How to Survive in the Trading Business | with Tushar Chande

imagine spending an hour with the world's greatest traders imagine learning from their experiences their successes and their failures imagine no more welcome to top traders unplugged the place where you can learn from the best hedge fund managers in the world so you can take your manager due diligence or investment career to the next level before we begin today's conversation remember to keep two things in mind all the discussion will have about investment performance is about the past and past performance does not guarantee or even infer anything about future performance also understand that there's a significant risk of financial loss with all investment strategies and you need to request and understand the specific risks from the investment manager about their products before you make investment decisions here's your host veteran hedge fund manager niels castro Larson thanks so much for tuning in today I really do appreciate it on today's show I'm talking to to Shonda co-founder and head of research at Rowe asset management to share is the author of a number of books on the topic of how to design rule based trading systems as well as having been actively trading these systems for more than 20 years so he brings real unique insight to what it takes to design and run a successful systematic trading program for those of you who are new to the show I just want to let you know that you can find all of the show notes including a full transcript of today's episode on the top traders on plucked calm website now let's get started with part 1 of my conversation I hope you will enjoy it - shark good morning it's nails morning meals how are you I'm doing very well things too sharp Before we jump into all the specific questions that we want to cover today and sort of the structure of our conversation I thought I would just try and go back a little bit because I think it's important that people when they are looking at an investment advisor also get to know you know the people behind the company and and some of the thoughts that have gone into where they are so perhaps with that in mind if you could just spend a little bit of time just to take us back to so sort of your own background and and how you got first into the whole world of trading and particular maybe of interest it would be why you ended up choosing the systematic route which is of course still the smallest part of the investment universe well my background is in engineering I came from a high-powered R&D background because I have a feed in engineering and I should do a lot of mathematical modeling and a lot of simulations try to understand and describe the underlying process so for me to the fairly natural way or natural transition to primarily use quantitative methods and therefore the systematic approach was in keeping with my training in my ability to solve problems now what we recognize is that when I worked as a scientist or an engineer I worked in a world of cause and effect that is we believed that there was some fun denying rules of the road or my natural process of phenomena that we needed to understand in order to predict what is going to happen now what I found interesting about trading or the markets was that fundamentally they were a random situation on a random event or a group a bunch of random events that is there was no fundamental cause and effect so and another way to think about it is for the same set of inputs in the you for a model of the equity markets you can get a very large range of outputs so it's very difficult to just make a relatively straightforward study of the markets and come up with some explanatory variables that are going to work that efficiently or that well for an ever and ever into the future so that sort of explains my background and also my general interest in trading in particular and then I'm also quite interested by the fact that there was there was an ability for you to be original by doing something interesting in terminal here research in order to deal with the inherent randomness in the markets so you know the markets were random you could still try to find some rules allowed you to react to the market differently than someone else so sort of a combination of the where my training and background came from and you know this the curiosity of somebody that was trying to move or deal with a structured situation versus a predominantly random situation and of course we've also done other things too sure I mean developing systems have been a very big part of of your life but but I guess also it's worth mentioning that you certain you also spend your fair share of time writing about it and trying to explain to other people how a good system should be designed and and and and how you can actually go about testing these things would that be a fair assessment yes I've published many articles over time and I have written a couple of books one of which has gone into a second edition called beyond technical analysis and has been translated into many languages across the world so I think it's been an interesting journey in terms of being able to communicate my understanding and models for the markets to a wider audience excellent well let's let's not keep away from all the good stuff we're gonna talk about today and and of course the program we're gonna be debating is the row I'll choose program and perhaps you could just give me a brief overview of the program that you run and and also just when it started and and how much assets on the management you run in the program today right so the program started in November of 2007 and we're running somewhere in the neighborhood of 40 million plus - on the program and then the program is a medium-term sort of trend-following program but what's interesting about the program is that we tried to think like discretionary traders and try to capture many different types of behavior into a single program so we have different groups of systems that represent different types of approaches to the market so for example if you are a bank and you said ok we want to start a trading desk and you'd go out and hire traders from other banks that might have individual niches or preferences or skills so you may buy you know you may hire somebody that's a good bond trader you may go out and buy somebody who's a good energy Strader may go out and hire somebody or find somebody who is a good at currencies and so on and each of these people would bring a different approach to their individual area of expertise and we've already discussed that the markets are random so what we did was we did something we wanted to follow that approach we would do something a little different we said we're going to use the same reason all markets so that makes it very robust so in a way we're not using different rules for different markets of each of your specialised traders might but we're trying to capture even in these uniform rules some of the behavioral styles of individual discretionary traders so for example one of the advantages that discretionary traders have is they can vary the size of their position that is the size of the initial risk based on their conviction about the trade so there won't be something in the information environment that allows them to change size so for example when the new Prime Minister was elected in Japan he was elected on a mandate to pump up the economy and increase the growth rate and the inflation rate so this is an external piece of information that a discretion trader could have used to increase his or her size inside the Japanese yen or the or the Nikkei equity market now as a mechanical system that uses all the same rules and all the markets we don't have the luxury of factoring in explicitly the different pieces of information for every new trade however we have some rules built in that allow us to change the size of the position going into a market so that's an example of where we tried to think about how a discretionary trader might approach a problem but we have converted that into some rule-based algorithms that we can apply over and over again without necessarily being able to incorporate a specific piece of news from the real world into the models sure well let's just go through some of these sort of the more tick box type questions that I think is important for people just to get familiar with and that's a little bit about the organization and how you've just you know structured row asset management perhaps you could just go into a little bit of detail about you know how it's done who does what and and also you know whether you use outside out sourced parties to to help you in certain areas or whether you do everything in-house right we're based in Switzerland and we have from day one use a very high level of automation for our day to day trading reconciliation back-office and record-keeping purposes so for example if you use the US regulatory standard as a reference they have various guidelines for how different bits of the trading process and we back off this process the record-keeping process should work so we've automated all of the mechanical or repetitive aspects of the business as much as possible but of course there's also value to outsourcing some functions because we get a third-party assessment or numbers or analysis of our performance so for example our administration is partly outsourced accounting is partly outsourced illegal in compliance is partly outsourced but you know we have a couple of lawyers and our board and so on so it's always a matter of striking a balance between doing everything in-house versus doing everything out sourced and we feel that since we are primarily traders and we have a strong regulatory responsibility to our regulator and our clients we try to automate all the immediate trading related tasks and processes so that we can have tremendous control and consistency and disciplined execution at all times and then of course even with that it's good to go outside for some of the things like accounting and legal because they can do it more efficiently there's a distance a standoff just in an arm's length measurement of the performance and of course we're not strictly lawyers we're primarily traders are not an interest so to the extent that the organization supports trading will put kept it in-house to the extent that there's areas of expertise and advantages to having a arm's length transaction we've gone out of house so in other words I guess you could say that this sort of the key functions of research trading software development and of course climb relations is kept in house whilst as you mentioned some of the other functions are our sourced in full or partly out sourced correct and when it comes to the underlying strategy that we're going to be discussing today in particular would you say that there is an an optimal size of the strategy that you see on the horizon and and and and at that point where you may say well we need to change the strategy or we made you do certain things differently in order to increase the capacity I would say that the the the capacity is well in excess of a billion dollars u.s. and certainly when we get to that point we may have to add more systems or look at the execution issues in order to get the appropriate control on slippage that we need to maintain the profitability of the system great Tushar you mentioned that the program started in November of 2007 and obviously since that time it's been an interesting journey not least four people in the CTA industry would you say that the program itself has performed in terms with your expectation or in line with your expectations and and obviously I guess we need to to take into account that many programs have performed quite differently before 2009 and after 2009 and is that something that the Alice program and they has experienced as well the short answer is yes that the fund is performed differently after 2009 than before 2009 as a generally performed as designed the short answer is yes and the long answer is we wish we had done better essentially what your to think about though here to think about the macro environment the external environment within which the fund will had to trade so to give you an idea if you are a fan of Formula One you know that the cars are very highly optimized for each circuit but the circuit is a static object we pretty much know where all their turns are going to be now if it rains during the on race day the performance of the f1 cars falls off dramatically why is that because even the best drivers need optimal conditions to perform at a world-class level another analogy I can give you is that of golfers if you look at the greatest golfers today or 50 years ago all of them are optimized to perform when the course conditions are perfect and they get to practice on the course for a week or longer so they pretty much know where every hazard is and where every bump arisen where every tree is now what happens with these world-class golfers when the wind picks up well they go this course pick up as well so if I told you that somebody scored a par for the third round you really can't evaluate that score without knowing how the rest of the field is done and what the environment was so for on a very windy day turning in a par score might actually be a phenomenally strong or successful round so the same thing happened to the CTA industry and to our program as a whole due to the incredible credit crisis and crack up of Lehman Brothers we had a massive breakdown in the entire world trading system so that the central banks had to intervene and so forth so essentially what happened was the markets were working in a particular way before say 2007-2008 and everybody systems including ours were sort of designed to perform under those kinds of markets where you had maybe a year or two of weak performance and then very strong trends so you could recover and make continue to make new equity highs what happened after 2009 or 2008 depending on weight or the line was that most virtually all of the trends were limited to the equity markets in the bond markets so the people who had very strong positions or exposure to these markets and very slow-moving systems tended to do better as a group than everyone else and we are part of that everyone else because we are highly diversified trader we don't overweight any particular sector relative to another sector we sort of have more or less well or significant exposure to all of the major groups in the market so so again to go back to the golf analogy of the fr analogy the trading environment has been unusually difficult for the last five years so just to give you an idea we use a row trend barometer it measures the percentage of markets that are trending or have reasonable trends at the end of every month and the break-even level is about safe forty two and a half to 45 percent so if the market so say if only 20 percent of the markets are trending then typically transfer laws tend to lose one standard deviation in terms of return for the month so the more months you have about breakeven the more likely you are to be profitable so if you look at the five year period ending in 2000 say nine and the five years after that what you find is that there are many more months below break-even after 2009 than they were before 2009 so that just tells you that since inception for our program more than two-thirds of the months have been below breakeven so that just gives you an idea of how difficult the environment has been for our program specifically and for diversified trend follows in general so it's raining during every race if you will if you want to use a f1 calendar or it's been a very windy condition for all the majors if you want to use a golf analogy so for us the system has done what it could given the very uniquely difficult trading circumstances but of course you know since we didn't have a large exposure to equities in bonds our performance in absolute terms looks worse when you compare two managers who overrated in those particular sectors so overall I think the system is done what it was designed to do and one thing we know from the CT industry is of course that many managers have very long and and two stiches track records but when you look at them of course we also have to be aware that there usually has been quite a lot of evolution and changes along the road in order to get to where they are today and therefore just looking at numbers historically I guess you know can give you a little bit of a false sense of comfort would you say that there are sort of particular ranges of time looking at your own track record that we should be aware and where maybe perhaps major research upgrades have happened just to put that into perspective and and so we know what we're dealing with in that sense yes sir when you look at somebody that has say a 20-year track record you can be almost sure that what they're doing today into in the systems and market weights are quite different from what they were doing 20 years ago and that's partly driven by the managers own research it's partly driven by the customers because they want us to do research all the time in quote made things better and then of course there's a natural reaction to what may be happening to AUM to liquidity as different markets are become available to trade or become too thin to trade and so on and so forth so when you look at a managers performance it's good to look at the environment within which the performance was produced using something like the ROE trend barometer or you certainly need to ask the question have there been you know significant changes or even small changes to the track record or to the systems and the portfolio weights and how that has played out over the time and how that correlate with the track record because for example with exactly the same system I could produce quite different looking track records by overweighting a particular sector or under weighting a particular sector so yes that's something that the user needs to be aware of and certainly need to inquire about and in case of the out use program would you say that there are two or three periods that are you know different because of research of grace in in in in that case well I'd say roughly two periods we tried not to make too many changes and in fact we we have we started with three systems and now we have six systems and most of the original systems we have continued to be used today with some minor changes so roughly I would say late 2010 early 2011 is a good time for us to differentiate a track record because we made some significant changes as a result of various things that happen in 2009 and 2010 but overall we so there's roughly two periods in our track record and I'm sure we're happy to provide you with more specific details if you desire but in general there has been the in going back to the previous question you do have to be aware of when changes are made and what was done and how that altered returns so that is not a if not the result of applying the same rules across the entire track record sure and and since we are talking about the track record maybe I could just ask you a few sort of very simple questions just again for the listeners to get a feel for what's inside the track record do you are you able to give us a hint of roughly in terms of the average fee structure that you have in your track record day today what would you say that that is it's very close to the industry benchmark of two-and-twenty that is a 2% of Miami fee and 20% of incentive fees okay and in terms of the Commission's being charged to the trading what would you say they are sort of approximately I'd say about say $10 u.s. you know approximately and of course there's also there's always a downside to the CTA and to any investment and so just highlighting those numbers as well in terms of the the maximum drawdown meaning from a a high to a low how has that been in the live trading since November Oh seven right so the rule of thumb that we use is that the worse drawdown is typically four times a monthly standard deviation so our multi standard deviation is something on the order of five point one percent so if you multiply that by four we should get something on the range of 20% so our worst drawdown has been 22 percent which was back in February of 2010 which is this a little bit higher than this 4x but it's in the basic range of say you know three to five times standard deviation so we've done a good job of controlling our risk in this extremely difficult environment now what does that mean if you look at many diversified CTAs we've not seen ours drawdown expand dramatically in the last year or two meaning we've stayed well with him this 22% drawdown whereas many of our cohorts in the business have had significant increases in the worst drawdown by a factor of 50% 100% even in the last few years so as a I'm an optimist so I like to say that your best month or the head of you and your worst month is also ahead of you and essentially in 2013 that came true for many programs but we were able to maintain and stay above I was wrong on even in the last year or two excellent and just just to sort of finalize these sort of short statistics and looking at your average winning month and your average losing month how does that compare one of the things we like to do is we like to use the statistic that we like to call offense defense ratio which is the ratio the average winning month divided by the average losing month now what does that mean well you're trying to get an idea for how quickly does the system or the program respond when they're good opportunities in the market so you want to put on positions increase risk when there are opportunities in the market so your average winning month will go up conversely when this things are difficult when there are no trends you want to shrink the number of positions or reduce your risk in the market so that the average losing month will go down so naturally the better you are at responding to opportunity or shrinking during adversity then this ratio will be more than one and will be relatively large compared to some people who are not as sensitive to responding to what's happening in the market so for example for us the average winning month is four point six five percent we have a losing month three point four two percent so to give us an offense defense ratio of one point three six in very very difficult market conditions and that's significantly better than many of the other programs especially when you compare diversify traders to diversify traders some of them are at one or a little bit less than one and it's not that their systems are not very good it just means that the trading conditions have been very difficult but even in these difficult rating conditions you can see that our systems have been able to respond aggressively when conditions are favorable and shrink or reduce our risk quickly when conditions are not and then finally I guess a number that's also somewhat relevant and that is of course the percentage of winning month even though I know the period has been probably appeared where the general CTA index have been also suffering from a lack of winning months but do you happen to know how how the altitude program have done yes our percentage in winning months is approximately 47 percent so it's a little bit lower than we would like but you know the markets Aminta and you know that's the way it shook out absolutely so let's talk a little bit about the trading program itself and perhaps you I know you mentioned the structure of it briefly at the beginning but perhaps you could just in your own words talk a little bit about the the overall structure the number of markets and sectors you trade and also given in say inside to the instruments that you've chosen for the algiers program okay Nilsa going back to where we were we are trying followers and we are medium term trend followers but we want to be a little bit different than the traditional trend follower now in terms of markets and portfolio we treat 44 markets we are very diversified in terms of and with roughly similar weights and all the media sectors so we trade you know currencies bonds interest rates equities which account on the primary sector that everybody does we also trade a lot of commodities or commodity weights is markets roughly more than half of the market period are commodity markets so we have a bit of a commodities orientation versus a orientation towards finance or financials compared to some of the larger managers now I haven't given you a sense for what we trade sort of a diversified portfolio the number of markets about 44 covering all the major sectors on the major futures exchanges let's talk a little bit about what are you trying to do and why doing what you do so we believe that the core benefit of a CTA or the core advantage of having a CTA in your portfolio is to be able to offset significant declines in the equity and bond markets that is declines it lasts for say a 1 month to 6 months or more so longer periods so we're not talking of necessarily providing a positive offset by that I mean equities are down bonds are down we are up on a day to day basis necessarily so we've done that from time to time but wearily talking of say one month and longer period when they are sustained declines in the equity markets esteem declines in the bond markets and then you want something in your portfolio that's going to offset that with positive returns and historically that has been the rule of CTAs and we tried to make sure that our systems are designed to deliver on that promise that CTAs were designed to provide so we sort of portfolio insurance so if you're a large manager or a large investor or a you know a small investor and you have some investments in equities like a long only strategy like an ETF or a mutual fund for you actually directly owned stocks and then you also have a portfolio of bonds we directly own bonds or a bond fund then you want something that's going to give you an offset if they're going to be prolonged declines in these markets so that's where CT is coming so roughly speaking we have three groups of systems groups one and two are primarily trend following that is they buy strength and sell weakness they don't move too rapidly because you have to allow the market some wiggle room but we've done something interesting in terms of how we are how much initial risk we are putting in how we design our entries our design their exits to allow us to differentiate ourselves and give a little different performance profile then other trend followers in the business or other momentum traders in the business so those are groups one in two and group three as we've discussed is primarily our counterweight to groups one and two that is that allows us to reinforce this offset function that CT is a meant to deliver so that sort of a quick overview of why we do what we do so we do what we do so that we can diversify a portfolio of stocks and bonds we've diversified by trading a lot of markets we have robust systems by using the same rules on all markets but we've we have different philosophies that can be grouped together into three groups of systems that allow us to react and we've already shown you that our offense defense ratio is more than one so that tells you that our systems have proven themselves proven is ability to take advantage of opportunities and expand positions and then shrink them just as quickly when and the trends are not there and of course we're not trying to to extract any of the secret sauce but I think it might be useful you mentioned you have six different models working inside the altruist program but I I do think it would be useful to try and and maybe talk through you know maybe not all of them but some of the key models and what kind of indicators is involved you know this volatility play a role and and just to give a little bit more insight as to how you've designed the individual models in order to achieve this overall goal that you just mentioned so let's talk about our Group one system which are primarily trend-following long short systems so a variety of ways you can do this you can do it using moving averages you can do it using break a style systems or you can use it using some sort of fundamental model or a predictive model from analytical for fundamental data you can do it using the term structure of interest rates or you can do it by looking at the the structure of the various contracts and you know the forward contracts versus the current contracts and so on and so on so in many different ways of looking at the market but fundamentally you were to decide do you want to be longer do you want to be short that is do you think prices are going to go up so you want to be long or do you think prices are going to go down and therefore you want to be short and then you decide how much to risk should I should you risk 1% 2% whatever 5 percent or whatever the magic number is may be 0.3% for you so you set some initial risk and then you decide what happens if you're wrong so say you put on the position and the market there's something else when you get out and then you also had aside what happens if things go your way you know you you think you're going to go short and the market obliges and go short moves lower very rapidly or very nicely conversely you think it's going to go higher in the market responds by going much higher so you also have to decide when you get out when you have a profitable trade so all be all these complex decisions they all interact with each other and one of the challenges about the temptation and the businesses that you could say that that many it's very easy to think in terms of having market specific models so as we talked about you could go to a bank and hire a bond trader so the bond trader may have a lot of market specific information and may be good at trading information flow but if you don't have an information flow then how do you design a market specific system so one of the choices we could make upfront was to have a series of market specific systems or have robust system so we've chosen to use the same rules in all markets so which means that we don't have any market specific systems so which means that we don't have a system that one only trains bonds or nothing else now there are some important reasons to do it this way but let this give you a sense for our philosophy in terms of what we're trying to do and how we are going about doing it and you mentioned the choice between say moving averages and and other types of trend following indicators which one did you choose and and is there a reason why that you could choose one over the other we've mostly gone with breakout style systems as opposed to a moving average crossover type systems to some extent it's a matter of individual preference but our but we had two reasons to do it the first reason is that when the markets are trading and a narrow trading range that is they're just sort of going up and down up and down in relatively narrow range in that situation a moving average system gives you a lot of unprofitable signals so want me to avoid trading during a consolidation or a narrow price range excuse break our style systems so that is one philosophical reason to avoid trading in a narrow trading range the other reason was that you can be more creative in terms of defining whether you should get in or not so one of the one of the philosophical questions you can ask is should you focus a lot of your energies in designing good entries or good exits or so on and we've spent a lot of time trying to design good that is if you look at the typical trading system a transforming trading system it may only have 30 to 35% profitable trades and we wanted to increase the percentage of winning trades so that is why we went with our break hostile strategy that we can combine with a small number of conditions maybe one or two or three to improve the percentage a winning trade so for most of our systems if you look at a long term test the percentage of winning trades is closer to forty five to fifty percent in that range rather than say twenty five to thirty five percent so while the key design features of our program is even though we use the same use and also same use in all markets we've still been able to structure our rules so that across a very broad set of very divergent or very different markets over a very long period of time we tend to get a higher proportion of winning trades compared to the typical trend following systems that you could easily find in the literature and and is this choice of using sort of price channels rather than moving averages is there also a little bit of a consequence as to how you want to manage your risk and the use of stops or not use of stops yes the if you have a moving average system you typically tend to be always longer always short so you have a large number of positions so which means that you have to somehow deal with equity curve with an equity curve that is full of markets that are not maybe going anywhere where as so that tends to reduce your offense defense ratio whether if you have a breakout star system you are trying to you're essentially changing the nature of your equity curve by saying that we're trying to avoid paying positions and markets that are not experiencing strong moves so the challenge of managing the equity curve is different because you don't have a continual equity curve composed of lots and lots of position and lots of lots of markets but you have a discontinuous equity curve with only a small number of positions that can expand or shrink so when the markets are trending you can have a large number of positions when the markets are shrinking small number of positions so the volatility of the equity curve is not constant or is not relatively stable as you would have if you had a moving average system because in that case you would have a lot of positions on all the time so some different set of challenges but on the other hand it's a way to differentiate ourselves and you know provide something different like a good offense defense ratio sure and and in terms of the inputs in the in your models when you run them every day what what what kind of input do you need in order to run the Algiers program we just need the daily price data of open high low and close so for example because we are a a algorithmic or discipline a systematic program that's all the information we need in terms of the de open high low close data we don't need to have fundamental data or other sources of data or multiple contract data in order to make our decisions and and how frequently to you then run the model in order to implement all these trades right we are an end-of-day trader so that means that we only have to run our model once a day after the trading is closed for the day as opposed to say if you are a moving average system you might get a crossover in the middle of the day then your decides we on a take it or not or if you're a very short-term trader you may be making all of intraday data updates in order to get generate new signals whereas we are really only updating the data once a day at the end of the day so we are an end-of-day trader that and we only have to do it once every a solid training cycle and so what kind of orders do you have to implement in order to to run the strategy what what kind of auto types to use we primarily use three kind water types that is we have spread orders when we have to roll over positions we are keen to use market orders to enter an exit position if we have a new account of some sort of trading error but most of the time we're just using stop orders so that these are which means that the price has to exceed a certain level like rise above or below a certain level called a stop level or stop price before the trade get executed and that means if I understand you correctly that you don't scale into any positions you basically want to be getting a full position on when you when when a signal is triggered correct again this is a matter of design of a matter of preference and the advantages or disadvantages to every approach but the approach you selected is to be all in or all out so we have stop orders and the entire position will be put on at one price point either put on or taken off at a single price point or ask your sourcing a price point as we can get sure so before putting on a position how do you go about calculating because I guess that is a much bigger part than many people are aware of the position sizing and the use of leverage is obviously a very important part in in getting a successful result over time in in the CTA industry how do you go about that side of things right now as we were talking about a couple of minutes ago we were talking about a moving average type system or break our style system and the difference in the equity curves so our risk control is embedded in our design process because the number of positions we have on at any one time is not constant conversely if you had a moving average time system where you already had a position in the market either long or short then you may have to adjust the positions over time more frequently in our case we have looked at the long term simulation of the system and determined a initial risk level at the market level or system level and the total standard deviation for the simulated returns over a very long term horizon and we've combined the two to come up with what gives us reasonable risk control and drawdown risk control over the course of the training and all the rules are automated so in terms of day to day we don't really have to think because that's all taken care of in our what we call our ITP or integrated training platform but as but really the data for the individual risk in anyone - for any one trait for any one system in any one market and the overall risk is all coming from our simulations which cover a long time period and have various checks and balances for robustness but more interestingly the regardless of what our testing may have been you can see and look at all real try and a real-time track record and see that we have controlled our risk very well on a daily basis thanks for listening to top traders unplugged if you feel you learned something of value from today's episode the best way to stay updated is to go on over to iTunes and subscribe to the show so that you'll be sure to get all the new episodes as they're released we have some amazing guests lined up for you and to insure our show continues to grow please leave us an honest rating and review on iTunes it only takes a minute and it's the best way to show us you love the podcast we'll see you next time on top traders unplugged you you

Channel: Top Traders Unplugged

Views: 3,852

Rating: 4.8709679 out of 5

Keywords: Tushar Chande, Rho Asset Management, trading, risk, top traders unplugged, investing, top investors, how to invest, investment strategies, top trading, top traders, money, investing interviews, successful traders, how to be a top trader, best traders, hedgefund, better trading, how to trade, analytics, managed futures, future of investing, investing strategies, investing 2018, investment advice, investment challenges, investing podcast, trading challenges

Id: FnZq5lO5Ue4

Length: 43min 16sec (2596 seconds)

Published: Fri Nov 04 2016