Non IID variables and SVM Classifier
The assumption regarding IID variables is to ensure a unique solution. By IID, it means they should be uncorrelated. You can't make sure data is identically distributed. Scaling and standardization are the obvious for you to find the solution. One justifies the use by getting satisfied by the solution found by the SVM algorithm (which is just one of the solution it has found).
But, you can the parameters for SVM and use a different kernel altogether. Remove redundant features yourself.
Or if you are looking at stock price prediction, look at Jane Market Street Prediction on Kaggle. I have seen heavy use of trees, mixture models and NNs.