Quantitative Analysis and Programming

Yang Zhang (Reinhard)

**** * ******* *** *******, IL 60615 312-***-**** **************@*****.*** EDUCATION

THE UNIVERSITY OF CHICAGO Chicago, IL

Master of Science in Financial Mathematics, GPA: 3.8/4.0 Sep. 2015- Dec. 2016 Coursework: Option Pricing, Portfolio Theory, Quant Trading Strategies, Time Series, Machine Learning, Python, C++ WUHAN UNIVERSITY Wuhan, China

B.A. in Finance & B.S. in Mathematics, GPA: 3.7/4.0 Sep. 2010- Jun. 2014 Coursework: Probability, Statistics, Stochastic Processes, Time Series, ODE, Econometrics, Economics, Machine Learning WORK EXPERIENCE

Auctos Capital Management Chicago, IL

Quantitative Research Intern Jan. 2017- Present

• Created Deep Learning and Regime Shift commodity trading strategy with Sharp ratio of 1.46 and 1.71

• Conducted feature selection methods such as Univariate statistical test, LASSO, recursive feature elimination and Tree method to select features from 2950 indicators

• Trained Feedforward Neural Network (MLP) in Maxout activation function and dropout regularization; using grid search to select the neurons in Python; got 59% and 57% for in and out of sample accuracy

• Built Hidden Markov Model (HMM) in Baum-Welch algorithm and Recurrent Neural Network (LSTM) Belvedere Trading, LLC Chicago, IL

Project Lab Researcher Sep. 2016- Dec. 2016

• Modeled the time-varying distribution of Eurodollar Futures in Normal, Lognormal and Mixed distribution

• Calibrated the parameters for Gaussian Mixture Model using EM algorithm and wrote the EM Python package

• Fitted return distribution with EM package and selected model based on AIC, BIC and KS test in Python

• Clustered interest rate regimes by DBSCAN algorithm and fitted mixture distribution term structure Aegea Capital Management Chicago, IL

Quantitative Research Intern Jun. 2016- Aug. 2016

• Predicted VIX open price with LASSO, Ridge and PC regression minimizing MSE in Cross-Validation

• Selected features to predict directional move of the VIX settlement price from the predicted price in LASSO

• Classified the directional move with Logistic regression, Decision Tree, SVM, RandomForest, Adaboost, GradientBoost and Multilayer Perceptron(MLP), achieving 76.54% accuracy with Cross-Validation in Python

• Analyzed the market structure of VIX derivative settlement; predicted implied volatility for SPX option ACADEMIC PROJECTS

Pair Trading Strategy in ETFS (R) Mar. 2016- May. 2016

• Conducted K-means clustering to extract the ETFs market structure and group correlated ETFs

• Selected pair trading ETFs with Engle–Granger two-step method based on co-integration relationship

• Analyzed structural relationship, and constructed time-varying hedging ratio in ETFs with Kalman Filter

• Calibrated the boundaries for long-short and loss stop in pairs; backtested pair trading in an ETF basket Option Pricing (Matlab) Jan. 2016- Mar. 2016

• Priced both American and European options with 2 methodologies, using Finited Difference Methods: Explict, Implict and Crank-Nicolson, and also Monte Carlo simulation with antithetic variable to reduce variance Copula-Based Currency Portfolio Risk Analysis (R) Nov. 2015- Jan. 2016

• Modeled marginal and joint distributions of currency returns in Mixture Model and Copula with BIC and KS test

• Conducted Monte Carlo method to simulate the joint distribution and calculated the VaR and CVaR of the portfolio

• Constructed portfolio based on min VaR and CVaR, and conducted the sensitivity analysis for the portfolio weights ADDITIONAL INFORMATION

Computer: Proficient in Python, MATLAB, C++, R, SAS, VBA, SQL, Bloomberg, SPSS

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