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Neuralnetwork

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 ! "
Pine Script® indicator
by Noldo
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 .
Pine Script® indicator
by Noldo
21
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.
Pine Script® indicator
by Noldo
1
ANN MACD BRENT CRUDE OIL (UKOIL) This script trained with Brent Crude Oil data including 7 basic indicators and 12 Guppy Exponential Moving Averages . Details : Learning cycles: 1 Training error: 0.006591 ( Smaller than 0.01 ! ) AutoSave cycles: 100 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 Note : Alerts added . Special thanks to wroclai for his great effort. Deep learning series will continue , stay tuned ! Regards.
Pine Script® indicator
by Noldo
4
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.
Pine Script® indicator
by Noldo
1