GoatsGlowingRSIGoatsGlowingRSI is a visually enhanced and feature-rich RSI (Relative Strength Index) indicator designed for deeper market insight and clearer signal visualization. It combines standard RSI analysis with gradient-colored backgrounds, glowing effects, and automated divergence detection to help traders spot potential reversals and momentum shifts more effectively.
Key Features:
✅ Multi-Timeframe RSI:
Calculate RSI from any timeframe using the custom input. Leave it blank to use the current chart's timeframe.
✅ Dynamic Gradient Background:
A smooth gradient fill is applied between RSI levels from the lower band (30) to the upper band (70). The gradient shifts from blue (oversold) to red (overbought), visually highlighting the RSI's position and strength.
✅ Glowing RSI Line:
A three-layered glow effect surrounds the main RSI line, creating a striking white core with a purple aura that enhances visibility against dark or light chart themes.
✅ Custom RSI Levels:
Dashed horizontal lines at RSI 70 (overbought), RSI 30 (oversold), and a dotted midline at 50 help you interpret trend momentum and strength.
✅ Automatic Divergence Detection:
Built-in logic identifies bullish and bearish divergences by comparing RSI and price pivot points:
🟢 Bullish Divergence: RSI makes a higher low while price makes a lower low.
🔴 Bearish Divergence: RSI makes a lower high while price makes a higher high.
Divergences are marked on the RSI line with colored lines and labels ("Bull"/"Bear").
✅ Alerts Ready:
Get notified in real-time with alert conditions for both bullish and bearish divergence setups.
אינדיקטורים ואסטרטגיות
WLSMA: fast approximation🙏🏻 Sup TV & @alexgrover
O(N) algocomplexity, just one loop inside. No, you can't do O(1) @ updates in moving window mode, only expanding window will allow that.
Now I have time series & stats models of my own creation, nowhere else available, just TV and my github for now, ain’t no legacy academic industry I always have fun about, but back in 2k20 when I consciously ain’t known much about quant, I remember seeing post by @alexgrover recreating Moving Regression Endpoint dropped on price chart (called LSMA here) as a linear filter combination of filters (yea yeah DSP terms) as 3WMA - 2SMA. Now it’s my time to do smth alike aye?
...
This script is remake of my 1st degree WLSMA via linear filter combo. It’s much faster, we aint calculate moving regression per se, we just match its freq response. You can see it on the screen (WLSMAfa) almost perfectly matching the original one (WLSMA).
...
While humans like to overfit, I fw generalizations. So your lovely WMA is actually just one case of a more general weight pattern: pow(len - i, e), where pow is the power function and e is the exponent itself. So:
- If e = 0, then we have SMA (every number in 0th power is one)
- If e = 1, we get WMA
- If e = 2, we get quadratic weights.
We can recreate WLSMA freq response then by combining 2 filters with e = 1 and e = 2.
This is still an approximation, even tho enormously precise for the tasks you’ve shared with me. Due to the non-linear nature of the thing it’s all we can do, and as window size grows, even this small discrepancy converges with true WLSMA value, so we’re all good. Pls don’t try to model this 0.00xxxx discrepancy, it’s not natural.
...
DSP approach is unnatural for prices, but you can put this thing on volume delta and be happy, or on other metrics of yours, if for some reason u dont wanna estimate thresholds by fitting a distro.
All good TV
∞
P.S.: strangely, the first script made & dropped in the location in Saint P where my actual quant way has started ~5 years ago xD, very thankful
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.
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.
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.
BK AK-Scope🔭 Introducing BK AK-Scope — Target Locked. Signal Acquired. 🔭
After building five precision weapons for traders, I’m proud to unveil the sixth.
BK AK-Scope — the eye of the arsenal.
This is not just an indicator. It’s an intelligence system for volatility, signal clarity, and rate-of-change dynamics — forged for elite vision in any market terrain.
🧠 Why “Scope”? And Why “AK”?
Every shooter knows: you can’t hit what you can’t see.
The Scope brings range, clarity, and target distinction. It filters motion from noise. Purpose from panic.
“AK” continues to honor the man who trained my sight — my mentor, A.K.
His discipline taught me to wait for alignment. To move with reason, not emotion.
His vision lives in every code line here.
🔬 What Is BK AK-Scope?
A Triple-Tier TSI Correlation Engine, fused with adaptive opacity logic, a volatility scoring system, and real-time signal clarity. It’s momentum dissected — by speed, depth, and rate of change.
Built to serve traders who:
Need visual hierarchy between fast, mid, and slow TSI responses.
Want adaptive fills that pulse with volatility — not static zones.
Require a volatility scoring overlay that reads the battlefield in real time.
⚙️ Core Systems: How BK AK-Scope Works
✅ Fast/Mid/Slow TSI →
Three layers of correlation: like scopes with zoom levels.
You track micro moves, mid swings, and macro flow simultaneously.
✅ Rate-of-Change Adaptive Opacity →
Momentum fills fade or flash based on speed — giving you movement density at a glance.
Bull vs. Bear zones adapt to strength. You feel the market’s pulse.
✅ Volatility Score Intelligence →
Custom algorithm measuring:
Range expansion
Rate-of-change differentials
ATR dynamics
Standard deviation pressure
All combined into a score from 0–100 with live icons:
🔥 = Extreme Heat (70+)
🧊 = Cold Zone (<30)
⚠️ = ROC Warning
• = Neutral drift
✅ Auto-Detect Volatility Modes →
Scalp = <15min
Swing = intraday/hourly
Macro = daily/weekly
Or override manually with total control.
🎯 How To Use BK AK-Scope
🔹 Trend Continuation → When all three TSI layers align in direction + volatility score climbs, ride with the trend.
🔹 Early Reversals → Opposing TSI + rapid opacity change + volatility shift = sniper reversal zone.
🔹 Consolidation Filter → Neutral fills + score < 30 = stay out, wait for signal surge.
🔹 Signal Confluence → Pair with:
• Gann fans or angles
• Fib time/price clusters
• Elliott Wave structure
• Harmonics or divergence
To isolate entry perfection.
🛡️ Why This Indicator Changes the Game
It's not just momentum. It’s TSI with depth hierarchy.
It’s not just color. It’s real-time strength visualization.
It’s not just volatility. It’s rate-weighted market intelligence.
This is market optics for the advanced trader — built for vision, clarity, and discipline.
🙏 Final Thoughts
🔹 In honor of A.K., my mentor. The man who taught me to see what others miss.
🔹 Inspired by the power of vision — because execution without clarity is chaos.
🔹 Powered by faith — because Gd alone gives sight beyond the visible.
“He gives sight to the blind and wisdom to the humble.” — Psalms 146
Every tool I build is a prayer in code — that it helps someone trade with clarity, integrity, and precision.
⚡ Zoom In. Focus Deep. Trade Clean.
BK AK-Scope — Lock on the target. See what others don’t.
🔫 Clarity is power. 🔫
Gd bless. 🙏
Z-Score Adaptive Connors RSIZ-Score Adaptive Connors RSI blends the classic three-component Connors RSI (RSI, Up/Down streak RSI, and Percentile Rank of 1-bar ROC) with a dynamic z-score filter that distinguishes trending vs. mean-reverting market regimes.
When the indicator detects an extreme deviation (|z-score| > threshold) , it switches to “trending” mode and tightens entry thresholds for capturing momentum. When markets are in a more neutral regime, it reverts to wider thresholds, hunting for overbought/oversold reversals.
Key Features
Connors RSI Core: Combines price momentum, streak measurements, and velocity for a robust baseline oscillator. Z-Score Regime Filter: Computes the z-score of the Connors RSI over a lookback window to adapt your trading style to trending vs. reverting environments.
Dynamic Thresholds: Separate user-configurable thresholds for trending (“tight” entries) and mean-reverting (“wide” entries) scenarios.
Inputs & Parameters
Connors RSI Settings
RSI Source: Price series for RSI calculation (default: Close)
RSI Length: Period for price‐change RSI (default: 24)
Up/Down Length: Period for streak RSI (default: 20)
ROC Length: Period for percentile‐rank of 1-bar return (default: 75)
Z-Score Filter
Lookback: Number of bars to compute mean and standard deviation of Connors RSI (default: 14)
Threshold: Minimum |z-score| to enter “trending” mode (default: 1.5)
Entry Thresholds
Trending Long/Short: Upper and lower RSI Thresholds when trending
Reverting Long/Short: Upper and lower RSI Thresholds when reverting
Two Candle Theory (Filtered) - Labels & ColorsOverview
This Pine Script classifies each candle into one of nine sentiment categories based on how the candle closes within its own range and in relation to the previous candle’s high and low. It optionally filters the strongest bullish and bearish signals based on volume spikes.
The script is designed to help traders visually interpret market sentiment through configurable labels and candle colors.
⸻
Classification Logic
Each candle is assessed using two metrics:
1. Close Position – where the candle closes within its own high-low range (High, Mid, Low).
2. Close Comparison – how the current close compares to the previous candle’s high and low (Bull, Bear, or Range).
Based on this, a short label is assigned:
• Bullish Bias: Strongest (SBu), Moderate (MBu), Weak (WBu), Slight (SlB)
• Neutral: Neutral (N)
• Bearish Bias: Slight (SlS), Weak (WBa), Moderate (MBa), Strongest (SBa)
⸻
Volume Filter
A volume spike filter can be applied to the strongest signals:
• SBu and SBa are only shown if volume is significantly higher than the average (SMA × threshold).
• The filter is optional and user-configurable.
⸻
Display Options
Users can control:
• Whether to show labels, bar colors, or both.
• Which of the nine label types are visible.
• Custom colors for each label and corresponding bar.
⸻
Visual Output
• Labels appear above or below candles depending on bullish or bearish classification.
• Bar colors reflect sentiment for quicker visual scanning.
⸻
Use Case
Ideal for identifying momentum shifts, validating trade entries, and highlighting candles that break out of previous ranges with conviction and/or volume.
⸻
Summary
This script simplifies price action by translating each candle into an interpretable sentiment label and color. With optional volume filtering and full display customization, it offers a practical tool for discretionary and systematic traders alike.
MirPapa:ICT:HTF: FVG OB Threeple# MirPapa:ICT:HTF: FVG OB (Fair Value Gap Order Block)
**Version:** Pine Script® v6
**Author:** © goodia
**License:** MPL-2.0 (Mozilla Public License 2.0)
---
## Overview
“FVG OB” (Fair Value Gap Order Block) identifies higher-timeframe candle ranges where a gap (imbalance) exists between two non-consecutive candles, signaling potential institutional order blocks. This module draws bullish or bearish FVG OB boxes on your lower-timeframe chart, extends them until price interacts a specified number of times, and then finalizes (recolors) the box.
---
## Inputs
- **Enable FVG OB Boxes** (`bool`)
Toggle drawing of HTF FVG OB boxes on the chart.
- **Enable FVG OB Midlines** (`bool`)
Toggle drawing of a midpoint line inside each FVG OB box.
- **FVG OB Close Count** (`int` 1–10)
Number of HTF closes beyond the FVG range required to finalize (recolor) the box.
- **FVG OB Bull Color** (`color`)
Fill & border color for bullish FVG OB boxes.
- **FVG OB Bear Color** (`color`)
Fill & border color for bearish FVG OB boxes.
- **FVG OB Box Transparency** (`int` 1–100)
Opacity level for FVG OB box fills (higher = more transparent).
---
## How It Works
1. **HTF Data Retrieval**
- The script uses `request.security()` (via `GetHTFrevised()`) to fetch HTF OHLC and historical values:
- `_htfHigh3` (high three bars ago) and `_htfLow1` (low one bar ago) for bullish FVG OB.
- `_htfLow3` (low three bars ago) and `_htfHigh1` (high one bar ago) for bearish FVG OB.
- It also tracks the HTF `bar_index` on the lower timeframe to align drawing.
2. **FVG OB Detection**
- **Bullish FVG OB**: Occurs when the HTF low of the previous bar (`low `) is strictly above the HTF high of three bars ago (`high `), creating a gap.
- **Bearish FVG OB**: Occurs when the HTF high of the previous bar (`high `) is strictly below the HTF low of three bars ago (`low `), creating a gap.
3. **Box Creation**
- On each new HTF bar (`ta.change(time(HTF)) != 0`), if a bullish or bearish FVG OB condition is met, the script calls `CreateBoxData()` with:
- **Bullish**: `bottom = HTF low `, `top = HTF high `, `_isBull = true`.
- **Bearish**: `bottom = HTF low `, `top = HTF high `, `_isBull = false`.
- Midline toggled by input.
- A `BoxData` struct is created and stored in either the Bull or Bear array.
4. **Box Extension & Finalization**
- On **every LTF bar**, `ProcessBoxDatas(...)` iterates over all active FVG OB boxes:
1. **Extend Right Edge**: `box.set_right(bar_index)` ensures the box follows the latest bar.
2. **Record Volume Delta**: Tracks buy/sell volume inside the box.
3. **Touch Stage Update**: `modBoxUpdateStage()` increments `_stage` when price touches its “basePoint” (for FVG OB, the basePrice is one side of the gap).
4. **Finalize**: `setBoxFinalize()` checks if the configured number of closes beyond the FVG gap (`FVG OB Close Count`) has occurred. If so:
- `_isActive := false`
- Border and background colors are changed to the “Box Close Color” (input).
- Finalized boxes remain on screen semi-transparent, indicating that the FVG OB zone has been tested.
5. **Midline (Optional)**
- If “Enable FVG OB Midlines” is checked, `ProcessBoxDatas()` also extends a horizontal midpoint line inside the box with `line.set_x2(bar_index)`.
---
## Usage Instructions
1. **Installation**
- Copy the FVG OB section of the Pine Script into TradingView’s Pine Editor (ensure the library import is included).
- Click “Add to Chart.”
2. **Configure Inputs**
- Choose a Higher Time Frame via the dropdown (e.g., “4시간” maps to a 4H timeframe).
- Toggle “Enable FVG OB Boxes” and “Enable FVG OB Midlines.”
- Select colors for bullish and bearish boxes and set transparency.
- Adjust “FVG OB Close Count” to control how many closes beyond the gap finalize the box.
3. **Interpretation**
- **Active FVG OB Boxes** extend to the right until price closes beyond the gap range the specified number of times.
- When finalized, each box changes to the “Box Close Color,” signaling that institutional orders in that gap have likely been filled.
Enjoy precise visualization of higher-timeframe Fair Value Gap Order Blocks on your lower-timeframe chart!
Shooting Star Detector[cryptovarthagam]🌠 Shooting Star Detector
The Shooting Star Detector is a powerful price action tool that automatically identifies potential bearish reversal signals using the well-known Shooting Star candlestick pattern.
Ideal for traders who rely on candlestick psychology to spot high-probability short setups, this script works across all markets and timeframes.
🔍 What is a Shooting Star?
A Shooting Star is a single-candle pattern that typically forms at the top of an uptrend or resistance zone. It’s characterized by:
A small body near the candle's low,
A long upper wick, and
Little or no lower wick.
This pattern suggests that buyers pushed price higher but lost control by the close, hinting at potential bearish momentum ahead.
✅ Indicator Features:
🔴 Accurately detects Shooting Star candles in real-time
🔺 Plots a red triangle above every valid signal candle
🖼️ Optional background highlight for visual clarity
🕵️♂️ Strict ratio-based detection using:
Wick-to-body comparisons
Upper wick dominance
Optional bearish candle confirmation
⚙️ Detection Logic (Rules Used):
Upper wick > 60% of total candle range
Body < 20% of total candle
Lower wick < 15% of candle range
Bearish candle (optional but included for accuracy)
These rules ensure high-quality signals that filter out false positives.
📌 Best Use Cases:
Spotting trend reversals at swing highs
Confirming entries near resistance zones
Enhancing price action or supply/demand strategies
Works on: Crypto, Forex, Stocks, Commodities
🧠 Trading Tip:
Pair this detector with volume confirmation, resistance zones, or bearish divergence for higher-probability entries.
📉 Clean, minimal, and non-repainting — designed for traders who value accuracy over noise.
Created with ❤️ by Cryptovarthagam
Follow for more real-time price action tools!
Volume-Enhanced Candlestick Patterns 1
Overview
Scans for four major candlestick reversal patterns:
Harami
Engulfing
Morning/Evening Star
Piercing Line/Dark Cloud Cover
Underlying logic assumes that, at a turning point, the dominant side (bulls or bears) often delivers a “final” push—either a last surge of buying or selling—before the reversal truly takes hold.
Pattern Toggles
Each individual pattern can be turned on or off in the inputs.
Enable only the patterns you want to monitor to reduce chart clutter and speed up performance.
Volume Filter Toggle
On: Requires volume-based exhaustion or climax to confirm each pattern.
Off: Relies purely on price-action candlestick logic (no volume checks).
Grouped Labels & Confluence
When one or more patterns trigger on the same bar close, a single label is drawn:
Grouping multiple confirmed patterns on one bar increases confluence and signal strength.
Climax Volume × Multiplier
Adjusting this input affects signal frequency and conviction:
Higher multiplier → fewer signals but with stronger volume confirmation
Lower multiplier → more signals, each with a looser volume requirement
Alerts
Built-in alert condition for each individual pattern (bullish/bearish Harami, Engulfing, Star, Piercing, Dark Cloud Cover), so you can receive real-time notifications whenever a confirmation occurs.
Follow for Weekly Scripts
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Disclaimer
Not Financial Advice. This script is for educational and research purposes only.
Use as Part of a Larger System. It should not be used in isolation; combine it with your own risk management rules, additional indicators, and broader market analysis.
No Guarantees. Candlestick patterns and volume filters can improve signal quality, but they do not guarantee profitable trades. Always perform your own due diligence before entering any position.
Uptrick: Z-Trend BandsOverview
Uptrick: Z-Trend Bands is a Pine Script overlay crafted to capture high-probability mean-reversion opportunities. It dynamically plots upper and lower statistical bands around an EMA baseline by converting price deviations into z-scores. Once price moves outside these bands and then reenters, the indicator verifies that momentum is genuinely reversing via an EMA-smoothed RSI slope. Signal memory ensures only one entry per momentum swing, and traders receive clear, real-time feedback through customizable bar-coloring modes, a semi-transparent fill highlighting the statistical zone, concise “Up”/“Down” labels, and a live five-metric scoring table.
Introduction
Markets often oscillate between trending and reverting, and simple thresholds or static envelopes frequently misfire when volatility shifts. Standard deviation quantifies how “wide” recent price moves have been, and a z-score transforms each deviation into a measure of how rare it is relative to its own history. By anchoring these bands to an exponential moving average, the script maintains a fluid statistical envelope that adapts instantly to both calm and turbulent regimes. Meanwhile, the Relative Strength Index (RSI) tracks momentum; smoothing RSI with an EMA and observing its slope filters out erratic spikes, ensuring that only genuine momentum flips—upward for longs and downward for shorts—qualify.
Purpose
This indicator is purpose-built for short-term mean-reversion traders operating on lower–timeframe charts. It reveals when price has strayed into the outer 5 percent of its recent range, signaling an increased likelihood of a bounce back toward fair value. Rather than firing on price alone, it demands that momentum follow suit: the smoothed RSI slope must flip in the opposite direction before any trade marker appears. This dual-filter approach dramatically reduces noise-driven, false setups. Traders then see immediate visual confirmation—bar colors that reflect the latest signal and age over time, clear entry labels, and an always-visible table of metric scores—so they can gauge both the validity and freshness of each signal at a glance.
Originality and Uniqueness
Uptrick: Z-Trend Bands stands apart from typical envelope or oscillator tools in four key ways. First, it employs fully normalized z-score bands, meaning ±2 always captures roughly the top and bottom 5 percent of moves, regardless of volatility regime. Second, it insists on two simultaneous conditions—price reentry into the bands and a confirming RSI slope flip—dramatically reducing whipsaw signals. Third, it uses slope-phase memory to lock out duplicate signals until momentum truly reverses again, enforcing disciplined entries. Finally, it offers four distinct bar-coloring schemes (solid reversal, fading reversal, exceeding bands, and classic heatmap) plus a dynamic scoring table, rather than a single, opaque alert, giving traders deep insight into every layer of analysis.
Why Each Component Was Picked
The EMA baseline was chosen for its blend of responsiveness—weighting recent price heavily—and smoothness, which filters market noise. Z-score deviation bands standardize price extremes relative to their own history, adapting automatically to shifting volatility so that “extreme” always means statistically rare. The RSI, smoothed with an EMA before slope calculation, captures true momentum shifts without the false spikes that raw RSI often produces. Slope-phase memory flags prevent repeated alerts within a single swing, curbing over-trading in choppy conditions. Bar-coloring modes provide flexible visual contexts—whether you prefer to track the latest reversal, see signal age, highlight every breakout, or view a continuous gradient—and the scoring table breaks down all five core checks for complete transparency.
Features
This indicator offers a suite of configurable visual and logical tools designed to make reversal signals both robust and transparent:
Dynamic z-score bands that expand or contract in real time to reflect current volatility regimes, ensuring the outer ±zThreshold levels always represent statistically rare extremes.
A smooth EMA baseline that weights recent price more heavily, serving as a fair-value anchor around which deviations are measured.
EMA-smoothed RSI slope confirmation, which filters out erratic momentum spikes by first smoothing raw RSI and then requiring its bar-to-bar slope to flip before any signal is allowed.
Slope-phase memory logic that locks out duplicate buy or sell markers until the RSI slope crosses back through zero, preventing over-trading during choppy swings.
Four distinct bar-coloring modes—Reversal Solid, Reversal Fade, Exceeding Bands, Classic Heat—plus a “None” option, so traders can choose whether to highlight the latest signal, show signal age, emphasize breakout bars, or view a continuous heat gradient within the bands.
A semi-transparent fill between the EMA and the upper/lower bands that visually frames the statistical zone and makes extremes immediately obvious.
Concise “Up” and “Down” labels that plot exactly when price re-enters a band with confirming momentum, keeping chart clutter to a minimum.
A real-time, five-metric scoring table (z-score, RSI slope, price vs. EMA, trend state, re-entry) that updates every two bars, displaying individual +1/–1/0 scores and an averaged Buy/Sell/Neutral verdict for complete transparency.
Calculations
Compute the fair-value EMA over fairLen bars.
Subtract that EMA from current price each bar to derive the raw deviation.
Over zLen bars, calculate the rolling mean and standard deviation of those deviations.
Convert each deviation into a z-score by subtracting the mean and dividing by the standard deviation.
Plot the upper and lower bands at ±zThreshold × standard deviation around the EMA.
Calculate raw RSI over rsiLen bars, then smooth it with an EMA of length rsiEmaLen.
Derive the RSI slope by taking the difference between the current and previous smoothed RSI.
Detect a potential reentry when price exits one of the bands on the prior bar and re-enters on the current bar.
Require that reentry coincide with an RSI slope flip (positive for a lower-band reentry, negative for an upper-band reentry).
On first valid reentry per momentum swing, fire a buy or sell signal and set a memory flag; reset that flag only when the RSI slope crosses back through zero.
For each bar, assign scores of +1, –1, or 0 for the z-score direction, RSI slope, price vs. EMA, trend-state, and reentry status.
Average those five scores; if the result exceeds +0.1, label “Buy,” if below –0.1, label “Sell,” otherwise “Neutral.”
Update bar colors, the semi-transparent fill, reversal labels, and the scoring table every two bars to reflect the latest calculations.
How It Actually Works
On each new candle, the EMA baseline and band widths update to reflect current volatility. The RSI is smoothed and its slope recalculated. The script then looks back one bar to see if price exited either band and forward to see if it reentered. If that reentry coincides with an appropriate RSI slope flip—and no signal has yet been generated in that swing—a concise label appears. Bar colors refresh according to your selected mode, and the scoring table updates to show which of the five conditions passed or failed, along with the overall verdict. This process repeats seamlessly at each bar, giving traders a continuous feed of disciplined, statistically filtered reversal cues.
Inputs
All parameters are fully user-configurable, allowing you to tailor sensitivity, lookbacks, and visuals to your trading style:
EMA length (fairLen): number of bars for the fair-value EMA; higher values smooth more but lag further behind price.
Z-Score lookback (zLen): window for calculating the mean and standard deviation of price deviations; longer lookbacks reduce noise but respond more slowly to new volatility.
Z-Score threshold (zThreshold): number of standard deviations defining the upper and lower bands; common default is 2.0 for roughly the outer 5 percent of moves.
Source (src): choice of price series (close, hl2, etc.) used for EMA, deviation, and RSI calculations.
RSI length (rsiLen): period for raw RSI calculation; shorter values react faster to momentum changes but can be choppier.
RSI EMA length (rsiEmaLen): period for smoothing raw RSI before taking its slope; higher values filter more noise.
Bar coloring mode (colorMode): select from None, Reversal Solid, Reversal Fade, Exceeding Bands, or Classic Heat to control how bars are shaded in relation to signals and band positions.
Show signals (showSignals): toggle on-chart “Up” and “Down” labels for reversal entries.
Show scoring table (enableTable): toggle the display of the five-metric breakdown table.
Table position (tablePos): choose which corner (Top Left, Top Right, Bottom Left, Bottom Right) hosts the scoring table.
Conclusion
By merging a normalized z-score framework, momentum slope confirmation, disciplined signal memory, flexible visuals, and transparent scoring into one Pine Script overlay, Uptrick: Z-Trend Bands offers a powerful yet intuitive tool for intraday mean-reversion trading. Its adaptability to real-time volatility and multi-layered filter logic deliver clear, high-confidence reversal cues without the clutter or confusion of simpler indicators.
Disclaimer
This indicator is provided solely for educational and informational purposes. It does not constitute financial advice. Trading involves substantial risk and may not be suitable for all investors. Past performance is not indicative of future results. Always conduct your own testing and apply careful risk management before trading live.
MirPapa_Library_ICTLibrary "MirPapa_Library_ICT"
GetHTFoffsetToLTFoffset(_offset, _chartTf, _htfTf)
GetHTFoffsetToLTFoffset
@description Adjust an HTF offset to an LTF offset by calculating the ratio of timeframes.
Parameters:
_offset (int) : int The HTF bar offset (0 means current HTF bar).
_chartTf (string) : string The current chart’s timeframe (e.g., "5", "15", "1D").
_htfTf (string) : string The High Time Frame string (e.g., "60", "1D").
@return int The corresponding LTF bar index. Returns 0 if the result is negative.
IsConditionState(_type, _isBull, _level, _open, _close, _open1, _close1, _low1, _low2, _low3, _low4, _high1, _high2, _high3, _high4)
IsConditionState
@description Evaluate a condition state based on type for COB, FVG, or FOB.
Overloaded: first signature handles COB, second handles FVG/FOB.
Parameters:
_type (string) : string Condition type ("cob", "fvg", "fob").
_isBull (bool) : bool Direction flag: true for bullish, false for bearish.
_level (int) : int Swing level (only used for COB).
_open (float) : float Current bar open price (only for COB).
_close (float) : float Current bar close price (only for COB).
_open1 (float) : float Previous bar open price (only for COB).
_close1 (float) : float Previous bar close price (only for COB).
_low1 (float) : float Low 1 bar ago (only for COB).
_low2 (float) : float Low 2 bars ago (only for COB).
_low3 (float) : float Low 3 bars ago (only for COB).
_low4 (float) : float Low 4 bars ago (only for COB).
_high1 (float) : float High 1 bar ago (only for COB).
_high2 (float) : float High 2 bars ago (only for COB).
_high3 (float) : float High 3 bars ago (only for COB).
_high4 (float) : float High 4 bars ago (only for COB).
@return bool True if the specified condition is met, false otherwise.
IsConditionState(_type, _isBull, _pricePrev, _priceNow)
IsConditionState
@description Evaluate FVG or FOB condition based on price movement.
Parameters:
_type (string) : string Condition type ("fvg", "fob").
_isBull (bool) : bool Direction flag: true for bullish, false for bearish.
_pricePrev (float) : float Previous price (for FVG/FOB).
_priceNow (float) : float Current price (for FVG/FOB).
@return bool True if the specified condition is met, false otherwise.
IsSwingHighLow(_isBull, _level, _open, _close, _open1, _close1, _low1, _low2, _low3, _low4, _high1, _high2, _high3, _high4)
IsSwingHighLow
@description Public wrapper for isSwingHighLow.
Parameters:
_isBull (bool) : bool Direction flag: true for bullish, false for bearish.
_level (int) : int Swing level (1 or 2).
_open (float) : float Current bar open price.
_close (float) : float Current bar close price.
_open1 (float) : float Previous bar open price.
_close1 (float) : float Previous bar close price.
_low1 (float) : float Low 1 bar ago.
_low2 (float) : float Low 2 bars ago.
_low3 (float) : float Low 3 bars ago.
_low4 (float) : float Low 4 bars ago.
_high1 (float) : float High 1 bar ago.
_high2 (float) : float High 2 bars ago.
_high3 (float) : float High 3 bars ago.
_high4 (float) : float High 4 bars ago.
@return bool True if swing condition is met, false otherwise.
AddBox(_left, _right, _top, _bot, _xloc, _colorBG, _colorBD)
AddBox
@description Draw a rectangular box on the chart with specified coordinates and colors.
Parameters:
_left (int) : int Left bar index for the box.
_right (int) : int Right bar index for the box.
_top (float) : float Top price coordinate for the box.
_bot (float) : float Bottom price coordinate for the box.
_xloc (string) : string X-axis location type (e.g., xloc.bar_index).
_colorBG (color) : color Background color for the box.
_colorBD (color) : color Border color for the box.
@return box Returns the created box object.
Addline(_x, _y, _xloc, _color, _width)
Addline
@description Draw a vertical or horizontal line at specified coordinates.
Parameters:
_x (int) : int X-coordinate for start (bar index).
_y (int) : float Y-coordinate for start (price).
_xloc (string) : string X-axis location type (e.g., xloc.bar_index).
_color (color) : color Line color.
_width (int) : int Line width.
@return line Returns the created line object.
Addline(_x, _y, _xloc, _color, _width)
Parameters:
_x (int)
_y (float)
_xloc (string)
_color (color)
_width (int)
Addline(_x1, _y1, _x2, _y2, _xloc, _color, _width)
Parameters:
_x1 (int)
_y1 (int)
_x2 (int)
_y2 (int)
_xloc (string)
_color (color)
_width (int)
Addline(_x1, _y1, _x2, _y2, _xloc, _color, _width)
Parameters:
_x1 (int)
_y1 (int)
_x2 (int)
_y2 (float)
_xloc (string)
_color (color)
_width (int)
Addline(_x1, _y1, _x2, _y2, _xloc, _color, _width)
Parameters:
_x1 (int)
_y1 (float)
_x2 (int)
_y2 (int)
_xloc (string)
_color (color)
_width (int)
Addline(_x1, _y1, _x2, _y2, _xloc, _color, _width)
Parameters:
_x1 (int)
_y1 (float)
_x2 (int)
_y2 (float)
_xloc (string)
_color (color)
_width (int)
AddlineMid(_type, _left, _right, _top, _bot, _xloc, _color, _width)
AddlineMid
@description Draw a midline between top and bottom for FVG or FOB types.
Parameters:
_type (string) : string Type identifier: "fvg" or "fob".
_left (int) : int Left bar index for midline start.
_right (int) : int Right bar index for midline end.
_top (float) : float Top price of the region.
_bot (float) : float Bottom price of the region.
_xloc (string) : string X-axis location type (e.g., xloc.bar_index).
_color (color) : color Line color.
_width (int) : int Line width.
@return line or na Returns the created line or na if type is not recognized.
GetHtfFromLabel(_label)
GetHtfFromLabel
@description Convert a Korean HTF label into a Pine Script timeframe string via handler library.
Parameters:
_label (string) : string The Korean label (e.g., "5분", "1시간").
@return string Returns the corresponding Pine Script timeframe (e.g., "5", "60").
IsChartTFcomparisonHTF(_chartTf, _htfTf)
IsChartTFcomparisonHTF
@description Determine whether a given HTF is greater than or equal to the current chart timeframe.
Parameters:
_chartTf (string) : string Current chart timeframe (e.g., "5", "15", "1D").
_htfTf (string) : string HTF timeframe (e.g., "60", "1D").
@return bool True if HTF ≥ chartTF, false otherwise.
CreateBoxData(_type, _isBull, _useLine, _top, _bot, _xloc, _colorBG, _colorBD, _offset, _htfTf, htfBarIdx, _basePoint)
CreateBoxData
@description Create and draw a box and optional midline for given type and parameters. Returns success flag and BoxData.
Parameters:
_type (string) : string Type identifier: "fvg", "fob", "cob", or "sweep".
_isBull (bool) : bool Direction flag: true for bullish, false for bearish.
_useLine (bool) : bool Whether to draw a midline inside the box.
_top (float) : float Top price of the box region.
_bot (float) : float Bottom price of the box region.
_xloc (string) : string X-axis location type (e.g., xloc.bar_index).
_colorBG (color) : color Background color for the box.
_colorBD (color) : color Border color for the box.
_offset (int) : int HTF bar offset (0 means current HTF bar).
_htfTf (string) : string HTF timeframe string (e.g., "60", "1D").
htfBarIdx (int) : int HTF bar_index (passed from HTF request).
_basePoint (float) : float Base point for breakout checks.
@return tuple(bool, BoxData) Returns a boolean indicating success and the created BoxData struct.
ProcessBoxDatas(_datas, _useMidLine, _closeCount, _colorClose)
ProcessBoxDatas
@description Process an array of BoxData structs: extend, record volume, update stage, and finalize boxes.
Parameters:
_datas (array) : array Array of BoxData objects to process.
_useMidLine (bool) : bool Whether to update the midline endpoint.
_closeCount (int) : int Number of touches required to close the box.
_colorClose (color) : color Color to apply when a box closes.
@return void No return value; updates are in-place.
BoxData
Fields:
_isActive (series bool)
_isBull (series bool)
_box (series box)
_line (series line)
_basePoint (series float)
_boxTop (series float)
_boxBot (series float)
_stage (series int)
_isStay (series bool)
_volBuy (series float)
_volSell (series float)
_result (series string)
LineData
Fields:
_isActive (series bool)
_isBull (series bool)
_line (series line)
_basePoint (series float)
_stage (series int)
_isStay (series bool)
_result (series string)
Neural Adaptive VWAPNeural Adaptive VWAP with ML Features is an advanced trading indicator that enhances traditional Volume Weighted Average Price (VWAP) calculations through machine learning-inspired adaptive algorithms and predictive volume modeling.
🌟 Key Features:
🧠 Machine Learning-Inspired Adaptation
Dynamic weight adjustment system that learns from prediction errors
Multi-feature volume prediction using time-of-day patterns, price momentum, and volatility
Adaptive learning mechanism that improves accuracy over time
📊 Enhanced VWAP Calculation
Combines actual and predicted volume for forward-looking VWAP computation
Session-based reset with proper daily anchoring
Confidence bands based on rolling standard deviation for dynamic support/resistance
🎯 Advanced Signal Generation
Volume-confirmed crossover signals to reduce false entries
Color-coded candle visualization based on VWAP position
Multi-level strength indicators (strong/weak bullish/bearish zones)
⚙️ Intelligent Feature Engineering
Normalized volume analysis with statistical z-score
Time-series pattern recognition for intraday volume cycles
Price momentum and volatility integration
Sigmoid activation functions for realistic predictions
📈 How It Works:
The indicator employs a sophisticated feature engineering approach that extracts meaningful patterns from:
Volume Patterns: Normalized volume analysis and historical comparisons
Temporal Features: Time-of-day and minute-based cyclical patterns
Market Dynamics: Price momentum, volatility, and rate of change
Adaptive Learning: Error-based weight adjustment similar to neural network training
Unlike static VWAP indicators, this system continuously adapts its calculation methodology based on real-time market feedback, making it more responsive to changing market conditions while maintaining the reliability of traditional VWAP analysis.
🔧 Customizable Parameters:
VWAP Length (1-200 bars)
Volume Pattern Lookback (5-50 periods)
Learning Rate (0.001-0.1) for adaptation speed
Prediction Horizon (1-10 bars ahead)
Adaptation Period for weight updates
📊 Visual Elements:
Blue Line: Adaptive VWAP with predictive elements
Red/Green Bands: Dynamic confidence zones
Colored Candles: Position-based strength visualization
Signal Arrows: Volume-confirmed entry points
Info Table: Real-time performance metrics and weight distribution
🎯 Best Use Cases:
Intraday Trading: Enhanced execution timing with volume prediction
Institutional-Style Execution: Improved VWAP-based order placement
Trend Following: Adaptive trend identification with confidence zones
Support/Resistance Trading: Dynamic levels that adjust to market conditions
MirPapa_Handler_HTFLibrary "MirPapa_Handler_HTF"
High Time Frame Handler Library:
Provides utilities for working with High Time Frame (HTF) and chart (LTF) conversions and data retrieval.
IsChartTFcomparisonHTF(_chartTf, _htfTf)
IsChartTFcomparisonHTF
@description
Determine whether the given High Time Frame (HTF) is greater than or equal to the current chart timeframe.
Parameters:
_chartTf (string) : The current chart’s timeframe string (examples: "5", "15", "1D").
_htfTf (string) : The High Time Frame string to compare (examples: "60", "1D").
@return
Returns true if HTF minutes ≥ chart minutes, false otherwise or na if conversion fails.
GetHTFrevised(_tf, _case)
GetHTFrevised
@description
Retrieve a specific bar value from a Higher Time Frame (HTF) series.
Supports current and historical OHLC values, based on a case identifier.
Parameters:
_tf (string) : The target HTF string (examples: "60", "1D").
_case (string) : A case string determining which OHLC value and bar offset to request:
"b" → HTF bar_index
"o" → HTF open
"h" → HTF high
"l" → HTF low
"c" → HTF close
"o1" → HTF open one bar ago
"h1" → HTF high one bar ago
"l1" → HTF low one bar ago
"c1" → HTF close one bar ago
… up to "o5", "h5", "l5", "c5" for five bars ago.
@return
Returns the requested HTF value or na if _case does not match any condition.
GetHTFfromLabel(_label)
GetHTFfromLabel
@description
Convert a Korean HTF label into a Pine Script-recognizable timeframe string.
Examples:
"5분" → "5"
"1시간" → "60"
"일봉" → "1D"
"주봉" → "1W"
"월봉" → "1M"
"연봉" → "12M"
Parameters:
_label (string) : The Korean HTF label string (examples: "5분", "1시간", "일봉").
@return
Returns the Pine Script timeframe string corresponding to the label, or "1W" if no match is found.
GetHTFoffsetToLTFoffset(_offset, _chartTf, _htfTf)
GetHTFoffsetToLTFoffset
@description
Adjust an HTF bar index and offset so that it aligns with the current chart’s bar index.
Useful for retrieving historical HTF data on an LTF chart.
Parameters:
_offset (int) : The HTF bar offset (0 means current HTF bar, 1 means one bar ago, etc.).
_chartTf (string) : The current chart’s timeframe string (examples: "5", "15", "1D").
_htfTf (string) : The High Time Frame string to align (examples: "60", "1D").
@return
Returns the corresponding LTF bar index after applying HTF offset. If result is negative, returns 0.
OA - Sigma BandsDescription:
The OA - Sigma Bands indicator is a fully adaptive, volatility-sensitive dynamic band system designed to detect price expansion and potential breakouts. Unlike traditional fixed-width Bollinger Bands, OA - Sigma Bands adjust their boundaries based on a combination of standard deviation (σ) and Average Daily Range (ADR), making them more responsive to real market behavior and shifts in volatility.
Key Concepts & Logic
This tool constructs three distinct band regions:
Sigma Bands (±σ):
Calculated using the standard deviation of the closing price over a user-defined lookback period. This acts as the core volatility filter to identify statistically significant price deviations.
ADR Zones (±ADR):
These zones provide an additional layer based on the percentage average of daily price ranges over the last 20 bars. They help visualize intraday or short-term expected volatility.
Dynamic Adjustment Logic:
When price breaks outside the upper/lower sigma or ADR boundaries for a defined number of bars (user input), the system recalibrates. This ensures that the bands evolve with volatility and don’t remain outdated in trending markets.
Inputs & Customization
Sigma Multiplier: Set how wide the sigma bands should be (default: 1.5).
Lookback Period: Controls how many bars are used to calculate the standard deviation (default: 200).
Break Confirmation Bars: Determines how many candles must close beyond a boundary to trigger band recalibration.
ADR Period: Internally fixed at 20 bars for stable short-term volatility measurement.
Full Color Customization: Customize the band colors and fill transparency to suit your chart style.
Benefits & Use Cases
Breakout Trading: Detect when price exits statistically significant ranges, confirming trend expansion.
Mean Reversion: Use the outer bands as potential reversion zones in sideways or low-volatility markets.
Volatility Awareness: Visually identify when price is compressed or expanding.
Dynamic Structure: The auto-updating nature makes it more reliable than static historical zones.
Overlay-Ready: Designed to sit directly on price charts with minimal clutter.
Disclaimer
This script is intended for educational and informational purposes only. It does not constitute investment advice, financial guidance, or a recommendation to buy or sell any security. Always perform your own research and apply proper risk management before making trading decisions.
If you enjoy this script or find it useful, feel free to give it or leave a comment!
Liquidity Sweep Candlestick Pattern with MA Filter📌 Liquidity Sweep Candlestick Pattern with MA Filter
This custom indicator detects liquidity sweep candlestick patterns—price action events where the market briefly breaks a previous candle’s high or low to trap traders—paired with optional filters such as moving averages, color change candles, and strictness rules for better signal accuracy.
🔍 What is a Liquidity Sweep?
A liquidity sweep occurs when the price briefly breaks the high or low of a previous candle and then reverses direction. These events often occur around key support/resistance zones and are used by institutional traders to trap retail positions before moving the price in the intended direction.
🟢 Bullish Liquidity Sweep Criteria
The current candle is bullish (closes above its open).
The low of the current candle breaks the low of the previous candle.
The candle closes above the previous candle’s open.
Optionally, in Strict mode, it must also close above the previous candle’s high.
Optionally, it can be filtered to only show if the candle changed color from the previous one (e.g., red to green).
Can be filtered to only show when the price is above or below a moving average (if MA filter is enabled).
🔴 Bearish Liquidity Sweep Criteria
The current candle is bearish (closes below its open).
The high of the current candle breaks the high of the previous candle.
The candle closes below the previous candle’s open.
Optionally, in Strict mode, it must also close below the previous candle’s low.
Optionally, it can be filtered to only show if the candle changed color from the previous one (e.g., green to red).
Can be filtered to only show when the price is above or below a moving average (if MA filter is enabled).
⚙️ Features & Customization
✅ Signal Strictness
Choose between:
Less Strict (default): Basic wick break and close conditions.
Strict: Must close beyond the wick of the previous candle.
✅ Color Change Candles Only
Enable this to only show patterns when the candle color changes (e.g., from red to green or green to red). Helps filter fake-outs.
✅ Moving Average Filter (optional)
Supports several types of MAs: SMA, EMA, WMA, VWMA, RMA, HMA
Choose whether signals should only appear above or below the selected moving average.
✅ Custom Visuals
Show short (BS) or full (Bull Sweep / Bear Sweep) labels
Plot triangles or arrows to represent bullish and bearish sweeps
Customize label and shape colors
Optionally show/hide the moving average line
✅ Alerts
Includes alert options for:
Bullish sweep
Bearish sweep
Any sweep
📈 How to Use
Add the indicator to your chart.
Configure the strictness, color change, or MA filters based on your strategy.
Observe signals where price is likely to reverse after taking out liquidity.
Use with key support/resistance levels, order blocks, or volume zones for confluence.
⚠️ Note
This tool is for educational and strategy-building purposes. Always confirm signals with other indicators, context, and sound risk management.
RSI Multi-TF TabRSI Multi-Timeframe Table 📊
A tool for multi-timeframe RSI analysis with visual overbought/oversold level highlighting.
Description
This indicator calculates the Relative Strength Index (RSI) for the current chart and displays RSI values across five additional timeframes (15m, 1h, 4h, 1d, 1w) in a dynamic table. The color-coded system simplifies identifying overbought (>70), oversold (<30), and neutral zones. Visual signals on the chart enhance analysis for the current timeframe.
Key Features
✅ Multi-Timeframe Analysis :
Track RSI across 15m, 1h, 4h, 1d, and 1w in a compact table.
Color-coded alerts:
🔴 Red — Overbought (potential pullback),
🔵 Blue — Oversold (potential rebound),
🟡 Yellow — Neutral zone.
✅ Visual Signals :
Background shading for oversold/overbought zones on the main chart.
Horizontal lines at 30 and 70 levels for reference.
✅ Customizable Settings :
Adjust RSI length (default: 14), source (close, open, high, etc.), and threshold levels.
How to Use
Table Analysis :
Compare RSI values across timeframes to spot divergences (e.g., overbought on 15m vs. oversold on D).
Use colors for quick decisions.
Chart Signals :
Blue background suggests bullish potential (oversold), red hints at bearish pressure (overbought).
Always confirm with other tools (volume, trends, or candlestick patterns).
Examples :
RSI(1h) > 70 while RSI(4h) < 30 → Possible reversal upward.
Sustained RSI(1d) above 50 may indicate a bullish trend.
Settings
RSI Length : Period for RSI calculation (default: 14).
RSI Source : Data source (close, open, high, low, hl2, hlc3, ohlc4).
Overbought/Oversold Levels : Thresholds for alerts (default: 70/30).
Important Notes
No direct trading signals : Use this as an analytical tool, not a standalone strategy.
Test strategies historically and consider market context before trading.
Swing High Low Detector by RV5📄 Description
The Swing High Low Detector is a visual indicator that automatically detects and displays swing highs and swing lows on the chart. Swings are determined based on configurable strength parameters (number of bars before and after a high/low), allowing users to fine-tune the sensitivity of the swing points.
🔹 Current swing levels are shown as solid (or user-defined) lines that dynamically extend until broken.
🔹 Past swing levels are preserved as dashed/dotted lines once broken, allowing traders to see previous support/resistance zones.
🔹 Customizable line colors, styles, and thickness for both current and past levels.
This indicator is useful for:
Identifying key market structure turning points
Building breakout strategies
Spotting trend reversals and swing zones
⚙️ How to Use
1. Add the indicator to any chart on any timeframe.
2. Adjust the Swing Strength inputs to change how sensitive the detector is:
A higher value will filter out smaller moves.
A lower value will capture more frequent swing points.
3. Customize the line styles for visual preference.
Choose different colors, line styles (solid/dashed/dotted), and thickness for:
Current Swing Highs (SH)
Past Swing Highs
Current Swing Lows (SL)
Past Swing Lows
4. Observe:
As new swing highs/lows are detected, the indicator draws a new current level.
Once price breaks that level, the line is archived as a past level and a new current swing is drawn.
✅ Features
Fully customizable styling for all lines
Real-time updates and automatic level tracking
Supports all chart types and instruments
👨💻 Credits
Script logic and implementation by RV5. This script was developed as a tool to improve price action visualization and trading structure clarity. Not affiliated with any financial institution. Use responsibly.
Pin Bar Reversal StrategyStrategy: Pin Bar Reversal with Trend Filter
One effective high-probability setup is a Pin Bar reversal in the direction of the larger trend. A pin bar is a candlestick with a tiny body and a long wick, signaling a sharp rejection of price
By itself, a pin bar often marks a potential reversal, but not all pin bars lead to profitable moves. To boost reliability, this strategy trades pin bars only when they align with the prevailing trend – for example, taking a bullish pin bar while the market is in an uptrend, or a bearish pin bar in a downtrend. The trend bias can be determined by a long-term moving average or higher timeframe analysis.
Why it works: In an uptrend, a bullish pin bar after a pullback often indicates that sellers tried to push price down but failed, and buyers are resuming control. Filtering for pin bars near key support or moving averages further improves odds of success. This aligns the entry with both a strong price pattern and the dominant market direction, yielding a higher win rate. The pin bar’s own structure provides natural levels for stop and target placement, keeping risk management straightforward.
Example Setup:
USDCHF - 4 Hour Chart
Trend SMA 12
Max Body - 34
Min Wick - 66
ATR -15
ATR Stop Loss Multiplier - 2.3
ATR Take Profit Multiplier - 2.9
Minimum ATR to Enter - 0.0025
[Top] Simple Position + SL CalculatorThis indicator is a user-friendly tool designed to help traders easily calculate optimal position sizing, determine suitable stop-loss levels, and quantify maximum potential losses in dollar terms based on their personalized trading parameters.
Key Features:
Position Size Calculation: Automatically computes the number of shares to purchase based on the trader’s total account size and specified percentage of the account allocated per trade.
Stop-Loss Level: Suggests an appropriate stop-loss price point calculated based on the trader’s defined risk percentage per trade.
Max Loss Visualization: Clearly displays the maximum potential loss (in dollars) should the stop-loss be triggered.
Customizable Interface: Provides the flexibility to place the calculation table in different chart positions (Top Left, Top Right, Bottom Left, Bottom Right) according to user preference.
How to Use:
Enter your total Account Size.
Set the desired Position Size as a percentage of your account. (Typically, 1%–5% per trade is recommended for cash accounts.)
Define the Risk per Trade percentage (commonly between 0.05%–0.5%).
Choose your preferred Table Position to comfortably integrate with your trading chart.
Note:
If you identify a technical support level below the suggested stop-loss point, consider reducing your position size to manage the increased risk effectively.
Keep in mind that the calculations provided by this indicator are based solely on standard industry best practices and the specific inputs entered by you. They do not account for market volatility, news events, or any other factors outside the provided parameters. Always complement this indicator with sound technical and fundamental analysis.
Canuck Trading Projection IndicatorCanuck Trading Projection Indicator
Overview
The Canuck Trading Projection Indicator is a powerful PineScript v6 tool designed for TradingView to project potential bullish and bearish price trajectories based on historical price and volume movements. It provides traders with actionable insights by estimating future price targets and assigning confidence levels to each outlook, helping to identify probable market directions across any timeframe. Ideal for both short-term and long-term traders, this indicator combines momentum analysis, RSI filtering, support/resistance detection, and time-weighted trend analysis to deliver robust projections.
Features
Bullish and Bearish Projections: Forecasts price targets for upward (bullish) and downward (bearish) movements over a user-defined projection period (default 20 bars).
Confidence Levels: Assigns percentage confidence scores to each outlook, reflecting the likelihood of the projected price based on historical trends, volatility, and volume.
RSI Filter: Incorporates a 14-period Relative Strength Index (RSI) to validate trends, requiring RSI > 50 for bullish and RSI < 50 for bearish signals.
Support/Resistance Detection: Adjusts confidence levels when projections are near key swing highs/lows (within 2% of average price), boosting confidence by 5% for alignments.
Time-Based Weighting: Prioritizes recent price movements in trend analysis, giving more weight to newer bars for improved relevance.
Customizable Inputs: Allows users to tailor lookback period, projection bars, RSI period, confidence threshold, colors, and label positioning.
Forced Label Spacing: Prevents overlap of bullish and bearish text labels, even for tight projections, using fixed vertical slots when price differences are small (<2% of average price).
Timeframe Flexibility: Works seamlessly across all TradingView timeframes (e.g., 30-minute, hourly, daily, weekly, monthly), adapting projections to the chart’s resolution.
Clean Visualization: Displays projections as green (bullish) and red (bearish) dashed lines, with non-overlapping text labels at the projection endpoints showing price targets and confidence levels.
How It Works
The indicator analyzes historical price and volume data over a user-defined lookback period (default 50 bars) to calculate:
Momentum: Combines price changes and volume to assess trend strength, using a weighted moving average (WMA) for directional bias.
Trend Analysis: Counts bullish (price up, volume above average, RSI > 50) and bearish (price down, volume above average, RSI < 50) trends, weighting recent bars more heavily.
Projections:
Bullish Slope: Positive or flat when momentum is upward, scaled by price change and momentum intensity.
Bearish Slope: Negative or flat when momentum is downward, amplified by bearish confidence for stronger projections.
Projects prices forward by 20 bars (default) using current close plus slope times projection bars.
Confidence Levels:
Base confidence derived from the proportion of bullish/bearish trends, with a 5% minimum to avoid zero confidence.
Adjusted by volatility (lower volatility increases confidence), volume trends, and proximity to support/resistance levels.
Visualization:
Draws projection lines from the current close to the 20-bar future target.
Places text labels at line endpoints, showing price targets and confidence percentages, with forced spacing for readability.
Input Parameters
Lookback Period (default: 50): Number of bars for historical analysis (minimum 10).
Projection Bars (default: 20): Number of bars to project forward (minimum 5).
Confidence Threshold (default: 0.6): Minimum confidence for strong trend indication (0.1 to 1.0).
Bullish Projection Line Color (default: Green): Color for bullish projection line and label.
Bearish Projection Line Color (default: Red): Color for bearish projection line and label.
RSI Period (default: 14): Period for RSI momentum filter (minimum 5).
Label Vertical Offset (%) (default: 1.0): Base offset for labels as a percentage of price range (0.1% to 5.0%).
Minimum Label Spacing (%) (default: 2.0): Minimum vertical spacing between labels for tight projections (0.5% to 10.0%).
Usage Instructions
Add to Chart: Copy the script into TradingView’s Pine Editor, save, and add the indicator to your chart.
Select Timeframe: Apply to any timeframe (e.g., 30-minute, hourly, daily, weekly, monthly) to match your trading strategy.
Interpret Outputs:
Green Line/Label: Bullish price target and confidence (e.g., "Bullish: 414.37, Confidence: 35%").
Red Line/Label: Bearish price target and confidence (e.g., "Bearish: 279.08, Confidence: 41.3%").
Higher confidence indicates a stronger likelihood of the projected outcome.
Adjust Inputs:
Modify Lookback Period to focus on shorter/longer historical trends (e.g., 20 for short-term, 100 for long-term).
Change Projection Bars to adjust forecast horizon (e.g., 10 for shorter, 50 for longer).
Tweak RSI Period or Confidence Threshold for sensitivity to momentum or trend strength.
Customize Colors for visual preference.
Increase Minimum Label Spacing if labels overlap in volatile markets.
Combine with Analysis: Use alongside other indicators (e.g., moving averages, Bollinger Bands) or fundamental analysis to confirm signals, as projections are probabilistic.
Example: TSLA Across Timeframes
Using live TSLA data (close ~346.46 USD, May 31, 2025), the indicator produces:
30-Minute: Bullish 341.93 (13.3%), Bearish 327.96 (86.7%) – Strong bearish sentiment due to intraday volatility.
1-Hour: Bullish 342.00 (33.9%), Bearish 327.50 (62.3%) – Bearish but less intense, reflecting hourly swings.
4-Hour: Bullish 345.52 (73.4%), Bearish 344.44 (19.0%) – Flat outlook, indicating consolidation.
Daily: Bullish 391.26 (68.8%), Bearish 302.22 (31.2%) – Bullish bias from recent uptrend, bearish tempered by longer lookback.
Weekly: Bullish 414.37 (35.0%), Bearish 279.08 (41.3%) – Wide range, reflecting annual volatility.
Monthly: Bullish 396.70 (54.9%), Bearish 296.93 (10.2%) – Long-term bullish optimism.
These results align with market dynamics: short-term intervals capture volatility, while longer intervals smooth trends, providing balanced outlooks.
Notes
Accuracy: Projections are estimates based on historical data and should be used with other analysis tools. Confidence levels indicate likelihood, not certainty.
Timeframe Sensitivity: Short-term intervals (e.g., 30-minute) show larger price swings and higher confidence due to volatility, while longer intervals (e.g., monthly) are more stable.
Customization: Adjust inputs to match your trading style (e.g., shorter lookback for day trading, longer for swing trading).
Performance: Tested on volatile stocks like TSLA, NVIDIA, and others, ensuring robust performance across markets.
Limitations: May produce conservative bearish projections in strong uptrends due to momentum weighting. Adjust lookback or projection_bars for sensitivity.
Feedback
If you encounter issues (e.g., label overlap, projection mismatches), please share your timeframe, settings, or a screenshot. Suggestions for enhancements (e.g., additional filters, visual tweaks) are welcome!
Disclaimer
The Canuck Trading Projection Indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves significant risks, and past performance is not indicative of future results. Always perform your own due diligence and consult a qualified financial advisor before making trading decisions.
Trend Scanner ProTrend Scanner Pro, Robust Trend Direction and Strength Estimator
Trend Scanner Pro is designed to evaluate the current market trend with maximum robustness, providing both direction and strength based on statistically reliable data.
This indicator builds upon the core logic of a previous script I developed, called Best SMA Finder. While the original script focused on identifying the most profitable SMA length based on backtested trade performance, Trend Scanner Pro takes that foundation further to serve a different purpose: analyzing and quantifying the actual trend state in real time.
It begins by testing hundreds of SMA lengths, from 10 to 1000 periods. Each one is scored using a custom robustness formula that combines profit factor, number of trades, and win rate. Only SMAs with a sufficient number of trades are retained, ensuring statistical validity and avoiding curve fitting.
The SMA with the highest robustness score is selected as the dynamic reference point. The script then calculates how far the price deviates from it using rolling standard deviation, assigning a trend strength score from -5 (strong bearish) to +5 (strong bullish), with 0 as neutral.
Two detection modes are available:
Slope mode, based on SMA slope reversals
Bias mode, based on directional shifts relative to deviation zones
Optional features:
Deviation bands for visual structure
Candle coloring to reflect trend strength
Compact table showing real-time trend status
This tool is intended for traders who want an adaptive, objective, and statistically grounded assessment of market trend conditions.
