I'm trying to build a classifier to predict stock prices. I generated extra features using some of the well-known technical indicators and feed these values, as well as values at past points to the machine learning algorithm. I have about 45k samples, each representing an hour of ohlcv data.
The problem is actually a 3-class classification problem: with buy, sell and hold signals. I've built these 3 classes as my targets based on the (%) change at each time point. That is: I've classified only the largest positive (%) changes as buy signals, the opposite for sell signals and the rest as hold signals.
However, presenting this 3-class target to the algorithm has resulted in poor accuracy for the buy & sell classifiers. To improve this, I chose to manually assign classes based on the probabilities of each sample. That is, I set the targets as 1 or 0 based on whether there was a price increase or decrease. The algorithm then returns a probability between 0 and 1(usually between 0.45 and 0.55) for its confidence on which class each sample belongs to. I then select some probability bound for each class within those probabilities. For example: I select p > 0.53 to be classified as a buy signal, p < 0.48 to be classified as a sell signal and anything in between as a hold signal.
This method has drastically improved the classification accuracy, at some points to above 65%. However, I'm failing to come up with a method to select these probability bounds without a large validation set. I've tried finding the best probability values within a validation set of 3000 and this has improved the classification accuracy, yet the larger the validation set becomes, it is clear that the prediction accuracy in the test set is decreasing.
So, what I'm looking for is any method by which I could discern what the specific decision probabilities for each training set should be, without large validation sets. I would also welcome any other ideas as to how to improve this process. Thanks for the help!
