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Project template

Use case

Code snippet

Real-time feature computation

A real-time feature pipeline that computes essential features (open-high-low-close prices) that you can pass to an ML-powered trading system for training and inference. This template was contributed by Pau Labarta Bajo.

Use cases:

Feature Engineering

Created by:

Paulescu

A diagram of a real-time feature pipeline

Main project components

Trade producer

A source connector that reads Bitcoin trades from the Kraken Websocket API and writes them in a Kafka topic.

Trade to OHLC

A transformation that reads trading data from a Kafka topic, computes Open-High-Low-Close candles (OHLC) using Stateful Window Operators, and saves them in another Kafka topic.

OHLC to feature store

A sink connector that saves the computed OHLC data to Hopsworks—an external feature store.

Streamlit Dashboard: last 20-minutes

A dashboard that visualizes the last 20 minutes of the real-time OHLC data based on trading activity in Kraken (computed on 10-second windows)

Technologies used

Using this template

This project could be easily adapted for use cases such as:

More advanced trading analysis
Other window-based pipelines such as temperature analysis

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