# Modelling market mode (trending vs about to reverse)

I am interested in stochastically modeling whether the market is likely to go on in the same direction(trend), or reverse and head back. This is all for intraday purposes, next 1-2 ticks kind of strategy with 30sec - 3 mins holding times. How can I attack this problem? Where do I start?

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You might want to look at the behavioral finance literature of fundamentalists and chartists in markets. Especially the papers where they apply it to actual data. –  user13253 Sep 10 '12 at 4:27
Thanks, anything more concrete? –  Viktor Sep 10 '12 at 5:39

Here's an R model of "trending and reversing":

#Plot a random time series
x <- cumsum(rnorm(1000))
plot(x, type="l", main="Trend versus Reverse")


Notice that you'll always get trending and reversing, 100% of the time (run the code over and over). And, those so-called "trends" and "reversals" were generated from a random sequence. So, how can you tell the difference between "trending" and "reversing" that's just random, versus "trending" and "reversing" that is due to some trading situation? If you think about it for a while, you'll come to the conclusion that you can't. The whole concept is a waste of time (and it's not just trends/reversals that are a waste of time).

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I wouldnt jump to the conclusion so quick, even if you can prove that stock price time series is i.i.d (which is obviously not), it still doesnt mean you cannot develop better investment strategy that beat the market with probability one. –  user55647 Jan 22 at 6:00
@user55647 - The question was about trends/reversals. All I can say is, if you're trading some trend/reversal scheme and you can't tell the difference in a so-called "meaningful" trend/reversal versus a random trend/reversal, then the scheme, by definition, is worthless. The same argument holds for trading schemes based on some other indicator that can't be separated from chance. A scheme "works" if it has a positive return and can be differentiated from chance. –  bill_080 Jan 22 at 19:01
Well, it can, althrough maybe not the way you thought, also, your example is flawed, since we see trends from a collections of thousands of indivial stocks, that demonstrated not much less significant trends comparing to the trends of each indivial stock, and there is very strong correlation between each stock in the market, whilst if trends are made purely by chance like you implied, then the collection of thousands or more stocks should not show any serious "trends", and indivial stock's trend should NOT correlated to each other the way we see in the market. –  user55647 Jan 22 at 19:26
@user55647 - Trying to stick with the original question, the issue is short term trends/reversals (1 to 2 ticks, 30 second to 3 minute holding times). Again, if you can't separate chance versus actual, then you have nothing to work with. In terms of correlations, don't all financial instruments respond in some way to the news (i.e. chance) of the week/day/hour/minute/second? Aren't those responses responsible for the correlations? Don't get me wrong, there are ways to make money in the market. It's just not from intra-day trend/reversal schemes. –  bill_080 Jan 23 at 15:07

Finding turn points is a very difficult problem. Having a covariate that is correlated at a leading time would help. Covariates in conjunction with noticing a leveling off in the market index would be even better.

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This is something that I'm looking into at the moment, although I'm using using daily OHLC data rather than tick/intraday data. The approach I've taken is to postulate 5 market types and then generate a large amount of synthetic data that conform to models underlying each respective market type. Using this data, I'm currently training a classification neural net to identify which model/market type "real" data most closely resembles over a specified look back period. Based on this classification one would then choose the most appropriate trading strategy: attempt to pick tops and bottoms in a mean reverting environment, and go with the flow in a trending environment. I'm blogging about my progress with this project on Dekalog Blog.

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