Machine Learning and Deep Learning Algorithm Trading, How to Train and Tune the GBM Model

In modern financial markets, algorithmic trading plays an important role. Especially with the advancement of machine learning and deep learning, it has become possible to develop more sophisticated and efficient trading strategies. In this course, we will explain in detail how to analyze and train financial data using the Gradient Boosting Machine (GBM) model.

1. Understanding Algorithmic Trading

Algorithmic trading is a method of automatically executing trades based on a specific algorithm. In this process, various data (price, volume, technical indicators, etc.) are analyzed to generate optimal buy and sell signals. Machine learning algorithms help learn patterns from this data and perform predictions using them.

1.1 Difference Between Machine Learning and Deep Learning

Machine learning is a modeling technique based on data, with various methods like supervised learning, unsupervised learning, and semi-supervised learning. On the other hand, deep learning is an approach based on artificial neural networks, generally suitable for more complex data (e.g., images, natural language processing). However, in the case of financial data, machine learning models are also widely used for efficient predictions.

2. Understanding the GBM Model

The Gradient Boosting Machine (GBM) is an ensemble learning technique based on decision trees. GBM learns by correcting the errors of previous trees. This process has the following advantages:

  • High accuracy: GBM provides strong predictive performance.
  • Flexibility: Various loss functions can be used, making it applicable to various problems.
  • Interpretability: The model can be interpreted, allowing for the evaluation of feature importance.

2.1 How GBM Works

GBM essentially follows these steps:

  1. Set initial estimates.
  2. Calculate the residuals for each sample.
  3. Train a new decision tree to predict the residuals.
  4. Add this new tree to the existing model to update the predictions.
  5. Finally, repeat the above steps to improve prediction accuracy.

3. Data Preparation

To train the GBM model, it is necessary to prepare financial data to use as input for the model. In the case of stocks, it is important to collect historical price data and related indicators. Generally, the following types of data are prepared:

  • Stock price data (open, high, low, close, volume)
  • Technical indicators (moving averages, RSI, MACD, etc.)
  • Financial indicators (dividend yield, PER, PBR, etc.)

3.1 Data Collection and Preprocessing

The process of collecting and preprocessing data proceeds through the following steps:

  1. Data collection: Collect financial data using APIs like Yahoo Finance, Alpha Vantage, etc.
  2. Handling missing values: Maintain data completeness by removing or substituting missing values.
  3. Data normalization: Normalizing the input data shortens the training time of the model and improves performance.

4. Implementing the GBM Model

We will learn how to implement and train the GBM model using Python. The main libraries are scikit-learn and XGBoost. First, we need to install the necessary libraries:

pip install numpy pandas scikit-learn xgboost

4.1 Training the GBM Model

Now let’s look at an example of loading data and training the GBM model.

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier

# Load data
data = pd.read_csv('financial_data.csv')

# Define input variables and target variable
X = data.drop(columns=['target'])
y = data['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train GBM model
model = XGBClassifier()
model.fit(X_train, y_train)

4.2 Model Evaluation

Evaluate the trained model to check its performance. Commonly used metrics include accuracy, precision, and recall:

from sklearn.metrics import accuracy_score, classification_report

# Perform predictions
y_pred = model.predict(X_test)

# Model evaluation
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')
print(classification_report(y_test, y_pred))

5. Hyperparameter Tuning

To optimize the model’s performance, hyperparameter tuning is performed. Hyperparameters are parameters that need to be set before model training. In the case of GBM, the following parameters are important:

  • learning_rate: learning rate
  • n_estimators: number of trees
  • max_depth: depth of the trees

5.1 Using GridSearchCV

We will use GridSearchCV to explore the optimal hyperparameters:

from sklearn.model_selection import GridSearchCV

param_grid = {
    'learning_rate': [0.01, 0.1, 0.2],
    'n_estimators': [100, 200],
    'max_depth': [3, 5, 7]
}

grid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring='accuracy', cv=3)
grid_search.fit(X_train, y_train)

print("Best parameters found: ", grid_search.best_params_)

6. Applying to Real Trading

To apply the trained GBM model to real trading, trading decisions must be made based on the model’s predictions. The main strategies are as follows:

  1. Buy the asset when the model generates a buy signal.
  2. Sell the asset when the model generates a sell signal.
  3. Decide on portfolio rebalancing and stop-loss strategies to manage risk.

6.1 Backtesting

Backtesting is performed to validate the model’s performance. Based on historical data, it is possible to evaluate how the model actually performed:

def backtest(model, data):
    predictions = model.predict(data)
    returns = np.where(predictions == 1, data['close'].pct_change(), 0)
    cumulative_returns = (1 + returns).cumprod() - 1
    return cumulative_returns

cumulative_returns = backtest(model, X_test)
print(cumulative_returns)

7. Conclusion

The GBM model can be a powerful tool in algorithmic trading using machine learning approaches. This course explained how to train and tune the GBM model, and through this, we learned how to perform predictions based on financial data and apply them to real trading. The world of algorithmic trading is constantly changing, and it is important to learn new data and techniques. If you want to move forward, you should research various algorithms and continually learn by gaining backtesting experience.

References

  • https://scikit-learn.org/stable/
  • https://xgboost.readthedocs.io/en/latest/
  • https://www.quantinsti.com/blog/gradient-boosting-in-python/