AI-Driven EURUSD Trading Forecasting

Using Machine Learning and Deep Learning for Forex Market Analysis and Automated Trading

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Table of Contents

1. Project Overview
2. Features
3. Technologies Used
4. Model Architecture
5. Installation
6. Usage
7. Results
8. Future Enhancements
9. License

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Project Overview

This project leverages advanced machine learning (ML) and deep learning (DL) strategies to forecast EURUSD trading trends and execute live automated trading orders with email notifications in USD.

At its core, the project features:

• An LSTM (Long Short-Term Memory) model for time series forecasting.
• Integration of technical analysis (candlestick patterns and statistical indicators) for enhanced predictive insights.
• Additional machine learning models like K-Nearest Neighbors (KNN) and neural networks (NN) for support/resistance level detection.

Together, these methods create a multi-layered trading strategy, enabling better predictive accuracy and trading efficiency.

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Features

🎯 Core Functionalities

• LSTM Model: Captures sequential dependencies in financial market data.
• Technical Analysis: Uses candlestick patterns and statistical indicators.
• Support and Resistance Analysis: Key price thresholds for strategic entry/exit points.
• Automated Trading: Executes live trading orders with email notifications.

📈 Advanced Prediction Techniques

• Classification Models: Categorize market movements into target price directions.
• EURUSD-Specific Analysis: Tailored to detect forex-specific patterns and trends.

⚡ Additional Highlights

• Immediate Alerts: Email notifications for trading actions.
• Multi-Model Approach: Combines ML and DL models for improved accuracy.
• Automated Execution: Real-time trading functionality.

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Technologies Used

• Programming Languages: Python (3.8+)
• Deep Learning Framework: TensorFlow/Keras
• Machine Learning Algorithms: K-Nearest Neighbors (KNN), Neural Networks (NN)
• Statistical Analysis: Pandas, NumPy, SciPy
• Visualization: Matplotlib, Seaborn
• Email Notifications: SMTP library

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LSTM for Time Series Forecasting

• Sequential Model with layers:
  • Input Layer: Takes processed time series data.
  • LSTM Layers: Captures sequential patterns in market data.
  • Dense Output Layer: Produces forecasts for price movement.

Complementary ML Models

• KNN and Neural Networks: Detect key support/resistance levels and classify trends.

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Results

• Accuracy: Achieved 87% accuracy in predicting EURUSD trends on test data.

• Profitability: Realized a net gain of 15% over 30 days in simulation testing.

  

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Future Enhancements

1. Additional Indicators: Incorporate RSI, MACD, Bollinger Bands.
2. Real-Time Simulations: Extend testing with simulated environments.
3. Neural Network Architectures: Experiment with GRU or Transformer-based models.
4. Multi-Currency Support: Expand analysis beyond EURUSD.

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