Machine Learning and Deep Learning Algorithm Trading, GRU

Hello! Today we will delve deeply into GRU (Gated Recurrent Unit), one of the important tools in machine learning and deep learning algorithmic trading. GRU shows strong performance in time series data prediction, making it widely used in automated trading systems in financial markets. We will learn more about GRU and discuss how to build advanced trading strategies using it.

1. What is Algorithmic Trading?

Algorithmic Trading is a trading method where a computer program automatically executes trades based on pre-set rules. It generally utilizes artificial intelligence (AI) and machine learning (ML) technologies to analyze historical data, predict trends, and execute trades in real-time.

  • Speed and Efficiency: Executes trades at high speeds while minimizing human emotional involvement.
  • Data-driven Decision Making: Analyzes vast amounts of data to find optimal trading points.
  • Risk Management: Executes strategies that minimize losses based on pre-set rules.

2. Definitions of Machine Learning and Deep Learning

Machine learning is a field of artificial intelligence that learns based on data to recognize patterns. In contrast, deep learning focuses on learning more complex data structures using neural network technology. It is particularly effective in time series prediction and pattern recognition.

2.1 Types of Machine Learning

  • Supervised Learning: Learns using labeled data.
  • Unsupervised Learning: Discovers patterns through unlabeled data.
  • Reinforcement Learning: Learns strategies to maximize rewards through interaction with the environment.

2.2 Advancements in Deep Learning

Deep learning has advanced significantly since the late 2000s and is being applied in various fields such as image recognition, natural language processing, and time series prediction. GRU is one of these deep learning models specializing in processing sequence data.

3. What is GRU (Gated Recurrent Unit)?

GRU operates similarly to LSTM (Long Short-Term Memory) but has a simpler and more efficient structure. GRU is designed to focus on specific parts of input data and not remember unnecessary data. It consists of a combination of bit stack (float) and state vector.

3.1 Structure of GRU

GRU uses two main gates: the Update Gate and the Reset Gate. These two gates interact to determine the next state.

  • Update Gate: Determines how much of the current state is needed.
  • Reset Gate: Determines how much information to forget by combining the previous state and current input.

3.2 Mathematics of GRU

The operation of GRU can be expressed with the following equations:


z_t = σ(W_z * x_t + U_z * h_{t-1})  # Update Gate
r_t = σ(W_r * x_t + U_r * h_{t-1})  # Reset Gate
h_t' = tanh(W * x_t + U * (r_t * h_{t-1}))  # Current state calculation
h_t = (1 - z_t) * h_{t-1} + z_t * h_t'  # Final state calculation

4. Predicting Financial Data Using GRU

Now, let’s explore how GRU is used for financial data prediction. To train a GRU model, historical price data must first be collected and preprocessed.

4.1 Data Collection

Financial data can be collected from various sources. For example, stock data and forex data can be collected in real-time through exchange APIs.

4.2 Data Preprocessing

The collected data undergoes preprocessing through missing value handling, normalization, exploratory data analysis (EDA), etc. Generally, the following steps are necessary:

  • Remove missing values and outliers
  • Data normalization (e.g., Min-Max Scaling)
  • Data splitting (training set, validation set, test set)

4.3 Model Building

In the model building step, we design a neural network that includes GRU layers. You can implement the model using TensorFlow or PyTorch libraries.


import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GRU, Dense

model = Sequential()
model.add(GRU(50, return_sequences=True, input_shape=(timesteps, features)))
model.add(GRU(50))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')

4.4 Model Training

The model is trained using the training dataset. Typically, 20–80% of the data is used for training, and the rest is used for validation.


model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_val, y_val))

4.5 Results Evaluation and Prediction

This stage involves using the trained model to predict future data and evaluating the model’s performance. Metrics such as RMSE and MAE are used for evaluation.


predictions = model.predict(X_test)
rmse = np.sqrt(np.mean((predictions - y_test) ** 2))

5. Building Trading Strategies Based on GRU

Trading strategies based on GRU models can be implemented in various ways. Here, we will generate simple trading signals.

5.1 Generating Trading Signals

Trading signals are generated by comparing predicted prices with actual prices. Common methods include:

  • Buy on price increase: A buy signal occurs when the predicted price is higher than the current price.
  • Sell on price decrease: A sell signal occurs when the predicted price is lower than the current price.

5.2 Strategy Backtesting

The constructed trading strategy is backtested to evaluate its performance on historical data. This is an important step to verify the strategy’s reliability.


def backtest(strategy, data):
    results = []
    for date in data.index:
        # Trading signal and position management logic
        results.append(strategy.make_decision(date))
    return results

5.3 Implementing a Real Trading System

Now we will build a real trading system. Using APIs, we automate trading and update the model based on real-time market data.


import requests

def execute_trade(signal):
    if signal == 'buy':
        requests.post("https://api.broker.com/buy", data={'amount': 100})
    elif signal == 'sell':
        requests.post("https://api.broker.com/sell", data={'amount': 100})

6. Conclusion

GRU is a deep learning model that exhibits strong capabilities in time series data prediction and is a highly useful tool in algorithmic trading. By utilizing GRU for financial market prediction and trading strategy development, one can make smarter investment decisions. However, all investments carry risks, so careful attention to model performance and risk management is essential.

We hope to continue researching various financial technologies utilizing machine learning and deep learning to develop better investment strategies.

Thank you!