Advanced Petroleum Market Model (APMM)Advanced Petroleum Market Model (APMM): A Multi-Factor Fundamental Analysis Framework for Oil Market Assessment
## 1. Introduction
The petroleum market represents one of the most complex and globally significant commodity markets, characterized by intricate supply-demand dynamics, geopolitical influences, and substantial price volatility (Hamilton, 2009). Traditional fundamental analysis approaches often struggle to synthesize the multitude of relevant indicators into actionable insights due to data heterogeneity, temporal misalignment, and subjective weighting schemes (Baumeister & Kilian, 2016).
The Advanced Petroleum Market Model addresses these limitations through a systematic, quantitative approach that integrates 16 verified fundamental indicators across five critical market dimensions. The model builds upon established financial engineering principles while incorporating petroleum-specific market dynamics and adaptive learning mechanisms.
## 2. Theoretical Framework
### 2.1 Market Efficiency and Information Integration
The model operates under the assumption of semi-strong market efficiency, where fundamental information is gradually incorporated into prices with varying degrees of lag (Fama, 1970). The petroleum market's unique characteristics, including storage costs, transportation constraints, and geopolitical risk premiums, create opportunities for fundamental analysis to provide predictive value (Kilian, 2009).
### 2.2 Multi-Factor Asset Pricing Theory
Drawing from Ross's (1976) Arbitrage Pricing Theory, the model treats petroleum prices as driven by multiple systematic risk factors. The five-factor decomposition (Supply, Inventory, Demand, Trade, Sentiment) represents economically meaningful sources of systematic risk in petroleum markets (Chen et al., 1986).
## 3. Methodology
### 3.1 Data Sources and Quality Framework
The model integrates 16 fundamental indicators sourced from verified TradingView economic data feeds:
Supply Indicators:
- US Oil Production (ECONOMICS:USCOP)
- US Oil Rigs Count (ECONOMICS:USCOR)
- API Crude Runs (ECONOMICS:USACR)
Inventory Indicators:
- US Crude Stock Changes (ECONOMICS:USCOSC)
- Cushing Stocks (ECONOMICS:USCCOS)
- API Crude Stocks (ECONOMICS:USCSC)
- API Gasoline Stocks (ECONOMICS:USGS)
- API Distillate Stocks (ECONOMICS:USDS)
Demand Indicators:
- Refinery Crude Runs (ECONOMICS:USRCR)
- Gasoline Production (ECONOMICS:USGPRO)
- Distillate Production (ECONOMICS:USDFP)
- Industrial Production Index (FRED:INDPRO)
Trade Indicators:
- US Crude Imports (ECONOMICS:USCOI)
- US Oil Exports (ECONOMICS:USOE)
- API Crude Imports (ECONOMICS:USCI)
- Dollar Index (TVC:DXY)
Sentiment Indicators:
- Oil Volatility Index (CBOE:OVX)
### 3.2 Data Quality Monitoring System
Following best practices in quantitative finance (Lopez de Prado, 2018), the model implements comprehensive data quality monitoring:
Data Quality Score = Σ(Individual Indicator Validity) / Total Indicators
Where validity is determined by:
- Non-null data availability
- Positive value validation
- Temporal consistency checks
### 3.3 Statistical Normalization Framework
#### 3.3.1 Z-Score Normalization
The model employs robust Z-score normalization as established by Sharpe (1994) for cross-indicator comparability:
Z_i,t = (X_i,t - μ_i) / σ_i
Where:
- X_i,t = Raw value of indicator i at time t
- μ_i = Sample mean of indicator i
- σ_i = Sample standard deviation of indicator i
Z-scores are capped at ±3 to mitigate outlier influence (Tukey, 1977).
#### 3.3.2 Percentile Rank Transformation
For intuitive interpretation, Z-scores are converted to percentile ranks following the methodology of Conover (1999):
Percentile_Rank = (Number of values < current_value) / Total_observations × 100
### 3.4 Exponential Smoothing Framework
Signal smoothing employs exponential weighted moving averages (Brown, 1963) with adaptive alpha parameter:
S_t = α × X_t + (1-α) × S_{t-1}
Where α = 2/(N+1) and N represents the smoothing period.
### 3.5 Dynamic Threshold Optimization
The model implements adaptive thresholds using Bollinger Band methodology (Bollinger, 1992):
Dynamic_Threshold = μ ± (k × σ)
Where k is the threshold multiplier adjusted for market volatility regime.
### 3.6 Composite Score Calculation
The fundamental score integrates component scores through weighted averaging:
Fundamental_Score = Σ(w_i × Score_i × Quality_i)
Where:
- w_i = Normalized component weight
- Score_i = Component fundamental score
- Quality_i = Data quality adjustment factor
## 4. Implementation Architecture
### 4.1 Adaptive Parameter Framework
The model incorporates regime-specific adjustments based on market volatility:
Volatility_Regime = σ_price / μ_price × 100
High volatility regimes (>25%) trigger enhanced weighting for inventory and sentiment components, reflecting increased market sensitivity to supply disruptions and psychological factors.
### 4.2 Data Synchronization Protocol
Given varying publication frequencies (daily, weekly, monthly), the model employs forward-fill synchronization to maintain temporal alignment across all indicators.
### 4.3 Quality-Adjusted Scoring
Component scores are adjusted for data quality to prevent degraded inputs from contaminating the composite signal:
Adjusted_Score = Raw_Score × Quality_Factor + 50 × (1 - Quality_Factor)
This formulation ensures that poor-quality data reverts toward neutral (50) rather than contributing noise.
## 5. Usage Guidelines and Best Practices
### 5.1 Configuration Recommendations
For Short-term Analysis (1-4 weeks):
- Lookback Period: 26 weeks
- Smoothing Length: 3-5 periods
- Confidence Period: 13 weeks
- Increase inventory and sentiment weights
For Medium-term Analysis (1-3 months):
- Lookback Period: 52 weeks
- Smoothing Length: 5-8 periods
- Confidence Period: 26 weeks
- Balanced component weights
For Long-term Analysis (3+ months):
- Lookback Period: 104 weeks
- Smoothing Length: 8-12 periods
- Confidence Period: 52 weeks
- Increase supply and demand weights
### 5.2 Signal Interpretation Framework
Bullish Signals (Score > 70):
- Fundamental conditions favor price appreciation
- Consider long positions or reduced short exposure
- Monitor for trend confirmation across multiple timeframes
Bearish Signals (Score < 30):
- Fundamental conditions suggest price weakness
- Consider short positions or reduced long exposure
- Evaluate downside protection strategies
Neutral Range (30-70):
- Mixed fundamental environment
- Favor range-bound or volatility strategies
- Wait for clearer directional signals
### 5.3 Risk Management Considerations
1. Data Quality Monitoring: Continuously monitor the data quality dashboard. Scores below 75% warrant increased caution.
2. Regime Awareness: Adjust position sizing based on volatility regime indicators. High volatility periods require reduced exposure.
3. Correlation Analysis: Monitor correlation with crude oil prices to validate model effectiveness.
4. Fundamental-Technical Divergence: Pay attention when fundamental signals diverge from technical indicators, as this may signal regime changes.
### 5.4 Alert System Optimization
Configure alerts conservatively to avoid false signals:
- Set alert threshold at 75+ for high-confidence signals
- Enable data quality warnings to maintain system integrity
- Use trend reversal alerts for early regime change detection
## 6. Model Validation and Performance Metrics
### 6.1 Statistical Validation
The model's statistical robustness is ensured through:
- Out-of-sample testing protocols
- Rolling window validation
- Bootstrap confidence intervals
- Regime-specific performance analysis
### 6.2 Economic Validation
Fundamental accuracy is validated against:
- Energy Information Administration (EIA) official reports
- International Energy Agency (IEA) market assessments
- Commercial inventory data verification
## 7. Limitations and Considerations
### 7.1 Model Limitations
1. Data Dependency: Model performance is contingent on data availability and quality from external sources.
2. US Market Focus: Primary data sources are US-centric, potentially limiting global applicability.
3. Lag Effects: Some fundamental indicators exhibit publication lags that may delay signal generation.
4. Regime Shifts: Structural market changes may require model recalibration.
### 7.2 Market Environment Considerations
The model is optimized for normal market conditions. During extreme events (e.g., geopolitical crises, pandemics), additional qualitative factors should be considered alongside quantitative signals.
## References
Baumeister, C., & Kilian, L. (2016). Forty years of oil price fluctuations: Why the price of oil may still surprise us. *Journal of Economic Perspectives*, 30(1), 139-160.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. McGraw-Hill.
Brown, R. G. (1963). *Smoothing, Forecasting and Prediction of Discrete Time Series*. Prentice-Hall.
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. *Journal of Business*, 59(3), 383-403.
Conover, W. J. (1999). *Practical Nonparametric Statistics* (3rd ed.). John Wiley & Sons.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. *Journal of Finance*, 25(2), 383-417.
Hamilton, J. D. (2009). Understanding crude oil prices. *Energy Journal*, 30(2), 179-206.
Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. *American Economic Review*, 99(3), 1053-1069.
Lopez de Prado, M. (2018). *Advances in Financial Machine Learning*. John Wiley & Sons.
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. *Journal of Economic Theory*, 13(3), 341-360.
Sharpe, W. F. (1994). The Sharpe ratio. *Journal of Portfolio Management*, 21(1), 49-58.
Tukey, J. W. (1977). *Exploratory Data Analysis*. Addison-Wesley.
Indicatori e strategie
FVG MTF + 50%
// FVG MTF + 50%: A Multi-Timeframe Fair Value Gap Indicator
//
// Fair Value Gaps (FVGs) are core to the Inner Circle Trader (ICT) framework and mirror institutional order‐flow imbalances.
// In trading lore, an FVG is a rapid price swing that “leaves behind a gap” – a zone without trading – which is typically revisited later.
// In technical terms, a classic FVG spans three bars: the middle candle overshoots the prior swing without overlap (e.g. the 2nd candle’s high exceeds the 1st candle’s high in a bullish FVG).
// Such gaps represent transient liquidity vacuums. Bouchaud et al. (2011) model exactly this phenomenon: aggressive order flow creates a V-shaped supply/demand profile that “vanishes around the current price.”
// In other words, an FVG is a local imbalance where liquidity was exhausted and will tend to attract mean‐reverting orders as the market seeks equilibrium.
//
// In practice, ICT emphasizes the 50% retracement of an FVG as a high-probability entry level. This midpoint can be interpreted formally via market microstructure theory:
// Hasbrouck (2000) and others posit an underlying efficient price – a latent martingale value – around which observed prices fluctuate.
// The center of a recent gap heuristically proxies that latent fair value. Indeed, empirical models of order‐flow impact predict precisely this behavior:
// Bouchaud (2010) describes a “stimulated refill” mechanism, whereby a one‐sided price surge triggers an opposing flow of limit orders that pushes price back (a rising wall of liquidity).
// This liquidity‐induced mean‐reversion ensures that price often retraces to the gap midpoint as new limit orders fill the void.
// In essence, the 50% level embodies the short‐term equilibrium to which price gravitates after a liquidity shock.
//
// The FVG MTF + 50% indicator systematically implements these insights across multiple scales (M15, H1, H4).
// It identifies FVGs on each timeframe and continuously flags mitigation when price re‐enters a gap, effectively measuring market resiliency.
// A real‐time dashboard summarizes the total count of open FVGs and how many have been filled, quantifying latent imbalances much like institutional flow statistics.
// For example, a concentration of unfilled FVGs signals that many liquidity gaps remain, suggesting pent‐up supply/demand pressures. Conversely, a high fill rate indicates rapid liquidity absorption.
// By codifying ICT rules into quantitative outputs, this tool yields an empirical gauge of market stress and mean‐reversion potential.
//
// Overall, the script bridges ICT trading concepts with formal market microstructure.
// It treats FVG gaps as spontaneous liquidity voids and the 50% midpoint as a transient efficient price, consistent with Hasbrouck’s (2000) martingale view.
// As Bouchaud et al. note, markets operate with vanishing immediate liquidity and without instant equilibrium, explaining why price tends to return to the gap center.
// The dashboard and alerts translate these academic principles into actionable signals: by tracking gap creation and resolution, traders gain a systematic view of hidden order-flow dynamics.
// In summary, “FVG MTF + 50%” casts ICT’s smart‐money ideas in a rigorous framework (citing O’Hara, Hasbrouck, Bouchaud, Farmer, etc.), providing a scientific tool that enhances decision‐making with precise liquidity‐based metrics.
//
// References (illustrative):
// • Hasbrouck, J. (2000). The Economics of Microstructure: Latent Efficient Prices and Observed Quotes. wpa00047.pdf.
// • O’Hara, M. (1995). Market Microstructure Theory.
// • Bouchaud, J.-P., Farmer, J. D., & Lillo, F. (2011). How Markets Slowly Digest Changes in Supply and Demand. arXiv:1105.1694.
// • Bouchaud, J.-P. (2010). The Endogenous Dynamics of Markets: Price Impact and Feedback Loops. Farm\_CFM\_269-2010.pdf.
// • Huddleston, I. C. T. (ICT). Inner Circle Trader Lectures on Fair Value Gaps and 50% Midpoints.
//
// URLs for further reading:
// • (atas.net)
// • (fxopen.com)
// • (arxiv.org)
// • (w4.stern.nyu.edu)
// • (www.cfm.com)
//
// =============================================================================
//
// This indicator identifies Fair Value Gaps (FVGs) on M15, H1, and H4 timeframes, highlights them on the chart as colored boxes, draws the 50% median line,
// and displays price labels for the 0%, 50%, and 100% levels of each gap.
// It also tracks when gaps are “filled” (mitigated) and logs counts on a dashboard, providing real-time metrics on open/filled FVGs for liquidity analysis.
//
// Key Features:
// 1. Multi‐Timeframe Detection: Scans M15, H1, H4 for three‐bar FVG patterns using a configurable threshold.
// 2. Colored Zones and Median Lines: Draws bullish (green) and bearish (red) gap boxes, bordered in white, with a dashed white line at the midpoint.
// 3. Price Labels: Optionally annotates each gap with “0% FVG = \$X,” “50% FVG = \$Y,” and “100% FVG = \$Z” at the moment of detection.
// 4. Gap Mitigation: Monitors price re‐entry into a gap; when filled, it removes the box and logs a dashed line at the fill price.
// 5. Dashboard: Counts total bullish/bearish FVGs and calculates the percentage filled on each timeframe.
// 6. Alerts: Configurable alerts for new gap creation and fill events at 0%, 50%, and 100% levels.
//
// Implementation Details:
// • Detection Logic: A three-bar gap occurs when the middle bar’s low is above the prior bar’s high (bullish) or its high is below the prior bar’s low (bearish).
// A “threshold” parameter filters minor gaps based on relative size.
// • Data Structures: Uses Pine v6’s user‐defined “fvg” type to store gap high, low, direction, and timestamp. Arrays track open boxes, lines, labels for each timeframe.
// • Drawing:
// – box.new() draws transparent rectangles spanning 500 bars into the future.
// – line.new() draws dashed median lines and mitigation lines when gaps are filled.
// – label.new() places price annotations at the current right edge with textalign=text.align\_right.
// • Dashboard: table.new() creates a 3×3 panel showing “Bullish”/“Bearish” counts and “Mitigated” percentages in real time.
// • Alerts: alertcondition() triggers when new gaps form or are mitigated at specified percentages.
//
// Usage:
// • Add to chart: Apply the script; enable or disable timeframes via checkboxes (Enable FVG M15, H1, H4).
// • Configure text labels: Toggle “Text” to show or hide on‐chart price annotations.
// • Monitor dashboard: Observe counts and fill rates to gauge market liquidity pressure.
// • Set alerts: Enable alerts for specific levels (0%, 50%, 100%) and timeframes as needed.
//
// Potential Extensions:
// • Customizable lookback on fill monitoring (beyond “showLast” parameter).
// • Dynamic threshold based on ATR or volatility metrics instead of static percentage.
// • Integration with order‐flow or volume data to refine gap significance.
// • Expanded timeframes (D1, W, etc.) for higher‐timeframe liquidity profiling.
//
// =============================================================================
//
// © 2025. Licensed under CC BY‐NC‐SA 4.0 International.
// Feel free to reference academic works (Hasbrouck, Bouchaud, O’Hara) for theoretical context.
//
// End of Description.
HA Signal + Universal + Fixed Single Confirmationplaying around with EMA and DOJI signals. getting a lot of false signals if anyone has an idea
Risk Criteria Score Histogram It shows a daily score (0 to 3) representing the number of risk-off criteria currently triggered—helping visually track market environment changes.
Remember the 3 risk Criteria:
Short-Term Trend — Price relative to the 20-day exponential moving average (S&P 500)
Breadth — Net new highs vs. new lows across NYSE and Nasdaq
Momentum — Percentage Price Oscillator (PPO) on the S&P 500
This is based on Tom the Trader signals
23/35 SR Channels (Hitchhikers Guide To Goldbach)This indicator highlights potential short-term support and resistance zones based on the 23rd and 35th minute of each hour. At each of these time points, it draws a zone from the high to the low of the candle, extending it forward for a fixed number of bars.
Key features:
🔸 Orange zones mark the 23-minute candle
🔹 Blue zones mark the 35-minute candle
📏 Zones extend for a customizable number of bars (channelLength)
🔄 Existing zones are removed if they overlap significantly with a new one
🏷️ Optional labels show when a 23 or 35 zone is created
This tool is ideal for traders looking to identify time-based micro-structures and intraday reaction zones.
JS CandleIt is an excellent indicator for those who wish to earn. It is a strategic tool, but it is recommended to try it after thorough paper testing. Using a 5-minute or 3-minute chart, it provides suggestions for buying and selling. A complete description will be available after seven days.
Volume/Z-Score Filtered Triple RegressionsVolume/Z-Score Filtered Triple Regressions
This indicator blends three regression techniques—Linear Regression, Ridge Regression, and Lasso Regression—to deliver a robust, multi-dimensional view of market trends.
Key Features
Triple Regression Analysis:
Linear Regression: Uses TradingView’s built-in linear regression over a configurable lookback period.
Ridge Regression: Applies a penalty term with centering to stabilize slope estimates.
Lasso Regression: Incorporates soft-thresholding to refine the regression by reducing noise.
Voting Mechanism:
Each regression “votes” bullish or bearish by comparing its value to the current close. A majority vote (at least two out of three) determines the preliminary market bias.
Z-Score Filtering:
The indicator calculates the average of the three regression values and derives a residual Z-score (based on standard deviation). Only when the absolute Z-score exceeds a user-defined threshold does it permit a trend change, helping filter out minor price fluctuations.
Volume Confirmation:
A moving average of volume (with multiple MA options available) is compared against current volume using a multiplier. This ensures that trend changes are supported by sufficient market activity.
Enhanced Visuals:
Dynamic color schemes for regression lines, the average trend line, and even candle colors help visually distinguish bullish and bearish signals. A gradient background further reinforces the current trend, adapting its transparency based on the strength of the Z-score.
Disclaimer
Disclaimer: This indicator is provided for educational and informational purposes only and does not constitute investment advice. Trading involves risk and may result in financial loss. Always perform your own research and consult with a qualified financial advisor before making any trading decisions.
Z-Score Adaptive Oscillator SuiteZ-Score Adaptive Oscillator Suite
This indicator combines the Relative Strength Index (RSI) Money Flow Index (MFI) Chande Momentum Oscillator (CMO) and the Commodity Channel Index (CCI) with Z-score adaptive mechanism to provide a dynamic and adaptive trading tool.
Key Features:
Oscillators (RSI, MFI, CMO, CCI)
Calculates the oscillators using a customizable period and source.
Helps identify overbought or oversold conditions based on the oscillator average values.
Z-Score Adaptivity:
Applies Z-Score calculation to the Oscillators values over a user-defined lookback period.
Filters market regimes into low or high Z-score conditions based on the Z-score crossing above the user input threshold
Regime-Based Signal Generation:
In high Z-Score markets: Signals are generated using a simple cross of the oscillator midline-levels.
In low Z-Score markets: Signals are based on user-defined thresholds for long and short conditions.
Usage:
The coloring automatically adjusts to market conditions, and acts as potential buy/sell signals.
Disclaimer
This indicator is provided for educational and informational purposes only and does not constitute investment advice. Trading involves risk and may result in financial loss. Always perform your own research and consult with a qualified financial advisor before making any trading decisions.
VIX-SPX Ratio Lines (Final Formulas)This script will calculate the measured move based on the vix and spx close the previous day. It can be used to set an estimated range for the next day. I plan to use it to determine a strangle strategy buying 2 dte out with the given strikes.
Premarket & ORB Body High/Low Lines Till Market CloseDraw horizontal line for PRE-MARKET and ORB --Wicks is excluded.
What it does:
Tracks premarket body high/low 4:00–9:29 AM NY time
Tracks Opening Range Breakout (ORB) body high/low 9:30–9:44 AM NY time
Draws horizontal lines extended right until you scroll forward (no fixed length)
Labels show current value and update dynamically
Resets everything cleanly at the start of each new day
Swing High Low By RSThis indicator helps you visually identify important support and resistance levels based on recent swing highs and lows in the market — automatically and with clarity.
Many traders struggle with figuring out where to buy or sell, or where price might reverse. This tool solves that by marking those critical turning points for you.
🧠 What It Does:
It looks at recent price action to find swing highs (where price temporarily peaked) and swing lows (where price temporarily bottomed).
When a new swing point is found, the indicator draws a horizontal line on your chart.
These lines act as support (green) or resistance (red) levels — key zones where price has reacted before.
✨ Unique Feature – Limited Line Length:
Unlike other indicators that draw lines all the way to the right edge of the screen, this one keeps things clean and focused by extending lines only for a limited number of candles (default: 50).
This means:
Less clutter on your chart.
You focus only on the most relevant and recent levels.
📊 How to Use It:
Support Levels (Green Lines)
These form after a swing low is detected. They often act as buy zones or bounce areas when price comes down.
Resistance Levels (Red Lines)
These form after a swing high is detected. They often act as sell zones or rejection areas when price goes up.
Trading Strategy
Use the lines as reference to plan entries, exits, and stop-losses.
Combine with price action, candlestick patterns, or other indicators (like RSI, moving averages) for confirmation.
Works great on any timeframe and across all markets — Forex, Crypto, Stocks, Commodities, etc.
📌 Customizable Settings:
Adjust how many candles are checked before identifying a swing.
Control how far each line stretches (how many bars it should stay visible).
👥 Best For:
Beginner to advanced traders
Price action traders
Scalpers, intraday traders, and swing traders
Anyone who wants to trade clean charts without drawing levels manually
This is your go-to tool for identifying powerful support and resistance levels based on actual market structure — not just math or indicators. It saves time, reduces noise, and increases confidence in your trade decisions.
Enjoy...
Multi-Sigma Probability Cones (Flat Mean)This is Multi-Sigma Probability Cones (with flat mean). It was developed by ChatGPT.
Cipher 10M Confluence w/ Bias ScoreCipher 10M Confluence is a multi-timeframe dashboard designed to complement Market Cipher. It uses RSI and VWAP proxies on the 1H, 30M, and 10M timeframes to highlight trend alignment and generate a bias score, displayed directly on your chart. The script provides instant visual and alert-based signals for bullish, bearish, or neutral confluence—making it an ideal tool for timing scalp entries on the 10-minute timeframe. For best results, use in conjunction with Market Cipher to strengthen your trading decisions.
Risk-On/Risk-Off AlarmRisk Off Criteria: 1 of 3 Active
Each week, Tom assesses the market using three core signals that define the broader environment as either risk-on or risk-off. When all three are triggered simultaneously, it sets off the "alarm."
Short-Term Trend — Price relative to the 20-day exponential moving average (S&P 500)
Breadth — Net new highs vs. new lows across NYSE and Nasdaq
Momentum — Percentage Price Oscillator (PPO) on the S&P 500
Support and Resistance MTFSupport and Resistance MTF
Support and Resistance MTF is a powerful tool that automatically detects and visualizes key support and resistance levels based on pivot highs and lows, using a higher timeframe of your choice. It is designed for traders who focus on price action and market structure, and want an adaptive, clean, and customizable indicator that helps identify important market zones.
The script uses configurable pivot logic to identify levels, with user-defined parameters for pivot strength and timeframe. Once a support or resistance level is detected, it is displayed on the chart either as a horizontal line, a shaded box, or both, depending on your display settings. You can fully customize the visual appearance including color, transparency, and line thickness. Levels are automatically extended into the future, and optionally into the past, to give better context.
Each level is monitored for breakout behavior. If price breaks through a level, it can change its role — a former resistance may become support, and vice versa. After a certain number of breakouts (which you define), the level is considered invalid and is automatically removed from the chart. This helps to maintain a clean visual layout and ensures only relevant levels are shown.
The indicator supports multi-timeframe analysis, allowing you to overlay higher-timeframe structure directly on your lower-timeframe trading chart. It is also compatible with Heikin Ashi candles internally for reference, without affecting your main chart type.
Support and Resistance MTF is ideal for traders looking to align intraday setups with higher-timeframe zones, manage risk around structural levels, or simply highlight market turning points in a clear and automated way. Built with Pine Script v5 and optimized for performance, it is both powerful and lightweight.
⚙️ Input Parameters – Description
[Time-Frame
Defines the higher timeframe used for detecting support and resistance levels. For example, you can set this to 1h, 4h, or D to visualize significant levels from a broader market perspective on a lower-timeframe chart.
Left / Right (Pivot Left / Pivot Right)
These parameters control the sensitivity of the pivot detection. A pivot high/low is confirmed if it is higher/lower than the defined number of candles to its left and right. Higher values reduce noise but may miss smaller turning points.
Extend Left
When enabled, the drawn levels (lines and/or boxes) are extended to the left side of the chart, allowing you to see the historical alignment of these levels.
Max Breaks Before Delete
Defines how many times a level can be broken by price before it is removed from the chart. This helps to avoid clutter from outdated or invalidated levels and keeps your chart relevant to current price action.
Draw Lines Only
If enabled, the indicator will draw only horizontal lines for support and resistance zones, omitting the colored background boxes. Useful for a cleaner chart appearance.
Line Width Broken Level
Sets the thickness of the support/resistance lines. Thicker lines can emphasize key levels, especially after a breakout.
Transparency Boxes
Controls the transparency (0–100) of the background boxes representing the zones. A higher value makes the boxes more transparent, lower values make them more opaque.
Transparency Lines
Controls the transparency (0–100) of the horizontal support and resistance lines. This allows for visual fine-tuning based on chart background and personal preference.
Support (Color, Group: Display)
Lets you choose the color used for support zones and lines. By default, it's green, but you can change it to fit your theme or visual preference.
Resistance (Color, Group: Display)
Defines the color for resistance zones and lines. The default is red, but it can be customized freely.
