GreenCrypto Swing Trade Indicator - GC02Overview: This is a swing trading Indictor works using support & resistance and market trend, it is designed for all type of markets (crypto, forex, stock etc.) and works on all commonly used timeframes (preferably on 1H, 4H Candles).
How it works:
Core logic behind this indicator is to finding the Support and Resistance, we find the Lower High (LH) and Higher Low (HL) to find the from where the price reversed(bounced back) and also we use a custom logic for figuring out the peak price in the last few candles (based on the input "Strength" ). Based on the multiple previous Support and Resistance (HH, HL, LL LH) we calculate a price level, this price level is used a major a factor for entering the trade. Once we have the price level we check if the current price crosses that price level, if it crossed then we consider that as a long/short entry (based on whether it crosses resistance or support line that we calculated). Once we have pre long/short signals we further filter it based on the market trend to prevent too early/late signals, this trend is calculated based on the value from the input field "Factor". Along with this if we don't see a clear trend we do the filtering by checking how many support or resistance level the price has bounced off.
Stop Loss and Take Profit : We have also added printing SL and TP levels on the chart to make the it easier for everyone to find the SL/TP values. Script calculates the SL value by checking the previous support level for LONG trade and previous resistance level for SHORT trades. Take profit are calculated in 1:1 ratio as of now.
Available Inputs:
Strength : Define the strength of the support resistance that we calculate. The lower value means less number of candles used for calculating the support & resistance and vice versa
Factor : Specify what level of trend to use. Using higher value will result script looking using the larger trend (zoomed out trend) and using lesser value will result in using the short trends
Note: For most of the charts you don’t need to change the default values. However, feel free to try it out.
How to use:
Add the script to the chart and once the indicator is load it will display the "long" and "short" entry points along with the stopLoss and takeProfit points.
How to get access:
Send a DM to us for getting access to the script.
AI
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
Relativity BULLS FOREX 50X 4H Algorithm
Hello, this script is the correction of my bull script related to Forex. (Bull market)
Old script :
4H was chosen as the time frame.
Thus, larger pips are at our disposal and we benefit more from the hedge effect of the leverage.
Commissions per trade have been removed to get more realistic commissions.
Because every wrong trade deletes all the 1% position size.
(with leverage effect)
Use the link below to obtain access to this indicator :
KBL PLAY-ZONE PLOTTER - MCX CRUDE OIL
► How To Use This Indicator ?
• New Intraday Trading Levels Will Be Generated At 09:30 AM (UTC +05:30)
• Buy If 5 Minutes Candle Close Above '' BreakOut Buy Here '' Level.
• Sell If 5 Minutes Candle Close Below '' BreakOut Sell Here '' Level.
• Book Profits At Breakout Buy or BreakOut Sell Targets.
• If 1st Call Target Hit , Then Do Not Trade More On That Day.
• If 1st Call StopLoss Hit , Then Only Trade On 2nd Call.
PM us to obtain access.
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 !
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.
ANN BTC MTF Golden Cross Period MACDHi, 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.
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.
Relativity Adaptive Stop-LossRelativity Adaptive Stop-Loss is a stop-loss technique that uses the Relativity Autonomous Distribution Blocks algorithm.
For detailed info about Relativity Autonomous Distribution Blocks :
*** Features
This structure is different from standard stop-losses.
The base frame is based on "Market Adaptive Stop-Loss" script.
For detailed information about Market Adaptive Stop-Loss:
This script uses the Relativity Autonomous Distribution Blocks as cross method.
Tradeable / Non Tradeable Region Detector :
This script separates tradeable and non-tradeable regions with a coloring method.
Plotting Rules :
* Maroon : Uncorfirmed Short Positions
* Teal : Unconfirmed Long Positions
* Green : Confirmed Long Positions
* Red : Confirmed Short Positions
This script can be used in only 1W time frame. (TF = 1W )
Does not repaint on 1W and larger time frames. ( Source = close )
*** Settings :
The only option here is the ATR multiplier.
The default use value of this ATR multiplier, which is of the standard of stop-loss, is 2.You can set it from the menu.
No alert is set.
Because the positive and negative regions are the same as Relativity Autonomous Distribution Blocks.
Since the traders can trade according to the support and resistance outside the definite regions, the unnecessary signal was confused and the alerts were removed.
*** USAGE
The Stop-Loss indicator can slide on the chart.
So you have to make sure you put it in right place.
Using this script in a new pane below will radically solve slip problems.
Stop-Loss values do not slip definitely.The values can select from the alignment.
NOTE :
Some structures (Market Adaptive Stop-Loss) and design in this script are inspired by everget's Chandelier Exit script :
Best regards.
Relativity Autonomous Distribution Blocks
The relativity method is a method of trade inspired by the Theory of Relativity of Albert Einstein , which argues that trade is a relative concept and, according to the case it advocates, creates the values to be evaluated relatively by using various engineering methods, and converts these values to factors to ensure the highest efficiency.
Many layers are common with Autonomous LSTM.
For more information about Autonomous LSTM :
But there are additional layers that are much higher than that.
These systems use COT (Commitment of Traders) data positively in trade and significantly increase the hit rate compared to conventional methods.
And in all traded instruments, it decides the degree of scoring by linking with global markets.
The more liquidity of the selected parities, the higher the success rate, the higher liquidity in the markets.
***STRUCTURE
Feature Layer 1 : Formulation : Common Layer with Autonomous LSTM
Feature Layer 2: Forecast Algorithm : Common Layer with Autonomous LSTM
Feature Layer 3 : Composite of Two Layers : Adaptive Period (Length) Algorithm : Common Layer with Autonomous LSTM
Feature Layer 4 : High - Low Selection Algorithm : Common Layer with Autonomous LSTM
Feature Layer 5 : Volume (Ticker ) - Open Interest (Global Market) Power Factor according to Global Markets and Related instrument (Ticker)
Feature Layer 6 : Quantum Equations including COT Commercial Positions (Communicate with layer 5)
Feature Layer 7 : World's Price/Earnings Ratio (This layer is automatically added to layer 6 as a factor each week.)
Feature Layer 8 : Distribution Blocks : The design of script as a histogram, with distributional buying and selling points and positive/negative zone coloring, with alerts.
Uses the relativity algorithm. This will contribute not only to leveraged transactions but also to portfolio management and will give a more realistic perspective.
Informs the trading points within the regions.
In this way, it allows for gradual buying and selling and reduces the risk to a much lower level.
These feature allows a difference perspective especially for traders who act with portfolio logic and / or add regular income.
The educational idea I shared in order to set an example for this logic:
***SETTINGS
Menu
1. * Market Type
The menu is divided into 5 different algorithms and covers all instruments around the world.
For example:
Futures : XAUUSD , GC , XAGUSD , SUGARUSD , SB1! , XAGUSD
Stocks : All Stocks and Modified Parities (Example : AAPL/EUR , XAU/XAG , AAPL , MT , BAC)
Forex Excluding USD/X : CHFUSD , EURUSD , EURJPY , AUDNZD
Forex USD/X : USDJPY , USDTRY , USDMXN
Crypto : BTCUSD , ETHUSD , ADAUSD or BTCETH , ETHBTC
2. * Barcolor
Barcolor Plotting Rules : On / off section with these rules when barcolor on :
Orange : Distributional Sell Signal ( Not Short )
Blue : Distributinaol Buy Signal
*** FEATURES
Indicator Features :
Red Background with Cross : Short Signal
Green Background with Cross : Buy Signal
Blue Histogram Color : Distributional Buy Signal
Orange Histogram Color : Distributional Sell Signal
Alerts
Long Alert
Short Alert
Distributional Buy Alert
Distributional Sell Alert
*** USAGE
Since the script uses various Commitment of Traders data, it is designed only for the weekly time frame. ( TF = 1W )
Script does not repaint 1 Week and above time frames . (Source = close )
NOTE :
The script design was inspired by one of RafaelZioni's script :
Best regards.
Autonomous LSTM Stop-LossStructure
Autonomous LSTM Stop-Loss is a stop-loss technique that uses the Autonomous LSTM algorithm.
For detailed info about Autonomous LSTM :
*** Features
This structure is different from standard stop-losses.
The base frame is based on "Market Adaptive Stop-Loss" script.
For detailed information about Market Adaptive Stop-Loss:
This script uses the Autonomous LSTM as cross method.
Tradeable / Non Tradeable Region Detector :
This script separates tradeable and non-tradeable regions with a coloring method.
Plotting Rules :
* Maroon : Uncorfirmed Short Positions
* Teal : Unconfirmed Long Positions
* Green : Confirmed Long Positions
* Red : Confirmed Short Positions
This script can be used in all time frames.
Does not repaint. ( Source = close )
*** Settings :
The only option here is the ATR multiplier.
The default use value of this ATR multiplier, which is of the standard of stop-loss, is 2.You can set it from the menu.
No alert is set.
Because the positive and negative regions are the same as Autonomous LSTM.
Since the traders can trade according to the support and resistance outside the definite regions, the unnecessary signal was confused and the alerts were removed.
*** Usage
The Stop-Loss indicator can slide on the chart.
So you have to make sure you put it in place.
Since this is a region scan from the OHLC levels, indicator contains small blue dots to the ohlc levels and made it serve as a guide.
However, since we cannot know the OHLC values precisely, it is best to use them as follows:
Because it is often forgotten to put it in place:
(OHLC : Average of Open, High, Low, and Closing prices for each period)
Using this script in a new pane below will radically solve slip problems.
Stop-Loss values do not slip definitely.The values are selected from the alignment.
NOTE :
Some structures (Market Adaptive Stop-Loss) and design in this script are inspired by everget's Chandelier Exit script :
Best regards.
Autonomous LSTM [Noldo] Structure
Feature Layer 1 : Formulation :
The Autonomous LSTM adaptive period equation is a multivariate equation created by averaging a table based on market weights and optimizing it for each time period, by specially Artificial Neural Networks (ANN) training and taking note of the instruments chosen from Foreign exchange instruments, Stock markets , Futures and Commodities , Interest Rates and Yields all over the Global Markets.
Market weights and liquidities were taken into consideration and included in the calculations.
Feature Layer 2: Forecast Algorithm :
When we apply only the first item, we only get the buy and sell signals in reverse.
In other words, since we measure the expectation, the positive signal informs the bear market and the negative scenario informs the bull market.
If we only act according to the expectations market, our system will be very sensitive.
When we associate this with real prices, both our accuracy increases and the reverse market returns to the normal market.
In other words, as in the indicators with standard average, the upward crosses are buy and the downward crosses are sell signal.
Examples:
a -) The normal deep learning script (ANN), which is only created according to expectations:
Unlike standard market, it gives reverse signals.
Original script :
b-) Script with Forecast Algorithm but it only uses valid and standard periods for certain instruments :
Original script :
Feature Layer 3 : Composite of Two Layers : Adaptive Period (Length) Algorithm
This layer is the most important layer.
Outputs the period.
It adjusts itself to market conditions and provides a more agile trading environment under all circumstances.
Display of smart period function and standard period :
Where the market is stagnant, the period increases automatically and reduces unnecessary trade, while in trendy markets the period decreases automatically and allows to see positions first.
The degree of stagnation of the instrument concerned is not calculated solely by volatility.
We may perceive this in relation to several factors, but yes volatility is one of these factors.
When we put the script system under the MACD (Moving Average Convergence Divergence) roof, I did the tests.
Where both averages were positive, they could report accurate harsh trend news, or vice versa.
But I decided to give it up and put it on the Stochastic Money Flow Index .
First of all , Stochastic Money Flow Index function takes the volume into account.
The reason for this is a very important factor, which is naturally contained in the structure of High - Low conditions related codes.
And by using this factor, it could be superfast adaptive in both stagnant and trendy markets.
Feature Layer 4 : High - Low Selection Algorithm
The High-Low Selection Algorithm does not depend on a specific period but scans all periods backwards.(Lookback Function - Lkb )
Outputs the lowest or highest values in the specified new period.
This algorithm was written by me with the concern that if everyone trades according to the same threshold values, it will cause problems and choosing between values of the whole period length will slow down the signals.
This algorithm consists of two functions.
a - Lkb (Lookback Function) :
The lookback function scans back all periods from 0 to Smart Period bars at the same time.
In order to show the effect of the function, it was done between 0 and 84 bars.
However, the scan period of the function is normally at the same time: 0 to adaptive period time.
If the adaptive period includes a fractional day, it can also scan it.
There is no need to be an integer.
All functions are written to make mutable variables appropriate.
And what this function will scan depends on the second feature.
The special selection algorithm is in this function.And the output is given in this function.
b-) High - Low Selection Algorithm
Outputs the lowest or highest values in the specified new period.
This function allows you to select the most advantageous low or high values, even though the adaptive period remains the same.
And the signals are even more accurate.
This is a comparison of the High-Low selection algorithm and the Function: Stochastic Money Flow Index in the standard period.
For the codes of the Stochastic Money Flow Index function:
Speed may not be clear here.
So let's take a look at on chart.
So I would like to show a comparison values of the standard and special selection algorithms on Standard Highest - Lowest Function (All effort goes to RicardoSantos)
Note: This function is the standard function and freed from integer loads.
Blue = Function Highest - Lowest (length = 10 )
Yellow = Smart High-Low Selection Algorithm (length = 10 )
You can better observe the different results in the same period on the chart.
***
4 layers are interdependent.
And when the inter-layer operations are completed, output is given.
*** - Usage of Autonomous LSTM
Plot Rules
Blue Zones = Crossover condition where the average of long and short lines is less than 50.
Orange Zones = Crossunder condition where long and short lines averages more than 50.
Green Zones = Crossover condition where the average of long and short lines is greater than 50.
Red Zones = Crossunder condition where long and short lines averages less than 50.
*** Autonomous LSTM Settings :
It is just the barcolor to be colored according to the crossover and crossunder conditions or not (I / 0) option.
*** Autonomous LSTM Alerts :
As an alert, it only reports crossover and crossunder status as "Long Signal" and "Short Signal" as a warning after the first bar closure.
*** CONCLUSION :
Autonomous LSTM Designed to be used in any time frame.
Does not repaint in any time frame.
Script is independent of constant coefficients.No period adjustment is necessary.
Each layer transfers the information in its own layer to the next layer and the results are reflected in the Stochastic Money Flow Index function built on the resultant.
Regards.
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.
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!
ANN Forecast Dependent Variable Odd GeneratorHello , this script is the ANN Forecast version of my "Dependent Variable Odd Generator " script.
I went to simplify a bit because the deep learning calculations are too much for this command.
The latest instruments included:
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
VIX : S & P 500 Volatility Index (VX1! , VIX ) Average error : 0.009999
ES : S&P 500 E-Mini Futures ( ES1! ) Average error : 0.010709
SSE : Shangai Stock Exchange Composite (Index ) ( 000001 ) Average error : 0.011287
XRPUSD : Ripple (XRPUSD , XRPUSDT ) Average error : 0.009803
Simply select the required instrument from the tradingview analysis screen, then add this command and select the same instrument from the settings section.
The codes are not open-source because they contain forecast algorithm codes a little that I will use commercially in the future.
However, I will never remove this script, and you can use it for free unlimitedly.
For more information about my artificial neural network forecast series:
For more information about my dependent variable odd generator :
For more information about simple artificial neural networks :
(detailed information about ANN )
(25 in 1 version )
I hope it helps in your analysis. Regards , Noldo .
NOTE : In the first pass bar of the definite positive and negative zone, alerts are added for both conditions.
Easy Loot BandsEasy Loot Bollinger Bands:
This indicator is a bollinger band that also auto-generates a trendline, giving you the best opportunities to buy & sell. That's it.
You can use this on any time-frame, any stock, and any cryptocurrency.
ANN Forecast Stochastic Oscillator [Noldo] In this script, I tried to integrate ANN Forecast Algorithm on Stochastic Oscillator.
It took me quite a while, but i guess it worth.
After selecting the ticker, select the instrument from the menu and the system will automatically turn on the appropriate Forecast Stoch system.
The system is trained with ANN values of ANN MACD 25 in 1.
The Forecast algorithm is not open-source.
But I'm never remove this script.
You can use it forever for free.
As you can see in the presentation, although it is in the same period, it is more accurate and agile than standard Stochastic Oscillator .
I think even a bar is important in trade.
For those who don't see that command,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
ES : S&P 500 E-Mini Futures ( ES1! ) Average error : 0.010709
SSE : Shangai Stock Exchange Composite (Index ) ( 000001 ) Average error : 0.011287
XRPUSD : Ripple (XRPUSD , XRPUSDT ) Average error : 0.009803
Extras :
- Crossover and crossunder alerts
- Switchable barcolor
NOTE :
Australian Dollar / US Dollar ( AUDUSD ) removed due to high average error. (Average error > 0.013 )
Timeframe advice :
I suggest you to use that system TF >= 1D
My favorite is 1 week bars. (1W)
More info about forecast series (My last forecast example ) :
Special thanks :
Special thanks to dear wroclai for his great effort .
NOTE : I decided to build Autonomous LSTM on Stochastic Oscillator , i think Stochastic Oscillator one of the best and it contains naturally high-lows.
ANN Forecast MACD [Noldo] In this script, I tried to convert ANN MACD to MACD Forecast.
It took me quite a while, but it was fun.
After selecting the ticker, select the instrument from the menu and the system will automatically turn on the appropriate Forecast MACD system.
The system is trained with ANN values of ANN MACD 25 in 1.
But because the system is overloaded, only the most popular instruments are left.
The others were unfortunately eliminated.
The only difference is that it was built on the forecast algorithm of my own creation.
The Forecast algorithm is not open-source.
The codes are a nice framework for some of my most valuable systems about ANN . (Working on them. )
But I'm never remove this script.
You can use it forever for free.
As you can see in the presentation, although it is in the same period, it is more accurate and agile than normal MACD.
I think even a bar is important in trade.
For those who don't see that command,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
ES : S&P 500 E-Mini Futures ( ES1! ) Average error : 0.010709
SSE : Shangai Stock Exchange Composite (Index ) ( 000001 ) Average error : 0.011287
XRPUSD : Ripple (XRPUSD , XRPUSDT ) Average error : 0.009803
Extras :
- Crossover and crossunder alerts
- Switchable barcolor
NOTE :
Australian Dollar / US Dollar (AUDUSD ) removed due to high average error. (Average error > 0.013 )
Timeframe advice :
I suggest you to use that system TF >= 1D
My favorite is 1 week bars. (1W)
Info about forecast series :
www.sciencedirect.com
Special thanks :
Special thanks to dear wroclai for his great effort .
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 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.
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.
