Swing Trading Model + Q-Score – August 2025

[00:00:15.13] - Speaker 1
The first thing that I want to show is we showed this slide in last week's training when we were talking about gamma levels. I think it's very important because what we're going to show you today is how you can actually think about trading like in institutions. So what you see in this slide is how institutional traders trade. They would use a large amount of data to create a strategy and to improve their alpha. Right?

[00:00:43.11] - Speaker 1
So what we're going to show you today is a similar approach. So they would use maybe Bloomberg Terminal, they would use alternative data, they would use other data. And the goal is really to add more factors that can provide a better risk and reward and better return to your strategy. So let's go into some backtesting results from last week. So we looked at last week.

[00:01:11.06] - Speaker 1
The reason why we did that because it was a very, very interesting week. We had a lot of earnings, we had the big tech companies reporting. We also had a very, very volatile week. So what we're going to show you today is how the model perform and how you can use it in conjunction with some other of our tool. So we're going to talk about our Q score.

[00:01:28.26] - Speaker 1
But before we do that, we're going to start with the first step which is really beginning with the foundational tool which is the swing trading model indicator. So again, what the model gives you is directional bias, bullish or bearish. We have our levels, we have our lower band, our upper band and our wrist trigger. And basically just alone, just using this alone, it can give you a high probability roadmap for where the price is likely to move. Right.

[00:02:00.19] - Speaker 1
But again we're going to look at it on how we can add some other models and how we can actually tilt our strategies based on the models and mentor Q provide. So here first we are looking at some assumptions. So we took the data that you will see today and this is also available on our blog. I'm going to share the link is looking at the levels as of the end of day Friday July 25th. So the levels that we take are looking at July, Friday close.

[00:02:34.11] - Speaker 1
So we took the levels and then we use the levels to then trade at the open of Monday, so 28 July and simply close at the, at the close of Friday 1st August. There's no risk management involved. There's no, there's no take profit, there's no stop loss. So the goal really of this exercise very, very simple is to show you how good the levels were and then how can we add more factors to potentially add Alpha to our strategy. The strategies that we're going to look for are two types of strategies.

[00:03:10.08] - Speaker 1
One is a directional trading strategy. So if a bias is bearish, we then go short at the open on Monday. So we take the levels from the previous Friday, we go short at the open of Monday, and we exit at the close of Friday. Very, very simple. If the, if the swing model bias is bullish, then we go long at the open of Monday and we exit at the close of Friday.

[00:03:34.07] - Speaker 1
The second strategy is using credit spreads. So if the bias is bearish, then we use a call credit spread. If the bias is bullish, we then use a put credit spreads using the levels as our short strikes. So using the upper and lower band as our short strike. And again, we are not looking at trade management.

[00:03:55.21] - Speaker 1
We're just looking to see if the price at the close of Friday of the asset closed above the lower band or below the upper band. Right. So the first really exercise is starting with just the swing trading model alone. This is our baseline framework. So what you see here, and then I'm gonna go over into the file as well and show you the data and show you where you can find this exercise.

[00:04:26.03] - Speaker 1
So we looked at about 904 assets between stocks, ETFs and indices. And we looked at a portfolio size of $1,000 per trade. So the total portfolio size was 904k. And the reason why we do that is because we want to have an equally weighty portfolio to understand the accuracy of the model. In this case, the win rate was 33.85% with a return of -1.37%.

[00:04:55.23] - Speaker 1
Just to note, last week was a very, very volatile week, was probably one of the most volatile week of the year. We had a lot of news, we had a lot of events, we had a lot of earnings. So what we want to show you is how you can then use our other models to then potentially add values to that to that exercise. If we look at a simple option selling strategy over 904 assets, our win rate was 75.44%. That means that the price at the end of Friday closed above the lower band or below the upper band on 75.44% of the cases.

[00:05:36.05] - Speaker 1
So what does that mean? That means that if you were to sell credit spreads, you would have been right on 75.44% of the cases. And of course, there's risk management involved. And we're not going to go into that. This is really simply to show you the accuracy of the data.

[00:05:53.23] - Speaker 1
The next step is really to segment this. So how can we add one step forward similar to what do, how can we then add additional factors that can help me first narrow down my strategy and potentially have a better return and better results? So the second step is really using the Mentor Q score. So the Mentor Q score, and let me go into where you can find it is we're going to go into details of the score this week. We're going to have a separate product training, but you can find the Mentor Q score right here on the dashboard.

[00:06:32.21] - Speaker 1
And we have four different factors that we use. We're going to talk about option, volatility, momentum and seasonality. So basically what this tells me is if we are bullish or bearish on an asset by looking at different factors, we're going to talk about option, momentum and seasonality in this exercise. So let's go back here.

[00:06:56.14] - Speaker 1
So the first, the first exercise and we call this tilting our strategy. So we started from our baseline which was simply using our swing trading model. So we had about 904 assets. How can we narrow it down? By adding some factors.

[00:07:14.21] - Speaker 1
So the first factor that we are going to add is our Q score, options and momentum. So what we do here is we filter out those 904 assets by our Q score. And we only take a bullish trade if the option and the momentum scores are equal to five. And we only take bearish trade if the option and momentum score are below 1. That means that both options and momentum, if they are five, they're very bullish and if they're one, they're very bearish.

[00:07:48.00] - Speaker 1
So what happens here is that we now only have a universe of 120 trades. The portfolio size stays the same and as you can see, our win rate improved from 33.85 to 41.67. And also our return improved from minus 1.37% to minus 0.09. If we look at our option selling strategy, then we are kind of like in a similar environment, we have a 75.83 success rate. Okay.

[00:08:22.09] - Speaker 1
If you have any questions, please send them over.

[00:08:26.21] - Speaker 1
Then we add another tilt to the strategy. So in this case we want to test out the momentum Q score and the seasonality Q score would perform together. So in this case what we do is we only take bullish trades if the momentum Q score is 5, which is really bullish. And our seasonality score is greater than 2. And we only take bearish trades if our momentum score is lower than one.

[00:08:55.18] - Speaker 1
So very bearish. And our seasonality score is lower. Than minus one. So just remember the seasonality score goes from minus five to plus five. Okay, so what happens here is that now we have only 24 trades, the portfolio size stays the same, and our win rate went again from 35, 33.85% to 41.

[00:09:19.12] - Speaker 1
And now we are at 58.33% with the return of plus 1.31%. If we look at our option credit selling strategy, our win rate also went up from about 75% to. To about 87.5%. Okay, so very, very important, we see a great improvement by combining momentum and seasonality score. But then we want to take it a step further.

[00:09:49.15] - Speaker 1
So let's now put everything together. And now we are adding our option score, our momentum score, and our season ID score. So we only take bullish trade if our option key score is greater than 4, our momentum score is equal to 5, and our seasonality score is greater than 2. And we only take bearish trades if our option score is lower than one, our momentum score is lower than one, and our seasonality score is lower than minus one. So we are now combining the three scores together.

[00:10:21.13] - Speaker 1
So as a result, what we see is that now the universe is also smaller. We only have 14 assets that match this criteria. Our win rate is now 57.14 and our return percentage is 3.18, which is great compared to also the previous exercise where we have a similar win rate but a better performance. But the most important part is that we now have a 100% win rate on our option selling strategy. So I'm going to show you where you can find this data.

[00:11:00.20] - Speaker 1
One second.

[00:11:05.12] - Speaker 1
So, first of all, this is the Excel file. This will be available for you guys on our blog. And I'm going to show you where it's going to be. So we have our first tab, which is really just our full coverage. Here is what you see the assets.

[00:11:21.04] - Speaker 1
Here is all the calculation. Here is all the prices, the SKU score data, the swing trading levels. And then we have the different exercise where it's adding our option and momentum score, momentum and seasonality, and then option, momentum and seasonality. Right? So all this is available within our blog.

[00:11:42.21] - Speaker 1
So if you come to our financial wiki page, just click on Quant strategies, swing levels. And then here is the exercise that we did. This is available for all of you. I'm just gonna also copy the link. I'm gonna paste it in the comment here so you guys can access.

[00:12:02.00] - Speaker 1
And basically within this document, you will also be able to download the file right here. And then it's gonna show you the same exact data that I showed you before.