20-Day SMA BIAS%20-day Bias is a commonly used indicator in technical analysis. It is used to measure the gap between the stock price and its 20-day moving average to determine whether the stock price deviates from the normal state and whether there is an overbought or oversold phenomenon.
How to calculate the 20-day deviation value:
The calculation formula of the deviation rate is: ((closing price of the day - 20-day moving average price) / 20-day moving average price) * 100%.
Interpretation of 20-day deviation value:
Positive deviation rate:
Indicates that the stock price is higher than the 20-day moving average, which means that the stock price is high and may face correction pressure.
Negative deviation rate:
Indicates that the stock price is lower than the 20-day moving average, which means that the stock price is low and there may be a rebound opportunity.
Absolute value of the deviation rate:
The larger the absolute value, the higher the deviation of the stock price, and the higher the degree of overbought or oversold.
Apply the deviation rate to determine the buying and selling opportunities:
Positive deviation rate is too large:
When the positive deviation rate of the stock price from the 20-day moving average is too large, and the stock price is already at a high level, this may be a sell signal.
Negative deviation rate is too large:
When the negative deviation rate of the stock price from the 20-day moving average is too large, and the stock price is already at a low level, this may be a buy signal.
Stock price fluctuates around the moving average:
Stock price usually fluctuates around the moving average and adjusts after over-rising or over-falling.
Practical operation suggestions:
The standards of the market and individual stocks are different:
When the positive and negative deviation rate of the market and the quarterly line is greater than 5%, there is a greater chance of correction; large-cap stocks are between 5% and 10%; small and medium-sized stocks may be above 15% to 20%.
Combined with other indicators:
The deviation rate is only one of the technical analysis indicators. It is recommended to combine it with other indicators, such as KD indicators, RSI, etc., to make a comprehensive judgment and improve accuracy.
Reference to historical experience:
You can refer to the situation where the deviation rate of the stock was too large in the past to determine whether the current deviation rate is also too large.
Summary:
The 20-day deviation value is an indicator to determine whether the stock price is overbought or oversold, which can help investors determine the timing of buying and selling, but it needs to be combined with other indicators and historical data, and adjusted according to market conditions.
Regressions
Quantum Harmonic Oscillator Overlay🧪 Quantum Harmonic Oscillator Overlay
A visual model of price behavior using quantum harmonic oscillation principles
📜 Indicator Overview
The Quantum Harmonic Oscillator Overlay applies concepts from both classical physics (harmonic motion) and quantum mechanics (energy states) to model and visualize how price orbits around a central trend line. It overlays a Linear Regression line (representing the “mean position” or ground state of price) and calculates surrounding energy levels (σ-zones) akin to quantum shells that price can "jump" between.
This indicator is particularly useful for visualizing mean reversion, volatility compression/expansion, and momentum-driven price breakthroughs.
🧠 Core Concepts
Linear Regression Line (LSR): This is the calculated center of gravity or equilibrium path of price over a user-defined period. Think of it like the lowest energy state or central axis around which price vibrates.
Standard Deviation Zones (σ-levels):
1σ: The majority of normal price activity; within this range, price tends to fluctuate if in balance.
2σ: Indicates volatility or possible breakout pressure.
3σ: Represents extreme movement — a phase shift in energy, potentially leading to reversal or continuation with higher momentum.
Quantum Analogy: Just like in a quantum harmonic oscillator, particles (here, prices) move probabilistically between discrete energy states. The further the price moves from the center, the more "energy" (momentum, volume, volatility) is implied.
⚙️ Input Parameters
Setting Description
Linear Regression Length The number of bars used to calculate the regression trend (default 100). Affects the central path and responsiveness.
σ Multipliers (1σ, 2σ, 3σ) Determine how far each band is from the regression line. Adjusting these can highlight different price behaviors.
Show Energy Level Zones Toggle visibility of the colored bands around the regression line.
Show LSR Center Line Toggles visibility of the white Linear Regression line itself.
🎨 Visual Components
Color Zone Interpretation
✅ Green ±1σ Normal oscillation / mean reversion area. Ideal for range-bound strategies.
🟧 Orange ±2σ Warning zone; price may be gaining momentum or volatility.
🔴 Red ±3σ High-momentum state or anomaly. These regions may imply trend exhaustion, reversals, or breakouts.
White Line: The LSR — the average trajectory of the price movement.
Pink Dots: Appear when price exceeds Zone 3 (outside ±3σ) — a signal of extreme behavior or a possible regime shift.
📈 How to Use This Indicator
1. Detect Overextensions
When price touches or breaches the 3σ zone, it is likely overextended. This can be used to anticipate potential snapbacks or strong breakout trends.
2. Identify Mean Reversion Trades
If price exits the 2σ or 3σ zones and returns toward the center line, this signals a likely mean reversion setup.
3. Volatility Compression or Expansion
Flat zones between σ levels suggest calm markets; widening bands suggest expanding volatility.
4. Use with Confirmation Tools
Combine with momentum oscillators (MACD, RSI) or volume-based signals to confirm reversals or continuation outside Zone 3.
🔮 Philosophical Note
This indicator embodies the metaphor that the market behaves like a quantum oscillator — price particles exist in a probabilistic field and jump between discrete zones of volatility and energy. Tracking these transitions allows the trader to see price behavior as rhythmic, wave-like, and multidimensional rather than purely linear.
Fibonacci Spectrum + Regression Channel + ConfirmationsA versatile multi-strategy tool for technical traders using Fibonacci levels, regression channels, and dynamic confirmations.
📘 Overview
This TradingView strategy script helps traders detect high-probability breakouts, reversals, and trend continuations using:
🔢 Fibonacci retracement zones
📉 Regression channels (local & multi-timeframe)
✅ Multiple confirmations (Volume, RSI, MACD, Candlestick)
🔄 Preset strategy modes (Trend-follow, Mean-reversion, Breakout, Custom)
📊 Visual dashboard for real-time analysis
🔔 Alerts for breakout and breakdown signals
🛠️ Inputs & Configuration
🎛️ Preset Modes
Choose from 4 trading modes:
Custom — manually enable/disable confirmations
Trend-follow — emphasizes RSI & MACD alignment
Mean-reversion — tight channels, ignores volume
Breakout — aggressive setup, tighter fib lookback & wider bands
🔧 Changing preset automatically adjusts parameters like regression length, fib lookback, and confirmation rules.
🔢 Fibonacci Settings
Fib Lookback: Number of bars to calculate the high/low range.
Fib Ratios A-E: Defines retracement levels (0.236 to 0.786 by default).
Zones are shaded for clarity:
🟧 0.236–0.382
🟨 0.382–0.5
🟩 0.5–0.618
🟦 0.618–0.786
📉 Regression Channel
Reg Channel Length: Period used for linear regression.
StdDev Multiplier: Defines channel width.
Multi-Timeframe Support: Choose a higher timeframe (like 1h) to overlay broader trends.
✅ Confirmations (toggle ON/OFF or preset controlled)
Volume: Must be above its 20-bar average.
RSI: Must be above 50 and rising (or below 50 and falling for shorts).
MACD: Line must cross above Signal (bull) or below (bear).
Candlestick Pattern: Looks for Bullish or Bearish Engulfing candles.
📅 Backtest Settings
Enable/disable strategy entries and exits for simulation.
Entries:
Long when price breaks above Fib 0.618 with all confirmations met
Short when price breaks below Fib 0.382 with all bearish confirmations
Exits:
Long exits when price breaks below Fib 0.382
Short exits when price breaks above Fib 0.618
🔍 How to Use the Strategy
🔹 Step 1: Choose a Preset
Pick one of the four Preset Modes:
Want to follow a trend? Select Trend-follow
Expect a price bounce? Try Mean-reversion
Expect volatility? Use Breakout
Prefer full control? Use Custom
🔹 Step 2: Enable Confirmations (if in Custom mode)
Activate/deactivate:
Volume
RSI
MACD
Candlestick Patterns
These filters increase signal quality.
🔹 Step 3: Watch for Signals
Look for:
💠 "READY" labels (potential breakout or breakdown)
Color-coded Fibonacci zones and channel bounds
📈 Entry/Exit signals (when backtesting is enabled)
🔹 Step 4: Use the Dashboard
Located in the top-right, the table shows:
Indicator values
Trend direction (Up/Down)
Status (Bull/Bear, High/Low, Above/Below)
Closest Fibonacci level and candlestick patterns
Colors:
🟢 Green = Bullish or Positive
🔴 Red = Bearish or Negative
🟡 Yellow = Close to a key Fib level
🔔 Alerts (Included)
You can set alerts on the following conditions:
Pre-Breakout — all bullish confirmations + Fib 0.618 cross
Pre-Breakdown — all bearish confirmations + Fib 0.382 break
Position Opened — any strategy entry (for backtest tracking)
To activate:
Right-click the chart → Add Alert
Select this strategy and the condition (e.g., “Pre-Breakout Signal”)
📊 Example Use Cases
🟢 Breakout Trader
Set to Breakout
Watch for “READY” label above Fib 0.618
Confirm with strong volume & bullish MACD
Alert triggers → enter trade
🔴 Mean Reversion Trader
Set to Mean-reversion
Price hits lower channel + Fib 0.382
Weak volume, but RSI rebounds → consider long
⚖️ Trend Follower
Set to Trend-follow
RSI, MACD, and price all moving up above midline
Enter on Fib 0.618 bounce
🧪 Tips & Notes
Combine with multi-timeframe analysis by enabling the MTF Channel
Tweak Fib ratios if you're using alternative levels (e.g., 0.886)
Use strategy.percent_of_equity for dynamic position sizing in backtests
Set proper timeframes (e.g., 15m, 1h) based on your trading style
Not financial advice at all. Strategy still WIP, i mainly think the indicator is ready.
Asset Premium/Discount Monitor📊 Overview
The Asset Premium/Discount Monitor is a tool for analyzing the relative value between two correlated assets. It measures when one asset is trading at a premium or discount compared to its historical relationship with another asset, helping traders identify potential mean reversion opportunities, or pairs trading opportunities.
🎯 Use Cases
Perfect for analyzing:
NASDAQ:MSTR vs CRYPTO:BTCUSD - MicroStrategy's premium/discount to Bitcoin
NASDAQ:COIN vs BITSTAMP:BTCUSD - Coinbase's relative value to Bitcoin
NASDAQ:TSLA vs NASDAQ:QQQ - Tesla's premium to tech sector
Regional banks AMEX:KRE vs AMEX:XLF - Individual bank stocks vs financial sector
Any two correlated assets where relative value matters
Example of a trade: MSTR vs BTC - When indicator shows MSTR at 95% percentile (extreme premium): Short MSTR, Buy BTC. Then exit when the spread reverts to the mean, say 40-60% percentile.
🔧 How It Works
Core Calculation
Ratio Analysis: Calculates the price ratio between your asset and the correlated asset
Historical Baseline: Establishes the "normal" relationship using a 252-day moving average. You can change this.
Premium Measurement: Measures current deviation from historical average as a percentage
Statistical Context: Provides percentile rankings and standard deviation bands
The Math
Premium % = (Current Ratio / Historical Average Ratio - 1) × 100
🎨 Customization Options
Correlated Asset: Choose any symbol for comparison
Lookback Period: Adjust historical baseline (50-1000 days)
Smoothing: Reduce noise with moving average (1-50 days)
Visual Toggles: Show/hide bands and percentile lines
Color Themes: Customize premium/discount colors
📊 Interpretation Guide
Premium/Discount Reading
Positive %: Asset trading above historical relationship (premium)
Negative %: Asset trading below historical relationship (discount)
Near 0%: Asset at fair value relative to correlation
Percentile Ranking
90%+: Near recent highs - potential selling opportunity
10% and below: Near recent lows - potential buying opportunity
25-75%: Normal trading range
Signal Classifications
🔴 SELL PREMIUM: Asset expensive relative to recent range
🟡 Premium Rich: Moderately expensive, monitor for reversal
⚪ NEUTRAL: Fair value territory
🟡 Discount Opportunity: Moderately cheap, potential accumulation zone
🟢 BUY DISCOUNT: Asset cheap relative to recent range
🚨 Built-in Alerts
Extreme Premium Alert: Triggers when percentile > 95%
Extreme Discount Alert: Triggers when percentile < 5%
⚠️ Important Notes
Works best with highly correlated assets
Historical relationships can change - monitor correlation strength
Not investment advice - use as one factor in your analysis
Backtest thoroughly before implementing any strategy
🔄 Updates & Future Features
This indicator will be continuously improved based on user feedback. So... please give me your feedback!
Adj Momentum (3M / 6M / 12M)Mirza Salman Volatility Adjusted Momentum.
The Volatility Adjusted Momentum Indicator distills a security’s recent performance into a single, decision-ready metric that captures both the velocity and the reliability of its trend. By simultaneously rewarding sustained price appreciation and discounting erratic fluctuations, the indicator highlights those stocks that are not only advancing but doing so with a consistent, low-volatility profile—attributes typically favoured by quantitative momentum and trend-following frameworks. A high positive reading points to instruments exhibiting strong, orderly upward trajectories, making them prime candidates for capital allocation in momentum-oriented portfolios. Conversely, muted or negative readings reveal markets whose returns have been lacklustre, unstable, or downward-sloping, signalling that they warrant caution or exclusion. In practice, this indicator enables portfolio managers and traders to rank broad watch-lists swiftly, focus due-diligence on the most robust price leaders, and enforce systematic discipline in scaling back exposure to choppier, less reliable names—all without resorting to subjective chart interpretation or ad-hoc volatility filters.
Logistic Regression ICT FVG🚀 OVERVIEW
Welcome to the Logistic Regression Fair Value Gap (FVG) System — a next-gen trading tool that blends precision gap detection with machine learning intelligence.
Unlike traditional FVG indicators, this one evolves with each bar of price action, scoring and filtering gaps based on real market behavior.
🔧 CORE FEATURES
✨ Smart Gap Detection
Automatically identifies bullish and bearish Fair Value Gaps using volatility-aware candle logic.
📊 Probability-Based Filtering
Uses logistic regression to assign each gap a confidence score (0 to 1), showing only high-probability setups.
🔁 Real-Time Retest Tracking
Continuously watches how price interacts with each gap to determine if it deserves respect.
📈 Multi-Factor Assessment
Evaluates RSI, MACD, and body size at gap formation to build a full context snapshot.
🧠 Self-Learning Engine
The logistic regression model updates on each bar using gradient descent, refining its predictions over time.
📢 Built-In Alerts
Get instant alerts when a gap forms, gets retested, or breaks.
🎨 Custom Display Options
Control the color of bullish/bearish zones, and toggle on/off probability labels for cleaner charts.
🚩 WHAT MAKES IT DIFFERENT
This isn’t just another box-drawing indicator.
While others mark every imbalance, this system thinks before it draws — using statistical modeling to filter out noise and prioritize high-impact zones.
By learning from how price behaves around gaps (not just how they form), it helps you trade only what matters — not what clutters.
⚙️ HOW IT WORKS
1️⃣ Detection
FVGs are identified using ATR-based thresholds and sharp wick imbalances.
2️⃣ Behavior Monitoring
Every gap is tracked — and if respected enough times, it becomes part of the elite training set.
3️⃣ Context Capture
Each new FVG logs RSI, MACD, and body size to provide a feature-rich context for prediction.
4️⃣ Prediction (Logistic Regression)
The model predicts how likely the gap is to be respected and assigns it a probability score.
5️⃣ Classification & Alerts
Gaps above the threshold are plotted with score labels, and alerts trigger for entry/respect/break.
⚙️ CONFIGURATION PANEL
🔧 System Inputs
• Max Retests – How many times a gap must be respected to train the model
• Prediction Threshold – Minimum score to show a gap on the chart
• Learning Rate – Controls how fast the model adapts (default: 0.009)
• Max FVG Lifetime – Expiration duration for unused gaps
• Show Historic Gaps – Show/hide expired or invalidated gaps
🎨 Visual Options
• Bullish/Bearish Colors – Set gap colors to fit your chart style
• Confidence Labels – Show probability scores next to FVGs
• Alert Toggles – Enable alerts for:
– New FVG detected
– FVG respected (entry)
– FVG invalidated (break)
💡 WHY LOGISTIC REGRESSION?
Traditional FVG tools rely on candle shapes.
This system relies on probability — by training on RSI, MACD, and price behavior, it predicts whether a gap will act as a true liquidity zone.
Logistic regression lets the system continuously adapt using new data, making it more accurate the longer it runs.
That means smarter signals, fewer false positives, and a clearer view of where real opportunities lie.
Momentum Regression [BackQuant]Momentum Regression
The Momentum Regression is an advanced statistical indicator built to empower quants, strategists, and technically inclined traders with a robust visual and quantitative framework for analyzing momentum effects in financial markets. Unlike traditional momentum indicators that rely on raw price movements or moving averages, this tool leverages a volatility-adjusted linear regression model (y ~ x) to uncover and validate momentum behavior over a user-defined lookback window.
Purpose & Design Philosophy
Momentum is a core anomaly in quantitative finance — an effect where assets that have performed well (or poorly) continue to do so over short to medium-term horizons. However, this effect can be noisy, regime-dependent, and sometimes spurious.
The Momentum Regression is designed as a pre-strategy analytical tool to help you filter and verify whether statistically meaningful and tradable momentum exists in a given asset. Its architecture includes:
Volatility normalization to account for differences in scale and distribution.
Regression analysis to model the relationship between past and present standardized returns.
Deviation bands to highlight overbought/oversold zones around the predicted trendline.
Statistical summary tables to assess the reliability of the detected momentum.
Core Concepts and Calculations
The model uses the following:
Independent variable (x): The volatility-adjusted return over the chosen momentum period.
Dependent variable (y): The 1-bar lagged log return, also adjusted for volatility.
A simple linear regression is performed over a large lookback window (default: 1000 bars), which reveals the slope and intercept of the momentum line. These values are then used to construct:
A predicted momentum trendline across time.
Upper and lower deviation bands , representing ±n standard deviations of the regression residuals (errors).
These visual elements help traders judge how far current returns deviate from the modeled momentum trend, similar to Bollinger Bands but derived from a regression model rather than a moving average.
Key Metrics Provided
On each update, the indicator dynamically displays:
Momentum Slope (β₁): Indicates trend direction and strength. A higher absolute value implies a stronger effect.
Intercept (β₀): The predicted return when x = 0.
Pearson’s R: Correlation coefficient between x and y.
R² (Coefficient of Determination): Indicates how well the regression line explains the variance in y.
Standard Error of Residuals: Measures dispersion around the trendline.
t-Statistic of β₁: Used to evaluate statistical significance of the momentum slope.
These statistics are presented in a top-right summary table for immediate interpretation. A bottom-right signal table also summarizes key takeaways with visual indicators.
Features and Inputs
✅ Volatility-Adjusted Momentum : Reduces distortions from noisy price spikes.
✅ Custom Lookback Control : Set the number of bars to analyze regression.
✅ Extendable Trendlines : For continuous visualization into the future.
✅ Deviation Bands : Optional ±σ multipliers to detect abnormal price action.
✅ Contextual Tables : Help determine strength, direction, and significance of momentum.
✅ Separate Pane Design : Cleanly isolates statistical momentum from price chart.
How It Helps Traders
📉 Quantitative Strategy Validation:
Use the regression results to confirm whether a momentum-based strategy is worth pursuing on a specific asset or timeframe.
🔍 Regime Detection:
Track when momentum breaks down or reverses. Slope changes, drops in R², or weak t-stats can signal regime shifts.
📊 Trade Filtering:
Avoid false positives by entering trades only when momentum is both statistically significant and directionally favorable.
📈 Backtest Preparation:
Before running costly simulations, use this tool to pre-screen assets for exploitable return structures.
When to Use It
Before building or deploying a momentum strategy : Test if momentum exists and is statistically reliable.
During market transitions : Detect early signs of fading strength or reversal.
As part of an edge-stacking framework : Combine with other filters such as volatility compression, volume surges, or macro filters.
Conclusion
The Momentum Regression indicator offers a powerful fusion of statistical analysis and visual interpretation. By combining volatility-adjusted returns with real-time linear regression modeling, it helps quantify and qualify one of the most studied and traded anomalies in finance: momentum.
Navy Seal Trading - EdgarTrader📌 Navy Seal Trading – Asia, London, and NY Sessions
This indicator clearly displays the ranges of the Asia, London, and New York sessions, featuring:
✅ Full range visualization for each session
✅ Asia session high, low, and midline, with extended projection lines for precise reaction analysis
✅ Clean, minimalistic, and professional colors to keep your chart focused
🔷 Designed for the Navy Seal Trading community, focused on precision, discipline, and professional execution in the markets.
Use it to:
✔️ Mark liquidity zones
✔️ Identify Asia manipulation ranges
✔️ Prepare executions in London and NY with clear context
💡 Remember: Clarity in your zones gives you the confidence and discipline to execute like a true Navy Seal Trader.
Iceberg DetectorThis Pine-script indicator helps you spot potential “iceberg” order activity by highlighting bars where volume spikes well above its average while price movement remains unusually muted. It’s purely a heuristic—no true bid/ask or futures order‐flow data is used—so treat every signal as an invitation to investigate, not as a standalone buy/sell trigger.
How It Works • Volume vs. Volume-SMA: The script compares each bar’s total volume to an N-bar simple moving average. • Price Movement vs. Movement-SMA: It measures the bar’s percent change (|close–open|/open×100) against its own N-bar SMA. • Sensitivity Slider: From 1 (loose filter) to 10 (strict filter), you control how extreme the volume spike (and muted move) must be to fire a signal. • Pivot-Style Extremes Filter: Short signals only appear when price is at or very near a recent local high, and long signals only when price is at or very near a recent local low. This dramatically cuts down “noise” on lower timeframes—script execution halts on intraday charts below 1 H.
How to Use
Apply to an hourly (or higher) chart.
Tweak “Length” parameters for your preferred look-back on volume and movement SMAs.
Adjust “Sensitivity” from 1 (more signals, weaker divergences) up to 10 (very rare, extreme divergences).
Watch for red triangles above bars (Iceberg-Short) and green triangles below (Iceberg-Long).
Important Disclaimers • This is NOT a genuine order-flow or footprint tool—it only approximates delta by bar direction. • Always contextualize Short signals near the lower end of a range or support zone, and Long signals near the upper end of a range or resistance zone. • Use additional confirmation (price patterns, larger-timeframe pivots, traditional volume/price analysis) before risking real capital.
By combining volume spikes with muted price action at range extremes, you gain a fresh lens on where hidden large orders might be lurking—without needing a dedicated order-flow feed. Use it as an idea‐generator, not as gospel
LANZ Strategy 1.0🔷 LANZ Strategy 1.0 — Session-Based Directional Logic with Visual Multi-Account Risk Management
LANZ Strategy 1.0 is a structured and disciplined trading strategy designed for the 1-hour timeframe, operating during the NY session and executing trades overnight. It uses the directional behavior between 08:00 and 18:00 New York time to define precise limit entries for the following night. Ideal for traders who prefer time-based execution, clear visuals, and professional risk management across multiple accounts.
🧠 Core Components:
1. Session Direction Confirmation:
At 18:00 NY, the system evaluates the market direction by comparing the open at 08:00 vs the close at 18:00:
If the direction matches the previous day, it is reversed.
If it differs, the current day’s direction is kept.
This logic is designed to avoid trend exhaustion and favor potential reversal opportunities.
2. EP Level & Risk Definition:
Once direction is defined:
For BUY, EP is set at the Low of the session.
For SELL, EP is set at the High of the session.
The system automatically plots:
SL fixed at 18 pips from EP
TP at 3.00× the risk → 54 pips from EP
All levels (EP, SL, TP) are shown with visual lines and price labels.
3. Time-Restricted Entry Execution:
The entry is only valid if price touches the EP between 19:00 and 08:00 NY.
If EP is not touched before 08:00 NY, the trade is automatically cancelled.
4. Multi-Account Lot Sizing:
Traders can configure up to five different accounts, each with its own capital and risk percentage.
The system calculates and displays the lot size per account, based on SL distance and pip value, in a dynamic floating label.
5. Outcome Tracking:
If TP is hit, a +3.00% profit label is displayed along with a congratulatory alert.
If SL is hit, a -1.00% label appears with a loss alert.
If the trade is still open by 09:00 NY, it is manually closed, and the result is shown as a percentage of the initial risk.
📊 Visual Features:
Custom-colored angle and guide lines.
Dynamic angle line starts at 08:00 NY and tracks price until 18:00.
Shaded backgrounds for key time zones (e.g., 08:00, 18:00, 19:00).
BUY/SELL signals shown at 19:00 based on match/divergence logic.
Label panel showing risk metrics and lot size for each active account.
⚙️ How It Works:
08:00 NY: Marks the session open and initiates a dynamic angle line.
18:00 NY: Evaluates the session direction and calculates EP/SL/TP based on outcome.
19:00 NY: Activates limit order monitoring.
During the night (until 08:00 NY): If EP is touched, the trade is triggered.
At 08:00 NY: If no touch occurred, trade is cancelled.
Overnight: TP/SL logic is enforced, showing percentage outcomes.
At 09:00 NY: If still open, trade is closed manually and result is labeled visually.
🔔 Alerts:
🚀 EP execution alert when touched
💢 Stop Loss hit alert
⚡ Take Profit hit alert
✅ Manual close at 09:00 NY with performance result
🔔 Daily reminder at 19:00 NY to configure and prepare the trade
📝 Notes:
Strategy is exclusive to the 1-hour timeframe.
Works best on assets with clean NY session movement.
Perfect for structured, semi-automated swing/overnight trading styles.
Fully visual, self-explanatory, and backtest-friendly.
👨💻 Credits:
Developed by LANZ
A strategy created with precision, discipline, and a vision for traders who value time-based entries, clean execution logic, and visual confidence on the chart.
Special thanks to Kairos — your AI assistant — for the detailed structure, scripting, and documentation of the strategy.
CAGR + Log Slope ColorThis custom TradingView indicator combines two important analytical concepts to help traders identify strong trends with visual clarity:
CAGR (Compound Annual Growth Rate):
Measures the geometric average annual return of the asset over a specified period. It smooths out short-term fluctuations and provides a long-term growth perspective.
Logarithmic Slope Coloring:
The slope of the log-scaled price is calculated and visualized with color gradients. Steeper upward slopes indicate stronger momentum and are highlighted with more intense colors. Downward slopes are shown in contrasting colors for easier identification of bearish trends.
Linear Regression Forecast (ADX Adaptive)Linear Regression Forecast (ADX Adaptive)
This indicator is a dynamic price projection tool that combines multiple linear regression forecasts into a single, adaptive forecast curve. By integrating trend strength via the ADX and directional bias, it aims to visualize how price might evolve in different market environments—from strong trends to mean-reverting conditions.
Core Concept:
This tool builds forward price projections based on a blend of linear regression models with varying lookback lengths (from 2 up to a user-defined max). It then adjusts those projections using two key mechanisms:
ADX-Weighted Forecast Blending
In trending conditions (high ADX), the model follows the raw forecast direction. In ranging markets (low ADX), the forecast flips or reverts, biasing toward mean-reversion. A logistic transformation of directional bias, controlled by a steepness parameter, determines how aggressively this blending reacts to price behavior.
Volatility Scaling
The forecast’s magnitude is scaled based on ADX and directional conviction. When trends are unclear (low ADX or neutral bias), the projection range expands to reflect greater uncertainty and volatility.
How It Works:
Regression Curve Generation
For each regression length from 2 to maxLength, a forward projection is calculated using least-squares linear regression on the selected price source. These forecasts are extrapolated into the future.
Directional Bias Calculation
The forecasted points are analyzed to determine a normalized bias value in the range -1 to +1, where +1 means strongly bullish, -1 means strongly bearish, and 0 means neutral.
Logistic Bias Transformation
The raw bias is passed through a logistic sigmoid function, with a user-defined steepness. This creates a probability-like weight that favors either following or reversing the forecast depending on market context.
ADX-Based Weighting
ADX determines the weighting between trend-following and mean-reversion modes. Below ADX 20, the model favors mean-reversion. Above 25, it favors trend-following. Between 20 and 25, it linearly blends the two.
Blended Forecast Curve
Each forecast point is blended between trend-following and mean-reverting values, scaled for volatility.
What You See:
Forecast Lines: Projected future price paths drawn in green or red depending on direction.
Bias Plot: A separate plot showing post-blend directional bias as a percentage, where +100 is strongly bullish and -100 is strongly bearish.
Neutral Line: A dashed horizontal line at 0 percent bias to indicate neutrality.
User Inputs:
-Max Regression Length
-Price Source
-Line Width
-Bias Steepness
-ADX Length and Smoothing
Use Cases:
Visualize expected price direction under different trend conditions
Adjust trading behavior depending on trending vs ranging markets
Combine with other tools for deeper analysis
Important Notes:
This indicator is for visualization and analysis only. It does not provide buy or sell signals and should not be used in isolation. It makes assumptions based on historical price action and should be interpreted with market context.
Zero-Lag Linear Regression Candles🚀 Zero-Lag Linear Regression Candles
📊 What It Does
The Zero-Lag Linear Regression Candles change traditional candlestick analysis by creating smoothed, predictive candles that eliminate the lag inherent in standard linear regression methods. Instead of waiting for price confirmation, this indicator anticipates market movements using advanced mathematical modeling.
🎯 Key Features
Tri-Layer Super Responsive System
Layer 1: Weighted Linear Regression with exponential decay weighting
Layer 2: Zero-lag correction algorithm that projects future price direction
Layer 3: Adaptive intelligence that adjusts to current market volatility and momentum
Smart Market Adaptation
Automatically adjusts sensitivity based on market volatility (ATR)
Responds to momentum changes in real-time
Filters out market noise while preserving important signals
Customizable
Regression Length: Fine-tune responsiveness (2-50 periods)
Weight Decay Factor: Control how much emphasis to place on recent vs. historical data
Zero-Lag Periods: Adjust the aggressiveness of lag elimination
Adaptive Factor: Set market adaptation strength
🛠️ Usage Instructions
1. Add to Chart: Apply the indicator to any timeframe
2. Configure Settings: Adjust regression length and sensitivity to match your trading style
3. Interpret Signals:
- Green Candles: Bullish linear regression trend
- Red Candles: Bearish linear regression trend
Created by B3AR_Trades
LRHA Trend Shift DetectorLRHA Trend Shift Detector (TSD)
The LRHA Trend Shift Detector is an advanced momentum exhaustion indicator that identifies potential trend reversals and changes by analyzing Linear Regression Heikin Ashi (LRHA) candle patterns. TSD focuses on detecting when strong directional moves begin to lose momentum.
🔬 Methodology
The indicator employs a three-stage detection process:
LRHA Calculation: Applies linear regression smoothing to Heikin Ashi candles, creating ultra-smooth trend-following candles that filter out market noise
Extended Move Detection: Identifies sustained directional moves by counting consecutive bullish or bearish LRHA candles
Momentum Exhaustion Analysis: Monitors for significant changes in candle size compared to recent averages
When an extended move shows clear signs of momentum exhaustion, the indicator signals a potential trend shift with red dots plotted above or below your candlesticks.
⚙️ Parameters
Core Settings
LRHA Length (11): Linear regression period for smoothing calculations. Lower values = more responsive, higher values = smoother trends.
Minimum Trend Bars (4): Consecutive candles required to establish an "extended move." Higher number detects longer term trend changes.
Exhaustion Bars (3): Number of consecutively smaller candles needed to signal exhaustion. Lower is more sensitive.
Size Reduction Threshold (40%): Percentage decrease in candle size to qualify as "exhaustion." Lower is more sensitive.
Trend Trading
Pullback Entries: Identify exhaustion in counter-trend moves for trend continuation
Exit Strategy: Recognize when main trend momentum is fading
Position Sizing: Reduce size when seeing exhaustion in your direction
🎛️ Optimization Tips
For More Signals (Aggressive)
- Decrease LRHA Length (7-9)
- Reduce Minimum Trend Bars (2-3)
- Lower Size Reduction Threshold (25-35%)
For Higher Quality (Conservative)
- Increase LRHA Length (13-18)
- Raise Minimum Trend Bars (5-6)
- Higher Size Reduction Threshold (45-55%)
⚠️ Important Notes⚠️
- **Not a Complete Strategy**: Use as confluence with other analysis methods
- **Market Context Matters**: Consider overall trend direction and key support/resistance levels
- **Risk Management Essential**: Always use proper position sizing and stop losses
- **Backtest First**: Optimize parameters for your specific trading style and instruments
Oculus Ultra Parallel S/R Channel**Oculus Ultra Parallel S/R Channel**
*Version 1.0 | Pine Script v6*
**Overview**
This indicator overlays a statistically-driven support/resistance channel on your chart by fitting a linear regression (median) line and plotting parallel bands at a configurable multiple of standard deviation. It adapts dynamically to both trend and volatility, highlights potential reaction zones, and offers optional alerts when price touches key levels.
**Key Features**
* **Median Regression Line**
Fits a best-fit line through the chosen lookback of price data, showing the underlying trend.
* **Volatility-Based Bands**
Upper and lower bands offset by *N*× standard deviation of regression residuals, capturing dynamic S/R zones.
* **Dynamic Coloring**
* Median line turns **teal** when sloping up, **orange** when sloping down.
* Bands tinted green or red depending on their position relative to the median.
* **Channel Fill**
Optional shaded area between the bands for immediate visual context.
* **Touch Alerts**
Precision alerts and on-chart markers when price touches the support or resistance band, with configurable tick tolerance.
* **Clean Layout**
Minimal lines and plots to avoid chart clutter, adjustable via toggle inputs.
**How to Use**
1. **Apply the Script** – Add to any timeframe in overlay mode.
2. **Configure Inputs** –
* **Channel Length**: Number of bars for regression and volatility calculation.
* **Deviation Factor**: Multiplier for band width (in standard deviations).
* **Show/Hide Elements**: Toggle median line, bands, fill, and touch alerts.
* **Color by Slope**: Enable slope-based median coloring.
* **Touch Tolerance**: Number of ticks within which a band touch is registered.
3. **Interpret the Channel** –
* **Trend**: Follow the slope and color of the median line.
* **Support/Resistance**: Bands represent dynamic zones where price often reacts.
* **Alerts**: Use touch markers or alert pop-ups to time entries or exits at band levels.
**Inputs**
* **Channel Length** (default: 100)
* **Deviation Factor** (default: 1.0)
* **Show Median Regression Line** (true/false)
* **Show Channel Bands** (true/false)
* **Fill Between Bands** (true/false)
* **Color Median by Slope** (true/false)
* **Alert on Band Touch** (true/false)
* **Touch Tolerance (ticks)** (default: 2)
**Version History**
* **1.0** – Initial release with dynamic regression channel, slope coloring, band fill, and touch alerts.
**Disclaimer**
This indicator is intended for educational purposes. Always backtest with your own settings and apply sound risk management before trading live.
VWAP Supply & Demand Zones PRO**Overview:**
This script represents a major evolution of the original "VWAP Supply and Demand Zones" indicator. Initially created to explore price interaction with VWAP, it has now matured into a robust and feature-rich tool for identifying high-probability zones of institutional buying and selling pressure. The update introduces volume and momentum validation, dynamic zone management, alert logic, and a visual dashboard (HUD) — all designed for improved precision and clarity. The structural improvements, anti-repainting logic, and significant added utility warranted releasing this as a new script rather than a minor update.
---
### What It Does:
This indicator dynamically detects **supply and demand zones** using VWAP-based logic combined with **volume** and **momentum confirmation**. When price crosses VWAP with strength, it identifies the potential zone of excess demand (below VWAP) or supply (above VWAP), marking it visually with colored regions on the chart.
Each zone is extended for a user-defined duration, monitored for touch interactions (tests), and tracked for possible breaks. The script helps traders interpret price behavior around these institutional zones as either **reversal** opportunities or **continuation** confirmation depending on context and strategy preference.
---
### How It Works:
* **VWAP Basis**: Zones are anchored at VWAP at the time of a significant cross.
* **Volume & Momentum Filters**: Crosses are only considered valid if backed by above-average volume and notable price momentum.
* **Zone Drawing**: Validated supply and demand zones are drawn as boxes on the chart. Each is extended forward for a customizable number of bars.
* **Touch Counting**: Zones track the number of price touches. Alerts are issued after a user-defined number of tests.
* **Break Detection**: If price closes significantly beyond a zone boundary, the zone is marked as broken and visually dimmed.
* **Visual Dashboard (HUD)**: A compact real-time HUD displays VWAP value, active zone counts, and current market bias.
---
### How to Use It:
**Reversal Trading:**
* Look for price **rejecting** a zone after touching it.
* Use rejection candles or secondary indicators (e.g., RSI divergence) to confirm.
* These setups may offer low-risk entries when price respects the zone.
**Continuation Trading:**
* A **break of a zone** suggests strong directional bias.
* Use confirmed zone breaks to enter in the direction of momentum.
* Ideal in trending environments, especially with high volume and ATR movement.
---
### Key Inputs:
* **VWAP Length**: Moving VWAP period (default: 20)
* **Zone Width %**: Percentage size of zone buffer (default: 0.5%)
* **Min Touches**: How many times price must test a zone before alerts trigger
* **Zone Extension**: How far into the future zones are projected
* **Volume & ATR Filters**: Ensure only strong, valid crossovers create zones
---
### Alerts:
You can enable alerts for:
* **New zone creation**
* **Zone tests (after minimum touch count)**
* **Zone breaks**
* **VWAP crosses**
* **Active presence inside a zone (entry conditions)**
These alerts help automate market monitoring, making it suitable for discretionary or systematic workflows.
---
### Why It's a New Script:
This is not a cosmetic update. The internal logic, signal generation, filtering methodology, visual engine, and UX framework have been entirely rebuilt from the ground up. The result is a highly adaptive, precision-oriented tool — appropriate for intraday scalpers and swing traders alike. It goes far beyond the original in terms of functionality and reliability, justifying a fresh release.
---
### Suitable Markets and Timeframes:
* Works across all liquid markets (crypto, equities, futures, forex)
* Best used on timeframes where volume data is stable (5m and above recommended)
* Recalibrate inputs for optimal detection across instruments
Wavelet Filter with Adaptive Upsampling [BackQuant]Wavelet Filter with Adaptive Upsampling
The Wavelet Filter with Adaptive Upsampling is an advanced filtering and signal reconstruction tool designed to enhance the analysis of financial time series data. It combines wavelet transforms with adaptive upsampling techniques to filter and reconstruct price data, making it ideal for capturing subtle market movements and enhancing trend detection. This system uses high-pass and low-pass filters to decompose the price series into different frequency components, applying adaptive thresholding to eliminate noise and preserve relevant signal information.
Shout out to Loxx for the Least Squares fitting of trigonometric series and Quinn and Fernandes algorithm for finding frequency
www.tradingview.com
Key Features
1. Frequency Decomposition with High-Pass and Low-Pass Filters:
The indicator decomposes the input time series using high-pass and low-pass filters to separate the high-frequency (detail) and low-frequency (trend) components of the data. This decomposition allows for a more accurate analysis of underlying trends, while mitigating the impact of noise.
2. Soft Thresholding for Noise Reduction:
A soft thresholding function is applied to the high-frequency component, allowing for the reduction of noise while retaining significant market signals. This function adjusts the coefficients of the high-frequency data, removing small fluctuations and leaving only the essential price movements.
3. Adaptive Upsampling Process:
The upsampling process in this script can be customized using different methods: sinusoidal upsampling, advanced upsampling, and simple upsampling. Each method serves a unique purpose:
Sinusoidal Upsample uses a sine wave to interpolate between data points, providing a smooth transition.
Advanced Upsample utilizes a Quinn-Fernandes algorithm to estimate frequency and apply more sophisticated interpolation techniques, adapting to the market’s cyclical behavior.
Simple Upsample linearly interpolates between data points, providing a basic upsampling technique for less complex analysis.
4. Reconstruction of Filtered Signal:
The indicator reconstructs the filtered signal by summing the high and low-frequency components after upsampling. This allows for a detailed yet smooth representation of the original time series, which can be used for analyzing underlying trends in the market.
5. Visualization of Reconstructed Data:
The reconstructed series is plotted, showing how the upsampling and filtering process enhances the clarity of the price movements. Additionally, the script provides the option to visualize the log returns of the reconstructed series as a histogram, with positive returns shown in green and negative returns in red.
6. Cumulative Series and Trend Detection:
A cumulative series is plotted to visualize the compounded effect of the filtered and reconstructed data. This feature helps traders track the overall performance of the asset over time, identifying whether the asset is following a sustained upward or downward trend.
7. Adaptive Thresholding and Noise Estimation:
The system estimates the noise level in the high-frequency component and applies an adaptive thresholding process based on the standard deviation of the downsampled data. This ensures that only significant price movements are retained, further refining the trend analysis.
8. Customizable Parameters for Flexibility:
Users can customize the following parameters to adjust the behavior of the indicator:
Frequency and Phase Shift: Control the periodicity of the wavelet transformation and the phase of the upsampling function.
Upsample Factor: Adjust the level of interpolation applied during the upsampling process.
Smoothing Period: Determine the length of time used to smooth the signal, helping to filter out short-term fluctuations.
References
Enhancing Cross-Sectional Currency Strategies with Context-Aware Learning to Rank
arxiv.org
Daubechies Wavelet - Wikipedia
en.wikipedia.org
Quinn Fernandes Fourier Transform of Filtered Price by Loxx
Note on Usage for Mean-Reversion Strategy
This indicator is primarily designed for trend-following strategies. However, by taking the inverse of the signals, it can be adapted for mean-reversion strategies. This involves buying underperforming assets and selling outperforming ones. Caution: This method may not work effectively with highly correlated assets, as the price movements between correlated assets tend to mirror each other, limiting the effectiveness of mean-reversion strategies.
Final Thoughts
The Wavelet Filter with Adaptive Upsampling is a powerful tool for traders seeking to improve their understanding of market trends and noise. By using advanced wavelet decomposition and adaptive upsampling, this system offers a clearer, more refined picture of price movements, enhancing trend-following strategies. It’s particularly useful for detecting subtle shifts in market momentum and reconstructing price data in a way that removes noise, providing more accurate insights into market conditions.
Curved Trend Channels (Zeiierman)█ Overview
Curved Trend Channels (Zeiierman) is a next-generation trend visualization tool engineered to adapt dynamically to both linear and non-linear market behavior. It introduces a novel curvature-based channeling system that grows over time during trending conditions, mirroring the natural acceleration of price trends, while simultaneously leveraging adaptive range filtering and dual-layer candle trend logic.
This tool is ideal for traders seeking smooth yet reactive dynamic channels that evolve with market structure. Whether used in curved mode or traditional slope mode, it provides exceptional clarity on trend transitions, volatility compression, and breakout development.
█ How It Works
⚪ Adaptive Range Filter Foundation
The core of the system is a volatility-based range filter that determines the underlying structure of the bands:
Pre-Smoothing of High/Low Data – Highs and lows are smoothed using a selectable moving average (SMA, EMA, HMA, KAMA, etc.) before calculating the volatility range.
Volatility Envelope – The range is scaled using a fixed factor (2.618) and further adjusted by a Band Multiplier to form the primary envelope around price.
Smoothed Volatility Curve – Final bands are stabilized using a long lookback, ensuring clean visual structure and trend clarity.
⚪ Curved Channel Logic
In Curved Mode, the trend channel grows over time when the trend direction remains unchanged:
Base Step Size (× ATR) – Sets the minimum unit of slope change.
Growth per Bar (× ATR) – Defines the acceleration rate of the channel slope with time.
Trend Persistence Recognition – The longer a trend persists, the more pronounced the slope becomes, mimicking real market accelerations.
This dynamic, time-dependent logic enables the channel to "curve" upward or downward, tracking long-standing trends with increasing confidence.
⚪ Trend Slope
As an alternative to curved logic, traders can activate a regular Trend slope using:
Slope Length – Determines how quickly the trend line adapts to price shifts.
Multiplicative Factor – Amplifies the sensitivity of the slope, useful in fast-moving markets or lower timeframes.
⚪ Candle Trend Confirmation
A robust second-layer trend detection method, the Candle Trend System evaluates directional pressure by analyzing smoothed price action:
Multi-tier Smoothing – Trend lines are derived from short-, medium-, and long-term candle movement.
█ How to Use
⚪ Trend Identification
When the Trend Line direction and Candle Colors are in agreement, this indicates strong, persistent directional conviction. Use these moments to enter with trend confirmation and manage risk more confidently.
⚪ Retest
During ongoing trends, the price will often pull back into the dynamic channel. Look for:
Support/resistance interactions at the upper or lower bands.
█ Settings
Scaled Volatility Length – Controls the historical depth used to stabilize the volatility bands.
Smoothing Type – Choose from HMA, KAMA, VIDYA, FRAMA, Super Smoother, etc. to match your asset and trading style.
Volatility MA Length – Smoothing length for the calculated range; shorter = more reactive.
High/Low Smoother Length – Additional smoothing to reduce noise from spikes or false pivots.
Band Multiplier – Widens or tightens the band range based on personal preference.
Enable Curved Channel – Toggle between curved or regular trend slope behavior.
Base Step (× ATR) – The starting point for curved slope progression.
Growth per Bar (× ATR) – How much the slope accelerates per bar during a sustained trend.
Slope – Reactivity of the standard trend line to price movements.
Multiplicative Factor – Sensitivity adjustment for HyperTrend slope.
Candle Trend Length – Lookback period for trend determination from candle structure.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Regression Channel (Interactive)Weighted Interactive Regression Channel (WIRC)
Overview
The Weighted Interactive Regression Channel improves on traditional regression channels by emphasizing key price points through intelligent weighting. Instead of treating all candles equally, WIRC adapts to market dynamics for better trend detection and channel accuracy.
Key Differences from Standard Channels
Weighted vs. Equal: Prioritizes significant events over uniform weighting
Dynamic vs. Static: Adapts in real time to market changes
Accurate vs. Basic: Reduces noise, enhances signal clarity
Customizable vs. Fixed: Full control over weights and visuals
Weighting Methods
Direction Change – Highlights reversal points via local peaks/troughs
Volume-Based – Emphasizes high-volume candles, ideal for breakouts
Price Range – Weights wide-range candles to capture volatility
Time Decay – Prioritizes recent data for current market relevance
Interactive Features
Data Range: Set channel start/end over 1–500 bars
Visuals: Line styles, color coding, fill options, reference lines
Stats: Slope, R², standard deviation, point count, weight method
Technical Implementation
Weighted Regression Formula: Uses weights for slope, intercept, and deviation
Channel Lines: Center = weighted regression; bounds = ± deviation × multiplier
Usage Scenarios
Trend Analysis: Use Direction Change + longer range
Breakouts: Use Volume weighting + fill + boundary watching
Volatility: Apply Price Range weighting + monitor standard deviation
Current Market: Use Time Decay + shorter ranges + stat display
Parameter Tips
Channel Width:
Narrow (1.0–1.5): Responsive
Standard (1.5–2.0): Balanced
Wide (2.0–3.0+): Conservative
Weighting Intensity:
Conservative (1.5–2.0)
Moderate (2.0–3.0)
Aggressive (3.0+)
Advanced Use
Multi-Timeframe: Use different weightings per timeframe
Market Structure: Detect swings, institutional zones
Risk Management: Dynamic S/R levels, volatility-driven sizing
Best Practices
Start with Direction Change
Test different ranges
Monitor stats
Combine with other indicators
Adjust to market context
Recalibrate regularly
Conclusion
WIRC delivers a smarter, more adaptive view of price action than standard regression tools. With real-time customization and multiple weighting options, it’s ideal for traders seeking precision across strategies—trend tracking, breakout confirmation, or volatility insight.
WLSMA: fast approximation🙏🏻 Sup TV & @alexgrover
O(N) algocomplexity, just one loop inside. No, you can't do O(1) @ updates in moving window mode, only expanding window will allow that.
Now I have time series & stats models of my own creation, nowhere else available, just TV and my github for now, ain’t no legacy academic industry I always have fun about, but back in 2k20 when I consciously ain’t known much about quant, I remember seeing post by @alexgrover recreating Moving Regression Endpoint dropped on price chart (called LSMA here) as a linear filter combination of filters (yea yeah DSP terms) as 3WMA - 2SMA. Now it’s my time to do smth alike aye?
...
This script is remake of my 1st degree WLSMA via linear filter combo. It’s much faster, we aint calculate moving regression per se, we just match its freq response. You can see it on the screen (WLSMAfa) almost perfectly matching the original one (WLSMA).
...
While humans like to overfit, I fw generalizations. So your lovely WMA is actually just one case of a more general weight pattern: pow(len - i, e), where pow is the power function and e is the exponent itself. So:
- If e = 0, then we have SMA (every number in 0th power is one)
- If e = 1, we get WMA
- If e = 2, we get quadratic weights.
We can recreate WLSMA freq response then by combining 2 filters with e = 1 and e = 2.
This is still an approximation, even tho enormously precise for the tasks you’ve shared with me. Due to the non-linear nature of the thing it’s all we can do, and as window size grows, even this small discrepancy converges with true WLSMA value, so we’re all good. Pls don’t try to model this 0.00xxxx discrepancy, it’s not natural.
...
DSP approach is unnatural for prices, but you can put this thing on volume delta and be happy, or on other metrics of yours, if for some reason u dont wanna estimate thresholds by fitting a distro.
All good TV
∞
P.S.: strangely, the first script made & dropped in the location in Saint P where my actual quant way has started ~5 years ago xD, very thankful
Support and Resistance Logistic Regression | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Logistic Regression Support / Resistance indicator! This tool leverages advanced statistical modeling "Logistic Regressions" to identify and project key price levels where the market is likely to find support or resistance. For more information about the process, please check the "HOW DOES IT WORK ?" section.
Logistic Regression Support / Resistance Features :
Intelligent S/R Identification : The indicator uses a logistic regression model to intelligently identify and plot significant support and resistance levels.
Predictive Probability : Each identified level comes with a calculated probability, indicating how likely it is to act as a true support or resistance based on historical data.
Retest & Break Labels : The indicator clearly marks on your chart when a detected support or resistance level is retested (price touches and respects the level) or broken (price decisively crosses through the level).
Alerts : Real-time alerts for support retests, resistance retests, support breaks, and resistance breaks.
Customizable : You can change support & resistance line style, width and colors.
🚩 UNIQUENESS
What makes this indicator truly unique is its application of logistic regression to the concept of support and resistance. Instead of merely identifying historical highs and lows, our indicator uses a statistical model to predict the future efficacy of these levels. It analyzes underlying market conditions (like RSI and body size at pivot formation) to assign a probability to each potential S/R zone. This predictive insight, combined with dynamic, real-time labeling of retests and breaks, provides a more robust and adaptive understanding of market structure than traditional, purely historical methods.
📌HOW DOES IT WORK ?
The Logistic Regression Support / Resistance indicator operates in several key steps:
First, it identifies significant pivot highs and lows on the chart based on a user-defined "Pivot Length." These pivots are potential areas of support or resistance.
For each detected pivot, the indicator extracts relevant market data at that specific point, including the RSI (Relative Strength Index) and the Body Size (the absolute difference between the open and close price of the candle). These serve as input features for the model.
The core of the indicator lies in its logistic regression model. This model is continuously trained on past pivot data and their subsequent behavior (i.e., whether they were "respected" as support/resistance multiple times). It learns the relationship between the extracted features (RSI, Body Size) and the likelihood of a pivot becoming a significant S/R level.
When a new pivot is identified, the model uses its learned insights to calculate a prediction value—a probability (from 0 to 1) that this specific pivot will act as a strong support or resistance.
If the calculated probability exceeds a user-defined "Probability Threshold," the pivot is designated a "Regression Pivot" and drawn on the chart as a support or resistance line. The indicator then actively tracks how price interacts with these levels, displaying "R" labels for retests when the price bounces off the level and "B" labels for breaks when the price closes beyond it.
⚙️ SETTINGS
1. General Configuration
Pivot Length: This setting defines the number of bars used to determine a significant high or low for pivot detection.
Target Respects: This input specifies how many times a level must be "respected" by price action for it to be considered a strong support or resistance level by the underlying model.
Probability Threshold: This is the minimum probability output from the logistic regression model for a detected pivot to be considered a valid support or resistance level and be plotted on the chart.
2. Style
Show Prediction Labels: Enable or disable labels that display the calculated probability of a newly identified regression S/R level.
Show Retests: Toggle the visibility of "R" labels on the chart, which mark instances where price has retested a support or resistance level.
Show Breaks: Toggle the visibility of "B" labels on the chart, which mark instances where price has broken through a support or resistance level.
TrueTrend MaxRThe TrueTrend MaxR indicator is designed to identify the most consistent exponential price trend over extended periods. It uses statistical analysis on log-transformed prices to find the trendline that best fits historical price action, and highlights the most frequently tested or traded level within that trend channel.
For optimal results, especially on high timeframes such as weekly or monthly, it is recommended to use this indicator on charts set to logarithmic scale. This ensures proper visual alignment with the exponential nature of long-term price movements.
How it works
The indicator tests 50 different lookback periods, ranging from 300 to 1280 bars. For each period, it:
- Applies a linear regression on the natural logarithm of the price
- Computes the slope and intercept of the trendline
- Calculates the unbiased standard deviation from the regression line
- Measures the correlation strength using Pearson's R coefficient
The period with the highest Pearson R value is selected, meaning the trendline drawn corresponds to the log-scale trend with the best statistical fit.
Trendline and deviation bands
Once the optimal period is identified, the indicator plots:
- A main log-scale trendline
- Upper and lower bands, based on a user-defined multiple of the standard deviation
These bands help visualize how far price deviates from its core trend, and define the range of typical fluctuations.
Point of Control (POC)
Inside the trend channel, the space between upper and lower bands is divided into 15 logarithmic levels. The script evaluates how often price has interacted with each level, using one of two selectable methods:
- Touches: Counts the number of candles crossing each level
- Volume: Weighs each touch by the traded volume at that candle
The level with the highest cumulative interaction is considered the dynamic Point of Control (POC), and is plotted as a line.
Annualized performance and confidence display
When used on daily or weekly timeframes, the script also calculates the annualized return (CAGR) based on the detected trend, and displays:
- A performance estimate in percentage terms
- A textual label describing the confidence level based on the Pearson R value
Why this indicator is useful
- Automatically detects the most statistically consistent exponential trendline
- Designed for log-scale analysis, suited to long-term investment charts
- Highlights key price levels frequently visited or traded within the trend
- Provides objective, data-based trend and volatility insights
- Displays annualized growth rate and correlation strength for quick evaluation
Notes
- All calculations are performed only on the last bar
- No future data is used, and the script does not repaint
- Works on any instrument or timeframe, with optimal use on higher timeframes and logarithmic scaling
