๐Ÿ“Œ AI-Powered Quantitative Investment System

1. Project Overview

1.1 Project Name

AI-Powered Quantitative Investment System

1.2 Project Objective*

This project aims to develop a quantitative investment system leveraging AI and data analysis to optimize stock market strategies. The initial phase focuses on stock price prediction using OpenAI API, with a long-term goal of incorporating machine learning (XGBoost, LSTM) models for automated investment strategies.

2. Project Goals

2.1 Phase 1 (MVP Development)

โœ… Build a stock prediction system using OpenAI API
โœ… Develop a FastAPI-based backend for real-time stock data retrieval and analysis
โœ… Implement a React frontend to visualize prediction results

2.2 Phase 2 (Expanding the Quantitative Investment System)

โœ… Enhance stock prediction with machine learning (XGBoost, LSTM)
โœ… Implement backtesting functionality to evaluate investment strategies
โœ… Optimize portfolio allocation and integrate automated trading system

3. System Architecture

3.1 Overview

Frontend (React) โ†’ Backend (FastAPI + OpenAI API + ML models) โ†’ Data Storage (PostgreSQL, Redis) โ†’ Automated Trading (Future Integration)

3.2 Technology Stack

  • Backend: FastAPI (Python), OpenAI API, yFinance (Stock Data), Binance API (Crypto Data)
  • Frontend: React.js, Chart.js (Data Visualization), TailwindCSS
  • Database: PostgreSQL (Stock Data Storage), Redis (Caching)
  • Machine Learning: Scikit-learn, XGBoost, TensorFlow (LSTM)
  • Deployment: Docker, AWS EC2, Vercel (Frontend Hosting)

4. Core Features

4.1 Backend (FastAPI-based API)

Feature Description
Stock Data Retrieval Fetch real-time and historical stock data from Yahoo Finance API
AI Stock Prediction Use OpenAI API to predict future stock price trends
ML-Based Prediction Enhance forecasts using XGBoost and LSTM models
Investment Strategy Recommendations AI-powered investment strategy suggestions

4.2 Frontend (React-based UI)

Feature Description
Stock Charts Visualize stock data using Chart.js
Prediction Results Display AI-generated stock predictions in real-time
User Input Allow users to input stock symbols for predictions

4.3 Advanced Quantitative Investment Features (Future Development)

Feature Description
Backtesting Evaluate investment strategies using historical data
Portfolio Optimization AI-driven asset allocation recommendations
Automated Trading Integration with Binance API for automated order execution

5. API Design (Phase 1)

API Endpoint Method Description
/api/stock/{symbol} GET Retrieve real-time stock price data
/api/predict/{symbol} GET Predict stock trends using OpenAI API
/api/train_model/{symbol} POST Train machine learning models for improved predictions

6. Project Timeline

Phase Tasks Estimated Duration
1๏ธโƒฃ Setup environment & API integration (FastAPI + OpenAI API) 1 week
2๏ธโƒฃ Implement stock data visualization (React + Chart.js) 1 week
3๏ธโƒฃ Develop & test OpenAI-based prediction system 2 weeks
4๏ธโƒฃ Apply machine learning models (XGBoost/LSTM) for stock forecasts 3 weeks
5๏ธโƒฃ Add investment strategy recommendations & backtesting functionality 3 weeks
6๏ธโƒฃ Integrate automated trading system (Binance API) 4 weeks

7. Expected Benefits

๐Ÿ”น Leverage data-driven investment strategies for objective decision-making
๐Ÿ”น Enhance stock price prediction accuracy using AI and machine learning
๐Ÿ”น Verify investment strategies through backtesting before real-world application

8. Conclusion

This project aims to build an AI-powered stock prediction system leveraging OpenAI API in the initial phase, followed by machine learning integration to create a comprehensive quantitative investment system.

Phase 1: Develop stock prediction using OpenAI API
Phase 2: Enhance predictions with machine learning models
Phase 3: Implement backtesting and automated trading for a fully operational quant investment platform

๐Ÿ“ข Next Step: Confirming the core features and beginning the development process! ๐Ÿš€