ES vs Bond ROCThis Pine Script plots the Relative Rate of Change (ROC) between the S&P 500 E-mini Futures (ES) and 30-Year Treasury Bond Futures (ZB) over a specified period. It helps identify when equities are overperforming or underperforming relative to long-term bonds—an insight often used to detect risk-on/risk-off sentiment shifts in the market.
Cari dalam skrip untuk "美国标普500指数成分股"
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Low Liquidity Zones [PhenLabs]📊 Low Liquidity Zones
Version: PineScript™ v6
📌 Description
Low Liquidity Zones identifies and highlights periods of unusually low trading volume on your chart, marking areas where price movement occurred with minimal participation. These zones often represent potential support and resistance levels that may be more susceptible to price breakouts or reversals when revisited with higher volume.
Unlike traditional volume analysis tools that focus on high volume spikes, this indicator specializes in detecting low liquidity areas where price moved with minimal resistance. Each zone displays its volume delta, providing insight into buying vs. selling pressure during these thin liquidity periods. This combination of low volume detection and delta analysis helps traders identify potential price inefficiencies and weak structures in the market.
🚀 Points of Innovation
• Identifies low liquidity zones that most volume indicators overlook but which often become significant technical levels
• Displays volume delta within each zone, showing net buying/selling pressure during low liquidity periods
• Dynamically adjusts to different timeframes, allowing analysis across multiple time horizons
• Filters zones by maximum size percentage to focus only on precise price levels
• Maintains historical zones until they expire based on your lookback settings, creating a cumulative map of potential support/resistance areas
🔧 Core Components
• Low Volume Detection: Identifies candles where volume falls below a specified threshold relative to recent average volume, highlighting potential liquidity gaps.
• Volume Delta Analysis: Calculates and displays the net buying/selling pressure within each low liquidity zone, providing insight into the directional bias during low participation periods.
• Dynamic Timeframe Adjustment: Automatically scales analysis periods to match your selected timeframe preference, ensuring consistent identification of low liquidity zones regardless of chart settings.
• Zone Management System: Creates, tracks, and expires low liquidity zones based on your configured settings, maintaining visual clarity on the chart.
🔥 Key Features
• Low Volume Identification: Automatically detects and highlights candles where volume falls below your specified threshold compared to the moving average.
• Volume Delta Visualization: Shows the net volume delta within each zone, providing insight into whether buyers or sellers were dominant despite the low overall volume.
• Flexible Timeframe Analysis: Analyze low liquidity zones across multiple predefined timeframes or use a custom lookback period specific to your trading style.
• Zone Size Filtering: Filters out excessively large zones to focus only on precise price levels, improving signal quality.
• Automatic Zone Expiration: Older zones are automatically removed after your specified lookback period to maintain a clean, relevant chart display.
🎨 Visualization
• Volume Delta Labels: Each zone displays its volume delta with “+” or “-” prefix and K/M suffix for easy interpretation, showing the strength and direction of pressure during the low volume period.
• Persistent Historical Mapping: Zones remain visible for your specified lookback period, creating a cumulative map of potential support and resistance levels forming under low liquidity conditions.
📖 Usage Guidelines
Analysis Timeframe
Default: 1D
Range/Options: 15M, 1HR, 3HR, 4HR, 8HR, 16HR, 1D, 3D, 5D, 1W, Custom
Description: Determines the historical period to analyze for low liquidity zones. Shorter timeframes provide more recent data while longer timeframes offer a more comprehensive view of significant zones. Use Custom option with the setting below for precise control.
Custom Period (Bars)
Default: 1000
Range: 1+
Description: Number of bars to analyze when using Custom timeframe option. Higher values show more historical zones but may impact performance.
Volume Analysis
Volume Threshold Divisor
Default: 0.5
Range: 0.1-1.0
Description: Maximum volume relative to average to identify low volume zones. Example: 0.5 means volume must be below 50% of the average to qualify as low volume. Lower values create more selective zones while higher values identify more zones.
Volume MA Length
Default: 15
Range: 1+
Description: Period length for volume moving average calculation. Shorter periods make the indicator more responsive to recent volume changes, while longer periods provide a more stable baseline.
Zone Settings
Zone Fill Color
Default: #2196F3 (80% transparency)
Description: Color and transparency of the low liquidity zones. Choose colors that stand out against your chart background without obscuring price action.
Maximum Zone Size %
Default: 0.5
Range: 0.1+
Description: Maximum allowed height of a zone as percentage of price. Larger zones are filtered out. Lower values create more precise zones focusing on tight price ranges.
Display Options
Show Volume Delta
Default: true
Description: Toggles the display of volume delta within each zone. Enabling this provides additional insight into buying vs. selling pressure during low volume periods.
Delta Text Position
Default: Right
Options: Left, Center, Right
Description: Controls the horizontal alignment of the delta text within zones. Adjust based on your chart layout for optimal readability.
✅ Best Use Cases
• Identifying potential support and resistance levels that formed during periods of thin liquidity
• Spotting price inefficiencies where larger players may have moved price with minimal volume
• Finding low-volume consolidation areas that may serve as breakout or reversal zones when revisited
• Locating potential stop-hunting zones where price moved on minimal participation
• Complementing traditional support/resistance analysis with volume context
⚠️ Limitations
• Requires volume data to function; will not work on symbols where the data provider doesn’t supply volume information
• Low volume zones don’t guarantee future support/resistance - they simply highlight potential areas of interest
• Works best on liquid instruments where volume data has meaningful fluctuations
• Historical analysis is limited by the maximum allowed box count (500) in TradingView
• Volume delta in some markets may not perfectly reflect buying vs. selling pressure due to data limitations
💡 What Makes This Unique
• Focus on Low Volume: Unlike some indicators that highlight high volume events particularly like our very own TLZ indicator, this tool specifically identifies potentially significant price zones that formed with minimal participation.
• Delta + Low Volume Integration: Combines volume delta analysis with low volume detection to reveal directional bias during thin liquidity periods.
• Flexible Lookback System: The dynamic timeframe system allows analysis across any timeframe while maintaining consistent zone identification criteria.
• Support/Resistance Zone Generation: Automatically builds a visual map of potential technical levels based on volume behavior rather than just price patterns.
🔬 How It Works
1. Volume Baseline Calculation:
The indicator calculates a moving average of volume over your specified period to establish a baseline for normal market participation. This adaptive baseline accounts for natural volume fluctuations across different market conditions.
2. Low Volume Detection:
Each candle’s volume is compared to the moving average and flagged when it falls below your threshold divisor. The indicator also filters zones by maximum size to ensure only precise price levels are highlighted.
3. Volume Delta Integration:
For each identified low volume candle, the indicator retrieves the volume delta from a lower timeframe. This delta value is formatted with appropriate scaling (K/M) and displayed within the zone.
4. Zone Management:
New zones are created and tracked in a dynamic array, with each zone extending rightward until it expires. The system automatically removes expired zones based on your lookback period to maintain a clean chart.
💡 Note:
Low liquidity zones often represent areas where price moved with minimal participation, which can indicate potential market inefficiencies. These zones frequently become important support/resistance levels when revisited, especially if approached with higher volume. Consider using this indicator alongside traditional technical analysis tools for comprehensive market context. For best results, experiment with different volume threshold settings based on the specific instrument’s typical volume patterns.
Volume Patterns [SS]Hey everyone,
Been a while since doing anything with Pinescript.
Here is my iteration of a Volume Pattern identification, inspired by Bulkowski's work on patterns and volume.
The indicator aims to identify the 4 major types of volume patterns, these are:
Bullish Breakout Volume
Bearish Breakout Volume
Inverted Domes
Domes
Classification
These patterns are all assigned to a classification based on theory. For example, dome volume is usually bearish, inverted dome is usually bullish, etc. etc. However, in order to accommodate changing sentiments and volatility, I have coded logic into the indicator to assess for the actual sentiment associated with these patterns itself.
The indicator calculates the average return associated with each pattern, scaling the data into a percent return. It then has the ability to re-scale the target using the close price associated with the pattern at the time of pattern signaling, to calculate the target price and plot the target on the chart for you.
Additionally, it provides you with the following:
Labels to signal when a pattern has happened
A table that shows you the average returns associated with the 4 major patterns
Target lines with labels that visually show you the target price associated with the pattern, as well as which pattern they are associated with.
All of these things can be toggled on or off depending on your preference.
Customizing the indicator
In addition to being able to toggle the visuals on or off depending on what you want to see or not see, there are some minor customization abilities in terms of training the indicator to recognize the patterns and predict the TP.
The first one is the Training length
In the settings, you will see "Train", and the default is 500. This is the amount the indicator is looking back in history to learn the patterns and returns associated with them. This 500 is appropriate in most cases and on most timeframes.
Lastly, the Lookforward Length
The look forward length represents the number of bars forward you want to determine the returns for. It is defaulted to 10, but you can modify it.
So, if you are on the 1-Minute chart and have the look forward set to 10, then once a signal happens, the target price is calculated based on 10 minutes from the time of signal. You can increase this or decrease this based on your preference.
Longer look forwards can be good for swingers but should be used on the larger timeframes, shorter are good for scalpers but should be used on the shorter timeframes.
The indicator's use is incredibly simple, you'll pick it up in no time!
Hope you enjoy it and as always, safe trades!
Just an FYI for those who may have questions:
The indicator is open source. This means you are free to take it and modify it as you wish. You do not need to ask me.
Please read the description carefully, as 100% of questions I am asked about indicators are covered in the description. ;-)
Have a good one guys and gals! 🚀🚀🚀
Machine Learning Trendlines Cluster [LuxAlgo]The ML Trendlines Cluster indicator allows traders to automatically identify trendlines using a machine learning algorithm based on k-means clustering and linear regression, highlighting trendlines from clustered prices.
For trader's convenience, trendlines can be filtered based on their slope, allowing them to filter out trendlines that are too horizontal, or instead keep them depending on the user-selected settings.
🔶 USAGE
Traders only need to set the number of trendlines (clusters) they want the tool to detect and the algorithm will do the rest.
By default the tool is set to detect 4 clusters over the last 500 bars, in the image above it is set to detect 10 clusters over the same period.
This approach only focuses on drawing trendlines from prices that share a common trading range, offering a unique perspective to traditional trendlines. Trendlines with a significant slope can highlight higher dispersion within its cluster.
🔹 Trendline Slope Filtering
Traders can filter trendlines by their slope to display only steep or flat trendlines relative to a user-defined threshold.
The image above shows the three different configurations of this feature:
Filtering disabled
Filter slopes above threshold
Filter slopes below threshold
🔶 DETAILS
K-means clustering is a popular machine-learning algorithm that finds observations in a data set that are similar to each other and places them in a group.
The process starts by randomly assigning each data point to an initial group and calculating the centroid for each. A centroid is the center of the group. K-means clustering forms the groups in such a way that the variances between the data points and the centroid of the cluster are minimized.
The trendlines are displayed according to the linear regression function calculated for each cluster.
🔶 SETTINGS
Window Size: Maximum number of bars to get data from
Clusters: Maximum number of clusters (trendlines) to detect
🔹 Optimization
Maximum Iteration Steps: Maximum loop iterations for cluster computation
🔹 Slope Filter
Threshold Multiplier: Multiplier applied to a volatility measure, higher multiplier equals higher threshold
Filter Slopes: Enable/Disable Trendline Slope Filtering, select to filter trendlines with slopes ABOVE or BELOW the threshold
🔹 Style
Upper Zone: Color to display in the top zone
Lower Zone: Color to display in the bottom zone
Lines: Style for the lines
Size: Line size
FOMO Indicator - % of Stocks Above 5-Day AvgThe FOMO Indicator plots the breadth indicators NCFD and S5FD below the price chart, representing the percentage of stocks in the Nasdaq Composite (NCFD) or S&P 500 (S5FD) trading above their respective 5-day moving averages.
This indicator identifies short-term market sentiment and investor positioning. When over 85% of stocks exceed their 5-day averages, it signals widespread buying pressure and potential FOMO (Fear Of Missing Out) among investors. Conversely, levels below 15% may indicate oversold conditions. By analyzing these breadth metrics over a short time window, the FOMO Indicator helps traders gauge shifts in investor sentiment and positioning.
Clean OHLC Lines | BaksPlots clean, non-repainting OHLC lines from higher timeframes onto your chart. Ideal for tracking key price levels (open, high, low, close) with precision and minimal clutter.
Core Functionality
Clean OHLC Lines = Historical Levels + Non-Repainting Logic
• Uses lookahead=on to anchor historical lines, ensuring no repainting.
• Displays OHLC lines for customizable timeframes (15min to Monthly).
• Optional candlestick boxes for visual context.
Key Features
• Multi-Timeframe OHLC:
Plot lines from 15min, 30min, 1H, 4H, Daily, Weekly, or Monthly timeframes.
• Non-Repainting Logic:
Historical lines remain static and never recalculate.
• Customizable Styles:
Adjust colors, line widths (1px-4px), and transparency for high/low/open/close lines.
• Candle Display:
Toggle candlestick boxes with bull/bear colors and adjustable borders.
• Past Lines Limit:
Control how many historical lines are displayed (1-500 bars).
User Inputs
• Timeframe:
Select the OHLC timeframe (e.g., "D" for daily).
• # Past Lines:
Limit historical lines to avoid overcrowding (default: 10).
• H/L Mode:
Draw high/low lines from the current or previous period.
• O/C Mode:
Anchor open/close lines to today’s open or yesterday’s close.
• Line Styles:
Customize colors, transparency, and styles (solid/dotted/dashed).
• Candle Display:
Toggle boxes/wicks and adjust bull/bear colors.
Important Notes
⚠️ Alignment:
• Monthly/weekly timeframes use fixed approximations (30d/7d).
• For accuracy, ensure your chart’s timeframe ≤ the selected OHLC timeframe (e.g., use 1H chart for daily lines).
⚠️ Performance:
• Reduce # Past Lines on low-end devices for smoother performance.
Risk Disclaimer
Trading involves risk. OHLC lines reflect historical price levels and do not predict future behavior. Use with other tools and risk management.
Open-Source Notice
This script is open-source under the Mozilla Public License 2.0. Modify or improve it freely, but republishing must follow TradingView’s House Rules.
📈 Happy trading!
Liquidity Zones [ActiveQuants]The Liquidity Zones indicator detects price areas where high trading volume coincides with below-average volatility , critical zones where large players often accumulate or distribute positions. Ideal for spotting potential reversal points and strategic liquidity pools.
Core Detection Formula
Liquidity Zone = (Volume > SMA(Volume, Length) × Multiplier) AND (Short-Term Volatility < 0.5 × Average Volatility)
Volume Surge Detection
Compares current volume to its SMA (user-defined length).
Multiplies threshold with " Volume Threshold Multiplier " parameter.
Volatility Contraction Filter
Calculates 5-bar volatility (standard deviation of closes).
Compares to average volatility over " Price Std. Dev. Length " period.
Requires short-term volatility < 50% of average.
█ KEY FEATURES
Merging Consecutive Zones
If the " Merge Consecutive Zones " option is enabled, the indicator will:
Calculate the number of consecutive bars that meet the liquidity zone criteria.
Sum the volume of these consecutive bars.
Display only the most recent label for the merged zone (previous labels in the sequence are removed).
Displays volume in either
Raw units (" Units ").
Dollar-equivalent (" Currency Value ") using closing price.
Alerts
An alert condition is built into the script. Traders can selectively enable alerts via TradingView’s alert system. Whenever a liquidity zone is detected, an alert is triggered with the message: " High-volume and low-volatility zone detected! ".
█ USER INPUTS
- Liquidity Zones Color
Sets the background color for liquidity zones.
Default: Orange (with 70 transparency).
- Volume SMA Length
Determines the number of bars over which the volume simple moving average is calculated.
Default: 20 bars.
- Volume Threshold Multiplier
Multiplies the volume SMA to establish a threshold. A bar’s volume must exceed this product to be considered high volume.
Default: 2.0.
- Price Std. Dev. Length
The period used to calculate the standard deviation of the closing prices. This is the basis for measuring average volatility.
Default: 14 bars.
- Zone Volume
A toggle to display a label with the volume value on liquidity zones.
Allows you to choose how the volume is displayed: Units (shows raw volume) or Currency Value (multiplies volume by the current closing price).
Allows you to choose the font size of the volume label.
- Merge Consecutive Zones
When enabled, volumes from consecutive liquidity zones are summed into a single total, and only the most recent label is displayed (previous labels in the sequence are removed).
Default: Enabled.
- Show Last
Specifies the number of bars back that the indicator will evaluate and plot liquidity zones.
Default: 500 bars.
- Timeframe
Analysis period.
Default: Chart.
█ CONCLUSION
The Liquidity Zones indicator is a powerful tool for traders seeking to identify key areas on the chart where liquidity is concentrated, characterized by high volume and low volatility . With customizable settings for volume analysis and volatility measurement , this indicator can be integrated into a wide range of trading strategies. It not only highlights these zones visually but also provides volume data labels and alerts for timely decision-making.
█ IMPORTANT NOTES
⚠ Volume and Volatility Settings: Adjust the Volume SMA Length , Volume Threshold Multiplier , and Price Std. Dev. Length to suit the typical trading volume and volatility of the asset you are analyzing.
⚠ Confirmed Bars Only: Signals are generated only on confirmed bars. This minimizes false signals due to intra-bar noise and also prevents indicator repainting .
⚠ Risk Management: Liquidity zones may signal areas of potential accumulation or distribution, but they should be used in conjunction with other technical analysis tools (e.g., support/resistance levels, trendlines, or momentum indicators). Trading involves risk, and it is recommended to combine this indicator with proper risk management techniques.
█ RISK DISCLAIMER
Trading involves substantial risk of loss. Liquidity zones indicate potential interest areas but don't guarantee price reactions. Always confirm with additional analysis and proper risk management. Past performance is not indicative of future results.
📈 Happy trading! 🚀
CAPM Alpha & BetaThe CAPM Alpha & Beta indicator is a crucial tool in finance and investment analysis derived from the Capital Asset Pricing Model (CAPM) . It provides insights into an asset's risk-adjusted performance (Alpha) and its relationship to broader market movements (Beta). Here’s a breakdown:
1. How Does It Work?
Alpha:
Definition: Alpha measures the portion of an investment's return that is not explained by market movements, i.e., the excess return over and above what the market is expected to deliver.
Purpose: It represents the value a fund manager or strategy adds (or subtracts) from an investment’s performance, adjusting for market risk.
Calculation:
Alpha is derived from comparing actual returns to expected returns predicted by CAPM:
Alpha = Actual Return − (Risk-Free Rate + β × (Market Return − Risk-Free Rate))
Alpha = Actual Return − (Risk-Free Rate + β × (Market Return − Risk-Free Rate))
Interpretation:
Positive Alpha: The investment outperformed its CAPM prediction (good performance for additional value/risk).
Negative Alpha: The investment underperformed its CAPM prediction.
Beta:
Definition: Beta measures the sensitivity of an asset's returns relative to the overall market's returns. It quantifies systematic risk.
Purpose: Indicates how volatile or correlated an investment is relative to the market benchmark (e.g., S&P 500).
Calculation:
Beta is computed as the ratio of the covariance of the asset and market returns to the variance of the market returns:
β = Covariance (Asset Return, Market Return) / Variance (Market Return)
β = Variance (Market Return) Covariance (Asset Return, Market Return)
Interpretation:
Beta = 1: The asset’s price moves in line with the market.
Beta > 1: The asset is more volatile than the market (higher risk/higher potential reward).
Beta < 1: The asset is less volatile than the market (lower risk/lower reward).
Beta < 0: The asset moves inversely to the market.
2. How to Use It?
Using Alpha:
Portfolio Evaluation: Investors use Alpha to gauge whether a portfolio manager or a strategy has successfully outperformed the market on a risk-adjusted basis.
If Alpha is consistently positive, the portfolio may deliver higher-than-expected returns for the given level of risk.
Stock/Asset Selection: Compare Alpha across multiple securities. Positive Alpha signals that the asset may be a good addition to your portfolio for excess returns.
Adjusting Investment Strategy: If Alpha is negative, reassess the asset's role in the portfolio and refine strategies.
Using Beta:
Risk Management:
A high Beta (e.g., 1.5) indicates higher sensitivity to market movements. Use such assets if you want to take on more risk during bullish market phases or expect higher returns.
A low Beta (e.g., 0.7) indicates stability and is useful in diversifying risk in volatile or bearish markets.
Portfolio Diversification: Combine assets with varying Betas to achieve the desired level of market responsiveness and smooth out portfolio volatility.
Monitoring Systematic Risk: Beta helps identify whether an investment aligns with your risk tolerance. For example, high-Beta stocks may not be suitable for conservative investors.
Practical Application:
Use both Alpha and Beta together:
Assess performance with Alpha (excess returns).
Assess risk exposure with Beta (market sensitivity).
Example: A stock with a Beta of 1.2 and a highly positive Alpha might suggest a solid performer that is slightly more volatile than the market, making it a suitable pick for risk-tolerant, return-maximizing investors.
In conclusion, the CAPM Alpha & Beta indicator gives a comprehensive view of an asset's performance and risk. Alpha enables performance evaluation on a risk-adjusted basis, while Beta reveals the level of market risk. Together, they help investors make informed decisions, build optimal portfolios, and align investments with their risk-return preferences.
[S1B] Engulfing Orderblock
The Engulfing Orderblock indicator is a custom script designed to visually highlight and track bullish and bearish engulfing patterns on a price chart. These patterns are widely used in technical analysis to identify potential reversal points. The indicator dynamically draws colored boxes around the previous candle involved in the engulfing event, making it easier for traders to spot these setups in the price action.
Key Features:
Bullish Engulfing Pattern:
When a bearish candle (one where the open is higher than the close) is followed by a candle whose close is above the previous candle’s open, the indicator detects a bullish engulfing pattern. A green box is drawn around the previous candle.
• Box Style Options: Users can choose whether the box represents the candle’s body (from open to close) or its wick (from open to low).
Bearish Engulfing Pattern:
When a bullish candle (one where the open is lower than the close) is followed by a candle whose close is below the previous candle’s open, a bearish engulfing pattern is identified. A red box is drawn around the previous candle.
• Box Style Options: The box can be drawn using the candle’s body (from close to open) or its wick (from high to open), according to the user’s preference.
Dynamic Box Management:
Once a box is drawn, the indicator continuously monitors the price. If the price moves beyond the box’s range, the box is either deleted or its color changes to gray, indicating that the pattern’s relevance may be diminishing.
Max Pattern Tracking:
To prevent clutter, the indicator limits the number of displayed engulfing boxes to 500 by default. Older boxes are removed as new patterns are detected.
Customization:
Users can adjust the number of previous bars scanned for engulfing patterns as well as the maximum number of patterns displayed. An option is also provided to select whether the box should reflect the candle’s body or include the wick.
How It Works:
Pattern Detection:
The script compares the current price with the previous candle’s data to detect either a bullish or bearish engulfing pattern.
Box Creation:
When a pattern is detected, a colored box is drawn around the previous candle’s price range (using the user-selected style) to visually highlight the orderblock.
Pattern Expiry and Cleanup:
The indicator monitors each drawn box, deleting or modifying it (changing the color to gray) if the price moves significantly beyond the box’s range.
Remark:
The original concept for this indicator is from daisukeburn .
Liquidity Hunt SwiftEdgeThe "Liquidity Hunt Dashboard By SwiftEdge" indicator is designed to assist traders in identifying potential liquidity zones by placing a dynamic target line based on swing points and weighted liquidity. It leverages technical analysis tools such as SMA (Simple Moving Average), pivot points, and volume to predict market movements and provides daily statistics on hits and success rate. The target line updates automatically when the price hits it, adapting to the market trend (up, down, or neutral). A dashboard displays the current price, target level, prediction, and trend, making it easy to make informed trading decisions.
Features:
Target Line: A yellow dashed line marks the next expected liquidity level (up to approximately 20 pips away on 1m).
Prediction: Displays "Up (Chasing Sell Liquidity)," "Down (Chasing Buy Liquidity)," or "Neutral" based on trend and liquidity.
Daily Statistics: Tracks hits and success rate, resetting daily.
Trend Indicator: Shows market direction ("Up," "Down," or "Neutral") in the dashboard.
Dynamic Updates: The line moves to a new target level when the price hits the current target.
Recommended Settings for 1-Minute Timeframe:
For Indices (e.g., S&P 500):
Lookback Period: 180 (3 hours to capture more stable swing points).
Max Distance (%): 0.015 (approximately 15 pips, suitable for indices).
Cooldown Period: 5 (stabilizes after hits).
Line Duration: 60 (displays the line for 1 hour).
For Crypto (e.g., BTC/USD):
Lookback Period: 120 (2 hours to capture short-term swing points).
Max Distance (%): 0.024 (approximately 20 pips, suitable for volatile crypto markets).
Cooldown Period: 5.
Line Duration: 60.
For Forex (e.g., EUR/USD):
Lookback Period: 180 (3 hours for greater data density in less volatile markets).
Max Distance (%): 0.012 (approximately 10-12 pips, suitable for forex).
Cooldown Period: 5.
Line Duration: 60.
Guide for Higher Timeframes:
This indicator can be adapted for higher timeframes (e.g., 5m, 15m, 1H) by adjusting the settings to account for larger price movements and slower market dynamics. Follow these steps:
Select Your Timeframe: Switch your chart to the desired timeframe (e.g., 5m, 15m, or 1H).
Adjust Lookback Period: Increase the "Lookback Period" to cover a longer historical period. For example:
5m: Set to 360 (equivalent to 6 hours).
15m: Set to 480 (equivalent to 8 hours).
1H: Set to 720 (equivalent to 12 hours).
Adjust Max Distance (%): Higher timeframes require larger targets to account for bigger price swings. For example:
5m: Increase to 0.05 (approximately 50 pips).
15m: Increase to 0.1 (approximately 100 pips).
1H: Increase to 0.2 (approximately 200 pips).
Adjust Cooldown Period: On higher timeframes, you may want a longer cooldown to avoid frequent updates. For example:
5m: Set to 10.
15m: Set to 15.
1H: Set to 20.
Adjust Line Duration: Extend the duration the line is displayed to match the timeframe. For example:
5m: Set to 120 (equivalent to 10 hours).
15m: Set to 240 (equivalent to 60 hours).
1H: Set to 480 (equivalent to 20 days).
Monitor the Dashboard: The dashboard will still show the target level, prediction, and trend, but the values will now reflect the larger timeframe's dynamics.
Usage Instructions:
Set your chart to a 1-minute timeframe (or follow the higher timeframe guide).
Adjust the settings based on the market and timeframe (see recommendations above).
Monitor the dashboard for the current price, target level, and prediction.
Use the yellow line as a potential entry or exit level, and adjust your strategy based on the trend and statistics.
Notes:
This indicator is intended solely for educational and analytical purposes and should not be considered financial advice.
Test the indicator on a demo account before using it with real funds.
The indicator complies with TradingView guidelines by not providing trading advice, automated trading signals, or guarantees of profit.
aivance_Multi-Index Performance Comparison# Multi-Index Performance Comparison
This indicator allows traders and investors to easily compare the performance of multiple global market and sector indexes from a user-defined start date. All indexes are normalized to 100% at the specified start date, making relative performance comparisons straightforward.
## Features:
- Customizable start date for performance comparison
- Toggleable global market indexes (S&P 500, MSCI World, DAX, Nasdaq 100, EURO STOXX 50, Japan, Hong Kong)
- Toggleable sector indexes (Materials, Health Care, Financial, Technology, AI & Robotics)
- Clear visualization with distinct colors for each index
- Reference line at 100% for easy benchmark comparison
## How to Use:
1. Set your desired start date for normalizing performance
2. Toggle indexes on/off under the "Inputs" tab
3. Compare relative performance across different markets and sectors
Perfect for identifying relative strength, sector rotation, or global market correlations over your specific timeframe of interest.
Economic Crises by @zeusbottradingEconomic Crises Indicator by @zeusbottrading
Description and Use Case
Overview
The Economic Crises Highlight Indicator is designed to visually mark major economic crises on a TradingView chart by shading these periods in red. It provides a historical context for financial analysis by indicating when major recessions occurred, helping traders and analysts assess the performance of assets before, during, and after these crises.
What This Indicator Shows
This indicator highlights the following major economic crises (from 1953 to 2020), which significantly impacted global markets:
• 1953 Korean War Recession
• 1957 Monetary Tightening Recession
• 1960 Investment Decline Recession
• 1969 Employment Crisis
• 1973 Oil Crisis
• 1980 Inflation Crisis
• 1981 Fed Monetary Policy Recession
• 1990 Oil Crisis and Gulf War Recession
• 2001 Dot-Com Bubble Crash
• 2008 Global Financial Crisis (Great Recession)
• 2020 COVID-19 Recession
Each of these periods is shaded in red with 80% transparency, allowing you to clearly see the impact of economic downturns on various financial assets.
How This Indicator is Useful
This indicator is particularly valuable for:
✅ Comparative Performance Analysis – It allows traders and investors to compare how different assets (e.g., Gold, Silver, S&P 500, Bitcoin) performed before, during, and after major economic crises.
✅ Identifying Market Trends – Helps recognize recurring patterns in asset price movements during times of financial distress.
✅ Risk Management & Strategy Development – Understanding how markets reacted in the past can assist in making better-informed investment decisions for future downturns.
✅ Gold, Silver & Bitcoin as Safe Havens – Comparing precious metals and cryptocurrencies against traditional stocks (e.g., SPY) to analyze their performance as hedges during economic turmoil.
How to Use It in Your Analysis
By overlaying this indicator on your Gold, Silver, SPY, and Bitcoin chart (for example), you can quickly spot historical market reactions and use that insight to predict possible behaviors in future downturns.
⸻
How to Apply This in TradingView?
1. Click on Use on chart under the image.
2. Overlay it with Gold ( OANDA:XAUUSD ), Silver ( OANDA:XAGUSD ), SPY ( AMEX:SPY ), and Bitcoin ( COINBASE:BTCUSD ) for comparative analysis.
⸻
Conclusion
This indicator serves as a powerful historical reference for traders analyzing asset performance during economic downturns. By studying past crises, you can develop a data-driven investment strategy and improve your market insights. 🚀📈
Let me know if you need any modifications or enhancements!
CCI with Signals & Divergence [AIBitcoinTrend]👽 CCI with Signals & Divergence (AIBitcoinTrend)
The Hilbert Adaptive CCI with Signals & Divergence takes the traditional Commodity Channel Index (CCI) to the next level by dynamically adjusting its calculation period based on real-time market cycles using Hilbert Transform Cycle Detection. This makes it far superior to standard CCI, as it adapts to fast-moving trends and slow consolidations, filtering noise and improving signal accuracy.
Additionally, the indicator includes real-time divergence detection and an ATR-based trailing stop system, helping traders identify potential reversals and manage risk effectively.
👽 What Makes the Hilbert Adaptive CCI Unique?
Unlike the traditional CCI, which uses a fixed-length lookback period, this version automatically adjusts its lookback period using Hilbert Transform to detect the dominant cycle in the market.
✅ Hilbert Transform Adaptive Lookback – Dynamically detects cycle length to adjust CCI sensitivity.
✅ Real-Time Divergence Detection – Instantly identifies bullish and bearish divergences for early reversal signals.
✅ Implement Crossover/Crossunder signals tied to ATR-based trailing stops for risk management
👽 The Math Behind the Indicator
👾 Hilbert Transform Cycle Detection
The Hilbert Transform estimates the dominant market cycle length based on the frequency of price oscillations. It is computed using the in-phase and quadrature components of the price series:
tp = (high + low + close) / 3
smooth = (tp + 2 * tp + 2 * tp + tp ) / 6
detrender = smooth - smooth
quadrature = detrender - detrender
inPhase = detrender + quadrature
outPhase = quadrature - inPhase
instPeriod = 0.0
deltaPhase = math.abs(inPhase - inPhase ) + math.abs(outPhase - outPhase )
instPeriod := nz(3.25 / deltaPhase, instPeriod )
dominantCycle = int(math.min(math.max(instPeriod, cciMinPeriod), 500))
Where:
In-Phase & Out-Phase Components are derived from a detrended version of the price series.
Instantaneous Frequency measures the rate of cycle change, allowing the CCI period to adjust dynamically.
The result is bounded within a user-defined min/max range, ensuring stability.
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence Setup:
Price makes a lower low, while CCI forms a higher low.
Buy signal is confirmed when CCI shows upward momentum.
Bearish Divergence Setup:
Price makes a higher high, while CCI forms a lower high.
Sell signal is confirmed when CCI shows downward momentum.
👾 Trailing Stop & Signal-Based Trading
Bullish Setup:
✅ CCI crosses above -100 → Buy signal.
✅ A bullish trailing stop is placed at Low - (ATR × Multiplier).
✅ Exit if the price crosses below the stop.
Bearish Setup:
✅ CCI crosses below 100 → Sell signal.
✅ A bearish trailing stop is placed at High + (ATR × Multiplier).
✅ Exit if the price crosses above the stop.
👽 Why It’s Useful for Traders
Hilbert Adaptive Period Calculation – No more fixed-length periods; the indicator dynamically adapts to market conditions.
Real-Time Divergence Alerts – Helps traders anticipate market reversals before they occur.
ATR-Based Risk Management – Stops automatically adjust based on volatility.
Works Across Multiple Markets & Timeframes – Ideal for stocks, forex, crypto, and futures.
👽 Indicator Settings
Min & Max CCI Period – Defines the adaptive range for Hilbert-based lookback.
Smoothing Factor – Controls the degree of smoothing applied to CCI.
Enable Divergence Analysis – Toggles real-time divergence detection.
Lookback Period – Defines the number of bars for detecting pivot points.
Enable Crosses Signals – Turns on CCI crossover-based trade signals.
ATR Multiplier – Adjusts trailing stop sensitivity.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Sniper Trade Pro (ES 15-Min) - Topstep Optimized🔹 Overview
Sniper Trade Pro is an advanced algorithmic trading strategy designed specifically for E-mini S&P 500 (ES) Futures on the 15-minute timeframe. This strategy is optimized for Topstep 50K evaluations, incorporating strict risk management to comply with their max $1,000 daily loss limit while maintaining a high probability of success.
It uses a multi-confirmation approach, integrating:
✅ Money Flow Divergence (MFD) → To track liquidity imbalances and institutional accumulation/distribution.
✅ Trend Confirmation (EMA + VWAP) → To identify strong trend direction and avoid choppy markets.
✅ ADX Strength Filter → To ensure entries only occur in trending conditions, avoiding weak setups.
✅ Break-Even & Dynamic Stop-Losses → To reduce drawdowns and protect profits dynamically.
This script automatically generates Buy and Sell signals and provides built-in risk management for automated trading execution through TradingView Webhooks.
🔹 How Does This Strategy Work?
📌 1. Trend Confirmation (EMA + VWAP)
The strategy uses:
✔ 9-EMA & 21-EMA: Fast-moving averages to detect short-term momentum.
✔ VWAP (Volume-Weighted Average Price): Ensures trades align with institutional volume flow.
How it works:
Bullish Condition: 9-EMA above 21-EMA AND price above VWAP → Confirms buy trend.
Bearish Condition: 9-EMA below 21-EMA AND price below VWAP → Confirms sell trend.
📌 2. Liquidity & Money Flow Divergence (MFD)
This indicator measures liquidity shifts by tracking momentum changes in price and volume.
✔ MFD Calculation:
Uses Exponential Moving Average (EMA) of Momentum (MOM) to detect changes in buying/selling pressure.
If MFD is above its moving average, it signals liquidity inflows → bullish strength.
If MFD is below its moving average, it signals liquidity outflows → bearish weakness.
Why is this important?
Detects when Smart Money is accumulating or distributing before major moves.
Filters out false breakouts by confirming momentum strength before entry.
📌 3. Trade Entry Triggers (Candlestick Patterns & ADX Filter)
To avoid random entries, the strategy waits for specific candlestick confirmations with ADX trend strength:
✔ Bullish Entry (Buy Signal) → Requires:
Bullish Engulfing Candle (Reversal confirmation)
ADX > 20 (Ensures strong trending conditions)
MFD above its moving average (Liquidity inflows)
9-EMA > 21-EMA & price above VWAP (Trend confirmation)
✔ Bearish Entry (Sell Signal) → Requires:
Bearish Engulfing Candle (Reversal confirmation)
ADX > 20 (Ensures strong trending conditions)
MFD below its moving average (Liquidity outflows)
9-EMA < 21-EMA & price below VWAP (Trend confirmation)
📌 4. Risk Management & Profit Protection
This strategy is built with strict risk management to maintain low drawdowns and maximize profits:
✔ Dynamic Position Sizing → Automatically adjusts trade size to risk a fixed $400 per trade.
✔ Adaptive Stop-Losses → Uses ATR-based stop-loss (0.8x ATR) to adapt to market volatility.
✔ Take-Profit Targets → Fixed at 2x ATR for a Risk:Reward ratio of 2:1.
✔ Break-Even Protection → Moves stop-loss to entry once price moves 1x ATR in profit, locking in gains.
✔ Max Daily Loss Limit (-$1,000) → Stops trading if total losses exceed $1,000, complying with Topstep rules.
Cumulative New Highs - New Lows IndicatorThis indicator is designed to track market momentum by calculating and plotting the cumulative sum of 52 weeks High-Low for different indices, alongside a customizable moving average.
Index Selection:
Users can choose from multiple indices, including:
Total Stock Market (default)
NYSE Composite
Nasdaq Composite
S&P 500
Nasdaq 100
Russell 2000
Moving Average Customization:
The script allows you to select between a Simple Moving Average (SMA) or an Exponential Moving Average (EMA) for smoothing the cumulative data. The window length of the moving average is also adjustable, letting you tailor the sensitivity of the trend analysis.
Dynamic Background Plotting:
With the background plot option enabled, the indicator changes the chart's background color dynamically:
Green: When the cumulative sum is above its moving average, suggesting bullish momentum.
Red: When it is below the moving average, indicating bearish conditions.
Visual Representation:
Two key lines are plotted:
Cumulative Index Line: Displayed in a subtle blue, representing the aggregated market movement.
Moving Average Line: Shown in an orange tone, offering a smoothed perspective that aids in identifying trend shifts.
Inspiration:
I took inspiration from the indicator made by YoxTrades (I can't put links, but you can check their profile) and added a few features I wanted on top of it.
[TehThomas] - ICT Volume ImbalanceThis script is a Volume Imbalance (VI) detector and visualizer for use on the TradingView platform. The goal of the script is to automatically identify areas where there are significant imbalances in the volume of trades between consecutive candlesticks and visually highlight these areas. These imbalances can provide traders with valuable insights about the market’s current condition, often signaling potential reversal or continuation points based on price and volume action.
ICT (Inner Circle Trader) Concept of Volume Imbalances
Volume imbalances are a critical concept in the ICT trading methodology. They refer to situations where there is an unusual or significant difference in volume between two consecutive candlesticks, which might indicate institutional or large player activity. According to ICT principles, these imbalances can show us areas of market inefficiency or potential price manipulation. By identifying these imbalances, traders can gain an edge in understanding where the market is likely to move next.
Bullish and Bearish Volume Imbalances:
Bullish Volume Imbalance: This occurs when there is a strong increase in buying pressure, typically indicated by a higher volume on a candle that closes significantly above the previous one, often leaving a gap or larger price movement. The market could be preparing to push higher, and the volume shows a clear shift in buying demand.
Bearish Volume Imbalance:
Conversely, a bearish imbalance occurs when there is a strong increase in selling pressure, typically signaled by a candle that closes significantly lower than the previous one, again with higher volume. This could indicate that large players are offloading positions, and the price is likely to drop further.
Key Features and Functions of the Script
The script automates the process of detecting these volume imbalances and visually marking them on a price chart. Let’s explore its functionality in detail.
1. Inputs Section
The script allows for significant customization through its input options, which help traders adjust the detection and visualization of volume imbalances based on their individual preferences and trading style. Below are the details:
lookback (250 bars): This input specifies the number of bars (or candles) the script should look back when analyzing the volume imbalance. By setting this to 250, the user is looking at the last 250 bars on the chart to detect any significant volume imbalances. This period is adjustable between 50 to 500 bars.
volumeThreshold (1.0 multiplier): This input helps set the sensitivity for identifying volume imbalances. The script compares the volume of the current candle with the previous one, and if the current volume exceeds the previous volume by this threshold multiplier (in this case, 1.0 means at least equal to the previous volume), then it triggers an imbalance. Users can adjust the multiplier to suit different market conditions.
showBoxes (true/false): This toggle determines whether the boxes representing volume imbalances are drawn on the chart. When enabled, the script visually highlights the imbalances with colored boxes.
fillBaseColor (orange with 80% opacity): This is the color setting for the background of the imbalance boxes. A softer color (like orange with opacity) ensures the imbalance is highlighted without obscuring the price action.
borderColor (gray): The color of the border around the imbalance boxes. This adds a visual distinction to make the imbalance areas more visible.
borderWidth (1 pixel): This controls the width of the box's border to adjust how prominent it appears.
rightOffset (30 bars): This input controls how far the imbalance box extends to the right on the chart. It helps users anticipate the potential continuation of the imbalance beyond the current candle.
allowWickOverlap (true/false): This setting allows imbalances to be identified even if the wicks of the two consecutive candlesticks overlap. If set to false, only imbalances where the bodies of the candlesticks don’t overlap are considered.
showBrokenBoxes (true/false): If enabled, once a volume imbalance no longer holds true (i.e., the price breaks through the box), the box is marked as "broken." If disabled, the box is deleted when the imbalance condition no longer applies.
brokenBoxColor (red): This controls the color of the box when it is broken, which can be used as a visual cue that the imbalance was invalidated or no longer valid for analysis.
2. Volume Imbalance Function
This is the core function of the script, where the logic to detect bullish and bearish volume imbalances is implemented.
Bullish Imbalance Condition:
The first condition checks if the low of the current candle is greater than the high of the previous candle. This suggests that the market is moving upward with buying pressure.
The second condition checks whether the volume of the current candle is higher than the previous candle by the volumeThreshold multiplier. If both conditions are satisfied, a bullish imbalance is detected.
Bearish Imbalance Condition:
The first condition checks if the high of the current candle is lower than the low of the previous candle. This suggests downward price action with selling pressure.
The second condition checks whether the current volume exceeds the previous volume by the threshold
Allow Wick Overlap: If allowWickOverlap is set to true, the script will still detect imbalances if the wicks of the two candles overlap (common in volatile markets). If false, imbalances are only considered if the wicks do not overlap.
3. Box Creation and Management
When a volume imbalance is detected, the script creates a box on the chart:
The bullish imbalance box is drawn using the minimum of the open and close of the current bar as the top boundary and the maximum of the open and close of the previous bar as the bottom boundary.
Conversely, the bearish imbalance box is drawn in reverse, using the maximum of the current bar’s open and close as the top boundary and the minimum of the previous bar’s open and close as the bottom boundary.
Once the box is created, it is displayed on the chart with the specified background color, border color, and width.
4. Processing Existing Boxes
After detecting a new imbalance and drawing a box, the script checks whether the box should still remain on the chart:
If the price moves beyond the boundaries of the imbalance box, the box is marked as broken (if showBrokenBoxes is enabled), and its color is changed to red, signifying that the imbalance is no longer valid.
If the box remains intact (i.e., the price has not broken the defined boundaries), the script keeps the box extended to the right as the market continues to evolve.
5. Removing Outdated Boxes
Lastly, the script removes boxes that are older than the specified lookback period. For example, if a box was created 250 bars ago, it will be deleted after that period. This ensures the chart stays clean and only focuses on relevant imbalances.
Why This Script is Useful for Traders
This script is extremely valuable for traders, especially those following the ICT methodology, because it automates the process of detecting market inefficiencies or imbalances that might signal future price action. Here’s why it’s particularly useful:
Identifying Key Areas of Interest: Volume imbalances often point to areas where institutional or large-scale traders have entered the market. These areas could provide clues about the next significant move in the market.
Visualizing Market Structure: By automatically drawing boxes around volume imbalances, the script helps traders visually identify potential areas of support, resistance, or turning points, enabling them to make informed trading decisions.
Time Efficiency: Instead of manually analyzing each candlestick and volume spike, this script does the heavy lifting, saving traders valuable time and allowing them to focus on other aspects of their strategy.
Enhanced Trade Entries and Exits: By understanding where volume imbalances are occurring, traders can time their entries (buying during bullish imbalances and selling during bearish ones) and exits (as imbalances break) more effectively, thus improving their chances of success.
Conclusion
In summary, this script is a powerful tool for traders looking to implement volume imbalance strategies based on the ICT methodology. It automates the identification and visualization of significant imbalances in price and volume, offering traders a clear visual representation of potential market turning points. By customizing the settings, traders can tailor the script to their preferred timeframes and sensitivity, making it a flexible and effective tool for any trading strategy.
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Thanks for your support!
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Opening Range BoxIndicator Name: Opening Range Box with Extensions
Author: YanivBull
Description:
The Opening Range Box with Extensions is a powerful tool designed to visualize the trading range established during the first 30 minutes of a market session, a critical period for setting the day's trend. This indicator plots a box representing the high and low prices formed within this opening range, with dashed extension lines projecting these levels forward throughout the session.
Its primary purpose is to identify the boundaries of the initial trend at the start of trading. When these boundaries are breached, it serves as a trigger for potential trading opportunities: a breakout above the box high signals a possible long entry, while a breakdown below the box low indicates a potential short entry. The indicator also includes historical boxes for up to 5 previous days (configurable), allowing traders to analyze past opening ranges and their extensions for context and pattern recognition.
Key Features:
Customizable session start time (hour and minute) to adapt to various markets (e.g., NYSE, DAX, etc.).
Displays the current session's opening range box in blue and historical boxes in gray.
Plots dashed extension lines from the high and low of each box, limited to 500 bars or the end of the trading day.
Adjustable number of historical days (1-20, default 5).
Usage:
Set the Session Start Hour and Session Start Minute according to your market's opening time (relative to your chart's timezone, e.g., UTC+2). Watch for price action around the box boundaries—breakouts above the high or below the low can be used as signals for initiating long or short trades, respectively. Combine with other technical analysis tools for confirmation.
This indicator is ideal for day traders looking to capitalize on early session momentum and breakout strategies.
Astro: Moon SizeThe Astro: Moon Size indicator, built using AstroLib , calculates the distance and visualizes the apparent size of the Moon based on astronomical positioning. This script is tailored for the 1D timeframe and provides insights into lunar perigees (closest approach) and apogees (farthest distance), making it useful for astrologically-informed trading strategies.
New Astro Indicators Feature:
By setting the Julian Date to X number of days in the future, and offsetting the plot by X number of bars accordingly, it is now possible to visualize future projections of TradingView indicators that reference the AstroLib . This feature has been long requested and is far overdue, so thank you to everyone who pushed for this feature release. Enjoy, time travelers from the future!!
Key Features:
Moon Size Calculation: Uses Julian Date (J2000) conversion and AstroLib functions to determine the Moon's apparent distance.
Future Projection: Displays the Moon's distance from 28 up to 500 days ahead, with color gradients indicating proximity/size.
Pivot Identification: Marks local maxima (apogees) and minima (perigees) with labeled date stamps for easy reference.
Dynamic Labeling: Adapts label positioning and size based on the Moon's current trend and relative size.
Usage Notes:
⚠️ Timeframe Restriction: For now, the script only functions on the 1D timeframe and will prompt an error otherwise.
⚠️ Asset Restriction: This script is meant to be loaded on charts for assets that trade 24/7, like BTCUSD historical index.
Multi-Ticker RS vs SPYThis Pine Script, titled "Multi-Ticker RS vs SPY," is a clean and efficient indicator designed for TradingView, enabling traders to monitor the relative strength (RS) of up to 10 ticker symbols compared to the S&P 500 ETF (SPY) on a single chart. Ideal for options traders, such as those managing a $1,400 account, it provides a simple way to assess which stocks are outperforming or underperforming the broader market. As of February 26, 2025, the script supports any chart timeframe, such as 5-minute or daily intervals, and calculates RS based on a user-defined lookback period, defaulting to 1 bar for real-time insights.
Users can input ticker symbols via customizable settings, with defaults set to popular stocks like AAPL, TSLA, NVDA, GOOGL, AMZN, MSFT, FB, NFLX, INTC, and PYPL. The script fetches closing prices for each ticker and SPY, computes their percentage changes over the lookback period, and determines RS as the ratio of each ticker’s change to SPY’s change, handling division by zero gracefully. It displays each ticker’s current RS score in a vertical column of labels on the chart’s top-left corner, updated on the last bar to avoid clutter. Users can adjust label size (tiny, small, normal, large) and text color for visibility, ensuring a tailored, error-free experience for quick market analysis.
Gann & Fibonacci Analysis for MSTRYes! Below is a step-by-step guide to performing Gann Analysis on MSTR in TradingView for the last year.
Step 1: Set Up the Chart
Open TradingView and search for MSTR (MicroStrategy).
Select the 1-day (D) timeframe to analyze the past year.
Set your chart to logarithmic scale (⚙ Settings → Scale → Log).
Enable grid lines for alignment (⚙ Settings → Appearance → Grid Lines).
Step 2: Identify Key Highs and Lows (Last Year)
Find the 52-week high and 52-week low for MSTR.
As of now:
52-Week High: ~$999 (March 2024).
52-Week Low: ~$280 (October 2023).
Step 3: Plot Gann Angles
Using TradingView's Gann Fan Tool:
Select "Gann Fan" (Press / and type “Gann Fan” to find it).
Start at the 52-week low (~$280, October 2023) and drag upwards.
Adjust the angles to match key levels:
1x1 (45°) → Main trendline
2x1 (26.5°) → Strong uptrend
4x1 (15°) → Weak trendline
1x2 (63.75°) → Strong resistance
Repeat the process from the 52-week high (~$999, March 2024) downward to see bearish angles.
Step 4: Apply Fibonacci & Gann Retracement Levels
Using Fibonacci Retracement:
Select "Fibonacci Retracement" tool.
Draw from 52-week high ($999) to 52-week low ($280).
Enable key Fibonacci levels:
23.6% ($816)
38.2% ($678)
50% ($640)
61.8% ($550)
78.6% ($430)
Watch for price reactions near these levels.
Using Gann Retracement Levels:
Select "Gann Box" in TradingView.
Draw from 52-week high ($999) to low ($280).
Enable key Gann retracement levels:
12.5% ($912)
25% ($850)
37.5% ($768)
50% ($640)
62.5% ($550)
75% ($480)
87.5% ($350)
Identify confluences with Gann angles and Fibonacci levels.
Step 5: Identify Significant Dates & Time Cycles
Use "Date Range" Tool in TradingView.
Mark major turning points:
High → Low: ~180 days (Half-year cycle).
Low → High: ~90 days (Quarter cycle).
Use Square-Outs (Time = Price method):
Example: If MSTR hit $500, check 500 days from key events.
Mark key anniversaries of past highs/lows for possible reversals.
Step 6: Analyze and Trade Execution
✅ If MSTR is at a Gann angle + Fibonacci level + key date → Expect a reaction.
✅ Use RSI, MACD, and Volume for extra confirmation.
✅ Set Stop-Loss at nearest Gann support/resistance.
Standardized Leveraged ETF Fund of FlowsThis indicator tracks and standardizes the 3-month fund flows of major leveraged ETFs across different asset classes, including equities, gold, and bonds.
The fund flows are summed over a 3-month period (63 trading days) and then standardized using a 500-day rolling mean and standard deviation.
The resulting normalized fund flow values are plotted in three distinct colors:
Blue for Equities Fund Flows
Yellow for Gold Fund Flows
Green for Bond Fund Flows
