TradeCore AI is an end-to-end full-stack application that predicts Bitcoin prices using Machine Learning and simulates a live trading environment with real-time scenario analysis.
TradeCore AI bridges the gap between static Data Science notebooks and reactive web applications. Unlike traditional dashboards that rely solely on expensive WebSocket feeds, TradeCore implements a Hybrid Architecture to deliver a "tick-by-tick" experience using resource-efficient methods.
- Data Ingestion → The Backend (FastAPI) fetches real-time macro data and historical prices from the CoinGecko API.
- ML Inference → A pre-trained Random Forest model processes technical indicators (RSI, SMA, Lag Features) to generate a base price prediction.
- Hybrid Synchronization → The Frontend receives the "Truth" from the backend and initiates a Client-side Stochastic Simulation (Geometric Brownian Motion) to model micro-volatility.
- Scenario Analysis → Users interact with sliders (RSI, Volatility), sending "What-If" parameters to the Python backend, which applies financial heuristics to adjust the forecast dynamically.
- Visualization → Next.js renders the data in a responsive "Bento Grid" layout with real-time charting via Recharts.
- Hybrid Real-Time Architecture: Combines server-side truth with client-side stochastic simulation to bypass API rate limits while maintaining a live market feel.
- ML-Powered Predictions: Uses a Random Forest Regressor trained on historical BTC data to forecast price trends.
- Interactive Scenario Simulator: A "What-If" analysis tool that allows users to stress-test the market (e.g., "What happens if RSI hits 90?"), generating "Strong Buy" or "Panic Sell" signals.
- Bento Grid UI: A modern, dark-mode interface built with Tailwind CSS, optimized for complex financial data visualization.
- Full Stack & Cloud Native: Deployed architecture using Vercel (Frontend) and Render (Backend) with automated CI/CD.
TradeCore AI is built with:
Python&FastAPI(Backend & API Layer)Scikit-Learn&Pandas(Machine Learning Pipeline)Next.js(React framework for the Frontend)TypeScript(Type safety)Tailwind CSS(Styling & Bento Grid layout)Recharts(Data Visualization)Geometric Brownian Motion(Mathematical modeling for simulation)
This is a monorepo containing both Client and Server. Follow these steps to run it locally:
Navigate to the backend folder and install Python dependencies:
cd backend
pip install -r requirements.txt
uvicorn main:app --reloadOpen a new terminal, navigate to the frontend folder, and install Node dependencies:
cd frontend
npm install
npm run dev