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Noldo
2019年12月10日午前11時27分

Macroeconomic Artificial Neural Networks 

詳細


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

Grid

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

Network

Input nodes connected: 19

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

Output nodes: 1

Controls

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.


コメント
rogeriomgrillo
Great indicator!!
MOMINCKS
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?
MOMINCKS
@MOMINCKS, BTW a great leading indicator, especially with high tf like 1M!
Noldo
@MOMINCKS, Thanks a lot!
RainerRocks
@Noldo, Hi, not working for me ,shows red exclamation saying "Study Error". Thanks.
Noldo
@MOMINCKS,
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.
MOMINCKS
@Noldo, That will be great! I am looking forward to it!
jdalber
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
Noldo
@jdalber, First of all thanks for your interest. You can find more information on my first Artificial Neural Network script :

tradingview.com/script/kPYANAD1-ANN-MACD-Future-Forecast-SPY-1D/
jdalber
@Noldo, thanks
詳細