[ChasinAlts] Best Volatility Indicator I hope you all enjoy this one as it does a great job at finding runners I did try to search for an example script to reference for quite a while when i first dreamt up this idea bc needed assistance implementing it. This script in particular was one that I began long ago but got put on the back-burner because I couldn't figure out how to implement the flow of logic until I came across a library titled 'Conditional Averages' and published by the “Pinecoders" account. Thus, the logic in this code is partially derived from that () . To understand what the functions/logic do in the beginning of the 'Functions'' section, you must understand how TV presents it's data through the charts.
Wether on the 1sec TF or the 1day (or ANY other), the only time TV prints a bar/candle is when a trade occurs for that asset (i.e. a change in volume). Even if Open=Close on the same candle, the candle will print with the updated price. The % of candles printed out of the TOTAL possible amount that COULD HAVE been printed is the ultimate output that’s calculated in the script. So, if the lookback setting=10min on the 1min TF and only 7 out of the last 10 candles have printed then the value will appear as 70(%). There are MANY benefits to using this method to measure volatility but its vital to recall that the indicator does nothing to provide the direction of future price movement. One thing I’ve noticed is that when a coin is just beginning it’s ascent and its move is considerably larger/longer than all the other coins OR the plots angle is very steep, it is usually the end of a move and the direction is about to abruptly reverse, continuing with it’s volatility. As volatility increases more and more the plot gets brighter and brighter…and also vise versa.
The settings are as follows:
1) which set of Kucoin’s Margin Coins to use (8 possible sets with 32 coins in each set).
2) input how many minutes ago to start counting the total printed candles from (i.e. if setting is input as 1440, count begins from exactly 24hrs(1440min) ago to present candle.
3) there are 3 different lines to choose from to be able to plot:
i. ‘Includes Open==Close’ = adds to count when bar prints but price does NOT change (=t1)
ii. ‘Does NOT include Open==Close’ = count ONLY updates upon price movement (=t2)
iii. ‘Difference’ = (( t1 - t2 ) / t1 ) *100
*** I’ve got some more great ones I will be uploading soon. Just have to create a description for them
Peace out,
- ChasinAlts
Tìm kiếm tập lệnh với "one一季度财报"
CFB-Adaptive, Williams %R w/ Dynamic Zones [Loxx]CFB-Adaptive, Williams %R w/ Dynamic Zones is a Jurik-Composite-Fractal-Behavior-Adaptive Williams % Range indicator with Dynamic Zones. These additions to the WPR calculation reduce noise and return a signal that is more viable than WPR alone.
What is Williams %R?
Williams %R , also known as the Williams Percent Range, is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. The Williams %R may be used to find entry and exit points in the market. The indicator is very similar to the Stochastic oscillator and is used in the same way. It was developed by Larry Williams and it compares a stock’s closing price to the high-low range over a specific period, typically 14 days or periods.
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.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
3 signal variations w/ alerts
Divergences w/ alerts
Loxx's Expanded Source Types
Natural Market Mirror (NMM) and NMAs w/ Dynamic Zones [Loxx]Natural Market Mirror (NMM) and NMAs w/ Dynamic Zones is a very complex indicator derived from Sloman's Ocean Theory. This indicator contains 3 core outputs and those outputs, depending on the one you select to be used to crate a long/short signal, will be highlighted and bound by Dynamic Zones. Pre-smoothing of source input is available, you only need to increase the period length to greater than 1. The smoothing algorithm used here it's Ehlers Two-pole Super Smoother. This indicator should be used as you would use the popular QQE, the difference being this indicator is multi-level momentum adaptive, and QQE is fixed RSI-based. This indicator is multilayer adaptive.
The three core indicators calculations are as follows:
NMM = Natural Market Mirror, solid line
NMF = Natural Moving Average Fast, dashed line (white when off)
NMA = Natural Moving Average Regular, dashed line (yellow when off)
Whichever one you select to be used as the signal output base, that line with increased in width and change color to match the price inputted trend. The Dynamic Zones will then readjust around that selected output and form a new bounding zone for signal output.
What is the Ocean Natural Market Mirror?
Created by Jim Sloman, the NMA is a momentum indicator that automatically adjusts to volatility without being programed to do so. For more info, read his guide "Ocean Theory, an Introduction"
What is the Ocean Natural Moving Average?
Also created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included
Bar coloring
3 types of signal output options
Alerts
Loxx's Expanded Source Types
Futures Exchange Sessions 3.0Description
The ultimate conclusion to the Futures Exchange Sessions 2.0 indicator. In version 3.0 the user gets full control of the start and end times of three separate dynamic boxes and one horizontal line. If the user wants to visually keep track of killzones, lunches, or any other time span in a trading day, version 3.0 will dynamically expand and keep track of price within the time specified by the user.
Inputs and Style
Everything about the three dynamic boxes and one horizontal line can but independently configured. Color, style, border, width can all be adjusted. In the Settings each box has a text box so the user can give each one a unique name.
Timezone
All of the start and end times are in EST. Additionally, each box and line need a dependent start of each day. This is controlled by a setting where the user can specify a timezone called Start Day Timezone which would be midnight of the respective timezone. In general if a box or line resides within a particular Session pick the corresponding timezone. If the users box/line fits in the Asian Session then choose Asia/Shanghai. If the box/line is within the London Session then choose Europe/London. And the same goes for the New York Session.
Special Notes
If start time is within one period of the Start Day Timezone in the Settings, then the line/box won't display
Boxes and time lines only display when timeframe is <= 30 minute
To turn off box text label set opacity to 0%
Goertzel Cycle Period [Loxx]Goertzel Cycle Period is an indicator that uses Goertzel algorithm to extract the cycle period of ticker's price input to then be injected into advanced, adaptive indicators and technical analysis algorithms.
The following information is extracted from: "MESA vs Goertzel-DFT, 2003 by Dennis Meyers"
Background
MESA which stands for Maximum Entropy Spectral Analysis is a widely used mathematical technique designed to find the frequencies present in data. MESA was developed by J.P Burg for his Ph.D dissertation at Stanford University in 1975. The use of the MESA technique for stocks has been written about in many articles and has been popularized as a trading technique by John Ehlers.
The Fourier Transform is a mathematical technique named after the famed French mathematician Jean Baptiste Joseph Fourier 1768-1830. In its digital form, namely the discrete-time Fourier Transform (DFT) series, is a widely used mathematical technique to find the frequencies of discrete time sampled data. The use of the DFT has been written about in many articles in this magazine (see references section).
Today, both MESA and DFT are widely used in science and engineering in digital signal processing. The application of MESA and Fourier mathematical techniques are prevalent in our everyday life from everything from television to cell phones to wireless internet to satellite communications.
MESA Advantages & Disadvantage
MESA is a mathematical technique that calculates the frequencies of a time series from the autoregressive coefficients of the time series. We have all heard of regression. The simplest regression is the straight line regression of price against time where price(t) = a+b*t and where a and b are calculated such that the square of the distance between price and the best fit straight line is minimized (also called least squares fitting). With autoregression we attempt to predict tomorrows price by a linear combination of M past prices.
One of the major advantages of MESA is that the frequency examined is not constrained to multiples of 1/N (1/N is equal to the DFT frequency spacing and N is equal to the number of sample points). For instance with the DFT and N data points we can only look a frequencies of 1/N, 2/N, Ö.., 0.5. With MESA we can examine any frequency band within that range and any frequency spacing between i/N and (i+1)/N . For example, if we had 100 bars of price data, we might be interested in looking for all cycles between 3 bars per cycle and 30 bars/ cycle only and with a frequency spacing of 0.5 bars/cycle. DFT would examine all bars per cycle of between 2 and 50 with a frequency spacing constrained to 1/100.
Another of the major advantages of MESA is that the dominant spectral (frequency) peaks of the price series, if they exist, can be identified with fewer samples than the DFT technique. For instance if we had a 10 bar price period and a high signal to noise ratio we could accurately identify this period with 40 data samples using the MESA technique. This same resolution might take 128 samples for the DFT. One major disadvantage of the MESA technique is that with low signal to noise ratios, that is below 6db (signal amplitude/noise amplitude < 2), the ability of MESA to find the dominant frequency peaks is severely diminished.(see Kay, Ref 10, p 437). With noisy price series this disadvantage can become a real problem. Another disadvantage of MESA is that when the dominant frequencies are found another procedure has to be used to get the amplitude and phases of these found frequencies. This two stage process can make MESA much slower than the DFT and FFT . The FFT stands for Fast Fourier Transform. The Fast Fourier Transform(FFT) is a computationally efficient algorithm which is a designed to rapidly evaluate the DFT. We will show in examples below the comparisons between the DFT & MESA using constructed signals with various noise levels.
DFT Advantages and Disadvantages.
The mathematical technique called the DFT takes a discrete time series(price) of N equally spaced samples and transforms or converts this time series through a mathematical operation into set of N complex numbers defined in what is called the frequency domain. Why would we what to do that? Well it turns out that we can do all kinds of neat analysis tricks in the frequency domain which are just to hard to do, computationally wise, with the original price series in the time domain. If we make the assumption that the price series we are examining is made up of signals of various frequencies plus noise, than in the frequency domain we can easily filter out the frequencies we have no interest in and minimize the noise in the data. We could then transform the resultant back into the time domain and produce a filtered price series that hopefully would be easier to trade. The advantages of the DFT and itís fast computation algorithm the FFT, are that it is extremely fast in calculating the frequencies of the input price series. In addition it can determine frequency peaks for very noisy price series even when the signal amplitude is less than the noise amplitude. One of the disadvantages of the FFT is that straight line, parabolic trends and edge effects in the price series can distort the frequency spectrum. In addition, end effects in the price series can distort the frequency spectrum. Another disadvantage of the FFT is that it needs a lot more data than MESA for spectral resolution. However this disadvantage has largely been nullified by the speed of today's computers.
Goertzel algorithm attempts to resolve these problems...
What is the Goertzel algorithm?
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of the individual terms of the discrete Fourier transform (DFT). It is useful in certain practical applications, such as recognition of dual-tone multi-frequency signaling (DTMF) tones produced by the push buttons of the keypad of a traditional analog telephone. The algorithm was first described by Gerald Goertzel in 1958.
Like the DFT, the Goertzel algorithm analyses one selectable frequency component from a discrete signal. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. For covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms, but for computing a small number of selected frequency components, it is more numerically efficient. The simple structure of the Goertzel algorithm makes it well suited to small processors and embedded applications.
The main calculation in the Goertzel algorithm has the form of a digital filter, and for this reason the algorithm is often called a Goertzel filter
Where is Goertzel algorithm used?
This package contains the advanced mathematical technique called the Goertzel algorithm for discrete Fourier transforms. This mathematical technique is currently used in today's space-age satellite and communication applications and is applied here to stock and futures trading.
While the mathematical technique called the Goertzel algorithm is unknown to many, this algorithm is used everyday without even knowing it. When you press a cell phone button have you ever wondered how the telephone company knows what button tone you pushed? The answer is the Goertzel algorithm. This algorithm is built into tiny integrated circuits and immediately detects which of the 12 button tones(frequencies) you pushed.
Future Additions:
Bartels test for cycle significance, testing output cycles for utility
Hodrick Prescott Detrending, smoothing
Zero-Lag Regression Detrending, smoothing
High-pass or Double WMA filtering of source input price data
References:
1. Burg, J. P., ëMaximum Entropy Spectral Analysisî, Ph.D. dissertation, Stanford University, Stanford, CA. May 1975.
2. Kay, Steven M., ìModern Spectral Estimationî, Prentice Hall, 1988
3. Marple, Lawrence S. Jr., ìDigital Spectral Analysis With Applicationsî, Prentice Hall, 1987
4. Press, William H., et al, ìNumerical Receipts in C++: the Art of Scientific Computingî,
Cambridge Press, 2002.
5. Oppenheim, A, Schafer, R. and Buck, J., ìDiscrete Time Signal Processingî, Prentice Hall,
1996, pp663-634
6. Proakis, J. and Manolakis, D. ìDigital Signal Processing-Principles, Algorithms and
Applicationsî, Prentice Hall, 1996., pp480-481
7. Goertzel, G., ìAn Algorithm for he evaluation of finite trigonometric seriesî American Math
Month, Vol 65, 1958 pp34-35.
Dual Fibonacci Zone & Ranged Vol DCA Strategy - R3c0nTraderWhat does this do?
This is for educational purposes and allows one to backtest two Fibonacci Zones simultaneously. This also includes an option for Ranged Volume as a parameter.
Pre-requisites:
First off, this is a Long only strategy as I wrote it with DCA in mind. It cannot be used for shorting. Shorting defeats the purpose of a DCA bot which has a goal that is Long a position not Short a position. If you want to short, there are plenty of free scripts out there that do this.
You must have some base knowledge or experience with Fibonacci trading, understanding what is ADX, +DI (and -DI), etc.
You can use this script without a 3Commas account and see how 3Commas DCA Bot would perform. However, I highly recommend inexperienced uses get a free account and going through the tutorials, FAQ's and knowledgebase. This would give you a base understanding of the settings you will see in this strategy and why you will need to know them. Only then should you try testing this strategy with a paper bot.
Background
After I had created and released "Fibonacci Zone DCA Strategy", I began expanding and testing other ideas.
The first idea was to add Ranged Volume to the Fibonacci Zone DCA strategy which I wanted for providing further confirmation before entering a trade. The second idea was to add a second Fibonacci Zone that was just as configurable as the first Fibonacci Zone. I managed to add both and they can be easily enabled or disabled via the strategy settings menu.
Things Got Real Interesting
Things got real interesting when I started testing strategies with two Fibonacci zones. Here's a quick list of what I found I was able to do:
Mix and match exit strategies. I could set the Fib-1 zone strategy to exit with a take profit % and separately set the Fib-2 zone strategy to exit when the price crosses the top-high fib border
Trade the trend. A common phrase amongst traders is "the Trend is your friend" and with the help of an additional Fib Zone, I was able to trade the trend more often by using two different Fib Zone strategies which if configured properly can shorten time to re-deploy capital, increase number of closed trades, and in some cases increase net profit.
Trade both bull market uptrends and bear market downtrends in the same strategy. I found I could configure one Fib Zone strategy to be really good in uptrends and another Fib Zone strategy to be really good in downtrends. In some cases, with both Fib Zone strategies enabled together in a single strategy I got better results than if the strategies were backtested separately.
There are many other trade strategies I am finding with this. One could be to trade a convergence or divergence of the two different Fib Zones. This could possibly be achieved by setting one strategy to have different Fibonacci length.
Credits:
Thank you "EvoCrypto" for granting me permission to use "Ranged Volume" to create this strategy
Thank you "eykpunter" for granting me permission to use "Fibonacci Zones" to create this strategy
Thank you "junyou0424" for granting me permission to use "DCA Bot with SuperTrend Emulator" which I used for adding bot inputs, calculations, and strategy
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
Multiple EMAAn exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average (SMA), which applies an equal weight to all observations in the period.
Here, i have merged multiple EMA into one indicator. traders would find it very convenient as multiple widely used EMA`s are merged into 1 indicator. one can also change the time and color from its settings as per their convenience.
About the practicality of this EMA`s:
Every EMA suggests the sentiments in a period of time.
The longer-day EMAs (i.e. 50 and 200-day) tend to be used more by long-term investors, while short-term investors tend to use 8 and 20 day EMAs.
One may prefer to short or to hedge their position when 200 day moving average is broken downside. vise-versa for long. Normally in one may expect around 2-3% move on either side when broken with volumes supporting it.
Bjorgum Double Tap█ OVERVIEW
Double Tap is a pattern recognition script aimed at detecting Double Tops and Double Bottoms. Double Tap can be applied to the broker emulator to observe historical results, run as a trading bot for live trade alerts in real time with entry signals, take profit, and stop orders, or to simply detect patterns.
█ CONCEPTS
How Is A Pattern Defined?
Doubles are technical formations that are both reversal patterns and breakout patterns. These formations typically have a distinctive “M” or a “W” shape with price action breaking beyond the neckline formed by the center of the pattern. They can be recognized when a pivot fails to break when tested for a second time and the retracement that follows breaks beyond the key level opposite. This can trap entrants that were playing in the direction of the prior trend. Entries are made on the breakout with a target projected beyond the neckline equal to the height of the pattern.
Pattern Recognition
Patterns are recognized through the use of zig-zag; a method of filtering price action by connecting swing highs and lows in an alternating fashion to establish trend, support and resistance, or derive shapes from price action. The script looks for the highest or lowest point in a given number of bars and updates a list with the values as they form. If the levels are exceeded, the values are updated. If the direction changes and a new significant point is made, a new point is added to the list and the process starts again. Meanwhile, we scan the list of values looking for the distinctive shape to form as previously described.
█ STRATEGY RESULTS
Back Testing
Historical back testing is the most common method to test a strategy due in part to the general ease of gathering quick results. The underlying theory is that any strategy that worked well in the past is likely to work well in the future, and conversely, any strategy that performed poorly in the past is likely to perform poorly in the future. It is easy to poke holes in this theory, however, as for one to accept it as gospel, one would have to assume that future results will match what has come to pass. The randomness of markets may see to it otherwise, so it is important to scrutinize results. Some commonly used methods are to compare to other markets or benchmarks, perform statistical analysis on the results over many iterations and on differing datasets, walk-forward testing, out-of-sample analysis, or a variety of other techniques. There are many ways to interpret the results, so it is important to do research and gain knowledge in the field prior to taking meaningful conclusions from them.
👉 In short, it would be naive to place trust in one good backtest and expect positive results to continue. For this reason, results have been omitted from this publication.
Repainting
Repainting is simply the difference in behaviour of a strategy in real time vs the results calculated on the historical dataset. The strategy, by default, will wait for confirmed signals and is thus designed to not repaint. Waiting for bar close for entires aligns results in the real time data feed to those calculated on historical bars, which contain far less data. By doing this we align the behaviour of the strategy on the 2 data types, which brings significance to the calculated results. To override this behaviour and introduce repainting one can select "Recalculate on every tick" from the properties tab. It is important to note that by doing this alerts may not align with results seen in the strategy tester when the chart is reloaded, and thus to do so is to forgo backtesting and restricts a strategy to forward testing only.
👉 It is possible to use this script as an indicator as opposed to a full strategy by disabling "Use Strategy" in the "Inputs" tab. Basic alerts for detection will be sent when patterns are detected as opposed to complex order syntax. For alerts mid-bar enable "Recalculate on every tick" , and for confirmed signals ensure it is disabled.
█ EXIT ORDERS
Limit and Stop Orders
By default, the strategy will place a stop loss at the invalidation point of the pattern. This point is beyond the pattern high in the case of Double Tops, or beneath the pattern low in the case of Double Bottoms. The target or take profit point is an equal-legs measurement, or 100% of the pattern height in the direction of the pattern bias. Both the stop and the limit level can be adjusted from the user menu as a percentage of the pattern height.
Trailing Stops
Optional from the menu is the implementation of an ATR based trailing stop. The trailing stop is designed to begin when the target projection is reached. From there, the script looks back a user-defined number of bars for the highest or lowest point +/- the ATR value. For tighter stops the user can look back a lesser number of bars, or decrease the ATR multiple. When using either Alertatron or Trading Connector, each change in the trail value will trigger an alert to update the stop order on the exchange to reflect the new trail price. This reduces latency and slippage that can occur when relying on alerts only as real exchange orders fill faster and remain in place in the event of a disruption in communication between your strategy and the exchange, which ensures a higher level of safety.
👉 It is important to note that in the case the trailing stop is enabled, limit orders are excluded from the exit criteria. Rather, the point in time that the limit value is exceeded is the point that the trail begins. As such, this method will exit by stop loss only.
█ ALERTS
Five Built-in 3rd Party Destinations
The following are five options for delivering alerts from Double Tap to live trade execution via third party API solutions or chat bots to share your trades on social media. These destinations can be selected from the input menu and alert syntax will automatically configure in alerts appropriately to manage trades.
Custom JSON
JSON, or JavaScript Object Notation, is a readable format for structuring data. It is used primarily to transmit data between a server and a web application. In regards to this script, this may be a custom intermediary web application designed to catch alerts and interface with an exchange API. The JSON message is a trade map for an application to read equipped with where its been, where its going, targets, stops, quantity; a full diagnostic of the current state and its previous state. A web application could be configured to follow the messages sent in this format and conduct trades in sync with alerts running on the TV server.
Below is an example of a rendered JSON alert:
{
"passphrase": "1234",
"time": "2022-05-01T17:50:05Z",
"ticker": "ETHUSDTPERP",
"plot": {
"stop_price": 2600.15,
"limit_price": 3100.45
},
"strategy": {
"position_size": 0.1,
"order_action": "buy",
"market_position": "long",
"market_position_size": 0,
"prev_market_position": "flat",
"prev_market_position_size": 0
}
}
Trading Connector
Trading Connector is a third party fully autonomous Chrome extension designed to catch alert webhooks from TradingView and interface with MT4/MT5 to execute live trades from your machine. Alerts to Trading Connector are simple; just select the destination from the input drop down menu, set your ticker in the "TC Ticker" box in the "Alert Strings" section and enter your URL in the alert window when configuring your alert.
Alertatron
Alertatron is an automated algo platform for cryptocurrency trading that is designed to automate your trading strategies. Although the platform is currently restricted to crypto, it offers a versatile interface with high flexibility syntax for complex market orders and conditions. To direct alerts to Alertatron, select the platform from the 3rd party drop down, configure your API key in the ”Alertatron Key” box and add your URL in the alert message box when making alerts.
3 Commas
3 Commas is an easy and quick to use click-and-go third party crypto API solution. Alerts are simple without overly complex syntax. Messages are simply pasted into alerts and executed as alerts are triggered. There are 4 boxes at the bottom of the "Inputs" tab where the appropriate messages to be placed. These messages can be copied from 3 Commas after the bots are set up and pasted directly into the settings menu. Remember to select 3 Commas as a destination from the third party drop down and place the appropriate URL in the alert message window.
Discord
Some may wish to share their trades with their friends in a Discord chat via webhook chat bot. Messages are configured to notify of the pattern type with targets and stop values. A bot can be configured through the integration menu in a Discord chat to which you have appropriate access. Select Discord from the 3rd party drop down menu and place your chat bot URL in the alert message window when configuring alerts.
👉 For further information regarding alert setup, refer to the platform specific instructions given by the chosen third party provider.
█ IMPORTANT NOTES
Setting Alerts
For alert messages to be properly delivered on order fills it is necessary to place the following placeholder in the alert message box when creating an alert.
{{strategy.order.alert_message}}
This placeholder will auto-populate the alert message with the appropriate syntax that is designated for the 3rd party selected in the user menu.
Order Sizing and Commissions
The values that are sent in alert messages are populated from live metrics calculated by the strategy. This means that the actual values in the "Properties" tab are used and must be set by the user. The initial capital, order size, commission, etc. are all used in the calculations, so it is important to set these prior to executing live trades. Be sure to set the commission to the values used by the exchange as well.
👉 It is important to understand that the calculations on the account size take place from the beginning of the price history of the strategy. This means that if historical results have inflated or depleted the account size from the beginning of trade history until now, the values sent in alerts will reflect the calculated size based on the inputs in the "Properties" tab. To start fresh, the user must set the date in the "Inputs" tab to the current date as to remove trades from the trade history. Failure to follow this instruction can result in an unexpected order size being sent in the alert.
█ FOR PINECODERS
• With the recent introduction of matrices in Pine, the script utilizes a matrix to track pivot points with the bars they occurred on, while tracking if that pivot has been traded against to prevent duplicate detections after a trade is exited.
• Alert messages are populated with placeholders ; capability that previously was only possible in alertcondition() , but has recently been extended to `strategy.*()` functions for use in the `alert_message` argument. This allows delivery of live trade values to populate in strategy alert messages.
• New arguments have been added to strategy.exit() , which allow differentiated messages to be sent based on whether the exit occurred at the stop or the limit. The new arguments used in this script are `alert_profit` and `alert_loss` to send messages to Discord
Aroon Oscillator of Adaptive RSI [Loxx]Aroon Oscillator of Adaptive RSI uses RSI to calculate AROON in attempt to capture more trend and momentum quicker than Aroon or RSI alone. Aroon Oscillator of Adaptive RSI has three different types of RSI calculations and the choice of either fixed, VHF Adaptive, or Band-pass Adaptive cycle measures to calculate RSI.
Arron Oscillator:
The Aroon Oscillator was developed by Tushar Chande in 1995 as part of the Aroon Indicator system. Chande’s intention for the system was to highlight short-term trend changes. The name Aroon is derived from the Sanskrit language and roughly translates to “dawn’s early light.”
The Aroon Oscillator is a trend-following indicator that uses aspects of the Aroon Indicator (Aroon Up and Aroon Down) to gauge the strength of a current trend and the likelihood that it will continue.
Aroon oscillator readings above zero indicate that an uptrend is present, while readings below zero indicate that a downtrend is present. Traders watch for zero line crossovers to signal potential trend changes. They also watch for big moves, above 50 or below -50 to signal strong price moves.
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.
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.
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.
VHF Adaptive Cycle:
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Band-pass Adaptive Cycle
Even the most casual chart reader will be able to spot times when the market is cycling and other times when longer-term trends are in play. Cycling markets are ideal for swing trading however attempting to “trade the swing” in a trending market can be a recipe for disaster. Similarly, applying trend trading techniques during a cycling market can equally wreak havoc in your account. Cycle or trend modes can readily be identified in hindsight. But it would be useful to have an objective scientific approach to guide you as to the current market mode.
There are a number of tools already available to differentiate between cycle and trend modes. For example, measuring the trend slope over the cycle period to the amplitude of the cyclic swing is one possibility.
We begin by thinking of cycle mode in terms of frequency or its inverse, periodicity. Since the markets are fractal ; daily, weekly, and intraday charts are pretty much indistinguishable when time scales are removed. Thus it is useful to think of the cycle period in terms of its bar count. For example, a 20 bar cycle using daily data corresponds to a cycle period of approximately one month.
When viewed as a waveform, slow-varying price trends constitute the waveform's low frequency components and day-to-day fluctuations (noise) constitute the high frequency components. The objective in cycle mode is to filter out the unwanted components--both low frequency trends and the high frequency noise--and retain only the range of frequencies over the desired swing period. A filter for doing this is called a bandpass filter and the range of frequencies passed is the filter's bandwidth.
Included:
-Toggle on/off bar coloring
-Customize RSI signal using fixed, VHF Adaptive, and Band-pass Adaptive calculations
-Choose from three different RSI types
Happy trading!
Double RSI FilterI've seen several youtubers using 2 RSI's on top of one another to filter trades for their strategies. I figured I would just code it up as an all-in-one indicator for people who have the basic package. This way they have an extra slot for another indicator if they need one and also for convenience.
Longs only when RSI 1 is above RSI 2 and shorts only when opposite. The arrows show where crosses of the RSI's occur.
Let me know if there is something else like this where it would just be very convenient to have 2 indicators on one window or other such things and I'll see if I can do something for you guys in my spare time. I'm just an amateur coder, but learning as I do more of these for people.
Thank you!
Hope this helps someone! :)
V/T Ratio: Onchain BTC MetricThis is a New Onchain metric that is designed for bitcoin by myself Mjshahsavar (Ghoddusifar), and it is published for the first time in this trading view in this post.
I think this metric has a very high capability to determine the ATH and bottom of the market. This metric can solve a problem that channels are unable to solve. this could be the equivalent of what is known in the stock market as P/E
Calculations:
V/T RATIO = MA (7) of Log ((THE TOTAL VOLUME OF BITCOIN TRANSFERRED ONCHAIN IN USD)/(THE TOTAL AMOUNT OF TRANSACTIONS))
INTERPRETATION:
What is the long-term price channel of Bitcoin? Have you ever thought that maybe drawing a price channel is not right and maybe we should look for something else?
Channel drawing for the price is a subjective and interpretive subject. Look at the charts below, they are all correct in terms of drawing, but no one can say which one will happen. There is no certainty because drawing them is objective.
But who can say which one will definitely work?
We need something more objective. I think V/T Ratio does that.
Just draw the channel. There is only one channel for it. And it has worked historically well to this day.
Compare the drawn channel with the price chart. It works right. When the metric reaches the top line of the channel, it indicates the new ATH and the end of the cycle.
When it reaches the bottom line of the channel, it indicates that the price has reached the bottom.
A Market Cycle:
According to this metric, the bitcoin cycle has 5 stages:
1- Bottom Price: which V/T Ratio touches the bottom line of the channel: In this case, we expect the price to reach the bottom.
2- Semi-high price: that the metric reaches the middle line of the channel: In this case, Bitcoin creates a local top in the MID-Term and Long-Term timeframes
3- Semi-low price: which has a metric return to the lower part of the channel (but the price can still increase)
4- ATH: that Bitcoin reaches its highest historical price
5- It starts after the ATH until the metric reaches the bottom part of the channel again.
Measure Volume, Momentum, Trend, VolatilityThis script displays the following indicators in one pane to quickly determine several important factors regarding price action. It allows the user to quickly see all of most important factors surrounding price action in one pane with one quick glance. This should be incredibly helpful and allow things like double divergence and trend confirmation to be spotted much more quickly. I personally use the data in this indicator to replace four separate indicators and it has brought my win rate and profit factor significantly higher. I hadn't seen any place where all of the best J. Welles Wilder indicators such as RSI, Parabolic SAR, and DMI/ADX were brought into one easy to use interface. This is my attempt at fixing that gap. For a much deeper understanding of how to use these indicators, I recommend reading New Concepts in Technical Trading Systems written by J. Welles Wilder.
Momentum via RSI (Relative Strength Index)
Volume via MFI (Money Flow Index)
Volatility via DMI/ADX (Direction Movement Index/Average Directional Index)
Trend via Parabolic SAR (Parabolic Stop and Reverse)
It is worth noting that DMI/ADX and Parabolic SAR can both help determine trend strength and volatility.
The Volatility mechanism is measured by DMI and ADX and displayed at the top of the pane using circles. The top, tiny circles reflect if show if positive DI or negative DI has a higher value. The small circles directly underneath indicate whether or not the ADX is above 20 (configurable, some may choose to increase this to 25 or even 30).
The Momentum mechanism is shown as standard RSI with the default being a white line and default period of 14, which is all configurable.
The Volume mechanism is shown as standard MFI with the default being a fuchsia line and default period of 14, which is also configurable.
The momentum and volume oscillators should be used in conjunction to help spot whether the trend is strong or weak using divergences and the middle, overbought, and oversold levels. These levels are also configurable.
The Trend mechanism is measured by Parabolic SAR and displayed at the bottom of the pane using diamonds. The default is red diamonds when in a bear trend, green when in an uptrend which is configurable. When price is above the Parabolic SAR, it is considered to be an uptrend. When price is below the Parabolic SAR, it is considered to be a downtrend. The way price is measured is also configurable (i.e. open, close, ohlc4, hlc3, etc.). When price crossed above or below the Parabolic SAR, the diamonds will change colors.
All the indicators displayed should be used in a well rounded strategy. For instance, I only trade when ADX is above 20 and rarely trade against the trend shown via PSAR. When trend shifts and divergences helped indicate a trend shift would occur using the RSI and MFI, it can be a great spot to take an entry. RSI/MFI can also confirm the trend is strong when they are not showing divergences and inline with price action. All of this data should be used in conjunction with good fundamental data and technical levels. Divergences with RSI and MFI on double tops or bottoms can also be incredibly powerful. There is no right or wrong way to use all the data displayed in this indicator, however using all four pillars of trading (Momentum, Volume, Trend, Volatility) will help ensure only the best trades are taken.
ADR label/
// To quote @qullamaggie: " High ADR is Gold, low ADR is shit..."
// Hence we display the ADR (Average Daily Range) in percent.
//
// We also calculate and display Long Stop-Loss suggestions.
// 1. Using the ATR times a multiple.
// 2. Using a percentage.
//
// We also calculates the Trend Template as described by Mark Minervini in his book: "Trade like a stock market wizard"
// For a stock to be considered all the rules has to be fullfilled.
// The only rule this indicator doesn't implement is the IBD Relative Strength ranking.
//
// Rules:
// close > MA-50 > MA-150 > MA-200 , each condition that is true gets one point.
// The current stock price is at least 30 percent above its 52-week low, gets one point
// The current stock price is within at least 25 percent of its 52-week high, gets one point.
// The 200-day moving average line is trending up for at least 1 month (preferably 4–5 months), gets one point.
//
// When we get 6 points, all the rules are fullfilled and we display an OK;
// else we show the number of points (X).
//
Bar By Bar ATR [upslidedown]After seeing strategy after strategy refer to calculating ATR values using a "calculator" (how barbaric!), I thought I'd take a stab at one possible solution to the "problem" as an overlay indicator on the main chart that replaces traditional standard ATR bands. This indicator presents ATR within a channel with a slick trick: invisible hover-able tooltips for you to know the ATR value for your strategy from bar to bar. Just zoom in and hover over the high ATR range and you'll see take profit and stop loss values for whatever strategy you might be running. I defaulted the indicator to a 1:1.5 ATR standard setup because that is good for many strategies but this is as configurable as you'd like to make it. One notable improvement for this indicator over standard ATR bands is that many existing ATR bands only use integers and this one uses a float value, so you can endlessly customize based on whatever strategy you might be running.
Note: Because labels are limited by default, the best way to historically see ATR values is to use TV's replay feature. I did this on purpose to limit resource usage. One could certainly print more labels but I felt it unnecessary to go beyond the default number of labels.
MTF Custom Moving AveragesThis user-friendly indicator allows up to 8 moving averages ( EMA or SMA ) from any timeframe, on any time frame. There are plenty of other MTF MA indicators, each with their own pros and cons. I wanted to make one without the cons:
- Independently set each MA to Exponential or Simple
- No preset lengths
- No preset timeframes
- Optional labels to help keep track of the period/length/type of each plot
- Clean, intuitive input layout
- More than enough MAs available to use one indicator for several use cases... just check/uncheck the ones that are relevant to each chart
Watch for death crosses on the 4hr while monitoring the "Bull Market Support band" (Weekly 21 EMA and 20 SMA ) and checking the Monthly 10 EMA for major support or resistance. Toggle between half of the available MAs for long term BTC trends and use the others for your alts. Use this one indicator to support multiple strategies.
Please leave a comment if you find it useful or have suggestions!
Inspired by the first MTF indicator I found: Weekly Moving Average by TommyTompsen.
CHOPperIt is based on the Choppiness Index indicator. It can show you when the market is in range. If the lines are below the lower band, it can be a strong trend, if it is inside the 2 bands, it is considered to be a choppy market, and if it is crossed down the upper band, it can be a developing trend.
This indicator does not show you the trend direction! This may be used as a confirmation indicator.
The improvements this indicator provides over the original:
It uses ATR instead of just TR (if ATR length is 1, it is the original TR)
It uses my ATRWO (ATR Without Outliers) indicator inside, which can remove extreme highs and lows from calculation. You can tune this by the "ATRWO STDev Mult" parameter. Higher value means more outliers are allowed.
It has 2 lines, one uses ATR(WO) (the blue one), which can be similar to the original Choppiness Index, the other uses standard deviation (the teal one).
The 2 lines can be used together, or you can hide one of them.
MA total distance on chartNOTE:
The name I used for this indicator was created by me and I’m not sure if it has been used or created by any other trader/creator in the past or not!
Motivation to create:
One of the most important uses of “moving averages” is indicating the trend! There are different ways you can distinguish trend by using moving averages and one of the most popular type of it is comparing closing price to a MA. In this case if close is higher than the MA, trend is bullish and if close is lower than MA, it’s bearish. This method is really useful and I see great results in my long-term back-tests, especially SMA-100 in 1H chart filter so many fake signals in many different indicator-based strategies (Personal experience). There are so many problems with using indicators that sometimes have difficult solutions but one of them is fake breakout!
Looking at the top picture, you’ll get a breakout has happened but trend did not change!
A super bearish trend is obviously visible in the chart and we know a small break out might be a fake one, but what if we have an indicator make conditions of a trend change a little harder?
Introduction:
I was careful about how I used moving averages and I got that I will take not only the last candle close price into consideration, so in these kind of false breakouts I will not fall into trap of them, On the contrary, I find a good opportunity to enter the market opposite of the MA break! (In this case short trade). I calculate the total distance of last 40 candles and divide them to 40, to get the average distance, to each a mathematical score for power of our trend comparing to the MA!
Number are just default you can change them.
In the picture below you can see how well it filtered the false breakout.
As it is obvious, Timeframe, MA length, MA source and MA type are editable.
Since I do not tested this indicator enough (for me enough means more than 5000 trades and 10 years) I can’t suggest any settings as the best one.
The distance length, which means number of candles that their distance to MA is considered in our calculations, the distance source and also smoothing of the MATD is editable too.
And without editing it will look like something like this!
Multi-Timeframe TTM Squeeze Pro
IMPORTANT NOTE:
-> The timeframe for this indicator must be set at 1 minute;
-> If the chart timeframe is higher than 1 minute, the results shown in the table for timeframes lower than the chart will not be correct;
-> Tradingview's own documentation explains this as follows: " It is not recommended to request data of a timeframe lower that the current chart timeframe, for example 1 minute data from a 5 minutes chart. The main problem with such a case is that some part of a 1 minute data will be inevitably lost, as it’s impossible to display it on a 5 minutes chart and not to break the time axis. In such cases the behavior of security can be rather unexpected "; and
-> It is therefore recommended that this indicator is placed in a standalone 1min chart window, and the window resized to only show the table to avoid any issues.
Credits:
-> John Carter creating the TTM Squeeze and TTM Squeeze Pro
-> Lazybear's original interpretation of the TTM Squeeze: Squeeze Momentum Indicator
-> Makit0's evolution of Lazybear's script to factor in the TTM Squeeze Pro upgrades - Squeeze PRO Arrows
This is my version of their collective works, with amendments primarily to the Squeeze Conditions to more accurately reflect the color coding used by the official TMM Squeeze Pro indicator.
TTM Squeeze Guide
For those unfamiliar with the TTM Squeeze, it is simply a visual way of seeing how Bollinger Bands (standard deviations from a simple moving average ) relate to Keltner Channels ( average true range bands) compared with the momentum of the price action. The concept is that as Bollinger Bands compress within Keltner Channels , price volatility decreases, giving way for a potential explosive price movement up or down.
Differences between the original TTM Squeeze and TTM Squeeze Pro:
-> Both use a 2 standard deviation Bollinger Band ;
-> The original squeeze only used a 1.5 ATR Keltner Channel; and
-> The pro version uses 1.0, 1.5 and 2.0 ATR Keltner Channels .
The pro version therefore helps differentiate between levels of squeeze (compression) as the Bollinger Bands moves through the Keltner Channels i.e. the greater the compression, the more potential for explosive moves - less compression means more squeezing.
The Histogram shows price momentum whereas the colored dots (along the zeroline) show where the Bollinger Bands are in relation to the Keltner Channels:
-> Cyan Bars = positive, increasing momentum;
-> Blue Bars = positive, decreasing momentum (indication of a reversal in price direction);
-> Red Bars = negative, increasing momentum;
-> Yellow Bars = negative, decreasing momentum (indication of a reversal in price direction);
-> Orange Dots = High Compression / large squeeze (One or both of the Bollinger Bands is inside the 1st (1.0 ATR) Keltner Channel);
-> Red Dots = Medium Squeeze (One or both of the Bollinger Bands is inside the 2nd (1.5 ATR) Keltner Channel);
-> Black Dots = Low compression / wide squeeze (One or both of the Bollinger Bands is inside the 3rd (2.0 ATR) Keltner Channels );
-> Green Dots = No Squeeze / Squeeze Fired (One or both of the Bollinger Bands is outside of the 3rd (2.0 ATR) Keltner Channel).
Ideal Scenario:
As the ticker enters the squeeze, black dots would warn of the beginning of a low compression squeeze. As the Bollinger bands continue to constrict within the Keltner Channels , red dots would highlight a medium compression. As the price action and momentum continues to compress an orange dot shows warning of high compression. As price action leaves the squeeze, the coloring would reverse e.g. orange to red to black to green. Any compression squeeze is considered fired at the first green dot that appears.
Note: This is an ideal progression of the different types of squeezes, however any type of squeeze (and color sequence) may appear at anytime, therefore the focus is primarily on the green dots after any type of compression.
Entry and Exit Guide:
-> John Carter recommends entering a position after at least 5 black dots or wait for 1st green dot ; and
-> Exit on second blue or yellow bar or, alternatively, remain in the position after confirming a continuing trend through a separate indicator.
Standalone Indicator:
The indicator (which can be used on any timeframe) can be found here:
The Witcher [30MIN] - AlertsHello,
This is the Witcher Bot
This bot is got best performance at BTCUSDTPERP BINANCE FUTURES
this is bot for leverage 1x,
I tried focusing at highest % profitable trades, bot could be optimalised to even higher profit net.
TP: 1.1
SL: 8.2
Stop-loss unfortunelly have to be high to avoid bear/bull traps
The core of this strategy is trend strenght ( MONEY FLOW INDKES)
Strategy can only open position on strong price movment, to avoid wrong decision
Settings are set for highest profitable trades %
Bot using 10 indicators to trigger basic condtition for long and short :
1) ADX - Is one of the most powerful and accurate trend indicators. ADX measures how strong a trend is, and can give valuable information on whether there is a potential trading opportunity.
2) RSI - value helps strategy to stop trade in right time. When RSI is overbought strategy don't open new longs , also when RSI is oversold strategy don't open new shorts
3) TREND STRENGHT
4) JURIK MOVING AVERAGE - The Jurik Moving Average indicator is one of the surest ways to smoothen price curves within a minimum time lag. The indicator offers currency traders one of the best price filters during strong price moves. In this time, when bitcoin price action is so strong, this indicator is necessary.
5) SAR - The parabolic SAR is a technical indicator used to determine the price direction of an asset, as well as draw attention to when the price direction is changing. SAR supporting bot, to not open new trades when the trends are slowly changing
6) TREND INDICATOR
7) MOMENTUM - Indicator istechnical analysis tool used to determine the strength or weakness of a stock's price. Momentum measures the rate of the rise or fall of stock prices. Common momentum indicators include the relative strength index ( RSI ) and moving average convergence divergence ( MACD ).
8) OBV - On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict changes in stock price.
9) FAST MA - like previous ones this is for better view of trends, and correctly define the trends, also Speed_MA are using for predict the future price action.
10) RANGE FILTER - this indicator is for the better view of trends, define trends, that is important for every bull/bear traps which helps a lot becouse of the very variable trends.
I decided to add momentum indicator to strategy, to make a fast-reacting decision on lower timeframes at extremly price volatility
Also bot got additional EMA scalping option, which increase profit net but, in some situation, that could be risky.
For max security I recommend to turn off this option.
Commision are set at standard binancefutures VIP-0 = 0.04%
After converting strategy into study version, bot is ready for automation.
All the ploting color depends of adx value.
Strategy are not Repainting
For the source code I tried to keep as clean as I could
Enjoy
+ Magic Carpet BandsFun name for an indicator, eh? Well, it is true, I think; they look like magic carpets. They're actually pretty simple actually. They're Keltner Channels smoothed with a moving average. If you go down to the lookback period for the bands and set it to 1, you'll recognize them immediately.
Digging a bit deeper you see there are four magic carpets on the chart. The inner ones are set to a multiplier of 2, and the outer to a multiplier of 4. Each "carpet" is composed of two smoothed upper or lower Keltner Channels bounds, both with an optional offset, one of which is set to 13, and the other to 0 by default; and an optional color fill between these. There is also a color fill between the outer and inner carpets which gives them an interesting 3-dimensional aspect at times. They can look a bit like tunnels by default.
My thinking around the idea of using an offset with the bands is that if we assume these things to provide a dynamic support and resistance, and previous support and resistance maintains status as support and resistance until proven otherwise, then by putting an offset to past data we are creating a more obvious visual indication of that support or resistance in the present. The default offset is set to 13 bars back, so if price found resistance at some point around 13 bars ago, and price is currently revisiting it we assume it is still resistance, and that offset band is there to give us a strong visual aid. Obviously it's not foolproof, but nothing is.
Beyond that most interesting part of the indicator you have a nice selection of moving averages which the bands are calculated off of. By default it's set to my UMA. The bands themselves also have a selection of moving averages for how the keltner channels are smoothed. And a note: because the UMA and RDMA are averages of different length MAs, they can not be adjusted other than via the multiplier that sets the distance from the moving average.
The indicator is multi-timeframe, and the moving average can be colored based on a higher timeframe as well.
I popped in the divergence indicator here too. You can choose from RSI and OBV, and the divergences will be plotted on the chart. Working on finding a way to be able to have the bands/MA set to a higher timeframe while plotting the divergences on the chart timeframe, but don't have an answer to that yet.
Alerts for moving average crosses, band touches, and divergences.
I like this one a lot. Enjoy!
Pictures below.
s3.tradingview.com
One interesting thing about this indicator is that band twists often occur at areas of support or resistance. Simply drawing horizontal lines from previous twisted points can provide places from which you may look for strength or weakness to enter into a trade, or which you might use as targets for taking profits. The vertical lines are just showing the point on the chart when the cross occurred.
s3.tradingview.com
Above is a Jurik MA with a bunch of adjustments made to the bands, and the moving average itself. Everything is super adjustable, so you can play around and have fun with them quite a bit.
s3.tradingview.com
Just a different MA and bands.
s3.tradingview.com
TTM Squeeze Pro BarsCredits:
-> John Carter creating the TTM Squeeze and TTM Squeeze Pro
-> Lazybear's original interpretation of the TTM Squeeze: Squeeze Momentum Indicator
-> Makit0's evolution of Lazybear's script to factor in the TTM Squeeze Pro upgrades - Squeeze PRO Arrows
This is my version of their collective works, with amendments primarily to the Squeeze Conditions to more accurately reflect the color coding used by the official TMM Squeeze Pro indicator.
Rather than having a separate indicator window, the TTM Squeeze Pro is now overlaid on the price bars for easier viewing.
For those unfamiliar with the TTM Squeeze, it is simply a visual way of seeing how Bollinger Bands (standard deviations from a simple moving average ) relate to Keltner Channels ( average true range bands) compared with the momentum of the price action. The concept is that as Bollinger Bands compress within Keltner Channels , price volatility decreases, giving way for a potential explosive price movement up or down.
Differences between the original TTM Squeeze and TTM Squeeze Pro:
-> Both use a 2 standard deviation Bollinger Band ;
-> The original squeeze only used a 1.5 ATR Keltner Channel; and
-> The pro version uses 1.0, 1.5 and 2.0 ATR Keltner Channels .
The pro version therefore helps differentiate between levels of squeeze (compression) as the Bollinger Bands moves through the Keltner Channels i.e. the greater the compression, the more potential for explosive moves - less compression means more squeezing.
The Histogram shows price momentum whereas the colored dots (along the zeroline) show where the Bollinger Bands are in relation to the Keltner Channels:
-> Cyan Bars = positive, increasing momentum;
-> Blue Bars = positive, decreasing momentum (indication of a reversal in price direction);
-> Red Bars = negative, increasing momentum;
-> Yellow Bars = negative, decreasing momentum (indication of a reversal in price direction);
-> Orange Dots = High Compression / large squeeze (One or both of the Bollinger Bands is inside the 1st (1.0 ATR) Keltner Channel);
-> Red Dots = Medium Squeeze (One or both of the Bollinger Bands is inside the 2nd (1.5 ATR) Keltner Channel);
-> Black Dots = Low compression / wide squeeze (One or both of the Bollinger Bands is inside the 3rd (2.0 ATR) Keltner Channels );
-> Green Dots = No Squeeze / Squeeze Fired (One or both of the Bollinger Bands is outside of the 3rd (2.0 ATR) Keltner Channel).
Ideal Scenario:
As the ticker enters the squeeze, black dots would warn of the beginning of a low compression squeeze. As the Bollinger bands continue to constrict within the Keltner Channels , red dots would highlight a medium compression. As the price action and momentum continues to compress an orange dot shows warning of high compression. As price action leaves the squeeze, the coloring would reverse e.g. orange to red to black to green. Any compression squeeze is considered fired at the first green dot that appears.
Note: This is an ideal progression of the different types of squeezes, however any type of squeeze (and color sequence) may appear at anytime, therefore the focus is primarily on the green dots after any type of compression.
Entry and Exit Guide:
-> John Carter recommends entering a position after at least 5 black dots or wait for 1st green dot ; and
-> Exit on second blue or yellow bar or, alternatively, remain in the position after confirming a continuing trend through a separate indicator.
TTM Squeeze ProCredits:
-> John Carter creating the TTM Squeeze and TTM Squeeze Pro
-> Lazybear's original interpretation of the TTM Squeeze: Squeeze Momentum Indicator
-> Makit0's evolution of Lazybear's script to factor in the TTM Squeeze Pro upgrades - Squeeze PRO Arrows
This is my version of their collective works, with amendments primarily to the Squeeze Conditions to more accurately reflect the color coding used by the official TMM Squeeze Pro indicator.
For those unfamiliar with the TTM Squeeze, it is simply a visual way of seeing how Bollinger Bands (standard deviations from a simple moving average ) relate to Keltner Channels (average true range bands) compared with the momentum of the price action. The concept is that as Bollinger Bands compress within Keltner Channels, price volatility decreases, giving way for a potential explosive price movement up or down.
Differences between the original TTM Squeeze and TTM Squeeze Pro:
-> Both use a 2 standard deviation Bollinger Band ;
-> The original squeeze only used a 1.5 ATR Keltner Channel; and
-> The pro version uses 1.0, 1.5 and 2.0 ATR Keltner Channels .
The pro version therefore helps differentiate between levels of squeeze (compression) as the Bollinger Bands moves through the Keltner Channels i.e. the greater the compression, the more potential for explosive moves - less compression means more squeezing.
The Histogram shows price momentum whereas the colored dots (along the zeroline) show where the Bollinger Bands are in relation to the Keltner Channels:
-> Cyan Bars = positive, increasing momentum;
-> Blue Bars = positive, decreasing momentum (indication of a reversal in price direction);
-> Red Bars = negative, increasing momentum;
-> Yellow Bars = negative, decreasing momentum (indication of a reversal in price direction);
-> Orange Dots = High Compression / large squeeze (One or both of the Bollinger Bands is inside the 1st (1.0 ATR) Keltner Channel);
-> Red Dots = Medium Squeeze (One or both of the Bollinger Bands is inside the 2nd (1.5 ATR) Keltner Channel);
-> Black Dots = Low compression / wide squeeze (One or both of the Bollinger Bands is inside the 3rd (2.0 ATR) Keltner Channels );
-> Green Dots = No Squeeze / Squeeze Fired (One or both of the Bollinger Bands is outside of the 3rd (2.0 ATR) Keltner Channel).
Ideal Scenario:
As the ticker enters the squeeze, black dots would warn of the beginning of a low compression squeeze. As the Bollinger bands continue to constrict within the Keltner Channels , red dots would highlight a medium compression. As the price action and momentum continues to compress an orange dot shows warning of high compression. As price action leaves the squeeze, the coloring would reverse e.g. orange to red to black to green. Any compression squeeze is considered fired at the first green dot that appears.
Note: This is an ideal progression of the different types of squeezes, however any type of squeeze (and color sequence) may appear at anytime, therefore the focus is primarily on the green dots after any type of compression.
Entry and Exit Guide:
-> John Carter recommends entering a position after at least 5 black dots or wait for 1st green dot ; and
-> Exit on second blue or yellow bar or, alternatively, remain in the position after confirming a continuing trend through a separate indicator.
