ATR Adaptive RSI OscillatorThe " ATR Adaptive RSI Oscillator " is a versatile technical analysis tool designed to help traders make informed decisions in dynamic market conditions. It combines the Relative Strength Index (RSI) with the Average True Range (ATR) to provide adaptive and responsive insights into price trends.
Key Features :
Adaptive RSI Periods : The indicator introduces the concept of adaptive RSI periods based on the ATR (Average True Range) of the market. When enabled, it dynamically adjusts the RSI calculation period, offering longer periods during high volatility and shorter periods during low volatility. This adaptability enhances the accuracy of RSI signals across varying market conditions.
Volume-Based Smoothing : The indicator includes a smoothing feature that computes a time-decayed weighted moving average of RSI values over the last two bars, using volume-based weights. This approach offers a time-sensitive smoothing effect, reducing noise for a clearer view of trend strength compared to the standard RSI.
Divergence Detection : Traders can enable divergence detection to identify potential reversal points in the market. The indicator highlights regular bullish and bearish divergences, providing valuable insights into market sentiment shifts.
Customizable Parameters : Traders have the flexibility to customize various parameters, including RSI length, adaptive mode, ATR length, and divergence settings, to tailor the indicator to their trading strategy.
Overbought and Oversold Levels : The indicator includes overbought (OB) and oversold (OS) boundary lines that can be adjusted to suit individual preferences. These levels help traders identify potential reversal zones.
The "ATR Adaptive RSI Oscillator" is a powerful tool for traders seeking to adapt their trading strategies to changing market dynamics. Whether you're a trend follower or a contrarian trader, this indicator provides valuable insights to support your decision-making process.
Adaptiversi
Adaptive RSI/Stochastic (ARSIS)As a trader, one of the most important aspects of technical analysis is identifying the dominant cycle of the market. The dominant cycle, also known as the market's "heartbeat," can provide valuable information on the current market trend and potential future price movements. One way to measure the dominant cycle is through the use of the MESA Adaptation - MAMA Cycle function, which is a part of the Dominant Cycle Estimators library.
I have developed an "Adaptive RSI/Stochastic" indicator that incorporates the MAMA Cycle function to provide more accurate and reliable signals. The indicator uses the MAMA Cycle function to calculate the period of the data, which is then used as a parameter in the calculation of the RSI and Stochastic indicators. By adapting the calculation of these indicators to the dominant cycle of the market, the resulting signals are more in tune with the current market conditions and can provide a more accurate representation of the current trend.
The MAMA Cycle function is a powerful tool that utilizes advanced mathematical techniques to accurately calculate the dominant cycle of the market. It takes into account the dynamic nature of the market and adapts the calculation of the period to the current conditions. The result is a more accurate and reliable measurement of the market's dominant cycle, which can be used to improve the performance of other indicators and trading strategies.
In conclusion, the Adaptive RSI/Stochastic indicator that I have developed, which incorporates the MAMA Cycle function, is a valuable tool for any trader looking to improve their technical analysis. By adapting the calculation of the RSI and Stochastic indicators to the dominant cycle of the market, the resulting signals are more in tune with the current market conditions and can provide a more accurate representation of the current trend.
Huge thank you to @lastguru for making this possible!
Global & local RSI / quantifytoolsAs the terms global and local imply, global RSI describes broad relative strength, whereas local RSI describes local relative strength within the broad moves. A macro and micro view of relative strength so to speak. Global and local RSI are simply regular RSI and stochastic RSI. Local RSI extremes ( stochastic RSI oversold/overbought) often mark a pivot in RSI which naturally reflects to price. Local RSI extremes are visualized inside the global RSI bands (upper band for overbought, lower band for oversold) in a "heat map" style.
By default:
Stochastic RSI >= 75 = yellow
Stochastic RSI >= 87 = orange
Stochastic RSI >= 100 = pink
Users also have the ability smooth the RSI with their preferred smoothing method ( SMA , EMA , HMA , RMA, WMA ) and length. This leads to different behavior in RSI, rendering the typical RSI extremes (> 70 or < 30) suboptimal or even useless. By enabling adaptive bands, the extremes are readjusted based on typical RSI pivot points (median pivots ), which gives much more relevant reference points for oversold/overbought conditions in both global and local RSI. This feature can be used without smoothing, but it rarely provides a meaningful difference, unless the RSI calculation length is messed with.
Global RSI can be plotted as candles, bars or a line. Candles and bars can be useful for detecting rejections (wicks) in relative strength, the same you would with OHLC data. Sometimes there are "hidden rejections" that are visible in relative strength but not on OHLC data, which naturally gives an advantage. All colors can be adjusted in the input menu. You also have a real-time view of the current RSI states in top right corner. Available alerts are the following: global RSI overbought, global RSI oversold, local RSI overbought and local RSI oversold.
Ehlers Adaptive Relative Strength Index (RSI) [Loxx]Ehlers Adaptive Relative Strength Index (RSI) is an implementation of RSI using Ehlers Autocorrelation Periodogram Algorithm to derive the length input for RSI. Other implementations of Ehers Adaptive RSI rely on the inferior Hilbert Transformer derive the dominant cycle.
In his book "Cycle Analytics for Traders Advanced Technical Trading Concepts", John F. Ehlers describes an implementation for Adaptive Relative Strength Index in order to solve for varying length inputs into the classic RSI equation.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, 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, as we have seen in previous chapters, 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 Adaptive RSI?
From his Ehlers' book mentioned above, page 137:
"The adaptive RSI starts with the computation of the dominant cycle using the autocorrelation periodogram approach. Since the objective is to use only those frequency components passed by the roofing filter, the variable "filt" is used as a data input rather than closing prices. Rather than independently taking the averages of the numerator and denominator, I chose to perform smoothing on the ratio using the SuperSmoother filter. The coefficients for the SuperSmoother filters have previously been computed in the dominant cycle measurement part of the code."
Happy trading!
