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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!
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Noldo tarafından
5
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.
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Noldo tarafından
13
BTC FRACTAL ANN S-R LEVELS (Fixed ANN MACD) This script is an adaptation of my deep learning system for Bitcoin to fractals. Fractal codes are not belong to me. Original : The code for the Deep learning (ANN MACD BTC) work belongs to me. Original: I didn't get license for this script because the fractal codes don't belong to me.You can use it for any purpose. This command can be a very helpful guide.You can use that fractals with your indicators for Bitcoin. You can also combine these levels with ANN - MACD - BTC script. Scripts about Artificial Neural Networks (ANN) will continue soon ! I hope it will help us to gain insight into technical analysis. Best regards. Noldo.
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Noldo tarafından
ANN MACD (BTC) Logic is correct. But I prefer to say experimental because the sample set is narrow. (300 columns) Let's start: 6 inputs : Volume Change , Bollinger Low Band chg. , Bollinger Mid Band chg., Bollinger Up Band chg. , RSI change , MACD histogram change. 1 output : Future bar change (Historical) Training timeframe : 15 mins (Analysis TF > 4 hours (My opinion)) Learning cycles : 337 Training error: 0.009999 Input columns: 6 Output columns: 1 Excluded columns: 0 Grid Training example rows: 301 Validating example rows: 0 Querying example rows: 0 Excluded example rows: 0 Duplicated example rows: 0 Network Input nodes connected: 6 Hidden layer 1 nodes: 8 Hidden layer 2 nodes: 0 Hidden layer 3 nodes: 0 Output nodes: 1 Learning rate : 0.6 Momentum : 0.8 More info : 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
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Noldo tarafından
13
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!
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Noldo tarafından
15
Ichimoku trendfollowingIchimoku più che un indicatore potrebbe essere considerato un sistema di trading. Ho quindi voluto implementare una strategia che utilizzasse soltanto i segnali che offre per l'ingresso ed uscita per verificarne l'efficacia nel lungo periodo su i diversi strumenti. I segnali che offre sono i seguenti: 1) il prezzo taglia la tenkan (segnale molto debole per un ingresso) 2) il prezzo taglia la kijun (possibile trade in controtrend fino alla kumo) 3) tenkan incrocia la linea kijun 4) uscita della linea chikou span dal prezzo: se la linea è sotto il prezzo valutare solo short, se sopra valutare solo long, se sopra il prezzo si è in correzione ed è meglio restare flat 5) prezzo entra dentro la kumo. (Non si entra in posizione dentro la kuno) 6) la kumo cambia colore (conferma direzione del trend in atto) 7) uscita dal prezzo dalla kumo 8) uscita della tenkan dalla kumo (conferma situazione) 9) uscita kijun dalla kumo (conferma situazione) 10) trend in corso con chiusura del prezzo fra la chikou span da una parte, tenkan, kijun e kumo dall'altra. Ichimoku è solitamente utilizzato come sistema trendfollowing, quindi considererò solo i segenti possibili ingressi: 1) breakout kumo (inizio trend di lungo periodo) 2) pullback (inizio trend di medio periodo o ripresa trend dopo correzione profonda con pullback su kumo) le uscite tradizionali sono: 1) prezzo chiude sotto la kijun 2) SL inizialmente su minimo candela che rompe il livello Filtri da applicare ai singoli ingressi: Filtro long: Close>kumo and chikou span > prezzo corrispondente and Filtro short Close
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Enry78 tarafından
Bollinger Bands V2 [Super Trend]################################## Anglais ###################################### With the News Bollinger Bands V2 , you can choose the source of the color (Color with Price or Color with the Super Trend ATR). You can also view the Super Trend on the chart and the configure. this allows you to quickly identify trends and the acceleration phase and accumulation Sorry for my basic English //J.Dow ################################## Français ###################################### Avec les Nouvelles Bollinger Bands V2 , vous pouvez choisir la source de la couleur ( couleur avec le prix ou la couleur avec le Super Tendance ATR ) . Vous pouvez aussi consulter le Super Tendance sur le graphique et le configure. cela vous permet d'identifier rapidement les tendances et la phase d'accélération et d'accumulation //J.Dow
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NomadandTrader tarafından
8