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Roadmap

Each roadmap phase is treated as a separate development sprint.
The roadmap is intentionally iterative: each sprint should leave the project in a usable and testable state.

Sprint 1 — Product Foundation & First Public Release

Status: In progress

Build a small but usable Python application with a clear structure, tests, documentation and first release readiness.

Scope:

  • Modular Python package structure
  • REST API client for live exchange rates
  • Calculator and currency conversion logic
  • Input validation and error handling
  • Tkinter GUI prototype
  • Legacy CLI/debug interface
  • First pandas/matplotlib analytics prototype with mock time-series data
  • Basic test suite
  • README update
  • First release instructions
  • API key setup instructions
  • Collaboration documentation through CONTRIBUTING.md, CODE_OF_CONDUCT.md and LICENSE

Outcome: ARGUS can be run locally, tested, understood by other developers and used as a small desktop analytics prototype.

Sprint 2 — Market Analytics & Data Source Expansion

Status: Planned

Move from simple FX conversion toward broader market analytics.

Scope:

  • Add stronger market metrics:
    • cumulative return
    • strongest / weakest day
    • rolling volatility
    • performance analytics
    • risk analytics
  • Extend the current dashboard without adding unnecessary chart noise
  • Add or evaluate new data clients:
    • Frankfurter for historical FX data
    • yfinance for broader market data
  • Replace or reduce dependency on the current ExchangeRate API where needed
  • Improve pandas-based analysis workflows
  • Add tests for metric calculations and data transformations
  • Document metric definitions, assumptions and chart behavior

Outcome: ARGUS becomes a basic market analytics tool, not only a converter.

Sprint 3 — Storage, Web-Ready UI & Data Architecture

Status: Planned

Prepare ARGUS for persistent data workflows and a stronger product interface.

Scope:

  • Add local storage layer:
    • PostgreSQL, DuckDB, SQLite or Parquet depending on use case
  • Store historical market data
  • Separate ingestion, transformation, analytics and presentation layers more clearly
  • Start NiceGUI as the main web-ready UI direction
  • Keep Tkinter as legacy/prototype unless still useful
  • Keep CLI as internal/debug interface only
  • Add clearer architecture documentation
  • Prepare the project for larger data workflows and external contributors

Outcome: ARGUS has a clearer data architecture and starts moving from local prototype toward a scalable analytics application.

Sprint 4 — Cloud, Pipelines & Portfolio-Grade Data Engineering

Status: Future

Turn ARGUS into a stronger end-to-end data engineering project.

Scope:

  • Docker / Docker Compose
  • Scheduled data ingestion
  • Cloud storage or cloud database
  • CI/CD improvements
  • Data quality checks
  • Basic pipeline orchestration
  • Reporting layer
  • Architecture diagram
  • Deployment documentation

Target workflow:

API → Ingestion → Storage → Transformation → Analysis → Visualization → CI/CD

Sprint 5 — AI-Assisted Research & Agentic Monitoring

Status: Future vision

Add AI support only after the data, storage, service and reporting layers are stable.

Scope:

  • LLM-assisted report summaries
  • Explanation of unusual movements
  • RAG over stored market notes, reports or documentation
  • Agentic checks for data quality, anomalies and recurring market scans
  • Human-in-the-loop signal review
  • Automated monitoring workflows

Outcome:

ARGUS starts behaving like its name: a system that continuously watches market data, evaluates it and helps generate useful signals.