Griffin, Gabriel Kevin

Norén, Daniel Vestmar

Ørum, Patrick Thomas

Statistical arbitrage is a trading strategy that aims to exploit price discrepancies between financial instruments. It can be a risky endeavor but if done correctly, has the potential to generate consistent profits with relatively low risk.Machine learning and technical indicators are two key tools that work well in supporting statistical arbitrage. Machine learning algorithms are valuable in their capacity to analyze large amounts of data and identify patterns and relationships often invisible to the human eye. When combined with mathematical calculations based on price and volume of a tradeable asset, it could provide a powerful means of identifying and exploiting statistical arbitrage opportunities.Ideally, through triggers, events, and some clever architecture, a pipeline involving sequential analysis of historical market data could run and make trades continuously. An automated cloud environment would bring that idea from formulation to reality, all the while handling infrastructure and scaling without further intervention.These points allow us to formulate our research question: Can machine learning and technical indicators be united in an automated cloud environment to perform risk controlled statistical arbitrage?In this paper, we will divide our research question into sub questions, using research and applying it to concrete examples. Once we have expanded our sub questions, they will be assembled to form a final answer to our research question.The result of the solution shows significant potential but requires further studies and work if it should have any hopes of real-world adaptation. The results show consecutively positive ROIs across multiple weeks, but this also requires additional work and testing before any adaptation can take place.<br/

Gaining an edge in statistical arbitrage by applying data science and machine learning

Abstract

Similar works

Full text

Available Versions

Roskilde Universitet