Python Data

KEILUNG CHOY

+1-646-***-**** *************@*****.*** 212 W 109th St, New York, NY 10025

EDUCATION

Columbia University New York, NY

Master of Arts (GPA: 3.90/4.00), Mathematics of Finance Sep 2018-Feb 2020

• Coursework: Numerical Methods in Finance, Non-Linear Option Pricing, Modeling and Trading Derivatives

• Implemented Monte Carlo Methods and Longstaff-Schwartz algorithm to price Asian and American put options and used various regression methods (including Kernel Regression and Local Linear Regression) to calculate conditional expectation of payoffs

• Introduced Particle Algorithm and Cubic Splines to calibrate stochastic local volatility model based on market volatility smiles

• Used ARMA model to predict monthly log returns of S&P 500 index and identified outliers in data by Bayesian inference

• Instructed students to realize models including Neville's algorithm and Merrill Lynch Exponential Spline in Excel VBA Peking University Beijing, China

Bachelor of Economics (GPA:3.70/4.00), Double Major in Mathematics and Applied Mathematics Sep 2014-Jul 2018

• Coursework: Statistics, Data Structure and Algorithm, Fixed-income Securities, Stochastic Analysis and its Applications PROFESSIONAL EXPERIENCE

BAM.Money, Inc. New York, NY

Data Scientist May 2019-Present

• Created code in Python to formulate trading strategies based on random forest model and supervised their performance on U.S. equities, calculated stock weights based on Black-Litterman Model to optimize portfolios and evaluated their statistical properties

• Constructed a system to collect daily S&P 500 sector indices and assets’ credit ratings automatically and performed linear regression between assets’ historical performance and these factors to generate signals indicating assets’ potential market value

• Transformed sentences in over 2000 articles related to bond market into vectors using Word2vec and trained RNN to conduct semantic analysis on sentences and observed daily changes of investors’ attitude towards market

• Refined an Optical Character Recognition tool by training Convolutional Neural Networks to extract text from images Derivatives China Beijing, China

Intern, Data Scientist Sep 2017-Dec 2017

• Initiated option pricing system using Monte Carlo simulation and promoted its performance by incorporating uncertain volatility model and interpolating volatility surface; validated trading strategies on options based on system

• Established models including Elastic Net, SVM and Random Forest to predict intra-day volatility in FX market

• Wrote Python and MySQL scripts for database maintenance and manipulated data to fit specific requirements of models GuoDu Securities Beijing, China

Intern, Data Scientist Jun 2017-Aug 2017

• Developed a multi-factor model using factors including industries’ dividend yield ratio to choose stocks and charted efficient frontier using historical volatility and expected return to adjust stock weights in portfolios

• Imported stock data from Bloomberg to produce vectors composed of binary variables; implemented Naïve Bayesian model on vectors to devise investment strategies on China equities and evaluated strategies based on Sharpe ratio

• Built Log Periodic Power Laws model in Python to simulate stock price movements and decided timing of trades based on simulation; applied Genetic Algorithm to adjust parameters and increase model accuracy CITIC Securities Beijing, China

Intern, Data Scientist Apr 2017-Jun 2017

• Designed Hull & White Model to predict changes in interest rates and priced vanilla options based on model prediction

• Wrote MySQL scripts to extract location information from 10K+ customers’ credit card transaction history and applied K-Means Clustering Algorithm in Python to analyze customers’ preferences and purchasing habits

• Established LDA model to group users based on users’ tweets and recorded keywords for each users’ group, selected groups relevant to financial services industry by filtering keywords and matched assets to these groups SKILLS & INTERESTS

• Skills: Programming (Python, C++/C, MATLAB, STATA, MySQL, R, Scala), Machine Learning (Neural Networks, CART, Adaptive Boosting, Clustering, SVM, PCA), Others (TensorFlow, AWS, Tableau, Jupyter Notebook), CFA Level I Candidate

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