Reversal & Breakout Strategy with ORB### Reversal & Breakout Strategy with ORB
This strategy combines three distinct trading approaches—reversals, trend breakouts, and opening range breakouts (ORB)—into a single, cohesive system. The goal is to capture high-probability setups across different market conditions, leveraging a mashup of technical indicators for confirmation and risk management. Below, I’ll explain why this combination works, how the components interact, and how to use it effectively.
#### Why the Mashup?
- **Reversals**: Identifies overextended moves using RSI (overbought/oversold) and SMA50 crosses, filtered by VWAP and SMA200 trend direction. This targets mean-reversion opportunities in trending markets.
- **Breakouts**: Uses EMA9/EMA20 crossovers with VWAP and SMA200 confirmation to catch momentum-driven trend continuations.
- **Opening Range Breakout (ORB)**: Detects early momentum by breaking the high/low of a user-defined opening range (default: 15 bars) with volume confirmation. This adds a time-based edge, ideal for intraday trading.
The synergy comes from blending these methods: reversals catch pullbacks, breakouts ride trends, and ORB exploits early volatility—all filtered by trend (SMA200) and anchored by VWAP for context.
#### How It Works
1. **Indicators**:
- **EMA9/EMA20**: Fast-moving averages for breakout signals.
- **SMA50**: Medium-term trend filter for reversals.
- **SMA200**: Long-term trend direction to align trades.
- **RSI (14)**: Measures overbought (>70) or oversold (<30) conditions.
- **VWAP**: Acts as a dynamic support/resistance level.
- **ATR (14)**: Sets stop-loss distance (default: 1.5x ATR).
- **Volume**: Confirms ORB breakouts (1.5x average volume of opening range).
2. **Entry Conditions**:
- **Long**: Triggers on reversal (SMA50 cross + RSI < 30 + below VWAP + uptrend), breakout (EMA9 > EMA20 + above VWAP + uptrend), or ORB (break above opening range high + volume).
- **Short**: Triggers on reversal (SMA50 cross + RSI > 70 + above VWAP + downtrend), breakout (EMA9 < EMA20 + below VWAP + downtrend), or ORB (break below opening range low + volume).
3. **Risk Management**:
- Risks 5% of equity per trade (based on the initial capital set in the strategy tester).
- Stop-loss: Based on lowest low/highest high over 7 bars ± 1.5x ATR.
- Targets: Two exits at 1:1 and 1:2 risk:reward (50% of position at each).
- Break-even: Stop moves to entry price after the first target is hit.
4. **Backtesting Settings**:
- Commission: Hardcoded at 0.1% per trade (realistic for most brokers).
- Slippage: Hardcoded at 2 ticks (realistic for most markets).
- Tested on datasets yielding 100+ trades (e.g., 2-min or 5-min charts over months).
#### How to Use It
- **Timeframe**: Works best on intraday (2-min, 5-min) or daily charts. Adjust `Opening Range Bars` (e.g., 15 bars = 30 min on 2-min chart) for your timeframe.
- **Settings**:
- Set your initial equity in the TradingView strategy tester’s "Properties" tab under "Initial Capital" (e.g., $10,000). The script automatically risks 5% of this equity per trade.
- Adjust `Stop Loss ATR Multiplier` or `Risk:Reward Targets` based on your risk tolerance.
- Note that commission (0.1%) and slippage (2 ticks) are fixed in the script for backtesting consistency.
- **Execution**: Enter on signal, monitor plotted stop (red) and targets (green/blue). The strategy supports pyramiding (up to 2 positions) for scaling into trends.
#### Backtesting Notes
Results are realistic with commission (0.1%) and slippage (2 ticks) included. For a sufficient sample, test on volatile instruments (e.g., stocks, forex) over 3-6 months on lower timeframes. The default 1.5x ATR stop may seem wide, but it’s justified to avoid premature exits in volatile markets—feel free to tweak it with justification. The script assumes an initial capital of $10,000 in the strategy tester for the 5% risk calculation (e.g., $500 risk per trade); adjust this in the "Properties" tab as needed.
This mashup isn’t just a random mix; it’s a deliberate fusion of complementary strategies, offering traders flexibility across market phases. Questions? Let me know!
Indicadores e estratégias
PSP - Precision CandlePSP Precision Spotting Points (PSP) Indicator
The PSP Precision Spotting Points (PSP) Indicator is designed for traders seeking to identify high-probability reversal zones by detecting PSP setups with precision. PSPs are Potential Swing Points that often precede market reversals or significant price reactions. This indicator simplifies the process of spotting these opportunities by highlighting areas of interest based on market structure, correlation imbalances, and wick rejections.
📌 Key Features:
PSP Detection: Accurately identifies Potential Swing Points by scanning for candle patterns that suggest a shift in momentum.
Precision Entry Zones: Marks areas where price is likely to react, offering clear visual cues for optimal trade execution.
Smart Filtering: Filters out low-quality signals using advanced volatility and liquidity analysis.
Wick Confirmation: Validates PSP setups using wick rejections and correlation cracks, enhancing the probability of a successful trade.
Customizable Alerts: Stay informed with real-time notifications when a PSP is detected.
🛠️ How It Works:
Candle Analysis: Scans for specific price action patterns where candle body-to-wick ratios and volatility suggest a Potential Swing Point.
Correlation Cracks: Detects discrepancies between correlated instruments, adding confluence to PSP setups.
POI Alignment: Highlights areas near Points of Interest (POIs) like Fair Value Gaps (FVGs), previous highs/lows, and session kill zones.
Rejection Confirmation: Ensures PSPs are validated through wick-based rejection patterns, minimizing false signals.
Momentum Based RSIThe Momentum Based RSI is an enhancement to the RSI. it incorporates 2 sections:
MA Ratio (Fast/Slow)
RSI
at the end both of those are multiplied to create a more responsive RSI which reacts fast to market moves while still providing a whip ressistant tool.
Momentum Calculation
The "MA Ratio" as i like to call it results from comparing 2 MAs (both can be set to whatever type you like) against eachother, which, in the end, provides a Ratio that visualizes the difference. It is simple yet effective
RSI
An Old yet popular tool which dates back to 1978. In and out of itself it is a great tool, however it still can be enhanced.
The Combination
The RSI and the MARatio are multiplied together, which results in an RSI that is ampliefied by the speed of the market movements.
This proves highly effective, since the MA Ratio is hovering around at the same level. However during trends, it picks up speed in either of both directions which marginally increases the RSI's response the said movement.
Why its Creative, New and Good
While it is a super simple concept, it still holds a lot of power relative to its sophistication. Traders may use it like they used the Vanilla RSI (e.g Trend following, Mean-reversion or other).
Unlike RSI with momentum overlays, this indicator actively uses an MA Ratio multiplier for simplicity and responsiveness.
At last, Its primary goal is to detect trends faster while not creating more noise & false signals.
What not to do
if youre using this indicator, please do NOT change the Fast MA to be slower than to Slow MA or vice versa, since you'll be getting broken & noise induced signals which may not align with your goals.
Great inventions require great Care
As with anything, you should not use this tool without any other confluence. As great as the backtests may be, you dont know what the future holds, be careful!
This indicator is not a guaranteed predicition tool. If youre going to use it for investment decisions, please use it in coherence with other tools.
Thank you for reading!
HEMA Trend Levels [AlgoAlpha]OVERVIEW
This script plots two Hull-EMA (HEMA) curves to define a color-coded dynamic trend zone and generate context-aware breakout levels, allowing traders to easily visualize prevailing momentum and identify high-probability breakout retests. The script blends smoothed price tracking with conditional box plotting, delivering both trend-following and mean-reversion signals within one system. It is designed to be simple to read visually while offering nuanced trend shifts and test confirmations.
█ CONCEPTS
The Hull-EMA (HEMA) is a hybrid moving average combining the responsiveness of short EMAs with the smoothness of longer ones. It applies layered smoothing: first by subtracting a full EMA from a half-length EMA (doubling the short EMA's weight), and then by smoothing the result again with the square root of the original length. This process reduces lag while maintaining clarity in direction changes. In this script, two HEMAs—fast and slow—are used to define the trend structure and trigger events when they cross. These crossovers generate "trend shift boxes"—temporary support or resistance zones drawn immediately after trend transitions—to detect price retests in the new direction. When price cleanly retests these levels, the script marks them as confirmations with triangle symbols, helping traders isolate better continuation setups. Color-coded bars further enhance visual interpretation: bullish bars when price is above both HEMAs, bearish when below, and neutral (gray) when indecisive.
█ FEATURES
Bullish and bearish bar coloring based on price and HEMA alignment.
Box plotting at each crossover (bullish or bearish) to create short-term decision zones.
Real-time test detection: price must cleanly test and bounce from box levels to be considered valid.
Multiple alert conditions: crossover alerts, test alerts, and trend continuation alerts.
█ USAGE
Use this indicator on any time frame and asset. Adjust HEMA lengths to match your trading style—shorter lengths for scalping or intraday, longer for swing trading. The shaded area between HEMAs helps visually define the current trend. Watch for crossovers: a bullish crossover plots a green support box just below price, and a bearish one plots a red resistance box just above. These zones act as short-term decision points. When price returns to test a box and confirms with strong rejection (e.g., closes above for bullish or below for bearish), a triangle symbol is plotted. These tests can signal strong trend continuation. For traders looking for clean entries, combining the crossover with a successful retest improves reliability. Alerts can be enabled for all key signals: trend shift, test confirmations, and continuation conditions, making it suitable for automated setups or discretionary traders tracking multiple charts.
Optimized WPR Strategy with Filters (Debug)Identifying Market Trends:
The 200-period EMA is used to determine the short-term trend of the market.
When the price is above the 200-period EMA, it suggests a potential bullish market and an uptrend, and the strategy will only look for buying opportunities.
When the price is below the 200-period EMA, it suggests a potential bearish market and a downtrend, and the strategy will only look for selling opportunities.
When the 200-period EMA intersects with the price, it indicates that the market may be in a directionless consolidation phase.
Identifying Potential Reversal Points:
The strategy employs two Williams %R (WPR) indicators: one with a 9-period (fast WPR) and another with a 28-period (slow WPR).
WPR is a momentum indicator used to identify overbought and oversold conditions in the market. Its value oscillates between -100 and 0, with values near -100 indicating oversold conditions and values near 0 indicating overbought conditions.
In an uptrend (when the price is above the 200 EMA), the strategy seeks buying opportunities when both WPR indicators cross above -80 (the oversold zone) from below. This is considered a bullish signal, suggesting the market may be about to rebound. Sell signals are ignored at this time.
In a downtrend (when the price is below the 200 EMA), the strategy seeks selling opportunities when both WPR indicators cross below -20 (the overbought zone) from above. This is considered a bearish signal, suggesting the market may be about to reverse downward. Buy signals are ignored at this time.
Summary:
In summary, this strategy first uses the 200-period EMA to determine the overall trend direction of the market. Then, within the confirmed trend direction, it utilizes the simultaneous crossing of the overbought or oversold zones by the dual WPR indicators to identify potential reversal points as entry signals for trading. The strategy emphasizes that trading signals are only valid when both WPR indicators meet the conditions.
SMA7 Tail Reversal📌 Description:
The SMA7 Tail Reversal indicator is designed to identify potential counter-trend trading opportunities by checking if candle wicks (tails) respect a key moving average level (SMA7).
This indicator highlights price action where candles are clearly separated from the moving average, suggesting a possible reversal or temporary correction.
📌 How It Works:
Moving Average Calculation:
Calculates a simple moving average (SMA) of length 7 to act as the primary trend filter.
Candle Classification:
Bullish Candle: A candle where the closing price is higher than the opening price, with a short upper wick.
Bearish Candle: A candle where the closing price is lower than the opening price, with a short lower wick.
Conditions for Coloring Candles:
Long Condition (Green Candle):
High & Low are both below the SMA7 line.
Volume is above the 20-period average.
A bullish candle is detected.
Short Condition (Red Candle):
High & Low are both above the SMA7 line.
Volume is above the 20-period average.
A bearish candle is detected.
📌 Visual Representation:
Green Candles: Potential long signals when price action stays below the SMA7 line.
Red Candles: Potential short signals when price action stays above the SMA7 line.
Yellow Line: SMA7, used as the dynamic threshold for signal generation.
📌 Usage:
Best applied to volatile markets with clear trends.
Effective in detecting counter-trend opportunities where price diverges from the SMA7 line.
Works well with additional confirmation tools for better accuracy.
True Open CalculationsIndicator Description: True Open Calculations
This custom Pine Script indicator calculates and plots key "True Open" levels based on specific time intervals and trading sessions. The True Open levels represent significant price points on the chart, helping traders identify key reference points tied to various market opening times. These levels are important for understanding price action in relation to market sessions and trading cycles. The indicator is designed to plot lines corresponding to different "True Opens" on the chart and display labels with the associated information.
Key Features:
True Year Open:
This represents the opening price on the first Monday of April each year. It serves as a reference point for the yearly price level.
Plot Color: Green.
True Month Open:
This represents the opening price on the second Monday of each month. It helps in identifying monthly trends and provides a key reference for monthly price movements.
Plot Color: Blue.
True Week Open:
This represents the opening price every Monday at 6:00 PM. It gives traders a level to track weekly opening movements and can be useful for weekly trend analysis.
Plot Color: Orange.
True Day Open:
This represents the opening price at 12:00 AM (midnight) each day. It serves as a daily benchmark for price action at the start of the trading day.
Plot Color: Red.
True New York Session Open:
This represents the opening price at 7:30 AM (New York session start time). This level is crucial for traders focused on the New York trading session.
Plot Color: Purple.
Additional Features:
Labels: The indicator displays labels to the right of each plotted line to describe which "True Open" it represents (e.g., "True Year Open," "True Month Open," etc.).
Dynamic Plotting: The lines are only plotted on the current candle, and the lines are dynamically updated for each time period based on the corresponding "True Open."
Visual Cues: The colors of the plotted lines (green, blue, orange, red, purple) help quickly distinguish between different "True Open" levels, making it easy for traders to track price action and make informed decisions.
Use Cases:
Yearly, Monthly, Weekly, Daily, and Session Benchmarking: This indicator provides traders with important price levels to use as benchmarks for the current year, month, week, and day, helping to identify trends and potential reversals.
Session Awareness: It is particularly useful for traders who want to track key market sessions, such as the New York session, and their impact on price movement.
Long-term Analysis: By including the yearly open, this indicator helps traders gain a broader perspective on market trends and provides context for analyzing shorter-term price movements.
Benefits:
Helps identify important reference points for longer-term trends (yearly, monthly) as well as shorter-term moves (daily, weekly, and session).
Visually intuitive with color-coded lines and labels, allowing quick and easy identification of key market open levels.
Dynamic and real-time: The indicator plots and updates the True Open levels dynamically as the market progresses.
PriorHourRangeLevels_v0.1PriorHourRangeLevels_v0.1
Created by dc_77 | © 2025 | Mozilla Public License 2.0
Overview
"PriorHourRangeLevels_v0.1" is a versatile Pine Script™ indicator designed to help traders visualize and analyze price levels based on the prior hour’s range. It overlays key levels—High, Low, 75%, 50% (EQ), and 25%—from the previous hour onto the current price chart, alongside the current hour’s opening price. With customizable display options and time zone support, it’s ideal for intraday traders looking to identify support, resistance, and breakout zones.
How It Works
Hourly Reset: The indicator detects the start of each hour based on your chosen time zone (e.g., "America/New_York" by default).
Prior Hour Range: It calculates the High and Low of the previous hour, then derives three additional levels:
75%: 75% of the range above the Low.
EQ (50%): The midpoint of the range.
25%: 25% of the range above the Low.
Current Hour Open: Displays the opening price of the current hour.
Projection: Lines extend forward (default: 24 bars) to project these levels into the future, aiding in real-time analysis.
Alerts: Triggers alerts when the price crosses any of the prior hour’s levels (High, 75%, EQ, 25%, Low).
Key Features
Time Zone Flexibility: Choose from options like UTC, New York, Tokyo, or London to align with your trading session.
Visual Customization:
Toggle visibility for each level (High, Low, 75%, EQ, 25%, Open, and Anchor).
Adjust line styles (Solid, Dashed, Dotted), colors, and widths.
Show or hide labels with adjustable sizes (Tiny, Small, Normal, Large).
Anchor Line: A vertical line marks the start of the prior hour, with optional labeling.
Alert Conditions: Set up notifications for price crossings to catch key moments without watching the chart.
Usage Tips
Use the High and Low as potential breakout levels, while 75%, EQ, and 25% act as intermediate support/resistance zones.
Trend Confirmation: Watch how price interacts with the EQ (50%) level to gauge momentum.
Session Planning: Adjust the time zone to match your market (e.g., "Europe/London" for FTSE trading).
Projection Offset: Extend or shorten the lines (via "Projection Offset") based on your chart timeframe.
Inputs
Time Zone: Select your preferred market time zone.
Anchor Settings: Show/hide the prior hour start line, style, color, width, and label.
Level Settings: Customize visibility, style, color, width, and labels for Open, High, 75%, EQ, 25%, and Low.
Display: Set projection length and label size.
Close Price PercentileClose Price Percentile with Range Distribution
This indicator helps traders identify where the current price stands relative to its historical context using percentile ranking. It calculates and displays the current price's percentile position within a customizable lookback window (default: 365 periods).
Key Features:
• Percentile Ranking: Shows where the current price falls within its historical range (0-100%)
• Customizable Thresholds: Set your own high/low percentile boundaries for analysis
• Statistical Distribution: Tracks and displays the number and percentage of data points falling in each range (below threshold, between thresholds, above threshold)
• Visual References: Includes dashed lines at threshold levels for easy visual reference
• Clean Data Presentation: Organized table display in the top-right corner
Perfect for:
• Understanding price extremes
• Identifying potential overbought/oversold conditions
• Statistical analysis of price movements
• Mean reversion strategies
Fully customizable with adjustable window length, thresholds, and visual settings.
SessionRangeLevels_v0.1SessionRangeLevels_v0.1
Overview:
SessionRangeLevels_v0.1 is a customizable Pine Script (v6) indicator designed to plot key price levels based on a user-defined trading session. It identifies the high and low of the session and calculates intermediate levels (75%, 50% "EQ", and 25%) within that range. These levels are projected forward as horizontal lines with accompanying labels, providing traders with dynamic support and resistance zones. The indicator supports extensive customization for session timing, time zones, line styles, colors, and more.
Key Features:
Session-Based Range Detection: Tracks the high and low prices during a specified session (e.g., 0600-0900) and updates them dynamically as the session progresses.
Customizable Levels: Displays High, 75%, EQ (50%), 25%, and Low levels, each with independent toggle options, styles (Solid, Dashed, Dotted), colors, and widths.
Session Anchor: Optional vertical line marking the session start, with customizable style, color, and width.
Projection Offset: Extends level lines forward by a user-defined number of bars (default: 24) for future price reference.
Labels: Toggleable labels for each level (e.g., "High," "75%," "EQ") with adjustable size (Tiny, Small, Normal, Large).
Time Zone Support: Aligns session timing to a selected time zone (e.g., America/New_York, UTC, Asia/Tokyo, etc.).
Alert Conditions: Triggers alerts when the price crosses any of the plotted levels (High, 75%, EQ, 25%, Low).
Inputs:
Session Time (HHMM-HHMM): Define the session range (e.g., "0600-0900" for 6:00 AM to 9:00 AM).
Time Zone: Choose from options like UTC, America/New_York, Europe/London, etc.
Anchor Settings: Toggle the session start line, adjust its style (default: Dotted), color (default: Black), and width (default: 1).
Level Settings:
High (Solid, Black, Width 2)
75% (Dotted, Blue, Width 1)
EQ/50% (Dotted, Orange, Width 1)
25% (Dotted, Blue, Width 1)
Low (Solid, Black, Width 2)
Each level includes options to show/hide, set style, color, width, and label visibility.
Projection Offset: Number of bars to extend lines (default: 24).
Label Size: Set label size (default: Small).
How It Works:
The indicator detects the start and end of the user-defined session based on the specified time and time zone.
During the session, it tracks the highest high and lowest low, updating the levels in real-time.
At the session start, it plots the High, Low, and intermediate levels (75%, 50%, 25%), projecting them forward.
Lines and labels dynamically adjust as new highs or lows occur within the session.
Alerts notify users when the price crosses any active level.
Usage:
Ideal for traders who focus on session-based strategies (e.g., London or New York open). Use it to identify key price zones, monitor breakouts, or set targets. Customize the appearance to suit your chart preferences and enable alerts for real-time trading signals.
Notes:
Ensure your chart’s timeframe aligns with your session duration for optimal results (e.g., 1-minute or 5-minute charts for short sessions).
The indicator overlays directly on the price chart for easy integration with other tools.
NakInvest - Inside Bar no Eden dos Traders (Stormer)📌 NakInvest - Inside Bar Detector (Éden dos Traders Enhanced Detection)
This indicator is designed to identify Inside Bars that occur during strong trending conditions, following the popular Éden dos Traders strategy by Stormer, famous brazilian trader. It uses the relationship between two EMAs (Short EMA & Long EMA) to determine whether the market is in a bullish or bearish trend, and highlights Inside Bars that meet specific criteria.
⸻
🔍 What This Indicator Does:
1. EMA-Based Trend Detection:
• Identifies Uptrends when the Short EMA is above the Long EMA.
• Identifies Downtrends when the Short EMA is below the Long EMA.
2. Inside Bar Detection:
• An Inside Bar is detected when the entire candle (body & wicks) is contained within the body of the previous candle.
• This pattern suggests consolidation and potential breakouts, especially when found within a strong trend.
3. Debug Mode for Transparency:
• When enabled, provides visual markers to indicate when the conditions for trend detection and Inside Bars are met.
• Helps traders understand why certain candles are detected and others are not.
⸻
📈 How to Use:
• Apply this indicator to any market and timeframe, but it’s most effective on higher timeframes (H1, H4, Daily).
• Ideal for traders looking for trend-continuation setups or reversal signals after periods of consolidation.
• Combine this indicator with other tools (e.g., Volume Analysis, Price Action Patterns) for greater accuracy.
⸻
⚙️ Indicator Settings:
1. Short EMA Length: The period for the fast-moving average (default: 8).
2. Long EMA Length: The period for the slow-moving average (default: 80).
3. Enable Debug Mode: Toggle visibility of debug markers to better understand condition logic.
⸻
📢 Alerts:
This script includes labels for:
• IB (Up): Inside Bar detected during an uptrend.
• IB (Down): Inside Bar detected during a downtrend.
⸻
📌 Disclaimer:
This indicator is intended for educational purposes only and is not financial advice. Always perform your own research and consult with a financial professional before making any trading decisions.
Full Breakout Alert (Yesterday’s Open & Close)For this Script
Horizontal 1 will be your resistance from the opening candle of yesterday
Horizontal 2 will be your support from the closing candle of yesterday
If you manually input the value, the line will change. Then you can add alert and it will just trigger if the Body and the Wick is above/below (whatever you choose from) from the value itself
BTC & SPX vs Yield Curve: Recession Risk ZonesBTC & SPX vs Yield Curve – Recession Risk Zones
This tool helps you track Bitcoin (BTC) and the S&P 500 (SPX) against key macro signals from the U.S. yield curve to spot potential recession risks.
🟪 Color Legend:
🔴 Red = Yield curve is inverted (warning starts)
🟡 Yellow = Projected 6–18 month recession risk (if inversion still active)
🟠 Orange = Active 6–18 month risk window (after inversion ends)
💜 Fuchsia = Real historical U.S. recessions
📈 What’s Plotted:
🔵 BTCUSD (blue line) – Normalized price
🟢 S&P 500 (green line) – Normalized price
🟠 10Y–2Y Yield Spread – Macro signal for risk
✅ Use it to:
Spot macro pressure zones
See how BTC and SPX behave around economic stress
Stay cautious when red/orange/yellow areas appear
Let me know if you'd like to enable toggles to hide/show BTC or SPX independently!
Coinbase Premium IndexThe Coinbase Premium Index is a measure of the percentage difference between the price of any coin on Coinbase Pro (USD pair) and the price on Binance (USDT trading pair). It helps differentiate between global and US-specific market sentiment
Major benefits:
Choose between USD or USDC for the Coinbase pair — they can behave differently in rare but actionable situations.
Apply it to any coin, not just BTC. Open any USDT-based chart on any exchange, and the script will automatically compare it with Coinbase’s USD or USDC price.
Highlight only active U.S. trading hours, cutting out irrelevant noise.
Display key thresholds that signal buying or selling pressure.
ADX BoxDescription:
The ADX Box indicator provides traders with a quick and intuitive way to monitor the current trend strength based on the Average Directional Index (ADX), calculated with a customisable period (default: 7 periods).
This compact indicator neatly displays the current ADX value rounded to one decimal place, along with a clear directional arrow:
Green upward triangle (▲): Indicates that ADX is rising above its moving average, signaling increasing trend strength.
Red downward triangle (▼): Indicates that ADX is declining below its moving average, signaling weakening trend strength.
Key Features:
Small and clean visual representation.
Dynamically updates in real-time directly on the chart.
Ideal for quick trend strength assessment without cluttering your workspace.
Recommended Usage:
Quickly identifying whether market trends are strengthening or weakening.
Enhancing decision-making for trend-following or breakout trading strategies.
Complementing other indicators such as ATR boxes for volatility measurement.
Feel free to use, share, and incorporate this indicator into your trading setups for clearer insights and more confident trading decisions!
Dynamic Momentum Bands | AlphaAlgosDynamic Momentum Bands | AlphaAlgos
Overview
The Dynamic Momentum Bands indicator is an advanced technical analysis tool that combines multiple analytical techniques to provide a comprehensive view of market momentum and trend dynamics. By integrating RSI (Relative Strength Index), volatility analysis, and adaptive moving averages, this indicator offers traders a nuanced perspective on market conditions.
Key Features
Adaptive band calculation based on price momentum
Integrated RSI-driven volatility scaling
Multiple moving average type options (EMA, SMA, VWMA)
Smooth, gradient-based band visualization
Optional price bar coloring for trend identification
Technical Methodology
The indicator employs a sophisticated approach to market analysis:
1. Momentum Calculation
Calculates RSI using a customizable length
Uses RSI to dynamically adjust band volatility
Scales band width based on distance from the 50 RSI level
2. Band Construction
Applies a selected moving average type to the price source
Calculates deviation using ATR (Average True Range)
Smooths band edges for improved visual clarity
Configuration Options
Core Settings:
Price Source: Choose the price data used for calculations
RSI Length: Customize the RSI calculation period (1-50)
Band Length: Adjust the moving average period (5-100)
Volatility Multiplier: Fine-tune band width
Band Type: Select between EMA, SMA, and VWMA
Visual Settings:
Bar Coloring: Toggle color-coded price bars
Gradient-based band visualization
Smooth color transitions for trend representation
Trend Identification
The indicator provides trend insights through:
Color-coded bands (blue for bullish, pink for bearish)
Smooth gradient visualization
Optional price bar coloring
Trading Applications
Trend Following:
- Use band position relative to price as trend indicator
- Identify momentum shifts through color changes
- Utilize gradient zones for trend strength assessment
Volatility Analysis:
Observe band width changes
Detect potential breakout or consolidation periods
Use RSI-driven volatility scaling for market context
Best Practices
Adjust RSI length to match trading timeframe
Experiment with different moving average types
Use in conjunction with other technical indicators
Consider volatility multiplier for different market conditions
This indicator is provided for informational purposes only. Always use proper risk management when trading. Past performance is not indicative of future results. Not financial Advise
Multi-Fibonacci Trend Average[FibonacciFlux]Multi-Fibonacci Trend Average (MFTA): An Institutional-Grade Trend Confluence Indicator for Discerning Market Participants
My original indicator/Strategy:
Engineered for the sophisticated demands of institutional and advanced traders, the Multi-Fibonacci Trend Average (MFTA) indicator represents a paradigm shift in technical analysis. This meticulously crafted tool is designed to furnish high-definition trend signals within the complexities of modern financial markets. Anchored in the rigorous principles of Fibonacci ratios and augmented by advanced averaging methodologies, MFTA delivers a granular perspective on trend dynamics. Its integration of Multi-Timeframe (MTF) filters provides unparalleled signal robustness, empowering strategic decision-making with a heightened degree of confidence.
MFTA indicator on BTCUSDT 15min chart with 1min RSI and MACD filters enabled. Note the refined signal generation with reduced noise.
MFTA indicator on BTCUSDT 15min chart without MTF filters. While capturing more potential trading opportunities, it also generates a higher frequency of signals, including potential false positives.
Core Innovation: Proprietary Fibonacci-Enhanced Supertrend Averaging Engine
The MFTA indicator’s core innovation lies in its proprietary implementation of Supertrend analysis, strategically fortified by Fibonacci ratios to construct a truly dynamic volatility envelope. Departing from conventional Supertrend methodologies, MFTA autonomously computes not one, but three distinct Supertrend lines. Each of these lines is uniquely parameterized by a specific Fibonacci factor: 0.618 (Weak), 1.618 (Medium/Golden Ratio), and 2.618 (Strong/Extended Fibonacci).
// Fibonacci-based factors for multiple Supertrend calculations
factor1 = input.float(0.618, 'Factor 1 (Weak/Fibonacci)', minval=0.01, step=0.01, tooltip='Factor 1 (Weak/Fibonacci)', group="Fibonacci Supertrend")
factor2 = input.float(1.618, 'Factor 2 (Medium/Golden Ratio)', minval=0.01, step=0.01, tooltip='Factor 2 (Medium/Golden Ratio)', group="Fibonacci Supertrend")
factor3 = input.float(2.618, 'Factor 3 (Strong/Extended Fib)', minval=0.01, step=0.01, tooltip='Factor 3 (Strong/Extended Fib)', group="Fibonacci Supertrend")
This multi-faceted architecture adeptly captures a spectrum of market volatility sensitivities, ensuring a comprehensive assessment of prevailing conditions. Subsequently, the indicator algorithmically synthesizes these disparate Supertrend lines through arithmetic averaging. To achieve optimal signal fidelity and mitigate inherent market noise, this composite average is further refined utilizing an Exponential Moving Average (EMA).
// Calculate average of the three supertends and a smoothed version
superlength = input.int(21, 'Smoothing Length', tooltip='Smoothing Length for Average Supertrend', group="Fibonacci Supertrend")
average_trend = (supertrend1 + supertrend2 + supertrend3) / 3
smoothed_trend = ta.ema(average_trend, superlength)
The resultant ‘Smoothed Trend’ line emerges as a remarkably responsive yet stable trend demarcation, offering demonstrably superior clarity and precision compared to singular Supertrend implementations, particularly within the turbulent dynamics of high-volatility markets.
Elevated Signal Confluence: Integrated Multi-Timeframe (MTF) Validation Suite
MFTA transcends the limitations of conventional trend indicators by incorporating an advanced suite of three independent MTF filters: RSI, MACD, and Volume. These filters function as sophisticated validation protocols, rigorously ensuring that only signals exhibiting a confluence of high-probability factors are brought to the forefront.
1. Granular Lower Timeframe RSI Momentum Filter
The Relative Strength Index (RSI) filter, computed from a user-defined lower timeframe, furnishes critical momentum-based signal validation. By meticulously monitoring RSI dynamics on an accelerated timeframe, traders gain the capacity to evaluate underlying momentum strength with precision, prior to committing to signal execution on the primary chart timeframe.
// --- Lower Timeframe RSI Filter ---
ltf_rsi_filter_enable = input.bool(false, title="Enable RSI Filter", group="MTF Filters", tooltip="Use RSI from lower timeframe as a filter")
ltf_rsi_timeframe = input.timeframe("1", title="RSI Timeframe", group="MTF Filters", tooltip="Timeframe for RSI calculation")
ltf_rsi_length = input.int(14, title="RSI Length", minval=1, group="MTF Filters", tooltip="Length for RSI calculation")
ltf_rsi_threshold = input.int(30, title="RSI Threshold", minval=0, maxval=100, group="MTF Filters", tooltip="RSI value threshold for filtering signals")
2. Convergent Lower Timeframe MACD Trend-Momentum Filter
The Moving Average Convergence Divergence (MACD) filter, also calculated on a lower timeframe basis, introduces a critical layer of trend-momentum convergence confirmation. The bullish signal configuration rigorously mandates that the MACD line be definitively positioned above the Signal line on the designated lower timeframe. This stringent condition ensures a robust indication of converging momentum that aligns synergistically with the prevailing trend identified on the primary timeframe.
// --- Lower Timeframe MACD Filter ---
ltf_macd_filter_enable = input.bool(false, title="Enable MACD Filter", group="MTF Filters", tooltip="Use MACD from lower timeframe as a filter")
ltf_macd_timeframe = input.timeframe("1", title="MACD Timeframe", group="MTF Filters", tooltip="Timeframe for MACD calculation")
ltf_macd_fast_length = input.int(12, title="MACD Fast Length", minval=1, group="MTF Filters", tooltip="Fast EMA length for MACD")
ltf_macd_slow_length = input.int(26, title="MACD Slow Length", minval=1, group="MTF Filters", tooltip="Slow EMA length for MACD")
ltf_macd_signal_length = input.int(9, title="MACD Signal Length", minval=1, group="MTF Filters", tooltip="Signal SMA length for MACD")
3. Definitive Volume Confirmation Filter
The Volume Filter functions as an indispensable arbiter of trade conviction. By establishing a dynamic volume threshold, defined as a percentage relative to the average volume over a user-specified lookback period, traders can effectively ensure that all generated signals are rigorously validated by demonstrably increased trading activity. This pivotal validation step signifies robust market participation, substantially diminishing the potential for spurious or false breakout signals.
// --- Volume Filter ---
volume_filter_enable = input.bool(false, title="Enable Volume Filter", group="MTF Filters", tooltip="Use volume level as a filter")
volume_threshold_percent = input.int(title="Volume Threshold (%)", defval=150, minval=100, group="MTF Filters", tooltip="Minimum volume percentage compared to average volume to allow signal (100% = average)")
These meticulously engineered filters operate in synergistic confluence, requiring all enabled filters to definitively satisfy their pre-defined conditions before a Buy or Sell signal is generated. This stringent multi-layered validation process drastically minimizes the incidence of false positive signals, thereby significantly enhancing entry precision and overall signal reliability.
Intuitive Visual Architecture & Actionable Intelligence
MFTA provides a demonstrably intuitive and visually rich charting environment, meticulously delineating trend direction and momentum through precisely color-coded plots:
Average Supertrend: Thin line, green/red for uptrend/downtrend, immediate directional bias.
Smoothed Supertrend: Bold line, teal/purple for uptrend/downtrend, cleaner, institutionally robust trend.
Dynamic Trend Fill: Green/red fill between Supertrends quantifies trend strength and momentum.
Adaptive Background Coloring: Light green/red background mirrors Smoothed Supertrend direction, holistic trend perspective.
Precision Buy/Sell Signals: ‘BUY’/‘SELL’ labels appear on chart when trend touch and MTF filter confluence are satisfied, facilitating high-conviction trade action.
MFTA indicator applied to BTCUSDT 4-hour chart, showcasing its effectiveness on higher timeframes. The Smoothed Length parameter is increased to 200 for enhanced smoothness on this timeframe, coupled with 1min RSI and Volume filters for signal refinement. This illustrates the indicator's adaptability across different timeframes and market conditions.
Strategic Applications for Institutional Mandates
MFTA’s sophisticated design provides distinct advantages for advanced trading operations and institutional investment mandates. Key strategic applications include:
High-Probability Trend Identification: Fibonacci-averaged Supertrend with MTF filters robustly identifies high-probability trend continuations and reversals, enhancing alpha generation.
Precision Entry/Exit Signals: Volume and momentum-filtered signals enable institutional-grade precision for optimized risk-adjusted returns.
Algorithmic Trading Integration: Clear signal logic facilitates seamless integration into automated trading systems for scalable strategy deployment.
Multi-Asset/Timeframe Versatility: Adaptable parameters ensure applicability across diverse asset classes and timeframes, catering to varied trading mandates.
Enhanced Risk Management: Superior signal fidelity from MTF filters inherently reduces false signals, supporting robust risk management protocols.
Granular Customization and Parameterized Control
MFTA offers unparalleled customization, empowering users to fine-tune parameters for precise alignment with specific trading styles and market conditions. Key adjustable parameters include:
Fibonacci Factors: Adjust Supertrend sensitivity to volatility regimes.
ATR Length: Control volatility responsiveness in Supertrend calculations.
Smoothing Length: Refine Smoothed Trend line responsiveness and noise reduction.
MTF Filter Parameters: Independently configure timeframes, lookback periods, and thresholds for RSI, MACD, and Volume filters for optimal signal filtering.
Disclaimer
MFTA is meticulously engineered for high-quality trend signals; however, no indicator guarantees profit. Market conditions are unpredictable, and trading involves substantial risk. Rigorous backtesting and forward testing across diverse datasets, alongside a comprehensive understanding of the indicator's logic, are essential before live deployment. Past performance is not indicative of future results. MFTA is for informational and analytical purposes only and is not financial or investment advice.
VSA Vol Key VSA Signals
(1) No Demand – Bearish Signal
Low volume, narrow spread.
Price rises, but volume does not increase → Weak market, lack of buyers.
If this appears in an uptrend, it may indicate a potential reversal.
(2) No Supply – Bullish Signal
Low volume, narrow spread.
Price declines, but volume does not increase → Weak selling pressure.
If this appears in an uptrend, it may confirm the continuation of the uptrend.
(3) Stopping Volume – Bullish Reversal Signal
Strong price decline, but unusually high volume.
Candle shows a long lower wick, closing near the top.
Indicates Smart Money absorbing supply, signaling a potential reversal upwards.
(4) Climactic Volume – Possible Trend Reversal
Extremely high volume with a sharp price increase or decrease.
If this occurs after a long trend, it may indicate a trend reversal.
Smart Money may be taking profits after a prolonged price movement.
(5) Effort vs. Result
If volume is high but price movement is weak → Inefficient buying/selling, possible reversal.
If volume is high and price moves strongly in the same direction → Trend is likely to continue.
Multi-Anchored Linear Regression Channels [TANHEF]█ Overview:
The 'Multi-Anchored Linear Regression Channels ' plots multiple dynamic regression channels (or bands) with unique selectable calculation types for both regression and deviation. It leverages a variety of techniques, customizable anchor sources to determine regression lengths, and user-defined criteria to highlight potential opportunities.
Before getting started, it's worth exploring all sections, but make sure to review the Setup & Configuration section in particular. It covers key parameters like anchor type, regression length, bias, and signal criteria—essential for aligning the tool with your trading strategy.
█ Key Features:
⯁ Multi-Regression Capability:
Plot up to three distinct regression channels and/or bands simultaneously, each with customizable anchor types to define their length.
⯁ Regression & Deviation Methods:
Regressions Types:
Standard: Uses ordinary least squares to compute a simple linear trend by averaging the data and deriving a slope and endpoints over the lookback period.
Ridge: Introduces L2 regularization to stabilize the slope by penalizing large coefficients, which helps mitigate multicollinearity in the data.
Lasso: Uses L1 regularization through soft-thresholding to shrink less important coefficients, yielding a simpler model that highlights key trends.
Elastic Net: Combines L1 and L2 penalties to balance coefficient shrinkage and selection, producing a robust weighted slope that handles redundant predictors.
Huber: Implements the Huber loss with iteratively reweighted least squares (IRLS) and EMA-style weights to reduce the impact of outliers while estimating the slope.
Least Absolute Deviations (LAD): Reduces absolute errors using iteratively reweighted least squares (IRLS), yielding a slope less sensitive to outliers than squared-error methods.
Bayesian Linear: Merges prior beliefs with weighted data through Bayesian updating, balancing the prior slope with data evidence to derive a probabilistic trend.
Deviation Types:
Regressive Linear (Reverse): In reverse order (recent to oldest), compute weighted squared differences between the data and a line defined by a starting value and slope.
Progressive Linear (Forward): In forward order (oldest to recent), compute weighted squared differences between the data and a line defined by a starting value and slope.
Balanced Linear: In forward order (oldest to newest), compute regression, then pair to source data in reverse order (newest to oldest) to compute weighted squared differences.
Mean Absolute: Compute weighted absolute differences between each data point and its regression line value, then aggregate them to yield an average deviation.
Median Absolute: Determine the weighted median of the absolute differences between each data point and its regression line value to capture the central tendency of deviations.
Percent: Compute deviation as a percentage of a base value by multiplying that base by the specified percentage, yielding symmetric positive and negative deviations.
Fitted: Compare a regression line with high and low series values by computing weighted differences to determine the maximum upward and downward deviations.
Average True Range: Iteratively compute the weighted average of absolute differences between the data and its regression line to yield an ATR-style deviation measure.
Bias:
Bias: Applies EMA or inverse-EMA style weighting to both Regression and/or Deviation, emphasizing either recent or older data.
⯁ Customizable Regression Length via Anchors:
Anchor Types:
Fixed: Length.
Bar-Based: Bar Highest/Lowest, Volume Highest/Lowest, Spread Highest/Lowest.
Correlation: R Zero, R Highest, R Lowest, R Absolute.
Slope: Slope Zero, Slope Highest, Slope Lowest, Slope Absolute.
Indicator-Based: Indicators Highest/Lowest (ADX, ATR, BBW, CCI, MACD, RSI, Stoch).
Time-Based: Time (Day, Week, Month, Quarter, Year, Decade, Custom).
Session-Based: Session (Tokyo, London, New York, Sydney, Custom).
Event-Based: Earnings, Dividends, Splits.
External: Input Source Highest/Lowest.
Length Selection:
Maximum: The highest allowed regression length (also fixed value of “Length” anchor).
Minimum: The shortest allowed length, ensuring enough bars for a valid regression.
Step: The sampling interval (e.g., 1 checks every bar, 2 checks every other bar, etc.). Increasing the step reduces the loading time, most applicable to “Slope” and “R” anchors.
Adaptive lookback:
Adaptive Lookback: Enable to display regression regardless of too few historical bars.
⯁ Selecting Bias:
Bias applies separately to regression and deviation.
Positive values emphasize recent data (EMA-style), negative invert, and near-zero maintains balance. (e.g., a length 100, bias +1 gives the newest price ~7× more weight than the oldest).
It's best to apply bias to both (regression and deviation) or just the deviation. Biasing only regression may distort deviation visually, while biasing both keeps their relationship intuitive. Using bias only for deviation scales it without altering regression, offering unique analysis.
⯁ Scale Awareness:
Supports linear and logarithmic price scaling, the regression and deviations adjust accordingly.
⯁ Signal Generation & Alerts:
Customizable entry/exit signals and alerts, detailed in the dedicated section below.
⯁ Visual Enhancements & Real-World Examples:
Optional on-chart table display summarizing regression input criteria (display type, anchor type, source, regression type, regression bias, deviation type, deviation bias, deviation multiplier) and key calculated metrics (regression length, slope, Pearson’s R, percentage position within deviations, etc.) for quick reference.
█ Understanding R (Pearson Correlation Coefficient):
Pearson’s R gauges data alignment to a straight-line trend within the regression length:
Range: R varies between –1 and +1.
R = +1 → Perfect positive correlation (strong uptrend).
R = 0 → No linear relationship detected.
R = –1 → Perfect negative correlation (strong downtrend).
This script uses Pearson’s R as an anchor, adjusting regression length to target specific R traits. Strong R (±1) follows the regression channel, while weak R (0) shows inconsistency.
█ Understanding the Slope:
The slope is the direction and rate at which the regression line rises or falls per bar:
Positive Slope (>0): Uptrend – Steeper means faster increase.
Negative Slope (<0): Downtrend – Steeper means sharper drop.
Zero or Near-Zero Slope: Sideways – Indicating range-bound conditions.
This script uses highest and lowest slope as an anchor, where extremes highlight strong moves and trend lines, while values near zero indicate sideways action and possible support/resistance.
█ Setup & Configuration:
Whether you’re new to this script or want to quickly adjust all critical parameters, the panel below shows the main settings available. You can customize everything from the anchor type and maximum length to the bias, signal conditions, and more.
Scale (select Log Scale for logarithmic, otherwise linear scale).
Display (regression channel and/or bands).
Anchor (how regression length is determined).
Length (control bars analyzed):
• Max – Upper limit.
• Min – Prevents regression from becoming too short.
• Step – Controls scanning precision; increasing Step reduces load time.
Regression:
• Type – Calculation method.
• Bias – EMA-style emphasis (>0=new bars weighted more; <0=old bars weighted more).
Deviation:
• Type – Calculation method.
• Bias – EMA-style emphasis (>0=new bars weighted more; <0=old bars weighted more).
• Multiplier - Adjusts Upper and Lower Deviation.
Signal Criteria:
• % (Price vs Deviation) – (0% = lower deviation, 50% = regression, 100% = upper deviation).
• R – (0 = no correlation, ±1 = perfect correlation; >0 = +slope, <0 = -slope).
Table (analyze table of input settings, calculated results, and signal criteria).
Adaptive Lookback (display regression while too few historical bars).
Multiple Regressions (steps 2 to 7 apply to #1, #2, and #3 regressions).
█ Signal Generation & Alerts:
The script offers customizable entry and exit signals with flexible criteria and visual cues (background color, dots, or triangles). Alerts can also be triggered for these opportunities.
Percent Direction Criteria:
(0% = lower deviation, 50% = regression line, 100% = upper deviation)
Above %: Triggers if price is above a specified percent of the deviation channel.
Below %: Triggers if price is below a specified percent of the deviation channel.
(Blank): Ignores the percent‐based condition.
Pearson's R (Correlation) Direction Criteria:
(0 = no correlation, ±1 = perfect correlation; >0 = positive slope, <0 = negative slope)
Above R / Below R: Compares the correlation to a threshold.
Above│R│ / Below│R│: Uses absolute correlation to focus on strength, ignoring direction.
Zero to R: Checks if R is in the 0-to-threshold range.
(Blank): Ignores correlation-based conditions.
█ User Tips & Best Practices:
Choose an anchor type that suits your strategy, “Bar Highest/Lowest” automatically spots commonly used regression zones, while “│R│ Highest” targets strong linear trends.
Consider enabling or disabling the Adaptive Lookback feature to ensure you always have a plotted regression if your chart doesn’t meet the maximum-length requirement.
Use a small Step size (1) unless relying on R-correlation or slope-based anchors as the are time-consuming to calculate. Larger steps speed up calculations but reduce precision.
Fine-tune settings such as lookback periods, regression bias, and deviation multipliers, or trend strength. Small adjustments can significantly affect how channels and signals behave.
To reduce loading time , show only channels (not bands) and disable signals, this limits calculations to the last bar and supports more extreme criteria.
Use the table display to monitor anchor type, calculated length, slope, R value, and percent location at a glance—especially if you have multiple regressions visible simultaneously.
█ Conclusion:
With its blend of advanced regression techniques, flexible deviation options, and a wide range of anchor types, this indicator offers a highly adaptable linear regression channeling system. Whether you're anchoring to time, price extremes, correlation, slope, or external events, the tool can be shaped to fit a variety of strategies. Combined with customizable signals and alerts, it may help highlight areas of confluence and support a more structured approach to identifying potential opportunities.
LinearRegressionLibrary "LinearRegression"
Calculates a variety of linear regression and deviation types, with optional emphasis weighting. Additionally, multiple of slope and Pearson’s R calculations.
calcSlope(_src, _len, _condition)
Calculates the slope of a linear regression over the specified length.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period for the linear regression.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast for efficiency.
Returns: (float) The slope of the linear regression.
calcReg(_src, _len, _condition)
Calculates a basic linear regression, returning y1, y2, slope, and average.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) An array of 4 values: .
calcRegStandard(_src, _len, _emphasis, _condition)
Calculates an Standard linear regression with optional emphasis.
Parameters:
_src (float) : (series float) The source data series.
_len (int) : (int) The length of the lookback period.
_emphasis (float) : (float) The emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegRidge(_src, _len, lambda, _emphasis, _condition)
Calculates a ridge regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
lambda (float) : (float) The ridge regularization parameter.
_emphasis (float) : (float) The emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegLasso(_src, _len, lambda, _emphasis, _condition)
Calculates a Lasso regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
lambda (float) : (float) The Lasso regularization parameter.
_emphasis (float) : (float) The emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcElasticNetLinReg(_src, _len, lambda1, lambda2, _emphasis, _condition)
Calculates an Elastic Net regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
lambda1 (float) : (float) L1 regularization parameter (Lasso).
lambda2 (float) : (float) L2 regularization parameter (Ridge).
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegHuber(_src, _len, delta, iterations, _emphasis, _condition)
Calculates a Huber regression using Iteratively Reweighted Least Squares (IRLS).
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
delta (float) : (float) Huber threshold parameter.
iterations (int) : (int) Number of IRLS iterations.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegLAD(_src, _len, iterations, _emphasis, _condition)
Calculates a Least Absolute Deviations (LAD) regression via IRLS.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
iterations (int) : (int) Number of IRLS iterations for LAD.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegBayesian(_src, _len, priorMean, priorSpan, sigma, _emphasis, _condition)
Calculates a Bayesian linear regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
priorMean (float) : (float) The prior mean for the slope.
priorSpan (float) : (float) The prior variance (or span) for the slope.
sigma (float) : (float) The assumed standard deviation of residuals.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRFromLinReg(_src, _len, _slope, _average, _y1, _condition)
Calculates the Pearson correlation coefficient (R) based on linear regression parameters.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_average (float) : (float) The average value of the source data series.
_y1 (float) : (float) The starting point (y-intercept of the oldest bar) for the linear regression.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast for efficiency.
Returns: (float) The Pearson correlation coefficient (R) adjusted for the direction of the slope.
calcRFromSource(_src, _len, _condition)
Calculates the correlation coefficient (R) using a specified length and source data.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast for efficiency.
Returns: (float) The correlation coefficient (R).
calcSlopeLengthZero(_src, _len, _minLen, _step, _condition)
Identifies the length at which the slope is flattest (closest to zero).
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length to consider (minimum of 2).
_minLen (int) : (int) The minimum length to start from (cannot exceed the max length).
_step (int) : (int) The increment step for lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the slope is flattest.
calcSlopeLengthHighest(_src, _len, _minLen, _step, _condition)
Identifies the length at which the slope is highest.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the slope is highest.
calcSlopeLengthLowest(_src, _len, _minLen, _step, _condition)
Identifies the length at which the slope is lowest.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the slope is lowest.
calcSlopeLengthAbsolute(_src, _len, _minLen, _step, _condition)
Identifies the length at which the absolute slope value is highest.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the absolute slope value is highest.
calcRLengthZero(_src, _len, _minLen, _step, _condition)
Identifies the length with the lowest absolute R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the lowest absolute R value.
calcRLengthHighest(_src, _len, _minLen, _step, _condition)
Identifies the length with the highest R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the highest R value.
calcRLengthLowest(_src, _len, _minLen, _step, _condition)
Identifies the length with the lowest R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the lowest R value.
calcRLengthAbsolute(_src, _len, _minLen, _step, _condition)
Identifies the length with the highest absolute R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the highest absolute R value.
calcDevReverse(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the regressive linear deviation in reverse order, with optional emphasis on recent data.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevForward(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the progressive linear deviation in forward order (oldest to most recent bar), with optional emphasis.
Parameters:
_src (float) : (float) The source data array, where _src is oldest and _src is most recent.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept of the linear regression (value at the most recent bar, adjusted by slope).
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevBalanced(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the balanced linear deviation with optional emphasis on recent or older data.
Parameters:
_src (float) : (float) Source data array, where _src is the most recent and _src is the oldest.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept of the linear regression (value at the oldest bar).
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevMean(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the mean absolute deviation from a forward-applied linear trend (oldest to most recent), with optional emphasis.
Parameters:
_src (float) : (float) The source data array, where _src is the most recent and _src is the oldest.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevMedian(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the median absolute deviation with optional emphasis on recent data.
Parameters:
_src (float) : (float) The source data array (index 0 = oldest, index _len - 1 = most recent).
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns:
calcDevPercent(_y1, _inputDev, _condition)
Calculates the percent deviation from a given value and a specified percentage.
Parameters:
_y1 (float) : (float) The base value from which to calculate deviation.
_inputDev (float) : (float) The deviation percentage.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevFitted(_len, _slope, _y1, _emphasis, _condition)
Calculates the weighted fitted deviation based on high and low series data, showing max deviation, with optional emphasis.
Parameters:
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The Y-intercept (oldest bar) of the linear regression.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevATR(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates an ATR-style deviation with optional emphasis on recent data.
Parameters:
_src (float) : (float) The source data (typically close).
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The Y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcPricePositionPercent(_top, _bot, _src)
Calculates the percent position of a price within a linear regression channel. Top=100%, Bottom=0%.
Parameters:
_top (float) : (float) The top (positive) deviation, corresponding to 100%.
_bot (float) : (float) The bottom (negative) deviation, corresponding to 0%.
_src (float) : (float) The source price.
Returns: (float) The percent position within the channel.
plotLinReg(_len, _y1, _y2, _slope, _devTop, _devBot, _scaleTypeLog, _lineWidth, _extendLines, _channelStyle, _colorFill, _colUpLine, _colDnLine, _colUpFill, _colDnFill)
Plots the linear regression line and its deviations, with configurable styles and fill.
Parameters:
_len (int) : (int) The lookback period for the linear regression.
_y1 (float) : (float) The starting y-value of the regression line.
_y2 (float) : (float) The ending y-value of the regression line.
_slope (float) : (float) The slope of the regression line (used to determine line color).
_devTop (float) : (float) The top deviation to add to the line.
_devBot (float) : (float) The bottom deviation to subtract from the line.
_scaleTypeLog (bool) : (bool) Use a log scale if true; otherwise, linear scale.
_lineWidth (int) : (int) The width of the plotted lines.
_extendLines (string) : (string) How lines should extend (none, left, right, both).
_channelStyle (string) : (string) The style of the channel lines (solid, dashed, dotted).
_colorFill (bool) : (bool) Whether to fill the space between the top and bottom deviation lines.
_colUpLine (color) : (color) Line color when slope is positive.
_colDnLine (color) : (color) Line color when slope is negative.
_colUpFill (color) : (color) Fill color when slope is positive.
_colDnFill (color) : (color) Fill color when slope is negative.
Find Untapped LevelsYou can find your daily, weekly and monthly levels with this indicator. You still need to go through each time frame and draw them yourself
Nef33-Volume Footprint ApproximationDescription of the "Volume Footprint Approximation" Indicator
Purpose
The "Volume Footprint Approximation" indicator is a tool designed to assist traders in analyzing market volume dynamics and anticipating potential trend changes in price. It is inspired by the concept of a volume footprint chart, which visualizes the distribution of trading volume across different price levels. However, since TradingView does not provide detailed intrabar data for all users, this indicator approximates the behavior of a footprint chart by using available volume and price data (open, close, volume) to classify volume as buy or sell, calculate volume delta, detect imbalances, and generate trend change signals.
The indicator is particularly useful for identifying areas of high buying or selling activity, imbalances between supply and demand, delta divergences, and potential reversal points in the market. It provides specific signals for bullish and bearish trend changes, making it suitable for traders looking to trade reversals or confirm trends.
How It Works
The indicator uses volume and price data from each candlestick to perform the following calculations:
Volume Classification:
Classifies the volume of each candlestick as "buy" or "sell" based on price movement:
If the closing price is higher than the opening price (close > open), the volume is classified as "buy."
If the closing price is lower than the opening price (close < open), the volume is classified as "sell."
If the closing price equals the opening price (close == open), it compares with the previous close to determine the direction:
If the current close is higher than the previous close, it is classified as "buy."
If the current close is lower than the previous close, it is classified as "sell."
If the current close equals the previous close, the classification from the previous bar is used.
Delta Calculation:
Calculates the volume delta as the difference between buy volume and sell volume (buyVolume - sellVolume).
A positive delta indicates more buy volume; a negative delta indicates more sell volume.
Imbalance Detection:
Identifies imbalances between buy and sell volume:
A buy imbalance occurs when buy volume exceeds sell volume by a defined percentage (default is 300%).
A sell imbalance occurs when sell volume exceeds buy volume by the same percentage.
Delta Divergence Detection:
Positive Delta Divergence: Occurs when the price is falling (for at least 2 bars) but the delta is increasing or becomes positive, indicating that buyers are entering despite the price decline.
Negative Delta Divergence: Occurs when the price is rising (for at least 2 bars) but the delta is decreasing or becomes negative, indicating that sellers are entering despite the price increase.
Trend Change Signals:
Bullish Signal (trendChangeBullish): Generated when the following conditions are met:
There is a positive delta divergence.
The delta has moved from a negative value (e.g., -500) to a positive value (e.g., +200) over the last 3 bars.
There is a buy imbalance.
The price is near a historical support level (approximated as the lowest low of the last 50 bars).
Bearish Signal (trendChangeBearish): Generated when the following conditions are met:
There is a negative delta divergence.
The delta has moved from a positive value (e.g., +500) to a negative value (e.g., -200) over the last 3 bars.
There is a sell imbalance.
The price is near a historical resistance level (approximated as the highest high of the last 50 bars).
Visual Elements
The indicator is displayed in a separate panel below the price chart (overlay=false) and includes the following elements:
Volume Histograms:
Buy Volume: Represented by a green histogram. Shows the volume classified as "buy."
Sell Volume: Represented by a red histogram. Shows the volume classified as "sell."
Note: The histograms overlap, and the last plotted histogram (red) takes visual precedence, meaning the sell volume may cover the buy volume if it is larger.
Delta Line:
Delta Volume: Represented by a blue line. Shows the difference between buy and sell volume.
A line above zero indicates more buy volume; a line below zero indicates more sell volume.
A dashed gray horizontal line marks the zero level for easier interpretation.
Imbalance Backgrounds:
Buy Imbalance: Light green background when buy volume exceeds sell volume by the defined percentage.
Sell Imbalance: Light red background when sell volume exceeds buy volume by the defined percentage.
Divergence Backgrounds:
Positive Delta Divergence: Lime green background when a positive delta divergence is detected.
Negative Delta Divergence: Fuchsia background when a negative delta divergence is detected.
Trend Change Signals:
Bullish Signal: Green label with the text "Bullish Trend Change" when the conditions for a bullish trend change are met.
Bearish Signal: Red label with the text "Bearish Trend Change" when the conditions for a bearish trend change are met.
Information Labels:
Below each bar, a label displays:
Total Vol: The total volume of the bar.
Delta: The delta volume value.
Alerts
The indicator generates the following alerts:
Positive Delta Divergence: "Positive Delta Divergence Detected! Price is falling, but delta is increasing."
Negative Delta Divergence: "Negative Delta Divergence Detected! Price is rising, but delta is decreasing."
Bullish Trend Change Signal: "Bullish Trend Change Signal! Positive Delta Divergence, Delta Rise, Buy Imbalance, and Near Support."
Bearish Trend Change Signal: "Bearish Trend Change Signal! Negative Delta Divergence, Delta Drop, Sell Imbalance, and Near Resistance."
These alerts can be configured in TradingView to receive real-time notifications.
Adjustable Parameters
The indicator allows customization of the following parameters:
Imbalance Threshold (%): The percentage required to detect an imbalance between buy and sell volume (default is 300%).
Lookback Period for Divergence: Number of bars to look back for detecting price and delta trends (default is 2 bars).
Support/Resistance Lookback Period: Number of bars to look back for identifying historical support and resistance levels (default is 50 bars).
Delta High Threshold (Bearish): Minimum delta value 2 bars ago for the bearish signal (default is +500).
Delta Low Threshold (Bearish): Maximum delta value in the current bar for the bearish signal (default is -200).
Delta Low Threshold (Bullish): Maximum delta value 2 bars ago for the bullish signal (default is -500).
Delta High Threshold (Bullish): Minimum delta value in the current bar for the bullish signal (default is +200).
Practical Use
The indicator is useful for the following purposes:
Identifying Trend Changes:
The trend change signals (trendChangeBullish and trendChangeBearish) indicate potential price reversals. For example, a bullish signal near a support level may be an opportunity to enter a long position.
Detecting Divergences:
Delta divergences (positive and negative) can anticipate trend changes by showing a disagreement between price movement and underlying buying/selling pressure.
Finding Key Levels:
Imbalances (green and red backgrounds) often coincide with support and resistance levels, helping to identify areas where the market might react.
Confirming Trends:
A consistently positive delta in an uptrend or a negative delta in a downtrend can confirm the strength of the trend.
Identifying Failed Auctions:
Although not detected automatically, you can manually identify failed auctions by observing a price move to new highs/lows with decreasing volume in the direction of the move.
Limitations
Intrabar Data: It does not use detailed intrabar data, making it less precise than a native footprint chart.
Approximations: Volume classification and support/resistance detection are approximations, which may lead to false signals.
Volume Dependency: It requires reliable volume data, so it may be less effective on assets with inaccurate volume data (e.g., some forex pairs).
False Signals: Divergences and imbalances do not always indicate a trend change, especially in strongly trending markets.
Recommendations
Combine with Other Indicators: Use tools like RSI, MACD, support/resistance levels, or candlestick patterns to confirm signals.
Trade on Higher Timeframes: Signals are more reliable on higher timeframes like 1-hour or 4-hour charts.
Perform Backtesting: Evaluate the indicator's accuracy on historical data to adjust parameters and improve effectiveness.
Adjust Parameters: Modify thresholds (e.g., imbalanceThreshold or supportResistanceLookback) based on the asset and timeframe you are trading.
Conclusion
The "Volume Footprint Approximation" indicator is a powerful tool for analyzing volume dynamics and anticipating price trend changes. By classifying volume, calculating delta, detecting imbalances and divergences, and generating trend change signals, it provides traders with valuable insights into market buying and selling pressure. While it has limitations due to the lack of intrabar data, it can be highly effective when used in combination with other technical analysis tools and on assets with reliable volume data.
Nasan Ultimate Health Index (NUHI)The Nasan Ultimate Health Index (NUHI) is a technical indicator designed to measure the relative health of a stock compared to a benchmark index or sector. By incorporating price action, volume dynamics, and volatility, NUHI provides traders with a clearer picture of a stock’s performance relative to the broader market.
The NUHI is based on the idea that a stock’s relative strength and momentum can be assessed more effectively when adjusted for volume behavior and benchmark comparison. Instead of looking at price movement alone, this indicator factors in:
The stock’s price trend (via EMA)
Volume participation (green vs. red volume) and volume ratio - SMA(volume, 21)/ SMA(volume, 252)
Volatility-adjusted performance (ATR-based scaling)
Comparison with a selected benchmark (e.g., SPX, NDX, sector ETFs)
This results in a normalized and comparative score that helps traders identify outperforming, neutral, and underperforming stocks within a specific market environment.
The NUHI is constructed using the following elements:
1️⃣ Stock Raw Score (Unadjusted Momentum)
The exponential moving average (EMA) of the hlc3 (average of high, low, close) is used to define the price trend.
The difference between the current EMA and the EMA from n bars ago shows whether the stock is gaining or losing momentum.
This difference is divided by the ATR (Average True Range) to adjust for volatility.
2️⃣ Volume Behavior Adjustment
Volume is split into green volume (up candles) and red volume (down candles).
The ratio of green to red volume determines whether buyers or sellers dominate over the selected period (n bars).
If the stock is in an uptrend, green volume is weighted higher; if in a downtrend, red volume is weighted higher.
The stock’s Volume Ratio (short-term SMA divided by long-term SMA) is adjusted based on this weight.
3️⃣ Benchmark Comparison
A similar Raw Score calculation is performed on the selected benchmark (SPX, NDX, or sector ETF).
Benchmark price movements, volume behavior, and ATR adjustments mirror the stock’s calculations.
This provides a reference point for evaluating the stock’s relative strength.
4️⃣ Normalization Process
Both the stock and benchmark raw scores are min-max normalized over the past 252 bars (1-year lookback).
This scales values between 0 and 1, ensuring fair comparisons regardless of absolute price differences.
5️⃣ NUHI Calculation
The final NUHI value is computed using a logarithmic ratio between the normalized stock score and the normalized benchmark score:
This transformation ensures a more symmetrical representation of overperformance and underperformance.
Performance Zones
Strong Outperforming (NUHI between >0.41 and 0.69)
Leading (NUHI between >0.10 and 0.41)
Transitioning Outperformance (NUHI between 0.10 and 0)
Equilibrium (NUHI 0)
Transitioning Underperformance (NUHI between -0.10 and 0)
Lagging (NUHI between < -0.1 and -0.41)
Strong Underperforming (NUHI between< -0.41 and -0.69 )
How to Use NUHI
✅ Identifying Strong Stocks
If NUHI > 0, the stock is outperforming its benchmark.
If NUHI < 0, the stock is underperforming the benchmark.
✅ Trend Confirmation
A steadily rising NUHI and raw score (colored green) suggests sustained strength bullish conditions.
A falling NUHI and raw score (colored orange) indicates weakness and possible rotation into other assets.
✅ Finding Reversals
Bullish Divergence: If NUHI is improving while the stock’s raw score is negative, it may signal a bottoming opportunity.
Bearish Signs: If NUHI is dropping despite price strength, it could hint at underlying weakness.
Why a Stock in a Downtrend Can Have NUHI > 0 (and Vice Versa )
NUHI measures performance relative to both its own history and the benchmark.
A stock’s recent movement is compared to how it usually behaves and how the benchmark is performing.
Example Scenarios:
Stock in a Downtrend but NUHI > 0
The stock may still be in a downtrend (negative raw score), but it’s performing better relative to its past downtrend behavior and better than the benchmark over the same period.
This could mean it’s showing relative strength compared to the broader market or sector.
Stock in an Uptrend but NUHI < 0
Even in a uptrend (positive raw score), the stock might be underperforming relative to its past uptrend behavior and underperforming the benchmark.
What This Means:
NUHI > 0 in a downtrend → The stock is falling less aggressively than usual and/or holding up better than the benchmark.
NUHI < 0 in an uptrend → The stock is gaining less than expected based on its history and/or lagging behind the benchmark.
NUHI helps identify relative strength or weakness .
Forexsom MA Crossover SignalsA Trend-Following Trading Indicator for TradingView
Overview
This indicator plots two moving averages (MA) on your chart and generates visual signals when they cross, helping traders identify potential trend reversals. It is designed to be simple yet effective for both beginners and experienced traders.
Key Features
✅ Dual Moving Averages – Plots a Fast MA (default: 9-period) and a Slow MA (default: 21-period)
✅ Customizable MA Types – Choose between EMA (Exponential Moving Average) or SMA (Simple Moving Average)
✅ Clear Buy/Sell Signals – Displays "BUY" (green label) when the Fast MA crosses above the Slow MA and "SELL" (red label) when it crosses below
✅ Alerts – Get notified when new signals appear (compatible with TradingView alerts)
✅ Clean Visuals – Easy-to-read moving averages with adjustable colors
How It Works
Bullish Signal (BUY) → Fast MA crosses above Slow MA (suggests uptrend)
Bearish Signal (SELL) → Fast MA crosses below Slow MA (suggests downtrend)
Best Used For
✔ Trend-following strategies (swing trading, day trading)
✔ Confirming trend reversals
✔ Filtering trade entries in combination with other indicators
Customization Options
Adjust Fast & Slow MA lengths
Switch between EMA or SMA for smoother or more responsive signals
Why Use This Indicator?
Simple & Effective – No clutter, just clear signals
Works on All Timeframes – From scalping (1M, 5M) to long-term trading (4H, Daily)
Alerts for Real-Time Trading – Never miss a signal
