CFB Adaptive, Jurik-Filtered Gann HiLo Activator [Loxx]CFB Adaptive, Jurik-Filtered Gann HiLo Activator is a Composite-Fractal-Behavior-adaptive Gann HiLo activator that has been smoothed using Jurik Filtering to reduce noise and better identify trending markets. This indicator is the CFB adaptive version of Jurik-Filtered, Gann HiLo Activator .
What is Gann HiLo
The HiLo Activator study is a trend-following indicator introduced by Robert Krausz as part of the Gann Swing trading strategy. In addition to indicating the current trend direction, this can be used as both entry signal and trailing stop.
Here is how the HiLo Activator is calculated:
1. The system calculates the moving averages of the high and low prices over the last several candles. By default, the average is calculated using the last three candles.
2. If the close price falls below the average low or rises above the average high, the system plots the opposite moving average. For example, if the price crosses above the average high, the system will plot the average low. If the price crosses below the average low afterward, the system will stop plotting the average low and will start plotting the average high, and so forth .
The plot of the HiLo Activator thus consists of sections on the top and bottom of the price plot. The sections on the bottom signify bullish trending conditions. Vice versa, those on the top signify the bearish conditions.
What is Composite Fractal Behavior (CFB)?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Included
-Toggle bar color on/off
Cerca negli script per "algo"
Composite Fractal Behavior (CFB) [Loxx]Composite Fractal Behavior (CFB) is a supplementary indicator used to provide inputs into other indicators in your toolkit. The output of the CFB is price trend duration inputs. This output can be injected into standard indicators for the length inputs in order to make your indicators price trend adaptive. The raw calculation of CFB is doubly smoothed using a Jurik-Filter and then standardized to be greater than or equal to 1.
What is Composite Fractal Behavior ( CFB )?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Jurik CFB Adaptive QQE [Loxx]Jurik CFB Adaptive QQE is a Double Jurik-Filtered, Composite Fractal Behavior (CFB) adaptive, Qualitative Quantitative Estimation indicator. This indicator includes both fixed and the CFB adaptive calculations as well as three different types of RSI calculations including Jurik's RSX.
What is Qualitative Quantitative Estimation (QQE)?
The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index ( RSI ) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR).
There are many indicators for many purposes. Some of them are complex and some are comparatively easy to handle. The QQE indicator is a really useful analytical tool and one of the most accurate indicators. It offers numerous strategies for using the buy and sell signals. Essentially, it can help detect trend reversal and enter the trade at the most optimal positions.
What is Wilders' RSI?
The Relative Strength Index ( RSI ) is a well versed momentum based oscillator which is used to measure the speed (velocity) as well as the change (magnitude) of directional price movements. Essentially RSI , when graphed, provides a visual mean to monitor both the current, as well as historical, strength and weakness of a particular market. The strength or weakness is based on closing prices over the duration of a specified trading period creating a reliable metric of price and momentum changes. Given the popularity of cash settled instruments (stock indexes) and leveraged financial products (the entire field of derivatives); RSI has proven to be a viable indicator of price movements.
What is RSX RSI?
RSI is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurk RSX retains all the useful features of RSI , but with one important exception: the noise is gone with no added lag.
What is Rapid RSI?
Rapid RSI Indicator, from Ian Copsey's article in the October 2006 issue of Stocks & Commodities magazine.
RapidRSI resembles Wilder's RSI , but uses a SMA instead of a WilderMA for internal smoothing of price change accumulators.
What is Composite Fractal Behavior (CFB)?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Included
-Toggle bar color on/off
EMCHO Stochastic RangeCustom Stochastic Oscillator with range plot. Can be used to better identify overbought/oversold conditions within a single bar. In addition to the default Stochastic:
%K line smoothing algorithm selection;
%D line smoothing algorithm selection;
%K line high/low plotting;
%K line high/low calculation factor (in bars, default 1).
AlphaTrend For ProfitViewThis strategy is based on the AlphaTrend indicator by KivancOzbilgic A full description of this algorithm functionality may be found by clicking the linked image above.
Changes and/or additions:
It is now a backtestable strategy
Updated alert trigger logic
Easy integration with ProfitView to use this algorithm for automated trading
When you create an alert, and you are using ProfitView, select " alert() function calls only " as the condition option. If you would rather set your own custom alert message, select " Order fills only " instead.
There is a selectable setting in the options to trigger alert() function calls immediately, that you may use to see what text it will send.
least_squares_regressionLibrary "least_squares_regression"
least_squares_regression: Least squares regression algorithm to find the optimal price interval for a given time period
basic_lsr(series, series, series) basic_lsr: Basic least squares regression algorithm
Parameters:
series : int t: time scale value array corresponding to price
series : float p: price scale value array corresponding to time
series : int array_size: the length of regression array
Returns: reg_slop, reg_intercept, reg_level, reg_stdev
trend_line_lsr(series, series, series, string, series, series) top_trend_line_lsr: Trend line fitting based on least square algorithm
Parameters:
series : int t: time scale value array corresponding to price
series : float p: price scale value array corresponding to time
series : int array_size: the length of regression array
string : reg_type: regression type in 'top' and 'bottom'
series : int max_iter: maximum fitting iterations
series : int min_points: the threshold of regression point numbers
Returns: reg_slop, reg_intercept, reg_level, reg_stdev, reg_point_num
simple_squares_regressionLibrary "simple_squares_regression"
simple_squares_regression: simple squares regression algorithm to find the optimal price interval for a given time period
basic_ssr(series, series, series) basic_ssr: Basic simple squares regression algorithm
Parameters:
series : float src: the regression source such as close
series : int region_forward: number of candle lines at the right end of the regression region from the current candle line
series : int region_len: the length of regression region
Returns: left_loc, right_loc, reg_val, reg_std, reg_max_offset
search_ssr(series, series, series, series) search_ssr: simple squares regression region search algorithm
Parameters:
series : float src: the regression source such as close
series : int max_forward: max number of candle lines at the right end of the regression region from the current candle line
series : int region_lower: the lower length of regression region
series : int region_upper: the upper length of regression region
Returns: left_loc, right_loc, reg_val, reg_level, reg_std_err, reg_max_offset
Too Many Cooks trend indicatorToo many Cooks in The Kitchen
You have probably heard the adage "Too many cooks spoils the broth" before. The meaning behind it is obviously that when to many people are trying to work on the same task at once it simply devolves into a fight for control and creates a mess of the situation. But is this true for indicators is the question I had and thus I made this indicator, a simple combination of 8 random trend finding indicators I assembled (A list of these indicators and their authors will be available at the bottom of this page) . Is it any good though ? In short yes, it is a decent trend finding indicator and could likely be used in your strategy in the place of your current trend finding indicator if you so wish. However much of the versatility of the individual indicators IS lost and would not be possible to get back in this big mess of a broth, so this indicator will not be the be all end all of trend indicators nor will it be a free money machine like you may be expecting looking at the list of included indicators so the adage was correct to a degree.
List of Authors and their included indicators
Trading View defaults:
MACD (Modified by me)
Stochastic RSI (Modified by me)
Lazy Bear:
Wavetrend Oscilator (Modified by me)
Traders Dynamic Index (Modified by me)
HACOLT (Modified by me)
Algokid
AK Trend
Racer8
Average Force
KivancOzbilgic
Average Sentiment Osclilator
Critical Levels Mixing Price Action, Volatility and VolumeIntroduction
This indicator has the purpose of setting levels, automatically, basing its creation on three aspects of the market:
- price action
- volume
- volatility
Price Action Algorithm
I divided the candle into 3 parts:
- body => abs (close-open)
- lower tail => red candle (close-low) green candle (open-low)
- upper tail => red candle (high-open) green candle (high-close)
- total => high-low
to give the signal the following conditions must be respected
- the body must be smaller than a certain percentage ("MAX CORE SIZE%) and larger than a certain percentage (" MIN CORE SIZE%);
- furthermore, the shorter tail cannot be higher than a certain percentage ("MAXIMUM LENGTH FOR SHORTE TAIL%");
Volume Algorithm
The volume value must be greater than the volume EMA multiplied by a certain value ("Multiplier")
Volatility Algorithm
the True Range of the candle must be greater than the "ATR percentage" of the ATR
Trigger
If all these three conditions are met then and only then will the level be drawn that will include the prices of the longest tail of the candle (high/open or open/low or high/close or close/low).
How to use
Like any level, the situation in which the price is reached does not imply a market reaction, for this reason, the use together with moving averages or oscillators from which to extrapolate the divergences can be a valid tool.
Using this indicator alone you can enter the market by placing a pending order above the high or low of the candle touching the level.
Example:
a bearish candle touches a low level, we place a pending buy order above the high of the candle
a bullish candle touches a level located high, we place a pending sell order below the low of the candle
Gann FanHello All,
For long time I have been getting many requests about Gann Fan indicator. now we have linefill() function in Pine Language and I think it's right time to make Gann Fan Indicator. Many Thanks to Pine Team for adding many new features to the Pine Language!
How this indicator works:
- It calculates midline (1/1)
- By using midline it calculates other lines (1/2, 1/3, 1/4...etc)
- It calculates highest/lowest Pivot Points in last 280 bars.( by default it's 280 bars, you can change it and pivot period )
- It checks the location of highest/lowest Pivot Points
- After the calculation of the Gann Fan lines, it draws lines, puts Labels and paints the zones between the lines according to the colors set by the user
Long time ago I created a special algorithm for calculating the line with 45 degree and I used it for "1/1" line. Anybody who needs it can use this algorithm freely ;)
Options:
You can change following items;
- The colors
- Transparency. Possible values for transparency are from 0 (not transparent) to 100 (invisible)
- Line styles
- Loopback Period (by default it's 280)
- Pivot Period (by default it's 5)
- Enable/disable Labels
- Label location (by default it's 50
Tradingview Gann Fan page : The Gann Fan is a technical analysis tool created by WD Gann. The tool is comprised of 9 diagonal lines (extending indefinitely) designed to show different support and resistance levels on a chart. These angles -drawn from main tops and bottoms- divide time and price into proportionate parts and are often used to predict areas of support and resistance, key tops and bottoms and future price moves. Please note that the chart needs to be scaled properly to ensure the market has a square relationship....
Enjoy!
Kirill ChannelThis indicator shows overbought and oversold zones. Can be used on all time frames. I personally use 15m - 30m.
How to apply ?:
- There can be many strategies for use! I use this indicator to buy an asset in the green zone and then sell it in the middle of the channel or in the red zone.
- I strongly advise against entering counter-trend positions in a growing market if you have little trading experience and understanding of price action.
How do I place orders ?:
- I place orders in a grid.
- If the price is very close to the edge, but it is difficult to reach it, then it is better to open a position on the market and place orders deep into the grid.
- If the price is at the edge of the channel for a very long time, then you need to look at a higher timeframe.
Algorithm composition:
- ALMA
- Keltner Channel
- Fibonacci Retracement
- Custom price percent offset calculations and manipulations.
Settings:
- I strongly do not recommend changing ALMA. These numbers have been specially calculated.
- It's better not to change Borders either. The current algorithm dynamically changes the width of the extreme channels depending on the price movement.
- The Keltner Channel was specially selected.
- Fibonacci Retracement can be changed. This part of the algorithm can be modified to suit your needs. At the moment, there are settings for aggressive trading.
Channel type:
- Conservative: Fibonacci Retracement settings (100 ma, 100 atr, 8 mult, 100 smooth)
- Aggressive: Fibonacci Retracement settings (25 ma, 25 atr, 3.5 mult, 100 smooth)
Сonservative channel does not allow a large number of points to enter positions, however, it is more straightforward and safer for very large movements.
I prefer aggressive settings because they allow me to make more profit on the number of trades.
Try to use both modes and choose what is preferable for you.
TrendCalculusThis indicator makes visualising some of the core TrendCalculus algorithm's key information and features both fast and easy for casual analysis.
Interpretation:
a) The light blue channel is the lagged price channel calculated over the timeframe of your choosing for a period of N values. When the current price breaks out of this channel the previous price major high/low can be identified as a trend reversal. This helps in counting trend "waves" and is a rolling visual version of ideas I developed for counting Elliot Waves. For EW analysis, your mileage may vary depending on the asset inspected, but the chart allows you to clearly count waves on a particular scale of time (period) ignoring noise on other time scales.
b) The green/red channel is a support/resistance indicator region that shows the relationship of the current price to the key pivot points on this time scale (period) and these make for good visual indication that the current trend is up (green), or down (red). You may find them helpful for identifying breakouts and placing stops - but this was not their original intention. The pink line is the mid point of closing values in the lagged price channel, and the orange line the mid point of closing values in the current price channel.
About TrendCalculus (TC):
TC is implemented in several languages including Lua, Scala and Python. The Lua implementation is the reference and has the most advanced functionality and delivers a powerful data processing tool for both multi-scale trend reversal detection, reversal labelling, as well as trend feature production - all useful things helping it to produce training data for machine learning models that detect trend changes in real time.
This charting tool includes: (1) two consecutive lagged Donchian channels configured to a common period N, (2) the current price, and (3) the mid price of both Donchian channels. These calculations are all part of the TC codebase, and are brought to life in this charting tool.
Motivation:
By creating a TC charting tool - the machine learning model is swapped for *your eyes* and *your brain*. Using the same inputs as the machine, you can use this chart to learn to detect trend changes, and understand how time frame (long periods, short periods) affect your view of trend change. If you choose to use it to trade, or make investment decisions, do so at your own risk. This indicator does not deliver financial advice.
TrendCalculus is the invention of Andrew Morgan, author of Mastering Spark for Data Science (2017).
The original core TrendCalculus (TC) algorithm itself is published as open-source code on github under a GPL licence, and free to use and develop.
Jurik Moving Average//Sup TV. This script is inspired by (and dedicated to) closure of sales (today, Oct 20 '21) of the famous Jurik Research.
...
Jurik Research, the real people who been doing real things by using the real instruments, while many others been reading books "How to become a billionaire in 2 days", watching 5687 hours videos of how to use RSI, and studying+applying machine learning to everything cuz suddenly it became trendy xD
...
This is my remake of the original Jurik Moving Average (JMA) based on all the info I managed to get my hands on, some stuff is dated back to 2008 or smth.
The whole point of this filter, the point missed by other attempts of its remakes even posted there on TV, is that it takes into account volatility and adjusts its speed based on it.
Think about it as an EMA, where the alpha parameter is dynamic.
Now, by all means I'm not claiming that's this is the perfect replica of the original algo. I've tested it a lot, looks like it's working legit...
But we all can see together whether it's legit or nah, besides, the official sales are closed since today, you feel me?
...
@everget, does it differs from yours closed one?
...
Live Long And Prosper
FunctionSMCMCLibrary "FunctionSMCMC"
Methods to implement Markov Chain Monte Carlo Simulation (MCMC)
markov_chain(weights, actions, target_path, position, last_value) a basic implementation of the markov chain algorithm
Parameters:
weights : float array, weights of the Markov Chain.
actions : float array, actions of the Markov Chain.
target_path : float array, target path array.
position : int, index of the path.
last_value : float, base value to increment.
Returns: void, updates target array
mcmc(weights, actions, start_value, n_iterations) uses a monte carlo algorithm to simulate a markov chain at each step.
Parameters:
weights : float array, weights of the Markov Chain.
actions : float array, actions of the Markov Chain.
start_value : float, base value to start simulation.
n_iterations : integer, number of iterations to run.
Returns: float array with path.
Repeated Median Regression with Interactive Range SelectionGreetings to all!
As you probably know, TradingView now supports interactive inputs that can be directly set on a chart. I decided to build a tool that takes advantage of this incredible feature. This tool applies robust linear regression within a time interval on the chart that you can select interactively.
Method
The script uses an algorithm known as Repeated Median Regression . It belongs to the class of so-called robust regression methods. The reason they are called “robust” is that these methods are much less sensitive to outliers in the data than the ordinary least squares.
The calculation procedure is as follows: For each data point, this algorithm collects the slopes of the lines connecting that point to all other points in the sample, calculates the median slope, and then obtains the median value of these median slopes. Subsequently, it calculates the intercepts of the regression line and the mean absolute error (MAE) of the model.
Based on these results, a linear channel is plotted. The upper and lower channel boundaries are set by the MAE value multiplied by a user-defined coefficient.
Further reading
You can read more about robust linear regression on Wikipedia .
For more information on interactive inputs, see the User Manual's page .
Previous publication
I have already posted a script using the repeated median regression method. Although the core algorithm is essentially the same, interactive input provides fundamentally different functionality to the current script.
A word of caution
Currently, the interactive interval selection mode can be triggered only when the script is loaded to the chart. Thus, you might have to reload it when switching between different timeframes.
MathGeometryCurvesChaikinLibrary "MathGeometryCurvesChaikin"
Implements the chaikin algorithm to create a curved path, from assigned points.
chaikin(points_x, points_y, closed) Chaikin algorithm method, uses provided points to generate a smoothed path.
Parameters:
points_x : float array, the x value of points.
points_y : float array, the y value of points.
closed : bool, default=false, is the path closed or not.
Returns: tuple with 2 float arrays.
smooth(points_x, points_y, iterations, closed) Iterate the chaikin algorithm, to smooth a sample of points into a curve path.
Parameters:
points_x : float array, the x value of points.
points_y : float array, the y value of points.
iterations : int, number of iterations to apply the smoothing.
closed : bool, default=false, is the path closed or not.
Returns: array of lines.
draw(path_x, path_y, closed) Draw the path.
Parameters:
path_x : float array, the x value of the path.
path_y : float array, the y value of the path.
closed : bool, default=false, is the path closed or not.
Returns: array of lines.
Hx MTF Sorted MAs Panel with Freeze WarningThis script displays the close price and 4 sorted moving averages of your choice in a small repositionable panel and, when used on a higher timeframe, warns you when values may be different from actual values in the higher timeframe, inciting you to double check the actual values of the moving averages in the higher timeframe the panel is supposed to reflect.
The 4 moving averages and close are sorted together, providing you with a bird’s-eye view of their relative positions, the same way moving averages and last price values are displayed on the right scale.
The black header reminds of:
(1) the timeframe (resolution) used in the panel
(2) the remaining time before a new bar is created in the panel timeframe. Note that this remaining time is different from the one on the right scale, since it is only updated when a new transaction occurs.
Below, price and moving averages are sorted, color coded and followed by:
(1) a trend indicator ↗ or ↘ meaning that last change is up or down
(2) the number of bars since the moving average is above or below close (0 means current bar). This is obviously not displayed after the close price line (white background color).
Use
This panel was basically developed to display higher timeframe data but it can also be used with the same timeframe as chart for example if you do not want to plot moving averages on your chart but are still interested in their trends and relative positions vs price.
If you see something strange (like header is not black and displays NaN), it just means you requested moving averages that are not available in the panel timeframe. This may happen with newly introduced cryptos and “long” MA timeframes.
Different Timeframe
If you choose to use the panel on a different timeframe than the current one, be aware that you should only use timeframes higher than the current one, as per Tradingview recommendations.
If you select a lower timeframe than the current one, the panel timeframe header cell will turn to the alert color you set (fuchsia by default).
After tinkering for a while with the security function, I noticed that sometimes indicator values “freeze” (i.e. stop udating) and I have found no workaround.
What I mean is that when you look at a sma on a 5 minutes timeframe (the reference) and look at this same sma on a 5 minutes timeframe but from a lower timeframe through the security function set with a timeframe of 5 minutes, values returned by the security function are not always up to date and “freeze”. That’s the bad news.
Freeze warning
The better news is that this unexpected behaviour seems to be predictable, at least on minutes timeframes and I implemented an indicator that endeavors to detecting such situations. When the panel believes data may be frozen, the ‘Remaining Time’ header cell will turn to the alert color.
This feature is only implemented on minutes timeframes and can be switched on or off.
Other points of interest in this script
If you code, this function may also interest you:
sortWithIndexes (arrayToSort) returns a tuple (sortedArray, sortedIndexes) and therefore allows multi-dimensional arrays sorting without actually implementing a sorting algorithm 😉.
Default Settings
The default settings provide an example of commonly used moving averages with associated colors ranked from Hot (more nervous) to Cold (less nervous).
These settings are just an example and are NOT meant to be used as a trading system! DYOR!
Hope it will be useful.
Does the Freeze warning work for you? What do you think of my pseudo sorting algorithm?
Enjoy and please let me know what you think in the comments.
Six Moving Averages Study (use as a manual strategy indicator)I made this based on a really interesting conversation I had with a good friend of mine who ran a long/short hedge fund for seven years and worked at a major hedge fund as a manager for 20 years before that. This is an unconventional approach and I would not recommend it for bots, but it has worked unbelievably well for me over the last few weeks in a mixed market.
The first thing to know is that this indicator is supposed to work on a one minute chart and not a one hour, but TradingView will not allow 1m indicators to be published so we have to work around that a little bit. This is an ultra fast day trading strategy so be prepared for a wild ride if you use it on crypto like I do! Make sure you use it on a one minute chart.
The idea here is that you get six SMA curves which are:
1m 50 period
1m 100 period
1m 200 period
5m 50 period
5m 100 period
5m 200 period
The 1m 50 period is a little thicker because it's the most important MA in this algo. As price golden crosses each line it becomes a stronger buy signal, with added weight on the 1m 50 period MA. If price crosses all six I consider it a strong buy signal though your mileage may vary.
*** NOTE *** The screenshot is from a 1h chart which again, is not the correct way to use this. PLEASE don't use it on a one hour chart.
Easy Entry/Exit Trend Colors (With Alerts)This is an updated version of user Algokid's script called 'AK MACD BB INDICATOR V 1.00'. You can find that original script here:
I added many alerts along with the Bullish and Bearish alerts when the MACD crosses over the Upperband or crosses down on the Lowerband.
I personally use this indicator with Crypto charts (Bitcoin on a 15min, 1hour, and 4 hour timeframe) as one of many confirmations that it's a good time to enter a trade. This script was made to be easy to follow with the colors of GREEN triangles being a good uptrend or entry confirmation, and RED being a confirmation to sell/short or exit your trade.
It's important to use this indicator in combination with other indicators that can give you more confirmations to enter or exit a trade, and make sure you are on normal candles and not HA or any other candles as you can get wildly inaccurate results.
This script also has the Death & Golden crosses, which is the slow and fast moving averages crossing over each other. I don't use this as an additional confirmation, it's just nice to know where the cross happens.
Particle Physics Moving AverageThis indicator simulates the physics of a particle attracted by a distance-dependent force towards the evolving value of the series it's applied to.
Its parameters include:
The mass of the particle
The exponent of the force function f=d^x
A "medium damping factor" (viscosity of the universe)
Compression/extension damping factors (for simulating spring-damping functions)
This implementation also adds a second set of all of these parameters, and tracks 16 particles evenly interpolated between the two sets.
It's a kind of Swiss Army Knife of Moving Average-type functions; For instance, because the position and velocity of the particle include a "historical knowlege" of the series, it turns out that the Exponential Moving Average function simply "falls out" of the algorithm in certain configurations; instead of being configured by defining a period of samples over which to calculate an Exponential Moving Average, in this derivation, it is tuned by changing the mass and/or medium damping parameters.
But the algorithm can do much more than simply replicate an EMA... A particle acted on by a force that is a linear function of distance (force exponent=1) simulates the physics of a sprung-mass system, with a mass-dependent resonant frequency. By altering the particle mass and damping parameters, you can simulate something like an automobile suspension, letting your particle track a stock's price like a Cadillac or a Corvette (or both, including intermediates) depending on your setup. Particles will have a natural resonance with a frequency that depends on its mass... A higher mass particle (i.e. higher inertia) will resonate at a lower frequency than one with a lower mass (and of course, in this indicator, you can display particles that interpolate through a range of masses.)
The real beauty of this general-purpose algorithm is that the force function can be extended with other components, affecting the trajectory of the particle; For instance "volume" could be factored into the current distance-based force function, strengthening or weakening the impulse accordingly. (I'll probably provide updates to the script that incoroprate different ideas I come up with.)
As currently pictured above, the indicator is interpolating between a medium-damped EMA-like configuration (red) and a more extension-damped suspension-like configuration (blue).
This indicator is merely a tool that provides a space to explore such a simulation, to let you see how tweaking parameters affects the simulations. It doesn't provide buy or sell signals, although you might find that it could be adapted into an MACD-like signal generator... But you're on your own for that.
[blackcat] L2 Zero-lag EMA Swing TradeLevel: 2
Background
This script is a comprehensive work of mine, incorporating Ehlers zero-lag EMA and my first script published: MA fingerprint for long entries.
Function
Ehlers zero-lag EMA algorithm in this scripts is mainly used for short signal production, while my MA fingerprint algorithm is used for long entries.
Key Signal
a ---> Ehlers Zero-lag EMA fast line for subjective long jugement
b ---> Ehlers Zero-lag EMA slow line for subjective short jugement
long --> Swing long entry with partial postion
short --> Swing short entry with partial postion
Remarks
Feedbacks are appreciated. This script is optimized for 1D time frame.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Polynomial Regression Bands + Channel [DW]This is an experimental study designed to calculate polynomial regression for any order polynomial that TV is able to support.
This study aims to educate users on polynomial curve fitting, and the derivation process of Least Squares Moving Averages (LSMAs).
I also designed this study with the intent of showcasing some of the capabilities and potential applications of TV's fantastic new array functions.
Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as a polynomial of nth degree (order).
For clarification, linear regression can also be described as a first order polynomial regression. The process of deriving linear, quadratic, cubic, and higher order polynomial relationships is all the same.
In addition, although deriving a polynomial regression equation results in a nonlinear output, the process of solving for polynomials by least squares is actually a special case of multiple linear regression.
So, just like in multiple linear regression, polynomial regression can be solved in essentially the same way through a system of linear equations.
In this study, you are first given the option to smooth the input data using the 2 pole Super Smoother Filter from John Ehlers.
I chose this specific filter because I find it provides superior smoothing with low lag and fairly clean cutoff. You can, of course, implement your own filter functions to see how they compare if you feel like experimenting.
Filtering noise prior to regression calculation can be useful for providing a more stable estimation since least squares regression can be rather sensitive to noise.
This is especially true on lower sampling lengths and higher degree polynomials since the regression output becomes more "overfit" to the sample data.
Next, data arrays are populated for the x-axis and y-axis values. These are the main datasets utilized in the rest of the calculations.
To keep the calculations more numerically stable for higher periods and orders, the x array is filled with integers 1 through the sampling period rather than using current bar numbers.
This process can be thought of as shifting the origin of the x-axis as new data emerges.
This keeps the axis values significantly lower than the 10k+ bar values, thus maintaining more numerical stability at higher orders and sample lengths.
The data arrays are then used to create a pseudo 2D matrix of x power sums, and a vector of x power*y sums.
These matrices are a representation the system of equations that need to be solved in order to find the regression coefficients.
Below, you'll see some examples of the pattern of equations used to solve for our coefficients represented in augmented matrix form.
For example, the augmented matrix for the system equations required to solve a second order (quadratic) polynomial regression by least squares is formed like this:
(∑x^0 ∑x^1 ∑x^2 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 | ∑(x^2)y)
The augmented matrix for the third order (cubic) system is formed like this:
(∑x^0 ∑x^1 ∑x^2 ∑x^3 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 ∑x^4 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 ∑x^5 | ∑(x^2)y)
(∑x^3 ∑x^4 ∑x^5 ∑x^6 | ∑(x^3)y)
This pattern continues for any n ordered polynomial regression, in which the coefficient matrix is a n + 1 wide square matrix with the last term being ∑x^2n, and the last term of the result vector being ∑(x^n)y.
Thanks to this pattern, it's rather convenient to solve the for our regression coefficients of any nth degree polynomial by a number of different methods.
In this script, I utilize a process known as LU Decomposition to solve for the regression coefficients.
Lower-upper (LU) Decomposition is a neat form of matrix manipulation that expresses a 2D matrix as the product of lower and upper triangular matrices.
This decomposition method is incredibly handy for solving systems of equations, calculating determinants, and inverting matrices.
For a linear system Ax=b, where A is our coefficient matrix, x is our vector of unknowns, and b is our vector of results, LU Decomposition turns our system into LUx=b.
We can then factor this into two separate matrix equations and solve the system using these two simple steps:
1. Solve Ly=b for y, where y is a new vector of unknowns that satisfies the equation, using forward substitution.
2. Solve Ux=y for x using backward substitution. This gives us the values of our original unknowns - in this case, the coefficients for our regression equation.
After solving for the regression coefficients, the values are then plugged into our regression equation:
Y = a0 + a1*x + a1*x^2 + ... + an*x^n, where a() is the ()th coefficient in ascending order and n is the polynomial degree.
From here, an array of curve values for the period based on the current equation is populated, and standard deviation is added to and subtracted from the equation to calculate the channel high and low levels.
The calculated curve values can also be shifted to the left or right using the "Regression Offset" input
Changing the offset parameter will move the curve left for negative values, and right for positive values.
This offset parameter shifts the curve points within our window while using the same equation, allowing you to use offset datapoints on the regression curve to calculate the LSMA and bands.
The curve and channel's appearance is optionally approximated using Pine's v4 line tools to draw segments.
Since there is a limitation on how many lines can be displayed per script, each curve consists of 10 segments with lengths determined by a user defined step size. In total, there are 30 lines displayed at once when active.
By default, the step size is 10, meaning each segment is 10 bars long. This is because the default sampling period is 100, so this step size will show the approximate curve for the entire period.
When adjusting your sampling period, be sure to adjust your step size accordingly when curve drawing is active if you want to see the full approximate curve for the period.
Note that when you have a larger step size, you will see more seemingly "sharp" turning points on the polynomial curve, especially on higher degree polynomials.
The polynomial functions that are calculated are continuous and differentiable across all points. The perceived sharpness is simply due to our limitation on available lines to draw them.
The approximate channel drawings also come equipped with style inputs, so you can control the type, color, and width of the regression, channel high, and channel low curves.
I also included an input to determine if the curves are updated continuously, or only upon the closing of a bar for reduced runtime demands. More about why this is important in the notes below.
For additional reference, I also included the option to display the current regression equation.
This allows you to easily track the polynomial function you're using, and to confirm that the polynomial is properly supported within Pine.
There are some cases that aren't supported properly due to Pine's limitations. More about this in the notes on the bottom.
In addition, I included a line of text beneath the equation to indicate how many bars left or right the calculated curve data is currently shifted.
The display label comes equipped with style editing inputs, so you can control the size, background color, and text color of the equation display.
The Polynomial LSMA, high band, and low band in this script are generated by tracking the current endpoints of the regression, channel high, and channel low curves respectively.
The output of these bands is similar in nature to Bollinger Bands, but with an obviously different derivation process.
By displaying the LSMA and bands in tandem with the polynomial channel, it's easy to visualize how LSMAs are derived, and how the process that goes into them is drastically different from a typical moving average.
The main difference between LSMA and other MAs is that LSMA is showing the value of the regression curve on the current bar, which is the result of a modelled relationship between x and the expected value of y.
With other MA / filter types, they are typically just averaging or frequency filtering the samples. This is an important distinction in interpretation. However, both can be applied similarly when trading.
An important distinction with the LSMA in this script is that since we can model higher degree polynomial relationships, the LSMA here is not limited to only linear as it is in TV's built in LSMA.
Bar colors are also included in this script. The color scheme is based on disparity between source and the LSMA.
This script is a great study for educating yourself on the process that goes into polynomial regression, as well as one of the many processes computers utilize to solve systems of equations.
Also, the Polynomial LSMA and bands are great components to try implementing into your own analysis setup.
I hope you all enjoy it!
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NOTES:
- Even though the algorithm used in this script can be implemented to find any order polynomial relationship, TV has a limit on the significant figures for its floating point outputs.
This means that as you increase your sampling period and / or polynomial order, some higher order coefficients will be output as 0 due to floating point round-off.
There is currently no viable workaround for this issue since there isn't a way to calculate more significant figures than the limit.
However, in my humble opinion, fitting a polynomial higher than cubic to most time series data is "overkill" due to bias-variance tradeoff.
Although, this tradeoff is also dependent on the sampling period. Keep that in mind. A good rule of thumb is to aim for a nice "middle ground" between bias and variance.
If TV ever chooses to expand its significant figure limits, then it will be possible to accurately calculate even higher order polynomials and periods if you feel the desire to do so.
To test if your polynomial is properly supported within Pine's constraints, check the equation label.
If you see a coefficient value of 0 in front of any of the x values, reduce your period and / or polynomial order.
- Although this algorithm has less computational complexity than most other linear system solving methods, this script itself can still be rather demanding on runtime resources - especially when drawing the curves.
In the event you find your current configuration is throwing back an error saying that the calculation takes too long, there are a few things you can try:
-> Refresh your chart or hide and unhide the indicator.
The runtime environment on TV is very dynamic and the allocation of available memory varies with collective server usage.
By refreshing, you can often get it to process since you're basically just waiting for your allotment to increase. This method works well in a lot of cases.
-> Change the curve update frequency to "Close Only".
If you've tried refreshing multiple times and still have the error, your configuration may simply be too demanding of resources.
v4 drawing objects, most notably lines, can be highly taxing on the servers. That's why Pine has a limit on how many can be displayed in the first place.
By limiting the curve updates to only bar closes, this will significantly reduce the runtime needs of the lines since they will only be calculated once per bar.
Note that doing this will only limit the visual output of the curve segments. It has no impact on regression calculation, equation display, or LSMA and band displays.
-> Uncheck the display boxes for the drawing objects.
If you still have troubles after trying the above options, then simply stop displaying the curve - unless it's important to you.
As I mentioned, v4 drawing objects can be rather resource intensive. So a simple fix that often works when other things fail is to just stop them from being displayed.
-> Reduce sampling period, polynomial order, or curve drawing step size.
If you're having runtime errors and don't want to sacrifice the curve drawings, then you'll need to reduce the calculation complexity.
If you're using a large sampling period, or high order polynomial, the operational complexity becomes significantly higher than lower periods and orders.
When you have larger step sizes, more historical referencing is used for x-axis locations, which does have an impact as well.
By reducing these parameters, the runtime issue will often be solved.
Another important detail to note with this is that you may have configurations that work just fine in real time, but struggle to load properly in replay mode.
This is because the replay framework also requires its own allotment of runtime, so that must be taken into consideration as well.
- Please note that the line and label objects are reprinted as new data emerges. That's simply the nature of drawing objects vs standard plots.
I do not recommend or endorse basing your trading decisions based on the drawn curve. That component is merely to serve as a visual reference of the current polynomial relationship.
No repainting occurs with the Polynomial LSMA and bands though. Once the bar is closed, that bar's calculated values are set.
So when using the LSMA and bands for trading purposes, you can rest easy knowing that history won't change on you when you come back to view them.
- For those who intend on utilizing or modifying the functions and calculations in this script for their own scripts, I included debug dialogues in the script for all of the arrays to make the process easier.
To use the debugs, see the "Debugs" section at the bottom. All dialogues are commented out by default.
The debugs are displayed using label objects. By default, I have them all located to the right of current price.
If you wish to display multiple debugs at once, it will be up to you to decide on display locations at your leisure.
When using the debugs, I recommend commenting out the other drawing objects (or even all plots) in the script to prevent runtime issues and overlapping displays.
NNFX Baseline ToolNNFX All-in-One Baseline display / test tool.
This is usefull (hopefully) for the NNFX way of trading only. It's not intended to be used as a standalone tool.
Basically, this script displays and tests many types of Moving Averages as baselines.
It displays baseline signals, based on the NNFX ATR-related rule for baseline entries.
It can be used as a backtest tool, or plugged into the whole nnfx algo.
If signal display option is enabled, signals are displayed on chart : green for long, red for short, orange for crossovers beyond the ATR channel :
Many baselines available : SMA , EMA , WMA , VWMA , ALMA , AMA, SMMA , DEMA , FRAMA , HULL, KAMA , KIJUN, JURIK, LAGUERRE, MCGINLEY , TMA1, TMA2, VIDYA , MODULAR FILTER, VAMA , ZLEMA , T3, LSMA, etc.
Additional options :
- multiplying the ATR channel (and subsequent rule) by a factor (default = 1)
- plot the ATR channel (def = yes)
- fill it (def = yes)
- display signals (def = yes)
- option for add color to the baseline, for long/short territory (2 different options : baseline is colored, background is colored)
- darkmode / lightmode color option. (def = dark)
We also display panels, with general information and some test results. Tests are done within the test period.
I tried to test all the different MAs included in the script but some bugs might still be present, so use it at ur own risk.
If you'd like a new MA option added, please let me know in comments.
I included a "bad" signal detection, it can help for tweaking the settings. Signals are defined as "bad" when they are immediately followed by another signal.
When there is 2 or more bad signals next to another, you spotted a chopiness zone (a chopiness zone is defined as a zone where BL get eaten alive).
Example :
to do :
- plug it with the c1/c2 backtest tool (it's the whole point)
- add alerts,
- add more ma types
- stop to use the operator, it's not convenient at all
- add wr% calculation as a standalone feature (with TP / SL)
- add a way to measure chopiness in the test (dont know how yet)
- detect & display chopiness zones
I asked other users when I used their ideas (for some particular types of MAs). They all agreed.
