Master Algorithm

Project Overview

Our algorithmic trading solution seamlessly integrates traditional and ML-driven strategies, employing ensemble learning for heightened predictive accuracy. An NLP-powered chatbot ensures user friendly real-time insights, while the fusion of almost 50 proven strategies enhances adaptability. Transparency is prioritized through comprehensive backtesting reports, fostering trust and informed decision-making. Our innovative approach combines strategy diversity, machine learning, user-friendly interaction, and transparent reporting for a cutting-edge algorithmic trading solution.

Core Objectives

• To provide an algorithm for real time trading.
• To implement an automated system for anomaly detection and alert generation.
• To offer a comprehensive data visualization and analytics dashboard.
• To ensure seamless integration with existing quality management systems.

Technology Stack

• Frontend: Next.js, HTML/CSS
• Backend: Python, Flask
• Machine Learning: TensorFlow
• Data Processing: Pandas
• Database: PostgreSQL

Features

1. Real-Time Monitoring: Continuous tracking of stock market.
2. Anomaly Detection: Automated detection of signal using machine learning.
3. Interactive Dashboard: User-friendly interface for data visualization and analytics.
4. Integration Capability: Compatible with existing quality management frameworks.

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

• Git
• Python
• Node.js
• A suitable IDE (e.g., VSCode)

Installation

1. Clone the Repository

   git clone https://github.com/swarnsiddhi/Master-Algorithm

2. Install Dependencies

   • Navigate to the project directory and install Python dependencies:

     pip install -r requirements.txt

   • Install Node.js dependencies for the Next.js frontend:

     npm install

3. Run the Application

   • In a new terminal, launch the Next.js frontend:

     npm run dev

4. Access the Application

   • The dashboard is accessible at http://localhost:3000 (default Next.js port).

Contributors

• Mohit Verma
• Nitin Yadav
• Suryansh Yagnik
• Satwik Singh
• Nishita Singh

Acknowledgments

Special thanks to the team members and mentors at the Scale +91 Hackathon - 2024, who inspired and supported this project.

Project Insights

Developed during a hybrid hackathon, this project involved collaboration with a diverse team of innovators, addressing the challenge of identifying discrepancies in Quality Monitoring Data on OMMAS. Key outcomes include:

1. Machine Learning Algorithm: Developed to analyze data and identify signal.
2. Customizable Dashboard: For visualizing and comparing data from various sources.
3. Automated Data Processing Pipeline: For efficient handling of large data volumes.
4. NLP Techniques: Implemented for Helpbot.

Future Scope

• Enhancement of the machine learning model for broader anomaly detection.
• Expansion of the data processing pipeline to include additional data sources.
• Further development of the dashboard for more interactive features.
• A new model revamp using BaaS (Backend as a Service) and FaaS (Function as a Service) technologies.

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This README is designed to provide a comprehensive overview of the Master Algorithm, a full-stack project based on Next.js. It outlines the project's objectives, features, technology stack, installation guide, and acknowledgments, offering a clear guide for anyone interested in understanding or contributing to the project.