Intraday Trading Strategy for Forex: A Deep Learning Approach with GRU and CNN :Spacewink

In this article, we explore a comprehensive intraday trading strategy for the foreign exchange (Forex) market by leveraging deep learning methods, specifically the Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN). This approach aims to predict the next-day price direction in the Forex market, focusing on the EUR/USD currency pair. By utilizing advanced machine learning techniques and technical indicators, this strategy provides a systematic framework for traders looking to make informed decisions in a volatile market.

Introduction to Forex and the Need for Prediction Models

The Forex market is the largest financial market globally, where traders buy and sell currencies, profiting from exchange rate movements. The market's constant fluctuation due to macroeconomic and political factors makes it both lucrative and risky. Traditionally, traders rely on two forms of analysis:

1. Technical Analysis: Uses historical price and volume data to identify trends.

2. Fundamental Analysis: Focuses on macroeconomic factors, such as government policies and economic stability.

In recent years, machine learning has offered new avenues for enhancing prediction accuracy by processing large volumes of historical data. Here, we propose a CNN-GRU hybrid model that uses technical indicators to improve price direction forecasting, thereby reducing risks and potentially increasing profits.

Data Collection and Preprocessing

Dataset Composition

For this research, the dataset comprises historical data of EUR/USD exchange rates from 2014 to 2021, segmented into training and test sets to preserve time-series order. The primary features include:

- Open: Opening price

- High: Highest price of the day

- Low: Lowest price of the day

- Close: Closing price

- Volume: Trade volume

Target Variable: Signal Generation

The model aims to predict the next day’s price direction by generating a signal (BUY, SELL, HOLD) based on price movements. This signal is derived as follows:

One-hot encoding is applied to convert these categorical signals for model training.

Technical Indicators

Technical indicators provide additional insights into price trends. Key indicators used include:

- Simple Moving Average (SMA), Weighted Moving Average (WMA), Exponential Moving Average (EMA)

- Relative Strength Index (RSI), Average Directional Index (ADX), Commodity Channel Index (CCI)

- Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), Rate-of-Change (ROC)

These indicators are calculated over varying periods, allowing the model to detect both short-term and long-term trends effectively.

Feature Selection

In a machine learning context, excessive features can introduce noise, affecting model performance. Here, feature selection reduces inputs from over 150 variables to a refined set of around 30. This selection prioritizes high-impact features, improving model accuracy and interpretability.

Model Architecture

CNN-GRU Hybrid Model

The proposed model combines CNN and GRU, taking advantage of CNN's pattern recognition capabilities and GRU’s ability to capture sequence dependencies.

Model Layers

- Input Layer: Accepts a matrix with observations for specific days and variables.

- Convolutional Layer: Applies multiple filters to recognize patterns in price data.

- GRU Layer: Processes sequential data, predicting the next day’s price direction based on a many-to-one architecture.

- Output Layer: Uses dense layers to output a final trading signal.

Sliding-Window Approach

To minimize noise and capture trends, a sliding-window approach is used. This method considers the current day’s data and a specified number of prior days (e.g., 6–10 days), which enhances the model’s pattern recognition capabilities.

Trading Rules and Implementation

Simple Technical Trading Rules

The CNN-GRU model’s signals are further reinforced by simple trading rules based on technical indicators:

- SMA(10) Rule: Signals a BUY if the current price is above the 10-day SMA and a SELL if below.

- RSI Rule: BUY if RSI < 30 (indicating oversold conditions) and SELL if RSI > 70.

- ATR Rule: Uses the Average True Range to determine entry and exit points based on price volatility.

Investment Strategy

To make the strategy actionable, a leveraged investment approach (e.g., 1:100) is employed, magnifying returns but increasing risks. The model predicts trading actions based on log returns and a specified threshold \( \alpha \). The strategy follows these steps:

1. BUY if log return > \( \alpha \).

2. SELL if log return < -\( \alpha \).

3. HOLD if within \( \alpha \) boundaries.

Model Training and Optimization

Training Process

The CNN-GRU model undergoes training with 25 epochs—optimal as it reaches stability at this point. Back-testing on historical data evaluates the model's performance in real trading scenarios, ensuring robustness.

Parameter Tuning

Experimentation on window length (6–10 days) and threshold values (0–0.001) reveals optimal settings that maximize both prediction accuracy and profit accuracy.

Performance Metrics

To assess profitability, the model’s back-testing results are measured in terms of:

- Prediction Accuracy: Correct prediction of price directions.

- Profit Accuracy: Alignment of predicted signals with profitable trades.

Results indicate peak performance with a threshold of 0.0002 and a window length of 6–10 days, achieving approximately 65–70% accuracy.

Discussion and Results

The model demonstrates strong potential for improving trading profitability. Key findings include:

- Optimal Window and Threshold: A window size of 6–10 and threshold up to 0.0002 achieve the highest accuracy.

- Sliding-Window Benefits: Incorporating prior days’ data helps in recognizing price patterns, improving forecast reliability.

- Challenges with Market Volatility: The model’s efficacy fluctuates with market volatility, requiring adaptive thresholds to mitigate risk.

Back-testing confirms the model’s consistency in forecasting price direction, enhancing profitability while managing market noise.

Practical Considerations and Limitations

While the model shows promise, Forex markets are highly dynamic. It is crucial to:

- Recalibrate Parameters: Regularly fine-tune the model based on recent data to maintain accuracy.

- Risk Management: Implement stop-losses and monitor leverage ratios to prevent substantial losses.

- Adaptive Trading Rules: Modify technical rules in response to changing market conditions, particularly during economic upheavals.

Conclusion and Future Directions

The CNN-GRU-based trading strategy provides a promising solution for predicting Forex price directions with improved accuracy over conventional techniques. By integrating advanced technical indicators with a powerful hybrid model, this strategy offers a comprehensive approach to navigating the complexities of the Forex market.

Future Research

Expanding this strategy to additional currency pairs and applying reinforcement learning could further enhance adaptability and profitability. Additionally, incorporating sentiment analysis of economic news may improve the model’s responsiveness to external factors, establishing a more robust predictive system for global Forex markets.

This strategy equips traders with a refined tool to make data-driven decisions, combining cutting-edge machine learning with tried-and-true technical indicators. However, ongoing adaptation remains essential to sustain profitability amid market fluctuations.