Machine Learning and Deep Learning Algorithm Trading, Minute Frequency Signals with LightGBM

The financial markets today are rapidly changing, and building an effective automated trading system in this environment has become essential. This course will detail how to generate signals at minute-level frequency using machine learning and deep learning. In particular, we will discuss how to build models using LightGBM (a type of Gradient Boosting Decision Tree) and how to generate trading signals through this model.

1. Introduction

The success or failure of a trading strategy depends on how accurately it can generate signals. Thus, machine learning and deep learning are very useful for analyzing market data to identify trends and patterns. This course will cover the following topics:

  • Basic concepts of machine learning
  • Principles and advantages of LightGBM
  • Minute-level frequency data collection
  • Data preprocessing
  • Model building and evaluation
  • Implementing trading strategies

2. Basic Concepts of Machine Learning

Machine learning is a collection of algorithms that learn from data to make predictions or decisions. Representative machine learning algorithms include regression, decision trees, support vector machines (SVM), and neural networks. Machine learning can largely be divided into supervised learning and unsupervised learning, with supervised learning being primarily used in automated trading.

2.1 Supervised Learning

In supervised learning, input data and corresponding labels (target variables) are provided, and the model is trained based on this data. For example, in the case of predicting stock prices, past stock prices are the input data, while future prices are the labels.

2.2 Unsupervised Learning

Unsupervised learning uses data without labels. K-means clustering and PCA (Principal Component Analysis) are representative techniques of unsupervised learning. While unsupervised learning is useful for finding patterns in data, it is generally not used for decision-making in stock trading.

3. Principles and Advantages of LightGBM

LightGBM is a lightweight gradient boosting framework developed by Microsoft, optimized for fast and efficient learning from large-scale data. The main advantages of LightGBM are as follows:

  • Speed: Processing large amounts of data is fast.
  • High Performance: It often shows better performance than other algorithms.
  • Memory Efficiency: It uses less memory.
  • Versatile Features: It is useful for handling categorical variables.

3.1 Basic Principles of LightGBM

LightGBM uses a leaf-wise tree learning method, which is advantageous for finding optimal splits at each leaf, helping split the data efficiently and increasing learning speed.

4. Minute-Level Frequency Data Collection

The process of data collection for algorithm trading is very important. Commonly used data sources include:

  • Real-time data collection via API
  • Download of historical data (e.g., Yahoo Finance, Alpha Vantage)
  • Exchange data

For example, here is how to collect minute-level data for a stock using the yfinance library in Python: 

import yfinance as yf

# Download minute-level data for a specific stock
data = yf.download("AAPL", interval="1m", period="7d")
print(data.head())

5. Data Preprocessing

The collected data needs to be preprocessed to be suitable for machine learning models. The main steps include:

5.1 Handling Missing Values

If there are missing values in the dataset, they need to be removed or replaced. Here is how to handle missing values using Pandas: 

import pandas as pd

# Remove missing values
data = data.dropna()
# Or replace with a specific value
data = data.fillna(method='ffill')

5.2 Feature Engineering

To improve the model’s performance, various new features can be created. For example, indicators like moving averages or the Relative Strength Index (RSI) can be created and included in the input data: 

# Add moving average
data['SMA_5'] = data['Close'].rolling(window=5).mean()
# Add Relative Strength Index
delta = data['Close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
rs = gain / loss
data['RSI'] = 100 - (100 / (1 + rs))

6. Model Building and Evaluation

A model needs to be built and evaluated using the preprocessed data. The model can be built using LightGBM, going through the following processes:

6.1 Model Training

Here is how to create and train a LightGBM model: 

import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Split the data
X = data.drop(columns=['target_column'])  # Feature variables
y = data['target_column']  # Target variable
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Convert to LightGBM data format
train_data = lgb.Dataset(X_train, label=y_train)

# Set model parameters
params = {
    'objective': 'binary',
    'metric': 'binary_logloss',
    'verbose': -1,
    'boosting_type': 'gbdt',
}

# Train the model
model = lgb.train(params, train_data, num_boost_round=100)  # Set num_boost_round

6.2 Model Evaluation

Test data is used to evaluate the model's performance. Check the prediction results and measure accuracy: 

# Predictions
y_pred = model.predict(X_test)
y_pred_binary = [1 if i > 0.5 else 0 for i in y_pred]

# Accuracy evaluation
accuracy = accuracy_score(y_test, y_pred_binary)
print(f'Model accuracy: {accuracy * 100:.2f}%')

7. Implementing Trading Strategies

Trading strategies can be established using the built model. The following example shows a basic strategy:

7.1 Signal Generation

Generate buy or sell signals based on the model's predicted results. For example: 

data['Signal'] = 0
data.loc[data['RSI'] < 30, 'Signal'] = 1  # Buy signal
data.loc[data['RSI'] > 70, 'Signal'] = -1  # Sell signal

7.2 Position Management

Manage positions based on the generated signals. Set trading rules according to the trading strategy and apply them to actual trading.

8. Conclusion

Algorithmic trading using machine learning and deep learning offers the possibility to learn more complex patterns beyond simple technical analysis. In particular, LightGBM is a useful tool for building fast and efficient trading models. Through this course, I hope you understand the basic structure and build foundational knowledge that can be applied to actual trading systems.

9. References