Adaptivity: Measures of Dominant Cycles and Price Trend [Loxx]Adaptivity: Measures of Dominant Cycles and Price Trend is an indicator that outputs adaptive lengths using various methods for dominant cycle and price trend timeframe adaptivity. While the information output from this indicator might be useful for the average trader in one off circumstances, this indicator is really meant for those need a quick comparison of dynamic length outputs who wish to fine turn algorithms and/or create adaptive indicators.
This indicator compares adaptive output lengths of all publicly known adaptive measures. Additional adaptive measures will be added as they are discovered and made public.
The first released of this indicator includes 6 measures. An additional three measures will be added with updates. Please check back regularly for new measures.
Ehers:
Autocorrelation Periodogram
Band-pass
Instantaneous Cycle
Hilbert Transformer
Dual Differentiator
Phase Accumulation (future release)
Homodyne (future release)
Jurik:
Composite Fractal Behavior (CFB)
Adam White:
Veritical Horizontal Filter (VHF) (future release)
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman's adaptive moving average (KAMA) and Tushar Chande's variable index dynamic average (VIDYA) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
What is this Hilbert Transformer?
An analytic signal allows for time-variable parameters and is a generalization of the phasor concept, which is restricted to time-invariant amplitude, phase, and frequency. The analytic representation of a real-valued function or signal facilitates many mathematical manipulations of the signal. For example, computing the phase of a signal or the power in the wave is much simpler using analytic signals.
The Hilbert transformer is the technique to create an analytic signal from a real one. The conventional Hilbert transformer is theoretically an infinite-length FIR filter. Even when the filter length is truncated to a useful but finite length, the induced lag is far too large to make the transformer useful for trading.
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, pages 186-187:
"I want to emphasize that the only reason for including this section is for completeness. Unless you are interested in research, I suggest you skip this section entirely. To further emphasize my point, do not use the code for trading. A vastly superior approach to compute the dominant cycle in the price data is the autocorrelation periodogram. The code is included because the reader may be able to capitalize on the algorithms in a way that I do not see. All the algorithms encapsulated in the code operate reasonably well on theoretical waveforms that have no noise component. My conjecture at this time is that the sample-to-sample noise simply swamps the computation of the rate change of phase, and therefore the resulting calculations to find the dominant cycle are basically worthless.The imaginary component of the Hilbert transformer cannot be smoothed as was done in the Hilbert transformer indicator because the smoothing destroys the orthogonality of the imaginary component."
What is the Dual Differentiator, a subset of Hilbert Transformer?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 187:
"The first algorithm to compute the dominant cycle is called the dual differentiator. In this case, the phase angle is computed from the analytic signal as the arctangent of the ratio of the imaginary component to the real component. Further, the angular frequency is defined as the rate change of phase. We can use these facts to derive the cycle period."
What is the Phase Accumulation, a subset of Hilbert Transformer?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 189:
"The next algorithm to compute the dominant cycle is the phase accumulation method. The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle's worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio."
What is the Homodyne, a subset of Hilbert Transformer?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 192:
"The third algorithm for computing the dominant cycle is the homodyne approach. Homodyne means the signal is multiplied by itself. More precisely, we want to multiply the signal of the current bar with the complex value of the signal one bar ago. The complex conjugate is, by definition, a complex number whose sign of the imaginary component has been reversed."
What is the Instantaneous Cycle?
The Instantaneous Cycle Period Measurement was authored by John Ehlers; it is built upon his Hilbert Transform Indicator.
From his Ehlers' book Cybernetic Analysis for Stocks and Futures: Cutting-Edge DSP Technology to Improve Your Trading by John F. Ehlers, 2004, page 107:
"It is obvious that cycles exist in the market. They can be found on any chart by the most casual observer. What is not so clear is how to identify those cycles in real time and how to take advantage of their existence. When Welles Wilder first introduced the relative strength index (rsi), I was curious as to why he selected 14 bars as the basis of his calculations. I reasoned that if i knew the correct market conditions, then i could make indicators such as the rsi adaptive to those conditions. Cycles were the answer. I knew cycles could be measured. Once i had the cyclic measurement, a host of automatically adaptive indicators could follow.
Measurement of market cycles is not easy. The signal-to-noise ratio is often very low, making measurement difficult even using a good measurement technique. Additionally, the measurements theoretically involve simultaneously solving a triple infinity of parameter values. The parameters required for the general solutions were frequency, amplitude, and phase. Some standard engineering tools, like fast fourier transforms (ffs), are simply not appropriate for measuring market cycles because ffts cannot simultaneously meet the stationarity constraints and produce results with reasonable resolution. Therefore i introduced maximum entropy spectral analysis (mesa) for the measurement of market cycles. This approach, originally developed to interpret seismographic information for oil exploration, produces high-resolution outputs with an exceptionally short amount of information. A short data length improves the probability of having nearly stationary data. Stationary data means that frequency and amplitude are constant over the length of the data. I noticed over the years that the cycles were ephemeral. Their periods would be continuously increasing and decreasing. Their amplitudes also were changing, giving variable signal-to-noise ratio conditions. Although all this is going on with the cyclic components, the enduring characteristic is that generally only one tradable cycle at a time is present for the data set being used. I prefer the term dominant cycle to denote that one component. The assumption that there is only one cycle in the data collapses the difficulty of the measurement process dramatically."
What is the Band-pass Cycle?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 47:
"Perhaps the least appreciated and most underutilized filter in technical analysis is the band-pass filter. The band-pass filter simultaneously diminishes the amplitude at low frequencies, qualifying it as a detrender, and diminishes the amplitude at high frequencies, qualifying it as a data smoother. It passes only those frequency components from input to output in which the trader is interested. The filtering produced by a band-pass filter is superior because the rejection in the stop bands is related to its bandwidth. The degree of rejection of undesired frequency components is called selectivity. The band-stop filter is the dual of the band-pass filter. It rejects a band of frequency components as a notch at the output and passes all other frequency components virtually unattenuated. Since the bandwidth of the deep rejection in the notch is relatively narrow and since the spectrum of market cycles is relatively broad due to systemic noise, the band-stop filter has little application in trading."
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 59:
"The band-pass filter can be used as a relatively simple measurement of the dominant cycle. A cycle is complete when the waveform crosses zero two times from the last zero crossing. Therefore, each successive zero crossing of the indicator marks a half cycle period. We can establish the dominant cycle period as twice the spacing between successive zero crossings."
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 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.
Komut dosyalarını "relative strength" için ara
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
Adaptive, Jurik-Filtered, Floating RSI [Loxx]Adaptive, Jurik-Filtered, Floating RSI is an adaptive RSI indicator that smooths the RSI signal with a Jurik Filter.
This indicator contains three different types of RSI. They are following.
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.
This indicator also uses adaptive cycles to calculate input lengths
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Lastly, RSI is filtered and smoothed using a Jurik Filter
What is Jurik Volty?
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.
Usage
-Red fill color when RSI is in overbought zone means a possible bear trend is incoming
-Green fill color when RSI is in overbought zone means a possible bear trend is incoming
Included
-Bar coloring
Adaptive Qualitative Quantitative Estimation (QQE) [Loxx]Adaptive QQE is a fixed and cycle adaptive version of the popular Qualitative Quantitative Estimation (QQE) used by forex traders. This indicator includes varoius types of RSI caculations and adaptive cycle measurements to find tune your signal.
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.
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
Visuals:
-Red/Green line is the moving average of RSI
-Thin white line is the fast trend
-Dotted yellow line is the slow trend
Happy trading!
KINSKI Multi Trend OscillatorThe Multi Trend Oscillator is a tool that combines the ratings of several indicators to facilitate the search for profitable trades. I was inspired by the excellent indicator "Technical Ratings" from Team TradingView to create an alternative with a technically new approach. Therefore, it is not a modified copy of the original, but newly conceived and implemented.
The recommendations of the indicator are based on the calculated ratings from the different indicators included in it. The special thing here is that all settings for the individual indicators can be changed according to your own needs and displayed as a histogram and MA line. This provides an excellent visual control of your own settings. Alarms are also triggered.
Criteria for determining the rating
Relative Strength Index (RSI)
Buy - Crossover oversold level and indicator < oversold level and rising
Sell - Crossunder oversold level and indicator >= oversold level and falling
Neutral - neither Buy nor Sell
Relative Strength Index (RSI) Laguerre
Buy - Crossover Oversold Level and Indicator < Oversold Level and rising
Sell - Crossunder oversold level and indicator >= oversold level and falling
Neutral - neither Buy nor Sell
Noise free Relative Strength Index (RSX)
Buy - Crossover Oversold Level and Indicator < Oversold Level and rising
Sell - Crossunder oversold level and indicator >= oversold level and falling
Neutral - neither Buy nor Sell
Money Flow Index (MFI)
Buy - Crossover Oversold Level and Indicator < Oversold Level and rising
Sell - Crossunder oversold level and indicator >= oversold level and falling
Neutral - neither Buy nor Sell
Commodity Channel Index (CCI)
Buy - Crossover Oversold Level and Indicator < Oversold Level and rising
Sell - Crossunder oversold level and indicator >= oversold level and falling
Neutral - neither Buy nor Sell
Moving Average Convergence/Divergence (MACD)
Buy - values of the main line > values of the signal line and rising
Sell - values of the main line < values of the signal line and falling
Neutral - neither Buy nor Sell
Klinger
Buy - indicator >= 0 and rising
Sell - indicator < 0 and falling
Neutral - neither Buy nor Sell
Average Directional Index (ADX)
Buy - indicator > 20 and +DI line crosses over the -DI line and rising
Sell - indicator > 20 and +DI line crosses below the -DI line and falling
Neutral - neither Buy nor Sell
Awesome Oscillator
Buy - Crossover 0 and values are greater than 0, or exceed the zero line
Sell - Crossunder 0 and values are lower than 0, or fall below the zero line
Neutral - neither Buy nor Sell
Ultimate Oscillator
Buy - Crossover oversold level and indicator < oversold level and rising
Sell - Crossunder oversold level and indicator >= oversold level and falling
Neutral - neither Buy nor Sell
Williams Percent Range
Buy - Crossover Oversold Level and Indicator < Oversold Level and rising
Sell - Crossunder Oversold Level and Indicator >= Oversold Level and falling
Neutral - neither Buy nor Sell
Momentum
Buy - Crossover 0 and indicator levels rising
Sell - Crossunder 0 and indicator values falling
Neutral - neither Buy nor Sell
Total Ratings
The numerical value of the rating "Sell" is 0, "Neutral" is 0 and "Buy" is 1. The total rating is calculated as the average of the ratings of the individual indicators and are determined according to the following criteria:
MaxCount = 12 (depending on whether other oscillators are added).
CompareSellStrong = MaxCount * 0.3
CompareMid = MaxCount * 0.5
CompareBuyStrong = MaxCount * 0.7
value <= CompareSellStrong - Strong Sell
value < CompareMid and value > CompareSellStrong - Sell
value == 6 - Neutral
value > CompareMid and value < CompareBuyStrong - Buy
value >= CompareBuyStrong - Strong Buy
Understanding the results
The Multi Trend Oscillator is designed so that its values fluctuate between 0 and currently 12 (maximum number of integrated indicators). Its values are displayed as a histogram with green, red and gray bars. The bars are gray when the value of the indicator is at half of the number of indicators used, currently 12. Increasingly saturated green bars indicate increasing values above 6, and increasingly saturated red bars indicate increasingly decreasing values below 6.
The table at the end of the histogram shows details (can be activated in the settings) about the overall rating and the individual indicators. Its color is determined by the rating value: gray for neutral, green for buy or strong buy, red for sell or strong sell.
The following alarms are triggered:
Multi Trend Oscillator: Sell
Multi Trend Oscillator: Strong Sell
Multi Trend Oscillator: Buy
Multi Trend Oscillator: Strong Buy
Cyclic RSI High Low With Noise Filter█ OVERVIEW
This indicator displays Cyclic Relative Strength Index based on Decoding the Hidden Market Rhythm, Part 1 written by Lars von Thienen.
To determine true or false for Overbought / Oversold are unnecessary, therefore these should be either strong or weak.
Noise for weak Overbought / Oversold can be filtered, especially for smaller timeframe.
█ FEATURES
Display calculated Cyclic Relative Strength Index.
Zigzag high low based on Cyclic Relative Strength Index.
Able to filter noise for high low.
█ LEGENDS
◍ Weak Overbought / Oversold
OB ▼ = Strong Overbought
OS ▲ = Strong Oversold
█ USAGE / TIPS
Recommend to be used for Harmonic Patterns such as XABCD and ABCD.
Condition 1 (XABCD) : When ▼ and ▲ exist side by side, usually this outline XA, while the next two ◍ can be BC.
Condition 2 (ABCD) : When ▼ and ▲ exist side by side, usually this outline AB, while the next one ◍ can be BC, strong ABCD.
Condition 3 (ABCD) : When ▼ or ▲ exist at Point A, the next two ◍ can be Point B and Point C, medium ABCD.
Condition 4 (ABCD) : When ◍ exist at Point a, the next two ◍ can be Point b and Point c, weak ABCD usually used as lower case as abcd.
█ CREDITS
LoneSomeTheBlue
WhenToTrade
Indicators Combination Framework v3 IND [DTU]Hello All,
This script is a framework to analyze and see the results by combine selected indicators for (long, short, longexit, shortexit) conditions.
I was designed this for beginners and users to facilitate to see effects of the technical indicators combinations on the chart WITH NO CODE
You can improve your strategies according the results of this system by connecting the framework to a strategy framework/template such as Pinecoder, Benson, daveatt or custom.
This is enhanced version of my previous indicator "Indicators & Conditions Test Framework "
Currently there are 93 indicators (23 newly added) connected over library. You can also import an External Indicator or add Custom indicator (In the source)
It is possible to change it from Indicator to strategy (simple one) by just remarking strategy parts in the source code and see real time profit of your combinations
Feel free to change or use it in your source
Special thanks goes to Pine wizards: Trading view (built-in Indicators), @Rodrigo, @midtownsk8rguy, @Lazybear, @Daveatt and others for their open source codes and contributions
SIMPLE USAGE
1. SETTING: Show Alerts= True (To see your entries and Exists)
2. Define your Indicators (ex: INDICATOR1: ema(close,14), INDICATOR2: ema(close,21), INDICATOR3: ema(close,200)
3. Define Your Combinations for long & Short Conditions
a. For Long: (INDICATOR1 crossover INDICATOR2) AND (INDICATOR3 < close)
b. For Short: (INDICATOR1 crossunder INDICATOR2) AND (INDICATOR3 > close)
4. Select Strategy/template (Import strategy to chart) that you export your signals from the list
5. Analyze the best profit by changing Indicators values
SOME INDICATORS DETAILS
Each Indicator includes:
- Factorization : Converting the selected indicator to Double, triple Quadruple such as EMA to DEMA, TEMA QEMA
- Log : Simple or log10 can be used for calculation on function entries
- Plot Type : You can overlay the indicator on the chart (such ema) or you can use stochastic/Percentrank approach to display in the variable hlines range
- Extended Parametes : You can use default parameters or you can use extended (P1,P2) parameters regarding to indicator type and your choice
- Color : You can define indicator color and line properties
- Smooth : you can enable swma smooth
- indicators : you can select one of the 93 function like ema(),rsi().. to define your indicator
- Source : you can select from already defined indicators (IND1-4), External Indicator (EXT), Custom Indicator (CUST), and other sources (close, open...)
CONDITION DETAILS
- There are are 4 type of conditions, long entry, short entry, long exit, short exit.
- Each condition are built up from 4 combinations that joined with "AND" & "OR" operators
- You can see the results by enabling show alerts check box
- If you only wants to enter long entry and long exit, just fill these conditions
- If "close on opposite" checkbox selected on settings, long entry will be closed on short entry and vice versa
COMBINATIONS DETAILS
- There are 4 combinations that joined with "AND" & "OR" operators for each condition
- combinations are built up from compare 1st entry with 2nd one by using operator
- 1st and 2nd entries includes already defined indicators (IND1-5), External Indicator (EXT), Custom Indicator (CUST), and other sources (close, open...)
- Operators are comparison values such as >,<, crossover,...
- 2nd entry include "VALUE" parameter that will use to compare 1st indicator with value area
- If 2nd indicator selected different than "VALUE", value are will mean previous value of the selection. (ex: value area= 2, 2nd entry=close, means close )
- Selecting "NONE" for the 1st entry will disable calculation of current and following combinations
JOINS DETAILS
- Each combination will join wiht the following one with the JOIN (AND, OR) operator (if the following one is not equal "NONE")
CUSTOM INDICATOR
- Custom Indicator defines harcoded in the source code.
- You can call it with "CUST" in the Indicator definition source or combination entries source
- You can change or implement your custom indicator by updating the source code
EXTERNAL INDICATOR
- You can import an external indicator by selecting it from the ext source.
- External Indicator should be already imported to the chart and it have an plot function to output its signal
EXPORTING SIGNAL
- You can export your result to an already defined strategy template such as Pine coders, Benson, Daveatt Strategy templates
- Or you can define your custom export for other future strategy templates
ALERTS
- By enabling show alerts checkbox, you can see long entry exits on the bottom, and short entry exits aon the top of the chart
ADDITIONAL INFO
- You can see all off the inputs descriptions in the tooltips. (You can also see the previous version for details)
- Availability to set start, end dates
- Minimize repainting by using security function options (Secure, Semi Secure, Repaint)
- Availability of use timeframes
-
Version 3 INDICATORS LIST (More to be added):
▼▼▼ OVERLAY INDICATORS ▼▼▼
alma(src,len,offset=0.85,sigma=6).-------Arnaud Legoux Moving Average
ama(src,len,fast=14,slow=100).-----------Adjusted Moving Average
accdist().-------------------------------Accumulation/distribution index.
cma(src,len).----------------------------Corrective Moving average
dema(src,len).---------------------------Double EMA (Same as EMA with 2 factor)
ema(src,len).----------------------------Exponential Moving Average
gmma(src,len).---------------------------Geometric Mean Moving Average
highest(src,len).------------------------Highest value for a given number of bars back.
hl2ma(src,len).--------------------------higest lowest moving average
hma(src,len).----------------------------Hull Moving Average.
lagAdapt(src,len,perclen=5,fperc=50).----Ehlers Adaptive Laguerre filter
lagAdaptV(src,len,perclen=5,fperc=50).---Ehlers Adaptive Laguerre filter variation
laguerre(src,len).-----------------------Ehlers Laguerre filter
lesrcp(src,len).-------------------------lowest exponential esrcpanding moving line
lexp(src,len).---------------------------lowest exponential expanding moving line
linreg(src,len,loffset=1).---------------Linear regression
lowest(src,len).-------------------------Lovest value for a given number of bars back.
mcginley(src, len.-----------------------McGinley Dynamic adjusts for market speed shifts, which sets it apart from other moving averages, in addition to providing clear moving average lines
percntl(src,len).------------------------percentile nearest rank. Calculates percentile using method of Nearest Rank.
percntli(src,len).-----------------------percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks.
previous(src,len).-----------------------Previous n (len) value of the source
pivothigh(src,BarsLeft=len,BarsRight=2).-Previous pivot high. src=src, BarsLeft=len, BarsRight=p1=2
pivotlow(src,BarsLeft=len,BarsRight=2).--Previous pivot low. src=src, BarsLeft=len, BarsRight=p1=2
rema(src,len).---------------------------Range EMA (REMA)
rma(src,len).----------------------------Moving average used in RSI. It is the exponentially weighted moving average with alpha = 1 / length.
sar(start=len, inc=0.02, max=0.02).------Parabolic SAR (parabolic stop and reverse) is a method to find potential reversals in the market price direction of traded goods.start=len, inc=p1, max=p2. ex: sar(0.02, 0.02, 0.02)
sma(src,len).----------------------------Smoothed Moving Average
smma(src,len).---------------------------Smoothed Moving Average
super2(src,len).-------------------------Ehlers super smoother, 2 pole
super3(src,len).-------------------------Ehlers super smoother, 3 pole
supertrend(src,len,period=3).------------Supertrend indicator
swma(src,len).---------------------------Sine-Weighted Moving Average
tema(src,len).---------------------------Triple EMA (Same as EMA with 3 factor)
tma(src,len).----------------------------Triangular Moving Average
vida(src,len).---------------------------Variable Index Dynamic Average
vwma(src,len).---------------------------Volume Weigted Moving Average
volstop(src,len,atrfactor=2).------------Volatility Stop is a technical indicator that is used by traders to help place effective stop-losses. atrfactor=p1
wma(src,len).----------------------------Weigted Moving Average
vwap(src_).------------------------------Volume Weighted Average Price (VWAP) is used to measure the average price weighted by volume
▼▼▼ NON OVERLAY INDICATORS ▼▼
adx(dilen=len, adxlen=14, adxtype=0).----adx. The Average Directional Index (ADX) is a used to determine the strength of a trend. len=>dilen, p1=adxlen (default=14), p2=adxtype 0:ADX, 1:+DI, 2:-DI (def:0)
angle(src,len).--------------------------angle of the series (Use its Input as another indicator output)
aroon(len,dir=0).------------------------aroon indicator. Aroons major function is to identify new trends as they happen.p1 = dir: 0=mid (default), 1=upper, 2=lower
atr(src,len).----------------------------average true range. RMA of true range.
awesome(fast=len=5,slow=34,type=0).------Awesome Oscilator is an indicator used to measure market momentum. defaults : fast=len= 5, p1=slow=34, p2=type: 0=Awesome, 1=difference
bbr(src,len,mult=1).---------------------bollinger %%
bbw(src,len,mult=2).---------------------Bollinger Bands Width. The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band.
cci(src,len).----------------------------commodity channel index
cctbbo(src,len).-------------------------CCT Bollinger Band Oscilator
change(src,len).-------------------------A.K.A. Momentum. Difference between current value and previous, source - source . is most commonly referred to as a rate and measures the acceleration of the price and/or volume of a security
cmf(len=20).-----------------------------Chaikin Money Flow Indicator used to measure Money Flow Volume over a set period of time. Default use is len=20
cmo(src,len).----------------------------Chande Momentum Oscillator. Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period.
cog(src,len).----------------------------The cog (center of gravity) is an indicator based on statistics and the Fibonacci golden ratio.
copcurve(src,len).-----------------------Coppock Curve. was originally developed by Edwin Sedge Coppock (Barrons Magazine, October 1962).
correl(src,len).-------------------------Correlation coefficient. Describes the degree to which two series tend to deviate from their ta.sma values.
count(src,len).--------------------------green avg - red avg
cti(src,len).----------------------------Ehler s Correlation Trend Indicator by
dev(src,len).----------------------------ta.dev() Measure of difference between the series and its ta.sma
dpo(len).--------------------------------Detrended Price OScilator is used to remove trend from price.
efi(len).--------------------------------Elders Force Index (EFI) measures the power behind a price movement using price and volume.
eom(len=14,div=10000).-------------------Ease of Movement.It is designed to measure the relationship between price and volume.p1 = div: 10000= (default)
falling(src,len).------------------------ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output)
fisher(len).-----------------------------Fisher Transform is a technical indicator that converts price to Gaussian normal distribution and signals when prices move significantly by referencing recent price data
histvol(len).----------------------------Historical volatility is a statistical measure used to analyze the general dispersion of security or market index returns for a specified period of time.
kcr(src,len,mult=2).---------------------Keltner Channels Range
kcw(src,len,mult=2).---------------------ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel.
klinger(type=len).-----------------------Klinger oscillator aims to identify money flow’s long-term trend. type=len: 0:Oscilator 1:signal
macd(src,len).---------------------------MACD (Moving Average Convergence/Divergence)
mfi(src,len).----------------------------Money Flow Index s a tool used for measuring buying and selling pressure
msi(len=10).-----------------------------Mass Index (def=10) is used to examine the differences between high and low stock prices over a specific period of time
nvi().-----------------------------------Negative Volume Index
obv().-----------------------------------On Balance Volume
pvi().-----------------------------------Positive Volume Index
pvt().-----------------------------------Price Volume Trend
ranges(src,upper=len, lower=-5).---------ranges of the source. src=src, upper=len, v1:lower=upper . returns: -1 source=upper otherwise 0
rising(src,len).-------------------------ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output)
roc(src,len).----------------------------Rate of Change
rsi(src,len).----------------------------Relative strength Index
rvi(src,len).----------------------------The Relative Volatility Index (RVI) is calculated much like the RSI, although it uses high and low price standard deviation instead of the RSI’s method of absolute change in price.
smi_osc(src,len,fast=5, slow=34).--------smi Oscillator
smi_sig(src,len,fast=5, slow=34).--------smi Signal
stc(src,len,fast=23,slow=50).------------Schaff Trend Cycle (STC) detects up and down trends long before the MACD. Code imported from
stdev(src,len).--------------------------Standart deviation
trix(src,len) .--------------------------the rate of change of a triple exponentially smoothed moving average.
tsi(src,len).----------------------------The True Strength Index indicator is a momentum oscillator designed to detect, confirm or visualize the strength of a trend.
ultimateOsc(len.-------------------------Ultimate Oscillator indicator (UO) indicator is a technical analysis tool used to measure momentum across three varying timeframes
variance(src,len).-----------------------ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta.sma), and it informally measures how far a set of numbers are spread out from their mean.
willprc(src,len).------------------------Williams %R
wad().-----------------------------------Williams Accumulation/Distribution.
wvad().----------------------------------Williams Variable Accumulation/Distribution.
HISTORY
v3.01
ADD: 23 new indicators added to indicators list from the library. Current Total number of Indicators are 93. (to be continued to adding)
ADD: 2 more Parameters (P1,P2) for indicator calculation added. Par:(Use Defaults) uses only indicator(Source, Length) with library's default parameters. Par:(Use Extra Parameters P1,P2) use indicator(Source,Length,p1,p2) with additional parameters if indicator needs.
ADD: log calculation (simple, log10) option added on indicator function entries
ADD: New Output Signals added for compatibility on exporting condition signals to different Strategy templates.
ADD: Alerts Added according to conditions results
UPD: Indicator source inputs now display with indicators descriptions
UPD: Most off the source code rearranged and some functions moved to the new library. Now system work like a little bit frontend/backend
UPD: Performance improvement made on factorization and other source code
UPD: Input GUI rearranged
UPD: Tooltips corrected
REM: Extended indicators removed
UPD: IND1-IND4 added to indicator data source. Now it is possible to create new indicators with the previously defined indicators value. ex: IND1=ema(close,14) and IND2=rsi(IND1,20) means IND2=rsi(ema(close,14),20)
UPD: Custom Indicator (CUST) added to indicator data source and Combination Indicator source.
UPD: Volume added to indicator data source and Combination Indicator source.
REM: Custom indicators removed and only one custom indicator left
REM: Plot Type "Org. Range (-1,1)" removed
UPD: angle, rising, falling type operators moved to indicator library
RSI Trend LineI took a concept similar to the "Adaptive RSI" to get the RSI overlaid on a price chart. The problem I have with the Adaptive RSI is to me it sticks too closely to price. I wanted something much more visually helpful that can provide actual tradable signals and strategies.
The orange line you are seeing is the "RSI Trend Line"
The further the RSI moves away from a value of 50 (the "zero line"), the more you see this orange line move away from price. This helps visualize the strength of price pushing away from a neutral value to a position of strength or weakness-- if orange is below price then relative strength is high; if orange is above price then relative strength is low. When price is equal to the orange RSI line, the RSI is at a value of 50.
In addition to the trend line, you can enable bands which reflect Overbought and Oversold levels . If you leave the responsiveness to a value of 1.0 and removed any smoothing, these should pretty accurately reflect an actual RSI chart topping the OB and OS lines (default 70 and 30, respectively). (They're still very close with different responsiveness and smoothing values)
The conversion or scaling of RSI value onto price comes with a bit of a quirk which I decided to leave to the user to determine how they want it applied. So the setting "Responsiveness" will impact the sort of aggressiveness of the RSI trend line as well as the the size of the bands. You could think of this in some ways as the OPPOSITE of the multiple setting on a Bollinger or Keltner band-- 1.0 will make for the widest band, 2.0 is the default and my preference, and you can move it up to a value of 5.0.
Here are some examples of how you could use the indicator for trade signals--
And here's my thought on the current state (as of 10/06) on indices with regards to this indicator-
Combo Backtest 123 Reversal & Relative Momentum Index This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The Relative Momentum Index (RMI) was developed by Roger Altman. Impressed
with the Relative Strength Index's sensitivity to the number of look-back
periods, yet frustrated with it's inconsistent oscillation between defined
overbought and oversold levels, Mr. Altman added a momentum component to the RSI.
As mentioned, the RMI is a variation of the RSI indicator. Instead of counting
up and down days from close to close as the RSI does, the RMI counts up and down
days from the close relative to the close x-days ago where x is not necessarily
1 as required by the RSI). So as the name of the indicator reflects, "momentum" is
substituted for "strength".
WARNING:
- For purpose educate only
- This script to change bars colors.
Market Strength ScannerHey traders, this is a table-based market relative strength and true strength scanner, designed to allow the users to get data from multiple pairs without having to go onto that pair for their strength's. This indicator uses functions to fetch data from other pairs so that the code is optimised and prevents slow loading. Furthermore, the indicator is easy to understand and use as there isn't a lot of settings for it, you can adjust the length of the true strength index or the relative strength index through one input box, you can change the data type from RSI to TSI without changing the code, and you can customise what pairs you want to display. Furthermore, the user can set alerts for the pairs that they want to have such as setting alerts for overbought and oversold zones. That's all to this indicator and I hope it is of use to some people :)
RelativeStrengthComparative_IBD_YRKI am publising Relative Strength Comparative.
It is be used to compare a Stock's Performance against another stock/index (Default NIFTY50)
I also devised a Plot RS Rating which is inspired from IBD's RS Rating and matches to some extent. You can turn off/on the RS Rating as per need.
Example: ITC vs NIFTY 50 it will be ITC / NIFTY
The Indicator can be used in Multiple ways:
1) Check Relative Strength
2) Check RS Rating (This is not Accurate as of now since IBD compares the ratings of all the stocks in an Exchange)
3) Can be used as a Spread Chart for the Division (We need to not divide every time we change Stocks)
4) Design a Template exactly as MarketSmith by using the TradingView feature of "Move to --> Existing Pane Above"
The Formula i used for RS Rating is below with more weightage on the 3 month performance and lesser on 12 month Performance. I am open to Modification of this Formula if a better suggestion
// relative strength IBD style
ThreeMthRS = 0.4*(close/close)
SixMthRS = 0.2*(close/(close*2))
NineMthRS = 0.2*(close/(close*3))
TwelveMthRS = 0.2*(close/(close*4))
Choppiness Index and RSI by ceyhun
Choppiness Index and RSI by ceyhun
This indicator is based on the inverse relationship between CHOP and RSI.
Bar color
If the RSI is greater than CHOP, the Bar color will be blue.
If CHOP is greater than RSI, the bar color will be red.
CHOP
If CHOP is less than 38.2, the color will turn blue. positive
If the CHOP is between 38.2 and 61.8, the color will be yellow and neutral.
If CHOP is greater than 61.8, the color will turn red. negative
Rsi
If Rsi is greater than 61.8, the color will turn blue, positive
If Rsi is between 38.2 and 61.8, the color will be hexagonal and neutral
If Rsi is less than 38.2 the color will be red, negative
The Choppiness Index (CHOP) is an indicator designed to determine if the market is choppy (trading sideways) or not choppy (trading within a trend in either direction). The Choppiness Index is an example of an indicator that is not directional at all. CHOP is not meant to predict future market direction, it is a metric to be used to for defining the market's trendiness only. A basic understanding of the indicator would be; higher values equal more choppiness, while lower values indicate directional trending.
Relative Strength Index (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 the 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.
Rumpy's Dynamic Momentum IndexNote : I haven't been able to determine from the info I've found whether the variable length is used for the average gain/loss part of the calculation and/or for the relative strength portion of the calculation . If anyone knows for certain please let me know.
Type A only uses the variable length for the final relative strength calculation and the fixed RSI length for the average gain/loss.
Type B uses the variable length for both.
I do suspect that Type B is correct though as it is a lot more sensitive to momentum changes while Type A tends to just exaggerate normal RSI
-------------------------------------------------------------------------------------------------------------------------------------------
This indicator, developed by Tushar Chande and Stanley Kroll, is similar to the relative strength index (RSI). The main difference between the two is that the RSI uses a fixed number of time periods (usually 14) in its calculation, while the dynamic momentum index uses different time periods as volatility changes, typically between five and 30.
The dynamic momentum index uses fewer periods in its calculation when volatility is high, and more periods when volatility is low.
The number of time periods used in the dynamic momentum index decreases as volatility in the underlying security increases, making this indicator more responsive to changing prices than the RSI. This is particularly useful when an asset's price moves quickly as it approaches key support or resistance levels. Because the indicator is more sensitive, traders can potentially find earlier entry and exit points than with the RSI.
-------------------------------------------------------------------------------------------------------------------------------------------
If you find it useful please consider a tip/donation :
BTC - 3BMEXEDyWJ58eXUEALYPadbn1wwWKmf6sA
[ALERTS] ADX and DIThe average directional index (ADX) is a technical analysis metric. Analysts use it to determine the relative strength of a trend, with the direction of the trend either upwards or downwards.
The Average Directional Index (ADX) along with the Negative Directional Indicator (-DI) and the Positive Directional Indicator (+DI) are momentum strength indicators that evolved for use in stock trading. Commodities trader J. Welles Wilder pioneered their use. Technical traders who use charting techniques want to know when first spotting a shifting trend how strong that trend is and how likely it is to sustain itself over time. The ADX helps investors determine trend strength as they plan their investment strategies.
Confirmation on a chart and other momentum indicators help investors spot trend reversals. But some trends are more potent than others and investors want to better understand the strength of a trend. The ADX identifies a strong positive trend when the ADX is over 25 and a weak trend when the ADX is below 20. Investors can determine directional movement by analyzing the difference between two consecutive low prices and their correlated highs. The movement is +DM when the current high price, less the previous high price, is greater than the previous low price less the current low. The opposite applies in determining the negative or –DI.
When analyzing charts, stock price is the single most important variable to follow. ADX and other indicators are supplementary to price movements in providing additional directional information and support. For example, some of the best trends come about from price range consolidation. It is those tugs of war between buying and selling volumes that lead to breakouts and other trading opportunities.
The Inventor of the Average Directional Index
J. Welles Wilder, Jr. is a former American engineer and real estate developer who went on to revolutionize trading analysis by applying mathematical systems to the world of investing. In addition to developing the ADX, Wilder is also responsible for several other commonly used technical analysis tools including the Average True Range (ATR), the Relative Strength Index (RSI) and the Parabolic SAR.
www.investopedia.com
This script has alerts and includes the filter for markets with no trend defined.
Green Alert --> Long
Red Alert --> Short
Yellow Area --> Weak trend. ADX below threshold
Green candles --> Bullish Market
Red Candles --> Bearish Market
Orange candles --> No defined trend
Enjoy!
Relative Momentum Index Backtest The Relative Momentum Index (RMI) was developed by Roger Altman. Impressed
with the Relative Strength Index's sensitivity to the number of look-back
periods, yet frustrated with it's inconsistent oscillation between defined
overbought and oversold levels, Mr. Altman added a momentum component to the RSI.
As mentioned, the RMI is a variation of the RSI indicator. Instead of counting
up and down days from close to close as the RSI does, the RMI counts up and down
days from the close relative to the close x-days ago where x is not necessarily
1 as required by the RSI). So as the name of the indicator reflects, "momentum" is
substituted for "strength".
You can change long to short in the Input Settings
WARNING:
- For purpose educate only
- This script to change bars colors.
Relative Momentum Index Strategy The Relative Momentum Index (RMI) was developed by Roger Altman. Impressed
with the Relative Strength Index's sensitivity to the number of look-back
periods, yet frustrated with it's inconsistent oscillation between defined
overbought and oversold levels, Mr. Altman added a momentum component to the RSI.
As mentioned, the RMI is a variation of the RSI indicator. Instead of counting
up and down days from close to close as the RSI does, the RMI counts up and down
days from the close relative to the close x-days ago where x is not necessarily
1 as required by the RSI). So as the name of the indicator reflects, "momentum" is
substituted for "strength".
WARNING:
- This script to change bars colors.
Currency Relative Strengths V.2 [GM]Version 2 Updates
Speed has been increased by ~7X
Highest and lowest pairs now highlighted using brighter colors
Re-ordered pairs from highest to lowest 'flight to risk' rating
I created this tool for the purpose of determining strongest and weakest currencies over different periods of time. Each major currency is compared to the field of other majors and its average change is measured over a predetermined period of time. The result is displayed as a percentage. I use it for trend following but it can also be used to fade exhaustion.
Instructions
Add indicator to chart
Select a time frame under settings
Place cursor over period of interest
Click "Data Window" on right hand side bar
View % change avg values for each currency
Relative Momentum Index The Relative Momentum Index (RMI) was developed by Roger Altman. Impressed
with the Relative Strength Index's sensitivity to the number of look-back
periods, yet frustrated with it's inconsistent oscillation between defined
overbought and oversold levels, Mr. Altman added a momentum component to the RSI.
As mentioned, the RMI is a variation of the RSI indicator. Instead of counting
up and down days from close to close as the RSI does, the RMI counts up and down
days from the close relative to the close x-days ago where x is not necessarily
1 as required by the RSI). So as the name of the indicator reflects, "momentum" is
substituted for "strength".
MSTY-WNTR Rebalancing SignalMSTY-WNTR Rebalancing Signal
## Overview
The **MSTY-WNTR Rebalancing Signal** is a custom TradingView indicator designed to help investors dynamically allocate between two YieldMax ETFs: **MSTY** (YieldMax MSTR Option Income Strategy ETF) and **WNTR** (YieldMax Short MSTR Option Income Strategy ETF). These ETFs are tied to MicroStrategy (MSTR) stock, which is heavily influenced by Bitcoin's price due to MSTR's significant Bitcoin holdings.
MSTY benefits from upward movements in MSTR (and thus Bitcoin) through a covered call strategy that generates income but caps upside potential. WNTR, on the other hand, provides inverse exposure, profiting from MSTR declines but losing in rallies. This indicator uses Bitcoin's momentum and MSTR's relative strength to signal when to hold MSTY (bullish phases), WNTR (bearish phases), or stay neutral, aiming to optimize returns by switching allocations at key turning points.
Inspired by strategies discussed in crypto communities (e.g., X posts analyzing MSTR-linked ETFs), this indicator promotes an active rebalancing approach over a "set and forget" buy-and-hold strategy. In simulated backtests over the past 12 months (as of August 4, 2025), the optimized version has shown potential to outperform holding 100% MSTY or 100% WNTR alone, with an illustrative APY of ~125% vs. ~6% for MSTY and ~-15% for WNTR in one scenario.
**Important Disclaimer**: This is not financial advice. Past performance does not guarantee future results. Always consult a financial advisor. Trading involves risk, and you could lose money. The indicator is for educational and informational purposes only.
## Key Features
- **Momentum-Based Signals**: Uses a Simple Moving Average (SMA) on Bitcoin's price to detect bullish (price > SMA) or bearish (price < SMA) trends.
- **RSI Confirmation**: Incorporates MSTR's Relative Strength Index (RSI) to filter signals, avoiding overbought conditions for MSTY and oversold for WNTR.
- **Visual Cues**:
- Green upward triangle for "Hold MSTY".
- Red downward triangle for "Hold WNTR".
- Yellow cross for "Switch" signals.
- Background color: Green for MSTY, red for WNTR.
- **Information Panel**: A table in the top-right corner displays real-time data: BTC Price, SMA value, MSTR RSI, and current Allocation (MSTY, WNTR, or Neutral).
- **Alerts**: Configurable alerts for holding MSTY, holding WNTR, or switching.
- **Optimized Parameters**: Defaults are tuned (SMA: 10 days, RSI: 15 periods, Overbought: 80, Oversold: 20) based on simulations to reduce whipsaws and capture trends effectively.
## How It Works
The indicator's logic is straightforward yet effective for volatile assets like Bitcoin and MSTR:
1. **Primary Trigger (Bitcoin Momentum)**:
- Calculate the SMA of Bitcoin's closing price (default: 10-day).
- Bullish: Current BTC price > SMA → Potential MSTY hold.
- Bearish: Current BTC price < SMA → Potential WNTR hold.
2. **Secondary Filter (MSTR RSI Confirmation)**:
- Compute RSI on MSTR stock (default: 15-period).
- For bullish signals: If RSI > Overbought (80), signal Neutral (avoid overextended rallies).
- For bearish signals: If RSI < Oversold (20), signal Neutral (avoid capitulation bottoms).
3. **Allocation Rules**:
- Hold 100% MSTY if bullish and not overbought.
- Hold 100% WNTR if bearish and not oversold.
- Neutral otherwise (e.g., during choppy or extreme markets) – consider holding cash or avoiding trades.
4. **Rebalancing**:
- Switch signals trigger when the hold changes (e.g., from MSTY to WNTR).
- Recommended frequency: Weekly reviews or on 5% BTC moves to minimize trading costs (aim for 4-6 trades/year).
This approach leverages Bitcoin's influence on MSTR while mitigating the risks of MSTY's covered call drag during downtrends and WNTR's losses in uptrends.
## Setup and Usage
1. **Chart Requirements**:
- Apply this indicator to a Bitcoin chart (e.g., BTCUSD on Binance or Coinbase, daily timeframe recommended).
- Ensure MSTR stock data is accessible (TradingView supports it natively).
2. **Adding to TradingView**:
- Open the Pine Editor.
- Paste the script code.
- Save and add to your chart.
- Customize inputs if needed (e.g., adjust SMA/RSI lengths for different timeframes).
3. **Interpretation**:
- **Green Background/Triangle**: Allocate 100% to MSTY – Bitcoin is in an uptrend, MSTR not overbought.
- **Red Background/Triangle**: Allocate 100% to WNTR – Bitcoin in downtrend, MSTR not oversold.
- **Yellow Switch Cross**: Rebalance your portfolio immediately.
- **Neutral (No Signal)**: Panel shows "Neutral" – Hold cash or previous position; reassess weekly.
- Monitor the panel for key metrics to validate signals manually.
4. **Backtesting and Strategy Integration**:
- Convert to a strategy script by changing `indicator()` to `strategy()` and adding entry/exit logic for automated testing.
- In simulations (e.g., using Python or TradingView's backtester), it has outperformed buy-and-hold in volatile markets by ~100-200% relative APY, but results vary.
- Factor in fees: ETF expense ratios (~0.99%), trading commissions (~$0.40/trade), and slippage.
5. **Risk Management**:
- Use with a diversified portfolio; never allocate more than you can afford to lose.
- Add stop-losses (e.g., 10% trailing) to protect against extreme moves.
- Rebalance sparingly to avoid over-trading in sideways markets.
- Dividends: Reinvest MSTY/WNTR payouts into the current hold for compounding.
## Performance Insights (Simulated as of August 4, 2025)
Based on synthetic backtests modeling the last 12 months:
- **Optimized Strategy APY**: ~125% (by timing switches effectively).
- **Hold 100% MSTY APY**: ~6% (gains from BTC rallies offset by downtrends).
- **Hold 100% WNTR APY**: ~-15% (losses in bull phases outweigh bear gains).
In one scenario with stronger volatility, the strategy achieved ~4533% APY vs. 10% for MSTY and -34% for WNTR, highlighting its potential in dynamic markets. However, these are illustrative; real results depend on actual BTC/MSTR movements. Test thoroughly on historical data.
## Limitations and Considerations
- **Data Dependency**: Relies on accurate BTC and MSTR data; delays or gaps can affect signals.
- **Market Risks**: Bitcoin's volatility can lead to false signals (whipsaws); the RSI filter helps but isn't perfect.
- **No Guarantees**: This indicator doesn't predict the future. MSTR's correlation to BTC may change (e.g., due to regulatory events).
- **Not for All Users**: Best for intermediate/advanced traders familiar with ETFs and crypto. Beginners should paper trade first.
- **Updates**: As of August 4, 2025, this is version 1.0. Future updates may include volume filters or EMA options.
If you find this indicator useful, consider leaving a like or comment on TradingView. Feedback welcome for improvements!
✅ VMA Avg ATR + Days to Targets 🎯1) The trend filter: LazyBear VMA
You implement the well‑known “LazyBear” Variable Moving Average (VMA) from price directional movement (pdm/mdm).
Internally you:
Smooth positive/negative one‑bar moves (pdmS, mdmS),
Turn them into relative strengths (pdiS, mdiS),
Measure their difference/total (iS), and
Normalize that over a rolling window to get a scaling factor vI.
The VMA itself is then an adaptive EMA:
vma := (1 - k*vI) * vma + (k*vI) * close, where k = 1/vmaLen.
When vI is larger, VMA hugs price more; when smaller, it smooths more.
Coloring:
Green when vma > vma (rising),
Red when vma < vma (falling),
White when flat.
Candles are recolored to match.
Why this matters: The VMA color is your trend regime; everything else in the script keys off changes in this color.
2) What counts as a “valid” new trend?
A new trend is valid only when the previous bar was white and the current bar turns green or red:
validTrendStart := vmaColor != color.white and vmaColor == color.white.
When that happens, you start a trend segment:
Save entry price (startPrice = close) and baseline ATR (startATR = ATR(atrLen)).
Reset “extreme” trackers: extremeHigh = high, extremeLow = low.
Timestamp the start (trendStartTime = time).
Effect: You only study / trade transitions out of a flat VMA into a slope. This helps avoid chop and reduces false starts.
3) While the trend is active
On each new bar without a color change:
If green trend: update extremeHigh = max(extremeHigh, high).
If red trend: update extremeLow = min(extremeLow, low).
This tracks the best excursion from the entry during that single trend leg.
4) When the VMA color changes (trend ends)
When vmaColor flips (green→red or red→green), you close the prior segment only if it was a valid trend (started after white). Then you:
Compute how far price traveled in ATR units from the start:
Uptrend ended: (extremeHigh - startPrice) / startATR
Downtrend ended: (startPrice - extremeLow) / startATR
Add that result to a running sum and count for the direction:
totalUp / countUp, totalDown / countDown.
Target checks for the ended trend (no look‑ahead):
T1 uses the previous average ATR move before the just‑ended trend (prevAvgUp/prevAvgDown).
Up: t1Up = startPrice + prevAvgUp * startATR
Down: t1Down = startPrice - prevAvgDown * startATR
T2 is a fixed 6× ATR move from the start (up or down).
You increment hit counters and also accumulate time‑to‑hit (ms from trendStartTime) for any target that got reached during that ended leg.
If T1 wasn’t reached, it counts as a miss.
Immediately initialize the next potential trend segment with the current bar’s startPrice/startATR/extremes and set validTrendStart according to the “white → color” rule.
Important detail: Using prevAvgUp/Down to evaluate T1 for the just‑completed trend avoids look‑ahead bias. The current trend’s performance isn’t used to set its own T1.
5) Running statistics & targets (for the current live trend)
After closing/adding to totals:
avgUp = totalUp / countUp and avgDown = totalDown / countDown are the historical average ATR move per valid trend for each direction.
Current plotted targets (only visible while a valid trend is active and in that direction):
T1 Up: startPrice + avgUp * startATR
T2 Up: startPrice + 6 * startATR
T1 Down: startPrice - avgDown * startATR
T2 Down: startPrice - 6 * startATR
The entry line is also plotted at startPrice when a valid trend is live.
If there’s no history yet (e.g., first trend), avgUp/avgDown are na, so T1 is na until at least one valid trend has closed. T2 still shows (6× ATR).
6) Win rate & time metrics
Win % (per direction):
winUp = hitUpT1 / (hitUpT1 + missUp) and similarly for down.
(This is strictly based on T1 hits vs misses; T2 hits don’t affect Win% directly.)
Average days to hit T1/T2:
The script stores milliseconds from trend start to each target hit, then reports the average in days separately for Up/Down and for T1/T2.
7) The dashboard table (bottom‑right)
It shows, side‑by‑side for Up/Down:
Avg ATR: historical average ATR move per completed valid trend.
🎯 Target 1 / Target 2: the current trend’s price levels (T1 = avgATR×ATR; T2 = 6×ATR).
✅ Win %: T1 hit rate so far.
⏱ Days to T1/T2: average days (from valid trend start) for the targets that were reached.
8) Alerts
“New Trend Detected” when a valid trend starts (white → green/red).
Target hits for the active trend:
Uptrend: separate alerts for T1 and T2 (high >= target).
Downtrend: separate alerts for T1 and T2 (low <= target).
9) Inputs & defaults
vmaLen = 17: governs how adaptive/smooth the VMA is (larger = smoother, fewer trend flips).
atrLen = 14: ATR baseline for sizing targets and normalizing moves.
10) Practical read of the plots
When you see white → green: that bar is your valid entry (trend start).
An Entry Line appears at the start price.
Target lines appear only for the active direction. T1 scales with your historical average ATR move; T2 is a fixed stretch (6× ATR).
The table updates as more trends complete, refining:
The average ATR reach (which resets your T1 sizing),
The win rate to T1, and
The average days it typically takes to hit T1/T2.
Subtle points / edge cases
No look‑ahead: T1 for a finished trend is checked against the prior average (not including the trend itself).
First trends: Until at least one valid trend completes, T1 is na (no history). T2 still shows.
Only “valid” trends are counted: Segments must start after a white bar; flips that happen color→color without a white in between don’t start a new valid trend.
Time math: Uses bar timestamps in ms, converted to days; results reflect the chart’s timeframe/market session.
TL;DR
The VMA color defines the regime; entries only trigger when a flat (white) VMA turns green/red.
Each trend’s max excursion from entry is recorded in ATR units.
T1 for current trends = (historical average ATR move) × current ATR from entry; T2 = 6× ATR.
The table shows your evolving edge (avg ATR reach, T1 win%, and days to targets), and alerts fire on new trends and target hits.
If you want, I can add optional features like: per‑ticker persistence of stats, excluding very short trends, or making T2 a user input instead of a fixed 6× ATR.
WRAMA Channel (Weighted RSI ATR MA)OVERVIEW
The WRAMA Channel (Weighted RSI ATR MA) is an advanced technical analysis tool designed to react more quickly to price movements compared to indicators using conventional moving averages. It combines the Relative Strength Index (RSI), Average True Range (ATR), and a weighted moving average, resulting in the WRAMA. This indicator forms a dynamic price channel based on a weighted average that incorporates both trend strength (via RSI) and market volatility (via ATR). It helps traders identify trends, potential reversals, and breakout signals, while offering broad customization options.
Key Features
WRAMA Price Channel:
Generates a dynamic channel around the weighted moving average (WRAMA), adapting to market volatility and momentum, similar to Bollinger Bands. Users are encouraged to adjust channel width and length according to their strategy.
The upper and lower channel bands are calculated based on a percentage deviation from the baseline line.
The channel fill color changes depending on the price's position relative to the baseline (green above, red below), with an optional gradient for better visualization.
Weighted Moving Average (WRAMA):
WRAMA is a custom weighted moving average (MA1), where closing prices are weighted based on RSI and ATR, allowing it to dynamically adapt to market conditions.
Baseline: The WRAMA line calculated over a user-defined period.
WRAMA Calculation:
RSI Weight: Based on RSI value. When RSI is in extreme zones (below the lower threshold or above the upper threshold), an extreme weight is applied. Otherwise, the weight is based on the squared RSI value divided by 100, raised to a power defined by the rsi_weight_factor.
ATR Weight: Based on the ATR-to-average-ATR ratio. If ATR exceeds a threshold (atr_threshold × avg_atr), an extreme weight is applied. Otherwise, the weight is based on the squared ratio of ATR to average ATR, raised to the power of the atr_weight_factor.
Combined Weight: RSI and ATR weights are combined using a rsi_atr_balance parameter. Final weight = RSI weight × balance + ATR weight × (1 - balance).
WRAMA Calculation: The closing price is multiplied by the combined weight. The result is averaged over the ma_length period and divided by the average of the weights, forming the WRAMA line. For current WRAMA (ma_length = 1), the calculation simplifies to a single weighted price.
Additional Moving Averages:
For additional confirmations, the indicator supports up to five moving averages (MA1–MA5) with various types (SMA, EMA, WMA, HMA, ALMA) and customizable periods.
All additional MAs are calculated based on WRAMA or its baseline, ensuring consistency and enabling deeper analysis within a unified methodology. MA trend directions can be tracked in a built-in signal table.
Trading Signals:
Breakout Signals: Breakouts above/below the channel are optionally marked with triangle shapes (green for bullish, red for bearish).
MA Signals: Price position relative to MAs or their slope generates bullish/bearish signals. These are optionally visualized with default triangles (green up, red down).
A signal table in the top-right corner summarizes the status of each moving average – bullish, bearish, or neutral.
Customization Options
Channel Settings:
MA Period: Length of the WRAMA baseline (default: 100).
Channel Deviation : Percentage offset from the baseline for upper/lower bands (default: 1.5%).
RSI Settings:
RSI Period: Length of the RSI calculation (default: 14).
RSI Upper/Lower Threshold: Overbought/oversold levels (default: 70/30).
RSI Weight Factor: Influence of RSI on weighting (default: 2.0).
ATR Settings:
ATR Period: ATR calculation length (default: 14).
ATR Threshold: Volatility threshold as a multiple of average ATR (default: 1.5).
ATR Weight Factor: Influence of ATR on weighting (default: 2.0).
RSI & ATR Combined:
Extreme Weight: Weight applied in extreme RSI/ATR conditions (default: 3.0).
RSI/ATR Balance: Balance between RSI and ATR influence (default: 0.5).
Signal Settings:
Show Breakout Signals: Enable/disable breakout triangles.
Show MA Signals: Enable/disable MA-based signals.
MA Signal Source: Choose between current WRAMA or baseline.
MA Signal Analysis: Based on price position or slope.
Neutral Threshold : Minimum distance from MA for signal neutrality (default: 0.5%).
Minimum MA Slope : Minimum slope for trend direction signals (default: 0.01%).
Moving Averages (MA1–MA5):
Options to enable/disable, select type (SMA, EMA, WMA, HMA, ALMA), set period length, and choose color.
Style Settings:
Gradient Fill: Enable/disable gradient coloring within the channel.
Show Baseline: Enable/disable WRAMA baseline visibility.
Colors: Customize line, fill, and signal colors.
Use Cases
Trend Identification: The WRAMA channel highlights trend direction and potential reversal zones when price contacts the channel edges.
Breakout Signals: Channel breakouts may indicate trend shifts or momentum surges.
MA Analysis: The signal table provides a clear summary of market direction (bullish, bearish, or neutral) based on selected moving averages.
Trading Strategies: Suitable for trend-following, mean-reversion, and scalping strategies, depending on user preferences and settings.
Notes
The indicator offers a high degree of flexibility, making it adaptable to various trading styles, instruments, and timeframes.
It is recommended to adjust channel length and width to fit your trading strategy.
Backtesting settings on historical data is advised to optimize parameters for a specific strategy and market.
CAN INDICATORCAN Moving Averages Indicator - Feature Guide
1. Multiple Moving Averages (20 MAs)
- Supports up to 20 individual moving averages
- Each MA can be independently configured:
- Enable/Disable toggle
- Length (period) setting
- Type selection (SMA, EMA, DEMA, VWMA, RMA, WMA)
- Color customization
- Individual timeframe settings when global timeframe is disabled
Pre-configured MA Settings:
1. MA1-8: SMA type
- Lengths: 20, 50, 100, 200, 365, 489, 600, 1460
2. MA9-20: EMA type
- Lengths: 30, 60, 120, 240, 300, 400, 500, 700, 800, 900, 1000, 2000
2. Global Timeframe Settings
Location: Global Settings group
Features:
- Use Global Timeframe: Toggle to use one timeframe for all MAs
- Global Timeframe: Select the timeframe to apply globally
3. Label Display Options
Location: Main Inputs section
Controls:
- Show MA Type: Display MA type (SMA, EMA, etc.)
- Show MA Length: Display period length
- Show Resolution: Display timeframe
- Label Offset: Adjust label position
4. Cross Alerts System
Location: Cross Alerts group
Features:
1. Price Crosses:
- Alerts when price crosses any selected MA
- Select MA to monitor (1-20)
- Triggers on crossover/crossunder
2. MA Crosses:
- Alerts when one MA crosses another
- Select fast MA (1-20)
- Select slow MA (1-20)
- Triggers on crossover/crossunder
5. Relative Strength (RS) Analysis
Location: Relative Strength group
Features:
- Select any MA to monitor (1-20)
- Compares MA to its own average
- Adjustable RS Length (default 14)
- Visual feedback via background color:
- Green: MA above its average (uptrend)
- Red: MA below its average (downtrend)
- Customizable colors and transparency
6. Moving Average Types Available
1. **SMA** (Simple Moving Average)
- Equal weight to all prices
2. **EMA** (Exponential Moving Average)
- More weight to recent prices
3. **DEMA** (Double Exponential Moving Average)
- Reduced lag compared to EMA
4. **VWMA** (Volume Weighted Moving Average)
- Incorporates volume data
5. **RMA** (Running Moving Average)
- Smoother than EMA
6. **WMA** (Weighted Moving Average)
- Linear weight distribution
Usage Tips
1. **For Trend Following:**
- Enable longer-period MAs (MA4-MA8)
- Use cross alerts between long-term MAs
- Monitor RS for trend strength
2. **For Short-term Trading:**
- Focus on shorter-period MAs (MA1-MA3, MA9-MA11)
- Enable price cross alerts
- Use multiple timeframe analysis
3. **For Multiple Timeframe Analysis:**
- Disable global timeframe
- Set different timeframes for each MA
- Compare MA relationships across timeframes
4. **For Performance:**
- Disable unused MAs
- Limit active alerts to necessary pairs
- Use RS selectively on key MAs
RSI Candlestick Oscillator [LuxAlgo]The RSI Candlestick Oscillator displays a traditional Relative Strength Index (RSI) as candlesticks. This indicator references OHLC data to locate each candlestick point relative to the current RSI Value, leading to a more accurate representation of the Open, High, Low, and Close price of each candlestick in the context of RSI.
In addition to the candlestick display, Divergences are detected from the RSI candlestick highs and lows and can be displayed over price on the chart.
🔶 USAGE
Translating candlesticks into the RSI oscillator is not a new concept and has been attempted many times before. This indicator stands out because of the specific method used to determine the candlestick OHLC values. When compared to other RSI Candlestick indicators, you will find that this indicator clearly and definitively correlates better to the on-chart price action.
Traditionally, the RSI indicator is simply one running value based on (typically) the close price of the chart. By introducing high, low, and open values into the oscillator, we can better gauge the specific price action throughout the intrabar movements.
Interactions with the RSI levels can now take multiple forms, whether it be a full-bodied breakthrough or simply a wick test. Both can provide a new analysis of price action alongside RSI.
An example of wick interactions and full-bodied interactions can be seen below.
As a result of the candlestick display, divergences become simpler to spot. Since the candlesticks on the RSI closely resemble the candlesticks on the chart, when looking for divergence between the chart and RSI, it is more obvious when the RSI and price are diverging.
The divergences in this indicator not only show on the RSI oscillator, but also overlay on the price chart for clearer understanding.
🔹 Filtering Divergence
With the candlesticks generating high and low RSI values, we can better sense divergences from price, since these points are generally going to be more dramatic than the (close) RSI value.
This indicator displays each type of divergence:
Bullish Divergence
Bearish Divergence
Hidden Bullish Divergence
Hidden Bearish Divergence
From these, we get many less-than-useful indications, since every single divergence from price is not necessarily of great importance.
The Divergence Filter disregards any divergence detected that does not extend outside the RSI upper or lower values.
This does not replace good judgment, but this filter can be helpful in focusing attention towards the extremes of RSI for potential reversal spotting from divergence.
🔶 DETAILS
In order to get the desired results for a display that resembles price action while following RSI, we must scale. The scaling is the most important part of this indicator.
To summarize the process:
Identify a range on Price and RSI
Consider them as equal to create a scaling factor
Use the scaling factor to locate RSI's "Price equivalent" Upper, Lower, & Mid on the Chart
Use those prices (specifically the RSI Mid) to check how far each OHLC value lies from it
Use those differences to translate the price back to the RSI Oscillator, pinning the OHLC values at their relative location to our anchor (RSI Mid)
🔹 RSI Channel
To better understand, and for your convenience, the indicator includes the option to display the RSI Channel on the chart. This channel helps to visualize where the scaled RSI values are relative to price.
If you analyze the RSI channel, you are likely to notice that the price movement throughout the channel matches the same movement witnessed in the RSI Oscillator below. This makes sense since they are the exact same thing displayed on different scales.
🔹 Scaling the Open
While the scaling method used is important, and provides a very close view of the real price bar's relative locations on the RSI oscillator… It is designed for a single purpose.
The scaling does NOT make the price candles display perfectly on the RSI oscillator.
The largest place where this is noticeable is with the opening of each candle.
For this reason, we have included a setting that modifies the opening of each RSI candle to be more accurate to the chart's price candles.
This setting positions the current bar's opening RSI candlestick value accurately relative to the price's open location to the previous closing price. As seen below.
🔶 SETTINGS
🔹 RSI Candles
RSI Length: Sets the Length for the RSI Oscillator.
Overbought/Oversold Levels: Sets the Overbought and Oversold levels for the RSI Oscillator.
Scale Open for Chart Accuracy: As described above, scales the open of each candlestick bar to more accurately portray the chart candlesticks.
🔹 Divergence
Show on Chart: Choose to display divergence line on the chart as well as on the Oscillator.
Divergence Length: Sets the pivot width for divergence detection. Normal Fractal Pivot Detection is used.
Divergence Style: Change color and line style for Regular and Hidden divergences, as well as toggle their display.
Divergence Filter: As described above, toggle on or off divergence filtering.
🔹 RSI Channel
Toggle: Display RSI Channel on Chart.
Color: Change RSI Channel Color
