How Hedge Funds look at Systematic Strategies and Alpha

[00:00:18.07] - Speaker 1
All right. Good morning everyone. Happy Thursday. Welcome Nick. A pleasure to see you here again.

[00:00:27.19] - Speaker 2
Good morning, guys.

[00:00:28.27] - Speaker 1
Yeah, so this session is about quant trading strategy and Nick is a good friend of mine from Miami and we have a similar background. Obviously you have about 19 years experience in institutional and discretionary and systematic trading. So you work for like very large firms. And now you are the CIO and founder and owner of Robin Capital Group. And also you are now launching another venture which is New Zemia Capital, which is more like a quant trading hedge fund. So today we're going to talk about a lot of quantitative strategies, how to look at data. But before we do that, I'll let you introduce yourself, Nick, and maybe walk us through your experience.

[00:01:10.29] - Speaker 2
Yeah, yeah, of course. Thanks Fabio. And hi to everyone and the team. And congratulations to the whole mentor Q team also for just fantastic job. You guys been growing really fast. I've been keeping in touch with Fabio, you know, over the last few years. So it's exciting to see all the growth you guys had. And thanks for having me. I'll try to add as about as much value as I can here. Yeah, so my background really, it started a while ago. I'm engineer by design by trade. I started working out of school and master's in finance. I worked out of school in Santiago, Chile for a brokerage firm. I was PM there, you know, very, very junior, very green. I moved to the states in 07, started working for B of A, had some, had some experience and you know, basically arrived right around the financial crisis. So it's very humbling experience into derivatives in the entire market which happened to be where I fell in for my role, moved on to fixed income trading. I was on, on the desk there and then this was in Chicago by the way. So I spent after B of A, I spent quite a bit of time at, at a fund in, based out of Chicago, Citadel and ended up as a market maker at Citadel Securities.

[00:02:29.21] - Speaker 2
That got me out to London. So I've, that was kind of my, you know, my, my professional resume before I started my own thing in 2019 and again my background really I kind of ventured in all different aspects of trading on my own. I kind of went more on the discretionary side as a portfolio manager running books for high net worth individuals, family offices. And this all kind of arose as a byproduct of the pandemic being towards the end of a garden leave and trying to figure out life in a warm sunny spot. And that's why I ended up in Miami. But yeah, so, so again my background really is, is in rates. A rates trader, money trader as you would call them. Used to, used to make markets and interest rate swaps, dollar interest rate swaps, US Treasuries. And then prior to that I was really like overseeing a lot of algorithmic trading strategies, high frequency, primarily proprietary and market making strategies across all the exchanges globally. So I have some experience on how things work at a micro kind of nanosecond level. And then you know, adjusting expertise and adjusting portfolios to I guess what you would call more at, you know, retail consumer type, traditional type, investment style.

[00:03:58.25] - Speaker 2
Right. So building efficient portfolios and, and you know, using strategies to, to, to lean into certain exposures in that capacity. Yeah.

[00:04:09.19] - Speaker 1
And I think your background kind of aligns with also what we do because obviously you mentioned that you started from a discretionary perspective, so obviously actively managing funds, but now you are moving more towards the quant side so using more like data driven strategies. Do you want to maybe tell us a little bit more about like for example, why now data is more important than ever and how is the market changing and how can we.

[00:04:35.10] - Speaker 2
Yeah, yeah, yeah, absolutely. So I guess one thing I usually highlight is like a market structure, right. It, it always changes and you guys are all looking at the same environment every day. But you know, every day that passes, it's no longer the same environment. Right. So patterns in the marketplace, they repeat obviously, but they're not always identical. Right. So if, if you, if you really, really hone in and you try to like micro analyze, you know, a chart, a pattern or whatever it is that, that you're trading as a strategy, you, you really want to look at the deviations, find some sort of mid. Middle ground, some mid point and, and then deviations from there. The patterns are going to repeat to some degree. Right. But there's always errors from those mids. The. Yeah. So I, I guess I forget what I, what I was going to say here. Fabio, remind me.

[00:05:37.01] - Speaker 1
Yes. Like I think the, the importance of our finance is changing. Obviously you come from the market making business in Chicago. I'm sorry, I'm sorry.

[00:05:45.06] - Speaker 2
Yeah. The point I was going to make was I've always been data driven, right. So you can have discretionary reviews, but you can always back test and validate things with data. And, and that's, that's more reassuring approach, at least for me. So you might have an idea and you want to test this in, in some, in some way. Right. In some capacity. And, and again, data is very, very readily available these days. Perhaps didn't used to be markets are super efficient now. There's data feeds that you can get for nearly nothing. You know, there's tons of free data that you can use. So unless you're trying to be super highly sophisticated in, in, in your analysis in the data consumption, everybody should be using it. Right. And, and, and it doesn't necessarily mean that you're going to get edge from it because it, it's, it's data is essentially a commodity unless you have proprietary data these days. But it, it will make you feel a lot better if, if you've data tested, validated, you know, a lot of the ideas that you, that you are actually deploying in production, right. In the marketplace, your trades.

[00:06:55.22] - Speaker 2
So yeah, so again like we're, we're a massive consumer of data, particularly at the hedge fund Nazumio, which is all data, data driven. Right. Like we, and I'll talk a little bit about you know, the things that I look at at Robin Capital Group, but, and, and how we run strategies. But if, if you want a fully autonomous type fund manager, investment manager, PM if you will, you know, iterating and, and consuming tons of data is, is key. Right. So, and, and so, so we do quite a bit of that.

[00:07:35.06] - Speaker 1
Can you maybe walk us through like for those who are not familiar with it, like obviously every year in the news we hear about these big quant funds that are outperforming the market and they're having massive returns. And I also worked for a company that was selling data to large quant funds, right. So the biggest one in the world. So all these funds are always looking for alpha, right? The, the term alpha is like the main topic. Like we want to look for alpha. Alpha. Can you talk through like on how you approach this topic and what does it mean for you and when you look at data as well and how you are structuring your strategies and also what you do at the fund that can leverage data to generate alpha.

[00:08:19.10] - Speaker 2
Yeah, yeah, absolutely. Yeah. It's an interesting one. So I guess we should start with the fact, you know, we, I briefly mentioned changing market structure, right. That, that could be a, that could be something that decays alpha. Right. Market efficiencies are really, the higher alpha decay are out there. So like, and I mentioned patterns that change as well. Right. So you know, things are constantly evolving in the marketplace. So you, you do need to reinvent and retrain and recalibrate consistently. Right. So, so the way we thought about it, knowing especially when you work in like a high frequency environment, like it's the erosion of any edge you think you have is very quick. Have it be because it's low latency, you're not fast enough to the market so speed and infrastructure and technology or you're not fast enough because you know it takes a second to process the, the signal. Right. But whatever, whatever causes you to miss a trade or not participate as much as you'd like to that you know, decays your, your alpha. You, you really have to be finding multiple different ways to re engineer some of these things, right? So you want to have a lot of different alpha bits.

[00:09:40.12] - Speaker 2
You don't want to trade one pattern, you want to trade multiple patterns. Some work at certain times you want to have a, an inventory of these things. And knowing that is, is basically what went into how we specked out the hedge fund. It was, you know, we basically were thinking of this how can we create, we know the market's changing all the time. Every day is different things that worked yesterday might not work today. How do we, how do we engineer an alpha factory that at any given time we're going to have a plethora of options across asset classes, geographies to simplify data, data sets and universes, right. That we can extract these trades from. So, so we took the approach of let's use the readily available data and obviously we consume it, but let's use the, the cheap compute. And this is the, the big game changer for doing it this this way was just the advances in computation, cloud compute and everything else. It's, it's, it's affordable, you know, it didn't used to be and that's affordable. And you can iterate and run things all day long and, and trade tomorrow. Right? And so these were the motivations but, but we knew that we needed to tackle the alpha erosion problem because you know, some trades are short lived especially in very liquid markets like I said, right.

[00:11:06.13] - Speaker 2
So efficient markets you get, you run out of ideas, right? So that was, that was the motivation design for building a big universe of tradable alphas. And, and then what we do is we depending on the conditions of the market these things kind of like self select themselves, right? Well, we designed some selection scoring process but like we trim anything down and so the best, most consistent repeatable trades or alphas as we call them, we call the trades alphas in this case. But you know, depending on the forecast we might trade for a day, might trade for 5, 7, 30 days depending on the outlook. That is the trade that we're going to give capital to and we're going to allocate and then we're going to mark it out every day, right? So, so that's kind of the way we thought about this alpha erosion and, and constantly finding trades or strategies or ideas or alphas that, that work, right? So that was kind of, that was actually like the main thing we were trying to tackle just knowing how a lot of these funds, you know, they have a good year or they're, they're expecting certain market conditions to, to have a, a consistent performance.

[00:12:23.12] - Speaker 2
And you see it, right? You see last year, you know, the macro funds, you know, were, were semi, okay. A lot of the quant funds last year during the first half, like not great, right? Like correlations broke down. You had stocks and bonds moving same direction regularly. So you have all these, you know, all this noise in the market. And if you're relying on some mean reversion, which is by the way, most of these like quant shops, the underlying principle is like something has to go back to normal. Whatever that normal might be in whatever time frame, you know, they're expecting this to happen. And so when things move, you know, together, then these things fall, they break down, these models all break down. Right.

[00:13:02.20] - Speaker 1
So the most interesting part Nick, when I was selling data to large funds was that not only you need data that can give you alpha, but you also need data that is uncorrelated to maybe something that you already have because then you can build up on things that might move in a different direction if the market changes. So like a lot of these funds are looking for things that are not correlated at all with what they have and they build alpha with the non correlated factors.

[00:13:36.09] - Speaker 2
Yeah, yeah, I agree. So anything alternative data or like feeds that. And again this is where like the, the you know, today's compute machinery and you know, just all of the, all of the ease to market, if you will like to plug these things into different types of models really facilitates experimenting with these things. To be honest. We, we really stick to the core. We stick to, you know, price data, volume data on, on very, very liquid. This is the other bit you need. You need very rich data sets for, for anything on the quantitative bit to, to really provide some value, right? You need a lot of data points. So we stick to the, you know, the highly liquid bit. But, but, yeah, but introducing alternative data sets or even proprietary data sets or you know, first some sort of process data set that you might have, could be an input for, you know, so anything that continually is extracting some value, right? Some measurable value. Could enhance what you're looking at and.

[00:14:44.14] - Speaker 1
How important is of course leveraging technology, artificial intelligence, but also back testing some of these strategies. And what do you look for when you're back testing some of these factors?

[00:14:57.12] - Speaker 2
Yeah, I mean, yeah, Consistency. Right. So, so just by definition, you know, you do need to be careful when you're building these things to not overfit or overly massage like. You know, it's funny, my, my CEO at Nazumia, you know, we worked together in the past for, for quite a long time and you know, it's just like incredibly methodical and anybody who's like a quantitative researcher, like you know, big, big brain, like are, are this is a clash, right? Just, just for context, you know, I'm the guy, I'm the guy in the, in the front facing the street. I'm the trader, I'm the pm. And then you know, we, we would always be backed by these quantitative researchers who are overthinking the paid to overthink and like to, to just make everything as precise and smooth and, and as possible as possible. But a lot of the time markets aren't, you know, nice and smooth. Right. It depends on the asset class. But like markets are tend to be very choppy. Prices are very choppy. So like when you start to, you can, you can overly, you get too sophisticated if you will and, and it ends up working one, costing time, two, not really getting you much.

[00:16:18.29] - Speaker 2
The overfitting is a real thing in models. So you basically just have to be careful when you back test to make sure that it's just a good predictor of the future. You build something, don't train on data that you plan to be. It's very simple concept but don't train on data that your model has already seen. See how good a predictor your model might be of data it hasn't seen and look for consistency, minimize errors like just iterate about as much as possible in every which way. But yeah, I mean overfitting is a big risk in dealing with this stuff.

[00:17:04.03] - Speaker 1
Yeah, maybe Nick. So we do a lot of data on options and for us it's important to leverage our model because especially in the option market, market making is key in driving not only liquidity but also potentially price action. Given the, the market structure, how the market works, given your. Maybe you can share some insights about maybe what you've done when you were market making and I know you were more in the kind of like treasury space, but maybe it's interesting to understand the things that you were looking for and you know, how you were planning your day as a market maker. I think that could be interesting.

[00:17:42.18] - Speaker 2
Yeah, yeah. I mean I'm happy to share what I can, I guess like. Yeah, specific to futures, you know, one thing to consider is. Yeah, I guess what is market making like? To me by definition it's quite simple. It's, I'm just going to show you two prices, right? I might have some alpha that wants me to trade a certain, you know, on the data or on the ask at any given time I'm going to show you the price closest to mid that I want to trade, right? So and I'm going to charge you some bid offer for the one that I don't necessarily want as much, right. Within, within reason. And still in a competitive market, market makers like their business is to collect, bid, offer and, and, and exhaust risk and pass it around, right. Like they don't want to hold this stuff. So like leading into events, what if you're, if you're truly market making and not taking proprietary positions, you should expect them not to want to hold anything, right? Like during a risk event you wanna, you wanna charge more. You almost don't wanna encourage trading, right. So I mean you do at a price, but it has to be the right price.

[00:18:51.08] - Speaker 2
So like you know, if you're trading, you know, at a pennywide, you might in equities or anything else, but if you trade in a penny wide, you might widen out a of lot a little bit during that event, right? Because that instant volatility could, could potentially be costly, right? So, so by definition market makers don't really want to hold these things. It doesn't mean that they don't because they do have to exhaust it, right? They get filled in the position. They, you know, they need to skew their book and then start to start to show a more favorable, you know, bitter ass depending on the side of the trade they're on, right? Like just, just to exhaust it. So they want to, they, they want to, they want to transfer that risk out into the marketplace through subsequent trades. But it, you know, they're not, they shouldn't be really taking a position, a market making position on, on any event. It doesn't mean they won't get stuck with the position, but they're not leaning into it as opposed to proprietary firm, right? Like they might want to express that risk depending on what's going to happen, right?

[00:19:49.14] - Speaker 2
And then ties closer to, you know, a lot of what you guys see and what you guys are doing, right? You guys are trying to trade those Those events.

[00:19:58.16] - Speaker 1
Yeah.

[00:19:58.27] - Speaker 2
So yeah, yeah.

[00:20:01.11] - Speaker 1
The goal of our model is really to look at the, similar to what you do with your strategy. We are trying to use the same approach used by quant firms to extract alpha or insights from a data set which is very available but it's very hard sometimes to manage which is the option data. We also look at options on futures, so which is a key, key driver of our business. And basically we're trying to extract similar to what a quant fund would look at that data like how can we predict where the price could go tomorrow? Or how can we predict if the price should stay within a range in the next 20 days so that you can actually use options that are non directional flexible instrument where you can make money not only if the market goes up or down, but also if the market stays in the range. So this is kind of like the goal of what we do. So we, we build quant models for, for that and we also monitor closely what market makers might do when they are trying to hedge. So when you know, when the volatility increases, like what's going to happen to those events?

[00:21:08.01] - Speaker 1
And now how is the liquidity changing? So market positioning and stuff, right?

[00:21:14.17] - Speaker 2
So one thing that comes to mind instantly is if you offer this hopefully at some point because I think it'd be a great exercise. But if you have your levels and what you publish on a specific instrument, if you have a time series of it, if you have some historical, you can easily plug that into some sort of backtest and all of your users could potentially optimize your strategies and just see what works and when it doesn't, right? And then that, that's kind of your calibration to, to what you're doing, right? Because again like the, these, the one thing I know with certainty is that you know it, the market's always changing, right? Like, and, and, and so these patterns change. They constantly, they're dynamic, they evolve, right? Like participants change that, you know, sizes, trades. I mean you look at market structure today and just like, just look at the S P and the weights. Like it's, it's absurd, right? Like there's no, you know, the market is, is there's a massive, massive liquidity premium in, you know, or I call it a liquidity premium in the top seven to 10 stocks, right? Like it's, it's nuts.

[00:22:21.26] - Speaker 2
And that's why everyone kind of gravitates towards these things. But, but it's always changing. So you do need to evolve with it and constantly, you know, just pick away at what it is that works for you so that, you know, you still have a fighting chance, right? And then, and know what you're up against too, because that, that's the other thing, right? If you use one of these, like, you know, one of these little mouses, it, it's very hard to, to contend, right? To fight, you know, a lot of these sophisticated players, right? So like automation trying to be systematic. If there's a way to, if there's a way to encapsulate what you're doing on a discretionary basis and just translate that into some sort of like simple strategy, right? Just to execute quicker or more efficiently or, you know, it ends up saving quite a bit and you see that that compounds into your returns, right? So.

[00:23:23.23] - Speaker 1
One of the most important thing is also taking away the emotional bias because I think like, obviously as a human, we are obviously more inefficient than technology. So sometimes we have a bias and we don't see the data around it that tells us that we are wrong. Right? So I think using a systematic approach sometimes can be better because you're leveraging data instead of leveraging emotions, which sometimes can lead to mistakes.

[00:23:49.25] - Speaker 2
Yeah. Yeah, I couldn't agree more. That to me that's like the, that, that's the hardest one to like. Well, it's the easiest one to realize, but it's hardest one to, to overcome, right? Because yeah, we, we are emotional by definition humans, right? And so, you know, we, we all fall into this kind of, you know, all the bad that, that we have. And this is why, this is why alos are, are better at doing a lot of this stuff. You know, we fall into group think, we fall into confirmation bias. You know, we're late to things it as a discretionary PM or trader. Like, especially as a trader. That's really. And I, I, I, some of your podcasts, you guys mentioned this as well. It's, I mean, you really have to take all the emotion out of this, right? Like, it's not, you have to be systematic, even if not programmatic, right? Like you have to be, you have to have a system. Some kind of checklist manifesto is like a random book that, that kind of talks to this, but just be methodical, right? And then take the human element out of it. Like it's, I, I can't stress how important it is.

[00:24:58.10] - Speaker 2
And it, if anything, that, that really just makes you consistent and it starts to like, really build on your conviction as you do this for longer, right? Especially guys starting out, you know, it's, yeah, Just stick to the plan basically, is what it is and be honest with yourself. What, what a lot of people, you know in the street don't do is mark themselves out. Right. So you know, you should, you can always mark out your trades and see how you did. Did you enter at the right time or did you not. Right. Mark yourself out first? What was the price a minute later, five minutes later, day later, like whatever frequency. But because that's the only way to get better, right. So you know, these are things that, that are helpful. It doesn't eliminate the human bias, but, but it's helpful to kind of turn yourself into a robot, if you will, when you're looking at this because yeah, you don't, you don't want to be influenced by anything. It's also, I think the best traders also have this element of, of you know, long solitude. The more people you speak to, and I don't know if this happens to you guys, but the more people you speak to you one gravitates towards those that kind of confirm what you're thinking.

[00:26:13.15] - Speaker 2
Right. So you know, you'll find yourself having a beer with people that all agree on your, on your stock pick or your trade. And, and it's opposite to what you should want, right. You should wanna, you should want a dialog and like debate your position all the time. You should constantly be living up fear and paranoia, especially if you're running risk for a living. Right. Like, like I, I want people to tell me why I'm wrong and then, then, and then, you know, I feel better about that because I'll do the work to, to, to just make sure that that may or may not be the case. Right?

[00:26:45.20] - Speaker 1
Yeah.

[00:26:46.04] - Speaker 2
So yeah, it could be a lonely, it could be a very lonely. You're better when you're lonely, I guess, if that makes sense.

[00:26:53.11] - Speaker 1
Makes sense. So here we see like some parts from kind of like your internal tools, your fun. I think you do a lot of macro. So you, when we were talking in Miami, you obviously care a lot about macro because also your background is it macro? Do you want to maybe like share on some of the things that you look for at the macro environment?

[00:27:16.05] - Speaker 2
Yeah. So outside of the very data driven approach where you know, you assume everything is, is in a price, you know, so you know, interest rates, price of oil, geopolitics, fiscal, whatever, you, you, one can assume the price reflects that and then you take that, you build models and you look for statistical relationships. Right. At a discretionary level. The macro bit to me really starts with like the cost of money. Right. So where are policy rates, interest rates? And that's usually where I start from, like a top down type approach. So I, I, I look for relationships that you know, short and longer, that kind of, you know, historical correlations if you will. And there's just like a you know, silly looking correlation plot there with a lot of ETFs. So I, I run an ETF strategy that's, it's essentially long only and I didn't mention this but I, I run sma so like I've got restrictions on what I could do. Right. So given the confines of my investable universe and, and some of the client accounts, I basically have to optimize the portfolio with the trends and, and different things that I want to exposures that I want to put into the book and then just optimize to that given the fact that I can't necessarily take it short or you know, so, so I have to, I have to get creative.

[00:28:37.08] - Speaker 2
Right. Like it's a big kind of engineer and exercise for me but we carry cash, you know, to, to kind of offset some of the, the lack of shorts I can put on at given times. Right. But anyway so, so yeah, historical correlations, how these things change, deviate, you know, always looking for trends, money flow, international money flow from you know, one place to another. Um, that obviously translates to fx. I know you guys trade FX as well. But you know, looking at interest rate differentials between economies like obviously like is instantly reflected in the FX market. I, I don't tend to use a lot of technicals outside of very, very short type. Okay, we're, we're gonna execute here but these things, I'm also not day trading. I, I don't like, I'll trade daily but I don't day trade. Right. So like I've got like a core, I build a portfolio, have a core and I'll rebalance as things kind of fall out of line or in line or things materialize. So, so that's really it. I'll, I'll turn the books around, you know, three to four times a year on average. And I'll do that in rates, I'll do that less so in rates to be honest because that's, it's, it's a very conservative portfolio.

[00:29:53.22] - Speaker 2
But, but yeah, the macro strategy, I'll turn three to four times a year and then we have an equity portfolio, long only equity portfolio, that one's a bit more active and, and similarly so you know, I look for trending fundamentals. When I, when I look at Stocks so basically just have a, have sort semi, semi automatic kind of way to, to view the big picture and make sure that, you know, things are staying in the right place. Interest rates are high. You know, we don't want, we don't want companies with a lot of debt, debt maturing coming due. We want to see like growing revenues growing a bit. Does like, you know, nothing, nothing groundbreaking. But we do capture those trends and then hope that there's some, some sort of catalyst that sparks a little momentum in the underlier. Right. So those are kind of the, the things we're, we're looking for and then we weight them in a portfolio to, to kind of optimize and make it efficient. Right.

[00:30:53.00] - Speaker 1
How do you deal with like earnings and catalyst events like that? Like how do you maybe.

[00:30:59.10] - Speaker 2
Yeah, I mean again, it's, it's look, you try to predict but like all the predictions and, and you know, earnings. And again, I'm not going to pretend to be like a, an equity analyst who's like on the phone with all the companies all the time. I do occasionally like depending on, on, on how important that position might be to me. But yeah, I mean you just kind of, we kind of wait and see. Right. And it's, it's kind of a reaction function if, if the trend breaks or they, you know, there's seasonality so you can adjust by that. But you know, I, I'm, my guess is as good as anyone else's. You know what, you know, what Amazon's going to report. Right. Like, but, but again a lot of the trends do confirm, but I'm not trying to, I'm already in that trade. Right. Like it's, it's going to be a little bit longer term so I can hide behind that. If there's a big break and something falls apart, then you know, there's risk management obviously as well to, to kind of, to kind of COVID that, that gap. But I'm not, yeah, I'm not trying to predict earnings.

[00:32:06.29] - Speaker 2
Right. For a specific company. It's, and this is again, this is, this, this is the importance of having a, a structured portfolio that, you know, you can afford to, you know, have something go down and then it's going to be decently diversified at least.

[00:32:22.05] - Speaker 1
Right. Is there a specific sector that you look for? Like, of course, no.

[00:32:28.08] - Speaker 2
I mean I, I tend to look at anything that has good management companies, but no, no sector, not sector specific. You know, I've, I've had, I've had plays on, you know, oil producers, oil service companies, a lot of technology. Um, and mostly because I'm, I'm, I'm usually in it myself. So like I, I, I tend to explore and experiment a lot with like new companies and new things that come out. So alternative technology. Right. I'm not rarely ever owned any of the Mag 7s like you know, like last year these things ripped. I, I own nothing. We perform quite well without any of these things. But alternative companies, companies with small companies with talent, like I try to keep, I look for liquidity obviously. So I'm not looking at small, you know, very micro cap, you know, nano cap companies, you know, anything and you know, at least I mean small, maybe a billion market cap kind of thing and above. But it's more about what they're doing. It's more about the innovation in tech. But yeah, I mean I'll look at everything. Housing, like, yeah, retail, I'm not sector specific. Like it's more, you know, this is the bottom up approach really.

[00:33:43.02] - Speaker 2
Right. So it starts with raids, trickles all the way down to trends and then within those trends that I might refine in an etf, I might scrub through and look for some, some companies that specific companies that I think are, are on the right track. Right. So yeah, spend most of my day doing research really. That's what it is.

[00:34:03.12] - Speaker 1
That's awesome. And if you have any question for Nick, feel free to send them over. I think this is a good chance to learn from someone who was managing the fund, his own fund and also has been very successful in the industry. I remember you outperformed the market almost every year, so. Which is awesome.

[00:34:27.20] - Speaker 2
Yeah, look, knock on wood. Right? Like trying not to be a one trick pony. But yeah, it's like when you start off on your own. Yeah, it's, it's, it's a blessing and a curse. Right. Because it's usually your friends and family are the ones that support you the most and those are the last people you want to upset. But, but yeah, look, it's just, you know, we've been very, very, very methodical in terms of how we've grown. It's, it's been very natural and, and the other bit is, and I think this makes a difference just as a side note to you know, to anyone looking at it, but having skin in the game is, is the main thing. Right. I'm not in it for, you know, obviously we have to survive. We, we charge some, some small fees but like we're definitely way more affordable than your traditional, you know, financial advisory firm or you know, asset manager and Again, it's just a function of being small, but mostly skin in the game. Like, if I don't perform, you know, I, I've got a life as, you know, Fabio and, you know, kids to feed and all and all that.

[00:35:34.05] - Speaker 2
Right. So it's just about being consistent, methodical, really. But yeah, but fortunately we, we had a great year last year. Things, things worked out and, and I'll remember, I'll forever remember 2024 as the grindiest year I've ever had in markets because it just, it was impossible the first half and then like, you know, like most, I think know, most books kind of snapped out of it leading into the end of the year and it worked out. But yeah, you just got to stick to it, you know, it's.

[00:36:05.16] - Speaker 1
Yeah.

[00:36:05.29] - Speaker 2
And manage risk.

[00:36:09.11] - Speaker 1
So how do you see the future, Nick? Like, obviously, you know, we, we are now in the AI era. Right. You know, things are changing so fast compared to the past and also this is gonna be applicable to finance, so we're gonna get to a point where AI is going to be with us in the finance industry too. So like, how do you look at the future and how do you think about the future towards stay competitive?

[00:36:34.11] - Speaker 2
Because, yeah, you know, that's, that's a good question, man. You know, yeah, maybe one day my AI is competing against your AI, right. Like, and then, and maybe things start to go a little more black box, even more so than they are now, right. For like, it depends, right. Is it a speed game? Is it not? You got to remember there's a lot of middle middlemen here are going to suffer as well, right. Because as the exchanges get more sophisticated and a lot of the, the specialists and the market makers and liquidity providers, like these guys are all fighting for, you know, these trades, this bid offer and eventually like, bid offer is tiny, right? Like in equity is, it's tiny in rates. It's gotten very small. So it depends on the asset class. Like, so, so for these like, highly deep liquid markets that we all operate in, yeah, I think it's just more and more automation, you know, more sophistication, like, like humans are going to get squeezed out of this, this space more and more. It doesn't mean you can't trade in them, but you're gonna wanna, you're gonna wanna do it in a more automated way.

[00:37:41.13] - Speaker 2
So. Right. But yeah, it's. Yeah, I wish I could see a little further out in the future, but. But yeah, no, I, I think that the rate of change now is, is, is just, I mean, I See it, it's, it's just exponential, right. There's no reason why we're not using all the tools at hand to, to quickly get to market and expedite and optimize, right. Like you, you can almost get away not even being very technical these days, right. Like you don't have to know how to code per se. Right. Like, which was massive growing pain for me. Like you know, early on and eventually you do it enough and you learn but like you don't even have to do that now. So like you can really like outsource most things to the tools at hand. Like everything that's blown people's minds, right. All of these like you know, different types of platforms will, will do this for you. So I, I think the future is going to be like just, it's going to evolve even quicker now. Like the, you know, that's kind of where I am. So again, focus on what works and have a, have an inventory of tradable strategies, alphas and then just you know, every so often test them out, make sure that they suit the current market.

[00:38:59.10] - Speaker 2
Right. Because it's going to be different very quickly. So you just want to keep up, you know, and how you look at the world.

[00:39:09.08] - Speaker 1
I'm sorry, We've got some questions. Yeah guys, send us any comments, Questions. I don't know Nick, if there's anything we missed we should add. But.

[00:39:34.05] - Speaker 2
Yeah, look different iteration or you know, there's happy to take any questions on the side too. If you want to share my email, you know that's, that's fine too. This was kind of high level brushstrokes of, of the world but you know, there's a lot of details that, that go into a lot of these things. So you know, happy to share. Yes.

[00:39:56.27] - Speaker 1
Like most of our users and customers are retail investors. Many maybe like any advice that you have from your institutional background for somebody who is now approaching trading and obviously trying to leverage data, trying to create a strategy, trying to kind of like make trading as their full time profession. Some of like the things that you might.

[00:40:23.23] - Speaker 2
Yeah, I mean, yeah, I would say, I would say start small, start humble, start on a paper account, you know and you have to grow into, grow into your level of comfort. Right. Like it, it takes a little bit of time if there's one thing everyone has in common. And regardless like nobody likes to lose money, right. Like it's, and, and that will happen. The market will, it'll, it's happy to take it from you. So you know, just stay humble and don't get too confident ever. Like you got to stay on your toes, be paranoid, definitely don't be complacent when you start. But, but with that, that like learn studies, see what works and mostly like just again back test and make sure that you're, you're, you're, you make yourself as comfortable as you can with whatever strategy it is you're trying to deploy because the market will also show you opportunities where if you, if you gain that level of confidence and conviction, then you know the world's yours. So you can, you can trade that all day, right? So just be cautious, but cautious, not overly afraid is, is really I think the approach.

[00:41:38.10] - Speaker 2
And yet I would adapt this in integrate as much technology as you possibly can to validation and back testing and this only makes you sleep well. You know, like it's, this is equivalent to an equity analyst who knows the ins and outs of a company and stock goes down, you know, what are they going to do? They're going to buy more. Right. Because they know this, you know, it doesn't mean they can't, doesn't mean they're not wrong. But, but you know, this is an opportunity to give me a gift to buy something that I know very well. Right. And it's the same thing at any time slice that you trade in, right? So have that be sub second or you know, days or weeks or years. Doesn't matter. Yeah.

[00:42:18.20] - Speaker 1
That's awesome.

[00:42:21.27] - Speaker 2
Yeah.

[00:42:22.21] - Speaker 1
All right, let's see if we get some last question, guys.

[00:42:26.16] - Speaker 2
We either explained it well or, or everyone's out for a walk in a coffee.

[00:42:34.04] - Speaker 1
Yeah, I think it was, this was great, Nick.

[00:42:38.19] - Speaker 2
Look happy, happy to help. And again, I'll be, I'll be looking at your gamma levels too fairly soon.

[00:42:45.28] - Speaker 1
So I don't know if you can say anything about Peak 93.

[00:42:50.28] - Speaker 2
Oh, what type of trade the noise Market makers the most. Oh man. So, so a good market maker doesn't really get annoyed, right? Because their price is going to be just fine. Right. Like again, remember, like I'll make you a market right now on anything you want and if I don't want it, I'm just gonna skew my, my mid. Right? So like my midpoint is here. This is the fair market price. As a market maker, I'm, I'm just gonna show you a really good bid if I, if I want it because I know you want to take my offer, so you're just gonna have to pay me more for it. Right? And this is what market makers do all day, right. And it depends on how they're managing their, their inventory. If they're in a trade, they want to exhaust it, they want to get out, right? Like they'd rather not make money and just get out of it for free to keep the bid offer, right? So I mean, I could share, I could share. Like I was annoyed as a, as a rage trader briefly in swaps because you've got a lot of like very, very technical, highly special, very smart people like that work in hedge funds, right?

[00:43:55.22] - Speaker 2
And a lot of the time, you know, they just want to make a buck and they'll do it at the expense of a market maker. So when you're, when you're trading in size, you know, and it's like institutional size, like I traded dollar swaps, for example, around events, you know, prices Snap, right? You guys see that you're trading the event, but like think of all the prices at future Snap cash bonds, everything that you use to construct your curve, which is also ultimately my mid price, right? Like these things have these, they have these burps, right, where like you're, you don't get a price feed, you know, instantly your curve is messed up. You got to reprocess the whole thing. You might have a little kink in your curve. And so the most annoying guys would be the guys that come in and ask you for a price on that kink. And if you don't pick up on the fact that your, your market data is not like fully up to date, you might make a wrong price and that's going to cost you a pant load, right? So, so those spivy, spivy traders, I would say, I recall.

[00:44:56.00] - Speaker 2
And you, you learn, you know, you take them out for beer and then you realize who they are and then it gets a little better. But, but those guys were predictable over time. But you know, sometimes, you know, everyone gets caught on a few things. So yeah, the guys that come in like right around really noisy market data things and then they just know where it is because these things always snap back. You might make the wrong price as a market maker. And then by the second your curve snaps, you realize, oh man, you know, I price this off by a twentieth of a basis point, you know, a tenth of a basis point. And that's, that's very costly, right? Because we're operating in markets that, you know, at least swaps markets were 3/10 of base point wide. Like it's, there's no room for error there, right? So, so a little kink on a, on a, on a, on a fig Trader's Curve, and you get the. You get the hedge fund guy just trying to rip your face off. Not the nicest thing. Right. And there's not much you can do, aside from. Aside from next time you ask me for a price, you know what it's going to look like, right?

[00:45:54.20] - Speaker 2
It's going to be this wide. Let's see. That's a polite way to, you know, tell you where to shove it, basically. But, yeah, so I, I would say that's. That's probably the most annoying.

[00:46:11.03] - Speaker 1
Thank you for sharing that.

[00:46:13.05] - Speaker 2
Yeah.

[00:46:15.12] - Speaker 1
All right.

[00:46:17.00] - Speaker 2
All right.

[00:46:17.14] - Speaker 1
I think this was awesome, Nick, and I hope to have you again on another session, maybe.

[00:46:24.21] - Speaker 2
Yeah, I'd love to listen. I'd love to. Check us out. Robin Capital Group. We're based in Miami. We're always trying to grow, if you have any interest, from vanilla to as sophisticated as you want. And yeah, with Nazumia and, and, and. And my partner, Patrick Huggins, we're gonna be. We're shooting for a launch for this fund, hopefully come March. So we're gonna be, uh, we're gonna be doing a little roadshow and hitting the streets. And, uh, yeah, just follow us if, you know, any interest or questions. You know, we're. We're gonna, we're out there. We're coming out of stealth mode, which we've been in for quite a while, just enjoying the Miami Rays here. But we'll be. We'll be on the street pretty soon and back at it, so. Yeah, yeah, and happy to share and thank you. Thank you for, for having me. I don't. I don't tend to do this often, as you know, Fabio, but, uh, no worries.

[00:47:19.16] - Speaker 1
So check group and, uh, yeah, check out the site. Reach out to Nick if you have questions. I think it's great. I mean, I've seen your growth over the past four years, and you've been building technology, and now you're going full on the race, which is awesome. And good luck with everything.

[00:47:39.20] - Speaker 2
Thank you, my man. Thanks, Fabio.

[00:47:42.02] - Speaker 1
Good day.

[00:47:42.20] - Speaker 2
Thanks. Have a great day. Happy trading.