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Under the usual code revision, I sorted out with a small modification of the training set that seems to have a large impact on the stability of the neural networks in the model. And also in our trading behaviors. I was able to find some interesting patterns in the data, that show that it is possible to reduce a bit of profitability for more reliable information This is why we need to make sure that the training set has enough accuracy.

As I wrote, the difference is tiny, just 1 bar. All training positions have been adjusted. We now have a more reliable set of predictions and the most affected indicator is probably the rV.Target. It was estimating the maximum extension of the move. It now represents the level that when cleared gives a start to the close position procedure.

There is a small (averaged) loss in presumed profitability, compensated by what should be a more reliable set of provisions. On the other side, the rV.Signal indicator has not been affected by the modification, so it still nosy search for bottoms and tops. As usual, the indicators enforce each other.

As you know, I do not backtest anything, so let’s see how it preforms in the real world.