Sirui Zhu
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Email
sirui.zhu@essex.ac.uk -
Location
Colchester Campus
Profile
- Finance, Python, Stock Trader, Fintech, Computational Finance, Bank
Biography
Sirui Zhu obtained the Masters in Economics specializing in Finance at the Albert-Ludwigs-Universität Freiburg in Germany, on the March 2018. Before graduation, he was a stock trader in Shanghai for a while, working for Shanghai Hunlicar Investment Management Co.,Ltd. His early research interests are focusing on figuring out individual financial or economic behavior and their unique thinking process, he tends to write down codes in order to simulate their cognitive process and running backtests for its estimations and constructing normal trading strategies, involving with algorithm building and data system storing. From 2019 July to 2022 December, he was hired working at Shenzhen in China Guangfa bank as an Account manager. In 2023, he did teach courses about banking knowledges and how to use python to analyse financial data in the college of Foshan Polytechnic, while as an Advisor he led the students won the Second Prize of the 4th Sichuan Student Fintech Modelling Competition in China. He now works part-time as a research assistant on an AKT project (Iceni Economic Benefits AKT), which is automating the production of Economic Benefits Infographics to aid transparency in the planning process and to communicate benefits to communities. He has started his PhD in Computational Finance at the University of Essex, UK since January 2024.
Qualifications
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Bachelor of Laws & Management (Double Degree) Guangdong University of Foreign Studies (2015)
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M.Sc.in Economics Albert-Ludwigs-Universität Freiburg (2018)
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Exchange Student Université de Genève (2017)
Research and professional activities
Research interests
Application and Study of Image Matching Strategies in Quantitative Stock Market Trading
From a behavioral point of view, under roughly similar circumstances, roughly the same volume, roughly the same price action, etc. Investors, will make the same decision to long or short a particular stock as they did at some point in history. Rational people, under similar circumstances and receiving similar external signals, should make similar decisions. The research methodology is to match and calculate the similarity of the candlestick images by different image recognition technologies.
A Study of Machine Learning in Law Firms -Machine learning analysis of structured data to predict case win rate and case time consuming
At this stage, law firms are still a textual field of work, and it seems to be a forbidden area when it comes to the application of data and the application of machine learning. We should try to incorporate machine learning techniques in law firms to analyse all kinds of case data and client data as well as lawyers' personal information data, and to predict the win rate and possible length of time spent on similar cases from historical big data, etc.