Creating Interactive Web API for PorQua

[rishavtarway]

Detailed Issue : Interactive Web API for PorQua

Overview

PorQua is a powerful library for portfolio optimization and index replication that currently requires Python scripts or Jupyter notebooks for usage. My project aims to make PorQua more accessible by developing:

1. A RESTful Web API using FastAPI
2. An interactive Jupyter Notebook UI with ipywidgets
3. Deployment solutions with Docker and cloud services
4. Comprehensive documentation

Detailed Implementation Plan

REST API Development

I will implement a comprehensive FastAPI-based REST API with the following components:

• Core API Endpoints:

  • /upload: For uploading financial data in CSV/JSON formats
  • /optimize: For running various portfolio optimization routines
  • /results: For retrieving optimized portfolio allocations
  • /metrics: For accessing risk metrics and performance indicators

• API Features:

  • Authentication and rate limiting
  • Request validation and error handling
  • Asynchronous processing for computationally intensive tasks
  • Caching mechanism for frequent queries

• Data Processing Pipeline:

  • Preprocessing of financial data
  • Validation of input parameters
  • Integration with PorQua's core optimization algorithms

Interactive Jupyter UI

I will create an interactive Jupyter Notebook interface that will:

• Provide intuitive widgets for parameter selection
• Implement real-time visualization using Plotly
• Enable saving/loading of optimization configurations
• Include interactive tutorials for different optimization scenarios

Example UI Components:

• Asset selection multi-select widget
• Date range selector
• Optimization parameter sliders
• Results visualization tabs

Deployment and Scalability

• Docker containerization with multi-stage builds for efficiency
• Configuration for different deployment environments
• Horizontal scaling capabilities for handling multiple requests

Documentation

• Swagger documentation for all endpoints
• Step-by-step tutorials for common use cases
• Code documentation following Python best practices
• Video demonstrations of the API and UI in action

Technical Stack

• Backend: Python, FastAPI, PorQua
• UI: Jupyter Notebooks, ipywidgets, Plotly
• Deployment: Docker, GitHub Actions

Expected Outcomes

1. A production-ready REST API for PorQua
2. An intuitive Jupyter Notebook UI for portfolio optimization
3. Docker containers for easy deployment
4. Comprehensive documentation and tutorials

[rishavtarway]

I've started working on this feature to make PorQua more accessible to everyone interested in financial calculations and portfolio optimization. I'm excited about implementing the REST API and interactive Jupyter UI as outlined in my detailed . I welcome any feedback or suggestions to improve my implementation plan. I'll be raising a PR soon with initial progress.

[rishavtarway]

@cbachela Your expertise in quantitative portfolio management and optimization would be invaluable for this feature. I'd greatly appreciate your feedback on the proposed implementation.

@TolisChal Your experience in statistical software would be extremely helpful. Any suggestions for improvements would be most welcome as I begin development.

@vissarion Your insights in mathematical software and optimization would be very beneficial for this implementation. I would value your perspective on making this feature robust and user-friendly.