Esse Quam Videri
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One day team hackathon on analysing fraudulent credit card transactions data by building a prediction model using XGBoost, SMOTE and threshold optimisation
Designed a trading strategy on Uniswap by implementing trading indicators like EMA, MACD and RSI on Python and the company’s Dojo platform
Intensive 10-week course about bond markets, equity markets, cost of capital, capital structure and risk management
Developed and tested a trading algorithm while demonstrating my team working and analytical skills. Learnt about Financial Markets, Market Makers, Market Strategies and Systems Training which gave me a great insight into the trading and investment world
Participated in a 7-part lecture series on finance and investment – topics include equities, derivatives, fixed income
Develop fundamental knowledge in statistical modelling, numerical and Bootstrap methods as well as learn core theoretical concepts like Lebesgue integration and likelihood theory. Experienced with Hadoop, PySpark, TensorFlow through Keras API, PyTorch, Deep and Machine learning models.
Learnt about MCMC and SMC methods, SVMs, Neural Networks and CNNs, Dimensionality Reduction methods and applied them to real-world datasets using Python. Linear Least Squares, Gradient and Gauss-Newton Methods, Gradient Projection Method and KKT conditions. Studied micro- and macro-economic concepts like supply and demand functions, profit maximisation and cost minimisation, as well as pricing and hedging derivatives using Martingales and the Black and Scholes model.
London , GB
P: +44(0)7756549358 | E: martstef@hotmail.com
Linkedin Url: https://www.linkedin.com/in/martin-stefanov-7072611a0/
Develop fundamental knowledge in statistical modelling, numerical and Bootstrap methods as well as learn core theoretical concepts like Lebesgue integration and likelihood theory. Experienced with Hadoop, PySpark, TensorFlow through Keras API, PyTorch, Deep and Machine learning models.
Learnt about MCMC and SMC methods, SVMs, Neural Networks and CNNs, Dimensionality Reduction methods and applied them to real-world datasets using Python. Linear Least Squares, Gradient and Gauss-Newton Methods, Gradient Projection Method and KKT conditions. Studied micro- and macro-economic concepts like supply and demand functions, profit maximisation and cost minimisation, as well as pricing and hedging derivatives using Martingales and the Black and Scholes model.
Winner of Data Science Challenge[Imperial College London]
One day team hackathon on analysing fraudulent credit card transactions data by building a prediction model using XGBoost, SMOTE and threshold optimisation
Winner of DeFi Trading Competition, Encode London Hackathon[Compas Labs]
Designed a trading strategy on Uniswap by implementing trading indicators like EMA, MACD and RSI on Python and the company’s Dojo platform
Corporate Finance[Imperial Business School]
Intensive 10-week course about bond markets, equity markets, cost of capital, capital structure and risk management
Insight days[Optiver]
Developed and tested a trading algorithm while demonstrating my team working and analytical skills. Learnt about Financial Markets, Market Makers, Market Strategies and Systems Training which gave me a great insight into the trading and investment world
Securities Educational Certificate[Investment Society]
Participated in a 7-part lecture series on finance and investment – topics include equities, derivatives, fixed income
Introduction to UK Financial Regulation & Professional Integrity – CIFA Management – Level 4
Learning about the roles of the FCA and PRA, legal concepts and professional ethics (CIFA ID: 20350)
Math Competitions
Won international competitions such as WMTC, IMC, JBMO and ITMO; gained problem-solving and critical thinking skills and got used to being resilient and handling pressure
