Kernel Regression Bands SuiteMulti-Kernel Regression Bands
A versatile indicator that applies kernel regression smoothing to price data, then dynamically calculates upper and lower bands using a wide variety of deviation methods. This tool is designed to help traders identify trend direction, volatility, and potential reversal zones with customizable visual styles.
Key Features
Multiple Kernel Types: Choose from 17+ kernel regression styles (Gaussian, Laplace, Epanechnikov, etc.) for smoothing.
Flexible Band Calculation: Select from 12+ deviation types including Standard Deviation, Mean/Median Absolute Deviation, Exponential, True Range, Hull, Parabolic SAR, Quantile, and more.
Adaptive Bands: Bands are calculated around the kernel regression line, with a user-defined multiplier.
Signal Logic: Trend state is determined by crossovers/crossunders of price and bands, coloring the regression line and band fills accordingly.
Custom Color Modes: Six unique color palettes for visual clarity and personal preference.
Highly Customizable Inputs: Adjust kernel type, lookback, deviation method, band source, and more.
How to Use
Trend Identification: The regression line changes color based on the detected trend (up/down)
Volatility Zones: Bands expand/contract with volatility, helping spot breakouts or mean-reversion opportunities.
Visual Styling: Use color modes to match your chart theme or highlight specific market states.
Credits:
Kernel regression logic adapted from:
ChartPrime | Multi-Kernel-Regression-ChartPrime (Link in the script)
Disclaimer
This script is for educational and informational purposes only. Not financial advice. Use at your own risk.
Regressions
Open-Close / High-Low RibbonThis indicator visualizes smoothed Open, Close, High, and Low price levels as continuous lines, helping users observe underlying price structure with reduced noise. The Open and Close values are shaded to highlight bullish (green) or bearish (red) zones based on their relationship. Smoothing is applied using a simple moving average (SMA) over a user-defined length to make trends easier to interpret. This tool can be useful for identifying directional bias, trend shifts, or areas of support and resistance on any timeframe.
Linear Regression Volume | Lyro RSLinear Regression Volume | Lyro RS
⚠️Disclaimer⚠️
Always combine this indicator with other forms of analysis and risk management. Please do your own research before making any trading decisions.
The LR Volume | 𝓛𝔂𝓻𝓸 𝓡𝓢 indicator blends linear regression with volume-adjusted moving average s to dynamically outline price equilibrium and trend intensity. By integrating volume into its regression model, it highlights meaningful price movement relative to trading activity.
📌 How It Works:
Volume-Weighted Regression Baseline
Price is filtered through one of four volume-adjusted moving averages (SMA, RMA, HMA, ALMA) before being passed through a linear regression model, forming a dynamic fair value line.
Deviation Bands
The indicator plots 1x, 2x, and 3x standard deviation zones above and below the baseline, helping identify potential extremes, volatility spikes, and mean reversion areas.
Slope-Based Color Logic
The baseline and fill areas are dynamically colored:
- 🟢 Green for positive slope (uptrend)
- 🔴 Red for negative slope (downtrend)
- ⚪ Gray for neutral movement
⚙️ Inputs & Options:
Regression Length – Controls how many bars are used in the moving average and regression calculation.
Deviation Multiplier – Adjusts the width of the bands surrounding the regression baseline.
MA Type – Choose from 4 types:
SMA (Simple Moving Average)
RMA (Relative Moving Average)
HMA (Hull Moving Average)
ALMA (Arnaud Legoux Moving Average)
Band Colors – Customizable upper/lower band colors to match your visual style.
🔔 Alerts:
Long Signal – Triggers when the regression slope turns positive.
Short Signal – Triggers when the regression slope turns negative.
LANZ Strategy 3.0🔷 LANZ Strategy 3.0 — Asian Range Fibonacci Strategy with Execution Window Logic
LANZ Strategy 3.0 is a rule-based trading system that utilizes the Asian session range to project Fibonacci levels and manage entries during a defined execution window. Designed for Forex and index traders, this strategy focuses on structured price behavior around key levels before the New York session.
🧠 Core Components:
Asian Session Range Mapping: Automatically detects the high, low, and midpoint during the Asian session.
Fibonacci Level Projection: Projects configurable Fibonacci retracement and extension levels based on the Asian range.
Execution Window Logic: Uses the 01:15 NY candle as a reference to validate potential reversals or continuation setups.
Conditional Entry System: Includes logic for limit order entries (buy or sell) at specific Fib levels, with reversal logic if price breaks structure before execution.
Risk Management: Entry orders are paired with dynamic SL and TP based on Fibonacci-based distances, maintaining a risk-reward ratio consistent with intraday strategies.
📊 Visual Features:
Asian session high/low/mid lines.
Fibonacci levels: Original (based on raw range) and Optimized (user-adjustable).
Session background coloring for Asia, Execution Window, and NY session.
Labels and lines for entry, SL, and TP targets.
Dynamic deletion of untriggered orders after execution window expires.
⚙️ How It Works:
The script calculates the Asian session range.
Projects Fibonacci levels from the range.
Waits for the 01:15 NY candle to close to validate a signal.
If valid, a limit entry order (BUY or SELL) is plotted at the selected level.
If price structure changes (e.g., breaks the high/low), reversal logic may activate.
If no trade is triggered, orders are cleared before the NY session.
🔔 Alerts:
Alerts trigger when a valid setup appears after 01:15 NY candle.
Optional alerts for order activation, SL/TP hit, or trade cancellation.
📝 Notes:
Intended for semi-automated or discretionary trading.
Best used on highly liquid markets like Forex majors or indices.
Script parameters include session times, Fib ratios, SL/TP settings, and reversal logic toggle.
Credits:
Developed by LANZ, this script merges traditional session-based analysis with Fibonacci tools and structured execution timing, offering a unique framework for morning volatility plays.
ETHBTC Z-ScoreETHBTC Z-Score Indicator
Key Features
Z-Score Calculation: Measures how far ETHBTC deviates from its mean over a user-defined period.
Linear Regression Line: Tracks the trend of the Z-score using least squares regression.
Standard Deviation Bands: Plots ±N standard deviations around the regression line to show expected Z-score range.
Dynamic Thresholds: Highlights overbought (e.g. Z > 1) and oversold (e.g. Z < -2) zones using color and background fill.
Visual & Table Display: Color-coded bars, horizontal level fills, and optional table showing regression formula and R².
Usage
Spot overbought/oversold extremes when Z-score crosses defined thresholds.
Use the regression line as a dynamic baseline and its bands as range boundaries.
Monitor R² to gauge how well the regression line fits the recent Z-score trend.
Example
Z > 1: ETHBTC may be overbought — potential caution or mean-reversion.
Z < -2: ETHBTC may be oversold — possible buying opportunity.
Z near regression line: Price is in line with recent trend.
Machine Learning: ARIMA + SARIMADescription
The ARIMA (Autoregressive Integrated Moving Average) and SARIMA (Seasonal ARIMA) are advanced statistical models that use machine learning to forecast future price movements. It uses autoregression to find the relationship between observed data and its lagged observations. The data is differenced to make it more predictable. The MA component creates a dependency between observations and residual errors. The parameters are automatically adjusted to market conditions.
Differences
ARIMA - This excels at identifying trends in the form of directions
SARIMA - Incorporates seasonality. It's better at capturing patterns previously seen
How To Use
1. Model: Determine if you want to use ARIMA (better for direction) or SARIMA (better for overall prediction). You can click on the 'Show Historic Prediction' to see the direction of the previous candles. Green = forecast ending up, red = forecast ending down
2. Metrics: The RMSE% and MAPE are 10 day moving averages of the first 10 predictions made at candle close. They're error metrics that compare the observed data with the predicted data. It is better to use them when they're below 8%. Higher timeframes will be higher, as these models are partly mean-reverting and higher TFs tend to trend more. Better to compare RMSE% and MAPE with similar timeframes. They naturally lag as data is being collected
3. Parameter selection: The simpler, the better. Both are used for ARIMA(1,1,1) and SARIMA(1,1,1)(1,1,1)5. Increasing may cause overfitting
4. Training period: Keep at 50. Because of limitations in pine, higher values do not make for more powerful forecasts. They will only criminally lag. So best to keep between 20 and 80
BTC vs ALT Lag Detector [MEXC Overlay]This indicator monitors the price movement of Bitcoin (BTC) and compares it in real time to a customizable list of major altcoins on the MEXC exchange.
It helps you identify lagging altcoins — tokens that are underperforming or overperforming BTC’s price action over a selected timeframe. These temporary deviations can offer profitable entry or rotation opportunities, especially for scalpers, day traders, and arbitrage-style strategies.
Key Features:
- Real-time deviation detection between BTC and altcoins
- Customizable comparison timeframe: 1m, 6m, 12m, 30m, 1h, 4h, or 1d
- Deviation threshold alert: Highlights coins that lag BTC by more than 0.5%, 1%, 2%, or 3%
- Compact stats table embedded in the price chart
- Fully adjustable layout: Table position (Top/Bottom/Center + Left/Right), Font size (Tiny, Small, Medium)
- Built-in alert system when deviation exceeds your chosen threshold
How to Use It:
Set your desired timeframe for comparison (e.g., 1 hour).
Select a deviation threshold (e.g., 1.0%).
The table will show:
Each altcoin’s % change
BTC’s % change
The delta (deviation) vs BTC
Red highlights indicate alts whose deviation exceeded the threshold.
When at least one alt lags beyond your threshold, the indicator can trigger an alert — helping you capitalize on potential catch-up trades.
Please provide any feedback on it.
[Tradevietstock] Fair Value Channel – Premium/Discount ZonesThe Ultimate Tool for Value Traders
Fair Value Channel – Premium/Discount Zones (Polynomial Regression)
Hello again, it’s Tradevietstock ,
This time, we’re introducing a powerful long-term tool for value investors and swing traders — a visual framework that answers one key question:
i. Overview
1. 🧠 Logic Behind the Script
This script creates a Fair Value Channel using polynomial regression to model the upper and lower bounds of a stock's expected price range. The core idea is to estimate "fair value" zones that indicate whether the current price is at a premium (overvalued) or discount (undervalued) relative to its historical range.
The script uses fixed coefficients for third-degree (cubic) polynomial equations to define a top channel and bottom channel, then scales and shifts these curves to match the actual price data. Intermediate levels (25%, 50%, 75%) are calculated using geometric interpolation, offering a graded assessment of price positioning within the channel.
2. The Trading Theory
This indicator is based on the idea that markets move in repeatable cycles of overvaluation and undervaluation. Rather than relying on instinct to judge whether an asset is “cheap” or “expensive,” it uses mathematical modeling — specifically, a fixed third-degree polynomial regression — to identify structured price patterns over time. This regression captures the natural wave-like behavior of prices and defines a fair value channel, with upper and lower bounds representing premium and discount zones.
The lower zone signals undervalued conditions, ideal for accumulating positions, while the upper zone reflects overvalued areas, where it may be time to reduce exposure. These zones are scaled to align with the asset’s real price range, making them practical and adaptive.
Ultimately, the indicator brings logic and discipline to value investing. It helps traders recognize favorable buying opportunities within a cycle — and hold until the next major uptrend, instead of reacting emotionally. The strategy: buy low, hold smart, sell high — driven by data, not guesswork.
ii. How to use
1. Key terms
Lookback_period : Sets the historical period used to calculate the highest and lowest prices. Determines whether the analysis is short-term, mid-term, or long-term.
Timeframe_input : Specifies the timeframe used for polynomial regression calculations. Higher timeframes smooth out noise.
Extrapolation_bars : Defines how many bars into the future the fair value channel should be projected (forecasted). Helps visualize future zones.
Show_forecast : Enables or disables the display of forecasted (future) evaluation zones based on extrapolated regression curves.
🎯 Evaluation Zones Based on Fair Value Range
Each of these zones represents a valuation level relative to a stock's or asset's estimated fair value. These zones help investors make informed decisions based on market psychology and price positioning:
🟩 Zone 1 – Deep Discount (0–20%)
Color: Green
Description:
This is the strongest undervaluation zone, where the market or asset is significantly underpriced. It typically reflects extreme fear and pessimism among investors.
A great opportunity for long-term investors to accumulate high-potential assets at bargain prices.
For example, Tesla (TSLA) stock dropped into the Deep Discount Zone in 2019, offering an exceptional entry point. By 2020, the stock had surged approximately 430%, illustrating how powerful the recovery can be from this zone.
The Deep Discount Zone often appears only during recessionary periods or times of extreme market fear, making it one of the best opportunities to accumulate high-quality stocks.
However, due to the elevated risks and uncertainty in such conditions, it’s crucial to prioritize risk management and approach this zone with a mid- to long-term investment mindset, rather than seeking short-term gains.
🟩 Zone 2 – Undervalued (20–40%)
Color: Lime
Description:
Still considered a strong buying opportunity, this zone offers assets at meaningful discounts. While not as deeply undervalued as Zone 1, it remains attractive for value-seeking investors.
For example, Netflix (NFLX) stock experienced a sharp decline of nearly 80% in 2011, pushing it into the Undervalued Zone. This presented a prime buying opportunity for long-term investors.
After a period of consolidation, NFLX surged over 500% by 2013, demonstrating how deeply discounted zones can signal powerful reversal and growth potential when backed by strong fundamentals.
🟨 Zone 3 – Fair Value (40–60%)
Color: Yellow
Description:
This zone represents the true fair value range. Many high-growth or in-demand assets may only dip this low due to market optimism. Buying in this zone can still be wise—especially for fundamentally strong stocks or tokens—depending on broader conditions and expectations.
For example, Apple stock has historically never fallen below the Fair Value Zone, largely due to the company’s strong core values, resilient business model, and consistent performance. Whether a stock dips further into undervalued zones often depends on its intrinsic fundamentals and long-term growth potential.
Likewise, NVDA stock has only dipped into the Fair Value Zone, but not deeper, due to the company’s strong fundamentals and high growth potential.
🟧 Zone 4 – Overvalued (60–80%)
Color: Orange
Description:
In this range, prices are becoming expensive. This is generally a time to pause further buying and begin looking for potential exit or profit-taking opportunities.
Despite potential continued upside, staying disciplined here is key, as price increases may be driven more by speculation than fundamentals.
🟥 Zone 5 – Extended Premium (80–100%)
Color: Red
Description:
This is the extreme overvaluation zone, often driven by market euphoria, FOMO (Fear of Missing Out), and greed.
Avoid buying in this range. Instead, focus on exiting positions and securing profits. Risk of a reversal is high.
2. How to Use?
This indicator is not designed for short-term trading. Instead, it supports a value investing mindset, applicable across various financial instruments—including stocks, indices, tokens, and CFDs.
Investing based on fair value means focusing on the intrinsic worth of an asset and holding through market cycles—from fear to euphoria.
The goal is to accumulate positions during Deep Discount Zones (often during extreme fear or recession) and hold them patiently until the market reaches the FOMO and Extreme Greed stages.
At that point, those who bought during deep discounts become the true winners, having captured both value and long-term upside.
Trading Tutorial
The strategy is simple: Buy cheap, sell high.
Note:
Discount zones are based on the historical price behavior of each asset.
A strong stock may never drop into the lowest zones, while some tokens/indices/stocks might reach the Deep Discount Zone and still dip further before recovering.
Always analyze the asset’s history—does it usually bounce from the Fair Value Zone, or does it often fall deeper before reversing?
Your strategy should adapt to the specific behavior of the stock, token, or index you're trading.
This indicator works with stocks, crypto, indices, and CFDs.
You can adjust any input settings to match your own strategy and risk tolerance, as long as you understand what you're doing.
Market Manipulation Index (MMI)The Composite Manipulation Index (CMI) is a structural integrity tool that quantifies how chaotic or orderly current market conditions are, with the aim of detecting potentially manipulated or unstable environments. It blends two distinct mathematical models that assess price behavior in terms of both structural rhythm and predictability.
1. Sine-Fit Deviation Model:
This component assumes that ideal, low-manipulation price behavior resembles a smooth oscillation, such as a sine wave. It generates a synthetic sine wave using a user-defined period and compares it to actual price movement over an adaptive window. The error between the real price and this synthetic wave—normalized by price variance—forms the Sine-Based Manipulation Index. A high error indicates deviation from natural rhythm, suggesting structural disorder.
2. Predictability-Based Model:
The second component estimates how well current price can be predicted using recent price lags. A two-variable rolling linear regression is computed between the current price and two lagged inputs (close and close ). If the predicted price diverges from the actual price, this error—also normalized by price variance—reflects unpredictability. High prediction error implies a more manipulated or erratic environment.
3. Adaptive Mechanism:
Both components are calculated using an adaptive smoothing window based on the Average True Range (ATR). This allows the indicator to respond proportionally to market volatility. During high volatility, the analysis window expands to avoid over-sensitivity; during calm periods, it contracts for better responsiveness.
4. Composite Output:
The two normalized metrics are averaged to form the final CMI value, which is then optionally smoothed further. The output is scaled between 0 and 1:
0 indicates a highly structured, orderly market.
1 indicates complete structural breakdown or randomness.
Suggested Interpretation:
CMI < 0.3: Market is clean and structured. Trend-following or breakout strategies may perform better.
CMI > 0.7: Market is structurally unstable. Choppy price action, fakeouts, or manipulative behavior may dominate.
CMI 0.3–0.7: Transitional zone. Caution or reduced risk may be warranted.
This indicator is designed to serve as a contextual filter, helping traders assess whether current market conditions are conducive to structured strategies, or if discretion and defense are more appropriate.
Future Candle Reversal Projection (Mastersinnifty)Overview
This tool identifies potential future market reversal zones by dynamically projecting pivot-based swing patterns forward in time. Unlike traditional ZigZag indicators that only reflect past movements, this indicator anticipates probable future turning points based on historical swing periodicity.
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Key Features
- Forward Projections: Calculates and projects future swing zones based on detected pivot distances.
- Customizable Detection: Adjust the ZigZag depth for different trading styles (scalping, swing, position).
- Dynamic Updates: Real-time recalibration as new pivots form.
- Clean Visual Markers: Projects reversal estimates as intuitive labels and dotted lines.
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How it Works
The indicator identifies significant swing highs and lows using a user-defined ZigZag depth setting. It measures the time (bars) and price characteristics of the latest swing movement. Using this pattern, it projects forward estimated reversal points at consistent intervals. Midpoint price levels between the last high and low are used for each future projection.
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Who Can Benefit
- Intraday and swing traders seeking advanced planning zones.
- Technical analysts relying on pattern periodicity.
- Traders who wish to combine projected reversal markers with their own risk management strategies.
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Disclaimer
This tool is an analytical and educational utility. It does not predict markets with certainty. Always combine it with your own analysis and risk management. Past behavior does not guarantee future results.
Liquidity Trap Reversal Pro (Radar v2)Liquidity Trap Reversal Pro (Radar v2) is a non-repainting indicator designed to detect hidden liquidity traps at key swing highs and lows. It combines wick analysis, volume spike detection, and optional trend and exhaustion filters to identify high-probability reversal setups.
🔷 Features:
Non-Repainting: Pivots confirmed after lookback period, no future leaking.
Volume Spike Detection: Filters traps that occur during major liquidity events.
EMA Trend Filter (Optional): Focus on traps aligned with the prevailing trend.
Higher Timeframe Trend Filter (Optional): Confirm traps using a higher timeframe EMA bias.
Exhaustion Guard (Optional): Prevents traps after overextended moves based on ATR stretch.
Clean Visuals: Distinct plots for raw trap points vs confirmed traps.
Alerts Included: Set alerts for confirmed high/low liquidity traps.
📚 How to Use:
Watch for Trap Signals:
A Trap High signal suggests a potential bearish reversal.
A Trap Low signal suggests a potential bullish reversal.
Use Confirmed Signals for Best Entries:
Confirmed traps fire only after price moves opposite to the trap direction, adding reliability.
Use Trend Filters to Improve Accuracy:
In an uptrend (price above EMA), prefer Trap Lows (buy setups).
In a downtrend (price below EMA), prefer Trap Highs (sell setups).
Use the Exhaustion Guard to Avoid Bad Trades:
This filter blocks signals when price has moved too far from trend, helping avoid late entries.
Recommended Settings:
Best used on 15-minute, 1-hour, or 4-hour charts.
Trend filter ON for trending markets.
Exhaustion guard ON for volatile or stretched markets.
📈 Important Notes:
This script does not repaint once a pivot is confirmed.
Alerts trigger only on confirmed trap signals.
Always combine signals with sound risk management and trading strategy.
Disclaimer:
This script is for educational purposes only. It is not investment advice or a guarantee of results. Always do your own research before trading.
Auto Trend Channel + Buy/Sell AlertsThis indicator automatically detects trend channels using a linear regression line, and dynamically plots upper and lower channel boundaries based on standard deviation. It helps traders identify potential Buy and Sell zones with clear visual signals and customizable alerts.
💡 How It Works:
🧠 Regression-Based Channel: Calculates the central trend line using ta.linreg() over a user-defined length.
📏 Dynamic Boundaries: Upper and lower channel lines are offset by a multiplier of the standard deviation for precision volatility tracking.
✅ Buy Signals: Triggered when price crosses above the lower boundary — potential bounce entry.
❌ Sell Signals: Triggered when price crosses below the upper boundary — potential reversal exit.
🔔 Alerts Enabled: Get real-time alerts when price touches the channel lines.
TradeSmart Morning GloryThe Morning Glory Indicator by TradeSmart University is a pre-market volume visualization tool designed to help traders quickly assess the quality of a morning gap. By highlighting volume levels before the market opens, this indicator helps distinguish between a professional gap (likely to continue running) and a retail/news-driven gap (likely to fade or reverse).
💡 What It Does:
This indicator plots color-coded volume bars in the pre-market session and highlights when volume crosses two key thresholds:
Teal Bars – Low institutional interest
Yellow Bars – Medium institutional interest (100K+ volume)
Red Bars – High institutional interest (400K+ volume)
These thresholds are most effective on AMEX:SPY and other high-volume ETFs or stocks, but may be customized to fit your trading style. Consider using a 15-minute chart for the above settings.
🧠 How to Use It:
This indicator works best in conjunction with the Morning Glory Strategy and Qualified Trade Setup . On its own, the indicator gives a real-time read on pre-market strength , helping you:
Confirm gap-and-go setups (gap + high volume = likely continuation)
Fade the gap (gap + low volume = higher likelihood of reversal)
While the indicator focuses exclusively on volume, the full Morning Glory strategy adds an important price gap size filter to create powerful trade signals.
📊 Probabilities of Success (Based on Full Strategy):
When used as part of the Morning Glory Qualified Trade Setup, here are the historical win rates by day of the week:
Monday: 65%
Tuesday: 77%
Wednesday: 79%
Thursday: 82%
Friday: 78%
If used in conjunction with an artificial intelligence like the Deep Sky Trading Assistant™, win-loss ratios improve to 89% or better across all days of the week.
🔔 Note: For best results, activate premium ARCA data on your TradingView account. This ensures the most accurate and complete pre-market volume data.
NexAlgo AI with Dynamic TP/SLThe NexAlgo Indicator combines a Gaussian kernel regression engine with adaptive volatility thresholds to generate clear, data‑driven trade signals and built‑in risk levels. It predicts the next bar’s price relative to a simple moving average, then measures the average deviation between actual and forecasted values to form dynamic bands. Breakouts beyond these bands, aligned with the prediction’s direction, produce buy or sell signals directly on your chart.
How It Works & What You’ll See
Kernel Regression Forecast: A rolling “lookback” window builds a Gaussian similarity matrix of recent prices. This matrix is used to project the next price, smoothing around a moving average.
Adaptive Volatility Bands: The indicator computes the mean absolute error between actual and predicted prices, multiplies it by your chosen volatility factor, and plots upper and lower bands.
Signal Triggers: When price closes above the upper band while the prediction is rising, a green “BUY” label appears; when price closes below the lower band as the forecast falls, a red “SELL” label is shown.
Automatic SL/TP Levels: After each signal, the script scans recent swing highs/lows and applies an ATR buffer. Stop‑loss is set conservatively at the more protective of these levels, while take‑profit is calculated by your reward‑to‑risk ratio and capped near the opposite swing extreme.
Customizable Inputs
Lookback Period & Smoothing: Adjust how many bars the regression and volatility calculations use, and tune the noise regularization to suit fast or slow markets.
Volatility Multiplier: Widen or tighten the adaptive bands to control signal frequency and confidence.
Swing Lookback & ATR Options: Define how far back the indicator searches for swing points, and choose between ATR calculation methods.
Reward‑to‑Risk Ratio: Set your preferred multiple of stop‑loss distance for take‑profit targets.
What Makes NexAlgo Different
Hybrid Statistical Approach: Unlike fixed‑period moving averages or standard regression, the Gaussian kernel adapts locally to evolving price patterns and regimes.
Self‑Adjusting Thresholds: Volatility bands derive from prediction errors—so they expand in choppy markets and contract in trending conditions.
Integrated Risk Controls: Automatically calculated stop‑loss and take‑profit levels remove manual guesswork, yet remain grounded in both ATR and price structure.
Trader‑Driven Flexibility: Every parameter—from lookback length to risk ratio—can be dialed in for scalping, swing trading, or longer‑term strategies.
Getting Started
• Apply NexAlgo to your preferred timeframe (5–15 min for intraday scalps, 1 h–4 h for swings, daily for position plays).
• Begin with default settings and gradually adjust lookback and smoothing to balance responsiveness versus noise.
• Experiment with volatility multipliers: tighten in strong trends, widen when markets churn.
• Backtest different ATR buffers and reward ratios to discover your ideal risk‑reward profile.
TASC 2025.05 Trading The Channel█ OVERVIEW
This script implements channel-based trading strategies based on the concepts explained by Perry J. Kaufman in the article "A Test Of Three Approaches: Trading The Channel" from the May 2025 edition of TASC's Traders' Tips . The script explores three distinct trading methods for equities and futures using information from a linear regression channel. Each rule set corresponds to different market behaviors, offering flexibility for trend-following, breakout, and mean-reversion trading styles.
█ CONCEPTS
Linear regression
Linear regression is a model that estimates the relationship between a dependent variable and one or more independent variables by fitting a straight line to the observed data. In the context of financial time series, traders often use linear regression to estimate trends in price movements over time.
The slope of the linear regression line indicates the strength and direction of the price trend. For example, a larger positive slope indicates a stronger upward trend, and a larger negative slope indicates the opposite. Traders can look for shifts in the direction of a linear regression slope to identify potential trend trading signals, and they can analyze the magnitude of the slope to support trading decisions.
One caveat to linear regression is that most financial time series data does not follow a straight line, meaning a regression line cannot perfectly describe the relationships between values. Prices typically fluctuate around a regression line to some degree. As such, analysts often project ranges above and below regression lines, creating channels to model the expected extent of the data's variability. This strategy constructs a channel based on the method used in Kaufman's article. It measures the maximum distances from points on the linear regression line to historical price values, then adds those distances and the current slope to the regression points.
Depending on the trading style, traders might look for prices to move outside an established channel for breakout signals, or they might look for price action to reach extremes within the channel for potential mean reversion opportunities.
█ STRATEGY CALCULATIONS
Primary trade rules
This strategy implements three distinct sets of rules for trend, breakout, and mean-reversion trades based on the methods Kaufman describes in his article:
Trade the trend (Rule 1) : Open new positions when the sign of the slope changes, indicating a potential trend reversal. Close short trades and enter a long trade when the slope changes from negative to positive, and do the opposite when the slope changes from positive to negative.
Trade channel breakouts (Rule 2) : Open new positions when prices cross outside the linear regression channel for the current sample. Close short trades and enter a long trade when the price moves above the channel, and do the opposite when the price moves below the channel.
Trade within the channel (Rule 3) : Open new positions based on price values within the channel's range. Close short trades and enter a long trade when the price is near the channel's low, within a specified percentage of the channel's range, and do the opposite when the price is near the channel's high. With this rule, users can also filter the trades based on the channel's slope. When the filter is active, long positions are allowed only when the slope is positive, and short positions are allowed only when it is negative.
Position sizing
Kaufman's strategy uses specific trade sizes for equities and futures markets:
For an equities symbol, the number of shares traded is $10,000 divided by the current price.
For a futures symbol, the number of contracts traded is based on a volatility-adjusted formula that divides $25,000 by the product of the 20-bar average true range and the instrument's point value.
By default, this script automatically uses these sizes for its trade simulation on equities and futures symbols and does not simulate trading on other symbols. However, users can control position sizes from the "Settings/Properties" tab and enable trade simulation on other symbol types by selecting the "Manual" option in the script's "Position sizing" input.
Stop-loss
This strategy includes the option to place an accompanying stop-loss order for each trade, which users can enable from the "SL %" input in the "Settings/Inputs" tab. When enabled, the strategy places a stop-loss order at a specified percentage distance from the closing price where the entry order occurs, allowing users to compare how the strategy performs with added loss protection.
█ USAGE
This strategy adapts its display logic for the three trading approaches based on the rule selected in the "Trade rule" input:
For all rules, the script plots the linear regression slope in a separate pane. The plot is color-coded to indicate whether the current slope is positive or negative.
When the selected rule is "Trade the trend", the script plots triangles in the separate pane to indicate when the slope's direction changes from positive to negative or vice versa. Additionally, it plots a color-coded SMA on the main chart pane, allowing visual comparison of the slope to directional changes in a moving average.
When the rule is "Trade channel breakouts" or "Trade within the channel", the script draws the current period's linear regression channel on the main chart pane, and it plots bands representing the history of the channel values from the specified start time onward.
When the rule is "Trade within the channel", the script plots overbought and oversold zones between the bands based on a user-specified percentage of the channel range to indicate the value ranges where new trades are allowed.
Users can customize the strategy's calculations with the following additional inputs in the "Settings/Inputs" tab:
Start date : Sets the date and time when the strategy begins simulating trades. The script marks the specified point on the chart with a gray vertical line. The plots for rules 2 and 3 display the bands and trading zones from this point onward.
Period : Specifies the number of bars in the linear regression channel calculation. The default is 40.
Linreg source : Specifies the source series from which to calculate the linear regression values. The default is "close".
Range source : Specifies whether the script uses the distances from the linear regression line to closing prices or high and low prices to determine the channel's upper and lower ranges for rules 2 and 3. The default is "close".
Zone % : The percentage of the channel's overall range to use for trading zones with rule 3. The default is 20, meaning the width of the upper and lower zones is 20% of the range.
SL% : If the checkbox is selected, the strategy adds a stop-loss to each trade at the specified percentage distance away from the closing price where the entry order occurs. The checkbox is deselected by default, and the default percentage value is 5.
Position sizing : Determines whether the strategy uses Kaufman's predefined trade sizes ("Auto") or allows user-defined sizes from the "Settings/Properties" tab ("Manual"). The default is "Auto".
Long trades only : If selected, the strategy does not allow short positions. It is deselected by default.
Trend filter : If selected, the strategy filters positions for rule 3 based on the linear regression slope, allowing long positions only when the slope is positive and short positions only when the slope is negative. It is deselected by default.
NOTE: Because of this strategy's trading rules, the simulated results for a specific symbol or channel configuration might have significantly fewer than 100 trades. For meaningful results, we recommend adjusting the start date and other parameters to achieve a reasonable number of closed trades for analysis.
Additionally, this strategy does not specify commission and slippage amounts by default, because these values can vary across market types. Therefore, we recommend setting realistic values for these properties in the "Cost simulation" section of the "Settings/Properties" tab.
Pullback SARPullback SAR - Parabolic SAR with Pullback Detection
Description: The "Pullback SAR" is an advanced indicator built on the classic Parabolic SAR but with additional functionality for detecting pullbacks. It helps identify moments when the price pulls back from the main trend, offering potential entry signals. Perfect for traders looking to enter the market after a correction.
Key Features:
SAR (Parabolic SAR): The Parabolic SAR indicator is used to determine potential trend reversal points. It marks levels where the price could reverse its direction.
Pullback Detection: The indicator catches periods when the price moves away from the main trend and then returns, which may suggest a re-entry opportunity.
Long and Short Signals: Once a pullback in the direction of the main trend is identified, the indicator generates signals that could be used to open positions.
Simple and Clear Construction: The indicator is based on the classic SAR, with added pullback detection logic to enhance the accuracy of the signals.
Parameters:
Start (SAR Step): Determines the initial step for the SAR calculation, which controls the rate of change in the indicator at the beginning.
Increment (SAR Increment): Defines the maximum step size for SAR, allowing traders to adjust the indicator’s sensitivity to market volatility.
Max Value (SAR Max): Sets the upper limit for the SAR value, controlling its volatility.
Usage:
Swing Trading: Ideal for swing strategies, aiming to capture larger price moves while maintaining a safe margin.
Scalping: Due to its precise pullback detection, it can also be used in scalping, especially when the price quickly returns to the main trend.
Risk Management: The combination of SAR and pullback detection allows traders to adjust their positions according to changing market conditions.
Special Notes:
Adjusting Parameters: Depending on the market and trading style, users can adjust the SAR parameters (Start, Increment, Max Value) to fit their needs.
Combination with Other Indicators: It's recommended to use the indicator alongside other technical analysis tools (e.g., EMA, RSI) to enhance the accuracy of the signals.
Link to the script: This open-source version of the indicator is available on TradingView, enabling full customization and adjustments to meet your personal trading strategy. Share your experiences and suggestions!
ML Deep Regression Pro (TechnoBlooms)ML Deep Regression Pro is a machine-learning-inspired trading indicator that integrates Polynomial Regression, Linear Regression and Statistical Deviation models to provide a powerful, data-driven approach to market trend analysis.
Designed for traders, quantitative analysts and developers, this tool transforms raw market data into predictive trend insights, allowing for better decision-making and trend validation.
By leveraging statistical regression techniques, ML Deep Regression Pro eliminates market noise and identifies key trend shifts, making it a valuable addition to both manual and algorithmic trading strategies.
REGRESSION ANALYSIS
Regression is a statistical modeling technique used in machine learning and data science to identify patterns and relationships between variables. In trading, it helps detect price trends, reversals and volatility changes by fitting price data into a predictive model.
1. Linear Regression -
The most widely used regression model in trading, providing a best-fit plotted line to track price trends.
2. Polynomial Regression -
A more advanced form of regression that fits curved price structures, capturing complex market cycles and improving trend forecasting accuracy.
3. Standard Deviation Bands -
Based on regression calculations, these bands measure price dispersion and identify overbought/ oversold conditions, similar to Bollinger Bands. By default, these lines are hidden and user can make it visible through Settings.
KEY FEATURES :-
✅ Hybrid Regression Engine – Combines Linear and Polynomial Regression to detect market trends with greater accuracy.
✅ Dynamic Trend Bias Analysis – Identifies bullish & bearish market conditions using real-time regression models.
✅ Standard Deviation Bands – Measures price volatility and potential reversals with an advanced deviation model.
✅ Adaptive EMA Crossover Signals – Generates buy/sell signals when price momentum shifts relative to the regression trend.
[iQ]PRO Ultimate Financial Analysis Tool And System SynergyUltimate Financial Analysis Tool And System Synergy (UFATASS)
Advanced Market Insights with Cycle Analysis, Trend Forecasting, and Risk Monitoring
The Ultimate Financial Analysis Tool And System Synergy (UFATASS) is a powerful indicator designed to give traders a deeper understanding of market dynamics. By blending cutting-edge techniques from signal processing, statistics, and dynamical systems theory, UFATASS provides a unique, all-in-one solution for technical analysis.
Key Features
Cycle Detection:
Pinpoints dominant market cycles using advanced spectral analysis, helping you identify potential turning points.
Trend Analysis:
Delivers multiple regression lines to capture short-term and long-term market trends, with a customizable complexity setting for precision.
Probability Forecasts:
Uses Monte Carlo simulations to estimate the likelihood of future price movements, offering a probabilistic edge for decision-making.
Risk Monitoring:
Tracks volatility and market stability, featuring an experimental chaos indicator based on Lyapunov exponents to assess price predictability.
Customization Options
Adjust the indicator to fit your trading style:
Cycle and regression lookback periods
Complexity factor for regression sensitivity
Volatility calculation window
Forecast horizon for price predictions
Visual Outputs
Price and regression lines plotted on the main chart
Cycle details and wave visuals in a separate pane
A summary label on the last bar with key metrics (e.g., cycle length, probabilities)
Background color alerts to signal risk levels
How to Use
Incorporate UFATASS into your strategy to:
Anticipate reversals with cycle analysis
Confirm trends using regression insights
Plan entries and exits with probability forecasts
Monitor market conditions and adjust risk exposure
Adaptive Trend FinderAdaptive Trend Finder - The Ultimate Trend Detection Tool
Introducing Adaptive Trend Finder, the next evolution of trend analysis on TradingView. This powerful indicator is an enhanced and refined version of Adaptive Trend Finder (Log), designed to offer even greater flexibility, accuracy, and ease of use.
What’s New?
Unlike the previous version, Adaptive Trend Finder allows users to fully configure and adjust settings directly within the indicator menu, eliminating the need to modify chart settings manually. A major improvement is that users no longer need to adjust the chart's logarithmic scale manually in the chart settings; this can now be done directly within the indicator options, ensuring a smoother and more efficient experience. This makes it easier to switch between linear and logarithmic scaling without disrupting the analysis. This provides a seamless user experience where traders can instantly adapt the indicator to their needs without extra steps.
One of the most significant improvements is the complete code overhaul, which now enables simultaneous visualization of both long-term and short-term trend channels without needing to add the indicator twice. This not only improves workflow efficiency but also enhances chart readability by allowing traders to monitor multiple trend perspectives at once.
The interface has been entirely redesigned for a more intuitive user experience. Menus are now clearer, better structured, and offer more customization options, making it easier than ever to fine-tune the indicator to fit any trading strategy.
Key Features & Benefits
Automatic Trend Period Selection: The indicator dynamically identifies and applies the strongest trend period, ensuring optimal trend detection with no manual adjustments required. By analyzing historical price correlations, it selects the most statistically relevant trend duration automatically.
Dual Channel Display: Traders can view both long-term and short-term trend channels simultaneously, offering a broader perspective of market movements. This feature eliminates the need to apply the indicator twice, reducing screen clutter and improving efficiency.
Fully Adjustable Settings: Users can customize trend detection parameters directly within the indicator settings. No more switching chart settings – everything is accessible in one place.
Trend Strength & Confidence Metrics: The indicator calculates and displays a confidence score for each detected trend using Pearson correlation values. This helps traders gauge the reliability of a given trend before making decisions.
Midline & Channel Transparency Options: Users can fine-tune the visibility of trend channels, adjusting transparency levels to fit their personal charting style without overwhelming the price chart.
Annualized Return Calculation: For daily and weekly timeframes, the indicator provides an estimate of the trend’s performance over a year, helping traders evaluate potential long-term profitability.
Logarithmic Adjustment Support: Adaptive Trend Finder is compatible with both logarithmic and linear charts. Traders who analyze assets like cryptocurrencies, where log scaling is common, can enable this feature to refine trend calculations.
Intuitive & User-Friendly Interface: The updated menu structure is designed for ease of use, allowing quick and efficient modifications to settings, reducing the learning curve for new users.
Why is this the Best Trend Indicator?
Adaptive Trend Finder stands out as one of the most advanced trend analysis tools available on TradingView. Unlike conventional trend indicators, which rely on fixed parameters or lagging signals, Adaptive Trend Finder dynamically adjusts its settings based on real-time market conditions. By combining automatic trend detection, dual-channel visualization, real-time performance metrics, and an intuitive user interface, this indicator offers an unparalleled edge in trend identification and trading decision-making.
Traders no longer have to rely on guesswork or manually tweak settings to identify trends. Adaptive Trend Finder does the heavy lifting, ensuring that users are always working with the strongest and most reliable trends. The ability to simultaneously display both short-term and long-term trends allows for a more comprehensive market overview, making it ideal for scalpers, swing traders, and long-term investors alike.
With its state-of-the-art algorithms, fully customizable interface, and professional-grade accuracy, Adaptive Trend Finder is undoubtedly one of the most powerful trend indicators available.
Try it today and experience the future of trend analysis.
This indicator is a technical analysis tool designed to assist traders in identifying trends. It does not guarantee future performance or profitability. Users should conduct their own research and apply proper risk management before making trading decisions.
// Created by Julien Eche - @Julien_Eche
CAPM Alpha & BetaThe CAPM Alpha & Beta indicator is a crucial tool in finance and investment analysis derived from the Capital Asset Pricing Model (CAPM) . It provides insights into an asset's risk-adjusted performance (Alpha) and its relationship to broader market movements (Beta). Here’s a breakdown:
1. How Does It Work?
Alpha:
Definition: Alpha measures the portion of an investment's return that is not explained by market movements, i.e., the excess return over and above what the market is expected to deliver.
Purpose: It represents the value a fund manager or strategy adds (or subtracts) from an investment’s performance, adjusting for market risk.
Calculation:
Alpha is derived from comparing actual returns to expected returns predicted by CAPM:
Alpha = Actual Return − (Risk-Free Rate + β × (Market Return − Risk-Free Rate))
Alpha = Actual Return − (Risk-Free Rate + β × (Market Return − Risk-Free Rate))
Interpretation:
Positive Alpha: The investment outperformed its CAPM prediction (good performance for additional value/risk).
Negative Alpha: The investment underperformed its CAPM prediction.
Beta:
Definition: Beta measures the sensitivity of an asset's returns relative to the overall market's returns. It quantifies systematic risk.
Purpose: Indicates how volatile or correlated an investment is relative to the market benchmark (e.g., S&P 500).
Calculation:
Beta is computed as the ratio of the covariance of the asset and market returns to the variance of the market returns:
β = Covariance (Asset Return, Market Return) / Variance (Market Return)
β = Variance (Market Return) Covariance (Asset Return, Market Return)
Interpretation:
Beta = 1: The asset’s price moves in line with the market.
Beta > 1: The asset is more volatile than the market (higher risk/higher potential reward).
Beta < 1: The asset is less volatile than the market (lower risk/lower reward).
Beta < 0: The asset moves inversely to the market.
2. How to Use It?
Using Alpha:
Portfolio Evaluation: Investors use Alpha to gauge whether a portfolio manager or a strategy has successfully outperformed the market on a risk-adjusted basis.
If Alpha is consistently positive, the portfolio may deliver higher-than-expected returns for the given level of risk.
Stock/Asset Selection: Compare Alpha across multiple securities. Positive Alpha signals that the asset may be a good addition to your portfolio for excess returns.
Adjusting Investment Strategy: If Alpha is negative, reassess the asset's role in the portfolio and refine strategies.
Using Beta:
Risk Management:
A high Beta (e.g., 1.5) indicates higher sensitivity to market movements. Use such assets if you want to take on more risk during bullish market phases or expect higher returns.
A low Beta (e.g., 0.7) indicates stability and is useful in diversifying risk in volatile or bearish markets.
Portfolio Diversification: Combine assets with varying Betas to achieve the desired level of market responsiveness and smooth out portfolio volatility.
Monitoring Systematic Risk: Beta helps identify whether an investment aligns with your risk tolerance. For example, high-Beta stocks may not be suitable for conservative investors.
Practical Application:
Use both Alpha and Beta together:
Assess performance with Alpha (excess returns).
Assess risk exposure with Beta (market sensitivity).
Example: A stock with a Beta of 1.2 and a highly positive Alpha might suggest a solid performer that is slightly more volatile than the market, making it a suitable pick for risk-tolerant, return-maximizing investors.
In conclusion, the CAPM Alpha & Beta indicator gives a comprehensive view of an asset's performance and risk. Alpha enables performance evaluation on a risk-adjusted basis, while Beta reveals the level of market risk. Together, they help investors make informed decisions, build optimal portfolios, and align investments with their risk-return preferences.
Simple APF Strategy Backtesting [The Quant Science]Simple backtesting strategy for the quantitative indicator Autocorrelation Price Forecasting. This is a Buy & Sell strategy that operates exclusively with long orders. It opens long positions and generates profit based on the future price forecast provided by the indicator. It's particularly suitable for trend-following trading strategies or directional markets with an established trend.
Main functions
1. Cycle Detection: Utilize autocorrelation to identify repetitive market behaviors and cycles.
2. Forecasting for Backtesting: Simulate trades and assess the profitability of various strategies based on future price predictions.
Logic
The strategy works as follow:
Entry Condition: Go long if the hypothetical gain exceeds the threshold gain (configurable by user interface).
Position Management: Sets a take-profit level based on the future price.
Position Sizing: Automatically calculates the order size as a percentage of the equity.
No Stop-Loss: this strategy doesn't includes any stop loss.
Example Use Case
A trader analyzes a dayli period using 7 historical bars for autocorrelation.
Sets a threshold gain of 20 points using a 5% of the equity for each trade.
Evaluates the effectiveness of a long-only strategy in this period to assess its profitability and risk-adjusted performance.
User Interface
Length: Set the length of the data used in the autocorrelation price forecasting model.
Thresold Gain: Minimum value to be considered for opening trades based on future price forecast.
Order Size: percentage size of the equity used for each single trade.
Strategy Limit
This strategy does not use a stop loss. If the price continues to drop and the future price forecast is incorrect, the trader may incur a loss or have their capital locked in the losing trade.
Disclaimer!
This is a simple template. Use the code as a starting point rather than a finished solution. The script does not include important parameters, so use it solely for educational purposes or as a boilerplate.
Ethereum Logarithmic Regression Bands (Fine-Tuned)This indicator, "Ethereum Logarithmic Regression Bands (Fine-Tuned)," is my attempt to create a tool for estimating long-term trends in Ethereum (ETH/USD) price action using logarithmic regression bands. Please note that I am not an expert in financial modeling or coding—I developed this as a personal project to serve as a rough estimation rather than a precise or professional trading tool. The data was fitted to non-bubble periods of Ethereum's history to provide a general trendline, but it’s far from perfect.
I’m sharing this because I couldn’t find a similar indicator available, and I thought it might be useful for others who are also exploring ETH’s long-term behavior. The bands start from Ethereum’s launch price and are adjustable via input parameters, but they are based on my best effort to align with historical data. With some decent coding experience, I’m sure someone could refine this further—perhaps by optimizing the coefficients or incorporating more advanced fitting techniques. Feel free to tweak the code, suggest improvements, or use it as a starting point for your own projects!
How to Use:
** THIS CHART IS SPECIFICALLY CODED FOR ETH/USD (KRAKEN) ON THE WEEKLY TIMEFRAME IN LOG VIEW**
The main band (blue) represents the logarithmic regression line.
The upper (red) and lower (green) bands provide a range around the main trend, adjustable with multipliers.
Adjust the "Launch Price," "Base Coefficient," "Growth Coefficient," and other inputs to experiment with different fits.
Disclaimer:
This is not financial advice. Use at your own risk, and always conduct your own research before making trading decisions.
NeoTrend AI- Advanced Trading SignalsNeoTrend AI – Advanced Trading Signals
Overview
NeoTrend AI is an advanced trading signal indicator that uniquely integrates a kernel-based predictive model with adaptive volatility analysis. By processing historical price data through a Gaussian kernel matrix, NeoTrend AI produces a statistically informed predicted price. This prediction is then used to generate dynamic volatility bands that serve as adaptive support and resistance levels, leading to clear BUY and SELL signals.
Originality and Usefulness
Innovative Mashup: NeoTrend AI isn’t a mere combination of common indicators; it fuses a novel kernel-based forecasting method with volatility analysis. This creates a tool that not only tracks trends but also identifies key market zones with enhanced precision.
Actionable Insights: The indicator’s design helps traders understand both the underlying trend and the market’s volatility, providing a robust framework for making informed trading decisions.
Customizable Approach: With user-adjustable settings for lookback periods, prediction offsets, smoothness factors, and volatility multipliers, NeoTrend AI adapts to various markets and trading styles.
Omissions and Realistic Claims
Transparent Methodology: NeoTrend AI’s signals are generated solely from historical data analysis using well-established mathematical techniques. There are no unrealistic promises—past performance does not guarantee future results.
No Unsubstantiated Claims: All performance metrics and signal accuracy are clearly derived from the underlying methodology. This script is designed to provide useful insights rather than definitive trading outcomes.
Strategy Results
Kernel Forecasting:
The script builds a Gaussian kernel matrix over a chosen lookback period, smoothing historical price data and generating a predictive price that adjusts dynamically.
Adaptive Volatility Bands:
A volatility band is calculated based on the difference between the actual price and the predicted price, scaled by a user-defined multiplier. These bands change in real time, acting as dynamic support and resistance levels.
Signal Generation:
BUY Signal: Issued when the current price moves above the upper volatility band and the predicted price is trending upward.
SELL Signal: Issued when the price falls below the lower volatility band while the predicted price is trending downward.
Visual Examples
Buy and sell signals are generated, as clearly shown on the chart
Usage Tips
Parameter Customization: Adjust the lookback period, smoothness factor, and volatility multiplier to fit your trading timeframe and market conditions.
Combine with Other Tools: Use NeoTrend AI alongside additional technical indicators and robust risk management strategies for best results.
Backtest Thoroughly: Always perform comprehensive backtesting to understand how the indicator behaves under different market scenarios.
Final Remarks
NeoTrend AI is built to offer traders an original, data-driven insight into market trends without resorting to exaggerated or misleading claims. Its design emphasizes both innovation and practicality, ensuring that you receive actionable signals based on sound statistical methods.
