Yome Kill Zones ProPerfect for US30 Entry ## Yome Kill Zones Pro
**Yome Kill Zones Pro** is a precision trading tool designed for day traders and scalpers who focus on session-based setups, liquidity sweeps, and directional bias during the London–New York overlap.
---
### **Key Features**
- **Customizable Kill Zone Box**
- Marks session high/low from any user-defined time window (default: 6:00–11:30 UTC).
- **Swing Point Sweep Detection**
- Identifies significant highs/lows swept by price with momentum—ideal for supply/demand or S/R zones.
- **Independent Bias Kill Zone**
- Separate bias calculation window with adjustable start/end time to isolate market sentiment.
- **Bias Table (Always-On Display)**
- **Killzone Bias** – Shows direction based on price change during bias time.
- **Long-Term Bias** – Compares price vs. Open and EMA(50) from any selected timeframe (default: 15m).
- **Full Visual Customization**
- Editable sweep labels, line colors, line style, label visibility, and kill zone extensions.
---
### **How to Use**
1. **Set Your Session Times**
- Use the “Killzone Settings” to define high/low tracking time.
- Use “Bias Killzone Settings” to define when to calculate bias direction.
2. **Check the Bias Table**
- Use **Killzone Bias** for short-term session direction.
- Use **Long-Term Bias** to align with higher timeframe market structure.
3. **Watch for Liquidity Sweeps**
- Look for momentum-based breaks of swing highs/lows within your kill zone window.
- Use these levels to anticipate reversals, retests, or continuations.
4. **Customize It Your Way**
- Everything from line styles, sweep label visibility, thickness, and colors can be customized.
---
### **Best For**
- London & New York session scalpers
- Liquidity & structure-based traders
- Traders using ICT, Smart Money Concepts, or Wyckoff-style analysis
---
> **Tip:** Pair with volume or order block tools for enhanced sniper entries.
อินดิเคเตอร์และกลยุทธ์
Heikin Ashi Colored Regular OHLC CandlesHeikin Ashi Colored Regular OHLC Candles
In the world of trading, Heikin Ashi candles are a popular tool for smoothing out price action and identifying trends more clearly. However, Heikin Ashi candles do not reflect the actual open, high, low, and close prices of a market. They are calculated values that change the chart’s structure. This can make it harder to see precise price levels or use standard price-based tools effectively.
To get the best of both worlds, we can apply the color logic of Heikin Ashi candles to regular OHLC candles. This means we keep the true market data, but show the trend visually in the same smooth way Heikin Ashi candles do.
Why use this approach
Heikin Ashi color logic filters out noise and helps provide a clearer view of the current trend direction. Since we are still plotting real OHLC candles, we do not lose important price information such as actual highs, lows, or closing prices. This method offers a hybrid view that combines the accuracy of real price levels with the visual benefits of Heikin Ashi trend coloring. It also helps maintain visual consistency for traders who are used to Heikin Ashi signals but want to see real price action.
Advantages for scalping
Scalping requires fast decisions. Even small price noise can lead to hesitation or bad entries. Coloring regular candles based on Heikin Ashi direction helps reduce that noise and makes short-term trends easier to read. It allows for faster confirmation of momentum without switching away from real prices. Since the candles are not modified, scalpers can still place tight stop-losses and targets based on actual price structure. This approach also avoids clutter, keeping the chart clean and focused.
How it works
We calculate the Heikin Ashi values in the background. If the Heikin Ashi close is higher than the Heikin Ashi open, the trend is considered bullish and the candle is colored green. If the close is lower than the open, it is bearish and the candle is red. If they are equal, the candle is gray or neutral. We then use these colors to paint the real OHLC candles, which are unchanged in shape or position.
Test OHLCV LibraryThis indicator, "Test OHLCV Library," serves as a practical example of how to use the OHLCVData library to fetch historical candle data from a specific timeframe (like 4H) in a way that is largely impervious to the chart's currently selected time frame.
Here's a breakdown of its purpose and how it addresses request.security limitations:
Indicator Purpose:
The main goal of this indicator is to demonstrate and verify that the OHLCVData library can reliably provide confirmed historical OHLCV data for a user-specified timeframe (e.g., 4H), and that a collection of these data points (the last 10 completed candles) remains consistent even when the user switches the chart's time frame (e.g., from 5-second to Daily).
It does this by:
Importing the OHLCVData library.
Using the library's getTimeframeData function on every bar of the chart.
Checking the isTargetBarClosed flag returned by the library to identify the exact moment a candle in the target timeframe (e.g., 4H) has closed.
When isTargetBarClosed is true, it captures the confirmed OHLCV data provided by the library for that moment and stores it in a persistent var array.
It maintains a list of the last 10 captured historical 4H candle opens in this array.
It displays these last 10 confirmed opens in a table.
It uses the isAdjustedToChartTF flag from the library to show a warning if the chart's time frame is higher than the target timeframe, indicating that the data fetched by request.security is being aligned to that higher resolution.
Circumventing request.security Limitations:
The primary limitation of request.security that this setup addresses is the challenge of getting a consistent, non-repainting collection of historical data points from a different timeframe when the chart's time frame is changed.
The Problem: Standard request.security calls, while capable of fetching data from other timeframes, align that data to the bars of the current chart. When you switch the chart's time frame, the set of chart bars changes, and the way the requested data aligns to these new bars changes. If you simply collected data on every chart bar where request.security returned a non-na value, the resulting collection would differ depending on the chart's resolution. Furthermore, using request.security without lookahead=barmerge.lookahead_off or an offset ( ) can lead to repainting on historical bars, where values change as the script recalculates.
How the Library/Indicator Setup Helps:
Confirmed Data: The OHLCVData library uses lookahead=barmerge.lookahead_off and, more importantly, provides the isTargetBarClosed flag. This flag is calculated using a reliable method (checking for a change in the target timeframe's time series) that accurately identifies the precise chart bar corresponding to the completion of a candle in the target timeframe (e.g., a 4H candle), regardless of the chart's time frame.
Precise Capture: The indicator only captures and stores the OHLCV data into its var array when this isTargetBarClosed flag is true. This means it's capturing the confirmed, finalized data for the target timeframe candle at the exact moment it closes.
Persistent Storage: The var array in the indicator persists its contents across the bars of the chart's history. As the script runs through the historical bars, it selectively adds confirmed 4H candle data points to this array only when the trigger is met.
Impervious Collection: Because the array is populated based on the completion of the target timeframe candles (detected reliably by the library) rather than simply collecting data on every chart bar, the final contents of the array (the list of the last 10 confirmed 4H opens) will be the same regardless of the chart's time frame. The table then displays this static collection.
In essence, this setup doesn't change how request.security fundamentally works or aligns data to the chart's bars. Instead, it uses the capabilities of request.security (fetching data from another timeframe) and Pine Script's execution model (bar-by-bar processing, var persistence) in a specific way, guided by the library's logic, to build a historical collection of data points that represent the target timeframe's candles and are independent of the chart's display resolution.
S&P 500 Top 25 - EPS AnalysisEarnings Surprise Analysis Framework for S&P 500 Components: A Technical Implementation
The "S&P 500 Top 25 - EPS Analysis" indicator represents a sophisticated technical implementation designed to analyze earnings surprises among major market constituents. Earnings surprises, defined as the deviation between actual reported earnings per share (EPS) and analyst estimates, have been consistently documented as significant market-moving events with substantial implications for price discovery and asset valuation (Ball and Brown, 1968; Livnat and Mendenhall, 2006). This implementation provides a comprehensive framework for quantifying and visualizing these deviations across multiple timeframes.
The methodology employs a parameterized approach that allows for dynamic analysis of up to 25 top market capitalization components of the S&P 500 index. As noted by Bartov et al. (2002), large-cap stocks typically demonstrate different earnings response coefficients compared to their smaller counterparts, justifying the focus on market leaders.
The technical infrastructure leverages the TradingView Pine Script language (version 6) to construct a real-time analytical framework that processes both actual and estimated EPS data through the platform's request.earnings() function, consistent with approaches described by Pine (2022) in financial indicator development documentation.
At its core, the indicator calculates three primary metrics: actual EPS, estimated EPS, and earnings surprise (both absolute and percentage values). This calculation methodology aligns with standardized approaches in financial literature (Skinner and Sloan, 2002; Ke and Yu, 2006), where percentage surprise is computed as: (Actual EPS - Estimated EPS) / |Estimated EPS| × 100. The implementation rigorously handles potential division-by-zero scenarios and missing data points through conditional logic gates, ensuring robust performance across varying market conditions.
The visual representation system employs a multi-layered approach consistent with best practices in financial data visualization (Few, 2009; Tufte, 2001).
The indicator presents time-series plots of the four key metrics (actual EPS, estimated EPS, absolute surprise, and percentage surprise) with customizable color-coding that defaults to industry-standard conventions: green for actual figures, blue for estimates, red for absolute surprises, and orange for percentage deviations. As demonstrated by Padilla et al. (2018), appropriate color mapping significantly enhances the interpretability of financial data visualizations, particularly for identifying anomalies and trends.
The implementation includes an advanced background coloring system that highlights periods of significant earnings surprises (exceeding ±3%), a threshold identified by Kinney et al. (2002) as statistically significant for market reactions.
Additionally, the indicator features a dynamic information panel displaying current values, historical maximums and minimums, and sample counts, providing important context for statistical validity assessment.
From an architectural perspective, the implementation employs a modular design that separates data acquisition, processing, and visualization components. This separation of concerns facilitates maintenance and extensibility, aligning with software engineering best practices for financial applications (Johnson et al., 2020).
The indicator processes individual ticker data independently before aggregating results, mitigating potential issues with missing or irregular data reports.
Applications of this indicator extend beyond merely observational analysis. As demonstrated by Chan et al. (1996) and more recently by Chordia and Shivakumar (2006), earnings surprises can be successfully incorporated into systematic trading strategies. The indicator's ability to track surprise percentages across multiple companies simultaneously provides a foundation for sector-wide analysis and potentially improves portfolio management during earnings seasons, when market volatility typically increases (Patell and Wolfson, 1984).
References:
Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159-178.
Bartov, E., Givoly, D., & Hayn, C. (2002). The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics, 33(2), 173-204.
Bernard, V. L., & Thomas, J. K. (1989). Post-earnings-announcement drift: Delayed price response or risk premium? Journal of Accounting Research, 27, 1-36.
Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Chordia, T., & Shivakumar, L. (2006). Earnings and price momentum. Journal of Financial Economics, 80(3), 627-656.
Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
Johnson, J. A., Scharfstein, B. S., & Cook, R. G. (2020). Financial software development: Best practices and architectures. Wiley Finance.
Ke, B., & Yu, Y. (2006). The effect of issuing biased earnings forecasts on analysts' access to management and survival. Journal of Accounting Research, 44(5), 965-999.
Kinney, W., Burgstahler, D., & Martin, R. (2002). Earnings surprise "materiality" as measured by stock returns. Journal of Accounting Research, 40(5), 1297-1329.
Livnat, J., & Mendenhall, R. R. (2006). Comparing the post-earnings announcement drift for surprises calculated from analyst and time series forecasts. Journal of Accounting Research, 44(1), 177-205.
Padilla, L., Kay, M., & Hullman, J. (2018). Uncertainty visualization. Handbook of Human-Computer Interaction.
Patell, J. M., & Wolfson, M. A. (1984). The intraday speed of adjustment of stock prices to earnings and dividend announcements. Journal of Financial Economics, 13(2), 223-252.
Skinner, D. J., & Sloan, R. G. (2002). Earnings surprises, growth expectations, and stock returns or don't let an earnings torpedo sink your portfolio. Review of Accounting Studies, 7(2-3), 289-312.
Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Graphics Press.
QuantumTrend SwiftEdgeQuantumTrend SwiftEdge - A Trend-Following Indicator for TradingView
Overview:
QuantumTrend SwiftEdge is a visually engaging and customizable trend-following indicator that combines the power of Supertrend, Keltner Channels, and a 100-period EMA to generate precise buy and sell signals. Designed to help traders identify trends and breakouts, this indicator offers a unique blend of technical tools with a modern gradient color effect, making it both functional and visually appealing.
What It Does:
This indicator identifies trend directions and potential entry/exit points:
- Supertrend determines the overall trend direction, showing a green line below the price during uptrends and a red line above the price during downtrends. The line only appears when the price is close to it, indicating an active trend.
- Keltner Channels highlight volatility and breakouts, with the upper and lower bands dynamically adjusting to market conditions.
- A 100-period EMA provides a longer-term trend perspective, helping to filter out noise.
- Buy and sell signals are generated when specific conditions align across these indicators, ensuring robust trade setups.
How It Works:
The indicator uses three components to generate signals:
1. **Supertrend**: Calculates trend direction using the Average True Range (ATR) and a multiplier. It switches between uptrend (green) and downtrend (red) based on price movements relative to the Supertrend line.
2. **Keltner Channels**: Consists of an EMA (default 20 periods) with upper and lower bands based on ATR. A breakout above the upper band signals potential buying opportunities, while a breakout below the lower band signals potential selling opportunities.
3. **100-period EMA**: Acts as a trend filter, ensuring signals align with the broader market direction.
**Buy Signal**:
- Price is above the 100-period EMA (bullish market).
- Price breaks above the Keltner Channel upper band (indicating a breakout).
- Supertrend switches to an uptrend (trend changes from down to up).
**Sell Signal**:
- Price is below the 100-period EMA (bearish market).
- Price breaks below the Keltner Channel lower band (indicating a breakout).
- Supertrend switches to a downtrend (trend changes from up to down).
Visual Features:
- **Gradient Colors**: Supertrend lines and Keltner Channels use a smooth gradient color transition between green (uptrend) and red (downtrend), reflecting the trend's strength. The gradient is based on a smoothed trend value, creating a visually appealing effect.
- **Keltner Channel Fill**: The area between the upper and lower Keltner Channels is filled with a transparent gradient, enhancing the trend visualization.
- **Dynamic Supertrend Visibility**: Supertrend lines only appear when the price is close to the line (within an ATR-based threshold), indicating an active trend.
How to Use:
1. Add the "QuantumTrend SwiftEdge" indicator to your chart in TradingView.
2. Customize the settings:
- **Signal Sensitivity (1=Low, 5=High)**: Default is 3. Lower values (e.g., 1) make signals less frequent by using wider parameters, while higher values (e.g., 5) make signals more frequent by tightening parameters.
- **Use Manual Settings**: If enabled, you can manually adjust all parameters (ATR Period, ATR Multiplier, Keltner Channel Length, Keltner Channel Multiplier, Keltner ATR Length, EMA Length) to fine-tune the indicator.
- **Change ATR Calculation Method**: Toggle between standard ATR calculation and a simple moving average of true range.
- **Show Buy/Sell Signals**: Toggle to show or hide buy (green "Buy" label) and sell (red "Sell" label) signals.
- **Highlighter On/Off**: Toggle to show or hide the gradient fill between the price and Supertrend line when the line is visible.
3. Interpret the signals:
- A green "Buy" label below the price indicates a potential buying opportunity.
- A red "Sell" label above the price indicates a potential selling opportunity.
- Use the Keltner Channel gradient fill and Supertrend lines to confirm the trend direction and strength.
Why This Combination?
- **Supertrend** provides a robust trend-following mechanism, ensuring signals align with the market direction.
- **Keltner Channels** add a volatility component, identifying breakouts that often precede significant price movements.
- **100-period EMA** filters out noise, ensuring signals are generated in the context of the broader trend.
Together, these indicators create a balanced approach: Supertrend and EMA confirm the trend, while Keltner Channels pinpoint actionable entry and exit points. The gradient visuals and dynamic visibility make it easier to focus on active trends.
Originality:
QuantumTrend SwiftEdge stands out with its unique features:
- Gradient color transitions for a modern, dynamic look.
- A filled gradient between Keltner Channels, visually emphasizing the trend.
- Supertrend lines that only appear when the price is close, reducing clutter and focusing on active trends.
- Flexible settings with both sensitivity-based and manual adjustments for maximum customization.
Default Settings:
The default sensitivity is set to 3, providing a balanced approach for most markets and timeframes (e.g., 5-minute charts for crypto like BTC/USD). This setting uses moderate parameters (ATR Period=10, ATR Multiplier=3.0, Keltner Channel Length=20, Keltner Channel Multiplier=1.5, Keltner ATR Length=10, EMA Length=100). Users can adjust the sensitivity or switch to manual settings for more control.
Important Notes:
- This indicator is a tool to assist in identifying trends and potential entry/exit points. It does not guarantee profits and should be used in conjunction with other analysis and risk management practices.
- The signals are based on historical price data and do not predict future performance. Always test the indicator on a demo account before using it in live trading.
- The gradient effect is purely visual and does not affect the signal logic.
Climax Detector (Buy & Sell)This indicator identifies potential Buying Climax (BC) and Selling Climax (SC) events based on volume spikes relative to historical averages.
• Buying Climax (BC):
• Detected when a green candle forms with volume significantly higher than the average (default: 2×).
• Often signals the end of an uptrend or distribution phase.
• Selling Climax (SC):
• Detected when a red candle forms with very high volume (default: 2× average).
• Often occurs at the end of a downtrend, suggesting panic selling and potential accumulation.
How it works:
• Calculates a moving average of volume over a user-defined period (default: 20 candles)
• Flags a climax when current volume exceeds the defined multiplier (default: 2.0×)
• Marks:
• BC with an orange triangle above the bar
• SC with a fuchsia triangle below the bar
Customizable Settings:
• Volume spike sensitivity
• Lookback period for average volume
Use Cases:
• Spot possible trend exhaustion
• Confirm Wyckoff phases
• Combine with support/resistance for reversal entries
Disclaimer: This tool is designed to assist in identifying high-probability exhaustion zones but should be used alongside other confirmations or strategies.
Liquidity and S&R Zones╔══════════════════════════════════════════════════════════════════════╗
║ Description ║
╚══════════════════════════════════════════════════════════════════════╝
This indicator identifies liquidity zones and support/resistance (S&R) levels
using pivot points and volume analysis. Liquidity zones highlight areas of high
trading activity, while S&R levels mark key price levels where price may reverse
or break. Breakouts are confirmed with a volume oscillator and visualized with
shapes. Alerts are provided for significant S&R breakouts.
╔══════════════════════════════════════════════════════════════════════╗
║ User Guide ║
╚══════════════════════════════════════════════════════════════════════╝
#### Overview
This indicator detects liquidity zones and support/resistance (S&R) levels
using pivot points and volume analysis. Liquidity zones highlight areas of
high trading activity, often targeted by institutional traders. S&R levels
indicate key price levels where price may reverse or break, with breakouts
confirmed by a volume oscillator. The indicator is designed for traders
seeking to trade breakouts or reversals at critical levels.
#### Features
- **Liquidity Zones**: Identifies pivot highs/lows with high-volume confirmation.
- **Support/Resistance Levels**: Plots dynamic S&R lines based on pivot points.
- **Breakout Signals**: Displays shapes for price crossing S&R levels with volume confirmation.
- **Volume Oscillator**: Uses short/long EMA difference to confirm breakouts.
- **Alerts**: Notifies users of support/resistance breakouts.
#### Input Parameters
- **Liquidity Settings**:
- *Liquidity Lookback Period*: Bars for average volume (default: 50).
- *Liquidity Volume Threshold Multiplier*: Volume multiplier for liquidity zones (default: 1.5).
- *Liquidity Pivot Lookback*: Bars for pivot detection (default: 5).
- **S&R Settings**:
- *Show Breaks*: Toggle breakout shapes (default: true).
- *Left/Right Bars*: Bars for S&R pivot detection (default: 15).
- *S&R Volume Threshold*: Minimum oscillator value for breakouts (default: 20).
- **Style Settings**: Predefined colors for liquidity and S&R visualization.
#### Usage
1. Apply the indicator to a chart (e.g., 1H, 4H, or D timeframes recommended).
2. Adjust input parameters to suit the instrument and timeframe:
- Increase `liqLookback` for smoother volume averages on lower timeframes.
- Adjust `leftBars` and `rightBars` for more/less sensitive S&R levels.
- Set `srVolumeThresh` based on typical oscillator values (plot `osc` to calibrate).
3. Monitor liquidity zones (red/green/yellow crosses) and S&R lines (red/green).
4. Watch for breakout signals (shapes) when price crosses S&R levels with volume confirmation.
5. Set up alerts for "Support Broken" or "Resistance Broken" to receive notifications.
#### Recommended Settings
- **Timeframes**: 1H, 4H, or D for reliable signals.
- **Instruments**: Assets with good volume (e.g., crypto, forex, indices).
- **Liquidity**: Increase `liqVolumeThreshold` (e.g., 2.0) for stricter zones.
- **S&R**: Use `leftBars = rightBars = 10` for faster markets.
#### Cautions
- Ensure sufficient chart history for pivot and volume calculations.
- High `liqLookback` or `leftBars` may delay signals on lower timeframes.
- Volume oscillator requires accurate volume data; test on reliable instruments.
- Backtest breakout signals, as false breakouts can occur in choppy markets.
#### Customization Ideas
- Add Fibonacci levels to complement S&R zones.
- Integrate with trend indicators (e.g., EMA) to filter breakouts.
- Visualize volume oscillator as a histogram for calibration.
- Extend liquidity zones with boxes to highlight price ranges.
#### Notes
- Combine with other analysis for a complete trading system.
- Test thoroughly in a demo account before live trading.
- Contact the author for support or feature requests.
Happy trading, and may your trades align with the market’s key levels! 🚀
Future OI AnalysisThis indicator appears to be a Futures Open Interest (OI) Analysis Tool designed to help traders understand market sentiment (long/short bias), cost of carry dynamics, and rollover behaviour. Below is a comprehensive description along with the formulas used:
To analyze futures market trends using:
Price change
Open Interest (OI) data
Cost of Carry (COC)
Rollover percentage
Column Breakdown and Formulas:
Date : Trading date.
Close : Close price of the spot on that day.
OI Cur (Current Open Interest) : Total open interest of the current (near) month contract.
OI Next (Next Month Open Interest) : Open interest of the next month contract.
COI (Cumulative Open Interest) : OI Cur + OI Next
COI Chg = ((Current COI - Previous COI) / Previous COI) × 100
Price Cng = ((Current Close - Previous Close) / Previous Close) × 100
OI BuildUP (Interpreted sentiment based on price & OI change):
Long Buildup : Price ↑ & OI ↑
Short Buildup : Price ↓ & OI ↑
Short Covering : Price ↑ & OI ↓
Long Unwinding : Price ↓ & OI ↓
PoiBox# PoiBox: Advanced Market Structure and POI Visualization Tool
PoiBox is a comprehensive market structure analysis tool designed to identify high-probability trading zones through advanced internal market structure (IDM) detection and points of interest (POI) calculation.
## How It Works
The indicator uses a multi-step approach to analyze price action:
1. **Market Structure Identification**: The script identifies significant highs and lows within your selected time range to determine the overall market structure direction (up or down).
2. **IDM Pattern Detection**: It then analyzes internal market structure patterns within this range, focusing on significant price movements that create trading opportunities.
3. **POI Calculation**: Using adaptive ATR measurements across multiple timeframes, the indicator calculates precise POI zones where price is likely to react. These zones are calibrated based on the volatility profile of each identified structure.
4. **Timeframe Correlation**: The script automatically determines which timeframe best matches each structure's size, providing valuable context for your trading decisions.
5. **Technical Implementation**: The indicator uses a sophisticated algorithm to analyze price swings, identify pivot points, and calculate market structure connections. It maintains a database of significant highs/lows and uses these to determine trend direction and potential reversal zones.
## Display Modes
PoiBox offers three powerful display options:
- **Main BOS**: Shows only the most significant breakout structure with its associated POI zone
- **Leg**: Displays the largest price leg within the selected range along with percentage-based POI zones
- **All IDMs**: Reveals all detected internal market structures and their POI zones
## Advanced Features
- **QM Mode**: Visualizes important market structure relationships with dashed lines connecting significant highs and lows
- **Trick Display**: Identifies nested market structures (tricks) within larger patterns, perfect for precision entries
- **Customizable POI Labels**: Control which price labels appear to maintain chart clarity
- **Extensive Color Settings**: Fully customizable colors for all visual elements
- **Safety Functions**: Includes built-in buffer management and error prevention algorithms to ensure stable performance across all timeframes and market conditions
## Trading Examples
**Downtrend Example:**
When PoiBox identifies a downtrend structure (Higher High → High → Low → Lower Low), it creates POI zones based on the market structure. As shown in the chart, these zones provide excellent entry opportunities when price returns to test previous structure. In this example, entering at the red POI zone with a stop above the zone and target at the QM level resulted in a 3.45 risk/reward trade.
**How to Read QM Lines:**
The dashed lines connecting High → Low → Higher High → Lower Low reveal the market's true structure. These connections help you anticipate where price might head next. When price breaks below a significant Low and creates a Lower Low, it confirms the downtrend continuation and provides a trading opportunity when price retests the broken structure.
**POI Zone Interpretation:**
- Red zones indicate bearish POI areas (ideal for short entries)
- Green zones indicate bullish POI areas (ideal for long entries)
- Yellow zones highlight the identified market structure
## Practical Application Example
In the GBP/USD example shown in the chart:
1. PoiBox identified a downtrend structure with Higher High → High → Low → Lower Low
2. The yellow box shows the main market structure area
3. The red POI zone appeared when price returned to test previous structure
4. Entry was taken at the POI zone with stop loss above structure
5. Target was placed at the QM level, resulting in a 3.45 risk/reward ratio trade
6. The dashed QM lines showed the overall market flow and direction
This demonstrates how PoiBox automatically identifies optimal entry and exit points based on market structure, without requiring manual analysis of each price swing.
## Mathematical Approach
PoiBox uses several mathematical concepts to determine market structure and calculate POI zones:
1. **Adaptive ATR Integration**: The script analyzes ATR (Average True Range) across multiple timeframes (M1, M5, M15, H1, H4, D1, W1, MN1) to determine the appropriate volatility context for each structure.
2. **Height-to-ATR Ratio**: The indicator calculates the ratio between structure height and the closest matching ATR value to determine the structure's timeframe context.
3. **Dynamic POI Calculation**: POI values are calculated using the formula:
`POI = factor * (atr_trigger + atr_double_trigger)`
where `factor` is derived from the structure's height-to-ATR ratio.
4. **Self-Adjusting Limits**: If the calculated POI value exceeds certain thresholds relative to structure height, the script automatically applies proportional adjustments to maintain optimal zone sizing.
## What Makes PoiBox Unique
While many indicators use common concepts like support/resistance or trend analysis, PoiBox stands apart through its:
1. **Adaptive POI Calculation**: Unlike static indicators, PoiBox automatically calibrates POI zones based on each market structure's volatility profile by analyzing ATR across multiple timeframes.
2. **Smart Timeframe Detection**: The indicator automatically determines the most relevant timeframe for each structure, eliminating guesswork and helping you align your trading with the appropriate market cycles.
3. **QM Visualization System**: Our proprietary QM visualization method reveals hidden market structure relationships that standard indicators cannot detect, giving you an edge in anticipating price movements.
4. **Nested Pattern Recognition**: The "Trick" detection feature identifies high-probability setups where smaller patterns form within larger ones, creating precise entry opportunities missed by conventional tools.
5. **Self-Adjusting Analysis**: PoiBox dynamically adapts to changing market conditions without requiring manual parameter adjustments, saving you time and increasing accuracy.
These innovations combine to create a truly original trading system that transforms complex market structure concepts into clear, actionable signals.
## How To Use
1. Define your analysis area using the time range selectors (X1 and X2)
2. Choose your preferred display mode based on your trading style
3. Enable QM Mode for additional market structure context if needed
4. Use the POI zones as potential entry and exit areas for your trades
5. Reference the automatically detected timeframe indicators to align your trading with the appropriate timeframe
### Settings Explanation
**Display Settings:**
- Display Mode: Choose between Main BOS, Leg, or All IDMs visualization
- QM Mode: Enable to see market structure connections with dashed lines
**Trick Settings:**
- Trick Display: Show the main trick or all nested patterns
- Trick POI: Control which POI zones appear for trick patterns
**Label Settings:**
- Leg POI %: Customize percentage-based POI zones in Leg mode
- POI Labels: Control which price labels appear on your chart
**Time Range:**
- X1 and X2: Define the analysis area for market structure detection
**Colors:**
- TF Color: Color for timeframe labels
- H/L Color: Color for high/low labels
- QM Lines: Color for market structure connection lines
- Trick Color: Color for nested pattern visualization
This indicator is designed for traders who understand market structure concepts and want a powerful tool that automatically identifies high-probability trading zones based on structural price patterns and volatility-adjusted measurements.
FVG [TakingProphets]🧠 Purpose
This indicator is built for traders applying Inner Circle Trader (ICT) methodology. It detects and manages Fair Value Gaps (FVGs) — price imbalances that often act as future reaction zones. It also highlights New Day Opening Gaps (NDOGs) and New Week Opening Gaps (NWOGs) that frequently play a role in early-session price behavior.
📚 What is a Fair Value Gap?
A Fair Value Gap forms when price moves rapidly, skipping over a portion of the chart between three candles — typically between the high of the first candle and the low of the third. These zones are considered inefficient, meaning institutions may return to them later to:
-Rebalance unfilled orders
-Enter or scale into positions
-Engineer liquidity with minimal slippage
In ICT methodology, FVGs are seen as both entry zones and targets, depending on market structure and context.
⚙️ How It Works
-This script automatically identifies and manages valid FVGs using the following logic:
-Bullish FVGs: When the low of the current candle is above the high from two candles ago
-Bearish FVGs: When the high of the current candle is below the body of two candles ago
-Minimum Gap Filter: Gaps must be larger than 0.05% of price
-Combine Consecutive Gaps (optional): Merges adjacent gaps of the same type
-Consequent Encroachment Line (optional): Plots the midpoint of each gap
-NDOG/NWOG Tracking: Labels gaps created during the 5–6 PM session transition
-Automatic Invalidation: Gaps are removed once price closes beyond their boundary
🎯 Practical Use
-Use unmitigated FVGs as potential entry points or targets
-Monitor NDOG and NWOG for context around daily or weekly opens
-Apply the midpoint (encroachment) line for precise execution decisions
-Let the script handle cleanup — only active, relevant zones remain visible
🎨 Customization
-Control colors for bullish, bearish, and opening gaps
-Toggle FVG borders and midpoint lines
-Enable or disable combining of consecutive gaps
-Fully automated zone management, no manual intervention required
✅ Summary
This tool offers a clear, rules-based approach to identifying price inefficiencies rooted in ICT methodology. Whether used for intraday or swing trading, it helps traders stay focused on valid, active Fair Value Gaps while filtering out noise and maintaining chart clarity.
Weekly Moving Averages (MAs) to Intraday ChartThis indicator overlays key weekly timeframe moving averages onto your intraday chart, allowing you to visualize important long-term support and resistance levels while trading shorter timeframes. The indicator includes:
330-period Simple Moving Average (white): Ultra long-term trend indicator
200-period Simple Moving Average (fuchsia): Major long-term trend indicator often watched by institutional traders
100-period Simple Moving Average (purple): Medium-to-long term trend indicator
50-period Exponential Moving Average (blue): Medium-term trend indicator, more responsive to recent price action
21-period Exponential Moving Average (teal): Short-to-medium term trend indicator
9-period Exponential Moving Average (aqua): Short-term trend indicator, highly responsive to recent price movements
This multi-timeframe approach helps identify significant support/resistance zones that might not be visible on your current timeframe. When price interacts with these weekly moving averages during intraday trading, it often signals important areas where institutional orders may be placed.
The indicator uses color-coding with increasing line thickness to help you quickly distinguish between different moving averages. Consider areas where multiple MAs cluster together as particularly strong support/resistance zones.
Perfect for day traders and swing traders who want to maintain awareness of the bigger picture while focusing on shorter-term price action.
OTC COT / smart money Index 2.0 COT/ Smart money Indicator – Institutional Commitment & Position Sizing (Inspired by Bernd Skorupinski Methodology)
📈 Description:
This indicator focuses on visualizing net positions held by commercials (smart money) and other key market participants, using data from the Commitments of Traders (COT) report. Inspired by Bernd Skorupinski’s institutional approach, the tool works hand-in-hand with the COT Index to provide a full picture of institutional sentiment and positioning strength.
👉 Core Functionality:
Displays net-long and net-short positions over time, helping traders understand how heavily institutions are positioned in a market.
Highlights historical extremes in net positions, which can act as warning signs or entry points when combined with technical analysis.
Supports customizable timeframes and asset selection (commodities, forex, indices) for maximum flexibility.
Best used in combination with the COT Index, offering a layered view of both relative extremes (COT Index) and absolute exposure (Net Positions).
The tool is designed to act as a contextual filter—it should complement technical setups rather than provide standalone trade signals.
📊 Applied Example – Gold Trade Using COT Net Position Analysis
To show the practical application, here’s a breakdown of a Gold (GC1!) trade that leveraged both COT Index and COT Net Positions to identify a high-probability setup.
Step 1️⃣ – Identifying Technical Structure:
The analysis started with classic price action review: Gold was approaching a significant demand zone, a well-established area that has historically triggered institutional buying.
Step 2️⃣ – COT Index Confirmation:
Upon reviewing the COT Index, the data revealed a 312-week buying extreme—the most aggressive commercial buying seen in over six years, signaling strong institutional accumulation.
Step 3️⃣ – COT Net Positions Validation:
Next, the COT Net Position Indicator showed that commercials were holding their largest net-long position in over 15 years—a rare and powerful signal of institutional conviction.
Step 4️⃣ – Divergence Check:
For added confirmation, divergence between commercials and retail traders was assessed:
✅ Commercials: Strongly net-long.
❌ Retail traders: Heavily net-short.
This clear divergence between smart money and retail sentiment further validated the setup.
Step 5️⃣ – Trade Execution:
With everything aligned:
Demand zone identified,
312-week COT Index extreme,
15-year high in net positions,
Divergence between commercials and retail,
…the trade was entered with a stop-loss placed just below the demand zone and a target set at a significant prior high. The result: a risk-reward ratio of 1:14.8, reflecting the strength and precision of the setup.
⚙️ What Sets This Tool Apart:
Provides deep insight into institutional exposure, showing both the magnitude of positions and how they evolve over time.
Enhances decision-making by cross-validating positioning extremes with technical levels.
Flexible design allows use across multiple asset classes and timeframes.
📌 Best Practices:
Always pair COT Net Position data with the COT Index to gauge both relative and absolute strength.
Use in conjunction with demand/supply zones or key technical levels for the strongest setups.
Look for divergence signals (institutions vs. retail) to confirm potential reversals.
Indicators Used in the Example:
This trade combined:
🧠 COT Net Position Indicator – to measure institutional exposure.
📊 COT Index – to identify positioning extremes.
📅 Seasonality Forecasting Tool – for time-based confirmation.
Together, these indicators provided a robust, multi-layered framework for high-confidence trading decisions.
OTC - COT Net positions 2.0 COT Net Position Indicator – Institutional Commitment & Position Sizing (Inspired by Bernd Skorupinski Methodology)
📈 Description:
This indicator focuses on visualizing net positions held by commercials (smart money) and other key market participants, using data from the Commitments of Traders (COT) report. Inspired by Bernd Skorupinski’s institutional approach, the tool works hand-in-hand with the COT Index to provide a full picture of institutional sentiment and positioning strength.
👉 Core Functionality:
Displays net-long and net-short positions over time, helping traders understand how heavily institutions are positioned in a market.
Highlights historical extremes in net positions, which can act as warning signs or entry points when combined with technical analysis.
Supports customizable timeframes and asset selection (commodities, forex, indices) for maximum flexibility.
Best used in combination with the COT Index, offering a layered view of both relative extremes (COT Index) and absolute exposure (Net Positions).
The tool is designed to act as a contextual filter—it should complement technical setups rather than provide standalone trade signals.
📊 Applied Example – Gold Trade Using COT Net Position Analysis
To show the practical application, here’s a breakdown of a Gold (GC1!) trade that leveraged both COT Index and COT Net Positions to identify a high-probability setup.
Step 1️⃣ – Identifying Technical Structure:
The analysis started with classic price action review: Gold was approaching a significant demand zone, a well-established area that has historically triggered institutional buying.
Step 2️⃣ – COT Index Confirmation:
Upon reviewing the COT Index, the data revealed a 312-week buying extreme—the most aggressive commercial buying seen in over six years, signaling strong institutional accumulation.
Step 3️⃣ – COT Net Positions Validation:
Next, the COT Net Position Indicator showed that commercials were holding their largest net-long position in over 15 years—a rare and powerful signal of institutional conviction.
Step 4️⃣ – Divergence Check:
For added confirmation, divergence between commercials and retail traders was assessed:
✅ Commercials: Strongly net-long.
❌ Retail traders: Heavily net-short.
This clear divergence between smart money and retail sentiment further validated the setup.
Step 5️⃣ – Trade Execution:
With everything aligned:
Demand zone identified,
312-week COT Index extreme,
15-year high in net positions,
Divergence between commercials and retail,
…the trade was entered with a stop-loss placed just below the demand zone and a target set at a significant prior high. The result: a risk-reward ratio of 1:14.8, reflecting the strength and precision of the setup.
⚙️ What Sets This Tool Apart:
Provides deep insight into institutional exposure, showing both the magnitude of positions and how they evolve over time.
Enhances decision-making by cross-validating positioning extremes with technical levels.
Flexible design allows use across multiple asset classes and timeframes.
📌 Best Practices:
Always pair COT Net Position data with the COT Index to gauge both relative and absolute strength.
Use in conjunction with demand/supply zones or key technical levels for the strongest setups.
Look for divergence signals (institutions vs. retail) to confirm potential reversals.
Indicators Used in the Example:
This trade combined:
🧠 COT Net Position Indicator – to measure institutional exposure.
📊 COT Index – to identify positioning extremes.
📅 Seasonality Forecasting Tool – for time-based confirmation.
Together, these indicators provided a robust, multi-layered framework for high-confidence trading decisions.
SMT Divergences By RootkitHow It Works:
Heikin Ashi Smoothing
Instead of using raw OHLC price data, the script applies Heikin Ashi smoothing to reduce noise and better identify pivot highs and lows.
Pivot Detection
It looks for local swing highs and lows using a user-defined lookback period (Pivot Lookback). These are used to detect potential divergences.
SMT Divergence Logic
An SMT divergence is identified when:
A pivot low occurs on the main chart.
But one or more of the comparison symbols makes a higher low instead (or lower high for bearish SMT).
This signals a disagreement between instruments, often attributed to smart money accumulation or distribution.
Trend Filter
To reduce false signals, the script only allows:
Buy signals during an uptrend
Sell signals during a downtrend
The trend is determined by comparing Heikin Ashi highs/lows to previous bars.
Signal Markers
BUY labels appear below bars at valid bullish SMT divergences.
SELL labels appear above bars at valid bearish SMT divergences.
Optional trend lines visually connect the last significant pivot to the current one.
⚙️ Inputs:
Pivot Lookback – Number of bars for swing high/low detection.
Symbol A/B/C – Up to 3 comparison symbols (futures, indices, etc.).
Toggle Each Symbol – Enable or disable any comparison ticker.
Custom Colors – Choose your own line colors for buy/sell signals.
🧠 Why It Matters:
SMT divergence is used by professional traders to spot early signs of accumulation/distribution, often before price reverses. This script helps you detect these signals automatically while keeping them filtered by overall market trend.
OTC Seasonal forecasting tool 2.0Seasonality Forecasting Tool – Advanced Seasonal Pattern Analysis (Inspired by Bernd Skorupinski Methodology)
📈 Description:
This script provides a structured way to analyze seasonal trends across financial markets, helping traders identify historical patterns that tend to repeat at specific times of the year. Inspired by Bernd Skorupinski’s institutional strategy, it has been refined with enhanced smoothing and customization options to improve adaptability across asset classes like commodities, forex, and indices.
👉 Core Functionality:
Analyzes historical price data over multiple lookback periods (5, 10, and 15 years) to calculate average seasonal performance.
Generates a smoothed seasonal curve that visually highlights periods of expected strength or weakness.
Allows users to customize lookback periods and adjust smoothing parameters, offering flexibility based on market type and volatility.
This tool is designed to be used as a contextual filter rather than a trade trigger—adding a layer of time-based confluence to enhance decision-making.
📊 Applied Example – Crude Oil Seasonality & Demand Zone Alignment
To demonstrate practical usage, here’s an example using Light Crude Oil Futures (CL1!) where seasonal tendencies and price structure aligned to create a high-probability setup.
Setup Steps:
1️⃣ Structural Context – Price Reaching a Demand Zone:
The market had been in a decline and approached a well-defined institutional demand area, which historically attracts buying interest.
2️⃣ Seasonality Analysis – Bullish Bias Identified:
The Seasonality Tool was applied using three distinct lookback windows:
5-year average 🟢
10-year average 🔴
15-year average 🔵
All three seasonal curves showed consistent upward trends during the late December to February period, historically signaling accumulation phases in crude oil markets.
3️⃣ Execution – Trade Setup:
With both:
Price action confirming a technical demand zone,
and seasonality indicating a strong historical bullish period,
a long position was taken targeting the next significant supply zone.
Result:
The trade unfolded as anticipated, with price rebounding strongly and delivering a risk-reward ratio of approximately 1:5.8—an outcome consistent with historical seasonal performance patterns.
⚙️ What Sets This Tool Apart:
Combines multi-timeframe seasonal data into a unified, easy-to-interpret visual output.
Includes custom smoothing algorithms to reduce noise, making the seasonal curves clearer and more reliable in fast-moving markets.
Offers flexibility to analyze not only commodities but also forex, indices, and other instruments influenced by recurring cycles (e.g., agricultural products, metals).
📌 Best Practices for Use:
Apply the tool alongside key technical zones (demand/supply) to find optimal trade timing.
Look for confluence across at least two of the seasonal curves (e.g., 5-year and 10-year averages agreeing on direction).
Use in combination with other market analysis tools—such as valuation indicators, COT data, or smart money flow—for full confirmation.
OTC valuation indicator 2.0Valuation Indicator – Relative Asset Valuation Tool (Inspired by Bernd Skorupinski Methodology)
📈 Description:
This script is designed to analyze relative value shifts between two assets—such as Gold (GC1!) and the Dollar Index (DXY)—to identify overvalued and undervalued market conditions. It is inspired by principles from Bernd Skorupinski’s methodology but has been developed with custom adjustments and improvements to enhance flexibility and adaptability across various asset classes.
👉 How It Works:
The script calculates a normalized valuation index by measuring the percentage price deviation between a target asset (e.g., Gold) and a reference asset (e.g., Dollar Index).
A moving average baseline defines fair value, with deviations indicating potential overvaluation or undervaluation.
A volatility-adjusted filter dynamically smooths the output, reducing noise and improving signal accuracy across different market environments.
Parameters such as evaluation period and sensitivity are fully customizable, allowing traders to tailor the tool to commodities, forex, indices, or other asset pairs.
📊 Detailed Example – Gold & Dollar Index Setup:
To demonstrate how the indicator can be used, here’s an example based on a real market scenario:
Context : Identifying high-probability buy setups on Gold when undervaluation is confirmed relative to the Dollar Index.
Conditions :
1️⃣ Gold enters a significant demand zone (identified through traditional technical analysis).
2️⃣ The valuation index (from this script) drops below the -75 level, signaling strong undervaluation
In both October 2022 and October 2023, the valuation index dropped well below -75, and Gold was sitting at major demand zones. The result?
📈 Massive moves to the upside, with Risk-Reward ratios hitting 1:4 or more.
snapshot
This is a textbook Bernd Skorupinski strategy setup, combining macro fundamentals (valuation) with technical structure (demand zones).
This is not just theory — the same conditions repeated multiple times, delivering repeatable, high-probability trades.
This showcases how macro mispricing (Dollar overvalued, Gold undervalued) can be identified visually and quantitatively using the indicator, enabling traders to make more confident, data-backed entry decisions.
⚙️ What Makes It Unique:
Unlike standard correlation or spread indicators, this script combines dynamic volatility filtering with a multi-step comparative analysis to better handle market volatility and price extremes.
It offers flexible asset pairing, allowing traders to adapt the tool to various market scenarios beyond just Gold/DXY—such as Oil vs. Euro or Stocks vs. Forex.
📌 Recommended Use:
Best applied on weekly and daily charts.
Should be combined with other technical tools such as support/resistance levels or demand zones for added confirmation.
Not intended as a standalone signal; it works best as part of a broader market analysis strategy.
Hull Moving Average with Cloud📈 Hull Moving Average with Cloud – Adaptive Trend Visualization
This indicator combines the power of the Hull Moving Average (HMA) with a visual signal line and trend cloud, giving traders a clearer view of market direction, momentum shifts, and potential reversals.
🔍 Key Features:
Dynamic HMA Length (optional): Adjusts the HMA period based on ATR volatility, allowing the moving average to adapt to changing market conditions.
Custom Smoothing Options: Smooth the main HMA with your choice of SMA, EMA, or WMA for a tailored trend line.
Signal Line (Orange HMA): A shorter-period Hull MA that acts as a trigger line for crossovers and trend changes.
Color-Coded Trend Cloud:
🟩 Green Cloud: Bullish – main HMA is above the signal HMA.
🟥 Red Cloud: Bearish – main HMA is below the signal HMA.
Real-Time Trend Coloring: Both lines dynamically change color based on slope (green for rising, red/purple for falling).
Offset Capability: Shift the HMA forward to visualize trend development and potential future direction.
✅ Use Cases:
Identify trend direction with cloud coloration.
Spot early reversals through HMA crossover signals.
Filter trades with volatility-aware moving average responsiveness.
ConeCastConeCast is a forward-looking projection indicator that visualizes a future price range (or "cone") based on recent trend momentum and adaptive volatility. Unlike lagging bands or reactive channels, this tool plots a predictive zone 3–50 bars ahead, allowing traders to anticipate potential price behavior rather than merely react to it.
How It Works
The core of ConeCast is a dynamic trend-slope engine derived from a Linear Regression line fitted over a user-defined lookback window. The slope of this trend is projected forward, and the cone’s width adapts based on real-time market volatility. In calm markets, the cone is narrow and focused. In volatile regimes, it expands proportionally, using an ATR-based % of price to scale.
Key Features
📈 Predictive Cone Zone: Visualizes a forward range using trend slope × volatility width.
🔄 Auto-Adaptive Volatility Scaling: Expands or contracts based on market quiet/chaotic states.
📊 Regime Detection: Identifies Bull, Bear, or Neutral states using a tunable slope threshold.
🧭 Multi-Timeframe Compatible: Slope and volatility can be calculated from higher timeframes.
🔔 Smart Alerts: Detects price entering the cone, and signals trend regime changes in real time.
🖼️ Clean Visual Output: Optionally includes outer cones, trend-trail marker, and dashboard label.
How to Use It
Use on 15m–4H charts for best forward visibility.
Look for price entering the cone as a potential trend continuation setup.
Monitor regime changes and volatility expansion to filter choppy market zones.
Tune the slope sensitivity and ATR multiplier to match your symbol's behavior.
Use outer cones to anticipate aggressive swings and wick traps.
What Makes It Unique
ConeCast doesn’t follow price — it predicts a possible future price envelope using trend + volatility math, without relying on lagging indicators or repainting logic. It's a hybrid of regression-based forecasting and dynamic risk zoning, designed for swing traders, scalpers, and algo developers alike.
Limitations
ConeCast projects based on current trend and volatility — it does not "know" future price. Like all projection tools, accuracy depends on trend persistence and market conditions. Use this in combination with confirmation signals and risk management.
MA Crossover with Adaptive Trend Strength📘 MA Crossover with Adaptive Trend Strength —
📌 Overview
This TradingView indicator plots two moving averages (Fast & Slow) with user-selected types (T3, EMA, SMA, HMA), visual crossovers, and dynamically calculates an adaptive trend strength score using Z-scores of multiple features. Optional higher timeframe (HTF) confirmation is supported. A color-filled region between the MAs visually indicates momentum direction.
⚙️ Inputs & Controls
📈 Moving Average Settings
Fast MA Length: Length of the fast-moving average (default: 9).
Slow MA Length: Length of the slow-moving average (default: 21).
MA Type: Type of moving average used (T3, EMA, SMA, HMA).
Source: Input data source (default: close).
T3 Volume Factor: Only used when T3 is selected, controls smoothing (range: 0–1).
🎨 Visual Controls
Bullish Fill Color: Fill color when Fast MA is above Slow MA.
Bearish Fill Color: Fill color when Fast MA is below Slow MA.
Show Gradient Fill: Enable or disable the colored area between Fast & Slow MAs.
Trend Label Position: Choose where the trend strength label appears (top or bottom).
Label Update Interval: Number of bars between label updates (reduces clutter).
⏱ Multi-Timeframe Support
Higher Timeframe: Timeframe used for confirmation (default: 60 min).
Use HTF Confirmation: Enables filtering of trend score by higher timeframe trend direction.
📊 Lookback Configuration
Auto Lookback Based on Timeframe: Dynamically adapts scoring lookback period per chart timeframe.
Manual Lookback: Manual fallback lookback length when auto is off.
🧮 MA Calculation Options
T3 MA: Custom T3 function with exponential moving averages and volume factor.
EMA/SMA: Built-in Pine functions (ta.ema, ta.sma).
HMA: Hull Moving Average using WMA calculations.
📉 Trend Strength Calculation
🧠 Z-Score Inputs
Distance between MAs (zDist)
Slope of the Fast MA (zSlope)
Volume (zVol)
ATR (zATR)
📏 Choppiness & Adaptive Weighting
A Choppiness Index (based on ATR & price range) reduces score impact in sideways markets.
Dynamically adjusts Z-score weights:
W1: Distance
W2: Slope
W3: Volume
W4: ATR
🔁 HTF Confirmation
Optionally multiplies the trend score by the direction of the higher timeframe trend to filter noise.
🟩 Plot & Visual Elements
📊 MA Lines
Plots Fast and Slow MA lines in colors based on selected MA type.
🌈 Gradient Fill
Fills the area between Fast and Slow MAs with opacity proportional to their difference.
Colors based on bullish/bearish condition.
🏷️ Trend Strength Label
Updates every n bars (Label Update Interval).
Shows:
Trend Classification: Weak, Moderate, Strong
Numerical Score
Label position (top or bottom) is configurable.
🔔 Crossover Signals
Bullish Crossover ("B"): Fast MA crosses above Slow MA.
Bearish Crossover ("S"): Fast MA crosses below Slow MA.
Labels are plotted at crossover points.
Old labels are removed after a threshold (100) to reduce chart clutter.
📋 Score Summary Table
A table showing:
Max Score within the lookback period
Min Score
HTF Confirmation Status (ON / OFF)
Updates on the same user-defined interval as the trend label.
🚨 Alerts
Condition Description
Bullish MA Cross Fast MA crosses above Slow MA
Bearish MA Cross Fast MA crosses below Slow MA
These are provided via alertcondition() for use in alert creation.
📌 Customization Tips
Turn off the gradient fill for a cleaner chart.
Use HTF confirmation to reduce false positives in ranging markets.
Adjust label update frequency to prevent visual clutter on faster timeframes.
Use T3 MA with volume factor for smoother signals in volatile markets.
Central Bank Assets YoY % with StdDev BandsCentral Bank Assets YoY % with StdDev Bands - Indicator Documentation
Overview
This indicator tracks the year-over-year (YoY) percentage change in combined central bank assets using a custom formula. It displays the annual growth rate along with statistical bands showing when the growth is significantly above or below historical norms.
Formula Components
The indicator is based on a custom symbol combining multiple central bank balance sheets:
Federal Reserve balance sheet (FRED)
Bank of Japan assets converted to USD (FX_IDC*FRED)
European Central Bank assets converted to USD (FX_IDC*FRED)
Subtracting Fed reverse repo operations (FRED)
Subtracting Treasury General Account (FRED)
Calculations
Year-over-Year Percentage Change: Calculates the percentage change between the current value and the value from exactly one year ago (252 trading days).
Formula: ((current - year_ago) / year_ago) * 100
Statistical Measures:
Mean (Average): The 252-day simple moving average of the YoY percentage changes
Standard Deviation: The 252-day standard deviation of YoY percentage changes
Display Components
The indicator displays:
Main Line: YoY percentage change (green when positive, red when negative)
Zero Line: Reference line at 0% (gray dashed)
Mean Line: Average YoY change over the past 252 days (blue)
Standard Deviation Bands: Shows +/- 1 standard deviation from the mean
Upper band (+1 StdDev): Green, line with breaks style
Lower band (-1 StdDev): Red, line with breaks style
Interpretation
Values above zero indicate YoY growth in central bank assets
Values below zero indicate YoY contraction
Values above the +1 StdDev line indicate unusually strong growth
Values below the -1 StdDev line indicate unusually severe contraction
Crossing above/below the mean line can signal shifts in central bank policy trends
Usage
This indicator is useful for:
Monitoring global central bank liquidity trends
Identifying unusual periods of balance sheet expansion/contraction
Analyzing correlations between central bank activity and market performance
Anticipating potential market impacts from changes in central bank policy
The 252-day lookback period (approximately one trading year) provides a balance between statistical stability and responsiveness to changing trends in central bank behavior.
CVDD + Terminal Price Indicator + Z-SCOREThis custom indicator is designed to help identify major overvaluation and undervaluation zones in Bitcoin's market cycles by analyzing price behavior relative to long-term on-chain benchmarks.
It leverages two well-known valuation models — CVDD (Cumulative Value Days Destroyed) and Terminal Price — to define a dynamic valuation range that adapts over time. These reference points are combined with a custom Z-score framework to evaluate how extended price is compared to long-term value zones.
The result is a normalized oscillator that highlights extremes in market sentiment, ranging from –3 (potential deep value) to +3 (potential overheated conditions), with a gradient color scale for quick visual interpretation.
This tool is especially useful for long-term investors and macro analysts seeking signals of potential market tops and bottoms based on deep on-chain behavior — rather than short-term technicals.
Strong Body Candle (80% of Range)You said:
Strong body candle 80% range
ChatGPT said:
A strong body candle with an 80% range typically refers to a candlestick on a price chart (usually used in technical analysis) where the real body of the candle (the open-to-close price movement) makes up 80% or more of the entire candle’s range (the high-to-low movement).
Breakdown:
Candle Range = High − Low
Body Size = |Close − Open|
Body-to-Range Ratio = (Body Size ÷ Candle Range) × 100
If this ratio is ≥ 80%, the candle is considered to have a strong body.
Interpretation:
A strong bullish candle: Close is near the high, open is near the low — strong buying pressure.
A strong bearish candle: Close is near the low, open is near the high — strong selling pressure.
These candles often signal momentum or trend continuation, especially when occurring after consolidation or at breakout points.
Would you like a script or formula to help detect these on a chart?
2/2
You said:
Yeh indicator banaya hai is ki description Bana ker rain publish kernrehanhun trading view per
ChatGPT said:
Great! Here's a professional and clear description you can use to publish your indicator on TradingView:
🔥 Strong Body Candle (80% Range) — Indicator Description
This indicator highlights candlesticks where the real body constitutes 80% or more of the entire candle's range (High − Low). These candles often represent strong momentum and are useful for identifying potential trend continuations, reversals, or breakout confirmations.
📌 Key Features:
✅ Detects both bullish and bearish strong body candles.
✅ Customizable threshold (default: 80%) for fine-tuning to your strategy.
✅ Visual markers make it easy to spot high-momentum candles on any timeframe.
📊 How It Works:
The script calculates the ratio of the candle's body size (|Close − Open|) to its total range (High − Low). If the ratio is equal to or above 80%, the candle is flagged:
Green Marker = Strong Bullish Candle (Close > Open)
Red Marker = Strong Bearish Candle (Open > Close)
🛠️ Use Cases:
Identify momentum shifts and breakout confirmations.
Use with support/resistance zones or trendlines for stronger signals.
Combine with volume indicators for enhanced precision.
RSI-EMA-Crossing with Donchian-Stop-LossThe Donchian RSI Indicator is a visual tool that combines momentum and trend analysis to identify high-quality long opportunities based on RSI crossovers, price action, and Donchian channel dynamics.
How It Works
Momentum Signal: A bullish RSI crossover is detected when the RSI crosses above its moving average.
Trend Filter: A signal is only valid if the crossover occurs while the price is above its moving average – filtering out entries against the prevailing trend.
Signal Candle: The high of the crossover candle is stored.
Entry Trigger: A valid signal occurs when a later candle closes above that signal high.
Stop-Loss (Visual Only)
The lower band of the Donchian Channel acts as a visual reference for a dynamic stop-loss level.
Features
Customizable RSI, Donchian Channel, and moving average lengths
Selectable MA types: SMA, EMA, WMA, VWMA, HMA
Signal candle highlighted (yellow background)
Entry points labeled on the chart
Price MA and Donchian Channel plotted
Trend filter improves signal quality by confirming upward bias
Use Case
Designed for swing and position traders
Optimized for use on daily or 4H charts
Volume Peak RectangleOutlines the 'Latest' Highest Volume Bar. Typically High Volume bars create very good support and resistance levels. This is a draw off the Opening Range Breakout theory, with the idea that high volume candles create very good upper and lower levels of liquidity zones.
