scaling for SVM destroys my results I'm applying standard 0-1 scaling of features before SVM classification for financial data but the results are worse. This is the results before scaling
    NORMAL DATA AVERAGE RESULTS
      Profit           PF         avMC         avPP         avRC        totTP        totFP         PF>1     algosnum           SS          SSl
  4389060.90         6.85        -0.00        60.69         0.50        16086        10973            5            8            1            5

and this is after scaling
NORMAL DATA AVERAGE RESULTS
      Profit           PF         avMC         avPP         avRC        totTP        totFP         PF>1     algosnum           SS          SSl
  2256204.80      2044.51        -0.07        52.53         0.46        14577        12220            4            8            1            5

Scaling is performed in 0-1 range, test data is scaled according to scaling factor of train data. From the above results you can see that precision went down (avPP) from 60.69 to 52.53, average Mathew Correlation Index from 0 to -0.07 number of true positives went down from 16086 to 14577 and number of false positives grown from 10973 to 12220. The presented result is an outcome of 80 classifications on different financial instruments data for 80 data sets 20000x200 so i think result is quite significant.
So my question is: In such situation how I should proceed? Shall I stick to scaling? Or maybe I should generate different data set to check if this behavior is consistent? What sort of analysis of my features I can make?
My data set is a mix of binary and continuous features in different scales.
 A: Keep in mind why people typically scale features prior to estimating an SVM. The notion is that the data are on different scales, and this happenstance of how things were measured might not be desirable -- for example, measuring some length quantity in meters versus kilometers. Obviously one will have a much larger range even though both represent the same physical quantity.
However, there's no reason to believe that the new scaling is any better. While it's true that the rescaled features  will all vary in comparable units, it's also possible that the original scaling happened to encode the data such that some important features had more prominence in the model.
You don't mention what kernel function you're using, but I think it's illustrative to consider the example of two different versions of the Gauissian RBF kernel: $K_1(x,x^\prime)=\exp(-\gamma||x-x^\prime||^2_2).$ This is an isotropic kernel, meaning that the same scaling ($\gamma$) is applied in all directions. A more general kernel function might have the form $K_2(x,x^\prime)=\exp\big(-(x-x^\prime)\Gamma(x-x^\prime)\big);$ it is anisotropic as $\Gamma$ is a diagonal PSD matrix, with each element applying a different scaling to each direction. The advantage of this kernel function is that it will vary more strongly in some directions than others.
Coming back to your question, it's possible to imagine that your data have, for whatever reason, some features that are more important than others, and that this coincides with the scale on which they are measured. Placing them on the new scale where they all appear on similar scales and are all treated as equally important means that unimportant or noise features cloud the signal.
