4 Institutional Scalping Strategies Market Makers Keep HIDDEN (Automate Prop Firm Trading)

I made more than 30 million euros for the bank. 90% of institutional traders were going like in one place to get their trading strategies ideas. The place, guys, is the Social Science Research Network. And I want you guys that you steal this, like steal as much as you can because this one was something that really impressed me when I started.

He traded over $15 billion in total volume at one of Europe's largest banks. Introducing the one and only Matteo Conti.

Dan is an industry paper called VWAP: The Holy Grail of Day Trading Systems. The fact that the vast majority of institutional traders are using this type of algo amplifies these directional moves.

While trading at the banks, Matteo ran over 400+ strategies at one time. And in this episode, he reveals the exact place that institutions find their edges.

And this is, in a nutshell, the big difference between retail and institutional trading. What is the pipeline every single institutional traders follow? Every single trading strategy needs to have these 3 boxes filled. So you need to know why you're opening, you need to know where to close, and a 3rd condition is having live capital.

These exact edges and strategies have been used to get consistent prop firm payouts by moving towards automation. Matteo reveals all of this and so much more in this special episode Chart Fanatics.

Every single institutional trader that I know, at least once, if not every single day, they send an execution order to the market. They often use—

I guess starting off, like, where should we begin? What are we covering today?

Yeah, look, so let me start with some facts. Like, obviously, I started trading just like the majority of the people that are watching this, most likely, like being 16, 17 years old, wondering what trading actually is. How do you trade like an institutional trader? But like online, it's very hard to find proper information, especially like fresh information straight out of the trading floor.

Right.

And to get to know what is the actual process, the step that I went through was going to university, studying 12, 14 hours a day because like it's an extremely competitive type of job. So you really need to have top grades just to book yourself an interview with banks or hedge funds. And if you're lucky enough, like I was at the end of my 5 years of university, I got a seat on the trading floor. I joined the bank in 2018, getting my own books in 2019. And since then, like, there's been a process of Learning how institutional traders actually trade, and it is very much different from what you usually see online. The very first thing that I want to say is that the first difference between retail and institutional trading is that retail chase trades. So all retail traders go after trades. On the other end, institutional traders go after validated trading strategies.

Trading strategies.

And this is In a nutshell, the big difference between retail and institutional trading. And this is as well the reason why there is inconsistency between the profitability of retail traders versus like having institutional traders that make money basically every single month. We are going through what is the pipeline that every single institutional trader follows.

Mm-hmm.

Which— so pipeline. And this pipeline starts from getting the idea for a trading strategy.

Mm-hmm.

And this is just the beginning of this long journey to end up to actually having our trading strategies trading live. Because once we have the idea, the next step is defining a set of rules. This set of rules needs to be so specific that you can feed it to a machine. And to feed it to a machine, it needs to be encoded.

Now, I imagine at this point the audience might be thinking, oh, 2 things. One is, what's the difference between chasing trades and validated trading strategy? To them, that might sound like the same thing.

Yeah, well, chasing trades, you might see like one of the— usually like they point at one specific setup. They say this is a setup where like this can work out well for you, can make you a lot of money 70% of the times, right?

Mm-hmm.

But if you look for validated trading strategies, means that it's a strategy that if you apply it systematically, Every single time is going to win 70% of the trades. Do you see the difference?

So more so rather than the idea of a trade, it's actually validated with data, with actual specifics that you can look at and go and have not certainty, 'cause I don't wanna give across the wrong impression, but almost. It's the closest thing you can probably get to certainty within the markets.

Yeah, you can, you can understand what are the probabilities of success. And this is the main difference. It's not just about— because, like, a good strategy can have 70% win rate, but if you only trade that strategy as a single trade, it doesn't guarantee you that you're going to be right, that you're going to be on the right side of that trade in that specific moment.

Yes.

On the other hand, if you apply that strategy over 100 trades, statistically, you will win 70% of them. And this step of going from a set of well-defined rules to a piece of code, it has been for a long time the biggest barrier to entry for retail traders to start trading like an institutional trader. Lucky for you guys, in 2023, something magnificent happened, which was like having ChatGPT and the other large language models going mainstream, which—

Yeah.

completely remove this massive barrier to entry. And now that you have the ideas, or you can get the ideas, we will get there, where to get the ideas like an institutional trader. You will know shortly how to set the rules that every single trading strategy needs to have. You can encode it leveraging the power of your ChatGPT, Claude or your favorite large language model. And then the next step is to backtest. So here, what is backtesting? Backtesting is taking your set of rules and replaying history. So every time your entry conditions were satisfied, you would have like a simulated trade back in time. And obviously you don't backtest it manually like I've seen doing it online every now and then, but you leverage the fact that you translated it into a piece of code to just replay history. And this one is going to give you one single equity curve, which can be uprising, downrising if the strategy is not good enough. But as an output, what you get out of it is a set of statistics, as we were saying.

Mhm.

that can tell you if you are on the right path. Like, for example, if you have a positive net profit, what is the win rate of your trading strategy over the past, let's say, 5 years?

Mm-hmm.

What is the average trade win? So how much your strategy is winning every time you place a trade? What is the drawdown of your strategy? So Which drawdown is the— where drawdown is the peak to valley of your cumulative P&L when you have a rough patch. And all of these statistics are necessary to put you on the map. However, this one is not the last step because then we need to validate.

Validate.

Can you read it?

Yes, of course.

Okay. The validation is another extremely important step in institutional trading. It usually is divided in 2 phases. One is the split test. So splitting your data between in-sample and out-of-sample. So in-sample versus out-of-sample. Why is this one important? This one is important because when you work on your set of rules, when you are developing your trading strategy, putting code, you should always work with in-sample data. So let's say that you have 10 years of data here. You should purely focus, like, let's say we split this data.

Yeah.

In 80% in-sample, And then you have a remaining 20% out-of-sample. In your in-sample data is where you're going to work on your rules, adding, removing parameters, fine-tuning the parameters until you find a strategy that looks like it's behaving correctly in-sample.

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And afterwards, you feed the same rules, like frozen in time, to unseen data. So that if your strategy is still performing good out of sample, means that you didn't overfit it. Because overfitting is when you add too many rules or parameters just to make your equity curve look nice in sample. But as you apply to unseen data, you will see something like this, like an equity curve that gets destroyed. And this one is going to be the first one. If you have a good result, both in-sample and out-of-sample, it means that you didn't overfit and there is a high chance that your strategy will work well with live data as well. And this one is really to put it at a very high level, just to give a little bit of context.

Yes.

And the second step is the Monte Carlo analysis. Monte Carlo analysis is something that, like, when you look at the internet, look, extremely difficult to understand, but what it's really about is just taking all the trades of your backtest and then either reshuffling the order of the trades, which is what is known as Monte Carlo reshuffle. And basically, like, in this way you create multiple versions of the past, multiple equity paths.

Yeah.

So that you can understand if your equity curve in your backtest was looking good just because of the sequence of trades, or is that because you really have an underlying edge? And the— so let's just show, let's say that this one was the equity curve of your—

Original one.

Yeah, the original one. And then you start changing the order of trades, right? But you will always start from the same point and you will always and up to the end because the trades are always the same. However, the path to get there is going to be different. And then you repeat this process basically thousands of times. And the bigger is the dispersion from the top equity line to the bottom, or the weaker is your edge.

Yeah.

On that, right, like when you See that reshuffling the order of the trades, you have a very tight distribution around the average. It means that no matter what is the order to which your trades took place, you still were going to have a nice tight distribution of the outcomes.

So you're showing the edge is very strong.

Exactly.

Regardless of how the trades are shuffled. So when we say shuffled, for the audience, in terms of that, it's not changing the strategy or anything. It's literally a case of, let's say the original data was 1,000 trades, and of those 1,000 trades, like the average losing streak, let's say was 7, and the average winning streak was 5. But it shows you the peaks as well. So the peak losing, you know, number of losing trades in a row was 15 in the original test. And then the most wins in a row was 15, just for example, random numbers. But then the reshuffle was essentially putting all those trades into a random order. And it might be a case where, you know, the peak losing number of trades in a row was now 45.

Exactly.

And therefore, you know, to your point, the further away, that's how, when the further away those lines will get.

Exactly.

Which means then the edge is maybe isn't as strong versus let's say if that number was 16 instead of 15 on the reshuffle or 14, that's when they're going to be closer together. So it's showing you that regardless of how that data is redistributed and all those trades are mixed up, it's actually seeing consistent data.

Yeah, 100%. And this one, like, I really like the reshuffle because it's like something very simple that literally doesn't require a lot of computing power, but already like helps you to visualize like, How dependent were you to that order of trades? And gives you a first idea of the distribution of possible drawdowns. Because like this, the second common version of the Monte Carlo is the bootstrapping and resampling. So which is the classic spaghetti charts. I'm not saying because I'm Italian, but it's like— It is the classic spaghetti charts, right? Where you have multiple equity lines. And the difference between the reshuffling and the resampling is that the ending point of the equity curve, it is different, right? And the reason why is that is because you don't simply just reshuffle the trades.

Yeah.

But some of the trades, appears twice, so they can appear multiple times. Some others don't appear at all. And that's how you reproduce multiple versions, 10,000, 20,000 simulations of the past.

So that's where it comes back to your validation. So it's not just a trading strategy, an idea, it's then validated through these stress tests, if you will.

Correct. What is cool as well is that on top of that, doing something like this type of Monte Carlo, you do get as an output, like, a distribution of outcomes.

Mm.

So, like, you can understand what is the expected profit of the strategy, what is the expected return if I run this strategy 20,000 times, what is the probability of getting a drawdown larger than $10,000.

Mm-hmm.

And all of that, all these possible scenarios, they're given by the distribution of outcomes of the simulation, which can be used as well, like during live trading. For example, if I know that after 10 trades, okay, after 10 trades, I should expect as maximum drawdown Hmm. $10,000, if I see that there was only a 5% probability of getting a drawdown larger than $10,000, like that one would be a red flag for me saying, okay, maybe I need to step back and stop this strategy.

Okay.

Because it's very unlikely that after 10 trades I get a larger drawdown than $10,000. And all of these are yardsticks. That are provided by this validation phase that you can use to give you awareness while you're trading live. And once all of these steps are completed, so you checked that you didn't overfit it with, uh, where the simplest version is running, like a comparison between in-sample results versus out-of-sample, you did your Monte Carlo to see how strong your edge actually is running 10,000, 20,000 simulations. Only at that point we reach the final phase, which is live trading. And note that with live trading, it's not that the institutional trader sits in front of their monitors waiting for the entry conditions to be satisfied. Just like as we did, like we encoded the strategy. So like the live trading is actually all about automation where you still leverage your machine to just monitor the data that are fed to it.

Mm-hmm.

And every time that your conditions are met, opening or closing your position on your behalf. So at that point, like, the question might be like, so all this work to have your machine trading, and indeed, like, what is your job, right? Like, your job as an institutional trader, it is doing this research to make sure that the strategies that you're trading live, they do have positive expected returns. They do have a positive value. They most likely than not, they're going to have a positive P&L at the end of the day. And your job is only one, monitoring the performance, and 2, monitoring the risk. So this is what you're doing while your trading strategies are trading live. And given the fact that they are automated, you can go back and restart this process of going from Trading strategy idea, rules in code, backtest validation, and eventually having the strategy trading live.

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Look at this. Like, usually the classic retail traders, like, just has the idea and trades live with real money. the idea right after. On the other hand, like, as an institutional trader, you need to follow this pipeline. And once you have a strategy that has been validated, that is proven, that can make you money in the future, you finally trade it live.

So this, as you, to your point, like most people will learn an idea maybe from a YouTube video or just generally from some, somewhere. Very rare nowadays that people are coming up with their own ideas, but that's possible too. And but straight away from there, they're looking at going live most of the time. At most, someone might create a few rules, someone might, you know, backtest a tiny bit, but, you know, not the level of detail that is necessary to really have confidence and edge within the markets. In terms of the first 2, which I know we're gonna focus on, like the idea generation and rules, but when it comes to the idea generation and those rules, and then getting encoded, so then The encoding side of things is where, again, as you mentioned, that language models can, you know, really help with that for the average retail trader. But in terms of that, from that point on, a lot of like the backtest side of things is automated. Uh, obviously you are, you know, giving the inputs to make it all happen, but the backtesting and the validation, that's all an automated, like being done for you process to give you that data for you to review. Is that correct?

Yeah, correct. Like, that's the big advantage. Like, nowadays, even the average retail trader has access to so many well-designed tools that allow you to run backtests and run like simulations. My favorite one, I'm not affiliated or anything like that, but my favorite one personally is MultiCharts because like the programming language is extremely simple. It's very easy to understand what the code is doing, which is key to understand what you're doing if you want to make money in the market as an institutional trader. And then, like, the backtesting infrastructure is very solid. So you can rely on the backtest.

Mm-hmm.

You have the possibility of running simulations using your backtest, like a classic Monte Carlo. And really, like, it's not a matter that you as a retail trader, you don't have the tools. It is a matter of really, like, having access to this process, getting to know which are the steps and the boxes that you need to check before trading your strategy live. And you say that, I love that you said that it is nowadays harder that retail traders come up with their ideas.

Mm-hmm.

Because today I really want to focus on the first, 2 steps. Like, where do the ideas come from as an institutional trader? And I want you guys that you steal this, like steal as much as you can, because this one was something that really impressed me when I started, that so many institutional traders were going like in one place to get their trading strategies ideas. The place, guys, is the Social Science Research Network.

Interesting.

Okay. Why does this matter? Because 90% of institutional trading strategies or strategies applied by institutional traders on any single trading floor are coming from research papers. And the feedback that I got, like when I started like this journey of showing like the behind the curtains of institutional trading to the broader public was that, yeah, but you, cannot use research paper because it's hard to read them, or like the edge is already decayed. Fair enough. But you don't copy the paper. You look at its findings and you look for the underlying reason, the why that strategy can make you or is supposed to make you money in the future.

Mm-hmm.

Only triggers the idea that allows you to frame the rules for your trading strategy. And this is very important. I will write it down that the goal is you don't copy the paper, but what you do is analyzing the findings. And you look for the why. The reason why your strategy is supposed to make you money in the future.

Mhm.

I brought you 4 examples that I thought was a good, like, exercise to go from research paper to an actual testable strategy, which we can cover one by one. So let's write it down again, that 90 to 95% of the trading strategies ideas for institutional traders are coming from research. And in particular, like, there is 2 types of research. There is academic research. Which is research conducted by university professors or researchers within the academia, or industry, which might be hedge fund managers or traders which conduct some specific research around trading strategies like momentum, mean reversion, or strategies on gold. All of that and they publish them online. Websites like the Social Science Research Network is like this giant collection of all this research, which is 100% for free, where anyone can just go and their job is to dig and trying to find something interesting. Again, it's not a matter of reading 40 pages because Just like for the encoding, you can leverage your machine, leverage the help of large language models to crack these papers, trying to understand, okay, what is this research about? What are the findings? Why is it supposed to make money? You don't copy the research one-to-one, but it is an extremely important starting point to define your trading strategy's rules. Which are the rules? Let's move there and then let's have a look at the examples. Every single trading strategy needs to have 3 things.

Mm-hmm.

An entry, so the conditions on why you should open a position. An exit, why you should close the position after it has been opened, and position sizing. Every single trading strategy needs to have, needs to have these 3 boxes filled. So you need to know why you're opening, you need to know where to close, and generally is having a Take profit, stop loss, and a third condition, one that I find very often across trading strategies, is having like an exit linked to time.

Mm-hmm.

So for example, closing a position after 1 hour or at a specific point in time, like 3 PM. And this one is satisfied if you don't hit your take profit nor your stop loss.

Okay.

And the third component, position sizing, which is something that I feel like is not discussed enough across retail traders because it's really like knowing how you size your trading strategy, your position can make a huge difference in both reducing the risk of blowing up your account.

Yeah.

And improve the risk-adjusted return of your trading strategy. We will have a look at some very simple examples on that one as well.

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But now that we have all of this, now that we know that the rules need to have an entry, exit, position sizing, we need to have a reason why our strategy is supposed to make us money. We can move forward and have a look at the first example.

Definitely. Let's do that.

Should we do that?

Yeah, for sure.

So let's start from the first paper. And the idea here, I really want to show you how to go from a research paper to an actual tradable strategy. So let's start from the first paper, which is Market Intraday Momentum. So Market Intraday momentum. And this one is a paper from 2018, a paper that you can find on the Social Science Research Network 100% for free. And the reason why I wanted to take this one as first example is because at the end of this example, you should be able to understand why one of the most popular Retail trading strategies, the opening range breakout actually worked so well over the past six years. As I said, like the first thing that we need to know about any single paper is the findings and the why. Why this strategy? Is working, why they found these specific findings within the paper. So the findings of this paper is that overnight gap plus the first 30 minutes of trading from 9:30 to 10:00 AM It reveals the imbalance between buyers and sellers, and it predicts the performance of the last 30 minutes of the regular trading hour. What does it mean? It means that if you had a positive return between 4 PM to 10, you should have a positive return even in the last 30 minutes of trading.

Mm-hmm.

This is what they analyzed in the paper. This is the finding of the paper. And then you need to ask yourself, okay, but why? The reason why is because after the market is closed, there is all a new set of information that gets released.

Yeah.

Like news, earnings, global positioning. So news plus earnings Plus global positioning, which creates all of these orders between buyers and sellers, and these orders collide in the first 30 minutes of trading, revealing the imbalance.

Yeah.

And this imbalance, if there is a lot of buyers over sellers, the market is not able to digest it just in the first 30 minutes. And this imbalance is carried on in the remaining part of the day. And that's why you have an intraday momentum.

Yeah.

Is it clear?

Yeah. Yeah.

And now that we know the findings, we know what they did. Like, okay, you check what is the performance in the first 30 minutes, if there is more buyers, more sellers, and that one allows you to predict the last 30 minutes. You might be tempted to try to develop a strategy taking the entry The exit and the position sizing of the strategy and replicate it. But if you try to replicate it, you're going to have a very nice equity curve up to 2018, followed by a very poor equity curve because you would be victim of the alpha decay.

Yes.

However, we don't copy the paper. But we can take the fact that the imbalance gets revealed, a finding that they have in the first 30 minutes of trading, setting up an entry of a classic opening range breakout. But now we know why it is supposed to work, because overnight there is all this set of information and not all the players, all the market participants are going to trade. until the market is open. Yeah. And we can say that entry condition, open a long if the price closes above the high of the range between 9:30 and 10 AM, right? If the price closes above the high of the range, we open a long position.

Mm-hmm.

Exit, you could say for the long, the same like putting a stop loss at the low of the range. So it would be somewhere down here, right? So here we have our stop loss. And for the take profit, a classic reward to risk ratio, either between 1 or 2. Okay?

Yeah.

This needs to stay simple because we need to have the control over what we're doing. Take profit, let's say we have here the high of the range. So it would be, let's say if we take 1:1, the TP would be up here.

Mm-hmm.

And we know that we are going to open a position only if the price will reach or will close above the high of this specific range. And we can add even an additional condition like closing the position if we don't hit nor the stop loss, nor the take profit, let's say at 3:30 Eastern time. So a very simple set of rules, but very well-defined.

Yeah.

That allows us to translate it into a tradable trading strategy. Last one that is missing is position sizing.

Yeah.

What I always suggest during the development phase is to use a fixed amount of contracts of 1. Why? Because it makes it simple to develop the strategy. However, this is not what I would suggest to trade live, because when you have a fixed amount of contracts like 1, if you have a very wide range, Yeah. So high volatile days, like you're going to put at risk something like $30,000.

Yeah.

On that range, in Narrow days, you're only going to put at risk, let's say, $1,000. And academia research, again, with 2 very important papers, and this is something like it is done so often on the trading floor, is to make your position sizing conditional to the volatility of the instrument.

Yes.

The 2 papers I'm talking about is Volatility Managed Portfolios And the second one is the impact of volatility targeting. What is volatility targeting? Volatility targeting is to have your position sizing where you always put at risk the same amount of money, let's say $10,000 per trade.

Mm-hmm.

But the amount of contracts that you're going to trade with is going to be based On the volatility of the underlying. So like if you have a very wide range, so a highly volatile day, you're going to use a lower number of contracts, still risking $10,000.

Yes.

Okay. On the other hand, like if you have low volatility day with a very narrow range, you still put at risk $10,000 betting or like trading with a higher number of contracts. And in this way, you don't have that highly volatile days are going to dictate the outcome of your trading strategy, but overall, they're going to smooth out the outcome of your trading strategy, improving by definition the risk-adjusted performance of your trading strategy.

Mm-hmm.

And this is like something like that is never discussed across retail traders, but it's such a small, simple change. that can highly impact the performance of your trading strategy. And this one is just one version of the strategy, but given the fact that for now we're just brainstorming on what is the right strategy that we should test, we could have a version where we have a short version. So to go short if we break the low of the range.

Yes.

However, this one, I'll tell it from the experience, is that when you develop a strategy based on the imbalance between buyers and sellers in the first 30 minutes, that works better on long-only strategies. Because once you start breaking down the level, Yeah. you have a lot of buyers stepping in, removing that imbalance so easily on the trend. Like on trending days, like it's hard that more buyers step in and you have this drifting effect making this strategy fairly effective.

Mm-hmm.

We didn't copy the paper. It's not that just because the paper saw that we, if we have a good performance in the first 30 minutes, we're going to be long in the last 30 minutes. But we took the concept of taking this imbalance gets rebuilt itself in the first 30 minutes to start defining properly the rules of a strategy that we can actually translate into a piece of code.

Mm-hmm.

And that's like key on this process of going from research to a testable strategy. Because now, like, even if you're trading from home an opening range breakout, now you know that the reason why your breakout is actually has been working fairly well over the past 5 years, 6 years, and note that this paper is from 2018. Like, it is because you have information accumulating overnight.

Yep.

Imbalance revealing in the first 30 minutes, and that's what is pushing the price in your direction. So now you start getting, like, more knowledge, more awareness around the why your strategy should work and the why you're doing what you're doing. This is what I really find. fascinating about this process of justifying what you're doing based on leveraging the work of people that are much smarter than you at the spend.

So, well, they say like your why is so important, right? And just in life, let alone in terms of trading, like knowing why you're putting a strategy to use or why you're planning to trade a strategy is so important. You know, like you said, originally a lot of people have maybe an idea, but usually that idea is just something they found you know, come across and there's no validation, no trying. While this has not only got a thesis behind it, like an actual academic thesis, but taking the understanding of what that's representing, as you mentioned, you know, with the overnight action and all these orders and participants waiting to step in. And because they're waiting, that then creates, as you say, an imbalance in price.

Yeah.

And our job a lot of the time in terms of edge and profitability is because you notice an imbalance in price. And so, you know, it's showing you that thought process step by step, as you say. And as you say, the opening range breakout is very popular, I'd say over recent years in particular.

But one thing that is cool about these and opening range breakout, like in general, is that they are part of a structural limitation of the financial world in a way that there is so many players that are only allowed to trade after 9:30 Eastern time. And that's why they don't place orders before, because they can't. Because if they were allowed to place the order before, like this market anomaly, the fact that there is like the reveal of this imbalance in the first 30 minutes would disappear.

Mm-hmm.

So knowing why it is working, like let's say things changes and this, UCITS fund or some pension funds, they start trading after or like before 9:30 AM, the effect of this one might disappear. And knowing that it's working because of that—

They lose that inefficiency.

Exactly. So like, gives you a completely different perspective over what you're doing and why you're doing it and why you're making the money. Next example. Do you have any other question on this?

No, no, this one makes sense. Yeah, no, 100%. You know, like I said, because it's a very widely used strategy, maybe not properly tested or data collected, but that's, I guess, one of the interesting things is that, yeah, like if it is a validated strategy, even if you didn't, that I guess that's a strange one, right? Maybe an idea is validated not by you, but someone's obviously validated it to be able to publish, or someone potentially has validated it, but you're just using the idea of it, just the concept that you've learned online. You could still be profitable, but you might not have the confidence that you need.

Yeah, exactly.

Long, long term.

Exactly. Like, if you, on top of that, like, you get the confidence over what you're doing, Like, it changes your perspective on trading completely because, like, you might get, like, let's say we develop the strategy. By the way, guys, the academia is saying that the first 30 minutes are the range that is optimal to take advantage of for any opening range breakout.

Yeah.

So, and that one is, like, it's not Matteo Conti, they're saying that. I invite you to develop a strategy based on the rules that we discussed before to confirm that obviously the long-only type of strategy works better. And it's not just a matter because of the beta of the market, that obviously if you have long-only strategies and the market is just going higher, you get a better performance because that strategy performed well even in 2022 when the market was bleeding. And the other thing is like the risk to reward ratio to use is 1:1 or 1:2. Like, yeah, that one is like the optimal that you can find for this strategy. Yeah, for this strategy for academia. But again, the, the, the goal is to gain the independence of going and testing it yourself, like trying to get the idea, Frame it with entry, exit, and position sizing, and then use large language models to get versions of your strategy.

Exactly.

And try to test it yourself, because then, like, you gain independence from anyone. Like, you can go on the Social Science Research Network, spend the full day finding all the strategies that you want, testing as many strategies as you want, and If you keep digging, you find gold.

Of course.

That's how this game works. Not only at home, like in every single trading floor on Earth. The second strategy, or better, the second paper, I wanted to still be somehow close to something that is still understandable across all the retail traders, which is a paper from the industry this time. So the previous one was purely academic.

Mhm.

This one, on the other hand, is an industry paper called VWAP: The Holy Grail of Day Trading Systems. The biggest difference between this one and the other one, again, is that this one is based on research. And there is a beauty in that as well, like, it's very practical because you have the price, right? So we have our price, our time. And from the price and the trading activity at any given point in time, you can get the VWAP. Which is the volume weighted average price.

Yes.

Say it differently. The VWAP is the average price of everything traded up to any specific point weighted by the volume. Is it clear?

Yes, yes.

Cool. So this one is our VWAP, and this one is our price, or the stock, or of the index. Again, we need to go back to which were the findings of the paper, and we will need the why. The findings of the paper was that if you go long When the price is above the VWAP and you go short when the price is below the VWAP, closing the position when the price crosses back the VWAP. So as exit, we will have— and we will repeat this later— but as exit, we will have that when the price is equal to the VWAP, we close the position, we can get very good returns. Like, we can get systematically good returns. And what the research that they did was on QQQ, which is an exchange-traded fund.

Mm-hmm.

Tracking the performance of NASDAQ 100. And they used a timeframe of 1 minute. So, instrument they used, QQQ, on a 1-minute timeframe. Every time that a 1-minute bar was closing above the VWAP, was getting longer, and they were closing the position when the price was crossing back—

Below the VWAP.

Yeah, exactly, with the VWAP. Exit the position. Let's put it with an E. And the same like going short.

On the flip.

Yeah, exactly. On the flip. When a 1-minute bar was closing below the VWAP and closing the position, this one would have been actually a losing trade, but closing the position when it was crossing back to the VWAP line. The returns that the fund was actually like quite impressive. You can find it in the paper, something like in the order of 671% over 5 years.

Hmm.

But we need to go beyond the headlines. We don't care about the headlines. What we care is like understanding what they've been doing and why. Why is it possible that when the price crosses below or above, we have like these directional moves that allow them to generate 600% over 5 years? The reason why is behind the VWAP algos. So every single institutional trader that I know, at least once, if not every single day, they send an execution algo to, or they send an execution order to the market, they often use VWAP algos, which is a type of algorithm that tries to track and get the same execution or as close as possible to the VWAP price.

Yes.

Okay. So it's like a— so it is a volume participation algo. If there is a lot of volume traded at specific points, the algo will send more orders at that specific point in time. And that one causes some heavy directional moves because you have a lot of buyers or a lot of sellers that try to track the VWAP price. And in the moment, like, you have these crosses between above or below the VWAP price, the fact that the vast majority of institutional traders are using this type of algo creates, like, amplifies these directional moves.

Mm-hmm.

So now we know that It might make sense to follow a logic that is related to this VWAP logic. They did the research, they already tested a strategy, a version of a strategy using a 1-minute timeframe.

Yes.

So we could do exactly the same. What I would change as first thing to get a little bit of context and a little bit of different ideas is that we don't need to deploy the same strategy on QQQ, but we might decide to test the strategy of NQ. So NASDAQ 100 futures, if we want to use the same underlying.

Okay.

So it's still NASDAQ 100, but you could test it. You could test the same on ES. You could, so S&P 500 futures. You could test the same on crude oil. You could test the same on single stocks. Like again, you're writing the code once, And then you can apply that code to whatever instruments you want, right? As long as you know that you have institutional traders using these specific algos on that instrument. Yeah. Like this logic is supposed to work across all the instruments, right? This is the first important takeaway. And then like, obviously you need to define your entry. So again, it's going to be to go long. When the price closes above the VWAP, to go short when the price closes below the VWAP. Exit, we can decide to simply have one exit condition.

I'm guessing the VWAP is Plotted on from the open?

Yeah, yeah, that one is another thing that you might decide. Like, you can decide to use the VWAP from the open, or you can decide to use the previous day VWAP incorporated as well. Those are like the type of parameters that is up to you to use. What I strongly suggest is to use it from the open. Okay, so to start, uh, considering like tracking from 9:30 AM if you're trading US or some markets here in Europe, like starting tracking from 9. And then like after you get some data in, start like checking what is the good level at which, or better. And then like trying to understand even during the backtest, What is the number of minutes or hours that you need to have, which is the optimal, that allows you like to benefit the most out of this?

The most volume, I imagine, because it's from the start of the day.

Exactly, exactly. And lastly, like obviously like the position sizing. And just like we discussed before, always use one contract in the development process. If you are testing for stocks, you can, might decide to use a percentage of your portfolio. Let's say you have a $100,000 portfolio, to allocate 10% of it just to see how it would behave. Or again, like, having like, your position sizing conditional to the volatility of the underlying instrument, just as we have seen before. But this is, again, in the very first phase, development phase, always one contract, and then you can test different versions. Yeah, different versions, like afterwards, once you make sure that the strategy that you have on hand is actually pointing to the right direction of Profitability. Next one.

Yeah, we can.

Yeah. So now we've seen two examples, and both of them I decided to take them because is topics that the classic retail traders relate a lot. And on that right now we start like going like with the last two examples, um, with stuff that is one of them more academic, which is the one that we are covering now. Yeah. And the difference between this one and the previous one is that this one is the result of a collection of research papers.

Okay. So like multiple into one?

Yes. I don't know if some of you already heard of it, but this one is about the post-earnings announcement drift. The logic of the post-earnings announcement drift Is that you have the release of the earnings, which is set by this vertical line, earnings. And what has been found over literally like 60 years of research, this one is one of the very first inefficiencies properly documented and still alive today. So it's like to the ones that say, ah, Alpha decay. Yes, there is alpha decay, but there is always a different angle you can approach with, always basing your knowledge from research.

You have to get creative, essentially.

Yeah, a little bit. Like, you need to— that's why the advantage of testing different things and say, okay, this one is not working on this specific type of stock, but maybe can work on a different type of stock. which we will see with this one as well, where there is like a very good insight. But like you have the earnings. So until the earnings day, the stock can do whatever. Okay, let's say that it's rising in price. Then there is the earnings release. What the post-earnings announcement drift is saying on a very high level is that if there is a positive surprise in the earnings and as a consequence, a positive price reaction.

Okay.

Yeah. For the following days and weeks, the stock keeps drifting higher.

Would it be the same in reverse?

Exactly. And the same in reverse. If there is a miss on the earnings or a negative price reaction the day after of the earnings, there is a negative drift. That keeps going for the following days or weeks.

Mm-hmm.

And here are the findings of the research, which is split across 3 papers. So the first paper is from 1968. Wow. And is an empirical evaluation of accounting income numbers.

Empirical evaluation.

I was gonna say that. In terms of numbers. Okay. And this one was the very first observation that where they saw that if there was a positive increase in value, the following days and weeks, the price would keep drifting higher. And this one was like its purest form of the post-earnings announcement drift. or drifting lower when there was a negative price reaction. And this one was the first contribution to this price anomaly.

Yeah.

The second one was a paper from 1989, which is Delayed Price Response or Risk Premium. In full was PDRP, the Delayed Price Response or Risk Premium. Question mark, delayed price response or risk premium. And the biggest contribution of this paper was noticing that this drift was lasting for around 60 days.

Okay.

So 60 trading days, which is approximately exactly 3 months.

Understood. Okay. Yes. So including weekends, it would Exactly, exactly.

The 3rd paper was a paper from 2006 called Comparing the Post-Earnings Announcement Drift for Surprises Calculated from Analyst and Time Series Forecasts. That's a very long one. Yeah, but the main point of this one was the introduction of the analyst consensus.

And the general, obviously we'll probably have links in the description and We've shown them on screen, but for just the general idea of how much detail these papers have, like one of these papers, how long and detailed are they?

They are like 30 to 60 pages. But that's the key of taking the paper, downloading it, feeding it into the machine, and then extracting exactly what is the finding. Okay. What is the reason?

Why? Because for some people that might not sound like a lot, but in reality, you know, that's on one topic. There's literally one thesis on one topic. That's a 30 to 60 page—

Yeah, yeah. But that's—

And this particular one has multiple papers.

Yeah. And you need to start changing like the perspective that if it's a lot is because many people spend so many hours studying that. that the finding is reliable and you don't need to spend months on just to understand the finding.

So in reality, you don't need to, because of these papers and their availability as well, is that you don't need to trust it in the sense, or you don't have to worry about trusting it because the research has been done.

Yeah.

To your point, it's like, okay, you just got to find the angle.

Exactly.

Like where it's at now.

Yeah, 100%. Like research has been done. Often these papers are published in financial journals. So like they are peer-reviewed across different academics. So like they say, if it ends up being published on a journal, means yeah, what they found is actually true. And then like it's up to you like to take like each one of these elements and combining it.

Yeah.

on a working trading strategy. Like, again, on the first one, we have the price reaction. So if we have a gap up on the following day, Yeah. from open to close, you might have like the first layer of the post-earnings announcement drift. The second one, you know, they last for 60 days. The third one is introducing the concept of the analyst.

Yes.

The analysts, what they do is before the earnings are released, they give an estimate of the earnings per share. The insight that they had was, okay, let's not compare just on the price, but let's see what is like the drift, if it's stronger, the drift, if the actual earnings per share is larger than the estimate of the analyst. So if we have a larger or stronger positive drift in this case, and if the earnings per share is below the estimates from the analyst, so if there is a miss in the earnings, is the negative worse? So it is more strong, the negative reaction.

Mm-hmm.

And today, this is like the standard. So not just using the price reaction, but keeping in consideration what is the actual earning per share versus the expected earning per share as well.

Okay.

These are the insights from the papers. Now the why. Why is it that not the information are reflected immediately on the price. Because we need to keep in mind that if you go to any university course, they will talk to you about the efficient market hypothesis.

Yes.

The efficient market hypothesis is saying that as the news are released, all the information are reflected immediately on the price. But this one, like in front of us, we have 60 years of research to say, no, it's not the case. Because if that was the case, you wouldn't have this drift over the following days.

Interesting.

Or weeks, right? And the reason why is because, one, there might be a low coverage of the stocks.

Okay.

So not all the analysts might cover properly all the stocks. So we can say that information travels slowly. Yes.

And that lack of awareness almost.

Yeah, exactly. Like if, especially like if we focus on small cap, or mid-cap, it's not that these stocks are as much as followed as mega-cap. So Tesla, Nvidia, Apple.

Yeah.

Because they're like, the information is out, it is reflected right away. Indeed, like newer paper from 2021 showed that if you try to apply post-earnings announcement drift strategies to mega-cap, you basically have no edge. However, if you apply it to small cap or mid cap, the price is not reflected immediately because of the analyst having a smaller coverage across these stocks.

Right.

And the second reason is liquidity constraints. Constraints. What do I mean with that? Yes, maybe there is a stock that is followed by a fund manager that would like to build a larger position, but maybe it's not able to create or allocate all the capital that wants to allocate on a single day because there isn't enough liquidity. So it decides to spread the execution of the order over the following 10 days.

Yeah.

Or over the following couple of weeks. And that's what generates—

The drift.

This drift on the upside or the downside. If we now try to take all of this and translate it into a trading strategy, we need our entry. Which again can be if we have a positive price reaction.

Mm-hmm.

So the price increases and we have a beat on the earnings. So the actual earnings per share is larger than the expected earnings per share. Then we open a long position at the opening of the following day. Okay. Exit. Again, we take it from research suggesting that 60 days is optimal. So let's use 60 days position, taking them as starting point, as suggested starting point. And as position sizing, You could do something like either a percentage of your portfolio, so allocating for every single stock that you're monitoring, let's say 1 to 2% of your portfolio, or a specific dollar amount. Or again, like in this specific case, you could do as a position size conditional to either the price movement.

Mm-hmm.

Or like the deviation between the earning per share, the actual earning per share with the, from the expected earning per share. And you could try to check and you could try to test these different variations to understand which one has the best risk-adjusted returns.

Where would, in this particular one, 'cause like with the VWAP, it was under— it's quite easy to understand where would your stop be, right? And where would you, look to exit that trade, as we already saw with the diagram. And same with the opening range breakout. With this one in particular, what would that look like? Because you would essentially maybe be the low of the previous day?

No, not necessarily. Like that one, it needs to be tested. Like for that purpose, like given the fact that here we have a cross-sectional type of trade and monitoring, let's say you were monitoring 20 to 30 stocks. The simplest starting point for an exit is just focusing on time.

Okay.

So you take the time which was suggested from the research and you focus on that. Afterwards, you might add an additional layer for risk management purposes where you say, okay, let's put like, let's try to put like 5% lower than the opening of the day. Or like if we have a gap up, like using the closing before the earnings got released as a stop loss, for example. But these are just like examples that you—

Would you think that these are things that you would, when you get to the sort of encoding stage and so on, you would have these sort of rules so you can do your backtests and everything?

Yeah, I would start for the backtest, I will start exactly like with this simplified version. So just using a time exit of 60 days. And then like I would, once I divide between in-sample data and out-of-sample, in-sample is where I would add different rules. Like for example, what would happen if I include a stop loss? Would improve their risk-adjusted performance?

Okay.

There is where like you test, You stop adding more and more. Exactly. Because why do you do that? Why do you do the split? Because you don't want that you just curve fit your strategy. Then you notice that if you add a 1% stop loss, it is the best possible results that you can get in sample. But then like you apply it out of sample on unseen data. And in that way, like you understand, did I overfit or was actually the best possible version.

Understood.

And so it really is all a process that you need to follow to make sure that you're not fooling yourself. You see? And yeah, and one interesting point as well, like obviously, as I was saying, this one is a strategy that nowadays is working the best on small cap and mid cap, but not necessarily like, you need to focus on small cap and mid cap in US. You could take the same strategy and apply it on the European stock market, right? Because you might find that in Europe, there is a better performance across.

I can imagine maybe the analysts are even less, maybe.

Yeah, exactly. Like there is less coverage, there is less interest, there is less—

Maybe even the liquidity aspect. effects in terms of the constraints.

Exactly. So like, see, you're funny. Like, you're getting there. You're getting there. Like, all of that, like, is all points that you really start— this is the new way of how an institutional trader thinks, of like connecting these dots, thinking exactly just like you did right now, thinking, yeah, there is less liquidity in Europe, so maybe I could apply this strategy on European names rather than just focusing On US names. And, um, and, um, on the one thing that I want to mention, if you want to try to develop strategies on the post-earnings announcement drift, that the short side is becoming less effective for one simple reason, that when a company has negative results, Yes. they tend to pre-announce it.

Yes.

So that one is making a huge difference nowadays that you have the CEO saying, yeah, the numbers are going to be poor.

So like, you want to get ahead of it.

Yeah. Doing, doing a lot of risk management, PR beforehand. And that's why you do have to try and soften the blow. Yeah. And that's why you do have already like Already some down. A negative price reaction before the earnings is announced. And that's why you—

So those gaps won't be as dramatic potentially.

Exactly, exactly. And even the consequence might be not as dramatic.

Yeah, it might be a 30-day thing or just sideways because it's already priced in.

Exactly.

So essentially you're trying to— the thesis of this, the foundation is that it's something that's a surprise to the market almost.

Exactly.

Versus, And if they're announcing it, then it's already going to start getting priced into the market.

If they start putting some announcement of, yeah, it's going to be bad. So like doing some management of the expectations, that's where like the PAD tends to die out. And this is a tendency that the CEOs tend to do a lot before negative numbers. I've seen some cases where even in positive numbers, like they started like saying, Yeah, no, the numbers are going to be much better than what we're expecting.

So then it wouldn't be valid for that, right?

And that one, exactly. You will have—

So I guess like, even though it's the CEO, it's almost in the same category as the analysts, right? If the analysts are covering that, oh, you know, this is a, well, one, they're covering it a lot and they might be even covering it saying, hey, this is gonna be negative, it's gonna be this, giving their thesis. Again, it'll start to get priced in, it'll start to be expected. So similar thesis, if the CEOs talking about it on the positive side as well.

Yeah.

It's not going to be as much of a shock, if you will, in the market. And you probably won't see as strong of a gap. Maybe we still get a gap, but not as strong. Sometimes you might not even get a gap because it's already been spread out there to the masses.

Exactly. Exactly. But again, on the positive side, we see it rarely, but can happen. It is something that you just need to be aware of.

It's almost like because the CEOs don't want a very drastic negative impact on their stock because that won't come across well. But they love, no doubt, a surprise positive announcement because then, you know, it's all in the headlines. Oh, the stock's up 10%.

100%. Yeah, 100%. And it is just something that it's good to know if you are developing strategies like that.

What's interesting as well though is these strategies so far, They're not complexed, you know, they're not like something that's overly hard to understand. It's not even hard to understand necessarily. And a lot of the time when people talk about institutions and institutional trading, they automatically probably assume that it has to have crazy technology, crazy information, insider information. And so, and yeah, the thoughts instantly go there versus actually, wait, I can trade like this. Really, as you said, the difference being that rather than just being an idea and a strategy that you may have learned from somewhere, this is backed up by years of data, years of evidence and research to give more confidence and certainty and the framework to then build upon. And just again, find the angle for today. If it's like the, the last example being 60 years old, still works today.

Yeah, 100%.

But it's just about adapting. As you mentioned, maybe it's the markets. Maybe rather than large cap, you're going, uh, medium to low, uh, or small, sorry. Um, and then it might be instead of the US market, you're moving over to European market, could be the Asian market. Um, so these slight tweaks that again aren't complex, it's just for taking the thesis, the idea, the foundation of said strategy and concept, um, and then validating it for today.

Like 100%, like complexity Doesn't mean more profitable. It is something that you learn quickly on a trading floor, especially like once you have like incredible infrastructure, because you need to know what you're doing. Like complexity actually means fragility more often than not, because you have many points where your strategy can break. Like the I made more than thirty million euro for the bank and. 80% of it were out of extremely simple strategies. This is so key. Like, I cannot say exactly what the strategy was doing, but it wasn't complex at all.

Mm-hmm.

4th example is related to another extremely well-known anomaly, which is the overnight Market anomaly. Have you ever heard of this?

No, I haven't. Not this one.

Okay. So what is interesting about this one? Let me Yeah, Picasso, my Van Gogh. So the market closes at 4 PM. Market closes— market opens at 9:30. Closes at 4, opens at 9:30. Closes at 4, right?

Yep.

There is a paper from 2008, which is called The Return Difference Between Trading and Non-Trading Hours. Like night and day. What they found in 2008, like across multiple index, across multiple equity index, across multiple stocks, was noticing that the—

Mm-hmm.

returns, once you analyze what contributed the most on the performance of, for example, the S&P 500, which has been like amazing and constantly rising.

Yeah.

What they noticed was like that 90% of these returns was coming from overnight holdings. So regular trading session, 9:30 to 4, basically flat. And then overnight gap. Flat, overnight gap. And again, that one was in 2008.

Yeah.

Around 90%. If we break it, if at home you do exactly the same, like getting a strategy which has entry, it opens a position at, that goes long at 4 PM. And as exit, you close the position, so you exit at 9:30 AM when the markets open. Okay? You will see that if you do this and you apply this strategy on, let's say, NASDAQ using NQ contracts, Mm-hmm. from 2015 to today, like June 2026, You have again 90% of the returns that are coming from this strategy, from holding the position overnight, and only 10% if you were just holding during regular trading hours. It is something like extremely fascinating that has been there like literally for decades and not just on NASDAQ, but across multiple indices. And again, everyone at home can just do this simple test.

Exactly.

And the reason why is that 90% of the returns of equity indexes is coming from overnight gap. Right. And the reason why we have multiple lines of thoughts, there is multiple theories. There isn't like a single theory that is like saying—

This is exactly—

This is the exact reason why. But the first one is about overnight Risk premium. So the holders of the position overnight need to be rewarded by the fact that they are holding that position overnight.

Yes.

Where there is lower liquidity, the market is closed, so you need to be rewarded by that. What is weird, however, is that it's 90% of the returns. Which is, that's why like there is some academics that are saying it's a little bit high. Even like practitioners, even like professionals, they're saying it's not possible that all of that is explained.

Within that one.

Yeah, within like overnight risk premium. And the second one on the other hand is related to overnight liquidity. So, overnight liquidity. During the night, there is information that gets released, as we have seen before with the opening range breakout thing, that new information are out. There is news, there might be earnings. And given the fact that the liquidity is not as strong as during regular trading hours, the price reaction might be a little bit more aggressive than it would've been during regular trading hours. And then there is a reversal during regular trading hours.

Exactly.

So like this one, like once you see it across like multiple days, it might explain that overnight we have an overreaction.

Yeah.

And then we have like a movement towards the fair market value of the underlying trading instrument. And this one, like, I feel like it's a very strong why. And what is nice is that you can use, like, knowing this, knowing that 90% of the returns of the index are coming overnight, given a low liquidity.

Mm-hmm.

This is just a starting point. Like, this one is like one of those things that where I want to conclude with is that you could take this idea and you start like saying, okay, we have seen the opening range breakout, how it is working. But if you're telling me that 90% of the returns are happening overnight, what about trying to develop an opening range breakout that only takes place overnight, because that's where, like, you have the biggest directional moves if this research is right. And this is like the full process of, like, having an idea, having an idea triggered by research, and then, like, start framing your trading strategy around it.

I didn't know that.

And that's like how you go from idea, To rules. And the next steps would be to encode it and test your idea until you don't finally arrive to—

In terms of encoding, obviously you can't whiteboard that, but what does that look like? So if you've got the ideas, like the 4 ideas we've gone through, you have the rules around it. So the rules, I guess, would be the criteria, no?

Yeah.

And then once you have those 2 things, what is just a general idea? Maybe it's something we can do in the future, but a general idea, what does that look like in terms of the next step when inputting into a language model?

So we need to understand that the language model is a tool, right? We cannot pretend that it's a senior developer that is working for Palantir. I wish it was like that, but it's not there yet. They say it's going to be there in 6 months, so fingers crossed. But the idea is like to follow an iterative process. So let's say that our entry is supposed to get us long at 4 PM when markets close. So like first you develop the first part.

Yeah.

So like, okay, I want a strategy that goes long at 4 PM Eastern time. Then you check if it's working fine. If it's working fine, then you add the next condition. Okay, now encode an exit on the top of what you just developed, which exits the position at 9:30 AM.

Mm-hmm.

Then you apply to your data and you make sure that the logic is working fine. If the logic is working correctly, then you start working on the position sizing, like linking the position to the volatility of the instrument. Have a larger position when the volatility is low, have a smaller position when the volatility is high, and so on. So following all these steps with an iterative process until you don't have all the rules that you defined.

Yes.

Compiled, so into the piece of code, and that you know that is working the way it is supposed to work. So that one is like the process on a Very high level. And the good thing is, like, again, there is so many softwares. Um, in first place, these MultiCharts, that's where the programming language is literally called Easy Language because it's so, like, intuitive to understand, like, for a human on how it needs to be coded.

Okay.

You can use Pine Script, so like TradingView, that is getting quite popular. to follow exactly the same process of like, you can even feed to the machine like, uh, the manuals on how to code with Pine Script. And then like the machine—

To build its knowledge pool.

Exactly, to build the knowledge pool. Like right now we really are in this phase where retail traders can have this massive step to really start trading like an institutional trader would. And it's not that One thing, one point I want to make, that all of you need to start automating your trading strategies. But if you follow the process, even if you learn how to encode basic version of your strategy, to backtest it properly, and then like understand if what you're doing actually makes sense, if you might end up with an uprising equity line, then you might decide to only automate a portion of it. Or only to use it as confirmation and then still trading manually. So it's like, it's really just an additional knowledge base.

Validating.

Exactly. Toolbox that you can add to your arsenal. This is like one thing that I really thought that retail traders are missing and that now finally like—

They can start piecing it together.

Yeah.

In terms of, uh, As an institution, and generally what retail can do if they choose to, is you could have these 4 ideas, but you could have them all 4 running simultaneously.

100%. Like that one, you got me there. That one is the endgame because the endgame is not just to run one single trading strategy. The goal is to run uncorrelated trading strategies. And that's what allows institutional traders to make money every month because you might have a strategy that works well in trending market. Right? But if you are in a mean-reverting market, the strategy will perform poorly. But if you have another strategy which is taking advantage of mean-reverting conditions, then the other strategy— exactly. So like, that's what uncorrelated means, that you might have a set of strategy that works well in different market conditions, and overall they give you that beautiful—

Your job is to— really refine and learn over time how to manage them so that you limit drawdowns on one, maximize profits on the other, and vice versa.

Exactly. Exactly. Like, uh, yeah, the exact process that I follow is like once a month I review which are the strategies that are running live.

Are you able to give an insight in terms of, you know, when you were in your career, how many systems would be operational at once or, or you would be managing?

Yeah. So like what I suggest, as a retail trader to have at least 3, 4 strategies trading at the same time. Because in any case, you are leveraging the power of your machine to have these trading strategies trading automatically for you.

Yes.

Which is a massive advantage. Like, if you try to follow 4 strategies at the same time, it's very hard to do it manually. And that's why, like, it's important, like, to eventually trade everything as automated trading. While I was working for the bank, I had something like 400 strategies running simultaneously.

Really?

But that one is like, you get to that point, especially like once you are monitoring the performance across different instruments, or you are monitoring the trading activity across different instruments. To put a context, like, The day I left the bank, the total volume that was going through me was approximately €15 billion on a yearly basis. So, like, you need to have multiple systems running at the same time. Right now, like, personally, I use a maximum of between 25 and 100 systems, really depending on the type of market conditions that—

So would you, do you find yourself consistently developing, tweaking, researching?

100%. Like that one is the job. The job is really—

So it changes from manually trading and having to like maybe do analysis to more analyzing history, analyzing edge, refining performance.

Yeah, because obviously like trading strategy stops working. So you really need to have this organism that Starts from research, refining, validating, deploying, and back.

Something we'll go deeper in tomorrow's Words of Wisdom for sure, because I think that's the right place for it. But in terms of when we think about that, and as we probably move into an era where more retail traders start to implement such systems, do you feel like manual trading will always have a place? Or do you feel like as maybe more and more automation takes place, That it's almost gonna be necessary to have a portfolio of edges that you manage and the manual side becomes your research, the manual side becomes your refinement, your manual side becomes your review of your automated strategies? Or do you think the retail side in terms of manual discretionary trading will always be there?

I mean, I think it will always be there, but like the distribution will change. Because like once you understand that your computer can trade on your behalf and you can run, and if you run multiple systems and with multiple again, just 3 or 4 simultaneously can be enough to make you profits on a more consistent base.

Mm-hmm.

Like people will be like, okay, so you're telling me they can have 4 strategies running live. while I'm at work without the need of me sitting in front of the monitor to wait for a specific entry condition, people will move towards that direction. Because if freedom time gives them more confidence, they have no emotions interfering with their trading activity. So it's going to be some sort of natural step.

100%.

Moving towards that. That direction.

Definitely. I think obviously this first step is education like this, you know, being able to hear about the process, how to go about it, how to think. Excited to do a full masterclass over on Char Academy with you as well.

Oh, it'll be super fun.

But yeah, no, this has been incredible. Is there more to go over?

No, I think we're done.

It's incredible. I know it's something different for the audience there. As I said, we're going to sit down and do a words of wisdom, dive into your background and more. more of that experience that you've had at the institutions and then where you're heading moving forward as well. So if you're interested in that, that will be coming out very, very soon. Keep an eye out. Uh, but for now, of course, links for Matteo will be in the description below. Make sure you check them out. Drop a like. Again, this is, uh, knowledge and, and wisdom really that he doesn't have to share, but he's doing so because he wants to bring the new era of information. And I, I guess getting ahead, right? Getting ahead of what's to come, as you just said. Uh, so make sure you drop a comment with your biggest takeaway from this episode. Any questions you have, throw them in the chat. I'll tell you why, because when we do that masterclass, we can use some of those questions to really give you, uh, the answers that you're looking for, right? It'll be a perfect opportunity. And, uh, well, other episodes are on screen right now. And until next time, everyone, take care.