Fall 2023 Recap

Dear SIF Community,

The fall semester has been very productive and impactful for the SIF Quant Team. We have many exciting updates to share with you regarding recruitment, education, and ongoing projects.

Recruitment and Alpha Competition

Throughout September and October, we carried through a successful recruitment campaign. An Instagram account was created this semester (@sif_umd) to facilitate publicity, and over 200 students collectively participated in the two interest meetings held in September.

We received almost 90 competitive applications from UMD students, many of whom we interviewed to get a better understanding of their background and commitment. Based on their past experiences and interests, the strength of their application and data analysis, and their successful interviews, we accepted 15 new members to our club this year. These members bring a variety of unique skills and novel perspectives to our club, which will greatly benefit our infrastructure and research initiatives.

All new members participated in our annual Quantitative Finance curriculum, where they refined their mathematical, computational, and financial skills through engaging lectures and data analysis projects. At the close of the semester, these members competed in the annual Alpha Competition to create the best strategy using the material they learned and their research skills. Here are descriptions of some of the submitted alphas:

• Eileen Chen, Phoebe Dainer, and Stacy Sun considered successful existing alphas and created a strategy that determined risk-adjusted return based on support and resistance, which establishes a long position according to a trading signal calculated from the highest, lowest, and pivot values of stock price.

• Eckart Schneider and Neel Jay sought to determine which stocks are currently the most undervalued. They found the optimal Sharpe ratio of the ten most undervalued stocks as parameters based on their price-to-earnings ratios. 

• Lawrence Zhang, Raymond Yang, and Shrinav Loka analyzed price-to-earnings (P/E) and price-to-book (P/B) ratios to create a strategy that was also focused on undervalued stocks. They determined that stocks with lower P/E were undervalued as they typically would not have prices that match their respective potential earnings, and their alpha was enhanced with the consideration of the P/B ratio, which considers price relative to the stock’s book value of its assets. As potential improvements to their strategy, the team mentions utilizing efficient frontier theory and limiting the universe size.

• Ankit Nakhawa, Parth Kocheta, and Rajat Baldawa strategically combined three technical indicators in their alpha: stochastic oscillator, simple moving average (SMA), and exponential moving average (EMA). Overbought or oversold stocks, identified by the stochastic oscillator and a predetermined threshold, dictated buy and sell signals. Then, EMA and SMA were calculated to determine stocks that have the most potential for a high gain.

Progress on Projects

The Quant team strives to create meaningful contributions to our alpha and infrastructure pool. This semester, our members made significant progress on research projects and initiatives that will strengthen the ability of members to conduct research. Here are descriptions of the teams’ work:

SIF Search

The Internal Tools vertical primarily continued their work on SIFSearch, a search engine for all past work and projects within the SIF Quant team. Kaushik Vejju, Ravi Panguluri, and Julia Chen applied changes to both the front-end user interface and backend and introduced new innovative features. The frontend transitioned from standard Django templates to React-based components, while the backend adapted to a PostgreSQL database. The team also built a new login system for SIFSearch, which supports hashing and JWT (JSON Web Token) authentication. Next semester, they hope to make progress on a new internal tool that aims to create and deploy new SIF projects through pre-existing project templates.

Options

Kwabena Aduse-Poku sought to apply ideas developed from intra-day trading and different financial products to Options trading, creating an infrastructure that makes creating and testing new strategies more flexible. He chose an OOP approach to define the strategy platform with Tradier API, which has both data and execution services. OptionsClient is the object that interacts with Tradier API by managing the OptionsStrategy to pass market data, execute trades, and handle logging of trading events. OptionsStrategy is an abstract class defining how options are traded, requiring the implementation of the generate_trade method to create OptionsTrade objects based on market data, providing flexibility for diverse trading styles. Finally, OptionsTrade represents an open option trade, capable of containing up to four legs to represent various options strategies, with exit conditions based on market data fields and lambdas. Kwabena’s comprehensive infrastructure allows researchers to define conditions with creative strategy design and pursue innovative solutions in options trading.

Cryptocurrency

As part of the Cryptocurrency Trading Platform, Rohan Uttamsingh defined a new type of alpha better suited to the crypto environment. He created a real-time, fully functional forward tester to run an alpha against an exchange’s order book in real-time using paper money, which will allow the Quant Team to develop strategies this upcoming semester. The team is also working on integrating the Cryptocurrency Data Collection Platform with this new development in order to backtest cryptocurrency trading strategies on historical data. Rohan enjoys the multifaceted nature of crypto in navigating the absence of designated market makers, presence of multiple possible exchanges that list the same pairs of currencies, and the challenge that crypto trading presents compared to traditional equity trading. 

High Frequency Trading

The HFT vertical made progress on parsing minute candle data from their AWS S3 bucket and synchronously iterating over multiple tickers over a single day period in their backtester. In order for the orders and exit conditions created by abstract alphas to be understood by the supervisor, the team designed an OOP-based lifecycle for them so they could subsequently be fed to the execution API. Next semester, the team aims to finalize capital/position holdings logic and begin live trading implementation.

Nonlinear Stochastic Discount Factors

James Zhang worked alongside UMD Assistant Professor Serhiy Kozak to explore nonlinear stochastic discount factors, which can be crafted both by implementing nonlinearities into existing models as well as injecting neural networks to learn optimal nonlinear transformations that price stocks in the cross-section. Kozak's previous work in cross-sectional asset pricing has included questioning famous characteristic-sparse linear SDFs, such as the Capital Asset Pricing Model and Fama French Factor Models, as well as establishing conditions under which different factor construction methods span the linear SDF pricing individual stocks.`

Sports Betting

Mishael Roy developed a sports betting algorithm in his interest of seeking a burgeoning market to apply fundamental techniques. Hoping to replicate events in more efficient markets to the sports betting market, he chose to determine some notion of expected value of the random variable associated with the expected profit on each transaction. When tested over the past 252 days, the realized profits of the strategy are best shown at the beginning of the NFL, NBA, and NHL seasons, culminating in an overall win probability of ~57% and a daily ROI of ~5%. Because this strategy is focused on the principles behind the pricing of odds, rather than the sports that the odds represent, the algorithm works on a wide range of sportsbook markets, and the trader has an advantage in having an observable edge on these markets. Having personal experience trading on sports betting, Mishael enjoyed developing this strategy during the major sports league seasons and reflects that actually taking on risk was the most challenging aspect of this project.

Conclusion

The Quant Team is extremely proud of another productive semester, with the integration of a strong class of new members and the progress made on several exciting projects.

We would like to congratulate Kwabena Aduse-Poku and Leo (Yunsheng) Liu for graduating this semester. Kwabena and Leo made significant contributions to SIF’s infrastructure and research during their time on the team, and we are so grateful for their mentorship, expertise, and friendship. We wish them all the best in their bright future endeavors.

Next semester, we have an exciting schedule planned with lectures led by reputable UMD professors, senior members, and first-year members. Sumit Nawathe will continue as President, with James Zhang and Ocarina Lin as Vice Presidents, and Rohan Uttamsingh as a Senior Leadership Advisor.

Thank you once again to our members, alumni, and readers for your continued support of the Smith Investment Club. We look forward to the many exciting opportunities that await us in the coming semesters.

Best,

SIF Quant Executive Team