About

Sunil Guglani is a skilled quantitative researcher with over 20 years of experience in developing trading strategies and predictive models in the capital market segment. He has a strong educational background with a Bachelor of Science in Mathematics Honors and has completed executive programs in Algorithmic Trading and Quantitative Finance. Sunil has developed several models in NIFTY, BANKNIFTY, NASDAQ, and S&P500 that can predict the HIGH/LOW/CLOSE of the day within the range of +-0.2 standard deviation. He has successfully completed projects on Artificial Intelligence, Deep learning, Machine Learning, Neural Networks, Google Cloud AI, Big Query, Quantitative Analysis, Quantitative Finance, Quantitative Trading, and Data analysis using Python, R, Excel, and Tableau. Sunil is proficient in exploring new investment ideas and innovative concepts, and he has developed multiple strategies using fundamentals and quantitative analysis together. He has also developed multiple python libraries for Backtesting and Technical analysis and has backtested and developed 100s of hypothesis and trading strategies in various instruments Equity/Commodities/FX/Cryptos. Sunil has worked with companies like Share India Securities Ltd. and Quantinsti, where he held senior positions in Quantitative Research. He is currently the Director of Quantitative Research at a new venture in Quantitative Research. Sunil's technical skills include expertise in Research Scientist, Python, Sentiment Analysis, AI, Deep Learning, ML, Neural Networks, and Software Engineering. He has also used various methodologies like VWAP, TWAP, ATR, and Volatility for position distribution and sizing and has performed quantitative analysis for high-frequency trading using order book (bid, ask, price volume, oi) data, and sentiment analysis data. He has used performance measurement metrics like Sharpe ratio, Sortino, Kurtosis, etc. In summary, Sunil is a highly skilled Quantitative Researcher with a strong educational background and over 20 years of experience in developing trading strategies and predictive models in the capital market segment. He is proficient in exploring new investment ideas and innovative concepts and has a wide range of technical skills in Research Scientist, Python, Sentiment Analysis, AI, Deep Learning, ML, Neural Networks, and Software Engineering.