Machine Learning and Deep Learning Algorithm Trading, Feedforward

Hello. In this course, we will take a closer look at machine learning and deep learning algorithm trading for quantitative trading. In particular, we will focus on the basic concepts of deep learning and forward propagation. The mechanism of algorithm trading can be a powerful tool for analyzing and predicting market data, and mastering it can upgrade your trading strategy to the next level.

1. What is Algorithm Trading?

Algorithm trading is a continuous electronic trading system that uses computer programs to automatically execute trades in the market. These systems are based on algorithms that analyze market data and generate buy and sell signals. By utilizing machine learning and deep learning techniques in this process, we can recognize patterns in data and establish more sophisticated trading strategies.

1.1 Advantages of Algorithm Trading

  • Exclusion of Emotion: Computers do not feel emotions, allowing for patient trading.
  • Fast Execution: Algorithms can respond quickly to market changes, gaining an edge.
  • Data Analysis: Machine learning algorithms can analyze large amounts of data and make better judgments than humans.
  • Backtesting: Algorithms can validate the effectiveness of strategies by simulating based on historical data.

2. Basic Concepts of Machine Learning

Machine learning is a field of computer science that involves learning from data and making predictions. It develops algorithms to identify patterns in data and predict future outcomes. In the financial market, machine learning can be used in various ways, including price prediction, risk management, and investment strategy development.

2.1 Supervised Learning vs Unsupervised Learning

Machine learning can be broadly divided into supervised and unsupervised learning.

  • Supervised Learning: When input data and correct labels are provided, the model learns from them to make predictions on new data. For instance, in stock price prediction, a model is created by learning from historical stock price data and the corresponding accurate stock prices.
  • Unsupervised Learning: This is a method used to find patterns or structures in data when there are no labels present. Techniques such as clustering or dimensionality reduction in the stock market fall under unsupervised learning.

3. Basic Concepts of Deep Learning

Deep learning is a subset of machine learning based on artificial neural networks. It uses multi-layer neural networks to automatically extract features from data and perform prediction or classification tasks. Deep learning shows strong performance, especially in solving complex problems, and has been successfully applied in various fields such as image recognition and natural language processing.

3.1 Structure of Neural Networks

A neural network consists of an input layer, hidden layers, and an output layer.

  • Input Layer: This is the layer where the features of the data provided to the model are inputted. In trading, factors such as stock prices, trading volume, and news data can be inputted.
  • Hidden Layer: This is the intermediate layer that processes the input data. Multiple hidden layers can be used, increasing the complexity of the model and enabling more sophisticated learning.
  • Output Layer: This is the layer where the final results of the model are outputted. Stock prices and buy/sell decisions fall under this category.

4. Forward Propagation

Forward propagation refers to the process of processing input data in a neural network to produce output. In this process, the neurons in each layer multiply the inputs they receive from the previous layer by weights and add biases, then pass them through an activation function to generate the final output.

4.1 Steps of Forward Propagation

  1. Preparing Input Values: Prepare the characteristic data to be input into the model.
  2. Applying Weights and Biases: Each input value is multiplied by the corresponding weight and accumulated. Then, the bias is added.
  3. Applying Activation Function: An activation function is applied to the sum of weights and biases to generate output values. Common activation functions include Sigmoid, ReLU, and Hyperbolic Tangent (tanh).
  4. Generating Output Values: The final prediction results are generated in the last output layer.

4.2 Mathematical Representation

Let weights be W, biases be b, and input values be X, then the output Y of a neuron can be expressed as follows:

    Y = activation(W * X + b)

4.3 Importance of Activation Functions

Activation functions introduce non-linearity to the neural network, enabling it to learn complex patterns. For example, the ReLU function has the following formula:

    f(x) = max(0, x)

This function outputs 0 for negative inputs, maintaining non-linear characteristics and helping enhance the expressiveness of the neural network.

5. Training Neural Networks and Backpropagation

This method trains the model based on the error between the output values generated by forward propagation and the actual labels. Here, the backpropagation technique is introduced, distributing the error according to each weight and connection in the network to adjust the weights.

5.1 Loss Function

The loss function measures the difference between the model’s predicted values and the actual values. It generally has the following form:

    Loss(y_true, y_pred) = (y_true - y_pred)^2

5.2 Weight Updates

Weights are updated based on the gradients of the error obtained through backpropagation. The Gradient Descent algorithm is used to update each weight as follows:

    W_new = W_old - learning_rate * gradient

Here, learning_rate indicates the speed at which the weights are adjusted.

6. Practical Example: Stock Price Prediction

Now we will take a closer look at an example of stock price prediction using machine learning and deep learning. In this example, we will build a simple neural network model that learns from historical stock price data to predict future prices.

6.1 Data Collection

Stock price data can be collected through Yahoo Finance API or other financial data services. The collected data requires preprocessing, which includes the following steps:

  • Selecting date, closing price, and trading volume to create a data frame.
  • Handling missing values and performing normalization.
  • Splitting into training data and testing data.

6.2 Model Design and Implementation

We’ll design a simple forward propagation-based deep learning model. The following model can be constructed using Python’s Keras and TensorFlow libraries:

    from keras.models import Sequential
    from keras.layers import Dense

    model = Sequential()
    model.add(Dense(units=64, activation='relu', input_dim=number_of_features))
    model.add(Dense(units=32, activation='relu'))
    model.add(Dense(units=1, activation='linear'))

    model.compile(optimizer='adam', loss='mean_squared_error')
    model.fit(X_train, y_train, epochs=100, batch_size=32)

6.3 Result Visualization

The model’s prediction results can be visualized to check the differences from the actual values. The following charts can be generated using the Matplotlib library:

    import matplotlib.pyplot as plt

    plt.plot(y_test, label='True Price')
    plt.plot(predictions, label='Predicted Price')
    plt.legend()
    plt.show()

7. Conclusion

In this course, we learned about algorithm trading using machine learning and deep learning, especially focusing on the concept of forward propagation and a practical example. These techniques will greatly assist in making data-driven decisions in the financial market. Over the long term, they can form the foundation for improving your trading strategy and investment performance. I hope you continue to learn and experiment to develop your own model.

8. References

  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning”, MIT Press.
  • Alexander Elder, “Trading for a Living”.
  • Andreas C. Müller and Sarah Guido, “Introduction to Machine Learning with Python”.