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  • Hi everybody, this is Shaun Overton with OneStepRemoved.com

  • and in this video I have Michael Halls-Moore. Michael's the owner

  • of quantstart.com, a blog about algorithmic trading. Mike thanks for

  • joining me

  • Mike, you have one more interesting stories about how you got

  • involved in finance

  • You want to tell us a little bit about it? Yeah, I came out of

  • postgraduate school and essentially, one of my friends actually

  • after going through a start up

  • He actually said would you like to be involved in an investment of his

  • which was essentially a quant fund, a prop fund A nice guy to know.

  • He's a nice guy to know, exactly.

  • I became essentially they're systems developer or quant developer.

  • And then

  • with that we essentially traded for about two months

  • we really started from scratch. Everybody has this

  • impression of what a quant it is. You're a quant developer or quant trader

  • What is that? The quant word is overused quite a lot

  • what I think what people really means is something that involves generally maths or

  • statistics in finance and in a quite heavy

  • fashion. It can mean anything from

  • the guy programming the trading structure right through to the

  • the guy doing the hardcore trading research

  • Strategic back testing in a very

  • computational and statistical manner Right

  • it's very nerdy. You got the propeller heads. They're in the back room

  • Exactly. When you're developing a strategy, how

  • important is the actual rule for buying and selling?

  • Not as important probably as

  • other aspects. I mean, you can think of it as one big whole

  • system, right? You got you got pricing to deal with, data cleansing you've got the

  • what they call the Alpha signal generator right that you mentioned

  • and then you have

  • the whole execution and an exchange model and an execution process and

  • each those areas has its own

  • unique challenges essentially. I see that sorta

  • signal generators

  • are one bit of a much larger system, which is not as

  • The importance is on all of these systems working absolutely well

  • rather than one signal. I get to use to signals

  • not to highlight any system, but say have a moving average cross. something everybody

  • knows

  • You look at the signal and you think, "Oh, if I would coulda

  • should have done this then I would have make loads money," but

  • that's not what funds or quants do at all.

  • It depends on the frequency of what they're trading, which is a

  • very important factor. It probably differs quite a lot from retail. Retail

  • is very much at the lower end of the frequency.

  • There's still money to be made there of course, right?

  • Quant funds tend to be generally more of the higher frequency. A lot of

  • what they will be studying is optimizing of technology and

  • strategies that are really involved in market

  • microstructures and like say of the

  • particular assets they're trading. That is quite different approach

  • generally to

  • "You know, I've seen that. I could've bought there. I might've sold there." It's a very different sort of

  • approach. The signals are generated in quite different ways, I think.

  • What is the typical timeframe? What's the difference between a retail

  • trader

  • a guy that just has a trade robot that he bought off the internet and

  • a guy that's developed it and he has a statistics background and he is a true

  • quant?

  • Depending on what you're trading it could be anywhere from

  • the guy who has bought the robot

  • That sort of guy would want to be

  • checking himself making sure it's not doing anything stupid. And so, you really wouldn't

  • want it to be doing

  • more than a few trades every minute probably.

  • That is the absolute cap? It's whatever you can

  • cognitively handle. In the true quant sense

  • the ultra high-frequency stuff will be microsecond and even nanosecond

  • kind of scale

  • that obviously is not, you know, it takes three hundred milliseconds to

  • blink.

  • You can imagine the kind of latency that

  • you have to deal with in that space is a very different kind of mentality. It's all done with

  • a computer. When you're competing against

  • quants that are trading in microseconds and

  • your technology is cut streaming from the US to your broker server in the UK

  • or whatever

  • how do you compete? You essentially don't. You are playing on

  • a different playing field, I mean

  • They're dealing with

  • differences on these incredibly tiny time scales. They have their own little

  • games going on down to that level and

  • As a kind of longer-term, lower frequency trader, you're playing

  • You aren't really hugely sensitive to latency. If you're trading

  • once every week, latency is not gonna be your biggest

  • problem Having been on the inside and worked in funds

  • where do you think the retail trader has the biggest opportunity?

  • Ok, so there's

  • as a retail trader, you could go for more a higher frequency style

  • straetgy and there are plenty of

  • spaces out there where bigger funds, because they got quite a lot to

  • invest

  • aren't able to compete at the lower scale of the money.

  • As a retail trader, you can go there.

  • You can make money even with a relatively sophisticated

  • strategy. But you do need to be quite computationally

  • aware. You need to be good at programming and probably have a bit of a

  • background in math and stats to compete at that end.

  • When we talk about quants, for whatever reason, that is almost always associated

  • with high frequency trading or even ultra high frequency trading

  • in my mind anyway.

  • There are plenty of quant funds who tackle lower frequency stuff.

  • There's quantitative and there's

  • automated. You can have a very low frequency, automated strategy.

  • It's just that quant funds by virtue of generally who they hire

  • they'll be working at the higher end of the

  • spectrum because that's generally where they have the skills to compete.

  • Why is that? Why do all the funds focus on these short term strategies. Get the

  • money

  • right now, real quick. I think it's because they take

  • quite a scientific approach to how the trade so they'll be actually trying to

  • exploit

  • physical inefficiency in the system, some of the time. It won't be

  • quite different from, as you say, coming up with a technical signal and going for it.

  • They'll be saying, right, hang on

  • the exchange works like this, they use this infrastructure

  • Can we exploit that somehow? So, it's just pure arbitrage?

  • Or something very close to it?

  • There's lots of different strategies, one of them obviously being arbitrage

  • There's lots of we know the infrastructure better than the other guy.

  • It is done that way. You can see how it comes from

  • very much a sort of hypothesis first

  • trade signal second mentality rather than, "Oh, let's

  • see what indicator gives me a buy or sell." It's done in a very differnt

  • way

  • What is your opinion? Do you think you need

  • the science background first and then you try to figure out trading or is it

  • better to have the trading background and then you try to develop a strategy

  • I think you need a mix of both.

  • You can't just be a nerd and have a Ph.D.

  • No. You can come up with the best

  • forecasting system in the world.

  • If you don't understand risk management, if you don't understand position sizing

  • You don't understand these core

  • bread and butter ideas in trading, you're going to lose money.

  • You won't have done any statistical analysis on your

  • drawdown characteristics.

  • Or even know what drawdown is. Or even know what drawdown is.

  • You cannot, I think

  • I believe you need to have a mix of both, frankly, to do well. Before we

  • started recording, we talked a lot about risk management.

  • What is that in your mind and how do you best go about coming up with the best

  • risk-averse strategy that makes money?

  • I think retail and funds have a very different approach to how they deal with risk.

  • In retail from my experience it seems kind of a secondary

  • consideration to the the signal generator. In a bad way or good way?

  • In a bad way.

  • It seems like a dull, boring area of

  • trading. You can see why that is.

  • You're telling me I can't make as much money as I possibly can. I have to dial it

  • back. Exactly.

  • This really comes down to the differences in now retailers and

  • funds operate. Funds have institutional mandates

  • What is that? That is, they have to generally

  • present monthly performance for what they're doing. Some investors will actually ask for

  • daily

  • performance or even real time performance. You're really under

  • the magnifying glass a lot of the time. For that reason, the primary concern is

  • not losing money rather than gung-ho. This is for the institutions?

  • Yeah, this is for the institutions. Especially for your larger institution like pension

  • funds who the people are putting in Well, it's people's pensions. It's people's

  • pensions. They're not going to just throw

  • a hundred-million at you and say, "Go for it."

  • They will do an extensive amount of due diligence on you. Years of due diligence in some

  • cases. They will want to see very sophisticated

  • risk systems in that regard. I guess a few examples would be

  • They'd want to see exactly

  • how you go about leveraging up and down and what your actual

  • mentality is for leveraging up and down. It's not just picking it out of the air.

  • There's a protocol? Very much a

  • protocol and you know it when I see it I downside insurance policy would be

  • in that regard. A classic

  • put option to make sure your portfolio.

  • They're stress testing you.

  • Yeah. You'll also be stress testing yourself. Yeah, of course. All the time.

  • There's the industry standard like VaR, the classic

  • stress testing. It's more a culture difference I think. It's

  • a risk

  • first mentality. You're always thinking how much is this going to

  • affect my portfolio swing

  • How much of mine is going to be the average volume

  • that I'm trading. It's just constantly assessing risk.

  • Whereas in retail, it's mostly, "What's the signal, what's the signal?"

  • exactly so if you are retail and

  • obviously retail traders are primarily profit-driven

  • How does that change your approach? If you were in an institutional environment

  • your risk management focused in terms of you want to make money every day, even if

  • it's a fraction of the potential money

  • But if you're trading your own money, within certain

  • restrictions and you can only tolerate so much drawdown

  • within that drawdown. you wanna make as much money as possible. How do you go

  • about doing it?

  • There's a couple of actual, specific ways. The classic is

  • the Kelly

  • criterion, which is a means of adjusting your leverage based on the amount of

  • account equity you have.

  • It can get a bit mathematical so I won't delve too deeply into that.

  • You can Google it.

  • The more conservative uses of

  • leverage and cutting back on leverage. That would be very much controlling your

  • drawdown and your

  • growth rate as a sort of slide almost. You say, "Alright, well I'm happy to

  • reduce my drawdown at the expense of not gaining as much growth."

  • Obviously, any trader doesn't want to lose their entire equity.

  • That's called gambling. Exactly.

  • They'll all be fundamentally using some principle like

  • Kelly

  • It's quite a common one. You can go anywhere right up to

  • as I said, the put option mentality

  • basically only half your account and then literally put 50%

  • of your account aside

  • if you lose your entire amount, you've only lost

  • 50%. That in itself is actually quite a

  • good mentality to have in some regard. If you... I've used Kelly before

  • and sometimes you have systems that have done way too well in the past

  • and you plug in the numbers and the optimal Kelly is something like risk 30 percent

  • of your account on some

  • but I know from experience that's dumb. What do you do? How do you

  • handle that? I mean

  • You tend to have overlays on top of that.

  • Another actual mechanism that is used

  • a lot in quant funds is what's called leading risk indicators.

  • especially in equities where you've got these new shiny

  • exchange-traded funds popping up all over the place. Some of them will be

  • measuring

  • very interesting things like the VIX, which you've probably heard of

  • This is the implied volatility index of the options on the S&P 500

  • and some people use that as a mechanism for essentially saying what is the future

  • volatility goign to be looking like. You would incorporate a lot of these.

  • If you can imagine putting all of these different factors into a pot

  • and essentially saying well okay my overall leverage is gonna be

  • this, it's not just dominated by Kelly. It's not just dominated by the VIX.

  • Mixing them all together and getting a holistic view.

  • You're mixing. You're not just saying here is my entry signal.

  • Here's my system that goes within

  • the entry system to come up with

  • the best trading size.

  • The signal generator is piping information to

  • a sort of more important risk management system that says

  • okay yes, okay no, or downscale by this much

  • the signal generator is convincing the risk manager whether or not to do the

  • trade rather than

  • the signal generator being the be all and end all decision-maker

  • the risk management layer is generally much more important and much heavier

  • Do you think you could make money if

  • say that you're trading Forex or equities or whatever

  • and you're pushing through a hundred shares every trade and you have stop-loss of

  • fifty cents

  • every time. Do you think you could make money doing that?

  • Essentially, because stop losses

  • they are quite a controversial thing to do in

  • trading. Are they? Cause retail traders... In trend following

  • definitely, they are a fundamental part of the strategy

  • You have lots of little

  • mini losses and then all of a sudden. The mega-winner.

  • Obviously, if that reverses, you don't want to lose it. That's the whole point of a stop

  • loss, but in

  • mean reversion, they're a bit more difficult to utilize.

  • Even a lot of quant funds specialize in mean reversion

  • they may or may not be using. So you have these huge institutions

  • that are really prestigious and

  • they're not trading with stop losses? I think you'd assess it in a

  • different way/

  • You'd say to yourself, right, what is the kind of historical

  • distribution of returns we've seen in our backtest.

  • and then you'd say well I've seen a 50 percent drawdown

  • in the backtest. You might just say, let's get rid of the

  • backtest and start again.

  • but you you'll see some drawdown in the back testing. You say ok, well

  • in the future I'm likely to see that drawdown if not much worse

  • You would essentially cap it in that regard.

  • That brings up a good point because the signal works

  • and you back test it. You feel good. Unless you get really

  • unlucky, it should work up to some point in the future

  • and then it stops you so that begs the question

  • how do you know when an algorithm is in a drawdown

  • and when you need to turn it off?

  • Before you even begin trading, you've carried out the backtest phase.

  • You would then say, right, I've seen a 30 or 25 per drawdown

  • in my backtest, which to me would be pretty high.

  • That's hard to ride through. Let's just pick a figure - 25%. if I got

  • anywhere near 25 percent in my

  • in my walk forwards, in my actual out of sample test, I'd just turn it off.

  • Time to do some different?

  • The reason I would turn it off is not because I'm expecting a 25 per cent in

  • the future it's because

  • I'm aware that I do not know. There's this unknown unknowns that could creep in and

  • it may even get much worse than that.

  • You should be expecting worse drawdowns during the walk forward.

  • yes a you might you should it be expecting were stored and certainly in

  • That raises a good philosophical point.

  • You can't know that your strategy is going to make money in the future.

  • How important do you think it is to try to disprove the strategy?

  • what's the value in that? I would actually come at it from the point

  • of disproving it. I would use the backtest not as a means of

  • putting something into production. I would use the backtest as a means to filter stuff.

  • getting... imagine starting with 100 strategies.

  • You can then use a computer to backtest the lot. You would filter 95% of

  • them out immediately.

  • Just cause of drawdowns?

  • Drawdowns, you're not happy with the statistical properties

  • For instance, you might not like the fat tails

  • as they would call them. It might be too much for you.

  • You're always looking for a filtration mechanism. You want

  • to find a reason to get rid of it always. If you

  • If you're happy that you've not found

  • any reason get rid of it then you'd put it in, as opposed to I've start with this

  • let's try and find everything I can to keep it. Very different

  • mentality

  • You mentioned fat tails and we've talked about trends

  • we talked about range trading. What's the relationship between those trading styles

  • and the fat tails? What are fat tails for people that don't know. Fat tail would mean

  • A lot of the finance is based on the assumption that

  • returns aee normally distributed meaning

  • they have relatively thin tales. There's relatively less

  • extremes occurring then probably do in real life

  • You'd say they have fat tails. This is the kind of

  • Taleb approach. The Black Swan guy? The Black Swan, right.

  • there's more extreme events than would occur

  • than you would assume under a normal distribution. You would be

  • using a distribution that has

  • much more extreme events more commonly. In the quant world

  • because Taleb writes scientific papers and he writes Black Swan in pop

  • literature type stuff

  • how how popular is he? He's a character, certainly.

  • He has very interesting strategies that sort of exploit

  • his belief that these fat tails occur more often than not. They are difficult

  • strategies to carry out. How are they difficult?

  • They are the opposite of what I would call the pennies under a steamroller

  • approach.

  • You are taking, it's very similar to a

  • momentum or trending strategy where you take lots of mini losses

  • on selling these options, out of the money options and then

  • every now and then, an extreme event will occur and these options will come into the money.

  • He believes these options are mispriced such that

  • you can make more money. Do you ever know when

  • you're due for a winner?

  • No. You can only ever say

  • historically over this time period I've seen this amount and

  • you know you can never be certain. You're trying it always

  • to see if it fits the statistical distribution. You don't know what that distribution is in

  • real life

  • You can only sort of estimate as best you can that

  • distribution and then

  • hope for the best, right, in his case. In my opinion it's quite

  • a tricky strategy. If you, let's say that you have a suite of range trading

  • strategies and have a suite of trend trading strategies

  • What do you feel more comfortable with in in the future?

  • This is a discussion that goes

  • very much goes back and forth depending on which year we're in, right?

  • Where am I making money? Exactly.

  • I would say quants tend to be... some quants are

  • ambivalent. They will basically try and assess

  • what type of market regime they're in and then apply

  • Do they do that quantitatively? They do.

  • I mean this is quite a sophisticated area actually and it's a very tricky one to get right.

  • There's a lot of them filtering mechanisms that

  • kind of machine learning technology that would be used to

  • try and ascertain whether you're in a

  • range-bound or mean reverting

  • scenario or a trending market. There are lots of ways of doing that but

  • you gotta say, "What time scale am I talking about? Am I talking about years? Am I talking about days?"

  • When you have a market regime shift

  • I mean

  • it's an unknown unknown

  • in some sense you can have a kind of view on interest rates

  • You can model interest rates, but

  • that's kinda a purely random event in some sense. It's very

  • difficult to

  • keep it in mind. You'd have to kind of look over historically what the ECB had done

  • and say how likely are they to drop rates?

  • The more sophisticated firms will obviously be tracking all this fundamental data and

  • be putting it into their models.

  • In some sense they would have more of an edge

  • in that regard to be able to detect these things, but

  • It's tricky. Market regime detection is one of the hardest problems to

  • solve in quant finance.

  • for that reason, to actually be able to answer whether we're mean reverting or

  • trending strategy is better at any time is very asset dependent and very

  • dependant on which year you're in. It's a tricky one.

  • With trading, it depends on what you're trading. With forex

  • there's like twenty to choose from. But if you're trading equities

  • you have the opposite problem. There's five thousand to chose from. How do you

  • pick one?

  • I wouldn't be

  • going and picking stocks in the traditional sense. I would

  • try and find some kind of economic relationship between

  • a pair of socks or a triplet of stocks in ETFs generally because you can

  • take a more... Yeah, why are ETFs so popular?

  • They're really quite interesting because what they allow you to do is

  • a company, the fund that sets them up can basically say, "We're doing

  • this specific thing and we will then give you an exchange-traded

  • instrument that does this so you might have a a possible gold stocks right now

  • i'll settle at war you might have

  • any shifts the triple levels up the nasdaq fortune minus

  • triple letters the nasdaq or something like that and say time

  • you've got low sophistication to buy and sell the system is without having see

  • and because they will exchange traded a base in stop-start

  • celiacs time and see deborah may also use generally

  • very very liquid the on on certain things saying I'm

  • sorry that take okay it might be the biggest example I can think up with an

  • e.t.a

  • and tracking error is USO yes the oil

  • ETF in the US market and I don't know what it is now but years ago it was 10

  • percent two years enormous

  • are ETF's the dumb money for you know that they're gonna do something in you

  • exploit it when they first came out there was a heavy load index of charge

  • not try to

  • United special night live. the classic S&P 500 and you know the

  • St line 119 keep trying to exploit differences in that and

  • here that can still be done you're you're investing significant man you

  • technology to do that you

  • okay so what is the advantage in the current market with the TSC

  • I would say it's not so much the any kind of trolls on on that so much for

  • the retailer Lisa for talking

  • so it's stability say well like a hand

  • this ETF has some peace of mind this is something to do

  • oil and there's fucking a fundamental underlying relationship between these

  • two quantities

  • okay and the you you could come up with a clutch scientific hypothesis

  • for why say a company that has a basket gold stocks

  • plus another easy if that measures the spot price goal tracks as well for

  • school

  • put in some sense because places or you know and you can exploit high

  • correlation

  • and and try to fix excuses is a much more scientific approach

  • to you here you know basically take a punt on any firm

  • you basically trying to exploit some kind is economic relationship that

  • should

  • partnership the definition BTS stay there

  • okay so you aren't just running math models you do have know what the market

  • is and FBI mean YouTube the

  • euro is coming with economy's fundamental idea is a your I

  • it they're all she complains that he really questionable may just literally

  • from focus prices from prior prices without any

  • concern about I'm fundamentals i mean

  • my personal views you need to mix the two okay because these

  • Black Swan defense right i mean if if there's no you

  • prior horrific volatility in the prices reason new

  • base model not been you know your PC is a

  • you not to expect a pic downloadable says the even a one may have just

  • happened

  • prize you look back period say its Sam

  • I think going so im prices a cases

  • is a tricky one because you will you were prolly hurt you in the future

  • circa so we have do we talk about performance

  • a lot of people have again quite very high level

  • it's got it's a sexy german Finance what's that highest-performing

  • return that you've ever seen an institutional finance the highest public

  • 1 I've ever heard of his love it that the very things one is

  • Renaissance technology they're the biggest fund in the US

  • ultra seeks is highs the best mathematicians and statisticians and

  • I tight Concord I think they're casket think women are things with the better

  • I'm they have a huge but

  • 35 percent return every year of fees on every shot okay so that's where absolute

  • best the best with math PHD's their mind they're they're very

  • they got a lot on the management process a to them to get back

  • return with that my money is is incredibly impressive

  • because you know it's quite easy to get much higher return

  • if you think a small amount to investigate doing something in the car

  • high-frequency space

  • we talked a lot about I quants that leave and they go manage their own money

  • and their trading sixty your account balances which is a lot of money in real

  • life

  • but in in finance it's not a lot of money so a

  • what kinda returns if they're really good care are possible on those kinda

  • downturn

  • with larry i mean you know you hitting the grapevine and people doing twenty

  • 20-25 percent I guess and probably your yet PSAs

  • time be really wanna be looking at their risk characteristics as well as I suck

  • figure cannot be created in isolation no of course not but everybody

  • you know you see it here that use on how much money that I make exactly coming

  • I would obviously one look at all to see characteristic is a mainland delaney

  • see how the shop rationing more not but time

  • I think you you could make a lot of money but the next year you may blow up

  • busy tight-lipped savior I I think the question really becomes what's the

  • country's

  • good return on a sensible the average that is likely lost

  • long-term and I think you can gently 15 to 20 few really good in exploiting

  • something that is

  • actual exploitation %uh the process rather than

  • so the pub condition right so

  • with the with quants one thing that caught my attention is you don't have

  • a magic algorithm like the one algorithm that does everything for you

  • guy was a sex act upset that you have heart lies and lies about Lindsay got

  • plenty light is coming out as

  • the academic would let some stuff that you can begin trading you can

  • replicate its you tend to have struck you are times where this thing loss to

  • six months a year

  • but then you'll know that newbie you be prepared for two dials you have to sort

  • is

  • the Act would you even consider it I'm cut off for a trading strategy

  • night hunting in the woods Hewitt's I'm generally keep it going into

  • it's not forming disorders level you expect in your

  • why you would have a mandate you so you basically hoping it doesn't politic off

  • a cliff that is the case yet generally they do you that the solution slightly

  • lower ones do tend to just K like that

  • okay the high-frequency ones can have a bit of a cliff fourth and you can see

  • why because they're exploiting things like

  • underlying hardware you right now and if the exchange the size of greatness

  • hardware or some

  • some other as incomes and concern seems that so that just immediately destroys

  • your

  • your house I heard that's a tough way to what walk into the office and you're

  • like oh our strategy doesn't work today

  • hey I think that's why these high previously cuz he pays column ICUs

  • suffer through the

  • the the pain that is you know he's the sort six-month research periods where

  • it just compete in the rugby put up from the room

  • he's just like it daily do these guys have high-stress jobs

  • I wouldn't someone I know been in the IPCC's I'd say it's not

  • much more high stress than any other german Finance the hours on gonna be

  • crazy

  • because they need people to be able to think rating right

  • you know there's a list studies that say that anything beyond a 40-hour workweek

  • you

  • come to the ability to missus pretty rapidly and they know that they

  • allegedly dated June people say they're not gonna make you work $89.00 weeks is

  • points not look different okay

  • I'm well not only the investment no it knows that it's all here

  • and I mean that you know can you be more hours than ever it's certainly been

  • there with deadlines

  • clearly but its you know it's much more the

  • the you know for the performance-driven kind of environment where

  • was getting things done be making money he we're not gonna make you stay ACLs

  • with Dalian a height

  • like yet know is I think about I think there is an A just you know they worked

  • 90 hours a week in a fly around the world it's just that

  • sounds cool but it's not yet tough game yeah even matter

  • the so that we'll with a quantitative trait or be up at night thinking about

  • oh god I got and I i'm only made

  • percent half this month in my boss is expecting to

  • I mean its if they're a source smart funds

  • you gonna have monthly losses are some signs this is the way it is I mean it

  • and police at a lower frequency yes partly and you will

  • it's difficult because you know in your practice the really expecting these

  • periods but as you said you see a nice up with a critique over some

  • looks great in history but when you actually go through that process if you

  • to follow the equity curve itself

  • their periods in its is dropping a land and say

  • in a wall while you're expecting those things troops it's still painful the

  • time yes

  • is what's going on what's happening nothing may be happening because the

  • statistical properties

  • alright in school generally civil what you see in the back to assist you

  • going through the particular period with Israel so is

  • deeds you look at these tests you get really involved in and it's all

  • scientific in it has a very

  • mathematical approach are daequan summer panic

  • but your too funny I mean it's a you know it's here because he was worried

  • about what you don't know you have included in the model

  • this is the guy who are you see here where that you have not created the

  • perfect model

  • I think anyone you trusts that model implicitly is

  • probably not a not really thinking from a fully quantitative mines in another

  • because it I mean by definition we could be a model is not reality

  • say this silly things as Miss was a a.m.

  • and the same here as any proxy so there's always things you don't need any

  • incoming

  • if this is the whole ethos behind the Wall Street Crash

  • 2081 in a Salem it

  • at the models had everything in there the this probably would have been an

  • issue but

  • but yeah they did and I mean it there's really famous examples like long-term

  • Capital Management was one other

  • the classics they the if they had and it was a plus one hundred

  • Russian bombs right Russian putting me in that

  • sexually please him up I section group

  • yeah exactly but you know that would have been a model Dave expected russia's

  • the default missus

  • is quite a Black Swan style event its but he probably wouldn't put it into

  • practice

  • so does ok whatever get jumpy

  • penny I mean it ends what you do you're constantly researching new models in

  • your you very wears a performance all the time

  • continue the mission force for you you understand their draw down periods

  • Unitarians limine is on 07 investors

  • some cities and those who don't you they will understand if you can get you crazy

  • drawdown

  • but OK time it's never fun you know it i think is

  • she knows they really are lost money a kid except it

  • concern you know and its you just

  • you what you really wanna be saying so is the model

  • doing something was not the right now I'm not aware of is a lot

  • is whatever's happening summits do the unknown unknown right or is it something

  • that I'm just

  • I'm just saying about period and that's the way it might think we're telling me

  • as I can just come up with an algorithm and then

  • move to the beach and forget about there's no there's no sipping martinis

  • early I mean I've had

  • but this is really funny annotate know the country quite funny needs

  • two entities function right man in the dog than that in a day

  • the the man is there to feed the dog and the dog is there to make sure the man

  • doesn't touch the computer let this tragedy and

  • is that okay that's what I i mean that's really what I'm asking as I have this

  • big red button theory

  • where especially traders that by these robots that I just hit the eject button

  • at the moment something doesn't feel right

  • dick wants to the same thing and there's there's a there's a very natural

  • tendency to want to interfere

  • okay and is often the case the interfering will generally have a net an

  • adverse effects

  • sewing I mean you the classic situation would be

  • you know you think you models not fully dealing with the antics in the market

  • opened

  • quickly okay and say you feel that if you wait say 10 minutes

  • you might get a you might get better execution that's a that's a

  • discretionary is awesome senator discretionary overlay and

  • its is this not hearing to the model I mean you have to say to supplying me is

  • is my model then

  • accounting for these enough the the that the dynamics of the market open a

  • shortened

  • should I now be putting these into my models getting cool if you sensitive

  • during that country's micro-management I think it becomes

  • you know GV is the best as the words whether you're actually running

  • construction of the next year

  • I told Mike I think we're at a time yet i want to thank you for joining me yep

  • and everybody thanks for joining us

  • I and

  • I'm gonna cut their yeah done

  • you

  • theme

Hi everybody, this is Shaun Overton with OneStepRemoved.com

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B1 中級

什麼是量販子? (What is a quant trader?)

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    陳步芳 發佈於 2021 年 01 月 14 日
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