Gaspar Zhang

Performed a deep analysis on a real dataset of NPinter and characterized the factors related to lncRNA sequences
Programmed and optimized algorithm named Linear neighborhood propagation in MATLAB
Researched on related algorithms and conducted benchmark comparison among those algorithms: RWR, LPBNI, CF
Applied cross-validation to assess the performance of Linear neighborhood propagation and achieved AUPR and AUC 0.42 and 0.91 respectively

Utilized SQL Server to collect the stock factors and de-noised the raw data using Python
Developed Random Forest to predict stock trend with accuracy of 64%, and performed Stepwise Regression to optimized trading strategy and gained annual return over 25%

Applied logistic regression classification & SVM to predict the ups and downs of 50ETF in MATLAB
Implemented Skew Volatility strategy to Soybean meal option in Python
Performed regression analysis on futures and options prices during a specified time period after market opening and before market closing, and conducted preliminary data processing using Excel pivot tables and functions in VBA
Utilized the Linear Neighborhood Propagation method to predict the ups and downs of 50ETF and transform this algorithm into trading strategy in MATLAB, back-tested strategy to evaluate their returns