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

Discover 4 secret institutional scalping strategies used by market makers to automate prop firm trading and gain an edge in the markets.

Key Takeaways

Institutional traders rely on validated, data-driven strategies rather than chasing individual trades.
Automation and algorithmic coding are essential to implementing and scaling institutional trading strategies.
Understanding the rationale behind a strategy is crucial for long-term success and adapting to market changes.
Backtesting with proper data splits (in-sample/out-of-sample) ensures robustness and reduces overfitting.
Retail traders can leverage institutional knowledge and automation to improve consistency and profitability.

What the video covers

Matteo Conte, a former institutional trader with over €30 million profits and $15 billion traded volume, reveals insider scalping strategies.
Institutional traders source strategies mainly from the Social Science Research Network, a key resource for validated trading ideas.
The video explains the fundamental difference between retail traders chasing trades and institutions using validated, data-backed strategies.
Every institutional trading strategy must fulfill three criteria: clear entry, exit, and an edge supported by rigorous validation.
The process includes encoding strategies into algorithms, backtesting with in-sample and out-of-sample data, and continuous refinement.
Automation and coding have lowered barriers for retail traders to adopt institutional-grade trading methods.
Validated strategies show consistent profitability with known win rates, drawdowns, and risk metrics, unlike anecdotal retail setups.
The video covers the institutional trading pipeline from idea generation to live automated execution.
It highlights the importance of understanding why a strategy works, not just copying it, to adapt to changing market conditions.
Additional insights include managing multiple strategies simultaneously and the significance of alpha decay and market inefficiencies.

Chapters

What is the main difference between retail and institutional trading according to the video?

Retail traders tend to chase individual trades based on setups, while institutional traders use validated, data-backed trading strategies with known probabilities of success.

Where do institutional traders commonly find their trading strategies?

Institutional traders often source their strategies and ideas from the Social Science Research Network, a key repository of research papers and validated trading algorithms.

Why is backtesting important in institutional trading?

Backtesting with in-sample and out-of-sample data helps validate a trading strategy's robustness, ensuring it performs well historically and reduces the risk of overfitting.

Full Transcript — Download SRT & Markdown

Speaker A

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

Speaker A

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 [music] Conte. There is an industry paper called VWOF, the holy grail of day trading [music] systems. The fact that the vast

Speaker A

He traded over $15 billion in total volume at one of Europe's largest banks. Introducing the one and only Matteo [music] Conte. There is an industry paper called VWOF, the holy grail of day trading [music] systems. The fact that the vast majority of institutional traders are using this type of algo amplifies these directional moves.

Speaker A

difference between retail and institutional trading. What is the pipeline every single institutional traders follow? Every single trading strategy needs to have these three boxes filled.

Speaker A

While trading at the banks, Matteo ran over 400 plus 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 trader follows?

Speaker A

this special episode of 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. So let me start

Speaker A

Every single trading strategy needs to have these three boxes filled. So you need to know why you're opening. You need to know where to close. And a third condition is having these exact edges, and strategies have been used to get consistent prop firm payouts [music]. By moving towards automation, Matteo reveals all of this and so much more in this special episode of Chart Fanatics.

Speaker A

especially like fresh information straight out of the trading floor. And um 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,

Speaker A

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. So let me start with some facts.

Speaker A

and since there 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. Um the very first thing that I want to say is that the first difference between retail and institutional trading is that retail

Speaker A

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

Speaker A

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 traders follow. Mhm. which so pipeline and this pipeline starts from getting the idea for a trading strategy. And this is

Speaker A

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 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.

Speaker A

This set of rules needs to be so specific that you can feed it to a machine and to feed it a machine it needs to be encoded.

Speaker A

And if you're lucky enough, like I was at the end of my five 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, there's been a process of learning how institutional traders actually trade, and it is very much different from what you usually see online.

Speaker A

at one specific setup. They say this is a setup where like this uh can work out well for you, can make you a lot of money 70% of the times, right? But if you look for validated trading strategies means that is a strategy that if you apply it systematically every single time is

Speaker A

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 strategy.

Speaker A

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,

Speaker A

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 institutional traders that make money basically every single month.

Speaker A

70% of them. And this step of going from a set of well- definfined 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 uh in 2023 something u magnificant happened which

Speaker A

We are going through what is the pipeline that every single institutional trader follows. Mhm. Which, so pipeline, and this pipeline starts from getting the idea for a trading strategy. And this is just the beginning of this long journey to end up actually having our trading strategies trading live.

Speaker A

rules that every single training strategy needs to have, you can encode it leveraging the power of your CHP code or your favorite large language model. And then the next step is to back test.

Speaker A

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.

Speaker A

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 a pricing then rising if the strategy is not good

Speaker A

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

Speaker A

what is the win rate of your trading strategy over the past let's say five years? Um what is the average trade win? So how much your strategy is winning every time you place a trade? What is the draw down of your strategy? So which draw down is the where draw down is the peak to valley of

Speaker A

Well, chasing trades, you might see one of the usually like they point at one specific setup. They say this is a setup where this can work out well for you, can make you a lot of money 70% of the time, right? But if you look for validated trading strategies, it means that it is a strategy that if you apply it systematically every single time, it is going to win 70% of the trades.

Speaker A

Can you read it? Yes, of course. Yes. Okay, the validation is another extremely important step in institutional trading and usually is divided in two phases. One is the split test. Mhm. So splitting your data between in sample and out of sample. So in sample versus out of sample.

Speaker A

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 because I don't want to give across the wrong impression, but almost it's the closest thing you can probably get to certainty within the markets.

Speaker A

So let's say that you have 10 years of data here. You should purely focus like let's say we split this data 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

Speaker A

Yeah, you can understand what are the probabilities of success, and this is the main difference. It's not just about because a good strategy can have a 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.

Speaker A

breaking down charts and market structure. And Ninja Trader is an awardwinning futures trading platform built for exactly that. You get advanced charting, depth of market tools, and market replay so you can work through historical sessions and practice in a fully simulated environment,

Speaker A

In the end, 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 has been, for a long time, the biggest barrier to entry for retail traders to start trading like an institutional trader.

Speaker A

Trader as your platform at checkout. It's the best platform for the job. So, go check it out and let's get back to the episode. 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

Speaker A

Lucky for you guys, in 2023, something magnificent happened, which was like having ChatGPT and the other large language models going mainstream, which completely removed 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 encode the rules that every single trading strategy needs to have, leveraging the power of your ChatGPT code or your favorite large language model.

Speaker A

good result both in sample and out of sample, it means that you didn't overfeit 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 a very high level, we uh just to give a little bit of context. And the second step is the

Speaker A

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 a simulated trade back in time.

Speaker A

as Monte Carlo reshuffle and basically like in this way you create multiple version of the past multiple equity parts so that you can understand if your equity curve in your back test was looking good just because of the sequence of trades or is that because you really have an underlying edge

Speaker A

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 a pricing then rising if the strategy is not good enough.

Speaker A

you the path to get there is going to be different and then you repeat this process basically thousands of times [snorts] and the bigger is the dispersion from the top equity line to the bottom or the weakest is your edge. Yeah. on the dread like when you see that

Speaker A

But as an output, what you get out of it is a set of statistics, as we were saying, 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, five years?

Speaker A

regardless of how the trades are shuffled so when we say shuffled to 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 uh data was a thousand trades and of those thousand trades like the average losing

Speaker A

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?

Speaker A

reshuffle was essentially putting all those trades into a random order and it might be a case where you know the the peak losing number of trades in a row was now 45. Exactly. And therefore you know to your point the further that's how when the further away those lines will get. Exactly. uh

Speaker A

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.

Speaker A

seeing a 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

Speaker A

Can you read it? Yes, of course. Yes. Okay, the validation is another extremely important step in institutional trading and usually is divided into two phases. One is the split test. Mhm.

Speaker A

spaghetti charts, right? Where you have multiple equity lines. And um the difference between the uh reshuffling and the resampling is that the ending point of the equity curve it is different.

Speaker A

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.

Speaker A

And that's how you reproduce multiple version 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 is then validated through these stress tests if you will. Correct. And what is cool as well is that on top

Speaker A

So let's say that you have 10 years of data here. You should purely focus, like let's say we split this data 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.

Speaker A

down larger than $10,000? Mhm. And all of that all these possible scenarios they are 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 draw down

Speaker A

Yeah, let's take a break for a minute there, guys, because a quick word from our official platform sponsor, NinjaTrader. If you'v

Speaker A

I get a larger draw down than $10,000." And all of these are yard sticks that are provided by this validation phase that you can use to give you awareness while you're trading live.

Speaker A

And once all of these steps are completed, so you checked that you didn't overfeit it with uh where the simplest version is running like a comparison between insample results versus out of sample. You did your Monte Carlo to see how strong your edge actually is running

Speaker A

10,000 20,000 simulation. Only at that point we reach the final phase which is live trading.

Speaker A

And note that with live trading is not that distution trader sits in front of the 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

Speaker A

where you still leverage your machine to just monitor the data that are fed to it and every time that your conditions are met opening or closing your position on your behalf.

Speaker A

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 a positive

Speaker A

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 Okay. And your job is only one monitoring the performance and two monitoring the risk. So this is what you're doing while you're trading strategy are

Speaker A

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 and code back test validation and eventually having the strategy trading life. Futures traders, it's time to hear about Apex Trader Funding,

Speaker A

the largest futures firm in the industry. They have completely changed the game with their new evaluations. So far, they've already paid out over $600 million to traders around the world [music] just like me and you. Now, not only that, Apex actually holds the record

Speaker A

for the largest payout to traders, which is of course with Jadecap, no stranger to chart fanatics [music] with $2.5 million in a single payout. And the person before that was Trader Kane with $2 million in payouts. Now, what sets Apex apart? Apex, you can get up to 20 accounts

Speaker A

which no other firm offers. Now, not only that, they have extremely clear rules with their revamp.

Speaker A

There's no MAE rule, no 5:1 risk-to-reward rule, no hidden rules, no gray areas, clearly defined draw down models. So, you have two options now. end of day draw down and intraday trailing draw down which means no payout denials, no payout reviews, no discretionary decisions and no

Speaker A

surprises. What sets them apart as well? Traders receive 100% of approved payout requests up to 20 accounts as I mentioned and one day to pass. Now the other thing that I love about Apex, not only can you get 20 accounts, but there's different account sizes. So they have 20k accounts all the

Speaker A

way to 150k accounts. So you can get 20 of any of the account sizes that you choose. Not only that, you have various trading platforms from wealth charts to trade of to rhythmic. The choice is yours. So make sure you use the code CF [music] to get up to 90% off on evaluations right now using

Speaker A

the link in the description below. Trader Apex today. Let's get back to the episode. Look at this. Like usually the re classic retail traders like just as the idea and trades live with real money the idea right after on the other end like as an institutional trader you need to follow this

Speaker A

pipeline. Yes. 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

Speaker A

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 uh going live most of the time. At most someone might create a few rules. Someone might you know back test a tiny bit but you know not the level of

Speaker A

detail that is necessary to really have confidence and edge within the markets. In terms of the first two which I know we're going to focus on like the idea generation and rules but when it comes to the idea generation and those rules um and then getting encoded. So then the encoding side

Speaker A

of things is where again as you mentioned that language models can you really help with that for the average retail trader. But in terms of that from that point on a lot of like the back test side of things is automated. Obviously you're you know giving the inputs to make it all

Speaker A

happen but the back testing 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 uh that that's the big advantage. Like nowadays even the average retail trader has access to so many welldesigned

Speaker A

tools that allow you to run back test and run like simulations. My favorite one I'm not affiliated or anything like that but my favorite one personally is multi charts because like the programming language is extremely simple. It's very easy to understand what the code is doing which is key to

Speaker A

understand what you're doing if you want to make money in the market as an institutional trader and then like the back testing infrastructure is very solid. So you can rely on the back test.

Speaker A

you have the possibility of run um simulations using your back test like classic Monte Carlos and really like is 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

Speaker A

boxes that you need to check before trading your strategy life and you say that I I love that you said that it is nowadays is harder that retail traders come up with their ideas. uh because um today I really want to focus on the first two steps like uh where do the idea comes from as an

Speaker A

institutional trader and I want you guys that you steal this like steal as much as you can because uh this one was something that um really impressed me when I started that uh so many institutional traders were going like in one place to get their trading strategies ideas. The place guys is the

Speaker A

social science research network. interest. 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. Mh. And uh the feedbacks that I got like when I started

Speaker A

like this journey of showing like the behind the curtains obviously social 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

Speaker A

you look for the underlying reason. the why that strategy can make you or is supposed to make you money in the future 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

Speaker A

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.

Speaker A

Um I brought you four examples that uh I thought was a good like exercise to go from research paper to an actual testable strategy. Mhm. Uh which we can cover one by one. So let's write it down again

Speaker A

that 90 to 95% of the trading strategies ideas for institutional traders are coming from research.

Speaker A

And in particular like there is two types of research. There is academic research. Mhm. 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

Speaker A

trading strategies like momentum mean reversion or strategies on gold. um all of that and they publish them online. Website like the social science research network is like this giant collection of all this research which is 100% for free

Speaker A

where anyone can just go and their job is to dig and trying to find something interesting.

Speaker A

M 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 this uh these uh papers trying to understand okay what is this research about what are the findings why

Speaker A

is it supposed to make money you don't copy the research one to one but it is an extreme important starting point to define your trading strategies rules. Which are the rules?

Speaker A

Let's move there and then let's have a look at the examples. Every single trading strategy needs to have three things. Mhm. An entry. So the conditions on why you should open a position, an exit, why you should close the position after it has been open. Mhm. And position sizing.

Speaker A

Every single trading strategy needs to have needs to have these three 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

Speaker A

is having like an exit linked to time. So, for example, closing a position after 1 hour or at a specific point in time like 3 p.m. And this one is satisfied if you don't hit your take profit nor

Speaker A

your stop-loss. Okay. And the third component, position sizing, which is something that I feel like is not discussed enough across uh uh 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

Speaker A

risk of blowing up your account. Yeah. And uh improve the risk adjusted return of your trading strategy. We will have a look at some very simple examples on that one as well. Tradzella is the all-in-one platform built to turn you into a profitable trader. And at the center of it is an

Speaker A

AI co-pilot [music] that does the heavy lifting for you. It watches every session. It learns exactly how you trade and your strengths and your weaknesses, the patterns you keep repeating without even knowing it. And it tells you in plain language what's working [music] and what

Speaker A

needs to change. The moment you close a trade, your AI captures it automatically. Fully sync to your broker or prop firm. Zero manual entry. You can replay any trade tick by tick, pip by pip, to see the full picture. Back test your strategy on years of real data before you ever put up real

Speaker A

money behind it. Your AI co-pilot ties all of it together, surfacing insights you'd never find on your own and guiding every decision you make going forward. Hundreds of thousands of traders, one AI powered platform, [music] real lasting results. Now, if you want that AI co-pilot and see

Speaker A

your trading excel, the link for Tradzella is in the description below. Use the code CF20 for 20% off your yearly subscription or CF10 for 10% off your monthly subscription. If you're a trader, you have to know about Chart Academy. Chart Academy is the first all-in-one free trading education

Speaker A

platform. That's right, absolutely free. No catch, no upsells, no links to sell you something. All free. proven world-class traders providing an A to Z masterclass on their strategies, their concepts, and their processes that make them the best. From futures, options, crypto, stocks, and forex,

Speaker A

everything is covered on chartacademy.com. Whether you want to fix your trading psychology or learn from a market wizard directly, you can do it all on Chart Academy. Not only that, by just spending time on Chart Academy and bettering yourself as a trader, you can get points that unlock exclusive

Speaker A

discounts, free trials, and free accounts with your favorite prop firms and trading softwares. Don't take my word for it. Chart Academy is live. The link is in the description below. Now, let's get back to the episode. But now that we have all of this, now that we know that the rules

Speaker A

needs 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, sure. So, let's start from the first paper. And the idea here,

Speaker A

I really want to show you how to go from a research paper to an actual tradable strategy.

Speaker A

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

Speaker A

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

Speaker A

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 a.m. It reveals the imbalance between buyers

Speaker A

and sellers and it predicts the performance of the last 30 minutes of the regular trading hour.

Speaker A

What does it mean? It means that if you had a return a positive returns between 4 p.m. to 10, you should have a positive return even in the last 30 minutes of trading. This is what they analyzed in the paper. This is the finding of the paper. This is true. And then you need

Speaker A

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 uh news, earnings, global positioning. Mhm. So news plus earnings plus global positioning

Speaker A

which creates all of these orders between buyers and sellers and these orders collides in the first 30 minutes of trading revealing the imbalance. Yeah.

Speaker A

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. Yes. Is it clear? Yeah. Yeah. And uh now that

Speaker A

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,

Speaker A

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,

Speaker A

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

Speaker A

all the set of informations 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 10:00 a.m. Right. If the price

Speaker A

closes above the high of the range, we open a long position. Mhm. Exit. You could say for the long the same like putting a stop loss at the low of the range.

Speaker A

So would be somewhere down here. Right. So here we have our stop loss and for the takerit a classic reward to risk ratio either between one or two. Okay, this needs to stay simple because we need to have the control over what we're doing.

Speaker A

take profit. Let's say we have here the eye of the range. So would be let's say if we take one to one the TP would be up here. Mhm. And we know that we are going to open a position only if the price

Speaker A

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

Speaker A

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

Speaker A

What I always suggest during the development phase is to use a fixed amount of contracts of one. Why?

Speaker A

Because 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 one, 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

Speaker A

the end in narrow days you're going only going to put at risk let's say $1,000 and academia research again with the two 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.

Speaker A

Yes. The two papers I'm talking about is volatility managed portfolios and the second one is the impact of volatility targeting. What is the 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

Speaker A

per trade. Mhm. [clears throat] 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. Yeah. So a highly volatile day, you're going to use a lower number of contracts still risking 10,000.

Speaker A

Yes. Okay. M on that trend 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

Speaker A

overall, they're going to smooth out the outcome of your trading strategy, improving by definition, the risk adjusted performance of your trading strategy. And this is like something like that is never discussed across uh retail traders but it's such a small simple change that can highly impact

Speaker A

the performance of your trading strategy. And this one is just one version of the strategy. But given the fact that right now we're just brainstorming on what is the right strategy that we should test.

Speaker A

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 tell it from the experience is that when you develop a strategy

Speaker A

based on the imbalance between buyers and sellers in the first 30 minutes, that's works better on long go 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 case like

Speaker A

it's hard that more buyers steps in and you have this drifting effect making this strategy fairly effective. Mhm. We didn't copy the paper. It's not that just because the paper saw that uh we if we have a good performance in the first 30 minutes we're going to be long in the last 30 minutes. But

Speaker A

we took the concept of taking this imbalance gets reveals 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.

Speaker A

Mhm. 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 five years six years and not

Speaker A

that this paper is from 2018 like it is because you have informations accumulating overnight Yeah. 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

Speaker A

strategy should work and the why you're doing what you're doing. Mhm. 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 spent. So, well, they say like your why is so

Speaker A

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

Speaker A

something they found, you know, come across and there's no validation, no trying. But this is 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 uh overnight action and all these orders

Speaker A

and participants waiting to step in and because they're waiting that then creates as you say an imbalance in price 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 full process step by step as you

Speaker A

say and [snorts] 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 um this and over opening range breakout like in general is that they are part of a structural limitation of the financial world in a way that

Speaker A

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

Speaker A

the first 30 minutes would disappear. So knowing why it is working like let's say things changes and this uh usage fund or some pension funds they start trading after or like before 9:30 a.m. the effect of this one might disappears and knowing that it's working because of that they lose that

Speaker A

inefficiency. Exactly. M so like uh gives you a complete different perspective over what you're doing and why you're doing and why you're making the money. Next example. Do you have any other question on this? No no this one this one makes sense 100%. you know, like I said, because it's

Speaker A

a a very widely used strategy, maybe not properly tested or data collected, but um that's I guess one of the interesting things is that yeah, like uh if it's a if it is a validated strategy um even if you didn't I guess that's a strange one, right? Maybe an idea is validated not by you,

Speaker A

but someone's obviously validated it to be be able to publish or someone potentially has validated it, but you're just using the idea of it. Uh just the concept that you've learned online, you could still be profitable, but you might not have the confidence that you need. Yeah.

Speaker A

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

Speaker A

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 monte they 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

Speaker A

better and it's not just a matter because of the beta of the market that obviously if you have long 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

Speaker A

the risk toreward ratio to use is one one or one to2 like that one is like the optimal that you can find for for this strategy yeah for this strategy for academia but again the the the goal is to gain

Speaker A

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 and try to test it yourself because then like you gain independence from anyone like you can

Speaker A

go on the social science research network. Spend the full day finding all the strategies that do 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

Speaker A

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.

Speaker A

So the previous one was purely academic. Mhm. This one there is an industry paper called VWOP, the holy grail of day trading systems.

Speaker A

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 is very practical. Mhm. Because you have the price, right?

Speaker A

So we have our price and our time and from the price and the trading activity at any given point in time you can get the VWOP which is the volume weighted average price. Yes. Say differently, the VWOP is the average price of everything traded

Speaker A

up to any specific point weighted by the volume. Mhm. Is it clear? Yes. Yes. Cool. So this one is our VWOP and this one is our price or the stock or of the index. Again we need to go back to which

Speaker A

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 VWOP and you go short when the price is below the VWOP,

Speaker A

closing the position when the price crosses back the VWOP. So as exit we will have and we will repeat this later but as exit we will have that when the price is equal the VW whoop we close the position we can get very good returns like we can get

Speaker A

systematically good returns and what the research that they did was on QQQ which is an exchange traded fund. Mhm. tracking the performance of NASDAQ 100 and they used a time frame of 1 minute. So instrument they use QQQ on a one minute time

Speaker A

frame. Every time that a one minute bar was closing above the VWOP was getting longer and they were closing the position when the price was closing back below the VWOP. Yeah, exactly. With the VWOP. So exit the position. Let's put it with a and the same like going short on the flip. Yeah,

Speaker A

exactly. on the flip when um one minute bar was closing below the VWOP and closing the position. This one would have been actually a losing trade but closing the position when it was crossing back to the to the VWOP line. The returns that the fund was actually like

Speaker A

quite impressive. You can find it in the paper. Something like in the order of 671% over 5 years.

Speaker A

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 five years.

Speaker A

The reason why is behind the VWOP Argos. So every single institutional traders that I know at least once if not every single day they send an execution algorith or they send an execution order to the market. They often

Speaker A

use VWOP algos which is a type of algorithm that tries to track and get the same execution or as close as possible to the VWOP price. Yes. Okay. So it's like a so it is a volume participation algo. If there is a lot of volume traded at a specific point, the algo will send more orders

Speaker A

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 VO price and in the moment like you have this crosses between above or below the view price. The fact that the vast majority of

Speaker A

institutional traders are using this type of algo creates like amplifies these directional moves. So now we know that it might make sense to follow a logic that is related to this um uh vivop logic.

Speaker A

They did the research. They already tested a strategy a version of a strategy using a one minute time frame. 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

Speaker A

the same strategy on QQQ but we could might decide to test the strategy of ENQ so NASDAQ 100 futures if we want to use the same underlying so NASDAQ 100 but you could test it you could test the same

Speaker A

on ES you could so S&P 500 future 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

Speaker A

instrument you want, right? As long as you know that you have institutional traders using this 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

Speaker A

define your entry. So again it's going to be to go long when the price closes above the VWOP to go short when the price closes below the VWOP. Mhm. exit. We can decide to simply have one exit condition. I'm guessing the VWAP is uh plotted on from the open. Yeah. Yeah. That one is

Speaker A

another thing that you might decide like you can decide to use uh the VWOP from the open or you can decide to use the previous day VWOP incorporate 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

Speaker A

uh considering like tracking from 9:30 a.m. If you're trading US or some markets here in Europe like starting tracking from 9 and then like uh after you get some data in start like checking what is the good uh level at which uh um or better and and then like trying to understand even during

Speaker A

the back test 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 the most volume I imagine because it's from the start of the day. Exactly. Exactly. And uh lastly like obviously like uh the position sizing

Speaker A

[snorts] and just like we discussed before always use one contract in development process. Uh 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 uh

Speaker A

having like uh your position sizing conditional to the volatility of the underlying instrument just we have seen before. But this is again in the very first phase development phase always one contract and then you can test different version. Yeah, different version like afterwards

Speaker A

once you make sure that the strategy that you Yeah, exactly. that you have on end is actually pointing to the right direction of profitability. Next one. Yeah, we can. Yeah. So now we have seen two examples and both of them I decided to take them because is topics that the classic retail

Speaker A

traders release 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're covering now. Yeah.

Speaker A

And the difference between this one and the previous one is that this one is the results of a collection of research papers. Okay? So like multiple interviews. Yes. Don't know if some of you already heard of it, but this one is about the post earnings announcement drift.

Speaker A

The logic of the post earnings announcement drift is that you have the release of the earnings. Yep. Which is set by this vertical line earnings.

Speaker A

And what has been found over literally like 60 years of research. This one is one of the very first inefficiencies properly documented. Okay. And still alive today. So it's like to the ones I say ah alpha decay. Yes there is alpha decay but there is always a different

Speaker A

angle you can approach with always basing your 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. Yeah.

Speaker A

but maybe can work on a different type of stock. Okay, 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 let's say that is rising in price then there is the earnings release.

Speaker A

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.

Speaker A

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 missend earnings or a negative price reaction the day after of the earnings, there is a negative drift

Speaker A

that keeps going for the following days or weeks. Mhm. And here are [snorts] the findings of the research which split it across three papers. So the first paper is from 1968 and is an empirical evaluation of accounting income numbers. Empirical evaluation say [laughter] numbers. Okay.

Speaker A

And uh this one was the very first um observation that uh where they saw that if there was a positive increase in value the following days and weeks the price was keep drifting higher and this one was like its purest form of the post earnings announcement drift or drifting lower

Speaker A

when there was a negative price reaction. Yeah. And this one was the first contribution to this uh uh price anomaly. Yeah. The second one was a paper from 1989 which is delayed price response or risk premium in full was pied the delayed price response or risk premium question mark.

Speaker A

delayed price response or risk premium. And the biggest contribution of this paper was noticing that this drift Yeah. was lasting for around 60 days. Okay. So 60 trading days which is approximately exactly 3 months Okay. Yes. So including weekends. Exactly. Exactly. Um the

Speaker A

third paper was a paper from 2006 called comparing the post earnings announcement drift for surprises calculated from analyst and time series for that's a very long yeah but but the main point of this one was the introduction of the analyst consensus and the general obviously we'll probably have

Speaker A

links in the description and we've shown them on screen but for just the general general idea of how much detail these papers have. Like one of these papers, how 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

Speaker A

to 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.

Speaker A

There's literally one one thesis on one topic that's a 30 to 60 page. But but that's and that's like and this particular one has multiple papers. Yeah. And you need to you need to start changing like the perspective that it fits a lot is because many people spend so many hours studying that that

Speaker A

the finding is reliable and you don't need to spend months on just to understand the finding.

Speaker A

So in reality, you don't need to because of these papers and and their availability as well is that you don't need to trust it in the 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,

Speaker A

you know. Exactly. Like what 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 they they say if it turns up being published on a journal means yeah what

Speaker A

they found is actually true. And uh then like it's up to you like to take like each one of these elements. Yeah. And combining it 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.

Speaker A

Mhm. um you might have like the first layer of the post earnings announcement drift. The second one you know that it lasts for 60 days. The third one is introducing the concept of the analyst. Yes, the analyst what they do is before the earnings are released they give an estimate of the earnings

Speaker A

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 is stronger the drift if the actual earning per share is larger than the estimate of the analyst. Mhm. So if we have a larger or stronger positive drift in

Speaker A

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. And today this is like the standard. So not just using the price reaction but keeping consideration

Speaker A

what is the actual earning per share versus the expected earning pressure as well. Okay, these the insights from the papers. Now the why why is it that not the informations are reflected immediately on the price? Because we need to keep in mind that if you go to

Speaker A

any university course they will talk to you about deficient market hypothesis. Yes, the efficient market hypothesis is saying that as the news are released, all the informations are reflected immediately on the price. Mhm. But this one like in front of us,

Speaker A

we have 60 years of research. They say no, it's not the case because if that uh was the case, you wouldn't have this drift over the following days interest or weeks, right? Mhm. And the reason why is because one there might be a low coverage of the stocks. Okay. So not all the analyst

Speaker A

might cover properly all the stocks. So we can say that information travel slowly. Yes. And lack of awareness almost. Yeah. Exactly. like if especially like if we focus on small cap or midcap it's not that these stocks are as much as follow as mega cap so Tesla invidia Apple

Speaker A

because they're like the information is out it is reflected right away indeed like newer paper from 2021 show that if you try to apply I post earnings announcement drift strategies to mega cap you basically have no edge. However, if you apply it to small cap or midcap,

Speaker A

the price is not reflected immediately because of the analyst having a smaller coverage across the stocks. And the second reason is liquidity constraints.

Speaker A

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

Speaker A

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.

Speaker A

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. So the price increases and we have a bits on the earnings. So the actual earnings per share is larger than the expected earning

Speaker A

per share. Then we open a long position at the opening of the following day. Okay.

Speaker A

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. Yeah. as suggested starting point and as position sizing.

Speaker A

You could do something like either a percentage of your portfolio. So allocating for every single stocks that you're monitoring let's say one 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

Speaker A

movement or like the deviation between the earning per share the actual earning per share with the from the expected earning per share. [snorts] And you could try to check and and you could try to test these different variation to understand which one has the best risk adjusted returns. We in this

Speaker A

particular one cuz like with the VWAP uh it was un 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.

Speaker A

Uh and same with the opening range breakout with this one in particular. What would that look like?

Speaker A

because you would essentially maybe be the low of the previous day. No, not necessarily like um that one is needs to be tested like uh for that purpose like given the fact that here we have um cross-sectional type of trade and monitoring let's say we're monitoring 20 to 30 stocks.

Speaker A

The simplest starting point for an exit is just focusing on time. So you take the time which was suggested from the research and 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

Speaker A

than the opening of the day or like if we have a gap up like using the closing before the earnings got released. Okay, as a stop-loss for example. But these are just like examples that you would you would you think that these are things that you would when you get to the sort of encoding stage

Speaker A

and so on you would have these sort of rules so you can do your back test and everything. Yeah I would start for the big test I would start exactly like with this simplified version. to just using a time exit of 60 days. And then like I would once I divide between in sample data and out of

Speaker A

sample in sample is where I would add different rules like for example what would happen if I include a stop-loss. Okay. Would improve their risk adjusted performance. Okay. There is where like you test people. Exactly. Because um why do you do that? Why do you do this split? Because

Speaker A

you don't want that you just c 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

Speaker A

of sample on unseen data and in that way like you understand did I over fit 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 uh um you're not fooling yourself, you see. And um yeah and one one interesting point as

Speaker A

well like obviously as I was saying this one is a strategy that nowadays is working the best on small cap and midcap but not necessarily like you need to focus on small cap and midcap in US. You could take the same strategy and applying on the European stock market, right? Because you might

Speaker A

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 um maybe even the liquidity aspects in terms of the constraints. Exactly. So like see you're

Speaker A

funny like getting there. 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 dons thinking exactly just like you did right now thinking yeah there

Speaker A

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 postix announcement drift that the short side is

Speaker A

becoming less effective for one simple reason that when a company has negative results Yes.

Speaker A

they tend to pre-annon announce it. Yes. Mhm. So that one is making a huge difference nowadays that you have the CEO saying yeah the numbers are going to be poor. [laughter] So like you want to get ahead of it. Yeah. doing doing a lot of risk management EPR before end and that's why you do

Speaker A

have 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 potential. Exactly. Exactly. And even the consequence might be

Speaker A

not as dramatic. Yeah. It might be a 30-day thing or or just sideways because it's already priced.

Speaker A

Exactly. So essentially you're trying to the thesis of this the foundation is that it's something that's in a surprise to the market almost. Exactly. versus uh if they're announcing it then it's already going to start getting priced into them. if they start putting some announcement

Speaker A

of yeah it's going to be a bad so like doing some management of the expectations that's where like the p tends to die out and this is a tendency that the cos tends to do a lot before negative

Speaker A

numbers I've seen some cases even in positive numbers that they started like saying yeah not the numbers are going to be much better than what we're so then it wouldn't be valid for that and that one exactly when you will have um so I guess like even though it's the CEO it's almost

Speaker A

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 going to be

Speaker A

negative it's going to be this giving their thesis uh again it will start to get priced in it will start to be expected uh so similar thesis if the CEO is talking about it on the positive side as

Speaker A

well it's not going to be as much of a 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

Speaker A

gap because it's already been spread out there to the masses and 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 uh impact on their

Speaker A

stock because that won't come across well. But they love no doubt a a surprise positive announcement because then you know it's all in the headlines. Oh, the stock's up 10% today.

Speaker A

100%. 100%. And it is just something that it's a good to know if you're developing strategies like that. What's interesting as well though is uh these strategies so far, they're not complex, you know, they're not like uh something that's overly hard to understand. It's not even hard

Speaker A

to understand necessarily. And a lot of the time when people talk about institutions and institutional trading, they automatically probably assume that there has to have crazy technology, uh, crazy information, insider information and so and you know the thoughts instantly go there

Speaker A

versus actually 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 uh backed up by years of data, years of evidence and research to give more confidence and certainty

Speaker A

and the framework to then build upon and just uh 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 medium to low

Speaker A

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

Speaker A

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 a incredible infrastructure because you need to know what you're doing. Like complexity actually means fragility more often

Speaker A

than not because you have many points where your strategy can break like they I made more than 30 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. Mhm. Fourth

Speaker A

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?

Speaker A

Let me Yeah. Picasso my Bangok. So the market closes at 400 p.m. Mhm. Market closes Market opens at 9:30.

Speaker A

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,

Speaker A

across multiple equity index, across multiple stocks was noticing that the 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

Speaker A

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 around 90%.

Speaker A

If we break it, if at home you do exactly the same like getting a strategy which as entry it opens a position at that goes long at 4 p.m. And as exit, you close the position. So you exit

Speaker A

at 9:30 a.m. when the market's open. Okay, you will see that if you do this and you apply this strategy on let's say NASDAQ using NQ contracts from 2015 to today like June 2026 you have again 90% of the returns that are coming over from this strategy from holding the position overnight and

Speaker A

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 and the reason why

Speaker A

is that 90% of the returns of equity indexes is coming from overnight gap, right? And the reason why we have multiple line of thoughts. There is multiple theories. There isn't like a single theory that is like saying this is this is the exact reason why.

Speaker A

But the first one is about overnight risk premium. So the holders of the position overnight needs 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

Speaker A

is that it's 90% of the returns which is that's why like there is some academics that are saying is a little bit too high. Um even like pract um even like professionals they're saying it's not possible that all of that is explained within that. Yeah within like overnight risk

Speaker A

premium. And [snorts] the second one on the D is related to overnight liquidity. So overnight liquidity during the night there is informations that get released as we have seen before with the opening range breakout thing that uh new informations are out there is

Speaker A

news there might be earnings and given the fact that the liquidity is not as strong as uh during regular trading hours, the price reaction might be a little bit more aggressive than it would have been during regular trading hours and then there is a reversal during regular

Speaker A

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 and then we have like a movement towards the fair market value of the underlying uh trading instrument and this one like I feel like

Speaker A

it's a very strong why and what is nice is that you can use like knowing is knowing that 90% of the returns of the index are coming overnight given a low liquidity. 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

Speaker A

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.

Speaker A

Mhm. 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

Speaker A

and then like start framing your trading strategy around it. 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 it's can't whiteboard that uh but what does that look like

Speaker A

so if you've got the ideas like the four ideas we've gone through uh you have the rules around it so the rules I guess would be the criteria no and then once you have those two things. What is

Speaker A

just a general idea? Maybe it's something we can do in the future, but general idea, what does that look like in terms of the next step when when inputting into a language model? So with the we need to understand that the language model is a tool, right? You we cannot pretend that is a

Speaker A

senior developer that is working for Palanteer. I wish it was like that, but it's not there yet.

Speaker A

they say he's going to be there in six months. So fingers crossed. But um the the idea is like um to follow an iterative process. So let's say that our entry is supposed to get us long at 4

Speaker A

p.m. when [clears throat] markets close. So like first you develop the first part. So like okay, I want a strategy that goes long at 400 p.m. Eastern time. Then you check if it's working fine. If it's working fine, then you at the next condition. Okay. Now,

Speaker A

encode an exit on the top of what you just developed, which exit the position and 930M.

Speaker A

Mhm. 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.

Speaker A

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

Speaker A

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 multi charts that 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

Speaker A

be coded okay you can use uh pine script so like threading view 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 build knowledge pool. Yeah, exactly. To

Speaker A

build the knowledge pool like right now we really are in this space 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 needs to start automating your training strategies

Speaker A

but if you follow the process even if you learn how to encode basic version of your strategy to make test it properly and then like understanding 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

Speaker A

only to use it as confirmation and then still trade manually. So it's like it's really just an additional knowledge base validating. Exactly. Tool box 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

Speaker A

um they can start piecing it together. in terms of uh as an institution and generally what retail can do if they choose to is you could have these four ideas but you you could have them all four running simultaneously 100%. Like that one you got me there. That one is the end game because

Speaker A

the end game 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

Speaker A

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

Speaker A

market conditions and overall they give you that beautiful job is to really refine and learn over time how to manage them so that you limit draw downs on one maximize profits on the other and vice versa. Exactly like uh the exact process that I follow is like once a month I review which

Speaker A

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?

Speaker A

Yeah. So like what I suggest as a retail trader to have at least three four strategies trading at the same time because in a case you are leveraging the power of your machine to have this trading strategies trading automatically for you. Yes. which is a massive advantage like if you try to

Speaker A

follow four 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 uh as automated trading while I was working for the bank I had something like 400 strategies running simultaneously but that way it's like

Speaker A

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

Speaker A

15 billion euro on a yearly base. So like you need to have multiple systems running at the same time.

Speaker A

Um right now like personally I use a maximum of between 25 and 100 systems really depending on the type of market conditions that we are in. So would you do you find yourself consistently developing tweaking 100% like that one is the job the job is freely so changes from manually trading

Speaker A

and having to like maybe do analysis to more an analyzing history analyzing edge you 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

Speaker A

deeper in tomorrow's words of wisdom for sure because I think that's the right place for it.

Speaker A

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?

Speaker A

uh or do you feel like as maybe more and more automation takes place that it's almost going to be necessary to have a portfolio of edges that you manage and uh the manual side becomes your research the manual side becomes your refinement your man your manual side becomes your review of

Speaker A

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

Speaker A

can run and if you run multiple systems and with multiple again just three or four simultaneously can be enough to make your profits on a more consistent base like people will be like okay so you're telling me that I can have four strategies running live while I'm at work without the need of

Speaker A

me sitting in front of the monitor to wait for a specific entry condition. People will move towards that direction because if freedom time give them more confidence, they have no emotions interfering with their trading activity. So it's going to be some sort of natural step moving towards that

Speaker A

uh 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. Uh excited to do a full master class over on Char Academy with you as well. Super fun. Um but yeah, no,

Speaker A

this is this has been incredible. Is there is there more to go over or is No, I think we're done. It's incredible. I know something different for the audience there. As I said, we're going to sit down and do a Words of Wisdom, dive into your your background and more of that

Speaker A

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

Speaker A

out. Drop a like. Again, this is 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

Speaker A

biggest takeaway from this episode. Any questions you have, throw them in the chat. I'll tell you why. Because when we do that master class, 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

Speaker A

uh what other episodes are on screen right now? And until next time everyone, take

Topics:institutional tradingscalping strategiesmarket makersprop firm tradingalgorithmic tradingautomated tradingvalidated trading strategiesbacktestingtrading automationMatteo Conte

Get More with the SozAI App

Transcribe recordings, audio files, and YouTube videos — with AI summaries and speaker detection. 30 minutes free.

App Store Google Play

Or transcribe another YouTube video here →

Free tools: TXT to SRT · SRT Validator · Merge SRT · Subtitle to Text · All tools