Urika Trend StrengthThe Urika Directional Strength (UDS) indicator calculates and visualizes the strength of the directional trend in the price data. It helps traders see the strength and direction of the trend and allows them to make informed trading decisions based on trend changes.
Calculation:
The Simple Moving Average is used to determine the upper and lower directional bands by adding and subtracting the product of the standard deviation of the price data and the multiplier of the moving average.
Direction: The upward directional trend and downward directional trend are calculated by taking the absolute value of the difference between the price data and the upper and lower directional bands, divided by the product of the standard deviation and the multiplier.
Strength: It is calculated by taking the absolute value of the difference between the price data and the moving average, divided by the product of the standard deviation and the multiplier.
Interpretation:
Direction: The position of the long and short lines at the top indicates the direction of the ticker. Long line for long position and Short line for short position.
Strength: When the Strength line is below the directional lines, it is a weak trend or consolidating. If it stays in between the two directional lines, it is a strong trend.
Volatility
Squeeze Momentum DeluxeThe Squeeze Momentum Deluxe is a comprehensive trading toolkit built with features of momentum, volatility, and price action. This script offers a suite for both mean reversion and trend-following analysis. Developed based on the original TTM Squeeze implementation by @LazyBear, this indicator introduces several innovative components to enhance your trading insights.
🔲 Components and Features
Momentum Oscillator - as rooted in the TTM Squeeze, quantifies the relationship between price and its extremes over a defined period. By normalizing the calculation, the values become comparable throughout time and across securities, allowing for a nuanced assessment of Bullish and Bearish momentum. Furthermore, by presenting it as a ribbon with a signal line we gain additional information about the direction of price swings.
Squeeze Bars - The original squeeze concept is based on the relationship between the Bollinger Bands and Keltner Channel , once the BB resides inside the KC a squeeze occurs. By understanding their fundamentals a new form of calculation can be inferred.
method bb(float src, simple int len, simple float mult) => method kc(float src, simple int len, simple float mult) =>
float basis = ta.sma (src, len) float basis = ta.sma (src, len)
float dev = ta.stdev(src, len) float rng = ta.atr ( len)
float upper = basis + dev * mult float upper = basis + rng * mult
float lower = basis - dev * mult float lower = basis - rng * mult
Both BB and KC are constructed upon a moving average with the addition of Standard Deviation and Average True Range respectively. Therefore, the calculation can be transformed to when the Stdev is lower than the ATR a squeeze occurs.
method sqz(float src, simple int len) =>
float dev = ta.stdev(src, len)
float atr = ta.atr ( len)
dev < atr ? true : false
This indicator uses three different thresholds for the ATR to gain three levels of price "Squeeze" for further analysis.
Directional Flux- This component measures the overall direction of price volatility, offering insights into trend sentiment. Presented as waves in the background, it includes an OverFlux feature to signal extreme market bias in a particular direction which can signal either exhaustion or vital continuation. Additionally, the user can choose if to base the calculation on Heikin-Ashi Candles to bias the tool toward trend assessment.
Confluence Gauges - Placed at the top and bottom of the indicator, these gauges measure confluence in the relationship between the Momentum Oscillator and Directional Flux. They provide traders with an easily interpretable visual aid for detecting market sentiment. Reversal doritos displayed alongside them contribute to mean reversion analysis.
Divergences (Real-Time) - Equipped with a custom algorithm, the indicator detects real-time divergences between price and the oscillator. This dynamic feature enhances your ability to spot potential trend reversals as they occur.
🔲 Settings
Directional Flux Length - Adjusts the period of which the background volatility waves operate on.
Trend Bias - Bases the calculation of the Flux to HA candles to bias its behavior toward the trend of price action.
Squeeze Momentum Length - Calibrates the length of the main oscillator ribbon as well as the period for the squeeze algorithm.
Signal - Controls the width of the ribbon. Lower values result in faster responsiveness at the cost of premature positives.
Divergence Sensitivity - Adjusts a threshold to limit the amount of divergences detected based on strength. Higher values result in less detections, stronger structure.
🔲 Alerts
Sell Signal
Buy Signal
Bullish Momentum
Bearish Momentum
Bullish Flux
Bearish Flux
Bullish Swing
Bearish Swing
Strong Bull Gauge
Strong Bear Gauge
Weak Bull Gauge
Weak Bear Gauge
High Squeeze
Normal Squeeze
Low Squeeze
Bullish Divergence
Bearish Divergence
As well as the option to trigger 'any alert' call.
The Squeeze Momentum Deluxe is a comprehensive tool that goes beyond traditional momentum indicators, offering a rich set of features to elevate your trading strategy. I recommend using toolkit alongside other indicators to have a wide variety of confluence to therefore gain higher probabilistic and better informed decisions.
Volatility Visualizer by Oddbeaker LLCUse this to determine if a crypto pair has volatility suitable for your Oddbeaker Synthetic Miner. Draws entry/exit lines over the candles.
"Show me every place on the chart where I could have made X percent gains in Y days or less."
Inputs :
Percent Gain : Minimum percent gains to show on the chart.
Scan Bars : Maximum number of bars allowed to reach the profit target.
Notes :
Lines drawn on the chart indicate the entry and exit times and prices to reach the exact profit target.
The indicator only uses the low price of each candle to determine entry. It does not show every possible entry point.
When counting lines, count any group of lines that cross each other as one. Also, count any group of lines that do not cross but overlap in price over the same time period as one.
Tips :
For best results, set Percent Gain to double the amount of the sum of Min Profit and Min Stash on your Synth Miner. Example: If you have minProfit=5 and minStash=5, 5+5=10, so percentGain should be 20 on the chart.
Use a daily chart and set Scan Bars to 7 or less on highly volatile pairs.
Look for charts with the highest number of lines that don't overlap.
Use this indicator combined with the Synthetic Mining Channel for best results.
Grucha Percentage Index (GPI) V2Grucha Percentage Index originally created by Polish coder named Grzegorz Antosiewicz in 2011 as mql code. This code is adapted by his original code to tradingview's pinescript.
What Does it Do
GPI is an oscillator that finds the lowest/highest prices with certain depth and generates signals by comparing the bull and bear bars. It use two lines, one is the original GPI calculation, the other is the smoothed version of the original line.
How to Use
GPI can catch quick volatility based movements and can be used as a confirmation indicator along with your existing trading system. When GDI (default color yellow) crosses above the GDI MA (default colored blue) it can be considered as a bullish movement and reverse can be considered as bearish movement.
How does it Work
The main calculation is done via the code below:
for i=0 to length
if candleC < 0
minus += candleC
if candleC >= 0
plus -= candleC
Simply we are adding green and red bars seperately and then getting their percentage to the bullish movement to reflect correctly in a 0-100 z-score enviroment via the code below:
res = (math.abs(minus)/sum)*100
Rest is all about plotting the results and adding seperate line with smoothing.
Note
These kind of oscillators are not designed to be used alone for signal generation but rather should be used in combination with different indicators to increase reliability of your signals.
Happy Trading.
TTP Intelligent AccumulatorThe intelligent accumulator is a proof of concept strategy. A hybrid between a recurring buy and TA-based entries and exits.
Distribute the amount of equity and add to your position as long as the TA condition is valid.
Use the exit TA condition to define your exit strategy.
Decide between adding only into losing positions to average down or take a riskier approach by allowing to add into a winning position as well.
Take full profit or distribute your exit into multiple take profit exists of the same size.
You can also decide if you allow your exit conditions to close your position in a loss or require a minimum take profit %.
The strategy includes a default built-in TA conditions just for showcasing the idea but the final intent of this script is to delegate the TA entries and exists to external sources.
The internal conditions use RSI length 7 crossing below the BB with std 1 for entries and above for exits.
To control the number of orders use the properties from settings:
- adjust the pyramiding
- adjust the percentage of equity
- make sure that pyramiding * % equity equals 100 to prevent over use of equity (unless using leverage)
The script is designed as an alternative to daily or weekly recurring buys but depending on the accuracy of your TA conditions it might prove profitable also in lower timeframes.
The reason the script is named Intelligent is because recurring buy is most commonly used without any decision making: buy no matter what with certain frequency. This strategy seeks to still perform recurring buys but filtering out some of the potential bad entries that can delay unnecessarily seeing the position in profits. The second reason is also securing an exit strategy from the beginning which no recurring buy option offers out-of-the-box.
Supertrended RSI [AlgoAlpha]🚀📈 Introducing the Supertrended RSI Indicator by AlgoAlpha!
Designed to empower your trading decisions, this innovative Pine Script™ creation marries the precision of the Relative Strength Index (RSI) with the dynamic prowess of the SuperTrend methodology. Whether you’re charting the course of cryptos, riding the waves of stock markets, or navigating the futures landscape, our SuperTrended RSI Indicator is your go-to tool for uncovering unique trend insights and crafting trading strategies. 🌟
Key Features:
🔍 Enhanced RSI Analysis: Combines the traditional RSI with a supertrend calculation for a dynamic look at market trends.
🔄 Multiple Moving Averages: Offers a selection of moving averages including SMA, HMA, EMA, and more for tailored analysis.
🎨 Customizable Visuals: Choose your own color scheme for uptrends and downtrends to match your trading dashboard.
📊 Flexible Input Settings: Tailor the indicator with customizable lengths, factors, and smoothing options.
⚡ Real-Time Alerts: Set alerts for bullish and bearish reversals to stay ahead of market movements.
Quick Guide to Using the Supertrended RSI Indicator
Maximize your trading with the Supertrended RSI by following these streamlined steps! 🚀✨
🛠 Add the Indicator: Search for "Supertrended RSI " in TradingView's Indicators & Strategies. Customize settings like RSI length, MA type, and Supertrend factors to fit your trading style.
🎨 Visual Customization: Adjust uptrend and downtrend colors for clear trend visualization.
📊 Market Analysis: Watch for the Supertrend color change for trend reversals. Use the 70 and 30 lines to spot overbought/oversold conditions.
🔔 Alerts: Enable notifications for reversal conditions to capture trading opportunities without constant chart monitoring.
How It Works:
At the core of this indicator is the combination of the Relative Strength Index (RSI) and the Supertrend framework, it does so by applying the SuperTrend on the RSI. The RSI settings can be adjusted for length and smoothing, with the option to select the data source. The Supertrend calculation takes into account a specified trend factor and the Average True Range (ATR) over a given period to determine trend direction.
Visual elements include plotting the RSI, its moving average, and the Supertrend line, with customizable colors for clarity. Overbought and oversold conditions are highlighted, and trend changes are filled with distinct colors.
🔔 Alerts: Enable alerts for crossover and crossunder events to catch every trading opportunity.
🌈 Whether you're a seasoned trader or just starting, the Supertrended RSI offers a fresh perspective on market trends. 📈
💡 Tip: Experiment with different settings to find the perfect balance for your trading style!
🔗 Explore, customize, and enhance your trading experience with the Supertrended RSI Indicator! Happy trading! 🎉
ATR Grid Levels [By MUQWISHI]▋ INTRODUCTION :
The “ATR Levels” produces a sequence of horizontal line levels above and below the Center Line (reference level). They are sized based on the instrument's volatility, representing the average historical price movement on a selected higher timeframe using the average true range (ATR) indicator.
_______________________
▋ OVERVIEW:
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▋ IMPLEMENTATION:
The indicator starts by drawing a Center Line that is selected by the user from a variety of common levels. Then, it draws a sequence of horizontal lines above and below the Center Line, which are sized based on the most confirmed average true range (ATR) at the selected higher timeframe.
In the top right corner of the chart, there is a table displaying both the selected ATR (in the right cell) and the ATR of the current bar (in the left cell). This feature enables users to compare these two values. It's important to note that the ATR of the current bar may not be confirmed yet, as the market is still active.
_______________________
▋ INDICATOR SETTINGS:
# Section (1): ATR Settings
(1) ATR Period & Smoothing.
(2) Timeframe where ATR value imported from.
(3) To show/hide the table comparison between the current ATR and the ATR for the selected period. Also, ability to color the current ATR cell if it’s greater.
# Section (2): Levels Settings
(1) Selecting a Center Line level among a variety of common levels, which is taken as reference level where a sequence of horizontal lines plot above and below it.
(2) Size of grid in ATR unit.
(3) Number of horizontal lines to plot in a single side.
(4) Grid Side. Ability to plot above or below the Center Line.
(5) Lines colors, and mode.
(6) Line style.
(7) Label style.
(8) Ability to remove old lines, from previous HTF.
_____________________
▋ COMMENT:
The ATR Levels should not be taken as a major concept to build a trading decision.
Please let me know if you have any questions.
Thank you.
Donchian Channel Trend MeterInspired by the Chande Trend Meter (this is not the Chande Trend Meter), this indicator aims to show the trend so you can make trading decisions accordingly. This is calculated by looking at Donchian Channels over a number of lengths (20, 40, 60 periods, etc.), converting them to percent, and then applying a weighting and smoothing similar to the Know Sure Thing Indicator. This results in smooth trend line that is not disturbed by large fluctuations in price action.
When the line is below 20%, you have a strong down trend. Values between 20 - 40% are a weak down trend. Values between 40 - 60% are no trend (slightly bullish or bearish if above or below 50%). Similarly, 60 - 80% is a weak uptrend, and above 80% is a strong uptrend. Trade signals can be turned on or off that correspond to crosses over 50%. It can be useful in spotting divergence.
Bandwidth Volatility - Silverman Rule of thumb EstimatorOverview
This indicator calculates volatility using the Rule of Thumb bandwidth estimator and incorporating the standard deviations of returns to get historical volatility. There are two options: one for the original rule of thumb bandwidth estimator, and another for the modified rule of thumb estimator. This indicator comes with the bandwidth , which is shown with the color gradient columns, which are colored by a percentile of the bandwidth, and the moving average of the bandwidth, which is the dark shaded area.
The rule of thumb bandwidth estimator is a simple and quick method for estimating the bandwidth parameter in kernel density estimation (KSE) or kernel regression. It provides a rough approximation of the bandwidth without requiring extensive computation resources or fine-tuning. One common rule of thumb estimator is Silverman rule, which is given by
h = 1.06*σ*n^(-1/5)
where
h is the bandwidth
σ is the standard deviation of the data
n is the number of data points
This rule of thumb is based on assuming a Gaussian kernel and aims to strike a balance between over-smoothing and under-smoothing the data. It is simple to implement and usually provides reasonable bandwidth estimates for a wide range of datasets. However , it is important to note that this rule of thumb may not always have optimal results, especially for non-Gaussian or multimodal distributions. In such cases, a modified bandwidth selection, such as cross-validation or even applying a log transformation (if the data is right-skewed), may be preferable.
How it works:
This indicator computes the bandwidth volatility using returns, which are used in the standard deviation calculation. It then estimates the bandwidth based on either the Silverman rule of thumb or a modified version considering the interquartile range. The percentile ranks of the bandwidth estimate are then used to visualize the volatility levels, identify high and low volatility periods, and show them with colors.
Modified Rule of thumb Bandwidth:
The modified rule of thumb bandwidth formula combines elements of standard deviations and interquartile ranges, scaled by a multiplier of 0.9 and inversely with a number of periods. This modification aims to provide a more robust and adaptable bandwidth estimation method, particularly suitable for financial time series data with potentially skewed or heavy-tailed data.
Formula for Modified Rule of Thumb Bandwidth:
h = 0.9 * min(σ, (IQR/1.34))*n^(-1/5)
This modification introduces the use of the IQR divided by 1.34 as an alternative to the standard deviation. It aims to improve the estimation, mainly when the underlying distribution deviates from a perfect Gaussian distribution.
Analysis
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Modelling Requirements
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Pros of Bandwidth as a volatility measure
Robust to Data Distribution: Bandwidth volatility, especially when estimated using robust methods like Silverman's rule of thumb or its modifications, can be less sensitive to outliers and non-normal distributions compared to some other measures of volatility
Flexibility: It can be applied to a wide range of data types and can adapt to different underlying data distributions, making it versatile for various analytical tasks.
How can traders use this indicator?
In finance, volatility is thought to be a mean-reverting process. So when volatility is at an extreme low, it is expected that a volatility expansion happens, which comes with bigger movements in price, and when volatility is at an extreme high, it is expected for volatility to eventually decrease, leading to smaller price moves, and many traders view this as an area to take profit in.
In the context of this indicator, low volatility is thought of as having the green color, which indicates a low percentile value, and also being below the moving average. High volatility is thought of as having the yellow color and possibly being above the moving average, showing that you can eventually expect volatility to decrease.
Likelihood of Winning - Probability Density FunctionIn developing the "Likelihood of Winning - Probability Density Function (PDF)" indicator, my aim was to offer traders a statistical tool to quantify the probability of reaching target prices. This indicator, grounded in risk assessment principles, enables users to analyze potential outcomes based on the normal distribution, providing insights into market dynamics.
The tool's flexibility allows for customization of the data series, lookback periods, and target settings for both long and short scenarios. It features a color-coded visualization to easily distinguish between probabilities of hitting specified targets, enhancing decision-making in trading strategies.
I'm excited to share this indicator with the trading community, hoping it will enhance data-driven decision-making and offer a deeper understanding of market risks and opportunities. My goal is to continuously improve this tool based on user feedback and market evolution, contributing to more informed trading practices.
This indicator leverages the "NormalDistributionFunctions" library, enabling easy integration into other indicators or strategies. Users can readily embed advanced statistical analysis into their trading tools, fostering innovation within the Pine Script community.
Bandwidth Bands - Silverman's rule of thumbWhat are Bandwidth Bands?
This indicator uses Silverman Rule of Thumb Bandwidth to estimate the width of bands around the rolling moving average which takes in the log transformation of price to remove most of price skewness for the rest of the volatility calculations and then a exp() function is performed to convert it back to a right skewed distribution. These bandwidths bands could offer insights into price volatility and trading extremes.
Silverman rule of thumb bandwidth:
The Silverman Rule of Thumb Bandwidth is a heuristic method used to estimate the optimal bandwidth for kernel density estimation, a statistical technique for estimating the probability density function of a random variable. In the context of financial analysis, such as in this indicator, it helps determine the width of bands around a moving average, providing insights into the level of volatility in the market. This method is particularly useful because it offers a quick and straightforward way to estimate bandwidth without requiring extensive computational resources or complex mathematical calculation
The bandwidth estimator automatically adjust to the characteristics of the data, providing a flexible and dynamic measure of dispersion that can capture variations in volatility over time. Standard deviations alone may not be as adaptive to changes in data distributions. The Bandwidth considers the overall shape and structure of the data distribution rather than just focusing on the spread of data points.
Settings
Source
Sample length
1-4 SD options to disable or enable each band
Rate of Change RSIIndicator Name: Rate of Change RSI
Description:
The Rate of Change (ROC) of the Relative Strength Index (RSI) is a technical indicator designed to provide insights into the momentum of an asset's price movement. It combines the Relative Strength Index (RSI), a popular momentum oscillator, with the Rate of Change (ROC) concept to assess the speed at which RSI values are changing.
How It Works:
Relative Strength Index (RSI): The RSI measures the magnitude of recent price changes to evaluate overbought or oversold conditions in an asset. It oscillates between 0 and 100, with readings above 70 typically indicating overbought conditions and readings below 30 indicating oversold conditions.
Rate of Change (ROC): The ROC calculates the percentage change in a given indicator over a specified period. In this indicator, we apply the ROC to the RSI values to determine how quickly the RSI is changing over time.
Key Features:
Acceleration and Deceleration: The ROC of RSI helps traders identify whether the momentum of the RSI is accelerating or decelerating. Positive values suggest increasing momentum, while negative values indicate decreasing momentum.
Dynamic Color Change: The color of the ROC RSI line changes dynamically based on the RSI level. When the RSI is between 0 and 40, the line color is blue, indicating potential oversold conditions. When the RSI is between 40 and 60, the line color is yellow, suggesting neutral conditions. When the RSI is above 60, the line color changes to green, indicating potential overbought conditions.
How to Use:
Acceleration: When the ROC RSI is positive and increasing while the RSI is above 60 (green), it may signal strong upward momentum.
Deceleration: Conversely, if the ROC RSI is negative and decreasing while the RSI is below 40 (blue), it may indicate weakening downward momentum.
Originality and Usefulness:
This indicator combines the RSI, a well-known momentum oscillator, with the ROC concept to provide a unique perspective on momentum dynamics. By dynamically adjusting the color of the ROC RSI line based on RSI levels, traders can quickly assess potential overbought or oversold conditions in the market.
Chart:
The chart displayed alongside this script provides a clean and easy-to-understand visualization of the ROC RSI indicator. The ROC RSI line color changes dynamically based on RSI levels, allowing traders to visually identify potential market conditions at a glance.
VWAP Bands @shrilssVWAP Bands Integrates VWAP with standard deviation bands to provide traders with insights into potential support and resistance levels based on volume dynamics. VWAP is a key metric used by institutional traders to gauge the average price a security has traded at throughout the trading day, taking into account both price and volume.
This script calculates the VWAP for each trading session and overlays it on the price chart as a solid line. Additionally, it plots multiple standard deviation bands around the VWAP to indicate potential areas of price extension or contraction. These bands are derived from multiplying the standard deviation of price by predetermined factors, offering traders a visual representation of potential price ranges.
Scalper's Volatility Filter [QuantraSystems]Scalpers Volatility Filter
Introduction
The 𝒮𝒸𝒶𝓁𝓅𝑒𝓇'𝓈 𝒱𝑜𝓁𝒶𝓉𝒾𝓁𝒾𝓉𝓎 𝐹𝒾𝓁𝓉𝑒𝓇 (𝒮𝒱𝐹) is a sophisticated technical indicator, designed to increase the profitability of lower timeframe trading.
Due to the inherent decrease in the signal-to-noise ratio when trading on lower timeframes, it is critical to develop analysis methods to inform traders of the optimal market periods to trade - and more importantly, when you shouldn’t trade.
The 𝒮𝒱𝐹 uses a blend of volatility and momentum measurements, to signal the dominant market condition - trending or ranging.
Legend
The 𝒮𝒱𝐹 consists of a signal line that moves above and below a central zero line, serving as the indication of market regime.
When the signal line is positioned above zero, it indicates a period of elevated volatility. These periods are more profitable for trading, as an asset will experience larger price swings, and by design, trend-following indicators will give less false signals.
Conversely, when the signal line moves below zero, a low volatility or mean-reverting market regime dominates.
This distinction is critical for traders in order to align strategies with the prevailing market behaviors - leveraging trends in volatile markets and exercising caution or implementing mean-reversion systems in periods of lower volatility.
Case Study
Here we can see the indicator's unique edge in action.
Out of the four potential long entries seen on the chart - displayed via bar coloring, two would result in losses.
However, with the power of the 𝒮𝒱𝐹 a trader can effectively filter false signals by only entering momentum-trades when the signal line is above zero.
In this small sample of four trades, the 𝒮𝒱𝐹 increased the win rate from 50% to 100%
Methodology
The methodology behind the 𝒮𝒱𝐹 is based upon three components:
By calculating and contrasting two ATR’s, the immediate market momentum relative to the broader, established trend is calculated. The original method for this can be credited to the user @xinolia
A modified and smoothed ADX indicator is calculated to further assess the strength and sustainability of trends.
The ‘Linear Regression Dispersion’ measures price deviations from a fitted regression line, adding further confluence to the signals representation of market conditions.
Together, these components synthesize a robust, balanced view of market conditions, enabling traders to help align strategies with the prevailing market environment, in order to potentially increase expected value and win rates.
RSI Volatility Bands [QuantraSystems]RSI Volatility Bands
Introduction
The RSI Volatility Bands indicator introduces a unique approach to market analysis by combining the traditional Relative Strength Index (RSI) with dynamic, volatility adjusted deviation bands. It is designed to provide a highly customizable method of trend analysis, enabling investors to analyze potential entry and exit points in a new and profound way.
The deviation bands are calculated and drawn in a manner which allows investors to view them as areas of dynamic support and resistance.
Legend
Upper and Lower Bands - A dynamic plot of the volatility-adjusted range around the current price.
Signals - Generated when the RSI volatility bands indicate a trend shift.
Case Study
The chart highlights the occurrence of false signals, emphasizing the need for caution when the bands are contracted and market volatility is low.
Juxtaposing this, during volatile market phases as shown, the indicator can effectively adapt to strong trends. This keeps an investor in a position even through a minor drawdown in order to exploit the entire price movement.
Recommended Settings
The RSI Volatility Bands are highly customisable and can be adapted to many assets with diverse behaviors.
The calibrations used in the above screenshots are as follows:
Source = close
RSI Length = 8
RSI Smoothing MA = DEMA
Bandwidth Type = DEMA
Bandwidth Length = 24
Bandwidth Smooth = 25
Methodology
The indicator first calculates the RSI of the price data, and applies a custom moving average.
The deviation bands are then calculated based upon the absolute difference between the RSI and its moving average - providing a unique volatility insight.
The deviation bands are then adjusted with another smoothing function, providing clear visuals of the RSI’s trend within a volatility-adjusted context.
rsiVal = ta.rsi(close, rsiLength)
rsiEma = ma(rsiMA, rsiVal, bandLength)
bandwidth = ma(bandMA, math.abs(rsiVal - rsiEma), bandLength)
upperBand = ma(bandMA, rsiEma + bandwidth, smooth)
lowerBand = ma(bandMA, rsiEma - bandwidth, smooth)
long = upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50)
short= not (upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50))
By dynamically adjusting to market conditions, the RSI trend bands offer a unique perspective on market trends, and reversal zones.
Truncated ATRThis program uses SMA and Truncated Mean to calculate ATR.
A truncated mean or trimmed mean is a statistical measure of central tendency, much like the mean and median. It involves the calculation of the mean after discarding given parts of a probability distribution or sample at the high and low end, and typically discarding an equal amount of both.
TASC 2024.03 Rate of Directional Change█ OVERVIEW
This script implements the Rate of Directional Change (RODC) indicator introduced by Richard Poster in the "Taming The Effects Of Whipsaw" article featured in the March 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
In his article, Richard Poster discusses an approach to potentially reduce false trend-following strategy entry signals due to whipsaws in forex data. The RODC indicator is central to this approach. The idea behind RODC is that one can characterize market whipsaw as alternating up and down ZigZag segments. By counting the number of up and down segments within a lookback window, the RODC indicator aims to identify if the window contains a significant whipsaw pattern:
RODC = 100 * Segments / Window Size (bars)
Larger RODC values suggest elevated whipsaw in the calculation window, while smaller values signify trending price activity.
█ CALCULATIONS
• For each price bar, the script iterates through the lookback window to identify up and down segments.
• If the price change between subsequent bars within the window is in the direction opposite to the current segment and exceeds the specified threshold , the calculation interprets the condition as a reversal point and the start of a new segment.
• The script uses the number of segments within the window to calculate RODC according to the above formula.
• Finally, the script applies a simple moving average to smoothen the RODC data.
Users can change the length of the lookback window , the threshold value, and the smoothing length in the "Inputs" tab of the script's settings.
F.B_Consolidation Range Identifier
The "F.B_Consolidation Range Identifier" (F.B_CRI) is an indicator aimed at identifying consolidation areas in the price chart. Here is an explanation of the logic and usage of this indicator:
Calculation of Standard Deviation
This indicator analyzes the market's volatility by considering the standard deviation of price movements over a defined period. A higher standard deviation indicates larger price movement, while a lower standard deviation suggests potential consolidation, where price movements are limited.
Derivation of Standard Deviation
To track changes in volatility, the derivative of the standard deviation is calculated. Positive derivative values indicate increasing volatility, while negative values suggest a decrease in volatility. This allows for the identification of potential consolidation phases where volatility decreases, and the market may stabilize.
Identification of Consolidation Phase
The indicator signals potential consolidation phases when the standard deviation is low and/or the derivative of the standard deviation is negative. To represent consolidation phases on the chart, the standard deviation line, background, and candles are colored red. However, it's important to note that the display is customizable and can be configured according to individual needs.
🚨 Important 🚨
The indicator only indicates whether consolidation phases exist. If the standard deviation line, background, or candles are gray, it indicates that a trend exists in general, but not whether it is bullish or bearish. It is advisable to use other analytical tools to confirm the direction of the trend.
Supertrend & CCI Strategy ScalpThis strategy is based on 2 Super Trend Indicators along with CCI .
The longer factor length gives you the current trend and the deviation in the short factor length gives us the opportunity to enter in the trade .
CCI indicator is used to determine the overbought and oversold levels.
Setup :
Long : When atrLength1 > close and atrLength2 < close and CCI < -100 we look for long trades as the longer factor length will be bullish .
Short : When atrLength1 < close and atrLength2 > close and CCI > 100 we look for short trades as the longer factor length will be bearish .
Please tune the settings according to your use .
Trade what you see not what you feel .
Please consult with your financial advisor before you deploy any real money for trading .
Pivot Extremes BreakoutI created the "Pivot Extremes Breakout" (PEB) indicator to easily spot breakout zones using pivot points. This tool comes from my need to anticipate market direction and capitalize on breakouts. PEB uses the last two pivot points to predict price paths and highlights potential breakout areas, adjusting for any timeframe. It simplifies seeing where the market might move next with color-coded lines and zones, aiming to improve your trading decisions.
F.B_DI+ DI- Trend TrackerThe F.B_DI+ DI- Trend Tracker is an indicator developed based on Directional Movement and True Range to identify trends in the market and assess their strength. Here is the logic behind the indicator and how to use it for trading:
Direction Determination
Unlike traditional DI+ and DI- based on simple calculations, this indicator utilizes derived versions of these directional indicators. These derived versions offer a more precise measurement of price directional movements by specifically tailoring to market conditions and the chosen time frame.
Trend Strength
The derived directional indicators are generated by dividing smoothed Directional Movements by smoothed True Range and converting them into percentages. These values provide insights into the strength of the trend by considering directional movements relative to market volatility.
Identifying Trend Reversals
To capture changes in trend strength more accurately, the first derivative of DI+ and DI- is computed. A crossover of these derived versions could indicate a potential trend reversal, with a crossover of DI+ over DI- suggesting a possible uptrend and a crossover of DI- over DI+ indicating a potential downtrend.
Making Trading Decisions
Traders can use crossovers between the derived DI+ and DI- to receive signals for potential trend reversals. Additionally, changes in the color of candlesticks or background can be used as confirmation of the trend direction and its strength.
By utilizing these derived directional indicators, the F.B_DI+ DI- Trend Tracker indicator offers more precise trading signals and improved trend analysis, enabling traders to make informed trading decisions.
Volatility Adjusted Profit Target
In my 'Volatility Adjusted Profit Target' indicator, I've crafted a dynamic tool for calculating target profit percentages suitable for both long and short trading strategies. It evaluates the highest and lowest prices over the anticipated duration of your trade, establishing a profit target that shifts with market volatility. As volatility increases, the potential for profit follows, with the target percentage rising accordingly; conversely, it declines with decreasing volatility. As a trader, setting an optimal Take Profit level has always been a challenge. This indicator not only helps in determining that level but also dynamically adjusts it throughout the trade's duration, providing a strategic edge in volatile markets.
Implied Volatility and Historical VolatilityThis indicator provides a visualization of two different volatility measures, aiding in understanding market perceptions and actual price movements. Remember to combine it with other technical analysis tools and risk management strategies for informed trading decisions. The two measures of volatility:
Implied Volatility: Based on the standard deviation of recent price changes, it represents the market's expectation of future volatility.
Historical Volatility: Measured by the daily high-low range as a percentage of the closing price, it reflects the actual volatility experienced recently. It is intended to be used along side the Mean and Standard Deviation Lines indicator.
Inputs:
Period (Days): Defines the number of past bars used to calculate both types of volatility.
Calculations:
Interpretation:
Comparing the lines: Divergence between the lines can indicate potential mispricing:
If the Implied Volatility is higher than the Historical Volatility, the market might be overestimating future volatility.
Conversely, if the Implied Volatility is lower, the market might be underestimating future volatility.
Monitoring trends: Track changes in both lines over time to identify potential shifts in volatility expectations or actual market behavior.
Limitations:
Assumes normality in price distribution, which may not always hold true.
Historical Volatility only reflects past behavior, not future expectations.
Consider other factors like market sentiment and news events for comprehensive volatility analysis.
