High Power CandlesThis indicator provides information about the strength of the candles in favor of the trend, following the logical meaning: green (rising force), yellow (little force), red (downward force)
Regressions
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
Custom Paul MACD-likePaul MACD is an indicator created by David Paul. It is implemented to effectively represent trend periods and non-trend (sideways/consolidation) periods, and its calculation method is particularly designed to reduce whipsaw.
Unlike the existing MACD which uses the difference between short-term (12) and long-term (26) exponential moving averages (EMA), Paul MACD has a different calculation method. This indicator uses a "center value" or "intermediate value". Calculation occurs when this intermediate value is higher than the High value (specifically, the difference between the center and High is calculated) or lower than the Low value (specifically, the difference between the center and Low is calculated). Otherwise, the value becomes 0. Here, the High and Low values are intended to be smoothly reflected using Smoothed Moving Average (SMMA). The indicator's method itself (using SMMA and ZLMA) is aimed at diluting whipsaws.
Thanks to this calculation method, in sections where whipsaw occurs, meaning when the intermediate value is between High and Low, the indicator value is expressed as 0 and appears as a horizontal line (zero line). This serves to visually clearly show sideways/consolidation periods.
Linear Regression ForecastDescription:
This indicator computes a series of simple linear regressions anchored at the current bar, using look-back windows from 2 bars up to the user-defined maximum. Each regression line is projected forward by the same number of bars as its look-back, producing a family of forecast endpoints. These endpoints are then connected into a continuous polyline: ascending segments are drawn in green, and descending segments in red.
Inputs:
maxLength – Maximum number of bars to include in the longest regression (minimum 2)
priceSource – Price series used for regression (for example, close, open, high, low)
lineWidth – Width of each line segment
Calculation:
For each window size N (from 2 to maxLength):
• Compute least-squares slope and intercept over the N most recent bars (with bar 0 = current bar, bar 1 = one bar ago, etc.).
• Project the regression line to bar_index + N to obtain the forecast price.
Collected forecast points are sorted by projection horizon and then joined:
• First segment: current bar’s price → first forecast point
• Subsequent segments: each forecast point → next forecast point
Segment colors reflect slope direction: green for non-negative, red for negative.
Usage:
Apply this overlay to any price chart. Adjust maxLength to control the depth and reach of the forecast fan. Observe how shorter windows produce nearer-term, more reactive projections, while longer windows yield smoother, more conservative forecasts. Use the colored segments to gauge the overall bias of the fan at each step.
Limitations:
This tool is for informational and educational purposes only. It relies on linear regression assumptions and past price behavior; it does not guarantee future performance. Users should combine it with other technical or fundamental analyses and risk management practices.
LANZ Strategy 3.0 [Backtest]🔷 LANZ Strategy 3.0 — Asian Range Fibonacci Scalping Strategy
LANZ Strategy 3.0 is a precision-engineered backtesting tool tailored for intraday traders who rely on the Asian session range to determine directional bias. This strategy implements dynamic Fibonacci projections and strict time-window validation to simulate a clean and disciplined trading environment.
🧠 Core Components:
Asian Range Bias Definition: Direction is established between 01:15–02:15 a.m. NY time based on the candle’s close in relation to the midpoint of the Asian session range (18:00–01:15 NY).
Limit Order Execution: Only one trade is placed daily, using a limit order at the Asian range high (for sells) or low (for buys), between 01:15–08:00 a.m. NY.
Fibonacci-Based TP/SL:
Original Mode: TP = 2.25x range, SL = 0.75x range.
Optimized Mode: TP = 1.95x range, SL = 0.65x range.
No Trade After 08:00 NY: If the limit order is not executed before 08:00 a.m. NY, it is canceled.
Fallback Logic at 02:15 NY: If the market direction misaligns with the setup at 02:15 a.m., the system re-evaluates and can re-issue the order.
End-of-Day Closure: All positions are closed at 15:45 NY if still open.
📊 Backtest-Ready Design:
Entries and exits are executed using strategy.entry() and strategy.exit() functions.
Position size is fixed via capital risk allocation ($100 per trade by default).
Only one position can be active at a time, ensuring controlled risk.
📝 Notes:
This strategy is ideal for assets sensitive to the Asian/London session overlap, such as Forex pairs and indices.
Easily switch between Fibonacci versions using a single dropdown input.
Fully deterministic: all entries are based on pre-defined conditions and time constraints.
👤 Credits:
Strategy developed by rau_u_lanz using Pine Script v6. Built for traders who favor clean sessions, directional clarity, and consistent execution using time-based logic and Fibonacci projections.
Tangent Extrapolation ForecastTangent Extrapolation Forecast
This indicator visually projects price direction by drawing a smoothed sequence of tangent lines based on recent price movements. For each bar in a user-defined lookback window, it calculates the slope over a smoothing period and extends the projected price forward. The resulting polyline forecast connect the endpoints of the extrapolations, and is color-coded to reflect directional changes: green for upward moves, red for downward, and gray for flat segments. This tool can assist traders in visualizing short-term momentum and potential trend continuity without introducing artificial future gaps.
Inputs:
Bars to Use: Number of historical bars used in the forecast.
Slope Smoothing Window: The number of bars used to calculate slope for projection.
Source: Price input for calculations (default is close).
This indicator does not generate buy/sell signals. It is intended as a visual aid to support discretionary analysis.
Linear Volume MACD | Lyro RS📊 Linear Volume MACD | Lyro RS is an advanced momentum and trend detection tool that fuses price action with volume-weighted MACD logic and linear regression analysis . Designed for traders seeking deeper insights into market strength and directional conviction, this indicator highlights trend shifts, volume anomalies, and potential reversal zones with precision.
✨ Key Features :
🔁 Multi-Mode Analysis: Switch between Linear Regression , Strong/Weak Trend , or Volume MACD logic.
📐 Volume-Adjusted MACD: Incorporates volume for a more realistic momentum view.
📊 Linear Regression Signal: Smoother and more reactive trend analysis.
🎯 Dynamic Stdev Bands: Visualize ±1 and ±2 standard deviation thresholds for anomaly detection.
🌈 Custom Color Themes: Choose from built-in palettes or define your own bullish/bearish signal colors.
⚠️ Alert Conditions: Built-in alerts notify you of potential trend shifts across all signal modes.
📈 How It Works :
🧮 MACD Core: Uses volume-weighted price to generate fast and slow EMAs, forming the MACD and signal lines.
📉 Histogram Logic: Histogram is either the traditional MACD histogram or its linear regression version.
📊 Signal Modes:
• Linear Regression: Detect trend based on smoothed MACD behavior.
• Strong/Weak Trend: Identifies accelerating/decelerating trend strength.
• Volume MACD: Classic volume MACD behavior for divergence spotting.
📏 Stdev Bands: Calculated over a long period (default 200) to highlight statistically significant moves.
🎨 Color-coded Feedback: Bar and background colors adjust dynamically with market condition.
⚙️ Customization Options :
🔄 Choose your Signal Type from three unique analysis modes.
📏 Modify Fast/Slow/Signal lengths and Regression parameters to suit your strategy.
📈 Enable or disable Stdev Bands and adjust multiplier.
🎨 Select from Classic, Mystic, Accented, or Royal color palettes — or create your own.
📌 Use Cases :
🟢 Identify trend continuation or reversal zones with volume-adjusted signals.
🔴 Detect volatility breakouts using standard deviation bands.
🧭 Use in confluence with price structure, RSI, or market sentiment.
⚠️ Disclaimer :
This indicator is for educational purposes only. It is not financial advice. Always use in conjunction with your own research and risk management strategy.
Weighted Regression Bands (Zeiierman)█ Overview
Weighted Regression Bands is a precision-engineered trend and volatility tool designed to adapt to the real market structure instead of reacting to price noise.
This indicator analyzes Weighted High/Low medians and applies user-selectable smoothing methods — including Kalman Filtering, ALMA, and custom Linear Regression — to generate a Fair Value line. Around this, it constructs dynamic standard deviation bands that adapt in real-time to market volatility.
The result is a visually clean and structurally intelligent trend framework suitable for breakout traders, mean reversion strategies, and trend-driven analysis.
█ How It Works
⚪ Structural High/Low Analysis
At the heart of this indicator is a custom high/low weighting system. Instead of using just the raw high or low values, it calculates a midline = (high + low) / 2, then applies one of three weighting methods to determine which price zones matter most.
Users can select the method using the “Weighted HL Method” setting:
Simple
Selects the single most dominant median (highest or lowest) in the lookback window. Ideal for fast, reactive signals.
Advanced
Ranks each bar based on a composite score: median × range × recency. This method highlights structurally meaningful bars that had both volatility and recency. A built-in Kalman filter is applied for extra stability.
Smooth
Blends multiple bars into a single weighted average using smoothed decay and range. This provides the softest and most stable structural response.
⚪ Smoothing Methods (ALMA / Linear Regression)
ALMA provides responsive, low-lag smoothing for fast trend reading.
Linear Regression projects the Fair Value forward, ideal for trend modeling.
⚪ Kalman Smoothing Filter
Before trend calculations, the indicator applies an optional Kalman-style smoothing filter. This helps:
Reduce choppy false shifts in trend,
Retain signal clarity during volatile periods,
Provide stability for long-term setups.
⚪ Deviation Bands (Dynamic Volatility Envelopes)
The indicator builds ±1, ±2, and ±3 standard deviation bands around the fair value line:
Calculated from the standard deviation of price,
Bands expand and contract based on recent volatility,
Visualizes potential overbought/oversold or trending conditions.
█ How to Use
⚪ Trend Trading & Filtering
Use the Fair Value line to identify the dominant direction.
Only trade in the direction of the slope for higher probability setups.
⚪ Volatility-Based Entries
Watch for price reaching outer bands (+2σ, +3σ) for possible exhaustion.
Mean reversion entries become higher quality when far from Fair Value.
█ Settings
Length – Lookback for Weighted HL and trend smoothing
Deviation Multiplier – Controls how wide the bands are from the fair value line
Method – Choose between ALMA or Linear Regression smoothing
Smoothing – Strength of Kalman Filter (1 = none, <1 = stronger smoothing)
-----------------
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 Slope ShiftNormalized Regression Slope Shift + Dynamic Histogram
This indicator detects subtle shifts in price momentum using a rolling linear regression approach. It calculates the slope of a linear regression line for each bar over a specified lookback period, then measures how that slope changes from bar to bar.
Both the slope and its change (delta) are normalized to a -1 to 1 scale for consistent visual interpretation across assets and timeframes. A signal line (EMA) is applied to the slope delta to help identify turning points and crossovers.
Key features:
- Normalized slope and slope change lines
- Dynamic histogram of slope delta with transparency based on magnitude
- Customizable colors for all visual elements
- Signal line for crossover-based momentum shifts
This tool helps traders anticipate trend acceleration or weakening before traditional momentum indicators react, making it useful for early trend detection, divergence spotting, and confirmation signals.
Cointegration Heatmap & Spread Table [EdgeTerminal]The Cointegration Heatmap is a powerful visual and quantitative tool designed to uncover deep, statistically meaningful relationships between assets.
Unlike traditional indicators that react to price movement, this tool analyzes the underlying statistical relationship between two time series and tracks when they diverge from their long-term equilibrium — offering actionable signals for mean-reversion trades .
What Is Cointegration?
Most traders are familiar with correlation, which measures how two assets move together in the short term. But correlation is shallow — it doesn’t imply a stable or predictable relationship over time.
Cointegration, however, is a deeper statistical concept: Two assets are cointegrated if a linear combination of their prices or returns is stationary , even if the individual series themselves are non-stationary.
Cointegration is a foundational concept in time series analysis, widely used by hedge funds, proprietary trading firms, and quantitative researchers. This indicator brings that institutional-grade concept into an easy-to-use and fully visual TradingView indicator.
This tool helps answer key questions like:
“Which stocks tend to move in sync over the long term?”
“When are two assets diverging beyond statistical norms?”
“Is now the right time to short one and long the other?”
Using a combination of regression analysis, residual modeling, and Z-score evaluation, this indicator surfaces opportunities where price relationships are stretched and likely to snap back — making it ideal for building low-risk, high-probability trade setups.
In simple terms:
Cointegrated assets drift apart temporarily, but always come back together over time. This behavior is the foundation of successful pairs trading.
How the Indicator Works
Cointegration Heatmap indicator works across any market supported on TradingView — from stocks and ETFs to cryptocurrencies and forex pairs.
You enter your list of symbols, choose a timeframe, and the indicator updates every bar with live cointegration scores, spread signals, and trade-ready insights.
Indicator Settings:
Symbol list: a customizable list of symbols separated by commas
Returns timeframe: time frame selection for return sampling (Weekly or Monthly)
Max periods: max periods to limit the data to a certain time and to control indicator performance
This indicator accomplishes three major goals in one streamlined package:
Identifies stable long-term relationships (cointegration) between assets, using a heatmap visualization.
Tracks the spread — the difference between actual prices and the predicted linear relationship — between each pair.
Generates trade signals based on Z-score deviations from the mean spread, helping traders know when a pair is statistically overextended and likely to mean revert.
The math:
Returns are calculated using spread tickers to ensure alignment in time and adjust for dividends, splits, and other inconsistencies.
For each unique pair of symbols, we perform a linear regression
Yt=α+βXt+ε
Then we compute the residuals (errors from the regression):
Spreadt=Yt−(α+βXt)
Calculate the standard deviation of the spread over a moving window (default: 100 samples) and finally, define the Cointegration Score:
S=1/Standard Deviation of Residuals
This means, the lower the deviation, the tighter the relationship, so higher scores indicate stronger cointegration.
Always remember that cointegration can break down so monitor the asset over time and over multiple different timeframes before making a decision.
How to use the indicator
The heatmap table:
The indicator displays 2 very important tables, one in the middle and one on the right side. After entering your symbols, the first table to pay attention to is the middle heatmap table.
Any assets with a cointegration value of 25% is something to pay attention to and have a strong and stable relationship. Anything below is weak and not tradable.
Additionally, the 40% level is another important line to cross. Assets that have a cointegration score of over 40% will most likely have an extremely strong relationship.
Think about it this way, the higher the percentage, the tighter and more statistically reliable the relationship is.
The spread table:
After finding a good asset pair using heatmap, locate the same pair in the spread table (right side).
Here’s what you’ll see on the table:
Spread: Current difference between the two symbols based on the regression fit
Mean: Historical average of that spread
Z-score: How far current spread is from the mean in standard deviations
Signal: Trade suggestion: Short, Long, or Neutral
Since you’re expecting mean reversion, the idea is that the spread will return to the average. You want to take a trade when the z-score is either over +2 or below -2 and exit when z-score returns to near 0.
You will usually see the trade suggestion on the spread chart but you can make your own decision based on your risk level.
Keep in mind that the Z-score for each pair refers to how off the first asset is from the mean compared to the second one, so for example if you see STOCKA vs STOCKB with a Z-score of -1.55, we are regressing STOCKB (Y) on STOCKA (X).
In this case, STOCKB is the quoted asset and STOCKA is the base asset.
In this case, this means that STOCKB is much lower than expected relative to STOCKA, so the trade would be a long position on stock B and short position on stock A.
