Quote:
Originally Posted by ipixtlan
Implement a simple feed forward optimisation algo, stimulate winning trades with some form of asymmetrical MM . Access your real trading results with standardized statistical measurements, so you can know if the whole model or part of it is broken when start under performing. Go back to optimization/drawing board.
All this only if your network captured, some underlying cyclical exploitable behaviour. Hint that you are on path of a success is if your pattern is of fractal nature.
Looking on your previous posts, you probably suffer of over-training/fitting syndrome.
I would go for big number of lower quality trades, so I could wed them out with simpler procedures...
|
I've read about over-fitting and I'm assuming this is what has happened here. Would I be better splitting that ten year sample into 10 1 year samples and training on one year and testing on the next? It appears to me that the GBPJPY goes through a 5-10 year cycle. I'd chosen a 10 year sample because I'd hoped to train the network to be able to identify that cycle. The hope being that a network trained over that cycle would be able to identify what part of the cycle it was in and respond appropriately.
My previous strategies have involved waiting for big moves and then cashing in but I would hope to use a neural net as part of a scalping strategy. I'd be looking to trade nearly every tick based on whether the forecasted close was above or below the current.