Macroeconomic Artificial Neural Networks

This script was created by training 20 selected macroeconomic data to construct artificial neural networks on the S&P 500 index .
No technical analysis data were used.
The average error rate is 0.01.
In this respect, there is a strong relationship between the index and macroeconomic data.
Although it affects the whole world,I personally recommend using it under the following conditions: S&P 500 and related ETFs in 1W time-frame (TF = 1W SPX500USD , SP1! , SPY , SPX etc. )

Macroeconomic Parameters

Effective Federal Funds Rate ( FEDFUNDS )
Initial Claims ( ICSA )
Civilian Unemployment Rate ( UNRATE )
10 Year Treasury Constant Maturity Rate ( DGS10 )
Gross Domestic Product , 1 Decimal (GDP)
Trade Weighted US Dollar Index : Major Currencies ( DTWEXM )
Consumer Price Index For All Urban Consumers (CPIAUCSL)
M1 Money Stock ( M1 )
M2 Money Stock ( M2 )
2 - Year Treasury Constant Maturity Rate ( DGS2 )
30 Year Treasury Constant Maturity Rate (DGS30)
Industrial Production Index ( INDPRO )
5-Year Treasury Constant Maturity Rate ( FRED : DGS5 )
Light Weight Vehicle Sales: Autos and Light Trucks (ALTSALES)
Civilian Employment Population Ratio (EMRATIO)
Capacity Utilization (TOTAL INDUSTRY) (TCU)
Average (Mean) Duration Of Unemployment (UEMPMEAN)
Manufacturing Employment Index (MAN_EMPL)
Manufacturers' New Orders ( NEWORDER )
ISM Manufacturing Index (MAN : PMI)

Artificial Neural Network (ANN) Training Details :

Learning cycles: 16231
AutoSave cycles: 100


Input columns: 19
Output columns: 1
Excluded columns: 0

Training example rows: 998
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0


Input nodes connected: 19

Hidden layer 1 nodes: 2
Hidden layer 2 nodes: 0
Hidden layer 3 nodes: 0

Output nodes: 1


Learning rate: 0.1000
Momentum: 0.8000 (Optimized)
Target error: 0.0100

Training error: 0.010000

NOTE : Alerts added . The red histogram represents the bear market and the green histogram represents the bull market.
Bars subject to region changes are shown as background colors. (Teal = Bull , Maroon = Bear Market )

I hope it will be useful in your studies and analysis, regards.

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Thank you. It is great contribution. I am trying to understand the logic.
1. you calculate the second derivative of each indicator, right?
2. what function does the "ActivationFunctionTanh"?
3. and how do you get the coeficientes for n_19 and n_20?
Albert Ac
+2 Reply
Noldo jdalber
@jdalber, First of all thanks for your interest. You can find more information on my first Artificial Neural Network script :

jdalber Noldo
@Noldo, thanks
+1 Reply
Noldo jdalber
@jdalber, Your welcome !
Hello, thanks for sharing! I want to choose an ANN indicator for Hang Seng Index Futures, do you think this one, or other SPX indicators can apply? And did you try other activation functions?
+1 Reply
@MOMINCKS, BTW a great leading indicator, especially with high tf like 1M!
@MOMINCKS, Thanks a lot!
It may benefit Hang Seng, but indirectly.
While S&P is rising, all exchanges are major.
But it will fail in country-based problems.
Or vice-versa.
I have previously tried to train Hang Seng with the ANN method, but I have removed the error rate is too high.
In my spare time, I will try again with other methods.
+1 Reply
@Noldo, That will be great! I am looking forward to it!
could U make btc version of this?))
+1 Reply
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