PETRA BAKOSOVA | from Hull Tactical

I sat down with Petra Bakosova, portfolio manager at Hull Tactical to explore algorithmic trading and math-driven investing. Petra’s insights were sharp and accessible.

Petra’s a math whiz. She studied applied mathematics in Bratislava, Slovakia and financial mathematics at the University of Chicago. Finance, not teaching, became her passion. She joined Hull Tactical, founded by blackjack legend Blair Hull, to manage the HTUS ETF. This fund uses algorithms to time the S&P 500, not pick stocks.

I asked about algorithmic trading. Petra explained it’s math-driven investing. Hull Tactical’s models analyze 30-40 indicators—think inflation, VIX, and sentiment. They decide when to dive in (up to 200% long), pull back, or rarely short. It’s systematic, not emotional. Blair’s blackjack roots shape this: bet smart, make many bets, manage risk.

Retail investors often blame “algos” for market swings. Petra clarified: algorithms are everywhere, but they’re diverse. They don’t collude. They’re tools, not robots, amplifying human strategies. This eased my fears of the robots taking over.

The HTUS ETF was launched in 2015 and is Hull Tactical’s core offering. It’s actively managed, adjusting S&P 500 exposure daily. Petra shared it’s outperforming its benchmark across multiple periods. Transparency is key—holdings and performance are public. I admire this openness.

For new investors, Petra warned against concentration risk. Don’t bet big on one stock. Diversify, even with small accounts. She praised ETFs and fractional shares for lowering barriers. She also urged the industry to ditch jargon and welcome everyone.

Petra’s a triathlete, training before Chicago’s markets open. Triathlon mirrors investing: stay consistent, know your edge, focus on the basics. Her weakest leg? Swimming. But discipline technique all.

For women investors, Petra’s advice was clear: trust your gut. Women excel at rational, risk-averse investing. The industry’s opening up—jump in. Visit Hull Tactical for daily reports and blogs. They’re on LinkedIn and Twitter, eager for your ideas. Listen to the episode and share your thoughts—does algorithmic trading spark your interest?

TRANSCRIPT FOLLOWS AFTER THIS BRIEF MESSAGE

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EPISODE TRANSCRIPT

Petra: Blackjack players. Some of their mantras would be number one, always, but proportionally to your advantage. And that's sort of what we try to do. We try to, like, estimate how much edge we think is in the market and, you know, bet proportionally to the edge that we perceive. Second of their mantras, they would play a ton of hands. They would play, I think, 100 hints per hour for eight hours a day. So you can do the math. I think over the five years, Blair probably played 200,000 Black Jack Hands. And so for us, what's really important is to be able to make a lot of bets. So unlike many managers who may only rebalance their portfolios quarterly, we actually adjust our exposure to the market daily. And we are, you know, obviously always exploring ways like, could we trade more than just once a day in the fund.

Phil: G'day and welcome back to Shares for Beginners. I'm Phil Muscatello. What exactly is algorithmic trading and how can it work for everyday investors? And is it something to be scared of? Can high level math really give you an edge in picking the right stocks? Today I'm chatting with Petra Bakosova, portfolio manager at Hull Tactical, a company that helps people invest by using math and data to make decisions about buying and selling stocks. Hi, Petra.

Petra: Hi, Phil. Thanks for having me on your show.

Phil: Thanks very much for coming on the show. Hull Tactical was founded by legendary trader Blair Hull. It focuses on a fund that tries to grow money when markets are up and protect it when they're down. So tell us about your journey that led to investing from maths. Where did you study maths and how did you find an interest in investing?

Petra: I was always interested in math and I got my undergraduate degree in applied mathematics back in Bratislava, Slovakia. And then I pursued graduate studies at the University of Chicago. And my graduate degree was in financial mathematics. Because even though I knew I like math, early on I realized I did not want to teach math. So then finance seemed like the second best thing to do if you enjoy math. And then, you know, Chicago being a big hub for commodity trading in the US Just like many of my classmates, I ended up working as a quant at one of the high frequency commodity trading firms here in Chicago. And then three years later, I was hired by Blair hall to initially work for a company called Ketch, um Up Trading, which was a futures trading operation. But right around the time when I started their etf, just got approval from the SEC and was going to launch the next year. And me sort of being the new quant on the block, trying to make a name for myself, I volunteered to work on the ETF, and that was almost exactly 10 years ago.

Phil: What is a quant?

Petra: Quant is a short for quantitative researcher, which is what, uh, all the mathematicians and physicists who do research on these quantitative models and algorithmic strategies usually call themselves physicists.

Phil: So physicists get in on the financial action as well.

Petra: They do. They are really good at you modeling different processes and understanding all the statistics. So basically anybody with training in math or physics can end up in finance and work on one of these models.

Phil: So can you explain the simple strategies that are embedded in the algorithms to decide when to sell or when to pull back or when to invest more?

Petra: Absolutely. So we are a little bit unique at whole tactical that we don't try, at least for the fund. We do have a proprietary trading side of the business. But as far as the ETF goes, we don't necessarily try to predict individual stocks, like which stocks are good to buy or sell. What we try to predict is whether it's a good idea to be invested in the broad market or whether it's a good idea to pull back. And the way we go about it, in simple terms, we would collect 30 or 40 different indicators of publicly available information. So we would be looking at macroeconomic information. So think inflation, unemployment. We would be looking at fundamental information. We would look at, you know, what is the median earnings yield or what's the median dividend yield of, um, a company in the S&P 500. We would look at sentiment information. We would look at the fear index vix. We would look at like the level of speculation in the market. We would look at the different sentiment surveys, different indicators from the asset managers. And we would also look at different technical indicators, and we would look at different seasonal anomalies. And then

00:05:00

Petra: we use our models. This is where the math comes in. So you collect 40 or so different numbers. But then how do you know how much weight to place on different numbers? So let's say the unemployment goes from 3% to 4%. What should you do as far as your investment goes? And so we put all this historical data through these back tests, and we basically test like, what is the optimal reaction to all of these numbers changing? So historically when unemployment went from 3 to 4%, what would have been a good move? Like, should you have bought more, should you have sold less? And then once we have these models, we basically every day we accumulate new data and if there's new data points coming that we haven't seen previously, we adjust the exposure. So if we think on average everything's kind of normal, or if we have all the numbers canceling out, we would be 100% invested in the market. We would say we take our portfolio and just buy some kind of an S&P 500 instrument which could be like a spy ETF or E Minif futures. And then if we think that most of the information gives us some kind of a bullish signal that we think this is a good time to be buying, we could go up to two long. And on the other hand, if we think oh no, we should really pull back, this is the right time to sell, we could, you know, sell all of our holdings, have all of our assets in cash and in fact like we are allowed to even short the market. But that is a very extreme position that we are pretty unlikely to take.

VIX, otherwise known as a fear index, spikes during recessions

Phil: So it's interesting you mentioned the VIX amongst all of that otherwise known as a fear index. And we're recording today it's the 17th of April here in Australia and 16th of April there in the United States. The fix has been in the news a bit lately. What's it kind of indicating to you at the moment?

Petra: So vix, you know, I think we have many of people in the industry today have been in the industry for less than 15 years and they sort of didn't live through the global financial crisis and they didn't really live through the dot com bubble. And so people got a little bit used to seeing the VIX index in pretty low ranges. It might surprise your listeners that the average value of Vix is actually 19, the median value is something like 17 because for so long we have seen VIX in these like 12, 13 ranges. And so now when we saw it spike to 30 or 40, it seemed like an unprecedented number, but it's really not an unprecedented number. We've seen vix in the 60s when the 80s before. But generally speaking we see VIX this fear index kind of spike and increase when there's a lot of uncertainty in the market and when there's, you know, potentially a lot going on. Um, it's not exclusive to recessionary periods, but oftentimes the vic spikes coincide with broader recessions.

Phil: O just recessions, not general market tumult and turmoil like we're seeing at the moment.

Petra: Uh, yes, it's usually both and it's hard to discern which one it is.

Phil: So it's not making, you have to make any adjustments to your investment strategy at the moment.

Petra: So we do make certain adjustments to vix. So VIX and the Fear index, it's a little bit of a misnomer and maybe a little bit of a oversimplification because a high level of vix, uh, doesn't necessarily just mean that the market is going to drop. It just means that the price is moving around. And so on one hand you sort of want to pull back and be more conservative when the market is really volatile. On the other hand, a certain strategy and strategies like ours tend to do better when the market moves around more. So if you're trying to sort of distinguish yourself from the benchmark, uh, you need the prices to move. So for us this is also an opportunity to, you know, create some trades and create some activity. So for active managers and systematic managers, a high level of VIX is not necessarily a reason to like run for, run for cover and high. This is actually the time to let the strategy do what the strategy does and trade more actively.

There's a view among some retail investors that the market is run by algorithms

Phil: PETRA I think there's a view among some investors, retail investors, that the market is run significantly by algorithms. They sort say, uh, what are the algos are doing? Or you know, things are dropping. It's the algos how much of market action is algorithmic and what kind of general effect does it have?

Petra: There's a lot of players in the market and I would say everybody uses technology, maybe everybody's an uh, overstatement, but people more and more use

00:10:00

Petra: technology. You know, the market makers, the people providing liquidity in the markets, they obviously rely on the algorithms to do the automated quoting for them. But also people who take directional bets, like the money managers, they also use models just like we do. And I would say even though for the sake of this simplification, everybody does use algorithms and technology, they're not all the same and they don't give everybody the same signals. So like, even if some algorithms suggest that, okay, we should, we should sell really quickly, there's always going toa be people on the other side of the trade who will see it as a buying opportunity. So I would say with the rise of technology, we respond faster and maybe the algorithms are able to generate market moves faster. So it may feel more scary, but it's not substantially different than what the situation was 20, 30 years ago when we had fewer of those deployed.

Phil: So it is really just putting into the investing space. It's really the way people would have invested anyway, but it's just given to a computer to help make those decisions. Is that how it works?

Petra: I would say so. I think the computers and the way people build these algorithms, they try to mimic what people used to do manually, just do it faster, do it more efficiently. I do it with less emotion. But generally speaking, these are not some, you know, totally obscure, unexplainable trades that would have never happened otherwise.

Phil: It's not robots taking over then.

Petra: Yes. Are you confused about how to invest? Life Sherpa can ease the burden of having to decide for yourself. Head to lifeshhera.com.au to find out more.

Phil: Liferpa, uh, Australia's most affordable online financial advice.

Blair Hull founded the firm in 2014 with the mission of deploying internal strategies

So tell us about Blair Hull, the founder of the company and about how the company has formed in a bit of history there, please.

Petra: Blair hall is kind of a household name if you're an options trader. Um, but not everybody knows that before Blair was an optionsce trader, he was a blackjack player. So in the 70s he would join one of these card counting teams and would go around casinos in Las Vegas and deploy a basically trading strategy that revolved around, you know, counting cards, estimating how hot or not hot the different decks were, and then making bets. So in a sense, sometimes he refers to his first investment experience and you know, later on, once he was done playing blackjack, he founded a really famous trading company called Whole Trading, which was a premier option market making company that was acquired by Goldman Sachs in 1999. And then for a while he stepped away from uh, trading and focused on different activities and sort of the global financial crisis and the losses that his portfolio incurred and everybody else's portfolio incurred in 2008 prompted him to sort of revisit the investment space. And he kind of dove into academia looking into what does the research say about how investors should invest. Are there indicators that would be helpful to investors that could sort of dampen some of the losses in recessions or just overall generate better risk adjusted returns than just buying and holding. And with the results that the group of the quants and Blair found over the years, they found it hold tactical. So we were founded in 2014 with the mission of deploying some of the internal strategies in a publicly available investment vehicles. So I think sort of like the investment philosophy of our firm traces a little bit back to blackjack. So blackjack players, some of their mantuas would be number one always, but proportionally to your advantage. And that's sort of what we try to do. We try to estimate how much edge we think is in the market and bet proportionally to the edge that we perceive. Second of their mantras, they would play a ton of hands. They would play, I think 100 hands per hour for eight hours a day. So you can do the math. I think over the five years, Blair probably played 200,000 blackjack hands. And so for us, what's really important is to be able to make a lot of bets. So unlike many managers who may only rebalance their portfolios quarterly, we actually adjust our exposure to the market daily. And we are, you know, obviously always exploring ways like could we trade more than

00:15:00

Petra: just once a day in the fund and then the third always try to stay in the game. So risk management has always been really important. You can only recover from drawdowns if you don't blow up. So have a risk management strategy in place and be really disciplined about risk management.

Phil: So it all comes down to one fund now, uh, doesn't it?

HTUS ETF forecasts six month equity risk premium using proprietary algorithms

Htus tell us about that fund and how it operates using the algorithms that have been developed.

Petra: Absolutely. So HTUS ETF started in June 2015. So we are approaching our 10 year anniversary and the fund has evolved a little bit over time. We started with one model we were forecasting. We're trying to forecast the six month equity risk premium. And for those who are not familiar with the term, the equity risk premium is the excess return clients or investors realize when they invest in the stock market as opposed to keeping their money in cash. And over time we sort of develop new models and we found that we are actually able and more successful in forecasting next day's equity risk premium. So we sort of shorten the forecasting horizon of this fund. But generally from the outside, the fund looks very much like it did over the last 10 years, which means we acquire a exposure to the S&P 500 that is right now centered around 100%. And then again, based on the outputs of the different models that we run under the hood, we either are over investteed or under invested in the S&P 500. So in a sense you would think about this fund as a replacement for a large cap equity fund for investors who are interested in doing something a little bit different than just buying and holding. So especially in the US it's fairly well accepted premise that it's a good idea to be invested in the market or have some exposure to the market. And most of the time people acquire this exposure through passive index funds. And what we're saying is that that's great. But we think you can do a little bit better than just a passive index fund. We think there's information out there in the market that allows you to slightly vary your exposure to the broad stock market and you can realize better risk adjusted returns if you do.

Sharpe ratio is the return of your strategy minus the standard deviation

Phil: So I know in this mathematical space we often hear something called the Sharp ratio. Is that part of what you plug into the models?

Petra: Exactly. So Sharpe ratio is basically one of the performance metrics that we are very interested in and that we hope to improve. So I would say the Sharpe ratio, again, provide some, um, basic definitions. Is the return of your strategy minus the return on the risk free asset divided by the standard deviation of this excess return. So basically how much volatility do you have to accept in order to realize some return over the return on the risk free asset? And basically what we try to do, we try to not exceed the volatility of the stock market, like the long term volatility of the stock market. And we hope to provide some extra return. So in a sense we hope to improve the Sharpe ratio of the buy and hold strategy.

Phil: Yeah, it's an interesting model when you look at it and you have a look at a diagram of what it means because it actually shows you. And I know we're getting into the weeds here a bit, but I do like going in and seeing this. But there's returns that are greater than the average. You need to take on more risk, but then you also want to make sure that your overall portfolio is not as risky than it actually needs to be. Uh, am I sort of simplifying it a bit too much there?

Petra: I think you're right. I think generally speaking you can always generate more returns just by leveraging yourself. But sort of the interesting question is, can you generate more return without. Or what's the most optimal way you can generate the excess return without just blowing up your risk. And that's sort of the premise of this whole fund.

Phil: And then risk and volatility are two sides of the same coin, aren't they?

Petra: Yes, basically, generally speaking, more volatility means more risk. So that's usually the measure. I mean, you can kind of like go more into the weeds and instead of measuring something like Sharp ratio, you can measure a Soino ratio, which only looks at downside risk, which is sort of the bad risk, if you will. But I feel like for the average investor, I think comparing two strategies on their Sharpe ratio is going to give them a very good intuition about what kind of strategy they're looking at.

Active ETFs are anything that is not just passive index tracking

Phil: So ETFs generally have a history of being known for mirroring indexes and being index

00:20:00

Phil: funds. But HTUS is actively managed. What does active management mean in the context of an etf?

Petra: It means sort of precisely what you just mentioned is anything that is not just, uh, passive index tracking. So I think when the ETFs first started in the early 90s, they were sort of just viewed as sort of like building blocks for advisors. So, you know, you just wanted to basically track some kind of an index. And what was the peak ETF was. The closer you could track an index with the least amount of trading, least amount of transactions, the better. And I would say when we first launched, even in 2015, active ETFs were a little bit fringe and a little bit frowned upon. There wasn't a whole lot of appetite. And over the years I think certain areas and certain asset classes have become really aggressive in terms of wanting, uh, the active exposure. I think bonds and corporate bonds are one where active management has always been fairly well accepted. But now I would say over the last couple years, even active ETFs in equities and equity space have become sort of demanded by both advisors and actual retail clients. And so kind of like to go back to the definition, active ETFs can choose to track some kind of a benchmark or some kind of an index. They don't have to. So basically the portfolio managers of these active ETFs have the discretion to buy or sell stocks. Obviously they have to follow their investment strategy that they describe in the prospectus. But generally speaking you can sort of pick your own strategy and follow that strategy.

Why run an ETF as opposed to say a mutual fund or a managed fund

Phil: So why was the decision made to run an ETF as opposed to say a mutual fund or a managed fund, as we say here in Australia?

Petra: So we sort of, I would say, I sort of remember this discussion and this was just before we launched in 2015. There were a couple different vehicles. We considered, we considered a hedge fund strategy and we considered a mutual fund and we considered an etf. And the decision about like hedge fund versus a retail product such as ETF or a mutual fund, it was really important for us to be available to the retail client. We wanted to make these strategies available to the public. We didn't just want to be available to qualified investors and have a hundred fifty thousand dollars minimum. And then the second decision point, which distinguishes a little bit between a mutual fund and etf, we really wanted to be transparent. And transparency has been one of our mantras ever since the company was formed. We like the fact that ETF Publishes their basket every day. And we like the fact that the public can see the daily track record of the etf. It's really visible. Anybody with any kind of brokerage account can buy or sell this ETF as they please during the day, anytime they want. The fees are really transparent. The performance is really transparent. And partially why we wanted to do this. Aside from wanting to build a successful investment product for investors, we also want to contribute to the academic discussion about, is this even possible? Is there even a worthwhile endeavor? Can you do better than buy and hold by having some kind of a strategy? And we thought having like the most transparent vehicle out there would make the discussion a little easier. Be like, hey, we don't have to argue over some back test. We don't have to argue over some hypothetical scenario. This is the performance of the fund. These are the holdings. This is what we did. This is how we did it. And you can decide if you like it or not.

Phil: Yeah, you mentioned backtesting there, because that's one of the ways that these strategies are developed, isn't it? That you have a strategy and then you apply it to m hindsight and see how it actually works so that you can help you make decisions into the future. That's how it works, isn't it?

Petra: Yes, that's how I would say most active FS and most quantitative fs, most systematic fs. That's how they all begin. Everything starts with the backtest and then you start trading live and you hope that the live performance looks like the backtest. That's the best case scenario.

Phil: So, of course, active management costs a little bit more than an index fund for obvious reasons. There's a lot more work that goes into making decisions.

You use industry jargon to explain complex financial topics

What is the management fee? Or I only just realized now that we in Australia, we call it management expense ratio, but you call it the management fee.

00:25:00

What is the management fee?

Petra: So the management fee for the H2S ETF is 91 basis points. And then the strategy acquires some other expenses through the different funds that we hold. So overall, the expense ratio for this fund is 97 basis points.

Phil: And, um, that's 0.97%, isn't it? In layman's terms, yes. Yeah, I heard a joke about basis points. That's a way of making people in the finance industry feel superior about themselves amongst more normal mortals. Yes.

Petra: I kind of. I kind of just caught myself, you know, like, you get so used to the industry jargon and you don't do it on purpose. It's like one of my Pet peeves when people use um, unnecessary jargon to explain things that could have been explained by more common terms. And I was just guilty of doing that.

Phil: O no, no, no, it's okay. That's part of what I do is ask those silly questions, but've been really great at simplifying what's quite a complex topic.

Having a systematic strategy makes it easier to stick with your system

So does mathematics help you stay calm when you're making decisions? When things get wild in the M market, I mean, does it give you more conviction? I guess that's another one of those terms that's used in the industry.

Petra: I would say it makes it easier to stick with your system. I think I talked about a little bit about how important is to have a plan for managing a risk and how important it is to have a systematic strategy. And they're not always easy to stick with. You know, we kind of saw big moves over the last two weeks and last Tuesday when our strategy wanted to go 140% long for Wednesday, that was not easy decision to follow. And we were happy uh, with how Wednesday turned out. But at the same time having the math background, understanding how the model works under the hood, understanding what drives the model, it makes it easier to actually stick with the system.

Phil: And how has the ETF performed? Has it outperformed an index fund?

Petra: So I don't have today's numbers, but uh, last time I checked we were outperforming month to date, quarter to date, year to date. We were slightly outperforming on one year basis and we were outperforming on a three year and five year. So so far so good.

Phil: Yeah. And that's all transparent for people to see, isn't it? As you say you want maintain transparency.

Petra: Yep.

One of the risks smaller investors face is concentration risk

Phil: So let's get back to giving some not advice because we're not allowed to give advice, but just some tips. Someone with a small savings account wanting to invest, what's the biggest mistake they can avoid when coming into the market for the first time?

Petra: Say one of the risks smaller investors face is concentration risk. If you don't have a ton of capital to deploy, it might be really tempting to pick your favorite company and just invest everything in one or two stocks. And that can be really dangerous because even the best company can have a bad year, a bad five year period. Uh, so I think it's really important to try to diversify your holdings even with a small account. So I would advise investors not to put more than 10% of their assets into a single stack.

Phil: Yeah. Because it's very tempting, isn't it, to Think that there's going to be that one life changing trade that you're going to make that's going to create so much, I think the terms alpha, isn't it in the industry that you're going to be able to retire on, um, just that one decision.

Petra: And you always see somebody on the Internet just talking about their 1000% return and it seems like free money. And people never talk about their bad traits. People only talk about their good traits.

Phil: Yeah, I know. It's really easy to get trapped in that sort of mindset, isn't it?

Petra: Yes.

How do you think investing can be fairer so more people can get started

Phil: So how do you believe that investing can be fairer so more people can get started? I mean, it sounds like that's part of the DNA of the fund that you're running.

Petra: Yes. I would maybe point out two things. One would be we as an industry, we should keep trying to lower their entry barriers. And I think ETFs are a good vehicle for that. You can invest in your favorite fund for oftentimes you can start with $25, you can start with $50. So you don't have to have hundreds of thousands of dollars to invest. So I think lowering the entry barriers gives access to more investors and it gives them a chance to start accumulating returns even without having large holdings. And I think recently a lot of brokerage firms started offering fractional shares, which also has been helpful because even if investors do want to invest in their favorite stock, their Apple, they re Nvidia or something, they don't have to

00:30:00

Petra: even buy a full share of something, so they can still diversify with pretty low investment amounts. And the second thing I would say we should sort of lower the information barriers and kind of. We joked about these basis points and the industry jargon. And I think as an industry we can do a better job just being more accessible and less intimidating by not trying to overcomplicate everything and by not using three letter acronyms every time you go out and try to explain something. So I think just making it more accessible will make it less scary to the everyday investors.

People often approach you with ideas or misconceptions about finance

Phil: So I know people that work in finance are often, you know, when they're gathering at a party or some social event, people will approach them because they know that you work in the industry. Do people come with you to you with ideas or misconceptions or anything like that?

Petra: Uh, people do. And we sort of. This is not something that's discouraged. This is something very encourageding. Like every time we talk somewhere, if I'm giving a talk at the conference or if I'm talking to someone like you. I do encourage people to look at what we do. We do publish our signal daily. We published all the different articles about our models and we always solicit feedback. I want to know, okay, if you read the article, what am I missing? What factor have we not been thinking about? What should we be doing differently so people do approach us with anything from hey, have you looked at this indicator? Have you thought about this indicator? Or different modeling techniques? Then it's always welcome. I always enjoy the conversations.

30% of our indicators came from different people pitching different ideas

Phil: So are there any ideas that someone's just come to you with out of the blue that change the way that you think or you might have incorporated?

Petra: I'm going to go ahead and say all the time, obviously we don't just take somebody's word at the face value. When people bring us ideas we always verify them, try to see if we can find some kind of academic research to back up their ideas. And then we always gather the data and update the back test. But I would guess that probably at least 30% of the indicators in the model right now came from different people pitching different ideas.

Phil: I got an example of one.

Petra: I would say there's like uh, one example I could list is we work with a company called NETD Davis Research and they have a number of their models, especially sentiment models that we ended up using in our system.

Phil: What's the sentiment model? What does that mean?

Petra: So they would aggregate these composites, uh, composite sentiment scores. So they will say they will take options based indicators such as the CULP put ratio. They will do manager surveys. They will look at uh, the level of buying and selling, of leverage and inverse ETFs and they sort of like blend all these different sentiment indicators into a sentiment model. And we use a model within a model. So we end up incorporating an entire model of their own inside our model.

Phil: Is that kind of quantifying something that's more qualitative do you think?

Petra: I think so. I think that's a fair way to say it because it's hard to sort of capture the overall sentiment in the market. I think we feel it now. Everybody's really freaked out about tariffs. But how do you distill this uncertainty into a number? And that's essentially what all these sentiment models attempt to do.

Phil: That's great. Distilling uncertainty into a model. I love that concept.

Petra loves swimming in Lake Michigan, even in a Chicago winter

So Petra, you're a triathlete. Where do you get the time to train?

Petra: Uh, usually first thing in the morning. Markets in Chicago don't open until 8:30 so if I can get Myself out of the bed around 5ooCk. I usually have time to knock out all my workouts before the market opens.

Phil: Uh, swimming in a Chicago winter too.

Petra: Right now we're in the pool. We are going to be swimming in Lake Michigan, but I think it's still gonna be at least a month away before I go anywhere near that body of water.

Phil: I was really shocked. I was body surfing a few weeks ago and there was someone from Michigan there and uh, he was body surfing and I said, where did you learn to body surf? And he said lake Michigan. Didn't realize there were waves and that it was such a large body of water.

Petra: Oh, absolutely. I remember when I first moved here. If you don't know if, let's say somebody like drops you in Chicago and you don't know where you are and you're just looking at the body of water, like on many days you would not realize

00:35:00

Petra: that you're looking at a lake because you can't really see the other end of the lake. And depending on the wind and the weather, the waves can really be huge. I've seen 10 foot waves in Lake Michigan. It's really asideight to see.

Po says there are parallels between triathlon and investing

Phil: So how does being a triathlete and apart from keeping you fit, how does it help you in your investing?

Petra: I would say there's, you know, every time I train and I do my little TED talk in my own head about the lessons between triathlons and investing, there's a number of parallels. I would say like any kind of endurance training, any kind of endurance athletes know that the number of touches is more important than one really impressive training session. And I think in investing is the same thing, like being consistent, being there day in, day out. Don't hope for some kind of Hail Mary trade, like some get rich quick scheme, just kind of build your portfolio day by day and just be consistent over many, many years. That usually yields better outcomes. And then I would say as far as like competing, understand again, understand where your advantage is coming from. Like you have limited resources, meaning like limited energy and limited fitness. And so when you're racing it's really important to understand like, okay, if I'm not the best swimmer, there's no point for me trying to like sprint, um, on the swim leg, get completely gassed and then completely die on the bike and on the run. Understand where your advantage is and try to pace yourself where you're not super strong and then try to really compete where you can. And then I would say the last lesson is triathlon is really famous for how much money you can spend on equipment. There's all these crazy fancy bikes and carbon shoes and wetsuits and everything, and watches and computers and really fancy nutrition systems. And those are all great, don't get me wrong. But at the same time, the engine matters. So I feel like in trading and investing, like we get really mesmerized by all these like really fancy machine learning AI models and we get mesmerized by FPGA supercomputers and quantum computers. And at the end of the day, again, keep it simple. It's important to understand where your model is coming from. Sticking with the model, even a know simpler regression model can get you really far if you have good data and if you know what you're doing.

Phil: And what's your strongest leg.

Petra: I am usually pretty equal between a bike and a run. Kind of depends on the temperature and how I wake up feeling. Definitely not a, not a natural swimmer, but once I get out of the water, that's where the day usually begins.

Phil: I've noticed that I've got a few friends who are, I'm a bit of a swimmer myself, but I've got friends who have taken on triathlete and it always seems to be the swimming that's the weakest leg.

Petra: You know, I think there's, there's a little bit of truth to that because the swim leg is the shortest in.

Phil: A sense, it's such a different physical use. And most people, you know, they run, they get on bikes, but then it's a bit more specialized, isn't it? Swimming.

Petra: Swimming is completely different. And this is what kind of drives me crazy. Like with biking and running, if you want to go faster, you just kind of bike harder. If you want to like run faster, you just like, you know, take longer strides, increase your turnover. But it's very mechanical with swimming. If you just try to swim faster without having good technique, you're just gonna splash around, create a ton of drag. You may end up actually going slower. So it's like a completely different beast in terms of how much technique is involved in swimming relative to the other two sports.

Phil: Yeah, it'say you got to swim slower to swim faster. I think the way it's usually, usually.

Petra: Po definitely applies to, uh, my swim performance most of the time.

Phil: So is there anything, any particular words of advice that you would give women that are starting investing? Because women often have a different mindset.

Petra: About this, say just do it and stick with your gut. I think some of the research that's been coming out actually suggests that women tend to be really good investors because they do tend to be fairly risk averse, which is not a bad thing. They tend to, you know, not try to go for these moonshots. They tend to make good, rational decisions. They often tend to stay calm, um, under pressure. So I would just say don't feel like you don't belong. Don't get intimidated by the industry and by what the industry may look like today. I think it's changing pretty rapidly.

00:40:00

Petra: And even through the course of my career, I have seen more and more women enter the industry and be really successful.

Phil: So how can viewers and listeners find out more about yourself and the fund?

Petra: Definitely there's many ways how to stay in touch with us. We have two different websites. One is the fund website. Uh, that's wwwoltacticalf funds.com where you can find all the information about the ETF itself. And then the second website we have is www.wholetactical.com, which is the advisory site. And I think that site is a lot of fun. We have our daily report for on the site where we explain how we got to our daily signal. And you can subscribe to this report if you think it's interesting and you would like to see it in your mailbox every day. There's also our blog, which I think it's a lot of fun to read. One of my colleagues, you and Sinclair, writes most of our blog posts. That's a great way to read about it. We also posted all our articles so that people can, you know, if they interested in the math behind the models, they can read the articles and tell us if they have any suggestions and then obviously follow us on socials. We're on LinkedIn, we're on Twitter. We uh, are always happy to talk to people, so don't hesitate to just give us a call if you have any ideas.

Phil: Petro Bakosova, thank you very much for joining me today.

Petra: Wonderful. It was, uh, wonderful being on your show, Phil. I really appreciate the opportunity.

00:41:44

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