Louis Bachelier's Random WalkSeveral tests of market efficiency have been developed over the years. The very first test, constructed by Louis Bachelier in 1900, measured the probability of a number of consecutively positive or consecutively negative price changes, or “runs.”
The randomness of runs is rejected with 95 percent statistical confidence whenever the plotted value is greater than 0. The randomness of runs cannot be rejected if it's < 0.
Desvio Padrão
Z-Score (Close)A Z-score is a numerical measurement of a value's relationship to the mean in a group of values. If a Z-score is 0, it represents the score as identical to the mean score.
Z-scores may also be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean. Positive and negative scores also reveal the number of standard deviations that the score is either above or below the mean.
Standard Deviation Pivot pointsSupport Resistance points that are calculated based on the standard deviation of the traditional pivot point(previous session's high, low and close). More often stocks tend to oscillate between 3 levels of deviation forming day's high or low. A breakout of the 3rd SR level with volume indicates a strong trend day.
Auto-LineAn indicator inspired by the Renko chart.Instead of using a static box size we use standard deviation, this make the indicator more reactive to the market state.
If the indicator show no values then you have to round the price to the nearest integer, for that use the round parameter.
Hope you enjoy :)
Relative Volatility IndexCorrected Relative Volatility Index. This indicator was originally developed by Donald Dorsey (Stocks & Commodities V.11:6 (253-256): The Relative Volatility Index).
The indicator was revised by Dorsey in 1995 (Stocks & Commodities V.13:09 (388-391): Refining the Relative Volatility Index).
I suggest the refined RVI with optional settings. If you disabled Wilder's Smoothing and Refined RVI you will get the original version of RVI (1993, as built-in).
Also, you can choose an algorithm for calculating Standard Deviation.
Woodies CCI with ChopZone and Sidewinder indicatorExcelente indicador a mi parecer, bastante complejo pero muy bien acoplado; dejo aquí las fuentes para aprender a utilizarlo:
www.x-trader.net
www.x-trader.net www.x-trader.net www.x-trader.net
BBLathe2: Bollinger Band Lathe w/ Elder's Force Index [sclark39]Welcome to the second version of the BBLathe!
This shows Bollinger Bands centered on a horizontal basis, to make it easier to see how volatility is changing and identify squeeze opportunities. By default Bollinger bands are calculated using an exponential moving average and an improved higher precision stdev implementation, but this can be disabled. Version 2 also shows Elder's Force Index as a white histogram, so you can see some volume information to help confirm the power of the bears/bulls. The green/red shadow shows how the Bollinger's basis is changing, and when it is going up there will be a green shadow underneath the basis line (this can be inverted in the settings). There is also price line (yellow) showing the location of the price within the Bollinger Bands.
Use this indicator for trades at your own risk, I made this for fun and it is not a trade recommendation.
That being said, if you like my work please tip me!
ETH: 0xf8E0Ea503B5c833fD4546E7fa2c70EcE42A27C8A
Please comment with feedback and requests!
META: Kahan Summation (Scripting Exercise)I was curious to see what Pine uses to accumulate numbers. It looks like it uses the simple "add em up" approach, rather than a compensated summation. This means that especially for large numbers, there is an inherent error amount.
This script implements the Kahan Summation Algorithm, also known as compensated summation.
en.wikipedia.org
This is part 2 of my study into the builtin stdev function. I think this is why it differs so much from the simple two-pass solution.
META: STDEV Study (Scripting Exercise)While trying to figure out how to make the STDEV function use an exponential moving average instead of simple moving average , I discovered the builtin function doesn't really use either.
Check it out, it's amazing how different the two-pass algorithm is from the builtin!
Eventually I reverse-engineered and discovered that STDEV uses the Naiive algorithm and doesn't apply "Bessel's Correction". K can be 0, it doesn't seem to change the data although having it included should make it a little more precise.
en.wikipedia.org
Exponential Bollinger Bands [Updated Feb 2018]The same as my previous Exponential Bollinger Bands script, but now you can set a desired offset for the indicator. I have published this as a new script that way those who prefer the old script can continue to use it without seeing any changes.
Coefficient of Variation [DW]This is a simple gauge of volatility using the Coefficient of Variation.
COV is calculated by dividing standard deviation of price by the expected (average) price.
Custom color scheme indicates increases and decreases in volatility, which is indicated when the COV forms new half period highs and lows.
SigmaSpikes(R) per Adam H. GrimesEach bar’s return against a volatility-adjusted baseline, as a standard deviation of the last 20 bars’ returns as per Adam H. Grimes SigmaSpikes(R).
adamhgrimes.com
www.marketlifetrading.com
SDSpikePrice Change as Standard Deviation Spikes
Plots price changes scaled to daily StdDev for the period
The Close price change is plotted as a thick bar coloured green for up close, red for down close
The High price change is plotted as a thin bar coloured aqua
The Low price change is plotted as a thin bar coloured orange
Can be used to understand the statistical price behaviour of the symbol.
Very useful for earnings trades and in general for options trades.
BKSqueezeThis is a price volatility compression and expansion indicator that uses the ratio of the Bollinger Band and Keltner Ratio.
Red segments indicate extreme price volatility compression that can be ideal entry points for stock/futures/forex and/or options positions.
Aqua segments indicate price volatility is expanding.
Blue segments indicate price volatility is compressing - can be used as an exit point or partial scale out point.
Note that the indicator doesn't indicate direction. One suggestion is to use the DMI indicator for this purpose - really depends on how early you enter the trade.
Suggest using a time period of 15 bars for volatile stocks, such as TSLA for example, otherwise a period of 20 bars suits most stocks/futures/forex symbols.
OHLC Volatility Estimators by @Xel_arjonaDISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is by Creative-Commons as TradingView's regulations. Any use, copy or re-use of this code should mention it's origin as it's authorship.
WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED AS DEBUGING CODE The models included in the function have been taken from openly sources on the web so they could have some errors as in the calculation scheme and/or in it's programatic scheme. Debugging are welcome.
WHAT'S THIS?
Here's a full collection of candle based (compressed tick) Volatility Estimators given as a function, openly available for free, it can print IMPLIED VOLATILITY by an external symbol ticker like INDEX:VIX.
Models included in the volatility calculation function:
CLOSE TO CLOSE: This is the classic estimator by rule, sometimes referred as HISTORICAL VOLATILITY and is the must common, accepted and widely used out there. Is based on traditional Standard Deviation method derived from the logarithm return of current close from yesterday's.
ELASTIC WEIGHTED MOVING AVERAGE: This estimator has been used by RiskMetriks®. It's calculation is based on an ElasticWeightedMovingAverage Standard Deviation method derived from the logarithm return of current close from yesterday's. It can be viewed or named as an EXPONENTIAL HISTORICAL VOLATILITY model.
PARKINSON'S: The Parkinson number, or High Low Range Volatility, developed by the physicist, Michael Parkinson, in 1980 aims to estimate the Volatility of returns for a random walk using the high and low in any particular period. IVolatility.com calculates daily Parkinson values. Prices are observed on a fixed time interval. n=10, 20, 30, 60, 90, 120, 150, 180 days.
ROGERS-SATCHELL: The Rogers-Satchell function is a volatility estimator that outperforms other estimators when the underlying follows a Geometric Brownian Motion (GBM) with a drift (historical data mean returns different from zero). As a result, it provides a better volatility estimation when the underlying is trending. However, this Rogers-Satchell estimator does not account for jumps in price (Gaps). It assumes no opening jump. The function uses the open, close, high, and low price series in its calculation and it has only one parameter, which is the period to use to estimate the volatility.
YANG-ZHANG: Yang and Zhang were the first to derive an historical volatility estimator that has a minimum estimation error, is independent of the drift, and independent of opening gaps. This estimator is maximally 14 times more efficient than the close-to-close estimator.
LOGARITHMIC GARMAN-KLASS: The former is a pinescript transcript of the model defined as in iVolatility . The metric used is a combination of the overnight, high/low and open/close range. Such a volatility metric is a more efficient measure of the degree of volatility during a given day. This metric is always positive.
Daily Deviations (Self Input Version)
Plots the standard deviation resistance/support levels.
Input the previous settlement price and the implied volatility.
credit to u/UberBotMan and u/Living_Granger for the idea and formulas
(preview example is using settlement of 2420 and IV of 11)
VWAP Stdev Bands v2 Modoriginal script by /u/SandroTurriate/ - I just made some small changes.
Vwap + standard deviation bands. Good for reversal trading among other things. Used intraday.
Very useful when price is ranging.
I added the option to fill the spaces between the deviation lines with color and also the option to add some extra bands. That's about it. Color/length/style etc is customizable.
GEOMETRIC STANDARD DEVIATION BANDS v1 by @XeL_ArjonaGEOMETRIC STANDARD DEVIATION BANDS
Ver.1 By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
WHAT'S THIS?
This IS NOT the wheel "Re-Invention"... This is exactly what the name says: A pair of Envelope Bands to measure "volatility", constructed at statistical relation from within price series and their Rolling back MEAN (Simple Moving Average). YES, What Mr. Bollinger did and put it's name to this simple, cleaver and popular formula.
This time, I took the time to make another simple mod, but seems to me to be quite functional in REAL VOLATILE assets like in the example chart: TO USE THEIR GEOMETRIC MODE!!
Cheers!
Any feedback or public modification(s) are quite welcome to the community....!
@XeL_Arjona
Apr 28 2016
Standard Error Bands by @XeL_arjonaStandard Error Bands - Code by @XeL_arjona
Original implementation by:
Traders issue: Stocks & Commodities V. 14:9 (375-379):
Standard Error Bands by Jon Andersen
Version 1
For a quick and publicly open explanation of this Statistical indicator, you can refer at Here!
Extract from the former URL:
Standard Error bands are quite different than Bollinger's. First, they are bands constructed around a linear regression curve. Second, the bands are based on two standard errors above and below this regression line. The error bands measure the standard error of the estimate around the linear regression line. Therefore, as a price series follows the course of the regression line the bands will narrow, showing little error in the estimate. As the market gets noisy and random, the error will be greater resulting in wider bands.
