Blockchain Artificial Neural NetworksI found a very high correlation in a research-based Artificial Neural Networks.(ANN)
Trained only on daily bars with blockchain data and Bitcoin closing price.
NOTE: It does not repaint strictly during the weekly time frame. (TF = 1W)
Use only for Bitcoin .
Blockchain data can be repainted in the daily time zone according to the description time.
Alarms are available.
And you can also paint bar colors from the menu by region.
After making reminders, let's share the details of this interesting research:
INPUTS :
1. Average Block Size
2. Api Blockchain Size
3. Miners Revenue
4. Hash Rate
5. Bitcoin Cost Per Transaction
6. Bitcoin USD Exchange Trade Volume
7. Bitcoin Total Number of Transactions
OUTPUTS :
1. One day next price close (Historical)
TRAINING DETAILS :
Learning cycles: 1096436
AutoSave cycles: 100
Grid :
Input columns: 7
Output columns: 1
Excluded columns: 0
Training example rows: 446
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Network :
Input nodes connected: 7
Hidden layer 1 nodes: 5
Hidden layer 2 nodes: 0
Hidden layer 3 nodes: 0
Output nodes: 1
Controls :
Learning rate: 0.1000
Momentum: 0.8000
Target error: 0.0100
Training error: 0.010571
The average training error is really low, almost worth the target.
Without using technical analysis data, we established Artificial Neural Networks with blockchain data.
Interesting!
Artificial_neural_networks
ANN GOLD WORLDWIDE This script consists of converting the value of 1 gram and / or 1 ounce of gold according to the national currencies into a system with artificial neural networks.
Why did I feel such a need?
Even though the printed products in the market are digitally circulated, only precious metals are available in full or near full.
Silver is difficult to carry because you have to buy too much because the unit price is low.
Platinum is very difficult to find and used in industry.
Gold is both practical and has less volatile movements, even more balanced than dollars, to preserve the value of money.
Uncertainty and tensions benefit gold.
Obviously this is my own opinion and is not worth the investment advice:
If there is to be an economic crisis, it is obvious that the dollar will rise against the emerging currencies, but I expect a crisis where gold and the dollar will rise together.
The world has been on a mercantilist line more than ever!
Spot gold can be bought from goldsmiths and banks.
I think this command will benefit people everywhere but in economies that are subject to developing currencies.
Now we can look at the details:
All you have to do is load the appropriate chart and select it from the menu.
Thus, the system will adjust itself to that instrument.
MENU and Tickers :
"GOLD" : XAUUSD or GC1! or GOLD (Average error = 0.0128)
"GOLDSILVER" : XAUXAG or GOLDSILVER (Gold Silver Ratio ) ( Average error : 0.01 )
"GOLD CZK " : XAUUSD/USDCZK ( 1 Ounce Gold Czech Koruna) ( Average error = 0.010879 )
"GOLD NZD " : XAUUSD/USDNZD ( 1 Ounce Gold New Zealand Dollar ) (Average error = 0.010736 )
"GOLD EURO" : XAUUSD/USDEUR ( 1 Ounce Gold Euro) ( Average error = 0.010000 )
"GOLD HUF " : XAUUSD/USDHUF ( 1 Ounce Gold Hungarian Forint ) ( Average error = 0.010000 )
"GOLD INR " : XAUUSD/USDINR (1 Ounce Gold Indian Rupee ) (Average error = 0.010458 )
"GOLD DKK" : XAUUSD/USDDKK (1 Ounce Gold Danish Krone) (Average error = 0.010671 )
"GOLD CHF" : XAUUSD/USDCHF (1 Ounce Gold Swiss Franc ) (Average error = 0.010967 )
"GOLD CNH" : XAUUSD/USDCNH(1 Ounce Gold Offshore RMB) (Average error = 0.012017 )
"GOLD MXN" : XAUUSD/USDMXN(1 Ounce Gold Mexican Peso) (Average error = 0.010000 )
"GOLD PLN" : XAUUSD/USDPLN (1 Ounce Gold Polish Zloty ) (Average error = 0.010173 )
"GOLD ZAR" : XAUUSD/USDZAR (1 Ounce Gold South African Rand (Average error = 0.010484 )
"GOLD NOK" : XAUUSD/USDNOK (1 Ounce Gold Norwegian Krone ) (Average error = 0.010842 )
"GOLD TRY" : XAUUSD/USDTRY (1 Ounce Gold Turkish Lira ) (Average error = 0.010000 )
"GOLD THB" : XAUUSD/USDTHB (1 Ounce Gold Thai Baht ) (Average error = 0.011747 )
Important note : XAUUSD/USDCUR = 1 Ounce Gold , XAUUSD/31.1*USDCUR = 1 gram Gold (CUR = Currency )
If you want to physically hold it, look gram value, because as far as I know, all goldsmiths and jewelleries in the world are selling gram gold.
I think that this command is the most useful and the concrete one that I have ever written.
I end my sentences with this anonymous proverb :
"Even if gold falls into the mud, it's still gold ! "
ANN MACD : 25 IN 1 SCRIPTIn this script, I tried to fit deep learning series to 1 command system up to the maximum point.
After selecting the ticker, select the instrument from the menu and the system will automatically turn on the appropriate ann system.
Listed instruments with alternative tickers and error rates:
WTI : West Texas Intermediate (WTICOUSD , USOIL , CL1! ) Average error : 0.007593
BRENT : Brent Crude Oil (BCOUSD , UKOIL , BB1! ) Average error : 0.006591
GOLD : XAUUSD , GOLD , GC1! Average error : 0.012767
SP500 : S&P 500 Index (SPX500USD , SP1!) Average error : 0.011650
EURUSD : Eurodollar (EURUSD , 6E1! , FCEU1!) Average error : 0.005500
ETHUSD : Ethereum (ETHUSD , ETHUSDT ) Average error : 0.009378
BTCUSD : Bitcoin (BTCUSD , BTCUSDT , XBTUSD , BTC1!) Average error : 0.01050
GBPUSD : British Pound (GBPUSD,6B1! , GBP1!) Average error : 0.009999
USDJPY : US Dollar / Japanese Yen (USDJPY , FCUY1!) Average error : 0.009198
USDCHF : US Dollar / Swiss Franc (USDCHF , FCUF1! ) Average error : 0.009999
USDCAD : Us Dollar / Canadian Dollar (USDCAD) Average error : 0.012162
SOYBNUSD : Soybean (SOYBNUSD , ZS1!) Average error : 0.010000
CORNUSD : Corn (ZC1! ) Average error : 0.007574
NATGASUSD : Natural Gas (NATGASUSD , NG1!) Average error : 0.010000
SUGARUSD : Sugar (SUGARUSD , SB1! ) Average error : 0.011081
WHEATUSD : Wheat (WHEATUSD , ZW1!) Average error : 0.009980
XPTUSD : Platinum (XPTUSD , PL1! ) Average error : 0.009964
XU030 : Borsa Istanbul 30 Futures ( XU030 , XU030D1! ) Average error : 0.010727
VIX : S & P 500 Volatility Index (VX1! , VIX ) Average error : 0.009999
YM : E - Mini Dow Futures (YM1! ) Average error : 0.010819
ES : S&P 500 E-Mini Futures (ES1! ) Average error : 0.010709
GAZP : Gazprom Futures (GAZP , GZ1! ) Average error : 0.008442
SSE : Shangai Stock Exchange Composite (Index ) ( 000001 ) Average error : 0.011287
XRPUSD : Ripple (XRPUSD , XRPUSDT ) Average error : 0.009803
Note 1 : Australian Dollar (AUDUSD , AUD1! , FCAU1! ) : Instrument has been removed because it has an average error rate of over 0.13.
The average error rate is 0.1850.
I didn't delete it from the menu just because there was so much request,
You can use.
Note 2 : Friends have too many requests, it took me a week in total and 1 other script that I'll share in 2 days.
Reaching these error rates is a very difficult task, and when I keep at a low learning rate, they are trained for a very long time.
If I don't see the error rate at an average low, I increase the layers and go back into a longer process.
It takes me 45 minutes per instrument to command artificial neural networks, so I'll release one more open source, and then we'll be laying 70-80 percent of the world trade volume with artificial neural networks.
Note 3 :
I would like to thank wroclai for helping me with this script.
This script is subject to MIT License on behalf of both of us.
You can review my original idea scripts from my Github page.
You can use it free but if you are going to modify it, just quote this script .
I hope it will help everyone, after 1-2 days I will share another ann script that I think is of the same importance as this, stay tuned.
Regards , Noldo .
ANN MACD WTI (West Texas Intermediate) This script created by training WTI 4 hour data , 7 indicators and 12 Guppy Exponential Moving Averages.
Details :
Learning cycles: 1
AutoSave cycles: 100
Training error: 0.007593 ( Smaller than average target ! )
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 300
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: 6
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.7000
Momentum: 0.8000
Target error: 0.0100
Special thanks to wroclai for his great effort.
Deep learning series will continue. But I need to rest my eyes a little :)
Stay tuned ! Regards.
ANN MACD S&P 500 This script is formed by training the S & P 500 Index with various indicators. Details :
Learning cycles: 78089
AutoSave cycles: 100
Training error: 0.011650 (Far less than the target, but acceptable.)
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 300
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: 1
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.7000
Momentum: 0.8000
Target error: 0.0100
Note : Thanks for dear wroclai for his great effort .
Deep learning series will continue . Stay tuned! Regards.
ANN MACD EURUSD (FX) Hello , this script is trained with eurusd 4-hour data. (550 columns) Details :
Learning cycles: 8327
AutoSave cycles: 100
Training error: 0.005500 ( That's a very good error coefficient.)
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 550
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: 5
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.6000
Momentum: 0.8000
Target error: 0.0055
NOTE : Use with EURUSD only.
Alarms added.
Thanks dear wroclai for his great effort.
Deep learning series will continue ! Stay tuned.
Regards , Noldo .
ANN MACD ETHEREUM
This script is trained with Ethereum (Timeframe : 4 hours ).
Details :
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 300
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Input nodes connected: 19
Hidden layer 1 nodes: 8
Hidden layer 2 nodes: 1
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.7000
Momentum: 0.8000
Training error: 0.009378 ( That's a very good error coefficient. )
Many thanks to wroclai for help.
Deep learning series will continue!
ANN MACD BTC v2.0 This script is the 2nd version of the BTC Deep Learning (ANN) system.
Created with the following indicators and tools:
RSI
MACD
MOM
Bollinger Bands
Guppy Exponential Moving Averages:
(3,5,8,10,12,15,30,35,40,45,50,60)
Note: I was inspired by the CM Guppy Ema script.
Thank you very much to dear wroclai for his great help.
He has been a big help in the deep learning series.
That's why the licenses in this series are for both of us.
I'm sharing these series and thats the first. Stay tuned and regards!
Note : Alerts added.
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:
hackernoon.com
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 github.com
Stay tuned. Best regards!
