Introduction
When I was a Finance undergrad, one of the classes I most looked forward to was Financial Trading Strategies. While other classes were more academic with textbooks and PowerPoint slides, our professor was a former market maker who taught us through hands-on experience. Throughout that semester, our professor gave us — using the same software Citadel uses to train their traders — simulated trading “cases.” Each case taught us to trade from the perspective of a different market participant. As a result, our grades didn’t just depend on how much money we made, but on the PnL compared to our classmates! At the time, the stakes seemed higher than trading with real money.
Here are some things I learned during that semester.
Isolate your exposures and hedge whenever possible.
The Stat Arb Case
In the stat arb case, we were commodity traders trying to run a “location arbitrage” strategy. We could buy oil in cheaper locations and transport it to places where oil was more expensive. The market was compensating us for providing supply in markets with too much demand for oil and providing demand in places with too much supply. However, we couldn’t hedge our positions, so our risk was that oil prices worldwide would fall before we could offload our inventory.
This case emphasized the importance of hedging. Even though every trade I made had a positive expected value, many of my trades were losers when oil prices fell. I lost money because I wasn’t just trading the relative prices between locations, but I also had unhedged exposure to the global oil market.
The Portfolio Management Case
In the portfolio management case, we were PMs. Our edge was that we knew the fair value of each of them (in real life, firms might have a team of analysts studying each stock and not giving CNBC interviews). Our job was to take long positions in undervalued or short overvalued stocks. However, our risk was that the stocks might move even further from fair value in the short term.
I quickly learned to take opposing positions to limit my risk to the overall market. If I were only long a bunch of stocks, I could sustain heavy losses during market downturns. Similarly, rally would demolish a short-only portfolio. Combining long and short positions made my portfolio more or less neutral to the broader market. Even if there were no overvalued stocks, I made sure to short something – this insulated me from market crashes and allowed me to leverage my long positions in undervalued stocks safely.
In options trading, hedging is even more important. Many options traders who are “wheeling” —selling cash-secured puts and covered calls— have a lot of long equity, short volatility positions and should be hedging their portfolios. Maybe shorting an index to balance out the long equity exposure of all these puts may be a good idea? Delta hedging is also an option. Buying options elsewhere to hedge the short volatility exposure from these wheel positions can also protect your portfolio from drawdowns.
Size your trades based on your edge.
The ETF Arbitrage Case
In the ETF arbitrage simulation, we were APs who could create and redeem shares of an ETF. When the price of the ETF was higher than that of the stocks in the ETF, we could buy the stocks and create shares of the (expensive) ETF to sell. Similarly, when the ETF became cheaper than the stocks, we could buy the cheap ETF and break it down into expensive stocks and sell them. The main risk was execution risk; the chance that the algo we wrote didn’t buy and sell stock in the right amounts or proportions.
This trading simulation was reasonably straightforward but taught me one important lesson: when there is an arbitrage (an edge with nearly no risk), we must trade as much as possible before it’s gone. In most simulations, I learned to maintain reasonable position sizes. However, ETF arbitrage was risk-free, provided I wrote the algo correctly. If I didn’t trade aggressively, my classmates would be, and the arbitrage opportunity would disappear.
In the options markets, we should take many trades in small sizes. Systematic trading strategies such as selling weekly or monthly SPY options tend to be profitable over time, but only if the trader is careful with their position sizing.
Work harder than anyone else.
This should be obvious, but for some reason, it isn’t. Many compete to make money in the markets; my class was no different. The PnL across the various trading simulations determined what your grades were going to be. While we had some trading simulations that we could trade by hand, we could choose to write algorithms to trade for us instead. Having studied each case beforehand, I spent hours outside of class writing python scripts to trade on my behalf. This allowed me to execute trades faster (and more accurately) than my classmates ever could by hand.
The Liability Trading Case
In a liability trading simulation, we were brokers handling large orders for other institutional clients. Our edge was that, as brokers, we would receive bids for a large amount of stock at a slight premium or be offered large blocks of shares at a discount. However, because these trades were so large, we couldn’t just close our positions immediately. We had to split up our trades into several orders so they wouldn’t push the market around too much. Our risk was the possibility of the stock moving against us while we tried to sell our shares or cover our short positions over time.
Many of my classmates traded this simulation by hand. They would receive an order, use an excel spreadsheet to evaluate each trade, and offload their positions manually. Those who traded by hand needed up to a minute to process trades. As a result, they were exposed to the market for too long, and many trades moved against them. On the other hand, I processed orders in four to five seconds because I was using an algo to trade.
An edge exists for a reason. It’s always about providing value.
Not a single trading simulation involved technical analysis. In every trading simulation, we had a role and a service to provide. We had a reason our trading strategies were profitable: we provided value in the market.
Liability traders help execute large trades for clients lacking the infrastructure or patience. Market makers provide the best bid and offer, giving traders better prices when they want to enter and exit positions. Portfolio managers can help resolve supply and demand imbalances in the market buying oversupplied, oversold securities. They can also short highly demanded, overvalued stocks. Finally, APs help keep ETF prices in line with stock prices through arbitrage.
Retail traders should be no different. The easiest services we can provide to the market involve risk premiums and price inefficiencies.
Risk premiums involve holding risk that the market tends to compensate traders for. Risk premiums include long equity & fixed-income positions (supplying equity and debt capital) and short options positions (supplying options). Remember that not all risk is created equal; day trading is risky but arguably provides no value to the market.
Trading price inefficiencies generally involve providing liquidity to market participants who might be forced to trade at bad prices. We can get paid by taking the other side of forced trades, which helps our counterparty get a better price. More on this in the next point …
Not everyone is doing things for the same reason. Trade with people who are forced to.
In real markets, many people are forced to trade at bad prices. Many institutional funds have rules that require them to buy options as a hedge, no matter the price. Some hedge funds sell their lower-quality holdings before their annual reports so they don’t have to explain to investors why they’re bag-holding the latest meme stock. They then repurchase these stocks after their report. If enough hedge funds sell the same stocks at the same time, these stocks temporarily become too cheap.
The Market Making Case
The market-making simulation was one of the hardest. We provided quotes on three stocks, and our job was to earn money collecting the bid/ask spread. The hard part was keeping the spread; since we were often on the wrong side of trades; a wave of buying would leave us short stock while the market rallied, and a wave of selling would leave us holding the bag. While we collected the spread with every trade, keeping our positions neutral was extremely difficult.
I’m not going to lie. My market-making algorithm was terrible. I would get flooded with orders on one side and be forced to dump my inventory at even worse prices. However, I noticed that everyone was panic buying or dumping stock at the same times I was. So, in my next iteration, I decided to change my strategy. Rather than competing with other MMs for orders, I would wait for a wave of buy orders and join them. These orders, alongside my own, caused my classmates’ algorithms to shift their prices higher and higher as they desperately attempted to cover their short positions. I would then helpfully sell them my stock at inflated prices. Sometimes, I would see a wave of sell orders and short the stock, knowing I could buy back it at a discount as the other market makers tried to dump their inventory.
Did I end up successfully building a market-making program? Unfortunately, no. But I made a lot of money.
And that class was the sweetest A I’ve ever earned.
