Tag Archives: technique

Watchlist and position manager Event coming soon for automated trading with new algo technique

New update watchlist and position manager coming soon for automated trading with new algo technique

I have scheduled this Monday Aug 20 night for 2 separate events. This helps for automated trading with new global techniques. These include:

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Monday Aug 20 7PM EDT

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Intraday positions manager and watchlist

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Details on new architecture for intraday positions manager and automated watchlist
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New update watchlist and position manager coming soon for automated trading with new  algo technique

Note: I have removed all public access to these topics which are not part of my Python Infrastructure Building Blocks 

 

 

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Be a Wolf of Wall Street? Be a millionaire with technique? Trading? Yeh…sure

Be a Wolf of Wall Street? Be a millionaire with technique? Trading? Yeh…sure

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Quant analytics: good ways to test experimental automated trading systems – preferably, using a distribution-free technique ie. permutation tests

Any ideas on some good ways to test experimental automated trading systems – preferably, using a distribution-free technique (e.g. permutation tests) ?.

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I wonder what are u want to test? What is the hypothesis?

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is your question about technical side — which platform/software to use, or about testing methodologies?

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As in optimization, or robustness evaluation?

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I am primarily interested in the statistical evaluation methodologies that one uses to test the validity or (statistical) “significance” of a trading rule. A monte carlo approach ostensibly, seems like a sensible approach – (typically, an MC approach involves generating a random TS and testing the rule on the series) however, my trading rules are not based directly off a financial time series, so it is difficult to see how monte carlo framework fits in my particular case.

The question then remains, how to test a rule in a robust way – so that one can ascertain that success (or otherwise) is not due to random events.

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first of all I would suggest considering if the amount of data used in the test is statistically significant. That is, if it covers different market conditions and is simply long enough to generate at least 100 trades. Then I’d pay attention to the distribution of trade results — ideally they should be evenly distribute with no excessive outliers. In other words, if all your profit is generated by a couple of trades while other trades are around 0 I would be in doubt if the trading rule is really based on a repeating local market inefficiency and not on some occasionally caught random process.

As to MC method — I personally do not use it as I do not build mathematical models for trading systems, so either my systems provide more or less equal returns for any given period (and in this case it makes no sense to MC), or vice versa — they do use some significant information which could be simply lost when MCing. So, in both cases MC makes no sense for me. However there are a lot of people around who build their systems on mathematical models, and for them, I suppose, MC is crucial to test.

It’s a bit difficult to go any further because we don’t have any idea not only about your trading rules (and no one expects you to publish them for sure), but also about your approach: if it’s based only on abstract mathematical models or on some market facts. Your comment that your rules are not based directly on financial time series makes me think that you follow the latter approach. However any more detailed information from your side could help.

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thanks for your detailed response. Despite coming from a mathematical background, I believe that trying to create a parsimonous mathematical model that explains all (or even the majority) of market behaviour, is fundamentally and intrisically flawed (but thats a topic for a philosophical debate in itself).

I’m already doing most of what you recommend in your reply – including introducing a few “sample statistics” of my own. Like you, (on some reflection), a MC approach does not make sense for my approach either.

I suppose, what I’m trying to get at, is a methodology for determining how much better a particular rule is – compared to a trading rule comprising of ‘randomly generated signals’ – or perhaps a ‘trivial rule’ such as one based on SMA, Fibonnaci (or similar).

The (obvious?) approach I suppose, would be to propose null hypotheses stating that all the generated trades (from all rules being tested) are drawn from the same population (ergo; similar mean and distribution properties) and then test to see if those null hypotheses can be rejected.

Coming to think of it, that may well be the most appropriate approach (its sometimes good to think out aloud!). I would be interested though, if someone is using an alternative approach to testing whether a specific rule’s performance is largely due to chance, or due to somehow encapsulating some salient information regarding market behaviour.

 

 

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Data Segmentation Technique – Financial Modeling

Data Segmentation Technique – Financial Modeling

There are 6 financial ratios which are to be used in order to predict the probability of liquidation for defaulted firm. Since the data set has only 239 records, i need some technique/methodology to predict the dependent variable using all the 6 financial ratios. I tried using logistic regression but as number of observation are less, the approach doesn’t works. I am thinking on segmenting the variables and then assign scores to each segment based on the historical liquidation rate in each segment. Can anyone suggest some statistical technique for segmenting the variables or a better approach to solve the problem.

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Looks like you got a rough problem with only 239 observations, but the logistic regression might not be a good fit for your job as it is used with discrete dependent variables, yours looks like being continuous so I would use Multiple Regression Analysis. Keep an eye on the ratios as using all might not be the best option, specially with only 239 obs.

Good luck, I hope it helps

I’m confused by the initial question and the enxt comment. Your target/dependent variable is defaulted/not defaulted firm, and thus a logistic regression and not a linear regression is more appropriate.

What I don’t understand is the comment on number of observations (239) versus 6 predictors. What’s the problem about few observations?

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If you do not have enough observations, you can try reducing the number of variables via a technique such as principal components analysis. Or are your 6 variables truly uncorrelated? Also, how many defaulters do you have in your 236 sample

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The dependent variable is dichotomous and therefor thought of using logistic. However the results are not getting validated on the out of time sample. Also, i could have used Discriminant Analysis technique but the independent variables do not follow the assumptions such as variable should be normally distributed etc..

What i am looking for is some technique which either tell the importance of each variable or assign weight to each variable so that we can calculate a Z score and based on the cut-off, we will decide the high/medium/low liquidation buckets.Also, i will be using all the 6 variables and there is no need of reducing the number of variables.

The 6 variables are uncorrelated to each other. The number of defaulters in my data set is 31(13% event rate)

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Not matter how you approach it the analysis will be largely based on heuristics. If you know the debt rating of these companies or their SIC code, then S&P or Fitch publish the 20 year default rates for each. Poisson regression is more widely used than logistic by academics when faced with a sparse dependent variable. Oversampling of the DV is another widely used technique for this situation. That said, I’m with the others in wondering why logistic regression doesn’t work with 239 obs and 6 predictors. Were they all insignificant? If so, try dropping the most insignificant predictors until there’s one, two or a few predictors left that probably are significant. Then I would use the Wald Chi-Square to rank the variable importance or go to Ulrike Gromping’s website for his Relaimpo R macro to develop a more rigorous metric of importance. Finally, I would use the predicted likelihood of default to calculate your cutoffs.

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With a prior of 13% and no good model found, don’t bother with discriminant at least for now. Thomas’ suggestion of adding info from the recently maligned Fitch et al is a good one. But, if your logistic is already bad, why bother with ranking variable importance when they’re not important for what you’re doing?

If you still believe that there is a model somewhere, try trees for instance, or its present versions as boosting.

Also, while you mention that the 6 ratios are uncorrelated, that sounds a bit difficult. Try to obtain the VIFs (in a proc reg with default as the dependent variable). it could well be that some VIFs are very high and thus co-linearity in logistic would be happening. Notice that the usual rule of thumb of vif > 10 does not apply in the logistic case. instead, you have to run a regression with a weight = sqrt (probability predicted by the logistic). The resulting vifs ‘cutoff’s are about 2 or 3.

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I would still expect to see at least 10 defaulters for each
covariate in the equation.

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You can try bootstrap. More important, you will benefit a lot by reading Edward Altman’s seminar paper on the Z-score (just google it) in 1960s and other variation after that. He has done very similar things before.

 

 

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Data Segmentation Technique – Financial Modeling


Data Segmentation Technique – Financial Modeling

There are 6 financial ratios which are to be used in order to predict the probability of liquidation for defaulted firm. Since the data set has only 239 records, i need some technique/methodology to predict the dependent variable using all the 6 financial ratios. I tried using logistic regression but as number of observation are less, the approach doesn’t works. I am thinking on segmenting the variables and then assign scores to each segment based on the historical liquidation rate in each segment. Can anyone suggest some statistical technique for segmenting the variables or a better approach to solve the problem.

1 day ago

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Looks like you got a rough problem with only 239 observations, but the logistic regression might not be a good fit for your job as it is used with discrete dependent variables, yours looks like being continuous so I would use Multiple Regression Analysis. Keep an eye on the ratios as using all might not be the best option, specially with only 239 obs.

 

 

HOW DO YOU START A PROFITABLE TRADING BUSINESS? Read more NOW >>>

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!