Ann

ANN Trend Prediction This trend indicator utilizes an artificial neural network (ANN) to predict the next market reversal within a certain range of previous candles. The larger the range of previous candles you set, the fewer reversals will be predicted, and trends will tend to last longer. The ANN is trained on the BTCUSD 4-hour chart, so using it on other assets or timeframes may yield suboptimal results. It takes three input values: the closing price, the Stochastic RSI, and a Choppiness Indicator. Based on thes

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BerlinCode42の投稿

Machine Learning: Perceptron-based strategy Perceptron-based strategy Description: The Learning Perceptron is the simplest possible artificial neural network (ANN), consisting of just a single neuron and capable of learning a certain class of binary classification problems. The idea behind ANNs is that by selecting good values for the weight parameters (and the bias), the ANN can model the relationships between the inputs and some target. Generally, ANN neurons receive a number of inputs, weight each of those inputs, sum the weights,

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capissimoの投稿

Candlesticks ANN for Stock Markets TF : 1W Hello, this script consists of training candlesticks with Artificial Neural Networks (ANN). In addition to the first series, candlesticks' bodies and wicks were also introduced as training inputs. The inputs are individually trained to find the relationship between the subsequent historical value of all candlestick values 1.(High,Low,Close,Open) The outputs are adapted to the current values with a simple forecast code. Once the OHLC value is found, the exponential moving averages of 5 and

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ANN Next Coming Candlestick Forecast SPX 1D v1.0 WARNING: Experimental and incomplete. Script is open to development and will be developed. This is just version 1.0 STRUCTURE This script is trained according to the open, close, high and low values of the bars. It is tried to predict the future values of opening, closing, high and low values. A few simple codes were used to correlate expectation with current values. (You can see between line 129 - 159 ) Therefore, they are all individually trained. You can see in functions. Th

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ANN BTC MTF Golden Cross Period MACD Hi, this is the MACD version of the ANN BTC Multi Timeframe Script. The MACD Periods were approximated to the Golden Cross values. MACD Lengths : Signal Length = 25 Fast Length = 50 Slow Length = 200 Regards.

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ANN BTC MTF CM Sling Shot System Hi all, this script was created as a result of ANN training in all time frames of bitcoin data. Trained data is built on Chris Moody's Sling Shot system. CM Sling Shot System : This system automatically generates the ANN output for all time periods. Therefore, it has multi-time-frame feature. Artificial Neural Networks training details: Average Errors 1 minute = 0.005570 3 minutes = 0.006674 5 minutes = 0.007067 15 minutes = 0.010000

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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 Parame

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Blockchain Artificial Neural Networks I 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 interestin

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NAND Perceptron Experimental NAND Perceptron based upon Python template that aims to predict NAND Gate Outputs. A Perceptron is one of the foundational building blocks of nearly all advanced Neural Network layers and models for Algo trading and Machine Learning. The goal behind this script was threefold: To prove and demonstrate that an ACTUAL working neural net can be implemented in Pine, even if incomplete. To pave the way for other traders and coders to iterate on this script and push the boundari

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UnknownUnicorn2796841の投稿

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 h

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ANN MACD : 25 IN 1 SCRIPT In 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! Averag

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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

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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 lay

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ANN MACD GOLD (XAUUSD)This script aims to establish artificial neural networks with gold data.(4H) Details : Learning cycles: 329818 Training error: 0.012767 ( Slightly above average but negligible.) 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: 5 Hidden layer 2 nodes: 1 Hidden layer 3 nodes: 0 Output nodes: 1 Learnin

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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

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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

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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 coeffici

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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 reg

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SPY FRACTAL S-R LEVELS (FIXED ANN MACD) This is a fractal version of my deep learning script for SPY In addition, buy and sell conditions may appear in bar colors in green and red. You can choose from the menu if you wish. Fractal codes do not belong to me. So I didn't put any license. You can use it as you want, you can change and modify. Regards.Noldo

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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 a

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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 Trainin

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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 b

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ANN [Original Version Repainting Fixed]It's the ANN, the original version with repainting issue got taken care of. The result as you can see, it's might not be that good compare to original. If anyone want to put it into a strategy, feel free. I do not recommend you to use this as a stand alone. Trade with your own risk. Maybe trade the opposite way might do it lol

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Jittraの投稿

Improved ANN Yes, this is the improved ANN, very powerful, you can make a lot of money with this, very very strong and good.

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wpatte15の投稿