Shadowing Practice: How I Got to $500,000 Trading by 25 - Learn English Speaking with Video

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I'm 25, and when I added up every trading account I run, it came out to more than $500 ,000.

If that is all you literally care about, I don't want to make you watch the entire video just to figure that out.

But for the rest of the video, I want to explain how I got that much money trading at this age without a rich family, without one lucky coin, or anything like that.

Now, I started trading at 18 as a complete beginner, almost blowing up small accounts and chasing every single trade idea on YouTube.

I went full -time at 20 by building sysmax strategies in Python, and then I moved into automating everything across crypto, futures, and equities.

And today, I'm trading a fully algorithmic portfolio full -time from Dubai.

So here is what we're going to cover.

First, I'm going to show you the actual portfolio

and show you as much as possible without it being a security risk or leaking any information.

Just so you can see, I'm not making crap up.

And second, I want to walk you through every year what I was trading and how it evolved over the years.

So you can actually see how I actually got here.

And third, I'm going to talk about seven things that made the biggest difference, plus a few tactical ideas I stole from books along the way that you can use straight away in your own trading.

But first, there's just a few things I want to get out of the way up front.

Number one, did I really start at 18?

Yes, I really did.

I lost nearly $3 ,000 trading randomly on large caps and small caps, and I even tried to make money with a dividend strategy that would have made me just about $5 in profit.

At the time, that $3 ,000 was pretty much all the money I had from working and saving up.

It felt like a humongous loss at that point in time.

Second thing, I already know I'm going to get a lot of comments

on this video about how I only got to this number

because of maybe a crypto bull market or because I can code

or because I had money coming in from outside of trading.

And yes, it's a combination of all those things.

There have been bullish market regimes that I've taken advantage of.

I've sweared my ass off as well to bring in money, whether that be back in the day playing esports semi -professionally and bartendering and then starting businesses.

Because if you want to achieve something, you've got to use every single advantage you can get to actually make it a reality.

Which brings me to my third point.

If this video is going to make you feel bad about yourself, then you should just literally stop watching right now.

I don't make these videos to brag.

All things equal, I'd actually rather not publish my account balances online for thousands of people to see.

But when I was 18, videos like this one is what got me into trading and taking it actually seriously.

And it's completely changed my life so far.

So I make these videos for the small set of people who are going to get motivated by it

and potentially can learn something useful from my own journey.

But with all that out of the way, let me show you how I actually got to this $500 ,000.

So first is the actual portfolio.

This is August for the whole portfolio, trade by trade.

I ended up being up around only 3 .21 % across everything with a 3 .6 % drawdown along the way.

So not a great month, but overall still profitable.

And what you're looking at is 10 strategies and around 308 trades, all of them being automated.

And keep in mind, this number moves every single day.

It's a trading account, it's not a saving account.

It jumps up and down quite a lot.

The names are blurred because the name basically tells you what the strategy is, and I don't share these at least straight on YouTube.

Now, six out of the 10 actually made money and four lost money, but the portfolio still came out to a plus 3 .21 %

and the best strategy did 2 .29 % while the worst one lost around 1%.

I know to a lot of newcomers, this can sound really jarring.

How can you have losers and winners all in one portfolio and then still be profitable at the end?

And ultimately, it's going to be a point I'll come back to later in this video.

Now, one of these strategies is actually quite brand new.

I'm scaling into it slowly, but it is looking actually quite promising.

Now, keep in mind as well, at any one point, I could be testing maybe four to five other strategies

that don't show up in this number at all.

The reason for that is most of the time, these are trading on a smaller amount of cash, for example, like five or 10 grand.

Now, some of that testing I actually do with a friend and my trading partner, Pedner, where we sometimes split capital across different strategies.

Now, obviously, all of that is from my own records.

So here is a live login into Interactive Brokers, probably the most well -known broker out there.

And the live login on the mobile app isn't something you can really fake.

This is just showing the equity sleeve part of my portfolio.

I'm going to be showing you month to date as I'm recording this.

As you can see, it's slightly negative.

I'm down about 0 .13 % and I've got around 174 grand in the account.

Now, keep in mind, this is only around $170 ,000 of the total portfolio, and it's just simply dedicated for equities.

The main curve also has crypto and futures in it as well.

Now, let's get on to every year, because how it evolved is really the whole story.

At 18, I was a complete beginner.

I joined a day trading room, which was dedicated mainly for large caps, and especially for my small account size at that time, it was just trading way too slow for me with way too small percentages.

So, I found a guy in that group that was kind of focused on small caps, and I spent a whole year slamming them and pretty much making no money.

I was shorting every extension, completely over -leveraged, and that's when I nearly blew up $3 ,000.

Now, at 19, it turns to 2020.

Everything's printing, and I'm still losing, unfortunately.

That's when I see two traders on Twitter posting around data on gappers instead of opinions.

And ultimately, this was the moment.

Three months of tracking data by hand in Excel, and then I realized I'd never actually analyzed any of my strategies.

So, instead of doing it manually, I actually wrote code, and I had five years of data by the end of the weekend.

The first stat that really stuck with me was around that 70 % of gappers closed red.

So how in the world am I losing shorting these small caps?

Well, the other 30 % have violent extensions you often can't outlast, especially if you're using leverage.

Now, at 20, it did actually start to work.

I went full -time.

I was a fully systematic small cap trader in Python.

And to be clear about the timeline, I was still overall unprofitable for about two years.

Year one, I lost money.

And year two, I ended just above breakeven but from there on, it was profitable and it stayed that way so far, fingers crossed.

Now, all the rules have come from data and it took me about two years to actually get there.

At 21 and 22, this was all dedicated to automation.

I had futures algos running on their own and I had small caps, which was 80 % systematic and around 20 % discretion.

This is where I also started to add outside income.

For example, I started a dev shop, which primarily was just coding other trade assistance for them, which became Unbiased Trading, which has now worked with funds,

prop firms, and hundreds of retail traders with some of the most famous names even included in those.

And then I started AskEdgar, the AI business in October 2023.

Now at 23, I moved to Dubai.

And this is also similar to the time where crypto came into the book.

I had around seven strategies running and I was still doing a bit of manual small cap execution on purpose.

The main reason for this is that if you only ever look at data, it gets quite hard to find new ideas, or at least at the time, that's what I thought.

Then at 24 to 25, this is the time where I was just fully dedicated to the portfolio.

I'm out of execution completely now.

And that's when I realized I'm a really weak executionist in trading, but I was quite decent at building the models.

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So I doubled down on what I'm good at.

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This is where I've run up to around nine to 10 strategies.

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And my daily life is really checking correlation and trying to get everything to be as uncorrelated as possible.

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Now, if we noticed the pattern, I was discretionary.

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Then I had one system, then I had many systems, and then I had a portfolio.

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The money followed that structure as well.

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And the reason for it is it's because I followed these seven things.

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Number one, I only got here by being algorithmic.

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And the trigger for this was a tweet.

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This is actually one of his tweets or the earliest one I could really find.

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This was from 2021.

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Now, I was also inspired from a lot of other traders, but a lot of those accounts have simply disappeared, unfortunately.

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So this is the best one I can really show.

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This was a guy shorting at the open on gappers and closing at the end of the day.

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My first reaction back in 2020 was that that has to be ridiculous.

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There's no way a strategy can be profitable and just, you know, short it open, close it at the end of day.

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But then I actually looked at the numbers and there was a lot to it.

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The truth is I was incredibly bad at discretion trading.

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And even nowadays, seven years in, I'm not the best.

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So automation gave me free things pretty much overnight.

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It gave me consistent execution, a bit of emotional detachment.

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Obviously that came more over the years, but it definitely helped a lot compared to, you know, staring at the chart and executing by hand.

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And it allowed me to run many strategies at once instead of babysitting just one.

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Now it wasn't a completely clean switch.

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In 2023, I was still 80 % systematic and 20 % discretion.

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Today, the execution is 100 % automated.

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And that's just because at the time I was still learning.

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There was a lot of things I was still getting wrong in the process.

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Now, in my opinion, I would fully recommend just picking one and dedicating it fully to that.

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It's quite hard to do a mix of systematic and discretion.

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Most of the time, it's going to be more beneficial if you're 100 % automated, or you're going to be primarily using discretion, but maybe you add like 10 % of data

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and you run a couple of backtests here and there just for some signals.

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Now, number two is portfolio, and I will keep on saying this until the end of the time.

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Focus on portfolio, not holy grail strategies.

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If you watch this animation I created, there's 10 strategies, and each one on its own is noisy and has ugly stretches.

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But if you put them together, if they're uncorrelated, the combined curve is far smoother than any single one.

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For example, that's my August trading performance.

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I had four of the 10 losing money, and the portfolio still made 3 .21%.

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But if we look at the right -hand side, the same 10 strategies, but they're slightly correlated, the smoothing basically disappears.

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That's why I run a correlation matrix, which I'll show you in just a second.

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Primarily, if you have 10 strategies doing the same thing, it's really just one strategy with extra leverage.

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Now, this is my actual correlation matrix from August.

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As you can see, I have 10 strategies and green is what you want.

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It's primarily low correlation or sometimes even negative, which means one strategy is losing while another strategy is winning at the exact same time.

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Now, the brown cells are the higher pairs.

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These are the ones I normally have to keep an eye on.

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And this is when two strategies start moving together.

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And that's exactly how I actually had to cut one of my strategies earlier this year.

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If you kind of watch for a lot of my portfolio videos.

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Now, an important reminder here is this is just one month's correlation.

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And I'll always mention this, that one month's correlation doesn't necessarily mean, hey, you have to get rid of that strategy because it slightly ticks up in correlation.

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Correlation isn't fixed.

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It will constantly be moving.

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And it also depends on what particular metrics you're looking at.

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So correlation is a lot harder than many assume.

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And that's why so much time can be dedicated to it.

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I'm still learning an awful amount trying to build out my book even further.

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And then we get onto sizing.

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Every strategy gets the same volatility target.

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So no one strategy can dominate the book.

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Normally this is around 1 % risk per trade.

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And the main reason for this is you can have a great edge

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and still go broke if you size like an idiot.

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And believe me, I've done that many times in the past.

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Position sizing actually kills probably more accounts than bad entries ever will.

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And the payoff for running uncorrelated strategies is the whole book

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can safely run bigger than any single strategy could ever run on its own.

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Ideally, you want to be sizing for the worst month

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and not just for a backtest or what kind of returns you want.

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Now, number three is data over gut feelings.

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How I like to look at it is you're not really a trader.

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You're a researcher who occasionally maybe presses some buttons.

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The loop is ideally you want to have an idea, quantify it into if -then rules, pull maybe five to ten years of data, backtest it with cost, slippage, all those sort of things.

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Then you want to run your robustness tests and then maybe have a small amount of capital, or you can even do sim if you prefer, for a few weeks and then slowly scale it live as you see performance confirmed.

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And the story that made this click really for me is those three months of manual tracking.

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I spent probably two to three hours a night with a total amount of probably 200 hours collecting data by hand, and I did basically zero analysis.

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Whereas one weekend of code, I got five years of data from a data provider, and then I was able to start analyzing it straight away.

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Nowadays, especially with AI coding tools, your time should be spent analyzing data, not collecting it.

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And for a bit of context here, I'd been coding since I was around 12.

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Primarily, I got into coding for Minecraft servers back in the day, and that's how I made some of my first money ever online.

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But for trading, when I, at least I was introduced into it, I always thought everyone was just discretionary.

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I never really thought people were using code.

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Now, obviously nowadays, I think more and more people are getting into the idea, especially with AI, that, hey, you can use code to analyze things, create automations, all those sort of things.

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But back in the day, that's why I spent time collecting data by hand because it never crossed my mind that, hey, I could just query an API and actually download some data.

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And just a really quick one, this is the exact same loop from an idea to a live algo.

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And this is what we build inside the crypto momentum group.

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If you want to build your own in 2026, check the link in the description.

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Now, the trap that most people fall into.

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Here are 100 strategies with zero real edge, just pure random noise.

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The best one of the 100 still looks great in a backtest, but in out of sample, it's completely nothing.

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Timothy Masters calls this the selection bias.

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If you test 100 variants and keep the best, the best is biased upwards just by construction.

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Now, if you're not too sure who Timothy Masters is, I definitely recommend you looking him up and reading some of his books.

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They are a bit more complex, but they're some of the best backtesting books out there.

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Now, the fix for this is normally a walk -forward testing and a permutation test, which basically just asks whether your exit curve beats shuffle data at all.

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Most people actually never run this, and it's actually not very hard nowadays, especially with AI coding.

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And this is why I keep saying it normally takes about three to four years to know if you're actually any good.

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I know it's not the most motivating statement to hear.

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It's kind of just true, and it's around the sample size.

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Your t -stat is roughly the sharp times the square root of years, and you normally want about two.

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At a sharp of one, that's normally around four years, and at 0 .5, it's 16.

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So plan for that.

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Stop judging yourself on just one bad quarter or one good quarter.

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I've had plenty of great quarters, but I know that I'm aiming for three to four years of consistent performance.

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Additionally, you can of course just take more historical data and then test it on, you know, three to four years without a sample.

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But the real test is always going to be the live market.

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And the last one on data is you need to know why it works.

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Gray and Vogel's quantitative momentum book makes the point

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that momentum is an underreaction to news and it survives for so long because sometimes it fails spectacularly against the benchmark.

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So professionals normally can't really hold it in a hedge fund.

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And that's what they would label as kind of career risk premium.

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So if you can't really say who's on the other side of your trade and why they keep losing, in my opinion, I'd say you don't really have an edge.

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Or even put better, you need to understand why you're getting paid for a particular trade.

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Is it because you're taking on volatility for someone else?

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Is it because they can't allocate to this particular asset?

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Is it because it's too illiquid?

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But understanding that can really help you on creating your backtest and also verifying it.

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And what I mean here with you just have a backtest

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is most of the time people will take a very arbitrary idea, have no idea why it potentially could work, and simply just create a really nice pretty backtest by optimizing it a couple of times.

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Ultimately, all you have there is a very nice picture of historical data and nothing really else.

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Now, number four is other ways to add capital.

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In my opinion, don't quit your job on day one.

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And I say that even though I went full -time at 20.

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And the reason for this was, well, my first accounts were funded by esports, bartendering, classic 9 -to -5 work.

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And then later it was businesses, coding strategies for other traders, which started just as free DMs I would get online from Twitter and then turned into the whole business of unbiased trading.

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And then I started AskEdgar, which is an AI SEC business, which focuses on helping enterprises and funds use SEC data.

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And here's really the math most people skip.

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I know it's going to sound so basic.

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And once you hear it, you're going to think, hey, like obviously that's so obvious, but most people don't appreciate the fact of it, that the same system, let's say you make 15 % a year, you do really good.

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On 10K, that makes about $1 ,500.

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On 200K, it makes 30 ,000.

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The algos do not care where the really the capital comes from.

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So sometimes adding external capital is going to be a lot easier than upping your returns.

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Trading is capital intensive.

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The more capital you have, obviously the better you will get on everything, commissions, better actual dollar amount of returns.

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And additionally, then you could be able to set some budget aside for maybe hires, like having someone help you with research, all those sort of things.

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Now, number five is the cost speed limit.

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This one actually comes from Robert Carver's systematic trading book.

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It's the single most useful rule in probably the whole book.

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Every trade costs you in sharp units, commission plus slippage divided by the volatility of the thing you're trading.

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A single rule earns around 0 .4 sharp before cost on an actual good backtest.

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So ideally, you never want to be spending more than a third of that, or about 0 .13 a year on costs, which gives you a maximum turnover of 0 .13 divided by the cost of a round trip.

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For example, in a cheap futures market, that's around maybe 65 round trips a year.

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In BTC, perps with realistic fees and slippage made us around 26.

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Another video I actually did was the Crabbell breakout strategy, which made about 0 .13 R a trade and died the moment costs were added to it.

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And this is why I would actually recommend normally looking at higher timeframes, not just minute data and trying to become a high frequency firm.

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That's often 10 times harder, and actually to create a good momentum strategy on daily and weekly bars is way more possible, just because you're not fighting as much costs.

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Now, this is a very rule of thumb.

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I have strategies that take more trades than this, but there's different ways you can optimize for costs.

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And of course, if you have a strategy that's producing really high returns, you have more of a buffer for costs as well inside that.

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Now, number six is all the main strategy types have been found.

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Trend, momentum, mean reversion, carry, value, breakout, volatility, seasonal, arbitrage.

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Every one of them is in literature, some of them for close to a century.

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Now, the $60 billion fund system I actually rebuilt on this channel is four trend lines and a volatility scale.

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That's pretty much all it is.

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Now, I've said this before, most traders aren't unique.

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You've probably taken hundreds of things from hundreds of people, and I personally have never really invented a strategy type, and most likely neither will you.

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And there's nothing wrong with that.

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That's not really a job of a trader.

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The job is three things.

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A more efficient version of the signal, sizing it properly, and combining it with other types so the portfolio is smooth.

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My 10 strategies are versions of maybe three to four of these types.

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The edge is really in the version and the combination, not often the strategy type.

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And next is signal efficiency is real.

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It is maybe the small stuff that most people find boring, but it's probably one of the most valuable things you can spend time on once you have maybe a couple of strategies.

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For example, in Gray and Vogel's book, they go over the 12 -month momentum, but they skip the most recent month.

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They also have the whole frog in the pan method, which smooth trends made of many small moves beat jumpy ones, for example, which was another way they were able to improve the signal efficiency.

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Another classic one is you put every signal on the same scale before you wait

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and combine them and create an ensemble strategy.

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Additionally, sometimes signals can be super simple.

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One of the simplest things I've ever seen work was one indicator, two parameters, and it was consistent from 2012 to 2024.

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Often, simpler wins, way more than people want to believe.

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Now, number seven is add pure inefficiencies when you get the chance.

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Now, on top of risk premium strategies, let's say like trend and momentum, you then want to add when there's a real mispricing that's showing up.

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For example, someone is simply wrong and you can take advantage of it.

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In crypto, that's often going to look like cross -exchange spreads or funding rate arbitrages potentially, or potentially when a scenario where an exchange is in trouble and you can take advantage of that.

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Now, there's normally three rules for these.

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They're gonna have the highest shot by far, but they're probably gonna have the shortest lifespan as well.

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And sadly, especially with these kind of inefficiencies, nothing scales infinitely.

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And sometimes you can scale it too much and you ruin the whole edge.

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One of the most classic examples for this is Jim Simons.

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He capped his fund because they knew their strategies just couldn't process more than that amount.

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How I like to think of these is they are bonuses on top of your core portfolio.

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Traditionally, they're never going to be the foundation.

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For some traders, they can be if they just spend all day looking for these.

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But personally, how I like to form mine is like a pyramid structure where the bottom is risk premiums.

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And then at the top, I'm adding inefficiencies when I find them.

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So that's the seven.

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Now, none of these are really a secret, but all of them are a bit boring to actually get the work done and get them live.

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Now, if you want to build your first life algo the way I did, that's exactly what we do inside the Crypto Momentum Group.

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I help you build it step by step the code, the templates, the guidance to go from an idea to running something live.

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And all of this is linked in the description.

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It sounds like your thing.

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But anyway, this is how I got to $500 ,000 plus trading at 25.

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If it was useful, subscribe.

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The previous videos will be in the description and I'll see you in the next one.

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Learn Real-Life English from a Trader’s Journey

Imagine watching a video where a 25-year-old trader shares how they built a $500,000 portfolio—no rich family, no lucky breaks. This isn’t just a story about trading; it’s a goldmine of natural English for learners. The dialogue is packed with conversational phrases, storytelling flow, and practical vocabulary you’d hear in real life, making it perfect for IELTS speaking practice or everyday communication.

Useful Chunks & Collocations to Master

Blowing up small accounts: To lose all money in a trading account (great for talking about mistakes).
Chasing every single trade idea: To follow too many opportunities without focus (useful for describing overeagerness).
Taking advantage of bullish market regimes: Using favorable market conditions (excellent for business or finance talks).
Bring in money from outside: Earn income from other sources (practical for discussing side hustles).
Jumps up and down quite a lot: Fluctuates frequently (simple way to describe instability).

Your Shadowing Challenge: Speak Like a Pro

Ready to practice? Grab a shadowing app and try the shadowing technique with this video. Here’s how: Play a 10-second clip, pause, and repeat it immediately, mimicking the tone, speed, and stress. Focus on phrases like “I went full-time at 20 by building sysmax strategies” or “It felt like a humongous loss.” This “shadow speak” method trains your mouth to move like a native and improves your listening skills—key for IELTS success. Do this daily for a week, and you’ll notice your fluency skyrocket. Remember: Every small step gets you closer to sounding confident in English!

Grammar in this video

The structures the speaker uses most, with the exact words from the video:

Structure	In the video
Present perfect have/has + past participle — a past action that still matters now	have been · I've sweared · have come
Passive voice be + past participle — the focus is on what happens, not who does it	being automated · are blurred · was also inspired
Relative clauses who / which + clause — extra information about a person or thing	people who are · room, which was · caps, which was

What is the Shadowing Technique?

Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.

Shadowing technique: read the full step-by-step guide →

Shadowing Practice: How I Got to $500,000 Trading by 25 - Learn English Speaking with Video

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