Inside Algorithmic Trading with Anthony Markham, Vice President, Quantitative Developer at Deutsche Bank

Richie Cotton:

So I, Antony, thank you for joining me on the show.

Anthony Markham:

All right, Chris, thanks for the invitation. Very excited.

Richie Cotton:

Excellent. Yeah. So I think, guys, to begin with, it'd be really nice to have a bit of context on like what is algorithmic trading? Can you tell me what it involves?

Anthony Markham:

Yeah, So it's very much a mystery to a lot of people. I guess the idea behind algorithmic trading, quantitative finance, all these sort of times they get thrown around is essentially trading automatically. So trading with computers. So instead of a person executing a trade themselves like me or you want, it's a computer doing all that. So the computer's going to do a logic behind them on the back end and execute the trade itself without a human.Exactly telling it what to do. So. So it's all programed, so these strategies are all programed into the system. Data and strategies all developed by people once.

Richie Cotton:

Okay, maybe can you just expand a bit on like which parts of this are done by computers and like where the actual human involvement is.

Anthony Markham:

So humans really design the strategies. So humans, of course, have complete oversight of this sort of stuff. I'm sure we'll talk later about the risks involved in this, but you can imagine letting a computer just go Skynet and try it can be very, very risky. So that's not the case. So humans often design strategies. They'll do all the analysis behind it.They'll be examining the data that we testing strategies, all the historical data and all these sorts of things, whereas the system itself is executed, right? So it really depends on the strategy that the system might pick when to trade and what time to trade. It might be a profitable time, it might ingest data from another source and infer something from that.And further, it's a great topic, right, based on that. So yeah, it's very data driven.

Richie Cotton:

Okay. So it sounds like the humans are involved and maybe the more creative part of things and the oversight and then the computers do everything else.

Anthony Markham:

Very much so. I mean, of course the computers are programed to use alternate software engineers, data engineers and all those sorts of typical roles to build out these models. And these models are very mathematical for a statistical. So maybe creative is the wrong word, but yeah, there is an ethic of creativity and that you need to be understanding financial markets as a human and you need to be sort of designing strategies around that. Computers, of course, live.

Richie Cotton:

Okay. And can you tell me how this algorithmic trading is different from maybe more traditional or standard trading and investing?

Anthony Markham:

I guess the speed at which it happens is probably the biggest difference I'm really writing about. Writing can be at any pace. You could execute one trade a day for example, with algorithmic trading, the computer disables execute one day, or you could execute 1000 seconds. And so you can typically break that down into made frequency trading or high frequency trading, a sort of some of the terms you here where that refers to the frequency of rates and at that sort of top level or the high frequency level, yeah, it can be in the thousands of trades a second. So very, very high throughput.

Richie Cotton:

Okay. So this is really something that a human couldn't do that fast.

Anthony Markham:

Exactly. A human could never do this sort of stuff. And so it's a very different way of investing. You think if you're a you as a retail investor, you think we might buy a stock because we believe in the company. You know, we might say the new iPhone comes out tomorrow and therefore the price of Apple stock is going to rise.Because I believe in Apple as a company. I believe the iPhone is going to be awesome. That's value investing. So we're investing in the value of the company and that product that they're releasing. Whereas our trading strategies that people design, it's very hard to do that. If you're trading a thousand times a second. You can't you know, you can't sort of trade based on value. So you often on a much shorter talk time, See you might buy something and sell it 10 seconds later for a very small profit, for a $0.01 sort of margin or a spread. So it's a very different sort of investing and it's cool.

Richie Cotton:

So it sounds like there are these different strategies and maybe not this value investing or the sort of, but let's make a little bit of money in 10 seconds. There's a lot of different approaches. Can you talk me through like what the analyzes look like in order to accompany these strategies?

Anthony Markham:

Yeah, and of course a lot done there. So I suppose maybe the biggest goal obviously I profit from is for the companies that do this, but these companies are also the local market makers and the term is very literal. It means to make a market. So you're acting on both sides of the trade. So you might offer liquidity to the market, meaning offering value to the market by being on both the buy and sell side. So so in that example I gave you buying some Apple stock, we would probably buy that in reality from from a market maker, from a high frequency trading company. They'd be the ones on the side of the deal that we'd be buying from just because they're constantly trading back and forth, buying and selling, providing that sort of activity in the market. And so the activity certainly is there and the analysis is there in constantly being a part of the market. But then, of course, risk management is is sort of a key part of the analysis. It's not as well on these strategies and maybe we'll discuss etc. further along. But there has been cases in the past, very famous cases that some listeners might remember the flash crash of 2010 where sort of the US market lost about $1,000,000,000,000 in sort of a 30 minute period of and rebounded straight away based on some somewhat shady high frequency trading, some big stuffing and spoofing. So it sort of very competitive space. And so a lot of analysis is done and that's why you see people in this space, typically with math backgrounds, that's background because that's what you really want, draw people who can sort of solve these problems and sort of figure out a solution.

Richie Cotton:

$10 trillion and like they're doing. That's a number that I find very difficult to imagine. That's a lot of cash.

Anthony Markham:

The 1 trillion listeners should look it up. The flash crash of 2010. Yeah, pretty incredible.

Richie Cotton:

I find it interesting that it's not just, oh, let's see how much profit we can make, all how much money you can make as a goal that you also have these ideas of like, oh, we need to do things like masturbate with liquidity or mess about with like how do we minimize risk? So just like there are a few different goals that involved.

Anthony Markham:

That's right. Yeah. I mean, you certainly can't just focus on making money. You're saying all sorts of examples of companies that have had bad risk management practices than just throwing cash around that being very irresponsible with their investing. As a result, they've gone bust, they've caused instability in the markets. So, yes, their risk management is a key aspect of it.

Richie Cotton:

Okay. That's actually a really interesting idea. Like the different skills needed for risk management versus some of the other some of the other goals like the straight up trading.

Anthony Markham:

Yeah. Generally speaking, people who are in a risk management area have more of a finance background, so they might have an undergraduate degree in finance or experience in risk management, whereas people like my role, for example, are mostly software engineers, some of the very technical background in maths engineering. So for engineering, but there is still a lot of work, a lot of building on the risk management side as well. There's a lot of models that are built to sort of model exposure and model risk. So it's a combination. You'll have risk managers and analysts who will look at stuff from a finance point of view, and you have software engineers like myself who will build out models that can analyze the data.

Richie Cotton:

That's pretty interesting. The idea that you need both that sort of finance background and you need like a software engineering background, but also you need like some kind of math or stat background as well. In order to make this this work. I'd like to go into a bit more detail on like what the data looks like and what sort of techniques are used.

Richie Cotton:

So I guess maybe let's start with with the data. Yeah. Yeah. What's involved?

Anthony Markham:

A lot of data. So yes, I certainly can imagine, you know, trading a thousand times a second as sort of an upper tier example there. That means you need to get to the millisecond essentially, right? You need constant updated prices for exchanges. You know, other people in the market. You need to know what's going on. You can't be entering a trade blind, not knowing what the price of something is or what the volume is or what the market's doing. So you can sort of break it down into two types of data. You've got historical data which would be like decades, and you stall and organize somewhere in sort of cold storage, and that's often for backtesting Strategies. So you'll be running strategies that you might help with on that previous data, see how I would have formed in the past and give that sort of financial advice. Past performance is no guarantee of future results. That's, of course, true, but it's valuable data. You know, you should be testing out as much as you can to get as much success as you can. And then you've also got data, and that's where maybe some of the engineering challenges really coming in, where you've got these data feeds and pipelines, a feeding and pretty extreme amounts of data from a lot of different data sources as quickly as I can.

Richie Cotton:

Okay. So the having historical data on what the price of something is seems pretty important. What other data sources do you use in addition to that?

Anthony Markham:

Yeah, I mean, so obviously having the price of the asset itself is pretty critical. But it also look at, you know, the interest in that so the bid and ask spreads you look at the volume historically how much volume has been traded. If there's a sudden increase in volume, what does that mean for the price? You know, a lot of people are buying. All of a sudden the price might go. You can sort of get a correlation with the lowest latency possible. That's a really big challenge.

Richie Cotton:

And in terms of the techniques you use, it seems like time series analysis may be the obvious thing, But can you talk me through a bit about like what techniques are used in these analyzes?

Anthony Markham:

Yeah, that's right. So Tom series data is what we deal with. We deal with, as I mentioned, a lot of data, often down to the millisecond, so broken down by millisecond timestamps so a lot of data that you can sort of visualize and you can do some sort of analysis on that that really comes into trading strategy. So for example, you might use some and I'll pay to, to look at natural language processing and you can look at what's happening in the market as a result of that. You build all sorts of models off the back of this data. And generally speaking, you know, there are so many models that you can build to develop this data in some way. And this is something you have all sorts of different views on the market by risk point of view from a trading point of view, from a wider perspective, there's a lot of analysis that can be done to sort of look at the data.

Richie Cotton:

Actually, I remember seeing a talk on sort of financial analytics a few years ago where they were saying that actually, although supervised machine learning is like an obvious thing to use for making predictions, it's very hard to make it work. In the case of stocks. And and I'm wondering whether that's something you've experienced or whether that's old hat now.

Anthony Markham:

Yeah, that's very much the case. I haven't heard of any companies really leveraging machine learning for actually trading. So in other words, using model that would say this time of year bitcoin's going to double the price or something, It just doesn't take into account enough factors. Typically speaking, if you build out something like a date and you're on it, let's look at this. It's often too hard to interpret. I think one of the big issues with with the Internet works is that it's really a black box. And I think when it comes to articulating strategy, you need to be able to understand what's going on there and you need to bring it back to the fundamentals, the market fundamentals. And I think with any price prediction using machine learning, you often see it online, you see the sort of tutorials always can predict the price of bitcoin. It's going to double quickly by now and 12 months. Yeah, it's not the case. These models often just rely on them and you wouldn't want to use an active trading botnet.

Richie Cotton:

So I'd also like to talk a little bit about technology, and I know you've been using Julia as part of your strategy, so can you talk a bit about what you're using for data analysis?

Anthony Markham:

That's right, yes. I love Julia. I think it's a great language and I've always seen it as the best of both worlds in that, you know, I really hope there's more adoption of Julia in that 5 to 10 years because it really hasn't seen much adoption in industry in the past few years. What is driving? I see it and the reason I use it is I see it with the simplicity of possible. So relatively simple syntax, pretty easy to understand. It's not complex like C++ or something like that. It still maintains a lot of the concepts, like the lot of frames that you see in Python. Floating packages are quite similar. It's very similar there, but it's significantly faster, right? It's got top declarations, takes advantage of the just by law. It's not you know, the global entrepreneur law isn't the thing and Julia like it is in Python so it's a significantly faster language so you're getting a speed of that low level language, but you've got the simplicity of what's something like the possible. So that's obviously one language. I think one of the reasons is you have a lot of people from diverse backgrounds. So you of course got people like myself who are software engineers at once. We don't see us for something, you know, it's our job to write code. But there are also people in financial institutions who have finance backgrounds. They will have that finance degree. They will have done something like that, and they won't know a programing language. I certainly want as it was to us and I should it's not video. So they need a language that's relatively simple to learn that they can just plug up some dart. I think that's about it pretty quickly without taking them days or weeks. And Path, of course, is really the industry standard then it's simple. The support is amazing. Lots of packages. You can do anything quickly. But Julia I think is great alternative again. So for those reasons that I mentioned, I think it's great for an analyst who has no software engineering experience to pick up and quickly just to work with because assets.

Richie Cotton:

That's pretty interesting. It's only seems like, well, Python is the most popular language for a data analysis right now. And so the fact that you've got this mix of the two is really interesting.

Anthony Markham:

That's right. Yeah.

Richie Cotton:

Are there any particular areas or use cases where you feel that Julia has the advantage over Python?

Anthony Markham:

It's definitely the space that I mentioned. Yeah. So we're working with large datasets, which is of course sort of a key part of quantifying it and upgrading the datasets. A huge we looking at years of decades of data and it could be broken down millisecond that huge amount of data. So, so that's why Julia definitely has a big advantage. I wouldn't say that it's certainly better than Python, You know, I wouldn't want to sort of tell everyone goes to a linear rate of often Python has its advantages. It really does the support out there. For example, Python is a million times what you have for Julia. Julia is still very much in its infancy in terms of the online support out there tutorials, courses, resources, packages that some stuff, especially for a relatively niche field like one finance. Atlassian has some great packages. Julia doesn't really have that. So so Julia has that speed advantage, but it's not the be all and end all. And that's why you're seeing most companies stick with Python.

Richie Cotton:

Okay, so having that support and the broader ecosystem, it is very advantageous. And there's that tradeoff then between ecosystem and speed of you code running.

Anthony Markham:

Exactly. Yep.

Richie Cotton:

And you mentioned that you also have some finance people who have to get involved in analysis and then need something really simple. I'm curious as to what happens when you start using different languages and you've got teams with different levels of technical ability. How does that work?

Anthony Markham:

It's usually pretty segmented. So for example, my development team like myself would be separated from my team of analysts or I'd be working on very separate work development with building models on the back end, I'd be writing C++ something low level to actually work on the back end, whereas an analyst is very much working on pivoting on that data. So they're going to take the data from the database, going can be some analysis line, make some plots, make some charts for a report, that sort of thing. So typically there isn't isn't that sort of disconnect where developers talking to an analyst about something in C++ and I have no idea what's going on know that's very much segmented in that everyone has a clear role there.

Richie Cotton:

Okay. Yeah, I can imagine. You know, someone without any technical background try to like color datasets is not going to work out. That's right. Okay. All right. So before you mentioned about risk management and risk analysis being an important part of the job, and it does seem like there's a lot can go wrong with algorithmic trading. So can you give me an overview of how you go about managing risks?

Anthony Markham:

Yeah, that's right. I mean, I brought up sort of the flash crash of 2010 before things are going to go wrong, I think is the attitude to approach with thinking they're going to go wrong. You are going to be on the wrong side of it, right? At some point, some sort of global event is going to happen. That is a systemic risk. The market covered black swan events, things like that that almost no one is going to predict. Right. You're going to be in a bad position. So so the key is really to have proper risk management so that if these events happen, you can manage it yourself. You're not super exposed. Suddenly Nasdaq app actually has a really good course on quant finance with a few risk management models that actually mentioned it and one from that right course is the file model, the all the value of risk model. And that's a pretty good model that it's a little bit of any risk management management strategy. So so yeah, it really is just very similar. Again, a lot of mathematical statistical models that you can use to constantly track your position, but I think always, always go wrong is often attitude. It's not a lack of technical skill, it's not a lack of the models, but it's attitude to their investment. That's where they go wrong themselves.

Richie Cotton:

Trouble That's interesting. The problem isn't necessarily just the statistics behind things. It's a cultural thing. Could you maybe expand on that a bit?

Anthony Markham:

Yeah. I mean, do you think it was some sort of some of these classes that had happened previously? I suppose FDX is maybe a very global case that everybody listening to be aware of. FCX had incredibly smart people, right? They were incredibly smart developers. You know, SDF was a giant straight line sort of confiance that sort of hires very intelligent people. He was a smart guy, but it was an attitude problem that right very clearly they were doing things that was illegal. It wasn't that they didn't have smart people, it wasn't they couldn't build out these models. It was that they didn't want it and that was doing illegal things. So yeah, so it's very much an attitude to risk management that is really the foundation rather than you want to itself.

Anthony Markham:

That's the stepping stone.

Richie Cotton:

Okay. So in that case, it seemed like because there was illegal activity, they were just like, okay, we we believe what we're doing. We're just going to go for it regardless of anything else that's wrong. Yeah, I think that's maybe not the case in a lot of organizations. Like you're probably still doing legal things, but they're still vulnerable to treating risks badly.

Richie Cotton:

Are there any sort of cultural warnings signs you can think of where you think, okay, maybe risk management isn't being taken seriously?

Anthony Markham:

Yeah, I suppose most companies have a compliance department. Do you have compliance essentially reviewing trades and even trades themselves? We're reviewing trades. So a very common thing is post-trade analysis where you will look at your trades. And as I mentioned before, it's a machine learning. You understand them. You should understand why those trades happened if you lost money. I'm afraid that's okay. You're going to something on trade every single day. But why did it happen? Why did you inquisition? Why did it come out badly? I think that constant addictive self-reflection is really important. And I think if you see the absence of that, if you see that absence of not really caring what absence of not really wanting to reflect the debris, and that's a concern.

Richie Cotton:

So I find this very interesting. So at Datacom, we have this idea of like intellectual honesty where, you know, if you do something silly, you got to like acknowledge that you've done something wrong.

Anthony Markham:

That's right. Yeah, that's right.

Richie Cotton:

So, yeah, that's absolutely fascinating that you do need some kind of review process and you do need to acknowledge where you've made mistakes in order to stop them in the future. Just on the subject of risk, how how is risk dealt with by analysts? Is it a separate team that's involved in thinking about risk, or is it something that analysts would have to worry about themselves?

Anthony Markham:

Yes, generally separate to have a team members and professionals who will do that and then developers who are geared towards building risk models as well. So people who are pretty professional at that, it's always something that should be very front line to everyone and the report is pretty open and comes to risk management. So a lot of the reporting is seen by everyone or metrics on a track for risk management. I say to everyone that people can have a pretty transparent view of what's going on. But yeah, there are specialists into this sort of thing.

Richie Cotton:

And on the technical side of things, can you maybe tell me a bit about like what they used to say, sorry, what the statistical techniques are to measure risk or are there any kind of analysis settings there?

Anthony Markham:

Yeah, of course. Yeah. You know, again quantified it all about masses. That's, that's, that's the core point I would give people. Right. It's all the maths, it's all about stats, it's all about a quantitative approach to this sort of stuff. So you think of all the sort of statistical measures that are out there. Yeah, probably apply them all to it to try to a portfolio that the model I mentioned that was covered in the DOT account course, the value at risk model of our will essentially look at a whole portfolio. So rather than looking at one track, it'll happen in a day of a million, it'll look at the entire portfolio and it'll try to estimate the maximum potential loss on that portfolio over a certain period of time with a set of confidence interval. So you could say for example, a one day, 95% confidence interval of $1,000,000 means just 5% chance that portfolio will lose more than $80 in that one day period. So it's trying to put a confidence interval on something. It's using statistics to say, okay, we're 95% confident that we're not going to lose more today. But then the individual trades, there's all sorts of metrics as well. And of course that's taken into account for entering into it, right? You've got standard deviation of variance and pretty simple things like that. You've got the better of a stock. So it means how sensitive the stock is to the returns of a benchmark. So a market benchmark of the market index, you got different ratios of ratios, risk risk adjusted returns or adjust returns for risk and all sorts of methods. Yeah, this is something you could talk about forever. All sorts of analysis. You can do that.

Richie Cotton:

Okay. So just seemed like there's a pretty comprehensive suite of possible statistics and possible techniques to use.

Anthony Markham:

That's right. Yeah. There's never been any sort of shortage of that.

Richie Cotton:

And I know, like, there's always people who want to know how to get a job. So I'd like to talk a little bit about how you get into this field of of algorithmic trading. So maybe can you talk me through like, what are the different data rules that are available?

Anthony Markham:

Yeah, definitely. I think the structure is very similar to a small tech firm. Firm. Some of the time might be a bit different. Monsanto's quantitative developer really just main software engineer. Yeah, different sort of titles, but yeah, very similar to what all the tech firms do have data engineers who would build that data pipelines, manage the back end databases, things like that. The data scientists, just like you would any other tech company or big welcome machine models and build models and things like that. Data analysts from all the analysts sort of side of things. You have developers like myself, quant developers, software engineers, same sort of thing, who got traditional software engineers. There's a lot of roles very, very, very much the same as a sort of standard tech company. It's yeah, everyone sort of has a pretty diverse skillset, but, you know, even their recruitment process is quite similar to tech companies these days. It's very hard to find difference there.

Richie Cotton:

That's interesting. So you really do have quite a wide range of choices, seem to have a background which you can come either from a finance background or from that sort of STEM background or maybe from a Yeah, software.

Anthony Markham:

Yeah, yeah.

Richie Cotton:

Okay. Are there any particular qualifications you need to get into the field?

Anthony Markham:

Yeah, I think the one thing that sets sort of the sorts of firms, quant trading firms, apart from maybe a traditional bank or an investment bank, is that there is a big focus on those quant methods of course. So any quantitative degree is advantageous. So mastery engineering degree, if that's degree, physics degree, the vast majority of people I know came from an engineering class background, physics, background, side backgrounds, things like that, just because they understand those statistical methods than, say, maths straight away, the finance can be taught and it will be taught entirely at any one trading for ability due to finance. You don't need a finance background and I think most people that often fall into these roles are people who don't really know what exists. It's quite a messy area. People don't think algorithmic trading. What is this? You know, not many companies do this. They're not really talked about much, but they will particularly target mass students on university campuses because, know, my students are really sort of built as problem solvers. And I have the best mind for this sort of stuff. And they understand, like a patient to finance the finance can be taught, but the maths that's much harder to teach.

Richie Cotton:

That is interesting. It's easier to teach finance to a math person than Matthew at a finance, but very also.

Anthony Markham:

Yeah.

Richie Cotton:

And from a data point of view, what are the most important data skills that you need in the field?

Anthony Markham:

It's quite similar to any other sort of data engineering role that a scientist role, whatever you're applying for and all the big tech companies, I think the back end infrastructure is going to be the same. You should be understanding information, although devices, no central databases, you should be confident in data pipelines. I really think that sort of recruitment process is very, very similar to any other data in role. Obviously, being counsel with big data is is important, but I think everybody is working with huge, huge datasets, whether it's a finance company or not. So I think, yeah, it's quite standard on that sort of side of things.

Richie Cotton:

I see you mentioned things like data pipelines and some of the more sort of backend roles there. We talked a little bit about the analysis tools you saw with Julia and Python on the backend, like what are the sort of tools used there?

Anthony Markham:

Yeah, so many languages, first class and of course industry standard these days, some companies usually or whatever. But similarly Python is a pretty standard sort of thing for everyone to know, so not even to develop it. But if you're coming in as an analyst, lots of time, a little bit of often, you know, learn how to work with data firms, develop, build and make a plot, something like that extremely beneficial to house everyone. Then if you're in a role to develop a software engineer, of course low level language is the key. We're talking low latency, we're talking high throughput with lots of data. So C++ on C, Java, some companies take advantage of go language like that, but they're incredibly fast, pretty fast. And I mentioned Kataeb as well. Kataeb too. This is something that some companies that work as well for the Time series database, they're sort of homogenous environment and then go down by side bit more standard again Katy based database but both relational databases.

Anthony Markham:

So PostgreSQL Oracle is quite common, but then also NoSQL databases as well. So MongoDB and particularly Cassandra Apache, Cassandra is quite commonly saying.

Richie Cotton:

Okay, so quite a wide variety of tools and particularly with databases. So with the CDB for Time series and then you got the NoSQL databases as well.

Anthony Markham:

That's right. And you often say in one company, certainly one, yeah.

Richie Cotton:

Okay. And you said that the companies will typically teach you financial skills, but are there any particular sort of core financial concepts that are useful to now?

Anthony Markham:

Yeah, I think just understanding training is the key thing. So, you know, understanding what a futures, what options hat options work, you know, what's a cool option, what's a good option, and what's the palatable option? It's sort of pretty key. Fundamental concepts are the most important thing. Yeah, What is an order book? What is buying and selling bids? It asks what's a spread? And I think a lot of that comes down to knowing what the company does. If you apply to one of these companies, you should know what they do. You know, are they a market maker? They are frequently trading. What's the tech stuff? That's probably information you can find publicly as well. It's probably public information. Someone with lots of talks on YouTube, on the website, things like that. So I think understanding what the company does and then learning a little bit about that, do they trade bonds, fixed income securities, Do they trade commodities? What do they trade? And then learning a little bit about that is a way to stand out.

Richie Cotton:

Okay. Interesting. So all of it's really about understanding like what is the jargon mean and what are the core concepts there.

Anthony Markham:

That's why I like any role. There's a lot of jargon that's going to confuse you, but if you can get a head start on that, it's going to be beneficial.

Richie Cotton:

And it seems like there's a bit of overlap between what you're doing with algorithmic trading and sort of more general data and technology skills. So how much of what you do is, is sort of finance specific and how much of it do you think is sort of a transferable or usable in other industries?

Anthony Markham:

Yeah, I think very transferable. I mean, I think anyone if we start at a software engineering background, anyone who works in quant finance could also go work somewhere else and vice versa. If you know C++, you could go work at a company, right? So it's a widely adopted language and very similar for anyone who go data raw data scientist Data engineer Yes, there are key things for me to learn about finance, about financial markets that may not be applicable to a typical tech company or some other role, but the tech is going to be the same or very similar. You're going to be saying what you're going to be doing, the pipelines you're going to do with relational databases, NoSQL databases, in-memory databases, all those core concepts are going to be the same wherever you go. So a lot of transferable skills in the tech industry, maybe for analysts, not as much because I'm with a finance background, a finance degree, you probably need to work in finance unless you're sort of making some sort of change.

Anthony Markham:

But that's not even necessarily true these days either.

Richie Cotton:

All right. Super. And do you have any final advice for anyone who's interested in algorithmic trading?

Anthony Markham:

I think just learning a bit about it, like I said before, is like anything, you know, if you're someone who's currently studying, focus on maths and starts driving a good mathematical back or has to just go back around, even if you aren't doing that as a degree, try to learn a little bit the core concepts on hard. You don't need to be a math genius. I think there's often an image that you need your math genius to work at these places. You really don't. And I think even thinking about sort of the interview process, the questions often aren't incredibly hard, but they're designed to see, I think. So you're not going to be awesome, incredibly complex mathematical problem. The problem is probably actually quite simple, but it will just take time to think through it. And what we want to see is how you solve that problem and how you break down that problem, how you articulate it. It's it's more important to think things through correctly than it is to get it right at the end. We want to see your thought process. So focusing on things like that, getting a good background in maths and stats, if you're a finance student, lots of positive. You know, I think it's not a hard language, it's a really beneficial wherever you go. Every company that days using Python somewhere, if you're doing analytical work to work with data, talk about you a lot. So I think a little bit of upskilling there. If you're a math student, maybe lots of finance. If you're a finance student, maybe like the maths lesson. So for engineering really, really help you out.

Richie Cotton:

I can see how thinking things through before you start is incredibly important with algorithmic trading. Like you don't want to lose the money after the trade you want to see it's going to go wrong beforehand.

Anthony Markham:

That's very much correct.

Richie Cotton:

All right. Fantastic. Okay. Thank you very much for your time, Anthony.

Anthony Markham:

Thanks a lot, Rishi, Thank you.