frankie336 / omega_predictor

Omega Predictor

Omega Predictor is a sophisticated machine learning pipeline designed for real-time financial market prediction, specifically tailored for the forex market. Utilizing advanced deep learning techniques, the Omega Predictor aims to provide accurate and timely predictions to empower decision-making.

Features

• Real-Time Prediction: Leverage state-of-the-art models for live market analysis.
• Modular Architecture: A service-based architecture ensures easy maintenance and scalability. Components like ModelWrapper, DataHandler, and DataLoaderService provide clear separation of concerns and enhance modularity.
• Data Preprocessing: Robust preprocessing steps to prepare market data for prediction. The DataHandler service is designed to process and scale data efficiently.
• Logging and Monitoring: Comprehensive logging of predictions and system performance. Predictions are logged for easy tracking and analysis.
• Dynamic Model Management: The ModelRegistryService centralizes model management, allowing for dynamic loading and easy updates to model versions.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

• Python 3.6 or later.
• PyTorch, NumPy, Pandas, and other required libraries listed in requirements.txt.

Installation

1. Clone the repository:git clone https://github.com/frankie336/omega_predictor.git2. Navigate to the project directory:cd omega_predictor3. Install the required dependencies:pip install -r requirements.txt### Usage

To start using the Omega Predictor, ensure you have the necessary data and model files in the appropriate directories. Here is a basic example of how to use the predictor:

from omega_predictor.svc.model.simple_classifier import SimpleClassifier
from omega_predictor.svc.data.data_handler import DataHandler
from omega_predictor.svc.pred.model_wrapper import ModelWrapper

# Initialize the data handler
data_handler = DataHandler()

# Load your trained model
model = SimpleClassifier(input_size=10)  # Assuming '10' is the correct input size for your model
model.load_state_dict(torch.load('path_to_trained_model.pt'))
model.eval()

# Wrap the model with the ModelWrapper for prediction
model_wrapper = ModelWrapper(model)

# Process your input data
df = your_input_dataframe  # Replace this with your actual data in a pandas DataFrame
tensor_data = data_handler.df_to_tensor(df)

# Make predictions
prediction = model_wrapper.predict(tensor_data)

# The 'prediction' variable now holds the model's predictionThis script demonstrates the basic flow from data handling to prediction. Replace the placeholders with your actual data paths and parameters.ContributionAs a growing project, we welcome contributions of all forms. Please read through CONTRIBUTING.md for guidelines on how to submit contributions.AuthorsFrancis F Neequaye - Initial work - ProfileLicenseThis project is licensed under the Attribution 4.0 International License - see the LICENSE.md file for details.AcknowledgmentsThe Omega team wishes to extend our gratitude to all contributors and users, especially to David, whose insights and guidance were invaluable during the development phase.