• Visualized large structured datasets to drive analytics and automated generation of monthly MBS research reports by agency for clients using Python script with the loan level data generated by PostgreSQL queries from the database of Analyzers

• Developed and optimized web text parsing and data cleaning algorithms in Python Scrapy framework to obtain high quality data and manage relational database in Amazon Redshift

• Extracted and Cleaned more than 2,000,000 real estate properties purchase and assessment historical data from multiple government official websites’ data resource of, prepared for post real estate market analysis

• Involved in and studied about multiple MBS related projects from different industrial data resource including IMF Data, NSMO, fannie mae, freddie mac and ginnie mae

• Developed Python based quantitative models and gathered quantitative information from Bloomberg terminal for identifying and evaluating investment opportunities. Performed quantitative analysis of macroeconomic data and interpretation.

• Performed historical backtesting of alpha-generating investment strategies and asset allocation strategies, stress testing, scenario analysis, analysis of hedging strategies and portfolio construction models.

• Used regression analysis (Linear or Logistic) and machine learning algorithms (Neuron network) to calculate several effective factors for Portfolio Management decision-making used Python Tensorflow

• Tested data stationary, checked seasonality and calculated the annualized volatiles for commodity futures price over 20 years