System Requirements Document (SRD)

Project Name: garnet-model

1. Introduction

The garnet-model is a cutting-edge system designed to create financial indexes, with a specific focus on equities. This project aims to provide a robust, scalable, and user-friendly platform for generating and managing equity-based financial indexes. By leveraging advanced technologies and APIs, the garnet-model will empower analysts, investors, and businesses to make informed decisions based on accurate and timely equity market data.

This document outlines the system requirements for the garnet-model, incorporating the latest updates to include equities support as pre-resolved by the API supervisor.

2. System Overview

The garnet-model is a financial index creation platform tailored for equities. It will aggregate, process, and analyze equity market data to generate indexes that can track performance across various sectors, geographies, or stock types (e.g., growth vs. value). The system will integrate with APIs to ensure real-time data accuracy and provide users with customizable tools to create and manage their own indexes.

Key features include:

• Real-time data aggregation from multiple equity market sources.
• Customizable index creation based on user-defined parameters.
• Advanced analytics and visualization tools for index performance tracking.
• Scalable architecture to handle large volumes of equity data.

The garnet-model will adhere to UK-specific financial regulations and standards, ensuring compliance and reliability for users in the GB region.

3. Functional Requirements

• As a User, I should be able to create a financial index based on equities.
• As a User, I should be able to customize the index parameters (e.g., sector, geography, stock type).
• As a User, I should be able to view real-time performance data for my created indexes.
• As a User, I should be able to integrate external equity market APIs for data aggregation.
• As a User, I should be able to export index data in multiple formats (e.g., CSV, JSON).
• As an Admin, I should be able to manage API integrations and ensure data accuracy.
• As an Admin, I should be able to monitor system performance and scalability.

4. User Personas

4.1 Analyst

• Description: A financial analyst who uses the garnet-model to create and track equity indexes for research and reporting purposes.
• Goals: Generate accurate indexes, analyze performance trends, and export data for presentations.

4.2 Investor

• Description: An individual or institutional investor who uses the garnet-model to benchmark portfolio performance against custom equity indexes.
• Goals: Create personalized indexes, monitor real-time performance, and make informed investment decisions.

4.3 Admin

• Description: A system administrator responsible for managing the garnet-model's backend, including API integrations and system performance.
• Goals: Ensure data accuracy, maintain system uptime, and manage user access.

5. Visuals Colors and Theme

The garnet-model will feature a sleek and professional design, reflecting its focus on financial data and analytics. The color palette is designed to convey trust, clarity, and sophistication:

• Background: #F5F7FA (light grey-blue for a clean, modern look)
• Surface: #FFFFFF (pure white for contrast and readability)
• Text: #2C3E50 (dark slate for professional tone)
• Accent: #E74C3C (vivid garnet red for highlights and calls to action)
• Muted Tones: #BDC3C7 (soft grey for secondary elements)

6. Signature Design Concept

The garnet-model homepage will feature an interactive financial dashboard as its centerpiece.

Key Features:

• Dynamic Equity Index Globe: A 3D rotating globe displaying equity market data in real-time. Users can click on regions to zoom in and view sector-specific performance.
• Customizable Widgets: Users can drag and drop widgets (e.g., performance charts, heatmaps, news tickers) to personalize their dashboard.
• Animated Transitions: Smooth animations will guide users as they navigate between sections, creating a seamless and engaging experience.
• Data-Driven Visuals: Charts and graphs will animate in real-time, reflecting the latest equity market data.

This bold design concept ensures that users are immediately captivated by the platform's capabilities while providing an intuitive and interactive experience.

7. Non-Functional Requirements

• The system must handle up to 10,000 concurrent users without performance degradation.
• Data updates from APIs should occur within 5 seconds of availability.
• The system must comply with UK financial regulations and data privacy laws (e.g., GDPR).
• The platform should achieve 99.9% uptime.

8. Tech Stack

Frontend:

• React for Web

Backend:

• Python
• FastAPI

Database:

• MySQL (with Alembic for migrations)

AI Models:

• GPT 5.2 for user-friendly responses
• Claude 4.5 Opas for coding and academic tasks

AI Tools:

• Langchain
• Litellm for LLM Routing

Orchestration:

• Docker
• Kubernetes

9. Assumptions and Constraints

• The system assumes that all external APIs used for equity data aggregation are reliable and provide real-time updates.
• The system will be deployed in a cloud environment to ensure scalability and availability.
• Users are expected to have basic financial knowledge to effectively use the platform.

10. Glossary

• Equities: Shares of ownership in a company, representing a claim on part of the company's assets and earnings.
• Index: A statistical measure of the performance of a group of assets, such as stocks.
• API: Application Programming Interface, a set of protocols for building and interacting with software applications.
• GDPR: General Data Protection Regulation, a legal framework for data protection and privacy in the European Union.