RDX Relative Directional IndexRDX Relative Directional Index, Strength + Direction + Trend. This indicator is the combination of RSI and DMI or ADX. RDX aims at providing Relative direction of the price along with strength of the trend. This acts as both RSI and Average Directional Index. as the strength grows the RSI line becomes wider and when there is high volatility and market fluctuation the line becomes thinner. Color decides the Direction. This indicator provides sideways detection of RSI signal.
RDX Width: This determines the strength of RSI and Strength of ADX, The strength grows RDX band grows wider, as strength decreases band shrinks and merge into the RSI line. for exact working simply disable RSI plot on the indicator. when there is no strength the RSI vanishes..
Technical:
RSI : with default 14 period
ADX : Default 14 period
RDX=RSI+(ADX-20)/5
Color Code:
Red: Down Direction
Green: Up Direction
Sideways:
A rectangular channel is plotted on RSI 50 Level
Oversold Overbought:
Oversold and Overbought Levels are plotted for normal RSI Oversold and Overbought detection.
Buy/Sell:
Buy sell signals from ADX crossover are plotted and its easy to determine
Strength + Direction + Trend in one go
Hope the community likes this...
Contibute for more ideas and indicators..
M-oscillator
RiverFlow ADX ScreenerRiverFlow ADX Screener, Scans ADX and Donchian Trend values across various Timeframes. This screener provides support to the Riverflow indicator. Riverflow concept is based on Two indicators. Donchian Channel and ADX or DMI.
How to implement?
1.Donchian Channel with period 20
2. ADX / DMI 14,14 threshold 20
Entry / Exit:
1. Buy/Sell Signal from ADX Crossovers.
2. Trend Confirmation Donchian Channel.
3. Major Trend EMA 200
Buy/Sell:
After a buy/sell is generated by ADX Crossover, Check for Donchian Trend. it has to be in same direction as trend. for FTT trades take 2x limit. for Forex and Stocks take 1:1.5, SL must be placed below recent swing. One can use Riverflow indicator for better results.
ADX Indicator is plotted with
Plus: Green line
Minus: Red Line
ADX strength: plotted as Background area.
TREND: Trend is represented by Green and Red Area around Threshold line
Table:
red indicates down trend
green indicates up trend
grey indicates sideways
Weak ADX levels are treated sideways and a channel is plotted on ADX and PLUS and MINUS lines . NO TRADES are to be TAKEN on within the SIDEWAYS region.
Settings are not required as it purely works on Default settings. However Donchian Length can be changed from settings.
Timeframes below 1Day are screened. Riverflow strategy works on timeframe 5M and above timeframe. so option is not provided for lower timeframes.
Best suits for INTRADAY and LONG TERM Trading
Moonhub IndexMoonhub Index combines several popular technical indicators to create an aggregated index that aims to give a clearer overall picture of the market. The index takes into account the current market condition (trending, ranging, or volatile) to adjust its calculations accordingly.
The indicators used in this composite index are:
Hull Moving Average (HMA)
Fisher Transform (FT)
Williams Alligator
Moving Average Convergence Divergence (MACD)
Average True Range (ATR)
On-Balance Volume (OBV)
Money Flow Index (MFI)
Accumulation/Distribution (AD)
Pivot Points
True Strength Index (TSI)
Volume-Weighted Average Price (VWAP)
The script calculates the values of each indicator and then normalizes and weighs them according to predefined weights. The composite index is formed by summing the weighted values of each indicator. The final Moon Index is plotted on the chart, along with several other related lines like the exponential moving averages (EMA) and simple moving averages (SMA) of the index.
This custom index can be used by traders to get a more comprehensive view of the market and make better-informed trading decisions based on the combined insights of multiple indicators.
Moonhub Cycle IndexMoonhub Cycle Index is a composite index derived from three popular technical analysis indicators: Moving Average Convergence Divergence (MACD), Schaff Trend Cycle (STC), and Detrended Price Oscillator (DPO). The indicator is designed to help identify potential trends and market sentiment by combining the unique characteristics of each indicator.
Key components of the indicator include:
Input Parameters:
COEMA Length (len_DIema): The length of the Exponential Moving Average (EMA) applied to the Custom Index. Default is set to 9.
COSMA Length (len_DIsma): The length of the Simple Moving Average (SMA) applied to the Custom Index. Default is set to 30.
Indicators:
MACD: A momentum oscillator that shows the relationship between two moving averages of a security's price. It is calculated using the difference between the 12-period and 26-period EMA, and a 9-period EMA (signal line) of the MACD.
STC: A cyclic indicator that identifies cyclical trends in the market. It is calculated using the Stochastic oscillator formula applied to the close, high, and low prices over a 10-period lookback window.
DPO: A price oscillator that eliminates the trend from price data to focus on underlying cycles. It is calculated using a custom function that shifts the price by half the length and subtracts the SMA from the shifted price.
Custom Index: The composite index is calculated by taking the average of the MACD line, STC, and DPO.
COEMA and COSMA: Exponential and Simple Moving Averages applied to the Custom Index using the lengths specified by the input parameters (len_DIema and len_DIsma).
Plots: The Custom Index, COEMA, and COSMA are plotted with different colors and line widths to visualize their interaction and provide insights into potential market trends.
This Custom Index Indicator can be useful for traders who want to analyze the market using a combination of these indicators to make more informed decisions. It can also help identify potential trends and market sentiment by combining the unique characteristics of each indicator.
Momentum Covariance Oscillator by TenozenWell, guess what? A new indicator is here! Again it's a coincidence, as I experiment with my formula. So far it's less noisy than Autoregressive Covariance Oscillator, so possibly this one is better. The formula is much simpler, care me to explain.
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Yt = close - previous average
Val = Yt/close
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Welp that's the formula lol. Funny thing is that it's so simple, but it's good! What matters is the use of it haha.
So how to use this Oscillator? If the value is above 0, we expect a bullish response, if the value is below 0 we expect a bearish response. That simple. Ciao.
(Any questions and suggestions? feel free to comment!)
Sakura 2The oscillator uses an adaptive moving average as input to another RSI oscillator and is designed to provide a way to minimize the impact of corrections on the output of the oscillator without significant lag.
An additional trigger line is present in order to provide entry points from intersections between the oscillator and the trigger line.
I'll be working on the code to add and describe the privileges and the best settings
Settings
=Lengthy : period of the oscillator
=Power : controls the sensitivity of the oscillator to retracements, with higher values minimizing the sensitivity to retracements.
=Src : source input of the indicator
The indicator also includes the following graphical settings:
=Gradient : Determines the color mode to use for the gradient, options include "Red To Green", "Red To Blue" and "None", with "None" displaying no gradient.
=Color fill : Determines whether to fill the area between the oscillator and the trigger line or not, by default "On".
=Circles : Determines whether to show circles highlighting the crosses between the oscillator and the trigger line.
Weighted Momentum and Volatility Indicator (WMI)The Weighted Momentum and Volatility Indicator (WMI) is a composite technical analysis tool that combines momentum and volatility to identify potential trend changes in the underlying asset.
The WMI is displayed as an histogram that oscillates around a zero line, with increasing bars indicating a bullish trend and decreasing bars indicating a bearish trend.
The WMI is calculated by combining the Rate of Change (ROC) and Average True Range (ATR) indicators.
The ROC measures the percentage change in price over a set period of time, while the ATR measures the volatility of the asset over the same period.
The WMI is calculated by multiplying the normalized values of the ROC and ATR indicators, with the normalization process being used to adjust the values to a scale between 0 and 1.
Traders and investors can use the WMI to identify potential trend changes in the underlying asset, with increasing bars indicating a bullish trend and decreasing bars indicating a bearish trend.
The WMI can be used in conjunction with other technical analysis tools to develop a comprehensive trading strategy.
Do not hesitate to let me know your comments if you see any improvements to be made :)
Oscillator: Which follows Normal Distribution?When doing machine learning using oscillators, it would be better if the oscillators were normally distributed.
So I analyzed the distribution of oscillators.
The value of the oscillator was divided into 50 groups each from 0 to 100.
ex) if rsi value is 45.43 -> group_44, 58.23 -> group_58
Ocscillators : RSI, Stoch, MFI, WT, RVI, etc....
Caution: The normal distribution was verified through an empirical formula.
Fetch Buy And Hold StrategyThis script was created as an experiment using ChatGPT. I actually woudn't recommend using the ai program to help you with your Pinescripts, as it makes a fair amount of mistakes. It was a fun experiment however.
The script is a simple buy and hold tool. Here's what it does:
- Everytime the rsi enters below the set treshold, a counter increases.
- The second increase of the counter happens when the price goes above the treshold, and then dips below the treshold again.
- The program would fire off a buy signal when the counter hits the number 3.
- After the buy. the counter will reset.
Lets take a look at the following example where the rsi treshold is 30:
- So the rsi dips below 30 and the initial counter is set from 0 to 1.
- The price rises which brings the rsi back to 40.
- Then another dip happens and the rsi is now 25, increasing the counter from 1 two.
- Rsi now dips to 23 and nothing happens.
- Rsi goes back up to 31, and dips back to 28 which puts the counter at 3. A buy singal is now fired and the counter is set to 0.
Paranoia IndicatorThe Paranoia Indicator is a technical analysis tool that combines three popular indicators: Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Stochastic. The Paranoia Indicator formula is calculated by taking a weighted average of the three indicators, with the weights being 23.6%, 61.8%, and 14.6%, respectively.
The Paranoia Indicator is used to identify potential trend reversals and overbought/oversold conditions in the market. When the indicator is above zero, it is considered bullish, and when it is below zero, it is considered bearish. The Paranoia Indicator also has extreme bands that help to identify when the market is overbought or oversold.
Traders can use the Paranoia Indicator in conjunction with other technical analysis tools to confirm trading signals and make more informed trading decisions. The Paranoia Indicator is suitable for all types of markets, including stocks, forex, and commodities, and can be applied to any time frame.
Overall, the Paranoia Indicator is a useful tool for traders looking to identify potential trend reversals and overbought/oversold conditions in the market.
Local Model Kalman Market ModeIntroduction
Heyo guys, I made a new (repainting) indicator called Local Model Kalman Market Mode.
I created it, because I wanted a reliable market mode filter for a potential mean-reversion strategy (e. g. BB Scalping).
On the screenshot you can see an example of how to use it in a BB strategy.
E.g. you would enter long when you have bullish divergence, price is under lower BB, price is under PoC and this indicator here shows range-bound market phase.
You would exit long on cross of the middle band.
Description
The indicator attempts to model the underlying market using different local models (i.e., trending, range-bound, and choppy) and combines them using the T3 Six Pole Kalman Filter to generate an overall estimate of the market.
The Fisher Transform is applied on the price to reach a Gaussian distribution, which increases the accuracy of the indicator itself.
The script first defines state variables for each local model, which include trend direction, trend strength, upper and lower bounds of the range, volatility of the range, level of choppiness, and strength of noise.
Then, likelihood functions are defined for each local model based on the state variables.
Next, the script calculates weights for each local model based on their likelihoods and uses them to calculate state variables for the overall estimate.
Finally, the script combines the state variables using the T3 Six Pole Kalman Filter to generate the overall estimate of the market, which is plotted in blue.
Fundamental Knowledge
To understand the explanation of the indicator and the script, there are a few fundamental concepts that you need to know:
Market: A market is a place where buyers and sellers come together to exchange goods or services.
In the context of trading, the market refers to the exchange where financial instruments such as stocks, currencies, and commodities are bought and sold.
Local models: Local models are statistical models that attempt to capture the characteristics of a particular market regime.
For example, a trending market may have different characteristics than a range-bound market or a choppy market.
The indicator uses different local models to capture the different market regimes.
Trend direction and strength: The trend direction refers to the direction in which the market is moving, either up or down.
The trend strength refers to the magnitude of the trend and how likely it is to continue.
Range-bound market: A range-bound market is a market where prices are trading within a specific range, with a clear upper and lower bound.
Choppiness: Choppiness refers to the degree of irregularity in price movements, often seen in sideways or range-bound markets.
Volatility: Volatility refers to the degree of variation in the price of an asset over time. High volatility implies larger price swings, while low volatility implies smaller price swings.
Kalman filter: A Kalman filter is a mathematical algorithm used to estimate an unknown variable from a series of noisy measurements.
In the context of the indicator, the Kalman filter is used to generate an overall estimate of the market by combining the local models.
T3 Six Pole Kalman Filter: The T3 Six Pole Kalman Filter is a specific type of Kalman filter that is used to smooth and filter time-series data, such as the price data of a financial instrument.
Fisher Transform: The Fisher Transform is a mathematical formula used to transform any probability distribution into a Gaussian normal distribution. It is commonly used in technical analysis to transform non-Gaussian indicators into ones that are more suitable for statistical analysis.
By understanding these fundamental concepts, you should have a basic understanding of how the indicator works and how it generates an overall estimate of the market.
Usage
You can use this indicator on every timeframe.
Users can customize the parameters of the T3 Six Pole Kalman Filter (T3 length, alpha, beta, gamma, and delta) using input functions.
Try out different parameter combinations and use the one you like most.
Thank you for checking this out. Leave me a comment or boost the script, when you wanna support me! 👌
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Credits to:
▪@HPotter - Fisher Transform
▪@loxx - T3
▪ChatGPT - Helped me to make the research for this indicator and helped to build the core algorithm.
Autoregressive Covariance Oscillator by TenozenWell to be honest I don't know what to name this indicator lol. But anyway, here is my another original work! Gonna give some background of why I create this indicator, it's all pretty much a coincidence when I'm learning about time series analysis.
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Well, the formula of Auto-covariance is:
E{(X(t)-(t) * (X(t-s)-(t-s))}= Y_s
But I don't multiply both values but rather subtract them:
E{(X(t)-(t) - (X(t-s)-(t-s))}= Y_s?
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For arm_vald, the equation is as follows:
arm_vald = val_mu + mu_plus_lsm + et
val_mu --> mean of time series
mu_plus_lsm --> val_mu + LSM
et --> error term
As you can see, val_mu^2. I did this so the oscillator is much smoother.
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After I get the value, I normalize them:
aco = Y_s? / arm_vald
So by this calculation, I get something like an oscillator!
(more details in the code)
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So how to use this indicator? It's so easy! If the value is above 0, we gonna expect a bullish response, if the value is below 0, we gonna expect a bearish response; that simple. Be aware that you should wait for the price to be closed before executing a trade.
Well, try it out! So far this is the most powerful indicator that I've created, hope it's useful. Ciao.
(more updates for the indicator if needed)
Hull PressureThis amazing oscillator displays the difference between the hull average calculated on the close of the candles and the one calculated between the average of the highs and lows.
This allows the user to identify the pressure of the closing price over the average, useful to identify trends, divergences, and reversals.
This indicator also has two dynamic overbought and oversold areas, calculated over the past extreme highs and lows of the oscillator.
Rolling Candle Closes Summationscript to sum rolling 20 (default) period's prices together
use on volume indicators to get the likes of McClellan Summation
User selection: rolling periods to add
Degen Dominator - (Crypto Dominance Tool) - [mutantdog]A fairly simple one this time. Another crypto dominance tool, consider it a sequel to Dominion if you will. Ready to go out-of-the-box with a selection of presets at hand.
The premise is straightforward, rather than viewing the various marketcap dominance indexes as their standard percentage values, here we have them represented as basic oscillators. This allows for multiple indexes to be viewed in one pane and gives a decent overview of their relative changes and thus the flow of capital within the overall crypto market. As a general rule-of-thumb, when a plot is above zero then the dominance is climbing, thus capital is likely flowing in that direction. The inverse applies when below zero. When the market is quiet, all will be close to zero. Basic overbought/oversold conditions can also be inferred too.
Active as default are:
Bitcoin (0range): CRYPTOCAP:BTC.D
Ethereum (Blue): CRYPTOCAP:ETH.D
Stablecoins (Red): CRYPTOCAP:USDT.D + CRYPTOCAP:USDC.D
Altcoins (Green): 100 - (all of the above)
These are plotted according to the selected oscillator preset and it's length parameter. The default is set to 'EMA Centre'. An optional RMA(3) smoothing filter is also included and active as default. Each index plot has its own colour and opacity settings available on the main page.
Additionally, the following are also available (deactivated as default):
Total DeFi : CRYPTOCAP:TOTALDEFI.D
Current Symbol : Will try to match corresponding dominance index for the chart symbol if available.
Custom Input : Manual text input, will try to match if available.
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The included presets determine the oscillator type used, all are fairly simple and easy to interpret:
EMA Centre
SMA Centre
Median Centre
Midrange Centre
The first 4 are all variations on the same theme, simply calculated as the difference between the actual value and its respective average. EMA is the default and is my personal preference, if you generally favour using an SMA then perhaps that would be your better choice. Like the two MAs, median and midrange are also dependant on the length parameter. Midrange is calculated from the difference between highest and lowest values within the length period, with a little extra smoothing from an RMA(3).
Simple Delta
Weighted Delta
Running Delta
Often referred to as momentum, delta is just change over time. 'Simple' is the most basic of these, the difference between the current value and the value (length) bars prior. A more long-winded way of calculating this would be to take the difference between each bar and its previous then average them with an SMA which results in the same value. 'Weighted' adopts that principle but instead uses a WMA, likewise 'Running' is the same but using an RMA. The latter is actually the basis of RSI calculations before any normalisation is applied, as you can see in the next preset.
RSI
CMO
RSI really should not need explaining, it is however applied a little differently here to the usual, in this case centred around 0. The x100 multiplication factor has been dropped too for the sake of consistency. The same principle applies with CMO, which is basically a 'Simple Delta' version of RSI.
Hard Floor
Soft Floor
These last two are a little different but both can provide useful interpretations. The floor here is simply the lowest value within the chosen length period. 'Hard' plots the difference between the current value and the floor, thus giving a value that is always above 0. In this case, focus should be given to the relative heights of each with a simple interpretation that capital is flowing into those that are climbing and out of those descending. 'Soft' is essentially the same except that the floor is smoothed with an RMA(3), the result being that when new lows are made, the plot will break below 0 before the floor corrects a few bars later. This soft break provides additional information to that given by 'Hard' so is probably the more useful of the two.
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To finish it off, a bunch of preset alerts are included for the various 0 crossings.
So that just about covers everything then, all quite straightforward really. Future updates may include some extra stuff, the composition of the stablecoin index may change if necessary too. While this is not really a tweaker's tool like some of my other projects, there's still some room for experimentation here. The 'current' and 'custom' indexes can provide some useful data for compatible altcoins and the possibility to compare inter-related tokens (eg: Doge vs Shib). While i introduced this as a sort of sequel to Dominion, it is not intended as a replacement but more of a companion. This initially started as a feature intended for that one but it quickly grew into its own thing. Both the oscillator view here and the more traditional view have merits, i personally use this one primarily now but frequently refer to Dominion for confirmations etc.
That's it for now anyway. As always, feedback is welcome below. Enjoy!
Correlation AnalysisAs the name suggests, this indicator is a market correlation analysis tool.
It contains two main features:
- The Curve: represents the historic correlation coefficient between the current chart and the “Reference Market” input from the settings menu. It aims to give more depth to the current correlation values found in the second feature.
- The Screener: this second feature displays all correlation coefficient values between the (max) 20 markets inputs. You can use it to create several screeners for several market types (crypto, forex, metals, etc.) or even replicate your current portfolio of investments and gauge the correlation of its components.
Aside from these two previous features, you can visually plot the variation rate from one bar to another along with the covariance coefficient (both used in the correlation calculation). Finally, a simple “signal” moving average can be applied to the correlation coefficient .
I might add alerts to this script or even turn it into a strategy to do some backtesting. Do not hesitate to contact me or comment below if this is something you would be interested in or if you have any suggestions for improvement.
Enjoy!!
