Custom GPT - Portfolio Analytics

QRep Analysis

• Powered by QuantStats-Lumi package with bug fixes from the original library
• Provides risk-return ratios (CAGR, Sharpe, Sortino) for single symbol vs benchmark
• Generates professional HTML reports with visualizations

Technical Analysis

• Live price data with technical indicators using Finta
• Advanced charts with Matplotlib and Gemini Vision API analysis
• PDF and HTML reports with embedded visuals

Security Performance Report (SPR)

• Multi-symbol portfolio analysis using custom calculations + FFN library
• Interactive daily returns charts with comprehensive risk metrics
• Professional HTML reports with CSV exports for detailed analysis

Detailed methodology & validation: SPR vs QRep Comparison.

Ask the GPT to guide you with these examples:

QRep Analysis

• Example: "Compare AAPL against QQQ from January 2020 to March 2023"
• Specify symbol, benchmark (default: ^GSPC), and time range

Technical Analysis

• Example: "Analyze MSFT with RSI, MACD and Bollinger Bands for the past 6 months"
• Specify symbol, timeframe, and desired indicators

Security Performance Report

• Example: "Generate SPR for AAPL,MSFT,GOOG from 2020-01-01 to 2023-12-31"
• Provide multiple symbols and date range for comprehensive analysis

OpenAPI Schema Integration

• Each MCP server codebase includes the OpenAPI schema in its docs folder
• Add schemas as Custom Actions in the ChatGPT GPT builder for full integration

1. QRep Analysis

• Backend QRep MCP server powered by QuantStats-Lumi package
• GPT connects via OpenAPI schema to MCP server
• Returns formatted HTML report with risk-return metrics

2. Technical Analysis

• FastAPI Technical Analysis service processes requests
• Converts daily to weekly data, computes indicators with Finta
• Generates charts via Matplotlib, analyzes with Gemini Vision API
• Returns Markdown responses, converts to PDF/HTML reports

3. Security Performance Report (SPR)

• Dual methodology: custom calculations for core metrics + FFN library
• FastAPI backend with MCP integration for AI/LLM interactions
• Processes multiple symbols with data quality filters
• Generates HTML reports with matplotlib charts and CSV exports

4. Integration Layer

• Custom GPT connects to FastAPI endpoints via OpenAPI JSON schemas
• All servers use fastapi-mcp for MCP protocol support
• OpenAPI schemas available in the docs folder of each codebase

1. Deploy Backend Servers

• Deploy FastAPI-MCP servers:
  • QRep Analysis server (powered by QuantStats-Lumi)
  • Technical Analysis server
  • Security Performance Report (SPR) MCP server
• Deploy markdown-to-PDF conversion server
• All GitHub repos include build guides and installation instructions

2. Setup Custom GPT

• Create a new Custom GPT in ChatGPT
• Copy OpenAPI JSON schemas from the docs folder of each MCP server repo
• Configure Custom Actions to point to your deployed endpoints
• Set appropriate instructions to handle all analysis types