View Proposal

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Proposer

Hans Wolfgang Loidl

Title

Efficient stream processing and machine learning for stock market data (industry project)

Goal

Build an stream processing infrastructure for stock market data

Description

High volumes of data that are generated continuously are a challenge for many application domains. Efficient implementation of a streaming pipeline is crucial to make processing feasible, and opens the opportunity for applying machine learning techniques on the data stream. The goal in this project is to evaluate and extend an existing system for high-performance stream processing, and to then use simple machine learning techniques on the data stream. The main platform is a recently developed, open source library [1], [2]. In the first step a systematic evaluation of performance and throughput of this library, in comparison with alternatives such as Apache-Kafka [3] should be performed. Possible extensions and enhancements to the performance of the library should be considered. In the second step, simple machine learning techniques should be employed to learn characteristics about the data stream. As underlying data streams, publicly available market data should be used [4], [5], [6], though commercial data integrations could also be optimised [7], [8]. If you are interested in low latency and high throughput data pipelines and want to gain experience with advanced optimization techniques, this might be the project for you! [1] https://github.com/invesdwin/invesdwin-context-integration#synchronous-channels [2] https://github.com/invesdwin/invesdwin-context-persistence#timeseries-module [3] https://kafka.apache.org/ [4] https://github.com/fxcm/ForexConnectAPI [5] https://www.alphavantage.co/documentation/ [6] https://www.dukascopy.com/wiki/en/development/strategy-api/historical-data [7] http://www.iqfeed.net/daytradersetups/index.cfm?displayaction=developer&section=main [8] https://api.tradestation.com/docs/fundamentals/http-streaming

Resources

departmental Linux machines

Background

strong programming background; good Linux systems knowledge

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

1

Supervisor

Hans Wolfgang Loidl

Keywords

fintech, industry project

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Bachelor of Science in Information Systems
Bachelor of Science in Software Development for Business (GA)
Master of Engineering in Software Engineering
Master of Design in Games Design and Development
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Business Information Management
Master of Science in Computer Science for Cyber Security
Master of Science in Computer Systems Management
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Human Robot Interaction
Master of Science in Network Security
Master of Science in Robotics
Master of Science in Software Engineering
Bachelor of Science in Computing Science