Master ALGO Trading In Less Than 90 Minutes (NO Coding) | $1M+ Profits

He has done close to a million dollars in verified profits, of which over 250,000 is on Kinfo.

To execute this kind of surgical action, you need speed, you need emotionless execution. You don't write a single line of code, not one, but you can still make money with 40% success rate. So there is what we call this.

Algorithmic trader has created profitable trading algorithms without knowing how to code. And in this episode he will reveal exactly how you can do the same.

As a breakout trader. This is how I size my trade. A good trader caught his loss quickly, let his profit run to target, manage.

His position size based on introducing Nofel tafe. Nofel goes through the exact process of developing his trading algorithms.

I don't know about you, but I am a you. I'm imperfect and I prefer an algo that will handle all this action for me. This is a very important metric that you need to analyze as an algo trader. And you have a whole other metric that you know because he shares the.

Exact platform he uses in order to develop these and shows exactly how you.

Can do the same.

This software lets you code a bot without learning how to code, without writing single line of code. So as an algo trader it is important to know this metric because you will be more confident when you will trade with your own money.

Because you know you never have to take an execution again, you never have to manage your own trade again. Build, build, develop and manage algorithms instead. Welcome everyone back to Chart Fanatics, the go to channel for all of the very best strategy and concept breakdowns with some of the very best traders in the world. Talking of which, today we are back in Miami after la and we're back.

Home in the move Miami. And they're in a new place as you can tell. It's incredible.

But today we have a phenomenal trader. He has done close to a million dollars in verified profits of which over 250000 I think 270000 to be a.

Bit more accurate, is on Kinfo, which is incredible. A verification platform. There he is, an algorithmic trader. Our first both on Chart for an X and Words of Wisdom which we'll be shooting after this. But here on Chart for an extra wondering how do we showcase algorithmic trading? Well, you're gonna about to find out. And this is actually a masterclass into how to do so with no coding experience. And this is the future of trading in my opinion and in many professionals opinion as well.

So without further ado is the one.

And only now fell thanks for having me.

How does it say it? Correct. Yes, perfect. And AKA his name on X is Ultra Instinct, right? Yes, perfect. But here we are. So thank you for being here.

First of all, thanks for having me.

I'm super excited because again I've been recently having many conversations with professional traders and non professionals alike talking about how algorithmic trading is. One already changed the markets, but two is going to be even more prominent, popular and we're going to see a lot more changes in the market due to algorithmic trading and the advancements and especially with AI coming around, which I know we just briefly talked about. But where do you think is best to begin in terms of what we're going to be talking about today?

First we have to know the advantage of algorithmic trader. So. So a good trader cut his loss quickly, let his profit run to target, manage his position size based on volatility and trade setup. To execute this kind of surgical action, you need speed, you need emotionless execution and you need many hours per day of monitoring without fatigue. I don't know about you, but I am a human, I'm imperfect and I prefer an algo that will handle all this action for me. Another benefit of algo trading is you have to access the data. There is no gray zone, it is black or white. You define your entry logic. And you define your exit logic and you then backtest it. You want to backtest your strategy across many year and many market regime. This is very important. Yes. So when you backtest the strategy, you have access as an algo trader to multiple trading metric. One of them that is very important is what we call a risk adjusted return. Such as we call it the UPI or the Sharpe ratio. Just to explain to your audience what this mean, so let's say you have strategy a, it deliver 20% a year of return. Right. But it is exposed 100% of the time in the market. Right. And you have strategy B. Where it also deliver 20% return per year, but it is only exposed 10% of the time in the market. So as an algo trader you have the choice between these two strategies, right? Let me ask you a quick question. Which one would you choose?

I was looking at this face value. I would say this one.

This one. Good answer.

Why?

Because your capital is working only 10% of the time in the market, which means you are less exposed to black swan event and at the same time it deliver a nice 20% a year of return. This means also that 90% of the time you can deploy your capital on other Strategy on other market, you have the potential to maybe have better return than 20%. So if you have 3, 4, 5 strategy diversified across multiple instruments, multiple timeframe, you can achieve a pretty interested interesting annual return per year.

Right.

So this is what we call the risk adjusted approach in algo trading.

Hey guys, before we get into this incredible episode, I want to say a massive thank you for all of your support so far on both words of wisdom and Chart Fanatics. We have grown immensely and are still the fastest growing channels in the trading industry. Now a way to get back to every single one of you. If you want profitable strategies completely for free, go to chartfanatics.com the links in the description, put your email in and every single week we will send you a free PDF with a profitable strategy of the guests that we host as well as on the website. You can go straight there and you can go through the library of of strategies completely for free. Just input your email. On top of that we launched a Chart Fanatics free Discord community that has already over 10,000 members of traders across the world. We have our live traders from Chart Fanatics live in there. I'm documenting every single one of my trades in there and we have exclusive discounts, massive giveaways and so much more just to give back to every single one of you. Let's not forget updates on every episode and things that we are bringing to this industry that's going to change it forever. But for now the links for that are in the description.

Let's get into this episode. In terms of backtesting, we may go over it already but I wanted to ask you, is that also an automated approach to collecting that data?

Yeah.

And is there any is a specific platforms you use or is it something that you have to build yourself?

Yeah. So with the coming of AI, right now everybody can go to ChatGPT, write a prompt and it will generate a coding strategy. Right. But it doesn't mean that the strategy will work. You need to have a robust process to make sure that your strategy is robust. Right. And you have a software that is specialized for this. I use Strategic Want X sqx. It is a trading system builder. The benefit of this system trading builder is first you don't write a single line of code, not one. Right. You have access to already to all the data that you need, all the stock that you need. So it's a matter of clicking the rule that you want to choose. Backtest across the year that you want and you will have access to many metrics such as the risk Adjusted approach that I showed you. So it is pretty user friendly. But of course you need to have a robust process to make sure that the strategy that you will put your hard real earned money to make sure that it is robust enough. So just to continue another metric that said is important, we call the drawdown, right? This is very important.

Why?

Because pretty sure you saw this online. But if you have a 50% drawdown, right. For your audience, let's say you start with $100,000 account, your account dropped to $50,000. This represents 50% of drawdown. To make it back you need 100% return, right? It's pretty, pretty difficult, right. So the approach is to have lower drawdown. Hopefully realistically on my part between 20, 25% is pretty realistic, right? Normal across all the backtest that I did. This is pretty average, right. But if you want to make back this 20, 25%, I don't have a lot to make maybe 30%, 35% pretty achievable. Right. Across the future. So the idea is why we concentrate to dry down. Because people, when you see them participate in U.S. investing, championship or competition, they will show crazy return. Even the great Larry Williams, he beat the record. He made around 10,000% in one year. But he even himself said that at one point he was up $2 million, but he ended the competition with $1 million and he started with $10,000. The return is outstanding, but the drawdown is 50%.

Yeah.

So you have to ask yourself about your risk tolerance. Are you ready to trade a strategy with 50% to maybe potentially make 100 or more or 20, 25%. So it has to fit your risk tolerance for that. So this is a very important metric that you need to analyze as a NALGO trader and you have a whole other metric that you know because when you trade the strategy, right? When you know the inside in and inside out. I know for instance, as an algo trader, whereas your my max drawdown, what is my annual return? What is my percent of time that I am exposed to the market?

Right.

I know what is my maximum consecutive losses. I have all these metrics to manage my expectation when I trademate with my real money. So as an algo trader it is important to know this metric because you will be more confident when you will trade with your own money. Because you know the data, you know how it performed during the last bear market, bull market and so on.

Yeah, it's interesting you mentioned in terms of the metrics, in terms of the risk tolerance is very, very important. As an individual, because everyone's risk tolerance is going to be different. But equally, as you mentioned, that you have to think about what is the goal of the trader. Because if you're just an independent trader trading your own capital, and as long as you accept your risk tolerance and what the reality of that is, then that's fine. But then equally, if the goal of the trader is to say, get investor capital or trade someone else's money, then it's less still important what you can handle. But it's equally okay. What would a investor be happy with? Right? So like looking at the two examples on the board, an investor probably is going to be looking at the 20 25% as more acceptable. Even that might be slightly large for that investor.

Right.

So I think it does come down to as well the goal of your trading. You know, some people's goal is to attract investor capital and to trade other people's money. A lot of traders of course are really focused on the independent route. A lot of traders nowadays are trading, even funding companies and these prop firms, as they call it, or they might be traders who are trading up traditional prop firms.

Right?

And all of these different endeavors or paths will dictate as well what's acceptable or not. Of course, the independent route means whatever you're accepting is most important. And I think that is the goal of most traders out there. But that's definitely a really important thing you've highlighted there in terms of understanding the drawdown. So have you seen, you know, dependent on the strategy and how it's been configured, there are different drawdowns and different metrics that come out. And then do you then select what's acceptable for you or not?

Yes, this is a very, very good point, Riz, because as an algo trader, there is another concept that we use a lot, right? So trading the market, you either make money with three way, either with breakout, directional trading, mean reversion. Right. Or choppy market. Right. So I trade mainly breakout. This means that my success rate is around 40%, right? This may sound counterintuitive with a lot of people, but you can still make money with 40% success rate. So there is what we call the risk of rune. Right. And this pretty much will tell you how much you can size. So the first setting of this risk of rune calculate is what is your winning percentage? Mine is around 40%.

Yeah.

What is your risk to reward.

Ratio?

This means you risk $1,000 to make $2,000. So this situation mine is around two, sometimes three, but let's say two okay. Right. And the. The third setup is how. How much in percent term is your average loss? Right. Mine is around 0.5%. Okay, right. Sometime lower. Sometime. But the average is around 0.5%. So we what is the risk of run roughly? If you want to give me, is it 10%, 15%, 5%.

Yours I would say is. Would be quite low risk of ruin near zero. Yeah.

Between let's say zero to 2% of risk of Rune. And you can play with all of this parameter. There is a lot of calculator online that you can try it. If you have let's say a higher winning percentage rate, you will likely have a less risk reward to ratio. Yeah. Probably around 0.5, 0.4. But if you risk more than 1%, your risk of run will greatly increase.

Yes.

So on my end as a breakout trader, this is how I size my trade. So if I have a strategy, I will allocate let's say between 5 to 25% of my account on a single algo. But I have to make sure that my average loss is 0.5% or lower.

So when you do that, let's say you assign 20 to 25% of your capital to one algorithm. Right. So one strategy. But then the 0.5% is that of the 20 25% of the overall. Is that 0.5 of the total capital?

No. Yeah, it is on the allocated. Right. So if I let's say I have $100,000, I risk to be aggressive. 25%, $25,000. I want to make sure that my average loss does not exceed 0.5% of my $25,000, which when you look at the portfolio level, it's even lower the loss. So your risk of ruin is again lower than it should be.

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Let's say for example, you have a strategy that may not perform consistently through the year. Just going back to your original point of the two examples, strategy A, Strategy B. Say you have strategy B that's only in the market 10% of the time, but can provide a 20% return. With that example, does that mean that potentially the percentage of winners is higher for that particular strategy? Does the average loss or risk that you're willing to put onto those sort of that sort of strategy higher as well? Or is it, is there any metrics that allows you to then increase your risk size without having to maybe adjust your risk of ruin size? Because the strategy itself is what some may class as an A star setup, if you will. A stars strategy, is that something you do with algorithmic trading as well, where you grade your strategies or have strategies that may perform better but maybe frequency is less versus strategy where frequency is higher but the performance is not bad because obviously you wouldn't use it then, right? But the performance is say just a more average performing strategy.

A very good question, Riz. As an algo trader you have to be conservative. That means that you have to expect that the strategy that you will make, that you will allocate will fail.

Okay?

Right.

So there is a concept we call Monte Carlo. Monte Carlo. So basically the Monte Carlo test is you reshuffle your trade to see what is your worst outcome. Because nowadays every software will generate a beautiful equity curve. But it doesn't mean this is the equity curve that you will have in real time with your real money. So you have to have some process to turn off the strategy. I always use this analogy. When you manage a portfolio of strategy, it's like managing a sports team. Your goal is to let your A plus player on the field and removing the less performing one, right? So some people, some algo trader for instance, if the drawdown of the strategy that they deploy exceed one and a half times, Sorry, the usual drawdown that you see on the backtest, they will just turn it off.

Okay.

That's mean that it is failing, probably failing. Then it's the moment to replace it with a new one.

Okay.

Right. But the logic to go back to your question is the same. So if my algo have 40%, I will deploy it as you know, to respect my average loss of 0.5%.

Right.

But the beauty of the algorithmic trading is let's say you have an algo that trade based on volume and you have another algo that trade on based on price action. And the two have the same signal.

Okay.

Right. We call this like. It's like ensemble trading. An ensemble, it's like a voting system.

Okay.

So you have this guy that trade volume, this guy check price action, the other guy trade many reversion. And the three of them give you the signal to go long.

Okay.

Right.

Maybe this is the time to press heavier.

Okay.

Because it will the quality of the signal will likely be higher.

Right.

And this is where you can size a bigger amount. So let's say you size 10% of the volume guy, 10% on the price action on the other on the mini reversion. Now you have 3% of your account on this single trade. But they have of course different exit. But to get back to your your question. Yeah, you can definitely increase the size, but it has to be a robust system.

Yeah. So it has to be you know, in. In line with the data still it doesn't change of course which even to the discretionary manual traders is the same principle. Like it's all backed off data. They won't just do it based on feeling.

Right.

Which is a. Well to be. To completely honest, some of them have said there's a bit of intuition in there of course as well. But to your point, just to finish off on that topic, as you say. So they you notice that three are sort of converging to give the same signal. Do you manually have to increase our size or is that still something that's automated?

Automated, 100% automated.

Is it a case where let's say that if the signal is saying tick, tick, tick across all three, it will just automate that this decision that's been preplanned or you still need to even though it's automated in its delivery, do you have to make the adjustment or that adjustments already it is programmed, coded.

It is done completely automatically. And you have to look you will also look it in the backtest. That's why I always pound the table. You have to back this. Your strategy.

Yeah.

So a new portfolio. So at some point in portfolio, let's say you have a strategy on Apple and the three system that are supposed to be uncorrelated.

Right.

They all fire the same signal. But you will be heavier on Apple. Right. So you will go long apple based on this tree algo. Right. Hopefully these algo are not that exposed to the market. So it's going to be in and out. Right. You will not hold it for days or week. But yeah, it is completely done automatically.

I love that. So I know we could keep going with the whiteboard, no doubt and do some stuff. But I know everyone at home probably feeling the same way. This is great. But we want to see the code or the algorithm, the platforms. So should we. Should we pivot across to the.

Sure.

So now that we've gone through the whiteboard section where we've sort of mapped.

Out the mindset behind using algorithms and sort of how robust they need to be and the data collection and the risk of ruin and the risk calculations and the things that need to be thought out before implementing an algorithm, we're now going to go through A to Z step by step process of actually building an algorithm without using code. So no coding experience necessary.

The different parameters, we'll look at everything, all A to Z to actually have.

An algo that can trade for you in the markets. So where should we begin with that?

Perfect. So this is a trading system builder. It is called Strategiquant X. So this software lets you code a bot without learning how to code, without writing a single line of code. Right. So if we go here in the screen you can see that on this setting it says what to build. So for the sake of this example, we want to build along only we have the choice to build short, both long and short. But we're going to stick to only long. We're going to use genetic evolution. You can use random generation. So basically random generation, it will try randomly different set of rules. So let's say buy when RSI is below 30 and if it's Friday by the stocks. So this is by random generation or genetic evolution. Right.

Or genetic evolution.

Genetic evolution. It's because it will optimize the setting of the indicator. Right. So it will say okay by when RSI3 is below 1.5. You know what I mean? Right. So it will really curve fit the strategy. But this is not what we want because we will have after that a filtering process. Because we're going to test the strategy across multiple parameter. So we're going to take the same strategy. We're going to say, okay, I'm going to set the software tested when RSI is below 20, 30, 40, 15, 10 and so on. And if we have a wide range of profitability, then we know the strategy is robust. When the strategy is not robust, when it picks the setting that you want, it will fail. Yeah, because it cherry picked the data to have the perfect equity curve.

Okay.

Right. But this is not what we want. We want a robust one that work across multiple setup. For the number of condition to generate we have the choice of using 1 rune only 2 rule, 3 rule, 4 rule and so on. But the more rule that you will use, the likely this strategy will be overfit. Meaning it's gonna fit only the data will which will when you will be trading it with real money in real time, it will fail. Right. Because it's easy to generate nice looking equity curve. You want something robust. So we want to stick to maybe two entry rule, two, three entry rule. And for exit rule you want maybe one to two. I'll stick to one. Right. Because at the end we're going to have a maximum of three rule of entry. So we reduce the likelihood of the strategy not performing. Right. So we have a stop loss, it's going to be required. You have the choice of a percentage stop loss between 1 and 10%. You have ATR based. It's used volatility stop. Right. Because as you know when market are calm it doesn't move a lot. But when market are very volatile, the average moving per day is higher.

Yeah.

Right. So you want an ATR base and. Or it can only use an indicator which mean if the close is below a Ketner channel, just exit the trade. Basically.

Right.

So this is the parameter that's you give to sqx. Same logic is applied with profit target. Is it required? Yes. Person. Yes. And we can use also an indicator level. Right. So this is what we want to build. So this is the interesting part here. The building block. The building block. So here you have predefined condition.

Yeah.

You have, you see hammer pattern, you have a lot of rule Bollinger bar, close above lower band. All these are automatically generated. Let's say you want to go to rsi. You go to the letter R. We can see here. That's RSI is here. Right. Resize, change direction downward above level and so on. You can select them all or not. But I just selected it randomly. Just choose, choose random hundred block. I can add more if I want to. I can choose other indicator. Atr, edx, cci, you name it, they have it probably. So I have a lot of indicator that the software will try to generate a strategy with. So this is very interesting here you can use your type of stop, right? You can use maybe the daily low of the bar to exit the trade, Fibonacci, you name it. There is many, many indicator here.

In terms of the indicator, is it only what's available on the platform or are you able to import a custom made indicator? I know a lot of traders I know personally have started to build indicators around their trading. Even if it's like a specific metric like liquidity for example, or you know a particular pattern that they use and they use an indicator to make that easier for them to spot or automate that process. Are you able to say import that onto this platform?

Yes.

And then therefore, you know it would, would it automatically just pick up the code from that, et cetera and have the parameters in there?

Yes. You have the possibility to add what we call here in strategic quant X a custom block. Okay. So this block you will import your indicator and it will use it to generate strategy.

Amazing.

And it's the wibble. You can see here we have predefined condition indicator, the stop type. If you want limit market order and so on. Here you have the exit. You want to exit after two bar, three bar you have trailing stop. You can use an indicator for an exit rule. Right? You can enter at market stop sales throughout market. So you can choose your type of money management. Here we going to try it on Apple. That is a stock. So we're going to choose a stock size by price.

Okay.

I just put an initial capital of $25,000. But you can use, let's say you trade future market, you can use fixed size. So maybe just buy one contract. Right? Completely doable. You can try crypto too, right? You can risk a person amount of your account. There is a whole garden variety of money management method that you can choose from. Yeah, but for this example we're going to try stock size by price. Right. And the ranking, what is the ranking? Basically it is the filter that you force. Strategic 1x you said. Okay, I want my strategy to have at least a return to drawdown ratio. What we Talked earlier of 4, we want least a profit factor of more than 1.5, 1.3. Right. We want at least an average trade per month of two. Okay, Right. So you can add a whole range of, you know, filter you can choose from, let's say some. We talk about the risk adjusted return earlier. Right. We can use the Sharpe. See Sharp ratio is already here. You can use the UPI index, User performance index. This is another risk adjusted metric. So you have a lot of the metric that you can use and you.

Just input your desired.

Yeah. Yes. But there is a, an approach to this, right. You don't want to curfeit the strategy, you want to give a lot of space to the, the strategy context to generate good strategy. Because if you say okay I want a strategy that make 100 a year.

Yeah.

It would likely be curve fit. So you want to have.

When you say curve it does that mean essentially it's just making a strategy that you want but the data and the robustness of it isn't really there?

Exactly. Yes it will look good on the backtest but not in real life. So you don't want to lose money so you want to make sure that it is robust. And one method to do that is to not give strategic context too much severe filter. So this is the standard filter that we can do. But now we're gonna, I'm gonna just clear here strategy.

So those are strategies that before we started. Yes, testing.

Yes. Now the data bank is empty so we're gonna start it here. So now you see strategy quant X is starting to work, is starting to do million of simulation.

Okay.

Thousand even easily we can achieve million of simulation with this kind of stuff.

So right now it's doing it through trade station on Apple on the. Is that the D1 daily time frame, is it?

Yes, yes, yes. We're using the, we are using the engine Trade Station because we want it to generate the code of trade station or multi chart. But let's say you trade another platform, say metatrader. Okay. Or something else you can generate the code with metatrader.

So in this case was it's going to do this back test, collect all this data. We'll get there no doubt. Obviously when it gets to you know, embedding it onto the platform. Right, yeah, we'll come back to that little shortly. But in terms of what we've done here. So it's now showing you here the parameters of what you set.

Exactly right.

So if we just walk through this whilst it's still generating. So what to build is obviously simple strategy. Long only stop loss and profit. Profitability required. Yeah. Using ATR it has generic genetics. Sorry evolution. Yeah. Genetic evolution. 100 generations max 4 islands, 100 per island.

Yes. And the building block that you see here is the rule for generating the strategy as well as the indicator. So we have roughly 140 entry block money management. You can see here, it's stock size by price. Right. So as you can see here, it's already started to generate. Yeah, right. Obviously the more powerful the computer the better is because it will generate it very quickly. But you can see here, it generated a strategy here with a net profit of $7,000. 451 trade with a profit factor of 1.54. You can see the drawdown is about $1,600. Right. So all the metric that we spoke about, if you remember, we have a filter of having a return to drawdown ratio above 4.

Yes.

So it did respected it. So this is how we generate ago. So let's say an example for the strategy here. This is one strategy that Strategic Quantx has developed. Developed. Right.

So showing you the data through all those years.

Exactly, exactly. When you go trade analysis, you have all this metric. You can see net profit per year. Where is your profit factor per year? How much, how many, number of trade, what were, what were your winning percentage per year? Right. You can see which one is loser year. Winning year. Right, Interesting. What is your maximum profit by weekday? You can see here that Friday is a good day to trade it.

Right.

So when you look at this data, is there anything. Well, there's two questions or a couple of questions to go from here. One I know that the audience might be thinking is like, okay, if it's this easy to develop a strategy and automated strategy in algo, surely everyone would just do this and then implement. Right, but what was the reality then after that? At this stage we have all these strategies that's been developed off these parameters. What do you do from here? Like how are you then looking at this data and then making decisions? Are there tweaks you need to make? Is there certain things you need to be aware of? Are there things that you go, okay, yes, it's generated this many strategies, but these ones are no good to look at. And these are the ones I need to focus on more. Are there things that you adjust from here still?

These strategies are curfew, which mean most of them will not perform well in life environment. So you need a robust process to filter out this strategy. Right. Earlier we spoke that we want a robust strategy. For instance, we wanted to work on a whole variety of setting. Let me give you an example. If the strategy here is, okay, buy when RSI is below 15. You want the strategy to be profitable by when RSI is below 10, 5, 25, 30, right. You want to have a map of profitable parameter that give you confidence to go to the next robustness test. Because there is many, many robustness test that you have to go through. You can start with 5,000 for instance. On my part, I can have, you know, in the result part, more than 10,000 strategy. Okay. But at the end I'll end up with maybe 25 potential that I will trade life. So from these 25, I have, okay, I have the confidence. That's okay. This strategy, they all passed all the robustness tests. Because I always like to use this analogy, right. Building an algo is like preparing a fighter for the unknown, right. If you're training a fighter, you want to make sure that your fighter is able to box, to throw kicks, to fight on the ground. You want a training, a whole diverse set of training for your fighter because you will give him a better chance to win the fight. The same logic apply in algo trading, right? You want to have as much as robustness tests. Your goal basically is just to break the strategy. Yeah, right. You want to break it. You will test it on another market to see how it perform. You know that, okay, the strategy is profitable across a big set of parameter. That's good. It's work another market. That's good, right. Earlier we spoke about knowing what is the, the worst outcome.

Yeah.

With Monte Carlo test. Right. With Monte Carlo test, for instance, it will reshuffle the trade and see what is the worst outcome. So now here you see the equity curve look very nice. But when you, when you do the Monte Carlo test, the equity curve is very ugly. So you just throw it to the garbage. To the garbage.

You would have to, even after the Monte Carlo test, it would have to produce similar results like positive return.

Yes.

For it to be robust and to be more trustful.

Yes. In term of drawdown, in term of performance, the equity curve will be just uglier. You will look at it, right?

Like when you say reshuffle, what does that mean in terms of.

So in term of sequence? So let's say you have this, this is in term of trade, a win, win, loss, win, loss, loss, loss, win. So it will reshuffle the trade sequence to see what was your worst outcome. So as an algo trader, you will trade a strategy with real money and at some point it will just start with losses, right. If I know in my Monte Carlo test, my max consecutive loss were 10. I know that what that was the worst outcome. So I will continue to trade it even If I have 10 consecutive losses because I know at some point I'm gonna be on the winning trajectory. Okay, so this is with Monte Carlo test you can, you know, with robustness, there is many, many robustness tests. You can just modify the open, high, low, close price, having a synthetic price action and test the strategy to see if it work or not. So all these tests are available in sqx. You can do it automatically as a workflow. We call it a workflow. So let's say it's you want a 10,000 strategy to be in the data bank. When it finish, it will test it. Let's say it will do the Monte Carlo. So from the 10,000, let's say there is 5,000 left, then after that it will test it, let's say on another market.

Yeah.

So on the 5000, you have only 2000 available. So you have many, many, many promise tests that you go through before trading the strategy, because many of the strategy are curfew. You need to have a robust process to filter out.

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Now let's get back to the episode.

So yeah, so here, this is a strategy that is generated. So now we're gonna import the code and export it into the trading station that we use. And you will see that once it's imported and exported into the trading session, it will automatically send the trade.

Okay.

The process in itself is easy, but what is difficult is having a robust strategy.

Yeah.

So when you talk about that robustness as well in terms of the different criteria, is that where you would adjust it in the, the building phase that we originally started on, you would adjust these different RSI filters?

Yes.

And see the performance. Do you collate the data together? Was it all separate?

No, it's all separate. So here we have what you call a. We can do it here, but we have what we call a custom project. Right. So this is all workflow. Right. So let's take this workflow.

Right.

This is the build phase that we did. Right? Yeah. It's going to start building and after that it's going to retest it on another out of sample data. Right. And after that it will test it on another time frame.

Okay, so what, our original one was Apple.

Yeah.

On the daily time frame.

Yes.

So in this case it would then test it on say Tesla.

Yes. On a four hour, on the, on the nasdaq, on gold, on bonds.

So it would do different assets first and then it would do time frame after or same.

It depends on your workflow that you want. Okay, this is just a generic workflow, right?

Yeah.

This is on there, right?

Yeah, yeah, this is a generic workflow. But just for generic. Yeah, it's a generic thing. Yeah. So if we go to the custom project here and we click start, you will see first it will clear the data bank and it will start building strategy. Right. And this is es, it may take a little bit longer because we are on a hourly time frame. So there is a lot of data for strategy X, how far back does it go?

As much as the data allows it to.

Oh, for early 2000. Yeah. So you have 25 year old data. So if you have a strategy that make it to the end, you have more confidence because you have 25 year of data from 2000 to 2025. We have a lot of bear market, a lot of volatility events. And if your strategy survive this, you have high confidence to trade it live. It doesn't mean it would work. Don't get me wrong, it may fail. Yeah, right. Even if it fell, it's okay. We're gonna stop it. Because look, we have hundred, if not thousand of strategy. Let's will be running on the background. We call this incubation. Incubation is when you run your strategy with simulation money. Right. So what.

Okay, so that before you go live.

Yeah. Simulation money.

So and even when you go live, as you mentioned, it could start still not work.

Exactly.

You still have more in that incubation phase to phase in, phase out.

Exactly. So if the one doesn't work. I know a strategy that is working on. Simulation one. Okay. Is performing. So I have an additional confident because it work on and seen data.

Yeah.

New data. And it did well. So I'm going to just grab it, replace the one that is not performing and trade it.

Yeah.

Right.

In terms of. Right now it's doing the building strategies again.

Yes.

Does it then wait to complete that to then move on to the phase?

Exactly. So now it's going to go to the phase three. And after it finished from the phase three, probably now here we have 44 strategy. Right. We will probably have less strategy after that. It's going to go retest time frame here.

By the time it gets to final, you might only have five.

Exactly.

Those are robust at this point.

Exactly. Okay.

But you can customize all of these elements exactly there. So if we were to take just the example we were on. So we did Apple originally.

Yeah.

So let's say just run us through what that would look like for you. Not that we actually have to do it because it probably take a lot of time just generally for the flow for people at home who might be look thinking of doing this. What would that look like for you in terms of. Okay, Apple number one. We had daily timeframe. What would those adjustments look like for you if you were to be doing that yourself?

Okay.

Through that flow of like.

Yes. So this is a generic workflow. This is not my own work.

Of course.

Right. I have my own workflow. I have a lot more steps. Yes, more. Much more steps. And just to give you an idea here, why we're gonna. We already have 200 strategy. And if we look at. Let's say I'm gonna click this one. So yeah, so this one just hit $240.

So when you're looking at that, what would. What are you thinking? You're looking at this. When you're looking at the data, you're thinking, okay, what are the key markers you're looking at to go this is no good. This is good.

Well first it has to generate a lot of strategy because I think there is a bug here. But when you look for instance at the trading metrics such as profit factor, right. You can see that we have between 1.4, 1.5. We have interesting return, annual return. Look here, annual percent rate of return is about 77%. Right. And this is trading future.

Yeah.

Right. So this is a return on margin. Right. Look like this is crazy return 208 return. Like I've said, these strategies are most likely curfetted if you don't apply the robustness test on it. Right. But let's say I'm going to.

When you look at that, you wouldn't.

Go no, no, no, I'm not going.

Wow, you're going to go yeah, okay, something's wrong there.

Not something is. But I have to wait and to go to all this process to maybe judge it. So let's say I'm going to just stop it here.

Would you. Let's say for example, when you go through this process, let's say there's one there as you say 208%.

Yeah.

Going through that process and it still makes out the other end. Would it be, would it have adjusted do you think to then be more accurate? Let's say if it's gone down to 100%, but it's gone through your the robustness test and gone through all the steps, then is that something you go okay, this is more of a realistic.

Yes, I will have more confidence for sure. And I have to look at the drawdown. Right. So to be to size it, you can see here the drawdown of the 208%. We're talking about 13,000.

Right.

So if I'm gonna allocate a hundred thousand dollar to this strategy, then my return will be lower than this on thingy is this.

I know obviously we ran a random test. Is it on a hunt? I know the original one was on 25,000.

Yes, but this is our future.

Okay.

It's our future and it use the margin required to trade. So that's why you see a higher number because if you need $25,000 to trade the one es future contract, then it will calculate the return based on that.

Gotcha.

Right. So.

Yeah.

So here the initial capital is on 10,000. So the return is calculated based on $10,000. Right. But this is the first step of the workflow. It's not time to get excited. We have to continue.

It's Very easy to get excited.

So if I run the project, let's say from another out of sample test.

Okay.

So this is taking, is it going to take the number that we had stopped at and run it through this now?

Exactly.

Okay.

So it's. We're going to retest out of sample another time frame.

Where was it out of sample. Where is it testing you on this is the workflow.

So have multi step. We were in the build. Yeah, build step strategy. Now we are on the retest step strategy.

Is that the Monte Carlo?

No, Monte Carlo is here. Step 5 MC test.

So when it says out of sample, what does that mean? We were looking at es. Is it then, is that what it's doing on S P for example?

Yeah. No. So in algo trading when you build a strategy, you have to use the in sample and out of sample.

Okay?

Right. So you will say to sqx, look, build my strategy from 2000 to 2008. Okay? Right. And after that, once you build a strategy, I want you to test it on 2008-2025 to see how it's performed.

Okay.

This, this is a step to reduce curve fitting because you will see if the strategy is not curve fit, it will likely perform on unseen data out of sample. It is just insane data. The software doesn't look at it.

Right.

So then it'll be like raw. Just be like how did this actually perform in this?

Yes.

So you have the in sample, you have the strategy to say the one that have 200%. But if you tested on from 2008 to 2025 which is out of sample and seen data, the software did not see this data.

Okay.

You will see how it performed.

Okay.

If the performance is representative of the in sample, that's we may have a potential candidate start to build more confidence. Exactly, exactly.

So for you, for example, if you were to do that process for yourself right now, what, what data would you use as in sample and then what would you use as out sample?

Very good question. Very good question. I like to use a diverse range of market regime in both. You know the saying that say that everybody is a genius in a bull market.

Yeah.

Right. But the same logic apply with this kind of software. You have to select a data set that you have multiple bear market, bull market, very volatile event, even low volatility events. So you want to make sure that your strategy is doing okay during this period. And after that you're going to test it on a set that was not seen but by SQX to see if it's Performed well how does that, how.

Does that work in terms of. It wasn't seen by sqx. What does that mean? Well, surely it has all the data, right?

Yes, it has all the data, but the data that it's used to build a strategy, it will not use the out of sample data.

Okay, so then how does it. So it will just run the parameters. Does that make sense? Like in the outer sample, just run the parameters of the.

It will run the strategy on the out of sample data set. Right. So let me, let me just show you here, let's take that. So you can see here, this is the end sample. Yeah, out of sample. So here it used all the data, which is not good. So what we want to do. When we build the strategy, we go to data and we will select you see here data range part. Yep. So you see oos, this means out of sample test. This means that strategic QuantX will not use this data from 2014 to 2019 to build the strategy. It will only use from 2007 to 2014 to build the algo.

Okay.

And after that once it's build it here, it will test it here. Okay. Right. It is unseen data.

So when you say build it, it's going to build it using the parameters or checking the parameters that we selected.

Right.

So the ticker of Apple, let's say daily time frame and then we chose certain indicators or technical patterns for it to build it from. So like RSI.

Yeah.

Daily, high, low, close, the volume, etc. We selected these. So then they select the date range for the in sample. It uses that data to formulate the algo and show you, you know, Sharpe ratio expectations.

Right.

And then from that you then select the outer sample and it will run the algo on the outskirt.

Exactly. Okay. And this is when you see if your strategy is robust or no. Okay. Because if you just lose money, that's when that's it is not robust enough. Right. That's why it's important to have multiple strategy to see which one will perform an out of sample data.

Yeah.

So yeah, if we go back. This workflow generic one, they didn't use the outer sample, they just used the sample. But on my methodology I always use out of sample. It's important is the first step to make sure if your strategy is robust or not. Right. So this is the Monte Carlo test that I was talking you about. Right.

Let me just.

Here. Do you do remember that we had at some point $13,000 dry down by reshuffling the trade. The force outcome were $34,000. Right. So this means that you do not have to consider the first drawdown figure that that you see on the back test.

This one's more important.

Yeah, definitively, Definitively. Because this is the real, the worst case, the worst outcome at 95 confidence level. So we have 34,000 and here you can see different equity curve.

You.

Yes, this is the first one you can see, this one is the poorest one. Yeah. It doesn't mean that you lost money at the end. At the end you make money.

Yeah.

But look at the volatility of the equity curve.

And does it come back to as you said at the beginning.

Yeah.

What is your drawdown tolerance? Exit risk tolerance. So you look at that and go okay, this is the average over here we can see the cluster. But here is an outlier. However, that is a reality that could take place. So am I willing to accept this and if the answer is no, then you would obviously move on or retest some things, reshuffle some things.

Yes.

If the answer is yes, then.

Or maybe size more. Okay, right. So if Instead of allocating 30,000 and you1 still want to trade the strategy, instead of allocating 30,000 you can allocate $60,000 because if the draw down come the proportion will be lower by the fact that you sized bigger. I don't know if it makes sense.

Yeah.

So. Yeah, so let's say the workflow is done and you're satisfied with some strategy. You can see that we can access.

The code just here.

This is the source code already generated. You can see we have different platform. We have TradeStation MultiChart. We have Expert Advisor, we have MetaTrader. We have many platform. I use multichart. So now it will update the code. See, this is the full code. Yeah, we can see this is the rule. Do you do remember I wanted not a lot of rule. Right. We can see long entry signal. We have two value long XX signal to true value. Right. We copy the code. Copy here and we go to the trading platform. Here I am with multi chart. We'll say Apple chart test with. Okay, we paste it, right. And we compile the code. Once we compile the code, we just opened a chart. With Apple and we insert it and it will trade automatically. I didn't write any single line of code.

I know, I know. It is incredible. What is interesting and I think would. Well, not only is it that you can do that no code and have these parameters especially I think the indicator element that we talked about would change the game for a lot of people because they might not use your traditional indicators that are already loaded in there. And I know a lot of people are very much interested in their discretionary trading styles, that a lot of them are, like I said, building indicators for and are available to them. So then now, knowing that they can implement it into this platform, start developing backtests, using it even. You know, the interesting thing is using that data just from a backtesting data collection perspective as a discretionary manual trader is very beneficial, no doubt. And being able to see these different iterations and what the worst case scenario can be as a discretionary trader is very powerful, which a lot of discretionary traders avoid doing. Right. Collecting that data, especially on the backtest front. So that's beneficial. But equally being able to do that is 100% something that a lot of people will probably open their eyes to. But as I said, what's super interesting, however, is that even as an algo trader, there's a lot of work and input that's necessary on a manual perspective of doing these iterations, making sure to review the data. Is this robust enough or not doing those reflections is still necessary to get to the point where you have an algorithm trading for you. Because I think a lot of people assume algo trading hands off, not too much work. Right. They think it's easy.

Right.

And before we wrap up on this segment and we'll go to the podcast, we will dive deeper into the processes and then of course, your own journey. What's the reality when it comes to the changes you're making? You know, in terms of these algorithms, how often are you phasing some out, bringing some in? How often are you having to run these tests on a day to day basis?

A very good question. Monitoring my portfolio of algo is definitely the, the longest part of the job because as you see, generating the algo doesn't take a lot of time if you have a powerful computer enough. Right. But like I've said, it's like managing a sports team. I monitor on a portfolio level all, all the performance of my strategy doesn't mean that if I have a red month or two red month on a single strategy, I will just turn it off. As you saw with Monte Carlo test, we can have worse outcome. That's the drawdown that we saw in the backtest. So I will just let it play out. But in the meantime, I have my incubation bank of algo. I have 100, if not thousand. So I surveil my incubation list and I look at which strategy is performing and I try to do the comparison with my life algo. If I see that say I have three months of loss, my drydown is near the maximum dreading that I saw on my backtest. I will either reduce the size dramatically or just turn it off and grab a strategy from my incubation one and trade it with live money.

When turning off, is there an element where it's just sort of shelved and it can come back or is that one done now and you have to.

Definitively it can come back adjusting off, it will keep running, but it's going to be around on simulation money. Okay.

So it just essentially gets turned off from live back onto simulation. If you start to notice the data picking up again, you can then obviously turn it back live.

Definitely interest.

How many would you say on average algos do you have running at once?

On my end I have easily more than 150 algorithms. It's already. Yeah.

And what's the process of you managing all of those in terms of keeping track, etc.

Yeah. So as if you remember, we talk about exposure. So I have a lot of algo that will enter in and out quickly. So I like algo that have acceptable return with lower exposure. So this algo are watching on the sideline. It depends on the period. There's some period that where many of my algo fail and this is completely normal. And this is where I have to really work and do the job to reback this all the portfolio, look at my trading metric and do the replacement. But I can't give you an exact figure. But I will say if the ago goes all the way across the workflow, it has about 60% for it to perform life. Right. I know it's not 100% like some people may think, but it is around 60%. But if it failed, not a big deal. There's other one that will be working and like I always say that with quantity, quality emerge. Right. With a lot of algo that I have, at some point I will figure out an A plus algo that I will trade for many months, even many years because I have it in simulation. Yeah. So to get back to your question, it depends in some phases, some months I just do nothing at all, just execute trade. And some months I have to work a lot and do the change that I told you Here we showed an example of generating and I'll go for one ticker. You may have people that say okay, look me, I. I don't trade just one ticker, I Trade a whole universal stock, let's say the S P 500 there is 500 stock I want so I there is a possibility with strategic want X to trade a whole universe of stock. So if we go so let's say here it trade the S&P100 the 100 more capitalized stock in the US market it will rank so here it says okay, the open of the stock has to be above 250SMA. Okay. The RSI has to be below 30 the close has to be above $10 and the volume has to be at least 25,000 share. Right. And after that it will use what we call a ranking score. So here so let's say we apply this filter and we have 20 stocks available to buy. Right? But your strategy you said okay me I, I want only a max open position of 5. How will it rank these 20 stocks to choose only 5? It will rank them based on this example rate of change. So the rate of change the stock that has the biggest change positive for the last let's say 15 day it will select the five best then it will execute the trade on these five. I don't know if it makes sense.

That does make sense.

Yeah.

This is like a ranking filter. So taking say the hundred basket, bringing it down through the filters it brings it down to which ones are most in play and then so and until you get to obviously your exact results that are worth trading and then it will exactly accordingly.

Exactly. This is another style of algo trading. People prefer to trade a universe of stocks and you have people like me who prefer to trade individual individual stock because I noticed that not all stock have the same personality. Now I noticed that for instance Apple don't trade the same as Costco. Completely different price action. Right? So this is where you can use the power of strategic quant X to identify these edges.

We're going to build a second algorithm and this one is going to be on the S&P 500. We're going to exploit known property of this market. The S&P 500 has a tendency to mean revert most of the time. So the same logic apply as the gold algo. I'm going to click on new select single hazard strategy sample editor. We're going to make sure that we have the right instrument with the right engine and the right time frame. We're going to use a daily bar here and I reset the date and we're going to select the es e minis s and P500. As for the money management we're going to trade on fixed size one contract and we're going to use the initial capital of $25,000 around 1.5 to 2 times the initial margin requirement from your usual broker. This strategy has to have a market regime.

It's going to be a long only.

Strategy in this situation. We're going to use a simple market regime which mean that we want to buy only when the close of the market is above the 200 sample moving average, right? So we're gonna select close here you can see it's gonna appear here. Close is above moving average. Simple moving average 200.

Oops.

Then the logic here, close above the SMA 200. This is the first entry condition. We're going to add a second entry condition. Pretty simple. Also we're going to be using a pretty popular indicator which is rsi, the relative straight index. And we want to buy when the RSI is below or lower than a certain level. In this case we're going to use the level 20. We want it to be our SOL and we're gonna use this period period two. So when the RSI 2 is below 20, the algo will buy and it has also to be above the 200 moving average. We're gonna add an exit. We want to sold. Sorry, we want to sell when the RSI is above 70. So the same logic apply, RSI is.

Higher than level 70.

We're going to use the same period. So two entry condition right for entry and one exit condition with the sample. We're going to run the backtest. So here strategyQuantx generated the backtest pretty quickly. We can see that the yearly average return is 34% and the drawdown is around 22% and we are right 77% of the time. This is pretty nice.

And one other indicator that I like.

To see is exposure. We are only exposed 24%, say 25% in the market which is pretty good. So the backtest run from 2009 to 2026 say 25 because we just started the year and very interesting. We only have 1, 2, 3, 4 for losing gear across many many year with many market regime, many volatile event. When the backtest you have to look at many indicator. I like to look at as I've said the exposure. I like to look at my max consecutive loss, my average loss, my average win and I size. Based on all this information you can also have the list of your trade. You can see by yes one, you see the profit and loss. So once you are satisfied with your algo, here you go to the source code and you select your trading engine. Right on my end I trade on multi chart. You only copy the code and paste it into your trading engine and you're good to go. Very simple here. So we're going to build an algorithm with strategiquant X. This is a software that lets you build an algo without writing a single line of code. So we're going to build a simple strategy on gold future. So when you download the software you go into algo wizard and you just click new strategy. We're going to click select and here this is the user interface. So we're going to build it on gold. So we have to make sure that we have first the right engine. On my part I use multi chart. So let's click on gold future. Let's see any gold. And we're gonna trade it on the daily bar. We're gonna reset the dates. So the backtest will start on 2009 and it will end on 2026. We want to make sure that we're gonna be using a fixed size of one which means that every time the logic is true it's gonna buy one future contract and I like to use as initial capital 1 1.5 times the initial margin on gold future. Right. I'm gonna add one new entry condition. So we're gonna try to build the popular gold rush strategy. Basically this strategy will buy every Thursday. The logic behind is during the weekend there is a lot of volatility that happens such as a war, geopolitical stuff. So we want to select the Thursday gonna go day of the week. As you can see bar day of the week. As you can see here the bar.

Day of the week.

If we set it up to zero the algo will buy every Sunday. If we set it up to one it will buy every Monday. Two Tuesday, three, Wednesday. In our case we wanted to buy every Thursday. If the condition is true it's mean that's equal four. So we're gonna click confirm. We you can see here day of the week we want it to be equal. So we're gonna click comparison equal.

To four.

If you remember four is Thursday. Right. So here so this is the first logic. The second entry condition we want the market to be a little bit oversold so we're going to just click RSI and we want RSI to be lower than a certain level. So let's say 40. In that case going to use a standard stop loss of average true range.

I like this kind of stop loss.

Because it use the volatility of the current symbol. So if the average true range in the last day are let's say five dollars, it's going to use a five dollar stop. If it's $10, it's going to use a $10 stop. So in this situation I'm going to just use one unit of an ATR based value and we're going to exit the trade after three bar. In this case since we are trading a daily bar, it's going to exit the trade after three day. So before back testing we just want to make sure that we are using the correct symbol. Gold Multichart this is the start day. At the end day Money Management one future contract, initial capital of 25,000 and we're going to run the backtest.

So.

Here this is the equity curve. It is a little bit volatile.

Why?

Because we included the short. So when this is on it's mean that the reverse logic will be applied to go short. But since it is a long only strategy we're gonna remove all this rule and we're gonna re backtest it again. So yeah, so this is the correct backtest here we have a yearly average return of 22.62%. We have 167 trade. We have a drawdown of roughly 25%. If we go in details, the exposure here is really interesting, about 11.9%. We are not always exposed to the market. This is good because there is a lot of volatility then and you don't want to be 100% exposed to the market. And this is a strategy that I trade live with my real money. And you also can see that across many years, right from 2009 to 2025 we only have three negative years. The rest were always positive. So this is a very interesting well tested strategy. We call it the gold rush strategy. So for the third strategy, this is not really a strategy but this is a nice feature. On strategiquant X we can use AI to code and gener our logic. So on the previous algo you saw me select every entry condition by selecting the close, the moving average, the day of the week and the same thing for the exit. But with algo AI wizard here, right we can select, we can write what we want and the AI will generate the strategy logic for us. So let's prompt it. You are professional constensive trader and you want to practice the popular strategy that is turned around Tuesday on the ES future market. Generate the algo wizard logic. Then I click send. Now the AI is thinking and it is crafting the strategy.

So it's gonna take a little bit.

Of time to generate the code. But this is a pseudo code. That's it. We generate. So once it generates, you can simply just export it to Algo wizard, right? So here it's already generated the pseudocode right on es, right? This is the signal. So we're going to send it to Algo wizard, which is the platform where we code inside strategy font X. So now it is preparing the strategy for Algo Wizard. So this platform is what we call the Algo Wizard.

Gonna take a little bit of time.

But you can see here it took literally some minute to just generate all this line of code.

So we're gonna.

Save as a new strategy, gonna use the existing one.

Right.

When I go to the ES and we're gonna load it. So here you see that I clicked nothing. And the strategy has been fully generated with AI, right? We're gonna just make sure that we have the right market.

Here.

Fixed size one. I'm gonna use 25,000, the initial capital and let's see the backtest. See I generated the backtest for you, right? I didn't write a single line of code, but with AI it completely generated the logic and I just backtested, right? So this is a nice feature for the AI wizard inside strategiquant X. If you have a logic, a strategy, just type it and you can quickly backtest it. So you will let data guide your trading decision and you will know beforehand before taking the trade. And this is the benefit of being an algo trader. You can backtest different strategy, see how it performed during different market and you can then craft a portfolio with different strategy. You have gold, you have, yes, you have, let's say soybean and many, many different instruments. To have a well diversified portfolio to trade with your hard earn one well.

But everyone drop a takeaway or your biggest comment from this episode, any questions you may have even throw them in the chat there because of course we'll probably have you back again, no doubt to dive deeper into this because I think as I said at the very beginning, and as we know, algorithm trading is going to continuously grow and expand and change the nature of markets over time as it has done in the past and will continue to do so. So drop a comment of your biggest takeaway or questions right below there. Other episodes are on screen. Links all about will be in the description below. So check those out as well. And this has been char fanatics. Until next time. Take care.