ARGUS
A collaborative financial engineering platform to analyze FX rates, develop quantitative trading strategies, execute historical backtests, and monitor live volatility risks.
Collaborative Side-Quest · Financial Data & Strategy Analytics
01. Introduction
Core Objectives
ARGUS is a collaborative financial analytics and backtesting framework built to ingest, validate, and analyze market data without expensive enterprise terminals. It serves as an open research harness for quantitative strategy development, risk scoring, and automated alerts.
02. Tech Stack
Engine & Tools
03. Background
Why This Exists
Financial analytics systems often hide calculation logic behind proprietary black boxes. ARGUS was launched as a collaborative learning project to master end-to-end data processing, strict Pydantic validation, vectorized volatility metrics, historical backtesting, and rule-based risk triggers from first principles.
Modular Ingestion & Analytical Engine
Data collectors, schema validators, and strategy computation layers are strictly separated. Ingestion modules validate API payloads before passing sanitized dataframes to analytical downstream workers.
Volatility Signals & Strategy Backtesting
Calculates rolling moving averages, variance spikes, and market spread deviations. Backtesting runs evaluate trading strategies against market history before deploying paper trading sandboxes.
Data Engineering Flow
From Ticker Ingestion to Strategy Backtesting & Alerting
Market data streams sequentially through currency fetchers, Pydantic validation, strategy backtesting routines, and risk alert sinks.
FX & Ticker Ingestion
Ingest live FX rates & historical market feeds
Pydantic Schema Guard
Validates rate types & interpolates gaps
Vectorized Analytics & Backtesting
Evaluates trading rules & calculates drawdown metrics
Persistent PostgreSQL Storage
Stores historical trends & dispatches volatility alerts
Roadmap & Direction
Sprint Roadmap & Entwicklungsfortschritt
Sprint 1: Architecture Core
Modular pipeline architecture, live FX conversion, Tkinter testing harness & pytest suite.
Sprint 2: Historical Analytics
Historical market ingestion, rolling volatility metrics & vectorized trend calculations.
Sprint 3: Persistence & Automation
Persistent PostgreSQL database schema, automated batch schedules & alert webhooks.
Sprint 4: Strategy Backtesting Engine
Backtesting module to simulate historical trading strategies, drawdowns & risk scores.
Sprint 5: Paper Trading Sandbox
Paper trading environment for risk-free strategy evaluation with live telemetry.