Python Data

Qi Xue

949-***-**** ** Summer St, Malden, ***** ******@********.***

EDUCATION

Columbia University New York, NY

M.S. Operations Research Dec 2018

Coursework: Data Analytics, Optimization, Asset Allocation, Intro to FE, Machine Learning, Simulation, Deep Learning, Application for FE, Derivatives Marketing & Structuring, Risk Management University of California, Irvine Irvine, CA

B.S. Mathematics Specialized in Applied and Computational Math, Cum Laude Jun 2017 B.A. Quantitative Economics, Cum Laude

Coursework: Numerical Analysis, PDE, Probability, Econometrics, Mathematical Finance QUANT PROGRAMMING SKILLS

• Using python to build a back-testing platform to test strategies with varies frequencies

• Using python to build a in house library which capable of utilizing Bloomberg API to get data automatically

• Perform statistical analysis using R and Numerical analysis with MATLAB

• Various Option pricings using Monte Carlo, Finite difference methods under C++ framework and python frameworks

• Additional skills: SQL, Tableau, Julia

EXPERIENCE

3IC Research LLC Boston, MA

Quantitative Analyst Feb 2019 – Present

• Execute, monitor, evaluate of currently running strategies on daily basis using a combination of python, SQL, and tableau.

• Develop analytics and visualization tools for the quant team. Enhance the efficiency of the in house back testing platform.

• Developing a trading strategy using HMM clustering on the market Volatility regime and trade on SPX, VIX ETFs and equities.

• Developing a systematic stock selection framework based on fundamentals and in-house technical indicators.

• Trade idea generation on a weekly basis related with rationale based on in house research, market information or Bloomberg. Capstone Investment Advisors, LLC New York, NY

Summer Intern, Quantitative Research Jun 2018 - Aug 2018

• Implemented and maintained scalable python code for daily automated data update, technical indicators generations

• Implemented automated python back-testing framework focusing on testing various trading strategies and visualization of the result.

• Created prediction models using a Hidden Markov Model enabled compatibility of multi-observation datasets with various emission distributions. Doubled the speed of the model processing time

• Back tested more than 150 assets with over 30 different strategies using Bloomberg daily and intraday data. Stored and visualized back testing results using tableau and SQL. Analyzed results on 30 different strategies and reported to 3 trading desks every week PROJECT

Deep Learning Project Sep 2018 – Dec 2018

• Implemented a Resnet152 model to diagnose patient’s tendency of getting Alzheimer's Disease. Utilized the supercomputer provided by Columbia Medical Center to run the model. Cleaned and constructed a data set with over 3500 MRI images for training purpose. Developed pipeline for daily usage. Researched different methods to increase the accuracy. Resulted in a final accuracy of 57%-61%.

ACTIVITIES

Sports: Running, skiing, swimming, cycling.

Others: Texas hold’em

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