Oxford Competes with Cambridge to Win Algorithmic Crypto Trading Contest

Multi-Asset Multi-Asset Composed of varying asset classes, multi-asset is a blanket designation combining different classes such bonds, equities, cash equivalents, fixed income, and alternative investments.When compared to traditional balanced funds, multi-asset solutions differ because they target specific investment outcomes. This includes outcomes such as return above inflation as opposed to gauging performance against standardized benchmarks.Given the composition of multi-asset classes, they need to be dynamically Composed of varying asset classes, multi-asset is a blanket designation combining different classes such bonds, equities, cash equivalents, fixed income, and alternative investments.When compared to traditional balanced funds, multi-asset solutions differ because they target specific investment outcomes. This includes outcomes such as return above inflation as opposed to gauging performance against standardized benchmarks.Given the composition of multi-asset classes, they need to be dynamically Read this Term Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Read this Term provider, APEX: E3 announced that it has arranged an algorithmic crypto trading competition between students of the University of Oxford and the University of Cambridge. The winning team will keep their seed capital and returns.

According to the official announcement, APEX: E3 launched the contest on 16 November and invited 15 teams from the Mathematics and Computer Science Departments of both universities to participate in a month-long algorithmic crypto trading competition. The teams will use API provided by APEX: E3 to construct algorithms and execute trades on CoinbasePro and FTX.

APEX: E3 sponsored the competition along with other partners to provide API, technical support, trading mentorship, and seed capital to the students. The content will conclude in December and the students will be evaluated by a panel of top industry leaders.

Commenting on the content, Usman Khan, CEO at APEX: E3, said: “We are excited by the opportunity to bring real-world learning opportunities, extra-curricular activities, and financial market analysis to interested students under lockdown. We have been really energised by the response from Oxford and Cambridge students for this competition and look forward to expanding the scope and number of universities next year.”

Performance Evaluation

University teams will be evaluated on the basis of their return on investment, algorithmic design, and crypto trading strategies. The London-based analytics company mentioned that teams can develop strategies based on arbitrage, machine learning-based predictive analysis, and trend investing. “Student ideas for algorithms include arbitraging, neural networks trained on historic data to predict market movements, momentum-driven strategies, ARMA to model the time series for future price trend predictions, machine-learning and NLP aided algorithms, whale order trading, trend investing based on time sequence forecasting using deep learning models and high-frequency momentum trading on volatile assets,” the official announcement states.

Professor Mike Wooldridge, Head of Department of Computer Science at the University of Oxford said that it is a unique opportunity for students as well as academic staff to explore new opportunities for research partnerships.