Combo Strategy 123 Reversal & Fractal Chaos Bands This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
Stock market moves in a highly chaotic way, but at a larger scale, the movements
follow a certain pattern that can be applied to shorter or longer periods of time
and we can use Fractal Chaos Bands Indicator to identify those patterns. Basically,
the Fractal Chaos Bands Indicator helps us to identify whether the stock market is
trending or not. When a market is trending, the bands will have a slope and if market
is not trending the bands will flatten out. As the slope of the bands decreases, it
signifies that the market is choppy, insecure and variable. As the graph becomes more
and more abrupt, be it going up or down, the significance is that the market becomes
trendy, or stable. Fractal Chaos Bands Indicator is used similarly to other bands-indicator
(Bollinger bands for instance), offering trading opportunities when price moves above or
under the fractal lines.
The FCB indicator looks back in time depending on the number of time periods trader selected
to plot the indicator. The upper fractal line is made by plotting stock price highs and the
lower fractal line is made by plotting stock price lows. Essentially, the Fractal Chaos Bands
show an overall panorama of the price movement, as they filter out the insignificant fluctuations
of the stock price.
WARNING:
- For purpose educate only
- This script to change bars colors.
Indicators and strategies
Spearman Rank Correlation CoefficientI'm pleased to introduce this script in honor of the new array functions introduced to PineScript version 4.0. This update is a long time coming and opens the door to amazing scripting possibilities!
Definition
Named after Charles Spearman and denoted by the Greek letter ‘ ρ ’ (rho), the Spearman rank correlation coefficient is the nonparametric version of the Pearson correlation coefficient . Use the Spearman rank correlation when you have two ranked variables, and you want to see whether the two variables covary. That is, as one variable increases/decreases, the other variable tends to increase/decrease respectively.
It is best used to discover if two variables (and in this first version of the indicator, two ticker symbols) increase and decrease together. There are advantages to using this version of correlation vs. the Pearson R.
Interpretation
The value oscillates between +1 and -1.
A value of +1 means the two variables are perfectly correlated, that is they are increasing and decreasing together in perfect harmony.
A value of -1 means the two variables exhibit a perfect negative correlation, that is they increase and decrease oppositely.
A value of zero means the two variables are not correlated at all (noise).
Resampling Filter Pack [DW]This is an experimental study that calculates filter values at user defined sample rates.
This study is aimed to provide users with alternative functions for filtering price at custom sample rates.
First, source data is resampled using the desired rate and cycle offset. The highest possible rate is 1 bar per sample (BPS).
There are three resampling methods to choose from:
-> BPS - Resamples based on the number of bars.
-> Interval - Resamples based on time in multiples of current charting timeframe.
-> PA - Resamples based on changes in price action by a specified size. The PA algorithm in this script is derived from my Range Filter algorithm.
The range for PA method can be sized in points, pips, ticks, % of price, ATR, average change, and absolute quantity.
Then, the data is passed through one of my custom built filter functions designed to calculate filter values upon trigger conditions rather than bars.
In this study, these functions are used to calculate resampled prices based on bar rates, but they can be used and modified for a number of purposes.
The available conditional sampling filters in this study are:
-> Simple Moving Average (SMA)
-> Exponential Moving Average (EMA)
-> Zero Lag Exponential Moving Average (ZLEMA)
-> Double Exponential Moving Average (DEMA)
-> Rolling Moving Average (RMA)
-> Weighted Moving Average (WMA)
-> Hull Moving Average (HMA)
-> Exponentially Weighted Hull Moving Average (EWHMA)
-> Two Pole Butterworth Low Pass Filter (BLP)
-> Two Pole Gaussian Low Pass Filter (GLP)
-> Super Smoother Filter (SSF)
Downsampling is a powerful filtering approach that can be applied in numerous ways. However, it does suffer from a trade off, like most studies do.
Reducing the sample rate will completely eliminate certain levels of noise, at the cost of some spectral distortion. The lower your sample rate is, the more distortion you'll see.
With that being said, for analyzing trends, downsampling may prove to be one of your best friends!
Matrix functions - JD/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// The arrays provided in Pinescript are linear 1D strucures that can be seen either as a large vertical stack or
// a horizontal row containing a list of values, colors, bools,..
//
// With the FUNCTIONS in this script the 1D ARRAY LIST can be CONVERTED INTO A 2D MATRIX form
//
//
///////////////////////////////////////////
/// BASIC INFO ON THE MATRIX STRUCTURE: ///
///////////////////////////////////////////
//
// The matrix is set up as an 2D structure and is devided in ROWS and COLUMNS.
// following the standard mathematical notation:
//
// a 3 x 4 matrix = 4 columns
// 0 1 2 3 column index
// 0
// 3 rows 1
// 2
// row
// index
//
// With the use of some purpose-built functions, values can be placed or retrieved in a specific column of a certain row
// this can be done by intuitively using row_nr and column_nr coördinates,
// without having to worry on what exact index of the Pine array this value is located (the functions do these conversions for you)
//
//
// the syntax I propose for the 2D Matrix array has the following structure:
//
// - the array starts with 2 VALUES describing the DIMENSION INFORMATION, (rows, columns)
// these are ignored in the actual calculations and serve as a metadata header (similar to the "location, time,... etc." data that is stored in photo files)
// so the array always carries it's own info about the nr. of rows and columns and doesn't need is seperate "info" file!
//
// To stay consistent with the standard Pinescript (array and ) indexing:
// - indexes for sheets and columns start from 0 (first) and run up to the (total nr of sheets or columns) - 1
// - indexes for rows also start from 0 (most recent, cfr. ) and run up to the (total nr of rows) - 1
//
// - this 2 value metadata header is followed by the actual df data
// the actual data array can consist of (100,000 - 2) usable items,
//
// In a theoretical example, you can have a matrix with almost 20,000 rows with each 5 columns of data (eg. open, high, low, close, volume) in it!!!
//
//
///////////////////////////////////
/// SCHEMATIC OF THE STRUCTURE: ///
///////////////////////////////////
//
////// (metadata header with dimensions info)
//
// (0) (1) (array index)
//
Function Polynomial RegressionDescription:
A function that returns a polynomial regression and deviation information for a data set.
Inputs:
_X: Array containing x data points.
_Y: Array containing y data points.
Outputs:
_predictions: Array with adjusted _Y values.
_max_dev: Max deviation from the mean.
_min_dev: Min deviation from the mean.
_stdev/_sizeX: Average deviation from the mean.
Resources:
en.wikipedia.org
rosettacode.org
SMA Directional Matrix [LuxAlgo]This script was created in collaboration with alexgrover and displays a simple & elegant panel showing the direction of simple moving averages with periods in a user-selected range (Min, Max). The displayed number in the panel is the period of a simple moving average and the symbol situated at the right of it is associated with the direction this moving average is taking.
Settings
Min: Minimum period of the moving average
Max: Maximum period of the moving average
Src: Source input of the moving averages
Number Of Columns: Number of columns to be displayed in the panel, handy when using a large range of periods.
Usage
Looking at the direction of moving averages with different periods is extremely useful when it comes to having information about the short/mid/long term overall market sentiment, and can also tell us if the market is trending or ranging.
Here we use periods ranging from 25 to 50, we can see that shorter moving averages react to the recent upward price variation, longer-term moving averages however are still affected by the overall downward variation you can see on the image. We can as such get information about the presence of potentials divergences, with shorter-term moving averages reacting to the divergence while the longer-term moving averages will still display the direction of the main trend.
As such the indicator can give information about how clean a trend is, with a clean trend being defined as a variation containing no retracements. When our trend contains no retracement, the mid/long term moving averages will all have the same direction, however, when a retracement is present, the midterm moving averages might be affected by it, thus displaying a direction contrary to the main trend.
When the market is ranging we can expect the panel to display an equal number of decreasing/increasing moving averages.
Possible Issues
When using a large range of periods, you might have an error message showing: "String is too long", try lowering the range of periods by increasing Min or decreasing Max .
If the script displays the error message "Loop is too long to execute", try resetting the settings and change them back to the one you wanted to use.
McGinley Dynamic (Improved) - John R. McGinley, Jr.For all the McGinley enthusiasts out there, this is my improved version of the "McGinley Dynamic", originally formulated and publicized in 1990 by John R. McGinley, Jr. Prior to this release, I recently had an encounter with a member request regarding the reliability and stability of the general algorithm. Years ago, I attempted to discover the root of it's inconsistency, but success was not possible until now. Being no stranger to a good old fashioned computational crisis, I revisited it with considerable contemplation.
I discovered a lack of constraints in the formulation that either caused the algorithm to implode to near zero and zero OR it could explosively enlarge to near infinite values during unusual price action volatility conditions, occurring on different time frames. A numeric E-notation in a moving average doesn't mean a stock just shot up in excess of a few quintillion in value from just "10ish" moments ago. Anyone experienced with the usual McGinley Dynamic, has probably encountered this with dynamically dramatic surprises in their chart, destroying it's usability.
Well, I believe I have found an answer to this dilemma of 'susceptibility to miscalculation', to provide what is most likely McGinley's whole hearted intention. It required upgrading the formulation with two constraints applied to it using min/max() functions. Let me explain why below.
When using base numbers with an exponent to the power of four, some miniature numbers smaller than one can numerically collapse to near 0 values, or even 0.0 itself. A denominator of zero will always give any computational device a horribly bad day, not to mention the developer. Let this be an EASY lesson in computational division, I often entertainingly express to others. You have heard the terminology "$#|T happens!🙂" right? In the programming realm, "AnyNumber/0.0 CAN happen!🤪" too, and it happens "A LOT" unexpectedly, even when it's highly improbable. On the other hand, numbers a bit larger than 2 with the power of four can tremendously expand rapidly to the numeric limits of 64-bit processing, generating ginormous spikes on a chart.
The ephemeral presence of one OR both of those potentials now has a combined satisfactory remedy, AND you as TV members now have it, endowed with the ever evolving "Power of Pine". Oh yeah, this one plots from bar_index==0 too. It also has experimental settings tweaks to play with, that may reveal untapped potential of this formulation. This function now has gain of function capabilities, NOT to be confused with viral gain of function enhancements from reckless BSL-4 leaking laboratories that need to be eternally abolished from this planet. Although, I do have hopes this imd() function has the potential to go viral. I believe this improved function may have utility in the future by developers of the TradingView community. You have the source, and use it wisely...
I included an generic ema() plot for a basic comparison, ultimately unveiling some of this algorithm's unique characteristics differing on a variety of time frames. Also another unconstrained function is included to display some the disparities of having no limitations on a divisor in the calculation. I strongly advise against the use of umd() in any published script. There is simply just no reason to even ponder using it. I also included notes in the script to warn against this. It's funny now, but some folks don't always read/understand my advisories... You have been warned!
NOTICE: You have absolute freedom to use this source code any way you see fit within your new Pine projects, and that includes TV themselves. You don't have to ask for my permission to reuse this improved function in your published scripts, simply because I have better things to do than answer requests for the reuse of this simplistic imd() function. Sufficient accreditation regarding this script and compliance with "TV's House Rules" regarding code reuse, is as easy as copying the entire function as is. Fair enough? Good! I have a backlog of "computational crises" to contend with, including another one during the writing of this elaborate description.
When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members, I may implement more ideas when they present themselves as worthy additions. Have a profitable future everyone!
RSI cyclic smoothed v2Cyclic Smoothed Relative Strength Indicator
The cyclic smoothed RSI indicator is an enhancement of the classic RSI , adding
additional smoothing according to the market vibration,
adaptive upper and lower bands according to the cyclic memory and
using the current dominant cycle length as input for the indicator.
The cRSI is used like a standard indicator. The chart highlights trading signals where the signal line crosses above or below the adaptive lower/upper bands. It is much more responsive to market moves than the basic RSI.
You can also review this short idea where BTC went down from 4300 USD (3 Sept 17) to 3700 USD (15 Sept 17) after the idea was posted and showed the clear short exit with the next low:
The indicator uses the dominant cycle as input to optimize signal, smoothing and cyclic memory. To get more in-depth information on the cyclic-smoothed RSI indicator, please read Chapter 4 "Fine tuning technical indicators" of the book "Decoding the Hidden Market Rhythm, Part 1" available at your favorite book store.
This is the open-source code version of the requested script already published as protected indicator back in 2017 "RSI cyclic smoothed". Now made public as v2. Would love to receive feedback and see your ideas.
Relative Strength of Volume Indicators by DGTThe Relative Strength Index (RSI) , developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements.
• Traditionally the RSI is considered overbought when above 70 and may be primed for a trend reversal or corrective pullback in price, and oversold or undervalued condition when below 30. During strong trends, the RSI may remain in overbought or oversold for extended periods.
• Signals can be generated by looking for divergences and failure swings. If underlying prices make a new high or low that isn't confirmed by the RSI, this divergence can signal a price reversal. If the RSI makes a lower high and then follows with a downside move below a previous low, a Top Swing Failure has occurred. If the RSI makes a higher low and then follows with an upside move above a previous high, a Bottom Swing Failure has occurred
• RSI can also be used to identify the general trend. In an uptrend or bull market, the RSI tends to remain in the 40 to 90 range with the 40-50 zone acting as support. During a downtrend or bear market the RSI tends to stay between the 10 to 60 range with the 50-60 zone acting as resistance
This study aim to implement Relative Strength concept on most common Volume indicators, such as
• Accumulation Distribution is a volume based indicator designed to measure underlying supply and demand
• Elder's Force Index (EFI) measures the power behind a price movement using price and volume
• Money Flow Index (MFI) measures buying and selling pressure through analyzing both price and volume (used as it is)
• On Balance Volume (OBV) , created by Joe Granville, is a momentum indicator that measures positive and negative volume flow
• Price Volume Trend (PVT) is a momentum based indicator used to measure money flow
Plotting will be performed for regular RSI and RSI of Volume indicator (RSI(VOLX)) selected from the dialog box, where the possibility to apply smoothing is provided as option. Additionally, labels can be added optionally to display the value and name of selected volume indicator
Secondly, ability to present Volume Histogram within the same study along with its Moving Average or Volume Oscillator based on selection
Finally, Volume Based Colored Bars , a study of Kıvanç Özbilgiç is added to emphasis volume changes on top of the bars
Nothing excessively new, the study combines RSI with;
- RSI concept applied to some of the common Volume indicators presented with a highlighted over/under valued threshold area, optional labeling and smoothing,
- added Volume data with additional information and
- colored bars based on volume
Thanks @Vishant_Meshram for the inspiration 🙏
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have 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 dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Volume by SessionThis indicator shows volume separated by session. The aim is clarify which session a volume bar occurred in and how it compares to the average of that session - NOT ALL SESSIONS.
The logic is that there is little value comparing for example the volume of the London session against volume of the London-NY session - the London-NY will always have higher volume. But how does today's london compare to yesterdays london or the london average.
The indicator has moving averages that only consider the average of previous episodes of the same session - so the average on the london session is the average of the past 5 london sessions (NY and asia and London-NY are not included in the calculation of the MA).
The indicator goes as high as 1H timeframe and the average line does not function below 5 mins - thats a limitation on available data to calculate it.
There's also a 'dark screen' function. Thats because I like a black background so the colours need to be inverted.
Hope its useful. Good luck.
John
COT Net Non-Commercials vs Commercials (Updated MTF Non-Repaint)Hello there,
With this script, you can see CFTC COT Non Commercial and Commercial Positions together.
This way, you can analyze net values greater than 0 and smaller, as well as very dense and very shallow positions of producers and speculators.
Green - Non Commercials - Speculators
Red - Commercials - Producers
This script is multi time-frame and non-repaint script.
Data pulled through Quandl.
And the latest version codes have been used.
As time goes by, I will try to make useful modifications to this scheme.
Regards.
Grid Like StrategyIt is possible to use progressive position sizing in order to recover from past losses, a well-known position sizing system being the "martingale", which consists of doubling your position size after a loss, this allows you to recover any previous losses in a losing streak + winning an extra. This system has seen a lot of attention from the trading community (mostly from beginners), and many strategies have been designed around the martingale, one of them being "grid trading strategies".
While such strategies often shows promising results on paper, they are often subjects to many frictions during live trading that makes them totally unusable and dangerous to the trader. The motivations behind posting such a strategy isn't to glorify such systems, but rather to present the problems behind them, many users come to me with their ideas and glorious ways to make money, sometimes they present strategies using the martingale, and it is important to present the flaws of this methodology rather than blindly saying "you shouldn't use it".
Strategy Settings
Point determines the "grid" size and should be adjusted accordingly to the scale of the symbol you are applying the strategy to. Higher value would require larger price movements in order to trigger a trade, as such higher values will generate fewer trades.
The order size determines the number of contracts/shares to purchase.
The martingale multiplier determines the factor by which the position size is multiplied after a loss, using values higher to 2 will "squarify" your balance, while a value of 1 would use a constant position sizing.
Finally, the anti-martingale parameter determines whether the strategy uses a reverse martingale or not, if set to true then the position size is multiplied after any wins.
The Grid
Grid strategies are commons and do not present huge problems until we use certain position sizing methods such as the martingale. A martingale is extremely sensitive to any kind of friction (frictional costs, slippage...etc), the grid strategy aims to provide a stable and simple environment where a martingale might possibly behave well.
The goal of a simple grid strategy is to go long once the price crossover a certain level, a take profit is set at the level above the current one and stop loss is placed at the level below the current one, in a winning scenario the price reach the take profit, the position is closed and a new one is opened with the same setup. In a losing scenario, the price reaches the stop loss level, the position is closed and a short one is opened, the take profit is set at the level below the current one, and a stop loss is set at the level above the current one. Note that all levels are equally spaced.
It follows from this strategy that wins and losses should be constant over time, as such our balance would evolve in a linear fashion. This is a great setup for a martingale, as we are theoretically assured to recover all the looses in a losing streak.
Martingale - Exponential Decays - Risk/Reward
By using a martingale we double our position size (exposure) each time we lose a trade, if we look at our balance when using a martingale we see significant drawdowns, with our balance peaking down significantly. The martingale sequence is subject to exponential growth, as such using a martingale makes our balance exposed to exponential decays, that's really bad, we could basically lose all the initially invested capital in a short amount of time, it follows from this that the theoretical success of a martingale is determined by what is the maximum losing streak you can endure
Now consider how a martingale affects our risk-reward ratio, assuming unity position sizing our martingale sequence can be described by 2^(x-1) , using this formula we would get the amount of shares/contracts we need to purchase at the x trade of a losing streak, we would need to purchase 256 contracts in order to recover from a losing streak of size 9, this is enormous when you take into account that your wins are way smaller, the risk-reward ratio is totally unfair.
Of course, some users might think that a losing streak of size 9 is pretty unlikely, if the probability of winning and losing are both equal to 0.5, then the probability of 9 consecutive losses is equal to 0.5^9 , there are approximately 0.2% of chance of having such large losing streak, note however that under a ranging market such case scenario could happen, but we will see later that the length of a losing streak is not the only problem.
Other Problems
Having a capital large enough to tank 9any number of consecutive losses is not the only thing one should focus on, as we have to take into account market prices and trading dynamics, that's where the ugly part start.
Our first problem is frictional costs, one example being the spread, but this is a common problem for any strategy, however here a martingale is extra sensitive to it, if the strategy does not account for it then we will still double our positions costs but we might not recover all the losses of a losing streak, instead we would be recovering only a proportion of it, under such scenario you would be certain to lose over time.
Another problem are gaps, market price might open under a stop-loss without triggering it, and this is a big no-no.
Equity of the strategy on AMD, in a desired scenario the equity at the second arrow should have been at a higher position than the equity at the first arrow.
In order for the strategy to be more effective, we would need to trade a market that does not close, such as the cryptocurrency market. Finally, we might be affected by slippage, altho only extreme values might drastically affect our balance.
The Anti Martingale
The strategy lets you use an anti-martingale, which double the position size after a win instead of a loss, the goal here is not to recover from a losing strike but instead to profit from a potential winning streak.
Here we are exposing your balance to exponential gross but you might also lose a trade at the end a winning streak, you will generally want to reinitialize your position size after a few wins instead of waiting for the end of a streak.
Alternative
You can use other-kind of progressions for position sizing, such as a linear one, increasing your position size by a constant number each time you lose. More gentle progressions will recover a proportion of your losses in a losing streak.
You can also simulate the effect of a martingale without doubling your position size by doubling your target profit, if for example you have a 10$ profit-target/stop-loss and lose a trade, you can use a 20$ profit target to recover from the lost trade + gain a profit of 10$. While this approach does not introduce exponential decay in your balance, you are betting on the market reaching your take profits, considering the fact that you are doubling their size you are expecting market volatility to increase drastically over time, as such this approach would not be extremely effective for high losing streak.
Conclusion
You will see a lot of auto-trading strategies that are based on a grid approach, they might even use a martingale. While the backtests will look appealing, you should think twice before using such kind of strategy, remember that frictional costs will be a huge challenge for the strategy, and that it assumes that the trader has an important initial capital. We have also seen that the risk/reward ratio is theoretically the worst you can have on a strategy, having a low reward and a high risk. This does not mean that progressive position sizing is bad, but it should not be pushed to the extreme.
It is nice to note that the martingale is originally a betting system designed for casino games, which unlike trading are not subject to frictional costs, but even casino players don't use it, so why would you?
Thx for reading
Quadratic RegressionFit a quadratic polynomial (parabola) to the last length data points by minimizing the sum of squares between the data and the fitted results. The script can extrapolate the results in the future and can also display the R-squared of the model. Note that this script is subject to some limitations (more in the "Notes" section).
Settings
Length : Number of data points to use as input.
Offset : Determine the number of past fitted values to be displayed, if 0 only the extrapolated values are displayed, if 55 only the past fitted values are displayed.
Src : Input data of the indicator
Show R2 : Determine if the value of the R-squared must be displayed, by default true.
Usage
When the underlying trend in the price is not linear, we might use more advanced models to estimate it, this is where using a higher-degree regression model might be required, as such a quadratic model (second-degree) is appropriate when the underlying trend is parabolic.
Here we can see that the quadratic regression (in blue) offer a better fit than a linear one.
Another advantage of the quadratic regression is that a linear one will always have the same direction, that's not the case with the quadratic regression and as such, it is possible to forecast reversals.
Above a linear regression (in red) and two quadratic regression (in blue) with both length = 54. Note that for the sake of clarity, the above image uses a quadratic regression to show all the past fitted values and another one to show all the forecasted values.
The R-Squared is also extremely useful when it comes to measuring the accuracy of the model, with values closer to 1 indicating that the model is appropriate, and thus suggesting that the underlying trend in the price is parabolic. The R-squared can also measure the strength of the trend.
Notes
The script uses the function line.new , as such only a maximum of 54 observations are displayed, getting more observations can be done by using an additional quadratic regression like we did in the previous section. Another thing is that line.new use xloc.bar_time , as such it is possible to observe some errors with the displayed results of the indicator, such as:
This will happen when applying the indicator to symbols with session breaks, I apologize for this inconvenience and I'll try to find solutions. Note however that the indicator will work perfectly on cryptos.
Summary
That's an indicator I really wanted to make, even if it is important to note that such models are rarely useful in stock markets, however it is more than possible to create a quadratic regression (with severe limitations) with pinescript.
Today I turn 21, while I should be celebrating I still wanted to share something with the community, it's also some kind of present to myself that tells me that I am a bit better at using pinescript than last year, and I am glad I could progress (instead of regress, regression , got it?). Thx a lot for reading!
Spread by//Every spread & central tendency measure in 1 script with comfortable visualization, including scrips's status line.
Spread measures:
- Standard deviation (for most cases);
- Average deviation (if there are extreme values);
- GstDev - Geometric Standard Deviation (exclusively for Geometric Mean);
- HstDev - Harmonic Deviation (exclusively for Harmonic Mean).
These modified functions will calculate everything right, they will take source, length, AND basis of your choice, unlike the ones from TW.
Central tendency measures:
- Mean (if everything's cool & equal);
- Median (values clustering towards low/high part of the rolling window);
- Trimean (3/more distinguishable clusters of data);
- Midhinhe (2 distinguishable clusters of data);
- Geometric Mean ( |low.. ... ... .. .... ... . . . . . . . . . . . .high| this kinda data); <- Exp law
- Harmonic Mean { |low. . . . . . . . . . . . . . .. . . .high| kinda data). <- Reciprocal law
Listen:
1) Don't hesitate using Standard Deviation with non-mean, like "Midhinge Standard Devition", despite what ol' stats gurus gonna say, it works when it's appropriate;
2) Don't check log space while using Geometric Mean & Geometric Standard Deviation, these 2 implement log stuff by design, I mean unless u wanna make it double xd
3) You can use this script, modify it how you want, ask me questions whatever, just make money using it;
4) Use Midrange & Midpoints in tandem when data follows ~addition law (like this . . . . . . . . . . . . . . . . . . . . .). <- just addition law
Look at the data, choose spread measure first, then choose central tendency measure, not vice versa.
!!!
Ain't gonna place ® sign on standard deviations like one B guy did in 1980s lmao, but if your wanna use Harmonic Deviations in science/write about/cite it/whatever, pls give me a lil credit at least, I've never seen it anywhere and unfortunately had to develop it by myself. it's useful when your data develops by reciprocals law (opposite to exponential).
Peace TW
Pre-Market Volume ProfileThis indicator displays the pre-market volume (note: without the post-market of the previous day).
Unusual pre-market volume often indicates that institutional market makers are moving the market, which is a good sign for unusual high price movement.
The indicator helps me to spot stocks, if a pre-market gap is confirmed with enough (unusual) volume.
You can define, what "unusual" means by you, by adjusting the SMA length and the SMA multiplier.
The default is a length of 21 bars and a 2.5 multiplier, meaning I'm interested in a stock, if the pre-market volume exceeds the average pre-market volume by 2.5 times.
Linear Regression (All Data)The tool plots a linear regression line using the entire history of an instrument on chart. There are may be issues on intraday timeframes less then 1h. On daily, weekly and monthly charts it works without problem.
If an instrument has a lot of data points, you may not see the line (this is TV feature):
To fix that you need to scroll your chart to the left and find the starting point of the line:
And then do an auto-scroll to the last bar:
[PX] External LevelHello everyone,
today I'd like to share a script, which enables you to use external logic to plot levels on your chart.
How does it work?
The concept is based on two scripts. One script, which uses an external input as a trigger to print a new level and one script that calculates an output, which will be fetched.
Sounds complicated? It really is not! Let's take a closer look.
// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © paaax
//@version=4
study("RSI OS/OB")
l = input(14, "RSI Length")
ob = input(70, "Overbought")
os = input(30, "Oversold")
r = rsi(close, l)
hline(ob)
hline(os)
plot(r, "RSI", color=color.orange)
// The following plot produces an output, which will be fetched the "External Level"-script.
// It evaluates to one of the following three values: 1.0, -1.0 or 0.0
plot(crossover(r, ob) ? 1.0 : crossunder(r, os) ? -1.0 : 0.0, "Output", transp=100)
The example script above uses an RSI and two threshold levels (70 and 30). The logic here is, that whenever the RSI is crossing down the lower threshold or crossing up the upper threshold we'd consider the current movement to be either oversold or overbought. Therefore, it's a point of interest, which we could visualize with a level.
The script creates an output when the crossover or crossunder of a threshold happens. A crossover would result in a value of 1.0, a crossunder in a value of -1.0. In all other cases the value would be 0.0.
The output of the RSI script would then be used as an input of the External Level script, which has a "Source"-parameter in its input-section. If the fetched input shows 1.0, then the script prints a resistance level. If it shows -1.0 a support level will be printed. And that's basically it. A very simple approach to print levels on your chart with an infinite number of use cases.
For example, you could use fetch outputs from a MACD script, MA script, outputs based on volume or price movement. Just remember the output has to evaluate to either 1.0 or -1.0 and has to be selected in the input-section.
Hope that might be useful to some of you :)
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If you are looking for help with your custom PineScript development, don't hesitate to contact me directly here on Tradingview or through the link in my signature :)
Bernoulli Process - Binary Entropy FunctionThis indicator is the Bernoulli Process or Wikipedia - Binary Entropy Function . Within Information Theory, Entropy is the measure of available information, here we use a binary variable 0 or 1 (P) and (1-P) (Bernoulli Function/Distribution), and combined with the Shannon Entropy measurement. As you can see below, it produces some wonderful charts and signals, using price, volume, or both summed together. The chart below shows you a couple of options and some critical details on the indicator. The best part about this is the simplicity, all of this information in a couple of lines of code.
Using the indicator:
The longer the Entropy measurement the more information you are capturing, so the analogy is, the shorter the signal, the less information you have available to utilize. You'll run into your Nyquist frequencies below a length of 5. I've found values between 9 and 22 work well to gather enough measurements. You also have an averaging summation that measures the weight or importance of the information over the summation period. This is also used for highlighting when you have an information signal above the 5% level (2 sigma) and then can be adjusted using the Percent Rank Variable. Finally, you can plot the individual signals (Price or Volume) to get another set of measurements to utilize. As can be seen in the chart below, the volume moves before price (but hopefully you already knew that)
At its core, this is taking the Binary Entropy measurement (using a Bernoulli distribution) for price and volume. I've subtracted the volume from the price so that you can use it like a MACD, also for shorter time frames (7, 9, 11) you can get divergences on the histogram. These divergences are primarily due to the weekly nature of the markets (5 days, 10 days is two weeks,...so 9 is measuring the last day of the past two weeks...so 11 is measuring the current day and the past two weeks).
Here are a couple of other examples, assuming you just love BTC, Stocks, or FOREX. I fashioned up a strategy to show the potential of the indicator.
BTC-Strategy
Stock-Strategy (#loveyouNFLX)
FOREX - (for everyone hopped up on 40X leverage)
