Trading using Python with Jason Strimpel – PyQuant News

[00:00:25.21] - Speaker 1
Foreign.

[00:00:38.17] - Speaker 2
Team. Welcome back. We had a couple of live session earlier. Very excited for this one. We are here today with Jason from Piquant News and I'm just gonna just pull up your website the second Jason and then we're going to introduce yourself. Very excited to be here. I think we've been talking over chat and over email for the past few weeks. We've been doing some pretty cool stuff. Excited to be here and maybe I'll get you to introduce yourself and then we can go through also your website, what you do and then we can go into the session.

[00:01:12.05] - Speaker 1
Yeah, great, Fabio. Thank you, Jason. I'm the founder and content creator at Pyquant News. Basically, Pyquant News is a great way to get started with Python for algorithmic trading, quantitative finance, market data analysis. I teamed up with Fabio because he's got the data, he's got some great analytics and I've got the Python. So if you're interested in getting started with Python, and that could be if you're a trader, if you're a financial professional, if you're a complete beginner, each one of the things that you're seeing on the screen there, these are my newsletters that go out. It's a free, free newsletter, 140 issues, 138 issues over the last couple years. Each one is like a Python tutorial that you can copy and paste and run yourself. And this Saturday, Fabio and I teamed up. We're going to be using the mentor queue data to actually back test a gamma strategy. And I'm sure over the next hour or so, Fabio will talk a lot about that.

[00:02:05.29] - Speaker 2
Yeah, absolutely. So thank you. And we're very excited about that. So first of all, we actually have really nice and exciting data sets that can be used to build quantitative strategies. So the goal of today is really to show you some of the things that we do and then we're going to go into kind of like how JSON is kind of using the data to backtest. So we recently released a really new platform and some new models, some new data sets. So we have our new scoring system here. We're going to show you what we do with it. We have our new dashboard and we have a lot of like, really interesting data. But most of all, we provide gamma levels. So, for example, looking at option data, we provide you with levels on your chart that can help you understand where the market could go and where the market could bounce. So, for example, here I'm just pulling up the spx. The market was kind of in a downtrend, we bounce perfectly on this one day minimum, which is really using option data to predict the minimum level for the day. And now of course, maybe some news has just come out and we are now kind of breaking down and we are in a downtrend.

[00:03:16.08] - Speaker 2
So as a trader, basically having these levels on the chart can help you understand, okay, where can I take my profit? Where can I take my stop? So if you were short here, this could have been an interesting area to, to stop. You know what jax is what gamma levels are. We teach you all of that also as well. And basically then you can kind of look and see where the next target is. We do the same thing on all the other assets. So we have all this data on any trading companies that you care about. Like in this case, look at Nvidia, we bounce all the way down. Imagine you didn't have that sort of idea. Where do you stop, where you take your profit and where do you look for a reversal? So the goal of this data is really to use the power of options data to then basically define where can we go in, where can we go out and how we can manage our risk. So that's the whole point. We also introduced last week our Q score. So our Q score is basically a scoring or rating system. Think about it similar to what analysts in the industry produce when you have buy sell recommendation.

[00:04:25.27] - Speaker 2
This is not a buy sell recommendation, but the goal of the score is really to show you if an asset is bullish or bearish. So when we open the spx, we can immediately see we have a lot of red here. We are in a negative gamma. We are in a very low option options scoring tool. So 1 is the lowest, 5 is the highest. We are in a very high volatility environment and we are in a very bearish momentum. So as you can see today, the SPX kind of like it is in a very strong downtrend. Of course there's a lot of news in the, in, in the market. But this allows you to understand bas how to, how to look at the data. We also have historical data for each of the scores. So what's interesting is for example, looking at seasonality, when the seasonality here dropped from very positive score of 3 to very negative score of minus 3, we saw a few days later like the start of the drop, right? So looking at this kind of type of data could be very interesting. And then what we can do with that is we can then build strategies.

[00:05:32.05] - Speaker 2
So we're not going to show you how to build A strategy like this today. But with Jason, we're going to show you how you can then look at data and start using Python to potentially create some interesting insights and results. So here, for example, this is example. And Jason, we can do this maybe in another time using our score. So the way we do it is we select the universe, we create a set of rules. So we look at, in this case, one score only. We look at seasonality, and we look at seasonality score greater than zero. We buy at the market open and we sell at the market open the next day. So it's the day trading strategy is one day holding period. And then basically this is kind of like the performance return. We have the SPX as our benchmark and then our seasonality strategy here and there you can see all the quantitative metrics. And then of course, you can also look at return historically, seasonality and sensitivity to initial capital. Let me know if that makes sense. Let me know if you guys have any questions. And then Jason, I don't know, maybe like, talk about your process.

[00:06:43.10] - Speaker 2
We can talk about how you use Python to do this kind of analysis and the importance of using Python for this, for example.

[00:06:52.11] - Speaker 1
Yeah, it's great. If, if any of you have been following my content, you'll probably know that I'm not, not a huge fan of technical analysis. And I, and I just want to draw the distinction between what Fabio is showing and the kind of general idea around technical analysis. So when you, when you look at the levels on the charts, those are actually being generated based on the options market. And the options market is known to have a ton of information, right? Options traders are extremely sophisticated, very smart, much more so than equity traders, unfortunately for all of us and the information that they are giving us in the markets, the Mentor Q platform is consolidating that, putting it on charts. So these are actually really quite powerful ways to identify areas that you can start to adjust your portfolio or start trading. So this is interesting with Python because there's one thing to kind of look at these charts and trade manually. And when you're trading manually, you run into all the problems that traders face, which is emotional control psychology. If you're losing money and your account, you know, is flashing red, you know, people tend to panic, especially if you're just getting started.

[00:07:59.09] - Speaker 1
And the best part about algorithmic trading or trading is that there's none of this, right? You just build the system, you collect the data, and you let the algorithms do all of the hard work for you. And that's really why we love Python. And that's really why we love algorithmic trading. So when I think about this type of thing, I always try to look for an edge. And an edge is that market mispricing or that inefficiency that allows us to continuously capture profits without just getting lucky, right? If, if you flip the dice 50, 50, 50 chance you're going to get heads or tails. You don't want that in trading. Imagine at the end of the month when you get your paycheck, if your boss came to you and said, let's flip a coin, 50% chance you get your paycheck, 50% chance you don't. That's not a way to run a business, that's not a way to make consistent profits. So we want to find edge in the market where other people may not know, right? And this, what you're seeing on the screen, is an exceptional way to start finding that edge. So that's always where you start.

[00:09:00.23] - Speaker 1
And I like to link strategies to so called economic reality, right? I don't like to look at random data on charts and try to aggregate it into moving averages, etc, I like to look at economic links. And what you're seeing here is exactly that, right? You're seeing the options market giving us data and information on what they think these levels should be. And from there I'll create a simple back test. Fabio, I think you've got a Google Doc open. So this is a newsletter, this is a draft that's going out on Saturday against the Piquant newsletter and you can sign up for this for free. Fabio flashed the sign up page. But what that article will do was we'll walk through how to actually write the Python code to back test a very simple strategy that just looks at those zero GTE gamma exposure levels. So you'll see this kind of imports. And Fabio was kind enough to send me a CSV file. I think you guys can get that out of one of the pro subscriptions for Mentor queue. But all of this is paste data that you can or code that you can copy and paste.

[00:10:05.14] - Speaker 1
And if you want to skip to the very end, Fabio, you'll see kind of the performance chart of the strategy which does show an improvement over the S and P over this very short window of time. So this is the general way that I take when I build algorithmic trading strategies. Now there's of course a lot of nuance to this, but this is a very simple way to get 80% of the way there. Then you can kind of iterate on entries and exits, position Sizing, et cetera.

[00:10:31.06] - Speaker 2
Yeah, that makes sense. And also like going back to what Jason was saying, like the option market has a tremendous amount of information that for most traders is very hard to capture because of the complexity of the data. So this is why also we created Mentor Queue, because it's really like we want to use the same approach used by large institutions, which is really taking a lot of complex information coming from a lot of different sources. We are using options, but we are actually building out models that are going to look at macro data, like earnings return, like all this kind of stuff that can predict where the price could go, Right? So if we look at, for example, let's take an example here, right? We look at Tesla like we are in a very, very bad downtrend and we look at one of the models coming from the option data and we can see immediately that as soon as we see these spikes going from like an, like a balanced market to a put market, which you see here already is like put bias, green is called bias, you start seeing like the strong movement to the downside.

[00:11:42.15] - Speaker 2
So here is of course driven by catalyst, by news, by event, but at the same time, the option market reacts to that. So the option market can be a sentiment indicator that can tell you, okay, like there's a lot of fear in the market and everybody's like bidding on protection on Tesla. And as a result, the price kind of follows the trend and becomes like a magnet.

[00:12:04.11] - Speaker 1
So sorry, sorry Fabios, just you mentioned the complexity of the options data, right? I just looked up and it says the average daily trading volume for U.S. equity options reached 48 million contracts in January. There were 1.2 billion contracts traded. And on the Chicago Board Options Exchange, only average daily volume of 14.8 million options contracts. Imagine trying to compute and calculate and store all of that data every single day. Not only that, but compute the analytics like the gamma exposure. That's what you're seeing here, which is, which is pretty amazing. Having been in the Options Market for 20 years, it's pretty amazing to see. See that you guys can do this.

[00:12:47.01] - Speaker 2
And I'll show you a chart as well. Let me bring this up because, just gonna bring it here. Basically this one, this chart that we presented also last week is why as a trader you need to look at options because the trend does not stop. So we were, we started our company in about 2020 and 2021 is where we saw the first time when all the Gamestop saga started to happen. The option volume racer passed the activity for the first time. This was 2021 right here. And this is the data as of end of 2024. So we are already in 2025. And as Jason mentioned, the volume is still very, very strong. But as you can see, 2024 was really the strongest year for option volume. This is like the average daily volume for 2024. And then of course look at the big trend coming from the early 2000 where options were not as important. Right now they are and they tend to drive a lot of the market and a lot of the flows. So looking at option, I think is for sure 100 important right now.

[00:14:00.29] - Speaker 1
Fabio, why don't you talk a bit about what gamma exposure is all about and how you generate the levels with your, with your software?

[00:14:08.14] - Speaker 2
Yeah, sure. So we use of course our proprietary model. So what we do is we take the option chains of we cover about over a thousand assets. So those are the most liquid names, but we also cover futures. So we cover, if we go here, we have our futures levels. So we cover things like gold. Yes. Nq. So like forex futures, crypto, soft commodities. So if you are like a futures trader, you will be able to access our level on all these assets. And if you are a stock or index trader, we cover about a thousand names. So from the Max 7 to smaller companies to indices, ETFs. And basically what we do is we look at every day at the option chain and we come up with these models. So here, what we see here is our net gamma exposure chart, which really shows you the most important levels where there's going to be a reaction. And why is there going to be a reaction? Because of the way the market makers will need to hedge if those prices are reached and if those levels are respected. So basically we look at the full option chain on over a thousand companies.

[00:15:25.20] - Speaker 2
So a lot of data. And then we also create like all this chart looking at different expirations. So if you are like an option trader or an option seller and you are looking at maybe taking a position around March 21, then we can show you the expiration for March 21. And then we can also simplify the read of the option chains. This is really taking the whole option chain, aggregating all the data in a nice table and showing you the importance of this data. So for example, if we look at Tesla 31 of gamma expires tomorrow, right? So that means that after tomorrow a lot of option activity will expire and therefore there could be a reaction because of the way the market makers need to hedge. And these edges will be released so they will not need to hedge those 30 of gum anymore. And therefore that could cause some technical reactions, technical moves, obviously very, very important. And basically then what we do is we created an integration with different trading platforms and we create, we bring those charts that you see here into so these levels that you see in this chart into your trading application.

[00:16:40.19] - Speaker 2
So whenever you are looking for a company or an asset, you can simply go and look at the data right there. So here we have our watch list. We can open up, you know, our QQQ. We can open up all our different stocks, ETFs and indices. If you trade NQ, you can also use the levels on NQ. Yeah, so basically again, this is very powerful because what we've done is you come in, in the morning, you set up your indicators and then all the data is there for you. And on top of that we also have the intraday updates. If you click on our intraday data, we can also show you something very powerful I believe, which is the changing gamma throughout the day. And also you can also integrate the intraday levels directly into the chart. So what you see here for example, is the changing gamma from the beginning of the day. So here we're looking at 7:45am which is the pre market data. And now we're at 12:30. And then we can also go back in time 11:30, 10:30, 935. So you can kind of replay the day and see how gamma could have affected your price action for the day on the asset.

[00:17:57.29] - Speaker 1
Fabio, do you, do you guys have intraday futures?

[00:18:01.22] - Speaker 2
Not currently, no. So the way it works for intraday you can actually convert. So you can actually come here and access our intraday levels. You can update those. So this is all done on the back end. So you guys just have to add the indicator to the chart and then we can come here and convert Basically the, for example in the case of you, you could convert the NDX data or the QQQ data directly on nq. And this is like intraday data. Here you see the update and here you have your NDX intraday gamma levels directly on your NQ chart. You can overlay that with the end of the day data on the future. So we are looking at future option versus index option. And then see the reaction right there, which is pretty significant.

[00:18:55.23] - Speaker 1
Yeah, there's some, some questions from the audience is why I ask.

[00:19:01.23] - Speaker 2
Nice. Yeah, let's see.

[00:19:05.15] - Speaker 1
So Trader Cat was asking about Intraday. I think you've answered that. And then Mayank is asking about Commodities, futures. I think the same answer applies, right?

[00:19:14.08] - Speaker 2
Yeah, exactly. So for commodities, very simple. We let's say you trade gold. We can come here and within the indicator just convert for example GLD to gc, for example. So you here you have your, your gold future and then basically just tick in a box. It's very, very simple. You come to GC and now you have your intraday GLD levels right there. So very well, it takes like 20 seconds. If you're looking at crude oil, you could convert USO levels. If you're looking at for example the dow, you could convert DIA levels if you're looking at mbt. So for example, another one that is very interesting is converting levels on Bitcoin. So we have the levels on the etf, which is the ibit. And then we could actually go and convert these levels on Bitcoin. These are again intraday levels coming from the option chain of the ETF converted on Bitcoin.

[00:20:23.29] - Speaker 1
So we had a question also, Fabio. Can we use Python for Ninja Trader, Tradestation, Trader View, what's tv? I'm not sure what trader TV is. Which of them is better suited for algorithmic trading? So I've been a trader, I've been a interactive, interactive brokers trader for 15 years and I use the interactive brokers API. I think that for trading, algorithmic trading, Trader Workstation is probably one of the best. I like that they don't sell your order flows like for example, Robinhood. I know people like Robinhood, for example, they sell all the retail order flow to Citadel and then Citadel executes on behalf of Robinhood for the, for the client. And obviously you're just getting worser fills. I mean, there's been lots of studies that show a lot of these platforms you just get really bad fills, especially in options. So Tick Blaze, okay, yeah, Tick Blaze, I actually partnered with Tick Blaze, been talking to them quite a while. So I think that the key when you're algorithmic trading with Python is that all the algorithmic part you want to do in Python and then all you want to do is send your orders to your broker of choice.

[00:21:27.22] - Speaker 1
And the reason you like to do that is because you have infinite flexibility with Python. You know, Dan, you're mentioning some Python tools. You know, I call this the Python quant stack, right there's back testing tools, data analytics tools, machine learning, AI tools, risk pricing optimization tools. I write about these constantly in my newsletter if you want to check it out. I posted it in the, in the chat. And the benefit that you have is that you can do all of this market data analysis in Python with an infinite amount of flexibility, using any number of tools that you want. And then you just come up with your order, right, all the trading signals, you're streaming live data to Python. You can do all the real time calculations and when you hit a signal, you just send your order off to whatever broker that you want. At least that's the way I've been doing it for a very, very long time now. I should say that I've been around before Trader View and Trading View and I've been around before Mentor Q. So like, I didn't have these tools at my disposal when I Learned this stuff 15 years ago.

[00:22:28.17] - Speaker 1
Now with what you're seeing here, I think you have a lot of power at your fingertips just by looking at these levels and, and triggering your trade manually as well.

[00:22:38.01] - Speaker 2
Yeah. And also we are going to release our Tick Blaze integration very soon. So that's definitely one that is coming. So very excited about that and I agree with you. I think from my experience, even when I was working@Bloomberg TWS, I think Interactive Brokers is very good at their API. I don't particularly love the execution within the platform, so executing trades within the platform, but I think they're very, very strong when you look at APIs. So that's why you have platforms like Motive Wave that connect to Interactive Brokers and basically you just use the Interactive Broker as a destination. I know tinkorswim had a strong API. I don't know what's happening now because they were acquired by Schwab, so I'm not sure the status of that before, but I think a lot of traders were using the tinkorswim API as well. That was very strong and I would say yes, Interactive Broker is probably the best option for API and order flow.

[00:23:38.26] - Speaker 1
I guess at least for execution they've got like the smart routing technology, et cetera. But you're right, TWS is a little bit clunky. It's an old Java app, it's a little bit clunky. There's a lot of complexity in dealing with order, like the life cycle of the orders. But all that said, like, nobody said this was an easy game. So, you know, you can look at the chart like what you're seeing on the screen and probably get just about as good as results if you've got that kind of emotional control.

[00:24:07.08] - Speaker 2
Yeah. And I think the other thing, why, why also we're going in the direction of leveraging more data than emotion is because when we look at Emotion, we have an emotional bias that basically prohibits us to take that to take execution. Like for example, we were talking about this in the live trading yesterday. So this was when we had the -4% like a few days ago. I don't have the levels now, but I could actually go and get it. Basically like the, the during the day. We broke three important levels on these three occasions. So we broke our put support here, which was a strong move to the downside. We broke our Jackson and then we broke our one the minimum and then we went all the way down here. So a lot of traders struggle to take this move because they wanted to make sure that the price could rebound. So whenever you are putting your bias because you don't think the market can go further down, then that's when you maybe hit on losses and then you, obviously your emotion gets into play. But if you look at data, this was a very strong one time, two times, three times where the market was supposed to fall because of those big levels that were kind of it.

[00:25:24.03] - Speaker 2
So I think that's why it's important to embrace technology and that's why we're also partnering with, with you Jason, because I think what you do is actually in line with exactly what we do.

[00:25:34.25] - Speaker 1
So which is, yeah, you've got the, you guys have the, the data that's hard to generate in the, in the levels and then you know, we can wrap that around instead of something like TradingView, for example, you put that in Python and you can apply any number of different tools, calculations, transforms you want within the programming language to that data and you can modify it, tweak it at a premium, at a discount, you know, look for signals. I think the example that I've got coming out on Saturday, I take your zero dte, so your near term expiration, which I think you've caveated that it's not always zero day expirations or zero days to expiration, but it's like the near term expiration. And I basically said buy at the support levels and sell at the resistance levels and you end up with a very compelling equity curve over the six month period. And it's a very, very simple. So the blue line is the strategy against spy and the orange line is just the buy and hold. So you just buy spy. So in August of last year the strategy accelerated above and then it kind of came back in line over the last couple months.

[00:26:50.28] - Speaker 1
But you see a lot of potential in these types of strategies and it just comes from a simple CSV export from Fabio's team. And 30 or 40 lines of Python code using some data manipulation. It's a great win win. It's a tie up. It's a great combination of the data that Fabio's team puts together plus the ability to put the program language on top of it. Trader. I get some questions around integrating Quant strategies with some of the commercial platforms. I'm an open source person as you can probably tell. But nowadays guys, you just use ChatGPT and you can translate code from Python to Ninja Traders code or Pine Script or whatever the language is. So I think there's a huge opportunity for folks to take a lot of the Python stuff and apply that into some of the commercial software. Now I should add that a lot of the commercial software is not fully featured in the sense that Python. So for example, like Python you can run machine learning algorithms, clustering algorithms or supervised learning algorithms, which you can't run in Pine Script or in the Trader Workstation Easy language. So not all of it will translate, but I think some of it will.

[00:28:14.10] - Speaker 1
The more simple stuff in Python will translate. And ChatGPT is a great, or cloud or whatever your favorite LLM is is a great way to start looking at things that.

[00:28:22.09] - Speaker 2
Yeah, absolutely. Yeah.

[00:28:25.06] - Speaker 1
That, that said, I think learning Python is critical. If you guys are trading, you will 2x your productivity, you will, you will do well to spend a couple hours a day spending time learning Python. I'm positive of that.

[00:28:41.01] - Speaker 2
And I mean I can also relate to that. If you look at our finance is now changing the big asset managers a big like funds are changing the way they hire people and they're now looking more at, not more no longer analysts being able to use Excel but more like quant analysts like being able to use Python. Right. Because data is becoming very important. When you look at Portfolio manager is no longer the portfolio manager that knows how to do fundamental analysis and knows how to read the balance sheet of a company is the quant portfolio manager. Right. So so the one who is able to build a strategy using data. So this is, this trend is basically I've witnessed it for the past five, six years. So that's why also we we study Mentor Q Because like the way institutions are looking at data now is basically very different to what the retail flow does because of course the know how the technology available. But it's important to keep up with that because otherwise you, you'd be left at the disadvantage. You know, it's just more is becoming a more competitive space out there for sure.

[00:29:43.14] - Speaker 1
And, and I mean you're, you're basically providing tools and data at the institutional quality to retail traders. So like, you know, you're, you're leveling up retail traders to get ready for the institutional setting with this hardcore quantitative analysis. But yet you're giving kind of non professionals that same level of data, which is a superpower. Right. If you think about, you know, I get a question, if you don't mind me riffing for on edge for a second, Fabio, but I always get this question, you know, if you're publishing your trading strategy, then won't the market like remove the edge and won't it become unprofitable? That's typically true only when the institutions start trading that edge. And the reason is because as retail traders, I mean, I'm trading 1 lots, 5 lots, 10, 10 lots on options. Right. Institutions are trading thousands lots, Right. Or tens of thousands of shares. So they have this huge swell of money that's moving into the market and that's what erodes edge. Not the 10,000, not the 100 shares here, the 50 shares there. So what you're looking for are frictions where the institutions can't trade the edge. And there's all sorts of reasons you find these frictions.

[00:31:00.04] - Speaker 1
But ultimately what, what you're seeing here is data and analytics that institutions typically trade that are being exposed to retail. So in the retail kind of arena, you now have an edge because you're trading against retail traders with kind of institutional quality data, if you know what I mean. So it's kind of an informational edge. And using the data that you're getting here, or even starting to get Python as a tool in your tool belt, you can start to out compete your, your retail peers.

[00:31:31.13] - Speaker 2
Yeah. And it's also like, for example, there is also a reason why if you, if you read the large quant funds, you know, like the renaissance of the world, all those funds that have highly returned, they're all capped to a size, you know, it's not replicable. You cannot have a trillion dollar making 55% because your commission costs would have a very strong impact when you go into the market. So that's why like it's much easier for a retail to be able to go in and out, not moving the market and still get an edge, like you said. So.

[00:32:03.27] - Speaker 1
Absolutely, absolutely. So, so, Trader Cat 5, if you don't mind, I might drop a coupon code.

[00:32:10.29] - Speaker 2
Yeah.

[00:32:11.18] - Speaker 1
One of my courses. So I, I offer tons of free stuff, 138, you know, free. Yeah. If you want to click on that second link, the Python Foundation So Trader Cat asks if I've got a beginner friendly course for non programmers. That's what this is. It's typically 300 bucks. I'll give you guys a 50% discount coupon if you'd like to take the course. If you're not willing to spend the money, I think you sign up for the free newsletter and you're going to get tons of, tons of value for free. That's what I've been doing for two and a half years. I pump a lot of free value out into the world. That's my only marketing is high quality free content. So if you want to have a look at the course, then by all means you can do that. I'm going to drop a coupon code in there in a second. Again, if you're not into spending money, then please have a look at the free content that I put out. It's all quite good stuff. I don't think Fabio would partner with me if he didn't have the content was good.

[00:33:10.15] - Speaker 2
No, absolutely. Like, I think what you provide is absolutely amazing and I think for everyone who's coming into the trading world, you know, like we get a lot of questions like what are the skills that we would need to become a successful trader? I think learning how to manage data, it doesn't mean that you need to become a Python developer to be able to be a good trader, but the ability to be able to handle data would definitely help you and it's going to help you in the future for sure.

[00:33:37.07] - Speaker 1
Yeah, yeah, for sure. And having access to this stuff, I think like options data, like you said, it's complex, it's hard and it's also very expensive. I mean getting, getting the retail options data right off the exchange that you guys do, it's very difficult to actually get access to number one and then do anything with it, number two, if you can even get it right. So like, kudos to you and the team for being able to get this stuff, manage it, do the analysis and then make it available to folks. I think it's a huge, huge.

[00:34:06.28] - Speaker 2
I think the other part, JSON is not only being able to have it, but being able to deliver the data reliable because like, you know, like what happens if your code breaks and tomorrow you don't have the option data and you have an algorithm that is trading off that data, suddenly then your strategy breaks and you're basically bringing losses because of the consistency of the data. So whenever, for example, I was selling data to institutions, the biggest part was not, not the only the Good quality. But the reliability, like what happens if your system goes down and what happens if you can't deliver the data? Like we can't put a billion dollar into a strategy if the data is not reliable, you know, that's why, you know, Bloomberg is the, the leader in the space because they are reliable. It never goes down 100%.

[00:34:56.20] - Speaker 1
Yeah, there's, there's always stories about Bloomberg having really bad prints and. But you're right, it's always available, absolutely critical. You don't want to be in the market and having positions on, and not having data. I've got some horror stories from back in the day where not even the data would flow but the. Do you know, do you know tt the X trader program, the ladders, it's like what. Uses scalp ways to trade on that. We had our own version of that and the thing broke and so all these guys were in the market and they couldn't even see their positions and we had to call the exchange and just a nightmare. So very important to get that stuff right.

[00:35:29.20] - Speaker 2
Yeah, absolutely. And, yeah, and I think, yeah, having the models, having the intraday data, you know, and then of course I think that the key, the key thing is having the levels on the charts. As a retail trader, you can set up a TradingView account. You don't even need to have the premium one, you can have the free one. And then if you want, you could actually connect your TradingView account, Interactive Brokers and you could actually trade here from the levels. So it's, it's pretty, pretty nice what you can do nowadays with really not a lot of technical knowledge because you could set this up in a matter of 20 minutes.

[00:36:05.22] - Speaker 1
Yeah, yeah, it's brilliant. Is the newsletter included? If I get foundations? Yeah, that's right, Dan. So you can subscribe to the newsletter separately or if you buy the foundations, course you'll get the newsletter. In either case, the newsletter is always there, it's always free. Um, I would suggest at least getting plugged into the newsletter. There's really no downside. If you don't like it, then you just cancel the subscription. I mean, on, on Monday or, excuse me, on Saturday, the very first post is going to be using data from, from Fabio's team. It's going to be these, these gamma exposure levels and it'll show you how to do a super simple strategy with, with using Python in a very simple levels like strategy. And look, and if, and if you're like reading all this code and you're like, what is this? This makes no sense. To me, no problem. That's where you might want to explore taking the course. But if you have a little bit of exposure, then you can get up and running pretty quick. If you don't mind, Fabio, there's another question on one of the courses. I don't want to turn this into a marketing sales pitch, but I do want to answer.

[00:37:13.17] - Speaker 1
I have a bigger course called Getting Started with Python for Quant Finance. That's that one, if you click there. This is the kind of flagship course. This has got 15 or 1600 people in it, or 14, 10. Sorry. And this is similar, but I cover much more conceptual frameworks around finding edge. What is quantitative finance? Lots of content. I think there's 20 hours of content, 154 modules, 13 sections. I do cover the big, like Python first for beginners, the beginning, a little bit of overlap of foundations, but I go a little bit more quickly in this course. I do assume some exposure to Python. It's much more focused on considering how to find edge. Like what is alpha? What is principal component analysis? Starting to use these quantitative methods to actually find and build trading strategies. It does not, Dan, but I think if you get the big course, you'll be, you'll be good against the little course. You'll have enough to, you'll have enough to cover. If you want to email me separately, Dan, we can work something out. Otherwise, thanks, thanks.

[00:38:22.22] - Speaker 2
Yeah, no worries, no worries. And again, I think I strongly believe the quality of your content. You know, we followed you on Twitter, so the reason why we reach out is because you provide, you like publish a lot of really good content and we want to do more with you and we want to do more sessions. But I think the session today was really to show you guys the importance of data. How can you use it to be actionable? Like how can you use the data on any asset very easily? As a retail investors, this was not available like 10 years ago. Like when I was in the institutional world, this, this type of analysis was not really widely available for retail. So we're trying to give you guys an edge. And of course with, with the help of JSON, we're going to start building more and more backtesting tools, showing you how the data can become predictive and then obviously give you some ideas on how you can implement it yourself as well.

[00:39:18.27] - Speaker 1
Yeah, can't wait. It's going to be great.

[00:39:22.17] - Speaker 2
Let's see if we get more question, guys. Question for Jason, question for me, question about Python, question about option data. Let us know. This is for you guys.

[00:39:35.07] - Speaker 1
Yeah, we've had, we've had a couple good questions, but also keen to answer any more that y' all have, I think. Fabio, how can, how can folks find you on. On the socials?

[00:39:47.21] - Speaker 2
Yeah, sure. So basically, let me see. So first we. You can find [email protected] and then basically we are actually live almost every day. If you come to our YouTube channel and I'm going to share the link here as well, we are live maybe once or twice days. The reason why we go live is because we want to show you guys how you can take complex information. So some of these models might be overwhelming for a lot of people that have not seen it before. So we are trying to help you guys learn on how to use it. So for example, we have daily sessions with one of our traders that just looks at SPX. We're using.1 of our other traders is using advanced option strategy. We have a macro, macro update. We also had one of our users, a beginner trader, showing you how he went from knowing zero to learning now how to trade options and futures. We are partnering with, you know, people from the industry. So we have market makers that are coming live with us. We have. So for example, here we have quant strategy. So if you go through this, they're all here on YouTube.

[00:41:00.28] - Speaker 2
This is like one of my contacts is a portfolio manager in Miami. We also have a session with one of my ex colleague, Tarsi now works at Citadel. So obviously there's a lot of like really good stuff here that you can find and it's all available here on YouTube.

[00:41:17.06] - Speaker 1
So yeah, it's awesome. So much great free content you got. You guys, there's a question from Christopher Fabio. How far back does the levels data go? Yeah, there you go.

[00:41:29.21] - Speaker 2
So within. So right now, just to point out, we are working on an historical API that will provide you potentially at least three to five years of data. It's not yet available that the team has been working on within the dashboard. I believe you can go back up to, to six, seven months and get access to that data as well.

[00:41:52.06] - Speaker 1
Nice.

[00:41:53.15] - Speaker 2
Yeah. So we are working on, of course, an historical API to make it easy for you guys to access the data.

[00:42:00.16] - Speaker 1
I got a question for you. What's the meaning of the name of your company? Mentor Q.

[00:42:07.05] - Speaker 2
So Mentor is more like. We started this company as an educational company so we wanted to mentoring help people to learn finance. And then the Q stands for quant. So basically then we shifted our vision to become more like one platform. So then it kind of became like, so Our company is built on three principles. One is the education. So if you go to. So if you guys even create a free account, you can access all our academy. We have about 500 hours of content. So we basically, you know, for free you can access a lot of these courses. So this is like the base, so obviously sharing our experience. So me and my partner, we have obviously background. I work at Bloomberg for 11 years. My partners worked at market makings and banks. So we are trying to share this knowledge with you guys. So we do it through the academy and then the platform is now how can you then take this knowledge and bring it into an actionable strategy that you can use for trading? That's why we, why the queue stays there.

[00:43:14.14] - Speaker 1
Nice, nice. I'll share my links too if that's cool. Here's the, here's the website, here's my Twitter. I'm out here posting three times a day. Here's the YouTube also got some video content I think, I think Fabio and I will cross post this video. So I'm starting to get into some AI stuff. So for example, I think one of the last videos I did was basically build an AI agent that kind of writes code and does some analysis and it's pretty amazing what you can do with this stuff these days. Code samples to get options data with the IBKR API. So lots of good stuff here as well. Tons of free content for you guys. I mean it's kind of like, it's, it's totally overwhelming. I get it. But I think the, the advice that I give folks is like kind of pick something that's pretty narrow and then just go super deep and then pick the next thing and then just go super deep and then pick the next thing and then go super deep. So if that's like gamma exposure levels, consume all the content from Fabio you can about gamma exposure and then move on to the next thing and consume all the content.

[00:44:20.12] - Speaker 1
Because you got a lot amongst our two, our two groups here.

[00:44:24.12] - Speaker 2
And Jason, I have a question for you. Right, so now with all these new AI model, of course a few weeks ago there was deep seq bringing down the market with the new model. Then you have all the new chat GPT that is planning to replace potentially developers around the world with their new tools. Like what? Especially in finance for people that are looking at trading and of course integrating a lot of this stuff. What do you think would be kind of like something to look for that could be disruptive, especially for non institutional players. And what are the things that you look at for the future. Of course the future is going to change very fast. But what do you think are the key trends that are going to be important for us in the future?

[00:45:10.24] - Speaker 1
It's a great question. I'll start with telling you what I think it won't do with generative AI. Generative AI are language models, right? So I think all these folks that are telling you that these language models are building trading strategies, they're probably not right? These are not numerical models. They might write some code or they might be fine tuned very specifically to write these strategies. But I think in general, at least right now, that's not the right place to focus. I think a really interesting place to focus is literally like using the Deep Research tools to either research topics and get good at them or, or build ideas for Edge. You know, like if you go to deep research and you ask it to write a report on how you could implement a crack spread trading strategy, I mean it's like a five page in depth customized report that walks you basically through everything you need to know about that particular trading strategy. So you know Fabio, you've got some quantitative strategies here with some seasonality that you've explained in, in a post. You could like copy and paste that into ChatGPT and be like, tell me anything about this, right?

[00:46:19.29] - Speaker 1
Build me a trading strategy or I think that's a huge like underrated way to use these tools. I think at scale in the institutional world, they're using them to process text data, Right. So before you could you, you couldn't really go through accurately, you know, 20 years of 10Qs or 10Ks and extract some interesting information. I heard a podcast with Bill Ackman recently and what his team does is look back through previous filings to see if the CEOs keep their promises. So if like Q1 2024, a CEO said by the end of this year we're going to do these five things and they have done none of those five things. Well, that could be considered a weak manager. Right. And you would short that company, he's doing that manually. You can now do this at scale across any number of industries, any number of historical data. So I think all this like turning text data documents into data that you can combine and join with numerical or quantitative data is a huge room where the institutions are working and really, really interesting for people like us that now have access to these tools as well.

[00:47:33.26] - Speaker 2
Yeah, and I'll tell you an example. I was working for a startup around three to four years ago and we were selling alternative data to Hedge funds. And we were using knowledge graph technology, which is of course now it's probably outdated and there's more like technology that's been developed since. But like we were actually using 10Ks and 10Qs to create thematic indices to create like baskets of companies that were like investing in whatever technology could be or whatever trend or whatever. Like team, you know, we could build data scale and just leveraging really textual information. So I think with what we have today that's like even past, like we can do much more right now.

[00:48:18.16] - Speaker 1
Yeah, I mean the old NLP natural language processing techniques are like, have been lapped 10x100x with the LLMs. It's pretty, it's, it is pretty incredible. But just like keep, keep focus I think on, I should say make sure you're able to separate kind of the, the hype from reality. Like if, if somebody's going to tell you you can pump 2 gigabytes of numerical data into an LLM and spit profits out the other end. I mean maybe Rentac is doing that, but I don't think most mortals and retail people will be able to do that. The expense and et cetera. So I think you just use it for this text processing and kind of intelligence gathering and finding edges and finding trading ideas and explaining them in a way that you can understand. I think is a huge underrated way to use these tools.

[00:49:13.10] - Speaker 2
Yeah, absolutely. Makes sense.

[00:49:15.10] - Speaker 1
Yeah.

[00:49:19.17] - Speaker 2
Awesome. That was awesome. Thank you. Thank you, Jason. And again, we're excited for the newsletter on Saturday. So for those guys who want to join and receive. The newsletter is absolutely free. So just come back to Pycone News and just sign up with Jason. We're going to do more with Jason going forward as well. So we're going to have you live again and we're going to do more of these sessions. And of course the goal is to turn data that we provide into something that can be digested through, through Python and then that can give you like some sort of like insights on things that you could do, some training strategies or analysis or research. The goal is always like either make more money or not lose money. So risk management is the most important part. And research is of course like key because you want to learn things and you want to know how they could Adapt in the future.

[00:50:11.18] - Speaker 1
100. It's. It's pretty easy, right? Make money or lose less money. It's pretty easy when you put it that way.

[00:50:17.11] - Speaker 2
Yeah. Well, I think the most important is risk management. I think.

[00:50:23.16] - Speaker 1
Trade to live another day is what my mentor told me when I just started.

[00:50:27.18] - Speaker 2
Exactly.

[00:50:28.20] - Speaker 1
Yeah.

[00:50:29.23] - Speaker 2
All right. And if you guys want to find us and learn more about us, just join [email protected] if you sign up for our premium subscription, you can get access to all the data. And then of course, there's more to come. We are planning on a lot of new things coming in next few months. We are going to introduce, like, European futures. We're going to introduce like crypto gamma levels. We're going to introduce a lot of, you know, AI is going to be part of that. So stay tuned and follow us. And if you have any questions, send us an email infoentorq.com and for you, Jason, really appreciate your time. Thank you so much. Excited for the newsletter on Saturday and excited to do more with you in the next few weeks and months.

[00:51:15.26] - Speaker 1
Likewise, Fabio, it's been a pleasure. Thanks a lot for building this great tool and bringing me into the, into the circle.

[00:51:22.29] - Speaker 2
Thank you. Have a good guys.

[00:51:24.17] - Speaker 1
Cheers, guys.

[00:51:25.14] - Speaker 2
Bye.