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Поиск скриптов по запросу "AI技术更新"

Machine Learning: LVQ-based StrategyLVQ-based Strategy (FX and Crypto) Description: Learning Vector Quantization (LVQ) can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all learning-based approach. It is based on prototype supervised learning classification task and trains its weights through a competitive learning algorithm. Algorithm: Initialize weights Train for 1 to N number of epochs - Select a training example - Compute the winning vector - Update the winning vector Classify test sample The LVQ algorithm offers a framework to test various indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others. Note: TradingViews's playback feature helps to see this strategy in action. The algo is tested with BTCUSD/1Hour. Warning: This is a preliminary version! Signals ARE repainting. ***Warning***: Signals LARGELY depend on hyperparams (lrate and epochs). Style tags: Trend Following, Trend Analysis Asset class: Equities, Futures, ETFs, Currencies and Commodities Dataset: FX Minutes/Hours+++/Days
Индикатор Pine Script®
от capissimo
6
Machine Learning: Logistic RegressionMulti-timeframe Strategy based on Logistic Regression algorithm Description: This strategy uses a classic machine learning algorithm that came from statistics - Logistic Regression (LR). The first and most important thing about logistic regression is that it is not a 'Regression' but a 'Classification' algorithm. The name itself is somewhat misleading. Regression gives a continuous numeric output but most of the time we need the output in classes (i.e. categorical, discrete). For example, we want to classify emails into “spam” or 'not spam', classify treatment into “success” or 'failure', classify statement into “right” or 'wrong', classify election data into 'fraudulent vote' or 'non-fraudulent vote', classify market move into 'long' or 'short' and so on. These are the examples of logistic regression having a binary output (also called dichotomous). You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using log of odds as dependent variable. In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function. Basically, the theory behind Logistic Regression is very similar to the one from Linear Regression, where we seek to draw a best-fitting line over data points, but in Logistic Regression, we don’t directly fit a straight line to our data like in linear regression. Instead, we fit a S shaped curve, called Sigmoid, to our observations, that best SEPARATES data points. Technically speaking, the main goal of building the model is to find the parameters (weights) using gradient descent. In this script the LR algorithm is retrained on each new bar trying to classify it into one of the two categories. This is done via the logistic_regression function by updating the weights w in the loop that continues for iterations number of times. In the end the weights are passed through the sigmoid function, yielding a prediction. Mind that some assets require to modify the script's input parameters. For instance, when used with BTCUSD and USDJPY, the 'Normalization Lookback' parameter should be set down to 4 (2,...,5..), and optionally the 'Use Price Data for Signal Generation?' parameter should be checked. The defaults were tested with EURUSD. Note: TradingViews's playback feature helps to see this strategy in action. Warning: Signals ARE repainting. Style tags: Trend Following, Trend Analysis Asset class: Equities, Futures, ETFs, Currencies and Commodities Dataset: FX Minutes/Hours/Days
Индикатор Pine Script®
от capissimo
111
Machine Learning: Perceptron-based strategyPerceptron-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, and then transform that sum using a special function called an activation function. The output of that activation function is then either used as the prediction (in a single neuron model) or is combined with the outputs of other neurons for further use in more complex models. The purpose of the activation function is to take the input signal (that’s the weighted sum of the inputs and the bias) and turn it into an output signal. Think of this activation function as firing (activating) the neuron when it returns 1, and doing nothing when it returns 0. This sort of computation is accomplished with a function called step function: f(z) = {1 if z > 0 else 0}. This function then transforms any weighted sum of the inputs and converts it into a binary output (either 1 or 0). The trick to making this useful is finding (learning) a set of weights that lead to good predictions using this activation function. Training our perceptron is simply a matter of initializing the weights to zero (or random value) and then implementing the perceptron learning rule, which just updates the weights based on the error of each observation with the current weights. This has the effect of moving the classifier’s decision boundary in the direction that would have helped it classify the last observation correctly. This is achieved via a for loop which iterates over each observation, making a prediction of each observation, calculating the error of that prediction and then updating the weights accordingly. In this way, weights are gradually updated until they converge. Each sweep through the training data is called an epoch. In this script the perceptron is retrained on each new bar trying to classify this bar by drawing the moving average curve above or below the bar. This script was tested with BTCUSD, USDJPY, and EURUSD. Note: TradingViews's playback feature helps to see this strategy in action. Warning: Signals ARE repainting. Style tags: Trend Following, Trend Analysis Asset class: Equities, Futures, ETFs, Currencies and Commodities Dataset: FX Minutes/Hours+/Days
Индикатор Pine Script®
от capissimo
33
Machine Learning: kNN-based StrategykNN-based Strategy (FX and Crypto) Description: This strategy uses a classic machine learning algorithm - k Nearest Neighbours (kNN) - to let you find a prediction for the next (tomorrow's, next month's, etc.) market move. Being an unsupervised machine learning algorithm, kNN is one of the most simple learning algorithms. To do a prediction of the next market move, the kNN algorithm uses the historic data, collected in 3 arrays - feature1, feature2 and directions, - and finds the k-nearest neighbours of the current indicator(s) values. The two dimensional kNN algorithm just has a look on what has happened in the past when the two indicators had a similar level. It then looks at the k nearest neighbours, sees their state and thus classifies the current point. The kNN algorithm offers a framework to test all kinds of indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others. Note: TradingViews's playback feature helps to see this strategy in action. Warning: Signals ARE repainting. Style tags: Trend Following, Trend Analysis Asset class: Equities, Futures, ETFs, Currencies and Commodities Dataset: FX Minutes/Hours+++/Days
Индикатор Pine Script®
от capissimo
149
Candlesticks ANN for Stock Markets TF : 1WHello, 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 20 periods are used. Reminder : OHLC = (Open + High + Close + Low ) / 4 First version : Script is designed for S&P 500 Indices,Funds,ETFs, especially S&P 500 Stocks,and for all liquid Stocks all around the World. NOTE: This script is only suitable for 1W time-frame for Stocks. The average training error rates are less than 5 per thousand for each candlestick variable. (Average Error < 0.005 ) I've just finished it and haven't tested it in detail. So let's use it carefully as a supporter. Best regards !
Индикатор Pine Script®
от Noldo
2
ANN Next Coming Candlestick Forecast SPX 1D v1.0WARNING: 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. The average training error of each variable is less than 0.011. NOTE : This script is designed for experimental use on S & P 500 and connected instruments only on 1-day bars. The Plotcandle function is inspired by the following script of alexgrover : Since we estimate the next values, our error rates should be much lower for all candlestick values. This is just first version to show logic. I will continue to look for other variables to reach average error = 0.001 - 0.005 for each candlestick status. Feel free to use and improve , this is open-source. Best regards.
Индикатор Pine Script®
от Noldo
5
ANN BTC MTF CM Sling Shot SystemHi 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 30 minutes = 0.009398 45 minutes = 0.010000 1 Hour = 0.006848 2 Hours = 0.006901 3 Hours = 0.009608 4 Hours = 0.009774 1 Day = 0.010000 1 Week = 0.010000 The results look good (All Average Error <= 0.01 ), the Sling Shot Method is also good, but you can also refer to historically slower period averages to filter these arrows a bit more. I leave the decision to you. Best regards.
Индикатор Pine Script®
от Noldo
13
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 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.
Индикатор Pine Script®
от Noldo
40
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!
Индикатор Pine Script®
от Noldo
17
Open Source Ichimoku Kinkō hyō Keizen 改善Open Source Ichimoku Kinkō hyō Keizen 改善 First of all, thank you for using my work, making changes and continuing to share it for free. I chose as indicator name Ichimoku Kinkō hyō Keizen logically simply by what the word Keizen reflects a Japanese method which means continuous improvement and quality. The goal here is to correct already any offset faults that should not be present and to try to bring the indicator of new things that can serve and advance and provide additional support for the decision. A continuation will surely be planned which will bring its batch of new elements which will come naturally be grafted to this tool. Possibility of adding or not adding new lines Jun (76), Kan (226), Junkan A, B (676). - Junakan To be calculated on the average of Jun / Kan 151 periods. - Junkan B calculate over the 676 periods. - Possibility of adding or not adding a new kinkohoyo calculate on Jun (76) and Kan (226). - Possibility of adding or not adding a new Kumo calculate over 676 periods. - Possibility of coloring or not the kumos or the kinkohyos. - Possibility of adding or not Chikō 9,26,52,76,226,676. - Ability to display or not passive lines in the future, 5 shots ahead of the movement of lines, provided that the extremes are not broken. Thank you: Thanks to TomQSD to have tested all the different versions of this project and to have contributed his criticism that helped me a lot to develop the tool. Thanks to Herveo for having agreed to share his code on the passive lines. Remarks: Not all options are activated at the same time. Please do not uncheck the boxes in style, only change the colors. To enable or disable lines, go to input parameters. ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// Open Source Ichimoku Kinkō hyō Keizen 改善 Tous d'abord, merci d'utiliser mon travail, d'apporter des modification et de continuer à le partager gratuitement. J'ai choisi comme nom d’indicateur Ichimoku Kinkō hyō Keizen en toute logique tout simplement par ce que le mot Keizen reflète une méthode Japonaise qui veut dire amélioration en continue et qualité. Le but ici est de corriger déjà tout les défauts de décalage qui ne devraient pas être présents et d’essayer d’apporter sans trop charger l’indicateur des choses nouvelles qui peuvent servir et faire progresser et apporter une aide supplémentaire à la décision. Une suite sera sûrement prévue qui apportera son lot d'éléments nouveaux qui viendront naturellement se greffer à cet outil. - Possibilité d’ajout ou non de nouvelles lignes Jun (76) , Kan (226) , Junkan A,B (676). - Junakan A calculer sur la moyenne de Jun/Kan 151 périodes. - Junkan B calculer sur les 676 périodes. - Possibilité d’ajout ou non d’un nouveau kinkohoyo calculer sur le Jun (76) et le Kan (226). - Possibilité d’ajout ou non d’un nouveau Kumo calculer sur 676 périodes. - Possibilité de colorier ou non le ou les kumos et le ou les kinkohyos. - Possibilité d’ajout ou non de Chikō 9,26,52,76,226,676. - Possibilité d’afficher ou non de lignes passives dans le future, 5 coups d’avance sur les déplacement des lignes, a condition que les extrêmes ne soient pas rompu. Remerciement : Merci à TomQSD d'avoir tester toutes les différentes versions de ce projet et d’avoir apporter sa critique qui m'a beaucoup aidé à développer l’outil. Merci à Herveo pour sont accord d’avoir bien voulu partager son code sur les lignes passives. Remarques: Toutes les options ne sont pas toutes à activer en même temps. Merci de ne pas décocher les cases dans style, modifiez seulement les couleurs. Pour activer ou désactiver des lignes, allez dans paramètres en entrée.
Индикатор Pine Script®
от Canop2e_T
5
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 ! "
Индикатор Pine Script®
от 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 .
Индикатор Pine Script®
от 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.
Индикатор Pine Script®
от 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.
Индикатор Pine Script®
от Noldo
4
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 Learning rate: 0.7000 Momentum: 0.8000 Target error: 0.0100 NOTE : Alarms added. And special thanks to dear wroclai for his great effort. Deep learning series will continue . Stay tuned! Regards.
Индикатор Pine Script®
от Noldo
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.
Индикатор Pine Script®
от Noldo
1