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 earned a Master's degree in Economics, specializing in Finance, from Albert-Ludwigs-Universität Freiburg, Germany, in March 2018. Prior to his graduation, he worked as a stock trader in Shanghai at Shanghai Hunlicar Investment Management Co., Ltd. His initial research interests were centered around understanding individual financial and economic behaviors and their unique thought processes. He often wrote code to simulate these cognitive processes, running backtests on his estimates, and developing standard trading strategies that included algorithm creation and data system management. From July 2019 to December 2022, he worked as an Account Manager at China Guangfa Bank in Shenzhen, China. In 2023, he taught courses on banking knowledge and the use of Python for financial data analysis at Foshan Polytechnic College. As an Advisor, he also led students to win the Second Prize in the 4th Sichuan Student Fintech Modelling Competition. He is currently employed part-time as a research assistant on the AKT project (Iceni Economic Benefits AKT), which focuses on automating the production of Economic Benefits Infographics to enhance transparency in the planning process and communicate benefits to communities. Since January 2024, he has been pursuing a PhD in Computational Finance at the University of Essex, UK.
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
Boosting Financial Image Recognition Performance through Reverse Image Augmentation -- accepted at ECAI Workshop on AI in Finance (AIFin’24)
Merging machine learning with stock price prediction leverages two primary data types: time series and images. Time series data include Open, High, Low, Close, and Volume (OHLCV) points along with technical indicators, while images created from OHLCV data are used in Convolutional Neural Networks to detect trends in candlestick charts. This paper introduces a novel approach by inverting candlestick images to expand training datasets, reducing model loss and improving the classification of future
A Study of Machine Learning in Law Firms -Machine learning analysis of structured data to predict case win rate and case time consuming
Currently, law firms primarily operate in a text-based environment, and the application of data analysis and machine learning appears to be largely unexplored. However, there is significant potential for incorporating machine learning techniques to analyze various types of data within law firms, such as case files, client information, and lawyers' personal data. By leveraging historical big data, these techniques could predict outcomes such as win rates and the likely duration of similar cases.
Contact
Location:
Colchester Campus
Working pattern:
Friday 10am-11am/ Location: 5B.540
