Volume Exhaustion [AlgoAlpha]Introducing the Volume Exhaustion by AlgoAlpha, is an innovative tool that aims to identify potential exhaustion or peaks in trading volume , which can be a key indicator for reversals or continuations in market trends 🔶.
Key Features:
Signal Plotting : A special feature is the plotting of 'Release' signals, marked by orange diamonds, indicating points where the exhaustion index crosses under its previous value and is above a certain boundary. This could signify critical market points 🚨.
Calculation Length Customization : Users can adjust the calculation and Signal lengths to suit their trading style, allowing for flexibility in analysis over different time periods. ☝️
len = input(50, "Calculation Length")
len2 = input(8, "Signal Length")
Visual Appeal : The script offers customizable colors (col for the indicator and col1 for the background) enhancing the visual clarity and user experience 💡.
col = input.color(color.white, "Indicator Color")
col1 = input.color(color.gray, "Background Color")
Advanced Volume Processing : At its core, the script utilizes a combination of Hull Moving Average (HMA) and Exponential Moving Average (EMA) applied to the volume data. This sophisticated approach helps in smoothing out the volume data and reducing lag.
sv = ta.hma(volume, len)
ssv = ta.hma(sv, len)
Volume Exhaustion Detection : The script calculates the difference between the volume and its smoothed version, normalizing this value to create an exhaustion index (fff). Positive values of this index suggest potential volume exhaustion.
f = sv-ssv
ff = (f) / (ta.ema(ta.highest(f, len) - ta.lowest(f, len), len)) * 100
fff = ff > 0 ? ff : 0
Boundary and Zero Line : The script includes a boundary line (boundary) and a zero line (zero), with the area between them filled for enhanced visual interpretation. This helps in assessing the relative position of the exhaustion index.
Customizable Background : The script colors the background of the chart for better readability and to distinguish the indicator’s area clearly.
Overall, Volume Exhaustion is designed for traders who focus on volume analysis. It provides a unique perspective on volume trends and potential exhaustion points, which can be crucial for making informed trading decisions. This script is a valuable addition for traders looking to enhance their trading experience with advanced volume analysis tools.
Meanreversion
Simple Neural Network Transformed RSI [QuantraSystems]Simple Neural Network Transformed RSI
Introduction
The Simple Neural Network Transformed RSI (ɴɴᴛ ʀsɪ) stands out as a formidable tool for traders who specialize in lower timeframe trading.
It is an innovative enhancement of the traditional RSI readings with simple neural network smoothing techniques.
This unique blend results in fairly accurate signals, tailored for swift market movements. The ɴɴᴛ ʀsɪ is particularly resistant to the usual market noise found in lower timeframes, ensuring a clearer view of short-term trends.
Furthermore, its diverse range of visualization options adds versatility, making it a valuable tool for traders seeking to capitalize on short-duration market dynamics.
Legend
In the Image you can see the BTCUSD 1D Chart with the ɴɴᴛ ʀsɪ in Trend Following Mode to display the current trend. This is visualized with the barcoloring.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 2), which can indicate extremely rare situations which can lead to either a softening momentum in the trend or even a mean reversion situation.
Here you can also see the original Indicator line and the Heikin Ashi transformed Indicator bars - more on that now.
Notes
Quantra Standard Value Contents:
To draw out all the information from the indicator calculation we have added a Heikin-Ashi (HA) Candle Visualization.
This HA transformation smoothens out the indicator values and gives a more informative look into Momentum and Trend of the Indicator itself.
This allows early entries and exits by observing the HA transformed Indicator values.
To diversify, different visualization options are available, either a classic line, HA transformed or Hybrid, which contains both of the previous.
To make Quantra's Indicators as useful and versatile as possible we have created options
to change the barcoloring and thus the derived signal from the indicator based on different modes.
Option to choose different Modes:
Trend Following (Indicator above mid line counts as uptrend, below is downtrend)
Extremities (Everything going beyond the Deviation Bands in a Mean Reversion manner is highlighted)
Candles (Color of HA candles as barcolor)
Reversion (HA ONLY) (Reversion Signals via the triangles if HA candles change state outside of the Deviation Bands)
- Reversion Signals are indicated by the triangles in the Heikin-Ashi or Hybrid visualization when the HA Candles revert
from downwards to upwards or the other way around OUTSIDE of the SD Bands.
Depending on the Indicator they signal OB/OS areas and can either work as high probability entries and exits for Mean Reversion trades or
indicate Momentum slow downs and potential ranges.
Please use another indicator to confirm this.
Case Study
To effectively utilize the NNT-RSI, traders should know their style and familiarize themselves with the available options.
As stated above, you have multiple modes available that you can combine as you need and see fit.
In the given example mostly only the mode was used in an isolated fashion.
Trend Following:
Purely relied on State Change - Midline crossover
Could be combined with Momentum or Reversion analysis for better entries/exits.
Extremities:
Ideal entry/exit is in the accordingly colored OS/OB Area, the Reversion signaled the latest possible entry/exit.
HA Candles:
Specifically applicable for strong trends. Powerful and fast tool.
Can whip if used as sole condition.
Reversions:
Shows the single entry and exit bars which have a positive expected value outcome.
Can also be used as confirmation or as last signal.
Please note that we always advise to find more confluence by additional indicators.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
In the showcased trades the default settings were used.
Methodology
The Simple Neural Network Transformed RSI uses a simple neural network logic to process RSI values, smoothing them for more accurate trend analysis.
This is achieved through a linear combination of RSI values over a specified input length, weighted evenly to produce a neural network output.
// Simple neural network logic (linear combination with weighted aggregation)
var float inputs = array.new_float(nnLength, na)
for i = 0 to nnLength - 1
array.set(inputs, i, rsi1 )
nnOutput = 0.0
for i = 0 to nnLength - 1
nnOutput := nnOutput + array.get(inputs, i) * (1 / nnLength)
nnOutput
This output is then compared against a standard or dynamic mean line to generate trend following signals.
Mean = ta.sma(nnOutput, sdLook)
cross = useMean? 50 : Mean
The indicator also incorporates Heikin Ashi candlestick calculations to provide additional insights into market dynamics, such as trend strength and potential reversals.
// Calculate Heikin Ashi representation
ha = ha(
na(nnOutput ) ? nnOutput : nnOutput ,
math.max(nnOutput, nnOutput ),
math.min(nnOutput, nnOutput ),
nnOutput)
Standard deviation bands are used to create dynamic overbought and oversold zones, further enhancing the tool's analytical capabilities.
// Calculate Dynamic OB/OS Zones
stdv_bands(_src, _length, _mult) =>
float basis = ta.sma(_src, _length)
float dev = _mult * ta.stdev(_src, _length)
= stdv_bands(nnOutput, sdLook,sdMult/2)
= stdv_bands(nnOutput, sdLook, sdMult)
The Standard Deviation bands take defined parameters from the user, in this case sigma of ideally between 2 to 3,
to help the indicator detect extremely improbable conditions and thus take an inversely probable signal from it to forward to the user.
The parameter settings and also the visualizations allow for ample customizations by the trader.
For questions or recommendations, please feel free to seek contact in the comments.
Triple Confirmation Kernel Regression Overlay [QuantraSystems]Kernel Regression Oscillator - Overlay
Introduction
The Kernel Regression Oscillator (ᏦᏒᎧ) represents an advanced tool for traders looking to capitalize on market trends.
This Indicator is valuable in identifying and confirming trend directions, as well as probabilistic and dynamic oversold and overbought zones.
It achieves this through a unique composite approach using three distinct Kernel Regressions combined in an Oscillator.
The additional Chart Overlay Indicator adds confidence to the signal.
Which is this Indicator.
This methodology helps the trader to significantly reduce false signals and offers a more reliable indication of market movements than more widely used indicators can.
Legend
The upper section is the Overlay. It features the Signal Wave to display the current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 3), which can indicate extremely rare situations which can lead to either a softening momentum in the trend or even a mean reversion situation.
The lower one is the Base Chart.
The Indicator is linked here
It features the Kernel Regression Oscillator to display a composite of three distinct regressions, also displaying current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 2), which can indicate extremely rare situations.
Case Study
To effectively utilize the ᏦᏒᎧ, traders should use both the additional Overlay and the Base
Chart at the same time. Then focus on capturing the confluence in signals, for example:
If the 𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮 on the Overlay and the ᏦᏒᎧ on the Base Chart both reside near the extreme of an Oversold zone the probability is higher than normal that momentum in trend may soften or the token may even experience a reversion soon.
If a bar is characterized by an Oversold Shading in both the Overlay and the Base Chart, then the probability is very high to experience a reversion soon.
In this case the trader may want to look for appropriate entries into a long position, as displayed here.
If a bar is characterized by an Overbought Shading in either Overlay or Base Chart, then the probability is high for momentum weakening or a mean reversion.
In this case the trade may have taken profit and closed his long position, as displayed here.
Please note that we always advise to find more confluence by additional indicators.
Recommended Settings
Swing Trading (1D chart)
Overlay
Bandwith: 45
Width: 2
SD Lookback: 150
SD Multiplier: 2
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Fast-paced, Scalping (4min chart)
Overlay
Bandwith: 75
Width: 2
SD Lookback: 150
SD Multiplier: 3
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Notes
The Kernel Regression Oscillator on the Base Chart is also sensitive to divergences if that is something you are keen on using.
For maximum confluence, it is recommended to use the indicator both as a chart overlay and in its Base Chart.
Please pay attention to shaded areas with Standard Deviation settings of 2 or 3 at their outer borders, and consider action only with high confidence when both parts of the indicator align on the same signal.
This tool shows its best performance on timeframes lower than 4 hours.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
The trend following functionality is indicated through the "𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮" Line, with optional "Up" and "Down" arrows to denote trend directions only (toggle “Show Trend Signals”).
Methodology
The Kernel Regression Oscillator takes three distinct kernel regression functions,
used at similar weight, in order to calculate a balanced and smooth composite of the regressions. Part of it are:
The Epanechnikov Kernel Regression: Known for its efficiency in smoothing data by assigning less weight to data points further away from the target point than closer data points, effectively reducing variance.
The Wave Kernel Regression: Similarly assigning weight to the data points based on distance, it captures repetitive and thus wave-like patterns within the data to smoothen out and reduce the effect of underlying cyclical trends.
The Logistic Kernel Regression: This uses the logistic function in order to assign weights by probability distribution on the distance between data points and target points. It thus avoids both bias and variance to a certain level.
kernel(source, bandwidth, kernel_type) =>
switch kernel_type
"Epanechnikov" => math.abs(source) <= 1 ? 0.75 * (1 - math.pow(source, 2)) : 0.0
"Logistic" => 1/math.exp(source + 2 + math.exp(-source))
"Wave" => math.abs(source) <= 1 ? (1 - math.abs(source)) * math.cos(math.pi * source) : 0.
kernelRegression(src, bandwidth, kernel_type) =>
sumWeightedY = 0.
sumKernels = 0.
for i = 0 to bandwidth - 1
base = i*i/math.pow(bandwidth, 2)
kernel = kernel(base, 1, kernel_type)
sumWeightedY += kernel * src
sumKernels += kernel
(src - sumWeightedY/sumKernels)/src
// Triple Confirmations
Ep = kernelRegression(source, bandwidth, 'Epanechnikov' )
Lo = kernelRegression(source, bandwidth, 'Logistic' )
Wa = kernelRegression(source, bandwidth, 'Wave' )
By combining these regressions in an unbiased average, we follow our principle of achieving confluence for a signal or a decision, by stacking several edges to increase the probability that we are correct.
// Average
AV = math.avg(Ep, Lo, Wa)
The Standard Deviation bands take defined parameters from the user, in this case sigma of ideally between 2 to 3,
to help the indicator detect extremely improbable conditions and thus take an inversely probable signal from it to forward to the user.
The parameter settings and also the visualizations allow for ample customizations by the trader. The indicator comes with default and recommended settings.
For questions or recommendations, please feel free to seek contact in the comments.
Triple Confirmation Kernel Regression Base [QuantraSystems]Kernel Regression Oscillator - BASE
Introduction
The Kernel Regression Oscillator (ᏦᏒᎧ) represents an advanced tool for traders looking to capitalize on market trends.
This Indicator is valuable in identifying and confirming trend directions, as well as probabilistic and dynamic oversold and overbought zones.
It achieves this through a unique composite approach using three distinct Kernel Regressions combined in an Oscillator. The additional Chart Overlay Indicator adds confidence to the signal.
This methodology helps the trader to significantly reduce false signals and offers a more reliable indication of market movements than more widely used indicators can.
Legend
The upper section is the Overlay. It features the Signal Wave to display the current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 3), which can indicate extremely rare situations which can lead to either a softening momentum in the trend or even a mean reversion situation.
The lower one is the Base Chart - This Indicator.
It features the Kernel Regression Oscillator to display a composite of three distinct regressions, also displaying current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 2), which can indicate extremely rare situations.
Case Study
To effectively utilize the ᏦᏒᎧ, traders should use both the additional Overlay and the Base
Chart at the same time. Then focus on capturing the confluence in signals, for example:
If the 𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮 on the Overlay and the ᏦᏒᎧ on the Base Chart both reside near the extreme of an Oversold zone the probability is higher than normal that momentum in trend may soften or the token may even experience a reversion soon.
If a bar is characterized by an Oversold Shading in both the Overlay and the Base Chart, then the probability is very high to experience a reversion soon.
In this case the trader may want to look for appropriate entries into a long position, as displayed here.
If a bar is characterized by an Overbought Shading in either Overlay or Base Chart, then the probability is high for momentum weakening or a mean reversion.
In this case the trade may have taken profit and closed his long position, as displayed here.
Please note that we always advise to find more confluence by additional indicators.
Recommended Settings
Swing Trading (1D chart)
Overlay
Bandwith: 45
Width: 2
SD Lookback: 150
SD Multiplier: 2
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Fast-paced, Scalping (4min chart)
Overlay
Bandwith: 75
Width: 2
SD Lookback: 150
SD Multiplier: 3
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Notes
The Kernel Regression Oscillator on the Base Chart is also sensitive to divergences if that is something you are keen on using.
For maximum confluence, it is recommended to use the indicator both as a chart overlay and in its Base Chart.
Please pay attention to shaded areas with Standard Deviation settings of 2 or 3 at their outer borders, and consider action only with high confidence when both parts of the indicator align on the same signal.
This tool shows its best performance on timeframes lower than 4 hours.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
The trend following functionality is indicated through the "𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮" Line, with optional "Up" and "Down" arrows to denote trend directions only (toggle “Show Trend Signals”).
Methodology
The Kernel Regression Oscillator takes three distinct kernel regression functions,
used at similar weight, in order to calculate a balanced and smooth composite of the regressions. Part of it are:
The Epanechnikov Kernel Regression: Known for its efficiency in smoothing data by assigning less weight to data points further away from the target point than closer data points, effectively reducing variance.
The Wave Kernel Regression: Similarly assigning weight to the data points based on distance, it captures repetitive and thus wave-like patterns within the data to smoothen out and reduce the effect of underlying cyclical trends.
The Logistic Kernel Regression: This uses the logistic function in order to assign weights by probability distribution on the distance between data points and target points. It thus avoids both bias and variance to a certain level.
kernel(source, bandwidth, kernel_type) =>
switch kernel_type
"Epanechnikov" => math.abs(source) <= 1 ? 0.75 * (1 - math.pow(source, 2)) : 0.0
"Logistic" => 1/math.exp(source + 2 + math.exp(-source))
"Wave" => math.abs(source) <= 1 ? (1 - math.abs(source)) * math.cos(math.pi * source) : 0.
kernelRegression(src, bandwidth, kernel_type) =>
sumWeightedY = 0.
sumKernels = 0.
for i = 0 to bandwidth - 1
base = i*i/math.pow(bandwidth, 2)
kernel = kernel(base, 1, kernel_type)
sumWeightedY += kernel * src
sumKernels += kernel
(src - sumWeightedY/sumKernels)/src
// Triple Confirmations
Ep = kernelRegression(source, bandwidth, 'Epanechnikov' )
Lo = kernelRegression(source, bandwidth, 'Logistic' )
Wa = kernelRegression(source, bandwidth, 'Wave' )
By combining these regressions in an unbiased average, we follow our principle of achieving confluence for a signal or a decision, by stacking several edges to increase the probability that we are correct.
// Average
AV = math.avg(Ep, Lo, Wa)
The Standard Deviation bands take defined parameters from the user, in this case sigma of ideally between 2 to 3,
to help the indicator detect extremely improbable conditions and thus take an inversely probable signal from it to forward to the user.
The parameter settings and also the visualizations allow for ample customizations by the trader. The indicator comes with default and recommended settings.
For questions or recommendations, please feel free to seek contact in the comments.
Mean Reversion Watchlist [Z score]Hi Traders !
What is the Z score:
The Z score measures a values variability factor from the mean, this value is denoted by z and is interpreted as the number of standard deviations from the mean.
The Z score is often applied to the normal distribution to “standardize” the values; this makes comparison of normally distributed random variables with different units possible.
This popular reversal based indicator makes an assumption that the sample distribution (in this case the sample of price values) is normal, this allows for the interpretation that values with an extremely high or low percentile or “Z” value will likely be reversal zones.
This is because in the population data (the true distribution) which is known, anomaly values are very rare, therefore if price were to take a z score factor of 3 this would mean that price lies 3 standard deviations from the mean in the positive direction and is in the ≈99% percentile of all values. We would take this as a sign of a negative reversal as it is very unlikely to observe a consecutive equal to or more extreme than this percentile or Z value.
The z score normalization equation is given by
In Pine Script the Z score can be computed very easily using the below code.
// Z score custom function
Zscore(source, lookback) =>
sma = ta.sma(source, lookback)
stdev = ta.stdev(source, lookback, true)
zscore = (source - sma) / stdev
zscore
The Indicator:
This indicator plots the Z score for up to 20 different assets ( Note the maximum is 40 however the utility of 40 plots in one indicator is not much, there is a diminishing marginal return of the number of plots ).
Z score threshold levels can also be specified, the interpretation is the same as stated above.
The timeframe can also be fixed, by toggling the “Time frame lock” user input under the “TIME FRAME LOCK” user input group ( Note this indicator does not repain t).
Alpha Schaff [AlgoAlpha]Description:
The Alpha Schaff indicator is a proprietary technical analysis tool that incorporates a modified version of the Schaff Trend Cycle (STC) to generate trading signals. The indicator is designed to identify potential overbought and oversold conditions in the market. It utilizes a combination of exponential moving averages (EMAs) and price volatility to generate trading signals. The plot of the indicator is derived from the opening price adjusted by a factor that depends on the Alpha Schaff value. A color scheme is used to indicate whether the current value is higher or lower than the previous value.
What is Alpha Schaff?:
Alpha Schaff is a technical indicator used in trading to identify potential trend reversals and confirm the strength of a current trend. It combines multiple moving averages and oscillators to generate buy and sell signals. Traders use Alpha Schaff to make informed decisions about entering or exiting positions based on its indications of trend momentum and market conditions.
Calculation:
The Alpha Schaff indicator calculates the difference between fast and slow EMAs based on the specified input lengths. It then measures the highest and lowest values of the difference over a defined sensitivity period. The indicator normalizes these values to a percentage scale to provide insights into the current market conditions.
How to use it?:
Monitor the color of the indicator line. A change in color indicates a potential trend reversal. For example, a switch from white to a purple color suggests a possible bullish trend, while a switch from a purple color to white indicates a potential bearish trend. Points of reversal can also be indicated by distinctive arrows pointing upwards or downward as well as visualized in bullish/bearish colors. The Distance between the indicator plot and the source can be interpreted as a measurement of price volatility. The script includes alert conditions that trigger when specific criteria are met. These alerts can notify users of potential buying or selling opportunities based on the indicator's signals.
Utility:
The Alpha Schaff is a trend-following indicator suitable for traders operating in trending markets. It offers clear and precise signals that provide valuable insights into bullish or bearish price movements. Additionally, this indicator stands out by incorporating distinctive arrows, indicating potential retracement points and allowing traders to anticipate mean reversion.
Originality:
The Alpha Schaff indicator, developed by AlgoAlpha introduces a proprietary modification to the Schaff Trend Cycle (STC) by incorporating multiple moving averages and oscillators. While the concept of the Schaff Trend Cycle exists, the specific implementation and combination of elements in the Alpha vSchaff indicator are unique to this tool. The inclusion of color schemes, arrow indicators, and volatility measurements sets it apart from other technical analysis indicators. Traders can benefit from its originality by utilizing its distinctive features to make more informed trading decisions in trending markets.
Amazing Oscillator (AO) [Algoalpha]Description:
Introducing the Amazing Oscillator indicator by Algoalpha, a versatile tool designed to help traders identify potential trend shifts and market turning points. This indicator combines the power of the Awesome Oscillator (AO) and the Relative Strength Index (RSI) to create a new indicator that provides valuable insights into market momentum and potential trade opportunities.
Key Features:
Customizable Parameters: The indicator allows you to customize the period of the RSI calculations to fine-tune the indicator's responsiveness.
Visual Clarity: The indicator uses user-defined colors to visually represent upward and downward movements. You can select your preferred colors for both bullish and bearish signals, making it easy to spot potential trade setups.
AO and RSI Integration: The script combines the AO and RSI indicators to provide a comprehensive view of market conditions. The RSI is applied to the AO, which results in a standardized as well as a less noisy version of the Awesome Oscillator. This makes the indicator capable of pointing out overbought or oversold conditions as well as giving fewer false signals
Signal Plots: The indicator plots key levels on the chart, including the RSI threshold(Shifted down by 50) at 30 and -30. These levels are often used by traders to identify potential trend reversal points.
Signal Alerts: For added convenience, the indicator includes "x" markers to signal potential buy (green "x") and sell (red "x") opportunities based on RSI crossovers with the -30 and 30 levels. These alerts can help traders quickly identify potential entry and exit points.
Hurst ALMA Channels With Signals [UAlgo]
In the pursuit of identifying potential market pivots, a single measurement of Average True Range (ATR), may not provide sufficient information on its own, lacking directional insights. However, by employing a Moving Average (MA), specifically the Arnaud Legoux MA with Hurst C. calculation applied, a potential trading range can be visualized, taking recent volatility into account.
The underlying assumption is that if volatility remains relatively stable and the price extends beyond this ATR-derived range, there is a high probability of a reversion to the mean. At this point, it is postulated that available buying or selling pressure is depleted, prompting a pivot back to the mean.
To enhance the analysis, multiple MAs of different lengths are plotted. While individual MAs alone may not convey substantial information, observing reversions to the mean between MAs of varying lengths becomes insightful. Shorter MAs may oscillate above or below longer MAs, returning to the mean and creating crossover patterns.
The key innovation lies in combining these two concepts. By utilizing three different length MAs with corresponding ATR lengths, a dynamic system is established. The smallest band fluctuates within the medium band, and the medium band oscillates within the large band, creating approximate short, medium, and long trading ranges relative to the MAs.
For instance, in a theoretical scenario, when the smallest band reaches the upper limit of the medium band, and simultaneously, the medium band reaches the upper limit of the large band, and the price surpasses all of them, there is a heightened probability of a market reversal.
It's important to emphasize that these observations are based on historical volatility patterns and are subject to adjustments based on specific market conditions and the chosen instrument.
The developed indicator generates three distinct signal types, each providing valuable insights into potential market pivots without disclosing specific parameters:
Large Triangles : Representing a high-probability pivot, this signal occurs when the price surpasses all bands, either at the top or bottom. It suggests an extreme point where a pivot is likely.
Medium Triangles : Indicating a notable market event, this signal emerges when the price exceeds both the small and medium bands but falls short of surpassing the large band. Additionally, the small band must have exceeded the medium band. This configuration points to a significant market move with a potential for reversion.
Small Triangles : This signal is observed when the price surpasses both the small and medium bands, yet does not breach the large band. Notably, the small band should not have exceeded the medium band. This signal type suggests a distinctive market condition where a pivot may be imminent.
These triangle signals are designed to identify key points in the market where historical patterns indicate a likelihood of reversion or significant price movement. It is crucial to note that the interpretation of these signals should be adapted to specific market conditions and instruments.
Good luck to you all !
Trend Flow Profile [AlgoAlpha]Description:
The "Trend Flow Profile" indicator is a powerful tool designed to analyze and interpret the underlying trends and reversals in a financial market. It combines the concepts of Order Flow and Rate of Change (ROC) to provide valuable insights into market dynamics, momentum, and potential trade opportunities. By integrating these two components, the indicator offers a comprehensive view of market sentiment and price movements, facilitating informed trading decisions.
Rationale:
The combination of Order Flow and ROC in the "Trend Flow Profile" indicator stems from the recognition that both factors play critical roles in understanding market behavior. Order Flow represents the net buying or selling pressure in the market, while ROC measures the rate at which prices change. By merging these elements, the indicator captures the interplay between market participants' actions and the momentum of price movements, enabling traders to identify trends, spot reversals, and gauge the strength of price acceleration or deceleration.
Calculation:
The Order Flow component is computed by summing the volume when prices move up and subtracting the volume when prices move down. This cumulative measure reflects the overall order imbalance in the market, providing insights into the dominant buying or selling pressure.
The ROC component calculates the percentage change in price over a given period. It compares the current price to a previous price and expresses the change as a percentage. This measurement indicates the velocity and direction of price movement, allowing traders to assess the market's momentum.
How to Use It?
The "Trend Flow Profile" indicator offers valuable information to traders for making informed trading decisions. It enables the identification of underlying trends and potential reversals, providing a comprehensive view of market sentiment and momentum. Here are some key ways to utilize the indicator:
Spotting Trends: The indicator helps identify the prevailing market trend, whether bullish or bearish. A consistent positive (green) histogram indicates a strong uptrend, while a consistent negative (red) histogram suggests a robust downtrend.
Reversal Signals: Reversal patterns can be identified when the histogram changes color, transitioning from positive to negative (or vice versa). These reversals can signify potential turning points in the market, highlighting opportunities for counter-trend trades.
Momentum Assessment: By observing the width and intensity of the histogram, traders can assess the acceleration or deceleration of price momentum. A wider histogram suggests strong momentum, while a narrower histogram indicates a potential slowdown.
Utility:
The "Trend Flow Profile" indicator serves as a valuable tool for traders, providing several benefits. Traders can easily identify the prevailing market trend, enabling them to align their trading strategies with the dominant direction of the market. The indicator also helps spot potential reversals, allowing traders to anticipate market turning points and capture counter-trend opportunities. Additionally, the green and red histogram colors provide visual cues to determine the optimal duration of a long or short position. Following the green histogram signals when in a long position and the red histogram signals when in a short position can assist traders in managing their trades effectively. Moreover, the width and intensity of the histogram offer insights into the acceleration or deceleration of momentum. Traders can gauge the strength of price movements and adjust their trading strategies accordingly. By leveraging the "Trend Flow Profile" indicator, traders gain a comprehensive understanding of market dynamics, which enhances their decision-making and improves their overall trading outcomes.
Bollinger Bands Percentile + Stdev Channels (BBPct) [AlgoAlpha]Description:
The "Bollinger Bands Percentile (BBPct) + STD Channels" mean reversion indicator, developed by AlgoApha, is a technical analysis tool designed to analyze price positions using Bollinger Bands and Standard Deviation Channels (STDC). The combination of these two indicators reinforces a stronger reversal signal. BBPct calculates the percentile rank of the price's standard deviation relative to a specified lookback period. Standard deviation channels operate by utilizing a moving average as the central line, with upper and lower lines equidistant from the average based on the market's volatility, helping to identify potential price boundaries and deviations.
How it Works:
The BBPct indicator utilizes Bollinger Bands, which consist of a moving average (basis) and upper and lower bands based on a specified standard deviation multiplier. By default, it uses a 20-period moving average and a standard deviation multiplier of 2. The upper band is calculated by adding the basis to the standard deviation multiplied by the multiplier, while the lower band is calculated by subtracting the same value. The BBPct indicator calculates the position of the current price between the lower and upper Bollinger Bands as a percentile value. It determines this position by comparing the price's distance from the lower band to the overall range between the upper and lower bands. A value of 0 indicates that the price is at the lower band, while a value of 100 indicates that the price is at the upper band. The indicator also includes an optional Bollinger Band standard deviation percentage (%Stdev) histogram, representing the deviation of the current price from the moving average as a percentage of the price itself.
Standard deviation channels, also known as volatility channels, aid in identifying potential buying and selling opportunities while minimizing unfavorable trades. These channels are constructed by two lines that run parallel to a moving average. The separation between these lines is determined by the market's volatility, represented by standard deviation. By designating upper and lower channel lines, the channels demarcate the borders between typical and atypical price movements. Consequently, when the market's price falls below the lower channel line, it suggests undervaluation, whereas prices surpassing the upper channel line indicate overvaluation.
Signals
The chart displays potential reversal points through the use of red and green arrows. A red arrow indicates a potential bearish retracement, signaling a possible downward movement, while a green arrow represents a potential pullback to the positive, suggesting a potential upward movement. These signals are generated only when both the BBPct (Bollinger Bands Percentage) and the STDC (Standard Deviation Channel) indicators align with bullish or bearish conditions. Consequently, traders might consider opening long positions when the green arrow appears and short positions when the red arrow is plotted.
Usage:
This indicator can be utilized by traders and investors to effectively identify pullbacks, reversals, and mean regression, thereby enhancing their trading opportunities. Notably, extreme values of the BBPct, such as below -5 or above 105, indicate oversold or overbought conditions, respectively. Moreover, the presence of extreme STDC zones occurs when prices fall below the lower channel line or cross above the upper channel line. Traders can leverage this information as a mean reversion tool by identifying instances of peak overbought and oversold values. These distinctive characteristics facilitate the identification of potential entry and exit points, thus augmenting trading decisions and enhancing market analysis.
The indicator's parameters, such as the length of the moving average, the data source, and the standard deviation multiplier, can be customized to align with individual trading strategies and preferences.
Originality:
The BBPct + STDC indicator, developed by AlgoAlpha, is an original implementation that combines the calculation of Bollinger Bands, percentile ranking, the %Stdev histogram and the STDC. While it shares some similarities with the Bollinger Bands %B indicator, the BBPct indicator introduces additional elements and customization options tailored to AlgoAlpha's methodology. The script is released under the Mozilla Public License 2.0, granting users the freedom to utilize and modify it while adhering to the license terms.
Fourier Smoothed Volume Zone Oscillator (FSVZO) [AlgoAlpha]Description
The Fourier Smoothed Volume Zone Oscillator (FSVZO) is an implementation of the Discrete Fourier Transform in a Volume Zone Oscillator. Its purpose is to smooth price data and reduce noise to provide a more clear and accurate indication of price movement. This indicator also includes additional EMA smoothing to accurately depict reversals.
Discrete Fourier Transform
The Discrete Fourier Transform (DFT) is a mathematical algorithm used to convert discrete time-domain data into its frequency-domain representation. By decomposing a signal into its constituent frequencies, it reveals the amplitude and phase information associated with each frequency component.
Volume Zone Oscillator
The Volume Zone Oscillator is an indicator that combines volume and price data to provide insights into market trends and momentum. It calculates the difference between the volume traded above and below a specified price level and represents it as a line plot on the chart. The Volume Zone Oscillator helps traders identify periods of high buying or selling pressure and can be used to confirm trends, spot divergences, and generate trading signals. By analyzing the relationship between volume and price, traders can gain a deeper understanding of market dynamics and make more informed trading decisions.
Features
This indicator incorporates Ehler's Universal Oscillator concept and presents a histogram to provide valuable insights into the market's noise levels. Ehler's Universal Oscillator represents the statistical model that characterizes random and unpredictable market behavior. By utilizing this concept, the histogram enhances traders' ability to identify periods of increased or decreased volatility in the market.
How to use it?
Green dots and lines represent bullish price movement, while red dots and lines indicate bearish price movement. These signals gain additional strength when considering our oversold and overbought zones. Traders and investors can leverage these signals to initiate long positions when green signals coincide with oversold conditions, and vice versa. By combining these signals in synergy with Ehler's Universal Oscillator, a more precise representation of market trends can be achieved. To optimize its effectiveness, it is advisable to integrate this indicator with complementary technical analysis tools and incorporate it into a comprehensive trading strategy. Traders are encouraged to explore diverse settings and timeframes to align the indicator with their individual trading preferences and adapt it to prevailing market conditions.
Utility
By combining the FSVZO indicator with Ehler's white noise histogram, users gain a comprehensive perspective on volume-related market conditions. It empowers traders and investors to evaluate the intensity of buying or selling pressure, detect potential trend reversals or continuations, and ultimately make more informed trading decisions. This information can serve as confirmation or validation for other technical indicators, enabling traders to identify potential market turning points and enhance their comprehension of market dynamics.
The indicator offers several valuable applications, including the detection of divergence patterns between volume and price, identification of accumulation or distribution phases, and assessment of overall market trend strength. It accommodates various trading styles, such as swing trading, trend following, or mean reversion strategies. By leveraging these capabilities, traders can expand their toolkit and make more informed trading decisions.
Originality
The originality of the script lies in the combination of the Fourier analysis, white noise calculations, and the Volume Zone Oscillator. It provides a unique perspective on market dynamics and can be used to identify potential trading opportunities based on overbought and oversold conditions as well as trend reversals. Special thanks to @QuantiLuxe for their assistance in the development of this indicator
Limited Growth Stock-to-Flow (LGS2F) [AlgoAlpha]Description:
The "∂ Limited Growth Stock-to-Flow (LG-S2F)" indicator, developed by AlgoAlpha, is a technical analysis tool designed to analyze the price of Bitcoin (BTC) based on the Stock-to-Flow model. The indicator calculates the expected price range of BTC by incorporating variables such as BTC supply, block height, and model parameters. It also includes error bands to indicate potential overbought and oversold conditions.
How it Works:
The LG-S2F indicator utilizes the Stock-to-Flow model, which measures the scarcity of an asset by comparing its circulating supply (stock) to its newly produced supply (flow). In this script, the BTC supply and block height data are obtained to calculate the price using the model formula. The formula includes coefficients (a, b, c) and exponentiation functions to derive the expected price.
The script incorporates error bands based on uncertainty values derived from the standard errors of the model parameters. These error bands indicate the potential range of variation in the expected price, accounting for uncertainties in the model's parameters. The upper and lower error bands visualize potential overbought and oversold conditions, respectively.
Usage:
Traders can utilize the LG-S2F indicator to gain insights into the potential price movements of Bitcoin. The indicator's main line represents the expected price, while the error bands highlight the potential range of variation. Traders may consider taking long positions when the price is near or below the lower error band and short positions when the price is close to or above the upper error band.
It's important to note that the LG-S2F indicator is specifically designed for Bitcoin and relies on the Stock-to-Flow model. Users should exercise caution and consider additional analysis and factors before making trading decisions solely based on this indicator.
Originality:
The LG-S2F indicator, developed by QuantMario and AlgoAlpha, is an original implementation that combines the Stock-to-Flow model with error bands to provide a comprehensive view of BTC's potential price range. While the concept of Stock-to-Flow analysis exists, the specific calculations, incorporation of error bands, and customization options in this script are unique to QuantMario's methodology. The script is released under Mozilla Public License 2.0, allowing users to utilize and modify it while adhering to the license terms.
Major Currency RSI Indicator (MCRSI)Experience the power of multi-dimensional analysis with our Multi-Currency RSI Indicator (MCRSI). This innovative tool allows traders to simultaneously track and compare the Relative Strength Index (RSI) of eight different currencies in a single chart.
The MCRSI calculates the RSI for USD (DXY), EUR (EXY), JPY (JXY), CAD (CXY), AUD (AXY), NZD (ZXY), GBP (BXY), and CHF (SXY), covering a broad range of the forex market. Each RSI line is color-coded for easy differentiation and equipped with labels at the last bar for a clutter-free view.
Our indicator is designed with user-friendly customization features. You can easily adjust the length of the RSI and the time frame according to your trading strategy. It also handles gaps in the chart data with the barmerge.gaps_on option, ensuring accurate and consistent RSI calculations.
Whether you are a novice trader seeking to understand market dynamics better or an experienced trader wanting to diversify your technical analysis, the MCRSI offers a unique perspective of the forex market. This multi-currency approach can help identify potential trading opportunities that could be missed when analyzing currencies in isolation.
Harness the power of multi-currency RSI analysis with our MCRSI Indicator. It's time to step up your trading game!
Features:
Tracks 8 different currencies simultaneously
Color-coded RSI lines for easy identification
Customizable RSI length and time frame
Handles gaps in chart data
Last bar labels for a clutter-free view
Ideal for forex traders of all experience levels
How to Use:
Add the MCRSI to your TradingView chart.
Adjust the RSI length and time frame as needed.
Monitor the RSI lines and their intersections for potential trading signals.
Happy trading!
Re-Anchoring VWAP TripleThe Triple Re-Anchoring VWAP (Volume Weighted Average Price) indicator is a tool designed for traders seeking a deeper understanding of market trends and key price levels. This indicator dynamically recalibrates VWAP calculations based on significant market pivot points, offering a unique perspective on potential support and resistance levels.
Key Features:
Dynamic Re-anchoring at All-Time Highs (ATH) : The first layer of this indicator continuously tracks the all-time high and recalibrates the VWAP from each new ATH. This VWAP line, typically acting as a dynamic resistance level, offers insights into the overbought conditions and potential reversal zones.
Adaptive Re-anchoring to Post-ATH Lows : The second component of the indicator shifts focus to the market's reaction post-ATH. It identifies the lowest low following an ATH and re-anchors the VWAP calculation from this point. This VWAP line often serves as a dynamic support level, highlighting key areas where the market finds value after a significant high.
Re-anchoring to Highs After Post-ATH Lows : The third element of this tool takes adaptation one step further by tracking the highest high achieved after the lowest low post-ATH. This VWAP line can act as either support or resistance, providing a nuanced view of the market's valuation in the recovery phase or during consolidation after a significant low.
Applications:
Trend Confirmation and Reversal Signals : By comparing the price action relative to the dynamically anchored VWAP lines, traders can gauge the strength of the trend and anticipate potential reversals.
Entry and Exit Points : By highlighting significant support and resistance areas, it assists in determining optimal entry and exit points, particularly in swing trading and mean reversion strategies.
Enhanced Market Insight : The dynamic nature of the indicator, with its shifting anchor points, offers a refined understanding of market sentiment and valuation changes over time.
Why Triple Re-Anchoring VWAP?
Traditional VWAP tools offer a linear view, often missing out on the intricacies of market fluctuations. The Triple Re-Anchoring VWAP addresses this by providing a multi-faceted view of the market, adapting not just to daily price changes but pivoting around significant market events. Whether you're a day trader, swing trader, or long-term investor, this indicator adds depth to your market analysis, enabling more informed trading decisions.
Examples:
Standardized SuperTrend Oscillator
The Standardized SuperTrend Oscillator (SSO) is a versatile tool that transforms the SuperTrend indicator into an oscillator, offering both trend-following and mean reversion capabilities. It provides deeper insights into trends by standardizing the SuperTrend with respect to its upper and lower bounds, allowing traders to identify potential reversals and contrarian signals.
Methodology:
Lets begin with describing the SuperTrend indicator, which is the fundamental tool this script is based on.
SuperTrend:
The SuperTrend is calculated based on the average true range (ATR) and multiplier. It identifies the trend direction by placing a line above or below the price. In an uptrend, the line is below the price; in a downtrend, it's above the price.
pine_st(float src = hl2, float factor = 3., simple int len = 10) =>
float atr = ta.atr(len)
float up = src + factor * atr
up := up < nz(up ) or close > nz(up ) ? up : nz(up )
float lo = src - factor * atr
lo := lo > nz(lo ) or close < nz(lo ) ? lo : nz(lo )
int dir = na
float st = na
if na(atr )
dir := 1
else if st == nz(up )
dir := close > up ? -1 : 1
else
dir := close < lo ? 1 : -1
st := dir == -1 ? lo : up
SSO Oscillator:
The SSO is derived from the SuperTrend and the source price. It calculates the standardized difference between the SuperTrend and the source price. The standardization is achieved by dividing this difference by the distance between the upper and lower bounds of the SuperTrend.
float sso = (src - st) / (up - lo)
Components and Features:
SuperTrend of Oscillator - An additional SuperTrend based on the direction and volatility of the oscillator, behaving as the SuperTrend OF the SuperTrend. This provides further trend analysis of the underlying broad trend regime.
Reversion Tracer - The RSI of the direction of the original SuperTrend, providing a dynamic threshold for premium and discount price areas.
float rvt = ta.rsi(dir, len)
Heikin Ashi Transform - An option to apply the Heikin Ashi transform to the source price of the oscillator, providing a smoother visual representation of trends.
Display Modes - Choose between Line mode for a standard oscillator view or Candle mode, displaying the oscillator as Heikin Ashi candles for more in-depth trend analysis.
Contrarian and Reversion Signals:
Contrarian Signals - Based on the SuperTrend of the oscillator, these signals can act as potential buy or sell indications, highlighting potential trend exhaustion or premature reversals.
Reversion Signals - Generated when the oscillator crosses above or below the Reversion Tracer, signaling potential mean reversion opportunities or trend breakouts.
Utility and Use Cases:
Trend Analysis - Utilize the SSO as a trend-following tool with the added benefits of the oscillator's SuperTrend and Heikin Ashi transform.
Valuation Analysis - Leverage the oscillator's reversion signals for identifying potential mean reversion opportunities in the market.
The Standardized SuperTrend Oscillator enhances the capabilities of the SuperTrend indicator, offering a balanced approach to both trend-following and mean reversion strategies. Its customizable options and contrarian signals make it a valuable instrument for traders seeking comprehensive trend analysis and potential reversal signals.
Volume and Price Z-Score [Multi-Asset] - By LeviathanThis script offers in-depth Z-Score analytics on price and volume for 200 symbols. Utilizing visualizations such as scatter plots, histograms, and heatmaps, it enables traders to uncover potential trade opportunities, discern market dynamics, pinpoint outliers, delve into the relationship between price and volume, and much more.
A Z-Score is a statistical measurement indicating the number of standard deviations a data point deviates from the dataset's mean. Essentially, it provides insight into a value's relative position within a group of values (mean).
- A Z-Score of zero means the data point is exactly at the mean.
- A positive Z-Score indicates the data point is above the mean.
- A negative Z-Score indicates the data point is below the mean.
For instance, a Z-Score of 1 indicates that the data point is 1 standard deviation above the mean, while a Z-Score of -1 indicates that the data point is 1 standard deviation below the mean. In simple terms, the more extreme the Z-Score of a data point, the more “unusual” it is within a larger context.
If data is normally distributed, the following properties can be observed:
- About 68% of the data will lie within ±1 standard deviation (z-score between -1 and 1).
- About 95% will lie within ±2 standard deviations (z-score between -2 and 2).
- About 99.7% will lie within ±3 standard deviations (z-score between -3 and 3).
Datasets like price and volume (in this context) are most often not normally distributed. While the interpretation in terms of percentage of data lying within certain ranges of z-scores (like the ones mentioned above) won't hold, the z-score can still be a useful measure of how "unusual" a data point is relative to the mean.
The aim of this indicator is to offer a unique way of screening the market for trading opportunities by conveniently visualizing where current volume and price activity stands in relation to the average. It also offers features to observe the convergent/divergent relationships between asset’s price movement and volume, observe a single symbol’s activity compared to the wider market activity and much more.
Here is an overview of a few important settings.
Z-SCORE TYPE
◽️ Z-Score Type: Current Z-Score
Calculates the z-score by comparing current bar’s price and volume data to the mean (moving average with any custom length, default is 20 bars). This indicates how much the current bar’s price and volume data deviates from the average over the specified period. A positive z-score suggests that the current bar's price or volume is above the mean of the last 20 bars (or the custom length set by the user), while a negative z-score means it's below that mean.
Example: Consider an asset whose current price and volume both show deviations from their 20-bar averages. If the price's Z-Score is +1.5 and the volume's Z-Score is +2.0, it means the asset's price is 1.5 standard deviations above its average, and its trading volume is 2 standard deviations above its average. This might suggest a significant upward move with strong trading activity.
◽️ Z-Score Type: Average Z-Score
Calculates the custom-length average of symbol's z-score. Think of it as a smoothed version of the Current Z-Score. Instead of just looking at the z-score calculated on the latest bar, it considers the average behavior over the last few bars. By doing this, it helps reduce sudden jumps and gives a clearer, steadier view of the market.
Example: Instead of a single bar, imagine the average price and volume of an asset over the last 5 bars. If the price's 5-bar average Z-Score is +1.0 and the volume's is +1.5, it tells us that, over these recent bars, both the price and volume have been consistently above their longer-term averages, indicating sustained increase.
◽️ Z-Score Type: Relative Z-Score
Calculates a relative z-score by comparing symbol’s current bar z-score to the mean (average z-score of all symbols in the group). This is essentially a z-score of a z-score, and it helps in understanding how a particular symbol's activity stands out not just in its own historical context, but also in relation to the broader set of symbols being analyzed. In other words, while the primary z-score tells you how unusual a bar's activity is for that specific symbol, the relative z-score informs you how that "unusualness" ranks when compared to the entire group's deviations. This can be particularly useful in identifying symbols that are outliers even among outliers, indicating exceptionally unique behaviors or opportunities.
Example: If one asset's price Z-Score is +2.5 and volume Z-Score is +3.0, but the group's average Z-Scores are +0.5 for price and +1.0 for volume, this asset’s Relative Z-Score would be high and therefore stand out. This means that asset's price and volume activities are notably high, not just by its own standards, but also when compared to other symbols in the group.
DISPLAY TYPE
◽️ Display Type: Scatter Plot
The Scatter Plot is a visual tool designed to represent values for two variables, in this case the Z-Scores of price and volume for multiple symbols. Each symbol has it's own dot with x and y coordinates:
X-Axis: Represents the Z-Score of price. A symbol further to the right indicates a higher positive deviation in its price from its average, while a symbol to the left indicates a negative deviation.
Y-Axis: Represents the Z-Score of volume. A symbol positioned higher up on the plot suggests a higher positive deviation in its trading volume from its average, while one lower down indicates a negative deviation.
Here are some guideline insights of plot positioning:
- Top-Right Quadrant (High Volume-High Price): Symbols in this quadrant indicate a scenario where both the trading volume and price are higher than their respective mean.
- Top-Left Quadrant (High Volume-Low Price): Symbols here reflect high trading volumes but prices lower than the mean.
- Bottom-Left Quadrant (Low Volume-Low Price): Assets in this quadrant have both low trading volume and price compared to their mean.
- Bottom-Right Quadrant (Low Volume-High Price): Symbols positioned here have prices that are higher than their mean, but the trading volume is low compared to the mean.
The plot also integrates a set of concentric squares which serve as visual guides:
- 1st Square (1SD): Encapsulates symbols that have Z-Scores within ±1 standard deviation for both price and volume. Symbols within this square are typically considered to be displaying normal behavior or within expected range.
- 2nd Square (2SD): Encapsulates those with Z-Scores within ±2 standard deviations. Symbols within this boundary, but outside the 1 SD square, indicate a moderate deviation from the norm.
- 3rd Square (3SD): Represents symbols with Z-Scores within ±3 standard deviations. Any symbol outside this square is deemed to be a significant outlier, exhibiting extreme behavior in terms of either its price, its volume, or both.
By assessing the position of symbols relative to these squares, traders can swiftly identify which assets are behaving typically and which are showing unusual activity. This visualization simplifies the process of spotting potential outliers or unique trading opportunities within the market. The farther a symbol is from the center, the more it deviates from its typical behavior.
◽️ Display Type: Columns
In this visualization, z-scores are represented using columns, where each symbol is presented horizontally. Each symbol has two distinct nodes:
- Left Node: Represents the z-score of volume.
- Right Node: Represents the z-score of price.
The height of these nodes can vary along the y-axis between -4 and 4, based on the z-score value:
- Large Positive Columns: Signify a high or positive z-score, indicating that the price or volume is significantly above its average.
- Large Negative Columns: Represent a low or negative z-score, suggesting that the price or volume is considerably below its average.
- Short Columns Near 0: Indicate that the price or volume is close to its mean, showcasing minimal deviation.
This columnar representation provides a clear, intuitive view of how each symbol's price and volume deviate from their respective averages.
◽️ Display Type: Circles
In this visualization style, z-scores are depicted using circles. Each symbol is horizontally aligned and represented by:
- Solid Circle: Represents the z-score of price.
- Transparent Circle: Represents the z-score of volume.
The vertical position of these circles on the y-axis ranges between -4 and 4, reflecting the z-score value:
- Circles Near the Top: Indicate a high or positive z-score, suggesting the price or volume is well above its average.
- Circles Near the Bottom: Represent a low or negative z-score, pointing to the price or volume being notably below its average.
- Circles Around the Midline (0): Highlight that the price or volume is close to its mean, with minimal deviation.
◽️ Display Type: Delta Columns
There's also an option to utilize Z-Score Delta Columns. For each symbol, a single column is presented, depicting the difference between the z-score of price and the z-score of volume.
The z-score delta essentially captures the disparity between how much the price and volume deviate from their respective mean:
- Positive Delta: Indicates that the z-score of price is greater than the z-score of volume. This suggests that the price has deviated more from its average than the volume has from its own average. Such a scenario could point to price movements being more significant or pronounced compared to the changes in volume.
- Negative Delta: Represents that the z-score of volume is higher than the z-score of price. This might mean that there are substantial volume changes, yet the price hasn't moved as dramatically. This can be indicative of potential build-up in trading interest without an equivalent impact on price.
- Delta Close to 0: Means that the z-scores for price and volume are almost equal, indicating their deviations from the average are in sync.
◽️ Display Type: Z-Volume/Z-Price Heatmap
This visualization offers a heatmap either for volume z-scores or price z-scores across all symbols. Here's how it's presented:
Each symbol is allocated its own horizontal row. Within this row, bar-by-bar data is displayed using a color gradient to represent the z-score values. The heatmap employs a user-defined gradient scale, where a chosen "cold" color represents low z-scores and a chosen "hot" color signifies high z-scores. As the z-score increases or decreases, the colors transition smoothly along this gradient, providing an intuitive visual indication of the z-score's magnitude.
- Cold Colors: Indicate values significantly below the mean (negative z-score)
- Mild Colors: Represent values close to the mean, suggesting minimal deviation.
- Hot Colors: Indicate values significantly above the mean (positive z-score)
This heatmap format provides a rapid, visually impactful means to discern how each symbol's price or volume is behaving relative to its average. The color-coded rows allow you to quickly spot outliers.
VOLUME TYPE
The "Volume Type" input allows you to choose the nature of volume data that will be factored into the volume z-score calculation. The interpretation of indicator’s data changes based on this input. You can opt between:
- Volume (Regular Volume): This is the classic measure of trading volume, which represents the volume traded in a given time period - bar.
- OBV (On-Balance Volume): OBV is a momentum indicator that accumulates volume on up bars and subtracts it on down bars, making it a cumulative indicator that sort of measures buying and selling pressure.
Interpretation Implications:
- For Volume Type: Regular Volume:
Positive Z-Score: Indicates that the trading volume is above its average, meaning there's unusually high trading activity .
Negative Z-Score: Suggests that the trading volume is below its average, signifying unusually low trading activity.
- For Volume Type: OBV:
Positive Z-Score: Signifies that “buying pressure” is above its average.
Negative Z-Score: Signifies that “selling pressure” is above its average.
When comparing Z-Score of OBV to Z-Score of price, we can observe several scenarios. If Z-Price and Z-Volume are convergent (have similar z-scores), we can say that the directional price movement is supported by volume. If Z-Price and Z-Volume are divergent (have very different z-scores or one of them being zero), it suggests a potential misalignment between price movement and volume support, which might hint at possible reversals or weakness.
Stablecoin Supply Ratio Oscillator
The Stablecoin Supply Ratio Oscillator (SSRO) is a cryptocurrency indicator designed for mean reversion analysis and sentiment assessment. It calculates the ratio of CRYPTO:BTCUSD 's market capitalization to the sum of stablecoins' market capitalization and z-scores the result, offering insights into market sentiment and potential turning points.
Methodology:
The SSRO is calculated as follows-
method ssro(float src, array stblsrc, int len) =>
float ssr = src / stblsrc.sum() // Source of the underlying divided by the sum of stablecoin sources
(ssr - ta.sma(ssr, len)) / ta.stdev(ssr, len) // Z-Score Transformed
This ratio is Z-Scored to provide a standardized measure, allowing users to identify periods of market fear or greed based on the allocation of capital between the underlying and Stablecoins ( CRYPTOCAP:USDT , CRYPTOCAP:USDC , CRYPTO:TUSD , CRYPTOCAP:BUSD , CRYPTOCAP:DAI , CRYPTOCAP:USDD , CRYPTOCAP:FRAX ). The z-scored values indicate potential areas of discount (buying opportunities) or premium (selling opportunities) relative to historical patterns.
Customization:
Underlying Asset: SSRO is customizable to different underlying assets, offering a versatile tool for various cryptocurrencies.
Calculation Length: Users can adjust the length of the calculation, tailoring the indicator to short or long-term analysis.
Visualization: SSRO can be displayed as candles, providing a visual representation of premium and discount areas.
Interpretation:
Market Sentiment: Lower SSRO values may indicate market fear, suggesting a preference for stablecoins as a relatively safer haven for capital. Conversely, higher values may suggest market greed, as more capital is allocated to the underlying asset.
Utility and Use Cases:
1. Mean Reversion Analysis: SSRO identifies potential mean reversion opportunities, guiding traders on optimal entry and exit points.
2. Sentiment Analysis: The indicator provides insights into market sentiment, aiding traders in understanding market dynamics.
3. Macro Analysis: The majority of cryptos follow \ correlate to CRYPTO:BTCUSD , Therefore by assessing premium and discount areas of CRYPTO:BTCUSD relative to the chosen underlying asset, users gain insights into potential market tops and bottoms.
4. Divergence Analysis: SSRO divergence from price trends can signal potential reversals, providing traders with additional confirmation for their decisions.
The Stablecoin Supply Ratio Oscillator is a valuable tool for cryptocurrency traders, offering a nuanced perspective on market sentiment and mean reversion opportunities. Its customization options and visual representation make it a versatile and powerful addition to the crypto analyst's toolkit.
[dharmatech] KBDR Mean ReversionBased on the criteria described in the book "Mean Revision Trading" by Nishant Pant.
Bullish signal criteria:
Bollinger Bands must be outside Keltner Channel
Price near bottom bband
DI+ increasing
DI- decreasing
RSI near bottom and increasing
Bearish signal criteria:
Bollinger Bands must be outside Keltner Channel
Price near upper bband
DI+ decreasing
DI- increasing
RSI near upper and decreasing
A single triangle indicates that all 4 criteria are met.
If letters appear with the triangle, this indicates that there was a partial criteria match.
K : bbands outside Keltner
B : bbands criteria met
D : DI criteria met
R : RSI criteria met
You can use the settings to turn off partial signals. For example:
"Partial 3" means show signals where 3 of the criteria are met.
If you want more insight into the underlying criteria, load these indicators as well:
Bollinger Bands (built-in to TradingView)
Keltner Channels (built-in to TradingView)
RSI (built-in to TradingView)
ADX and DI
Warning:
Not meant to be used as a stand-alone buy/sell signal.
It regularly provides signals which would not be profitable.
It's meant to be used in conjunction with other analysis.
Think of this as a time-saving tool. Instead of manually checking RSI, DI+/DI-, bbands, distance, etc. this does all of that for you on the fly.
MeanReversion - LogReturn/Vola ZScoreShows the z-Score of log-return (blue line) and volatility (black line). In statistics, the z-score is the number of standard deviations by which a value of a raw score is above or below the mean value.
This indicator aggregates z-score based on two indicators:
MeanReversion by Logarithmic Returns
MeanReversion by Volatility
Change the time period in bars for longer or shorter time frames. At a daily chart 252 mean on trading year, 21 mean one trading month.
Dynamic GANN Square Of 9 BandsDynamic GANN Square Of 9 Bands
Created on 3 Sept 2023
Adjust Increment Value:
Customize increment to match symbol and price characteristics for accuracy.
Green Line:
200 EMA. Identifies trend direction; moves with the prevailing trend.
Red Lines:
Mark prominent reversal levels closer to the red range; ideal for mean reversion strategies.
Crossing red levels may indicate trend continuation to the next red level.
Grey Lines:
Show immediate target reversal levels; watch for potential reversals.
Key Features:
Levels are different from Standard Deviation Lines.
Levels remain fixed and parallel, unaffected by volatility.
Despite its dynamism, it can serve as a leading indicator, revealing potential trend changes.
Primarily designed for trend-following strategies.
Additional Tips:
Use additional confirmations
Manage predefined risk and quantity
Additional Resources:
GANN Square Of 9 Pivots:
Bollinger Band Percentile SuiteThe Bollinger Band Percentile Suite (𝐵𝐵𝒫𝒸𝓉 𝒮𝓊𝒾𝓉𝑒) is a comprehensive and customizable toolkit built upon the foundation of the %B indicator. The methodology behind this toolkit remains consistent with the original %B indicator, while introducing a host of powerful features to enhance its functionality and adaptability.
Key Features and Customization:
The 𝐵𝐵𝒫𝒸𝓉 offers a wide array of customizable options to suit your trading preferences and strategies. It includes a variety of 14 moving average types that can be chosen as the basis for the Bollinger Band calculation. Additionally, traders have the flexibility to set their upper and lower boundaries for mean reversion detection, allowing for analysis tailored to the user's preference.
Deviation Calculation:
The toolkit provides an option to choose between standard and weighted deviation calculation methods. This added customization ensures that the indicator's behavior aligns with your unique trading style and preferences.
Signals and Reversals:
The 𝐵𝐵𝒫𝒸𝓉 excels in identifying potential overbought and oversold market conditions. It highlights these levels on the chart and marks potential reversal signals with small circles positioned either at the top or bottom of the indicator pane, providing traders with actionable insights.
Trend and Color Coding:
Incorporating a color-coded approach, the BBpct Suite enhances your understanding of market dynamics. It offers bar coloring options based on trend, allowing traders to identify bullish or bearish market conditions as the percentile goes above or below the midline.
Extremities and Reversions:
Recognizing extreme market conditions is crucial for traders. The 𝐵𝐵𝒫𝒸𝓉 includes color-coded indicators for extremities, indicating when the percentile ventures above or below the predefined thresholds. Moreover, it promptly identifies reversions by marking the moment the percentile crosses under the upper threshold (overbought) or over the lower threshold (oversold).
The Bollinger Band Percentile Suite equips traders with a versatile toolkit to gain valuable insights into market overbought and oversold conditions, and potential reversal signals. Its extensive customization options and array of features empower traders to make well-informed decisions based on their unique trading strategies and risk tolerance.
Please note that while the BBpct Suite provides robust analysis, it is advisable to combine its insights with other technical indicators and tools for a comprehensive trading approach.
Example Chart:
Extreme Entry with Mean Reversion and Trend FilterThis non-repainting indicator is an improved version of my previous work, a more versatile tool designed to provide traders with dynamic and adaptive entry signals while incorporating a mean reversion and trend filtering mechanism. By combining RSI overbought/oversold, regular divergence and confirmatory momentum oscillator such as CCI or MOM, this indicator generates more precise and timely signals for entering trades.
The indicator offers a comprehensive set of entry conditions for both Buy and Sell entries:
• For Buy entries, it checks for oversold conditions based on RSI levels, and detects bullish divergence patterns while oversold and it identifies upward crossovers in the selected entry signal source (CCI or Momentum).
• Similarly, for Sell entries, it identifies downward crossovers of the CCI or Mom, after the recent overbought conditions, and bearish divergence patterns inside the overbought RSI.
To refine the entry signals even further, the indicator utilizes a mean reversion filter. Traders can choose to display signals that occur inside or outside the upper and lower mean reversion bands:
• Range Entries are indicating potential buying opportunities near the lower band and selling opportunities near the upper band. This is based on the concept of mean reversion, which suggests that prices tend to return to the average when they reach the upper or lower bands. By focusing on these signals, traders can take advantage of price movements that have a higher probability of reversing towards the mean.
• Extreme Entries, on the other hand, represent signals that occur outside of the bands, signaling potential pullbacks during strong trends. By entering positions only at extreme highs or lows, traders can avoid getting caught in the middle of the trend. This approach helps traders capitalize more favorable trading opportunities which have a high reward-risk ratio.
Trend Filter acts as a directional bias for the entry signals. When enabled, long and short entry conditions are filtered based on the relationship between the closing price and the EMA.
Traders have the flexibility to customize, tweak the indicator filter and values in the settings according to their preferences strategies and traded assets, tailoring the signals to their specific needs. The script sets alert conditions to trigger alerts for buy, sell, or both entry signals. This indicator can be used in conjunction with price action or other technical analysis tools for confirmation and better trading decisions.
I created this indicator for my own use, and I share this for informational purposes only. It does not constitute financial advice so use at your own risk and consider your financial situation before making any trading decisions. The indicator's accuracy is not guaranteed, and past performance is not indicative of future results.
I appreciate your feedback on this indicator. As I am new to script development, I am open to comments and suggestions to improve it. If you encounter any issues while using this indicator, please let me know in the comments section. If you find it helpful, I kindly ask for your support in boosting it. Thank you for your cooperation.
Reversion Zone IndexThe Reversion Zone Index (RZI) is an indicator that combines the Commodity Channel Index (CCI), Choppiness Index (CI), and Bollinger Bands Percentage (BBPct) to identify mean reversion signals in the market. It is plotted as an Exponential Moving Average (EMA) smoothed oscillator with overbought and oversold zones, and mean reversion signals are represented by red and green arrows.
The three indicators are combined to benefit from their complementary aspects and create a more comprehensive view of mean reversion conditions. Here's a brief overview of each indicator's benefits:
1. Commodity Channel Index (CCI): CCI measures the current price level relative to its average over a specified period. It helps identify overbought and oversold conditions, as well as potential trend retracements. By incorporating CCI, the RZI gains insights into momentum and potential turning points.
2. Choppiness Index (CI): CI quantifies the market's choppiness or trendiness by analyzing the range between the highest high and lowest low over a specific period. It indicates whether the market is in a trending or ranging phase. CI provides valuable information about the market state, which can be useful in mean reversion analysis.
3. Bollinger Bands Percentage (BBPct): BBPct measures the current price's position relative to the Bollinger Bands. It calculates the percentage difference between the current price and the bands, identifying potential overbought or oversold conditions. BBPct helps gauge the market's deviation from its typical behavior and highlights potential reversal opportunities.
The RZI combines the three indicators by taking an average of their values and applying further calculations. It smooths the combined oscillator using an EMA to reduce noise and enhance the visibility of the trends. Smoothing with EMA provides a more responsive representation of the overall trend and helps filter out short-term fluctuations.
The overbought and oversold zones are marked on the chart as reference levels. When the combined oscillator is above the overbought zone or below the oversold zone, it suggests a potential mean reversion signal. Red and green arrows are displayed to visually indicate these mean retracement signals.
The RZI is a valuable tool for identifying mean reversion opportunities in the market. It incorporates multiple indicators, each providing unique insights into different aspects of mean reversion, such as momentum, volatility, and price positioning. Traders can use this indicator to spot potential turning points and time their trades accordingly.
