Well, I can choose to use whatever structure in terms of number of layers and hidden neurons. The question is do you think there is SOME structure that will allow the NN to pick-up this relationship? And, what do you suspect it might look like?
Great! We are on the same page. My testing confirms that is does NOT matter.
Here is the next "thought experiment". I am trying to predict the future price change FPC.
If I supply the FPC as an input, then the NN uses this information and makes pretty-much perfect predictions. No surprise...
I am just reversing the bar order - no other changes. And Stock A is in a strong rally through the period, and therefore Stock AR is in a strong decline. I would expect the same performance because the only thing different is that the sign of the changes I feed to the neural net have flipped.
Here's an interesting thing. I have created a simple neural net with 1 input (20 day price change) on a stock A that go UP over time. I then created artificial data AR by sorting the prices in reverse time order i.e the stock now looks like it is gone down. My intuition tells me that the...
What accuracy are you targeting? 70% actually sound very high. AlphaGo only get 57% accuracy on matching a human's decisions. And many trend-following CTAs have win-rates in the low 50%.
I want to trade my simple neural net across 20 stocks. In simulation I could Train "walking-forward" based on a single stock - train for x months, paper-trade for y months and keep rolling this window forward. Or I could train across all stocks in each window. Do people think it is best to train...
I have introduced early stopping into my system. I do this by measuring the slope of the recent values of MSE over the past x iterations. However, this gets triggered very rarely - I had assumed it would trigger often and I would save time.