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Projects/Research

Table of Contents

The following provides an overview of projects and research that I have completed either as part of my graduate program or indenpendently.

Final Project From FINM 33150 - Quantitative Trading Strategies #

The final group project consisted of developing a trading strategy incorporating the following:

  • Creating criteria and evaluating potential trades
  • Establishing position sizes, entry, and exit rules
  • Minimum of 5 different assets (NOT asset classes)
  • Use of leverage
  • Produce a “paper” in Jupyter Notebook, HTML
  • Produce a PDF pitch book

From the abstract:

The final project seeks to develop a systematic trading/investment strategy that takes advantage of the time-tested market ideas that the market tends to increase in price/value over time, positive earnings growth tends to increase company valuation, companies with positive earnings growth tend to exhibit upward price momentum. When companies have lower earnings, they tend to have lower valuations which provides an opportunity for the value factor to take effect.

Final Project From FINM 35900 - Macro-Finance #

The final group project consisted of selecting a fund or company and improving the fund with respect to macro/multi-asset ideas. The deliverables included:

From the abstract:

This proposal examines the potential benefits of incorporating Bitcoin into the asset allocation strategy of the Illinois State Board of Investment (ISBI). With over $25 billion in assets under management and a significant responsibility for managing pension plans for State of Illinois employees, ISBI’s investment policy warrants periodic review and adjustment to optimize returns and manage risk. This study suggests initializing a 1% allocation of the fund’s assets to Bitcoin as part of a diversified portfolio, alongside current assets.

Final Project From FINM 35910 - Applied Algorithmic Trading #

The final group project was to “develop and present a fictional fund focusing on alternative algorithmic trading strategies.” The strategy was to provide “uncorrelated return relative to the S&P 500 Index through robust strategy creation, risk management, and performance metrics.”

From the fund prospectus:

Part-Time Trading LLC is an investment firm focusing on developing machine-learning models to actively predict prices within cryptocurrency markets. Our fundʼs objective is to extract predictive features from crypto market data and utilize these features to develop best in class predictive machine learning models. Currently, we are focusing our trading efforts on trading Bitcoin and Ethereum on the crypto exchange Kraken. As we continue to advance our proprietary research and models, we plan to expand into other cryptocurrencies and exchanges.

Our trading system is designed to deliver consistent alpha by exploiting inefficiencies and opportunities in the highly volatile and liquid crypto markets. Our objective is to maximize returns while minimizing risk through data-driven decision making and robust predictive analytics.