# How is quantitative system trading profitable?



## goponcho (28 July 2011)

Hi, only still in my early stages of learning about systems trading. I've been learning about the use of amibroker slowly.

One thing that I am still unclear on is where we profit in systems trading. In order for us to profit, we need to be exploiting an inefficiency in the market right? What process goes into discovering these systems which exploit these inefficiencies? Trial and error after having an idea?

I get that we can use Amibroker to explore, backtest and optimize a system, but what process is required to develop a system with a *positive expectation*, not just a system.

After I have spent more time studying and learning how to create a system, will i definitely be able to find a system that will gain money?

Dont know if i explained myself properly, but yeah. Thanks guys!!


Also, as it is probably relevant, i am aiming to eventually trade a 100-200k account in a medium term time frame.


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## skc (28 July 2011)

goponcho said:


> Hi, only still in my early stages of learning about systems trading. I've been learning about the use of amibroker slowly.
> 
> One thing that I am still unclear on is where we profit in systems trading. In order for us to profit, we need to be exploiting an inefficiency in the market right? What process goes into discovering these systems which exploit these inefficiencies? Trial and error after having an idea?
> 
> ...




You made a good point. Many people get fancy with their system using all sorts of parameter and indicators, without really understanding why such a combination of numbers worked in the past, or why should/would it continue to work in the future. 

And if and when the system stops working, they don't really know how to fix it, as they never knew why it worked in the first instance.

IMO you need to watch the market first, identify an inefficiency first before back testing. Otherwise you are creating a blackbox for yourself...


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## goponcho (28 July 2011)

skc said:


> You made a good point. Many people get fancy with their system using all sorts of parameter and indicators, without really understanding why such a combination of numbers worked in the past, or why should/would it continue to work in the future.
> 
> And if and when the system stops working, they don't really know how to fix it, as they never knew why it worked in the first instance.
> 
> IMO you need to watch the market first, identify an inefficiency first before back testing. Otherwise you are creating a blackbox for yourself...





Thanks for your response. What kind of inefficiencies are there that we can use? Can you give some examples, even if they're not true just so i can get an feel for what I'm looking for?

Are they just simple, like if MA1 crosses MA2 we buy, and MA2 crosses MA3 we sell and historically we profit? This example doesnt make sense logically, but is just a correlation which would have made us profit in the past. Are the 'inefficiencies' we are looking for logical ones?

I dont think i really know what the inefficiencies are that I'm tlaking about, just know that they exist.

I am planning to keep an eye on the markets after my initial reading, though i have been in the past.


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## tech/a (28 July 2011)

You need to find opportunities where demand outstrips supply which will cause price to rise.
You then need to identify where demand is outstripped by supply which causes price to stall or fall.

How you do that is wide and varied
You'll need to spend the 1000s of hrs investigating the art of technical analysis and how to apply it--- just like everyone else.


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## AlterEgo (28 July 2011)

I don’t think it’s really an inefficiency. You’re just recognising the way stocks tend to move due to human behaviour. If you’re aiming to trade over a medium term timeframe, then I think what you should probably be focusing on is *trends *– ie. identifying what the current trend is and how to determine when the trend has likely changed. Trends can often persist for a considerable amount of time. The idea is to ride the trend as far as it goes, then exit when it reverses. Maximise your profit when you’re right, and minimise your loss when you’re wrong.


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## goponcho (29 July 2011)

tech/a said:


> You need to find opportunities where demand outstrips supply which will cause price to rise.
> You then need to identify where demand is outstripped by supply which causes price to stall or fall.
> 
> How you do that is wide and varied
> You'll need to spend the 1000s of hrs investigating the art of technical analysis and how to apply it--- just like everyone else.




Hi tech,

I dont mind spending the hours learning about technical analysis, just wanted to know that there _are_ profitable ways of applying it find inbalances in supply and demand. There is a lot of noise for a beginner trader to filter through.

So the positive expectation comes from greater knowledge of techincal analysis than the market, at buying and selling points?



AlterEgo said:


> I don’t think it’s really an inefficiency. You’re just recognising the way stocks tend to move due to human behaviour. If you’re aiming to trade over a medium term timeframe, then I think what you should probably be focusing on is *trends *– ie. identifying what the current trend is and how to determine when the trend has likely changed. Trends can often persist for a considerable amount of time. The idea is to ride the trend as far as it goes, then exit when it reverses. Maximise your profit when you’re right, and minimise your loss when you’re wrong.




Thanks AlterEgo. I will probably be trading with the trend, as it seems simpler for a beginner. Yeah ill probably look for low risk situations and let it run with maybe a trailing stop.


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## skc (29 July 2011)

goponcho said:


> Thanks for your response. What kind of inefficiencies are there that we can use? Can you give some examples, even if they're not true just so i can get an feel for what I'm looking for?




Here are a couple of (ficticious) examples.

- CBA and WBC are both influenced by the domestic economy the same way. So their share price movements should and do more or less mirror each other. Now for no apparent reason the share price has diverged. You place a bet on the price reverting to the mean. This is pairs trading using statistical / quantitative tool backed by actual logic.

- The SPI has never risen more than 10 days in a row. That kind of over-enthusiasm is shown to be smacked back down 65% of the time after 7 days, 72% of the time after 8 days etc etc. You have an edge placing a short as supported by the statistics.

With chart patterns, trends, support and resistance etc, you can probably come up with similar thoughts and logic, and test quantitatively whether such logic prevailed.


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## goponcho (29 July 2011)

skc said:


> Here are a couple of (ficticious) examples.
> 
> - CBA and WBC are both influenced by the domestic economy the same way. So their share price movements should and do more or less mirror each other. Now for no apparent reason the share price has diverged. You place a bet on the price reverting to the mean. This is pairs trading using statistical / quantitative tool backed by actual logic.
> 
> ...




Coooool! Gives me a taste of what i will be looking for.
With the first example, it seems quite specific to a particular pair of stocks. Is it suitable to create an entire trading system with(ficticiously)? From my limited reading, trading systems usually involves applying an idea across a whole range of stocks to find multiple trades, can we do this with such a specific idea?

The second example seems much more testable, and clearer on how the system will form.

I like how you tied how the logic related to creating a trading system, rather than just scouring a whole bunch of data for trades that would have worked in the past, and happen to work in out-of-sample data, without a reason. This is kind of how it seems in my recent reading, which would not leave me confident trading.


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## wayneL (29 July 2011)

I don't know much about quantitative trading, but I always thought quantitative trading was something distinct from technical trading.


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## skc (29 July 2011)

goponcho said:


> Coooool! Gives me a taste of what i will be looking for.
> With the first example, it seems quite specific to a particular pair of stocks. Is it suitable to create an entire trading system with(ficticiously)? From my limited reading, trading systems usually involves applying an idea across a whole range of stocks to find multiple trades, can we do this with such a specific idea?
> 
> The second example seems much more testable, and clearer on how the system will form.
> ...




There's a whole thread on pairs trading here. https://www.aussiestockforums.com/forums/showthread.php?t=14508

On statistical testing etc here's a good source. http://quantifiableedges.blogspot.com/


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## tech/a (29 July 2011)

wayneL said:


> I don't know much about quantitative trading, but I always thought quantitative trading was something distinct from technical trading.




Yes you do!
Black Sholes ring a bell?

Technical analysis is simply the vehicle to derive the numbers used in quant analysis.
You can of course get numbers for quant analysis from other areas un related to tech analysis.
So I wouldn't call it distinct--- but then again the use of it (tech analysis) in quant would make it distinct.
It's basically what your doing when you use it in systems design.


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## wayneL (29 July 2011)

tech/a said:


> Yes you do!
> Black Sholes ring a bell?



I guess so LOL.  I Just never connected the dots. 



> Technical analysis is simply the vehicle to derive the numbers used in quant analysis.
> You can of course get numbers for quant analysis from other areas un related to tech analysis.
> So I wouldn't call it distinct--- but then again the use of it (tech analysis) in quant would make it distinct.
> It's basically what your doing when you use it in systems design.




Again yes I guess so.


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## village idiot (29 July 2011)

you play poker dont you, OP?


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## goponcho (29 July 2011)

village idiot said:


> you play poker dont you, OP?




Yes sir. Do you feel I am thinking about things in an incorrect way because of this?


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## village idiot (29 July 2011)

not neccessarily. you are thinking about it in the correct way in that you need to find situations that are +EV and act upon that in a structured manner, which is a long way ahead of many who start out.  

The line you are bringing from poker is that the job is to figure out which are the losing players and how best to exploit them. 

Here it doesnt matter who is losing or whether the market is inefficient or not, just concentrate on identifying situations where the balance of probablilities is something other than random, by enough for a bet to be +EV. Which is the track you are on anyway

this is not a bad start; 



> Trial and error after having an idea?




and you are definitely on the right track with this;



> I like how you tied how the logic related to creating a trading system, rather than just scouring a whole bunch of data for trades that would have worked in the past, and happen to work in out-of-sample data, without a reason. This is kind of how it seems in my recent reading, which would not leave me confident trading.


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## howardbandy (30 July 2011)

Greetings all--

Developing trading systems that are profitable is very hard.

Barriers to entry into trading are low, rewards for being right are high, the markets are very nearly efficient.  

The basis of any trading system is to identify some inefficiency in the issue being traded -- identify some pattern that precedes a profitable trading opportunity.  Making profitable trades removes a portion of the inefficiency making it more difficult to be profitable in the future. 

Your competition is well educated, well trained, well paid, well equipped.    

I agree with Geoff Colvin in "Talent is Overrated" and Malcolm Gladwell in "Outliers" that it takes on the order of 10,000 hours of high quality practice to become expert at anything.  If anything, that is an underestimate for trading.

Thanks for listening,
Howard


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## mazzatelli (30 July 2011)

tech/a said:


> Technical analysis is simply the vehicle to derive the numbers used in quant analysis.




Hi tech, 
Could you elaborate further? - I've hardly seen any technical analysis, unless you count plotting the underlying price on a 2d plane, used in quant analysis.

It's more akin to what skc describes - corr, auto corr,co-integration, residuals on corr etc , basically relative value metrics.


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## tech/a (30 July 2011)

mazzatelli said:


> Hi tech,
> Could you elaborate further? - I've hardly seen any technical analysis, unless you count plotting the underlying price on a 2d plane, used in quant analysis.
> 
> It's more akin to what skc describes - corr, auto corr,co-integration, residuals on corr etc , basically relative value metrics.




I'm attempting to simplify.
Probably wont do a great job as the topic of Quant analysis could be discussed for ever.

The numbers derived from *say* stochastic can be the input function or part a group of functions with variables that can be tested against a data set to determine any advantage in their repeated application over time to a similar data set.

I dont wish to narrow the answer to my suggestion but offer it up as a part of the whole.

Now I maybe wrong so I will consult my in house Rocket scientist.


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## mazzatelli (31 July 2011)

tech/a said:


> I'm attempting to simplify.
> Probably wont do a great job as the topic of Quant analysis could be discussed for ever.
> 
> The numbers derived from *say* stochastic can be the input function or part a group of functions with variables that can be tested against a data set to determine any advantage in their repeated application over time to a similar data set.
> ...




Hi tech/a,
I'd say 85% of quant space assumes the underlying is random via stochastic processes e.g. brownian motion - this assumption only useful for instruments with optionality.

This leaves the remaining, of which I have seen times series analysis and forecasts used to model for example momentum e.g. correcting for volatility differences to filter out random noise that might look like momentum, rather than using a stochastic oscillator calculation.

Or e.g. converting prices to log returns and running F-test in an attempt to define trendiness rather than use a moving average.

There is some crossover e.g. using breadth statistics but I wouldn't say ta provides the majority of input for quant analysis. 

I noticed you cited using simulation e.g. bootstrap or Monte Carlo to test if a systems returns are statistically significant, with a stochastic oscillator as the/a input. I agree it is a quantitative approach, but debate the perceived common use of traditional ta indicators.

Relevance to OP? goponcho can look into stat-arb [pairs trading] as suggested by skc or loose arbs [ - buy X at n and looking to sell Y at n+1], since this is classed under traditional quantitative trading.

All semantics, and by no means a dish on technical analysis. 

Is your rocket scientist now working in finance?


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## tech/a (31 July 2011)

Thanks Mazza
Beyond basics is above my fighting weight.

Rocket scientist is developing a cancer identification instrument through protien identification using light refraction,
So I guess you couldnt get further from finance!


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## goponcho (31 July 2011)

Thanks for relating back to the thread Mazza, i got a whiff of what you were saying but it was complex  Im still too new to care about semantics


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## skc (31 July 2011)

mazzatelli said:


> Hi tech,
> Could you elaborate further? - I've hardly seen any technical analysis, unless you count plotting the underlying price on a 2d plane, used in quant analysis.
> 
> It's more akin to what skc describes - corr, auto corr,co-integration, residuals on corr etc , basically relative value metrics.




Did I really say all that? I have no idea on what half those terms means.

But I do conduct most of my trading - technical, fundamental or quants - on relative value metrics. To me that offers higher accuracy and logic than without.


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## maximillian (2 August 2011)

I don't see any clear distinctions here of Technical Analysis compared to Quantitative Analysis, so here's my interpretation based on Howard's 10,000+ hours of studying and using both types of analysis.   I'll then address the question asked of this thread.


Any Technical Analysis is essentially reduced to finding statistically significant relationships.  Quantitative Analysis seeks to quantify how statistically significant those relationships are.  QA is aligned with mechanical systems trading as it seeks to execute trades based on precise information and timing.  Whereas TA allows for any type of logic, reasoning, rational and irrational, more art than science. 


How is quantitative system trading profitable?

Okay I'll give an answer that favours either method.
Take for instance, momentum.  Totally quantifiable 
and it's use also subject to a viewers interpretation.
Depending on your method of analysis you could
trade and be profitable or unprofitable.  Profitability
is not mutually exclusive to analysis.


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## mazzatelli (4 August 2011)

tech/a said:


> Rocket scientist is developing a cancer identification instrument through protein identification using light refraction,
> So I guess you couldn't get further from finance!




That is awesome!!! At least he is putting that great mind to good societal use other than providing liquidity :



skc said:


> But I do conduct most of my trading - technical, fundamental or quants - on relative value metrics. To me that offers higher accuracy and logic than without.




+1 e.g. index beta trackers, implied v realized vol, discount arbs, convergence of tenor in futures


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## tech/a (4 August 2011)

mazzatelli said:


> That is awesome!!! At least he is putting that great mind to good societal use other than providing liquidity :
> 
> 
> 
> +1 e.g. index beta trackers, implied v realized vol, discount arbs, convergence of tenor in futures




Mazz
Youve tweeked my curiosity
I dont use any of that.

Would you ( or anyone else) mind explaining how you go about this and what you look for.
Then how you apply it in a practical sense to your trading.

Appreciated.


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## mazzatelli (4 August 2011)

tech/a said:


> Would you ( or anyone else) mind explaining how you go about this and what you look for.
> Then how you apply it in a practical sense to your trading.
> 
> Appreciated.




Professionally, I've never worked in equities space, but the methods mentioned are fungible/transferable to this market.

Index Beta Trackers
What to look for?: Premise is to compare a custom index of products vs. target that tracks the counterpart well e.g. for index vs. custom basket of stocks. Identification can be based on beta, correlation &/or clustering e.g. k-means

Practical: Once the basket has been identified, you can take the moving average - above would be MA of index and custom basket and trade the crossovers as signals

Implied v Realized vol
When I mention vol = volatility, not volume.
Observe the response of implied vol in the options to changes in realized vol. Realized vol can be modeled with GARCH etc and implied vol data fit with some parametric skew to reflect the smile in the market you are dealing with. 

Practical: Involves trading the spread between the two measures. Analogous to pairs trading when correlations diverge/converge.
From a retail perspective, replicate the vols using ATM gamma in the options (short for converging spread, long for diverging spread).

Discount Arbs
The term is a misnomer since its not a pure arbitrage. The relative value being to basically you replicate positions [derivative] cheaper than current market value. From a buy side perspective, you'd have to take on some price &/or vol risk to replicate

Practical: E.g. replicate butterfly spread at less than mv, long the wings with a view to vol increase, then short the body

Convergence of tenors
Relationships between spread of the prices in futures contracts from month to month, being cognizant of the term structure and news released.

Practical: e.g historically max spread between May/Jun futures is $1.00 . As of today the May/Jun spread is $1.50. Play for convergence of spread to $1.00 [assuming flat term structure]


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## village idiot (4 August 2011)

mazza, 

I'm not quite clear on whether you are saying these are the main strats you trade; that is the implication of post #24;


> +1 e.g. index beta trackers, implied v realized vol, discount arbs, convergence of tenor in futures




but this quote confuses me a bit;


> Professionally, I've never worked in equities space, but the methods mentioned are fungible/transferable to this market.




so this is saying all your option strats are in anything but equities ie fx, commodities, Treasuries/rates etc, indices(stiil in equity space?), or are you saying here that you stick to options or strategies including options, as opposed to just long/short equities?

in short, if you are trading the 4 strats you list, what space are they in?



mind if I drill down a bit more on the strategies just to be clear?



> Implied v Realized vol - From a retail perspective, replicate the vols using ATM gamma in the options (short for converging spread, long for diverging spread).




is this a fair summary;
replicate (or just become exposed to) the IV by using the options, with the simplest way being using the ATM straddle (sell when IV relatively high, buy when IV relatively low)? Then replicate Realised vol by delta hedging the underlying (is there another way)? 
Close out position if spread converges by eg IV dropping to meet RV (in case where IV>RV), or run till expiry to harvest the difference if they never converge?
In comparing IV-->RV are you using an 'offset' of some sort to adjust RV to account for the fact that RV if measured in the usual way (eg HV45) usually underestimates potential RV because it uses a small sample which does not usually include a fair representation of the whole population of possible returns?
(ps I am using RV interchangably here with Statistical or Actual or vol, correct me if you mean something different)
If that is what you mean , what sort of securites do you find it works best in? 





> The relative value being to basically you replicate positions [derivative] cheaper than current market value. Practical: E.g. replicate butterfly spread at less than mv, long the wings with a view to vol increase, then short the body




how do you replicate a butterfly at less than current mv ? obv you can replicate it all sorts of ways but where do you find components out of line pricewise by enough for a retail punter to take advantage before the big boys do? 




> Practical: e.g historically max spread between May/Jun futures is $1.00 . As of today the May/Jun spread is $1.50. Play for convergence of spread to $1.00 [assuming flat term structure]




as most futures spreads are tied to the cost of carry, again, where do you find spreads out of line by enough to profit such as in the above example? surely any time it gets out of line enough it would be arbed back in pretty quickly, otherwise it would be free money. Either that, or there is a reason for it to be out of line (seasonal factors, maybe actual production or transportion issues?), but then it wouldnt be a relative value trade, rather a punt on whether the markets expectations are right.
The only exception to this that i know of the VIX curve, which i do trade on the premise you have given, as well as the time decay aspects. If there are others about would love to know about them.


thanks


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## mazzatelli (5 August 2011)

village idiot said:


> so this is saying all your option strats are in anything but equities ie fx, commodities, Treasuries/rates etc, indices (stiil in equity space?), or are you saying here that you stick to options or strategies including options, as opposed to just long/short equities?



I've traded equity + index ops in a personal account in the past, however for work, yes, its been anything but equities.



> replicate (or just become exposed to) the IV by using the options, with the simplest way being using the ATM straddle (sell when IV relatively high, buy when IV relatively low)? Then replicate Realized vol by delta hedging the underlying (is there another way)?
> Close out position if spread converges by e.g. IV dropping to meet RV (in case where IV>RV), or run till expiry to harvest the difference if they never converge?



Yes, but as you know the rub is transaction costs of dynamic hedging. I've found that if vol model signals are very short durations e.g. <7 days, trade the fly for exposure, with wings as static hedges and avoid dynamic hedging. 



> In comparing IV-->RV are you using an 'offset' of some sort to adjust RV to account for the fact that RV if measured in the usual way (e.g. HV45) usually underestimates potential RV because it uses a small sample which does not usually include a fair representation of the whole population of possible returns?



RV/stat vol would be calculated and analyzed using high frequency data to overcome the sample limitations of daily data w.r.t to vol (greater sample size -> central limit theorem for parameters). iirc correctly we discussed using an alternative rv measure (Parkinsons?) in another thread if you want to work exclusively with daily data.



> how do you replicate a butterfly at less than current mv ? obv you can replicate it all sorts of ways but where do you find components out of line pricewise by enough for a retail punter to take advantage before the big boys do?



In my previous post, I mentioned that if you're not on the sell side, for vanilla ops you'd have to take on some price &/or vol risk to replicate - basically legging into the position e.g. for the fly at small debit or credit [equivalence]. 

So it's strongly contingent on weighing vol &/or price model's edge vs. risk of vega.



> as most futures spreads are tied to the cost of carry, again, where do you find spreads out of line by enough to profit such as in the above example? surely any time it gets out of line enough it would be arbed back in pretty quickly, otherwise it would be free money. Either that, or there is a reason for it to be out of line (seasonal factors, maybe actual production or transportation issues?), but then it wouldn't be a relative value trade, rather a punt on whether the markets expectations are right.



It would be a combination of relative value and expectations, you wouldn't blindly trade a convergence/divergence output from your model if there is a valid reason for the [new] spread existing.

I mentioned this with VIX-like products in mind. The other market that the VIX has comparable structures are FI tenors, but the spreading will be on different classes e.g. govt and corporate spreads to converge (bullish play on economy), and matching of short/long legs would be duration (and possibly convexity) based where duration is to delta, and convexity is to gamma in ops for FI securities.


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## village idiot (5 August 2011)

mazza, thanks for taking the time to reply to my many questions. that clears up a few things for me
So you work in a bank in quant analysis or something like?



> *Yes, but as you know the rub is transaction costs of dynamic hedging*. I've found that if vol model signals are very short durations e.g. <7 days, trade the fly for exposure, with wings as static hedges and avoid dynamic hedging.




Indeed they are. I have tried diligently to trade vol from the long side as well as the short side for balance.

 however bearing in mind the following rule of thumb ;
- when long vol, you *have to* adjust/hedge frequently, as that is where your profit is coming from most of the time. failure to take a profitable hedge (round trip) is money lost forever and will see time decay (and possibly vega) eat away at you
- when short vol, every adjustment/hedge eats away at your potential profit, so you want to make as few adjustments as you can get away with

when long vol then, transaction costs in dynamic hedging at the optimum frequency are a big killer, meaning you need a much larger edge in the first place to attempt it. 
Transaction costs when short vol are less of a killer since we are trying to keep adjustments to a minimum anyway.  However I tend to take a hybrid 'two bob each way' approach, adding the wings to say half the position early on, then loosely dynamically hedging the rest when and if required. 




> RV/stat vol would be calculated and analyzed using high frequency data to overcome the sample limitations of daily data w.r.t to vol (greater sample size -> central limit theorem for parameters). iirc correctly we discussed using an alternative rv measure (Parkinsons?) in another thread if you want to work exclusively with daily data.



i dont like to work with daily data much at all, apart from being the base unit in a spreadsheet. I find the idea of using a small recent sample of daily fluctuations as a basis for the distribution of possible future returns over some longer period to be somewhat silly. It contain 2 flaws which compound each other;
1. in real life the SD of returns over a period longer than 1 day does not match the SD implied by using one day returns multiplied by the sqrt of time
2. the distribution of a small recent sample does not (usually) faily reflect the distribution of all possible outcomes

No method provides a definitive probability distribution, but I prefer to use the distribution of x day returns over a  larger sample (i use 10 years) as a starting point, influenced upwards or downwards by recent HV.  daily data HV also is useful as an indication of how many hedge adjustments you can expect, to either have to make, or have the opporunity to make, depending on which side youre on. 

But i would be interested in hearing more about how the 'using high frequency data' method works


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## goponcho (5 August 2011)

woooosh


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## mazzatelli (7 August 2011)

village idiot said:


> So you work in a bank in quant analysis or something like?



Yeah, in the past I was a quant at an IB



> - when long vol, you *have to* adjust/hedge frequently, as that is  where your profit is coming from most of the time. failure to take a  profitable hedge (round trip) is money lost forever and will see time  decay (and possibly vega) eat away at you
> - when short vol, every adjustment/hedge eats away at your potential  profit, so you want to make as few adjustments as you can get away with
> 
> when long vol then, transaction costs in dynamic hedging at the optimum  frequency are a big killer, meaning you need a much larger edge in the  first place to attempt it.
> Transaction costs when short vol are less of a killer since we are trying to keep adjustments to a minimum anyway.



Are you implying there is edge to short gamma side of a position?

What you say is more a truism for a choppy market. In a strongly  trending market, you'd rather the intervals between hedges be larger  [reducing frequency] for long vol and hedge more frequently for short  vol.



> No method provides a definitive probability distribution, but I prefer  to use the distribution of x day returns over a  larger sample (i use 10  years) as a starting point, influenced upwards or downwards by recent  HV.  daily data HV also is useful as an indication of how many hedge  adjustments you can expect, to either have to make, or have the  opportunity to make, depending on which side you're on.



What variables distribution are you trying to define here?
I ask because there is a difference between the distribution of asset returns and the sampling distribution of volatility.

Going  by what you've got above, you seem to be incorporating a lot of  redundant data. Basically all institutions run intra-day data for  volatility analysis e.g. for 20 days realized vol, by using tick data,  so the sample size increases significantly without changing the calendar  time as you have done [10 years]. 

Question boils down to how you are determining the adjustment factor [rhetorical].



goponcho said:


> woooosh



lol, sorry about all the bs. Let us know if you want it to move to another thread, since its moving away from systems trading


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## goponcho (8 August 2011)

mazzatelli said:


> lol, sorry about all the bs. Let us know if you want it to move to another thread, since its moving away from systems trading




Lol nope, go for it seems like u guys are gettin some solid discussion in


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## skc (8 August 2011)

mazzatelli said:


> Yeah, in the past I was a quant at an IB




I knew it. It was you who caused the GFC!


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## village idiot (8 August 2011)

haha , nothing like having a debate on a public forum with someone more knowledgeable than one's self to expose and focus one's thinking. 



> Are you implying there is edge to short gamma side of a position?



I am not implying there is any edge to either side of options per se. whether there is or not depends on the price of the particular option in question. 
 I havent done much with currency options, but from my brief look at them there was no edge anywhere that i could see, so no I am not suggesting any edge to either side. I have never really looked at commodities/FI etc  so cant comment but assume it would be the same there
In equity space; equities,  paticularly equity  indices , and even more particularly the SPX, the mean reverting bias outweighs the fat tails from an EV point of view,   and options on these seem to be overpriced more often than they are underpriced. 
Then in these cases yes I am implying there is often an edge to short side, although that clearly isnt always the case. My comments in the previous post were in reference to equity/index options only, which I should have made clear.  

if there was no edge you couldnt have a picture like this; underlying is SPX





> What you say is more a truism for a choppy market. In a strongly trending market, you'd rather the intervals between hedges be larger [reducing frequency] for long vol and hedge more frequently for short vol.




Agree. Again I was thinking of equity/indices which imo are choppy and mean reverting enough to favour hedging less if short vol. In above graph unhedged short vol strategy beats delta hedged short vol strategy. 


also agree; apologies for hijacking the thread, we can move it to the derivatives forum if others prefer.


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## village idiot (8 August 2011)

> What variables distribution are you trying to define here?
> I ask because there is a difference between the distribution of asset returns and the sampling distribution of volatility.




with this anaylsis; distribution of asset returns. I acknowledge that this is looking at the end result rather than the path taken to get there, so it is looking at only half the story. I do look at the path to get there as well, using data from daily returns. I see what you are saying in that that method of analysis is not useful in detremining the volatility one might experience between opening and expiry, which is more relevant to a discussion on hedging
now I am going to have to go and read up on sampling distribution of volatility. 



> Going by what you've got above, you seem to be incorporating a lot of redundant data. Basically all institutions run intra-day data for volatility analysis e.g. for 20 days realized vol, by using tick data, so the sample size increases significantly without changing the calendar time as you have done [10 years].




I did not know that. But if its a quieter 20 days than normal isnt the RV using tick data still going to be lower than 'normal'?



> Question boils down to how you are determining the adjustment factor [rhetorical].



I dont know to determinine it quantitively, I just know it needs to be (and is)  allowed for somehow,  i am curious how the pros do it which is why I was asking you.


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## mazzatelli (13 August 2011)

village idiot said:


> In equity space; equities, particularly equity  indices and even more  particularly the SPX, the mean reverting bias outweighs the fat tails  from an EV point of view,   and options on these seem to be overpriced  more often than they are underpriced.



Are you saying that when calculating expected value, the  effect of fat tails is offset and more by the tendency of the index to  mean revert? This is how I am reading that statement, and if that is the  case why worry about black swan events?

Either way vanilla  options are priced with risk neutral valuation. To incorporate jumps in  price, empirically there is a risk premium that exists for all options  (iv > hv, its about 400 basis points on index) regardless of market  e.g. currencies, interest rates, commodities etc. 

More often  than not there is a reason for the risk premium. If the ops are  overpriced, why does a volatility skew exist [in indices]?



> if there was no edge you couldnt have a picture like this; underlying is SPX



The  draw down in 2008 is quite large, and I'd imagine if using a geometric  position sizing model, it would be difficult to recover from those  losses.

I didn't see what the Sharpe ratio is for those  strategies? I'd imagine that it would be pretty low and wouldn't be  justifiable for any of the big boys to run a book based on this  strategy.

Studies have shown a random entry for stocks [long only] have positive expectancy over the long run. Edge?



> But if its a quieter 20 days than normal isnt the RV using tick data still going to be lower than 'normal'?




No, I think you are confusing long run variance with population variance for n days.

If you are asked what is the "true" variance for the period spanning dates 1/1 - 20/1?
You  can take the 20 days returns (sample = 20 observations) and calculate  by dividing the sum of squared returns from the mean, divided by n-1  degrees of freedom to obtain a variance measure.

Generally by law  of large numbers the greater the sample size, the closer the parameter  is to the statistic being estimated, so using tick data, from 1/1 - 20/1  the sample can contain >100 observations rather than 20 as before.

Once  this variance is calculated - then the process to contextualize it  historically begins. [regime changes (macro-events, earnings etc.),  current risk premium levels, forward vol as function of stat vol e.g.  what percentile is it vs. the 10 year variance etc]


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## village idiot (14 August 2011)

I am enjoying this discussion. Please treat my responses as answers to a test rather than trying to teach granny to suck eggs.



> Are you saying that when calculating expected value, the effect of fat tails is offset and more by the tendency of the index to mean revert? This is how I am reading that statement, and if that is the case why worry about black swan events?



yes, that is what I am saying, if you calculate the probabilities using the distribution from back data. however back data by definition doesnt account for the possibility of all jumps or black swan events. As I have said before I acknowledge the need to make some allowance for them.  Why worry about them? because even if a bet was known to be +EV it doesnt mean the worst possible outcome wont happen more than it 'should' over a small number of trials?



> Either way vanilla options are priced with risk neutral valuation. To incorporate jumps in price, empirically there is a risk premium that exists for all options (iv > hv, its about 400 basis points on index) regardless of market e.g. currencies, interest rates, commodities etc.



that would be the offset or 'adjustment factor' we have discussed in previous posts?  but how do we know that risk premium is 'correct', since we cant really know the probs or impact of all black swan events? since the risk premium the market puts on it is a bit of a guess , it is quite possible it is under or overstated.



> More often than not there is a reason for the risk premium. If the ops are overpriced, why does a volatility skew exist [in indices]?




It exists because the fat tails to the downside have historically meant that OTM puts have 'paid off' far more than would be priced in by using HV or ATM IV. However I am not seeing that that proves anything about whether ATM options are over or under or fairly  priced. 



> I didn't see what the Sharpe ratio is for those strategies? I'd imagine that it would be pretty low and wouldn't be justifiable for any of the big boys to run a book based on this strategy.




yes, that chart would have been more useful if it had included some sort of benchmark to compare it against, such as risk free rate or the underlying. There were no sharpe ratios included in the article i got it from and i am not familiar enough with the information ratio to know where IR=1.1 puts it?

Regardless of whether it could be traded as a system with +EV,  would it not be fair to say that if options were always fairly / risk neutral priced then the net result of such a strategy should be random swings ending up at zero over a long term, not an upward sloping equity line? (I will concede here that perhaps  1996 to 2009 is not a long enough period to draw a conclusion, and that it is possible that the positive equity  from such a strategy to date is in fact merely advance compensation for the risk of some black swan event that didnt happen yet ).
Or have I got it wrong and it is supposed to be that a short vol strategy 'should' make the risk free rate ( and by corrollary a long vol strat would expect to 'pay' it.?

Furthermore, it is my understanding that the whole BS model is based on the principle that the value of an option should be equivalent to the amount gained or lost by delta hedging (ignoring costs) if RV turned out to be equal to IV paid. If that was in fact true , then the result of an unhedged ATM straddle strategy should be the same as the delta hedged strategy (long or short) ? Yet in the graph I posted that has not been the case, there is divergence between the strategies,  which suggests there must be an edge somewhere. If we took the delta hedged equity line as being 'par' or zero EV (just equiv to risk free rate?), then unhedged must have an edge over it at least?



> Studies have shown a random entry for stocks [long only] have positive expectancy over the long run. Edge?




i am guessing that the positve expectancy would be equiv to either inflation or a risk free rate or somewhere round there? If we defined an edge as being positive expectancy over and above the risk free rate then no, no edge there. if it was above that, then, well; positive expectancy=edge, is there any other way to see it?




> No, I think you are confusing long run variance with population variance for n days.




quite possibly, I am not an expert on stats. i will take your word for it that using tick data is better, but I dont suppose I will ever be in a position get to use it


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## goponcho (17 August 2011)

Arrite guys, I have another question. (sorry to interrupt ur superdiscussion)

So we basically have a logical idea we want to trade, back test it, then test it on the out-of-sample data and if its profitable we trade it right.

Isnt this sort of curve fitting? If we do this procedure enough times wont we come across a system which is profitable in the OOS data just by pure chance?

How can we trust our system to have a positive expectation?


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## howardbandy (18 August 2011)

Hi GoPoncho --

You are correct.  There is always a bias.  Even systems designed and validated using walk forward out-of-sample testing can appear to be valid, when they are not, due to having examined so many that some will eventually pass the test.

What is the alternative?  Using less stringent testing procedures will allow a higher percentage of invalid systems to move from development to trading.  

I recommend continuously monitoring the health of every system, applying statistical tests to give a metric of system health, taking any system that appears to be performing poorly offline, and waiting for its performance to return to an acceptable level.  

The risk of just watching a good system is lost opportunity.  The risk of trading a broken system is lost money.

My newest book, Modeling Trading System Performance, addresses exactly this issue.

Thanks for listening,
Howard


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## goponcho (18 August 2011)

Hi Howard,

Thanks for the response.
I now see that creating a system around a trading idea is a tradeoff as you explained. As only a certain frequency of truly profitable systems should be expected it makes sense to have active monitoring.

I think I'm still not sure about where to look for the trading idea itself, but I'm slowly trudging through information  Getting there; very, very slowly.

Reading your Introduction to Amibroker at the moment, and will certainly read Modeling Trading System Performance later down the track.

Thanks!


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## mazzatelli (22 August 2011)

My post wasn't meant to be condescending.
I typed out a whole bunch but lost it in the refresh. Summary as follows:

1) Historically index related returns are leptokurtic (> mean    reversion than normal) and so edge is to short vol with random entry,   since this edge persists.

2) 







> that would be the offset or 'adjustment factor' we have    discussed in previous posts?  but how do we know that risk premium is    'correct', since we cant really know the probs or impact of all black    swan events? since the risk premium the market puts on it is a bit of a    guess , it is quite possible it is under or overstated.



Index   vols are systematically rich in general - i.e. traders  consistently   price a premium to realized vol, but thats not to say they  are rich at   any given point in time. Your question is absolutely valid,  most cases   we don't know if the risk premium is correct until after the  fact.   Empirically despite the risk premium it still underprices options,    hence vol skew. otm vols are a function of atm vols so when I discuss    them, they are not independent of each other when modeling the surface.

3) 







> i am guessing that the positive expectancy would be equiv to   either  inflation or a risk free rate or somewhere round there?



Yeah sorry, that originally should have read positive returns.   It's +ve expectancy if > inflation and risk free rate as you've   stated

4) 







> Regardless of whether it could be traded as a system with   +EV,  would it  not be fair to say that if options were always fairly /   risk neutral  priced then the net result of such a strategy should be   random swings  ending up at zero over a long term, not an upward sloping   equity line?



As stated in point one, if this "edge" persists  so  one can take random  entries every month, is this the case with   Barclay's strategy? Or do  they have a specific signal of which they   trade from? 
Looking at their long/short vol portfolio the triggers are based on 2    metrics a) component stocks of a sector are higher by x basis points    than a sector index (e.g. ETF) and b) implied vol vs. adjusted implied    vol (where they take out earnings days)
If the latter is the case, is the edge due to what you propose or their    ability to discern changes in implied vol vs relative iv of sector and    historical iv. I'm guessing their index signals would have some  metrics   linked to the VIX.

Another reason why I am interested, I see in one of their publications,  they are short the variance swap and hedged using the VIX.

http://www.scribd.com/doc/35884388/Barclays-Index-Volatility-Weekly-20100809
Here they look for put spreads, based on relative value to other sectors

5) 







> Furthermore, it is my understanding that the whole BS model is    based on the principle that the value of an option should be   equivalent  to the amount gained or lost by delta hedging (ignoring   costs) if RV  turned out to be equal to IV paid.
> If that was in fact true, then the result of an unhedged ATM straddle    strategy should be the same as the delta hedged strategy (long or    short)?



Assuming iv=rv and optimal hedges were possible, then   atm vol  theoretically equates to all payouts. The probability of payout   for the position whether hedged or not does not change.

The expected return could be different (as shown in your graph).

6) 







> quite possibly , I'm not the expert in stats



There's   no need to trust me, since I know you DYOR. This is though, stats 101    regarding increasing sample sizes to reduce sampling errors. With    financial data the errors are heteroskedastic, so increasing the scope    (in this case years) does not improve estimates hence all the  literature   surrounding volatility measures that include open, close,  high, low   when working with daily data and on the other side high  frequency data.

EDIT: lol bs this was a summary, half a page...sorry goponcho


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## village idiot (24 August 2011)

> 1) Historically index related returns are leptokurtic (> mean reversion than normal) and so edge is to short vol with random entry, since this edge persists.




Isn’t that what I have been saying and you have been disagreeing with?  Or are you now referring only to returns (ie at expiry)  rather than vol path on the way?

I think I maybe see the light – we have perhaps been talking at cross purposes about two slightly different things – volatility and returns.   In brownian motion theory, (distribution of) returns is a direct function of volatility, so they are two sides of  the same equation - if you know the vol , you can predict the return distn.  But we  know in the real world  the leptokurtic/ mean reverting distribution of returns does not match the assumed normal distn and is  not necessarily a direct function of volatility, so the two begin to diverge making two approaches possible

Where our approaches differ is that, you seem to concentrate on the volatility, I’m more focused on returns (but without ignoring the volatility path on the way).  

Why is this so? Well because I am a retail punter who has to pay significant transaction costs**, which largely prevents or at least restricts me from engaging in strategies involving too much trading in and out or dynamically adjusting hedges. Thus I pretty much had to concentrate on strategies that would only require minimal transactions, so I tend to look for edges in distribution of returns v vol paid or received. Then hedge as little as possible.

Whereas your background is from a bank, with minimal transaction costs and maximum scope to data crunch and take small edges, which is more conducive to straight vol or vol arbitrage plays.

**even using Interactive Brokers, transaction costs are large enough to impact significantly on a strategy requiring frequent hedging adjustments, much as I would like to use them


Here is the article I pulled that graph from

http://www.surlytrader.com/volatility-selling-strategies/

It is short on detail and it does vaguely refer to a Barclays ‘piece’ as the source of it,  which I haven’t seen but you might be able to find. 

But without that ‘piece’ I saw nothing to indicate it is for anything other than consistent periodic entries with no other filters or inputs. At least that is what I assumed, otherwise it wouldn’t be analysing the effectiveness of volatility selling strategies, it  would be analysing the effectiveness of  Barclay’s signals for selling vol, which as you say  is a different thing.  What do you see that indicates the metrics you suggest are being used, or are you referring to some other portfolio? edit'; could you post a link to  the long/short portfolio you mention. thanks

Going back to what I said above about the two approaches;   in the chart I posted,  the unhedged ATM short straddle equity line  is essentially the ‘asset returns v IV ’ approach, the delta hedged strategy equity line represents  the ‘volatility arbitrage; (RV v IV)’ approach


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## mazzatelli (2 September 2011)

village idiot said:


> Isn’t that what I have been saying and you have been disagreeing with?



Was just summarisizng what you said.
I agree about asset returns have the characteristic of leptokurtosis. Don't agree that in itself is an "edge".

Energy, fx etc all have leptokurtosis and a risk premium priced into the ops. But sure wouldn't sell ATM vols regardless every month.



> I think I maybe see the light – we have perhaps been talking at cross purposes about two slightly different things – volatility and returns.   In brownian motion theory, (distribution of) returns is a direct function of volatility, so they are two sides of  the same equation - if you know the vol , you can predict the return distn.  But we  know in the real world  the leptokurtic/ mean reverting distribution of returns does not match the assumed normal distn and is  not necessarily a direct function of volatility, so the two begin to diverge making two approaches possible



Leptokurtosis is still taken into account. For e.g. guy I knew on the equities desk priced ops under a smile model + Heston, but hedged under Black-Scholes. 
Another example :If you look at vol modeled under GARCH - it will produce a leptokurtic distribution.



> Where our approaches differ is that, you seem to concentrate on the volatility, I’m more focused on returns (but without ignoring the volatility path on the way).



It reminds me a lot of the systematic Iron Condor selling to take advantage of the fact the index "doesn't gap and tends to mean revert".



> But without that ‘piece’ I saw nothing to indicate it is for anything other than consistent periodic entries with no other filters or inputs. At least that is what I assumed, otherwise it wouldn’t be analysing the effectiveness of volatility selling strategies, it  would be analysing the effectiveness of  Barclay’s signals for selling vol, which as you say  is a different thing.  What do you see that indicates the metrics you suggest are being used, or are you referring to some other portfolio?



I was referring to another portfolio, and guessing since I cannot see the background to these pictures you posted.

I was surprised, because if it was a straight vol play as you say, then the variance swap should come out on top vs. a vanilla straddle. From the article it seems they didn't price the tails 'large" enough hence the larger loss comparatively.

So are they playing vol or distribution (despite the title of the article)?



> Going back to what I said above about the two approaches;   in the chart I posted,  the unhedged ATM short straddle equity line  is essentially the ‘asset returns v IV ’ approach, the delta hedged strategy equity line represents  the ‘volatility arbitrage; (RV v IV)’ approach



 I thought the trading signal was the same and comparing the results using different vehicles?


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## mazzatelli (8 February 2012)

I was looking at returns of CTA's this morning and remembered this thread. Results were a mixed bag for those trying to capture index volatility risk premium. 

LJM Partners, one of the larger ones ($300M AUM), YTD is about -4%. Ace Investments up 5%, although was -46% previous year (~both 10 year records). Cinamen Vol Arb Program opened in '09 and is ~-29% YTD.  Diamond Capital on the other hand is up 2.2% YTD (6 year record) and OpHedge is up 4.66%, although they do not focus entirely on index.

ML's index which tries to capture the implied/realized spread has taken a bit of beating (though they sell the 90-day var swap instead of the straddle). Clicking on the 3 year chart looks rosy, while the 5 year, not so much
http://www.bloomberg.com/quote/MLHFEV1:IND/chart

imo It's debatable whether the risk premium adequately compensates left tail risk.


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## village idiot (8 February 2012)

didnt know thos existed till now so thanks for the heads up. I had a look at LJM and ML. couple of points;

1.  LJM shows -4.12% for the full calendar 2011 year. i would say that is pretty acceptable for  a year with an outbreak of vol like we saw in august, when any years without such an outbreak return figs like 54% 42% 48% 21% etc. 

2008 to be sure was huge drawdown for any of these sorts of strategies (-49%), whether 1 huge drawdown is enough to write off a strategy that shows 17.7% annualised return over the longer term (i am using LJMs figures here,not ML) is debatable. On those figures it has a poor risk;reward but is nevertheless +EV

2. I note LJM have a 20% incentive and a 2% mgt fee, although it is not clear how often the 20% is raked.  To give away 20% of your profits when you have a win, (but not get back 20% of your losses), plus 2%pa of your capital regardless, would put such a handbrake on the whole scheme that it would probably turn a strategy that was +EV into -EV. That headwind must be built in to the published performance figures, so you would be expect the performance of the actual underlying strategy, which is what we are debating here,  to be considerably better than the reported fund performance. 

With such a negative edge added in it is a wonder they made anything at all.


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## mazzatelli (8 February 2012)

village idiot said:


> 1.  LJM shows -4.12% for the full calendar 2011 year. i would say that is pretty acceptable for  a year with an outbreak of vol like we saw in august, when any years without such an outbreak return figs like 54% 42% 48% 21% etc.
> 
> 2008 to be sure was huge drawdown for any of these sorts of strategies  (-49%), whether 1 huge drawdown is enough to write off a strategy that  shows 17.7% annualised return over the longer term (i am using LJMs  figures here,not ML) is debatable. On those figures it has a poor  risk;reward but is nevertheless +EV




Isn't that the nature of short gamma strategies? Personally I'd look at standard dev of returns in addition to expected value for this sort of strategy. I'd wager the Sharpe ratios of systematic vol selling is quite low. iirc there is a paper by Waggoner showing risk-adj returns to be positive, however the skew in returns is negative.

Nick Leeson of Barings bank infamy was also 'winning' if it wasn't for the Kobe earthquake. LTCM continued to sell index vol believing it 'overpriced' (but to be fair other issues contributed to its fall). 



> 2. I note LJM have a 20% incentive and a 2% mgt fee, although it is not clear how often the 20% is raked.  To give away 20% of your profits when you have a win, (but not get back 20% of your losses), plus 2%pa of your capital regardless, would put such a handbrake on the whole scheme that it would probably turn a strategy that was +EV into -EV. That headwind must be built in to the published performance figures, so you would be expect the performance of the actual underlying strategy, which is what we are debating here,  to be considerably better than the reported fund performance.
> 
> With such a negative edge added in it is a wonder they made anything at all.




 20% seems to be charged quarterly.

The program for LJM I quoted is 100% discretionary signals, and as far as I can see on my side, returns for all funds are quoted pre-mgmt/incentive fees and charges for consistency. Also they are selling index strangles as far as I can tell. 

Ace is 100% discretionary, Cinamen is 100% Systematic, Diamond is 80% Systematic, Ophedge is 100% discretionary.


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## village idiot (8 February 2012)

we might not be looking at the same thing of course - here is what I am looking at for LJM

http://www.ma-research.com/ctaprof2.php?cta_id=238

I would have thought it odd if performances was published pre fees - as post fees performance is what public actually gets.  to publish ex fees might be more level in terms of strategy perfomance comparison but pretty misleading to the public. But i have been wrong before..

Now here is something that will interest you - here is another fund i clicked on almost at random - this funds says it concentrates on vol skew opportunities although it does also sell strangles . although only running 4 years it is showing 33% annualised return with no yearly drawdowns and one monthly (out of 40) drawdown of 4%. Although it did conveniently start just after the 2008 GFC

http://www.ma-research.com/ctaprof2.php?cta_id=1307


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## village idiot (8 February 2012)

just been going thru the other funds you mentioned and realised  that the second link i put was for OptHedge which you had already pointed to. apologies


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## mazzatelli (8 February 2012)

I'd have to check. I'm using works portals and viewing it as a CTA/MMgr rather than an investor. 

LJM, their return is +ve, stdev is much larger compared to the second one although they invest only in index vols, while the other is spread across other markets.

Anyway, looks like I've destroyed the thread for the OP.


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## mazzatelli (19 February 2012)

mazzatelli said:


> I'd have to check. I'm using works portals and viewing it as a CTA/MMgr rather than an investor.




Checked this, appears to be net of fees. Apologies folks


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## mazzatelli (8 July 2012)

Updated YTD, figures pre-mgmt/incentive fees:
1) Discretionary: Stock Index Only

LJM: 31.02% 
Ace Investments: 2.52% 
 2) Discretionary: Diversified

K&Q Futures: -18.55% 
 3) Systematic: Stock Index Only

Diamond: 7.16% 
GrowthPoint: Iron Condor: -9.24 
Vantage: -2.39% 
 4) Systematic: Diversified

Clinamen: - 3.98%, Interesting note, one of the people involved in this fund is a strategist over at Condor Options 
Clinamen VIX portfolio Hedging: -15.22% 
OptHedge: 10.84% 
White Indian Trading: Straddle Program 4.17% 
 Chosen LJM distribution of monthly returns since inception, because it has the largest AUM and longest track record of the above

​


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## Wysiwyg (1 September 2014)

goponcho said:


> One thing that I am still unclear on is where we profit in systems trading. In order for us to profit, we need to be exploiting an inefficiency in the market right? What process goes into discovering these systems which exploit these inefficiencies? Trial and error after having an idea?



This website has some direction on system trading. The trend trading model is basic with the buy on any equal or new all time high and a sell using ATR(10) but no multiple given. 

Quantpedia


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