VIX-Heatmap [CrossTrade]The "VIX-Heatmap" is a sophisticated and informative indicator designed for traders who want to integrate volatility analysis into their trading strategy, especially focusing on the market's fear gauge, the VIX (Volatility Index). This tool is not just about plotting numbers; it's about visualizing market sentiment in a more intuitive and impactful way.
Key Features and Customization Options:
1. Primary Functionality:
At its core, the VIX-Heatmap tracks the daily closing price of the VIX. It provides a clear, line-based visualization, with the line color set to black for stark contrast and easy visibility.
2. Segmented Volatility Levels:
The indicator allows users to set multiple VIX levels: Danger Zone (super low VIX level), and Levels 1 through 5. These levels are represented as horizontal lines on the chart, offering a structured view of different volatility thresholds.
3. Customizable Thresholds:
Traders can input their preferred values for each level, tailoring the indicator to fit their perception of market risk and volatility. This customization makes the tool versatile for different trading styles and market conditions.
4. Heatmap Visualization:
The chart's background color changes based on the VIX level, creating a "heatmap" effect. This visual representation allows traders to quickly gauge the current market sentiment. The color intensity varies from white (for extremely low VIX values) through various shades of red, increasing in intensity with higher VIX levels. This gradient provides an immediate visual cue of rising or falling market anxiety.
5. Interactive Display:
The indicator includes an interactive table display at the bottom center of the chart that shows the current VIX level in large, bold text, ensuring that it catches the trader's eye.
6. Optional Background Coloring:
Users have the option to enable or disable the heatmap feature. When enabled, the chart's background reflects the VIX level with the corresponding color, enhancing the visual impact of the data.
Applications and Benefits:
The VIX-Heatmap is ideal for traders who base their decisions not only on price movements but also on market sentiment and volatility. Its color-coded heatmap approach simplifies the interpretation of the VIX data, making it accessible even to those who may not be deeply familiar with volatility indices. By offering a quick visual summary of current market fear levels, it aids in making informed decisions, especially in times of market uncertainty.
In summary, the VIX-Heatmap transforms the traditional VIX data into an interactive, visually engaging, and easy-to-interpret format.
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Price Delta HeatmapThe Price Delta Heatmap is an indicator designed to visualize the price changes of an asset over time. It helps traders identify and analyze significant price movements and potential volatility. The indicator calculates the price delta, which is the difference between the current close price and the previous close price. It then categorizes the price deltas into different color ranges to create a heatmap-like display on the chart.
The indicator uses user-defined thresholds to determine the color ranges. These thresholds represent the minimum price change required for a specific color to be assigned. The thresholds are adjustable to accommodate different asset classes and trading strategies. Positive price deltas are associated with bullish movements, while negative price deltas represent bearish movements.
The indicator plots bars color-coded according to the price delta range it falls into. The color ranges can be customized to match personal preferences or specific trading strategies. Additionally, the indicator includes signal shapes below the bars to highlight significant positive or negative price deltas. Traders can adjust the threshold values based on their preferred sensitivity to price changes. Higher threshold values may filter out minor price movements and focus on more significant shifts, while lower threshold values will capture even minor fluctuations.
****The default settings have the thresholds set to levels of 100, 50, 20, 10, 0, -10, -20, -50, and -100. These numbers are well-suited for assets such as Ethereum or Bitcoin which are larger in price than an asset that has a price of $1.50, for example. To compensate, adjust the thresholds in the settings to reflect the price delta on the desired asset. All coloration and horizontal line plots will adjust to reflect these changes.****
Traders can interpret the Price Delta Heatmap as follows:
-- Bright green bars indicate the highest positive price deltas, suggesting strong bullish price movements.
-- Green bars represent positive price deltas above the third threshold, indicating significant bullish price changes.
-- Olive bars indicate positive price deltas above the second threshold, suggesting moderate bullish price movements.
-- Yellow bars represent positive price deltas above the lowest threshold, indicating minor bullish price changes. This color is reflected on the negative side as well. Yellow bars below zero indicate negative price deltas below the lowest threshold, suggesting minor bearish price changes.
-- White bars represent zero price deltas, indicating no significant price movement.
-- Orange bars represent negative price deltas below the second threshold, indicating moderate bearish price movements.
-- Red bars indicate negative price deltas below the third threshold, suggesting significant bearish price changes.
-- Maroon bars represent the lowest negative price deltas, indicating strong bearish price movements.
The coloration of the Price Delta line itself is determined by the line's relation to the second positive and second negative thresholds (default +/- 20) - if the line is above the second positive threshold, the line is colored lime (and is reflected in a lime arrow at the bottom of the indicator); if the line is below the second negative threshold, the line is colored fuchsia (also reflected as an arrow); if the line is between thresholds, it is colored aqua.
The Price Delta Heatmap can be used in various trading strategies and applications. Some potential use cases include:
-- Trend identification : The indicator helps traders identify periods of high volatility and potential trend reversals.
-- Volatility analysis : By observing the color changes in the heatmap, traders can gauge the volatility of an asset and adjust their risk management strategies accordingly.
-- Confirmation tool : The indicator can be used as a confirmation tool alongside other technical indicators, such as trend-following indicators or oscillators.
-- Breakout trading : Traders can look for price delta bars of a specific color range to identify potential breakout opportunities.
However, it's important to note that the Price Delta Heatmap has certain limitations. These include:
-- Lagging nature : The indicator relies on historical price data, which means it may not provide real-time insights into price movements.
-- Sensitivity to thresholds : The choice of threshold values affects the indicator's sensitivity and may vary depending on the asset being traded. It requires experimentation and adjustment to find optimal values.
-- Market conditions : The indicator's effectiveness may vary depending on market conditions, such as low liquidity or sudden news events.
Traders should consider using the Price Delta Heatmap in conjunction with other technical analysis tools and incorporate risk management strategies to enhance their trading decisions.
Seasonality Heatmap [QuantAlgo]๐ข Overview
The Seasonality Heatmap analyzes years of historical data to reveal which months and weekdays have consistently produced gains or losses, displaying results through color-coded tables with statistical metrics like consistency scores (1-10 rating) and positive occurrence rates. By calculating average returns for each calendar month and day-of-week combination, it identifies recognizable seasonal patterns (such as which months or weekdays tend to rally versus decline) and synthesizes this into actionable buy low/sell high timing possibilities for strategic entries and exits. This helps traders and investors spot high-probability seasonal windows where assets have historically shown strength or weakness, enabling them to align positions with recurring bull and bear market patterns.
๐ข How It Works
1. Monthly Heatmap
How % Return is Calculated:
The indicator fetches monthly closing prices (or Open/High/Low based on user selection) and calculates the percentage change from the previous month:
(Current Month Price - Previous Month Price) / Previous Month Price ร 100
Each cell in the heatmap represents one month's return in a specific year, creating a multi-year historical view
Colors indicate performance intensity: greener/brighter shades for higher positive returns, redder/brighter shades for larger negative returns
What Averages Mean:
The "Avg %" row displays the arithmetic mean of all historical returns for each calendar month (e.g., averaging all Januaries together, all Februaries together, etc.)
This metric identifies historically recurring patterns by showing which months have tended to rise or fall on average
Positive averages indicate months that have typically trended upward; negative averages indicate historically weaker months
Example: If April shows +18.56% average, it means April has averaged a 18.56% gain across all years analyzed
What Months Up % Mean:
Shows the percentage of historical occurrences where that month had a positive return (closed higher than the previous month)
Calculated as:
(Number of Months with Positive Returns / Total Months) ร 100
Values above 50% indicate the month has been positive more often than negative; below 50% indicates more frequent negative months
Example: If October shows "64%", then 64% of all historical Octobers had positive returns
What Consistency Score Means:
A 1-10 rating that measures how predictable and stable a month's returns have been
Calculated using the coefficient of variation (standard deviation / mean) - lower variation = higher consistency
High scores (8-10, green): The month has shown relatively stable behavior with similar outcomes year-to-year
Medium scores (5-7, gray): Moderate consistency with some variability
Low scores (1-4, red): High variability with unpredictable behavior across different years
Example: A consistency score of 8/10 indicates the month has exhibited recognizable patterns with relatively low deviation
What Best Means:
Shows the highest percentage return achieved for that specific month, along with the year it occurred
Reveals the maximum observed upside and identifies outlier years with exceptional performance
Useful for understanding the range of possible outcomes beyond the average
Example: "Best: 2016: +131.90%" means the strongest January in the dataset was in 2016 with an 131.90% gain
What Worst Means:
Shows the most negative percentage return for that specific month, along with the year it occurred
Reveals maximum observed downside and helps understand the range of historical outcomes
Important for risk assessment even in months with positive averages
Example: "Worst: 2022: -26.86%" means the weakest January in the dataset was in 2022 with a 26.86% loss
2. Day-of-Week Heatmap
How % Return is Calculated:
Calculates the percentage change from the previous day's close to the current day's price (based on user's price source selection)
Returns are aggregated by day of the week within each calendar month (e.g., all Mondays in January, all Tuesdays in January, etc.)
Each cell shows the average performance for that specific day-month combination across all historical data
Formula:
(Current Day Price - Previous Day Close) / Previous Day Close ร 100
What Averages Mean:
The "Avg %" row at the bottom aggregates all months together to show the overall average return for each weekday
Identifies broad weekly patterns across the entire dataset
Calculated by summing all daily returns for that weekday across all months and dividing by total observations
Example: If Monday shows +0.04%, Mondays have averaged a 0.04% change across all months in the dataset
What Days Up % Mean:
Shows the percentage of historical occurrences where that weekday had a positive return
Calculated as:
(Number of Positive Days / Total Days Observed) ร 100
Values above 50% indicate the day has been positive more often than negative; below 50% indicates more frequent negative days
Example: If Fridays show "54%", then 54% of all Fridays in the dataset had positive returns
What Consistency Score Means:
A 1-10 rating measuring how stable that weekday's performance has been across different months
Based on the coefficient of variation of daily returns for that weekday across all 12 months
High scores (8-10, green): The weekday has shown relatively consistent behavior month-to-month
Medium scores (5-7, gray): Moderate consistency with some month-to-month variation
Low scores (1-4, red): High variability across months, with behavior differing significantly by calendar month
Example: A consistency score of 7/10 for Wednesdays means they have performed with moderate consistency throughout the year
What Best Means:
Shows which calendar month had the strongest average performance for that specific weekday
Identifies favorable day-month combinations based on historical data
Format shows the month abbreviation and the average return achieved
Example: "Best: Oct: +0.20%" means Mondays averaged +0.20% during October months in the dataset
What Worst Means:
Shows which calendar month had the weakest average performance for that specific weekday
Identifies historically challenging day-month combinations
Useful for understanding which month-weekday pairings have shown weaker performance
Example: "Worst: Sep: -0.35%" means Tuesdays averaged -0.35% during September months in the dataset
3. Optimal Timing Table/Summary Table
โ Best Month to BUY: Identifies the month with the lowest average return (most negative or least positive historically), representing periods where prices have historically been relatively lower
Based on the observation that buying during historically weaker months may position for subsequent recovery
Shows the month name, its average return, and color-coded performance
Example: If May shows -0.86% as "Best Month to BUY", it means May has historically averaged -0.86% in the analyzed period
โ Best Month to SELL: Identifies the month with the highest average return (most positive historically), representing periods where prices have historically been relatively higher
Based on historical strength patterns in that month
Example: If July shows +1.42% as "Best Month to SELL", it means July has historically averaged +1.42% gains
โ 2nd Best Month to BUY: The second-lowest performing month based on average returns
Provides an alternative timing option based on historical patterns
Offers flexibility for staged entries or when the primary month doesn't align with strategy
Example: Identifies the next-most favorable historical buying period
โ 2nd Best Month to SELL: The second-highest performing month based on average returns
Provides an alternative exit timing based on historical data
Useful for staged profit-taking or multiple exit opportunities
Identifies the secondary historical strength period
Note: The same logic applies to "Best Day to BUY/SELL" and "2nd Best Day to BUY/SELL" rows, which identify weekdays based on average daily performance across all months. Days with lowest averages are marked as buying opportunities (historically weaker days), while days with highest averages are marked for selling (historically stronger days).
๐ข Examples
Example 1: NVIDIA NASDAQ:NVDA - Strong May Pattern with High Consistency
Analyzing NVIDIA from 2015 onwards, the Monthly Heatmap reveals May averaging +15.84% with 82% of months being positive and a consistency score of 8/10 (green). December shows -1.69% average with only 40% of months positive and a low 1/10 consistency score (red). The Optimal Timing table identifies December as "Best Month to BUY" and May as "Best Month to SELL." A trader recognizes this high-probability May strength pattern and considers entering positions in late December when prices have historically been weaker, then taking profits in May when the seasonal tailwind typically peaks. The high consistency score in May (8/10) provides additional confidence that this pattern has been relatively stable year-over-year.
Example 2: Crypto Market Cap CRYPTOCAP:TOTALES - October Rally Pattern
An investor examining total crypto market capitalization notices September averaging -2.42% with 45% of months positive and 5/10 consistency, while October shows a dramatic shift with +16.69% average, 90% of months positive, and an exceptional 9/10 consistency score (blue). The Day-of-Week heatmap reveals Mondays averaging +0.40% with 54% positive days and 9/10 consistency (blue), while Thursdays show only +0.08% with 1/10 consistency (yellow). The investor uses this multi-layered analysis to develop a strategy: enter crypto positions on Thursdays during late September (combining the historically weak month with the less consistent weekday), then hold through October's historically strong period, considering exits on Mondays when intraweek strength has been most consistent.
Example 3: Solana BINANCE:SOLUSDT - Extreme January Seasonality
A cryptocurrency trader analyzing Solana observes an extraordinary January pattern: +59.57% average return with 60% of months positive and 8/10 consistency (teal), while May shows -9.75% average with only 33% of months positive and 6/10 consistency. August also displays strength at +59.50% average with 7/10 consistency. The Optimal Timing table confirms May as "Best Month to BUY" and January as "Best Month to SELL." The Day-of-Week data shows Sundays averaging +0.77% with 8/10 consistency (teal). The trader develops a seasonal rotation strategy: accumulate SOL positions during May weakness, hold through the historically strong January period (which has shown this extreme pattern with reasonable consistency), and specifically target Sunday exits when the weekday data shows the most recognizable strength pattern.
Cointegration Heatmap & Spread Table [EdgeTerminal]The Cointegration Heatmap is a powerful visual and quantitative tool designed to uncover deep, statistically meaningful relationships between assets.
Unlike traditional indicators that react to price movement, this tool analyzes the underlying statistical relationship between two time series and tracks when they diverge from their long-term equilibrium โ offering actionable signals for mean-reversion trades .
What Is Cointegration?
Most traders are familiar with correlation, which measures how two assets move together in the short term. But correlation is shallow โ it doesnโt imply a stable or predictable relationship over time.
Cointegration, however, is a deeper statistical concept: Two assets are cointegrated if a linear combination of their prices or returns is stationary , even if the individual series themselves are non-stationary.
Cointegration is a foundational concept in time series analysis, widely used by hedge funds, proprietary trading firms, and quantitative researchers. This indicator brings that institutional-grade concept into an easy-to-use and fully visual TradingView indicator.
This tool helps answer key questions like:
โWhich stocks tend to move in sync over the long term?โ
โWhen are two assets diverging beyond statistical norms?โ
โIs now the right time to short one and long the other?โ
Using a combination of regression analysis, residual modeling, and Z-score evaluation, this indicator surfaces opportunities where price relationships are stretched and likely to snap back โ making it ideal for building low-risk, high-probability trade setups.
In simple terms:
Cointegrated assets drift apart temporarily, but always come back together over time. This behavior is the foundation of successful pairs trading.
How the Indicator Works
Cointegration Heatmap indicator works across any market supported on TradingView โ from stocks and ETFs to cryptocurrencies and forex pairs.
You enter your list of symbols, choose a timeframe, and the indicator updates every bar with live cointegration scores, spread signals, and trade-ready insights.
Indicator Settings:
Symbol list: a customizable list of symbols separated by commas
Returns timeframe: time frame selection for return sampling (Weekly or Monthly)
Max periods: max periods to limit the data to a certain time and to control indicator performance
This indicator accomplishes three major goals in one streamlined package:
Identifies stable long-term relationships (cointegration) between assets, using a heatmap visualization.
Tracks the spread โ the difference between actual prices and the predicted linear relationship โ between each pair.
Generates trade signals based on Z-score deviations from the mean spread, helping traders know when a pair is statistically overextended and likely to mean revert.
The math:
Returns are calculated using spread tickers to ensure alignment in time and adjust for dividends, splits, and other inconsistencies.
For each unique pair of symbols, we perform a linear regression
Ytโ=ฮฑ+ฮฒXtโ+ฮต
Then we compute the residuals (errors from the regression):
Spreadtโ=Ytโโ(ฮฑ+ฮฒXtโ)
Calculate the standard deviation of the spread over a moving window (default: 100 samples) and finally, define the Cointegration Score:
S=1/Standard Deviation of Residuals
This means, the lower the deviation, the tighter the relationship, so higher scores indicate stronger cointegration.
Always remember that cointegration can break down so monitor the asset over time and over multiple different timeframes before making a decision.
How to use the indicator
The heatmap table:
The indicator displays 2 very important tables, one in the middle and one on the right side. After entering your symbols, the first table to pay attention to is the middle heatmap table.
Any assets with a cointegration value of 25% is something to pay attention to and have a strong and stable relationship. Anything below is weak and not tradable.
Additionally, the 40% level is another important line to cross. Assets that have a cointegration score of over 40% will most likely have an extremely strong relationship.
Think about it this way, the higher the percentage, the tighter and more statistically reliable the relationship is.
The spread table:
After finding a good asset pair using heatmap, locate the same pair in the spread table (right side).
Hereโs what youโll see on the table:
Spread: Current difference between the two symbols based on the regression fit
Mean: Historical average of that spread
Z-score: How far current spread is from the mean in standard deviations
Signal: Trade suggestion: Short, Long, or Neutral
Since youโre expecting mean reversion, the idea is that the spread will return to the average. You want to take a trade when the z-score is either over +2 or below -2 and exit when z-score returns to near 0.
You will usually see the trade suggestion on the spread chart but you can make your own decision based on your risk level.
Keep in mind that the Z-score for each pair refers to how off the first asset is from the mean compared to the second one, so for example if you see STOCKA vs STOCKB with a Z-score of -1.55, we are regressing STOCKB (Y) on STOCKA (X).
In this case, STOCKB is the quoted asset and STOCKA is the base asset.
In this case, this means that STOCKB is much lower than expected relative to STOCKA, so the trade would be a long position on stock B and short position on stock A.
Liqudation HeatMap [BigBeluga]๐ต OVERVIEW
An advanced liquidity visualization tool that plots horizontal heat zones to highlight where potential liquidations and volume clusters are most likely hiding beneath price action.
Liqudation HeatMap scans historical price movements for local highs and lows with elevated volume or candle range. It then draws dynamic heatmap boxesโshaded from lime (low interest) to yellow (high interest)โrevealing potential zones of trapped positions or stop clusters. A vertical scale on the right shows you the relative strength of volume behind each level, from 0 to the highest detected.
๐ต CONCEPTS
Maps areas of potential liquidity using volume or candle range (if volume is unavailable).
Identifies swing highs/lows (pivots) and extends heatmap boxes outward from these levels. Colors each zone based on the relative strength of volume concentration.
Fades or removes zones once price crosses their midpoints, simulating the idea of liquidity being โconsumed.โ
Displays a live vertical scale that shows the volume range for quick reference.
๐ต FEATURES
Dynamic Heatmap Zones:
Draws few boxes above and after pivot highs and below pivot lows, each shaded based on volume concentration.
Smart Coloring System:
Uses a gradient from lime (low) to yellow (high) to visually distinguish between weak and strong liquidity zones.
Adaptive ATR Widths:
Automatically adjusts zone thickness based on volatility (ATR), scaling intelligently across timeframes.
Liquidity Consumption Logic:
Zones are stope extending once price interacts with themโmimicking the behavior of real liquidation sweeps.
Volume Scale Legend:
A real-time scale is plotted on the right side, showing the min-max range of volume used for heat calculations.
๐ต HOW TO USE
Look for thick yellow zones to identify areas of concentrated stop losses or liquidation triggers.
Use these levels to anticipate mean reversion points or high-volatility zones.
Combine with your trend or structure tools to trade into or fade these liquidity pools.
On lower timeframes, use this tool to confirm entries around sweeps or deviations.
Use the right-side scale to compare relative zone strength instantly.
๐ต CONCLUSION
Liqudation HeatMap is a powerful visualization tool that uncovers where liquidity likely resides on the chart. By highlighting hidden traps and reactive levels in real-time, it gives traders a significant edge when it comes to spotting stop hunts, mean reversions, and areas of institutional interest. Whether youโre scalping or swing trading, this heatmap provides unmatched context on the marketโs hidden intent.
Simulated Liquidation Heatmap [QuantAlgo]๐ข Overview
This indicator visualizes where clusters of stop-loss orders and liquidation levels are likely located, displayed as a 'heatmap'. It's based on the concept of market structure liquidity: large groups of stop orders tend to gather around obvious technical levels (like swing highs and lows), and these pools of orders often attract price movement from institutional traders. The indicator uses a fractal-based algorithm to identify these high-probability liquidation zones and displays them as dynamic, color-coded boxes.
The key feature is the thermal color gradient, which indicates the freshness (age) and therefore the relative relevance of the liquidity zone. Hot colors (e.g., Red/Yellow) represent fresh clusters that have just formed, suggesting strong and immediate liquidity interest. Cold colors (e.g., Blue/Purple) represent aged or decaying clusters that are becoming less relevant over time. This visualization allows traders to anticipate potential liquidity sweeps (stop hunts) and understand areas of significant retail and institutional positioning.
๐ข Key Features
1. Liquidity Zone Heatmap
The core function is the identification of swing high and swing low price points using a user-defined Lookback period. These points are where retail traders are statistically most likely to place their stop-loss orders. The indicator simulates the clustering of these orders by drawing a zone (box) around the detected swing point, with the vertical size controlled by the Stop/Liquidation Zone Width (%) setting.
โถ Cluster Lookback: Defines the sensitivity of swing point detection. Lower values detect frequent, minor zones (scalping/intraday); higher values detect major, stronger swing points (swing trading).
โถ Zone Width (%): Sets the percentage range above and below the swing point where stops are simulated to cluster, accounting for slippage and typical stop placement spread.
โถ Liquidity Decay: Zones gradually fade in color intensity and are eventually removed after the user-defined Liquidity Decay Period (Bars), ensuring the heatmap only displays relevant, current liquidity areas.
โถ Round Number Filter: An optional filter that limits the display to liquidity zones occurring only at psychologically significant round numbers (e.g., $100, $1,500.00), which typically attract higher concentrations of orders.
2. Thermal Color Gradient
The heatmap's color is a direct function of the zone's age, providing a visual proxy for immediate relevance.
โถ Freshness: Newly created zones are displayed in the Hot Color (high relevance).
โถ Decay: As bars pass, the zone color transitions along the gradient toward the Cold Color and increased transparency (lower relevance), until it is removed entirely.
โถ Color Schemes: Multiple pre-configured and custom color schemes are available to optimize the visualization for different chart themes and color preferences.
3. Liquidity Heat Thermometer
An optional visual thermometer is displayed on the chart to provide an instant, overall assessment of the current liquidation heat level in the immediate vicinity of the price.
โถ Calculation: The thermometer calculates an aggregate heat score based on the age and proximity of all liquidity zones within a user-defined Zone Detection Range (%) of the current price.
โถ Visual Feedback: A marker (triangle) points to the corresponding level on the thermometer's color gradient (Hot to Cold). A high reading indicates price is close to fresh, dense stop clusters, suggesting high volatility or an imminent liquidity sweep is probable. A low reading indicates price is in a low-density or aged liquidity area.
โถ Customization: The thermometer's resolution, position, and text size are fully customizable for optimal chart placement and readability.
๐ข Practical Applications
โถ Anticipate Sweeps: Prioritize trading in the direction of Hot (fresh) liquidity zones. For example, a hot low-side zone suggests strong sell-side liquidity (stop-losses) is available for large buyers to sweep.
โถ Filter Noise: Use the Round Number Filter to focus only on the highest probability liquidation zones, which are often at clean, psychological price levels.
โถ Validate Entries: Combine the Heat Thermometer with price action analysis. A rising heat level indicates increasing proximity to a major stop cluster, signaling a potential turn or an aggressive market move to sweep those stops.
โถ Risk Management: Understand that price often acts dynamically around these zones. High heat levels imply high risk/reward setups; stops should be placed strategically beyond the defined Liquidation Zone Width.
โถ Multi-Timeframe Context: Higher timeframes (e.g., Daily, 4-Hour) often reveal more significant, major liquidity zones. Use this indicator on lower timeframes (e.g., 5-min, 15-min) for execution, but prioritize zones that align with higher-timeframe structures.
Correlation Heatmapโ OVERVIEW
This indicator creates a correlation matrix for a user-specified list of symbols based on their time-aligned weekly or monthly price returns. It calculates the Pearson correlation coefficient for each possible symbol pair, and it displays the results in a symmetric table with heatmap-colored cells. This format provides an intuitive view of the linear relationships between various symbols' price movements over a specific time range.
โ CONCEPTS
Correlation
Correlation typically refers to an observable statistical relationship between two datasets. In a financial time series context, it usually represents the extent to which sampled values from a pair of datasets, such as two series of price returns, vary jointly over time. More specifically, in this context, correlation describes the strength and direction of the relationship between the samples from both series.
If two separate time series tend to rise and fall together proportionally, they might be highly correlated. Likewise, if the series often vary in opposite directions, they might have a strong anticorrelation . If the two series do not exhibit a clear relationship, they might be uncorrelated .
Traders frequently analyze asset correlations to help optimize portfolios, assess market behaviors, identify potential risks, and support trading decisions. For instance, correlation often plays a key role in diversification . When two instruments exhibit a strong correlation in their returns, it might indicate that buying or selling both carries elevated unsystematic risk . Therefore, traders often aim to create balanced portfolios of relatively uncorrelated or anticorrelated assets to help promote investment diversity and potentially offset some of the risks.
When using correlation analysis to support investment decisions, it is crucial to understand the following caveats:
โโข Correlation does not imply causation . Two assets might vary jointly over an analyzed range, resulting in high correlation or anticorrelation in their returns, but that does not indicate that either instrument directly influences the other. Joint variability between assets might occur because of shared sensitivities to external factors, such as interest rates or global sentiment, or it might be entirely coincidental. In other words, correlation does not provide sufficient information to identify cause-and-effect relationships.
โโข Correlation does not predict the future relationship between two assets. It only reflects the estimated strength and direction of the relationship between the current analyzed samples. Financial time series are ever-changing. A strong trend between two assets can weaken or reverse in the future.
Correlation coefficient
A correlation coefficient is a numeric measure of correlation. Several coefficients exist, each quantifying different types of relationships between two datasets. The most common and widely known measure is the Pearson product-moment correlation coefficient , also known as the Pearson correlation coefficient or Pearson's r . Usually, when the term "correlation coefficient" is used without context, it refers to this correlation measure.
The Pearson correlation coefficient quantifies the strength and direction of the linear relationship between two variables. In other words, it indicates how consistently variables' values move together or in opposite directions in a proportional, linear manner. Its formula is as follows:
๐(๐ฅ, ๐ฆ) = cov(๐ฅ, ๐ฆ) / (๐๐ฅ * ๐๐ฆ)
Where:
โโข ๐ฅ is the first variable, and ๐ฆ is the second variable.
โโข cov(๐ฅ, ๐ฆ) is the covariance between ๐ฅ and ๐ฆ.
โโข ๐๐ฅ is the standard deviation of ๐ฅ.
โโข ๐๐ฆ is the standard deviation of ๐ฆ.
In essence, the correlation coefficient measures the covariance between two variables, normalized by the product of their standard deviations. The coefficient's value ranges from -1 to 1, allowing a more straightforward interpretation of the relationship between two datasets than what covariance alone provides:
โโข A value of 1 indicates a perfect positive correlation over the analyzed sample. As one variable's value changes, the other variable's value changes proportionally in the same direction .
โโข A value of -1 indicates a perfect negative correlation (anticorrelation). As one variable's value increases, the other variable's value decreases proportionally.
โโข A value of 0 indicates no linear relationship between the variables over the analyzed sample.
Aligning returns across instruments
In a financial time series, each data point (i.e., bar) in a sample represents information collected in periodic intervals. For instance, on a "1D" chart, bars form at specific times as successive days elapse.
However, the times of the data points for a symbol's standard dataset depend on its active sessions , and sessions vary across instrument types. For example, the daily session for NYSE stocks is 09:30 - 16:00 UTC-4/-5 on weekdays, Forex instruments have 24-hour sessions that span from 17:00 UTC-4/-5 on one weekday to 17:00 on the next, and new daily sessions for cryptocurrencies start at 00:00 UTC every day because crypto markets are consistently open.
Therefore, comparing the standard datasets for different asset types to identify correlations presents a challenge. If two symbols' datasets have bars that form at unaligned times, their correlation coefficient does not accurately describe their relationship. When calculating correlations between the returns for two assets, both datasets must maintain consistent time alignment in their values and cover identical ranges for meaningful results.
To address the issue of time alignment across instruments, this indicator requests confirmed weekly or monthly data from spread tickers constructed from the chart's ticker and another specified ticker. The datasets for spreads are derived from lower-timeframe data to ensure the values from all symbols come from aligned points in time, allowing a fair comparison between different instrument types. Additionally, each spread ticker ID includes necessary modifiers, such as extended hours and adjustments.
In this indicator, we use the following process to retrieve time-aligned returns for correlation calculations:
โ1. Request the current and previous prices from a spread representing the sum of the chart symbol and another symbol ( "chartSymbol + anotherSymbol" ).
โ2. Request the prices from another spread representing the difference between the two symbols ( "chartSymbol - anotherSymbol" ).
โ3. Calculate half of the difference between the values from both spreads ( 0.5 * (requestedSum - requestedDifference) ). The results represent the symbol's prices at times aligned with the sample points on the current chart.
โ4. Calculate the arithmetic return of the retrieved prices: (currentPrice - previousPrice) / previousPrice
โ5. Repeat steps 1-4 for each symbol requiring analysis.
It's crucial to note that because this process retrieves prices for a symbol at times consistent with periodic points on the current chart, the values can represent prices from before or after the closing time of the symbol's usual session.
Additionally, note that the maximum number of weeks or months in the correlation calculations depends on the chart's range and the largest time range common to all the requested symbols. To maximize the amount of data available for the calculations, we recommend setting the chart to use a daily or higher timeframe and specifying a chart symbol that covers a sufficient time range for your needs.
โ FEATURES
This indicator analyzes the correlations between several pairs of user-specified symbols to provide a structured, intuitive view of the relationships in their returns. Below are the indicator's key features:
Requesting a list of securities
The "Symbol list" text box in the indicator's "Settings/Inputs" tab accepts a comma-separated list of symbols or ticker identifiers with optional spaces (e.g., "XOM, MSFT, BITSTAMP:BTCUSD"). The indicator dynamically requests returns for each symbol in the list, then calculates the correlation between each pair of return series for its heatmap display.
Each item in the list must represent a valid symbol or ticker ID. If the list includes an invalid symbol, the script raises a runtime error.
To specify a broker/exchange for a symbol, include its name as a prefix with a colon in the "EXCHANGE:SYMBOL" format. If a symbol in the list does not specify an exchange prefix, the indicator selects the most commonly used exchange when requesting the data.
Note that the number of symbols allowed in the list depends on the user's plan. Users with non-professional plans can compare up to 20 symbols with this indicator, and users with professional plans can compare up to 32 symbols.
Timeframe and data length selection
The "Returns timeframe" input specifies whether the indicator uses weekly or monthly returns in its calculations. By default, its value is "1M", meaning the indicator analyzes monthly returns. Note that this script requires a chart timeframe lower than or equal to "1M". If the chart uses a higher timeframe, it causes a runtime error.
To customize the length of the data used in the correlation calculations, use the "Max periods" input. When enabled, the indicator limits the calculation window to the number of periods specified in the input field. Otherwise, it uses the chart's time range as the limit. The top-left corner of the table shows the number of confirmed weeks or months used in the calculations.
It's important to note that the number of confirmed periods in the correlation calculations is limited to the largest time range common to all the requested datasets, because a meaningful correlation matrix requires analyzing each symbol's returns under the same market conditions. Therefore, the correlation matrix can show different results for the same symbol pair if another listed symbol restricts the aligned data to a shorter time range.
Heatmap display
This indicator displays the correlations for each symbol pair in a heatmap-styled table representing a symmetric correlation matrix. Each row and column corresponds to a specific symbol, and the cells at their intersections correspond to symbol pairs . For example, the cell at the "AAPL" row and "MSFT" column shows the weekly or monthly correlation between those two symbols' returns. Likewise, the cell at the "MSFT" row and "AAPL" column shows the same value.
Note that the main diagonal cells in the display, where the row and column refer to the same symbol, all show a value of 1 because any series of non-na data is always perfectly correlated with itself.
The background of each correlation cell uses a gradient color based on the correlation value. By default, the gradient uses blue hues for positive correlation, orange hues for negative correlation, and white for no correlation. The intensity of each blue or orange hue corresponds to the strength of the measured correlation or anticorrelation. Users can customize the gradient's base colors using the inputs in the "Color gradient" section of the "Settings/Inputs" tab.
โ FOR Pine Scriptยฎ CODERS
โโข This script uses the `getArrayFromString()` function from our ValueAtTime library to process the input list of symbols. The function splits the "string" value by its commas, then constructs an array of non-empty strings without leading or trailing whitespaces. Additionally, it uses the str.upper() function to convert each symbol's characters to uppercase.
โโข The script's `getAlignedReturns()` function requests time-aligned prices with two request.security() calls that use spread tickers based on the chart's symbol and another symbol. Then, it calculates the arithmetic return using the `changePercent()` function from the ta library. The `collectReturns()` function uses `getAlignedReturns()` within a loop and stores the data from each call within a matrix . The script calls the `arrayCorrelation()` function on pairs of rows from the returned matrix to calculate the correlation values.
โโข For consistency, the `getAlignedReturns()` function includes extended hours and dividend adjustment modifiers in its data requests. Additionally, it includes other settings inherited from the chart's context, such as "settlement-as-close" preferences.
โข A Pine script can execute up to 40 or 64 unique `request.*()` function calls, depending on the user's plan. The maximum number of symbols this script compares is half the plan's limit, because `getAlignedReturns()` uses two request.security() calls.
โโข This script can use the request.security() function within a loop because all scripts in Pine v6 enable dynamic requests by default. Refer to the Dynamic requests section of the Other timeframes and data page to learn more about this feature, and see our v6 migration guide to learn what's new in Pine v6.
โโข The script's table uses two distinct color.from_gradient() calls in a switch structure to determine the cell colors for positive and negative correlation values. One call calculates the color for values from -1 to 0 based on the first and second input colors, and the other calculates the colors for values from 0 to 1 based on the second and third input colors.
Look first. Then leap.
RSI Screener / Heatmap - By LeviathanThis script allows you to quickly scan the market by displaying the RSI values of up to 280 tickers at once and visualizing them in an easy-to-understand format using labels with heatmap coloring.
๐ Source
The script can display the RSI from a custom timeframe (MTF) and custom length for the following data:
- Price
- OBV (On Balance Volume)
- Open Interest (for crypto tickers)
๐ Ticker Selection
This script uses a different approach for selecting tickers. Instead of inputting them one by one via input.symbol(), you can now copy-paste or edit a list of tickers in the text area window. This approach allows users to easily exchange ticker lists between each other and, for example, create multiple lists of tickers by sector, market cap, etc., and easily input them into the script. Full credit to @allanster for his functions for extracting tickers from the text. Users can switch between 7 groups of 40 tickers each, totaling 280 tickers.
๐ฅ๏ธ Display Types
- Screener with Labels: Each ticker has its own color-coded label located at its RSI value.
- Group Average RSI: A standard RSI plot that displays the average RSI of all tickers in the group.
- RSI Heatmap (coming soon): Color-coded rows displaying current and historical values of tickers.
- RSI Divergence Heatmap (coming soon): Color-coded rows displaying current and historical regular/hidden bullish/bearish divergences for tickers.
๐จ Appearance
Appearance is fully customizable via user inputs, allowing you to change heatmap/gradient colors, zone coloring, and more.
Correlation HeatMap Matrix Data [TradingFinder]๐ต Introduction
Correlation is a statistical measure that shows the degree and direction of a linear relationship between two assets.
Its value ranges from -1 to +1 : +1 means perfect positive correlation, 0 means no linear relationship, and -1 means perfect negative correlation.
In financial markets, correlation is used for portfolio diversification, risk management, pairs trading, intermarket analysis, and identifying divergences.
Correlation HeatMap Matrix Data TradingFinder is a Pine Script v6 library that calculates and returns raw correlation matrix data between up to 20 symbols. It only provides the data โ it does not draw or render the heatmap โ making it ideal for use in other scripts that handle visualization or further analysis. The library uses ta.correlation for fast and accurate calculations.
It also includes two helper functions for visual styling :
CorrelationColor(corr) : takes the correlation value as input and generates a smooth gradient color, ranging from strong negative to strong positive correlation.
CorrelationTextColor(corr) : takes the correlation value as input and returns a text color that ensures optimal contrast over the background color.
Library
"Correlation_HeatMap_Matrix_Data_TradingFinder"
CorrelationColor(corr)
โโParameters:
โโโโ corr (float)
CorrelationTextColor(corr)
โโParameters:
โโโโ corr (float)
Data_Matrix(Corr_Period, Sym_1, Sym_2, Sym_3, Sym_4, Sym_5, Sym_6, Sym_7, Sym_8, Sym_9, Sym_10, Sym_11, Sym_12, Sym_13, Sym_14, Sym_15, Sym_16, Sym_17, Sym_18, Sym_19, Sym_20)
โโParameters:
โโโโ Corr_Period (int)
โโโโ Sym_1 (string)
โโโโ Sym_2 (string)
โโโโ Sym_3 (string)
โโโโ Sym_4 (string)
โโโโ Sym_5 (string)
โโโโ Sym_6 (string)
โโโโ Sym_7 (string)
โโโโ Sym_8 (string)
โโโโ Sym_9 (string)
โโโโ Sym_10 (string)
โโโโ Sym_11 (string)
โโโโ Sym_12 (string)
โโโโ Sym_13 (string)
โโโโ Sym_14 (string)
โโโโ Sym_15 (string)
โโโโ Sym_16 (string)
โโโโ Sym_17 (string)
โโโโ Sym_18 (string)
โโโโ Sym_19 (string)
โโโโ Sym_20 (string)
๐ต How to use
Import the library into your Pine Script using the import keyword and its full namespace.
Decide how many symbols you want to include in your correlation matrix (up to 20). Each symbol must be provided as a string, for example FX:EURUSD .
Choose the correlation period (Corr\_Period) in bars. This is the lookback window used for the calculation, such as 20, 50, or 100 bars.
Call Data_Matrix(Corr_Period, Sym_1, ..., Sym_20) with your selected parameters. The function will return an array containing the correlation values for every symbol pair (upper triangle of the matrix plus diagonal).
For example :
var string Sym_1 = '' , var string Sym_2 = '' , var string Sym_3 = '' , var string Sym_4 = '' , var string Sym_5 = '' , var string Sym_6 = '' , var string Sym_7 = '' , var string Sym_8 = '' , var string Sym_9 = '' , var string Sym_10 = ''
var string Sym_11 = '', var string Sym_12 = '', var string Sym_13 = '', var string Sym_14 = '', var string Sym_15 = '', var string Sym_16 = '', var string Sym_17 = '', var string Sym_18 = '', var string Sym_19 = '', var string Sym_20 = ''
switch Market
'Forex' => Sym_1 := 'EURUSD' , Sym_2 := 'GBPUSD' , Sym_3 := 'USDJPY' , Sym_4 := 'USDCHF' , Sym_5 := 'USDCAD' , Sym_6 := 'AUDUSD' , Sym_7 := 'NZDUSD' , Sym_8 := 'EURJPY' , Sym_9 := 'EURGBP' , Sym_10 := 'GBPJPY'
,Sym_11 := 'AUDJPY', Sym_12 := 'EURCHF', Sym_13 := 'EURCAD', Sym_14 := 'GBPCAD', Sym_15 := 'CADJPY', Sym_16 := 'CHFJPY', Sym_17 := 'NZDJPY', Sym_18 := 'AUDNZD', Sym_19 := 'USDSEK' , Sym_20 := 'USDNOK'
'Stock' => Sym_1 := 'NVDA' , Sym_2 := 'AAPL' , Sym_3 := 'GOOGL' , Sym_4 := 'GOOG' , Sym_5 := 'META' , Sym_6 := 'MSFT' , Sym_7 := 'AMZN' , Sym_8 := 'AVGO' , Sym_9 := 'TSLA' , Sym_10 := 'BRK.B'
,Sym_11 := 'UNH' , Sym_12 := 'V' , Sym_13 := 'JPM' , Sym_14 := 'WMT' , Sym_15 := 'LLY' , Sym_16 := 'ORCL', Sym_17 := 'HD' , Sym_18 := 'JNJ' , Sym_19 := 'MA' , Sym_20 := 'COST'
'Crypto' => Sym_1 := 'BTCUSD' , Sym_2 := 'ETHUSD' , Sym_3 := 'BNBUSD' , Sym_4 := 'XRPUSD' , Sym_5 := 'SOLUSD' , Sym_6 := 'ADAUSD' , Sym_7 := 'DOGEUSD' , Sym_8 := 'AVAXUSD' , Sym_9 := 'DOTUSD' , Sym_10 := 'TRXUSD'
,Sym_11 := 'LTCUSD' , Sym_12 := 'LINKUSD', Sym_13 := 'UNIUSD', Sym_14 := 'ATOMUSD', Sym_15 := 'ICPUSD', Sym_16 := 'ARBUSD', Sym_17 := 'APTUSD', Sym_18 := 'FILUSD', Sym_19 := 'OPUSD' , Sym_20 := 'USDT.D'
'Custom' => Sym_1 := Sym_1_C , Sym_2 := Sym_2_C , Sym_3 := Sym_3_C , Sym_4 := Sym_4_C , Sym_5 := Sym_5_C , Sym_6 := Sym_6_C , Sym_7 := Sym_7_C , Sym_8 := Sym_8_C , Sym_9 := Sym_9_C , Sym_10 := Sym_10_C
,Sym_11 := Sym_11_C, Sym_12 := Sym_12_C, Sym_13 := Sym_13_C, Sym_14 := Sym_14_C, Sym_15 := Sym_15_C, Sym_16 := Sym_16_C, Sym_17 := Sym_17_C, Sym_18 := Sym_18_C, Sym_19 := Sym_19_C , Sym_20 := Sym_20_C
= Corr.Data_Matrix(Corr_period, Sym_1 ,Sym_2 ,Sym_3 ,Sym_4 ,Sym_5 ,Sym_6 ,Sym_7 ,Sym_8 ,Sym_9 ,Sym_10,Sym_11,Sym_12,Sym_13,Sym_14,Sym_15,Sym_16,Sym_17,Sym_18,Sym_19,Sym_20)
Loop through or index into this array to retrieve each correlation value for your custom layout or logic.
Pass each correlation value to CorrelationColor() to get the corresponding gradient background color, which reflects the correlationโs strength and direction (negative to positive).
For example :
Corr.CorrelationColor(SYM_3_10)
Pass the same correlation value to CorrelationTextColor() to get the correct text color for readability against that background.
For example :
Corr.CorrelationTextColor(SYM_1_1)
Use these colors in a table or label to render your own heatmap or any other visualization you need.
[DarkTrader] Pivot Point HeatmapThe indicator calculates pivot points using price data from different timeframes such as 12M, 1M, 1W, 3D, and 1D. For each timeframe, it retrieves the high, low, open, and close prices of the previous bar. The pivot point is calculated as the average of the high, low, and close prices, which provides a central level where market sentiment may shift. This calculation is repeated for each timeframe, ensuring a multi-dimensional view of potential interest zones.
Importance of Pivot Points :
Pivot points are essential tools in technical analysis, providing traders with levels that act as potential support and resistance zones. These zones help identify price levels where reversals or breakouts are more likely to occur.
Visual Representation :
The core feature of this indicator is its ability to visualize pivot points as a heatmap on the chart. Instead of showing just the latest pivot points, it tracks the historical pivot swipe, providing a dynamic view of how price interacts with these key levels. Each pivot point is represented by a line, color-coded based on its position relative to other points, creating a gradient effect that highlights the most critical price areas.
Customization Options :
Traders can customize various aspects of the heatmap to suit their preferences. The indicator offers options to toggle pivot swipe history, enabling traders to either focus on the most recent price interactions or consider how price has behaved over time. The background color and pivot line colors are fully customizable, making it easy to match the heatmap with your chart's theme or emphasize certain price levels.
Detecting Sweeps and Price Interaction :
Another important feature is the detection of price interactions with pivot levels. If the current bar's high and low cross a pivot point, it signals that the pivot level has been "swept" by price action, potentially indicating a change in market sentiment. The indicator either extends the line if the pivot point remains relevant or deletes it if price has broken through. This dynamic adjustment helps traders stay updated on which pivot levels are still valid.
Volume Analysis - Heatmap and Volume ProfileHello All!
I have a new toy for you! Volume Analysis - Heatmap and Volume Profile . Honestly I started to work to develop Volume Heatmap then I decided to improve it and add more features such Volume profile, volume, difference in Buy/Sell volumes etc. I tried to put my abilities into this script and tried to use some new Pine Languageโข features ( method, force_overlay, enum etc features ). I hope the usage of these new features would be an example for Pine Programmers.
Lets talk about how it works:
- It gets number of Rows/Columns from the user for each candle to create heatmap
- It calculates the number of the candles to analyze. Number of the candles may change by number of Rows/columns or if any volume / difference in volumes / volume profile is enabled
- It gets Closing/Opening price, Volume and Time info from lower time frame for each candle ( it can be up to 100K for each candle )
- After getting the data it calculates lower time frame to analyze
- Then it calculates how closing price moves, how much volume on each move and create boxes by the volume/move in each box
- The colors for each box calculated by volume info and closing price movements in the lower time frame
- It shows the boxes on Absolute places or Zero Line optionally
- it shows Volume, Cumulative volume, Difference between Buy/Sell volume for each column
- it changes empty box color by Chart background color, also you can change transparency
- At this time it creates Volume Profile with up to 25 rows
- As a new Pine Languageโข feature, it can show Volume Profile in the indicator window or in Main chart, shows Value Area, Value Area High (VAH), Value Area Low (VAL), and draw it and POC (Point Of Control) in the indicator window and/or in the main chart
- Honestly the feature I like is that: For the markets that are not open 24/7, it combines the data from the lower time period without any gaps. For example, if you work for a market that is closed on Saturdays and Sundays, it ensures data integrity by omitting weekends and holidays. so for example if the data is like "ABC---DEF-X---YL-Z" then it makes this data like "ABCDEFXYLZ". In this way, there will be no data breaks in the displayed boxes, there will be no empty colons, and it will appear as if data is coming in at any time.
- Finally it shows Info Panel to give info, its background color automatically changes by the Chart background color
- Important! You should set your "Plan" accordingly, your plan is "Premium or Higher" or "Lower tier". so the script can understand the minimum time frame it can get data!!
I tried to share many screenshots below to explain it much better
How it looks?
it shows Highest Buy/Sell volumes brighter, move volume -> brighter
Volume Profile ( up to 25 row s) ( number of contained candles should be more than 1 )
Volume Profile can be shown in the main chart optionally
How the main chart looks:
Closing price shown and you can enable it, change colors & line width
Can include many candles according to Row&Column number you set
Optionally it can show cumulative volume for each candle
Closing prices from lower time frame
Shows Candle Body by changing background colors
It can shows all included candles on Zero line
You can change the colors of many things
You can set Empty box and border transparency
Table, Empty box Colors adjustment done automatically by chart background color
Sometimes we can not get data from some historical candles if time frame is high such 2days, 1 week etc, and it looks like:
It also checks if Chart time frame and Chart type is suitable
Enjoy!
ATH Distance HeatmapThe "ATH Distance Heatmap" is a powerful visualization tool designed for traders and investors who seek to quickly assess the relative performance of assets against their All-Time Highs (ATH). By mapping the percentage distance of current prices from their historical peaks, this script provides a unique perspective on market sentiment, potential recovery opportunities, and overvaluation risks.
Key Features:
Visual Clarity: Utilize a color-coded heatmap to instantly recognize which assets are near or far from their ATHs. Colors transition smoothly from cool to warm tones, indicating smaller to larger distances respectively.
Real-Time Updates: The script updates dynamically with live market data, ensuring you have the most current information at your fingertips.
Versatile Application: Whether you're tracking stocks, cryptocurrencies, commodities, or indices, the "ATH Distance Heatmap" adapts to a wide array of assets, making it a versatile tool for your trading arsenal.
Insightful Analysis: Beyond mere visualization, this tool can help identify potential buying opportunities in assets that are significantly below their ATHs, or highlight caution for those nearing their peaks.
How to Use:
Configure Your Assets: Start by selecting the assets you wish to track. The script can be customized to monitor a broad market range or a specific segment.
Interpret the Colors: Use the color gradient to gauge the distance of each asset from its ATH. Cooler colors indicate assets closer to their ATH, while warmer colors highlight those further away.
Ideal for:
Traders looking for a quick visual guide to market trends and asset performance.
Investors aiming to capitalize on recovery opportunities or to evaluate entry and exit points.
Market analysts interested in a concise overview of asset health relative to historical performance.
Blockchain Fundamentals: 200 Week MA Heatmap [CR]Blockchain Fundamentals: 200 Week MA Heatmap
This is released as a thank you to all my followers who pushed me over the 600 follower mark on twitter. Thanks to all you Kingz and Queenz out there who made it happen. <3
Indicator Overview
In each of its major market cycles, Bitcoin's price historically bottoms out around the 200 week moving average.
This indicator uses a color heatmap based on the % increases of that 200 week moving average. Depending on the rolling cumulative 4 week percent delta of the 200 week moving average, a color is assigned to the price chart. This method clearly highlights the market cycles of bitcoin and can be extremely helpful to use in your forecasts.
How It Can Be Used
The long term Bitcoin investor can monitor the monthly color changes. Historically, when we see orange and red dots assigned to the price chart, this has been a good time to sell Bitcoin as the market overheats. Periods where the price dots are purple and close to the 200 week MA have historically been good times to buy.
Bitcoin Price Prediction Using This Tool
If you are looking to predict the price of Bitcoin or forecast where it may go in the future, the 200WMA heatmap can be a useful tool as it shows on a historical basis whether the current price is overextending (red dots) and may need to cool down. It can also show when Bitcoin price may be good value on a historical basis. This can be when the dots on the chart are purple or blue.
Over more than ten years, $BTC has spent very little time below the 200 week moving average which is also worth noting when thinking about price predictions for Bitcoin or a Bitcoin price forecast.
Notes
1.) If you do not want to view the legend do the following: Indicator options > Style tab > Uncheck "Tables"
2.) I use my custom function to get around the limited historical data for bitcoin. You can check out the explanation of it here:
MA heatmap (Double cross edition)Hello my friends
Sorry yesterday I couldn't post an indicator because I was travelling. So here's the today indicator inspired from that one Moving-Average-Heatmap-Visualization/
This will gives an interesting representation of a Double Moving Average cross
That's all for me
Let's resume the free indicators publishing next Monday with the MA heatmap (Triple cross edition) and then the 4 cross ... until the 100 cross edition .... "wait are you joking sir ?"... Totally YES :)
But the Triple cross edition will be released as it's interesting from a Pine script perspective
Enjoy your weekend and stay safe
Dave
FVG Heatmap [Hash Capital Research]FVG Map
FVG Map is a visual Fair Value Gap (FVG) mapping tool built to make displacement imbalances easy to see and manage in real time. It detects 3-candle FVG zones, plots them as clean heatmap boxes, tracks partial mitigation (how much of the zone has been filled), and summarizes recent โfill speedโ behavior in a small regime dashboard.
This is an indicator (not a strategy). It does not place trades and it does not publish performance claims. It is a market-structure visualization tool intended to support discretionary or systematic workflows.
What this script detects
Bullish FVG (gap below price)
A bullish FVG is detected when the candle from two bars ago has a high below the current candleโs low.
The zone spans from that prior high up to the current low.
Bearish FVG (gap above price)
A bearish FVG is detected when the candle from two bars ago has a low above the current candleโs high.
The zone spans from the current high up to that prior low.
What makes it useful
Heatmap zones (clean, readable FVG boxes)
Bullish zones plot below price. Bearish zones plot above price.
Partial fill tracking (mitigation progress)
As price trades back into a zone, the script visually shows how much of the zone has been filled.
Mitigation modes (your definition of โfilledโ)
โข Full Fill: price fully trades through the zone
โข 50% Fill: price reaches the midpoint of the zone
โข First Touch: price touches the zone one time
Optional auto-cleanup
Optionally remove zones once theyโre mitigated to keep the chart clean.
Fill-Speed Regime Dashboard
When zones get mitigated, the script records how many bars it took to fill and summarizes the recent environment:
โข Average fill time
โข Median fill time
โข % fast fills vs % slow fills
โข Regime label: choppy/mean-revert, trending/displacement, or mixed
How to use
Use FVG zones as structure, not guaranteed signals.
โข Bullish zones are often watched as potential support on pullbacks.
โข Bearish zones are often watched as potential resistance on rallies.
The fill-speed dashboard helps provide context: fast fills tend to appear in more rotational conditions, while slow fills tend to appear in stronger trend/displacement conditions.
Alerts
Bullish FVG Created
Bearish FVG Created
Notes
FVGs are not guaranteed reversal points. Fill-speed/regime is descriptive of recent behavior and should be treated as context, not prediction. On realtime candles, visuals may update as the bar forms.
Bollinger Heatmap [Quantitative]Overview
The Bollinger Heatmap is a composite indicator that synthesizes data derived from 30 Bollinger bands distributed over multiple time horizons, offering a high-dimensional characterization of the underlying asset.
Algorithm
The algorithm quantifies the current priceโs relative position within each Bollinger band ensemble, generating a normalized position ratio. This ratio is subsequently transformed into a scalar heat value, which is then rendered on a continuous color gradient from red to blue. Red hues correspond to price proximity to or extension below the lower band, while blue hues denote price proximity to or extension above the upper band.
Using default parameters, the indicator maps bands over timeframes increasing in a pattern approximating exponential growth, constrained to multiples of seven days. The lower region encodes relationships with shorter-term bands spanning between 1 and 14 weeks, whereas the upper region portrays interactions with longer-term bands ranging from 15 to 52 weeks.
Conclusion
By integrating Bollinger bands across a diverse array of time horizons, the heatmap indicator aims to mitigate the model risk inherent in selecting a single band length, capturing exposure across a richer parameter space.
Daily Performance HeatmapThis script displays a customizable daily performance heatmap for key assets across crypto, equities, bonds, commodities, currencies, and volatility indices.
Each cell shows the current price and the percent change since the daily open, color-coded using a gradient from negative to positive. Assets are arranged in a left-to-right, top-down grid, with adjustable layout and styling.
โ๏ธ Features:
๐ข Displays current price and daily % change
๐จ Color-coded heatmap using customizable gradients
๐งฑ Adjustable layout: number of columns, cell size, and text size
๐ง Smart price formatting (no decimals for BTC, Gold, etc.)
๐ช Clean alignment with padded spacing for UI clarity
๐ ๏ธ Future plans:
User-input asset lists and labels
Category grouping and dynamic sorting
Optional icons, tooltips, or alerts
ZVOL โ Z-Score Volume Heatmapโฉ ZVOL transforms raw volume into a statistically calibrated heatmap using Z-score thresholds. Unlike classic volume indicators that rely on fixed MA comparisons, ZVOL calculates how many standard deviations each volume bar deviates from its mean. This makes the reading adaptive across timeframes and assets, in order to distinguish meaningful crowd behavior from random volatility.
๐ The core display is a five-zone histogram, each encoded by color and statistical depth. Optional background shading mirrors these zones across the entire pane, revealing subtle compression or structural rhythm shifts across time. By grounding the volume reading in volatility-adjusted context, ZVOL inhibits impulsive trading tactics by compelling the structure, not the sentiment, to dictate the signal.
๐ฅตย Heatmap Coloration:
๐ Suppressed volume โย congestion, coiling phases
๐ฉฑ Stable flow โย early trend or resting volume
๐ High activity โย emerging pressure
๐ Extreme โย possible climax or institutional print
๐๏ธ A dynamic Fibonacci-based 21:34-period EMA ribbon overlays the histogram. The fill area inverts color on crossover, providing a real-time read on tempo, expansion, or divergence between price structure and crowd effort.
๐กย LTF Usage Suggestions:
โขย Confirm breakout legs when orange or red zones align with range exits
โขย Fade overextended moves when red bars appear into resistance
โขย Watch for rising EMAs and orange volume to front-run impulsive moves
โขย Combine with volatility suppression (e.g. ATR) to catch compression โ expansion transitions
๐ฅย Ideal Pairings:
โขย OBVX Conviction Biasย โย to confirm directional intent behind volume shifts
โขย SUPeR TReND 2.718ย โย for directional filters
โขย ATR Turbulence Ribbonย โย to detect compression phases
๐ฅ The OBVX Conviction Bias adds a second dimension to ZVOL by revealing whether crowd effort is aligning with price direction or diverging beneath the surface. While ZVOL identifies statistical anomalies in raw volume, OBVX tracks directional commitment using cumulative volume and moving average cross logic. Use them together to spot fake-outs, anticipate structure-confirmed breakouts, or time pullbacks with volume-based conviction.
๐ฌ ZVOL isnโt just a volume filter โ itโs a structural lens. It reveals when crowd effort is meaningful, when it's fading, and when something is about to shift. Designed for structure-aware traders who care about context, not noise.
Volume Heatmap 2024 | NXT2017 Christmas EditionHi big players around the world,
I wish you a merry christmas time.
Today I have a nice present for you: a new volume heatmap indicator for free using!
HISTORY
My first volume heatmap project got a lot of feedback and a big demand. You can find it here:
In this time pinescript version 4 was the newest one and I worked the first time with arrays.
Today we have pinescript version 5 and some new features. This is why I tried again with matrix function and the results are better than I expected.
HOW IT WORKS
The indicator calculates similar like the volume profile. It looks back and every volume where the close price is on the same row area, the volume will cumulated. How much rows the new chart view is showing, you can choose manually.
The mind behind this is to find high volume levels, where high volume catch the price in a range or get function as support/resistance line.
PICTURES
I hope it helps for your trading. You are welcome to give some comments.
Merry christmas and best regards
NXT2017
Supertrend MTF Heatmap V2Hello traders and aspiring Pinescripters
You might remember this script Supertrend-Heatmap-Multi-timeframes/ ?
A follower, asked me in a comment to do a version where YOU guys can select the timeframes
Well... what follower asks, follower (sometimes) gets. I'm not Santa Claus but this is Christmas with a few months in advance (#oh #oh #oh)
Unmitigated MTF High Low Pro - Cave Diving Bookmap Heatmap Plot
Unmitigated MTF High Low Pro - Cave Diving Bookmap Heatmap Plot
---
## ๐ Table of Contents
1. (#what-this-indicator-does)
2. (#core-concepts)
3. (#visual-components)
4. (#the-cave-diving-framework)
5. (#how-to-use-it-for-trading)
6. (#settings--customization)
7. (#best-practices)
8. (#common-scenarios)
---
## What This Indicator Does
The **Unmitigated MTF High Low v2.0** tracks unmitigated (untouch) high and low levels across multiple timeframes, helping you identify key support and resistance zones that the market hasn't revisited yet. Think of it as a sophisticated memory system for price action - it remembers where price has been, and more importantly, where it *hasn't been back to*.
### Why "Unmitigated" Matters
In futures trading, especially on instruments like NQ and ES, the market has a tendency to revisit levels where liquidity was left behind. An "unmitigated" level is one that hasn't been touched since it was formed. These levels often act as magnets for price, and understanding their age and proximity gives you a significant edge in:
- **Entry timing** - Waiting for price to approach tested levels
- **Exit planning** - Taking profits before ancient resistance/support
- **Risk management** - Avoiding entries when approaching multiple old levels
- **Liquidity mapping** - Visualizing where orders likely cluster
---
## Core Concepts
### 1. **Sessions & Age**
The indicator uses **New York trading sessions** (6:00 PM to 5:59 PM NY time) as the primary time measurement. This aligns with how futures markets naturally segment their activity.
**Age Categories:**
- ๐ข **New (0-1 sessions)** - Fresh levels, recently formed
- ๐ก **Medium (2-3 sessions)** - Tested by time, gaining significance
- ๐ด **Old (4-6 sessions)** - Highly significant, survived multiple days
- ๐ฃ **Ancient (7+ sessions)** - Extreme significance, major support/resistance
The longer a level remains unmitigated, the more significant it becomes. Think of it like compound interest - time adds weight to these zones.
### 2. **Multi-Timeframe Tracking**
You can set the indicator to track high/low levels from any timeframe (default is 15 minutes). This means you're watching for unmitigated 15-minute highs and lows while trading on, say, a 1-minute or 5-minute chart.
**Why this matters:**
- Higher timeframe levels have more weight
- You can see multiple timeframe structure simultaneously
- Helps you avoid fighting larger timeframe momentum
### 3. **Mitigation**
A level becomes "mitigated" (deactivated) when price touches it:
- **High levels** are mitigated when price reaches or exceeds them
- **Low levels** are mitigated when price reaches or goes below them
Once mitigated, the level disappears from view. The indicator only shows you the untouch levels that still matter.
---
## Visual Components
### ๐ The Dashboard Table
Located in the corner of your chart (configurable), the table shows:
```
โโโโโโโโโโโฌโโโโโโโโโโโโฌโโโโโโโโโฌโโโโโโฌโโโโโโโโ
โ Level โ Price โ Points โ Age โ % โ
โโโโโโโโโโโผโโโโโโโโโโโโผโโโโโโโโโผโโโโโโผโโโโโโโโค
โ โโโโโ โ 21,450.25 โ +45.50 โ 8 โ +0.21%โ โ 5th High (Ancient)
โ โโโโ โ 21,430.00 โ +25.25 โ 5 โ +0.12%โ โ 4th High (Old)
โ โโโ โ 21,420.50 โ +15.75 โ 3 โ +0.07%โ โ 3rd High (Medium)
โ โโ โ 21,412.00 โ +7.25 โ 1 โ +0.03%โ โ 2nd High (New)
โ โ โ ๏ธ โ 21,408.25 โ +3.50 โ 0 โ +0.02%โ โ 1st High (Proximity Alert!)
โโโโโโโโโโโผโโโโโโโโโโโโผโโโโโโโโโผโโโโโโผโโโโโโโโค
โ 15 mins โ ๐ข โ ฮ 8.75 โ 2U โ โ โ Status Row
โโโโโโโโโโโผโโโโโโโโโโโโผโโโโโโโโโผโโโโโโผโโโโโโโโค
โ โ โ ๏ธ โ 21,399.50 โ -5.25 โ 0 โ -0.02%โ โ 1st Low (Proximity Alert!)
โ โโ โ 21,395.00 โ -9.75 โ 2 โ -0.05%โ โ 2nd Low (Medium)
โ โโโ โ 21,385.25 โ -19.50 โ 4 โ -0.09%โ โ 3rd Low (Old)
โ โโโโ โ 21,370.00 โ -34.75 โ 6 โ -0.16%โ โ 4th Low (Old)
โ โโโโโ โ 21,350.75 โ -54.00 โ 9 โ -0.25%โ โ 5th Low (Ancient)
โโโโโโโโโโโผโโโโโโโโโโโโผโโโโโโโโโผโโโโโโผโโโโโโโโค
โ ๐ 15โ / 12โ โ โ Statistics (optional)
โโโโโโโโโโโดโโโโโโโโโโโโดโโโโโโโโโดโโโโโโดโโโโโโโโ
```
**Reading the Table:**
- **Level Column**: Number of arrows indicates position (1-5), color shows age
- **Price**: The actual price level
- **Points**: Distance from current price (+ for highs, - for lows)
- **Age**: Number of full sessions since creation
- **%**: Percentage distance from current price
- **โ ๏ธ**: Proximity alert - price is within threshold distance
- **Status Row**: Shows timeframe, direction (๐ข bullish/๐ด bearish), tunnel width (ฮ), and Strat pattern
### ๐ Visual Elements on Chart
**1. Level Lines**
- Horizontal lines showing each unmitigated level
- **Color-coded by age**: Bright colors = new, darker = older, deep purple/teal = ancient
- **Line style**: Customizable (solid, dashed, dotted)
- Automatically turn **yellow** when price gets close (proximity alert)
**2. Price Labels**
- Show the exact price and age: "21,450.25 (8d)"
- Fixed at small size for clean readability
- Positioned with configurable offset from current bar
**3. Bands (Optional)**
- Shaded zones between pairs of unmitigated levels
- Default: Between 1st and 2nd levels (the "tunnel")
- Can switch to 1st-3rd, 2nd-3rd, or disable entirely
- **Upper band** (pink/maroon) - Between unmitigated highs
- **Lower band** (blue/teal) - Between unmitigated lows
- These represent the "no man's land" or consolidation zones
---
## The Cave Diving Framework
This indicator is designed around the **Cave Diving Trading Framework** - a psychological and technical approach that maps cave diving safety protocols to futures trading risk management.
### ๐คฟ The Core Metaphor
**Cave diving has clear danger zones based on depth and overhead environment. Your trading should too.**
#### Shallow Water (New Levels, 0-1 Sessions)
- **Light**: Bright colors (bright red highs, bright green lows)
- **Psychology**: Fresh territory, recently tested
- **Trading**: Be aware but not overly concerned
- **Cave Diving Parallel**: You can see the surface, easy exit
#### Penetration Depth (Medium Levels, 2-3 Sessions)
- **Light**: Medium intensity colors
- **Psychology**: Building significance, market memory forming
- **Trading**: Start respecting these levels for entries/exits
- **Cave Diving Parallel**: Deeper in, need to track your line back
#### Deep Dive Zone (Old Levels, 4-6 Sessions)
- **Light**: Dark colors (deep maroon, dark blue)
- **Psychology**: Highly tested support/resistance
- **Trading**: Major decision points, plan accordingly
- **Cave Diving Parallel**: Significant overhead, careful navigation required
#### Overhead Environment (Ancient Levels, 7+ Sessions)
- **Light**: Very dark, purple/deep teal
- **Psychology**: Extreme caution required, major liquidity zones
- **Trading**: These are your "turn back" signals - don't fight ancient levels
- **Cave Diving Parallel**: Maximum danger, no room for error
### ๐ฏ The Proximity Alert System
Just like a cave diver's depth gauge that warns at critical thresholds, the proximity alerts (โ ๏ธ) tell you when you're entering a danger zone. When price gets within your configured threshold (default 5 points), the indicator:
- Highlights the level in **yellow** on the chart
- Shows **โ ๏ธ** in the table
- Signals: "You're entering a high-significance zone - adjust your position accordingly"
This prevents the trading equivalent of going deeper into a cave without checking your air supply.
---
## How to Use It for Trading
### ๐ฏ Entry Strategies
**1. The "Bounce Setup" (Mean Reversion)**
- Wait for price to approach an old or ancient unmitigated level
- Look for confluence: multiple levels nearby, bands narrowing
- Enter when price shows rejection (reversal candle patterns)
- **Example**: Price drops to a 6-session-old low, shows bullish engulfing โ Long entry
**2. The "Break and Retest" (Trend Following)**
- Wait for price to break through an unmitigated level (mitigates it)
- Enter on the retest of the newly broken level
- **Example**: Price breaks above 4-session-old high โ Wait for pullback to that level โ Long entry
**3. The "Tunnel Trade" (Range Trading)**
- When bands are active, trade the range between 1st-2nd levels
- Short near upper band resistance, long near lower band support
- Exit at opposite side or when bands break
### ๐จ Risk Management Rules
**The Ancient Level Rule**
> Never fight ancient levels (7+ sessions). If you're long and approaching an ancient high, take profits. If you're short and approaching an ancient low, take profits.
These levels have survived a full trading week without being touched - there's likely significant liquidity and institutional interest there.
**The Proximity Exit Rule**
> When you see โ ๏ธ proximity alerts on multiple levels above/below your position, tighten stops or scale out.
This is your "overhead environment" warning. You're in dangerous territory.
**The New Level Filter**
> Be cautious taking positions based solely on new levels (0-1 sessions). Wait for them to age or combine with other confluence.
Fresh levels haven't been tested by time. They're like unconfirmed support/resistance.
### ๐ Reading Market Structure
**Bullish Structure (๐ข in status row)**
- Unmitigated lows are aging and holding
- Price respecting the lower band
- Old lows below acting as strong support
- **Bias**: Look for long entries at lower levels
**Bearish Structure (๐ด in status row)**
- Unmitigated highs are aging and holding
- Price respecting the upper band
- Old highs above acting as strong resistance
- **Bias**: Look for short entries at higher levels
**The Tunnel Compression**
- When the ฮ (delta) in the status row is small, levels are tight
- This often precedes a breakout
- **Trading**: Wait for breakout direction, then trade the break
### ๐ Strat Integration
The indicator shows Strat patterns in the status row:
- **1** - Inside bar (consolidation)
- **2U** - Broke high only (bullish)
- **2D** - Broke low only (bearish)
- **3** - Broke both (wide range, volatility)
Use these with the unmitigated levels:
- **2U near old high** โ Potential resistance, watch for rejection
- **2D near old low** โ Potential support, watch for bounce
- **3 pattern** โ High volatility, respect wider stops
---
## Settings & Customization
### ๐
Session & Timeframe Settings
**HL Interval** (Default: 15 minutes)
- The timeframe for high/low calculation
- **Lower (1m, 5m)**: More levels, more noise, good for scalping
- **Higher (30m, 1H, 4H)**: Fewer levels, stronger significance, good for swing trading
- **Recommendation for NQ/ES**: 15m or 30m for day trading, 1H for swing trading
**Session Age Threshold** (Default: 2)
- How many sessions before a level is considered "old"
- Lower = more levels classified as old
- Higher = stricter definition of significance
### ๐ Level Display Options
**Show Level Lines**
- Toggle: Display horizontal lines for each level
- **Turn off** if you prefer a cleaner chart and only want the table
**Show Level Labels**
- Toggle: Display price labels on the chart
- **Turn off** for minimal visual clutter
**Label Offset**
- Distance (in bars) from current price bar to place labels
- Increase if labels overlap with price action
**Level Line Width & Style**
- Customize visual appearance
- **Thin solid**: Minimal distraction
- **Thick dashed**: High visibility
### ๐จ Age-Based Color Coding
Customize colors for each age category (high and low separately):
- **New (0-1 sessions)**: Default bright red/green
- **Medium (2-3 sessions)**: Default medium intensity
- **Old (4+ sessions)**: Default dark red/blue
- **Ancient (7+ sessions)**: Default deep purple/teal
**Color Strategy Tips:**
- Keep ancient levels in highly contrasting colors
- Use opacity (transparency) if you want subtler lines
- Match your chart's color scheme for aesthetic coherence
### ๐ฏ Band Settings
**Band Mode**
- **1st-2nd** (Default): The primary "tunnel" between most recent levels
- **1st-3rd**: Wider band, more room for price action
- **2nd-3rd**: Band between less immediate levels
- **Disabled**: No bands, lines only
**Band Colors & Borders**
- Customize fill color and border separately
- **Tip**: Keep bands very transparent (90-95% transparency) to avoid obscuring price action
### โ ๏ธ Proximity Alert Settings
**Enable Proximity Alerts**
- Toggle: Turn on/off the warning system
- When enabled, levels within threshold distance show โ ๏ธ and turn yellow
**Alert Threshold** (Default: 5.0 points)
- Distance in points to trigger the alert
- **For NQ**: 5-10 points is reasonable
- **For ES**: 2-5 points is reasonable
- **For MES/MNQ**: Scale down proportionally
**Alert Highlight Color**
- The color lines/labels turn when proximity is triggered
- Default: Yellow (high visibility)
### ๐ Table Settings
**Show Table**
- Toggle: Display the dashboard table
**Table Location**
- Top Left, Top Right, Bottom Left, Bottom Right
- Choose based on your chart layout and other indicators
**Text Size**
- Tiny, Small, Normal, Large
- **Recommendation**: Normal for 1080p monitors, Small for 4K
**Show % Distance**
- Toggle: Add percentage distance column to table
- Useful for comparing relative distances across different price ranges
**Show Statistics Row**
- Toggle: Show total count of unmitigated highs/lows
- Format: "๐ 15โ / 12โ" (15 unmitigated highs, 12 unmitigated lows)
- Useful for gauging overall market structure
### โก Performance Settings
**Enable Level Cleanup**
- Automatically remove very old levels to maintain performance
- **Keep on** unless you want unlimited history
**Max Lookback Levels** (Default: 10,000)
- Maximum number of levels to track
- 10,000 โ 6+ months of 15-minute bars
- **Increase** if you want more history
- **Decrease** if experiencing performance issues
**Max Boxes Per Band** (Default: 245)
- TradingView limit is 500 total boxes
- With 2 bands, 245 each = 490 total (safe maximum)
---
## Best Practices
### ๐ฏ Position Management
**1. Scaling In Near Old Levels**
```
Price approaching 5-session-old low:
- First position: 30% size at proximity alert (โ ๏ธ)
- Second position: 40% size at exact level
- Third position: 30% size if it shows strong rejection
```
**2. Scaling Out Near Ancient Levels**
```
Holding long position, approaching 8-session-old high:
- Exit 50% at proximity alert (โ ๏ธ)
- Exit 30% at exact level
- Trail stop on remaining 20%
```
### ๐ง Trading Psychology Integration
Drawing from principles in *The Mountain Is You*, this indicator helps you:
**1. Recognize Self-Sabotage Patterns**
- **The Premature Entry**: Entering before price reaches your planned level
- **Solution**: Set alerts at unmitigated levels, wait for proximity warnings
- **The Profit-Taking Problem**: Exiting too early from fear
- **Solution**: Identify the next unmitigated level and commit to holding until proximity alert
- **The Loss Holding**: Refusing to exit losing trades
- **Solution**: When price breaks through and mitigates your entry level, it's telling you the structure changed
**2. Building Better Habits**
The color-coded age system trains your brain to:
- Respect levels that have proven themselves over time
- Distinguish between noise (new levels) and structure (old levels)
- Make decisions based on objective data, not fear or greed
**3. Emotional Regulation**
The proximity alerts serve as:
- **Circuit breakers** - Forcing you to re-evaluate before dangerous zones
- **Permission to act** - Giving you objective signals to exit without second-guessing
- **Validation** - Confirming when you're in alignment with market structure
### ๐ Pre-Market Routine
**Daily Setup Checklist:**
1. โ
Identify the 3 nearest unmitigated highs above current price
2. โ
Identify the 3 nearest unmitigated lows below current price
3. โ
Note which are ancient (7+) - these are your "no-go" zones
4. โ
Check the tunnel width (ฮ in status row) - tight or wide?
5. โ
Set alerts at the 1st high and 1st low for proximity warnings
6. โ
Plan: "If we go up, I exit at ___. If we go down, I enter at ___."
### ๐ Timeframe Confluence
**Multi-Timeframe Strategy:**
Run the indicator on **three instances**:
- **15-minute** (short-term structure)
- **1-hour** (intermediate structure)
- **4-hour** (major structure)
**Strong Setup**: When all three timeframes show unmitigated levels converging at the same price zone.
**Example:**
- 15m: Old low at 21,400
- 1H: Ancient low at 21,398
- 4H: Ancient low at 21,395
- **Result**: 21,395-21,400 is a monster support zone
### โ ๏ธ What This Indicator Doesn't Do
**Not a Crystal Ball**
- It doesn't predict where price will go
- It shows you where price *hasn't been* and how long it's been avoided
- The trading decisions are still yours
**Not an Entry Signal Generator**
- It provides context and structure
- You need to combine it with your entry methodology (price action, indicators, order flow, etc.)
**Not Foolproof**
- Ancient levels get broken
- Proximity alerts can trigger early in strong trends
- The market doesn't "owe" you a reversal at any level
---
## Common Scenarios
### Scenario 1: "Level Cluster Ahead"
**Situation**: You're long at 21,400. The table shows:
- 1st High: 21,425 (2 sessions old)
- 2nd High: 21,428 (3 sessions old)
- 3rd High: 21,435 (6 sessions old)
**Interpretation**: There's a resistance cluster just 25-35 points away. The 6-session-old level is particularly significant.
**Action**:
- Set first profit target at 21,420 (before the cluster)
- Set second target at 21,426 (between 1st and 2nd)
- Trail remaining position, but be ready to exit on rejection at 21,435
**Cave Diving Analogy**: You're approaching an overhead section with limited clearance. Lighten your load (reduce position) before entering.
---
### Scenario 2: "Ancient Level Approaches"
**Situation**: The market is grinding higher. You see โ ๏ธ appear next to a 9-session-old high at 21,500.
**Interpretation**: This level has survived over a week without being touched. Massive potential liquidity zone.
**Action**:
- If long, this is your absolute exit zone. Take profits before or at level.
- If looking to short, wait for clear rejection (price taps and reverses)
- Don't try to buy the breakout until it clearly breaks and retests
**Cave Diving Analogy**: Your dive computer is beeping - you've reached your planned turn-back depth. No matter how interesting it looks ahead, honor your plan.
---
### Scenario 3: "Mitigated Levels Create New Structure"
**Situation**: Price breaks and mitigates the 1st High. The previous 2nd High becomes the new 1st High.
**Interpretation**: The structure just shifted. What was the 2nd level is now most relevant.
**Action**:
- Watch how price reacts to the newly-mitigated level
- If it holds below (acts as resistance), bearish
- If it reclaims and holds above (acts as support), bullish
- The NEW 1st High is your next target/resistance
**Cave Diving Analogy**: You've passed through a restriction - the cave layout ahead is different now. Update your mental map.
---
### Scenario 4: "Tight Tunnel, Upcoming Breakout"
**Situation**: The ฮ in the status row shows 3.25 points (very tight). Bands are converging.
**Interpretation**: Price is consolidating between very close unmitigated levels. Breakout likely.
**Action**:
- Don't try to predict direction
- Set alerts above 1st High and below 1st Low
- When break occurs, trade the retest
- Expect volatility - use wider stops
**Cave Diving Analogy**: You're in a narrow passage. Movement will be sudden and directional once it starts.
---
### Scenario 5: "Imbalanced Structure"
**Situation**: The statistics row shows "๐ 22โ / 7โ"
**Interpretation**: There are many more unmitigated highs than lows. This suggests:
- Price has been declining (hitting lows, leaving highs behind)
- Potential bullish reversal zone (lots of overhead supply mitigated)
- Or continued bearish structure (resistance everywhere above)
**Action**:
- Look at the age of those 22 highs
- If mostly new (0-2 sessions): Just a recent downmove, not significant yet
- If many old/ancient: Strong overhead resistance, be cautious on longs
- Compare to price action: Is price respecting the remaining lows?
**Cave Diving Analogy**: You've swam deeper than your starting point - most of your markers are above you now. Are you planning the ascent or going deeper?
---
## Final Thoughts: The Philosophy
This indicator is built on a simple but powerful principle: **The market has memory, and that memory has weight.**
Every unmitigated level represents:
- Liquidity left behind
- Orders waiting to be filled
- Institutional interest potentially parked
- Psychological significance for participants
The longer a level remains unmitigated, the more "charged" it becomes. When price finally revisits it, something significant usually happens - either a strong reversal or a definitive break.
Your job as a trader isn't to predict which outcome will occur. Your job is to:
1. **Recognize** when you're approaching these charged zones
2. **Respect** them by adjusting position size and risk
3. **React** appropriately based on how price behaves at them
4. **Remember** that ancient levels (like ancient wisdom) deserve extra reverence
The Cave Diving Framework embedded in this indicator serves as a constant reminder: Trading, like cave diving, requires rigorous respect for environmental hazards, meticulous planning, and the discipline to turn back when your limits are reached.
**Every proximity alert is the market asking you**: *"Do you really want to go deeper?"*
Sometimes the answer is yes - when your setup, confluence, and risk management all align.
Often, the answer should be no - and that's the trader avoiding the accident that would have happened to the gambler.
---
### ๐ฏ Quick Reference Card
**Color System:**
- ๐ข Bright colors = New (0-1 sessions) = Shallow water
- ๐ก Medium colors = Medium (2-3 sessions) = Penetration depth
- ๐ด Dark colors = Old (4-6 sessions) = Deep dive zone
- ๐ฃ Deep dark colors = Ancient (7+ sessions) = Overhead environment
**Symbols:**
- โ โโ โโโ โโโโ โโโโโ = High levels (1st through 5th)
- โ โโ โโโ โโโโ โโโโโ = Low levels (1st through 5th)
- โ ๏ธ = Proximity alert (danger zone)
- ๐ข = Bullish structure
- ๐ด = Bearish structure
- ฮ = Tunnel width (distance between 1st high and 1st low)
**Critical Rules:**
1. Never fight ancient levels (7+ sessions)
2. Respect proximity alerts (โ ๏ธ)
3. Scale out near old/ancient resistance
4. Wait for confluence when entering
5. Let mitigated levels prove their new role
---
**Remember**: The indicator gives you structure. The trading edge comes from your discipline in respecting that structure.
Trade safe, trade smart, and always know your exit before your entry. ๐ฏ
---
*"You don't become your best self by denying your patterns. You become your best self by recognizing them, understanding them, and choosing differently." - Adapted from The Mountain Is You*
In trading: You don't become profitable by ignoring market structure. You become profitable by recognizing it, understanding it, and choosing your entries accordingly.
MA Cross HeatmapThe Moving Average Cross Heatmap Created by Technicator , visualizes the crossing distances between multiple moving averages using a heat map style color coding.
The main purpose of this visualization is to help identify potential trend changes or trading opportunities by looking at where the moving averages cross over each other.
Key Features:
Can plot up to 9 different moving average with their cross lengths you set
Uses a heat map to show crossing distances between the MAs
Adjustable settings like crossing length percentage, color scheme, color ceiling etc.
Overlay style separates the heat map from the price chart
This is a unique way to combine multiple MA analysis with a visual heat map representation on one indicator. The code allows you to fine-tune the parameters to suit your trading style and preferences. Worth checking out if you trade using multiple moving average crossovers as part of your strategy.
Treasury Yields Heatmap [By MUQWISHI]โ INTRODUCTION :
The โTreasury Yields Heatmapโ generates a dynamic heat map table, showing treasury yield bond values corresponding with dates. In the last column, it presents the status of the yield curve, discerning whether itโs in a normal, flat, or inverted configuration, which determined by using Pearson's linear regression coefficient. This tool is built to offer traders essential insights for effectively tracking bond values and monitoring yield curve status, featuring the flexibility to input a starting period, timeframe, and select from a range of major countries' bond data.
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โ OVERVIEW:
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โ YIELD CURVE:
It is determined through Pearson's linear regression coefficient and consideredโฆ
R โฅ 0.7 โ Normal
0.7 > R โฅ 0.35 โ Slight Normal
0.35 > R > -0.35 โ Flat
-0.35 โฅ R > -0.7 โ Slight Inverted
-0.7 โฅ R โ Inverted
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โ INDICATOR SETTINGS:
#Section One: Table Setting
#Section Two: Technical Setting
(1) Country: Select countryโs treasury yields data
(2) Timeframe: Time interval.
(3) Fetch By:
โ(3A) Date: Retrieve data by beginning of date.
โ(3B) Period: Retrieve data by specifying the number of time series back.
Enjoy. Please let me know if you have any questions.
Thank you.
