occ3aka weighted fair price
The ultimate price source for all your stuff, unless you go completely nuts.
The ultimate way to build line charts & do pattern trading, unless you go completely nuts.
Why occ3?
You need a one-point estimate for every bar, a typical price of every bar aye? But then you see that every bar has a different distribution of prices. You can drop a stat test on every bar and pick median, mean, or whatever. But that's still prone to error (imagine borderline cases).
Instead, you can transform the task into a geometric one and say, "I wanna find the center of mass of all dem ticks within a particular interval (a day, a week, a century)". But lol ofc you won't do it, so lets's estimate it:
1) a straight line from Open to Close more/less estimates a regression line if you woulda dropped regression on all the ticks within a given interval;
2) centroid always lies on regression line, so it's always in between the endpoints of regression line. So that's why (open + close) /2;
3) Then, you remember that sequence matters, + generally the volume is higher near the close, so...;
4) Voila, (open + close + close) / 3
Why "fair" price?
Take a daily bar:
1) High & low were the best prices to sell & buy;
2) Opening & closing auctions had acceptable prices, in exchange for the the biggest potential to transact serious volume;
3) "Fair" price, logically, is somewhere in between the acceptable prices;
4) Market is fractal => the same principles propagate everywhere;
4) No, POCs and VPOCs don't make much sense as fair prices.
Nothing else to say, really advise to use it as a line chart if you trade price patterns.
Сферы применения Pine
Volume Weighted Standard Deviation (VWSD)The Volume Weighted Standard Deviation indicator is a custom technical analysis tool that uses the volume of trading to calculate the standard deviation of a stock's price. This indicator takes the source of data, the length of data, and the deviation as inputs, and calculates the volume weighted standard deviation using the values.
The indicator first calculates the mean price and mean volume by using simple moving average over the given length of data. Then it calculates the squared difference between the mean price and the actual price, multiplied by the volume. This gives a volume-weighted squared difference. The indicator then calculates the square root of the sum of the volume-weighted squared differences divided by the sum of the volumes over the given length of data. This gives the volume weighted standard deviation.
The indicator then plots the standard deviation and deviation as a band around the simple moving average of the source data, providing a clear view of the volatility of the stock.
In summary, the Volume Weighted Standard Deviation indicator is a powerful tool for measuring the volatility of a stock by taking into account the volume of trading. It uses the volume of trading to calculate the standard deviation of a stock's price, giving a more accurate representation of the volatility of the stock. It can be useful for traders to identify entry and exit points and make more informed trading decisions.
Chart Time and Price Range It is easy to loose track of time and price volatility when the chart automatically scales to the bars on the chart. This helps you keep track.
This is a very simple indicator that is designed to ensure that you're looking at a segment of the chart that is relevant to the trade you're considering in both price distance and time.
The Problem:
When looking at a chart the lowest price is at the bottom of the screen, the highest price is at the top. The time at the beginning of the chart is based on how many bars and what timeframe you're looking at.
But is the price difference between the two wide or narrow? Are you seeing minutes, hours, or days of price action?
You can get the measure tool out, but you'll change the zoom level and now its different. You change the timeframe and its different.
This Solution:
This indicator puts a table on the screen that will tell you the X/Y distance of everything that is on your chart. If your hold period is 5 minutes, why would you be looking at 3 days of price action to find s/r or make a decision on a trade?
This will show you how much price opportunity was available in the amount of time you are currently viewing. Using the PineCoders VisibleChart library, we're retrieving the time and bar_index of the beginning of the chart so that everything that is currently on the chart is measured and it adapts as that changes.
It will work with light and dark themes (you can change the colors) and can be positioned wherever you prefer to see the information.
Disclaimer: This was a quick release script. I wrote it and published the same day. There could be bugs, so send me a message or add a comment to report anything that isn't behaving correctly.
Probabilities Module - The Quant Science This module can be integrate in your code strategy or indicator and will help you to calculate the percentage probability on specific event inside your strategy. The main goal is improve and simplify the workflow if you are trying to build a quantitative strategy or indicator based on statistics or reinforcement model.
Logic
The script made a simulation inside your code based on a single event. For single event mean a trading logic composed by three different objects: entry, take profit, stop loss.
The script scrape in the past through a look back function and return the positive percentage probability about the positive event inside the data sample. In this way you are able to understand and calculate how many time (in percentage term) the conditions inside the single event are positive, helping to create your statistical edge.
You can adjust the look back period in you user interface.
How can set up the module for your use case
At the top of the script you can find:
1. entry_condition : replace the default condition with your specific entry condition.
2. TPcondition_exit : replace the default condition with your specific take profit condition.
3. SLcondition_exit : replace the default condition with your specific stop loss condition.
Super 8 - 30M BTCWelcome to Super 8, the ultimate automatic trading script for Pine!
This bad boy is designed to go both long and short, and it's equipped with all the tools you need to maximize your profits. Whether you're looking to take profit, set a trailing stop, or protect yourself with a stop loss, Super 8 has you covered.
But that's not all! Super 8 is also loaded with 8 powerful indicators to help you make informed decisions. We've got the EMA, ADX, SAR, MACD, VOLUME, BOLLINGER BANDS, DONCHIAN, and ATR all working together to give you the best possible trading experience.
And if you want to take it to the next level, Super 8 also has a feature that lets you use stepped entries in normal mode or incremental 1,2,3,... to improve your average price. Plus, if you're using trailing stop, you can activate the Backtest precision to use lower timeframes.
But what's in a name? Super 8 is called that because it's just that... super! It's tailored specifically for the OKX:BTCUSDT.P pair, so you know you're getting the best possible results. it's highly adjustable and can be used with any other pair. So no matter what market you're trading in, Super 8 has got you covered.
So if you want to level up your trading game, give Super 8 a try. You won't be disappointed.
Certain Risks of Live Algorithmic Trading:
Backtesting Cannot Assure Actual Results.
The relevant market might fail or behave unexpectedly.
Your broker may experience failures in its infrastructure, fail to execute your orders in a correct or timely fashion or reject your orders.
The system you use for generating trading orders, communicating those orders to your broker, and receiving queries and trading results from your broker may fail.
Time lag at various point in live trading might cause unexpected behavior.
The systems of third parties in addition to those of the provider from which we obtain various services, your broker, and the applicable securities market may fail or malfunction.
Lines and Table for risk managementABOUT THIS INDICATOR
This is a simple indicator that can help you manage the risk when you are trading, and especially if you are leverage trading. The indicator can also be used to help visualize and to find trades within a suitable or predefined trading range.
This script calculates and draws six “profit and risk lines” (levels) that show the change in percentage from the current price. The values are also shown in a table, to help you get a quick overview of risk before you trade.
ABOUT THE LINES/VALUES
This indicator draws seven percentage-lines, where the dotted line in the middle represents the current price. The other three lines on top of and below the middle line shows the different levels of change in percentage from current price (dotted line). The values are also shown in a table.
DEFAULT VALUES AND SETTINGS
By default the indicator draw lines 0.5%, 1.0%, and 1.5% from current price (step size = 0.5).
The default setting for leverage in this indicator = 1 (i.e. no leverage).
The line closest to dotted line (current price) is calculated by step size (%) * leverage (x) = % from price.
Pay attention to the %-values in the table, they represent the distance from the current price (dotted line) to where the lines are drawn.
* Be aware! If you change the leverage, the distance from the closest lines to the dotted line showing the current price increase.
SETTINGS
1. Leverage: set the leverage for what you are planning to trade on (1 = no leverage, 2 = 2 x leverage, 5 = 5 x leverage...).
2. Stepsize is used to set the distance between the lines and price.
EXAMPLES WITH DIFFERENT SETTINGS
1) Leverage = 1 (no leverage, default setting) and step size 0.5 (%). Lines plotted at (0.5%, 1%, 1.5%, and –0.5%, –1%, –1,5%) from the current price.
2) Leverage = 3 and stepsize 0.5(%). Lines plotted at (1.5%, 3.0%, 4.5%, and –1.5%, –3.0%, –4.5%) from the current price.
3) Leverage = 3 and stepsize 1(%). Lines plotted at (3%, 6%, 9%, and –3%, –6%, –9%) from the current price.
The distance to the nearest line from the current price is always calculated by the formula: Leverage * step size (%) = % to the nearest line from the current price.
Crypto Index (DXY) CandlesA modification of @loxx's "Dollar Index (DXY) Candles" allowing for a user selected basket of tickers using the same weights as the standard DXY. Ticker Inputs are in descending order so highest weight is at the top by default, although weighting can be changed manually by modifying the symbol's corresponding weight input in the settings. The Idea was to get a bird's eye view of the crypto space using some of the more relevant names in the space. User's can use the toggle in the settings for viewing the default DXY for comparison. Indicator should be used as a confirmation when looking at long term trend changes in the space, shorter time frames may, or may not be as useful.
Selected Dates Filter by @zeusbottradingWe are presenting you feature for strategies in Pine Script.
This function/pine script is about NOT opening trades on selected days. Real usage is for bank holidays or volatile days (PPI, CPI, Interest Rates etc.) in United States and United Kingdom from 2020 to 2030 (10 years of dates of bank holidays in mentioned countries above). Strategy is simple - SMA crossover of two lengts 14 and 28 with close source.
In pine script you can see we picked US and GB bank holidays. If you add this into your strategy, your bot will not open trades on those days. You must make it a rule or a condition. We use it as a rule in opening long/short trades.
You can also add some of your prefered dates, here is just example of our idea. If you want to add your preffered days you can find them on any site like forexfactory, myfxbook and so on. But don’t forget to add function “time_tradingday ! = YourChoosedDate” as it is writen lower in the pine script.
Sometimes the date is substituted for a different day, because the day of the holiday is on Saturday or Sunday.
Made with ❤️ for this community.
If you have any questions or suggestions, let us know.
The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold zeusbottrading TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script.
Manage tradeThis indicator is intended to simulate a second entry to adjust the average price.
INPUTS:
Current price || Coins -> Current price, your first entry || Initial amount of coins
Simulate entry || Coins -> New entry, from this entry the average price will be calculated || new amount of coins
Simulate partial % || Coins -> Percentage over current price or average price || Amount of coins withdrawn
* Currency conversion
Value $ || Target price -> amount or coins, example: 25 dolar or 25 busd || Acquisition target price, returns desired coin amount, visible in table.
Table information:
simbol = current symbol
current price = First entry price
total coins = Total coins from first entry
profit or loss = self explanatory
update value = increases or decreases according to profit or loss
Partial profit = partial simulation result,
$ ⮕ ₿ = total amount of currencies in the conversion
Multi-Polar WorldA new macro analysis tool for easily analyzing the multi-polar world's economic powerhouses / spheres of influence, making for an easy to use visual when comparing a number of statistics:
GDP, GDP per Capita, External Debt, Government Debt, Exports, Imports, Gold Reserves, Employed Persons, Military Expenditure, Population, Bank Lending Rate, Balance of Trade, Central Bank Balance Sheet, M2 Money Supply, and CPI . Includes option to provide the total for each pole, or view individually for more detailed comparison. Meant to be used when analyzing the macro-economic conditions/trends in conjunction with other "Big Picture" type indicators when adjusting your macro framework.
Band Pass Normalized Suite (BPNS)Outlier-Free Normalization and Band Pass Filtering
We present a technique for normalizing and filtering a given time series, source, in order to improve its stationarity and enhance its features. The technique includes two stages: outlier-free normalization and band pass filtering.
Outlier-Free Normalization:
In order to normalize source and reduce the impact of outliers, we first smooth the time series using an exponential moving average with a smoothing factor of alpha. The smoothed time series is then normalized by subtracting the minimum value within a given lookback period, dev_lookback, and dividing the result by the range (maximum - minimum) within the same lookback period. Outliers are detected and excluded from the normalization process by identifying values that are more than outlier_level standard deviations away from the exponentially smoothed average.
Band Pass Filtering:
After normalization, the time series is passed through a band pass filter to remove low and high frequency components. The specifics of the band pass filter implementation are not provided.
Code snippet:
bes(float source = close, float alpha = 0.7) =>
var float smoothed = na
smoothed := na(smoothed) ? source : alpha * source + (1 - alpha) * nz(smoothed )
max(source, outlier_level, dev_lookback)=>
var float max = na
src = array.new()
stdev = math.abs((source - bes(source, 0.1))/ta.stdev(source, dev_lookback))
array.push(src, stdev < outlier_level ? source : -1.7976931348623157e+308)
max := math.max(nz(max ), array.get(src, 0))
min(source, outlier_level, dev_lookback) =>
var float min = na
src = array.new()
stdev = math.abs((source - bes(source, 0.1))/ta.stdev(source, dev_lookback))
array.push(src, stdev < outlier_level ? source : 1.7976931348623157e+308)
min := math.min(nz(min ), array.get(src, 0))
min_max(src, outlier_level, dev_lookback) =>
(src - min(src, outlier_level, dev_lookback))/(max(src, outlier_level, dev_lookback) - min(src, outlier_level, dev_lookback)) * 100
To apply the outlier-free normalization and band pass filter to a given time series, source, the min_max() function can be called with the desired values for outlier_level and dev_lookback as arguments. For example:
normalized_source = min_max(source, 2, 50)
This will apply the outlier-free normalization and band pass filter to source, using an outlier_level of 2 standard deviations and a lookback period of 50 data points for both the normalization and outlier detection steps. The resulting normalized and filtered time series will be stored in normalized_source.
It is important to note that the choice of values for outlier_level and dev_lookback will have a significant impact on the resulting normalized and filtered time series. These values should be chosen carefully based on the characteristics of the input time series and the desired properties of the normalized and filtered output.
In conclusion, the outlier-free normalization and band pass filtering technique presented here provides a useful tool for preprocessing time series data and improving its stationarity and feature content. The flexibility of the method, through the choice of outlier_level and dev_lookback values, allows it to be tailored to the specific characteristics of the input time series.
Slope Normalized (SN)Introduction:
The Normalized Slope script is a technical indicator that aims to measure the strength and direction of a trend in a financial market. It does this by calculating the slope of the source data series, which can be any type of data (such as price, volume, or an oscillator) over a specified length of time. The slope is then normalized, meaning it is transformed to a scale between -1 and 1, with 0 representing a flat trend.
Methodology:
The Normalized Slope script uses an exponential smoothing function to smooth the source data series. The smoothing factor, or alpha, can be adjusted by the user through the input parameter "Pre Smoothing".
Next, the script calculates the slope of the smoothed data series by finding the average difference between the current value and the values of the previous "Length" periods. This slope is then normalized using a function that scales the data to a range of -1 to 1, with 0 representing a flat trend. The normalization function takes the minimum and maximum values of the slope, calculates the difference between them, and then scales the data to the range of -1 to 1.
The normalized slope is then smoothed again using another exponential smoothing function with a user-adjustable smoothing factor (the "Post Smoothing" input parameter). A center line representing a flat trend can also be plotted on the chart by enabling the "Center Line" input parameter. Additionally, the user can choose to display bounds at the -1 and 1 levels by enabling the "Bounds" input parameter.
Conclusion:
The Normalized Slope script provides traders with a visual representation of the strength and direction of a trend in a financial market. It can be used as a standalone indicator or in combination with other technical analysis tools to help traders make informed trading decisions.
Welford Bollinger Bands (WBB)The Welford method is an algorithm for calculating the running average and variance of a series of numbers in a single pass, without the need to store all the previous values. It works by maintaining an ongoing running average and variance, updating them with each new value in the series. The running average is updated using a simple formula that adds the new value to the previous average, weighed by the number of values that have been processed so far. The variance is updated using a similar formula that takes into account the deviation of the new value from the running average.
The Welford method has several advantages that make it a good fit for use in calculating Bollinger Bands. First, it is more numerically stable than other methods, as it avoids accumulating round-off errors and can handle large numbers of data points without overflow or underflow. This is important when working with financial data, which can contain large price movements and wide ranges of values.
Second, the Welford method is well-suited for use in real-time or streaming data scenarios where all the data may not be available upfront. This is useful in the context of Bollinger Bands, which are often used to identify trend changes and trading opportunities in real-time, as the bands are updated with each new data point.
Finally, the Welford method is simple and efficient, making it easy to implement and fast to compute. This is important when creating technical indicators and trading strategies, as performance is often a critical factor.
Overall, the Welford method is a reliable and efficient way to calculate the running average and variance of a series of numbers, making it a good fit for use in calculating Bollinger Bands and other technical indicators.
Noise GateThis Pine Script code defines an indicator called "Noise Gate" which filters out "noise" from a given signal. The indicator takes four input parameters: source, length, ratio, and level. The source parameter specifies the source data for the indicator (e.g., close prices), the length parameter specifies the length of a moving average, the ratio parameter specifies the attenuation ratio, and the level parameter specifies the threshold for attenuating the signal.
The core of the indicator is the noise_gate function, which takes three input parameters: signal, ratio, and level. The signal parameter represents the input signal that needs to be filtered. The ratio parameter specifies the amount by which the signal will be attenuated (reduced in amplitude) if it falls below the level parameter. The level parameter is a threshold that determines whether the signal will be attenuated or not.
The noise_gate function first calculates the absolute value of the signal using the math.abs() function. This is done because the filtering only applies to the magnitude of the signal, not its sign (positive or negative value).
The function then checks if the absolute value of the signal is above the level threshold using an if statement. If it is, the signal is returned as is. If the absolute value of the signal is below the level threshold, the function calculates a value called soft_knee_ratio using the formula 1 - (level - abs_signal) / level. This value represents the amount by which the signal will be attenuated. The signal is then reduced in amplitude by this soft_knee_ratio and the resulting value is returned as the output of the function.
The noise_gate function applies the transformation symmetrically to both positive and negative values of the signal parameter. This is because the transformation only depends on the absolute value of the signal, not its sign. The transformation first calculates the absolute value of the signal using the math.abs() function and then applies the filtering based on the magnitude of the signal. The sign of the signal is not taken into account in this process. As a result, the transformation is applied symmetrically to both positive and negative values of the signal.
The noise_gate function can be a valuable tool for anyone looking to filter out noise or unwanted variations from a signal. It is flexible and easy to use, and can be applied to a wide range of situations where signal noise reduction is needed. For example, it can be used to smooth out financial time series data or to remove background noise from an audio recording.
The noise_gate function in this code has been modified to include an additional input parameter called knee_type, which allows the user to specify whether to use a hard knee or a soft knee. A hard knee means that the compressor triggers simply at the threshold, whereas a soft knee means that the compressor triggers smoothly, gradually increasing the attenuation as the signal falls further below the threshold.
To use a hard knee, the user can set the knee_type parameter to "hard". To use a soft knee, the user can set the knee_type parameter to "soft". The default value for the knee_type parameter is "soft", so if the user does not specify a value for knee_type, the noise_gate function will use a soft knee by default.
The noise_gate function includes a check for the value of the knee_type parameter and applies the appropriate knee type. If the knee_type parameter is set to "hard", the function applies a hard knee by simply triggering at the threshold and dividing the input by the ratio if the signal falls below the threshold. If the knee_type parameter is set to "soft" (or if it is not specified and the default value is used), the function applies a soft knee by gradually increasing the attenuation of the signal as it falls further below the threshold.
The noise_gate function can be a valuable tool for anyone looking to filter out noise or unwanted variations from a signal. It is flexible and easy to use, and can be applied to a wide range of situations where signal noise reduction is needed. For example, it can be used to smooth out financial time series data or to remove background noise from an audio recording.
Session LiquidityThe “Session Liquidity” TradingView indicator by Infinity Trading creates dynamic horizontal lines at the high and low points of a specified time span within the trading day. This indicator gives the user control of three separate time spans so the user can dynamically see the highs and lows of their favorite daily time spans.
Purpose
This indicator is similar to my TradingView indicator “Futures Exchange Sessions 3.0”. In that indicator the user gets control of dynamic price boxes. For me, these boxes made it difficult to spot ICT’s Orderblocks. So instead of boxes I made independently controllable lines and now I can spot ICT Orderblocks and easily identify Liquidity Pools.
Inputs and Style
Everything about the three dynamic lines can but independently configured. Start & End Times, Line Color, Line Style, Line Width, Text Characters, Text Size, Text Color can all be adjusted. The high and low lines as well as their text labels can be individually toggled on or off for maximum control.
Timezone
All of the start and end times are in EST. Additionally, each time span line needs a specific start of each day. This is controlled by a setting called “Line Start Day Timezone” where the user sets a timezone that corresponds with the start time. In general if a timespan resides within a particular Session pick the corresponding timezone. If the users line fits in the Asian Session then choose Asia/Shanghai. If the line is within the London Session then choose Europe/London. And the same goes for the New York Session.
Special Notes
If the Line Start Time is within one candle of the Start Day Timezone in the Settings, then the line/box won’t display. So choose the previous timezone
Lines only display when the timeframe is <= 30 minute
Gallery
[-_-] Custom Type ExamplesDescription:
This script shows an example use of new Pinescript's feature called User Defined Types, which can be seen as analogue of from C++ or from Python. It is not an indicator for technical analysis, and serves only as an example of how to use the new feature mentioned above.
In the script I define 4 custom types and a custom initialisation function for each:
- Point (represents a coordinate with x -> bar_index, y -> price)
- Tria (creates a triangle using objects and objects as coordinates of 3 points)
- Path (creates a path-like object from an of )
- Trade (creates a visual representation of a Long/Short trade with set Take Profit and Stop Loss, and displays an info label with realized Profit/Loss)
I'd personally like to see this feature improved by adding methods (so that we could, for example, define functions inside a custom type), which could be an analogue of classes from other programming languages.
Cumulative Weighted Exponential Moving Average (CWEMA)This is a Pine Script for the "Cumulative Weighted Exponential Moving Average" (CWEMA) indicator. The script defines the CWEMA indicator using the ema() and wma() functions, which respectively calculate the exponential moving average and weighted moving average of a given data series. The cwema() function computes the weights for each value in the moving average and then uses the wma() function to calculate the weighted moving average of the exponentially weighted moving average of the input data series. The resulting CWEMA values are plotted on a chart.
Center Weighted Moving Average (CWMA)This is a pine script that defines and implements a custom weighted moving average (WMA) indicator. The script first defines a function called wma that calculates the weighted moving average of a given data source (src) with a specified set of weights (weights) over a specified length (len). The wma function first computes the weighted sum of the source data by multiplying the source data with the weights and summing over the length. It then calculates the sum of the weights. Finally, it divides the weighted sum by the sum of the weights to compute the weighted moving average.
The script then defines a second function called cwma that calculates a custom WMA by defining the weights for each value in the moving average as the length plus one divided by two minus the absolute value of the length minus the source data. It then uses the wma function to calculate and return the weighted moving average.
The script then specifies the input parameters for the cwma function: the source data (close) and the moving average length (length). It then plots the custom weighted moving average on the chart.
Time Zone / Market SessionsTime Zone / Market Sessions indicator is a handy tool to display current major Market Open / Close condition along with present time at that Exchange. Sydney, Tokyo, London, New York market sessions are included. Clear display of Overlap Zones and Kill Zones.. Can be used to estimate market Pumping and Dumping. Alerts can be set when a market opens.
Time is Displayed in Table Layout
Active Market is displayed as Dots on top and bottom
You can select as Timeline or Table display
Overlap is clearly displayed on top and bottom without any mess
Gray color in Table layout Shows Closed Markets
You are free to reuse this code No Limitations.
Kill Zones are indicated in Table format. When some of the markets are closed
Alerts included for market openings
[Mad] Active LineThis is a simple line tool that can give you active feedback based on the closing price.
It is intended for a live chart whose ticker is not changed by the user every 10 minutes, like a general overview of an asset in a higher time frame.
What can it provide:
Price difference from the price to the line
Price at the current collision point
How to use it:
When opened, it requests 2 input points from you.
After you set these 2 points, it will set the "Active Line" for you, like the normal TV line.
One thing to know is that you can't place into the future, so "extend" is always on by default.
and as always, have fun trading the assets up and down :-)
Custom Candle Body WidthOver the specified bar range, plots a vertical line from open to close using the specified colors and line width (in pixels).
In effect, these look like thinner/thicker candle bodies.
The default candle bodies are made transparent over this range to make them appear hidden.
The indicator is capable of referencing the bar colors from another plot, using a technique of encapsulating the RGBT color value in a float value (plot to data window only recommended).
Refer to example code (which is commented out) in the source code.
RSI Candle ColorI manually made a 100 point gradient for this one. Its just smooth sensitive rsi but it colors your candles based on the level of the rsi. I hope you find this useful even as a utility for the gradient.
True Range Outlier Detector (TROD)True Range Outlier Detector (TROD) shows you weather or not a candle is larger than normal. This works by taking the normalized true range and if the candle exceeds a score of 0.5 or -0.5 it triggers the outlier detection. This is great for building strategies if you want to refrain from buying larger than normal up or down ticks. The only feature is the ability to change the lookback period of the normalization. I hope you find this as useful as I do!
Enjoy!
