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Market data processing pipeline for stock market scanner

Project description

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kuhl-haus-mdp

Market data processing library.

Overview

The Kuhl Haus Market Data Platform (MDP) is a distributed system for collecting, processing, and serving real-time market data. Built on Kubernetes and leveraging microservices architecture, MDP provides scalable infrastructure for financial data analysis and visualization.

Key Features

  • Real-time market data ingestion and processing

  • Scalable microservices architecture

  • Automated deployment with Ansible and Kubernetes

  • Multi-environment support (development, staging, production)

  • OAuth integration for secure authentication

  • Redis-based caching layer for performance

Code Organization

The platform consists of four main packages:

  • Market data processing library (kuhl-haus-mdp) - Core library with shared data processing logic

  • Backend Services (kuhl-haus-mdp-servers) - Market data listener, processor, and widget service

  • Frontend Application (kuhl-haus-mdp-app) - Web-based user interface and API

  • Deployment Automation (kuhl-haus-mdp-deployment) - Docker Compose, Ansible playbooks and Kubernetes manifests for environment provisioning

Documentation

For architecture details, component descriptions, and API reference, see the full documentation on Read the Docs.

Configuration

Finlight Data Listener (FDL)

The FinlightDataListener component reads the following environment variables when constructing configuration at the application layer:

Variable

Required

Description

FINLIGHT_API_KEY

Yes

Finlight API key for authenticating WebSocket connections

FINLIGHT_QUERY

No

Boolean search query for filtering articles (e.g. "earnings AND revenue")

FINLIGHT_TICKERS

No

Comma-separated list of ticker symbols to filter (e.g. "AAPL,MSFT,GOOG")

FINLIGHT_SOURCES

No

Comma-separated list of news source domains (e.g. "reuters.com,bloomberg.com")

FINLIGHT_LANGUAGE

No

ISO 639-1 language code for filtering articles (e.g. "en")

FINLIGHT_RAW

No

Set to "true" to use the raw WebSocket stream (no AI enrichment; faster delivery)

Additional Resources

📖 Blog Series:

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