Range & Consolidation DetectorHello friends,
I’m excited to share my latest discovery with you — the Range & Consolidation Detector. This script is built on a unique methodology I’m truly proud of. It uses no traditional indicators like ADX, RSI, or ATR — just pure statistics and mathematics under the hood. No parameters to tweak, no settings to guess — it just works, right out of the box.
🛠️ How It Works
At its core is a proprietary formula that reliably identifies ranging conditions across all tickers and timeframes. It’s simple, robust, and consistent — exactly what traders need to spot sideways markets without noise or lag.
🔥 Key Features
Pine Script v6 – Uses the latest version for maximum performance
Zero configuration – No inputs to adjust, no hidden settings — the algorithm works automatically
Optimized performance – Runs efficiently for smooth charting
Universal compatibility – Works flawlessly on any asset and timeframe, in every market condition — from euphoric peaks to choppy ranges
📸 Visual Examples
If you’d like access or have any questions, feel free to reach out to me directly via DM.
👋 Good luck and happy trading!
Statistics
Alpha - Combined BreakoutThis Pine Script indicator, "Alpha - Combined Breakout," is a combination between Smart Money Breakout Signals and UT Bot Alert, The UT Bot Alert indicator was initially developer by Yo_adriiiiaan
The idea of original code belongs HPotter.
This Indicator helps you identify potential trading opportunities by combining two distinct strategies: Smart Money Breakout and a modified UT Bot (likely a variation of the Ultimate Trend Bot). It provides visual signals, draws lines for potential take profit (TP) and stop loss (SL) levels, and includes a dashboard to track performance metrics.
Tutorial:
Understanding and Using the "Alpha - Combined Breakout" Indicator
This indicator is designed for traders looking for confirmation of market direction and potential entry/exit points by blending structural analysis with a trend-following oscillator.
How it Works (General Concept)
The indicator combines two main components:
Smart Money Breakout: This part identifies significant breaks in market structure, which "smart money" traders often use to gauge shifts in supply and demand. It looks for higher highs/lows or lower highs/lows and flags when these structural points are broken.
UT Bot: This is a trend-following component that generates buy and sell signals based on price action relative to an Average True Range (ATR) based trailing stop.
You can choose to use these signals independently or combined to generate trading alerts and visual cues on your chart. The dashboard provides a quick overview of how well the signals are performing based on your chosen settings and display mode.
Parameters and What They Do
Let's break down each input parameter:
1. Smart Money Inputs
These settings control how the indicator identifies market structure and breakouts.
swingSize (Market Structure Time-Horizon):
What it does: This integer value defines the number of candles used to identify significant "swing" (pivot) points—highs and lows.
Effect: A larger swingSize creates a smoother market structure, focusing on longer-term trends. This means signals might appear less frequently and with some delay but could be more reliable for higher timeframes or broader market movements. A smaller swingSize will pick up more minor market structure changes, leading to more frequent but potentially noisier signals, suitable for lower timeframes or scalping.
Analogy: Think of it like a zoom level on your market structure map. Higher values zoom out, showing only major mountain ranges. Lower values zoom in, showing every hill and bump.
bosConfType (BOS Confirmation Type):
What it does: This string input determines how a Break of Structure (BOS) is confirmed. You have two options:
'Candle Close': A breakout is confirmed only if a candle's closing price surpasses the previous swing high (for bullish) or swing low (for bearish).
'Wicks': A breakout is confirmed if any part of the candle (including its wick) surpasses the previous swing high or low.
Effect: 'Candle Close' provides stronger, more conservative confirmation, as it implies sustained price movement beyond the structure. 'Wicks' provides earlier, more aggressive signals, as it captures momentary breaches of the structure.
Analogy: Imagine a wall. 'Candle Close' means the whole person must get over the wall. 'Wicks' means even a finger touching over the top counts as a breach.
choch (Show CHoCH):
What it does: A boolean (true/false) input to enable or disable the display of "Change of Character" (CHoCH) labels. CHoCH indicates the first structural break against the current dominant trend.
Effect: When true, it helps identify early signs of a potential trend reversal, as it marks where the market's "character" (its tendency to make higher highs/lows or lower lows/highs) first changes.
BULL (Bullish Color) & BEAR (Bearish Color):
What they do: These color inputs allow you to customize the visual appearance of bullish and bearish signals and lines drawn by the Smart Money component.
Effect: Purely cosmetic, helps with visual identification on the chart.
sm_tp_sl_multiplier (SM TP/SL Multiplier (ATR)):
What it does: A float value that acts as a multiplier for the Average True Range (ATR) to calculate the Take Profit (TP) and Stop Loss (SL) levels specifically when you're in "Smart Money Only" mode. It uses the ATR calculated by the UT Bot's nLoss_ut as its base.
Effect: A higher multiplier creates wider TP/SL levels, potentially leading to fewer trades but larger wins/losses. A lower multiplier creates tighter TP/SL levels, potentially leading to more frequent but smaller wins/losses.
2. UT Bot Alerts Inputs
These parameters control the behavior and sensitivity of the UT Bot component.
a_ut (UT Key Value (Sensitivity)):
What it does: This integer value adjusts the sensitivity of the UT Bot.
Effect: A higher value makes the UT Bot less sensitive to price fluctuations, resulting in fewer and potentially more reliable signals. A lower value makes it more sensitive, generating more signals, which can include more false signals.
Analogy: Like a noise filter. Higher values filter out more noise, keeping only strong signals.
c_ut (UT ATR Period):
What it does: This integer sets the look-back period for the Average True Range (ATR) calculation used by the UT Bot. ATR measures market volatility.
Effect: This period directly influences the calculation of the nLoss_ut (which is a_ut * xATR_ut), thus defining the distance of the trailing stop loss and take profit levels. A longer period makes the ATR smoother and less reactive to sudden price spikes. A shorter period makes it more responsive.
h_ut (UT Signals from Heikin Ashi Candles):
What it does: A boolean (true/false) input to determine if the UT Bot calculations should use standard candlestick data or Heikin Ashi candlestick data.
Effect: Heikin Ashi candles smooth out price action, often making trends clearer and reducing noise. Using them for UT Bot signals can lead to smoother, potentially delayed signals that stay with a trend longer. Standard candles are more reactive to raw price changes.
3. Line Drawing Control Buttons
These crucial boolean inputs determine which type of signals will trigger the drawing of TP/SL/Entry lines and flags on your chart. They act as a priority system.
drawLinesUtOnly (Draw Lines: UT Only):
What it does: If checked (true), lines and flags will only be drawn when the UT Bot generates a buy/sell signal.
Effect: Isolates UT Bot signals for visual analysis.
drawLinesSmartMoneyOnly (Draw Lines: Smart Money Only):
What it does: If checked (true), lines and flags will only be drawn when the Smart Money Breakout logic generates a bullish/bearish breakout.
Effect: Overrides drawLinesUtOnly if both are checked. Isolates Smart Money signals.
drawLinesCombined (Draw Lines: UT & Smart Money (Combined)):
What it does: If checked (true), lines and flags will only be drawn when both a UT Bot signal AND a Smart Money Breakout signal occur on the same bar.
Effect: Overrides both drawLinesUtOnly and drawLinesSmartMoneyOnly if checked. Provides the strictest entry criteria for line drawing, looking for strong confluence.
Dashboard Metrics Explained
The dashboard provides performance statistics based on the lines drawing control button selected. For example, if "Draw Lines: UT Only" is active, the dashboard will show stats only for UT Bot signals.
Total Signals: The total number of buy or sell signals generated by the selected drawing mode.
TP1 Win Rate: The percentage of signals where the price reached Take Profit 1 (TP1) before hitting the Stop Loss.
TP2 Win Rate: The percentage of signals where the price reached Take Profit 2 (TP2) before hitting the Stop Loss.
TP3 Win Rate: The percentage of signals where the price reached Take Profit 3 (TP3) before hitting the Stop Loss. (Note: TP1, TP2, TP3 are in order of distance from entry, with TP3 being furthest.)
SL before any TP rate: This crucial metric shows the number of times the Stop Loss was hit / the percentage of total signals where the stop loss was triggered before any of the three Take Profit levels were reached. This gives you a clear picture of how often a trade resulted in a loss without ever moving into profit target territory.
Short Tutorial: How to Use the Indicator
Add to Chart: Open your TradingView chart, go to "Indicators," search for "Alpha - Combined Breakout," and add it to your chart.
Access Settings: Once added, click the gear icon next to the indicator name on your chart to open its settings.
Choose Your Signal Mode:
For UT Bot only: Uncheck "Draw Lines: Smart Money Only" and "Draw Lines: UT & Smart Money (Combined)". Ensure "Draw Lines: UT Only" is checked.
For Smart Money only: Uncheck "Draw Lines: UT Only" and "Draw Lines: UT & Smart Money (Combined)". Ensure "Draw Lines: Smart Money Only" is checked.
For Combined Signals: Check "Draw Lines: UT & Smart Money (Combined)". This will override the other two.
Adjust Parameters:
Start with default settings. Observe how the signals appear on your chosen asset and timeframe.
Refine Smart Money: If you see too many "noisy" market structure breaks, increase swingSize. If you want earlier breakouts, try "Wicks" for bosConfType.
Refine UT Bot: Adjust a_ut (Sensitivity) to get more or fewer UT Bot signals. Change c_ut (ATR Period) if you want larger or smaller TP/SL distances. Experiment with h_ut to see if Heikin Ashi smoothing suits your trading style.
Adjust TP/SL Multiplier: If using "Smart Money Only" mode, fine-tune sm_tp_sl_multiplier to set appropriate risk/reward levels.
Interpret Signals & Lines:
Buy/Sell Flags: These indicate the presence of a signal based on your selected drawing mode.
Entry Line (Blue Solid): This is where the signal was generated (usually the close price of the signal candle).
SL Line (Red/Green Solid): Your calculated stop loss level.
TP Lines (Dashed): Your three calculated take profit levels (TP1, TP2, TP3, where TP3 is the furthest target).
Smart Money Lines (BOS/CHoCH): These lines indicate horizontal levels where market structure breaks occurred. CHoCH labels might appear at the first structural break against the prior trend.
Monitor Dashboard: Pay attention to the dashboard in the top right corner. This dynamically updates to show the win rates for each TP and, crucially, the "SL before any TP rate." Use these statistics to evaluate the effectiveness of the indicator's signals under your current settings and chosen mode.
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Set Alerts (Optional): You can set up alerts for any of the specific signals (UT Bot Long/Short, Smart Money Bullish/Bearish, or the "Line Draw" combined signals) to notify you when they occur, even if you're not actively watching the chart.
By following this tutorial, you'll be able to effectively use and customize the "Alpha - Combined Breakout" indicator to suit your trading strategy.
Floor and Roof Indicator with SignalsFloor and Roof Indicator with Trading Signals
A comprehensive support and resistance indicator that identifies premium and discount zones with automated signal generation.
Key Features:
Dynamic Support/Resistance Zones: Calculates floor (support) and roof (resistance) levels using price action and volatility
Premium/Discount Zone Identification: Highlights areas where price may find resistance or support
Customizable Signal Frequency: Control how often signals are displayed (every Nth occurrence)
Visual Signal Table: Optional table showing the last 5 long and short signal prices
Multiple Timeframe Compatibility: Works across all timeframes
Technical Details:
Uses ATR-based calculations for dynamic zone width adjustment
Combines Bollinger Bands with highest/lowest price analysis
Smoothing options for cleaner signal generation
Fully customizable colors and display options
How to Use:
Floor Zones (Blue): Potential support areas where long positions may be considered
Roof Zones (Pink): Potential resistance areas where short positions may be considered
Signal Crosses: Visual markers when price interacts with key levels
Signal Table: Track recent signal prices for analysis
Settings:
Length: Period for calculations (default: 200)
Smooth: Smoothing factor for cleaner signals
Zone Width: Adjust the thickness of support/resistance zones
Signal Frequency: Control signal display frequency
Visual Options: Customize colors and table position
Alerts Available:
Long signal alerts when price touches discount zones
Short signal alerts when price reaches premium zones
Educational Purpose: This indicator is designed to help traders identify potential support and resistance areas. Always combine with proper risk management and additional analysis.
This description focuses on the technical aspects and educational value while avoiding any language that could be interpreted as financial advice or guaranteed profits.
Jumping watermark# Jumping watermark
## Function description
- Dynamic watermark: Mainly used to add dynamic watermarks to prevent theft and transfer when recording videos.
- Static watermark: Sharing opinions can easily include information such as trading pairs, cycles, current time, and individual signatures.
### Static watermark:
Display the watermark related to the current trading pair in the center of the chart.
- Configuration items:
- You can choose to configure the display content: current trading pair code and name, cycle, date, time, and individual signature content
### Dynamic watermark
Display the configured watermark content in a dynamic random position.
- Configuration items:
- Turn on or off the display of watermark jumping
- Modify the display text content and style by yourself
----- 中文简介-----
# 跳动水印
## 功能描述
- 动态水印： 主要可用于视频录制时添加动态水印防盗、防搬运。
- 静态水印：观点分享是可方便的带上交易对、周期、当前时间、个签等信息。
### 静态水印：
在图表中心位置显示当前交易对相关信息水印。
- 配置项：
- 可选择配置显示内容：当前交易对代码及名称、周期、日期、时间、个签内容
### 动态水印
动态随机位置显示配置水印内容。
- 配置项：
- 开启或关闭显示水印跳动
- 自行修改配置显示文字内容和样式
NQ Hourly Stats - Detailed Prob (24h)Hourly Sweep Statistics - Probability Engine (Credits to nqstats.com)
Overview
This indicator is a powerful statistical tool designed for intraday traders, particularly those focused on session-based patterns and mean reversion strategies. It automatically tracks the previous hour's high, low, and open, and when a sweep of the high or low occurs, it instantly displays the historical probability of the price returning to the hourly open within that same hour.
The core of this indicator is a comprehensive probability model built on historical price data, providing traders with an objective, data-driven edge.
Key Concepts
The indicator operates on a simple but effective premise: after the high or low of the previous hour is taken, what is the statistical likelihood that price will revert back to the opening price of the current hour?
• Previous Hour High (PHH) & Previous Hour Low (PHL): These levels often act as key liquidity zones. A sweep of these levels can signify either a stop run before a reversal or the start of a strong continuation.
• Return to Open: This is a classic mean-reversion concept. The indicator quantifies the probability of this event happening based on the exact time the sweep occurs.
• Time-Based Probability: The probability of returning to the open is not static; it changes depending on when the sweep happens. A sweep in the first 5 minutes of the hour has a different statistical outcome than a sweep in the last 5 minutes. This indicator accounts for that variance by breaking down the hour into 12 distinct 5-minute buckets.
How It Works
1. Automatic Level Plotting: At the start of each new hour, the indicator automatically draws three lines on your chart:
o The Previous Hour's High (Teal, solid line)
o The Previous Hour's Low (Maroon, solid line)
o The Current Hour's Open (Gray, dotted line)
2. Sweep Detection & Labeling: The script constantly monitors price action. The moment the current price action sweeps (touches or breaks) the PHH or PHL, a label appears.
o High Sweep: A label will appear above the PHH line.
o Low Sweep: A label will appear below the PHL line.
3. Information-Rich Labels: Each label provides crucial, real-time information:
o Direction: "Took PHH" or "Took PHL".
o Time: The exact time (@ HH:MM) the sweep occurred.
o Probability: The historical probability ("Prob to Open: XX.XX%") of price returning to the hourly open after that specific sweep.
4. Dynamic Color-Coding: The labels are color-coded for at-a-glance interpretation:
o Green: High probability (>70%) - Strong statistical likelihood of returning to the open.
o Orange: Medium probability (40%-70%) - Neutral/moderate likelihood.
o Red: Low probability (<40%) - Weak statistical likelihood of returning to the open; may suggest trend continuation.
How to Use in Your Trading
This indicator is not a standalone signal generator but a powerful confluence tool to enhance your decision-making.
• Mean Reversion Setups: When a sweep occurs and a high-probability (green) label appears, it can serve as strong confirmation for a mean-reversion trade. You can look for entries on a lower timeframe, targeting the hourly open.
• Trend Continuation Setups: If a sweep generates a low-probability (red) label, it suggests that the move has strength and is less likely to reverse. This can be used to validate a breakout or trend-following strategy, or to avoid taking a counter-trend trade.
• Filtering Trades: Use the probabilities to filter your existing setups. You might choose to only take reversion trades when the probability is above a certain threshold (e.g., 70%) or avoid them entirely when the probability is low.
Features & Customization
• Full 24-Hour Data: The statistical model includes data for all 24 hours of the day, making it useful for trading any session (Asia, London, New York).
• Timezone Setting: Ensure you set the Chart Timezone input to match your chart's timezone (e.g., 'America/New_York') for the probabilities to be accurate.
• Custom Colors: All line colors are fully customizable to match your chart's theme.
Disclaimer: This indicator is based on historical statistics and does not guarantee future results. It should be used as part of a comprehensive trading plan that includes proper risk management. Always do your own research and backtesting.
Kelly Optimal Leverage IndicatorThe Kelly Optimal Leverage Indicator mathematically applies Kelly Criterion to determine optimal position sizing based on market conditions.
This indicator helps traders answer the critical question: "How much capital should I allocate to this trade?"
Note that "optimal position sizing" does not equal the position sizing that you should have. The Optima position sizing given by the indicator is based on historical data and cannot predict a crash, in which case, high leverage could be devastating.
Originally developed for gambling scenarios with known probabilities, the Kelly formula has been adapted here for financial markets to dynamically calculate the optimal leverage ratio that maximizes long-term capital growth while managing risk.
Key Features
Kelly Position Sizing: Uses historical returns and volatility to calculate mathematically optimal position sizes
Multiple Risk Profiles: Displays Full Kelly (aggressive), 3/4 Kelly (moderate), 1/2 Kelly (conservative), and 1/4 Kelly (very conservative) leverage levels
Volatility Adjustment: Automatically recommends appropriate Kelly fraction based on current market volatility
Return Smoothing: Option to use log returns and smoothed calculations for more stable signals
Comprehensive Table: Displays key metrics including annualized return, volatility, and recommended exposure levels
How to Use
Interpret the Lines: Each colored line represents a different Kelly fraction (risk tolerance level). When above zero, positive exposure is suggested; when below zero, reduce exposure. Note that this is based on historical returns. I personally like to increase my exposure during market downturns, but this is hard to illustrate in the indicator.
Monitor the Table: The information panel provides precise leverage recommendations and exposure guidance based on current market conditions.
Follow Recommended Position: Use the "Recommended Position" guidance in the table to determine appropriate exposure level.
Select Your Risk Profile: Conservative traders should follow the Half Kelly or Quarter Kelly lines, while more aggressive traders might consider the Three-Quarter or Full Kelly lines.
Adjust with Volatility: During high volatility periods, consider using more conservative Kelly fractions as recommended by the indicator.
Mathematical Foundation
The indicator calculates the optimal leverage (f*) using the formula:
f* = μ/σ²
Where:
μ is the annualized expected return
σ² is the annualized variance of returns
This approach balances potential gains against risk of ruin, offering a scientific framework for position sizing that maximizes long-term growth rate.
Notes
The Full Kelly is theoretically optimal for maximizing long-term growth but can experience significant drawdowns. You should almost never use full kelly.
Most practitioners use fractional Kelly strategies (1/2 or 1/4 Kelly) to reduce volatility while capturing most of the growth benefits
This indicator works best on daily timeframes but can be applied to any timeframe
Negative Kelly values suggest reducing or eliminating market exposure
The indicator should be used as part of a complete trading system, not in isolation
Enjoy the indicator! :)
P.S. If you are really geeky about the Kelly Criterion, I recommend the book The Kelly Capital Growth Investment Criterion by Edward O. Thorp and others.
Crypto Risk-Weighted Allocation SuiteCrypto Risk-Weighted Allocation Suite
This indicator is designed to help users explore dynamic portfolio allocation frameworks for the crypto market. It calculates risk-adjusted allocation weights across major crypto sectors and cash based on multi-factor momentum and volatility signals. Best viewed on INDEX:BTCUSD 1D chart. Other charts and timeframes may give mixed signals and incoherent allocations.
🎯 How It Works
This model systematically evaluates the relative strength of:
BTC Dominance (CRYPTOCAP:BTC.D)
Represents Bitcoin’s share of the total crypto market. Rising dominance typically indicates defensive market phases or BTC-led trends.
ETH/BTC Ratio (BINANCE:ETHBTC)
Gauges Ethereum’s relative performance versus Bitcoin. This provides insight into whether ETH is leading risk appetite.
SOL/BTC Ratio (BINANCE:SOLBTC)
Measures Solana’s performance relative to Bitcoin, capturing mid-cap layer-1 strength.
Total Market Cap excluding BTC and ETH (CRYPTOCAP:TOTAL3ES)
Represents Altcoins as a broad category, reflecting appetite for higher-risk assets.
Each of these series is:
✅ Converted to a momentum slope over a configurable lookback period.
✅ Standardized into Z-scores to normalize changes relative to recent behavior.
✅ Smoothed optionally using a Hull Moving Average for cleaner signals.
✅ Divided by ATR-based volatility to create a risk-weighted score.
✅ Scaled to proportionally allocate exposure, applying user-configured minimum and maximum constraints.
🪙 Dynamic Allocation Logic
All signals are normalized to sum to 100% if fully confident.
An overall confidence factor (based on total signal strength) scales the allocation up or down.
Any residual is allocated to cash (unallocated capital) for conservative exposure.
The script automatically avoids “all-in” bias and prevents negative allocations.
📊 Outputs
The indicator displays:
Market Phase Detection (which asset class is currently leading)
Risk Mode (Risk On, Neutral, Risk Off)
Dynamic Allocations for BTC, ETH, SOL, Alts, and Cash
Optional momentum plots for transparency
🧠 Why This Is Unique
Unlike simple dominance indicators or crossovers, this model:
Integrates multiple cross-asset signals (BTC, ETH, SOL, Alts)
Adjusts exposure proportionally to signal strength
Normalizes by volatility, dynamically scaling risk
Includes configurable constraints to reflect your own risk tolerance
Provides a cash fallback allocation when conviction is low
Is entirely non-repainting and based on daily closing data
⚠️ Disclaimer
This script is provided for educational and informational purposes only.
It is not financial advice and should not be relied upon to make investment decisions.
Past performance does not guarantee future results.
Always consult a qualified financial advisor before acting on any information derived from this tool.
🛠 Recommended Use
As a framework to visualize relative momentum and risk-adjusted allocations
For research and backtesting ideas on portfolio allocation across crypto sectors
To help build your own risk management process
This script is not a turnkey strategy and should be customized to fit your goals.
✅ Enjoy exploring dynamic crypto allocations responsibly!
Logistic Regression ICT FVG🚀 OVERVIEW
Welcome to the Logistic Regression Fair Value Gap (FVG) System — a next-gen trading tool that blends precision gap detection with machine learning intelligence.
Unlike traditional FVG indicators, this one evolves with each bar of price action, scoring and filtering gaps based on real market behavior.
🔧 CORE FEATURES
✨ Smart Gap Detection
Automatically identifies bullish and bearish Fair Value Gaps using volatility-aware candle logic.
📊 Probability-Based Filtering
Uses logistic regression to assign each gap a confidence score (0 to 1), showing only high-probability setups.
🔁 Real-Time Retest Tracking
Continuously watches how price interacts with each gap to determine if it deserves respect.
📈 Multi-Factor Assessment
Evaluates RSI, MACD, and body size at gap formation to build a full context snapshot.
🧠 Self-Learning Engine
The logistic regression model updates on each bar using gradient descent, refining its predictions over time.
📢 Built-In Alerts
Get instant alerts when a gap forms, gets retested, or breaks.
🎨 Custom Display Options
Control the color of bullish/bearish zones, and toggle on/off probability labels for cleaner charts.
🚩 WHAT MAKES IT DIFFERENT
This isn’t just another box-drawing indicator.
While others mark every imbalance, this system thinks before it draws — using statistical modeling to filter out noise and prioritize high-impact zones.
By learning from how price behaves around gaps (not just how they form), it helps you trade only what matters — not what clutters.
⚙️ HOW IT WORKS
1️⃣ Detection
FVGs are identified using ATR-based thresholds and sharp wick imbalances.
2️⃣ Behavior Monitoring
Every gap is tracked — and if respected enough times, it becomes part of the elite training set.
3️⃣ Context Capture
Each new FVG logs RSI, MACD, and body size to provide a feature-rich context for prediction.
4️⃣ Prediction (Logistic Regression)
The model predicts how likely the gap is to be respected and assigns it a probability score.
5️⃣ Classification & Alerts
Gaps above the threshold are plotted with score labels, and alerts trigger for entry/respect/break.
⚙️ CONFIGURATION PANEL
🔧 System Inputs
• Max Retests – How many times a gap must be respected to train the model
• Prediction Threshold – Minimum score to show a gap on the chart
• Learning Rate – Controls how fast the model adapts (default: 0.009)
• Max FVG Lifetime – Expiration duration for unused gaps
• Show Historic Gaps – Show/hide expired or invalidated gaps
🎨 Visual Options
• Bullish/Bearish Colors – Set gap colors to fit your chart style
• Confidence Labels – Show probability scores next to FVGs
• Alert Toggles – Enable alerts for:
– New FVG detected
– FVG respected (entry)
– FVG invalidated (break)
💡 WHY LOGISTIC REGRESSION?
Traditional FVG tools rely on candle shapes.
This system relies on probability — by training on RSI, MACD, and price behavior, it predicts whether a gap will act as a true liquidity zone.
Logistic regression lets the system continuously adapt using new data, making it more accurate the longer it runs.
That means smarter signals, fewer false positives, and a clearer view of where real opportunities lie.
EVaR Indicator and Position SizingThe Problem:
Financial markets consistently show "fat-tailed" distributions where extreme events occur with higher frequency than predicted by normal distributions (Gaussian or even log-normal). These fat tails manifest in sudden price crashes, volatility spikes, and black swan events that traditional risk measures like volatility can underestimate. Standard deviation and conventional VaR calculations assume normally distributed returns, leaving traders vulnerable to severe drawdowns during market stress.
Cryptocurrencies and volatile instruments display particularly pronounced fat-tailed behavior, with extreme moves occurring 5-10 times more frequently than normal distribution models would predict. This reality demands a more sophisticated approach to risk measurement and position sizing.
The Solution: Entropic Value at Risk (EVAR)
EVaR addresses these limitations by incorporating principles from statistical mechanics and information theory through Tsallis entropy. This advanced approach captures the non-linear dependencies and power-law distributions characteristic of real financial markets.
Entropy is more adaptive than standard deviations and volatility measures.
I was inspired to create this indicator after reading the paper " The End of Mean-Variance? Tsallis Entropy Revolutionises Portfolio Optimisation in Cryptocurrencies " by by Sana Gaied Chortane and Kamel Naoui.
Key advantages of EVAR over traditional risk measures:
Superior tail risk capture: More accurately quantifies the probability of extreme market moves
Adaptability to market regimes: Self-calibrates to changing volatility environments
Non-parametric flexibility: Makes less assumptions about the underlying return distribution
Forward-looking risk assessment: Better anticipates potential market changes (just look at the charts :)
Mathematically, EVAR is defined as:
EVAR_α(X) = inf_{z>0} {z * log(1/α * M_X(1/z))}
Where the moment-generating function is calculated using q-exponentials rather than conventional exponentials, allowing precise modeling of fat-tailed behavior.
Technical Implementation
This indicator implements EVAR through a q-exponential approach from Tsallis statistics:
Returns Calculation: Price returns are calculated over the lookback period
Moment Generating Function: Approximated using q-exponentials to account for fat tails
EVAR Computation: Derived from the MGF and confidence parameter
Normalization: Scaled to for intuitive visualization
Position Sizing: Inversely modulated based on normalized EVAR
The q-parameter controls tail sensitivity—higher values (1.5-2.0) increase the weighting of extreme events in the calculation, making the model more conservative during potentially turbulent conditions.
Indicator Components
1. EVAR Risk Visualization
Dynamic EVAR Plot: Color-coded from red to green normalized risk measurement (0-1)
Risk Thresholds: Reference lines at 0.3, 0.5, and 0.7 delineating risk zones
2. Position Sizing Matrix
Risk Assessment: Current risk level and raw EVAR value
Position Recommendations: Percentage allocation, dollar value, and quantity
Stop Parameters: Mathematically derived stop price with percentage distance
Drawdown Projection: Maximum theoretical loss if stop is triggered
Interpretation and Application
The normalized EVAR reading provides a probabilistic risk assessment:
< 0.3: Low risk environment with minimal tail concerns
0.3-0.5: Moderate risk with standard tail behavior
0.5-0.7: Elevated risk with increased probability of significant moves
> 0.7: High risk environment with substantial tail risk present
Position sizing is automatically calculated using an inverse relationship to EVAR, contracting during high-risk periods and expanding during low-risk conditions. This is a counter-cyclical approach that ensures consistent risk exposure across varying market regimes, especially when the market is hyped or overheated.
Parameter Optimization
For optimal risk assessment across market conditions:
Lookback Period: Determines the historical window for risk calculation
Q Parameter: Controls tail sensitivity (higher values increase conservatism)
Confidence Level: Sets the statistical threshold for risk assessment
For cryptocurrencies and highly volatile instruments, a q-parameter between 1.5-2.0 typically provides the most accurate risk assessment because it helps capturing the fat-tailed behavior characteristic of these markets. You can also increase the q-parameter for more conservative approaches.
Practical Applications
Adaptive Risk Management: Quantify and respond to changing tail risk conditions
Volatility-Normalized Positioning: Maintain consistent exposure across market regimes
Black Swan Detection: Early identification of potential extreme market conditions
Portfolio Construction: Apply consistent risk-based sizing across diverse instruments
This indicator is my own approach to entropy-based risk measures as an alterative to volatility and standard deviations and it helps with fat-tailed markets.
Enjoy!
Hour-Stats v2cHour-Stats Indicator
The Hour-Stats indicator is a powerful, data-driven tool designed specifically for NQ futures traders who rely on statistically significant hourly price action probabilities. While traditional indicators typically focus only on the likelihood of prices returning to the opening price, Hour-Stats distinguishes itself by offering detailed statistical analysis across multiple critical price points.
Leveraging over 15 years of historical data, this indicator provides traders with robust probabilities for three unique hourly metrics:
Return to Hourly Open – The percentage likelihood of price revisiting the hourly open after breaking the high or low.
Return to Previous Hour Midpoint (PHM) – Offers clear probabilities of price returning to the midpoint (50%) of the previous hour’s range, a valuable metric for gauging reversals and continuations.
Opposite Extreme Targeting – Calculates the statistical likelihood of price moving to the opposite end (high or low) of the previous hour’s candle range, offering actionable insights for range trading strategies.
Additionally, Hour-Stats presents the historical probabilities of hourly highs and lows forming within three distinct 20-minute segments of each trading hour. This breakdown gives traders a precise understanding of when peaks or troughs are most likely, enhancing entry and exit timing.
The indicator’s settings are highly customizable, allowing traders to personalize visuals such as vertical and horizontal line colors, line styles (dotted, dashed, solid), and line thickness. Further customization includes label sizing, label positioning, and the ability to adjust visual dimming of swept price levels, providing clarity and ease of use during live market conditions.
Inspired by NQ Stats' concept (details available at nqstats, Hour-Stats expands significantly upon the original idea, delivering a uniquely comprehensive suite of hourly probability analytics for informed decision-making in futures trading.
Disclaimer: Futures trading involves significant risk. Traders should conduct their own due diligence and are responsible for their trading outcomes. Historical probabilities do not guarantee future results.
CM EMA Crossover Price Probabilities customCM EMA Crossover Price Probabilities
This indicator combines Exponential Moving Average (EMA) crossovers with swing high/low detection to calculate and display the historical probability of price movements exceeding user-defined percentage thresholds. Unlike standard EMA crossover indicators, it quantifies the likelihood of specific price changes following bullish (fast EMA crossing above slow EMA) or bearish (fast EMA crossing below slow EMA) crossovers, providing traders with data-driven insights into potential price behavior.
How It Works:EMA Crossovers: Detects when the fast EMA crosses above (bullish) or below (bearish) the slow EMA, marking these events with chart labels.
Price Change Measurement: Measures the percentage price change from the crossover point to the next swing high (for bullish crossovers) or swing low (for bearish crossovers), using pivot point detection.
Probability Calculation: Analyses historical crossover data to compute the probability of price changes meeting or exceeding customizable percentage thresholds (e.g., 2.5%, 5%). Probabilities are displayed as labels on the last bar, showing both bullish and bearish outcomes.
Customization: Allows users to adjust EMA lengths, pivot lookback, historical data limit, and probability thresholds via inputs.
Inputs:Fast EMA Length (default: 20): Period for the fast EMA.
Slow EMA Length (default: 50): Period for the slow EMA.
Pivot Lookback (default: 15): Bars used to detect swing highs/lows.
Max Historical Crossovers (default: 100): Limits stored crossovers for performance.
Bin Thresholds (defaults: 2.5%, 4.6%, 8.4%, 21.0%, 100.0%): Five customizable percentage thresholds for probability calculations.
Usage:
Add the indicator to your chart and adjust inputs to match your trading style. Bullish and bearish crossover points are labeled on the chart, and probability labels appear in the top-right corner when sufficient data is available. Use these probabilities to assess the historical likelihood of price movements after EMA crossovers, aiding in trade planning or risk assessment.
Why It’s Useful:
By combining EMA crossovers with swing-based price change analysis, this indicator offers a unique perspective on market behaviour post-crossover. The customizable probability thresholds allow traders to focus on specific price movement targets, making it a versatile tool for studying trend strength and potential outcomes.
Notes:
Probabilities are based on historical data and do not predict future performance.
Set bin thresholds in ascending order for accurate probability calculations.
Designed for educational purposes to analyze EMA crossover patterns.
xGhozt Wickless Candle Streak ProbabilityThe xGhozt Wickless Candle Streak Probability is a custom Pine Script indicator designed to identify and quantify the occurrence of consecutive "wickless" candles of the same trend (either bullish or bearish).
Key Features:
Wickless Candle Detection: It first identifies candles that lack an upper or lower wick (meaning their open/close is equal to their high/low, respectively).
Consecutive Streak Tracking: The indicator tracks how many wickless bullish candles occur in a row, and similarly for wickless bearish candles.
User-Defined Streak Length: You can specify a Streak Length in the indicator's settings. This defines how many consecutive wickless candles are needed to register a "streak."
Probability Calculation: For the chosen Streak Length, the indicator calculates the historical probability (as a percentage) of encountering such a streak for both bullish and bearish wickless candles. This is done by dividing the number of times a streak of that length has occurred by the total number of candles scanned.
On-Chart Display: The results, including the total wickless candles, total scanned candles, and the calculated streak probabilities, are displayed in a convenient table directly on your chart.
Purpose:
This indicator helps traders and analysts understand the historical likelihood of sustained, strong directional moves as indicated by consecutive wickless candles. By quantifying these probabilities, it can provide insights into potential continuation patterns or extreme market conditions, which might be useful for developing trading strategies or confirming market biases.
xGhozt Wickless Candles with TailSimple script showing candles missing an upper or lower wick. As candles tend to have a low and a high, they will most certainly form wicks. It is rare to have wickless candles on longer time frames, so it's more relevant on 1h and above.
Additionally, this indicator now visually tracks these 'missing wicks' as horizontal 'tails'. These tails extend from the wickless candle's extreme (low for bullish, high for bearish) and continue to stretch to the right until price action finally touches that level. Once touched, the tail disappears, signifying that the 'missing wick' has been filled or 'mitigated'.
What can you do about it?
If you see for example a Bitcoin 4h candle that hasn't formed two wicks yet, there are high chances that the missing wick will be formed at one point or another. The persistent horizontal tail vividly highlights these unmitigated levels, allowing you to identify potential price magnets. You could therefore consider taking a trade in the direction of the missing wick. You can set alerts on wickless candles if needed.
Shift 3M - 30Y Yield Spread🟧 Shift 3M - 30Y Yield Spread
- This indicator visually displays the **inverse of the US Treasury short-long yield spread** (3-month minus 30-year spread reversal signal) in a "price chart-like" form.
- By default, the spread line is shifted by 1 year to help anticipate forward market moves (you can adjust this offset freely).
- Especially customized to be analyzed together with the movements of US indices like the S&P 500, and to help understand broader market cycles.
✅ Description
- Normalizes the spread based on a rolling window length you set (default: 500 bars).
- Both the normalization window and offset (shift) are fully customizable.
- Then, it scales the spread to match your chart’s price range, allowing you to intuitively compare spread movements alongside price action.
- Instantly see the **inverse (reversal) signals of the short-long yield spread**, curve steepening, and how they align with actual price trends.
⚡ By reading macro yield signals, you can **anticipate exactly when a market crash might come or when an explosive rally is about to start**.
⚡ A perfect tool for macro traders and yield curve analysts who want to quickly catch major market turning points!
copyright @invest_hedgeway
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🟧3개월 - 30년 물 장단기 금리차 역수
- 이 인디케이터는 미국 국채 **장단기 금리차 역수**(3개월물 - 30년물 스프레드의 반전 시그널)를 시각적으로 "가격 차트"처럼 표시해 줍니다.
- 기본적으로 스프레드 선은 **1년(365봉) 시프트**되어 있어, 시장을 선행적으로 파악할 수 있도록 설계되었습니다 (값은 자유롭게 조정 가능).
- 특히 S&P500 등 미국 지수 흐름과 함께 분석할 수 있도록 맞춤화되었으며, 시장 사이클을 이해하는 데에도 큰 도움이 됩니다.
✅ 설명
- 지정한 롤링 윈도우 길이(기본: 500봉)를 기준으로 스프레드를 정규화합니다.
- 정규화 길이와 오프셋(시프트) 모두 자유롭게 설정 가능
- 이후 현재 차트의 가격 레인지에 맞게 스케일링해, 가격과 함께 흐름을 직관적으로 비교할 수 있습니다.
- **장단기 금리차의 역전(역수) 시그널**, 커브 스티프닝 등과 실제 가격 움직임의 관계를 한눈에 확인
⚡ 거시 금리 신호를 통해 **언제 폭락이 올지, 언제 폭등이 터질지** 미리 감지할 수 있습니다.
⚡ 시장의 전환점을 빠르게 캐치하고 싶은 매크로 트레이더와 금리 분석가에게 완벽한 도구!
copyright @invest_hedgeway
BANKNIFTY Contribution Table [GSK-VIZAG-AP-INDIA]1. Overview
This indicator provides a real-time visual contribution table of the 12 constituent stocks in the BANKNIFTY index. It displays key metrics for each stock that help traders quickly understand how each component is impacting the index at any given moment.
2. Purpose / Trading Use Case
The tool is designed for intraday and short-term traders who rely on index movement and its internal strength or weakness. By seeing which stocks are contributing positively or negatively, traders can:
Confirm trend strength or divergence within the index.
Identify whether a BANKNIFTY move is broad-based or driven by a few heavyweights.
Detect reversals when individual components decouple from index direction.
3. Key Features and Logic
Live LTP: Current price of each BANKNIFTY stock.
Price Change: Difference between current LTP and previous day’s close.
% Change: Percentage move from previous close.
Weight %: Static weight of each stock within the BANKNIFTY index (user-defined).
This estimates how much each stock contributes to the BANKNIFTY’s point change.
Sorted View: The stocks are sorted by their weight (descending), so high-impact movers are always at the top.
4. User Inputs / Settings
Table Position (tableLocationOpt):
Choose where the table appears on the chart:
top_left, top_right, bottom_left, or bottom_right.
This helps position the table away from your price action or indicators.
5. Visual and Plotting Elements
Table Layout: 6 columns
Stock | Contribution | Weight % | LTP | Change | % Change
Color Coding:
Green/red for positive/negative price changes and contributions.
Alternating background rows for better visibility.
BANKNIFTY row is highlighted separately at the top.
Text & Background Colors are chosen for both readability and direction indication.
6. Tips for Effective Use
Use this table on 1-minute or 5-minute intraday charts to see near real-time market structure.
Watch for:
A few heavyweight stocks pulling the index alone (can signal weak internal breadth).
Broad green/red across all rows (signals strong directional momentum).
Combine this with price action or volume-based strategies for confirmation.
Best used during market hours for live updates.
7. What Makes It Unique
Unlike other contribution tables that show only static data or require paid feeds, this script:
Updates in real time.
Uses dynamic calculated contributions.
Places BANKNIFTY at the top and presents the entire internal structure clearly.
Doesn’t repaint or rely on lagging indicators.
8. Alerts / Additional Features
No alerts are added in this version.
(Optional: Alerts can be added to notify when a certain stock contributes above/below a threshold.)
9. Technical Concepts Used
request.security() to pull both 1-minute and daily close data.
Conditional color formatting based on price change direction.
Dynamic table rendering using table.new() and table.cell().
Static weights assigned manually for BANKNIFTY stocks (can be updated if index weights change).
10. Disclaimer
This script is intended for educational and informational purposes only. It does not constitute financial advice or a buy/sell recommendation.
Users should test and validate the tool on paper or demo accounts before applying it to live trading.
📌 Note: Due to internet connectivity, data delays, or broker feeds, real-time values (LTP, change, contribution, etc.) may slightly differ from other platforms or terminals. Use this indicator as a supportive visual tool, not a sole decision-maker.
Script Title: BANKNIFTY Contribution Table -
Author: GSK-VIZAG-AP-INDIA
Version: Final Public Release
Frahm Factor Position Size CalculatorThe Frahm Factor Position Size Calculator is a powerful evolution of the original Frahm Factor script, leveraging its volatility analysis to dynamically adjust trading risk. This Pine Script for TradingView uses the Frahm Factor’s volatility score (1-10) to set risk percentages (1.75% to 5%) for both Margin-Based and Equity-Based position sizing. A compact table on the main chart displays Risk per Trade, Frahm Factor, and Average Candle Size, making it an essential tool for traders aligning risk with market conditions.
Calculates a volatility score (1-10) using true range percentile rank over a customizable look-back window (default 24 hours).
Dynamically sets risk percentage based on volatility:
Low volatility (score ≤ 3): 5% risk for bolder trades.
High volatility (score ≥ 8): 1.75% risk for caution.
Medium volatility (score 4-7): Smoothly interpolated (e.g., 4 → 4.3%, 5 → 3.6%).
Adjustable sensitivity via Frahm Scale Multiplier (default 9) for tailored volatility response.
Position Sizing:
Margin-Based: Risk as a percentage of total margin (e.g., $175 for 1.75% of $10,000 at high volatility).
Equity-Based: Risk as a percentage of (equity - minimum balance) (e.g., $175 for 1.75% of ($15,000 - $5,000)).
Compact 1-3 row table shows:
Risk per Trade with Frahm score (e.g., “$175.00 (Frahm: 8)”).
Frahm Factor (e.g., “Frahm Factor: 8”).
Average Candle Size (e.g., “Avg Candle: 50 t”).
Toggles to show/hide Frahm Factor and Average Candle Size rows, with no empty backgrounds.
Four sizes: XL (18x7, large text), L (13x6, normal), M (9x5, small, default), S (8x4, tiny).
Repositionable (9 positions, default: top-right).
Customizable cell color, text color, and transparency.
Set Frahm Factor:
Frahm Window (hrs): Pick how far back to measure volatility (e.g., 24 hours). Shorter for fast markets, longer for chill ones.
Frahm Scale Multiplier: Set sensitivity (1-10, default 9). Higher makes the score jumpier; lower smooths it out.
Set Margin-Based:
Total Margin: Enter your account balance (e.g., $10,000). Risk auto-adjusts via Frahm Factor.
Set Equity-Based:
Total Equity: Enter your total account balance (e.g., $15,000).
Minimum Balance: Set to the lowest your account can go before liquidation (e.g., $5,000). Risk is based on the difference, auto-adjusted by Frahm Factor.
Customize Display:
Calculation Method: Pick Margin-Based or Equity-Based.
Table Position: Choose where the table sits (e.g., top_right).
Table Size: Select XL, L, M, or S (default M, small text).
Table Cell Color: Set background color (default blue).
Table Text Color: Set text color (default white).
Table Cell Transparency: Adjust transparency (0 = solid, 100 = invisible, default 80).
Show Frahm Factor & Show Avg Candle Size: Check to show these rows, uncheck to hide (default on).
Machine Learning Key Levels [AlgoAlpha]🟠 OVERVIEW
This script plots Machine Learning Key Levels on your chart by detecting historical pivot points and grouping them using agglomerative clustering to highlight price levels with the most past reactions. It combines a pivot detection, hierarchical clustering logic, and an optional silhouette method to automatically select the optimal number of key levels, giving you an adaptive way to visualize price zones where activity concentrated over time.
🟠 CONCEPTS
Agglomerative clustering is a bottom-up method that starts by treating each pivot as its own cluster, then repeatedly merges the two closest clusters based on the average distance between their members until only the desired number of clusters remain. This process creates a hierarchy of groupings that can flexibly describe patterns in how price reacts around certain levels. This offers an advantage over K-means clustering, since the number of clusters does not need to be predefined. In this script, it uses an average linkage approach, where distance between clusters is computed as the average pairwise distance of all contained points.
The script finds pivot highs and lows over a set lookback period and saves them in a buffer controlled by the Pivot Memory setting. When there are at least two pivots, it groups them using agglomerative clustering: it starts with each pivot as its own group and keeps merging the closest pairs based on their average distance until the desired number of clusters is left. This number can be fixed or chosen automatically with the silhouette method, which checks how well each point fits in its cluster compared to others (higher scores mean cleaner separation). Once clustering finishes, the script takes the average price of each cluster to create key levels, sorts them, and draws horizontal lines with labels and colors showing their strength. A metrics table can also display details about the clusters to help you understand how the levels were calculated.
🟠 FEATURES
Agglomerative clustering engine with average linkage to merge pivots into level groups.
Dynamic lines showing each cluster’s price level for clarity.
Labels indicating level strength either as percent of all pivots or raw counts.
A metrics table displaying pivot count, cluster count, silhouette score, and cluster size data.
Optional silhouette-based auto-selection of cluster count to adaptively find the best fit.
🟠 USAGE
Add the indicator to any chart. Choose how far back to detect pivots using Pivot Length and set Pivot Memory to control how many are kept for clustering (more pivots give smoother levels but can slow performance). If you want the script to pick the number of levels automatically, enable Auto No. Levels ; otherwise, set Number of Levels . The colored horizontal lines represent the calculated key levels, and circles show where pivots occurred colored by which cluster they belong to. The labels beside each level indicate its strength, so you can see which levels are supported by more pivots. If Show Metrics Table is enabled, you will see statistics about the clustering in the corner you selected. Use this tool to spot areas where price often reacts and to plan entries or exits around levels that have been significant over time. Adjust settings to better match volatility and history depth of your instrument.
[TCV] - Position Tool Position Tool by TheCryptoVizier is a trade-planning widget that lets you drop Entry, Take-Profit and Stop-Loss levels directly on the chart, instantly calculates risk-to-reward and position size, and shows only the numbers you actually need. It’s designed for traders who plan visually and don’t want to juggle spreadsheets or external calculators.
WHAT PROBLEM DOES IT SOLVE?
When you drag price levels on TradingView you still have to:
work out how many contracts / coins you can buy for a fixed $ risk,
check that your R:R is acceptable,
copy the final values somewhere else.
The Position Tool automates all of that inside the chart and keeps the screen clean.
HOW TO USE
Add the indicator to any chart.
Drag the blue (Entry), green (TP) and red (SL) lines to your desired levels.
Set your Risk in USDT and toggle the check-boxes to show / hide extra fields.
Read off the position size, risk and R:R in the corner table or copy the exact numbers from the Data Window.
Place your order with confidence – the maths is already done.
Whether you scalp lower-timeframes or swing trade higher ones, the Position Tool removes friction from trade preparation and lets you focus on execution.
KEY FEATURES
Drag-and-drop Entry / TP / SL lines – plan the trade visually.
Fixed-risk position sizing – enter how much you’re willing to lose in USDT (or account currency) and the script tells you the exact position value and quantity.
Live R-to-R ratio – instantly see whether the reward compensates the risk as you move levels.
Smart info panel – overlay table shows Entry, TP, SL, R:R and – optionally via check-boxes – position in USDT, position in $TICKER and risk in USDT. Hide what you don’t need.
Copy-ready Data Window values – the same numbers appear in TradingView’s Data Window, so you can click any cell to copy it straight to the clipboard.
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Disclaimer: This indicator is provided for educational purposes only. Trading involves substantial risk, and nothing here should be construed as financial advice or a recommendation to trade. Always do your own research and consult a qualified professional.
ReversoReverso – Moving Average Touch Statistics Tracker
Reverso indicator is a technical analysis tool that tracks and visualizes how price interacts with a selected Exponential Moving Average (EMA). It provides detailed statistics about price behavior before, during, and after each EMA touch event.
This script is suitable for both trend-following and mean-reversion traders who want to study EMA reactions, understand market tendencies, and refine entry/exit strategies based on price-memory dynamics.
Features and Functionality
Supported MAs: EMA 9, 20, or 50
Timeframe Support: Uses the chart’s timeframe
Touch Detection: Triggered when the price range (high to low) crosses or touches the EMA
Automatic Data Tracking
Tables for Quick Visual Summary
Visual Overlay: Optional EMA line plotted on chart
Timeframe Support: Uses the chart’s timeframe
Capped history: Most recent 50 touches
Automatic Data Tracking:
Number of EMA touches
Time intervals between touches
Price distance from last touch
Maximum price deviation (above/below EMA) between touches
Time spent above/below EMA
Tables for Quick Visual Summary:
Info Table: Live details about last and first touches, distance from touch, bars above/below, peak movements since last touch
Stats Table: Averages and extreme values for price behavior patterns across recent history
Core Metrics Tracked
Last Touch Price: The last price level where price touched the EMA
Distance from Last Touch: Current % change from the last touch price
Time Between Touches: Average and maximum intervals (in bars or time) between touch events
Max Distance Above/Below: Peak movement above/below EMA between touches
Bars Above/Below: How long price stayed above/below the EMA since last touch
Peak This Cycle: Max deviation above/below in current cycle since last touch
How It Works
Reverso monitors each bar to check if price intersects the selected EMA.
When a new touch occurs, it records the touch price and time, and resets the tracking cycle.
From that point forward, it tracks how far and how long price drifts above or below the EMA.
This process repeats with each new touch, building a detailed profile of how price behaves around the moving average.
The result is a visual and statistical framework for understanding price memory, market rhythm, and mean-reversion opportunities.
Customization Options
EMA Length: Choose from EMA 9, 20, or 50
Show MA Line: Toggle the EMA plot on the chart
Show Info Table: Enable/disable the current-touch summary
Show Statistics Table: Show aggregate data over the history
Table Positioning: Customizable placement for both tables
MA Color: Select custom color for EMA plot
Intended Use Cases
Identify reversal or continuation setups near EMAs
Validate strategies relying on mean reversion
Backtest the consistency of price respect to EMAs
Detect periods of volatility clustering around EMAs
Notes and Disclaimers
This script does not repaint: calculations are made on confirmed bars.
This indicator is educational in nature and should be used alongside other forms of analysis.
Time durations in the tables are approximated using bar timing and may vary across markets/timeframes.
SD Levels"SD Levels", is a powerful tool for technical analysis that automatically calculates and plots key price levels based on the price action within a user-defined time range. It functions by identifying a specific trading session, calculating the midpoint and half the range of that session's price action, and then using these values as a baseline and a standard deviation equivalent to project a series of customizable Fibonacci-style levels into the future.
These projected levels can act as potential support and resistance zones, helping traders identify significant price areas where the market might react. The indicator is highly customizable, allowing users to tailor its functionality and appearance to their specific trading strategies.
Key Features
• User-Defined Time Range: You can specify a particular time window (e.g., the first three hours of the New York session) and a corresponding timezone. The indicator will base all its calculations on the high, low, and closing prices within this defined period each day.
• Standard Deviation-Based Levels: The core of the indicator is its use of a "standard deviation" value, which is calculated as half the range (High - Low) of the specified session. The baseline, or "0" level, is the midpoint of this range.
• Customizable Fibonacci Levels: The script allows for the plotting of up to 11 distinct levels, each defined by a multiplier of the calculated standard deviation. Users have complete control over:
o The level's multiplier value.
o Whether the level is displayed.
o The color, style (solid, dashed, dotted), and thickness of the level line.
o The option to display a text label for each level.
• Mirrored Levels: An option is available to automatically "mirror" each level on the opposite side of the baseline. For example, if you have a level at 1.5 standard deviations above the baseline, enabling the mirror function will also plot a corresponding level at -1.5 standard deviations below it.
• Visual Customization: Beyond individual line styles, you can adjust the overall appearance of the levels, including:
o Adding a transparent background fill between the levels to enhance visibility.
o Adjusting the padding (extension) of the level lines to the right of the chart.
o Controlling the size of the labels and choosing to display the level value, the price value, or both.
• Historical Analysis: The indicator can display these calculated levels for a user-specified number of previous days, allowing for back-testing and analysis of how price has historically interacted with these zones.
How It Works
1. Session Identification: The indicator first identifies the bars on the chart that fall within the user-defined Range Time and Timezone.
2. Range Calculation: During this identified session, it records the highest high and the lowest low.
3. Baseline and Deviation Calculation: At the end of the session, it calculates two critical values:
o Baseline: The midpoint of the session's range, calculated as (range_high + range_low) / 2. This serves as the 0 level.
o Standard Deviation Value: Half of the session's total range, calculated as (range_high - range_low) / 2.
4. Level Plotting: Using the baseline and the standard deviation value, the indicator calculates and plots the various user-defined Fibonacci levels. For instance, a level with a multiplier of 2.0 would be plotted at baseline + (2 * stdev_val).
5. Drawing and Extension: The calculated levels are drawn starting from the beginning of the session and are extended forward in time, updating with each new bar. This allows traders to see how the current price is interacting with the levels derived from the earlier session.
In essence, the "SD Levels" indicator provides a structured and automated way to identify and visualize significant, data-driven price levels based on the volatility and price action of a specific, important trading period.
Liquidity Break Probability [PhenLabs]📊 Liquidity Break Probability
Version: PineScript™ v6
The Liquidity Break Probability indicator revolutionizes how traders approach liquidity levels by providing real-time probability calculations for level breaks. This advanced indicator combines sophisticated market analysis with machine learning inspired probability models to predict the likelihood of high/low breaks before they happen.
Unlike traditional liquidity indicators that simply draw lines, LBP analyzes market structure, volume profiles, momentum, volatility, and sentiment to generate dynamic break probabilities ranging from 5% to 95%. This gives traders unprecedented insight into which levels are most likely to hold or break, enabling more confident trading decisions.
🚀 Points of Innovation
Advanced 6-factor probability model weighing market structure, volatility, volume, momentum, patterns, and sentiment
Real-time probability updates that adjust as market conditions change
Intelligent trading style presets (Scalping, Day Trading, Swing Trading) with optimized parameters
Dynamic color-coded probability labels showing break likelihood percentages
Professional tiered input system - from quick setup to expert-level customization
Smart volume filtering that only highlights levels with significant institutional interest
🔧 Core Components
Market Structure Analysis: Evaluates trend alignment, level strength, and momentum buildup using EMA crossovers and price action
Volatility Engine: Incorporates ATR expansion, Bollinger Band positioning, and price distance calculations
Volume Profile System: Analyzes current volume strength, smart money proxies, and level creation volume ratios
Momentum Calculator: Combines RSI positioning, MACD strength, and momentum divergence detection
Pattern Recognition: Identifies reversal patterns (doji, hammer, engulfing) near key levels
Sentiment Analysis: Processes fear/greed indicators and market breadth measurements
🔥 Key Features
Dynamic Probability Labels: Real-time percentage displays showing break probability with color coding (red >70%, orange >50%, white <50%)
Trading Style Optimization: One-click presets automatically configure sensitivity and parameters for your trading timeframe
Professional Dashboard: Live market state monitoring with nearest level tracking and active level counts
Smart Alert System: Customizable proximity alerts and high-probability break notifications
Advanced Level Management: Intelligent line cleanup and historical analysis options
Volume-Validated Levels: Only displays levels backed by significant volume for institutional-grade analysis
🎨 Visualization
Recent Low Lines: Red lines marking validated support levels with probability percentages
Recent High Lines: Blue lines showing resistance zones with break likelihood indicators
Probability Labels: Color-coded percentage labels that update in real-time
Professional Dashboard: Customizable panel showing market state, active levels, and current price
Clean Display Modes: Toggle between active-only view for clean charts or historical view for analysis
📖 Usage Guidelines
Quick Setup
Trading Style Preset
Default: Day Trading
Options: Scalping, Day Trading, Swing Trading, Custom
Description: Automatically optimizes all parameters for your preferred trading timeframe and style
Show Break Probability %
Default: True
Description: Displays percentage labels next to each level showing break probability
Line Display
Default: Active Only
Options: Active Only, All Levels
Description: Choose between clean active-only view or comprehensive historical analysis
Level Detection Settings
Level Sensitivity
Default: 5
Range: 1-20
Description: Lower values show more levels (sensitive), higher values show fewer levels (selective)
Volume Filter Strength
Default: 2.0
Range: 0.5-5.0
Description: Controls minimum volume threshold for level validation
Advanced Probability Model
Market Trend Influence
Default: 25%
Range: 0-50%
Description: Weight given to overall market trend in probability calculations
Volume Influence
Default: 20%
Range: 0-50%
Description: Impact of volume analysis on break probability
✅ Best Use Cases
Identifying high-probability breakout setups before they occur
Determining optimal entry and exit points near key levels
Risk management through probability-based position sizing
Confluence trading when multiple high-probability levels align
Scalping opportunities at levels with low break probability
Swing trading setups using high-probability level breaks
⚠️ Limitations
Probability calculations are estimations based on historical patterns and current market conditions
High-probability setups do not guarantee successful trades - risk management is essential
Performance may vary significantly across different market conditions and asset classes
Requires understanding of support/resistance concepts and probability-based trading
Best used in conjunction with other analysis methods and proper risk management
💡 What Makes This Unique
Probability-Based Approach: First indicator to provide quantitative break probabilities rather than simple S/R lines
Multi-Factor Analysis: Combines 6 different market factors into a comprehensive probability model
Adaptive Intelligence: Probabilities update in real-time as market conditions change
Professional Interface: Tiered input system from beginner-friendly to expert-level customization
Institutional-Grade Filtering: Volume validation ensures only significant levels are displayed
🔬 How It Works
1. Level Detection:
Identifies pivot highs and lows using configurable sensitivity settings
Validates levels with volume analysis to ensure institutional significance
2. Probability Calculation:
Analyzes 6 key market factors: structure, volatility, volume, momentum, patterns, sentiment
Applies weighted scoring system based on user-defined factor importance
Generates probability score from 5% to 95% for each level
3. Real-Time Updates:
Continuously monitors price action and market conditions
Updates probability calculations as new data becomes available
Adjusts for level touches and changing market dynamics
💡 Note: This indicator works best on timeframes from 1-minute to 4-hour charts. For optimal results, combine with proper risk management and consider multiple timeframe analysis. The probability calculations are most accurate in trending markets with normal to high volatility conditions.
Logarithmic Moving Average (LMA) [QuantAlgo]🟢 Overview
The Logarithmic Moving Average (LMA) uses advanced logarithmic weighting to create a dynamic trend-following indicator that prioritizes recent price action while maintaining statistical significance. Unlike traditional moving averages that use linear or exponential weights, this indicator employs logarithmic decay functions to create a more sophisticated price averaging system that adapts to market volatility and momentum conditions.
The indicator displays a smoothed signal line that oscillates around zero, with positive values indicating bullish momentum and negative values indicating bearish momentum. The signal incorporates trend quality assessment, momentum confirmation, and multiple filtering mechanisms to help traders and investors identify trend continuation and reversal opportunities across different timeframes and asset classes.
🟢 How It Works
The indicator's core innovation lies in its logarithmic weighting system, where weights are calculated using the formula: w = 1.0 / math.pow(math.log(i + steepness), 2) The steepness parameter controls how aggressively recent data is prioritized over historical data, creating a dynamic weight decay that can be fine-tuned for different trading styles. This logarithmic approach provides more nuanced weight distribution compared to exponential moving averages, offering better responsiveness while maintaining stability.
The LMA calculation combines multiple sophisticated components. First, it calculates the logarithmic weighted average of closing prices. Then it measures the slope of this average over a 10-period lookback: lmaSlope = (lma - lma ) / lma * 100 The system also incorporates trend quality assessment using R-squared correlation analysis of log-transformed prices, measuring how well the price data fits a linear trend model over the specified period.
The final signal generation uses the formula: signal = lmaSlope * (0.5 + rSquared * 0.5) which combines the LMA slope with trend quality weighting. When momentum confirmation is enabled, the indicator calculates annualized log-return momentum and applies a multiplier when the momentum direction aligns with the signal direction, strengthening confirmed signals while filtering out weak or counter-trend movements.
🟢 How to Use
1. Signal Interpretation and Threshold Zones
Positive Values (Above Zero): LMA slope indicating bullish momentum with upward price trajectory relative to logarithmic baseline
Negative Values (Below Zero): LMA slope indicating bearish momentum with downward price trajectory relative to logarithmic baseline
Zero Line Crosses: Signal transitions between bullish and bearish regimes, indicating potential trend changes
Long Entry Threshold Zone: Area above positive threshold (default 0.5) indicating confirmed bullish signals suitable for long positions
Short Entry Threshold Zone: Area below negative threshold (default -0.5) indicating confirmed bearish signals suitable for short positions
Extreme Values: Signals exceeding ±1.0 represent strong momentum conditions with higher probability of continuation
2. Momentum Confirmation and Visual Analysis
Signal Color Intensity: Gradient coloring shows signal strength, with brighter colors indicating stronger momentum
Bar Coloring: Optional price bar coloring matches signal direction for quick visual trend identification
Position Labels: Real-time position classification (Bullish/Bearish/Neutral) displayed on the latest bar
Momentum Weight Factor: When short-term log-return momentum aligns with LMA signal direction, the signal receives additional weight confirmation
Trend Quality Component: R-squared values weight the signal strength, with higher correlation indicating more reliable trend conditions
3. Examples: Preconfigured Settings
Default: Universally applicable configuration balanced for medium-term investing and general trading across multiple timeframes and asset classes.
Scalping: Highly responsive setup with shorter period and higher steepness for ultra-short-term trades on 1-15 minute charts, optimized for quick momentum shifts.
Swing Trading: Extended period with moderate steepness and increased smoothing for multi-day positions, designed to filter noise while capturing larger price swings on 1-4 hour and daily charts.
Trend Following: Maximum smoothing with lower steepness for established trend identification, generating fewer but more reliable signals optimal for daily and weekly timeframes.
Mean Reversion: Shorter period with high steepness for counter-trend strategies, more sensitive to extreme moves and reversal opportunities in ranging market conditions.
Avg daily rangeThe Average Daily Range (ADR) is a technical indicator that measures the average price movement of a financial instrument over a specific period.
Price Reaction Analysis by Day of WeekOverview
The "Price Reaction Analysis by Day of Week" indicator is a tool that enables traders to analyze historical price reaction patterns to technical indicator signals on a selected day of the week. It examines price behavior on a chosen candle (from 1 to 30) in the next day or subsequent days after a signal, depending on the timeframe, and provides success rate statistics to support data-driven trading decisions. The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week comparisons. Lower timeframes generate more signals due to the higher number of candles per day.
Key Features
1. Flexible Technical Indicator Selection
Users can choose one of four technical indicators: RSI, SMI, MA, or Bollinger Bands. Each indicator has configurable parameters, such as:
RSI length, oversold/overbought levels.
SMI length, %K and %D smoothing, signal levels.
MA length.
Bollinger Bands length and multiplier.
2. Day-of-Week Analysis
The indicator allows users to select a day of the week (Monday, Tuesday, Wednesday, Thursday, Friday) for generating signals. It analyzes price reactions on a selected candle (from 1 to 30) in the next day or subsequent days after the signal. Examples:
On a daily timeframe, a signal on Monday can be analyzed for the first, fourth, or later candle (up to 30) in subsequent days (e.g., Tuesday, Wednesday).
On timeframes lower than 1 day (e.g., 12H, 8H, 6H, 4H, 1H, 15M), the analysis targets the selected candle in the next day or subsequent days. For example, on a 4H timeframe, you can analyze the second Tuesday candle following a Monday signal. The maximum timeframe is 1 day to ensure consistent day-of-week analysis.
3. Visual Signals
Signals for the analysis period are marked with background highlights in real-time when the indicator’s conditions are met. The last highlighted candle of the selected day is always analyzed. Arrows are displayed on the chart at the candle specified by the “Candles to Compare” setting (e.g., the first candle if set to 1):
Green upward triangles (below the candle) for successful buy signals (the closing price of the selected candle is higher than the signal candle’s close).
Red downward triangles (above the candle) for successful sell signals (the closing price of the selected candle is lower than the signal candle’s close).
Gray “x” marks for unsuccessful signals (no price reversal in the expected direction). Arrow positions are intuitive: buy signals below the candle, sell signals above. Highlights and arrows do not require waiting for future signals but are essential for calculating statistics.
Note: The first candle of the next day may appear shifted on the chart due to timezone differences, which can affect the timing of signal appearance.
4. Signal Conditions (Highlights) for Each Indicator
RSI: The oscillator is in oversold (buy) or overbought (sell) zones.
SMI: SMI returns from oversold (buy) or overbought (sell) zones.
MA: Price crosses the MA (upward for buy, downward for sell).
Bollinger Bands: Price returns inside the bands (from below for buy, from above for sell).
5. Success Rate Statistics
A table in the top-right corner of the chart displays:
The number of buy and sell signals for the selected day of the week.
The percentage of cases where the price of the selected candle in the next day or subsequent days reversed as expected (e.g., rising after a buy signal). Statistics are based on comparing the closing price of the signal candle with the closing price of the selected candle (e.g., first, fourth) in the next day or subsequent days.
Important: Statistics do not account for price movements within the candle or after its close. The price on the selected candle (e.g., fourth) may be lower than earlier candles but still higher than the signal candle, counting as a positive buy signal, though it does not guarantee profit.
6. Date Range
Users can specify the analysis date range, enabling strategy testing on historical data from a chosen period. Ensure the start and end dates are set correctly.
Applications
The indicator is designed for traders who want to leverage historical patterns for position planning. Examples:
On a 4-hour timeframe: If a sell signal highlight appears on Monday and statistics show an 80% chance that the fourth Tuesday candle is bearish, traders may consider playing a correction at the open of that candle.
On a daily timeframe: If a highlight indicates market overheating, traders may consider entering a position at the open of the first candle after the signal (e.g., Tuesday), provided statistics suggest an edge. Users can analyze the signal on the first candle and check later candles to validate results, increasing confidence in consistent patterns.
Key Settings
Indicator Type: Choose between RSI, SMI, MA, or Bollinger Bands.
Selected Day: Monday, Tuesday, Wednesday, Thursday, or Friday.
Candles to Compare: The number of the candle in the next day or subsequent days (from 1 to 30).
Indicator Parameters: Lengths, levels (e.g., oversold/overbought for RSI).
Background Colors: Configurable highlights for buy and sell signals.
Notes
Timeframes: The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week patterns. Timeframes lower than 1 day generate more signals due to the higher number of candles per day.
Candle Shift: The first candle of the next day may appear shifted on the chart due to timezone differences, affecting the timing of signals across markets or platforms.
Statistical Limitations: Results are based on the closing prices of the selected candle, ignoring fluctuations in earlier candles, within the candle, or subsequent price movements. Traders must assess whether entering at the open or after the close of the selected candle is profitable.
Testing: Effectiveness depends on historical data and parameter settings. Testing different configurations across markets and timeframes is recommended.
Who Is It For?
Swing and position traders who base decisions on technical analysis and historical patterns.
Market analysts seeking patterns in price behavior by day of the week.
TradingView users of all experience levels, thanks to an intuitive interface and flexible settings.
