noldo_taI'm a follower of Noldo, and I've learned almost all of his published scripts. I like some of the basic functions he wrote so much that I decided to collect them as a noldo_ta library file to share. Most of these functions are the same as Noldo's version, and there are some interesting algorithmic processing, which I also encapsulated into functions. Enjoy.
COURTESY OF NOLDO for these intersting functions!
Library "noldo_ta"
bton()
f_ema()
f_highest()
f_lowest()
f_rma()
f_rsi()
f_stoch()
f_kdj()
f_sum()
f_sma()
f_stdev()
f_bb()
f_pearson_corr()
f_multiple_corr()
f_adjusted_r_squared()
f_mfi()
dow()
pivothl()
f_adjusted_r_squared2()
linreg()
f_roc()
f_macd()
f_mom()
f_wma()
f_hull()
f_vwma()
f_obv()
f_sar()
f_stochastic()
f_stochrsi()
f_stochmfi()
f_kst()
f_smahist()
f_emahist()
f_fisher()
f_ao()
f_accdist()
f_highestbars()
f_lowestbars()
Noldo
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.
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 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 ! "
Function : Stochastic Money Flow IndexThis function is similar to the stochastic rsi function.
The only difference is that Money Flow Index is used instead of rsi.
Oversold and overbought values were changed to 80 and 20.
Because the MFI's overbought and overbought zones are 20 and 80.
In MFI, I think that it can be more beneficial in liquid markets than stochastic rsi since volume is taken into account in contrast to RSI.
Regards.
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 .
Distribution BlocksThis idea has been created by the combination of the two existing systems as a result of my efforts to create a distributional buying and selling guide that has plagued my head for a long time.
1st idea is Accumulation / Distribution Line :
2nd idea is Distribution Day :
These two ideas, the intellectual assistance of professional brokers, and my observations of cot data played a role in the formation of this idea.
Let's start.
No matter how often we divide our risk, both our minds are not comfortable and our capital may end at any moment, and if we do not use professional systems, our chances of success are 50 percent.
If we take this system as an aid to our classic systems, we can determine the amount of risk with those predictions and gradually trade.
If we don't use leverage and we have a little predictive ability, our chances of success go above 50 percent.
But for the first time, we can keep our first lot very low and increase the number of positions in the same order of orders (example: buy and buy and buy).
If we keep the first amount low, the folds won't hurt us.
When we catch up with the trend, purchases with larger position sizes than lower prices lower our average price, so that we can make a good profit when the rising trend starts.
By accepting the zone changes as the reset point just like in the martingale system, we enter the folds in the new zone with our first lot weight.
Although we cannot catch the trend, we determine the stoploss level by adding the first point we entered or the first point we entered and the commission cost.
In fact, this method is the method of buying and selling very large traders and producers, banks, pro-brokers, hedge funds and in other words the new popular phrase "whales".
Because if he trades otherwise, he cannot find buyers because his goods are too big.
I like the comfort of mind in this way.
Finally, your methods separating the negative and positive regions (macd, rsi, interpretation observation etc.)
the stronger you are, the higher your success rate.
I think the Accumulation Distribution method is very successful, but it can be adjusted for the period.
I can't wait to integrate my relativity system on this.
And when my deep learning series is over, I will integrate them on ANN series and share them publicly.
To start with, I can say briefly.
If your capital is 100:
(first lot + (increase multiplier * first lot) + (increase multiplier * increase multiplier * first lot) + .....) = 100
I tell you that you can have the same position in this series 10 - 15 times,
this will help you decide how small a position size is to be used as the starting rate and choose a low increment multiplier!
I think that this idea cannot be converted into strategy, because when our expectations come true, we may want to free all positions and start again.And I think that's better.
And in sudden movements and developments we take action with different expectations.
I'm going to talk about this script's calculations and profits on educational ideas.
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.
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.
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
Learning rate: 0.6000
Momentum: 0.8000
Target error: 0.0055
NOTE : Use with EURUSD only.
Alarms added.
Thanks dear wroclai for his great effort.
Deep learning series will continue ! Stay tuned.
Regards , Noldo .
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!
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.
COT Commercial Positions (Updated)
This script aims to look at the markets from a manufacturer's point of view.
Producers or large enterprises gradually sell their goods as the price increases.
Because both the amount of product and position in their hands is too high, otherwise they can not find buyers, and they have to make a safe profit.
Therefore, I have shown short positions in green and long positions in red.
Blue is the net position formed by subtracting long positions from short positions.
This script is created with the latest Quandl data number codes.
Please let me know if you see a missing or a code update.
I recommend using it in a weekly (1W) time frame.
CAUTION : Since Bitcoin producer positions are very sparse, speculative long positions have been preferred in Bitcoin.
If you're looking for Bitcoin,
select Bitcoin from the menu.
Regards.
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
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.
Dow Factor RSI Fractals with Risk Detector
This script is only the fact that the fractals are set according to my Dow Factor RSI .
Bar color and background change where risk factor is high.
You can turn it off or on from the menu.
I'm not licensing because "fractal support and resistances" are not my own codes.
The rest are my own products.
For more information about the Dow Factor RSI:
For more information on the DVOG Risk Detector:
For information about Dependent Variable Odd Generator :
To see the script in a storm theme with Quadruple Exponential Moving Averages:
Best wishes.
Storm Trading System This script is inspired by the following :
Fractal Dow RSI Support and Resistance ;
Moving Average Clouds ;
Let's start.
This command is based on a fun description of where we are.
Technical analysis methods are likened to a storm.
Clouds as moving average,risk factor as lightning,
fractals were taken as green and red rain.
In this system:
4 Exponential Moving Averages, ( EMA15, EMA30 , EMA45 , EMA60 ),
interpretation of my own work, Dow Factor RSI, as Fractal Support and Resistance,
interpretation of my own work , DVOG Risk Factor : with changeable background and bar color.
Fractal support resistance level codes do not belong to me.
So I'm not putting a license.
But the other codes are my labor.
Consider the risk factor not as a stop, but as a region of high attention.
It is a warning before hard movements.
And watch out for turbulence in the clouds :)
The regions above and below the clouds are major trend zones, which may take a long time.
Guide the fractals in these areas.
It allows you to comment on this and tons of similar things.
And you see where you are in the big trade from a different perspective.
Repaint issue :
Firstly our source is close . Repaint will only cause the following issue and solution:
There may be a time difference between countries as the dow factor depends on the indexes.
Do not use a low graph time frame in stocks.
Dependent Variable Odd Generator Risk Detector
In fact, I wrote this script for detect Bollinger and Linear Regression Bands squeeze.
It's a side script.
Logic works like this:
Only the stagnant market probability is drawn from the Bollinger bandwidth by Dependent Variable Odd Generator and MFI index is calculated taking into account the volume.
This value ranges from 0 to 100.
To be sure, this value is averaged over a small period.
If you break the average and exceed 50, the bollinger band is too narrow and the risk is too high.
This means more commissions, more transactions, and vain work.
Or, when in position, the warning is not ignored due to unnecessary signals.
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.
Function : Multiple Correlation
This script was written to calculate the correlation coefficient (Adjusted R-Squared) for one dependent and two independent variables.(3-way)
Pearson correlation method was used with exponential moving averages as the correlation calculation method.
Use your source ( i use "close" generally ) as the dependent variable.
Inspired by this article : www.real-statistics.com
The Adjusted R-Squared coefficient is used as output, but the R-Squared coefficient is also available in the code.
Adjusted R-Squared is often used for multiple correlations.
It also gives better results in large samples.
Here is the article about the difference of the two coefficients : www.investopedia.com
I wrote this function to increase the efficiency of my Dow Factor I used before.
When my research is over, I will apply the 3-factor correlation to my scripts.
I hope that I will achieve more efficient indicators and oscillators and even strategies.
In this command, I gave a few variable values and plotted them as an example.
I hope this function is useful in your work.
Finally, you can use periods as mutable variables.
The function is recovered from integer loads.
Best regards. Noldo
Dow Factor Stoch RSIThe indicator was generated by adding the Dow Factor to the Stochastic Relative Strength Index.( Stoch RSI )
The Dow factor is the effect of the correlation coefficient, which determines the relationship between volume and price, on the existing indicators.
With these codes we are able to integrate them numerically into the indicators.
For more information on the Dow factor, please see my indicator:
This code is open source under the MIT license. ( github.com )
My dow factor updates will continue.We adapted the indicators and saw successful results, now it is time to examine and develop the factor itself.
Stay tuned , best regards.
Function : Stochastic Oscillator Here is the Stochastic Oscillator function.
Now you can easily use length with mutable variables !!
