The Forex Prediction System is a Django-based web application designed to predict Forex rates using a combination of ensemble models. It fetches historical and current trend data, processes it through various technical, machine learning, and risk management models, and makes predictions about future Forex rates.

• Fetches and processes historical Forex data.
• Uses a variety of models including Simple Moving Average (SMA), RSI, MACD, Bollinger Bands, and several machine learning models.
• Employs ensemble techniques to improve prediction accuracy.
• Integrates risk management models for better decision-making.
• Provides a user-friendly interface for making predictions.

• Simple Moving Average (SMA)
• Relative Strength Index (RSI)
• Moving Average Convergence Divergence (MACD)
• Bollinger Bands

• Linear Regression
• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• ARIMA

• Fixed Fraction Model
• Kelly Criterion Model
• Expected Value Model

• Mean Reversion Model
• Carry Trade Model
• Volatility Model

• Combines predictions from various models using a weighted approach to enhance overall prediction accuracy.

1. Clone the repository:

   git clone https://github.com/rootcreator/trader.git
   cd forex-prediction-system
   python -m venv venv
   pip install -r requirements.txt
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   pip install -r requirements.txt
   python manage.py migrate
   python manage.py runserver