- Machine Learning for Algorithmic Trading
- Stefan Jansen
- 308字
- 2021-06-11 18:47:23
About the reviewers
Prem Jebaseelan has about 20 years of experience in handling different financial data and enjoys the challenge of organizing, storing, retrieving, and analyzing large volumes of data. Prem has designed and implemented several enterprise-level solutions for front office trading strategies, middle office, and back office applications for funds, and has good experience in applying machine learning and AI-based solutions. Prem has an engineering degree.
Prem is currently the co-founder and CEO of Zentropy Technologies, a fintech company that specializes in creating machine learning based solutions in the financial domain. Prior to this, Prem worked in one of the leading hedge funds as a technology solutions provider.
I would like to thank all my previous employers who have helped me in developing real-world solutions that bring technology and finance together. I would specifically like to thank Dr Yves Hilpisch for our work together in the application of machine learning to real-world trading strategies.
Ramanathan Ramakrishnamoorthy is one of the co founders and directors at Zentropy Technologies. Ramanathan started his professional career with a leading hedge fund and in his latest position, he worked as a project manager responsible for building tools and technologies required by the middle and back office. At Zentropy, Ramanathan is primarily responsible for better understanding project requirements and converting them to technical specs. alongside executing them. Having a keen eye for subtle data patterns, Ramanathan also has a good understanding of the machine learning and data science domain, particularly with expertise in the time series analysis domain. Ramanathan's experience has primarily been around building trading systems, quant warehouses, and backtesting engines for capital markets.
Ramanathan is also an active core group member in the Hyderabad Python group. He leads some of the most important activities of the community, like organizing conferences, monthly meetups, and conducting Python sessions at colleges.
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