Fusion Traders - RSI Overbought/Oversold + Divergence IndicatorFusion Traders - RSI Overbought/Oversold + Divergence Indicator - new version
This indicator has lots of various add ons.
RSI overbought / oversold with changeable inputs
Divergence indicator
DESCRIPTION:
This script combines the Relative Strength Index ( RSI ), Moving Average and Divergence indicator to make a better decision when to enter or exit a trade.
- The Moving Average line (MA) has been made hidden by default but enhanced with an RSIMA cloud.
- When the RSI is above the selected MA it turns into green and when the RSI is below the select MA it turns into red.
- When the RSI is moving into the Overbought or Oversold area, some highlighted areas will appear.
- When some divergences or hidden divergences are detected an extra indication will be highlighted.
- When the divergence appear in the Overbought or Oversold area the more weight it give to make a decision.
- The same colour pallet has been used as the default candlestick colours so it looks familiar.
HOW TO USE:
The prerequisite is that we have some knowledge about the Elliot Wave Theory, the Fibonacci Retracement and the Fibonacci Extension tools.
We are hoping you like this indicator and added to your favourite indicators. If you have any question then comment below, and I'll do my best to help.
FEATURES:
• You can show/hide the RSI .
• You can show/hide the MA.
• You can show/hide the lRSIMA cloud.
• You can show/hide the Stoch RSI cloud.
• You can show/hide and adjust the Overbought and Oversold zones.
• You can show/hide and adjust the Overbought Extended and Oversold Extended zones.
• You can show/hide the Overbought and Oversold highlighted zones.
HOW TO GET ACCESS TO THE SCRIPT:
• Favorite the script and add it to your chart.
ابحث في النصوص البرمجية عن "THE SCRIPT"
Squeeze & Release [AlgoAlpha]Introduction:
💡The Squeeze & Release by AlgoAlpha is an innovative tool designed to capture price volatility dynamics using a combination of EMA-based calculations and ATR principles. This script aims to provide traders with clear visual cues to spot potential market squeezes and release scenarios. Hence it is important to note that this indicator shows information on volatility, not direction.
Core Logic and Components:
🔶EMA Calculations: The script utilizes the Exponential Moving Average (EMA) in multiple ways to smooth out the data and provide indicator direction. There are specific lengths for the EMAs that users can modify as per their preference.
🔶ATR Dynamics: Average True Range (ATR) is a core component of the script. The differential between the smoothed ATR and its EMA is used to plot the main line. This differential, when represented as a percentage of the high-low range, provides insights into volatility.
🔶Squeeze and Release Detection: The script identifies and highlights squeeze and release scenarios based on the crossover and cross-under events between our main line and its smoothed version. Squeezes are potential setups where the market may be consolidating, and releases indicate a potential breakout or breakdown.
🔶Hyper Squeeze Detection: A unique feature that detects instances when the main line is rising consistently over a user-defined period. Hyper squeeze marks areas of extremely low volatility.
Visual Components:
The main line (ATR-based) changes color depending on its position relative to its EMA.
A middle line plotted at zero level which provides a quick visual cue about the main line's position. If the main line is above the zero level, it indicates that the price is squeezing on a longer time horizon, even if the indicator indicates a shorter-term release.
"𝓢" and "𝓡" characters are plotted to represent 'Squeeze' and 'Release' scenarios respectively.
Standard Deviation Bands are plotted to help users gauge the extremity and significance of the signal from the indicator, if the indicator is closer to either the upper or lower deviation bands, this means that statistically, the current value is considered to be more extreme and as it is further away from the mean where the indicator is oscillating at for the majority of the time. Thus indicating that the price has experienced an unusual amount or squeeze or release depending on the value of the indicator.
Usage Guidelines:
☝️Traders can use the script to:
Identify potential consolidation (squeeze) zones.
Gauge potential breakout or breakdown scenarios (release).
Fine-tune their entries and exits based on volatility.
Adjust the various lengths provided in the input for better customization based on individual trading styles and the asset being traded.
41-80 F&O MA ScreenerThis Pine Script is a TradingView indicator named "41-80-F&O EMA Screener." It calculates and displays four moving averages (MA1, MA2, MA3, and MA4) and the Relative Strength Index (RSI) on a chart. The script generates buy and short signals based on certain conditions involving the moving averages and RSI. Additionally, it includes a screener section that displays a table of symbols with buy and short signals.
Here's a breakdown of the key components:
Moving Averages (MAs):
MA1: Simple Moving Average with length len1 (green line).
MA2: Simple Moving Average with length len2 (red line).
MA3: Simple Moving Average with length len3 (orange line).
MA4: Simple Moving Average with length len4 (black line).
Relative Strength Index (RSI):
The RSI is calculated with a length of rsiLengthInput and a source specified by rsiSourceInput.
Conditions for Buy and Short Signals:
Buy Signal: When MA1 is above MA2 and MA3, and RSI is above 50.
Short Signal: When MA1 is below MA2 and MA3, and RSI is below 50.
Signal Plots:
Buy signals are plotted as "B" below the corresponding bars.
Short signals are plotted as "S" above the corresponding bars.
Background Coloring:
Bars are colored based on their opening and closing prices.
Screener Section:
The script defines a watchlist (gticker) with 40 predefined symbols.
It then calls the getSignal function for each symbol to identify buy and short signals.
The results are displayed in a table with long signals in green and short signals in red.
Table Theming:
The script allows customization of the table's background, frame, and text colors, as well as the text size.
The table's location on the chart can also be customized.
Please note that the script uses the Mozilla Public License 2.0. Make sure to review and comply with the terms of this license if you plan to use or modify the script.
Candle size in pipsDescription
Enhance your trading strategy with precision using this script, designed to measure the range of a candle from wick to wick in pips. Whether you're implementing a specific pip requirement within a candle for your strategy, or simply seeking to better understand market dynamics, this tool provides valuable insights. The script is calculating the amount of pips between the high and the low then compares it to the minimal size you declared. If the amount of pips is more or equal to minimal size it will show the label.
Features
Alert Functionality: Opt to receive alerts by checking the checkbox (default: false).
Customizable Pip Threshold: Tailor the script to your needs by setting the minimum required pips to display on the screen (default: 12).
Different shape: circle, triangle up, triangle down, none
How to Use
Personalize your trading approach by integrating this script with your preferred strategy. For instance, in my strategy involving a 3M continuation, I leverage this tool to determine the pip count of the M15 candle before making entry decisions.
Note: Ensure you understand your strategy's requirements and adjust the script settings accordingly for optimal result s.
Feel free to reach out if you have any questions or require further assistance in maximizing the utility of this script.
COT MCIThe COT MCI script is a market indicator based on the data from the Commitment of Traders Reports.
Integration of COT Report Data:
The script sources COT data from futures contracts, including:
Treasury Bonds (ZB), Dollar Index (DX), 10-Year Treasury Notes (ZN)
Commodities like Soybeans (ZS), Soy Meal (ZM), Soy Oil (ZL), Corn (ZC), Wheat (ZW), Kansas City Wheat (KE), Pork (HE), Cattle (LE)
Precious Metals such as Gold (GC), Silver (SI), Palladium (PA), Platinum (PL)
Industrial Metals like Copper (HG), Aluminum (AUP), Steel (HRC)
Energy Products like Crude Oil (CL), Heating Oil (HO), Gasoline (RB), Natural Gas (NG), Brent Crude (BB)
Currencies such as AUD (6A), GBP (6B), CAD (6C), EUR (6E), JPY (6J), CHF (6S), NZD (6N), BRL (6L), MXN (6M), RUB (6R), ZAR (6Z)
Others: Sugar (SB), Coffee (KC), Cocoa (CC), Cotton (CT), Ethanol (EH), Rice (ZR), Oats (ZO), Whey (DC), Orange Juice (OJ), Lumber (LBS), Livestock (GF), E-mini S&P 500 (ES), E-mini Russell 2000 (RTY), E-mini Dow Jones (YM), E-mini NASDAQ-100 (NQ), VIX Futures (VX), S&P 500 (SP), DJIA (DJIA)
Cryptocurrencies such as Bitcoin (BTC) and Ethereum (ETH)
Functions and Logic of the Script:
COT Calculation: Determines the net positions for commercial actors and large speculators. Also Available are short and long positions of commercials or large speculators.
Position Change Analysis: Analyzes the percentage changes in net positions and open interest data over a period of 6 weeks (Weekly Chart).
Average Value Calculation: Determines short-term and long-term trend averages.
Trend Analysis: Buy and sell signals (represented in colors) are based on linear regressions and average calculations.
Usage and Application Examples:
Ideal for traders looking for a detailed analysis of market dynamics and position changes in the futures market. Suitable for decision-making in transaction timing and assessing market sentiment.
Usage Notes:
Users should be familiar with the interpretation of COT data and basic concepts of futures trading. Particularly suitable for medium to long-term trading strategies.
Intersection Value FunctionsWinning entry for the first Pinefest contest. The challenge required providing three functions returning the intersection value between two series source1 and source2 in the event of a cross, crossunder, and crossover.
Feel free to use the code however you like.
🔶 CHALLENGE FUNCTIONS
🔹 crossValue()
//@function Finds intersection value of 2 lines/values if any cross occurs - First function of challenge -> crossValue(source1, source2)
//@param source1 (float) source value 1
//@param source2 (float) source value 2
//@returns Intersection value
example:
value = crossValue(close, close )
🔹 crossoverValue()
//@function Finds intersection value of 2 lines/values if crossover occurs - Second function of challenge -> crossoverValue(source1, source2)
//@param source1 (float) source value 1
//@param source2 (float) source value 2
//@returns Intersection value
example:
value = crossoverValue(close, close )
🔹 crossunderValue()
//@function Finds intersect of 2 lines/values if crossunder occurs - Third function of challenge -> crossunderValue(source1, source2)
//@param source1 (float) source value 1
//@param source2 (float) source value 2
//@returns Intersection value
example:
value = crossunderValue(close, close )
🔶 DETAILS
A series of values can be displayed as a series of points, where the point location highlights its value, however, it is more common to connect each point with a line to have a continuous aspect.
A line is a geometrical object connecting two points, each having y and x coordinates. A line has a slope controlling its steepness and an intercept indicating where the line crosses an axis. With these elements, we can describe a line as follows:
slope × x + intercept
A cross between two series of values occurs when one series is greater or lower than the other while its previous value isn't.
We are interested in finding the "intersection value", that is the value where two crossing lines are equal. This problem can be approached via linear interpolation.
A simple and direct approach to finding our intersection value is to find the common scaling factor of the slopes of the lines, that is the multiplicative factor that multiplies both lines slopes such that the resulting points are equal.
Given:
A = Point A1 + m1 × scaling_factor
B = Point B1 + m2 × scaling_factor
where scaling_factor is the common scaling factor, and m1 and m2 the slopes:
m1 = Point A2 - Point A1
m2 = Point B2 - Point B1
In our cases, since the horizontal distance between two points is simply 1, our lines slopes are equal to their vertical distance (rise).
Under the event of a cross, there exists a scaling_factor satisfying A = B , which allows us to directly compute our intersection value. The solution is given by:
scaling_factor = (B1 - A1)/(m1 - m2)
As such our intersection value can be given by the following equivalent calculations:
(1) A1 + m1 × (B1 - A1)/(m1 - m2)
(2) B1 + m2 × (B1 - A1)/(m1 - m2)
(3) A2 - m2 × (A2 - B2)/(m1 - m2)
(4) B2 - m2 × (A2 - B2)/(m1 - m2)
The proposed functions use the third calculation.
This approach is equivalent to expressions using the classical line equation, with:
slope1 × x + intercept1 = slope2 × x + intercept2
By solving for x , the intersection point is obtained by evaluating any of the line equations for the obtained x solution.
🔶 APPLICATIONS
The intersection point of two crossing lines might lead to interesting applications and creations, in this section various information/tools derived from the proposed calculations are presented.
This supplementary material is available within the script.
🔹 Intersections As Support/Resistances
The script allows extending the lines of the intersection value when a cross is detected, these extended lines could have applications as support/resistance lines.
🔹 Using The Scaling Factor
The core of the proposed calculation method is the common scaling factor, which can be used to return useful information, such as the position of the cross relative to the x coordinates of a line.
The above image highlights two moving averages (in green and red), the cross-interval areas are highlighted in blue, and the intersection point is highlighted as a blue line.
The pane below shows a bar plot displaying:
1 - scaling factor = 1 -
Values closer to 1 indicate that the cross location is closer to x2 (the right coordinate of the lines), while values closer to 0 indicate that the cross location is closer to x1 .
🔹 Intersection Matrix
The main proposed functions of this challenge focus on the crossings between two series of values, however, we might be interested in applying this over a collection of series.
We can see in the image above how the lines connecting two points intersect with each other, we can construct a matrix populated with the intersection value of two corresponding lines. If (X, Y) represents the intersection value between lines X and Y we have the following matrix:
| Line A | Line B | Line C | Line D |
-------|--------|--------|--------|--------|
Line A | | (A, B) | (A, C) | (A, D) |
Line B | (B, A) | | (B, C) | (B, D) |
Line C | (C, A) | (C, B) | | (C, D) |
Line D | (D, A) | (D, B) | (D, C) | |
We can see that the upper triangular part of this matrix is redundant, which is why the script does not compute it. This function is provided in the script as intersectionMatrix :
//@function Return the N * N intersection matrix from an array of values
//@param array_series (array) array of values, requires an array supporting historical referencing
//@returns (matrix) Intersection matrix showing intersection values between all array entries
In the script, we create an intersection matrix from an array containing the outputs of simple moving averages with a period in a specific user set range and can highlight if a simple moving average of a certain period crosses with another moving average with a different period, as well as the intersection value.
🔹 Magnification Glass
Crosses on a chart can be quite small and might require zooming in significantly to see a detailed picture of them. Using the obtained scaling factor allows reconstructing crossing events with an higher resolution.
A simple supplementary zoomIn function is provided to this effect:
//@function Display an higher resolution representation of intersecting lines
//@param source1 (float) source value 1
//@param source2 (float) source value 2
//@param css1 (color) color of source 1 line
//@param css2 (color) color of source 2 line
//@param intersec_css (color) color of intersection line
//@param area_css (color) color of box area
Users can obtain a higher resolution by modifying the provided "Resolution" setting.
The function returns a higher resolution representation of the most recent crosses between two input series, the intersection value is also provided.
New York Sessions Morning, Lunch and afternoon. AMKDescription
The script is designed to highlight the New York Stock Exchange's trading day, broken down into three specific sub-sessions: morning, lunchtime, and afternoon. Each sub-session is color-coded to provide an immediate visual cue about which portion of the trading day is currently active. Additionally, this script allows the user to adjust the time zone offset, making it adaptable for traders in different time zones around the world.
Originality
While there are scripts that highlight the entire trading day or specific market hours, this script adds granularity by breaking down the New York trading session into its typical behavioral parts: the morning rush, the lunchtime lull, and the afternoon action. The addition of an adjustable time zone offset is a unique feature that makes the tool more versatile and accommodating to a global user base.
Usefulness
The ability to visualize these different trading sessions can be valuable for various types of traders:
Day Traders: The script helps to immediately identify which session they are in, aiding in their trading strategy as market behavior can vary between these periods.
Swing Traders: They may use these sub-sessions to time their entries or exits, especially if they're based in different time zones.
Market Analysts: The color-coded sessions provide a quick way to analyze the historical performance and volatility of an asset during different trading periods.
Global Traders: The time zone adjustment feature makes it easy for traders outside of the Eastern Time Zone to customize the script according to their local time, increasing its utility across different markets.
Educational Purpose: For new traders, this could serve as an educational tool to understand the typical behavior of the stock market at different times of the day.
So, whether you're timing an intraday entry or looking for patterns tied to specific market sessions, this script offers a straightforward, visual way to keep track of where you are in the trading day.
Liquidation Estimates (Real-Time) [LuxAlgo]The Liquidation Estimates (Real-Time) experimental indicator attempts to highlight real-time long and short liquidations on all timeframes. Here with liquidations, we refer to the process of forcibly closing a trader's position in the market.
By analyzing liquidation data, traders can gauge market sentiment, identify potential support and resistance levels, identify potential trend reversals, and make informed decisions about entry and exit points.
🔶 USAGE
Liquidation refers to the process of forcibly closing a trader's position. It occurs when a trader's margin account can no longer support their open positions due to significant losses or a lack of sufficient margin to meet the maintenance requirements.
Liquidations can be categorized as either a long liquidation or a short liquidation. A long liquidation is a situation where long positions are being liquidated, while short liquidation is a situation where short positions are being liquidated.
The green bars indicate long liquidations – meaning the number of long positions liquidated in the market. Typically, long liquidations occur when there is a sudden drop in the asset price that is being traded. This is because traders who were bullish on the asset and had opened long positions on the same will now face losses since the market has moved against them.
Similarly, the red bars indicate short liquidations – meaning the number of short positions liquidated in the futures market. Short liquidations occur when there is a sudden spike in the price of the asset that is being traded. This is because traders who were bearish on the asset and had opened short positions will now face losses since the market has moved against them.
Liquidation patterns or clusters of liquidations could indicate potential trend reversals.
🔹 Dominance
Liquidation dominance (Difference) displays the difference between long and short liquidations, aiming to help identify the dominant side.
🔹 Total Liquidations
Total liquidations display the sum of long and short liquidations.
🔹 Cumulative Liquidations
Cumulative liquidations are essentially the cumulative sum of the difference between short and long liquidations aiming to confirm the trend and the strength of the trend.
🔶 DETAILS
It's important to note that liquidation data is not provided on the Trading View's platform or can not be fetched from anywhere else.
Yet we know that the liquidation data is closely tied in with trading volumes in the market and the movement in the underlying asset’s price. As a result, this script analyzes available data sources extracts the required information, and presents an educated estimate of the liquidation data.
The data presented does not reflect the actual individual quantitative value of the liquidation data, traders and analysts shall look to the changes over time and the correlation between liquidation data and price movements.
The script's output with the default option values has been visually checked/compared with the liquidation chart presented on coinglass.com.
🔶 SETTINGS
🔹Liquidations Input
Mode: defines the presentation of the liquidations chart. Details are given in the tooltip of the option.
Longs Reference Price: defines the base price in calculating long liquidations.
Shorts Reference Price: defines the base price in calculating short liquidations.
🔶 RELATED SCRIPTS
Liquidation-Levels
Liquidity-Sentiment-Profile
Buyside-Sellside-Liquidity
Anchored Average Price by Atilla Yurtseven (AAP)Anchored Average Price indicator is designed to pinpoint a specific date and price in a given financial instrument's price chart. Once anchored to the desired date and price level, the script calculates and displays the average price from that anchor point to the current day.
Features
Customizable Source: Allows users to choose the source data for calculations. By default, it uses hlc3, which is the average of high, low, and close prices.
Start Date Input: The script includes a timestamp-based input that allows the user to specify the anchor date easily.
Customizable Color: Users can change the color of the plotted average line, adding an additional layer of customization to the visual representation.
Code Mechanics
Initialization: Declares the variables and arrays required for calculations and display. The array is used to store price data.
Condition Check: Only starts storing and calculating data if the chart's time is equal to or greater than the user-defined start date.
Data Storing: Once the condition is met, the script pushes the src price data into the array for future averaging.
Average Calculation: It calculates the average price of the values stored in the array.
Data Clearing: If the condition is not met, the array is cleared, and no average is plotted.
Plotting: The average price is plotted on the chart with the user-defined color.
By incorporating these features and mechanics, AAP provides traders and investors with a powerful tool for assessing average prices anchored to a specific date or swing.
Disclaimer:
This TradingView script is intended for educational and informational purposes only and should not be considered as investment or trading advice. Past performance is not indicative of future results. Trading and investing carry a high level of risk, and you should consult with a qualified financial advisor before making any financial decisions. The creator of this script, Atilla Yurtseven, is not responsible for any losses or damages incurred as a result of using this script.
Trade smart, stay safe
Atilla Yurtseven
Spot-Vol CorrelationSpot-Vol Correlation Script Guide
Purpose:
This TradingView script measures the correlation between percentage changes in the spot price (e.g., for SPY, an ETF that tracks the S&P 500 index) and the changes in volatility (e.g., as indicated by the VIX, the Volatility Index). Its primary objective is to discern whether the relationship between spot price and volatility behaves as expected ("normal" condition) or diverges from the expected pattern ("abnormal" condition).
Normal vs. Abnormal Correlation:
Normal Correlation: Historically, the VIX (or volatility) and the spot price of major indices like the S&P 500 have an inverse relationship. When the spot price of the index goes up, the VIX tends to go down, indicating lower volatility. Conversely, when the index drops, the VIX generally rises, signaling increased volatility.
Abnormal Correlation: There are instances when this inverse relationship doesn't hold, and both the spot price and the VIX move in the same direction. This is considered an "abnormal" condition and might indicate unusual market dynamics, potential uncertainty, or impending shifts in market sentiment.
Using the Script:
Inputs:
First Symbol: This is set by default to VIX, representing volatility. However, users can input any other volatility metric they prefer.
Second Symbol: This is set to SPY by default, representing the spot price of the S&P 500 index. Like the first symbol, users can substitute SPY with any other asset or index of their choice.
Length of Calculation Period: Users can define the lookback period for the correlation calculation. By default, it's set to 10 periods (e.g., days for a daily chart).
Upper & Lower Bounds of Normal Zone: These parameters define the range of correlation values that are considered "normal" or expected. By default, this is set between -0.60 and -1.00.
Visuals:
Correlation Line: The main line plot shows the correlation coefficient between the two input symbols. When this line is within the "normal zone", it indicates that the spot price and volatility are inversely correlated. If it's outside this zone, the correlation is considered "abnormal".
Green Color: Indicates a period when the spot price and VIX are behaving as traditionally expected (i.e., one rises while the other falls).
Red Color: Denotes a period when the spot price and VIX are both moving in the same direction, which is an abnormal condition.
Shaded Area (Normal Zone): The area between the user-defined upper and lower bounds is shaded in green, highlighting the range of "normal" correlation values.
Interpretation:
Monitor the color and position of the correlation line relative to the shaded area:
If the line is green and within the shaded area, the market dynamics are as traditionally expected.
If the line is red or outside the shaded area, users should exercise caution as this indicates a divergence from typical behavior, which can precede significant market moves or heightened uncertainty.
Intraday Volatility Bands [Honestcowboy]The Intraday Volatility Bands aims to provide a better alternative to ATR in the calculation of targets or reversal points.
How are they different from ATR based bands?
While ATR and other measures of volatility base their calculations on the previous bars on the chart (for example bars 1954 to 1968). The volatility used in these bands measure expected volatility during that time of the day.
Why would you take this approach?
Markets behave different during certain times of the day, also called sessions.
Here are a couple examples.
Asian Session (generally low volatility)
London Session (bigger volatility starts)
New York Session (overlap of New York with London creates huge volatility)
Generally when using bands or channel type indicators intraday they do not account for the upcoming sessions. On London open price will quickly spike through a bollinger band and it will take some time for the bands to adjust to new volatility.
This script will show expected volatility targets at the start of each new bar and will not adjust during the bar. It already knows what price is expected to do at this time of day.
Script also plots arrows when price breaches either the top or bottom of the bands. You can also set alerts for when this occurs. These are non repainting as the script knows the level at start of the bar and does not change.
🔷 CALCULATION
Think of this script like an ATR but instead it uses past days data instead of previous bars data. Charts below should visualise this more clearly:
The scripts measure of volatility is based on a simple high-low.
The script also counts the number of bars that exist in a day on your current timeframe chart. After knowing that number it creates the matrix used in it's calculations and data storage.
See how it works perfectly on a lower timeframe chart below:
Getting this right was the hardest part, check the coding if you are interested in this type of stuff. I commented every step in the coding process.
🔷 SETTINGS
Every setting of the script has a tooltip but I provided a breakdown here:
Some more examples of different charts:
Liquidity Sentiment Profile (Auto-Anchored) [LuxAlgo]
The Liquidity Sentiment Profile (Auto-Anchored) is an advanced charting tool that measures by combining PRICE and VOLUME data over specified anchored periods and highlights the distribution of the liquidity and the market sentiment at specific price levels. This version is a variation of the previously published Liquidity Sentiment Profile , wherewith this version allows users to select a variety of different anchoring periods, such as 'Auto', 'Fixed Range', 'Swing High', 'Swing Low', 'Session', 'Day', 'Week', 'Month', 'Quarter', and 'Year'
Liquidity refers to the availability of orders at specific price levels in the market, allowing transactions to occur smoothly.
🔶 USAGE
A Liquidity Sentiment Profile (Auto-Anchored) is a combination of liquidity and a sentiment profile, where the right side of the profile highlights the distribution of the traded activity at different price levels, and the left side of the profile highlights the market sentiment at those price levels
The liquidity profile is categorized by assigning different colors based on the significance of the traded activity of the specific price levels, allowing traders to reveal significant price levels, such as support and resistance levels, supply and demand zones, liquidity gaps, consolidation zones, etc
The Liquidity Sentiment Profiles aim to present Value Areas based on the significance of price levels, thus allowing users to identify value areas that can be formed more than once within the range of a single profile
Level of Significance Line - displays the changes in the price levels with the highest traded activity (developing POC)
Buyside & Sellside Liquidity Zones - displays Liquidity Levels, also known as Supply and Demand Zones
🔶 SETTINGS
The script takes into account user-defined parameters and plots the profiles, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip.
🔹 Liquidity Sentiment Profile
Anchor Period: The indicator resolution is set by the input of the Anchor Period.
Fixed Period: Applicable if the Anchor Period is set to 'Fixed Range' then the period of the profile is defined with this option
Swing Detection Length: Applicable if the Anchor Period is set to 'Swing High' or 'Swing Low' then the length required to detect the Swing Levels is defined with this option which is then used to determine the period of the profile
🔹 Liquidity Profile
Liquidity Profile: Toggles the visibility of the Liquidity Profiles
High Traded Nodes: Threshold and Color option for High Traded Nodes
Average Traded Nodes: Color option for Average Traded Nodes
Low Traded Nodes: Threshold and Color option for Low Traded Nodes
🔹 Sentiment Profile
Sentiment Profile: Toggles the visibility of the Sentiment Profiles
Bullish Nodes: Color option for Bullish Nodes
Bearish Nodes: Color option for Bearish Nodes
🔹 Buyside & Sellside Liquidity Zones
Buyside & Sellside Liquidity Zones: Toggles the visibility of the Liquidity Levels
Buyside Liquidity Nodes: Color option for Buyside Liquidity Nodes
Sellside Liquidity Nodes: Color option for Sellside Liquidity Nodes
🔹 Other Settings
Level of Significance: Toggles the visibility of the Level of Significance Line
Price Levels, Color: Toggles the visibility of the Profile Price Levels
Number of Rows: Specify how many rows each profile histogram will have. Caution, having it set to high values will quickly hit Pine Script™ drawing objects limit and fewer historical profiles will be displayed
Profile Width %: Alters the width of the rows in the histogram, relative to the profile length
Profile Range Background Fill: Toggles the visibility of the Profiles Range
🔶 RELATED SCRIPTS
Liquidity-Sentiment-Profile
Buyside-Sellside-Liquidity
ICT-Concepts
Candle Close AlertCandle Close Alert (CCA) :
The "Candle Close Alert" (CCA) is a custom technical analysis tool. It operates as an overlay on price charts and serves to detect and notify users about significant changes in consecutive candle closes. The script calculates the difference between the closing price of the current candle and the previous candle, referred to as the "close difference." It then compares this close difference against a user-specified threshold value.
When the close difference exceeds the threshold, the script triggers an alert, notifying users of a potential noteworthy event. This alert can serve as a prompt for traders and investors to investigate the current price action further or to consider possible trading decisions .
Additionally, the script enhances visualization by plotting the close differences on the price chart. Positive close differences exceeding the threshold are plotted in green, while negative close differences exceeding the threshold in magnitude are plotted in red. This color-coded visualization helps users quickly identify periods of significant price movement and potential market trends.
However, it's important to note that the CCA script is a standalone tool and should be used in conjunction with comprehensive market analysis. Trading decisions should not be solely based on the alerts and visualizations provided by this script. Instead, they should be considered within the broader context of other technical indicators, fundamental analysis, and risk management strategies. Enjoy it!
Liquidity Levels/Voids (VP) [LuxAlgo]The Liquidity Levels/Voids (VP) is a script designed to detect liquidity voids & levels by measuring traded volume at all price levels on the market between two swing points and highlighting the distribution of the liquidity voids & levels at specific price levels.
🔶 USAGE
Liquidity is a fundamental market force that shapes the trajectory of assets.
The creation of a liquidity level comes as a result of an initial imbalance of supply/demand, which forms what we know as a swing high or swing low. As more players take positions in the market, these are levels that market participants will use as a historical reference to place their stops. When the levels are then re-tested, a decision will be made. The binary outcome here can be a breakout of the level or a reversal back to the mean.
Liquidity voids are sudden price changes that occur in the market when the price jumps from one level to another with little trading activity (low volume), creating an imbalance in price. The price tends to fill or retest the liquidity voids area, and traders understand at which price level institutional players have been active.
Liquidity voids are a valuable concept in trading, as they provide insights about where many orders were injected, creating this inefficiency in the market. The price tends to restore the balance.
🔶 SETTINGS
The script takes into account user-defined parameters and detects the liquidity voids based on them, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip.
🔹 Liquidity Levels / Voids
Liquidity Levels/Voids: Color customization option for Unfilled Liquidity Levels/Voids.
Detection Length: Lookback period used for the calculation of Swing Levels.
Threshold %: Threshold used for the calculation of the Liquidity Levels & Voids.
Sensitivity: Adjusts the number of levels between two swing points, as a result, the height of a level is determined, and then based on the above-given threshold the level is checked if it matches the liquidity level/void conditions.
Filled Liquidity Levels/Voids: Toggles the visibility of the Filled Liquidity Levels/Voids and color customization option for Filled Liquidity Levels/Voids.
🔹 Other Features
Swing Highs/Lows: Toggles the visibility of the Swing Levels, where tooltips present statistical information, such as price, price change, and cumulative volume between the two swing levels detected based on the detection length specified above, Coloring options to customize swing low and swing high label colors, and Size option to adjust the size of the labels.
🔹 Display Options
Mode: Controls the lookback length of detection and visualization.
# Bars: Lookback length customization, in case Mode is set to Present.
🔶 RELATED SCRIPTS
Liquidity-Voids-FVG
Buyside-Sellside-Liquidity
Swing-Volume-Profiles
AI SuperTrend Clustering Oscillator [LuxAlgo]The AI SuperTrend Clustering Oscillator is an oscillator returning the most bullish/average/bearish centroids given by multiple instances of the difference between SuperTrend indicators.
This script is an extension of our previously posted SuperTrend AI indicator that makes use of k-means clustering. If you want to learn more about it see:
🔶 USAGE
The AI SuperTrend Clustering Oscillator is made of 3 distinct components, a bullish output (always the highest), a bearish output (always the lowest), and a "consensus" output always within the two others.
The general trend is given by the consensus output, with a value above 0 indicating an uptrend and under 0 indicating a downtrend. Using a higher minimum factor will weigh results toward longer-term trends, while lowering the maximum factor will weigh results toward shorter-term trends.
Strong trends are indicated when the bullish/bearish outputs are indicating an opposite sentiment. A strong bullish trend would for example be indicated when the bearish output is above 0, while a strong bearish trend would be indicated when the bullish output is below 0.
When the consensus output is indicating a specific trend direction, an opposite indication from the bullish/bearish output can highlight a potential reversal or retracement.
🔶 DETAILS
The indicator construction is based on finding three clusters from the difference between the closing price and various SuperTrend using different factors. The centroid of each cluster is then returned. This operation is done over all historical bars.
The highest cluster will be composed of the differences between the price and SuperTrends that are the highest, thus creating a more bullish group. The lowest cluster will be composed of the differences between the price and SuperTrends that are the lowest, thus creating a more bearish group.
The consensus cluster is composed of the differences between the price and SuperTrends that are not significant enough to be part of the other clusters.
🔶 SETTINGS
ATR Length: ATR period used for the calculation of the SuperTrends.
Factor Range: Determine the minimum and maximum factor values for the calculation of the SuperTrends.
Step: Increments of the factor range.
Smooth: Degree of smoothness of each output from the indicator.
🔹 Optimization
This group of settings affects the runtime performances of the script.
Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
Historical Bars Calculation: Calculation window of the script (in bars).
SuperTrend AI (Clustering) [LuxAlgo]The SuperTrend AI indicator is a novel take on bridging the gap between the K-means clustering machine learning method & technical indicators. In this case, we apply K-Means clustering to the famous SuperTrend indicator.
🔶 USAGE
Users can interpret the SuperTrend AI trailing stop similarly to the regular SuperTrend indicator. Using higher minimum/maximum factors will return longer-term signals.
The displayed performance metrics displayed on each signal allow for a deeper interpretation of the indicator. Whereas higher values could indicate a higher potential for the market to be heading in the direction of the trend when compared to signals with lower values such as 1 or 0 potentially indicating retracements.
In the image above, we can notice more clear examples of the performance metrics on signals indicating trends, however, these performance metrics cannot perform or predict every signal reliably.
We can see in the image above that the trailing stop and its adaptive moving average can also act as support & resistance. Using higher values of the performance memory setting allows users to obtain a longer-term adaptive moving average of the returned trailing stop.
🔶 DETAILS
🔹 K-Means Clustering
When observing data points within a specific space, we can sometimes observe that some are closer to each other, forming groups, or "Clusters". At first sight, identifying those clusters and finding their associated data points can seem easy but doing so mathematically can be more challenging. This is where cluster analysis comes into play, where we seek to group data points into various clusters such that data points within one cluster are closer to each other. This is a common branch of AI/machine learning.
Various methods exist to find clusters within data, with the one used in this script being K-Means Clustering , a simple iterative unsupervised clustering method that finds a user-set amount of clusters.
A naive form of the K-Means algorithm would perform the following steps in order to find K clusters:
(1) Determine the amount (K) of clusters to detect.
(2) Initiate our K centroids (cluster centers) with random values.
(3) Loop over the data points, and determine which is the closest centroid from each data point, then associate that data point with the centroid.
(4) Update centroids by taking the average of the data points associated with a specific centroid.
Repeat steps 3 to 4 until convergence, that is until the centroids no longer change.
To explain how K-Means works graphically let's take the example of a one-dimensional dataset (which is the dimension used in our script) with two apparent clusters:
This is of course a simple scenario, as K will generally be higher, as well the amount of data points. Do note that this method can be very sensitive to the initialization of the centroids, this is why it is generally run multiple times, keeping the run returning the best centroids.
🔹 Adaptive SuperTrend Factor Using K-Means
The proposed indicator rationale is based on the following hypothesis:
Given multiple instances of an indicator using different settings, the optimal setting choice at time t is given by the best-performing instance with setting s(t) .
Performing the calculation of the indicator using the best setting at time t would return an indicator whose characteristics adapt based on its performance. However, what if the setting of the best-performing instance and second best-performing instance of the indicator have a high degree of disparity without a high difference in performance?
Even though this specific case is rare its however not uncommon to see that performance can be similar for a group of specific settings (this could be observed in a parameter optimization heatmap), then filtering out desirable settings to only use the best-performing one can seem too strict. We can as such reformulate our first hypothesis:
Given multiple instances of an indicator using different settings, an optimal setting choice at time t is given by the average of the best-performing instances with settings s(t) .
Finding this group of best-performing instances could be done using the previously described K-Means clustering method, assuming three groups of interest (K = 3) defined as worst performing, average performing, and best performing.
We first obtain an analog of performance P(t, factor) described as:
P(t, factor) = P(t-1, factor) + α * (∆C(t) × S(t-1, factor) - P(t-1, factor))
where 1 > α > 0, which is the performance memory determining the degree to which older inputs affect the current output. C(t) is the closing price, and S(t, factor) is the SuperTrend signal generating function with multiplicative factor factor .
We run this performance function for multiple factor settings and perform K-Means clustering on the multiple obtained performances to obtain the best-performing cluster. We initiate our centroids using quartiles of the obtained performances for faster centroids convergence.
The average of the factors associated with the best-performing cluster is then used to obtain the final factor setting, which is used to compute the final SuperTrend output.
Do note that we give the liberty for the user to get the final factor from the best, average, or worst cluster for experimental purposes.
🔶 SETTINGS
ATR Length: ATR period used for the calculation of the SuperTrends.
Factor Range: Determine the minimum and maximum factor values for the calculation of the SuperTrends.
Step: Increments of the factor range.
Performance Memory: Determine the degree to which older inputs affect the current output, with higher values returning longer-term performance measurements.
From Cluster: Determine which cluster is used to obtain the final factor.
🔹 Optimization
This group of settings affects the runtime performances of the script.
Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
Historical Bars Calculation: Calculation window of the script (in bars).
