Zhonghao Wang

347-***-**** ad3ztu@r.postjobfree.com www.linkedin.com/in/ZhonghaoWang

EDUCATION

Columbia University New York, NY

Master of Science in Operations Research GPA 3.83 Relevant Courses: Algorithmic Trading, Data Analytics (Python), Applied Analytics (SQL), Probability and Statistics, Stochastic Models, Machine Learning, Simulation, Business Analytics, Asset Allocation Dec 2023

Shanghai Jiao Tong University Shanghai, CN

Bachelor of Engineering in Industrial Engineering GPA 3.72 Bachelor of Economics in Finance GPA 3.88

Jun 2021

Jun 2021

WORK EXPERIENCE

Intrua Financial New York, NY

Data Science Intern Jun 2023 - Aug 2023

● Led the development of a comprehensive optimization model using Matlab to identify the mathematically optimal portfolio weight combination, based on mean-variance efficient allocation. Improved the average monthly return by 5% compared to benchmark groups in the back testing during 5-year (2018-2022) historical data.

● Collaborated with data engineering team to collect and preprocess stock price data, as well as ACWI & AGG benchmark data, sourced from Bloomberg database. Optimized data accuracy, integrity, and increased data reading speed by 10%.

● Analyzed the model’s robustness under various thematic tilts simulating the ESG investments in diverse investment environments. Demonstrated increases in monthly returns ranging from 1% to 10%, highlighting the model's strong adaptability, and attracted billion dollars in these strategies from potential investment advisors. Dorfman Value Investment Shanghai, CN

Statistical Analyst Intern Jun 2021 - Aug 2021

● Restructured and enhanced a trading framework using Pandas, NumPy, and Scikit-learn packages, transforming the trading problem into a stochastic control problem by applying Bellman’s principle of optimality. Reduced the model calculation time by over 10%, which significantly improved the efficiency of financial analysis and decision-making process.

● Adapted the Hamilton-Jacobi-Bellman equation to describe individuals’ optimal control problem, and the Fokker-Planck equation to describe the agents’ aggregate distribution dynamics, building a strong basis for the automatic trading framework.

● Delivered weekly presentations to the project director, managing director and other related teams on the latest updates of the framework's development, ensuring consistent alignment on projects' goals and milestones. RESEARCH EXPERIENCE

Recommendation System Research Based on Neural Collaborative Filtering New York, NY Columbia University Apr 2023 - May 2023

● Implemented the Neural Collaborative Filtering method, integrating matrix factorization and neural networks to enhance recommendation accuracy for implicit data within e-commerce user-item interaction datasets. Tested the model in the Amazon and Alibaba datasets and achieved a notable hit rate of 30%.

● Conducted research on three distinct data cleaning techniques and employed comprehensive model selection and hyperparameter Grid Search using Python, resulting in a 20% improvement in hit rate for implicit recommendations. Data Analysis on Relationship among New York Taxi, Uber Trip and Weather Data New York, NY Columbia University Dec 2022

● Collected and cleaned last five years’ yellow taxi trip data, Uber trip data, and weather data of New York State. Designed and implemented an exhaustive dimensional database system utilizing SQLAlchemy, optimizing data organization and querying, which facilitated efficient analysis.

● Established a versatile interactive interface, featuring seven distinct groups of diagrams and an integrated dashboard utilizing Looker, that effectively conveyed the dynamical relationships of trips and weather, presenting a holistic view of data. SSE 50ETF Option Trading Strategy Based on Genetic Algorithm Shanghai, CN Shanghai Jiao Tong University Mar 2021 - Jun 2021

● Utilized the GRACH model to predict future stock variance and applied the Black-Scholes equation to calculate option prices. Developed an automated option trading strategy by integrating these models as a robust foundation.

● Employed the genetic algorithm to fine-tune and optimize the trading strategy, with a primary focus on price-based signals. Achieved a Sharpe ratio of 0.57 and a maximum drawdown of 30% by using this strategy, following a rigorous two-year back- testing process.

SKILLS

Programming Languages: Python (Package: Pandas, SciPy, NumPy, TensorFlow, Matplotlib, …), SQL, R, C++, C# Software: BigQuery, Looker, Tableau, Matlab, Linux, Bloomberg Terminal, Jupyter Notebook Tools: Machine Learning, Data Visualization (Dashboard), Statistical Analysis, Optimization

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