Support Engineer

• Jan 2021 - Present

Quantitative Analyst & System Developer

• Jan 2020 - Jan 2021

• Designed Python scripts to download all the U.S based companies financial data from Bloomberg and FactSet terminal, sort out and dump into SQL database, achieving an efficiency raise by 45%. • Accomplished relationship researches between Hard-To-Borrow rate and future short returns in past decade, created a stock picking strategy by using H2b rate as a significant alpha factor, realizing a 20% growth of seasonal profits. • Completed Python scripts to capture all new-listed companies… Show more • Designed Python scripts to download all the U.S based companies financial data from Bloomberg and FactSet terminal, sort out and dump into SQL database, achieving an efficiency raise by 45%. • Accomplished relationship researches between Hard-To-Borrow rate and future short returns in past decade, created a stock picking strategy by using H2b rate as a significant alpha factor, realizing a 20% growth of seasonal profits. • Completed Python scripts to capture all new-listed companies and information monthly, made analysis on relationship between future profits and significant information (industry, bookrunners, deal advisors, market value), carried out an evaluation of new listed companies in 3 minutes. Show less • Designed Python scripts to download all the U.S based companies financial data from Bloomberg and FactSet terminal, sort out and dump into SQL database, achieving an efficiency raise by 45%. • Accomplished relationship researches between Hard-To-Borrow rate and future short returns in past decade, created a stock picking strategy by using H2b rate as a significant alpha factor, realizing a 20% growth of seasonal profits. • Completed Python scripts to capture all new-listed companies… Show more • Designed Python scripts to download all the U.S based companies financial data from Bloomberg and FactSet terminal, sort out and dump into SQL database, achieving an efficiency raise by 45%. • Accomplished relationship researches between Hard-To-Borrow rate and future short returns in past decade, created a stock picking strategy by using H2b rate as a significant alpha factor, realizing a 20% growth of seasonal profits. • Completed Python scripts to capture all new-listed companies and information monthly, made analysis on relationship between future profits and significant information (industry, bookrunners, deal advisors, market value), carried out an evaluation of new listed companies in 3 minutes. Show less

Quantitative Researcher

• Jul 2017 - Jun 2018

• Completed macro economics research on the national financial policy to evaluate stock investment risk via studying industry trend, collecting financial data, and performing statistic analysis. • Collaborated with cross-functional teams in combining Moving Average Convergence and Divergence indicators of different windows to effectively select stocks in SAS and R, increasing trading profits by 50%. • Tailored portfolio allocation by building predictive modeling (Regression, GBM, Random… Show more • Completed macro economics research on the national financial policy to evaluate stock investment risk via studying industry trend, collecting financial data, and performing statistic analysis. • Collaborated with cross-functional teams in combining Moving Average Convergence and Divergence indicators of different windows to effectively select stocks in SAS and R, increasing trading profits by 50%. • Tailored portfolio allocation by building predictive modeling (Regression, GBM, Random Forest) in Python and R, increasing trading profits by 20%. • Designed algorithm in C ++ to improve financial derivatives pricing efficiency and accuracy. • Predicted Bitcoin price change with articles, using XGBoost with corpora-based statistical NLP and sentiment analysis, achieving the accuracy of 0.83. Show less • Completed macro economics research on the national financial policy to evaluate stock investment risk via studying industry trend, collecting financial data, and performing statistic analysis. • Collaborated with cross-functional teams in combining Moving Average Convergence and Divergence indicators of different windows to effectively select stocks in SAS and R, increasing trading profits by 50%. • Tailored portfolio allocation by building predictive modeling (Regression, GBM, Random… Show more • Completed macro economics research on the national financial policy to evaluate stock investment risk via studying industry trend, collecting financial data, and performing statistic analysis. • Collaborated with cross-functional teams in combining Moving Average Convergence and Divergence indicators of different windows to effectively select stocks in SAS and R, increasing trading profits by 50%. • Tailored portfolio allocation by building predictive modeling (Regression, GBM, Random Forest) in Python and R, increasing trading profits by 20%. • Designed algorithm in C ++ to improve financial derivatives pricing efficiency and accuracy. • Predicted Bitcoin price change with articles, using XGBoost with corpora-based statistical NLP and sentiment analysis, achieving the accuracy of 0.83. Show less

Risk Management Researcher

• Jun 2017 - Jul 2017

• Completed in-depth quantitative analysis utilizing Matlab and Python to notify fund managers for identified risks. • Led a team to alert fund managers timely by improving algorithm to calculate percentage of stock holdings, reducing cost by 30%. • Completed in-depth quantitative analysis utilizing Matlab and Python to notify fund managers for identified risks. • Led a team to alert fund managers timely by improving algorithm to calculate percentage of stock holdings, reducing cost by 30%.

Analyst

• Jan 2017 - Jun 2017

• Performed statistical hypothesis tests and identified the relationship between the time customers spent in the exchange and their investment size. • Developed marketing strategy targeting prospective clients, decreasing human capital expenditure by 65% while keeping investment commitments from current clients. • Performed statistical hypothesis tests and identified the relationship between the time customers spent in the exchange and their investment size. • Developed marketing strategy targeting prospective clients, decreasing human capital expenditure by 65% while keeping investment commitments from current clients.