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Common Probability Rules to Employ in Automated Trading Strategies?

Good Day Quants!
Need to ask: What are the most common or top probability rules to employ in a Algo trading strategy’s building-block i.e. Standard Deviation, Regression etc. am I missing anymore? Thanks for any suggestions or ideas what works.

4 responses

My in-sample backtest must have at least 100 trades or 300 periods. For example, if I am making a daily interval strategy, the in-sample backtest must generate around 100 trades or the time period must be around 300 days. Just something I do, not really taught anywhere specifically.

interesting but has this "in-sample backtest" got to do with Probability / Predictive analytics?

I think we he is getting at is that when you use probability to perform predictive analytics the data that you are using to draw your conclusions from must be of a certain minimum size. If the parent population is normal (i.e. normal means a random sampling would fall along the bell curve) then a sample size of 10 or 20 will often be large enough to draw conclusions. However, since this data is not normal Kory puts forth that the sampling size should include 100 trades or have a population of 300 days, to be able to provide enough data to draw any viable conclusions.

It's a very important concept, covered well in the Overfitting lecture.


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