Quantitative Investment Research Suite

This project is dedicated to building an interconnected system that bridges traditional investment principles with advanced AI techniques, delivering a comprehensive decision-support framework.

The repository offers a fully integrated pipeline for data-driven investment strategies, spanning stock screening, portfolio optimization, forecasting, and algorithmic trading, with a user-friendly LLM-powered dashboard for interaction and insights. The workflow is designed to create a seamless flow of information between modules:

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Features

1. Stock Screening

• Methodology: Implements the CANSLIM framework to identify high-potential stocks.
• Key Inputs: Fundamental and technical indicators.
• Output: A ranked list of stocks ready for optimization.

2. Portfolio Optimization

• Approach: Uses a mean-variance optimization framework.
• Goal: Balance risk and expected return to construct an efficient portfolio.
• Output: Optimal asset allocation weights.

3. Forecasting

• Model: SARIMAX, N-BEATS, and Informer and with Exogenous features (e.g., compressed news sentiment, macroeconomic indicators).
• Purpose: Predict short-term price trends and market dynamics.
• Output: Forecasted price changes or returns.

4. Algorithmic Trading

• Technique: Reinforcement learning (e.g., Q-Learning).
• Use Case: Design automated trading strategies based on forecasting outputs and real-time portfolio states.
• Output: Dynamic buy/sell/hold signals.

5. LLM RAG Dashboard

• Functionality: A retrieval-augmented generation (RAG) dashboard powered by large language models.
• Purpose:
  • Interactive insights presentation.
  • Real-time query answering.
  • Re-run analyses with adjusted assumptions.
• Output: User-friendly decision-support system.

Steps

1. Clone the repository:

   git clone https://github.com/your-username/QuantitativeInvestmentResearchSuite.git