9:15 Range with 0.09% BufferThis strategy is based on the first 9:15 AM candle for Nifty, which is considered a key reference point (also called the "GAN level entry"). It defines a range around the high and low of the 9:15 candle with a 0.09% buffer on both sides.
The upper buffer level acts as a potential resistance.
The lower buffer level acts as a potential support.
When the price crosses above the upper buffer, it signals a possible entry for a Call option (CE) or a long position.
When the price crosses below the lower buffer, it signals a possible entry for a Put option (PE) or a short position.
This approach helps traders identify early breakout opportunities based on the opening candle range, aiming to capture momentum moves in either direction during the trading session.
Indicadores e estratégias
Pre Market High/Low LevelsPre Market High & Pre Market Low By Jadra
Pre Market High/Low Levels Indicator
This indicator automatically identifies pre-market high and low levels (4:00-9:30 AM ET) and marks them with blue horizontal lines that extend throughout the entire trading session. Perfect for NYSE and NASDAQ traders who use these key levels as support and resistance. Features color-coded backgrounds: yellow for pre-market, transparent for regular hours, and blue for post-market. Lines remain visible from pre-market through market close, providing constant visual references for making trading decisions based on these important psychological levels. Essential tool for day traders focusing on overnight price action and gap analysis in US equity markets.
Ethereum Rainbow Chart (9 Levels with Legend)The Ethereum Rainbow Chart is a long-term, color-coded chart that displays Ethereum’s price on a logarithmic scale to show historical trends and growth patterns. It uses colored bands to highlight different price zones, helping to visualize how ETH’s price has moved over time without focusing on short-term fluctuations.
Essa - Multi-Timeframe LevelsEnhanced Multi‐Timeframe Levels
This indicator plots yearly, quarterly and monthly highs, lows and midpoints on your chart. Each level is drawn as a horizontal line with an optional label showing “ – ” (for example “Apr 2025 High – 1.2345”). If two or more timeframes share the same price (within two ticks), they are merged into a single line and the label lists each timeframe.
A distance table can be shown in any corner of the chart. It lists up to five active levels closest to the current closing price and shows for each level:
level name (e.g. “May 2025 Low”)
exact price
distance in pips or points (calculated according to the instrument’s tick size)
percentage difference relative to the close
Alerts can be enabled so that whenever price comes within a user-specified percentage of any level (for example 0.1 %), an alert fires. Once price decisively crosses a level, that level is marked as “broken” so it does not trigger again. Built-in alertcondition hooks are also provided for definite breaks of the current monthly, quarterly and yearly highs and lows.
Monthly lookback is configurable (default 6 months), and once the number of levels exceeds a cap (calculated as 20 + monthlyLookback × 3), the oldest levels are automatically removed to avoid clutter. Line widths and colours (with adjustable opacity for quarterly and monthly) can be set separately for each timeframe. Touches of each level are counted internally to allow future extension (for example visually emphasising levels with multiple touches).
HARSI PRO v2 - Advanced Adaptive Heikin-Ashi RSI OscillatorThis script is a fully re-engineered and enhanced version of the original Heikin-Ashi RSI Oscillator created by JayRogers. While it preserves the foundational concept and visual structure of the original indicatorusing Heikin-Ashi-style candles to represent RSI movementit introduces a range of institutional-grade engines and real-time analytics modules.
The core idea behind HARSI is to visualize the internal structure of RSI behavior using candle representations. This gives traders a clearer sense of trend continuity, exhaustion, and momentum inflection. In this upgraded version, the system is extended far beyond basic visualization into a comprehensive diagnostic and context-tracking tool.
Core Enhancements and Features
1. Heikin-Ashi RSI Candles
The base HARSI logic transforms RSI values into open, high, low, and close components, which are plotted as Heikin-Ashi-style candles. The open values are smoothed with a user-controlled bias setting, and the high/low are calculated from zero-centered RSI values.
2. Smoothed RSI Histogram and Plot
A secondary RSI plot and histogram are available for traditional RSI interpretation, optionally smoothed using a custom midpoint EMA process.
3. Dynamic Stochastic RSI Ribbon
The indicator optionally includes a smoothed Stochastic RSI ribbon with directional fill to highlight acceleration and reversal zones.
4. Real-Time Meta-State Engine
This engine determines the current market environmentneutral, breakout, or reversalbased on multiple adaptive conditions including volatility compression, momentum thrust, volume behavior, and composite reversal scoring.
5. Adaptive Overbought/Oversold Zone Engine
Instead of using fixed RSI thresholds, this engine dynamically adjusts OB/OS boundaries based on recent RSI range and normalized price volatility. This makes the OB/OS levels context-sensitive and more accurate across different instruments and regimes.
6. Composite Reversal Score Engine
A real-time score between 0 and 5 is generated using four components:
* OB/OS proximity (zone score)
* RSI slope behavior
* Volume state (burst or exhaustion)
* Trend continuation penalty based on position versus trend bias
This score allows for objective filtering of reversal zones and breakout traps.
7. Kalman Velocity Filter
A Kalman-style adaptive smoothing filter is applied to RSI for calculating velocity and acceleration. This allows for real-time detection of stalls and thrusts in RSI behavior.
8. Predictive Breakout Estimator
Uses ATR compression and RSI thrusting conditions to detect likely breakout environments. This logic contributes to the Meta-State Engine and the Breakout Risk dashboard metric.
9. Volume Acceleration Model
Real-time detection of volume bursts and fades based on VWMA baselines. Volume exhaustion warnings are used to qualify or disqualify reversals and breakouts.
10. Trend Bias and Regime Detection
Uses RSI slope, HARSI body impulse, and normalized ATR to classify the current trend state and directional bias. This forms the basis for filtering false reversals during strong trends.
11. Dashboard with Tooltips
A clean, table displays six key metrics in real time:
* Meta State
* Reversal Score
* Trend Bias
* Volume State
* Volatility Regime
* Breakout Risk
Each cell includes a descriptive tooltip explaining why the value is being shown based on internal state calculations.
How It Works Internally
* The system calculates a zero-centered RSI and builds candle structures using high, low, and smoothed open/close values.
* Volatility normalization is used throughout the script, including ATR-based thresholds and dynamic scaling of OB/OS zones.
* Momentum is filtered through smoothed slope calculations and HARSI body size measurements.
* Volume activity is compared against VWMA using configurable multipliers to detect institutional-level activity or exhaustion.
* Each regime detection module contributes to a centralized metaState classifier that determines whether the environment is conducive to reversal, breakout, or neutral action.
* All major signal and context values are continuously updated in a dashboard table with logic-driven color coding and tooltips.
Based On and Credits
This script is based on the original Heikin-Ashi RSI Oscillator by JayRogers . All visual elements from the original version, including candle plotting and color configurations, have been retained and extended. Significant backend enhancements were added by AresIQ for the 2025 release. The script remains open-source under the original attribution license. Credit to JayRogers is preserved and required for any derivative versions.
The LEAP Contest - Symbol & Max Position Table TrackerDescription:
This indicator tracks the maximum contracts allowed to be traded for TradingView’s *"The Leap"* Contest. It displays a horizontal table at the bottom right of your chart showing up to 20 symbols along with their maximum allowable open contract positions.
Use case:
Designed specifically for traders participating in *The Leap* Contest on TradingView.
Users need to enter the symbol and the maximum contracts allowed for that symbol in the settings menu for each new contest.
It provides a quick reference to ensure compliance with contest rules on maximum position sizes.
How it works:
The table shows two rows: the top row displays the symbol name, and the bottom row shows the max contract limit.
If the currently loaded chart symbol matches any symbol in the list, its text color changes to yellow .
Customization:
Symbols and limits must be updated in the indicator’s settings before each contest to reflect the current rules.
Interpolated Median Volatility LSMA | OttoThis indicator combines trend-following and volatility analysis by enhancing traditional LSMA with percentile-based linear interpolation applied to both the Least Squares Moving Average (LSMA) and standard deviation. Rather than relying on raw values, it uses the interpolated median (50th percentile) to smooth out noise while preserving sensitivity to significant price shifts. This approach produces a cleaner trend signal that remains responsive to real market changes, adapts to evolving volatility conditions, and improves the accuracy of breakout detection.
Core Concept
The indicator builds on these core components:
LSMA (Least Squares Moving Average): A linear regression-based moving average that fits line using user selected source over user defined period. It offers a smoother and more reactive trend signal compared to standard moving averages.
Standard Deviation shows how much price varies from the mean. In this indicator, it’s used to measure market volatility.
Volatility Bands: Instead of traditional Bollinger-style bands, this script calculates custom upper and lower bands using percentile-based linear interpolation on both the LSMA and standard deviation. This method produces smoother bands that filter out noise while remaining adaptive to meaningful price movements, making them more aligned with real market behavior and helping reduce false signals.
Percentile interpolation estimates a specific percentile (like the median — the 50th percentile) from a set of values — even when that percentile doesn't fall exactly on one data point. Instead of selecting a single nearest value, it calculates a smoothed value between nearby points. In this script, it’s used to find the median of past LSMA and standard deviation values, reducing the impact of outliers and smoothing the trend and volatility signals for more robust results.
Signal Logic: A long signal is identified when close price goes above the upper band, and a short signal when close price goes below the lower band.
⚙️ Inputs
Source: The price source used in calculations
LSMA Length: Period for calculating LSMA
Standard Deviation Length: Period for calculating volatility
Percentile Length: Period used for interpolating percentile values of LSMA and standard deviation
Multiplier: Controls the width of the bands by scaling the interpolated standard deviation
📈 Visual Output
Colored LSMA Line: Changes color based on signal (green for bullish, purple for bearish)
Upper & Lower Bands: Volatility bands calculated using interpolated values (green for bullish, purple for bearish)
Bar Coloring: Price bars are colored to reflect signal state (green for bullish, purple for bearish)
Optional Candlestick Overlay: Enhances visual context by coloring candles to match the signal state (green for bullish, purple for bearish)
How to Use
Add the indicator to your chart and look for signals when close price goes above or below the bands.
Long Signal: close Price goes above the upper band
Short Signal: close Price goes below the lower band
🔔 Alerts:
This script supports alert conditions for long and short signals. You can set alerts based on band crossovers to be notified of potential entries/exits.
⚠️ Disclaimer:
This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate strategies before applying them in live markets. Use at your own risk.
ATR-InfoWHAT IT SHOWS
- ATR (): Average True Range of the chosen timeframe, printed with the instrument’s native tick precision (format.mintick).
- ATR % PRICE: ATR divided by the latest close, multiplied by 100 – the range as a percentage of current price.
- LEN / TF: The ATR length and timeframe you selected (shown in small print).
INPUTS
- ATR Length (default 14)
- ATR Timeframe (for example 60, D, W)
- Design settings: table position, font size, colours, border
EXAMPLES
BTC-USD: price 67 800, ATR 2 450, ATR % 3.6
NQ E-Mini: price 18 230, ATR 355, ATR % 1.9
CL WTI: price 76.40, ATR 2.10, ATR % 2.8
EUR-USD: price 1.0860, ATR 0.0075, ATR % 0.69
USE CASES
Volatility-adjusted stops: place your stop roughly one ATR beyond the entry price.
Position sizing: money at risk divided by ATR gives the number of contracts or coins.
Market selection: trade assets only when their ATR % sits in your preferred range.
Strategy filter: trigger entries or exits only when ATR % crosses a chosen threshold.
LIMITS
ATR is descriptive; it does not predict future moves.
Illiquid symbols may show exaggerated ATR spikes.
ATR % ignores differing session lengths (24/7 crypto versus exchange-traded hours).
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.
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
If you find this helpful, please hit Follow and 🚀button —I release a new scripts every week.
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.
