[Hassam]

I forgot to mention Naive Bayes and Nearest Neighbors algorithm. Nearest Neighbors algorithm might help. What these algorithms will do predict based on the past history. If history doesn't repeat itself than the predictions will be wide off the market. When building these models you should take care of class imbalance. Class imbalance means one or more classes can be much lower or higher than the other classes. If there is class imbalance, these algorithms will select more from the majority classes so the results will be wrong most of the times. You can take care of class imbalance by binning the target variable into bins having almost equal classes.

I think you haven't given much importance to non stationary nature of financial time series. The probability distribution generating the financial time series data is constantly changing. Above classification algorithms fail when the underlying distribution generating the data is non stationary. Non stationary in simple terms means that the mean and volatility of the financial time series is constantly changing. If the time series is non stationary than the Law of Large Numbers cannot be used to calculate the mean of the time series. Building models can be a time consuming process requiring a lot of backtesting.