"Automated Trading Secrets" - Martyn Tinsley | Trader Interview

you know in terms of self-improvement there's always something you can do and then i suppose the benefit for me as an algo trader is that when you come across one of those things and you realize there's something that you can change to improve that you can then implement that into all of your strategies and you can benefit from that moving forward welcome back to volunteering i'm sitting down with martin tingsley from training for darwin's at the moment a go traders it's gonna be interesting to hear his perspective on argos his website's called trade like a machine so martin welcome on the podcast how's it going today it's great thank you thank you for having me out here it's a pleasure to be here yeah good to connect with you i've heard a lot of of what you do i've seen kind of the work you do online in a bit but i want to kind of go back in time and tell us a bit more about how you begin trading in the first place sure okay well sort of going back a long time now um i got very interested in the concepts of predictive analysis so predicting different events um so that could be for example football matches it could be even you know my own rudimentary attempting weather forecasts things like that and anything that i could get data for i would basically crunch the numbers and try to come up with a predictive model that would predict what would happen next and i was doing this as a teenager so when i was a teenager the two sort of main computers on the market were the spectrum 48k and the commodore 64. and i had a commodore 64 and that's how i learned to program and so i was programming those models as a teenager just really as a as a hobby and i think it was that those early days that eventually led to me becoming becoming an algo trader many many years later now my my first job as a in my career was as a software developer so that taught me again many of the skills i needed to eventually become an elder trader and i guess in the early days when when i first started to investigate trading i i was very much trading in a manual manner and i think many people do that even when they do become algo traders eventually because it helps you to learn about the mechanics of trading and how trading works how charts work and so i learned a lot from those early days as a manual trader but unfortunately also lost a lot of money um because manual trading just simply was not for me um i think to be a successful manual trader you need to have the right psychology you need to have patience you need to be able to look at charts and make valid decisions without emotions taking over and for me that was a real problem and i i struggled with that immensely and so eventually when i'd learned what i needed to learn i then transferred those skills over to to algo trading interesting now what made you want to go into trading as a software developer because you can make a pretty good living like creating softwares and stuff what made training so appealing to you compared to that kind of previous job i think that it was the came back to this desire to build models that could predict future events based on past historical data and although i i loved my career you know and i got a lot a lot of value from my career it never really took me in that direction and so um trading firstly as a hobby gave me something where i could you know use those skills and and you know it was something that i enjoyed doing and so i ran you know my my career as in in the software world with um with the trading as a hobby in parallel for many many years and it was only maybe two and a half three years ago that i i made the jump and became a full-time trader what made the shift of you being from having training as a heartbeat something you do kind of decide to then deciding you want to do it like a main thing a full-time thing what made that shift i think it was because you know i was relatively successful during that time where i was trying to juggle both of those both of those careers if you like um but it reached the point where i felt i i'd reached a plateau and with the trading and i think i needed to dedicate that extra time if you like to push myself on on further because this really did become my passion that was where i chose to go rather than continue the career in the software industry so how did that turn out did you just like from the start one to be profitable in trading imagine you have some issues with psychology in the mindset around it what was some of the biggest part to becoming a good trader overall so so for me almost instantly when i when i transferred from being a manual trader to an algorithmic trader that was the key moment that things began to change because i knew the rules that i'd i'd come up with through my analysis of past data and so on i knew they were good trading rules but the problem i had was the lack of patience so i knew in some instances for example i should wait longer before i i opened a position but because of my lack of patience i just i couldn't and i was trying to get into the into the position too early and likewise you know when you're in a position and you may be going to profit i cut those profits too short because i was i was fearful if you like that i would then you know the market would turn i'd lose lose that even though i knew it wasn't the right time and so by building the rules into an algorithm and literally just letting the algorithm do the work instantly i saw a shift in my performance and i went from from loss making to you know break even pretty quickly and then over time refined that to become become profitable so that was the key moment for me i'm not saying that's right for everyone but it's right for me because because of my psychology and the algos take away the the issues that i had as a manual trader for some people who are not too experienced with argos and might think they get something i can not make money for a long time that he has to lose money because it's like it's in the rules they think that if they they'll go around too long it's going to stop working what would those people who think that agua is just like a temporary thing that can make money for a while but cannot sustain this for a long time well you know the way i look at this is that if you're a a manual trader and you come up with a set of rules that you try to adhere to in order to take money out of the market then over time if you keep those rules constant and the same that also might start to lose its edge as the markets begin to change and the dynamics to the markets change and so for me there's actually no difference so just like you would expect a a manual trader to adapt their rules and adapt the way that they approach the markets for me as an algo trader there's nothing different i simply will adapt the rules the only difference is that those rules are being run but in an automated way instead of me sitting and waiting to click a buy and a sell button so for me there's no difference between those and i think both approaches need to be adapted over time to cope with those dynamics that change in the markets and they do you know you you'll have you'll have markets that tend to be more mean reverting at some times and then they'll change to more momentum based dynamics and you have to take account of that regardless of what type of trader you are and how do you change those rules because there's a kind of sweet spot you have to find between changing it like every week all the time and then changing it like never at all so how do you find that sweet spot of like when to change things for me there are two there are two ways the first is is what i would call sort of dynamic changes and so i will actually build rules into the algorithms to automatically detect what type of market it is so you know is the market currently mean reverting is it more momentum based and it will actually automatically tweak the rules dynamically without me touching the algorithm at all there are some maybe longer term changes in the markets which are more subtle which might mean that you maybe need to you know come right back to the beginning look at the rules see if any rules need to be changed um and that type of change is something that i would maybe do every one or two years no no more frequently than that now i think a key part of also trading with tacos is building the right portfolio because you could you can try go something that are all correlated you're going to have a lot of losses i know this is something you're good at and like building a good portfolio for our goals and being able to kind of run things all together for having good performance how do you kind of deal with that and set that up correctly yeah so so it's a good point actually because again you know i mentioned earlier on one of the big shifts for me was transferring from a manual to an algo trade but another another huge shift was the way that i i handled the portfolio of positions in in the account at any time and this is still very much a work in progress for me but i'm already starting to see some of the benefits of that coming through so the reason i started to investigate this in the first place was because what i noticed about my my track record was that you know in the long term i had an equity curve that went up uh that you know had steady growth over time but at certain points i would suffer significant drawdowns and for me as as a trader myself that was that was fine i could i could handle those drawdowns um and you know the reason for that is because obviously i knew what was happening behind the scenes i knew that eventually my strategies would come out of those drawdowns but when it really started to become an issue i guess was when i started to get investors in my in my strategies so i i'm a trader on the the darwinex platform just like atu i think who you you interviewed a couple of weeks ago the issue there is that you know for investors it's different they don't know what's going through you know my head how my algorithms are working on a day-to-day basis and so when investors see drawdowns you know understandably they start to panic they don't like big drawdowns and so i realized i had to do something about this so when i went back and i started to look at when those drawdowns were occurring it was at times when the different assets and asset classes in the market tended to exhibit high levels of correlation and so what was happening is that my you know my my strategies would have open positions but because there was a lot of correlation in the market when the market turned against me that caused a big drawdown and so that really piqued my interest in terms of trying to investigate how i could put together a diversified portfolio which is really really important you know diversification is so important but also pay attention to when the markets do become correlated and take action when they do in the form of putting together a more balanced portfolio and thinking about things like um you know risk of the portfolio as a whole and the return of the portfolio as a whole so up until that point i'd done i think what probably most traders do and i'd thought about risk on a position by position basis so for example i'd have a rule that said you know i will risk a maximum of point five percent of my account on each position or point two five percent or whatever it whatever it is for you know in in individuals uh risk appetites and everything was done on that basis of you know controlling risk on a single position and doing that for each of the positions but what i realized was that actually there's a huge amount of benefit in thinking about risk completely differently and thinking about it on a portfolio basis and also the return on a portfolio basis and when you do that you're then able to position size each of your possessions to maximize the the return over the risk ratio on that portfolio basis so what what i've got here is a an equity curve that shows that individual position risk management that i talked about a moment ago and as you can see in the first half of this performance is very good but then performance starts to flatten out and there's you know some pretty serious drawdowns especially this one at the at the end here so let me just explain the the different um colors here behind the lines so obviously the blue is the the equity curve the orange line here represents the high water mark and then the gray line i've set that here at a 10 drawdown from that high watermark so you can sort of see what the kind of drawdowns are here and pretty much up until the end here you know the drawdowns are staying broadly speaking within that that 10 region but then here at the end you know the drawdown exceeds that and probably goes to something in the region of 30 which you know for for investors is is is a big issue when you or when i transferred the the risk management of this over to the portfolio basis this is what happens so bear in mind here that all of the trades are absolutely identical from these two charts so the the trade entries the same the trade exits are the same the only difference here is the position sizing and the relative position sizing between the different positions that are open in the portfolio at any point in time and so there's a fairly you know dramatic reduction here in the drawdowns because what's happening now is that when the algorithms recognize that the markets are very correlated and and the positions that are being held at any point in time are correlated themselves it dramatically reduces the position sizes at those times and then at other times when the portfolio is much more diversified and you know potentially negatively correlated between some of the positions there then it then increases the position sizing in relation to what it would have done previously and so this is the kind of effects that i've realized you can get from that so you know a massive improvement so i think i've got here just a this is sort of a side-by-side comparison so the the orange line here is what you have with individual position sizing and then the blue line with with portfolio position sizing and interestingly you can see that you know early on when there weren't particularly any major drawdowns and the markets weren't correlated the performance is actually very similar it's comparable but where this tends to really come into its own is in those times when when markets are correlated and that's when you see the the difference in the in the equity curve and this is exactly what i set out to to achieve now you know this is as i said this is a still a work in progress for me i've still got things i need to investigate here so i've started to implement this in some of my strategies and still got i guess fairly long way to go but i think with trading that's that's always the case there's always things you can improve there's always new things that you can learn and so you know i realize that over time i'll hone this and make it better and better um but that's the that's the sort of effect that i'm looking at now so hopefully you know i'll start to see much much smaller drawdowns in the equity curve going forward that's a really good example of problem solving as traders we take trades we get some feedback but then we have like issues coming up then it's our job to deal with those issues find ways to do things better that's a really good example of like how you've been able to do that look at what works well what doesn't go well then try to fix it in any way you can that's a really good example what kind of indicator or factor do you look at when you say the market are correlated is it just based on how price moves together or yeah some other kind of indicators you look at for this yeah so it's it's purely correlation calculations so interestingly what i did from you know my analysis of this i went through i went through many stages i guess of of attempting to produce these kind of improvements in the in the equity curve and initially you know i went down several dead ends ended up with you know equity curves that didn't really achieve what i i was after and and ultimately in the end i i actually turned my attention to some of the techniques that are used in much longer term investment houses um in terms of things concepts like the efficient frontier so so so these are concepts that are used from modern portfolio theory that are designed for much longer term holding of of of assets and putting together and constructing the portfolio that maximizes that return over risk and so when i sort of read and investigated these techniques my my first question to myself was well can these techniques be transferred to a much shorter term trading type um environment and so i started to you know read about the financial risk management techniques get hold of any research papers that i could that talked about modern portfolio theory and looked at adapting those to to to a much shorter term time frame so um if i just show you again some of the sort of the concepts around that so so so this is what's called the minimal minimal variance frontier and along the the x-axis here you've got risk as measured by portfolio standard deviation now you don't have to use standard deviation but it's one of the probably one of the most common ways of doing this and then on the y-axis we've got the sort of expected return and when you crunch the numbers and you look at the correlation and you look at the return and risk this is what it actually looks like if you plot that okay so the point from this end of the chart here upwards is what's called the efficient frontier and this is where you should be aiming to get your position sizing relative to each other to get you onto this efficient frontier now it's quite a complex subject and it takes a bit of getting your head around um but once you understand it you start to understand the real value of position sizing in order to get onto that efficient frontier now this part of the curve down here i don't really know if this has got a name but i call it the inefficient frontier and when you're down here with your position sizing what happens is that if you think about it the reason it's inefficient is because you've got two points over each other here that have the same level of risk okay but the difference is that one has a much lower expected return than the one at the top that's why this is called the efficient frontier and why i call the lower part the inefficient frontier now there's there's way too much i guess to explain if people aren't familiar with the efficient frontier already but you know i i interestingly i have put together a series of of videos um that explained this i think this in the region of about 30 episodes that explain the concepts of portfolio management so i'd be happy to share a link with you etienne if you you know if you think it would be worth putting that into the description of this this podcast but but that's the whole basis that i now take in order to produce if you like an optimal portfolio with that the right position sizes to achieve the maximum return over risk and so obviously it's a very very different concept than just looking at risk on a single position basis where you say 0.5 risk per position so it's a whole mind shift that's required to do this but i think the the results i showed you a moment ago sort of speak for themselves in terms of the value of this if you can get your head around it yeah we'll definitely add a link below in the description people to check it out for sure that's interesting thank you now this kind of curve is going to be the same for every strategy or like similar like a similar curve or does it depend on the strategy the kind of trade you take and everything of the sort the the only thing i guess that that inform you always get the shape of curve but obviously different strategies would have different levels of expected return they'd have different levels of standard deviation and risk associated to them and so the you know the exact values on the curve would be different but the shape of the curve is always as you see the one on the chart here interesting then how can the same risk per trade give you different returns as you adjust the the weightings of the positions in a portfolio obviously the the overall portfolio risk changes and the expected return changes now in terms of the dynamics of how this curve evolves yeah i must admit i don't really know but when you when you run the numbers through this is this is the shape of the curve you get i i think the reason for this is that if you are too heavily invested in one particular position and all of your your risk if you like is being driven by that one position and the other position has a very small size that doesn't really drive the um the risk as much and so what happens is that if if the market turns against that position you're not getting the benefits of diversification because the position on the other asset is so small and vice versa if you had all of the weighting in the second position then equally that would mean you know if the market turned against that you don't get that level of diversification and so by getting the right blend means you optimize the diversification to minimize that risk for a set level of return and i think that's what drives the shape of the curve that you see here interestingly this this is an example just for two positions if you have three positions or more then instead of getting a simple curve you get a surface okay so you get this efficient frontier surface that if you look at this it appears to sort of bend round towards you and and so you know you're looking for the optimal place on that surface with with more than two positions but the concepts are exactly the same it's about maximizing that return over risk for the for the surface in this case they're interesting stuff for sure that's something that i know most traders don't do even agua traders don't do it it only makes you suggest if you trade algos really because otherwise it's too hard and it will take a long time to gather the data but i think it's something that can be worth doing to kind of understand your data better and also optimize your portfolio better it's not just about like training for yourself especially with investors something you have to think about where how you're gonna maximize the value for the investor as well yeah so so this this as i said this these sort of techniques tend to be used in institutional investing and you know where portfolio might be held for six months and so there i guess these they can be done in a more manual way but but for me when my portfolio changes on a you know on an hourly basis as different trades are closing and opening then i have to re-evaluate this as every trade opens and closes and so the only way of doing that for me is is in an automated way sure now tell us a bit more about kind of what you traded diago is it's more a reversal system is it what are some things you look at with this kind of toggle in terms of the i guess the types of strategies that i develop i'll develop absolutely anything that i think has an edge that can be programmed and you know i'm i'm a pretty firm believer actually in that anything if you if you're a good enough developer you can code pretty much anything i mean and if you can't code something you could probably use ai in order to do it as an alternative way which again is something that i'll is on my to-do list and i'm going to be looking at in in the years to come um so so anything really i think that that you can get an edge from the markets i will i will attempt to you know develop systems for but but pretty much you know i'm looking at things like very simple techniques you know mean reversion type techniques so is the market currently oversold is the market currently overbought and you know taking counter trades in relation to to that also you know i i have strategies which are trend following strategies trend continuation strategies so you know looking for existing established trends that are currently in a pullback from that i'm looking to get into a continuation of that breakout strategies so really any any of i would say i don't specialize in any type of strategy and i think there are benefits in that because in terms of diversification you know you can diversify across assets obviously we've just been talking about that but also you can diversif diversify across time frames but you can also diversify across strategy types and so very often when mean reversion strategies are performing well in the market trend following strategies probably won't be and vice versa other times you know trend following is what's bringing in the the profits whereas with mean reversion strategies they might be losing at that time and so by by trading different types of strategy in the same account can actually help again to smooth out um the equity curve and to you know to diversify in a different way in order to to achieve a better return over risk what's the sweet spot though because you could have a thousand strategies if you wish but that might be too much to manage or you could have like 50 or something how many strategies do you think someone should have to get a decent kind of amount of return decent like diversification but not be too overwhelmed but all these strategies running in the same time i think you know when you're starting out you start out with one you know you you get one pro one strategy that you can make a profit with over the long term and you focused on that and you get that strategy working as well as you possibly can and then yeah you know you then maybe think okay so if that's a trend following strategy my next strategy that i'm going to look at maybe should be something like a mean reversion strategy where i'm looking for overbought oversight conditions and and then combined when you've got that strategy right combine them together into a into a into an account to get some of the benefits of that um so what's right and what's wrong well if you're a manual trader i would say the number's going to be fairly small you know because if you're if you're you're doing your analysis on charts there's only so much you're going to be able to do as an algo trader you can probably trade more different strategies because you know once once you have that strategy up and running it pretty much looks after itself however even when you're an algo trader there's still there are still limits here you know you still have to monitor to make sure everything's working as it should be you've got to monitor for any errors that are raised in the in the platform you know you just in terms of simple things like you're managing your infrastructure you're going to need more infrastructure the more strategies that you you have to be able to you know some of these strategies take a lot of processing power especially when you're doing things like efficient frontier calculations and so there are limits i think even when you're an algo trader so so although i said to you you know i broadly speaking i will will trade maybe four or five different styles of trading strategy i still probably only have about six strategies now that i'm trading live um and i would increase that but but yeah you know there are limits i think but for me five or six strategies i would say in terms of what i'm trading at the moment what's the kind of work you do on a daily basis because some people might think we just let it run then you're good you can go out enjoy the beach enjoy the go out if you want so what's the kind of amount of work you put in and what kind of tasks you focus on and what kind of tests are just like fully automated with diago so i think when you're a trader there's always there's always something else that you can learn you can you know in terms of self-improvement there's always something you can do and then i suppose the benefit for me as an algo trader is that when you come across one of those things and you realize there's something that you can change to improve that you can then implement that into all of your strategies and you can benefit from that moving forward um so so for me no it's you know there's there's no beaches involved and it's it's it's pretty much you know a full full working day i work some weekends as well and so as an example for the last 18 months my my primary project has been this one that we talked about initially which is that portfolio management side of things and you know for the first i guess first nine months of that i just hit dead end after dead end and didn't really progress and it was only in the most recent 12 nine months that i've started to see some of the benefits and i i've i think i've really cracked that portfolio management side of things um but there's still a way to go you know there's still there's still ways that i can improve that you know if you looked at my my to-do list you'd have a heart attack i've probably got about 200 things on there at the moment of things that i want to investigate in the future um i alluded to one earlier on which was artificial intelligence and although i have coded in my career in the past i've cr i i've coded neural networks and and um and similar kinds of ai technology i've never actually got to the point yet of incorporating that into my trading um so that's probably in about a year's time i might start to look at look at that but now the the to-do list is is fairly significant and um there's lots of room for improvement i think that's a good thing you've got to talk about the fact that you are funded by darwinix so you created your own fund and darwin actually people can actually fund your your trading how did that go how did that kind of progress yeah so i think i was i was actually one of the first traders to go on to the to the darwinix platform i think i was trading number seven or eight or something like that and um when i came across darwinex it it was perfect timing really because i'd i got to the point where i i'd become profitable i'd started to build a a track record and i started to think about well you know how do i take this to the to the next level now so i've got my own capital which was relatively limited at the time and i was thinking you know if i can make if i can make 15 20 year on year with the capital i've got that really isn't going to enable me to become a full-time trader in in the future it isn't going to enable me to i guess exploit the the skills that i'd built up in in an effective way um and and so when when i found out about darwinex and that concept of you know traders can can effectively just do what they do normally they just trade in the way that they always trade and if they can demonstrate a good track record those traders will get investment and investments in investors rather on the on the darwin x platform can see all of the traders track records on there they can choose themselves how to build their own uh diversified portfolio um of different trader strategies and for me this was the this was the one of the key moments in terms of again taking trading to the next level because when you get investors obviously you know you have to make profit for those investors but you can then also charge success fees and the success fees that that you get can far outweigh the individual profits you can make on your own capital and so it really gives traders that sort of opportunity to for want of a better term to manage their own hedge fund in a really low cost effective way so if if i were to start my own hedge fund you know that would carry significant costs i'd have to think about regulation where we're going to be regulated and the the initial costs would be significant and inhibitive i think for for a lot of traders and so with darwinex that's i guess all taken care of so you know they have the the regulation covered so that traders on the platform are are covered without having to deal with any of that themselves so it was a great opportunity for me when darwinix came along i'm sure you know many of your uh your viewers might might consider it as well because it's it's that really you know i was going to say low-cost but in fact i think it's no cost you know there's you can sign up for a free account and just start to start trading and if your track record is good enough you you can get investment and if you know for traders that have already built up a track record i believe they can actually migrate that into the platform so you're not starting from scratch how long a tracker could you need for darwinix is it like six months a year or more than that it's a long time since i since i was in that position but but it's all based on so darwin x has these things called investable attributes which are they are metrics that measure if you like the effectiveness of your strategy and one of those metrics is called experience and that just basically measures you know how much track record do you have how much trading activity have you had and you have to get your experience to a certain level before you can then open up your strategy for investment but you know so that that very much depends i think on how frequently you trade so if you only trade very infrequently and you might only have one trade a week it would take you longer to build up that track record in order to make it available to investors if you're trading like like i do maybe 20 40 positions every week you'll build that track up track record very quickly and you know within possibly a matter of weeks you could you could be in a position where you could be um opening up that strategy for investment if that's what you choose to do you don't have to you know you can just trade with darwinex as a regular broker you don't have to open up your strategy for investment but if you if you want to that's your choice you you can do that so so it all depends on on how much track record you have obviously if you're importing a track record then it might be that you can you know make your your system available for investors immediately if you've if you built up enough track record with an alternative broker so it all depends but yeah relatively quickly i think but of course you know getting investors i guess is a different matter and but i've seen i've seen many strategies on the platform that have got a good solid track record over let's say six months and even they can start to get some level of of investment and then as time goes on and the track record increases you know that that investment will grow over time how do you feel the pressure increases once you start to manage more and more capital do you feel like you're more pressured you have to kind of deal with your mindset a little bit more than before or how did that kind of evolve yeah it's it's definitely different um so so you know i mentioned before that some of the drawdowns that i've experienced in the past i was probably quite comfortable with those you know they they they weren't necessarily a problem for me i knew that the reason for the drawdowns i knew it was because of the the correlation and things like that that were causing them and i knew that would end and the strategy would become profitable again but when you get investors you've always got to put yourself in their shoes and sort of say well what's an investor thinking now and from an investor they don't have that assurance of why is this drawdown occurring and so yes it does need a mind shift and and you know you're responsible for investors capital and the growth behind that so a very definite shift as an elgo trader i think it's less so because as long as you have the commitment to just let your algorithms do what they do you know you're not going to start making any crazy decisions if you're a manual trader and you notice a trade going against you i guess the temptation is there is to change your behavior because you know that investors are being affected whereas you might just let that carry on and hopefully recover if you're only trading your own capital there is that mind shift and so you have to be able to cope with that and my way of coping it was doing exactly what i said to you before you know over the last 18 months i've been looking at how i can reduce those drawdowns for investors by by looking at those you know modern portfolio theory techniques and adapting those to trading so that was my response to it but different traders will have different responses but yes there is it is different and the psychology is different for sure interesting definitely a learning curve you have to go through at first and you got to be able to get used to it but if you can get used to not touching your goals and changing your wrist then it's for sure better the journey is important so so you know the scenario i mentioned to you a moment ago you know you might start getting a small amount of investment after six months and when it's only a small amount of investment you know that's something that you can probably handle if all of a sudden you have 10 million dollars of investment that would be very scary so if you went from nothing to 10 million i know atu who you spoke to um a couple of weeks ago he's currently managing 10 million but he didn't go straight to that he went you know he he gradually built up that investment over time and so i think because of that that enables you to handle it more effectively so you know if you start off you might start off with a thousand dollars of investment and that you can handle it's fine and then it will gradually grow new acclimatize to that which helps um rather than just going in big bang so i think that's that that's a good side of the nx platform that you you will see growth gradually over time definitely good point there for sure so what can people find if they're connect with your reach out after the interview here if they want to see your work or see what you're doing so my website is tradelikemachine.com i also produce didn't do from the beginning but now i produce a lot of videos for for darwinix as well and so people can can hook up with me through the darwinix channel um i'll look at all the comments on the videos there and so there's a number of ways people can get in touch if they need to yeah definitely an interesting journey so i hope we can connect the futures you know you're doing with the argos and kind of on that journey too with the argos as well so definitely a fun game to learn it takes some but i'm getting their shoulders that's good and yeah that's let's see how you perform over time and you can connect back and see how you're doing in the future yeah it's been a real pleasure etienne thank you thank you very much for having me and i hope some of that was was useful to your to your viewers so until next time there's something you comment below in the comment section tell us if you like this interview a bit more than that than before but i think it's that's useful sometimes it's worth it to go more in depth on these topics is that's getting much more perspective how do people do things good to grow of course could become better so i appreciate that

Channel: Etienne Crete - Desire To TRADE

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Id: 3c8ji5uDPjg

Length: 44min 39sec (2679 seconds)

Published: Sun Sep 18 2022