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Following on from lehalle's answer I ended up with the following solution (in case this is useful to anyone). First for the spread (expressed as an ask:bid ratio) I used the Corwin & Schultz paper as follows (in php): // Gamma: square of the ratio of the logs of the highest highs and lows $gamma = pow(log(max($curBar['high'], $prevBar['high']) / max($...


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You need to remove the call to range. In Python it’s necessary but here it just returns the smallest and largest element of the vector.


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There is an old literature on estimating the BA-spread using transaction prices: have a look at Estimation of the bid–ask spread and its components: A new approach The Review of Financial Studies, 4(4), 623-656, by George, T. J., Kaul, G., & Nimalendran, M. (1991). It relies on the fact that a bid-ask spread adds a quantity to the autocorrelation of ...


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I think what you have in mind is similar to the Cowles Test, described by Alfred Cowles in his 1933 paper Can Stock Market Forecasters Forecast? (link) Cowles wanted to evaluate the trading performance of an advisor who tells people when to be in the stock market and when to get out. He compared the actual performance to random switching in and out of the ...


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False positives and false negatives are concepts from Frequentist statistical inference. They depend on people not data mining. Using Keynesian notation, they test $\Pr(X|\theta)$. That is to say, they test the probability of observing a result as extreme or more extreme than the sample given that the model is true. In more common notation they test $P_\...


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