Earlier today, I had the privilege to speak with Patrick Chi, a current student at Columbia University who has worked closely with a handful cryptocurrency and trading firms, such as Optiver. According to Mr. Chi, his experience rarely intersected with A.I in trading algorithms, since his work was primarily done with pricing options, cryptocurrency arbitrage, and market making. This was quite an interesting and useful perspective to learn from, as Mr. Chi is a statistics major who only recently began dabbling with financial instruments and trading (a 180 degree turn away from my path, which has been learning trading strategies first and then studying necessary statistical and mathematical models as they appear). Also, this conversation familiarized me with the concept of market making, which is explained later on through Mr. Chi’s own words.
So, without further ado, here is the transcript of our conversation:
S.A: Hi, thanks a lot for offering to talk, I really appreciate it. The reason I reached out to you is because this year, I have been working to get familiarized with quantitative finance in general, as this is something I’ve always been interested in and will likely pursue at college and beyond. As part of this, I’ve been trying to write my own trading algorithm as well. So, first off, I’m wondering how you got familiarized with this work in quant finance? Was it more from personal interest, or was it from school?
P.C: I actually became interested in trading because of the interview process most trading companies had, which was very focused on problem solving like you would see in math competitions and AMCs. A lot of their problems are similar to trading and probability problems, and the interviews are very interesting, and I’ve always liked this type of thinking and solving challenging math questions. Some examples were finding optimal trades to make given a certain scenario, making decisions to maximize expected value given certain constraints. Like, no trades = 0 expected value, good trades and hedging = positive expected value, but at the same time, a lot of trades which aren’t well calculated = negative expected value.
S.A: Working at a company focused on algorithmic trading strategies, is there a stronger emphasis on mathematical modeling, such as statistical probabilities, or on A.I strategies when creating your algorithms?
P.C: In terms of actual building algos with A.I, I’m not the most experienced, because my work is mostly in market making and options. Also, it’s important to clarify what you mean by A.I, because most companies nowadays claim to use A.I because it’s such a buzzword. In reality though, A.I is not used as much as people think it is in trading.
S.A: Could you tell me a bit about what market-making and arbitrage are?
P.C: From how I see things, there are three main types of trading: Hedge funds, HFTs, and market makers. Hedge funds trade long term positions and try to find bigger trends in the market as a whole. Hedge funds like Two Sigma are known for using statistical relationships. Another thing about these funds is that they try to find correlations which have not already been exploited, and there’s a variety of methods for finding these correlations. For example, and this is not true at all by the way, a hedge fund might notice that whenever Apple’s stock goes up, Amazon’s goes down, and then they will make investments based off this correlation.
High frequency trading companies look at a lot of data, and establish a bunch of positions which they take in seconds and then sell a couple seconds later. They also retrain their models every couple days, so there’s a lot of active management going on. The reason why they retrain is because there’s so much of this intraday data, and the patterns might change or new patterns might emerge. An example of how a HFT would work would be: Oh, we’ve seen this pattern of behavior in intraday data before, and 90% of the time this will return profit. Mind you, the profits are usually a couple cents, but they’re in such high volume that there’s a lot of money to be made. Using current market conditions, they make this judgement, and this is all happening in a matter of seconds.
Market making is where you are the market itself, so we set buy and sell prices based on what people are willing to pay. Essentially, the spread between two assets is calculated by market makers, and we are the ones who provide and enable liquidity. Prices are pretty much determined based off of expected value, and I set a price where there will be an equivalent number of buy and sells. This is probably one of the safest form of trading, because there’s less risk and dependence on volatility.
S.A: So what’s one thing you guys consider most heavily when gauging the value of an option? My only experience with options is that they’re used to hedge other investments, so I’m wondering if you guys had other uses for them?
P.C: Options are much harder to price than stocks. We look at order books and other probability models. It kind of works by asking what do you think is the probability that an asset will be above/below a price at a certain time, and then we calculate a distribution of prices. It’s funny because the models we use are not very applicable to real life, as in reality, markets just keep going up long term. What’s harder to price is the probability that we finish above a threshold, which depends a lot on volatility. Nowadays, bigger companies mostly trade in options, because they’re way more profitable since everyone knows what the value of a stock is.
As a novice investor, you would be taking the prices, rather than making them. There’s a lot less volume with a personal investor, so you can’t really have an influence on the market, which means you are kind of powerless in changing these prices like a bigger company might be.
S.A: What do you think all of this says about the premise of trying to match larger patterns in the markets?
P.C: From what you’ve described, which is using A.I to match patterns, it sounds like your work is more related to HFT. I’d recommend starting out trading with crypto, since you can start off with small amounts of money and get used to how the markets work. You can also learn a lot about expected value vs. current value doing this, which all boils back down to expected value and probability.
I don’t mean to discourage you, but macro scale is already priced-in, as long term trading is pretty much finding correlation between two instruments. This is primarily because long term doesn’t have enough data, and not nearly as much data as HFT does. There’s just too much noise with the data you’re working with.
S.A: What are some good ressources you recommend I look at, as a beginner in quantitative trading? Or some other people I could reach out to?
P.C: Well, I do have some friends working in HFT, which would probably useful to you considering your focus on A.I and data analysis. But, it’s always hard with high frequency traders, as they’re the ones who need to hide trade secrets to preserve their advantages over competition. Also, you might not know this, but HFT is actually a much smaller, much more specialized field than most people think. There’s actually not that many companies out there doing HFT, just because it’s a pretty new thing and there’s so much competition over who receives data the fastest, among other things. I’ll definitely try to connect you with them, though.
There’s a lot of useful information to unpack in this conversation, so I’ll give a rundown of what I found most intriguing.
First off, Mr.Chi’s explanation of high frequency trading strategies was worth noting. According to him, HFT works by finding patterns in intraday market data, which makes sense. There must be some trends in this type of data, like the book pressure that Mr. Conerly mentioned in my interview with him. A simple example of such a pattern is whenever there are more buy offers than sell offers, the price will go up. This may seem self-evident, but it is still a form of repeating pattern which a neural network would probably pick up on. This is important because this acknowledges the existence of patterns in market behavior, and acknowledges that A.I is being used to identify these patterns. This is more affirmation than I can ask for in reference to my project. Like I’ve mentioned, my work with using embeddings for mapping market behavior depends entirely on the existence of repeating patterns in the markets.
A second intriguing takeaway is Mr. Chi’s take on the usefulness and applicability of options. According to him, most big traders nowadays rely on options because of several reasons. For instance, options are more versatile, in that you can purchase a put if you predict a downturn and you can purchase a call if you predict the opposite. To add, options allow you to choose what price you set your buy/sell point at. You don’t simply buy an option, you buy a call at $280 for March 22nd, for example. This means you agree to sell your call by March 22nd at the latest, and if the price is $280 or above, you will make profit. So, there’s plenty of factors that come into play when purchasing an option which give you more control over your investments. Purchasing stock is rather one-dimensional, as you only profit if the price goes up (unless you’re shorting), and all the investors in the world know this, meaning that you have much less edge over your competition.
With all that in mind, I might consider purchasing options rather than stocks through my trading algorithm, but only after I see positive results. Options, as Mr. Chi mentioned, can be much more profitable and versatile than simple stock positions, but at the same time, they are much riskier and more expensive. They’re expensive because they are sold in bundles of 100, meaning if an option is priced at $2.00, then the least you can expect to pay is $200.
Finally, the “no trades = 0 expected value, good trades and hedging = positive expected value, but at the same time, a lot of trades which aren’t well calculated = negative expected value” reference reminded me of my post on loud silence, and how most of the time in trading, it’s best to remain uninvolved until you find a great entry point.