compilation du modèle
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=['accuracy'])

# Entraînement du modèle
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

Please note that this is a simple example to illustrate the creation of a neural network. In a trading context, you will need to adapt the model’s architecture and parameters to your specific needs. Additionally, data preprocessing, time series data management, and other considerations will also need to be taken into account for a more sophisticated business model.

2/ Now let’s use PyTorch to create a simple feedforward neural network for binary classification. The model has three fully connected (linear) layers with ReLU activations between them and sigmoid activation at the output layer.

The model is trained using binary cross-entropy loss and the Adam optimizer. We loop through the training data in batches and update the model parameters by backpropagation.

pip install torch
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# Generate sample data
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Generate example data
X, y = make_classification(n_samples=1000, n_features=10, random_state=42)

# Convert data to PyTorch tensors
X = torch.tensor(X, dtype=torch.float32)
y = torch.tensor(y, dtype=torch.float32)

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define the neural network model
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.layer1 = nn.Linear(10, 64)
        self.layer2 = nn.Linear(64, 32)
        self.layer3 = nn.Linear(32, 1)
        self.activation = nn.ReLU()
        self.output_activation = nn.Sigmoid()

    def forward(self, x):
        x = self.activation(self.layer1(x))
        x = self.activation(self.layer2(x))
        x = self.output_activation(self.layer3(x))
        return x

# Create the model instance
model = NeuralNetwork()

# Define the loss function and optimizer
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
num_epochs = 10
batch_size = 32

for epoch in range(num_epochs):
    for i in range(0, len(X_train), batch_size):
        batch_X = X_train[i:i + batch_size]
        batch_y = y_train[i:i + batch_size]

        optimizer.zero_grad()
        outputs = model(batch_X)
        loss = criterion(outputs, batch_y.view(-1, 1))
        loss.backward()
        optimizer.step()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")

# Evaluate the model
with torch.no_grad():
    predictions = model(X_test)
    predicted_labels = (predictions >= 0.5).float()
    accuracy = (predicted_labels == y_test.view(-1, 1)).sum().item() / len(y_test)
    print(f"Test Accuracy: {accuracy:.4f}")

Finally, we evaluate the accuracy of the model trained on the test data.

Remember that this is still a basic example. In a trading context, you will need to adjust model architecture and parameters, manage financial time series data, and implement risk management strategies to create a reliable trading system.

3/ In this example, we use Keras to create a sequential neural network for regression. The architecture of the model is similar to previous examples, with three layers: an input layer with 10 neurons, two hidden layers with ReLU activations, and an output layer for regression tasks.

The model is compiled with the Adam optimizer and the mean squared error loss function, which is commonly used for regression tasks.

pip install keras
import numpy as np
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense

# Generate example data
X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, random_state=42)

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create the neural network model
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=10))
model.add(Dense(units=32, activation='relu'))
model.add(Dense(units=1))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

We then train the model using the training data.

Remember again that this example is simplified for demonstration purposes.

These examples given in raw with Python, are not directly compatible with MQL5, but you can potentially use external libraries to bridge the communication between the two languages. A common approach is to create a dynamic link library (DLL) in a language like C/C++ that can be called from MQL5 to interact with Python code. However, this process can be quite complex for a beginner.

If you are looking for a trading robot including the latest technologies, please visit my profile:

https://www.mql5.com/en/users/incepline

If you want to learn a bit more about how to pair Python with MQL5, keep reading.

I can provide you with a high-level overview of the steps you could take to create a DLL for this purpose, but keep in mind that this involves more advanced programming concepts:

Create a C/C++ DLL:

You would need to write C/C++ code to create a DLL that acts as an interface between MQL5 and your Python code. This DLL should include functions that can be called from MQL5 and that interact internally with your Python code.

Use the Python C API:

In your C/C++ DLL, you can use the Python C API to embed Python code. This allows you to run Python functions and scripts from your DLL.

Compile the DLL:

Compile the C/C++ code to create the DLL file that MQL5 can use.

Import the DLL into MQL5:

In your MQL5 code, you can use the #import directive to import functions from the DLL. These functions will essentially act as bridges between your MQL5 code and the Python code running in the DLL.

Call the Python code:

You would call functions imported from MQL5, which in turn execute Python code through the DLL.

This is a high level overview of the process. Creating a DLL that communicates effectively between MQL5 and Python requires a solid understanding of both languages, as well as experience with inter-process communication and API usage.

If you are relatively new to programming or it seems daunting, you might want to consider buying a trading robot outright.

Check out my profile below:

https://www.mql5.com/en/users/incepline

Thank you for reading this article.

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