Trading Like a Quant: Swing Trading + Q-Score

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Sam foreign.

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Welcome everyone to our Trading Like a Quant series. This is a new video format that we are going to start from now on and we're very excited about that. Today we're going to uncover our institutions in particular quant funds actually trade and more importantly how you as a retail trader can actually use the same models and tools. So feel free to send any questions in the comment and we'll go over Q and A at the end.

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So let's start with really the, the big questions, right? Quants don't trade on emotions, they don't trade on social media hype, they don't trade looking at charts but they actually use data driven rule based system to generate consistent return. It's a systematic game, not an emotional game, right? It's very, very important. Quants trade with data and they rinse and repeat the process, right?

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Very important.

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Always actually trading this way. And for those who don't know, these are some, some names that you guys might have, might know or might have read in the news. We always talk about Citadel, we have Two Sigma, Jane Street Millenniums. These are some of the biggest quant funds that I've seen really a systemic growth in AUM and also growth in their performance, right. And if you, if you read the articles we always see at the end of the quarter the outstanding results coming from large quantitative firms, Macro fund, you know, systematic funds, big market makers flow and all of that.

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So we see that this type of investors have proven to be very successful and they have a very, very strong track record, right? So but how do they achieve this and how do they actually trade?

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So in my experience, so I worked very closely with large quant funds that we were selling. I was working for a start them but the, the reality is very, very simple. The quant funds start always from a baseline strategy. This is the foundation of their systematic trading, right? So they start with something simple, proven and repeatable.

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A baseline strategy could be a trend following strategy, could be a mean reversion, could be a volatility based system, but could also be a mandate that a fund might have. So let's imagine that we start seeing an increase in consumer companies, right? Or AI companies. So the baseline of the strategy could be like I want to trade AI stocks and then I want to build an edge on that. So the baseline would be find a strategy that can work with AI companies and then make sure the strategy is consistent.

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So the baseline strategy is really the backbone of their trading approach. The baseline allows you to have an historical track record, back testing and all of that. They don't simply trade with no strategy. Right. There's no foundation behind it.

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And as a retail trader you always have to think about in the same way. So where are you successful at? Are you good at day trading? Are you good at swing trading? What, what are, you know, are you better with the 10 minutes time frame?

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Are you better with the 5 minutes time frame? And this could be like the baseline of your strategy. Do you use technical indicators? Do you use options data, do you use news feeds and all of that? So that is the baseline, right?

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So you start from that and then you can augment by adding factors. So just think about that, think about how hedge funds are measured, think about how their performance is evaluated. There is a very big concept of alpha, which is the excessive return of a strategy compared to a benchmark. So let's imagine that The S&P 500 returns 8% a year on average and a hedge fund is able to return 10%. Right?

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So that would be that you have a 2% alpha on top of simply following the US market, which obviously is the benchmark. So their goal is really to add more factors to potentially increase their alpha. So they start from a baseline strategy again and then basically they add factors to, to create basically this alpha factor. Right. But today we're going to show you some example how you can also use the same approach as retail trader.

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So as a retail trader of course we don't have access to the same, the same level of technology, the same level of data each ones have, but we can actually rely on models. For example, we're going to show you what we built for you guys. We're going to show you how to use some of the mentor to models there. But the idea behind it is really the same. You know, you are trading based on data.

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You let data guide you. You don't trade with foremost, so you don't get into a trade just because you think it's going to be a good idea. That's a bias and you could be wrong and you could lose all the profit that you made in the past. Right? And then you can add filters to refine your edge.

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Right? And the goal always is never think about profit, but think about how you can manage risk. That's the most important part. So you don't need really to have a PhD or billions of dollars in infrastructure to be able to use the same approach. And today we're going to show you an example.

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So we're going to go live in the platform and we're going to Go over some example.

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All right, so what we're going to use today, we're going to use our baseline strategy. Our baseline strategy is going to be based on our swing trading models. So we're going to base it on our swing trading levels, our bias. So if we are on a bullish or bearish bias, we're going to use that baseline strategy to integrate our trade through ideas. We're going to do it on a very large set of companies.

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We're going to show you how statistically this is very important. And then we're going to use our Q score as our alpha factors. So the Q score can help us determine and can help us add filters on top of our baseline strategy. So we're going to do that through Excel. And then I'm going to share the, this file as well within our, within our, our blog and also within this code.

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So let's go into our Excel file. And this is very simple. I've done this just by using Excel, using the data that we, we provide from Mentor Queue. And then that's going to help you understand the process. So the first step is really we, we do a strategy that is a weekly strategy.

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So we, we take the data coming from last Friday. This is just a Backtest. We are 26-9-2025. Today is October, is October 3rd, so it's Friday. So the market doesn't close yet.

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So we're going to use the opening price of Monday and the closing price of Thursday. And then obviously we can refine this once the market closed later today. We have about 1194 tickers that we analyze. This is the full coverage of Mentor Q. We have our market cap and then this section here highlights our baseline strategy.

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So our baseline strategy is following the bias of our string model. So if our string model is bullish or bearish to see that, we can come into the dashboard and we can see if we have a lower band or an upper band. And if you need to, if you want to learn more about the string model, we have dedicated videos on it as we're not going to go over it today, today, but we're gonna answer any question that you might have. Then we use our swing level to base our strategy. Right.

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So whether we have a lower band or an upper band, we're gonna base a strategy based on that and we're going to calculate the return based on that. To the right, we have our Q score. So we have our scores for each of the stocks. We have our momentum, our option seasonality and volatility. Those are the same score that you can find here in the dashboard.

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You can access them on a daily basis, we can go back in time, you can backtest that and you also have, you have also the historical chart. So for example, just to give you an example backtesting of this, this is the option score for Tesla. Just take a look. When the option score went from bearish to bullish, this was really the start of a really massive uptrend from Tesla going from $300 to over 450, etc. Right.

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So again let's go back to our file and let's go through the steps. Then we look at the opening price. So we trade at the open of Monday and we close at the close of Thursday. We are going to run this again once the market closed today. Today is Friday, so we're going to run that.

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So it's going to become a weekly strategy. Here you have the return in percentage and the return for a $1,000 investment. So if you had $1,000 just put in this position, this would be your return in dollars. Right. Then we, not only we have, we always do that.

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This with the two types of strategies. We do this with the long short strategy, meaning I buy at the open if my swing model is bullish and I sell at the close of, in this case Thursday and I calculate the return. Simple. My size is $1,000 per position. We have 1164 positions.

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So my total portfolio, and this is just an exercise again would be 1.16 million. Right. MARP was 7,106. This is a 0.61% return. This is simply without applying any risk management, just trail the open trade at the close.

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We don't do any interdimovement, we don't do any adjustment, no stop loss taken and so on. Very simple. The second type of strategy is can we use options. So can we sell options by using the string levers? Right, so there are two.

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So you can actually take exposure to the underlying by going long or short. But again what happens if the market moves sideways? Then you could risk of losing money even if your idea was correct. Or we can use options and leverage neutral strategy by selling credit spreads or put spread core spreads and so on. And then we use the levels for our strike.

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So the goal here is that whenever we see a win rate, it means that if I'm selling a put spread, I want to make sure that the price at the close of Thursday is above my level. So it's above my swing level. That means that I Could have pocketed potentially 100% of premium and my strategy would have been successful. If we see a call spread, then the success means that the price at the close has to be below my upper band level. So in this case we have our 2798 and our upper band is at 2984.

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So if we sold calls above this level, we would have been successful because the price closed below that level at the end of Thursday. Right. So let's go over the results and then let's go on to how we can then add alpha factor. So this first file is just using our baseline strategy. Our baseline strategy is simply using our bias from our swing level and calculating the return using two types of strategy.

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Just a long short strategy, buying or selling at the open and exiting the position at the close of Thursday, four days after. Just by doing that, our success rate, which is actually pretty, pretty interesting, is 53% almost. And our return would have been 0.61%. Again, this one is without applying any stop loss, any risk management, any take profit target and so on. What if we did the opposite strategy?

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So if we actually sold options on all these 1164 companies, we would have been right on 92.27% of the cases. Clearly it's not feasible to be able to trade 1100 stocks. So this is just an exercise. But the idea behind this is showing you that even with the very large basket of companies, you can also have a really, really big data driven success rate just by not applying any filters, not applying any, any, any risk management and so on. And even just by looking at the long short strategy.

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Right. We were, we were right on 50, almost 53% of the cases. Right. And the return of also was positive and it was a very, very large basket of companies. This is just without applying anything.

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So then the next step is, okay, so now I have my baseline. So there is a lot of potential in this strategy because the data seems to be very good. But how can I make it better, right? How can I increase, how can I increase this? And I can maybe even reduce the number of trades I cannot trade, possibly 11, 1100 stocks.

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So let's go on how we can customize that. So, so the next step is we add what we call alpha factors, right? So we start from our baseline strategy. So the goal is the same. We are going to use the bullish or bearish bias coming from a swing level and we are going to add some alpha factor.

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And in this case the alpha factors are my option and momentum score for our bullish Trade and my option and momentum score for our Bearish trademark. I will only take a bullish trade if my option score which you see here is greater than 4. So it's 4 or 5 and if my momentum as well is 4 or 5. So I want, only want to see companies that are showing me a strong option score and a strong momentum. Right.

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I don't wanna, I don't want to trade the other ones. So the, currently the alpha factor that we're using are just two of the scores which are these two scores. So I'm only using two of them. Right. So by doing that I do the same.

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I calculate the price at the open and at the close, I calculate the return. I calculate if it's a put spread or a curve spread right here. So we have bearish and bullish trades right there. So we have all the stuff. And then now we can see something really interesting.

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So we went from 1164 companies to 339. We increase our success rate to 57.23% which is great if you just consider the number of assets that we're looking for. And we had a return of 0.78% in, in four days. Right. Looking at the overall basket.

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So very, very good. If we were to trade our option strategy, our win rate in this case would have been 92%. If we, we just go back to our baseline is in line with our baseline. But at the same time we're actually trading less names. So we actually reduce the number of trades.

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Right. So again, we were able to start from one basket with just one factor which was a swing model. We are now adding two alpha factors which is our momentum and option score. And the goal is to then refine and filter down potential better return or potential less risk or also reduce the number of trades. Let's, let's go one step further and let's now add market cap.

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So what if we don't want to trade small names? Right? So we have the market cap here. We can filter by market cap. And what we did here, we only traded companies that have 100 billion plus market cap.

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So just big companies. And as you can see, we now reduce the number from 339 to 39 stocks. Right. So it's more manageable, right? Is it's, it's easier to, it's easier to, to do that.

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But at the same time, let's go and see the results. So the results actually kind of decrease the return from my previous strategy. So my win rate 56%. My P L is only 0.11. And again, this is not looking at trade management.

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We're going to go into that in a second. Is purely looking at open to close. Right. The stock, the option strategy actually decreased. So we, we went from a 92% success rate to an 82% success rate.

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Right. So again, still good, but maybe we can do, we can do better. Right. So let's go over and let's augment that again. So now we're going to use a different alpha factor.

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So now we're going to just see and test. Okay, so what if we start from our baseline strategy and we use a different factor, which is our seasonality score. So I'm only gonna go long or trade the long upside if my seasonality score is greater than 2 and my bias is bullish and I'm only going to trade to the downside is my seasonality is lower than minus 3 and my bias is bearish. Right. So here you can see bullish and bearish trade.

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So you have here. So as a result, we are narrowing down now to 36 stocks. So only from 1164 to 36, our win rate was 52%. But we had a slightly negative return just by doing a long short strategy there. But on the option side, what's incredible is that our success rate was 97%.

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Right. So if we look at all the trades, there was only one out of those that would have lead to kind of like a loss if we sold spreads above or below those levels right here. Right. So again, interesting, Right. We're now using seasonality.

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But let's now go back one step further. So what if we now add two alpha factors? So we are adding in this case, momentum and seasonality. Right. So we're now using my momentum score here and my seasonality score here.

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We're only going long or we are bullish if the bullish bias coming from the swing model is there. And if our Momentum score is 5 and our seasonality is greater than 2. So we see our bullish trade right here we have a 5 momentum and a greater than 2 seasonality. So 2 and above. Right.

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Bearish side. So short side, we are only going short if the string model bias is bearish and if the Momentum is lower than 1. So 0 or 1 like we see here. And our seasonality is lower than minus three. So minus three, minus four and minus five.

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Right. So let's see how that perform. Right. Okay, so this is incredible. So we see now we reduce the number of trade to 11 companies.

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We went from 1164 to 11. Our win rate is 60, almost 64%. And basically we would have made a 1.21 return in one week, in four days, actually. And on the option side, our win rate is actually 100. So if we followed the bias of the swing level and we sold call or put spreads above or below these levels, then we would have been right on 100 of the cases.

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Right. Very important.

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The last one. And then I'm gonna open up for some question is now adding three factors, right? So now we're adding our option score has to be greater than 4, our momentum score has to be greater than 4, and our seasonality has to be greater than 2 for our bullish trades, and then for our bearish trade, we have an option score lower than 1, momentum score lower than lower than 2, and a seasonality score lower than minus 3. So again, the factors that we are considering are these three. This is the data here.

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And then let's go over and look at the results. So again, we now narrow it down to 17 stocks win rate of 53%. But our overall return is -0.53. On the option side, we still were right on 100% of the cases. Right.

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So the, the strategy that kind of performed better would have been the momentum plus seasonality where we see stronger kind of return and obviously the least number of trades. So always have to account number of trades, commission cost, all of that stuff. But the idea behind is really like we started from 11, 64 companies to narrow it down to 30, 40, 11 companies like we do here. Right. So this is exactly the same approach that a large fund would use.

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Clearly the factor that they use might be more, might be more complex. They might use machine learning models, they might use like AI. Of course, they might use a lot of data sets that we might not have access to, which is fair. They have budget, they have technology, they have a big team for that. But the process is the same.

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Extract excessive return from a data set, add it to my baseline strategy, and make that strategy more effective and more performing.

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Let me know guys if you have questions. So this is kind of like the backbone of what systematic trading is. In simple, in simple terms, as retail traders, again, we can also apply the same principle by leveraging up and, and by using data. Right. So instead of just relying on lagging indicators or on, without looking at context, just make sure you are familiar with how some of these models work.

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Because the idea behind it is that again, this, this we use the same approach used by big funds. We take data, we create A factor based model, we overlay that factor onto an asset and again just follow how this can change and how you can find ideas around it. Right. So just make sure you understand how to apply those models and then how to apply them on your swing trading. So the other point is again we didn't really look at trade management, so we didn't really manage the trade intraday.

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So you could have actually got greater. So if we look at Nike for example, this was one of the companies in the, in the, in the screener. You know, if you look at, you know, trade execution, we went from 76 yesterday to around 73. So if you were able to execute at a better price than the closing, then again you could have improved the strategy even further. So again just some, some, some thoughts there and, and then it becomes really up to the trader to define when to enter to exit.

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But you have the tools available at your disposal to understand when could be a good time to exit to enter. Or is the market changing, is the regime changing? Right, so we had a question about some of the models. So if we go and look for example at the crypto market, so we look at Bitcoin for example, we are also at all time high. So we passed the 120 level again, we touched 124.

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Right. So if you now come to, to your dashboard first you have our swing levels here. So this was, if we add also gamma levels, this was a very big break yesterday. Break of the core resistance. Then the market broke the one, the max and now it's kind of retracing.

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If you look at the quant models, just look at some of these models and look at for example the direction model. Again this could have really signal a strong upside move from 1 110, 115 right here and now all the way to 1 123. So some of this data can actually be used for you to trade your asset. Right, let's go back another example. Right, let's go back and let's go back to our Tesla example.

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Right, so let's say that we see the data and we are bullish on Tesla. Okay. The option market is very strong, the momentum has been very strong. But we now see an increase in volatility and slightly negative seasonality. Right.

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So let's say that you think Tesla could actually go higher. Right. And you decide to just. I want to just buy call options. Right?

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I want to buy call because I'm going to use leverage. I want to get the exposure in case there's an Upside potential. So if you buy calls without understanding the risk, you probably would risk of losing a lot of the premium. So what we see here is, for example, this is our volatility risk premium model. And what this is telling you is putting volatility and implied volatility versus historical volatility in a context.

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Right? So we are now seeing the 61. The implied volatility of Tesla is 62%. Right. The historical volatility is 48.

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What does this mean? Is this high? Is this low? How has the volatility of Tesla historically been compared to its historical volatility? So what we see here is we see the volatility risk premium, which is the difference between implied volatility and historical volatility.

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We're seeing that yesterday we were almost at the 100%. So the implied volatility of Tesla minus the historical volatility yesterday was very high. I think it was about 19%. We are now at 13.5%. Right.

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So that means that implied volatility went down and this gap got filled. But we're still at a very, very high premium compared to the past three months, right? We are the 88%, right. So what does that mean? That if you buy call option or if you buy put option, it doesn't matter if you buy premium, you're actually overpaying for premium.

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So that means that if the price doesn't move in your direction, just the drop in implied volatility will cause your premium to potentially lose and all. It's all depending. Okay, option pricing is more complicated. But how, what was the situation actually? If we go back two months ago, right?

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If we go back to August.

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So in August we can see that the implied volatility was actually 45. So now we are at 61, 62%. Two months ago it was actually 20 less. Right. So in this time it actually was probably a good time to potentially looking at buying premium instead of selling.

[00:28:51.28] - Speaker 1
And with the vrp you can actually see, see that. And if we go back to our October data, We can see how the volatility risk premium back in August was actually very, very low. So at that time implied volatility was actually very low compared to historical. And now it historically has been. And then of course we saw this big uptrend and now implied volatility is actually very high.

[00:29:30.20] - Speaker 1
So it's pricing a higher premium again. So just to bear in mind, those are some of the tools that you can use to kind of understand how to position yourself. So again, we're starting from a baseline Strategy, maybe we're adding the Q score and then now we are actually managing the type of strategy by looking at volatility. So again, instead of maybe going long or short on Tesla, maybe it's good to sell option, right? You can still get the exposure and you actually getting more premium because you look at the volatility models.

[00:30:05.05] - Speaker 1
So this is just some ideas. There's a lot, there's a lot there, there's a lot to cover there. But this is how big hedge funds are trading and this is how we are trying to educate our users and help them basically leverage data to potentially be successful and be like, manage better manage the risk.

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I'm gonna pause here and I'm gonna ask if you guys have any questions. Thank you, Brooke. Welcome. Welcome to the group. Excited.

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If you have any questions, please let us know. Send us an email at any time and we'll be able to help you.

[00:30:56.01] - Speaker 1
While we're getting some questions, I just want to remind everyone that from next Monday we are going to start with a lot of live sessions on top of the ones that we do here on YouTube and X because we want to help you guys really leverage the data from now to the end of the year. So it's the last quarter, it's going to be a very volatile quarter. So we want to make sure that we provide you with the assistance that you need. So we are introducing three new sessions for our Premium members. Masiai, which is also here in the room, is going to lead our European session with Patrick as well on Gold and Forex.

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So if you are in Europe and you want to, if you train in the European session, you'll be able to join as a premium member. Our golden forex section at 2:30am Eastern Time or this time on Eastern Eastern time. Then we're gonna go over Monday to Friday. Our NASDAQ NNQ playbook also available for premium members at 8:45am this is a great way to start the day, great way to prepare with the levels. And then of course the market will start on 9:30 if you guys are interested.

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Then we also have our Pro Pro membership which we are trading live every morning and afternoon from 9:15 to 11:30. Focus mostly on futures. And then we have our power hour and market close Monday to Thursday at 3pm on top of that we are also adding another session for Premium members and Pro members Monday to Thursday at 2:30 to just go over and recap the price action for the SPX so that you guys can actually be better prepared for for the next session and get some insights on how to better leverage our tools. And of course for those who want to join our Pro room so our Pro room is really, you are getting access not only to our data and models but you also get access to the time of three professional traders with 20 plus years experience. So you'd be able to be in the room for about 24 hours per week.

[00:33:05.01] - Speaker 1
So it's a lot, it's a lot of time. Of course you might not join all the sessions but the team is actually there for you to help. So you can actually access our Pro for $299 for three months. This is only available until the end of Sunday and this offer will not come back on Black Friday. So if you guys are interested there you have the link to potentially join.

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There's a QR code as well for some of our links but basically yeah, it's going to be, it's going to be a great quarter, there's going to be a lot of volatility, there's going to be a lot of opportunities. So we want to make sure that we are there for you guys to help you and we are providing you the resources for the last quarter to be successful and learn how to use the data as well.

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There's any questions or if you have any questions, any questions about, about the deal about the, the live event, just send us an [email protected] and we'll be able to answer even over the weekend. So just don't hesitate to contact us infoentorq.com and if not we are going to be live again next week. We're gonna be ready in the morning at 2:30am Eastern. Excited to have a new member of the team on board for that session together with Patrick and then we'll be live or next week and from there to till the end of the year. So stay tuned.

[00:34:36.21] - Speaker 1
Don't forget to also follow us on on YouTube. So here is our YouTube channel. We also have live sessions during the day so we have a lot of these available for you. These are all stream on YouTube. Just make sure you, you subscribe there and then all the other session will be on top of the ones that we have there.

[00:35:02.23] - Speaker 1
So with that I really want to thank you guys for listening. Hope this was insightful. Any questions please reach out to us and then I wish you everyone a nice weekend. Have a great day.