During my undergraduate studies in Finance, one of the courses I eagerly anticipated was Financial Trading Strategies. While other classes relied on academic materials such as textbooks and PowerPoint presentations, our professor, a former market maker, chose a hands-on approach. Throughout the semester, we were immersed in simulated trading “cases,” utilizing the same software as professional traders at Citadel, among others. Our grades weren’t solely determined by profits but by how our performance compared to our classmates’. At times, the pressure felt greater than trading with real money.

Isolate your exposures and hedge whenever possible.

In this case, we assumed the role of commodity traders engaging in “location arbitrage” strategies. We sought to exploit price differences in oil across various locations by buying from cheaper markets and selling to more expensive ones. While the market rewarded us for addressing supply-demand imbalances, we faced a significant risk: global oil price fluctuations. This underscored the criticality of hedging. Despite positive expected values, unmitigated exposure to the global oil market led to losses when prices fell.

As portfolio managers managing a trio of stocks, we leveraged our knowledge of their fair values. Our task was to take long positions in undervalued stocks and short overvalued ones. However, we had to contend with the risk of short-term deviations from fair value. To mitigate overall market risk, I adopted a strategy of taking opposing positions, ensuring my portfolio remained neutral to broader market movements. This approach safeguarded against losses during market downturns and enabled leveraging of undervalued positions.

In options trading, effective hedging is paramount, especially for traders employing strategies like “wheeling.” Delta hedging and buying options elsewhere to offset short volatility exposure can protect portfolios from adverse movements.

As authorized participants in an ETF arbitrage simulation, we capitalized on price disparities between ETFs and their underlying assets. The key lesson here was to seize arbitrage opportunities aggressively before they vanished. While caution in position sizing was prudent in most cases, ETF arbitrage, with its minimal risk, demanded swift and substantial trades to capitalize fully.

In options markets, frequent trades in small sizes are advisable, particularly for systematic strategies like selling SPY options.

Despite the competitive nature of trading simulations, success depended on effort. While some classmates traded manually, I dedicated hours to developing Python scripts for automated trading. This allowed for faster and more accurate execution, giving me a competitive edge.

Handling large orders for institutional clients as brokers, we faced execution challenges due to trade size. Automating trades reduced execution time, mitigating exposure to adverse market movements. While classmates using manual methods took up to a minute to process orders, algorithmic trading enabled me to execute trades in mere seconds.

An edge exists for a reason. It’s always about providing value.

Every trading simulation underscored the importance of providing value in the market. Regardless of the strategy, profitability stemmed from meeting market needs.

Market making, while lucrative, posed challenges in maintaining neutral positions amid market fluctuations. Recognizing patterns in classmates’ trading behavior, I adjusted my strategy to capitalize on their actions. By aligning with prevailing market sentiment, I capitalized on inflated prices during buying frenzies and bought at discounts during sell-offs.

In conclusion, while I didn’t perfect a market-making algorithm, the lessons learned in that class were invaluable, culminating in a well-earned A grade.