Ultimate Reversion BandsURB – The Smart Reversion Tool
URB Final filters out false breakouts using a real retest mechanism that most indicators miss. Instead of chasing wicks that fail immediately, it waits for price to confirm rejection by retesting the inner band—proving sellers/buyers are truly exhausted.
Eliminates fakeouts – The retest filter catches only genuine reversions
Triple confirmation – Wick + retest + optional volume/RSI filters
Clear visuals – Outer bands show extremes, inner bands show retest zones
Works on any timeframe – From scalping to swing trading
Perfect for traders tired of getting stopped out by false breakouts.
Core Construction:
Smart Dynamic Bands:
Basis = Weighted hybrid EMA of HLC3, SMA, and WMA
Outer Bands = Basis ± (ATR × Multiplier)
Inner Bands = Basis ± (ATR × Multiplier × 0.5) → The "retest zone"
The Unique Filter: The Real Retest
Step 1: Identify an extreme wick touching the outer band
Step 2: Wait 1-3 bars for price to return and touch the inner band
Why it works: Most false breakouts never retest. A genuine reversal shows seller/buyer exhaustion by allowing price to come back to the "halfway" level.
Optional Confirmations:
Volume surge filter (default ON)
RSI extremes filter (optional)
Each can be toggled ON/OFF
How to Use:
Watch for extreme wicks touching the red/lime outer bands
Wait for the retest – price must return to touch the inner band (dotted line) within 3 bars
Enter on confirmation with built-in volume/RSI filters
Set stops beyond the extreme wick
Meanreversion
Mutanabby_AI | ONEUSDT_MR1
ONEUSDT Mean-Reversion Strategy | 74.68% Win Rate | 417% Net Profit
This is a long-only mean-reversion strategy designed specifically for ONEUSDT on the 1-hour timeframe. The core logic identifies oversold conditions following sharp declines and enters positions when selling pressure exhausts, capturing the subsequent recovery bounce.
Backtested Period: June 2019 – December 2025 (~6 years)
Performance Summary
| Metric | Value |
|--------|-------|
| Net Profit | +417.68% |
| Win Rate | 74.68% |
| Profit Factor | 4.019 |
| Total Trades | 237 |
| Sharpe Ratio | 0.364 |
| Sortino Ratio | 1.917 |
| Max Drawdown | 51.08% |
| Avg Win | +3.14% |
| Avg Loss | -2.30% |
| Buy & Hold Return | -80.44% |
Strategy Logic :
Entry Conditions (Long Only):
The strategy seeks confluence of three conditions that identify exhausted selling:
1. Prior Move Filter:*The price change from 5 bars ago to 3 bars ago must be ≥ -7% (ensures we're not entering during freefall)
2. Current Move Filter: The price change over the last 2 bars must be ≤ 0% (confirms momentum is stalling or reversing)
3. Three-Bar Decline: The price change from 5 bars ago to 3 bars ago must be ≤ -5% (confirms a significant recent drop occurred)
When all three conditions align, the strategy identifies a potential reversal point where sellers are exhausted.
Exit Conditions:
- Primary Exit: Close above the previous bar's high while the open of the previous bar is at or below the close from 9 bars ago (profit-taking on strength)
- Trailing Stop: 11x ATR trailing stop that locks in profits as price rises
Risk Management
- Position Sizing:Fixed position based on account equity divided by entry price
- Trailing Stop:11× ATR (14-period) provides wide enough room for crypto volatility while protecting gains
- Pyramiding:Up to 4 orders allowed (can scale into winning positions)
- **Commission:** 0.1% per trade (realistic exchange fees included)
Important Disclaimers
⚠️ This is NOT financial advice.
- Past performance does not guarantee future results
- Backtest results may contain look-ahead bias or curve-fitting
- Real trading involves slippage, liquidity issues, and execution delays
- This strategy is optimized for ONEUSDT specifically — results may differ on other pairs
- Always test before risking real capital
Recommended Usage
- Timeframe:*1H (as designed)
- Pair: ONEUSDT (Binance)
- Account Size: Ensure sufficient capital to survive max drawdown
Source Code
Feedback Welcome
I'm sharing this strategy freely for educational purposes. Please:
- Drop a comment with your backtesting results any you analysis
- Share any modifications that improve performance
- Let me know if you spot any issues in the logic
Happy trading
As a quant trader, do you think this strategy will survive in live trading?
Yes or No? And why?
I want to hear from you guys
Market Analysis Pro [Trademy]OVERVIEW
Trademy Market Analysis Pro is a professional-grade trading system that combines advanced momentum analysis with institutional-level Supply/Demand zone mapping. This indicator is designed to provide crystal-clear market analysis with precise risk management tools, creating a complete trading framework within a single, streamlined interface.
Unlike complex indicators that overwhelm traders with information, Trademy focuses on what matters: high-probability setups with clear entry points, defined risk levels, and multiple profit targets. The system is built to eliminate guesswork and provide actionable signals that work across multiple timeframes and asset classes eg: ( INDEX:BTCUSD , NASDAQ:NVDA and more )
CORE CONCEPTS
Advanced Momentum Engine: The foundation of Trademy Market Analysis Pro is a proprietary momentum detection system that identifies true directional shifts in the market. The algorithm analyzes price behavior relative to volatility-adjusted dynamic levels, generating signals only when genuine momentum reversals occur. The "Signal Sensitivity" control allows you to adapt the system from conservative (fewer, higher-quality signals) to aggressive (more frequent opportunities) based on your trading style and market conditions.
Institutional Supply/Demand Zones: The system automatically identifies and plots key institutional levels where significant buying (Demand) or selling (Supply) pressure has occurred. These zones are calculated using advanced price structure analysis, filtered through intelligent overlap detection to ensure only the most relevant zones appear on your chart. When price approaches these levels, they often act as strong support or resistance, providing logical areas for entries and exits.
Intelligent Signal Classification: Not all signals are created equal. Trademy categorizes every signal as either "Normal" or "Strong" based on its alignment with the broader market structure and trend context. Strong signals represent higher-conviction setups where momentum and trend align perfectly, while normal signals indicate counter-trend or early reversal opportunities.
Non-Repainting Architecture: Every signal is locked in at bar close (when enabled), and all TP/SL levels are calculated using volatility measurements captured at the moment of signal generation.
KEY FEATURES
Precision Signal System
Dual Signal Modes: Choose between Normal signals (standard momentum reversals) or Strong signals (high-conviction trend-aligned setups), or view both simultaneously
Wait for Bar Close: Optional no-repaint mode ensures signals only appear after candle confirmation
Visual Signal Hierarchy: Normal signals shown with standard arrows (▲/▼), Strong signals marked with distinctive colors for instant recognition
Adjustable Arrow Sizes: Customize signal display from tiny to large based on your chart preferences
Professional Risk Management
Automated TP/SL Calculation: Three take-profit levels (TP1, TP2, TP3) and one stop-loss level automatically calculated using advanced volatility measurement
Fixed Risk Levels: TP/SL lines are locked at signal generation and never move—providing consistent, reliable risk parameters
Visual Risk Zones: Optional colored zones highlight your risk and reward areas for instant position assessment
Adjustable Risk Multiplier: Scale your targets up or down with a single parameter while maintaining proper risk-reward ratios
Clear On-Chart Labels: Every level displays exact price values in an easy-to-read format
Supply/Demand Zone Mapping
Automatic Zone Detection: System identifies high-probability supply and demand zones using advanced price structure analysis
Anti-Overlap Algorithm: Intelligent filtering prevents zone clutter by removing overlapping levels
Extended Zone Projection: Zones extend into the future, showing you key levels before price reaches them
Break-of-Structure Tracking: Monitors when zones are broken and removes invalidated levels
Fully Customizable: Adjust zone colors, swing length, history depth, and box width to match your analysis style
Visual Customization
Flexible Color Schemes: Customize colors for bull/bear signals, TP/SL levels, and supply/demand zones
Trend Background: Optional background coloring to instantly visualize the current market bias
Support/Resistance Lines: Toggle automatic S/R level plotting from key price pivots
Multiple Arrow Sizes: Choose from tiny, small, normal, or large signal arrows
WHAT MAKES TRADEMY MARKET ANALYSIS PRO DIFFERENT
✅ Simplicity Meets Power
✅ TP/SL Levels
✅ Institutional Zone Integration
✅ Universal Indicator for all markets
✅ Multi-Timeframe Flexibility
BEST PRACTICES
📌 Always Use Stop-Loss: Enable the TP/SL system and respect your stop-loss levels,risk management is key to long-term success
📌 Backtest First: Before live trading, replay historical charts to understand signal behavior on your specific asset and timeframe
📌 Combine Timeframes: Use higher timeframe signals as your bias, enter on lower timeframe signals in the same direction
📌 Watch the Zones: Highest probability setups occur when signals align with supply/demand zones (buy near demand, sell near supply)
📌 Don't Chase: If you miss a signal, wait for the next one,forcing trades leads to losses
📌 Partial Profits: Consider taking partial profits at TP1, moving stop to breakeven, and letting the rest run to TP2/TP3
📩 ACCESS & SUPPORT
This is an invite-only indicator. For access inquiries, please contact via TradingView private message.
Important Disclaimers:
This indicator is a tool for technical analysis and does not constitute financial advice
Past performance does not guarantee future results
Always practice proper risk management and never risk more than you can afford to lose
Trading carries substantial risk of loss and is not suitable for all investors
Bollinger Bands Mean Reversion using RSI [Krishna Peri]How it Works
Long entries trigger when:
- RSI reaches oversold levels, and
- At least one bullish candle closes inside the lower Bollinger Band
Short entries trigger when:
- RSI reaches overbought levels, and
- At least one bearish candle closes inside the upper Bollinger Band
This approach aims to capture exhaustion moves where price pushes into extreme deviation from its mean and then snaps back toward the middle band.
Important Disclaimer
This is a mean-reversion strategy, which means it performs best in sideways, ranging, or slowly oscillating market conditions. When markets shift into strong trends, Bollinger Bands expand and volatility increases, which may cause some signals to become inaccurate or fail altogether.
For best results, combine this script with:
- Price action
- Market structure
- Higher-timeframe trend context
- Previous day/week/month highs & lows
- Untested liquidity levels or imbalance zones
- Session timing (Asia, London, NY)
Using these confluences helps filter out low-probability trades and significantly improves consistency and precision.
Trend Tracer [AlgoAlpha]🟠 OVERVIEW
This tool builds a two-stage trend model that reacts to structure shifts while also showing how strong or weak the move is. It uses a mid-price band (from the highest high and lowest low over a lookback) and applies two Supertrend passes on top of it. The first pass smoothens the basis. The second pass refines that direction and produces the final trail used for signals. A gradient fill between the two trails uses RSI of price-to-trail distance to show when price is stretched or cooling off. The aim is to give traders a simple way to read trend alignment, pressure, and early turns without guessing.
🟠 CONCEPTS
The script starts with a mid-range basis. This is the average of the rolling highest high and lowest low. It acts as a stable structure reference instead of raw close or typical price. From there, two Supertrend layers are applied:
• The first Supertrend uses a shorter ATR period and lower factor. It reacts faster and sets the main regime.
• The second Supertrend uses a slightly longer ATR and higher factor. It filters noise, waits for confirmed continuation, and generates the signal line.
The interaction between these trails matters. The outer Supertrend provides context by defining the broader regime. The inner Supertrend provides timing by flipping earlier and marking possible shifts. The gradient fill uses RSI of (close − supertrend value) to display when price stretches away from the trail. This shows strength, exhaustion, or compression within the trend.
🟠 FEATURES
Bullish and bearish flip markers placed at recent highs/lows
Rejection signals off the trend tracer line
Alerts for bullish and bearish trend changes
🟠 USAGE
Setup : Add the script to your chart. Timeframe is flexible; lower timeframes show more flips while higher ones give cleaner swings. Adjust Length to change how wide the basis range is. Use the two ATR settings and factors to match the volatility of the market you trade.
Read the chart : When the refined trail (stv_) sits above price the regime is bearish; when below, it is bullish. The wide trail (stv) confirms the larger move. Watch the gradient fill: darker colors appear when price is stretched from the trail and lighter colors appear when the move is weakening. Flip markers ▲ or ▼ highlight the first clean shift of the refined trail.
Settings that matter : Increasing the Main Factor slows main-trend flips and filters chop. Increasing the Signal Factor delays the timing trail but reduces noise. Shortening Length makes the basis more reactive. ATR periods change how sensitive each Supertrend pass is to volatility.
Trend Step Channel [BigBeluga]🔵 OVERVIEW
Trend Step Channel identifies directional bias by forming a dynamic volatility-based step channel. It detects trend shifts when candle lows close above the upper band (bullish) or when candle highs drop below the lower band (bearish). A step-style midline tracks the trend evolution, while an integrated dashboard shows price positioning percentages across multiple timeframes.
🔵 CONCEPTS
ATR-Based Channel — The indicator constructs upper and lower channel boundaries using ATR distance around a single adaptive trend line, providing automatic scaling with volatility.
Trend Direction Logic —
• Low above upper band → uptrend confirmation.
• High below lower band → downtrend confirmation.
Step Trend Line — A reactive midline that locks onto price swings, stepping upward or downward as new trend confirmations occur.
Channel Width — Defines the total volatility range around the midline; a wider channel smooths market noise, while a narrower one reacts faster.
Price Position Ratio — Calculates the relative position of the close within the channel, from 0% (bottom) to 100% (top).
🔵 FEATURES
Volatility-Adaptive Channel — Expands and contracts dynamically to match market volatility, maintaining consistent distance scaling.
Configurable MA Source — Choose from SMA, EMA, SMMA, WMA, or VWMA as the base smoothing method.
Color-Coded Step Line —
• Green indicates an uptrend.
• Orange indicates a downtrend.
Channel Fill Visualization — Semi-transparent fills highlight active volatility zones for clear trend identification.
Price Position Label — Displays a “<” marker and percentage at the channel edge showing how far the current close is from the lower or upper band.
Multi-Timeframe Dashboard —
• Displays alignment across 1H–5H charts.
• Each cell shows an arrow (↑ / ↓) with price % positioning.
• Cell background color reflects bullish or bearish bias.
Real-Time Updating — The channel, midline, and dashboard refresh dynamically every bar for continuous feedback.
🔵 HOW TO USE
Trend Confirmation —
• Bullish trend forms when candle low closes above the upper band.
• Bearish trend forms when candle high closes below the lower band.
Trend Continuation — Maintain bias while the step line color remains consistent.
Volatility Breakouts — Sudden candle breaks outside the band suggest new directional strength.
Dashboard Alignment — Confirm trend consistency across multiple timeframes before entering trades.
Entry Planning — In uptrends, consider entries near the lower band; in downtrends, focus on upper-band rejections.
Price Position Insight — Use the % label to judge whether price is extended (near 100%) or compressed (near 0%) within the channel.
🔵 CONCLUSION
Trend Step Channel delivers a precise, volatility-driven view of trend structure using ATR-based boundaries and a step-line framework. The integrated dashboard, color-coded channel, and live positioning metrics give traders a complete picture of market direction, trend strength, and price location within evolving conditions.
SMC Statistical Liquidity Walls [PhenLabs]📊 SMC Statistical Liquidity Walls
Version: PineScript™ v6
📌 Description
The SMC Statistical Liquidity Walls indicator is designed to visualize market volatility and potential reversal zones using advanced statistical modeling. Unlike traditional Bollinger Bands that use simple lines, this script utilizes an “Inverted Sigmoid” opacity function to create a “fog of war” effect. This visualizes the density of liquidity: the further price moves from the equilibrium (mean), the “harder” the liquidity wall becomes.
This tool solves the problem of over-trading in low-probability areas. By automatically mapping “Premium” (Resistance) and “Discount” (Support) zones based on Standard Deviation (SD), traders can instantly see when price is overextended. The result is a clean, intuitive overlay that helps you identify high-probability mean reversion setups without cluttering your chart with manual drawings.
🚀 Points of Innovation
Inverted Sigmoid Logic: A custom mathematical function maps Standard Deviation to opacity, creating a realistic “wall” density effect rather than linear gradients.
Dynamic “Solidity”: The indicator is transparent at the center (Equilibrium) and becomes visually solid at the edges, mimicking physical resistance.
Separated Directional Bias: distinct Red (Premium) and Green (Discount) coding helps SMC traders instantly recognize expensive vs. cheap pricing.
Smart “Safe” Deviation: Includes fallback logic to handle calculation errors if deviation hits zero, ensuring the indicator never crashes during data gaps.
🔧 Core Components
Basis Calculation: Uses a Simple Moving Average (SMA) to determine the market’s equilibrium point.
Standard Deviation Zones: Calculates 1SD, 2SD, and 3SD levels to define the statistical extremes of price action.
Sigmoid Alpha Calculation: Converts the SD distance into a transparency value (0-100) to drive the visual gradient.
🔥 Key Features
Automated Premium/Discount Zones: Red zones indicate overbought (Premium) areas; Green zones indicate oversold (Discount) areas.
Customizable Density: Users can adjust the “Steepness” and “Midpoint” of the sigmoid curve to control how fast the walls become solid.
Integrated Alerts: Built-in alert conditions trigger when price hits the “Solid” wall (2SD or higher), perfect for automated trading or notifications.
Visual Clarity: The center of the chart remains clear (high transparency) to keep focus on price action where it matters most.
🎨 Visualization
Equilibrium Line: A gray line representing the mean price.
Gradient Fills: The space between bands fills with color that increases in opacity as it moves outward.
Premium Wall: Upper zones fade from transparent red to solid red.
Discount Wall: Lower zones fade from transparent green to solid green.
📖 Usage Guidelines
Range Period: Default 20. Controls the lookback period for the SMA and Standard Deviation calculation.
Source: Default Close. The price data used for calculations.
Center Transparency: Default 100 (Clear). Controls how transparent the middle of the chart is.
Edge Transparency: Default 45 (Solid). Controls the opacity of the outermost liquidity wall.
Wall Steepness: Default 2.5. Adjusts how aggressively the gradient transitions from clear to solid.
Wall Start Point: Default 1.5 SD. The deviation level where the gradient shift begins to accelerate.
✅ Best Use Cases
Mean Reversion Trading: Enter trades when price hits the solid 2SD or 3SD wall and shows rejection wicks.
Take Profit Targets: Use the Equilibrium (Gray Line) as a logical first target for reversal trades.
Trend Filtering: Do not initiate new long positions when price is deep inside the Red (Premium) wall.
⚠️ Limitations
Lagging Nature: As a statistical tool based on Moving Averages, the walls react to past price data and may lag during sudden volatility spikes.
Trending Markets: In strong parabolic trends, price can “ride” the bands for extended periods; mean reversion should be used with caution in these conditions.
💡 What Makes This Unique
Physics-Based Visualization: We treat liquidity as a physical barrier that gets denser the deeper you push, rather than just a static line on a chart.
🔬 How It Works
Step 1: The script calculates the mean (SMA) and the Standard Deviation (SD) of the source price.
Step 2: It defines three zones above and below the mean (1SD, 2SD, 3SD).
Step 3: The custom `get_inverted_sigmoid` function calculates an Alpha (transparency) value based on the SD distance.
Step 4: Plot fills are colored dynamically, creating a seamless gradient that hardens at the extremes to visualize the “Liquidity Wall.”
💡 Note
For best results, combine this indicator with Price Action confirmation (such as pin bars or engulfing candles) when price touches the solid walls.
The Oracle: Dip & Top Adaptive Sniper [Hakan Yorganci]█ OVERVIEW
The Oracle: Dip & Top Adaptive Sniper is a precision-focused trend trading strategy designed to solve the biggest problem in swing trading: Timing.
Most trend-following strategies chase price ("FOMO"), buying when the asset is already overextended. The Oracle takes a different approach. It adopts a "Sniper" mentality: it identifies a strong macro trend but patiently waits for a Mean Reversion (pullback) to execute an entry at a discounted price.
By combining the structural strength of Moving Averages (SMA 50/200) with the momentum precision of RSI and the volatility filtering of ADX, this script filters out noise and targets high-probability setups.
█ HOW IT WORKS
This strategy operates on a strictly algorithmic protocol known as "The Yorganci Protocol," which involves three distinct phases: Filter, Target, and Execute.
1. The Macro Filter (Trend Identification)
* SMA 200 Rule: By default, the strategy only scans for buy signals when the price is trading above the 200-period Simple Moving Average. This ensures we are always trading in the direction of the long-term bull market.
* Adaptive Switch: A new feature allows users to toggle the Only Buy Above SMA 200? filter OFF. This enables the strategy to hunt for oversold bounces (dead cat bounces) even during bearish or neutral market structures.
2. The Volatility Filter (ADX Integration)
* Sideways Protection: One of the main weaknesses of moving average strategies is "whipsaw" losses during choppy, ranging markets.
* Solution: The Oracle utilizes the ADX (Average Directional Index). It will BLOCK any trade entry if the ADX is below the threshold (Default: 20). This ensures capital is only deployed when a genuine trend is present.
3. The Sniper Entry (Buying the Dip)
* Instead of buying on breakout strength (e.g., RSI > 60), The Oracle waits for the RSI Moving Average to dip into the "Value Zone" (Default: 45) and cross back up. This technique allows for tighter stops and higher Risk/Reward ratios compared to traditional breakout systems.
█ EXIT STRATEGY
The Oracle employs a dynamic dual-exit mechanism to maximize gains and protect capital:
* Take Profit (The Peak): The strategy monitors RSI heat. When the RSI Moving Average breaches the Overbought Threshold (Default: 75), it signals a "Take Profit", securing gains near the local top before a potential reversal.
* Stop Loss (Trend Invalidated): If the market structure fails and the price closes below the 50-period SMA, the position is immediately closed to prevent deep drawdowns.
█ SETTINGS & CONFIGURATION
* Moving Averages: Fully customizable lengths for Support (SMA 50) and Trend (SMA 200).
* Trend Filter: Checkbox to enable/disable the "Bull Market Only" rule.
* RSI Thresholds:
* Sniper Buy Level: Adjustable (Default: 45). Lower values = Deeper dips, fewer trades.
* Peak Sell Level: Adjustable (Default: 75). Higher values = Longer holds, potentially higher profit.
* ADX Filter: Checkbox to enable/disable volatility filtering.
█ BEST PRACTICES
* Timeframe: Designed primarily for 4H (4-Hour) charts for swing trading. It can also be used on 1H for more frequent signals.
* Assets: Highly effective on trending assets such as Bitcoin (BTC), Ethereum (ETH), and high-volume Altcoins.
* Risk Warning: This strategy is designed for "Long Only" spot or leverage trading. Always use proper risk management.
█ CREDITS
* Original Concept: Inspired by the foundational work of Murat Besiroglu (@muratkbesiroglu).
* Algorithm Development & Enhancements: Developed by Hakan Yorganci (@hknyrgnc).
* Modifications include: Integration of ADX filters, Mean Reversion entry logic (RSI Dip), and Dynamic Peak Profit taking.
Volatility Signal-to-Noise Ratio🙏🏻 this is VSNR: the most effective and simple volatility regime detector & automatic volatility threshold scaler that somehow no1 ever talks about.
This is simply an inverse of the coefficient of variation of absolute returns, but properly constructed taking into account temporal information, and made online via recursive math with algocomplexity O(1) both in expanding and moving windows modes.
How do the available alternatives differ (while some’re just worse)?
Mainstream quant stat tests like Durbin-Watson, Dickey-Fuller etc: default implementations are ALL not time aware. They measure different kinds of regime, which is less (if at all) relevant for actual trading context. Mix of different math, high algocomplexity.
The closest one is MMI by financialhacker, but his approach is also not time aware, and has a higher algocomplexity anyways. Best alternative to mine, but pls modify it to use a time-weighted median.
Fractal dimension & its derivatives by John Ehlers: again not time aware, very low info gain, relies on bar sizes (high and lows), which don’t always exist unlike changes between datapoints. But it’s a geometric tool in essence, so this is fundamental. Let it watch your back if you already use it.
Hurst exponent: much higher algocomplexity, mix of parametric and non-parametric math inside. An invention, not a math entity. Again, not time aware. Also measures different kinds of regime.
How to set it up:
Given my other tools, I choose length so that it will match the amount of data that your trading method or study uses multiplied by ~ 4-5. E.g if you use some kind of bands to trade volatility and you calculate them over moving window 64, put VSNR on 256.
However it depends mathematically on many things, so for your methods you may instead need multipliers of 1 or ~ 16.
Additionally if you wanna use all data to estimate SNR, put 0 into length input.
How to use for regime detection:
First we define:
MR bias: mean reversion bias meaning volatility shorts would work better, fading levels would work better
Momo bias: momentum bias meaning volatility longs would work better, trading breakouts of levels would work better.
The study plots 3 horizontal thresholds for VSNR, just check its location:
Above upper level: significant Momo bias
Above 1 : Momo bias
Below 1 : MR bias
Below lower level: significant MR bias
Take a look at the screenshots, 2 completely different volatility regimes are spotted by VSNR, while an ADF does not show different regime:
^^ CBOT:ZN1!
^^ INDEX:BTCUSD
How to use as automatic volatility threshold scaler
Copy the code from the script, and use VSNR as a multiplier for your volatility threshold.
E.g you use a regression channel and fade/push upper and lower thresholds which are RMSEs multiples. Inside the code, multiply RMSE by VSNR, now you’re adaptive.
^^ The same logic as when MM bots widen spreads with vola goes wild.
How it works:
Returns follow Laplace distro -> logically abs returns follow exponential distro , cuz laplace = double exponential.
Exponential distro has a natural coefficient of variation = 1 -> signal to noise ratio defined as mean/stdev = 1 as well. The same can be said for Student t distro with parameter v = 4. So 1 is our main threshold.
We can add additional thresholds by discovering SNRs of Student t with v = 3 and v = 5 (+- 1 from baseline v = 4). These have lighter & heavier tails each favoring mean reversion or momentum more. I computed the SNR values you see in the code with mpmath python module, with precision 256 decimals, so you can trust it I put it on my momma.
Then I use exponential smoothing with properly defined alphas (one matches cumulative WMA and another minimizes error with WMA in moving window mode) to estimate SNR of abs returns.
…
Lightweight huh?
∞
Change in State of Delivery CISD [AlgoAlpha]🟠 OVERVIEW
This script tracks how price “changes delivery” after failed attempts to push in one direction. It builds swing levels from pivots, watches for those levels to be wicked, and then checks if price delivers cleanly in the opposite direction. When the pattern meets the script’s tolerance rules, it marks a Change in State of Delivery (CISD). These CISD levels are drawn as origin lines and are used to spot shifts in intent, failed pushes, and continuation attempts. A CISD becomes stronger when it forms after opposing liquidity is swept within a defined lookback.
🟠 CONCEPTS
The script first defines structure using swing highs/lows. These levels act as potential liquidity points. When price wicks through a swing, the script registers a mitigation event. After this, it looks for a reversal-style candle sequence: a failed push, followed by a counter-move strong enough to pass a tolerance ratio. This ratio compares how far price expanded away from the failed attempt versus the counter-move that followed. If the ratio is high enough, this becomes a CISD. The idea is simple: liquidity interaction sets context , and the tolerance logic identifies actual intent . CISD levels and sweep markers combine these two ideas into a clean map of where delivery flipped.
🟠 FEATURES
Liquidity tracking: marks swing highs/lows and updates them until expiry
Liquidity sweep confirmation when CISD aligns with recent mitigations
Alert conditions for all key events: mitigations, CISDs, and strong CISDs
🟠 USAGE
Setup : Add the script to your chart. Use it on any timeframe where swing behavior matters. Set the Swing Period for how wide a pivot must be. Set Noise Filter to control how strict the CISD detection is. Liquidity Lookback defines how recent a wick must be to confirm a sweep.
Read the chart : Origin lines mark where the CISD began. A green line signals bullish intent; a red line signals bearish intent. ▲ and ▼ shapes show CISDs that form after liquidity is swept, these mark strong signals for potential entry. Swing dots show recent swing highs/lows. Candle colors follow the latest CISD trend.
Settings that matter : Increasing Swing Period produces fewer but stronger swings. Raising Noise Filter requires cleaner counter-moves and reduces false CISDs. Liquidity Lookback controls how strict the sweep confirmation is. Expiry Bars decides how long swing levels remain active.
Uptrick: Dynamic Z-Score DivergenceIntroduction
Uptrick: Dynamic Z-Score Divergence is an oscillator that combines multiple momentum sources within a Z-Score framework, allowing for the detection of statistically significant mean-reversion setups, directional shifts, and divergence signals. It integrates a multi-source normalized oscillator, a slope-based signal engine, structured divergence logic, a slope-adaptive EMA with dynamic bands, and a modular bar coloring system. This script is designed to help traders identify statistically stretched conditions, evolving trend dynamics, and classical divergence behavior using a unified statistical approach.
Overview
At its core, this script calculates the Z-Score of three momentum sources—RSI, Stochastic RSI, and MACD—using a user-defined lookback period. These are averaged and smoothed to form the main oscillator line. This normalized oscillator reflects how far short-term momentum deviates from its mean, highlighting statistically extreme areas.
Signals are triggered when the oscillator reverses slope within defined inner zones, indicating a shift in direction while the signal remains in a statistically stretched state. These mean-reversion flips (referred to as TP signals) help identify turning points when price momentum begins to revert from extended zones.
In addition, the script includes a divergence detection engine that compares oscillator pivot points with price pivot points. It confirms regular bullish and bearish divergence by validating spacing between pivots and visualizes both the oscillator-side and chart-side divergences clearly.
A dynamic trend overlay system is included using a Slope Adaptive EMA (SA-EMA). This trend line becomes more responsive when Z-Score deviation increases, allowing the trend line to adapt to market conditions. It is paired with ATR-based bands that are slope-sensitive and selectively visible—offering context for dynamic support and resistance.
The script includes configurable bar coloring logic, allowing users to color candles based on oscillator slope, last confirmed divergence, or the most recent signal of any type. A full alert system is also built-in for key signals.
Originality
The script is based on the well-known concept of Z-Score valuation, which is a standard statistical method for identifying how far a signal deviates from its mean. This foundation—normalizing momentum values such as RSI or MACD to measure relative strength or weakness—is not unique to this script and is widely used in quantitative analysis.
What makes this implementation original is how it expands the Z-Score foundation into a fully featured, signal-producing system. First, it introduces a multi-source composite oscillator by combining three momentum inputs—RSI, Stochastic RSI, and MACD—into a unified Z-Score stream. Second, it builds on that stream with a directional slope logic that identifies turning points inside statistical zones.
The most distinctive additions are the layered features placed on top of this normalized oscillator:
A structured divergence detection engine that compares oscillator pivots with price pivots to validate regular bullish and bearish divergence using precise spacing and timing filters.
A fully integrated slope-adaptive EMA overlay, where the smoothing dynamically adjusts based on real-time Z-Score movement of RSI, allowing the trend line to become more reactive during high-momentum environments and slower during consolidation.
ATR-based dynamic bands that adapt to slope direction and offer real-time visual zones for support and resistance within trend structures.
These features are not typically found in standard Z-Score indicators and collectively provide a unique approach that bridges statistical normalization, structure detection, and adaptive trend modeling within one script.
Features
Z-Score-based oscillator combining RSI, StochRSI, and MACD
Configurable smoothing for stable composite signal output
Buy/Sell TP signals based on slope flips in defined zones
Background highlighting for extreme outer bands
Inner and outer zones with fill logic for statistical context
Pivot-based divergence detection (regular bullish/bearish)
Divergence markers on oscillator and price chart
Slope-Adaptive EMA (SA-EMA) with real-time adaptivity based on RSI Z-Score
ATR-based upper and lower bands around the SA-EMA, visibility tied to slope direction
Configurable bar coloring (oscillator slope, divergence, or most recent signal)
Alerts for TP signals and confirmed divergences
Optional fixed Y-axis scaling for consistent oscillator view
The full setup mode can be seen below:
Input Parameters
General Settings
Full Setup: Enables rendering of the full visual system (lines, bands, signals)
Z-Score Lookback: Lookback period for normalization (mean and standard deviation)
Main Line Smoothing: EMA length applied to the averaged Z-Score
Slope Detection Index: Used to calculate directional flips for signal logic
Enable Background Highlighting: Enables visual region coloring in
overbought/oversold areas
Force Visible Y-Axis Scale: Forces max/min bounds for a consistent oscillator range
Divergence Settings
Enable Divergence Detection: Toggles divergence logic
Pivot Lookback Left / Right: Defines the structure of oscillator pivot points
Minimum / Maximum Bars Between Pivots: Controls the allowed spacing range for divergence validation
Bar Coloring Settings
Bar Coloring Mode:
➜ Line Color: Colors bars based on oscillator slope
➜ Latest Confirmed Signal: Colors bars based on the most recent confirmed divergence
➜ Any Latest Signal: Colors based on the most recent signal (TP or divergence)
SA-EMA Settings
RSI Length: RSI period used to determine adaptivity
Z-Score Length: Lookback for normalizing RSI in adaptive logic
Base EMA Length: Base length for smoothing before adaptivity
Adaptivity Intensity: Scales the smoothing responsiveness based on RSI deviation
Slope Index: Determines slope direction for coloring and band logic
Band ATR Length / Band Multiplier: Controls the width and responsiveness of the trend-following bands
Alerts
The script includes the following alert conditions:
Buy Signal (TP reversal detected in oversold zone)
Sell Signal (TP reversal detected in overbought zone)
Confirmed Bullish Divergence (oscillator HL, price LL)
Confirmed Bearish Divergence (oscillator LH, price HH)
These alerts allow integration into automation systems or signal monitoring setups.
Summary
Uptrick: Dynamic Z-Score Divergence is a statistically grounded trading indicator that merges normalized multi-momentum analysis with real-time slope logic, divergence detection, and adaptive trend overlays. It helps traders identify mean-reversion conditions, divergence structures, and evolving trend zones using a modular system of statistical and structural tools. Its alert system, layered visuals, and flexible input design make it suitable for discretionary traders seeking to combine quantitative momentum logic with structural pattern recognition.
Disclaimer
This script is for educational and informational purposes only. No indicator can guarantee future performance, and trading involves risk. Always use risk management and test strategies in a simulated environment before deploying with live capital.
Baseline Deviation Oscillator [Alpha Extract]A sophisticated normalized oscillator system that measures price deviation from a customizable moving average baseline using ATR-based scaling and dynamic threshold adaptation. Utilizing advanced HL median filtering and multi-timeframe threshold calculations, this indicator delivers institutional-grade overbought/oversold detection with automatic zone adjustment based on recent oscillator extremes. The system's flexible baseline architecture supports six different moving average types while maintaining consistent ATR normalization for reliable signal generation across varying market volatility conditions.
🔶 Advanced Baseline Construction Framework
Implements flexible moving average architecture supporting EMA, RMA, SMA, WMA, HMA, and TEMA calculations with configurable source selection for optimal baseline customization. The system applies HL median filtering to the raw baseline for exceptional smoothing and outlier resistance, creating ultra-stable trend reference levels suitable for precise deviation measurement.
// Flexible Baseline MA System
ma(src, length, type) =>
if type == "EMA"
ta.ema(src, length)
else if type == "TEMA"
ema1 = ta.ema(src, length)
ema2 = ta.ema(ema1, length)
ema3 = ta.ema(ema2, length)
3 * ema1 - 3 * ema2 + ema3
// Baseline with HL Median Smoothing
Baseline_Raw = ma(src, MA_Length, MA_Type)
Baseline = hlMedian(Baseline_Raw, HL_Filter_Length)
🔶 ATR Normalization Engine
Features sophisticated ATR-based scaling methodology that normalizes price deviations relative to current volatility conditions, ensuring consistent oscillator readings across different market regimes. The system calculates ATR bands around the baseline and uses half the band width as the normalization factor for volatility-adjusted deviation measurement.
🔶 Dynamic Threshold Adaptation System
Implements intelligent threshold calculation using rolling window analysis of oscillator extremes with configurable smoothing and expansion parameters. The system identifies peak and trough levels over dynamic windows, applies EMA smoothing, and adds expansion factors to create adaptive overbought/oversold zones that adjust to changing market conditions.
1D
3D
1W
🔶 Multi-Source Configuration Architecture
Provides comprehensive source selection including Close, Open, HL2, HLC3, and OHLC4 options for baseline calculation, enabling traders to optimize oscillator behavior for specific trading styles. The flexible source system allows adaptation to different market characteristics while maintaining consistent ATR normalization methodology.
🔶 Signal Generation Framework
Generates bounce signals when oscillator crosses back through dynamic thresholds and zero-line crossover signals for trend confirmation. The system identifies both standard threshold bounces and extreme zone bounces with distinct alert conditions for comprehensive reversal and continuation pattern detection.
Bull_Bounce = ta.crossover(OSC, -Active_Lower) or
ta.crossover(OSC, -Active_Lower_Extreme)
Bear_Bounce = ta.crossunder(OSC, Active_Upper) or
ta.crossunder(OSC, Active_Upper_Extreme)
// Zero Line Signals
Zero_Cross_Up = ta.crossover(OSC, 0)
Zero_Cross_Down = ta.crossunder(OSC, 0)
🔶 Enhanced Visual Architecture
Provides color-coded oscillator line with bullish/bearish dynamic coloring, signal line overlay for trend confirmation, and optional cloud fills between oscillator and signal. The system includes gradient zone fills for overbought/oversold regions with configurable transparency and threshold level visualization with automatic label generation.
snapshot
🔶 HL Median Filter Integration
Features advanced high-low median filtering identical to DEMA Flow for exceptional baseline smoothing without lag introduction. The system constructs rolling windows of baseline values, performs median extraction for both odd and even window lengths, and eliminates outliers for ultra-clean deviation measurement baseline.
🔶 Comprehensive Alert System
Implements multi-tier alert framework covering bullish bounces from oversold zones, bearish bounces from overbought zones, and zero-line crossovers in both directions. The system provides real-time notifications for critical oscillator events with customizable message templates for automated trading integration.
🔶 Performance Optimization Framework
Utilizes efficient calculation methods with optimized array management for median filtering and minimal computational overhead for real-time oscillator updates. The system includes intelligent null value handling and automatic scale factor protection to prevent division errors during extreme market conditions.
🔶 Why Choose Baseline Deviation Oscillator ?
This indicator delivers sophisticated normalized oscillator analysis through flexible baseline architecture and dynamic threshold adaptation. Unlike traditional oscillators with fixed levels, the BDO automatically adjusts overbought/oversold zones based on recent oscillator behavior while maintaining consistent ATR normalization for reliable cross-market and cross-timeframe comparison. The system's combination of multiple MA type support, HL median filtering, and intelligent zone expansion makes it essential for traders seeking adaptive momentum analysis with reduced false signals and comprehensive reversal detection across cryptocurrency, forex, and equity markets.
INMERELO EMA Reclaim HighlighterOverview
The INMERELO EMA Reclaim indicator highlights intraday candles reclaiming a configurable EMA on any timeframe. It identifies candles based on customizable candle geometry filters and confirms momentum using a custom MACD setup.
Features
Configurable Intraday EMA
Any EMA length and timeframe. Default: 6-period EMA on chart timeframe.
Highlights when price reclaims the EMA after a configurable number of prior closes below it.
Candle Geometry Filters (ORB-Style)
Open Position: Maximum position of open relative to candle range (0–1). Default: 0.40
Close Position: Minimum position of close relative to candle range (0–1). Default: 0.70
Body Fraction: Minimum body size relative to candle range. Default: 0.50
Custom MACD Filter
Fast line above slow line.
Configurable: Fast (default 6), Slow (default 20), Signal (default 9).
Prior Closes Below EMA Filter
Configurable minimum number of prior closes below EMA. Default: 2
Visual Options
Paint candle with configurable color.
Optional arrow display above reclaim candle (toggleable).
Flexible
Works on any intraday timeframe, including 5-minute, 2-minute, 15-minute, etc.
Settings Overview
Setting Default Notes
EMA Length 6 EMA used for reclaim detection
EMA Timeframe Chart TF Can be set to any intraday timeframe
Open ≤ 0.40 ORB-style filter
Close ≥ 0.70 ORB-style filter
Body Fraction 0.50 ORB-style filter
Min Prior Closes Below EMA 2 Minimum closes below EMA before reclaim
MACD Fast 6 Custom MACD fast line
MACD Slow 20 Custom MACD slow line
MACD Signal 9 Custom MACD signal line
Paint Candle True Highlights valid candles
Candle Color Lime Configurable
Show Arrow False Optional visual
Summary:
The INMERELO EMA Reclaim indicator identifies intraday candles reclaiming a configurable EMA, filtered by customizable candle geometry and MACD momentum. Visual options include painted candles and optional arrows, and all settings are fully configurable.
Market Extreme Zones IndexThe Market Extreme Zones Index is a new mean reversion (valuation) tool focused on catching long term oversold/overbought zones. Combining an enhanced RSI with a smoothed Z-score this indicator allows traders to find oppurtunities during highly oversold/overbought zones.
I will separate the explanation into the following parts:
1. How does it work?
2. Methodologies & Concepts
3. Use cases
How does it work?
The indicator attempts to catch highly unprobable events in either direction to capture reversal points over the long term. This is done by calculating the Z-Score of an enhanced RSI.
First we need to calculate the Enhanced RSI:
For this we need to calculate 2 additional lengths:
Length1 = user defined length
Length2 = Length1/2
Length3 = √Length
Now we need to calculate 3 different RSIs:
1st RSI => uses classic user defined source and classic user defined length.
2nd RSI => uses classic user defined source and Length 2.
3rd RSI => uses RSI 2 as source and Length 2
Now calculate the divergence:
RSI_base => 2nd RSI * 3 - 1st RSI - 3rd RSI
After this we need to calculate the median of the RSI_base over √Length and make a divergence of these 2:
RSI => RSI_base*2 - median
All that remains now is the Z-score calculations:
We need:
Average RSI value
Standard Deviation = a measure of how dispersed or spread out a set of data values are from their average
Z-score = (Current Value - Average Value) / Standard Deviation
After this we just smooth the Z-score with a Weighted Moving average with √Length
Methodology & Concepts
Mean Reversion Methodology:
The methodology behind mean reversion is the theory that asset prices will eventually return to their long-term average after deviating significantly, driven by the belief that extreme moves are temporary.
Z-Score Methodology:
A Z-score, or standard score, is a statistical measure that indicates how many standard deviations a data point is from the mean of a dataset. A positive z-score means the value is above the mean, a negative score means it's below, and a score of zero means the value is equal to the mean.
You might already be able to see where I am going with this:
Z-Score could be used for the extreme moves to capture reversal points.
By applying it to the RSI rather than the Price, we get a more accurate measurement that allow us to get a banger indicator.
Use Cases
Capturing reversal points
Trend Direction
- while the main use it for mean reversion, the values can indicate whether we are in an uptrend or a downtrend.
Advantages:
Visualization:
The indicator has many plots to ensure users can easily see what the indicator signals, such as highlighting extreme conditions with background colors.
Versatility:
This indicator works across multiple assets, including the S&P500 and more, so it is not only for crypto.
Final note:
No indicator alone is perfect.
Backtests are not indicative of future performance.
Hope you enjoy Gs!
Good luck!
Screener (ILPAC) [AlgoAlpha]🟠 OVERVIEW
This script is a powerful multi-symbol scanner designed to work as a companion to the "Institutional Liquidity & PA Concepts" (ILPAC) indicator. It allows you to monitor the key price action and liquidity signals from the ILPAC suite across a watchlist of up to 18 assets, all from a single dashboard. The primary goal of this tool is to provide a high-level market overview, enabling you to efficiently spot assets that are showing strong structural trends, interacting with key liquidity zones, or exhibiting signs of FOMO-driven volatility.
Instead of switching between dozens of charts, you can use this screener to quickly filter for assets that meet your specific trading criteria based on the advanced concepts of market structure, liquidity analysis, trend lines, and market sentiment.
🟠 CONCEPTS
The screener is built upon the core analytical engine of the "Institutional Liquidity & PA Concepts" indicator. It applies the proprietary algorithms of the ILPAC indicator to each symbol in your watchlist and presents the results in an easy-to-digest table. The concepts are combined to create a holistic view of the market.
Each column in the table is a window into a specific trading concept:
Market Structure: This is the foundation of price action analysis. The screener identifies the current market trend (bullish or bearish) by tracking swing highs and lows. It also flags critical events like a Break of Structure (BOS), which signals trend continuation, and a Change of Character (CHoCH), which suggests a potential trend reversal.
Liquidity Analysis: The screener analyzes order flow to determine where significant liquidity is resting. The "Liquidity Bias" column shows the net direction of this pressure, while the "Liquidity Event" column alerts you when price interacts with these key zones, either by forming a new one or mitigating an old one.
Trend Lines: This concept automates the classic technical analysis technique of drawing trend lines. The screener identifies significant swing points to form trend lines and then monitors them, alerting you to potential trend continuations or breakouts.
FOMO Bubbles: This concept measures crowd psychology by identifying sudden spikes in volume and price movement that are characteristic of "Fear of Missing Out." These signals can help identify potential trend exhaustion points or the start of a speculative rally.
By presenting these distinct but interconnected concepts together, the screener provides a multi-faceted view that allows traders to build a strong, confluence-based trading thesis.
🟠 FEATURES
This screener organizes a vast amount of data into a simple, color-coded table. Here is a breakdown of each column and the values you can expect to see:
Asset: Displays the ticker symbol for the asset being analyzed.
Market Structure: Shows the dominant trend based on swing highs and lows.
Bull: The asset is in a structural uptrend (making higher highs and higher lows).
Bear: The asset is in a structural downtrend (making lower highs and lower lows).
Detecting: The trend is neutral or a clear structure has not yet been established.
Structure Event: Flags the most recent significant market structure event.
Bull CHoCH: A bullish Change of Character, signaling a potential shift from a downtrend to an uptrend.
Bear CHoCH: A bearish Change of Character, signaling a potential shift from an uptrend to a downtrend.
Bull BOS: A bullish Break of Structure, confirming the continuation of an uptrend.
Bear BOS: A bearish Break of Structure, confirming the continuation of a downtrend.
–: No significant event has occurred recently.
Latest Swing Label: Identifies the most recently confirmed swing point.
HH: Higher High.
HL: Higher Low.
LH: Lower High.
LL: Lower Low.
–: No new swing point has been confirmed.
Liquidity Bias: Measures the net direction of liquidity and its relative strength.
▲ : A bullish liquidity bias, where the number indicates the strength.
▼ : A bearish liquidity bias, where the number indicates the strength.
Balanced: Liquidity is relatively balanced between buyers and sellers.
Liquidity Event: Indicates recent interactions with key liquidity zones.
New▲: A new bullish liquidity zone has just formed.
New▼: A new bearish liquidity zone has just formed.
Mit▲: Price has just tested (mitigated) a key bullish liquidity zone.
Mit▼: Price has just tested (mitigated) a key bearish liquidity zone.
–: No recent interaction.
Trend Line: Displays the status of automatically drawn trend lines.
Break▲: Price has broken above a key bearish trend line.
Break▼: Price has broken below a key bullish trend line.
Bull TL: Price is respecting an active bullish trend line.
Bear TL: Price is respecting an active bearish trend line.
–: No significant trend line is currently active.
FOMO: Detects sentiment-driven price moves of varying intensity.
Big▲/Med▲/Small▲: A bullish FOMO bubble has been detected (large, medium, or small).
Big▼/Med▼/Small▼: A bearish FOMO bubble has been detected (large, medium, or small).
–: No FOMO activity detected.
🟠 USAGE
The primary way to use this screener is to quickly scan your watchlist for assets that exhibit a confluence of bullish or bearish signals, which can significantly improve the probability of a trade.
1. Setup and Configuration:
Add the screener to your chart.
Open the settings and populate the "Watchlist" section with the symbols you want to track.
Fine-tune the input settings for each component (Market Structure, Liquidity, etc.) to match your preferred trading style. These settings will apply to all symbols in the table.
2. Interpreting the Columns for Trading Decisions:
Market Structure Columns: Use the first three structure columns to define your trading bias. For a high-probability long setup, you would look for an asset with a "Bull" structure, a recent "Bull BOS" event, and a "HL" as the latest swing point. This confirms the uptrend is healthy and ongoing.
Liquidity Columns: These are crucial for identifying key price levels. A strong "Liquidity Bias" can confirm your directional bias. A "Mit▲" (mitigation) event at a support level can be a powerful entry trigger, as it shows that institutional buy orders are defending that zone.
Trend Line Column: This is ideal for breakout traders. A "Break▲" signal can serve as an excellent entry confirmation, especially if the overall "Market Structure" is already "Bull".
FOMO Column: This column is best used for identifying potential exhaustion points. For instance, if you are in a long trade and a "Big▲" FOMO signal appears after a strong rally, it could be a sign that the move is overextended and it's a good time to consider taking profits.
Script a pagamento
Screener (MC) [AlgoAlpha]🟠 OVERVIEW
This script is a multi-symbol scanner that works as a companion to the "Momentum Concepts" indicator. It provides a comprehensive dashboard view, allowing traders to monitor the momentum signals of up to 18 different assets in real-time from a single chart. The main purpose is to offer a bird's-eye view of the market, helping you quickly identify assets with strong momentum confluence or potential reversal opportunities without having to switch between different charts.
The screener displays the status of all key components from the Momentum Concepts indicator, including the Fast Oscillator, Scalper's Momentum, Momentum Impulse Oscillator, and Hidden Liquidity Flow, organizing them into a clear and easy-to-read table.
🟠 CONCEPTS
The core of this screener is built upon the analytical framework of the "Momentum Concepts" indicator, which evaluates market momentum across multiple layers: short-term, medium-term, and long-term. This screener applies those complex, proprietary calculations to each symbol in your watchlist and visualizes the current state of each component.
Each column in the table represents a specific aspect of momentum analysis:
Fast Oscillator Columns: These columns reflect the short-term momentum. They show the immediate trend direction, whether the asset is in an overbought or oversold condition, and flag high-probability events like divergences, reversals, or diminishing momentum.
Scalper's Momentum Column: This column gives insight into medium-term momentum. It distinguishes between strong, sustained moves and weakening, corrective moves, which is useful for gauging the health of a trend.
Momentum Impulse Column: This column represents the dominant, long-term trend bias. It helps you understand the underlying market regime (bullish, bearish, or consolidating) to align your trades with the bigger picture.
Hidden Liquidity Flow Column: This column provides a unique view into the market's underlying liquidity dynamics. It signals whether there is net buying or selling pressure and uses special coloring to highlight periods of unusually high liquidity activity, which often precedes volatile price movements.
By combining these perspectives, the screener justifies its utility by enabling traders to make more informed decisions based on multi-layered signal confluence.
🟠 FEATURES
This screener organizes momentum data into several key columns. Here is a breakdown of each column and its possible values:
Asset: Displays the symbol for the asset being analyzed in that row.
Fast Oscillator Trend: Shows the immediate, short-term momentum direction.
▲: Indicates a bullish short-term trend.
▼: Indicates a bearish short-term trend.
–: Indicates a neutral or transitional state.
Fast Oscillator Valuation: Measures whether the asset is in a short-term overbought or oversold state.
OB: Signals an "Overbought" condition, often associated with bullish exhaustion.
OS: Signals an "Oversold" condition, often associated with bearish exhaustion.
Neutral: The asset is trading in a neutral zone, neither overbought nor oversold.
Scalper's Momentum: Assesses the strength and direction of medium-term momentum.
Strong▲: Strong bullish momentum.
Weak▲: Bullish momentum exists but is weakening or corrective.
Strong▼: Strong bearish momentum.
Weak▼: Bearish momentum exists but is weakening or corrective.
–: Neutral or no clear medium-term momentum.
Momentum Impulse: Identifies the dominant, long-term trend bias. A colored background indicates that the momentum is in a strong "impulse" phase.
▲: Indicates a bullish long-term bias.
▼: Indicates a bearish long-term bias.
0: Indicates a neutral or ranging market condition.
Hidden Liquidity Flow: Tracks underlying buying and selling pressure. The background color highlights periods of unusual liquidity activity.
▲: Positive liquidity flow, suggesting net buying pressure.
▼: Negative liquidity flow, suggesting net selling pressure.
–: Neutral liquidity flow.
Dim. Momentum: Provides an early warning that short-term momentum is beginning to fade.
● (Bullish Color): Bullish momentum is weakening.
● (Bearish Color): Bearish momentum is weakening.
–: No diminishing momentum detected.
Divergence: Flags classic or hidden divergences between price and the Fast Oscillator.
Div▲: A bullish divergence has been detected.
Div▼: A bearish divergence has been detected.
–: No active divergence signal.
Reversal: Signals a potential reversal when the Fast Oscillator crosses its trend line from an overbought or oversold zone.
Rev▲: A bullish reversal signal has occurred.
Rev▼: A bearish reversal signal has occurred.
–: No active reversal signal.
🟠 USAGE
The primary function of this screener is to quickly identify trading opportunities and filter setups based on momentum confluence across your watchlist.
1. Setup and Configuration:
Add the indicator to your chart.
Go into the script settings and populate the "Watchlist" group with the symbols you wish to monitor.
Adjust the settings for the various momentum components (Fast Oscillator, Scalper's Momentum, etc.) to align with your trading strategy. These settings will be universally applied to all symbols in the screener.
2. Interpreting the Columns for Trading Decisions:
Momentum Impulse & Hidden Liquidity Flow: Use these columns to establish a directional bias. A bullish "▲" in both columns on an asset suggests a strong underlying uptrend with supportive buying pressure, making it a good candidate for long positions.
Scalper's Momentum: Use this for entry timing and trend health. A "Strong▲" reading can confirm the strength of an uptrend, while a shift to "Weak▲" might suggest it's time to tighten stops or look for an exit.
Fast Oscillator Trend & Valuation: These are best for precise entry triggers. For a "buy the dip" strategy in an uptrend, you could wait for the Fast Oscillator to show "OS" (Oversold) and then enter when the "Trend" column flips back to "▲".
Dim. Momentum: This is an excellent take-profit signal. If you are in a long position and a bullish-colored "●" appears, it's a warning that the upward move is losing steam, and you might consider closing your trade.
Divergence & Reversal: These columns are for identifying potential turning points. A "Div▲" or "Rev▲" signal is a strong alert that a downtrend might be ending, making the asset a prime candidate to watch for a long entry.
3. Finding High-Probability Setups:
Trend Confluence: Look for assets where multiple components show alignment. For example, an ideal long setup might show a bullish "Momentum Impulse" (▲), a "Strong▲" reading in "Scalper's Momentum," and a bullish trend in the "Fast Oscillator." This indicates that the long-term, medium-term, and short-term momentums are all in agreement.
Reversal and Exhaustion: Use the "Divergence" and "Reversal" columns to spot potential turning points. A "Div▲" signal appearing in an asset that is in an oversold "Fast Oscillator Valuation" zone can be a strong indication of an upcoming bounce.
Script a pagamento
Consolidation Value Zones (Recio)Consolidation Value Zones introduces an original algorithm to identify consolidation ranges and locate areas of importance within them. This new method "looks" at the chart and draws zones based on price with the goal of producing actionable zones which appear natural, as if they were found through a human analysis.
> Consider the following...
The chart image above displays Bitcoin, at no specific date, for no specific reason. What I have done here is simply glanced at the chart for about 5 seconds, and circled a few areas which stood out as "obvious" consolidation. It does not take a savant to look at a chart and circle ranging price. However, what we have just done defies many common systems for identifying consolidation. We have located ranges of various zone lengths, as small as roughly 25 bars to as large as roughly 100 bars. Regardless of this, we still determined these zones with our eyes and brain in a few seconds, for some it's practically instant. The issue with us humans doing this, is that we are subjective. We did not really use any concrete rules to determine these areas with our eyes. So the problem becomes "How do we identify these zones in a way which seems natural to us with a repeatable system?" Because of this, my approach is simply a logical attempt to reverse engineer our human intuition.
> Consolidation Value Zones
The name of this indicator is generic. To dissect it, we are identifying consolidation ranges, then using a volume profile to determine the value zone within that range. The specific method used to identify these consolidation zones is something I've personally been referring to as the "skewer" method. Another name that may fit better is "Linear Range Alignment/Overlap".
Ultimately, the goal is to locate a single price level or range that overlaps many adjacent bars.
This should, in theory, return areas of visually obvious consolidation.
> The Skewer Method (Identification Method & Bar Gap Allowances)
One consistent concept across the different identification methods for determining consolidation is time. How long do we chop around before calling it consolidation? This is the "Identification Threshold". Once we have located a consolidation zone "this" wide, we will then consider it as consolidation.
In the chart image above, we are considering a six-bar consolidation formation. The figure on the left shows an example of a perfect raw bar overlap, we can see that the six bars all overlap at one price range. This is a perfect example of what we are looking to identify as consolidation. Unfortunately, if this was all we looked at, we would have a very scarce identification method.
For that reason, we have the example on the right, which shows the additional allowances for the identification of these ranges. At most, the example on the right shows a gapless three-bar overlap. However, if we allow the identification to bridge across the gaps, we are able to draw a zone directly through the center and still be within our parameters. This allowance is the "Bar Gap Allowance" and will determine the leniency of the identification.
Between our identification threshold and bar gap allowance, we can start to piece together how the script is "looking" at our chart.
> Detecting Consolidation (Live Detection)
To aid in transparency and user understanding, the live detection calculation can be seen on the chart as a box, skewering the recent historical bars with a number next to it, indicating the number of bars found as potential consolidation.
As we can see in the chart image above, the script, by default, is looking for a 15-bar consolidation, with a 5-bar gap allowance. In the image, the specific gap count is labeled, we can see the script scan backwards as far as it can before counting five gaps in the data. Once that occurs, the detection stops.
Notice how the zone found is a range, consisting of all price levels which meet the parameters. The lower level of the range only had two gaps, but the upper level reached five.
> Consolidation Range and Value Zones (Volume Profiles)
Once the script has identified the consolidation formation, it calculates a volume profile across the identified consolidation range. From this it calculates and draws the Point of Control (POC) and Value Area in addition to the full consolidation range.
Once we have our zones drawn, and understand what they identify, we can go one step further and apply concepts from volume profile trading.
Range High/Low: Displays the current extent of the identified consolidation.
Value High/Low: Shows the specific area within the consolidation where buyers and sellers found the most value.
POC: The single point, where the most volume was transacted during consolidation.
In a balanced market, we would anticipate price to rotate around POC, oscillating from Value High (VAH) to Value Low (VAL). In contrast, a market in motion moves directionally, building volume at new price levels as value, naturally the POC shifts with it.
> Zone Extensions
Unlike many other scripts, there is no mitigation logic at play here, since crossing a zone simply tells us "buyers and sellers are not currently active here", but it does not guarantee that value cannot return or react from previous areas of value.
Obviously the current zone will always be most relevant, but historical zones can retain relevance depending on the context of the market.
Remember: Each area of consolidation is an area where buyers and sellers were once facing off, resulting in price's consolidation. Amidst this, the value zone was the area of greatest agreement between the participants at that time. When moving outside of a range, we would typically look at historical value areas and price's interaction with them for further context.
Due to the ever changing market, there is no fixed extension lookback that will cover every scenario. By default, the Extension Lookback is "1", meaning the script will extend the most recent zone forward until a new zone is detected.
Note: For clarity, zone extensions are colored differently from core zones.
The following chart image shows a few examples of these unique interactions.
As seen in the chart image, looking to previous areas of value as well as POC can provide context in the form of acceptance or rejection at these levels, providing further insight into the auction for us to respond to.
The zones do contain logic to maintain a clean display. By default, the zones extend conditionally when price returns to the previous consolidation range. If desired, the zones can be extended regardless of price action; this can be toggled with the option "Regardless Extension Mode", as seen below.
> Hollow Candles & Zone Merging
When consolidation is identified, a hollow candle is drawn; these can be used to see exactly when each zone is identified. It is important to understand that consolidation zones stemming from the same origin are merged into one zone. This is a frequent occurrence when the consolidation threshold is passed, but the consolidation continues. For this reason you will often see multiple hollow candles in the later areas of the zones.
Similarly, zones from different origin points that overlap are also merged into one consolidation zone. This ensures that no core zones overlap.
Additionally, every time a zone is merged, a new volume profile for the area is calculated.
> Bar Gap Allowance Type (Technical Explanation)
The specific bar gap allowance value can be altered, but so can the type of allowance being used. While some analyses may benefit from counting the total amount of bar gaps within the consolidation, others may benefit from detecting based on consecutive bar gaps.
The chart image above displays the gap counts for each gap allowance type.
The total bar gap allowance type will count until the gap amount is reached, then terminate detection once the allowed number of gaps has been exceeded.
The consecutive bar gap allowance type resets its count once it finds a valid bar within range, by doing so, it only counts the bars that separate each island of in-range bars.
Both methods have merit.
> Implementation
This identification method has proven effective to identify consolidation across market types. As a result, there cannot be one configuration of settings to fit every application. Adapting the detection type and method for each trader's specific market conditions is highly recommended.
When determining parameters, it is helpful to consider time, as it plays a major role in the identification method.
On a 1D chart, the default threshold of 15 corresponds to 15 days, or about 3 weeks depending on the ticker. To identify periods of one-week consolidation, a threshold of 5 would be suitable. To detect perfect gapless weeks, a bar gap allowance of 0 could be used, as seen in the chart image below.
Additional Example:
In the chart image above, we see a 15-second forex chart over the span of a few hours. The detection parameters are set up to detect 15-minute consolidation with a 2-minute max dead zone (consecutive bar gap).
> Detection Source
By default, the script detects consolidation ranges using the full extent of candle wicks. While this is traditional, detection can also be done using only the candle bodies. These identifications are much more nuanced, detecting only from confirmed candle price action; they do not trigger at the same frequency as wick detection.
Optionally, a "Wick/Body Average" can be chosen as the source for detection; as the name implies, this uses the average value between the candle body and its respective wick.
> Additional Settings
The settings mentioned thus far serve as core parameters for identifying consolidation. The following parameters are simply included for the benefit of the advanced user. It is not recommended to adjust these settings under normal circumstances.
- Value Area Percent: Default = 68.26, while traditionally 70 for volume profiles, 68.26 is accurate to the values of a standard bell-curve distribution. The differences are minimal in application.
- VP Rows: Default = 99, Sets the number of rows to be used when calculating the Volume Profiles (VP); note that higher values will lead to a slower calculation. Max value: 999
> Final Notes
If you have made it this far, thank you for reading.
I hope you find value in this new consolidation identification system and understand the logic behind it.
That's it.
Script a pagamento
Stochastic + Bollinger Bands Multi-Timeframe StrategyThis strategy fuses the Stochastic Oscillator from the 4-hour timeframe with Bollinger Bands from the 1-hour timeframe, operating on a 10-hour chart to capture a unique volatility rhythm and temporal alignment discovered through observational alpha.
By blending momentum confirmation from the higher timeframe with short-term volatility extremes, the strategy leverages what some traders refer to as “rotating volatility” — a phenomenon where multi-timeframe oscillations sync to reveal hidden trade opportunities.
🧠 Strategy Logic
✅ Long Entry Condition:
Stochastic on the 4H timeframe:
%K crosses above %D
Both %K and %D are below 20 (oversold zone)
Bollinger Bands on the 1H timeframe:
Price crosses above the lower Bollinger Band, indicating a potential reversal
→ A long trade is opened when both momentum recovery and volatility reversion align.
✅ Long Exit Condition:
Stochastic on the 4H:
%K crosses below %D
Both %K and %D are above 80 (overbought zone)
Bollinger Bands on the 1H:
Price reaches or exceeds the upper Bollinger Band, suggesting exhaustion
→ The long trade is closed when either signal suggests a potential reversal or overextension.
🧬 Temporal Structure & Alpha
This strategy is deployed on a 10-hour chart — a non-standard timeframe that may align more effectively with multi-timeframe mean reversion dynamics.
This subtle adjustment exploits what some traders identify as “temporal drift” — the desynchronization of volatility across timeframes that creates hidden rhythm in price action.
→ For example, Stochastic on 4H (lookback 17) and Bollinger Bands on 1H (lookback 20) may periodically sync around 10H intervals, offering unique alpha windows.
📊 Indicator Components
🔹 Stochastic Oscillator (4H, Length 17)
Detects momentum reversals using %K and %D crossovers
Helps define overbought/oversold zones from a mid-term view
🔹 Bollinger Bands (1H, Length 20, ±2 StdDev)
Measures price volatility using standard deviation around a moving average
Entry occurs near lower band (support), exits near upper band (resistance)
🔹 Multi-Timeframe Logic
Uses request.security() to safely reference 4H and 1H indicators from a 10H chart
Avoids repainting by using closed higher-timeframe candles only
📈 Visualization
A plot selector input allows toggling between:
Stochastic Plot (%K & %D, with overbought/oversold levels)
Bollinger Bands Plot (Upper, Basis, Lower from 1H data)
This helps users visually confirm entry/exit triggers in real time.
🛠 Customization
Fully configurable Stochastic and BB settings
Timeframes are independently adjustable
Strategy settings like position sizing, slippage, and commission are editable
⚠️ Disclaimer
This strategy is intended for educational and informational purposes only.
It does not constitute financial advice or a recommendation to buy or sell any asset.
Market conditions vary, and past performance does not guarantee future results.
Always test any trading strategy in a simulated environment and consult a licensed financial advisor before making real-world investment decisions.
McMillan Volatility Bands (MVB) – with Entry Logic// McMillan Volatility Bands (MVB) with signal + entry logic
// Author: ChatGPT for OneRyanAlexander
// Notes:
// - Bands are computed using percentage volatility (log returns), per the Black‑Scholes framing.
// - Inner band (default 3σ) and outer band (default 4σ) are configurable.
// - A setup occurs when price closes outside the outer band, then closes back within the inner band.
// The bar that re‑enters is the "signal bar." We then require price to trade beyond the signal bar's
// extreme by a user‑defined cushion (default 0.34 * signal bar range) to confirm entry.
// - Includes alertconditions for both setups and confirmed entries.
Reversals & Pullbacks PRO🚀 Reversals & Pullbacks Pro — Predict Market Turning Points with Precision
Stop chasing trends — start anticipating them.
The Reversals & Pullbacks Pro indicator identifies high-probability reversal and pullback zones before they happen, using advanced mean reversion logic and momentum change signals.
What it does:
✅ Detects major reversals and minor pullbacks in real time
✅ Uses dynamic mean reversion algorithms to spot over-extended price moves
✅ Highlights premium entry zones for counter-trend and trend-reversal setups
✅ Works across many markets — Designed for Forex and Indices but can be used on Crypto
✅ Clean visuals with smart alerts (no repainting after candle close)
💡 Perfect for:
Swing traders, scalpers, and day traders who want to catch price turning points before everyone else.
⏱️ Don’t react — predict.
Upgrade your trading with Reversals & Pullback Pro and trade market reversals like a PRO!
BB SPY Mean Reversion Investment StrategySummary
Mean reversion first, continuation second. This strategy targets equities and ETFs on daily timeframes. It waits for price to revert from a Bollinger location with candle and EMA agreement, then manages risk with ATR based exits. Uniqueness comes from two elements working together. One, an adaptive band multiplier driven by volatility of volatility that expands or contracts the envelope as conditions change. Two, a bias memory that re arms the same direction after any stop, target, or time exit until a true opposite signal appears. Add it to a clean chart, use the markers and levels, and select on bar close for conservative alerts. Shapes can move while the bar is open and settle on close.
Scope and intent
• Markets. Currently adapted for SPY, needs to be optimized for other assets
• Timeframes. Daily primary. Other frames are possible but not the default
• Default demo. SPY on daily
• Purpose. Trade mean reversion entries that can chain into a longer swing by splitting holds into ATR or time segments
Originality and usefulness
• Novelty. Adaptive band width from volatility of volatility plus a persistent bias array that keeps the original direction alive across sequential entries until an opposite setup is confirmed
• Failure modes mitigated. False starts in chop are reduced by candle color and EMA location. Missed continuation after a take profit or stop is addressed by the re arm engine. Oversized envelopes during quiet regimes are avoided by the adaptive multiplier
• Testability. Every module has Inputs and visible levels so users can see why a suggestion appears
• Portable yardstick. All risk and targets are expressed in ATR units
Method overview in plain language
The engine measures where price sits relative to Bollinger bands, confirms with candle color and EMA location, requires ADX for shorts(in our case long close since we use it currently as long only), and optionally requires a trend or mean reversion regime using band width percent rank and basis slope. Risk uses ATR for stop, target, and optional breakeven. A small array stores the last confirmed direction. While flat, the engine keeps a pending order in that direction. The array flips only when a true opposite setup appears.
Base measures
• Range basis. True Range smoothed over a user defined ATR Length
• Return basis. Not required
Components
• Bollinger envelope. SMA length and standard deviation multiplier. Entry is based on cross of close through the band with location bias
• Candle and EMA filter. Close relative to open and close relative to EMA align direction
• ADX gate for shorts. Requires minimum trend strength for short trades
• Adaptive multiplier. Band width scales using volatility of volatility so envelopes breathe with conditions
• Regime gate optional. Band width percent rank and basis slope identify trend or mean reversion regimes
• Risk manager. ATR stop, ATR target, optional breakeven, optional time exit
• Bias memory. Array stores last confirmed direction and re arms entries while flat
Fusion rule
Minimum satisfied gates count style. All required gates must be true. Optional gates are controlled in Inputs. Bias memory never overrides an opposite confirmed setup.
Signal rule
• Long setup when close crosses up through the lower band, the bar closes green, and close is above the long EMA
• Short setup when close crosses down through the upper band, the bar closes red, close is below the short EMA, and ADX is above the minimum
• While flat the model keeps a pending order in the stored direction until a true opposite setup appears
• IN LONG or IN SHORT describes states between entry and exit
What you will see on the chart
• Markers for Long and Short setups
• Exit markers from ATR or time rules
• Reference levels for entry, stop, and target
• Bollinger bands and optional adaptive bands
Inputs with guidance
Setup
• Signal timeframe. Uses the chart timeframe
• Invert direction optional. Flips long and short
Logic
• BB Length. Typical 10 to 50. Higher smooths more
• BB Mult. Typical 1.0 to 2.5. Higher widens entries
• EMA Length long. Typical 10 to 50
• EMA Length short. Typical 5 to 30
• ADX Minimum for short. Typical 15 to 35
Filters
• Regime Type. none or trend or mean reversion
• Rank Lookback. Typical 100 to 300
• Basis Slope Length and Threshold. Larger values reduce false trends
Risk
• ATR Length. Typical 10 to 21
• ATR Stop Mult. Typical 1.0 to 3.0
• ATR Take Profit Mult. Typical 2.0 to 5.0
• Breakeven Trigger R. Move stop to entry after the chosen multiple
• Time Exit. Minimum bars and extension when profit exceeds a fraction of ATR
Bias and rearm
• Bias flips kept. Array depth
• Keep rearm when flat. Maintain a pending order while flat
UI
• Show markers and levels. Clean defaults
Usage recipes
Alerts update in real time and can change while the bar forms. Select on bar close for conservative workflows.
Properties visible in this publication
• Initial capital 25000
• Base currency USD
• If any higher timeframe calls are enabled, request.security uses lookahead off
• Commission 0.03 percent
• Slippage 3 ticks
• Default order size method Percent of equity with value 5
• Pyramiding 0
• Process orders on close On
• Bar magnifier Off
• Recalculate after order is filled Off
• Calc on every tick Off
Realism and responsible publication
No performance claims. Costs and fills vary by venue. Shapes can move intrabar and settle on close. Strategies use standard candles only.
Honest limitations and failure modes
High impact releases and thin liquidity can break assumptions. Gap heavy symbols may require larger ATR. Very quiet regimes can reduce contrast in the mean reversion signal. If stop and target can both be touched inside one bar, outcome follows the TradingView order model for that bar path.
Regimes with extreme one sided trend and very low volatility can reduce mean reversion edges. Results vary by symbol and venue. Past results never guarantee future outcomes.
Open source reuse and credits
None.
Backtest realism
Costs are realistic for liquid equities. Sizing does not exceed five percent per trade by default. Any departure should be justified by the user.
If you got any questions please le me know
Ornstein-Uhlenbeck Trend Channel [BOSWaves]Ornstein-Uhlenbeck Trend Channel - Adaptive Mean Reversion with Dynamic Equilibrium Geometry
Overview
The Ornstein-Uhlenbeck Trend Channel introduces an advanced equilibrium-mapping framework that blends statistical mean reversion with adaptive trend geometry. Traditional channels and regression bands react linearly to volatility, often failing to capture the natural rhythm of price equilibrium. This model evolves that concept through a dynamic reversion engine, where equilibrium adapts continuously to volatility, trend slope, and structural bias - forming a living channel that bends, expands, and contracts in real time.
The result is a smooth, equilibrium-driven representation of market balance - not just trend direction. Instead of static bands or abrupt slope shifts, traders see fluid, volatility-aware motion that mirrors the natural pull-and-release dynamic of market behavior. Each channel visualizes the probabilistic boundaries of fair value, showing where price tends to revert and where it accelerates away from its statistical mean.
Unlike conventional envelopes or Bollinger-type constructs, the Ornstein-Uhlenbeck framework is volatility-reactive and equilibrium-sensitive, providing traders with a contextual map of where price is likely to stabilize, extend, or exhaust.
Theoretical Foundation
The Ornstein-Uhlenbeck Trend Channel is inspired by stochastic mean-reversion processes - mathematical models used to describe systems that oscillate around a drifting equilibrium. While linear regression channels assume constant variance, financial markets operate under variable volatility and shifting equilibrium points. The OU process accounts for this by treating price as a mean-seeking motion governed by volatility and trend persistence.
At its core are three interacting components:
Equilibrium Mean (μ) : Represents the evolving balance point of price, adjusting to directional bias and volatility.
Reversion Rate (θ) : Defines how strongly price is pulled back toward equilibrium after deviation, capturing the self-correcting nature of market structure.
Volatility Coefficient (σ) : Controls how far and how quickly price can diverge from equilibrium before mean reversion pressure increases.
By embedding this stochastic model inside a volatility-adjusted framework, the system accurately scales across different markets and conditions - maintaining meaningful equilibrium geometry across crypto, forex, indices, or commodities. This design gives traders a mathematically grounded yet visually intuitive interpretation of dynamic balance in live market motion.
How It Works
The Ornstein-Uhlenbeck Trend Channel is constructed through a structured multi-stage process that merges stochastic logic with volatility mechanics:
Equilibrium Estimation Core : The indicator begins by identifying the evolving mean using adaptive smoothing influenced by trend direction and volatility. This becomes the live centerline - the statistical anchor around which price naturally oscillates.
Volatility Normalization Layer : ATR or rolling deviation is used to calculate volatility intensity. The output scales the channel width dynamically, ensuring that boundaries reflect current variance rather than static thresholds.
Directional Bias Engine : EMA slope and trend confirmation logic determine whether equilibrium should tilt upward or downward. This creates asymmetrical channel motion that bends with the prevailing trend rather than staying horizontal.
Channel Boundary Construction : Upper and lower bands are plotted at volatility-proportional distances from the mean. These envelopes form the “statistical pressure zones” that indicate where mean reversion or acceleration may occur.
Signal and Lifecycle Control : Channel breaches, mean crossovers, and slope flips mark statistically significant events - exhaustion, continuation, or rebalancing. Older equilibrium zones gradually fade, ensuring a clear, context-aware visual field.
Through these layers, the channel forms a continuously updating equilibrium corridor that adapts in real time - breathing with the market’s volatility and rhythm.
Interpretation
The Ornstein-Uhlenbeck Trend Channel reframes how traders interpret balance and momentum. Instead of viewing price as directional movement alone, it visualizes the constant tension between trending force and equilibrium pull.
Uptrend Phases : The equilibrium mean tilts upward, with price oscillating around or slightly above the midline. Upper band touches signal momentum extension; lower touches reflect healthy reversion.
Downtrend Phases : The mean slopes downward, with upper-band interactions marking resistance zones and lower bands acting as reversion boundaries.
Equilibrium Transitions : Flat mean sections indicate balance or distribution phases. Breaks from these neutral zones often precede directional expansion.
Overextension Events : When price closes beyond an outer boundary, it marks statistically significant disequilibrium - an early warning of exhaustion or volatility reset.
Visually, the OU channel translates volatility and equilibrium into structured geometry, giving traders a statistical lens on trend quality, reversion probability, and volatility stress points.
Strategy Integration
The Ornstein-Uhlenbeck Trend Channel integrates seamlessly into both mean-reversion and trend-continuation systems:
Trend Alignment : Use mean slope direction to confirm higher-timeframe bias before entering continuation setups.
Reversion Entries : Target rejections from outer bands when supported by volume or divergence, capturing snapbacks toward equilibrium.
Volatility Breakout Mapping : Monitor boundary expansions to identify transition from compression to expansion phases.
Liquidity Zone Confirmation : Combine with BOS or order-block indicators to validate structural zones against equilibrium positioning.
Momentum Filtering : Align with oscillators or volume profiles to isolate equilibrium-based pullbacks with statistical context.
Technical Implementation Details
Core Engine : Stochastic Ornstein-Uhlenbeck process for continuous mean recalibration.
Volatility Framework : ATR- and deviation-based scaling for dynamic channel expansion.
Directional Logic : EMA-slope driven bias for adaptive mean tilt.
Channel Composition : Independent upper and lower envelopes with smoothing and transparency control.
Signal Structure : Alerts for mean crossovers and boundary breaches.
Performance Profile : Lightweight, multi-timeframe compatible implementation optimized for real-time responsiveness.
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Reactive equilibrium tracking for short-term scalping and microstructure analysis.
15 - 60 min : Medium-range setups for volatility-phase transitions and intraday structure.
4H - Daily : Macro equilibrium mapping for identifying exhaustion, distribution, or reaccumulation zones.
Suggested Configuration:
Mean Length : 20 - 50
Volatility Multiplier : 1.5× - 2.5×
Reversion Sensitivity : 0.4 - 0.8
Smoothing : 2 - 5
Parameter tuning should reflect asset liquidity, volatility, and desired reversion frequency.
Performance Characteristics
High Effectiveness:
Trending environments with cyclical pullbacks and volatility oscillation.
Markets exhibiting consistent equilibrium-return behavior (indices, majors, high-cap crypto).
Reduced Effectiveness:
Low-volatility consolidations with minimal variance.
Random walk markets lacking definable equilibrium anchors.
Integration Guidelines
Confluence Framework : Pair with BOSWaves structural tools or momentum oscillators for context validation.
Directional Control : Follow mean slope alignment for directional conviction before acting on channel extremes.
Risk Calibration : Use outer band violations for controlled contrarian entries or trailing stop management.
Multi-Timeframe Synergy : Derive macro equilibrium zones on higher timeframes and refine entries on lower levels.
Disclaimer
The Ornstein-Uhlenbeck Trend Channel is a professional-grade equilibrium and volatility framework. It is not predictive or profit-assured; performance depends on parameter calibration, volatility regime, and disciplined execution. BOSWaves recommends using it as part of a comprehensive analytical stack combining structure, liquidity, and momentum context.
