Ella Powell@ella_powell55

The project aims to develop an advanced AI-powered tool using the Gemma 2 language model to assist financial advisors in comprehensive portfolio management, market analysis, and client communication. The solution will leverage machine learning techniques to provide nuanced insights into portfolio performance, market trends, and personalized investment strategies. Key Problem Statement: Financial advisors face significant challenges in: 1. Efficiently analyzing complex market information 2. Correlating diverse financial data sources 3. Providing personalized portfolio insights 4. Navigating complex market news and its potential impacts 5. Optimizing portfolio performance across multiple dimensions Technical Approach: The project will utilize a multi-step approach: - Fine-tune the Gemma 2 large language model - Create a comprehensive training dataset - Develop custom data processing pipelines - Implement advanced financial metric calculations - Build an intelligent query-response system Core Capabilities: 1. Portfolio Composition Analysis - Detect direct and indirect stock holdings - Analyze asset allocation across stocks, ETFs, and mutual funds - Identify potential investment opportunities and risks 2. Performance Metric Computation - Calculate advanced financial metrics: * Sharpe Ratio * Beta * Price-to-Earnings (P/E) Ratio * Price-to-Book (P/B) Ratio * Momentum Indicators (RSI, MACD) 3. Market News Correlation - Interpret market news in context of specific portfolios - Predict potential impacts on individual client investments - Provide personalized market insights Data Sources and Integration: - Financial APIs (Alpha Vantage, potentially Bloomberg, Quandl) - Custom-generated training datasets - Historical market data - Fundamental company financial information