235: How traders can compete in the markets and find profitable edges - Jason Strimpel

I've been following you for a while and I can't wait to dig into some of that content today. Now our official title is uh it's gone through a couple of revisions, but here's what we've got at the moment. How traders can compete in the markets and find profitable edges.

Now I saw you actually say this, I think in a more eloquent way. Um, how can non professionals compete in the markets? It's a very good question. Uh but before we even get into all that, how about um share us share with us a little bit of background on yourself just so that we can get some context on our discussion today.

ended up doing a quantitative credit risk analysis for derivatives business and then market risk analysis and then kind of left the the grind of trading, moved up the career ladder, got into quantitative risk analysis, um, moved out to Singapore where I did that for a egg egg trader.

Did some capital uh venture investing in crypto, um, built a data and analytics team for a metals trader, and all the while kind of using Python, learning Python, and of course trading, trading typically options uh US stocks and options. In the background.

But the the earliest memory I have was my friend's father was a stockbroker and he drove a Cadillac. And to my to my own dad, the Cadillac was like the creme de la creme. That was the vehicle that everybody wanted. And I was like, oh, this gets dad, stocks, Cadillac. That's gotta be good. Um, and kind of ever since then, it was just a just a hunt for uh hunt for complexity, hunt for alpha um to this day.

backgrounds and experience. We could go any direction. And y you when you said something about um how to compete in the markets, that really kind of resonated with me because, you know, we've seen the stats out there. Yeah, most traders fail, right? It's a very Kraftmarke. Well sorry, it's a very tough way to make money. The markets are really competitive.

Um, there are people in the markets who have a lot more resources than than you and I have, or maybe you've got more resources than me, but you know, it's really uh really a difficult way to uh make a living. Uh why do you think um yeah why do you think it's important to have a discussion like this outside of you know just the high failure rate.

Uh whereas on the discretionary side, you're looking at charts using your intuition, using your experience. And I think a lot of time when we hear that stats, it's a lot of the discretionary traders that aren't up for it. And I think when you're just starting, the easiest way to get started in trading is like pull up your charting software, look at two technical indicators.

see where they cross and then you enter your trade. And I I think where people get into trouble is they assign luck to skill. And as many experienced people will know, there's very little edge in any strategy. And I would posit that most for most beginners, there's zero edge in any sort of discretionary trading. And if you're making money, you're probably just getting

Um, so that's kind of the harsh truth. I think that it takes a lot of market tuition to learn. Um so on the other end of the spectrum, like. when we think about algorithmic trading, we think about using data to capture market inefficiencies and try to then determine with statistical significance if you're getting lucky or if you've actually found something interesting.

Uh a lot of the time it's not sustainable to look at charts depending on, you know, what time frames you look at and and there's no real way to to verify that what you're doing actually works. And Yeah. Um, you know, one of my experiences is when I I joined a prop trading firm and they uh yeah, part of the package was we'll teach you all these trading strategies and you know, I was trying to apply them and they didn't work. And then I actually went and back tested them.

And uh they were rubbish. And there's always this thing like, oh, there's nuances to this and that. You've got to get a feel for it. But like, how can you test that kind of stuff? So um in your um your work with traders, I know you've worked with um thousands of traders, do you see do they just start in quantum? or algorithmic trading or is there a progression to get there? Like how do they how do other people that you work with get to that space?

But you then start to automate those strategies. You start to use, you know, there's very good accessible trading software that is based on these kinds of things. You can do your technical analysis, you can do your indicators, and you can automate a lot of that. Um but I I tend to find that the longer people are in it

the more creative they get in the way that they think about finding these repeatable patterns in the market. Um and they get more creative in the way that they can tell stories. And I'm kind of segueing a bit into like this concept of edge, but They they get very, very uh they get outside of just price, right? And I think that's the biggest inflection point that I found in most experienced and successful people in this is that.

you move away from just looking at price and you start to look at bigger relationships. And the more you look at the bigger relationships, the more you need data, the more you need po more powerful tools, not just Excel or your Trading View to start to bring all of this together. And with all of this data, then you need more s sophisticated techniques. So that's, you know, where you start to get into that quant side, which is statistics.

and even machine learning, um, to apply patterns and detect those patterns. So that that's kind of the evolution that I've generalized over time for most of the people that are, you know, through my courses and that I talk

And then try to trade that out of sample. And the good part of that is like it's easy. Relatively speaking, you can get data pretty easily. You can run some loops and, you know, novice programmers, intermediate programmers can kind of do this thing. But of course the downside is every single there's there's thousands or not millions of computers doing exactly this same thing.

And they're doing it at scale and they're doing it every second and every nanosecond, twenty four hours a day. So you know, all of these market inefficiencies and all these kind of nuances in the market gets squeezed out and there's nothing there left to compete against. On the other end of the spectrum, you've got these this kind of concept of a market inefficiency.

But I I typically shy away from the data mining approach. I'm not trying to brute force optimize anything. I much, you know, I'm I'm trying to take a more creative approach and more thoughtful approach. uh to come up with a story and then test that story, test that hypothesis and find evidence to suggest that there's something.

all of the constituent refined products and they can sell all of those products for it's called$120, then there's a$40 margin, right? So this is the crack. And as that crack spread increases. You would expect that the refiner margins are improving. So the therefore you would expect stocks with a high level of exposure to these refined products and these input products, you would expect that those stocks would increase in value.

So, a great example of this is that doesn't always happen. Now, if the crack spread starts to expand, you can measure quantitatively. whether the stock is increasing or it is not increasing. Now you can look at it on a chart, of course, but you know there's some fairly straightforward statistical methods that you can use to determine whether that stock is increasing as well. So that is a market inefficiency if you think about it, whereas

You know, the the fact that this crack spread is increasing implies that the my the refiner margins shouldn't be improving and that should be priced into the market immediately. There are times where that's not priced into the market and you can take advantage. 拜拜

understanding of the markets or some other concepts to even get to this point where you're like, oh, here's an idea. I'm going to test this. Like where where do these ideas come from?

This isn't all about looking at the markets and all about charts. It's all about like being very curious about the links between the markets, like the cause and effect that happens. There's a lot that goes on outside of the price aspect. And as soon as you can get your head out of the price action, out of the limit book and start looking more holistically, you kind of put yourself into this um this idea creation mode.

And you can start to get creative and start to tell these stories about how this stuff should behave. Um, now it sounds like very kind of lofty and theoretical, but You know, you've been doing this for a very long time. I've been doing this for a very long time. And eventually you just look for ideas.

So like you said, it's not easy. So if you if you kind of just expect to rock up and like have a list of 30 ideas of which 25 will work, it's not going to work that way. It really takes time and a high level of curiosity to kind of dive into

in the markets tend to rally and the first couple of days. Sorry, the last couple of days in the month tend to rally and also the first couple of days. No idea why. It just happens. It's a repeatable pattern, 80% win rate or whatever, depending how you trade it. Um So there's this observation. Trader B, he may work in a fund. Maybe he's like, Okay, we get all this money that we need to deploy, um, capital deployment last week of the month and the first couple of days.

uh you know, and all my fundmates do the same thing. So, you know, the the market rallies. I've got this story. I understand why. Now both of these traders have observe the same pattern coming from different methods. One has a story or a reason behind it and one doesn't care. It's just like the data's the data. How do you think about that? Like how do you rationalise? Is one more accurate than the other? Or more correct. Correct?

And yes, there are cases where there is just pure statistical anomalies and you can take advantage of just pure statistical anomalies and there's no real explanation for it. But in your example, if you're seeing like a rally at the end of the month or something that's repeatable like that, you might conclude that there's something driving that force. Okay. Um so I think in that case, the curious trader, trader A, would go on, do data analysis, assess

Collect evidence whether that this is actually persistent. Um, I think it would actually they would act they would also uh try to test whether statistical significance. So to your point, you know, maybe it happened the last three months or the last six months

Well, there's a difference between observing something and actually being to able to express your view to take advantage of that thing, right? So the algo person would go run some back tests. They would try to model that market inefficiency. And once they have a model for it, they can continuously back to us and improve and see if there are statistical. Um I think on the other end of that spectrum of trader A, they'll just be like, Oh, this seems to work. Let me like

Let me just wait until Tuesday, two weeks before the month end and I'll just wait for the pattern and kind of click it. There's no real curiosity there. There's just kind of like, let me look at the prices and and go. On the other end of the spectrum with the um

with the person who understands the rationale why, I mean, ultimately they will probably take the following steps. They might just have a head start because they have that intuition as to why that's happening, but they should ultimately still try to model that. run it through their analysis to try to determine if there's statistical uh statistical significance in a way that they will continuously be able to exploit that pattern for for process.

Um, so that's the kind of ideal scenario, I think, for folks. But it's not always true. Like in principle component analysis, which is a statistical technique, I get this all the time. You run a bunch of data, you get n and number of components that explain the variation in your data. And people are always at, well, what is it? Well, there it's nothing. It's a it's a statistical pattern in the data that we can't necessarily assign a label to, and that's uncomfortable.

Uh and if you're trading on this thing and you don't have necessarily a label to it and you don't know why it's reacting the way it is, that can be very uncomfortable because you don't know how to handle it if things go wrong. Um so I think there's some real practical reasons why working backwards from like an economic reality is is

air quote better than just coming up with a pattern statistically and trading that. But in the end of the day, if you're consistently making money in a pattern, I mean, that's kind of what

So it definitely makes it, I think, more um psychologically manageable in a drawdown, which is, you know, one of the I guess the biggest challenges for traders is how to manage. yourself in a drawdown, but um I think that's you touched on a really good point there. D do you think that um

D do you think that so the example you gave before about the crack spread, uh y you're essentially, I guess, doing intermarket or inter asset analysis, right? Looking at the relationships. Do you think by expanding into those uh and in other markets or assets, does that make an idea more robust if you're you're getting confirmation from here and here, combining it all together?

um as a proxy. And that's a pretty innovative way to think about things. I I I also think that beginner folks or even experienced folks, they kind of get a little bit narrow minded. It's like, this is my market. This is what I trade. I might look at two uranium stocks or I might look at two oil stocks or I might do some relative value thing between two assets or between two futures or I look at the futures curve.

But if you can start to construct, you know, spreads in futures markets that reflect some economic proxy and use that as a way to signal the buy or sell of an asset that responds to that economic proxy. I mean that's a very powerful, um very power powerful mechanism. And you know, what what your question kind of made me think of something else. There's

dozens of different ways to express your view in that one trait. And that's something else that that I think um non professionals can focus on is if your if your signal is saying go long Valero. Well, you can sell a call spread on Valero, or you can buy a call, or you can sell put.

You can express your view in many, many different ways. You can trade something other than Valeria, you can trade Phillips, or you can put the crack spread on in a different way, like instead of a 3-2-1 crack spread, a three-two-three crack spread or some different combination. So there's many, many different variants.

in in how you can express your view in the market based on that same strategy. I mean, we're not even talking about volatility here. Like you could even look for signals of volatility and put on straddles in the options market. di dozens of different ways to express your view in this single strategy. So not only can you use intercommodity prices to try to get an edge, but you can actually express your view in totally different markets.

And I think that's one of the most exciting things for retail traders now is like you spin up your interactive brokers account, your thinkorswim account, you have basically access to the entire world's markets. And you can do this type of analysis now with the tools that we have at our disposal.

And I I uh at first it was kind of like a throwaway comment, but it's it rings really true. Like if you are risk on, twenty-six years old and risk on, then go ahead and and punt on out of the money options, right? If that's your Um, if you are 42 like me and you want something slower, then you just run a monthly factor strategy where you push a button once a month and rebalance your portfolio. So I think you can you know diversify by running multiple strategies.

you have the mental capacity and wherewithal to deal with that added complexity. I think something people forget about is like the operational risk that you introduce. by trying to do sophisticated strategies. Like if you sit down at your computer and your discussion of your trader, it's just you in the market and your mouse and you're clicking and looking at the ladders and the limit book and I'm simplifying it obviously. Um and but if you're running like an end-to-end automated system.

then not only do you have to like find and capture edge in the market, but you have to make sure all your code's working and you're not making mistakes and your hard drive doesn't crash. So I think there's like added complexity at some point becomes not worth that added risk to take on the operational side. Um, but I guess to more succinctly answer your question, yeah, if you can measure and if you're looking for diversification, you can run a intra week option strategy.

And try to collect premium and run, you know, collect cash, and then every month rebalance a stock portfolio and you get some pretty good uncorrelated returns that way. Um, again, if you can deal with the the added complexity of running.

Maybe that's why my trading my training's slowing down and everything else. Yeah. Um, but uh yeah, I I like I like what you're saying there. So I wanna um go back now to a a comment you made very early on and I said I was gonna revisit this one.

Can't remember the exact wording you used, but you said you're hunting for complexity. Um What what does that mean?

Tons of this overly complicated stuff that I put in. And just try to simplify it. And I've learned through many, many thousands of dollars of lost money that you hit diminishing returns by adding a lot. So I think it's my personality that I've just been on the hunt for like hard, complex things to deal with and Um but now I'm trying to trying my best to keep things as simple as possible.

But how how would you apply this actually maybe you want to explain first what first principles are, what that concept is, and then um you know how how would you apply that to quant-based or algo-based trading?

Learning that from first principles would be going through the underlying theory, the underlying mathematics, understanding what the test results mean on a very, very low level before you start implementing. Um so applied to kind of algorithmic trading, I mean, I I kind of see it like first of all, what is algorithmic trading, right? It's taking I see it as like using computers, obviously, using statistics, using mathematics. To automate

Expression of edge in the market. And that can take many different forms. So, what are kind of the first principles that you need to know? I think Data is important. I think on getting a very good understanding of like market microstructure is also very important. This is like basically how the limit book operates.

Um spend a month like scalping futures contracts and a paper trading account on like your ladder screen, and you'll get a really good view of how the market behaves your time. You have to understand like Uh you know, if you're aggressively trying to get a price, what are the trade offs? If you're passively getting a price where the

I think next level are the tools. So, you know, things like literally basic statistics that you learned in undergrad university. So you can get really far with linear aggression or multivariate aggression. Um, I think a lot of people overcomplicate it and they look at very, very high-end, you know, countless times people are trying to pump data into a neural network. It it's just like Start simple, start with the underlying statistical like clustering.

um classification, regressions, those three techniques alone, you it opens up a whole world to you. So those techniques plus the tools to do it, like SciPy, SciKit Learn, Stats Models, all powerful Python tools that I talk a lot about. I call it the Python Quant Stack. It's like the ten libraries that that kind of you use for this stuff. Um, I think some basic machine learning can get you also very far. So I mentioned like clustering, classification, these things can get

They're pretty simple, but they can get you very far. Um, and then I think obviously this ability to think about the economic relationships. And think about why Valero, for example, might be rallying with no news, right? It's not because some 50-day s moving moving average crossed over 200-day moving average. Look at the oil markets and look at the Arbob and look at gasoline and see what that crack spread is doing. So that foundational kind of curiosity and the ability to link.

the economics throughout these markets I think is also super important. Uh and then programming. So You know, I I like to say Python is the new Excel. Python is kind of like table stakes these days, or you know, your your language of choice R Python. I'm not gonna get into the flame or over what language is best. Obviously PyQuant news, so I'm a Python person, but um Once you can get good enough at these tools.

you just open up an entirely new spectrum of possibilities to yourself. So, you know, you kind of asked about first principles. I I think it's these buckets of tools, these buckets of activities. that are are really critical for the non professional investor and trader to really get

might hear that and go, Well, that's so overwhelming. How do I do that? But it's really I think um Uh what I've figured out from you by following all your content is you can take a lot of these complex ideas and really simplify them. So I'm gonna come back to that at the end and ask you for a simple answer to how people can can get started there. But um we got a we got a bunch of questions in the chat as well which I'm I'm going to uh

raised you in a minute. Cool. But first I wanted to kind of switch gears a little bit more towards finding signal. Especially in the noise. There's some we got some very noisy markets. Um and you've you've talked about finding signal a lot. Um first of all, what do you even mean by that? What what is finding signal?

Signal, yeah.

That appears in an otherwise random process. Okay. And that is what I consider a market inefficiency. It's some structural issue. in the market that's causing a consistent pattern to repeat itself. And it's our job to amplify and detect that pattern and then figure out how to exploit it for profit and then of course measuring if it's actually random or if it's actually repeating. So with that on the with that kind of on the uh on the table.

A lot of the concepts that folks are used to hearing about, you could consider market inefficiency. So think about a mean reversion process, right? We all know that volatility means. And if you consider volatility as a random process and you witness me uh this mean reversion all the time, well the question then is how do we Exploit, we're seeing the signal. So, how do we measure this and how do we exploit it? So, a very, very simple example could be like

take this is the z-score of your volatility, right? And if that z-score it goes above or below three or minus three, it's at some extreme. So this should catch your Now whether that's gonna work.

Consistently or not, I'm making no comment on that, but that's just an example. Um, you know, another way you could do that is try to forecast volatility in a way that is better than the market. So there's these arch models, there's modified arch models, there's been Thousands and thousands of pages of the yeah.

academic research on forecasting volatility. But if you can model volatility in a way that's improved over the market, then you can price your options more accurately and you can find yourself in a better position. So these are the types of things that that you think about. Now I'm it's not easy, right? I'm not trying to make it sound easy. Um I think there's often a lot of signal in these economic relationships.

Uh pairs trading is is very popular. Uh pairs trading is you can imagine two assets that are linked. Economically linked, think like Apple and Foxconn, and that that relationship breaks, right? There's a dislocation in that relationship. Well, that relationship tends to be together. So can we exploit that temporary break?

uh for for an opportunity to profit. Um that could be a signal for you. So, you know, these are the kind of spaces where you want to look, you know, these economic you find the pairs that are economically linked. geopolitical shocks to everything that's happening in in Eastern Europe right now causes all sorts of opportunity to look for big market moves based on these geopolitical shocks, supply and demand.

So there's some counter positioning that you can take advantage of there. Now, of course, the question is, can you consistently do that in a way to consistently make money non-random? Um, I think that's that next level question. But to your point, yeah, absolutely. There's this hurting behavior. We've seen this in most trends are because of this behavioral aspect, right? Like

New information isn't priced in fast enough, then all of a sudden it is. And then you've got this herd behavior like NVIDIA, right? All of a sudden the world woke up and realized that every single generative AI application is based on GPUs. And guess who's the best provider of GPUs? NVIDIA. The whole world woke up to that. I mean, NVIDIA's gone up, what, a trillion dollars in market cap in like 12 months? Cl clearly there's some hurting going on there, be some behavioral thing.

Yeah, so there's there's definitely that.

um then you can absolutely capture some edge. Now this is a portfolio-based strategy as opposed to necessarily a you know single asset or spread strategy. I think the pairs trade is always a good one. It just depends on how big of a universe you can search. Uh and then I'll say it again, the c the crack spread is still working. Um there's a uranium trade that's been quite good. So if you can find all the uranium names.

Um there's some regulatory constraints that impact certain uranium producers from exporting to the US that you might be able to exploit. So we've seen that trade work pretty well over the past couple months too.

So the crack spread, I keep going back to this because it's like taking money from a baby. Um, but you can trade the futures, you can trade the options. Obviously, if you're an options trader, you know that there's an infinite number of ways you can express your view in the options market. Um that's such an underrated way to continuously capture edge on an idea. Um For sure. You know, some somebody said something. I I read it somewhere. It's this concept of a luck surface area.

Like the more you read, the more you consume, the more you know in an intentional way, the broader the surface area that you have that helps you get lucky with stuff. And I kind of see it with trading the same way. The more you know.

the more opportunities you'll be able to put together. You'll be able to piece the puzzle together, put two and two together in more ways than your competition. And that's what edge is, right? It's that it's making money on the margins where you have some informational advantage for some Um so that's kind of the perspective I take. The more I can consume in an intentional way, the more opportunities will present themselves.

uh what's the word? Not sector, but yeah, one domain and and applying it to another and you know revolutionizing that. So I really like that you uh what did you call it? Luck surface What's the concept? Lux surface area. Yeah, I really like that. It's pretty neat. Yeah.

Once a week, like let's look at a strategy and go five thousand words on that strategy. Um, but yeah, he's he's back.

Hey Jason, I fully appreciate that the correct way of doing quant trading is having a trading strategy based on some economic relationship and then improving it using machine learning or AI. My curiosity got the best. Of me and I've tried using data mining to build trading models using economic features without providing a economic relationship. All validation tests have passed and the models keep working during live trading. My question is

The models cannot be fully understood, but they make money. Does that invalidate them?

you know, the appropriate level of risk management just to protect the downside. I I don't want, you know, I I talked about data mining and and I don't want folks to get so caught up on definitions, right? Like ultimately, if you've got a model that is statistically significant. And you've got enough data points to show over the long run it can consistently make money on a non-random basis.

It's just important that you understand the theory and most importantly the assumptions behind that model. So for example, does your model assume a normal distribution of asset return? Well if the answer is yes, then you're violating that model because your asset returns are not

to be normally distributed. I mean that's a pretty known thing. That's why a lot of time series don't work in the markets. And that's why the markets are hard because they're non-stationary, right? And and all these things, you just need to be careful that you're not violating the assumptions of the model that you're using. and make sure that you're doing the appropriate testing on the back end to show statistical significance.

And put on your risk management. And who really cares if you can explain what this column of data means if your model is performing and it's um and you're sure that it's correctly, accurately representing what you think it is. Yep.

And it means two very different things. If it's a part-time hobby thing, then you've got to manage your expectations that you know you've you've got to carve out the time. If it's a full-time thing, then you've got to treat it like a business. Okay. So that's kind of like the very first conclusion that you have to draw for yourself. Now more practically, I think the best way to do this is to get the market intuition by literally staring at the markets for like a month.

Get yourself a paper trading account and just Just stare at the limit book on these things and actually enter limit orders and enter market orders and you know in your paper trading account buy the offer and sell the bid and see what happens and see how the market reacts and see how Like, you know, all of a sudden there'll be a five tick spread in a market because some news comes out and the liquidity dries up. Well, what if you had a position on it? What are you going to do about it?

the more intuition that you can get on how the market behaves, the more comfortable you will be. Okay. So after that, I think you gotta learn the tooling. And again, if you wanna get into technical analysis, then figure out like how these things are calculated, talk to people, understand what works, understand what a breakout means, understand what consolidation means, understand what patterns to look for, a lot of rote memorization, a lot of pattern recognition.

Um if you're trying to go the quant route, learn Python. Full stop. Learn Python. That's it. Take a take a$19 Udemy course. You know, you can read my newsletter. I publish, I think I've got 80 weekly newsletters out now. You can read them all. You can join the newsletter and get them in your inbox.

You can take a$19 Unity course. You can do whatever you want, but get yourself very comfortable with with the Python syntax. Loads and loads of free content out there, free courses, paid courses, paid content. Um spend three months. getting good at Python, spend three months getting good at Pandas, P-A-N-D-A-S. So p pandas is the data manipulation library. It's at the heart of everything we do in quant, everything we do in in data science. is this pandas library.

And then all the while, just read. You know, read about the markets. You know, listen to better system trader, listen to people with experience, listen to people who have done this, read about their exploits, read about their losses, just read, expand that luck surface area. And then after three or six months, you'll be in a pretty powerful position to start. Okay. Then you can kind of start thinking about how to apply all this knowledge to the

Um, we go all the way through algorithmic trading where you're literally uh connecting to an interactive brokers account with Python, entering trades, downloading data. Um it's a pretty broad and I would say relatively deep treatment of all the tools that you will become very familiar with in a career in in um in quant with with Python.

Um the balance curve is the amount of money I'm winning. So I assume that means open equity. What I mean is I'm making money consistently, but my equity curve, or maybe closed e equity, struggles to meet the balance curve. Well What what would you recommend they I'm trying to

It's for my camera. Um, you might be, you might like I used to trade options when I just started and I was dealing with like 50 cent spread. So I'd get stuck and have to pay up to get out of these trades and I would just get smashed because of that. So are you trading illiquid securities? Are you trading liquid securities? are you know are you buy you know are you are trading market orders. If you're experiencing a lot of averse, you know, market reaction, try limit orders, try trailing

Stops where if you buy at fifty, the stop loss will follow you up the price curve. Um, these are types of things that you can do to kind of reduce the volatility in your equity curve. Um, if you're In an okay, so I think I know what you're saying now, Andrew. So a lot of unrealized gains, but then when you go to realize them, maybe a loss, right? So That's probably a discipline problem. Uh which is not unique, right? Getting in is the easy part, getting out is the hard

Um, so it's probably risk management. Double down on your risk management. Think carefully about how you're managing your risk, think carefully about the types of orders that you're executing, the size of your transactions, and the securities that you're trading. Make sure you're trading liquid assets. Make sure you, you know, start with some tight stops. Try trailing stops. Um, and make sure that you're not um you know trading market orders all the time and getting smashed on the spread.

So if you're trading a portfolio of assets, so I think the best use case for Zipline Reloaded is these factor-based portfolio strategies because it has a factor mechanism that you can rank factors and build these portfolios. And it but it's slow. Um Vector Bt is exceptionally fast. Like talk about three or four million simulations in three seconds. I've written four or five newsletters on Vector Bt. I know the developer Oleg, but you get yourself into trouble.

because you can easily data snoop and look ahead into the future because you're applying your calculation. So it's more about the type of strategy that you're employing. And um the right tool for the job. So I teach both of those in the course that I mentioned to give you a very good insight into when to use each, what an event-based backtester is, what a vector-based backtester is, et cetera. That's a good question.

And I was doing this every week and I did it successfully. I had the same strategy. I was selling like fifteen net delta cost uh iron condors. I was covering them at ten days to expiration, the exact same trade. Every week for eighteen months and I think I lost money twice.

I've also got this course that's closing on Saturday night US Eastern Time. If you're interested in that, jump on. And then you can email me at Jason at PyQuant News if you'd like to get in touch more personally.