Session 4. Q-Score

[00:00:50.20] - Speaker 1
Welcome everyone. Welcome to another product training session today. Very excited to talk about the Q score. I am Fabio, founder of Mentor Q and we're going to talk about a lot of interesting things today. So let's go over the agenda first.

[00:01:09.14] - Speaker 1
So we are going to introduce the importance of a very important concept which is factor investing and how it's finally possible for retail traders to leverage the same approach that is actually used by quantitative funds. We will show you what the Q score is, how to use it, show some examples, we will go over our screeners and we will also go over backtesting results strategies and how can we create basically a strategy based on quantitative factor like the Q score. At the end of the session we will also have a Q and A. So please send us all your questions in the comments and and we will answer them at the end.

[00:01:56.17] - Speaker 1
All right, so let's be honest. Most retail traders chase price jump on news and often trade without a real framework. Meanwhile, on the other side, professional traders rely on factors which are measurable signals that consistently drive price behavior across time and market regimes. These factors include things like momentum, volume, volatility, options, positionings, macro factor and so on. They are not emotions, they are not opinions.

[00:02:27.14] - Speaker 1
They are actually data backed hedges. Right. Very, very important.

[00:02:34.29] - Speaker 1
So today we're going to talk about the Mentor Q scores which actually brings the power of institutional factor investing directly to the retail traders for the first time. This approach is traditionally used by hedge funds and asset managers. On the left hand side, for example, you see some of the things that they look for. They basically when I was working with the hedge funds, they're actually creating strategies based on multiple factors and they combine them together to be able to identify quantifiable characteristics like momentum, volatility, sentiment, positioning and risk regimes and so on. Now with the Q score you can actually access the same framework so you don't actually have to build your models.

[00:03:19.06] - Speaker 1
We've done the work for you and we're going to show you during the presentation what the Q score is and how you can use it. Each asset is scored based on multiple weighted factors to help you assess conviction, identify high probability setups and avoid noise. The Q score can also be used to confirm your theory, your strategy, so you can use it in conjunction with your own methodology as well. And that can actually help you add additional value to your strategy. So whether you're trading crypto stocks indices, the Q score has been designed to give you clear data back analysis by looking at multiple factor.

[00:04:03.29] - Speaker 1
So you're not trading with emotions, you're actually trading with data and using the same approach leveraged by institutions. So how can traders use the Q score? So the Q score is not a crystal ball, is not about predicting the future, is about knowing when not to fight the conditions. Right? So here is an example on how you can use the Q score in your workflow.

[00:04:33.02] - Speaker 1
For example, if we have AIQ scores, you could actually potentially think to size up, focus, or even act with confidence. You can also avoid forcing trades when the Q score is low and when the regime is unclear. So when you are thinking about going into a trade, you can use the Q score to confirm whether you could be on the right side of the trade or not. It can also help you to filter your trade ideas. So let's imagine that your chart looks great, but the Q score disagrees.

[00:05:06.21] - Speaker 1
Maybe take a second look and analyze the data further. You don't want to jump on a trade when you have conflicting data that can potentially put your position at risk. Of course, you need to combine it with your own playbook, right? So whether you're using supply and demand, trend lines, Fibonacci score, whatever, whatever indicator you use, the Q score can actually help you validate your setup. We're not here to override it.

[00:05:34.06] - Speaker 1
Again, if you're in the trading business, you already have a successful strategy. You already, you already have an edge. The Q score can actually help you get that edge further and increase potentially your, your accuracy, increase your strategy.

[00:05:52.25] - Speaker 1
So it can be applied by a lot of different traders. So if you are a momentum trader, you can actually use the Q score to filter out maybe choppy setups in a strategy. If you are a reversal trader, then maybe wait for a high score to come to an exhaustion. Right? So if you are using our momentum or options score and we're gonna go into details in a second, maybe you can use that to potentially find reversal point.

[00:06:21.05] - Speaker 1
And we're gonna show you some example. Again, the Q score does not replace your strategy. It's trying to make it smarter, make it better, and help you be more confident in your trade. So why did we develop. Because you don't need to code your own models and we have done the work for you.

[00:06:39.15] - Speaker 1
We take in a lot of data and we basically then created this score to help you basically assess the risk and profitability of your strategy. We're going to show you some example. We have some backtesting results as well. So yeah, so this is what the Q score can do for you. But now let's go into the details.

[00:06:59.17] - Speaker 1
We're going to show you how the Q score works and we're going to go into each of the factors that we analyze one by one. So the Q score is based on four factors. Currently we have momentum options, seasonality and volatility. So let's go into each of them.

[00:07:21.04] - Speaker 1
We're going to start with our momentum score and let's see what it represents. Right. So the momentum score measures the strength and direction of an asset price trend using our proprietary quant model that is looking to analyze price action and technical indicators. The score ranges from zero where we have, when we have zero, we have a bearish momentum and then it goes all the way to 5, which is a bullish momentum. When we see a 3, this is a neutral and of course, so these are kind of the ranges from zero bearish to five bullish.

[00:07:59.22] - Speaker 1
So what does it mean? A high score can indicate a strong upward price momentum and a low score can signal weakness or potential downside. So you can actually use this in conjunction with your momentum strategies and understand when we are in a long term trend or maybe when the price action is actually going to reverse.

[00:08:27.05] - Speaker 1
The second one is our option score and basically what the options score does and we do a lot of. For those who are using our gamma levels and our option models, Options data is a great sentiment indicator and it can actually reflect the sentiment of the market, the sentiment of the traders, the positioning and basically is looking at option flow and activity. The score again ranges from zero to five. Where we have a zero, we have a strong bearish sentiment and five, we have a strong bullish sentiment coming from the option market. That indicates.

[00:09:03.23] - Speaker 1
So how can we use this score? It indicates if the market expectations and directional bias from option traders are confirming the trends or maybe they are anticipating a shift in the market. And we're going to show you some really nice examples in a second.

[00:09:24.02] - Speaker 1
Then we have our third score which is the seasonality score. We do seasonality a little bit different from the traditional way of looking at seasonality, but we do look at historical price performance over a specific time frame, typically the last 20 years of data, if that is available. And what the, what the seasonality score is looking to do is looking to predict the next five days of price action. So we are looking at the past to potentially predict the future. The score goes from minus 5, which is bearish seasonality, to plus 5, which is bullish seasonality.

[00:09:59.22] - Speaker 1
When we see a zero, it means that there is not a significant seasonal trend. So maybe the, the indicator does not provide a significant signal, but we need to look for the change in seasonality as well. So the idea behind the seasonality is actually looking at past past data to potentially determine when we could be in a potential bullish or bearish price trend and price movement.

[00:10:36.16] - Speaker 1
And then the last one is our volatility score. Volatility is very key, especially for options trader and for momentum traders and future traders. And basically it measures the magnitude of recent price fluctuation to indicate whether we are in a high volatility environment or a low volatility environment. The score goes from zero where we see a low volatility or a calm market, so we are in a very low volatile market or A5, which is a high volatility, where we're going to see large price swings. The volatility score is basically is telling us whether we are in a low volatility environment that can actually favor trend following strategies.

[00:11:25.02] - Speaker 1
Or if we are in a high volatility environment, maybe that can actually be a good time to potentially look at selling premiums and looking at option strategies. So again, you can use the volatility to adjust your strategy based on whether you are a directional trader or an option trader and so on. So how can you use it as a trader? So the Q score combines these four factors into a structure quantitative framework to provide a view of an asset. So whenever you log into our dashboard, you're going to immediately see our Q score and you're going to get a glance in less than 30 seconds on where the asset stands, whether we are in a bullish or bearish bias and whether we are in a high and low volatility environment, you can use it to confirm trends, identify seasonal patterns, or adjust your strategy based on volatility regimes and so on.

[00:12:24.03] - Speaker 1
So if you are for example, a futures trader and you have very, very tight stop loss and you see that the volatility score on NQ or yes is getting higher, then maybe you also want to adjust your risk management because you might be chopped out very fast given that the volatility is increasing.

[00:12:44.02] - Speaker 1
And then finally, it can help you support for time entries and exit, help you refine the strategy by integrating momentum, seasonality and volatility. And of course option positioning.

[00:13:02.24] - Speaker 1
All right, so who is the Q score for and what type of trader can use it? So the Q score was designed to support various type of investors and traders. Swing traders can use the Q score to identify favorable favorable entry points. We're going to show you some examples. We also did some really cool example last week on our session on swing trading models.

[00:13:27.05] - Speaker 1
If you are an option trader, you can use the Q score to understand market sentiment and positioning from the option market. And it can help you identify bullish or bearish setups. If you are a futures trader, you can use the Q score to set tighter stops, avoid trades when scores are not favorable for your trading style and managing risk in volatile market. And then finally, if you are a longer term position trader, you can also use the Q score to identify assets that can potentially have a strong trend and can also identify, for example, when we are seeing strong performance. And you can also use the Q score to actually manage your active portfolio.

[00:14:10.10] - Speaker 1
So if you see changes in the Q score on your long term positions, then you can also adjust your risk accordingly based on that.

[00:14:26.07] - Speaker 1
All right, so now let's look at some example on how you can use the different scores to take advantage for our trading style.

[00:14:36.20] - Speaker 1
Start with the option score, right? So if you're a swing trader, the option score is really a great market intelligence tools that quantify quantifies options market sentiment from 0 to 5. So 0 obviously very bearish, 5 very bullish. So interpreting the changes in the score can help you understand shift in the expectation of the market in positioning and can also be a good indicator of selecting some trade ideas and also selecting the timing. So typically when we see a rising option score, this means that we are seeing an increasing bullish sentiment among option traders and positions.

[00:15:23.23] - Speaker 1
So this suggests growing confidence in an upside potential. And this can also be a signal of market positioning at higher prices. So again, this is a sentiment indicator. It's not a crystal ball as always. But this takes in consideration what's going on in the option market.

[00:15:44.11] - Speaker 1
When we see a falling Q score, this suggests actually the growth of a bearish sentiment or maybe like an increase in hedging activity in the asset. So that means that the market is kind of expecting potential downside moves. So they are either positioning for a downside or they are hedging themselves from a potential downside move. If the option score, for example, moves from neutral to which is a score of three to a score of four and five, which is bullish, a swing trader could interpret this as a potential position for a rally or maybe look for upside momentum on the asset. If the score drops from high to low, this could reflect a very strong increase in bearish bats.

[00:16:38.27] - Speaker 1
Right? So if you see for example an option score going from 5 to 0, then again this could be a warning signal on the asset.

[00:16:49.24] - Speaker 1
We're gonna look at some example. This is a very cool example that we showed Many, many times. And this is taken as of. Well, I mean, this is the history of Nvidia. But around April 22nd or 23rd, we were in a very, very bearish market, right?

[00:17:08.11] - Speaker 1
We were. There was a very high volatility. Nvidia dropped below the $100 range. So that was very, very strong downside move that you see. And then suddenly the narrative change.

[00:17:21.12] - Speaker 1
So we saw obviously more positive news coming in. But at the same time, all the AI narrative and the AI trend on Nvidia. But we also saw that the market completely changed positioning. So we saw that the option score went from zero to four in a matter of a couple of days. This was the beginning of the recent trend that we saw from $100 to now at all time high on Nvidia.

[00:17:51.03] - Speaker 1
So again, this could have been a very, very nice signal of option sentiment changing. And the option Q score could have actually helped you understand this very, very easily. The second example is also on Google. We had recent earnings there, and also the Q score dropped during that period. But then we went bullish again.

[00:18:16.17] - Speaker 1
So we moved very, very strongly to an upside score that you see here. And again, this was another start of a trend. Now, I think Google is quoting at around 200. So we saw like a strong increase in the price there from around 150. So that was a very, very, very nice move.

[00:18:41.18] - Speaker 1
Now let's look at the momentum score. All right, so the momentum score is really trying to quantify the strength and direction of the price trend. And again, we go from a scale of zero, which is a bearish momentum, to a scale of five, which is a bullish momentum. And basically understanding the changes can really help you confirm the validity of the trend and align with your trade selection and timing. So when we see a rising momentum, that indicates that we see a strengthening in price trends.

[00:19:17.14] - Speaker 1
And, you know, we see an increase in bullish sentiment as well coming from the price action. And basically this could mean that the price is gaining upward momentum. And then again, this can support how you place your trades and how you can align with the trend on the other side. If we see a falling momentum score, this could signal that we are seeing a weakening trend. And basically bearish pressure is emerging.

[00:19:45.26] - Speaker 1
And again, we could see potential pullbacks. And again, this could actually confirm potentially a reversal trade and align with your. With your strategy. Of course, course.

[00:19:59.16] - Speaker 1
So let's go and see again some other example. Right? So this is another good example of the momentum score. And this was around February time when we saw basically when we had the tariff Announcement at the beginning. And we saw obviously the S p dropping around 20% here you can see clearly that the momentum score drops drastically in a couple of days.

[00:20:27.19] - Speaker 1
And again, that was the beginning and it could have been a good signal for a downside trend right here. The market lost about 20 there. And basically you can see how the momentum score could have kind of like showed this ahead of the the move or right at the beginning of the move.

[00:20:55.08] - Speaker 1
The third score is the seasonality score. And again we're looking at past price action to potentially predict the next five days of price movement. We go from a, from a score of minus 5 to plus 5. So minus 5 is, is bearish seasonality and plus 5 is bullish seasonality. And what does it mean?

[00:21:20.02] - Speaker 1
How can you read this score? Right, so when we see a rising seasonality score that can indicate that the asset is entering a period with historically stronger price performance, suggesting a higher probability of upward movement based on past seasonal trends. Again, we are using the history to potentially predict the future. On the other hand, if we see a falling seasonality, then that could be a signal that the asset is moving into an historically weaker seasonal period. And again this could actually imply increased risk or potentially downside or consolidation.

[00:21:58.12] - Speaker 1
So how can you use the seasonality score? You can actually use it as a timing tool to align with your trades. A rising score can potentially encourage prioritizing setups that align with seasonal strength, improving the odds of success in that case. And a falling score can actually suggest that you should be more, maybe more cautious and look at risk and maybe be careful of potential pullbacks or sideways trend as well.

[00:22:32.04] - Speaker 1
Let's do another example here. Again here we also have the QQQ here. This is what we see the price of QQQ In February, again before the move, we saw a massive downs down move on the seasonality score. What you see in the red quadrant there, and basically that was like few days before the price actually dropped in February. So this, the seasonality score in this case that was very, very bullish on the left hand side, went very bearish in a matter of a couple of days.

[00:23:08.25] - Speaker 1
That could have been an interesting signal of hey, maybe something is happening, I should be worried about what's going on. And then of course we had the announcement, we had the tariff. But then the seasonality score was able to kind of precede the downside move that we saw in February.

[00:23:29.05] - Speaker 1
All right, the last one is the volatility score. Right. So we are looking, this is not a directional indicator. We're actually looking at Understanding market volatility from low to high. So Obviously we have 0, which is a low volatility environment, and a 5, which is a high volatility environment.

[00:23:52.09] - Speaker 1
Volatility is key for traders to manage risk time trades and obviously select the appropriate strategy. So whenever you see a rising volatility score, it generally indicates that we are seeing an increase in market volatility, which can also increase the uncertainty. We could also see larger price swings and potentially more trading opportunities. But at the same time we are going to see an increase in risk. When we see a falling volatility score, we are obviously in a low volatility environment, meaning that the market is very calm and obviously we have tighter price ranges and of course less opportunities for large moves.

[00:24:37.17] - Speaker 1
So based on the type of strategy that you're running, you can also be accountable for that. And of course we are going to see lower risk as well because of the lower volatility. So how can you use the volatility score? So if you are looking at a rising score, you could expect wider price swings and of course that can help you potentially adjust not only your position sizing, but also your risk management. So you could actually have tighter stops or, sorry, larger stops and larger take profit targets.

[00:25:14.05] - Speaker 1
When you see a falling score, you can actually focus on strategies that might perform in a stable or trending market with lower volatility. And when you see a significant shift in the score, that can actually be a warning sign that something is changing in terms of market regime. And of course you might have to reassess your strategy and the way you trade.

[00:25:42.00] - Speaker 1
So the volatility score is really a market risk sentiment tool that can help you not only take trades, but also understand how to manage your trade by looking at volatility. And of course the most important part is always risk, risk management. So let's look at an example. So we're going to look at an example from last week. The ticker is Team T, which is Atlassian Corporation, It's a technology company.

[00:26:14.07] - Speaker 1
And this stock came up in one of our screener. We're gonna go into the screener later in the presentation. But what we see is that the Q score is on. The volatility Q score was very, very high. We had a 5 volatility Q score.

[00:26:32.17] - Speaker 1
When we open the dashboard, we also see that, that we are in a negative gamma condition and we can also see the other Q score in one place. So we are very, very bearish option score, very, very bearish momentum score, no seasonality signal and a very, very high volatility. All right, next we can look at our net gamma exposure chart to understand the overall positioning.

[00:27:01.08] - Speaker 1
So we see a lot of, like, negative gamma to the downside there with the very, very large, wide red bars that you see. And then finally, we can look at our swing trading model. Our swing trading model also confirms we are in a bearish bias. So you have, again, three of our models or four of our models. Here you have our negative gamma environment that we know, of course, what that means.

[00:27:26.08] - Speaker 1
We have our Q scores with the four factors. We have a net gamma exposure chart, and we have our swing trading model. So what could we do in this case? Right, so we know that the volatility is very high, and we know that the option activity is very bearish and the momentum is very bearish. So this environment could be a favorable environment for selling premium.

[00:27:50.08] - Speaker 1
But what do we do? And again, we could have sold credit spreads by looking at the swing trading model. And then if we look at the price action from last week, the stock is down now almost 15%. So if you were to, for example, sell credit spreads there on the call side, in this case, because of the bearish momentum, you probably could have pocketed the full premium in the last. In a few days or in a couple of days, depending on the move.

[00:28:20.05] - Speaker 1
But this is kind of like an analysis here.

[00:28:26.12] - Speaker 1
All right, so now we're going to go into the dashboard and we're going to go into the demo. But please let us know, guys, if you have any questions.

[00:28:42.07] - Speaker 1
All right, so from the dashboard, whenever you come in, you will see the Q score right here. So the Q score will appear next to the liquidity summary or the liquidity snapshot here. So you see immediately our gamma condition of the asset. And then to the right side, you will see the Q score simply by clicking on one of the tickers. This will open up the dashboard for that ticker.

[00:29:11.20] - Speaker 1
And then we're going to see the score for the same ticker as well. We also have historical time series. So what you see here is also the momentum score going back historically with the option score, then together with all our models. If you want to go back further, you can also click on the calendar here and go back up to six months in the past to understand also how the Q score change past the days that you see here. But let's open up, open up another ticket here.

[00:29:43.29] - Speaker 1
And then, of course, everything change based on the score. The score is updated once a day after market close, so you will have one value per day and it will be recalculated at the end of the day after the market close. All right, the next thing is we're gonna go into is, okay, so now we have the Q score, but how can we actually find trade ideas and trading opportunities? So what we've done is if you scroll down to the dashboard here on the left, we've created a section of our screeners which is about our Q score. So clicking here we have four screeners for every factor.

[00:30:25.15] - Speaker 1
So we have the highest option score and lowest option score. So that's going to give you a list of asset with the highest score. So maybe assets that are on a very, very bullish bias coming from the option market lowest score, but we also have the highest option score increase and decrease. That means what are the assets that saw the biggest change in option score from the previous day? Right, and we're going to look at some example and we do the same thing on volatility, momentum and seasonality.

[00:31:00.11] - Speaker 1
Right. So four types of screeners for every factor. Let's look at the first example. This is an example. You can also go back in time.

[00:31:10.09] - Speaker 1
So we're looking at last week, August 4, 2025. So here what we see is we have the highest option score here. So here we have a list of assets. We have the score right here. We have our different scores as well.

[00:31:24.08] - Speaker 1
So you can actually filter by by the different scores. It's very, very easy to run and manage it from there. And then we can see the asset here. So let's look for for example, as some examples. So we took credit as an example.

[00:31:39.14] - Speaker 1
So this is the dashboard of Reddit as of last week. So if you go back in time, you can see basically our option score. You can see how the option score has changed. And then you can also look at all the different models on Reddit. We can look at our swing trading model.

[00:31:58.04] - Speaker 1
And then of course, if we then go and see the price action on Reddit from last week, we saw almost a 12% increase right there. So again, this came up on the scanner with a very, very high option score. And then you could actually come here and look at the other ticker there as well and make your analysis based on the option score.

[00:32:25.23] - Speaker 1
The second screener that we want to see is the again we're going back to last week to show some example is the highest option score, one day increase. So we want to see what are the assets that saw the largest increase in option score. And we have a lot of different here. We have of course, vix, we have Expedia, but we're going to look at Costco. So Costco.

[00:32:52.05] - Speaker 1
So an increase of 3. So the current option score at that time on August 4th was 4, but the previous day was 1. So we saw an increase of 3 on. On the asset. So we can also go and look at the.

[00:33:09.00] - Speaker 1
The asset here. We can also look at all the different models right there. And then finally we can go and look at the chart and see what change.

[00:33:21.20] - Speaker 1
And you can see how the price moved from last week to now. So we are about 3.3% up on Costco. So very, very interesting there. But let's flip it to the other side. So let's go and see again, same from last week.

[00:33:39.09] - Speaker 1
Let's go and see actually bearish setups. And we're going to look at the lowest option score, right? So here you have a list of assets with a score of zero. Right here we're going to look at Salesforce and again we have the data from last week. We open up Salesforce and immediately we see that we are in negative gamma condition.

[00:34:01.14] - Speaker 1
We have a very low option score and a very low momentum score. And we have a very, very high volatility environment, the matrix. Again, we are in a very, very heavy negative gamma positioning there. We can also see our net gum exposure chart right here. And we can look, for example at our swing model.

[00:34:24.15] - Speaker 1
Our swing model was also in a bearish bias with an upper band of 266 and a lower band of 238. And you can also look at the success rate of the model right here. But now let's look at what happened to the price.

[00:34:41.25] - Speaker 1
So since that day, the stock is now down about 8% roughly. We see this movement here and basically, again, this is how you can kind of use it in conjunction with your. With your analysis. So, yeah, please let us know if you have, if you have any questions. But basically this is how you can then find the different scores.

[00:35:05.09] - Speaker 1
And again, click on the end of day for every asset, whether it's crypto, futures, stocks, ETFs, indices. The Q score will be available for you right here.

[00:35:25.03] - Speaker 1
All right, so the next step of the presentation is actually looking at quant strategies and how can you find an edge using the Q score. So let's dig right into it. So we've developed and we've backtested about five different strategies that are available on the website. And I'm going to show you where they are using the Q score to add value to the selection. So what you see here is one of the strategies we're Going to go over the five of them very, very easily.

[00:35:59.29] - Speaker 1
This strategy is actually looking at outperforming the market by selecting sector ETFs. So the asset coverage that you see here are some sector ETFs that you, that we have. So energy, financial, utility and so on. And we are going to look at seasonality and momentum. So the goal is to combine the two scores in this case seasonality and momentum and what the score does or what the strategy does.

[00:36:30.26] - Speaker 1
You can see the entry conditions on the left side and you can see the exit condition on the right side. So we are looking to identify every day within the list of assets, within this coverage list that you see at the top here, the ETF that have the highest seasonality score. And the seasonality score must be greater than zero. And if multiple ETF have the same score then we look at the one with the highest average return over the past week. We buy at the market open and we sell at the market open the next day unless the same ETF is also selected for the next day.

[00:37:14.11] - Speaker 1
And then in that case we, we keep it, we account some trading cost and we also look at back testing period of about 10 years plus. So what you see here is the performance of the strategy there. So we're looking just a seasonality in this case. The green is the strategy, the red is the spx and you can find all the relevant data there.

[00:37:42.07] - Speaker 1
The second strategy is using the momentum score. So again we look at the same ETFs. So the coverage is the same as the one that we had here. So one strategy is just using seasonality and one strategy is using momentum. So what we do is we identify the ETF with the highest difference between today momentum and the momentum score five days ago.

[00:38:12.02] - Speaker 1
If we only consider ETFs with a positive difference, we buy at the market open and we sell at the at the market open the next day unless the same ETF matches the criteria for the next day. In that case we would keep the trade and so on. And again this is the momentum strategy right there with the the green is the momentum strategy, the red is the SPX.

[00:38:38.29] - Speaker 1
The third strategy is looking at Max 7. So we only look at Max 7 company and we look at momentum and seasonality together. So we filter the max 7 stocks with the seasonality score greater than 0 and momentum score greater than 3. Again we buy the market open, we sell at the market, the market open the next day unless the same stock is also part of the selection for the next day. And then here you see the SPX versus the max strategy there in green, right?

[00:39:15.02] - Speaker 1
SPX in red, max 7 strategy in green. And then the other one is really looking at futures. So we are looking at seasonality on futures. And again we filter out futures contract coming from ES&Q, CL, GC and ZN. These are typical future that experience good seasonality patterns.

[00:39:38.18] - Speaker 1
So that's why we select those. And basically these are the entry and exit conditions. And then what you see here is the performance versus the SPX right there.

[00:39:55.00] - Speaker 1
All right, let me show you where you can find this information on, on the website. So if we go back to, if we go back to a website, come to our, our dashboard. I'm going to show you where to go from here. Come under our finance Wiki section. And then you have the first options which is Quant strategies.

[00:40:19.27] - Speaker 1
You can come here under strategies and you can access all the documentation on, on the strategy that we showed you here. So this is all available for you guys. And here you can see basically all the different data points, the chart that I showed you with the returns, all of that seasonality pattern, sensitivity to capital and so on. So everything is available under our finance wiki page here. Quant Strategies Strategies.

[00:40:52.28] - Speaker 1
And here you see the different backtesting results. We are also going to go into how you can then combine our quant models or our Q score with our swing trading. But let's go back to the presentation first.

[00:41:17.15] - Speaker 1
All right, so the next step is going back to an exercise that we did last week where we are looking to combine a few different models to potentially find an edge. Right. And we're going to go into the details. But before we do that, we always need, and I will not stress this enough, we always need to think like an institution. Just think about our institution trades and basically try to leverage the same approach.

[00:41:45.29] - Speaker 1
So if you see like a quant fund or a large institutions, they would have multiple data sets they will actually buy a lot of data from whether it's a Bloomberg Terminal alternative data, price action option data and they will try to combine those factors together to potentially increase the alpha of their strategy. So they're always looking for an edge that can actually add value to the strategy. So whether you can increase your return by 1%, that makes a very, very big difference. So they would actually use a similar approach to what we're going to show you today where they are actually looking at data to potentially find an edge. So we're going to go back and we have videos here as well on the back testing of the swing model but together with the Q score.

[00:42:36.20] - Speaker 1
So we, we did a really nice exercise that you can also find on the website and I'm going to show you where you can find it. Where we took our back testing, where we took our swing model levels from July 25, we were trading on July 28, which is a Monday, and we were exiting on August 1, 2025. And we were looking at two types of strategies. One was directional strategy. So we wanted to see if the string model had a bullish or bearish bias.

[00:43:10.14] - Speaker 1
Can we go longer short and how much can that return to our strategy? If we also want to use a string model as option sellers, we can also use the same data to potentially sell credit spreads. So the second strategy was looking at the bias of the model. And if the bias was bearish, then we will sell a credit spread or call credit spread. And if the bias is bullish, then we would sell a put credit spread.

[00:43:39.29] - Speaker 1
So the step one, again, this is doing exactly the same. This is the same approach that would an institution would use.

[00:43:48.06] - Speaker 1
The step one is really start from the baseline of our strategy. So the baseline of our strategy was looking at the swing trading model. So we had two types of strategies. One is just simply long, short, directional trading, the equity, the universe of all our coverage was around 904 assets. And again we were trading an equally weighted portfolio.

[00:44:13.16] - Speaker 1
So one $1,000 per trade. So the total portfolio size would have been $904,000. And we can see that simply by going long or short based on based on the signal from the swing trading model, our win rate would have been around 34 and our return was negative 1.37%. On the other hand, if we actually used an option selling strategies with combining the bias of the swing model with the swing level, and we would have used the swing level for the strikes of our credit spreads, we would have had a win rate of about 75.44%. Right.

[00:44:56.18] - Speaker 1
But then we said, okay, let's take this a step further. So how can we actually add value, reduce the number of trades and basically add potential return to our strategy? So we started adding and tilting the strategy by adding the Q score. So we now went and what we are looking now is we are adding the options and momentum Q score. So we only take, we were only taking bullish trades if the option and momentum score were equal to five.

[00:45:28.27] - Speaker 1
And we were only taking bearish trades if the option and momentum score were lower than one. So what that result is we went from around 904 assets or 904 trades. To around 120 trades. The size of the portfolio remained the same. So $1,000 per trade, our win rate went from 33% to 41.67% and our return went from minus 1.37 to minus 0.09.

[00:46:00.18] - Speaker 1
Again, what we're doing here is simply adding an additional factor to our strategy. If we look at our options selling strategy, we went from 75.44 to 75.83. So in this case, the option and momentum score added value to our directional trading strategy and kind of like made our option strategy roughly in the same same win rate that we saw there. So then what we did is we added another, another tilt of that strategy. So what if we now look at momentum and seasonality, right?

[00:46:38.28] - Speaker 1
So we are now adding our third score. So we only take bullish trades if the momentum score was equal to 5. So very bullish momentum and the seasonality score was greater than 2. So bullish seasonality and we only took bearish trades if the momentum score was lower than one and the seasonality score was lower than minus one. So as a result, again, let's look at the difference.

[00:47:06.16] - Speaker 1
We went from around 904 trades to around 24 trades. Portfolio size is the same. And now our win rate went from 33.85 to 58.33, resulting on a plus 1.31 return compared to a minus 1.37%. We, if we also look at our win rate, we went from 75 to around 87.5%. If we look at our options selling strategy, which is great, this is a great way of adding factors to potentially increase the return.

[00:47:46.12] - Speaker 1
But we went another step further. So the the last step was, okay, what if we combine our option, momentum and seasonality score together? So we take bullish trades if the option score is greater than 4, if the momentum score is equal to 5, and if the seasonality score is greater than 2. On the other side, we only take bearish trades if the option score is lower than 1, the momentum score is lower than 1, and the seasonality score is lower than minus 1. So as a result, we now only have 14 trades.

[00:48:20.08] - Speaker 1
We have a 57 win rate and a return of 3.18. So if we compare it to our initial setup, we went from 904 trades to about 14 trades, win rate went doubled and the return also tripled or more. If we look at our options selling strategy now we see a 100 win rate. That means that if we sold credit spreads using the swing levels, we would have pocketed the premium on all the trades because the price never touched those levels. Right.

[00:49:01.19] - Speaker 1
So let me go back to the website and let me show you where you can also find the documentation about this. But please let me know if you guys have any questions.

[00:49:20.18] - Speaker 1
All right, so if you want to look for, for the results and the, the data that we did last week, just come under quant strategies, go under swing levels and just take a look at the latest document here. I'm also gonna, if you look at our YouTube channel, the, the link is also there here we also have a video that we, we also have a video in the, in the website as well. And here you can find all the different data and you can also download the file with all, with all the levels, with all the data, with all the Q score data as well. So you can actually look at it there. But please let me know if you have questions.

[00:50:32.15] - Speaker 1
All right, so I don't see any question from the audience. So please let us know if you have any questions, any doubts or feedback. But if you want to reach out to us and ask more information about the different models, if you want to learn more about what we do, just send us an [email protected] you can also look at our website mentor Q.com and you will find all the information about the Q score, about the different models. But basically the goal of this presentation was to, to show you how you can leverage the same approach that large institution would use when it's coming down to factor based trading. So adding quantitative data to your strategy, whether you're a swing trader, position trader or futures trader, this model can actually help you understand how to manage your risk, how to manage your position and of course potentially provide value to your strategy.

[00:51:32.08] - Speaker 1
So with that said, I really want to thank you guys for watching and stay tuned because this week we're also going to have more and more session on integration on blind spots. We're going to do a session on volatility next week. So please stay tuned and join us. And for, for, for, for now, have a great rest of the day. Thank you guys.