What to feed to the input of the neural network? Your ideas... - page 73

You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Thanks for the ideas
Others are dealing with MO, they have their own branch.
The level is easier here.
N.S.A. needs weights, if I understand correctly.
That's what you need to submit.
NSka needs weights, if I understand correctly.
That's what you need to submit.
Rena, you misunderstand. Some data is fed to the NS input, but not weights. And weights of NS synapses are determined by its type, architecture, training...
and have nothing to do with input data. Weights can even change in dynamics.
Rena, you misunderstand. The input of the NS is some data, but not weights. And weights of NS synapses are determined by its type, architecture, training ...
and have nothing to do with input data. Weights can even change in dynamics.
Do weights change over time? That's a fantasy I've never heard before
Playing with parameters using creative methods (mixing formulas by poking and prodding) can sometimes improve results, again - conditionally.
In the example below we managed to increase the number of forward trades again, the chart started to look better, but at the end it started to fade. + The backtest outside the optimisation period (from 2000 to 2012) became a victim, it failed.
Forward 2021-2025 for EUR
I still have a feeling that the market is described by some kind of looped formula (some chart parameters should be connected with each other somehow) and such an owl will work almost everywhere. Thoughts aloud
What are weights?
Do weights change over time? I've never heard that fantasy before.
What's so surprising? The sample has changed --> model parameters have changed. Moreover, even on the same sample the parameters may differ due to differences in parameter initialisation and as a consequence different convergence (the neural network will simply converge to a neighbouring local minimum, for example).