Through my experience in algorithmic trading competitions, I have developed skillsto exploit predictive patterns in historical data and develop algorithms accordingly to catch the trading opportunities. And I will illustratethesebya curve trading experience at the Chicago Midwest Trading Competition.
I processedlarge data sets and exploitedtrading opportunities intuited by financial theories. To make the data more readable and predictable, I first convertedthe high-frequency price data on interest rate futuresinto interest rates by terms. Inspired by the business cycle theory, I designed the strategy from time-series characteristics of the interest rate term structure because, empirically, the curve hasthe trend to become steeper or flatter in different phases and even inverted atthe end of the cycle, which indicated a mean-reversion property.Therefore, we modeled the long-term convergent interest rate term structure using the Nelson-Siegel function and backtested to find the parameters that maximized the profits.
I developed fast algorithms to compete in the fast-changing market. To boost the processing speed during live trading, we simulated possible market scenarios beforehand and prepared different model parameters accordingly. While the algorithm spent some time identifying the market scenario at the beginning, it was much faster afterward and could send orders more quickly, and won better deals than other teams.
I thought systematically and refined the algorithms to improve stability. Aside from making the strategy lucrative, I also introduced hedging and market liquidity considerations to enhance the performance further. Specifically, we enteredlong and short positions in different terms simultaneously in a cash-neutral way to profit from the curve’s slope changes, avoiding losses due to shifts. Additionally, I added liquidity signals into the framework to help deduct the order size when the market is inactive. These features reduced the unnecessary risk exposures, making the strategy more stable in different rounds.
During many similar trading competition experiences, I witnessed the perfect combination of finance theories and computational techniques. These skillscan be useful in algorithmic trading positions, as they help quantify trading ideas and builddisciplined investment systems.
If I were only allowed to give one reason for choosing quantitative finance, it would be its appealing ability to “make ideas happen.” When I was a sophomore, I happened to read a book called 20 for twenty, a quantitative research paper collection published by AQR Capital, in which an article named Buffett’s Alpha drew my attention. In this article, rigorous researchers decomposed Buffett’s returns into well-defined investment features, and they even created a systematic portfolio that tracked Buffett’s market exposures and stock-selection themes, surprisingly outperforming the original. That was when I was intrigued by how quantitative methods could turn a profound investment philosophy into concrete analysis that is defended by mathematics, statistics, and empirical tests.
As mentioned in my previous article and resume, I spent the next two years exploring my quantitative finance interests through internships, researches, and trading competitions. Then I starteda two-year journeyat ABC Companyas an intern, then a full-time analyst. The industry experience was tremendously enriching, but as a risk analyst, I am not satisfied with knowing only one side of the business, and I want to get more perspectives on how investment decisions are made. This drives me to find answers through further education and fine-tune my career path to get closer to my original goal.
Machine learning and computational skills are the two aspects I am seeking from the MSCF program. Machine learning is such a hot research field today,and it has become an essential skill for quantitative research. As I had no systematic training on Machine learning at College, I am thrilled to explore its interaction with finance at the definite top school in this subject. The machine learning courses and capstone projects at the MSCF program are incredibly attractive, and I plan to take a Deep Learning course as an elective. Furthermore, the MSCF program has aunique emphasis on computational skills. As this is what I found the most differentiating ability to solve practical problems at work, I hope to build my competitive edge on this.
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