ctndLibrary "ctnd"
Description:
Double precision algorithm to compute the cumulative trivariate normal distribution
found in A.Genz, Numerical computation of rectangular bivariate and trivariate normal
and t probabilities”, Statistics and Computing, 14, (3), 2004. The cumulative trivariate
normal is needed to price window barrier options, see G.F. Armstrong, Valuation formulae
or window barrier options”, Applied Mathematical Finance, 8, 2001.
References:
link.springer.com
www.tandfonline.com
citeseerx.ist.psu.edu
The Complete Guide to Option Pricing Formulas, 2nd ed. (Espen Gaarder Haug)
CTND(LIMIT1, LIMIT2, LIMIT3, SIGMA1, SIGMA2, SIGMA3)
Returns the Cumulative Trivariate Normal Distribution
Parameters:
LIMIT1 : float,
LIMIT2 : float,
LIMIT3 : float,
SIGMA1 : float,
SIGMA2 : float,
SIGMA3 : float,
Returns: float.
Cari dalam skrip untuk "如何用wind搜索股票的发行价和份数"
Multiple Frequency Volatility CorrelationThis is a complex indicator that looks to provide some insight into the correlation between volume and price volatility.
Rising volatility is depicted with the color green while falling volatility is depicted with purple.
Lightness of the color is used to depict the length of the window used, darker == shorter in the 2 -> 512 window range.
FunctionDynamicTimeWarpingLibrary "FunctionDynamicTimeWarping"
"In time series analysis, dynamic time warping (DTW) is an algorithm for
measuring similarity between two temporal sequences, which may vary in
speed. For instance, similarities in walking could be detected using DTW,
even if one person was walking faster than the other, or if there were
accelerations and decelerations during the course of an observation.
DTW has been applied to temporal sequences of video, audio, and graphics
data — indeed, any data that can be turned into a linear sequence can be
analyzed with DTW. A well-known application has been automatic speech
recognition, to cope with different speaking speeds. Other applications
include speaker recognition and online signature recognition.
It can also be used in partial shape matching applications."
"Dynamic time warping is used in finance and econometrics to assess the
quality of the prediction versus real-world data."
~~ wikipedia
reference:
en.wikipedia.org
towardsdatascience.com
github.com
cost_matrix(a, b, w)
Dynamic Time Warping procedure.
Parameters:
a : array, data series.
b : array, data series.
w : int , minimum window size.
Returns: matrix optimum match matrix.
traceback(M)
perform a backtrace on the cost matrix and retrieve optimal paths and cost between arrays.
Parameters:
M : matrix, cost matrix.
Returns: tuple:
array aligned 1st array of indices.
array aligned 2nd array of indices.
float final cost.
reference:
github.com
report(a, b, w)
report ordered arrays, cost and cost matrix.
Parameters:
a : array, data series.
b : array, data series.
w : int , minimum window size.
Returns: string report.
PhinkTrade Risk Manager EssentialsHello there, fellow traders!
So, happy to bring you a new, free tool: my Risk Manager Essentials .
(To use it, click on "Add to favorite indicators" below, and then look for it in your charts’ "Indicators & Strategies" dialog window, inside "Favorites" tab.)
The main objective of this indicator is to help and incentivize as many traders as possible to adopt essential risk management practices .
First and foremost, it helps you define how much you can buy or sell, at your chosen price levels, in order to keep your risk always under control (in other words: in order to limit the amount you can potentially lose with a trade if your stop loss order is hit).
This is fundamental if you want to have a lasting and successful trading career: protect your capital, always . Because without it, you know: it’s game over.
Indicator also helps you visualize where minimum ideal target / take profit level is , given your risk, using the popular 3:1 Return/Risk ratio (R/R) .
3:1 R/R ratio is popular because with it you only need to “be right” (have price reach your targets) about 33% of the time, in order to be profitable : in other words, the fewer successful trades will pay you more than the sum of your unsuccessful ones will take from you.
So, make sure your strategy has a success rate greater than 33% and apply 3:1 R/R to your trades . This indicator will help you that, and with developing the necessary discipline . For example, by knowing where the ideal target should be, given your choices, you can assess the likelihood of it being reached in current price context. If that would look like a hard to happen scenario, it would probably be a good idea to avoid taking that particular trade.
Now, let’s see how it works:
When you deploy the indicator to the chart for the first time, you’ll be asked to define:
Your 1st entry price (interactively: you can define and adjust levels directly on the chart, thanks to the new Interactive Mode introduced by TradingView (ty, TV team!))
Your stop loss price (likewise)
Your 1st target price (likewise)
Your starting capital (via initial Input dialog)
Your risk (likewise)
Your risk is how much of your starting capital you are willing to lose if your stop loss is hit (define it as a % of your starting capital).
There’s a good practice here too: to risk only 1 percent of your capital per trade . This way, you can reinforce the odds of making more money than you lose and keep your peace of mind in all trades – and avoid messing up with your plans – and statistics – along the way.
Successful trading is a statistics-based endeavor. So, you want to implement and maintain consistency. Again, this indicator helps with that.
After initial setup:
You can also define additional entries and targets (up to 3 each) . Just open indicator’s Settings window and adjust accordingly.
If you have more than one entry – or target, the amounts involved will be split evenly between them. You can also enable the display of the Average Entry and Average Target labels , to see the equivalent, should you have taken (or take) a single order for each.
You can also define (via Settings, then interactively) a particular date and time for the trade . This way, labels will be presented near that moment, instead of constantly show near the latest bar.
Finally, you can personalize some other display settings: levels precision (number of decimal places), labels positions , and labels colors .
In conclusion:
You are very welcome to check it out – and adopt it on your daily use!
Please let me know your feedbacks as well. If you find any issues, or have any suggestions, I’ll be glad to hear. You can contact me here, via TradingView, or Telegram.
Finally, check the updates section below , as new stuff may show from time to time.
Thank you very much for your attention, and enjoy!
PhinkTrade
Pivot Points High Low & Missed Reversal Levels [LuxAlgo]Pivot Points High/Low, sometimes referred to as "Bar Count Reversals" allows highlighting market prices local tops and bottoms. This method compares the price value at a certain index within a user-specified window length and tests whether it is the highest (pivot high) or lowest (pivot low).
This method can return successive pivots of the same type, thus missing certain reversals, as such, we aimed to highlight those missed reversals and provide a level at their location to determine if they had any significant role to play. A zig-zag is additionally built, connecting regular pivots with missed reversals.
Settings
Pivot Length: Determine the "Bar Count Reversals" window size. higher values will highlight more significant reversals.
Other settings control the look of the displayed graphical elements within the indicator.
Usage
Missed reversals are highlighted by labels with a ghost emoji, a Zig-Zag line connecting a missed reversal is dashed. Note that labels are offset by -Pivot Length bars (with the exception of the most recent one), and as such are detected further ahead of their location.
When a missed reversal is detected, a level is displayed, starting at the location of the missed reversal and ending when a new missed reversal is detected.
These levels can be used as support and resistance.
The most recent label estimates the possible location of a confirmed reversal, and will continuously readjust whether price makes a new higher high/lower low depending on the type of previously detected pivot. A level is also made out of this estimated reversal.
Levels Of Greed
The Levels Of Greed indicator is based on the same idea as the Levels Of Fear one and was suggested by several traders in the comment section. It helps analyze price advances to find the best levels for closing a long position in an asset after a quick surge or longer up-trend. In finance, volatility is a term that describes the degree of variation of an asset price over time. It is usually denoted by the letter σ (sigma) and estimated as the standard deviation of the asset price or price returns. The Levels Of Greed indicator helps measure the current price advance in the standard deviation units. It plots seven levels at distances of 1, 2, 3, 4, 5, 6, and 7 standard deviations (sigmas) above the base price (the recent lowest price or lower bound of the established range). In what follows, we will refer to these levels as levels of greed.
HOW TO USE
When the price in its surge reaches a certain level of greed, it means that it has surged from its recent lowest value by a corresponding number of standard deviations. The indicator helps traders see the maximum levels to which the price may rise and estimate the potential height of the current surge. Five-seven sigma surges are relatively rare events and correspond to significant market exuberance. Careful traders and shorter-term ones would not want to participate in the bandwagon effect and herd behavior that drive market bubbles. They prefer to take their profits when the market is not exceedingly overbought.
SETTINGS
Window : the averaging window or period of the indicator. The algorithm uses this parameter to calculate the base level and standard deviations. Higher values are better for measuring deeper and longer surges.
Levels Stability : the parameter used in the up-move detection. The higher the value is, the more stable and long the greed levels are, but at the same time, the lag increases. The lower it is, the faster the indicator responds to the price changes, but the greed levels are recalculated more frequently and are less stable. This parameter is mostly for fine-tuning. It does not change the overall picture much.
Mode : the parameter that defines the style for the labels. In the Cool Guys Mode, the indicator displays the labels as emojis. In the Serious Guys Mode, labels show the distance from the base level measured in standard deviation units or sigmas.
Multi-Timeframe 10XIMPORTANT NOTE:
-> The timeframe for this indicator must be set at 1 minute;
-> If the chart timeframe is higher than 1 minute, the results shown in the table for timeframes lower than the chart will not be correct;
-> Tradingview's own documentation explains this as follows: " It is not recommended to request data of a timeframe lower that the current chart timeframe, for example 1 minute data from a 5 minutes chart. The main problem with such a case is that some part of a 1 minute data will be inevitably lost, as it’s impossible to display it on a 5 minutes chart and not to break the time axis. In such cases the behavior of security can be rather unexpected "; and
-> It is therefore recommended that this indicator is placed in a standalone 1min chart window, and the window resized to only show the table to avoid any issues.
Credits:
-> J. Welles Wilder creating the Directional Movement System (DMS) (1978); and
-> John Carter applying the DMS to create the popular Simpler Trading 10X Bars indicator.
Introduction:
Quickly see the quality and strength of a trend based on Directional Movement Index (DMI).
The Average Directional Index (ADX), Minus Directional Indicator (-DI) and Plus Directional Indicator (+DI) represent a group of directional movement indicators that form a trading system developed by Welles Wilder. Although Wilder designed his Directional Movement System with commodities and daily prices in mind, these indicators can also be applied to stocks. Wilder determined directional movement by comparing the difference between two consecutive lows with the difference between their respective highs.
+DI and -DI are derived from smoothed averages of these differences and measure trend direction over time. These two indicators are often collectively referred to as the DMI. ADX is in turn derived from the smoothed averages of the difference between +DI and -DI; it measures the strength of the trend (regardless of direction) over time.
Trade Signals:
-> Green indicates an uptrend i.e. when +DI is above -DI and ADX is greater than 20 - there is more upward pressure than downward pressure in the price;
-> Red indicates a downtrend i.e. when -DI is above +DI and ADX is greater than 20 - there is more downward pressure on the price; and
-> Yellow indicates no strong directional trend and potential for a reversal.
Standalone Indicator:
The 10X Bars version of the indicator can be found here:
Multi-Timeframe TTM Squeeze Pro
IMPORTANT NOTE:
-> The timeframe for this indicator must be set at 1 minute;
-> If the chart timeframe is higher than 1 minute, the results shown in the table for timeframes lower than the chart will not be correct;
-> Tradingview's own documentation explains this as follows: " It is not recommended to request data of a timeframe lower that the current chart timeframe, for example 1 minute data from a 5 minutes chart. The main problem with such a case is that some part of a 1 minute data will be inevitably lost, as it’s impossible to display it on a 5 minutes chart and not to break the time axis. In such cases the behavior of security can be rather unexpected "; and
-> It is therefore recommended that this indicator is placed in a standalone 1min chart window, and the window resized to only show the table to avoid any issues.
Credits:
-> John Carter creating the TTM Squeeze and TTM Squeeze Pro
-> Lazybear's original interpretation of the TTM Squeeze: Squeeze Momentum Indicator
-> Makit0's evolution of Lazybear's script to factor in the TTM Squeeze Pro upgrades - Squeeze PRO Arrows
This is my version of their collective works, with amendments primarily to the Squeeze Conditions to more accurately reflect the color coding used by the official TMM Squeeze Pro indicator.
TTM Squeeze Guide
For those unfamiliar with the TTM Squeeze, it is simply a visual way of seeing how Bollinger Bands (standard deviations from a simple moving average ) relate to Keltner Channels ( average true range bands) compared with the momentum of the price action. The concept is that as Bollinger Bands compress within Keltner Channels , price volatility decreases, giving way for a potential explosive price movement up or down.
Differences between the original TTM Squeeze and TTM Squeeze Pro:
-> Both use a 2 standard deviation Bollinger Band ;
-> The original squeeze only used a 1.5 ATR Keltner Channel; and
-> The pro version uses 1.0, 1.5 and 2.0 ATR Keltner Channels .
The pro version therefore helps differentiate between levels of squeeze (compression) as the Bollinger Bands moves through the Keltner Channels i.e. the greater the compression, the more potential for explosive moves - less compression means more squeezing.
The Histogram shows price momentum whereas the colored dots (along the zeroline) show where the Bollinger Bands are in relation to the Keltner Channels:
-> Cyan Bars = positive, increasing momentum;
-> Blue Bars = positive, decreasing momentum (indication of a reversal in price direction);
-> Red Bars = negative, increasing momentum;
-> Yellow Bars = negative, decreasing momentum (indication of a reversal in price direction);
-> Orange Dots = High Compression / large squeeze (One or both of the Bollinger Bands is inside the 1st (1.0 ATR) Keltner Channel);
-> Red Dots = Medium Squeeze (One or both of the Bollinger Bands is inside the 2nd (1.5 ATR) Keltner Channel);
-> Black Dots = Low compression / wide squeeze (One or both of the Bollinger Bands is inside the 3rd (2.0 ATR) Keltner Channels );
-> Green Dots = No Squeeze / Squeeze Fired (One or both of the Bollinger Bands is outside of the 3rd (2.0 ATR) Keltner Channel).
Ideal Scenario:
As the ticker enters the squeeze, black dots would warn of the beginning of a low compression squeeze. As the Bollinger bands continue to constrict within the Keltner Channels , red dots would highlight a medium compression. As the price action and momentum continues to compress an orange dot shows warning of high compression. As price action leaves the squeeze, the coloring would reverse e.g. orange to red to black to green. Any compression squeeze is considered fired at the first green dot that appears.
Note: This is an ideal progression of the different types of squeezes, however any type of squeeze (and color sequence) may appear at anytime, therefore the focus is primarily on the green dots after any type of compression.
Entry and Exit Guide:
-> John Carter recommends entering a position after at least 5 black dots or wait for 1st green dot ; and
-> Exit on second blue or yellow bar or, alternatively, remain in the position after confirming a continuing trend through a separate indicator.
Standalone Indicator:
The indicator (which can be used on any timeframe) can be found here:
Levels Of Greed [AstrideUnicorn]The Levels Of Greed indicator is based on the same idea as the Levels Of Fear one and was suggested by several traders in the comment section. It helps analyze price advances to find the best levels for closing a long position in an asset after a quick surge or longer up-trend. In finance, volatility is a term that describes the degree of variation of an asset price over time. It is usually denoted by the letter σ (sigma) and estimated as the standard deviation of the asset price or price returns. The Levels Of Greed indicator helps measure the current price advance in the standard deviation units. It plots seven levels at distances of 1, 2, 3, 4, 5, 6, and 7 standard deviations (sigmas) above the base price (the recent lowest price or lower bound of the established range). In what follows, we will refer to these levels as levels of greed.
HOW TO USE
When the price in its surge reaches a certain level of greed, it means that it has surged from its recent lowest value by a corresponding number of standard deviations. The indicator helps traders see the maximum levels to which the price may rise and estimate the potential height of the current surge. Five-seven sigma surges are relatively rare events and correspond to significant market exuberance. Careful traders and shorter-term ones would not want to participate in the bandwagon effect and herd behavior that drive market bubbles. They prefer to take their profits when the market is not exceedingly overbought.
SETTINGS
Window : the averaging window or period of the indicator. The algorithm uses this parameter to calculate the base level and standard deviations. Higher values are better for measuring deeper and longer surges.
Levels Stability : the parameter used in the up-move detection. The higher the value is, the more stable and long the greed levels are, but at the same time, the lag increases. The lower it is, the faster the indicator responds to the price changes, but the greed levels are recalculated more frequently and are less stable. This parameter is mostly for fine-tuning. It does not change the overall picture much.
Mode : the parameter that defines the style for the labels. In the Cool Guys Mode , the indicator displays the labels as emojis. In the Serious Guys Mode , labels show the distance from the base level measured in standard deviation units or sigmas.
Retail Insider Trend Following (HTF ONLY)Before I begin, here is a disclaimer: None of this is financial advice, and I'll recommend you to do your own research or talk to your financial advisor, if you want to use this. And also make sure you understand the risks properly before taking any trades. This particular indicator is a work of experiment, and I am publishing the optimized code. Please leave a comment below if you have any queries.
As per the logic, I am taking the highest point in a particular time window (used the in built ta.highest function) , and the lowest point in a particular time window (used the ta.lowest), and averaged it using the in built function (
RMA(which is the B33 Mean.)
For the offset, I am simply calculating and adding some values (which can also be input by the user.)
and this user input is in percentage.
So if you observe the lines, the Red line in between is the mean, and the Yellow lines are the offsets. (Everything can be changed in settings)
In simple layperson terms, if the price goes above the Red line, it's an uptrend,
and if the price goes below the Red line, it is in a downtrend.
Now I just wanted to keep the offset because I wanted more confirmation before actually entering a trade. (the offset can be changed again, from the settings, and the offset is in percentage)
A lot of times, you will see that the price is kind of going sideways, where, a lot of traders get trapped, as there is no clear trend.
So in order to eliminate that choppy price action and stay out, I'm using this offset. This should probably save a lot of bad trades.
So basically, if the price goes and closes above the higher offset, it will confirm a trend change, and a possible bull market.
Similarly, if the price goes and closes below the lower offset, it will confirm the end of the bull market, or a corrective phase, or a bear market.
A few things to note, however...
If you change the timeframe, you will see that the lines are not shifting/changing that much. This is because, it will consider the highest and the lowest points and average it.
So, basically, if you do the math, you will understand why,... and this logic is purely for a higher timeframe analysis/confirmation.
I'll personally recommend this kind of a setup for swing trading/confirmation on the daily or the 4H charts, mostly for longer timeframes. (If you are on the pro/pro+ or premium, you can try out 6H or 12H timeframes as well)
If you are looking for scalping, setups and indicators, this is not the right one.
If you liked it, don't forget to give a follow :)
The Divergent LibraryLibrary "TheDivergentLibrary"
The Divergent Library is only useful when combined with the Pro version of The Divergent - Advanced divergence indicator . This is because the Basic (free) version of The Divergent does not expose the "Divergence Signal" value.
Usage instructions:
1. Create a new chart
2. Add The Divergent (Pro) indicator to your chart
3. Create a new strategy, import this library, add a "source" input, link it to "The Divergent: Divergence Signal", and use the library to decode the divergence signals from The Divergent (You can find example strategy code published in our profile)
4. Act on the divergences signalled by The Divergent
---
isRegularBullishEnabled(context) Returns a boolean value indicating whether Regular Bullish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Regular Bullish divergence detection is enabled in The Divergent.
isHiddenBullishEnabled(context) Returns a boolean value indicating whether Hidden Bullish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Hidden Bullish divergence detection is enabled in The Divergent.
isRegularBearishEnabled(context) Returns a boolean value indicating whether Regular Bearish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Regular Bearish divergence detection is enabled in The Divergent.
isHiddenBearishEnabled(context) Returns a boolean value indicating whether Hidden Bearish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Hidden Bearish divergence detection is enabled in The Divergent.
getPivotDetectionSource(context) Returns the 'Pivot Detection Source' setting of The Divergent. The returned value can be either "Oscillator" or "Price".
Parameters:
context : The context of The Divergent Library.
Returns: One of the following string values: "Oscillator" or "Price".
getPivotDetectionMode(context) Returns the 'Pivot Detection Mode' setting of The Divergent. The returned value can be either "Bodies" or "Wicks".
Parameters:
context : The context of The Divergent Library.
Returns: One of the following string values: "Bodies" or "Wicks".
isLinked(context) Returns a boolean value indicating the link status to The Divergent indicator.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating the link status to The Divergent indicator.
init(firstBarSignal, displayLinkStatus, debug) Initialises The Divergent Library's context with the signal produced by The Divergent on the first bar. The value returned from this function is called the "context of The Divergent Library". Some of the other functions of this library requires you to pass in this context.
Parameters:
firstBarSignal : The signal from The Divergent indicator on the first bar.
displayLinkStatus : A boolean value indicating whether the Link Status window should be displayed in the bottom left corner of the chart. Defaults to true.
debug : A boolean value indicating whether the Link Status window should display debug information. Defaults to false.
Returns: A bool array containing the context of The Divergent Library.
processSignal(signal) Processes a signal from The Divergent and returns a 5-tuple with the decoded signal: [ int divergenceType, int priceBarIndexStart, int priceBarIndexEnd, int oscillatorBarIndexStart, int oscillatorBarIndexEnd]. `divergenceType` can be one of the following values: na → No divergence was detected, 1 → Regular Bullish, 2 → Regular Bullish early, 3 → Hidden Bullish, 4 → Hidden Bullish early, 5 → Regular Bearish, 6 → Regular Bearish early, 7 → Hidden Bearish, 8 → Hidden Bearish early.
Parameters:
signal : The signal from The Divergent indicator.
Returns: A 5-tuple with the following values: [ int divergenceType, int priceBarIndexStart, int priceBarIndexEnd, int oscillatorBarIndexStart, int oscillatorBarIndexEnd].
A simple trading strategy for XTZ/EUR (December 2021)This is my current trading strategy for XTZ/EUR for this month of December.
It tries to avoid pumps/dumps (i.e. does not trade on big candles).
It always performs one order in each candle for the trading window of the rebalance bear/bull market indicator (check my profile for it).
It has alerts configured so that you can use it in your server/broker (just pass along the `{{strategy.order.alert_message}}` in the alert message, it will include a positive number of XTZ when to buy, or a negative number when to sell).
It does not repaint.
The amount of crypto and fiat in the portfolio can be configured in the cog.
It does not outperform buy/hold for the bull months.
Check the results in the Data Window of Trading View (please avoid the Strategy Tester, it has too many bugs and is not intended for out of the box strategies such a this one).
All code is open source.
XTZ trading strategy for Bear monthsThis is my 3rd approach into trading XTZ. It is tuned into bear months and needs to work for at least 30 days.
It looks at the rebalance oscillator and bear market indicator and tries to find a position in the market that does not lose money within the month.
Use the cog icon to update the trading window to fit previous months, and check out the data window for its real performance. Do not rely on the Strategy Tester since this strategy is performing only with `strategy.order` making the values of the Strategy Tester irrelevant and inconsistent with its performance.
40+ Coin Screener (workaround to 40 Security Limit Per Script) This is a far inferior method for a screener/scanner (compared to my first publication) but after looking at that script from a noobs eyes again, I could see how this form would be a lot easier to take in/understand so wanted to publish it. Everything that I could think of to mention about this is in my 1st pub so ill leave it to you to check it out...though I did include some comments in the script. It is pretty straight forward but if you have any questions don't hold them in. I'll answer them if I can. The only thing that is not in this one is setting up the alert feature so that you only have to create 1 alert per iteration of the script and it takes care of all of the coins for that iteration/set that is chosen in the settings (so please see previous script if would like to do this for your screener/scanner).
To be PERFECTLY CLEAR, the workaround is to the issue of not being able to scan but only 40 coins per script. You can scan more than 40 per script but only if you create "batches" or "sets" that the user can select within the settings which set to use for each iteration of the script on the chart. That being, you have to the script multiple times to the chart and merge them into 1 window and merge the scales (instructions in first publications). Here in this script I am scanning 72 different coins that are the Margin Coins on KUCOIN. I have split them up into 3 sets (24 coins per set). I could have made 2 sets but the script will be slower to load and to respond (like, when it comes to receiving alerts), thus I split them up the way I did. If you want to change any of this there are slightly more details in the previous script.
One great use-case that I LOVE about this particular version (and the way I use it) is right at the end of when I see a whole market dump/pump coming to an end and want to know which horse to bet on. Used to think whichever coin come out the fastest from the dump was the one to bet on but quickly learned that 1-2 (or even a few) hrs needs to go by first bc the ones that look the strongest in the beginning are NOT the ones to have performed the best when viewing the results 12 hrs later. IN FACT, many instances of using this exact script for reasons as such has taught me that the manipulators (I believe this to be the case as least) WANT everyone to bet on these that come out the gate the hardest and thus they make them move REALLY hard in the beginning then they QUICKLY become stagnant (moreso, they become WORSE than stagnant, they actually quickly retrace to put you into the negative so that you get out to get into the others now moving (to provide the market with more liquidity. They WANT you to get into a coin thats moving crazy hard so that they can then cease that movement once many fall for the trick just to then make that once strong looking coin now stagnant and make others move crazy hard. They wait for you to get out of the 1st and into the next set of movers just to do this time and time again bc hey, what are we sheep good for other than to provide the big guns with liquidity, am I right? Thats rhetorical, which you would know if you've ever had this happen to you (without a doubt MANY of you have). Let this script (above all other things) provide good evidence to back up this cynical way of viewing the markets to anyone that is questioning it.
This prolonged time between when the dump is over and when the ACTUAL movers REALLY start moving can actually be of great benefit to us sheep if used correctly, Firstly, it gives us some time to determine if when we thought was the bottom, ACTUALLY was the bottom. That bottom is easily determined if there are no (or very few) coins that went any lower than the point in time that the script began calculating on. Secondly, it allows us time to wait for the REAL movers and shakers to start moving and shaking.
One new feature that I LOVE that TV has implemented is the ability (once the script is added to the chart) to be able to click a point in time on the chart where you want the script to begin its calculations. If this point needs to be changed at any point in time then you can either go into the setting and input the time you wish or simply remove the script and add it again so that you are prompted to select another point in time. Ok, I think that everything I wanted to say. The next version that I will add will be probably my favorite and most used by yours truly...not to mention unique in a way that I have yet to see an implementation anything like it in all of TV's public library. Not to say its not there, but I have yet to come across it and I have DEFINITELY done my fair share of searching for it when I couldn't figure out how to code it for the longest time (though, I was and still am a noob so might get some great feedback on better ways to approach it, but we'll save that jabbering for the next of the publications.
I hope each and every one of ya'll (yes, Im from the South) have the GREATEST of Thanksgivings (if in the US that is...I graced my parents with the best gift anyone could have given them 35 years ago on Thanksgiving....MEEEE ;) So I will sure as hell be having a great holiday. Thanks for checking out my script...you can "like" and leave a comment if you so feel the urge to...or not. Im not doing this for me, but rather to stretch my arms out as far as possible to benefit the most people as possible and more people would see the script if it has more likes/comments/traffic pointing towards it...not to mention as other publishers have...it IS gratifying to see a few likes in my side window, which btw, I have MANY more variations and completely diff types of scanners/screeners Ill be publishing in the future and to know that they've become of use....I"VE become of use to the community is very....pleasing to me and does (as I've also seen many publishers mention as well) drive me to want to publish ones that I originally thought I would keep for myself. Peace out people.
ArrayGenerateLibrary "ArrayGenerate"
Functions to generate arrays.
sequence_int(start, end, step) returns a sequence of int numbers.
Parameters:
start : int, begining of sequence range.
end : int, end of sequence range.
step : int, step, default=1 .
Returns: int , array.
sequence_float(start, end, step) returns a sequence of float numbers.
Parameters:
start : float, begining of sequence range.
end : float, end of sequence range.
step : float, step, default=1.0 .
Returns: float , array.
sequence_from_series(src, length, shift, direction_forward) Creates a array from a series sample range.
Parameters:
src : series, any kind.
length : int, window period in bars to sample series.
shift : int, window period in bars to shift backwards the data sample, default=0.
direction_forward : bool, sample from start to end or end to start order, default=true.
Returns: float array
normal_distribution(size, mean, dev) Generate normal distribution random sample.
Parameters:
size : int, size of array
mean : float, mean of the sample, (default=0.0).
dev : float, deviation of the sample from the mean, (default=1.0).
Returns: float array.
log_spaced(length, start_exp, stop_exp) Generate a base 10 logarithmically spaced sample sequence.
Parameters:
length : int, length of the sequence.
start_exp : float, start exponent.
stop_exp : float, stop exponent.
Returns: float array.
linear_range(stop, start) Generate a linearly spaced sample vector within the inclusive interval (start, stop) and step 1.
Parameters:
stop : float, stop value.
start : float, start value, (default=0.0).
Returns: float array.
periodic_wave(length, sampling_rate, frequency, amplitude, phase, delay) Create a periodic wave.
Parameters:
length : int, the number of samples to generate.
sampling_rate : float, samples per time unit (Hz). Must be larger than twice the frequency to satisfy the Nyquist criterion.
frequency : float, frequency in periods per time unit (Hz).
amplitude : float, the length of the period when sampled at one sample per time unit. This is the interval of the periodic domain, a typical value is 1.0, or 2*Pi for angular functions.
phase : float, optional phase offset.
delay : int, optional delay, relative to the phase.
Returns: float array.
sinusoidal(length, sampling_rate, frequency, amplitude, mean, phase, delay) Create a Sine wave.
Parameters:
length : int, The number of samples to generate.
sampling_rate : float, Samples per time unit (Hz). Must be larger than twice the frequency to satisfy the Nyquist criterion.
frequency : float, Frequency in periods per time unit (Hz).
amplitude : float, The maximal reached peak.
mean : float, The mean, or DC part, of the signal.
phase : float, Optional phase offset.
delay : int, Optional delay, relative to the phase.
Returns: float array.
periodic_impulse(length, period, amplitude, delay) Create a periodic Kronecker Delta impulse sample array.
Parameters:
length : int, The number of samples to generate.
period : int, impulse sequence period.
amplitude : float, The maximal reached peak.
delay : int, Offset to the time axis. Zero or positive.
Returns: float array.
Arnaud Legoux Moving Average Set [DM]Greetings Colleagues
As it could not be otherwise in the soul series could not miss its own moving average.
Here you can enjoy 3 soul moving averages with color variables and alerts on all crosses of the three moving averages
/// Quick Explained ALMA ///
//Window size: The window size is the look-back period and it is a basic setting of ALMA.
//Experienced traders can change this setting according to their preference.
//But if you are using this indicator for the first time, it is recommended to go with the default setting.
//Offset: The offset value is used to tweak the ALMA so that it will be more inclined
//towards responsiveness or smoothness. You can set the offset in decimals between the 0 to1.
//The value of 0.01 makes it smoother, while a setting of 0.99 makes the indicator more responsive.
//Sigma: The sigma is used for the filter. Any value less than 6 makes the indicator
//more focused, whereas the setting of 6 makes the filter large. According to Mr Arnaud,
//a sigma value of 6 is offer good performance.
"DSS" Bessert Double Smooth Stochastic Alma Variant Hi colleagues I share this time DSS bressert.
As usual they have all the adjustable colors and signals. I hope you enjoy them and leave here below the length of signals that best suit your needs so that we can continue sharing content
Description
One after the other, William Blau and Walter Bressert each presented a version of the Double Smoothed Stochastics. Two exponentially smoothed MAs are used to even out the input values (H, L and C), in a similar way to the well-known stochastic formula.
Parameters
The adjustable period length can be chosen from 2 to 500. The most common settings will have a period length ranging from 5 to 30. In addition, the indicator can be smoothed in the interval from 1 to 50. Meaningful smoothing values lie in the short-term range.
Interpretation
The application of the DSS is comparable with that of the stochastic method. Accordingly, values above 70 or 80 must be regarded as overbought and values below 20 or 30 as oversold. A rise of the DSS above its center line should be viewed as bullish, and a fall of the DSS below its center line as bearish.
/// Quick Explained ALMA ///
//Window size: The window size is the look-back period and it is a basic setting of ALMA.
//Experienced traders can change this setting according to their preference.
//But if you are using this indicator for the first time, it is recommended to go with the default setting.
//Offset: The offset value is used to tweak the ALMA so that it will be more inclined
//towards responsiveness or smoothness. You can set the offset in decimals between the 0 to1.
//The value of 0.01 makes it smoother, while a setting of 0.99 makes the indicator more responsive.
//Sigma: The sigma is used for the filter. Any value less than 6 makes the indicator
//more focused, whereas the setting of 6 makes the filter large. According to Mr Arnaud,
//a sigma value of 6 is offer good performance.
/// Explain DSS ///
//Parameters
//The adjustable period length can be chosen from 2 to 500.
//The most common settings will have a period length ranging from 5 to 30.
//In addition, the indicator can be smoothed in the interval from 1 to 50.
//Meaningful smoothing values lie in the short-term range.
//Interpretation
//The application of the DSS is comparable with that of the stochastic method.
//Accordingly, values above 70 or 80 must be regarded as overbought and values below 20 or 30 as oversold.
//A rise of the DSS above its center line should be viewed as bullish,
//and a fall of the DSS below its center line as bearish
/// End Of The Code///
Strategy TemplateTrying to include few basic things which is needed for strategy which can be used as template.
Few important components
Strategy parameters
Few important parameters include - initial_capital, default_qty_type, default_qty_value, commission_type, pyramiding and commission_value. All my strategies will have similar settings with initial captial set to 20000 to 100000. 100% of equity per trade with no pyramiding (set to 1) and minimal commission.
margin_long and margin_short can be used for leveraged trading. But, since we are not using pyramiding, it will make no effect.
Trade Limiting parameters
Two types of limiting is available in the scripts
Limiting trading direction : this is done through method strategy.risk.allow_entry_in and input parameter tradeDirection
Limiting trades to particular time window : This is achieved through adding start time and end time parameters of type input.time and check whether time is within this window
Custom Methods
customized security method to get higher timeframe data
customized moving average method to get moving average of any type
Custom Parameters
Moving average Type option list which I use quite often. Any strategy where there is need to use moving average, I try to scan through different moving average types and lengths to see which one is more appropriate for the given strategy. Hence, keeping this parameter in template to make it readily available when I start with new strategy
waitForCloseBeforeExit - this is used if trailing stop need to activated as soon as price hits the stop or only on close price. This is again something I switch quite often based on strategy. Hence, keeping this as part of the template.
Entry and Exit statements for long and short
These statements from line (57 to 62) can remain as is even with new strategy. Only thing to be set are variables - buyCondition, sellCondition, closeBuyCondition and closeSellCondition
Last but not the least
In pinescript, a long and short position cannot coexist in a strategy at any point of time. Any short positions created will automatically stop long positions and vice versa. Hence, it is important make short and long trades mutually exclusive. In this example, I have used 200 weekly moving average as trend bias. No short positions are taken when price is trading above 200 weekly moving average low/close and no long positions are taken when price is less than 200 weekly moving average high/close. Any rule built on top of this (In this case a simple supertrend rules) ensures that there are no conflicting signals and hence avoids confusing trades on the stratgy.
Portfolio Backtester Engine█ OVERVIEW
Portfolio Backtester Engine (PBTE). This tool will allow you to backtest strategies across multiple securities at once. Allowing you to easier understand if your strategy is robust. If you are familiar with the PineCoders backtesting engine , then you will find this indicator pleasant to work with as it is an adaptation based on that work. Much of the functionality has been kept the same, or enhanced, with some minor adjustments I made on the account of creating a more subjectively intuitive tool.
█ HISTORY
The original purpose of the backtesting engine (`BTE`) was to bridge the gap between strategies and studies . Previously, strategies did not contain the ability to send alerts, but were necessary for backtesting. Studies on the other hand were necessary for sending alerts, but could not provide backtesting results . Often, traders would have to manage two separate Pine scripts to take advantage of each feature, this was less than ideal.
The `BTE` published by PineCoders offered a solution to this issue by generating backtesting results under the context of a study(). This allowed traders to backtest their strategy and simultaneously generate alerts for automated trading, thus eliminating the need for a separate strategy() script (though, even converting the engine to a strategy was made simple by the PineCoders!).
Fast forward a couple years and PineScript evolved beyond these issues and alerts were introduced into strategies. The BTE was not quite as necessary anymore, but is still extremely useful as it contains extra features and data not found under the strategy() context. Below is an excerpt of features contained by the BTE:
"""
More than `40` built-in strategies,
Customizable components,
Coupling with your own external indicator,
Simple conversion from Study to Strategy modes,
Post-Exit analysis to search for alternate trade outcomes,
Use of the Data Window to show detailed bar by bar trade information and global statistics, including some not provided by TV backtesting,
Plotting of reminders and generation of alerts on in-trade events.
"""
Before I go any further, I want to be clear that the BTE is STILL a good tool and it is STILL very useful. The Portfolio Backtesting Engine I am introducing is only a tangental advancement and not to be confused as a replacement, this tool would not have been possible without the `BTE`.
█ THE PROBLEM
Most strategies built in Pine are limited by one thing. Data. Backtesting should be a rigorous process and researchers should examine the performance of their strategy across all market regimes; that includes, bullish and bearish markets, ranging markets, low volatility and high volatility. Depending on your TV subscription The Pine Engine is limited to 5k-20k historical bars available for backtesting, which can often leave the strategy results wanting. As a general rule of thumb, strategies should be tested across a quantity of historical bars which will allow for at least 100 trades. In many cases, the lack of historical bars available for backtesting and frequency of the strategy signals produces less than 100 trades, rendering your strategy results inconclusive.
█ THE SOLUTION
In order to be confident that we have a robust strategy we must test it across all market regimes and we must have over 100 trades. To do this effectively, researchers can use the Portfolio Backtesting Engine (PBTE).
By testing a strategy across a carefully selected portfolio of securities, researchers can now gather 5k-20k historical bars per security! Currently, the PTBE allows up to 5 securities, which amounts to 25k-100k historical bars.
█ HOW TO USE
1 — Add the indicator to your chart.
• Confirm inputs. These will be the most important initial values which you can change later by clicking the gear icon ⚙ and opening up the settings of the indicator.
2 — Select a portfolio.
• You will want to spend some time carefully selecting a portfolio of securities.
• Each security should be uncorrelated.
• The entire portfolio should contain a mix of different market regimes.
You should understand that strategies generally take advantage of one particular type of market regime. (trending, ranging, low/high volatility)
For example, the default RSI strategy is typically advantageous during ranging markets, whereas a typical moving average crossover strategy is advantageous in trending markets.
If you were to use the standard RSI strategy during a trending market, you might be selling when you should be buying.
Similarily, if you use an SMA crossover during a ranging market, you will find that the MA's may produce many false signals.
Even if you build a strategy that is designed to be used only in a trending market, it is still best to select a portfolio of all market regimes
as you will be able to test how your strategy will perform when the market does something unexpected.
3 — Test a built-in strategy or add your own.
• Navigate to gear icon ⚙ (settings) of strategy.
• Choose your options.
• Select a Main Entry Strat and Alternate Entry Strat .
• If you want to add your own strategy, you will need to modify the source code and follow the built-in example.
• You will only need to generate (buy 1 / sell -1/ neutral 0) signals.
• Select a Filter , by default these are all off.
• Select an Entry Stop - This will be your stop loss placed at the trade entry.
• Select Pyamiding - This will allow you to stack positions. By default this is off.
• Select Hard Exits - You can also think of these as Take Profits.
• Let the strategy run and take note of the display tables results.
• Portfolio - Shows each security.
• The strategy runs on each asset in your portfolio.
• The initial capital is equally distributed across each security.
So if you have 5 securities and a starting capital of 100,000$ then each security will run the strategy starting with 20,000$
The total row will aggregate the results on a bar by bar basis showing the total results of your initial capital.
• Net Profit (NP) - Shows profitability.
• Number of Trades (#T) - Shows # of trades taken during backtesting period.
• Typically will want to see this number greater than 100 on the "Total" row.
• Average Trade Length (ATL) - Shows average # of days in a trade.
• Maximum Drawdown (MD ) - Max peak-to-valley equity drawdown during backtesting period.
• This number defines the minimum amount of capital required to trade the system.
• Typically, this shouldn’t be lower than 34% and we will want to allow for at least 50% beyond this number.
• Maximum Loss (ML) - Shows largest loss experienced on a per-trade basis.
• Normally, don’t want to exceed more than 1-2 % of equity.
• Maximum Drawdown Duration (MDD) - The longest duration of a drawdown in equity prior to a new equity peak.
• This number is important to help us psychologically understand how long we can expect to wait for a new peak in account equity.
• Maximum Consecutive Losses (MCL) - The max consecutive losses endured throughout the backtesting period.
• Another important metric for trader psychology, this will help you understand how many losses you should be prepared to handle.
• Profit to Maximum Drawdown (P:MD) - A ratio for the average profit to the maximum drawdown.
• The higher the ratio is, the better. Large profits and small losses contribute to a good PMD.
• This metric allows us to examine the profit with respect to risk.
• Profit Loss Ratio (P:L) - Average profit over the average loss.
• Typically this number should be higher in trend following systems.
• Mean reversion systems show lower values, but compensate with a better win %.
• Percent Winners (% W) - The percentage of winning trades.
• Trend systems will usually have lower win percentages, since statistically the market is only trending roughly 30% of the time.
• Mean reversion systems typically should have a high % W.
• Time Percentage (Time %) - The amount of time that the system has an open position.
• The more time you are in the market, the more you are exposed to market risk, not to mention you could be using that money for something else right?
• Return on Investment (ROI) - Your Net Profit over your initial investment, represented as a percentage.
• You want this number to be positive and high.
• Open Profit (OP) - If the strategy has any open positions, the floating value will be represented here.
• Trading Days (TD) - An important metric showing how many days the strategy was active.
• This is good to know and will be valuable in understanding how long you will need to run this strategy in order to achieve results.
█ FEATURES
These are additional features that extend the original `BTE` features.
- Portfolio backtesting.
- Color coded performance results.
- Circuit Breakers that will stop trading.
- Position reversals on exit. (Simulating the function of always in the market. Similar to strategy.entry functionality)
- Whipsaw Filter
- Moving Average Filter
- Minimum Change Filter
- % Gain Equity Exit
- Popular strategies, (MACD, MA cross, supertrend)
Below are features that were excluded from the original `BTE`
- 2 stage in-trade stops with kick-in rules (This was a subjective decision to remove. I found it to be complex and thwarted my use of the `BTE` for some time.)
- Simple conversion from Study to Strategy modes. (Not possible with multiple securities)
- Coupling with your own external indicator (Not really practical to use with multiple securities, but could be used if signals were generated based on some indicator which was not based on the current chart)
- Use of the Data Window to show detailed bar by bar trade information and global statistics.
- Post Exit Analysis.
- Plotting of reminders and generation of alerts on in-trade events.
- Alerts (These may be added in the future by request when I find the time.)
█ THANKS
The whole PineCoders team for all their shared knowledge and original publication of the BTE and Richard Weismann for his ideas on building robust strategies.
═════════════════════════════════════════════════════════════════════════
Zig Zag Channels [LuxAlgo]The Zig Zag indicator is a useful indicator when it comes to visualizing past underlying trends in the price and can make the process of using drawing tools easier. The indicator consists of a series of lines connecting points where the price deviates more than a specific percentage from a maximum/minimum point ultimately connecting local peaks and troughs.
This indicator by its very nature backpaints by default, meaning that the displayed components are offset in the past.
🔶 USAGE
The Zig Zag indicator is commonly used to returns points of references for the usage of specific drawing tools, such as Fibonacci retracements, fans, squares...etc.
The proposed indicator estimates peaks and troughs by using rolling maximums/minimums with a window size determining their significance. This window size approach allows us to have an indicator that works with a certain regularity no matter the scale of the price, something the percentage-based approach struggles with. Additionally, one upper and lower extremity are displayed, highlighting the price point that deviates the most from the Zig Zag lines.
A common usage also includes the easy determination of Elliot wave patterns in the price.
The Zig Zag indicator above highlights a downtrending motive wave.
🔹 Extremities
The novel approach taken by this Zig Zag indicator is the addition of two extremities derived from the distance between the price and the Zig Zag line, thus returning channels. It is uncommon seeing extremities in Zig Zag indicators since the line connecting peaks and troughs has rarely any other utility than seeing trend variations with more clarity and is not meant to provide an accurate estimate of underlying local trends in the price.
This channel can be useful to study the potential relationship between underlying trends and the Zig Zag line. A low width between the Zig Zag and the upper extremity indicates price variations mostly located below the Zig Zag while equal width indicates more linear trends.
When the indicator is extended to the last line, the extremities provide potential support and resistances, thus making this indicator able to forecast price variations.
🔶 SETTINGS
Length: Determines the significance of the detected peaks and troughs.
Extend To Last Bar: Extend the most recent line to the most recent closing price value.
Show Extremities: Displays the extremities.
Show Labels: Display labels highlighting the high/low prices located at peaks and troughs.
🔹 Style
Upper Extremity Color: Color of the upper extremity displayed by the indicator.
Zig Zag Color: Color of the ZigZag lines.
Lower Extremity Color: Color of the lower extremity displayed by the indicator.
[Unxi]McClellan Summation Index for DAX 30 (GER30) [modified]About McClellan Summation Index
The McClellan Summation Index is a market breadth indicator which was developed by Sherman and Marian McClellan. It is based on the McClellan Oscillator and add its values together, effectively running a total. The index goes up when the McClellan Oscillator is positive and goes down when it is negative. Signals can be derived from the index crossing the middle line (bullish when it's crossing up and bearish when it's crossing down). Other potential signals include divergences and overbought and oversold conditions. The indicator is best used in combination with other analysis techniques.
About this implementation
This version here is a modification of the McClellan Summation Index.
It runs the simple version of the McClellan Oscillator and uses the simple method to calculate the Summation Index. No ratios are used in this implementation.
Further information:
- It can only be used on the DAX index ( DAX 30 or GER 30)
- It only considers the DAX 30 stocks
- The data window will provide a summary about rising and declining stocks
- The data window will output the last change for each of the 30 stocks
- The script is pretty slow because it has to calculate the change for each bar individually (instead of receiving a complete calculation from the stock exchange).
DISCLAIMER
This script was mainly written for educational purposes (training myself how to write custom indicatotors).
As you can see, the code is really messy.
FOR YOUR INFORMATION: This script will work on any time period. It is recommended to use it with timeperiod = 1d, though. Just use whatever timeperiod you are comfortable with, the indicator will automatically adjust accordingly.
Credits
Based on the simple version of aftabmk and of code from lazybear.
[Unxi]McClellan Oscillator for TECDAX [modified]This version is for use with the German TecDax index only!
About McClellan Oscillator
Developed by Sherman and Marian McClellan, the McClellan Oscillator is a breadth indicator derived from Net Advances, the number of advancing issues less the number of declining issues. Subtracting the 39-day exponential moving average of Net Advances from the 19-day exponential moving average of Net Advances forms the oscillator.
As the formula reveals, the McClellan Oscillator is a momentum indicator that works similar to MACD .
McClellan Oscillator signals can be generated with breadth thrusts, centerline crossovers, overall levels and divergences.
About my version
This version here is a modification, though:
- It can only be used on the German TECDAX index
- It only considers the TECDAX stocks
- The data window will provide a summary about rising and declining stocks
- The data window will output the last change for each of the 30 stocks
The script is pretty slow because it has to calculate the change for each bar individually (instead of receiving a complete calculation from the stock exchange).
This script will work on any time period. Just use whatever timeperiod you are comfortable with, the indicator will automatically adjust accordingly. It is recommended to use it with timeperiod = 1d, though.
DISCLAIMER
This script was mainly written for educational purposes (training myself how to write custom indicatotors).
As you can see, the code is really messy. Feel free to provide your feedback in the comments!
Credits
Based on the simple version of aftabmk
You can find the original version by searching for McClellan Oscillator for nifty 50.
Also got some inspiration from lazybear's version and from danarn's "Crypto McClellan Oscillator".
McClellan Oscillator for DAX (GER30) [aftabmk modified]About McClellan Oscillator
Developed by Sherman and Marian McClellan, the McClellan Oscillator is a breadth indicator derived from Net Advances, the number of advancing issues less the number of declining issues. Subtracting the 39-day exponential moving average of Net Advances from the 19-day exponential moving average of Net Advances forms the oscillator.
As the formula reveals, the McClellan Oscillator is a momentum indicator that works similar to MACD .
McClellan Oscillator signals can be generated with breadth thrusts, centerline crossovers, overall levels and divergences.
About my version
This version here is a modification, though:
- It can only be used on the DAX index (DAX 30 or GER 30)
- It only considers the DAX 30 stocks
- The data window will provide a summary about rising and declining stocks
- The data window will output the last change for each of the 30 stocks
BUG
I am only publishing this version because I am not sure if my current version is saved when I leave tradingview.com without publishing the script.
This version still contains a bug - the if/else clauses do not correctly recognize declining stocks. So the oscillator should not be used as it is.
Working on it these days. Feel free to provide feedback!
Stuff I am working on
- Coloring the area green/red according to the value
- Fixing this bug/making this script more efficient
DISCLAIMER
This script was mainly written for educational purposes (training myself how to write custom indicatotors).
As you can see, the code is really messy.
Credits
Based on the simple version of aftabmk
You can find the original version by searching for McClellan Oscillator for nifty 50.
