Enhance Trading Bot with AI Logic
Budget: $10 – $30 USD
Objective:
Implement a focused enhancement to an already developed trading bot within 3–4 hours, utilizing AI logic and the MERN stack.
Scope of Work:
Option A: AI Signal Module Integration
- Add a lightweight AI-based signal generation module (e.g., simple moving average crossover prediction using pre-trained Python model).
- Expose the AI inference through a Flask/FastAPI microservice and integrate it into the existing Node.js backend via HTTP request.
Option B: Frontend Upgrade (React.js)
- Add a new React component to display AI-generated signals, trading confidence scores, and allow toggling between "AI Mode" and "Manual Mode".
- Include real-time chart updates (using Chart.js) for predictions overlay.
Option C: Backend Extension (Express + MongoDB)
- Modify the backend to accept and store AI-generated trading signals, update the schema, and expose new REST endpoints for signal history.
- Implement logging for bot actions based on AI decisions.
Tech Used:
- Frontend: React.js, Chart.js
- Backend: Node.js, Express.js
- Database: MongoDB
- AI Microservice (Optional): Python + Flask/FastAPI
- Communication: REST API or WebSocket
Expected Outcome:
A lightweight, testable AI-enhanced feature added to the trading bot system that demonstrates real-time integration, decision tracking, and user interaction—within 3–4 hours of development time.
Implement a focused enhancement to an already developed trading bot within 3–4 hours, utilizing AI logic and the MERN stack.
Scope of Work:
Option A: AI Signal Module Integration
- Add a lightweight AI-based signal generation module (e.g., simple moving average crossover prediction using pre-trained Python model).
- Expose the AI inference through a Flask/FastAPI microservice and integrate it into the existing Node.js backend via HTTP request.
Option B: Frontend Upgrade (React.js)
- Add a new React component to display AI-generated signals, trading confidence scores, and allow toggling between "AI Mode" and "Manual Mode".
- Include real-time chart updates (using Chart.js) for predictions overlay.
Option C: Backend Extension (Express + MongoDB)
- Modify the backend to accept and store AI-generated trading signals, update the schema, and expose new REST endpoints for signal history.
- Implement logging for bot actions based on AI decisions.
Tech Used:
- Frontend: React.js, Chart.js
- Backend: Node.js, Express.js
- Database: MongoDB
- AI Microservice (Optional): Python + Flask/FastAPI
- Communication: REST API or WebSocket
Expected Outcome:
A lightweight, testable AI-enhanced feature added to the trading bot system that demonstrates real-time integration, decision tracking, and user interaction—within 3–4 hours of development time.