Python, C++, SQL, R, Matlab, Vba, Machine Learning, Linux, Aws, Git

Shiqi (Ciela) Ma

*******@***.*** +1-914-***-**** https://www.linkedin.com/in/shiqi-ciela-ma/

EDUCATION

New York University New York, NY

M.S. in Financial Engineering, GPA: 3.8/4.0 08/23 – 05/25 l Relevant Courses: Options Pricing & Stochastic Calculus, Machine Learning, Financial Computing (C++), NLP, Big Data, Algorithmic Trading, Fixed Income & Interest Rate Derivatives, Risk Management, Valuation. Central University of Finance and Economics Beijing, China B.S. in Statistics (major) & Finance (minor), GPA: 4.0/4.0 09/19 – 06/23 l Relevant Courses: Time Series Analysis, Econometrics, Data Mining, Quantitative Investing, Stochastic Process, Numerical Analysis, Probability Theory, Calculus, Linear Algebra, Data Structures & Algorithms, Python, C++/C. Awards & Honors: Overall Development Scholarship (Top 2%), 1st Prize of Mathematical Modeling Competition (Top 3%) PROFESSIONAL EXPERIENCE

Maoyuan Capital Haikou, China

Quantitative Research Intern 06/24 – 08/24

l Engineered an alpha factor mining framework, exploring factors such as trend funds, momentum, reversal, risk (VaR), and higher moments, generating 3,000+ factors with a 90% reduction in computation time through multiprocessing and Numba. l Built Lasso and LSTM (PyTorch) models to synthesize alpha factors, using the log-signature method to extract time series features from 5-minute data; achieved an information coefficient (IC) of 0.06 and was integrated into trading models. Haitong Securities Shanghai, China

Quantitative Research Intern 02/23 – 05/23

l Backtested a CTA strategy for U.S. natural gas futures using Carry and momentum signals, achieving Sharpe ratio of 1.67. l Enhanced the predictive power of overnight return factors by 60% through detailed data analysis and refined definitions. l Quantified futures’ slippage with tick data, analyzed trend strength with ER and ADX indicators, and evaluated pros and cons of portfolio weighting methods, providing actionable insights to traders for futures selection and portfolio optimization. Latham Street Capital Beijing, China

Quantitative Research Intern 05/22 – 11/22

l Built a market making model for crypto based on Avellaneda-Stoikov model, and optimized mid-price calculations, position balancing, and capital allocation, achieving 25%+ return in live trading, collaborating with developers for deployment. l Developed Random Forest and XGBoost models using LOB data to predict Bitcoin price direction, achieving 57% accuracy. l Backtested funding rate arbitrage strategy and conducted profitability and risk analysis across exchanges and currencies. Lakefront Asset Management Beijing, China

Quantitative Risk Analyst Intern 09/21 – 12/21

l Developed Barra risk model (CNE6) to calculate individual stock factor exposures for 40+ risk factors, leveraging SQL and Python to perform linear regression analysis and significance tests, enhancing portfolio risk identification. l Conducted factor-based performance attribution analysis, presenting findings to cross-functional team to monitor portfolio sensitivities, identify risk drivers, and optimize risk exposure to align with portfolio objectives. PROJECTS

Financial Text Analysis New York, NY

Advisor: Prof. Dan Rodriguez 09/24 – 10/24

l Collected FOMC documents via web scraping and quantified hawkish/dovish sentiment using Factor Similarity and RoBERTa. Discovered a link between sentiment and Treasury yields/gold prices via regression, enhancing macro forecasting. l Automated the download of 10-Q reports from EDGAR using Python and retrieved stock returns from WRDS via SQL. Calculated TF-IDF of negative words and analyzed the relationship between TF-IDF quintiles and average excess returns. Thematic Investing New York, NY (Remote)

Collaboration with Acadia Data 05/24 – 08/24

l Used Elasticsearch (Exact, Vector, BM25, ELSER) to quantify stock thematic exposure and applied Markowitz model to build AI and renewable energy portfolios, achieving returns over twice those of the S&P 500 and related ETFs. l Deployed AutoGen to streamline sector report generation, utilizing a multi-agent system to improve efficiency and accuracy. l Developed a Discord chatbot integrated with APIs, providing a user-friendly platform for stock investment decisions. Building an Options Pricing System New York, NY

Advisor: Prof. Song Tang 03/24 – 04/24

l Constructed CRR and Snell models in C++ for European and American options, supporting implied volatility, Greeks, and pricing of complex options like binary, butterfly, and straddle. Deployed on AWS with version control via Git. l Designed a Monte Carlo module with variance reduction for Asian options pricing, achieving 70% improvement in accuracy. SKILLS & OTHERS

Coding Languages: Python (Pandas, NumPy, SciPy, statsmodels), C++/C, SQL, R, MATLAB, VBA Skills & Tools: Machine Learning (scikit-learn, PyTorch), Linux, AWS, Git, Tableau, MS Office (Excel), Bloomberg Certifications: Akuna Capital Options 101, Deep Learning Specialization, Bloomberg Market Concepts Extracurricular Activities: Peking University Hedge Fund Association, Green Finance Research Club, 85+ volunteer hours.

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