Financial Engineering

Alejandro Ruiz

*****@*******.*** phone: +52-55-600*-****

EDUCATION

Cornell University, College of Engineering, New York, NY Master of Engineering in Financial Engineering (Financial Data Science Certificate) December 2020 Instituto Tecnologico Autonomo de Mexico (ITAM), Mexico City, Mexico Bachelor of Science in Industrial Engineering May 2018 Coursework: Financial Engineering with Stochastic Calculus, Fixed-Income Securities, Monte Carlo Simulation, Big Data Technologies, Optimization Modeling for Financial Engineering, Derivatives, Portfolio Management, Machine Learning SKILLS

Technical: Python(expert), R(proficient), SQL(fluent), C++(prior experience), Go(prior experience) EXPERIENCE

Quantitative Summer Analyst, Citi, New York, NY July – September 2020

• Slashed by 80% the time needed to generate test cases by designing an application with Python and Microsoft Excel that automates the generation of spoofing sequences, modifying notional amount, price and cancellation time, reducing the bottleneck and improving the financial surveillance model Debt Capital Markets Analyst, Actinver Investment Banking, Mexico City, Mexico August 2016 - December 2018

• Analyzed competitive market dynamics and trends on the company’s weekly analytical report in order to find new business opportunities and product development leading to an increase of $8 million USD of the original budget

• Developed a VBA application to automatize the fulfilling of Word documents needed for the issuance of short-term bonds. Diminishing the time this process took by 80%

• Participated in the origination and execution of debt transactions of more than $2.5B USD, including corporate bonds and asset backed securities

PROJECTS/COMPETITIONS

How to Catch People who Cheat the Stock Market Project, Project Sponsor: Citi, Cornell University, New York, NY September – December 2020

• Analyze the behavior of simulated market orders and identify patterns of suspicious trading activity (i.e. spoofing)

• Design algorithms that heuristically and systematically identifies spoofing sequences, with accuracy over 90%

• Investigate the accuracy of these algorithms into CME and Citi real one-day data Trading Strategy with Satellite Imagery Project, Cornell University, New York, NY October 2020

• Analyzed NYC Metro area satellite imagery, two weeks sparse

• Applied a Random Forest classifier to the images and extracted a trading signal, having S&P500 as a benchmark

• Built an algorithmic trading strategy based on this signal, obtaining a 30% annualized return. Citadel Data Open, Citadel, New York, NY September 2020

• Performed an EDA and Feature Selection on different movie datasets to determine interesting and meaningful implications about consumer preferences

• Computed several ML models (Linear Regression, SVM, Random Forest, XGBoost, Dense Neural Network) to predict a movie Box Office based on the selected features with 70% accuracy Optimization Statistical Arbitrage with RNN Project, Cornell University, New York January – May 2020

• Predicted the spread between stock prices by training a Recurrent Neural Network model with Keras in Python

• Implemented a Bayesian Optimization and a Spectral Analysis algorithm into a pairs-trading portfolio

• Optimized the trading strategy, for 5 financial sectors, setting the buy/sell signal and stop-loss thresholds obtaining a profit of 18%

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