Skip to main content

Market data processing pipeline for stock market scanner

Project description

License PyPI Downloads Build Status CodeQL Advanced codecov GitHub issues GitHub pull requests Documentation

kuhl-haus-mdp

Market data processing library.

TL;DR

Non-business Massive (AKA Polygon.IO) accounts are limited to a single WebSocket connection per asset class and it has to be fast enough to handle messages in a non-blocking fashion or it'll get disconnected. The market data processing pipeline consists of loosely-coupled market data processing components so that a single WebSocket connection can handle messages fast enough to maintain a reliable connection with the market data provider.

Per, https://massive.com/docs/websocket/quickstart#connecting-to-the-websocket:

By default, one concurrent WebSocket connection per asset class is allowed. If you require multiple simultaneous connections for the same asset class, please contact support.

Components Summary

Non-business Massive (AKA Polygon.IO) accounts are limited to a single WebSocket connection per asset class and it has to be fast enough to handle messages in a non-blocking fashion or it'll get disconnected. The Market Data Listener (MDL) connects to the Market Data Source (Massive) and subscribes to unfiltered feeds. MDL inspects the message type for selecting the appropriate serialization method and destination Market Data Queue (MDQ). The Market Data Processors (MDP) subscribe to raw market data in the MDQ and perform the heavy lifting that would otherwise constrain the message handling speed of the MDL. This decoupling allows the MDP and MDL to scale independently. Post-processed market data is stored in the MDC for consumption by the Widget Data Service (WDS). Client-side widgets receive market data from the WDS, which provides a WebSocket interface to MDC pub/sub streams and cached data.

[Market Data Processing C4-V1.drawio.png]

Component Descriptions

Market Data Listener (MDL)

The MDL performs minimal processing on the messages. MDL inspects the message type for selecting the appropriate serialization method and destination queue. MDL implementations may vary as new MDS become available (for example, news).

MDL runs as a container and scales independently of other components. The MDL should not be accessible outside the data plane local network.

Market Data Queues (MDQ)

Purpose: Buffer high-velocity market data stream for server-side processing with aggressive freshness controls

  • Queue Type: FIFO with TTL (5-second max message age)
  • Cleanup Strategy: Discarded when TTL expires
  • Message Format: Timestamped JSON preserving original Massive.com structure
  • Durability: Non-persistent messages (speed over reliability for real-time data)
  • Independence: Queues operate completely independently - one queue per subscription
  • Technology: RabbitMQ

The MDQ should not be accessible outside the data plane local network.

Market Data Processors (MDP)

The purpose of the MDP is to process raw real-time market data and delegate processing to data-specific handlers. This separation of concerns allows MDPs to handle any type of data and simplifies horizontal scaling. The MDP stores its processed results in the Market Data Cache (MDC).

The MDP:

  • Hydrates the in-memory cache on MDC
  • Processes market data
  • Publishes messages to pub/sub channels
  • Maintains cache entries in MDC

MDPs runs as containers and scale independently of other components. The MDPs should not be accessible outside the data plane local network.

Market Data Cache (MDC)

Purpose: In-memory data store for serialized processed market data.

  • Cache Type: In-memory persistent or with TTL
  • Queue Type: pub/sub
  • Technology: Redis

The MDC should not be accessible outside the data plane local network.

Widget Data Service (WDS)

Purpose:

  1. WebSocket interface provides access to processed market data for client-side code
  2. Is the network-layer boundary between clients and the data that is available on the data plane

WDS runs as a container and scales independently of other components. WDS is the only data plane component that should be exposed to client networks.

Service Control Plane (SCP)

Purpose:

  1. Authentication and authorization
  2. Serve static and dynamic content via py4web
  3. Serve SPA to authenticated clients
  4. Injects authentication token and WDS url into SPA environment for authenticated access to WDS
  5. Control plane for managing application components at runtime
  6. API for programmatic access to service controls and instrumentation.

The SCP requires access to the data plane network for API access to data plane components.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kuhl_haus_mdp-0.2.8.tar.gz (45.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

kuhl_haus_mdp-0.2.8-py3-none-any.whl (40.7 kB view details)

Uploaded Python 3

File details

Details for the file kuhl_haus_mdp-0.2.8.tar.gz.

File metadata

  • Download URL: kuhl_haus_mdp-0.2.8.tar.gz
  • Upload date:
  • Size: 45.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for kuhl_haus_mdp-0.2.8.tar.gz
Algorithm Hash digest
SHA256 c90ae7600c599cbbfd6e4fa497d96408793abe0c8bf2cba8193204527e43bf09
MD5 89f5028fda3ca4204afc354f8b0f3e94
BLAKE2b-256 6d1c7d8500e9ce5f7988880507914a85d2807295f766b904015f7890217de66c

See more details on using hashes here.

Provenance

The following attestation bundles were made for kuhl_haus_mdp-0.2.8.tar.gz:

Publisher: publish-to-pypi.yml on kuhl-haus/kuhl-haus-mdp

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file kuhl_haus_mdp-0.2.8-py3-none-any.whl.

File metadata

  • Download URL: kuhl_haus_mdp-0.2.8-py3-none-any.whl
  • Upload date:
  • Size: 40.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for kuhl_haus_mdp-0.2.8-py3-none-any.whl
Algorithm Hash digest
SHA256 1ed38a3662e96f65a085ca773bcfd495bfa68635fef9ab83ffd8e58ff99fecb2
MD5 b3f38ccbafd6b5835a1b6fde5e84a5ee
BLAKE2b-256 3e10b8201558107c17b50235f88a415985b4187b6db85fb5078ffba0d991e932

See more details on using hashes here.

Provenance

The following attestation bundles were made for kuhl_haus_mdp-0.2.8-py3-none-any.whl:

Publisher: publish-to-pypi.yml on kuhl-haus/kuhl-haus-mdp

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page