ANN MACD Future Forecast (SPY 1D)

NOTE : Deep learning was conducted in a narrow sample set for testing purposes. So this script is Experimental .

This system is based on the following article and is inspired by an external program:

None of the artificial neural networks in Tradingview work and are not based on completely correct logic. Unlike others in this system:

IMPORTANT NOTE: If the tangent activation function is used, the input data must also have tangent values (compared to the previous values of 1 bar).
Inputs were prepared according to this judgment.

1. The tangent function which is the activation function is written correctly. (The tangent function in the article: ActivationFunctionTanh (v) => (1 - exp (-2 * v)) / (1 + exp (-2 * v)))
2. Missing bias parts in the formulas were added.
3. The output function is taken from the next day (historical), so that the next bar can be predicted, which is the truth.
4.The forecast value of the next bar is subtracted from the current bar change and the market direction is determined.
5.When the future forecast and the current close are added together, the resulting data is called seed.
The seed carries data both from the present and from yesterday and from the future.
6.And this seed was subjected to the MACD method.
Thus, due to exponential averages, more importance will be given to recent developments and
The acceleration situations will show us the direction.
However, a short position should be taken for crossover and a long position for crossunder .
Because the predicted values ​​work in reverse.Even though we use the same period (9,12,26) it is much faster!
7. There is no future code that can cause Repaint.
However, the color after closing should be checked.

The system is completely correct.
However, a very narrow sample was selected.
100 data: Tangent diffs ; volume change, bollinger bands values changes (Upband , Midband , Lowband) and LazyBear's Squeeze Momentum Indicator (SQZMOM_LB) change and the next bar data (historical) price change were put into the deep learning test.

IMPORTANT NOTE : The larger the sample set and the more effective dependent variables, the higher the hit rate of the deep learning test!
EDIT : This code is open source under the MIT License. If you have any improvements or corrections to suggest, please send me a pull request via the github repository

Stay tuned. Best regards!
เอกสารเผยแพร่: Added max_bars_back for reduce calculation loads. Thanks @rhanna for warn me .
Note : Use that only SPY (AMEX:SPY) . Because deep learning calculations based on this chart.
I m working with BTC Deep Learning script . I will publish it soon ! Best regards.
เอกสารเผยแพร่: Plot codes updated.
เอกสารเผยแพร่: Switchable barcolor preference added.
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pine cannot determine the referencing length of the series. try setting max_bars_back.. i get this error
Beautiful script. Is it possible to turn this into a strategy?
HedgedTrader HedgedTrader
@Noldo this would help quantify the success rate. Thank you.
Noldo HedgedTrader
@HedgedTrader, Hello, I don't believe in any strategy without logical automatic support resistance levels and automatic trend line detectors. )
BTC version :
Thanks for sharing, but when i try to add it to my chart then i'm getting the following error

How can i fix this?
Noldo rhanna
rhanna Noldo
@Noldo, no i tried it on ETHUSD, can i use this script only for SPY?
Noldo rhanna
@rhanna, Yes , because this script calculated with SPY data and indicators on SPY. But thanks for that feedback. The calculations are so heavy for pine so i may add max_bars_back function. BTW I'm working on BTC Deep Learning script now .(15 mins ) I ll post it after i finish. (y)
rhanna Noldo
@Noldo, thanks buddy, i appreciate it. keep up the good work.
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