Algo Trading Week Day 1: How to become a successful quant - Q&A Session with Dr Ernest Chan

i hope all of you can see me as well just type in yes in the questions panel and i'll know all right thank you michael thank you kenya thank you prashant thank you deepak thank you patrick all right all right so i see that all of you can uh see me and hear me so uh as you can see here uh i'm sharing the screen so first of all uh welcome everyone to uh algo trading week now this algo trading week uh we kick started yesterday uh with a session a panel discussion on before you get into quant and algorithmic trading it was a very interesting one and as you can see here the recordings are live so you can go check it out for today's session we have here with us our very own dr ernest chan so it will be a q and a session where we'll be asking a lot of questions to dr chan about how to become successful quant now before we start off with the q and a session today i just wanted to highlight another thing so that is our algo trading competition now i'll go reading competition as you can see here if you are interested you can go to our algo trading week page and then you can click on get started the moment you click on get started you will see this page all right now here there is all the details about how you can join it what is the format how to prepare for this competition and how it is going to end but the very important thing that i want to highlight about this session this competition sorry is that this is going to be a very good learning opportunity for those who are just starting out off with algorithmic trading and are really keen uh to learn the basics of algorithmic trading the it's not a very trading competition sort of competition but it's most about learning how you can learn and basics of algorithmic trading if you are already an expert or already a knowledgeable person in the algo trading or quantitative trading space then this can be a good opportunity for you to brush up your knowledge so as simple as that and the important thing is the winners of the top three winners of this competition are going to get uh an amazon voucher 15 scholarship on uh epads next anniversary batch then a certificate of achievement and top 10 percentile of the participants of this competition are going to get 10 scholarship in our epad anniversary batch that starts next month and also a certification of achievement now you can just check out all these details and there is a lot of learning material as you can see here there will be three tests and you have to uh you know complete those three tests on given date and then uh yeah on the last day uh day seven competition winners will be announced now uh moving on to i'm going to stop sharing my screen all right so yeah moving on to today's session so uh first of all welcome again and uh i would like to uh make a quick introduction to dr understand i'm not sure if it is required or not but for the uh formalities i'll do that so dr chan is founder and ceo of predict now dot ai and managing member of qts capital management he has worked for various investment banks and hedge funds since 1997. dr chen received his phd in physics from cornell university and was a member of ibm's human language technologies group before joining the financial industry he was a principal of exp capital management which is a chicago-based investment firm and dr chan is also the author of three some of the amazing books in quantitative trading if i name them the first one is quantitative trading how to build your own algorithmic trading business the second one is algorithmic trading winning strategies and the rationale and the third and the latest one is uh called machine learning deploying computer algorithm to conquer the markets uh yeah so that's a quick very brief introduction there is a lot i can talk about dr chan but uh i think i'll make a quick one so again so join our hands and i think uh uh welcome to algo trading week dr chan are you there with us hi kishidi thank you for kind introduction very happy to be here wonderful wonderful so i'm just going to exp quickly explain everyone this entire audience the format so uh uh i would like to highlight that i have a set of uh some of the most common questions here with me and if any of you in the audience have any questions related to the algo trading or quantitative trading for dr chan feel free to ask them in the questions panel so we'll randomly pick uh almost all of the questions we'll try to all address all of them uh the most uh popular ones and then uh yeah i think that's about it so i'll just uh straight away jump off on the first question uh and that is uh dr chan why is quantitative trading considered the next big thing well i wouldn't even call it the next big thing because it has already been a big thing uh i uh you know has been really uh a uh a dominant form of trading uh for the last 10 years i would say and from some estimates i've heard numbers that as high as 90 of the trading volume on u.s exchanges are due to algorithmic trading granted some much of it is due to high frequency market making but you know hyphen c market making is a form of algorithmic trading so we should not really exclude them so um now that does not mean in my mind that uh you know the you know every discretionary trader should become a quantitative trader or that you know concentrating quantitative trading is better than discretionary trading but uh all one can say is that um um you know to [Music] to ignore uh the um uh the opportunity to automate one strategy to systematize one strategy even if one word is discretionary trading would not be wise okay so discretionary trading okay so uh it's basically uh your preference but yeah the quantitative trading has its own edges that's what i think what you mean okay all right so all right uh the next question uh is uh how competitive is quantitative trading for uh individual uh traders and uh institutions yes so um as i've expressed uh in my first book quantitative trading and i still believe uh in it that um the main opportunity for a um individual independent trader uh that can you know really succeed and compete against the big institution is in niche strategies that is to say those strategies that has very limited capacity right if there are many strategies where it is possible to deploy a hundred thousand dollars account on it or even a million dollars account on it but anything more than that there would not be enough uh liquidity to uh really um uh continue to to support the the revenue growth and um so those are the opportunities you know for example in uh otc stocks trading that is very popular among a lot of day traders and such such thing as um maybe in certain seasonal trade that you might conduct once a quarter or once a year even and those are really not attractive to hedge fund you know who who can survive on a once a quarter kind of trade right unless you make uh 50 in one trade but um but those can be quite um suitable for independent traders because uh you know you might make a trade once a quarter and then you have found another strategy that make another trade a quarter uh and that would be a good um you know sort of income supplement unless you are intending to become a trader full-time that that's not a bad opportunity to pursue so i would say that any strategy that are unattractive to a large institution small capacity strategies in frequent trading strategies would be would still have a lot of opportunity in them okay einstein so i have heard a lot of questions like uh so here the com the question was very simple in terms of how competitive it is but uh a lot of people whom i meet uh they usually say that uh an individual trader or a retail trader how that individual trader can compete against institutions those who have all those uh technology power or stuff like that so how how does that happen i mean can they compete against the big institutions or are they even you know in a competition well you know that's exactly what i described is that you have to know look for this niche where the big institutions are not competing you do not want to compete with them you do you want to avoid competition at all costs i i like the uh a book that is um written by peter thiel i think he's the founder of co-founder of paypal right and and uh currently uh i think uh also a co-founder parenter so peter in his book called i think it's called one two zero um put it very well he said highly lucrative enterprise didn't come about because of competition they come about because of the avoided competition you do not want to compete that that is the key to success try not to have any competitors okay so get into a competition when there is no competition okay okay all right so all right moving to the next question and it is also one of the most common question is do you need a phd to be a quant ah definitely not okay so uh so on the similar line i think uh romaine has asked this question he is asking hello dr chan uh how important do you think is a phd for quantitative and algo trading and if doing one how do you think a phd student should approach the research thanks in advance um i think that um you know there are of course tons of research out there on different algorithmic trading strategies um but uh the key uh of uh any uh you know of turn them from purely theoretical and academic interest to practical interest uh is that um you should backtest them yourself before trading them and also to develop a uh intuitive understanding and understand all the various nuances of the market to to tell what paper is just based on very shaky foundation so you know because there's so much so many papers out there uh you cannot possibly read them more and um so one needs to develop some kind of a filter to select the ones that are meaningful and uh that filter is to be gained from reading books and trading yourself you will you'll soon develop a sense of what papers are just overfitting wallpapers rely on 40 data and which paper has a backtest methodology backtest methodology that is flawed and there are many you know well-known books that and blogs uh that uh discuss these kind of pitfalls and you would be you know well advised to read them first before pursuing all the full voluminous research that are being put out every day i think that's a really good piece of advice for phd students all right so the next question that i have for you is a very basic and very generic question that is how can i become a successful quant um i think the key is to have skin in the game so a lot of um you know i i have mentored and hired a lot of highly educated people you know phds and and uh you know pg candidates and so forth and i i have uh found that they you know they had they could become great consultant in in providing support to the traders to support to provide analysis to provide top uh you know most advanced programs and algorithm but in order to be the person who actually originated a successful strategy and not to improve or provide analytics or support or analysis for the astrology the key is that you have to put your own money on the line without skin in the game you never learn to trade and you your mind will always be focused on secondary matters such as mathematics or uh data science or machine learning or whatever latest and greatest theories but once your own money is on the line it focuses the mind and it focuses on the most basic aspects of trading and and that is why you know as i said earlier there is no need to get a phd because when your mind is focused on the simple things a good factory degree is more than enough to succeed in fact i have known numerous highly successful traders people who manage billions who barely graduate from college and they are quantitative traders does not mean that there's not intelligent but it does mean that they are less focused on the minutiae of mathematics of data science of machine learning than on the market itself there need to be singular focus on the markets not on all these theoretical knowledge that you might gain from a phd so doing practical and uh trying things hands-on and developing that experience is something that is uh required one cannot become successful quant overnight that is what dr chan is sharing his experience here with us okay very interesting and uh so talking about becoming a successful quant now as an individual trader okay uh you know there are a lot of things i think i'm also getting a lot of questions from individual traders here uh but given that we are talking about becoming a successful quant let's first uh i would like to pick a question on the career side so um so the question that i see here is uh [Music] how to prepare so this is by roger so roger asks how to prepare and distinguish yourself to join the best quant funds so you have been working with a lot of corn funds and hedge funds in the past so given your experience like how can you prepare and distinguish um i think the one of the more effective ways is to um write a white paper uh on a topic uh with your special insights you know you can write a white paper on a trading strategy that you develop or a particular analysis of a particular market and phenomenon that you become intrigued on the key is original research you don't want to report on other people's research the key is that you maybe you heard about a certain phenomenon in the market uh and you set about gathering the data to validate whether that uh opportunity is real or not maybe provide a back test and and so forth so um you know you you original research would be the best way uh to get in the door and uh i've seen it many of my mentees and advisees gaining excellent positions in various headphones and maybe also excellent programs in the master financial engineering program at most prestigious universities through this uh path you know there's nothing that can beat original research now even you know some people might say hey you know why don't i just trade an account and uh you know i earn 100 return on that account shouldn't i be able to use that uh as a um proof well that helps certainly you know the fact that you should show that you have taken your foot skin in your own game and traded like that certainly have like i said nobody can become good trader without skin in the game but um i would say that uh you know people would also come you know come with you at the with a dose of skepticism because anybody can be make a lucky bet and say oh you know last year i just bought the bitcoin when it hit the dip and then i saw it when it doubled recovery right and and that's a you know maybe a stroke of good luck so you know a short track record so it does tell people something it's not sufficient in itself to to prove that you are a consistent and knowledgeable trader okay all right so so there is a follow-up question so ah so then how to incorporate order flow data if even if if even accessible if we are retail traders the same question by roger um order flow is indeed a good indicator but it is not sufficient as a standalone indicator so there are many indicators out there that people these describe and and promote and and and love and uh but all of them become well known and they are not by itself strong enough to to build the trading strategy upon so what you would need oftentimes is to incorporate it as a one of many features in the machine learning program if that's what you want to use it you know any one of this indicator by itself are now not strong enough unfortunately because of the large number of people that uses them all right so i hope that answers your question roger uh a very interesting question and this one uh is by ayal uh uh have you noticed an ageism bias within the quant industry similar to what sometimes can be seen in the larger tech industry well that's only a problem if you are trying to get a job right so i um have you know all my books main theme is that you don't need to get a job if you are good at trading so uh there's no such thing as ageism if you are a sole proprietor great i hope that answers your question yes uh there is a question by bruno bruno asks hello dr chan uh our algos still profitable these days or does one need to add machine learning to the mix i do think that uh if you are relying on simple models uh if there is a offer decay because simply because of sheer volume of people trading these simple strategies so i would suggest that indeed machine learning is a great addition as at least as a risk management layer so that that's what i've been promoting actually in the last two years is that machine learning is it is not uh the best tool to generate offer because of the easy overfitting i mean some people are successful you know kudos to them but i'm i'm myself not one of those people who who can generate offer directly using machine learning but i am quite impressed by the utility of machine learning as a risk management tool very successfully using it and hope that uh you know and you know that's the reason we launched predict now.ai is to also enable other people to use it as a risk management tool we highly discommand using it for direct offer generation because think about it if we were to launch the system that can tell you whether to buy or sell tesla tomorrow and we sell it to a hundred thousand dollars our prediction will immediately um be inaccurate because every one of the hundred thousand people will compete against each other and the signal will disappear overnight right so you you cannot trust a machine learning system that provide trading signals to the public but you can definitely use that for managing your own risk so i think machine learning for risk management is the way to go all right so yes there is a combination that you can use all right so moving to the next question um one asks does it make sense to use quant trading for low latency trading well in some sense quantitative trading is a necessity for low latency trading you you know no human can operate in a millisecond domain so the only way that can work is is if it's fully automated and not only with you know typically c software is being used in that domain all right the next question is uh again from the preparing for a career in quantitative trading point of view so chetan asks hi sir i just need to know what skills one required one requires to start quant based hedge fund well obviously you need to have a successful track record and i don't again don't mean that just profitable for one year or half a year you hope hopefully you have a longer track record than that but beyond that you need to have management skills because you know you won't hopefully be the only person managing the fund you need to hire technologists you need to hire researchers you need to hire business development staff and you need to manage them so just having trading skills is not sufficient to make a conference successful you also need to know something about business management because there are many many aspects of running a hedge fund that is beyond um just uh you know right yeah okay so it's basically a business so you need a lot of management skills as well yes maybe an mba and that is the sense also if you look at the read a book um called you know on jim simon the founder of renaissance technology you get the same sense you know jim simon of course is a famous mathematician he's still writing papers and he's extremely smart but you will get the sense that after the you know he certainly personally involved himself in trading strategy environment when he first started the fund but very soon his focus is on hiring other people you read between the lines in the book that he doesn't do any research on his own on trading certainly not involved at the detail level in encoding or anything but his one of his great skills is in identifying talent and hiring them and put and managing them publicly so this is a solely neglected skill in with a lot of contrary they think that they can do everything but they can't the most successful headphones are built by people who know how to manage other people yes so leadership qualities are must in order to set up your own hedge fund as well this is something new that happened with you okay all right so uh the next question now this is a very interesting one because uh it's sort of uh amalgamation of multiple markets so uh have you tried to understand the indian market quantitatively if so what perspective do you have related to indian market as compared to u.s markets or european markets um i confess that i have no knowledge of the indian market and uh you know partly is because um uh you know we we um you know do not have a uh trading staff in india partly because of regulations i think that you know even when we were trying to see about trading in index futures um you know i don't think we are allowed you know until perhaps maybe things have changed but we were not allowed to trade in the indian market at all so i confess i know nothing about the indian market very well location so okay next question it's about how much in terms of time do you think we should spend on paper trading a strategy before going live with it yes that's a very good question and it depends on the efficiency of trading so if you have a high frequency trading strategy that in the now you know every second uh practically you need two weeks to test and you should go live if it works because that you you know for that high frequency strategy every day should be profitable but if you have a strategy that holds for days for precision then chances are you need three months of uh paper trading to go live because you would need to have enough statistics uh to justify that uh to to to earn a confidence you know you you need to verify that there's statistical significance in the paper trading just as you need to earn statistical significance in the back test paper trading also requires statistical significance so uh it's a statistical significance based on the number of traits so you have to make sure that you have enough number of traits i hope that answers your question siddhesh uh okay um this one is uh also very interesting so uh a recent study by quan pedia shown that most alphas deemed profitable are mostly non-price based should the time series approach still be the heart of one's alpha portfolio um actually i don't quite grasp the chase of the question do you mind repeating it yeah so a recent study by quan pedia shown that most alphas deemed profitable are mostly non-price based so the alphas that they create are not on price based data and should be uh so so should the time series approach uh still be the heart of one's alpha portfolio well i don't um know um who published this study and i have not uh you know heard of it and have not read it you know maybe they have their good reasons to suggest i would i'm actually quite skeptical of of that conclusion uh because how do they determine that this is not uh price based they don't have access to everybody's propriety strategies maybe they tested their own price based strategy and found it to be you know subpar that hardly means that there's no signal in prices so i'm extremely skeptical of this research so i but i'd be happy to take a look at it yeah it would need some study obviously more data then that can you know that then we can come up to a conclusion that is true uh a follow-up question on the machine learning uh for risk management part so so when you say machine learning works well in risk management do you mean something like a risk filter or more on a position sizing both yes so to me they you know these simple uh prediction that a machine learning system can can make is the probability of loss for your strategy over some future period that's exactly what pre-technology ai performed of course so it's knowing coincidence that i think that's the most important function because from this probability of loss you can decide whether to trade at all or decide how much capital to allocate to that trade for the next period so there's nothing more simpler not nothing's simpler than getting a probability of loss for risk management purpose okay so i hope that answers the question the next one is i'm pursuing cfa studying fundamentals and technicals and already have programming experience in the vba excel what should be my next step to go in algo trading shall i go for python or c plus plus how artificial intelligence fits in trading like there are a lot of questions in this one but yeah so we can uh pick one by one so uh vb excel what should be my next step to go into algorithmic trading shall i go for python or c plus plus yeah i think python would be um preferred because some c plus plus is really necessary only for high frequency execution so based on your background as a you know fundamental you have strong fundamental knowledge it would seem that your talent is best deployed in developing astrology making use of these fundamental indicators not in developing a high performance low latency execution system which is best left to a professional software developer yes so python has more applications for an individual 100 degree on that one so okay uh the next one uh is also i'm also curious that why you ignore ow because um financial um you know a lot of the uh packages related to economic econometrics and finance in python are full of bugs um they are not to be trusted only our um are trusted by professionals when they are dealing with econometrics model that is some amazing good piece of advice thank you so much dr chan uh all right so the follow-up question was how artificial intelligence fits in trading from the same person yes so i as i said best apply to risk management and not after generation yes all right okay so now a shortened question uh all right so how to find some professional quant to train our beginner team no good reference is outside to learn from it i'm wondering if you can clarify it's a question of how to hire a good quan trader is that the question it's about how to train or how to how do we find yeah how to find some professional quant to train our beginner team oh okay well clearly the the person you hire um need to have demonstrated um expertise now you know when you say train it does not mean that the quantum trading team has to develop a new trading strategy they could be a team that is focused on risk management they could be a team that is focused on derivatives pricing that could be a team that is focused on you know data science so it is very highly dependent on what functions your team is doing in terms of quantitative trading so you would need to hire expert that has a strong track record in each function but if your goal is to have a quantum that develops strategies uh you know that are profitable um to be blunt my experience is that you can only hire people who already have a track record and that is very interesting um you know unless someone were able to build a successful strategy on their own it is very hard to teach them that skill i have tried and i failed so you would only want if your goal now again you know you're not if you're talking about hiring someone to do risk management or to do machine learning or data science that's a different question if you want to hire someone who actually can generate profit for you you better hire someone who has already generated profit for themselves because otherwise it's an uphill battle you can just you know i i strongly advise that if you're a small firm you don't waste your time and money on that only high people who have track record trading on their own okay yeah uh now that so there is a follow-up question on the same one so uh it is like uh which market should be selected for such beginner team let's say if they are into quantitative trading uh is there should there be any uh preference to start off with for beginners not really you know whatever market that you feel you can understand you know for example um you know i have a more typically understand the crypto market than the other person but you know so i don't trade in crypto but the other people are much more expert in crypto so they should trade quicker and i am much more familiar with um trading index futures than i'm uh in trading uh agricultural futures so i let the other traders take care of the agricultural market while focused on equity index future and vice versa you know i asked a very uh very very profitable skillful recovery trader about some questions about the equity index market and volatility market and i drew a blank so everybody have a very narrow glitch i'm afraid um and uh you know you don't uh you know you you should just pick whatever you know best and it's not that there's a market that is universally good for someone because if it's universally good everybody will be trading and overnight it will be completely efficient and there won't be any other charge opportunities all right moving to the next question this is a very long one so dr chan do hft traders have an edge in predicting the market direction then medium and low frequency trailers uh is it easier to predict using hft but not everyone can have hft setup as it will cost millions of dollars so given that high frequency traders can have expensive setup are they at an advantage to predict market detections yes generally speaking it is uh easier to predict market direction at a high at a very short time frame than at the long time frame you know i really challenge anyone to predict um the direction of the um uh i don't know oil market uh in one year you know is it gonna go up or down wow that's probably have an error bar that is you know maybe 20 plus or minus right so uh you know it's it's really hard to make long-term prediction yeah and that sense of reason you know you know if you even like predicting weather if i see that it's already raining i can with 99 accuracy predict that it will still be raining one minute from now but if you ask me as a you know even if i were a expert meteorologist to predict if it's going to rain 15 days from now i don't forget it right so same apply to market if you could you know it's easier to predict what whether the mid price will be next second than where it would be next day frankly so so yes definitely high frequency trading can be much more accurate but as you pointed out to take advantage of that prediction is a complete different matter okay very interesting so okay now this is uh like a very personal question it is pointed directly towards you so do you trade entirely based on elbow or some traits are discretionary too sorry sorry yes you first answered this question yeah so um you know i managed hedge fund and uh as i pointed out earlier um just you know not to compare myself with jim simon far far from it but i you know always try to learn from the giants and from what i learned is that you basically don't want to trade yourself after you have started the fund you want to hire people you want to apply your skill as a matter trader right it's funny because it sounds like a commercial product meta trader but yes you want to be a matter trader and the job of a matter trader is to hire traders not to trade and i have been extremely good at it lately for for some reason i have been able to hide traders i have hired you know seven traders and all of them are profitable since inception so we have developed a keen proceed a really unique way of hiring and and on boarding some advices that have in the last two years 100 hit rate it is quite remarkable so you know you you have to think about hiring traders is like picking stock right because when you pick a stock it's like picking the management uh picking the business which is direction is a lot of it is determined by the management team of course there are external factors you know if you pick a dying industry you can put a genius as a ceo and you can't really make it work so there are external factors but you know a lot of it is dependent on the management so but picking a trader is the same it's like making bets on stock right so you know it would be amazing if you buy tens you know seven stocks and all of them go up uh and that's what we did and uh you know so uh you know our meta trading strategy has been very good and uh but certainly you need to start as a trader before you can become a meta trader okay so talking about the hit rate okay so i'm sorry main question main point is what whether it's discretionary or quantitative and the short answer is we don't care as long as you have a strong track record i don't care you whatever you trade and accept crypto which is highly regulated in the u.s but uh other than that it is um you know we we we place bad space on the knowledge and the expertise and the track record of the trader not on the style and and and the particular style that they trade okay all right so yeah so yeah again so it you talked about uh hit ratio so with that so the following question is like and also in your successful journey uh till now what has been your ca done i'm sorry i missed the last the crucial part of the question the last part so yeah so what has been your uh cumulative annual growth rate uh returns so like i guess this question is about profitability so yes so um we are regulated by the sec and nfa and we do not disclose a growth rate of the fund because of regulations and you will you can ask any u.s manager and they will not tell you because of sec regulation all right yes i i understand that part so all right so uh now this one is by june ming he asks from your experience does quant traders normally use the same strategy across all asset classes um no definitely not i'm completely opposed to using the same strategy on every asset okay now now it's a very good comparison here so how does python compare to matlab for algo trading matlab scan all yeah and can all that can be done in python be done exclusively in matlab for sure i highly prefer matlab to python but i know different people have their own preferences you know language is not the most important part you can use any language you like you can use visual basic if you like but um to me as a beginning trader it is far easier to learn matlab than python and matlab has far fewer bugs than python and there are far more professionally prepared packages than python so i have no idea what beginning trader want to use python if they want to get a job that might make sense but if you're trading for yourself there's absolutely no reason to use python okay and how important is optimizing the transaction cost in trading strategies what is an optimal percentage max threshold for transaction cost and pnl well i think that the main thing about transaction cost is that you can't do much about it um frankly you know we we sometimes trade a thousand contracts with e-minis and uh we hire specialist firms that are famous for optimizing transaction costs and they couldn't do any better than market orders so i frankly don't know uh what you mean by optimizing transaction costs because you can't generally speaking you even if you leave it to the hands of experts oftentimes they can't you just take it down that's about it all right um the next one is uh when it comes to machine learning strategies how often or on which criteria would you revise or retrain the model uh sorry i didn't catch quite so so uh when it comes to machine learning strategies how often or on which criteria would you revise or retrain the model yes good question i think that you don't have to retrain it too often i would say that you want to retrain it when you feel that the market regime has changed or otherwise if everything remain the same you know once a quarter would be quite sufficient because remember machine learning takes a lot of data to train adding a few months of data is not going to make a big difference to the model if it does something is wrong with the model but on the other hand if there is a drastic change in market regime you want to take into account those changes and you would want to test whether the existing model still works in this new market regime and if not you might want to add new predictors and then therefore retrain the market so of course you know no machine learning system is static in terms of the number of predictors and the methodology so obviously you also continuously uh increase the number of predictors you are increasing you know improving your machine learning methodology so every time you make this kind of improvement you have to retrain the model so that's a natural process but assuming that everything is the same you don't make any improvement in your predictive cell you don't make any improvement in methodology you don't have market regime change and you just everything is the same uh except for the evolution of the market with you know all these daily prices and earnings and whatever then i think that uh you know once a quarter would be more than that but nobody does that so effectively you are forced to update your model as your research progresses all right so we have an individual so he mentions that i have uh uh learned um mql4 and excel vba do i still need to learn python or r to become a quant um so mql4 and what else excel vba okay yeah i think that again um you know if you are able to trade successfully using these tools all the power to you there's no urgent need for you to learn a new language but if you want to um you know up your game in like expanding the number of markets you trade and the opportunities that you can take advantage of definitely you should learn one of python matlab and i'll preferably matlab but you know i know you you may not you have your own preference that's fine but um [Music] so yeah so it's not an urgent necessity but we should always upgrade our skills right so we we should not be satisfied with what we have like me i i i frankly hate python but i learned python i'm you know so uh uh i and i i have a you know after i learned python i have more reason to hate it so uh so that's uh but you know if i don't learn python people would say that you know why do you hate it you haven't tried well i've tried i still hate it so that's that's you know you definitely want to expand your horizon uh even in direction that you may may think is not necessarily you know good so that you can have more evidence to say that it's not a good direction for you to pursue let's say you want to trade you are not good at trading options you would try to learn about option and dabble in option and then you can have a more informed opinion of whether the option market is for you all right uh there is a question that says will there be a second addition to your machine tray uh machine learning trading book i guess machine trading book so i'm in a second innovation yeah so as you know i've just published the second edition of the first book quantitative trading it has all numerous new materials and coded in python our and matlab with one some of the most advanced uh analytics that you can find i'm proud to say in the book um and um i'm currently focused on launching or you know basically making available more widely this machine learning based risk management system to everyone i have no time at the moment to write books so until i you know we are exit our firm uh i will unlikely to have time to write a new addition i'm afraid okay i think people are eagerly awaiting for your second edition of machine learning but yeah i think before that there is an algo trading book that you also have so you may also need to work on that second edition all right so uh this this next question is uh again about hiring so what is a unique skill that you are looking while picking the traders as in the asset of your team i'm sorry the the this minimum skills that we require for so what what is a unique skill so is there any unique skill that you are looking for while picking a trader for your asset team not really the unique skill that we look for is a long and consistent track record as i said i find it a uphill battle to to convert a quant to a trader so i would rather start hiring a trader instead of a quan but that trader can be a call of course but it doesn't have to be okay uh we have got a technical one here so uh what kind of uh statistical tools are helpful when you are trading at second level time frame is trading at this shorter time frame really random do you have any suggestions that are generally proven to work um i'm sorry can you repeat that question yeah yeah i will repeat that question so what kind of statistical tools are helpful when you are trading at second level time frame so that is the first question second level time frame i think that um you know any time serious technique like auto regression factor auto regression techniques states state space models uh those traditional econometric time series method would be highly useful you know common filter that sort of thing yeah okay and is is trading at this shorter time frame really random or do you have like any suggestions that are generally proven to work no no i as i mentioned before it's far from random at the shorter the time frame the less random it is so the only problem is how do you take advantage of it because of another question because of transacting costs so um yeah the two are they're all related highly predictable signals but greater transaction cost per per round trip return okay uh the next question is about uh execution so there are huge technical analysis and other quant methods for and for entries but uh would you recommend what would you recommend for exit so uh can get more of the profit of trend or higher profit so okay i'll again rephrase the question so the so someone in the audience is asking there are huge uh technical indicators or uh you know quantitative methods to get into uh the order but uh are there any recommendations for exit like while exiting the order exit depends on the strategy if this momentum strategy a stop uh exit is uh is suitable but not a profit gap uh if it's meaningful strategy a profit cap profit target is suitable but not a stop so it's highly dependent on the strategy yes okay it definitely depends on the strategy uh all right now someone is seeking a career advice from you okay so working you know i do need to jump uh off to my next meeting yeah oh yes yes all right so uh all right so i think uh uh i think there are tons of many questions but yeah we are running out of time so that's about it and uh thanks for your time uh douglas in fact i was just trying to keep track of time but it is like flawless so happen and thanks for your time and uh i think you asked answered a lot of questions today and these are really helpful and i hope the audience enjoyed the time and thank you so much again and uh that's for it today uh thank you thank you for inviting me all right thank you take care thank you all right so uh that's about it for uh today's session and uh the interesting thing is if your questions are uh some of your questions were not picked up uh you can just email us or share them with us over the survey that you are going to get after this webinar and we will make sure that all of your questions are going to get answered from our site and uh tomorrow we have another very interesting session by doctor who live so today is day one tomorrow is day two and uh we have to this session till next thursday day seven so i hope you will enjoy them and keep loving us keep supporting and thank you so much catch you in the next one thank you

Channel: QuantInsti Quantitative Learning

Views: 1,028

Rating: 5 out of 5

Keywords: learn algorithmic trading, algo trading course, Algo Trading Week, QuantInsti, Algo Trading Week 2021

Id: XMjsF5y3Oe4

Length: 57min 40sec (3460 seconds)

Published: Fri Sep 24 2021