Focus (usually) on returns rather than asset prices.
If you know that certain asset prices are correlated (because of rigid
arbitrage-like relationships or because of “soft” statistical correlations),
preprocess your data to take advantage of those correlations. Don’t make
your network “learn” those correlations. Your correlations need not be
rock-solid – it will generally be better to ask your network to “learn” the
deviations from the baseline correlations, rather than those correlations
Try to take into account relevant non-tradable information (i.e., things other
than prices) – for example, the “fundamentals” of companies whose stocks
you might want to trade or the observed liquidity of assets whose prices you
It’s quite hard and it’s a moving target. People are all the time trying to do
this (with or without neural networks). When some subset of these people
find a pattern that lets them make tradable and money-making predictions,
they make those trades (to make money, naturally) – and those trades push
the prices back to where the tradable pattern no longer exists.
Let’s say that both you and I build an MNIST digit classifier and yours is
quite good and mine is a little better. We both have good classifiers, so
we’re both happy. But if both you and I build a model that predicts some
stock’s price and mine is a little better, I’ll trade using my model (to make
money, naturally) and squeeze the predictability of that stock’s price out
of the market. I’ll end up with money, but you, maybe not so much …
I’m focusing on making a model that is able to to tell me if the percentage is 5% or higher.
It’s still in progress I can’t tell yet if it will work,
It was hard to predict the next day prices. so I’m to predict the percent change between the current day and the next day(future day)