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Tools being used by your trading competition

Discussion in 'Trading Strategies/Systems' started by howardbandy, Dec 10, 2016.

  1. craft

    craft Can't re Member

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    Hi Howard

    My answer to you regards your first post would be basically as Minwa has posted.

    Add to this your often cited definition of an investment as something you write into your will.

    and what I do is clearly a trader by your definition (as is nearly everybody) so take some interest in this thread which otherwise I may have left alone.

    It was Tech/a post that I replied too and objected too with his line on lack of comprehension and understanding. Dismissing the objections and agreeing the thread had reached its limit.

    There is plenty in this thread I disagree with and it’s not because of lack of understanding and comprehension.

    Lots to debate if you want to hear the other side – lets start with:

    You talk about the scientific method of learning then validating. The more standard definition would be hypothesizing and then attempting to invalidate.

    How do your assertions in this thread fit with the traditional definition of scientific method.

     
    systematic likes this.
  2. minwa

    minwa Well-Known Offender

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    I wasn't here when your Tech Trader but if I was and looking to learn from someone, I certainly would've looked into your system as opposed to other with absolutely no results. I started out questioning Radge as well but then someone posted some of their results with his system/subscription and it was solid. Not outstanding but definitely solid for the average trader and I have changed my views about him. I wouldn't actively recommend him but when askedwould not warn others away from him like I do with almost all other educators.

    The results are others and not you is true, but no one has the time to look into everything so at least learning from someone who actually does what they teach in the real markets over theorists is increasing your chances, in my opinion.
     
  3. DeepState

    DeepState Multi-Strategy, Quant and Fundamental

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    That's a goodie.

    Just some observations:

    In the past, some one used their fingers to add 2+8 to arrive at ten. Your PC can do around 10^9 times that now or more.

    In the past, a theory was created when someone saw something interesting in their natural setting and put forward an idea. Some could be tested, some were inherently unfalsifiable.

    Now, your PC together with a decent financial engineering package and a decent data source, bolted together with some machine learning methods, will produce a large number of theories which fit the data to a level that you specify. Large being arbitrarily big....but big.

    Computational power has increased the speed at which we can perform calculations. Machine learning and datamining/datascience has increased the speed at which we generate theories which 'fit' the data or other supervision criterion.

    Do you believe an edge in trading rests with finding some non-linear relationship on a combination of obvious and obtuse data? That's what ML does best. A normal fundie/trader has access to heaps of data so, just for this purpose, let's assume a level playing field except for ML algos which can find heaps of relationships that the other traders would not be able to generate quite as quickly.

    Is there an edge to be gained for having a special calculator which trawls over the data you already have to find relationships you would not have been able to find in a reasonable time frame? If so, how significant might it be?
     
  4. Trembling Hand

    Trembling Hand Can be found on the bid

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    DeepState the problem that Joe Average Trader is going to face is they are likely to apply ML to standard backtested systems or variations not that far from the standard decision tree approach on single instruments or at most a portfolio of EOD or min bar data. Then all ML is going to do is hyper optimise it. Therefor rendering the result useless in forward test.

    The reason why JP Morgan has many thousand python developers is because of the breadth of data their systems run on. Large data is what ML is all about. For example American futures exchanges generates over 100 billion order messages each day and the stock markets billions more day in day out, thats just crazy numbers. With 1.7 trillion under management there is value for them in large amount of data. I cannot imagine a trader having the resources to data mine exchange wide tick and quote data to build a ML system from. Then at the other end I'm yet to see a 'simplish' system that has been improved by ML. It doesn't find hidden patters in small data sets. It over fits it.
     
  5. DeepState

    DeepState Multi-Strategy, Quant and Fundamental

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    Your response re: JP Morgan suggests that there is some belief that ML does have benefits, even though it might be restricted primarily to those with the true savvy??

    I think it comes down to the insight being used. Does the idea produce an edge for reasons you can understand and which can be expected to prevail in the future? If so, I don't care much how you got it. Whether you came up with it in the bath or ran a CART over it, if it makes sense, it's valid to use (theory, empirical, sniff test etc etc). If you pile garbage upon garbage hoping that it might fuse into diamond, that seems less likely to be a successful thing to attempt...but the computational cost is small and the outcomes might sustain hope for another day for Joe Average.
     
  6. craft

    craft Can't re Member

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    I was reading Howard Mark's latest memo today and a quote contained in it pretty much sums things up for me.

     
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  7. craft

    craft Can't re Member

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    This is my point exactly - doesn't matter how you come up with it, quantitatively or qualitatively - if you are applying it to the future its subjective. More art than science.

    Don't get me wrong - I think statistical data analysis and computing power has huge benefit but more so in process control rather than predictive value.

    As far back as twenty years ago as part of my trading I was using statistical control charts to monitor my trading system performance using western electric rules as decision points to turn off systems. I don't know how wide spread that sort of thing was at the time but it was something I adapted from working in a processing industry and it worked well for me in trading. Getting more sophisticated in that area I can see as an advantage but when it comes to predicting the future you ain't ever going to turn subjective into objective no matter how smart or sophisticated you are and its a real danger to think you can.
     
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  8. odds-on

    odds-on Well-Known Member

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    I can believe this. I once watched an interview with James Simons from Renaissance Technologies and he mentioned how much data they process every day. Truly mind boggling.
     
  9. tech/a

    tech/a No Ordinary Duck!

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    Craft
    Why does data science have to be looking for predictive ability?

    As Howard has said all he cares is there is movement
    If you have a look at the way he trades and he spelled it out
    It makes sense

    If you could profit from movement regardless of it being long or short
    Wouldn't that be optimal?
     
  10. Wysiwyg

    Wysiwyg Everyone wants money

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    I heard on CNBC (Josh Brown) that algorithms read speeches (speech to text I presume) and make trade decisions based on keywords. Justifying the market selloff during Trumps utterances this morning. I thought data mining social media sites could be advantageous. Charting the frequency of keywords or something and checking for market correlation.
     
  11. craft

    craft Can't re Member

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    ???

    For a system to remain stationary it has to be, by definition, on balance predictive.

    I'll refer you to Howards post because your post makes no sense in light of it.

    I have absolutely no issue with how Howard advocates monitoring a system - I think its sound advice. I do have an issue with .
    I don't think he has proven his case on this paragraph. Nor have I disproved his opinion, but I am mighty happy to continue using qualitatively judgment of fundamentals as a basis for investing/trading over longer term holding periods, in spite of his prediction and if I or anybody else succeed using alternative methods to what that paragraph advocates then on a traditional scientific basis - his hypothesis is disproved.

    Again - my summary on this whole topic is the following quote.

     
    Last edited: Jan 12, 2017
  12. tech/a

    tech/a No Ordinary Duck!

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    I don't know made sense to me.
     
  13. craft

    craft Can't re Member

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    If you can not see the need for prediction here then I can not fathom how a duck comprehends things :banghead:
     
  14. skc

    skc Goldmember

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    Great discussions guys. It gets me thinking about how I should evolve/revolve as a trader. I think there are lots of places for data analysis in financial markets overall.
    - Things that Howard does with respect to position size, system monitoring etc.
    - Alternate data analysis like satellite image analysis for crops, gas fields, industrial production etc.
    - Behavioral analysis - insurance, credit, who panic sells during Brexit etc.

    But what this thread seems to be focusing on is using price/volume/time data to find insights and edge... and potentially consistently "true" insights and edge. And I agree with Craft that it is to be proven. I can see big data providing some unique edge that would last varying durations. But the rest of the market and players will always be revolving. There is no doubt that one machine will inevitably be trumped by people and other machines.

    Another example I was thinking about is in sports. Big data collected using new technology (like motion capture and analysis) are ever so popular in elite sports. One trend in the NBA (basketball league) has been the the trend in shot distribution - and in particular the rise of the 3 pointers. Having 3 point shooters at all 5 positions draws the defense out to the perimeters which opens up the driving lanes for easy shots close to the ring. So big players (who are traditionally slower) with speed and 3 point range are very important to any team. Yet, in less than 2 seasons, this edge is beginning to be eroded - every team is copying the model and and the slower big players are getting less and less playing time... which means the faster shooting big men have no slower player to exploit. The competition doesn't stand still, and the sport quants will need to find another edge.

    These few articles discuss the above much better than I have here.
    https://theringer.com/under-the-influence-of-moreyball-1ea4ba34b85c#.95wllsvjm
    https://theringer.com/inside-daryl-...basketball-laboratory-8f5ccb6c6302#.r7cujq66q
    https://theringer.com/the-nba-might-have-too-many-big-men-1a654d0b1dec#.hhl94n9z2

    There's something unique about the market that is not the same as predicting weather or having self driving cars. A machine learned system may be able to make some smart predictions, but such predictions can only become correct when someone else is also correct. Say my magic system found that the SPI closing price next week will be 6,000, against today's close of 5,722. In order for my prediction to be correct, someone else have to bid the price up to 6,000. This fact tells me that there is always scope for alternate methods to succeed. Indeed, it is impossible for machines to eat everyone else for lunch... the market will die and by definition the predictions cannot come true no matter how smart the machine is.

    So my summary on this topic is:
    - Machine learning / big data is an advanced tool that could discover a temporary edge.
    - Machines are unlikely to be universal winners. There will be winning machines, much like there will be winner system traders, discretionary traders, and winning dartboards.
    - Renaissance is consistently profitable because it is continuously evolving its strategy. It also gains an edge by using data outside of the markets itself to make predictions.
    - The chance of an aspiring trader discovering a magical edge using advanced computational techniques are perhaps no better than him/her discovering an edge in any other alternate method.
     
  15. tech/a

    tech/a No Ordinary Duck!

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    That's a good thing
     
  16. skyQuake

    skyQuake Well-Known Member

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    This! Be it charting/quantopia/stocktwits/Kosec report/amibroker its the same old story.

    In fact I would go one step further and say any easily backtestable strategy that you can think of has been done to death by hedge funds already. The ease of access of global markets allows the best/fastest guy in the room to steal everyone's lunch around the globe.
    P.S. wouldn't it be hilarious if all Ren Tech did all these years was to play simple breakout/breakdown trades across the entire stock universe... rather than go deep they just go super wide
     
  17. skc

    skc Goldmember

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    I am pretty sure that have a moving average cross over strategy as well.
     
  18. Trembling Hand

    Trembling Hand Can be found on the bid

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    Yeah well I suspect it should :confused:. For two reasons. Firstly as already stated the size and spread of their trading, literally everything, everywhere. And two its an arms race and they are one of the few with the resources to be on the top of the pile.
     
  19. tech/a

    tech/a No Ordinary Duck!

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    I don't think this is an issue.
    Retail Traders are using vastly smaller capital bases.
    As such they can be very nimble with their entire account.

    With the power and knowledge available to retail traders NOW and not before why wouldn't you incorporate it.

    Skyquake

    Your logic runs in the face of all Fundamental analysis as well.
    If retail can spot an under valued stock so too can the bigger players and the were doing it before computers as was everyone else.

    What has been highlighted is that edges are harder to define and find---and keeping any edge for any length of time harder again.

    But they are there as many have pointed out as they are trading them.
    Wether they can quantify them or not.

    I see most of this argument like comparing the way Retail Traders should trade to BUFFETT.
    Your just not in the same league. Principals may be similar but application isn't and cant be remotely close.
     
  20. Klogg

    Klogg Well-Known Member

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    That's not necessarily true. There are other reasons why F.A. is not replicated to the same extent at an institutional level as T.A. Some of these include:
    - The need for instant returns (due to different incentives). T.A. generally is on a shorter timeframe
    - Lack of specialisation within institutions. F/A positions are usually held for years, even decades. Try holding onto your staff that long...
    - Knowledge of the company/industry and how they interact - relating to the previous point, you cannot easily train someone to have this sort of working knowledge of all relevant information. An individual can train themselves to do it through years of work, however.

    I can go on, but my point is I don't necessarily agree with your bolded statement.
     
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