Transcription
We're in the research business, first and foremost. Trading is simply how we monetize that research. That's a direct quote from Ken Griffin, the founder of Citadel. It manages more than $65 billion as of January 2025 and is one of the biggest quant hedge funds in the world.
This is one of the key perspectives, I think, to long-term profitability as a trading business. And a lot of traders view trading as a hobby or even just, you know, using general instincts and that's how you're kind of monetizing, uh, in trading. I think, uh, this episode, we want to kind of dedicate to this particular quote, how we think about it, how we also do it in our own trading, uh, as we're both systematic and algorithmic traders as well. So a lot of this quote is like our flesh and blood. This probably is actually going to be one of my main quotes I always, uh, refer back to, especially in my own trading or, you know, to other people as well.
Um, Pedma, if you want, I guess you can go first with how you maybe monetize your research or how you look at research in general.
Yeah. So, um, like any business, you want to monetize the time that you are working, right? Most of our work, at least as systematic traders, is not a process of trade execution itself. It takes a bit of time each day, like I would say under, under one hour in most cases. Some, sometimes you have more trades, whatever, there's a more odd period, but in general, it doesn't take much time to to execute trades and manage positioning. So it's mostly around management and research. And then what I do is I track. We were talking about this even now. So, I track all my time on where I spend my time on when I'm at the desk. And I like to find out like how much time I spend on, let's say, on a research project and then how do I monetize that research project?
Okay. So you, let's say that I get a new trade idea or I'm trying to find if there's edge in whatever signal I'm trying to to work with. And then I against that research, I see how much like how much money, basically, I can make out of that research. Not only directly from trading, from other stuff also I do. But that's the purpose, right? You have to look at it at the business. How, how are you spending your time and what kind of, uh, ROI are you getting on that time? It's, it gets a bit clunky on the details because sometimes you like, you spend, I spend a lot of time on research that doesn't go anywhere. So probably 80% of the stuff that I work on doesn't go anywhere, and I spend a lot of time on that. So you need to understand that a lot of it is a wash or a loss or whatever you want to consider. But it's that 20% of stuff that I work on that I really find something good that I can squeeze money out of that compensates for those times. And that's it. It's thinking more and something I have to do better as well. Thinking more, how am I monetizing this time I'm spending on this piece of research.
I, I think that's put perfectly. I'd also say, um, how I look at it, especially when you're getting started in trading, it can be kind of hard to know where do you put your time, uh, and energy, right? Right? Because a lot of people think it's, you know, you need to be sit in front of the desk. You just have to be looking at, um, the charts all day. You have to be, I know, doing technical analysis all day, which is fair. Like if it's part of your research process, sure. But a lot of the time now, especially after hearing this quote, it's, it's very first and foremost and centered that, okay, a lot of my time is just simply researching edge, seeing where I can find edges or risk premiums or anything. And every moment of time that I'm spending capitalizing on those or setting up automation for those or backtesting those, that is useful, um, time that I'm getting paid for potentially in the future over a long enough kind of time horizon.
Um, so I, I definitely agree because I think when I first got started trading, I don't even really know what I spent time on. I think it was mainly like journaling. I journal trades a lot. Um, at the end of the day, collecting some data manually. Um, that was probably the main activities. Whereas nowadays, it's, well, trading side of things is all kind of automated, so I don't do much of that. But for the backtesting, I'm checking out new ideas, listening to other people's approaches. I do plenty of that kind of all day. And then also just coding here and there, general, um, simple ideas that maybe I want to test. Yeah.
Completely agree. It's like, again, we are running a business here, right? Even, even if it's just trading or trading and services and whatever we do on the side, you have a business to keep on and you need to track your spending. And we don't necessarily get a salary like a like a normal job. So, it's hard to understand what kind of hourly wage we get because it's so, uh, it's so different from a normal job. It's not about hours you put in in direct trading, but it's still good. Okay, I spent a hundred hours this this month on this piece of research or on this group, uh, of research. What did I get out of it? If I didn't get anything, then it's either you really didn't know if you had something there and you need to get, get better at the ideation, or you are just doing stuff for entertainment purposes, you know, which is fine. But you have to understand that you are putting that time to work and you need to get something out of it in the long term. And that's, that's how I'm also trying to change my mentality around it because a lot of times I'm just, yeah, looking through stuff like Twitter or whatever. And a lot of time is spent not directly on improving my business or improving the ROI on my time. And if I'm spending time on the desk and researching new ideas, new trades, whatever, I want that time to be compensated maximum. I don't want to waste a lot of time. So being just mindful of that and managing your business, trading business as a business, not just a hobby, I think it's very important in the long term.
Yeah, I, I agree. I also think, um, because you don't get paid by the hour, right? In trading sometimes, at least for me, sometimes it can be hard to kind of know, is what I'm doing right now actually useful to either my P&L in the future or like just to my trading in general? And if you kind of view it through the frame of like, okay, what are you researching right now? What are you testing? Those sort of things kind of bring it back to center where, okay, well, if I'm doing this, probably this is going to contribute to P&L or whatever kind of in the future.
Um, one thing I thought maybe we discuss is kind of what does research look like? So what are the sort of steps? Um, if you want, I can start with number one. I think number one's quite clear is like you have to find an idea. Um, an idea can come from almost anywhere, I would say. But for a lot of the time, at least from my experience, it's either coming from potentially books, uh, articles, um, people on Twitter, maybe they've discussed something or I've seen something that sort of looks like an idea and maybe I want to test it. Um, and then also a great one is network. So if you're in a group of other people or if you've just, you know, maybe a Twitter group or something like that, um, a lot of times you'll hear someone maybe discuss something you've never thought about and then you can kind of go from there and start testing. Um, if you want, do you want to go through the next step and we kind of ping pong back and forth?
Yeah, for sure. I think that the idea, like you just mentioned, just to briefly touch on it, is like the most important part is where do you get the idea and why should it be a a trade, basically? I think that's that's the most important part. And then you go to the next step and you quantify it. So, okay, I have this signal or this trade that is mispriced or whatever, or this flow that is going into the market and I can like front-run it, whatever idea you have. And you want to quantif, try to quantify it. Can I use some historical data to quantify this phenomenon? Okay, if you cannot, it's not necessarily a bad thing or like it doesn't necessarily mean you have to throw that trade under, like throw it away. But if you have some data backing it, it it gives you some sort of confidence, right? Because, okay, this happened X amount of times in the future and the expected value over the next 15 bars or 15 minutes or 15 hours is X and I can front-run it. So there's different approaches here. But yeah, you want to have some quantification around it. Okay, this is the expected value of me putting risk on the table right now.
Yeah, I, I will say to kind of enhance upon that, it doesn't have to be fully quantified, but you have to know the risks of if you can't quantify it, right? Um, and depending on the style of trader you are, maybe you'll take on that risk, maybe you won't. So like an easy example for me to think about is so much of time I wasted on trying to quantify like order flow and those sort of things back in like small cap days. Um, and because I, I knew and had seen many traders use this that sort of information to pretty, you know, pretty good P&L. Um, but at least for quantifying it in a way that actually showed to have positive expected value over a long enough time horizon, I just never really found it. So I think a lot of that was quite a discretionary aspect. Um, they had a certain feel by just looking at the order book for many, many years. Um, and that wasn't ever really able to be fully quantified unless you maybe were trying to go down to, you know, be a market maker or something like that, right? Which at the time I, I didn't really want to be. So I think as long as you understand the risk and how you're trading your time off for that, then I think it's fine if you don't have it quantified. But at least in my experience, all the time now, I always try and quantify it. And I'll even, um, take a worse performance compared to human execution if I can quantify it and then maybe automate or have it fully just via alerts or whatever. The reason being for that is I know I'm a terrible executor manually. So, I would prefer to have it fully systemized so I can just kind of turn off my brain on that side of things and just either execute it via alerts or execute it using code, uh, automatically.
Um, the next part I'd say, if we're kind of simplifying it down, is really just testing. Um, so testing most of the time, you're going to want to code that particular strategy or idea, um, and have it tested over a long enough time horizon. Now, if you don't know code, I would say the best thing you can do is take a very small sample and just see if it shows any sort of, um, decent results over a recent time horizon, right? That could be a month, maybe two months, depending on, you know, what sort of frequency the strategy is, etc. Um, but if you have a small sample like that, while it's definitely not, you know, going to qualify as a backtest and there may even be errors here and there, it at least can give you, um, a judgment on, is it worth to pursue this even more? Because a lot of the time, a lot of the ideas I test are really bad anyway. So if you can, you do one or two months and see, okay, at least it's performed decently well, then from there you can, you know, whatever, um, hire a developer or learn to code yourself or whatever you want to try and do to get a full proper backtest. For the kind of full backtest, I'd probably want multiple years of data, depending on obviously what it is. Um, and then you can do extra tests from there. You can do robustness testing and things like that. Um, most of the time, as we've talked about in other podcasts, a lot of that is going to depend on what the idea is. For example, like an arbitrage strategy, you don't really need to backtest that really. Uh, you can just kind of quickly see if it sort of works, test it out manually, and then most of the time you can see if the arbitrage is actually live. Whereas something that's like risk premium or, uh, trend following, all those sort of things, you can definitely properly backtest. Um, so first, you can understand how it works historically. Has it worked? Has it been profitable? Then you can understand the behavior side of things. So what is the average drawdown? Um, when does it work best? All those sort of things. So you can set the right expectations for when you get to, I guess, stage four, which I guess PML can go over.
Yeah. Then the next step is like deploying or testing with small size or whatever, like from an Excel sheet if you can, or even clicking it, uh, whatever. And and I, in in still in topic of the monetization of research, I would always track the time that I'm spending on specific strategies, right? So if it takes you one hour to execute that strategy, whatever, track that one hour every day and put it on an Excel. Then quantify the time you spend on that research, add it to the time that you spend, like deploying or trading it, and then, uh, track the time that, then all of that time that you spend on that piece of, uh, research and on that strategy. I would try to quantify it with your hourly wage or whatever you think your time is worth. And then see if you could, if you can monetize it above that, um, in terms of direct market monetization, right, trading it. And that's how you try to monetize your research. Is how much am I getting for the time I spent on this model, on automating this model, etc.? Can I bring positive ROI on this? And that's the essence of a good trading business or any business, right? You have a product in, in a store or whatever. If you are selling that product for a loss all the time, then your business will tank. But if you are selling that product for a for a for a positive ROI, then your business will grow. Same with trading. And that's why, uh, when we heard this video for the first time, it was so, I don't know, it's simple. We know about this intuitively, right? But we don't think about it like like that all the time. And and it's very interesting because that's how you make your business successful. It's thinking deeply about return on investment.
Yeah, that's personally why I love quotes because like, I know quotes can sometimes be cringe here and there, but a lot of times if it's a good quote, sort of like this, it's condensed down knowledge or a perspective that you, you sort of inherently know, but you've never been able to put it into words. And like those, I think make the the best quotes by far. And that's why, yeah, as I was saying, we both really like this. Um, I was also going to say, you left a really good note here, right? Is that capital as a means of monetization? So, do you, I don't know, do you want to expand upon that?
Yeah, sure. So, uh, a lot of traders, uh, I can give a a simple example is like, I, I wrote, I wrote an article a few months ago about, can you go, can you trade full-time or not? And and it goes into details like I put there a simple formula with taxes, with cost of living, with with what you need to live, etc. Um, costs and goes through all of those parameters to understand if you can, uh, live on trading income by itself. It's a very general formula. Trading income is not, uh, reliable all the times, right? But I think it fits this perfectly because even if you want to monetize your research, if you're going to monetize it with a $100 account, like you're not going to make a lot of money, right? You don't, the monetization. You can have a super profitable strategy, but if you don't have capital, it's hard to monetize it to a decent level of a first world country, right? Um, so, uh, the, the hard thing, one of the hard things about our business is that it is not a business that you can just start with no money. You need money to make money. Money is your tool, uh, in this business. It's your tool that you go to work with, right? And so that is also an important factor on monetization of research is capital because without capital, you can have the highest edge, you, you are not going to, uh, monetize it. So you can go at it from a bunch of ways. You can go from a, a job and funnel that, I did that, funnel that money into my trading account and over time expand that. It can go from a business and can go from outside capital. Now, the outside capital thing, I have no experience because I've never raised capital, but I know it's one way to do it. Uh, but yeah, capital is very important.
I, I think that was put perfectly. Um, one thing I just thought of while you were speaking is that the previous thing you were saying, right, like dedicate or, uh, calculate how much time you're spending on each strategy. One of probably the ones that I think for time to let's say potential return or like, you know, depending on your backtest, etc., is based on the previous video I did. I think it is the previous video after this podcast, um, was around kind of momentum and specifically like momentum in crypto because for the most part, crypto right now is more inefficient than all the classic sort of markets like, uh, equities, commodities, etc. So you can employ somewhat similar strategies, like a classic trend following or momentum strategy, and you're just getting better returns because you're in a more inefficient market. Now, you have to obviously give that with a caveat, right, that there's more volatility, there's more variance. Um, but normally you could get a higher Sharpe compared to something that's in, um, you know, let's say futures, like I'm currently running with my 10 automations. But you can then also have extra diversification in crypto, kind of like I'm doing now, because you should just get better returns overall. Now, obviously, you have to be, you know, comfortable with those sort of risks putting on the line. But I do think in the long term, it's always good to have an allocation to multiple markets because I know you were even considering doing equity soon, maybe like in a year or so.
Yeah, exactly. Um, like if you, if you are depending on on one just market, on just one market, which is fine initially because you need to focus on something that you know how to do. But eventually, I want to scale, I want to scale up and I need other markets that offer like different, uh, three different properties that are not necessarily highly correlated to what I do now and that can offer like a different, uh, source of returns that I'm not, uh, that I wouldn't get just in crypto, you know.
Yeah, I also think from this quote, it allows you to view research in the, it's like the function of you making money. Meaning, if you've already done research, let's say in crypto, right? Like you're already running a momentum strategy in crypto or in another market, deploying it into another market, as long as it, you know, you've tested it and it seems to work, you're getting higher returns on that research because you've barely put in any extra time into that research. You're kind of copy and pasting it, slash maybe modifying it a bit. And that's why I think for the most part, diversifying is always a benefit. Um, obviously, as you were kind of saying, you do need to hone down your first market first and then kind of grow from there. But it is, you're just getting kind of not free money because nothing in trading is free money. I'm trying to think of a better phrase, but you're able to, um, add to that research without much time or extra, uh, effort being put into it.
Yeah, for sure. It's like, how do you add, okay, you already spent all this time on this model or on this model to trade. How can I add some like a cherry on top so that it's a bit of alpha on top of it? It's, yeah, that's that's a more comp, that's a thing with more experience and a bit more complex, but definitely. And then I do think that the ultimate way to look at like ROI is on a risk-adjusted basis eventually, you know.
Yeah. No, I definitely agree. Well, that's why most people are looking at Sharpe or Sortino or one of those sort of metrics because that's how you want to view it. Yeah, for sure. Because you can have like, you can use like high leverage, whatever, just to get some money out of it and be fooled by short-term randomness. But over time, if we get a decent Sharpe above, that is like the sweet spot of