Multi SMA EMA VWAP1. Moving Average Crossover
This is one of the most common strategies with moving averages, and it involves observing crossovers between EMAs and SMAs to determine buy or sell signals.
Buy signal: When a faster EMA (like a short-term EMA) crosses above a slower SMA, it can indicate a potential upward movement.
Sell signal: When a faster EMA crosses below a slower SMA, it can indicate a potential downward movement.
With 4 EMAs and 5 SMAs, you can set up crossovers between different combinations, such as:
EMA(9) crosses above SMA(50) → buy.
EMA(9) crosses below SMA(50) → sell.
2. Divergence Confirmation Between EMAs and SMAs
Divergence between the EMAs and SMAs can offer additional confirmation. If the EMAs are pointing in one direction and the SMAs are still in the opposite direction, it is a sign that the movement could be stronger and continue in the same direction.
Positive divergence: If the EMAs are making new highs while the SMAs are still below, it could be a sign that the market is in a strong trend.
Negative divergence: If the EMAs are making new lows and the SMAs are still above, you might consider that the market is in a downtrend or correction.
3. Using EMAs as Dynamic Support and Resistance
EMAs can act as dynamic support and resistance in strong trends. If the price approaches a faster EMA from above and doesn’t break it, it could be a good entry point for a long position (buy). If the price approaches a slower EMA from below and doesn't break it, it could be a good point to sell (short).
Buy: If the price is above all EMAs and approaches the fastest EMA (e.g., EMA(9)), it could be a good buy point if the price bounces upward.
Sell: If the price is below all EMAs and approaches the fastest EMA, it could be a good sell point if the price bounces downward.
4. Combining SMAs and EMAs to Filter Signals
SMAs can serve as a trend filter to avoid trading in sideways markets. For example:
Bullish trend condition: If the longer-term SMAs (such as SMA(100) or SMA(200)) are below the price, and the shorter EMAs are aligned upward, you can look for buy signals.
Bearish trend condition: If the longer-term SMAs are above the price and the shorter EMAs are aligned downward, you can look for sell signals.
5. Consolidation Zone Between EMAs and SMAs
When the price moves between EMAs and SMAs without a clear trend (consolidation zone), you can expect a breakout. In this case, you can use the EMAs and SMAs to identify the direction of the breakout:
If the price is in a narrow range between the EMAs and SMAs and then breaks above the fastest EMA, it’s a sign that an upward trend may begin.
If the price breaks below the fastest EMA, it could indicate a potential downward trend.
6. "Golden Cross" and "Death Cross" Strategy
These are classic strategies based on crossovers between moving averages of different periods.
Golden Cross: Occurs when a faster EMA (e.g., EMA(50)) crosses above a slower SMA (e.g., SMA(200)), which suggests a potential bullish trend.
Death Cross: Occurs when a faster EMA crosses below a slower SMA, which suggests a potential bearish trend.
Additional Recommendations:
Combining with other indicators: You can combine EMA and SMA signals with other indicators like the RSI (Relative Strength Index) or MACD (Moving Average Convergence/Divergence) for confirmation and to avoid false signals.
Risk management: Always use stop-loss and take-profit orders to protect your capital. Moving averages are trend-following indicators but don’t guarantee that the price will move in the same direction.
Timeframe analysis: It’s recommended to use different timeframes to confirm the trend (e.g., use EMAs on hourly charts along with SMAs on daily charts).
VWAP
1. VWAP + EMAs for Trend Confirmation
VWAP can act as a trend filter, confirming the direction provided by the EMAs.
Buy Signal: If the price is above the VWAP and the EMAs are aligned in an uptrend (e.g., short-term EMAs are above longer-term EMAs), this indicates that the trend is bullish and you can look for buy opportunities.
Sell Signal: If the price is below the VWAP and the EMAs are aligned in a downtrend (e.g., short-term EMAs are below longer-term EMAs), this suggests a bearish trend and you can look for sell opportunities.
In this case, VWAP is used to confirm the overall trend. For example:
Bullish: Price above VWAP, EMAs aligned to the upside (e.g., EMA(9) > EMA(50) > EMA(200)), buy.
Bearish: Price below VWAP, EMAs aligned to the downside (e.g., EMA(9) < EMA(50) < EMA(200)), sell.
2. VWAP as Dynamic Support and Resistance
VWAP can act as a dynamic support or resistance level during the day. Combining this with EMAs and SMAs helps you refine your entry and exit points.
Support: If the price is above VWAP and starts pulling back to VWAP, it could act as support. If the price bounces off the VWAP and aligns with bullish EMAs (e.g., EMA(9) crossing above EMA(50)), you can consider entering a buy position.
Resistance: If the price is below VWAP and approaches VWAP from below, it can act as resistance. If the price fails to break through VWAP and aligns with bearish EMAs (e.g., EMA(9) crossing below EMA(50)), it could be a good signal for a sell.
Cerca negli script per "breakout"
Power Of 3 ICT 01 [TradingFinder] AMD ICT & SMC Accumulations🔵 Introduction
The ICT Power of 3 (PO3) strategy, developed by Michael J. Huddleston, known as the Inner Circle Trader, is a structured approach to analyzing daily market activity. This strategy divides the trading day into three distinct phases: Accumulation, Manipulation, and Distribution.
Each phase represents a unique market behavior influenced by institutional traders, offering a clear framework for retail traders to align their strategies with market movements.
Accumulation (19:00 - 01:00 EST) takes place during low-volatility hours, as institutional traders accumulate orders. Manipulation (01:00 - 07:00 EST) involves false breakouts and liquidity traps designed to mislead retail traders. Finally, Distribution (07:00 - 13:00 EST) represents the active phase where significant market movements occur as institutions distribute their positions in line with the broader trend.
This indicator is built upon the Power of 3 principles to provide traders with a practical and visual tool for identifying these key phases. By using clear color coding and precise time zones, the indicator highlights critical price levels, such as highs and lows, helping traders to better understand market dynamics and make more informed trading decisions.
Incorporating the ICT AMD setup into daily analysis enables traders to anticipate market behavior, spot high-probability trade setups, and gain deeper insights into institutional trading strategies. With its focus on time-based price action, this indicator simplifies complex market structures, offering an effective tool for traders of all levels.
🔵 How to Use
The ICT Power of 3 (PO3) indicator is designed to help traders analyze daily market movements by visually identifying the three key phases: Accumulation, Manipulation, and Distribution.
Here's how traders can effectively use the indicator :
🟣 Accumulation Phase (19:00 - 01:00 EST)
Purpose : Identify the range-bound activity where institutional players accumulate orders.
Trading Insight : Avoid placing trades during this phase, as price movements are typically limited. Instead, use this time to prepare for the potential direction of the market in the next phases.
🟣 Manipulation Phase (01:00 - 07:00 EST)
Purpose : Spot false breakouts and liquidity traps that mislead retail traders.
Trading Insight : Observe the market for price spikes beyond key support or resistance levels. These moves often reverse quickly, offering high-probability entry points in the opposite direction of the initial breakout.
🟣 Distribution Phase (07:00 - 13:00 EST)
Purpose : Detect the main price movement of the day, driven by institutional distribution.
Trading Insight : Enter trades in the direction of the trend established during this phase. Look for confirmations such as breakouts or strong directional moves that align with broader market sentiment
🔵 Settings
Show or Hide Phases :mDecide whether to display Accumulation, Manipulation, or Distribution.
Adjust the session times for each phase :
Accumulation: 1900-0100 EST
Manipulation: 0100-0700 EST
Distribution: 0700-1300 EST
Modify Visualization : Customize how the indicator looks by changing settings like colors and transparency.
🔵 Conclusion
The ICT Power of 3 (PO3) indicator is a powerful tool for traders seeking to understand and leverage market structure based on time and price dynamics. By visually highlighting the three key phases—Accumulation, Manipulation, and Distribution—this indicator simplifies the complex movements of institutional trading strategies.
With its customizable settings and clear representation of market behavior, the indicator is suitable for traders at all levels, helping them anticipate market trends and make more informed decisions.
Whether you're identifying entry points in the Accumulation phase, navigating false moves during Manipulation, or capitalizing on trends in the Distribution phase, this tool provides valuable insights to enhance your trading performance.
By integrating this indicator into your analysis, you can better align your strategies with institutional movements and improve your overall trading outcomes.
Confirmed market structure buy/sell indicatorOverview
The Swing Point Breakout Indicator with Multi-Timeframe Dashboard is a TradingView tool designed to identify potential buy and sell signals based on swing point breakouts on the primary chart's timeframe while simultaneously providing a snapshot of the market structure across multiple higher timeframes. This dual approach helps traders make informed decisions by aligning short-term signals with broader market trends.
Key Features
Swing Point Breakout Detection
Swing Highs and Lows: Identifies significant peaks and troughs based on a user-defined lookback period.
Breakout Signals:
Bullish Breakout (Buy Signal): Triggered when the price closes above the latest swing high.
Bearish Breakout (Sell Signal): Triggered when the price closes below the latest swing low.
Visual Indicators: Highlights breakout bars with colors (lime for bullish, red for bearish) and plots buy/sell markers on the chart.
Multi-Timeframe Dashboard
Timeframes Monitored: 1m, 5m, 15m, 1h, 4h, 1D, and 1W.
Market Structure Status:
Bullish: Indicates upward market structure.
Bearish: Indicates downward market structure.
Neutral: No clear trend.
Visual Table: Displays each timeframe with its current status, color-coded for quick reference (green for bullish, red for bearish, gray for neutral).
Operational Workflow
Initialization:
Sets up a dashboard table on the chart's top-right corner with headers "Timeframe" and "Status".
Swing Point Detection:
Continuously scans the main timeframe for swing highs and lows using the specified lookback period.
Updates the latest swing high and low levels.
Signal Generation:
Detects when the price breaks above the last swing high (bullish) or below the last swing low (bearish).
Activates potential buy/sell setups and confirms signals based on subsequent price movements.
Dashboard Update:
For each defined higher timeframe, assesses the market structure by checking for breakouts of swing points.
Updates the dashboard with the current status for each timeframe, aiding in trend confirmation.
Visualization:
Colors the bars where breakouts occur.
Plots buy and sell signals directly on the chart for easy identification.
Relative Measured Volatility (RMV) – Spot Tight Entry ZonesTitle: Relative Measured Volatility (RMV) – Spot Tight Entry Zones
Introduction
The Relative Measured Volatility (RMV) indicator is designed to highlight tight price consolidation zones , making it an ideal tool for traders seeking optimal entry points before potential breakouts. By focusing on tightness rather than general volatility, RMV offers traders a practical way to detect consolidation phases that often precede significant market moves.
How RMV Works
The RMV calculates short-term tightness by averaging three ATR (Average True Range) values over different lookback periods and then normalizing them within a specified lookback window. The result is a percentage-based scale from 0 to 100, indicating how tight the current price range is compared to recent history.
Here’s the breakdown:
Three ATR values are computed using user-defined short lookback periods to represent short-term price movements. An average of the ATRs provides a smoothed measure of current tightness. The RMV normalizes this average against the highest and lowest values over the defined lookback period, scaling it from 0 to 100.
This approach helps traders identify consolidation zones that are more likely to lead to breakouts.
Key Features of RMV
Multi-Period ATR Calculation : Uses three ATR values to effectively capture market tightness over the short term. Normalization : Converts the tightness measure to a 0-100 scale for easy interpretation. Dynamic Histogram and Background Colors : The RMV indicator uses a color-coded system for clarity.
How to Use the RMV Indicator
Identify Tight Consolidation Zones:
a - RMV values between 0-10 indicate very tight price ranges, making this the most optimal zone for potential entries before breakouts.
b - RMV values between 11-20 suggest moderate tightness, still favorable for entries.
Monitor Potential Breakout Areas:
As RMV moves from 21-30 , tightness reduces, signaling expanding volatility that may require wider stops or more flexible entry strategies.
Adjust Trading Strategies:
Use RMV values to identify tight zones for entering trades, especially in trending markets or at key support/resistance levels.
Customize the Indicator:
a - Adjust the short-term ATR lookback periods to control sensitivity.
b - Modify the lookback period to match your trading horizon, whether short-term or long-term.
Color-Coding Guide for RMV
ibb.co
How to Add RMV to Your Chart
Open your chart on TradingView.
Go to the “Indicators” section.
Search for "Relative Measured Volatility (RMV)" in the Community Scripts section.
Click on the indicator to add it to your chart.
Customize the input parameters to fit your trading strategy.
Input Parameters
Lookback Period : Defines the period over which tightness is measured and normalized.
Short-term ATR Lookbacks (1, 2, 3) : Control sensitivity to short-term tightness.
Histogram Threshold : Sets the threshold for differentiating between bright (tight) and dim (less tight) histogram colors.
Conclusion
The Relative Measured Volatility (RMV) is a versatile tool designed to help traders identify tight entry zones by focusing on market consolidation. By highlighting narrow price ranges, the RMV guides traders toward potential breakout setups while providing clear visual cues for better decision-making. Add RMV to your trading toolkit today and enhance your ability to identify optimal entry points!
Composite Z-Score with Linear Regression Bands [UAlgo]The Composite Z-Score with Linear Regression Bands is a technical indicator designed to provide traders with a comprehensive analysis of price momentum, volatility, and volume. By combining multiple moving averages with slope analysis, volume/volatility compression-expansion metrics, and Z-Score calculations, this indicator aims to highlight potential breakout and breakdown points with high accuracy. The inclusion of linear regression bands further enhances the analysis by providing dynamic support and resistance levels, which adapt to market conditions. This makes the indicator particularly useful in identifying overbought/oversold conditions, volume squeezes, and the overall direction of the trend.
🔶 Key Features
Multi-Length Slope Calculation: The indicator uses multiple Hull Moving Averages (HMA) across various lengths to calculate slope angles, which are then converted into Z-Scores. This helps in capturing both short-term and long-term price momentum.
Volume/Volatility Composite Analysis: By calculating a composite value derived from both volume and volatility, the indicator identifies periods of compression (squeezes) and expansion, which are crucial for detecting potential breakout opportunities.
Linear Regression Bands: The inclusion of dynamic linear regression bands provides traders with adaptive support and resistance levels. These bands are enhanced by the composite value, which adjusts the band width based on market conditions, offering a clearer view of possible price reversals.
Overbought/Oversold Detection: The indicator highlights overbought and oversold conditions by comparing Z-Scores against the upper and lower bounds of the regression bands, which can signal potential reversal points.
Customizable Inputs: Users can customize key parameters such as the lengths of the moving averages, the regression band period, and the number of deviations used for the bands, allowing for flexibility in adapting the indicator to different market environments.
🔶 Interpreting the Indicator
Z-Score Plots: The individual Z-Score plots represent the normalized slope of the Hull Moving Averages over different periods. Positive values indicate upward momentum, while negative values suggest downward momentum. The combined Z-Sum provides a broader view of the overall market momentum.
Composite Value: The composite value is a ratio of volume to volatility, which highlights periods of market compression and expansion. When the composite value rises, it suggests increasing market activity, often preceding a breakout.
Why are we calculating values for multiple lengths?
The Composite Z-Score with Linear Regression Bands indicator employs a multi-timeframe analysis by calculating Z-scores for various moving average lengths. This approach provides a more comprehensive view of market dynamics and helps to identify trends and potential reversals across different timeframes. By considering multiple lengths, we can:
Capture a broader range of market behaviors: Different moving average lengths capture different aspects of price movement. Shorter lengths are more sensitive to recent price changes, while longer lengths provide a smoother representation of the underlying trend.
Reduce the impact of noise: By combining Z-scores from multiple lengths, we can help to filter out some of the noise that can be present in shorter-term data and obtain a more robust signal.
Enhance the reliability of signals: When Z-scores from multiple lengths align, it can increase the confidence in the identified trend or potential reversal. This can help to reduce the likelihood of false signals.
In essence, calculating values for multiple lengths allows the indicator to provide a more nuanced and reliable assessment of market conditions, making it a valuable tool for traders and analysts.
Linear Regression Bands: The central line represents the linear regression of the Z-Sum, while the upper and lower bands represent the dynamic resistance and support levels, respectively. The deviation from the regression line indicates the strength of the current trend. When price moves beyond these bands, it may signal an overbought (above upper band) or oversold (below lower band) condition.
Volume/Volatility Squeeze: When the price moves between the regression bands and the volume/volatility-adjusted bands, the market is in a squeeze. Breakouts from this squeeze can lead to significant price moves, which are indicated by the filling of areas between the Z-Score plots and the bands.
Color Interpretation: The indicator uses color changes to make it easier to interpret the data. Teal colors generally indicate upward momentum or strong conditions, while red suggests downward momentum or weakening conditions. The intensity of the color reflects the strength of the signal.
Overbought/Oversold Signals: The indicator marks potential overbought and oversold conditions when Z-Scores cross above or below the upper and lower regression bands, respectively. These signals are crucial for identifying potential reversal points in the market.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Super IndicatorOverview of the Combined Indicator
This combined indicator leverages three major technical analysis tools:
Bollinger Bands
Linear Regression Channels
Scalping Strategy Indicators (RSI, MACD, SMA)
Each of these tools provides unique insights into market conditions, and their integration offers a comprehensive view of price movements, trends, and potential trading signals.
1. Bollinger Bands
Purpose:
Bollinger Bands are used to measure market volatility and identify overbought or oversold conditions.
Components:
Basis (Middle Band): Typically a 20-period Simple Moving Average (SMA).
Upper Band: Basis + (2 * Standard Deviation).
Lower Band: Basis - (2 * Standard Deviation).
Why They Complement:
Bollinger Bands expand and contract based on market volatility. When the bands are narrow, it indicates low volatility and potential for a significant move. Wide bands indicate high volatility. This helps traders gauge the strength of market moves and potential reversals.
2. Linear Regression Channels
Purpose:
Linear Regression Channels identify the overall trend direction and measure deviation from the mean price over a specific period.
Components:
Middle Line (Linear Regression Line): The line of best fit through the price data over a specified period.
Upper and Lower Lines: Channels created by adding/subtracting a multiple of the standard deviation or another deviation measure from the regression line.
Why They Complement:
Linear Regression Channels provide a clear visual representation of the trend direction and the range within which prices typically fluctuate. This can help traders identify trend continuations and reversals, making it easier to spot entry and exit points.
3. Scalping Strategy Indicators
Purpose:
The RSI, MACD, and SMA are used to generate short-term buy and sell signals, which are essential for scalping strategies aimed at capturing quick profits from small price movements.
Components:
RSI (Relative Strength Index): Measures the speed and change of price movements, typically over 14 periods. It helps identify overbought and oversold conditions.
MACD (Moving Average Convergence Divergence): Consists of the MACD line, Signal line, and histogram. It helps identify changes in the strength, direction, momentum, and duration of a trend.
SMA (Simple Moving Average): The average price over a specified period, used to smooth out price data and identify trends.
Why They Complement:
These indicators provide short-term signals that can confirm or refute the signals given by Bollinger Bands and Linear Regression Channels. For example, a buy signal might be more reliable if the price is near the lower Bollinger Band and the MACD crosses above its signal line.
How They Work Together
Scenario 1: Confirming Trend Continuations
Bollinger Bands: Price staying near the upper band suggests a strong uptrend.
Linear Regression Channels: Price staying above the middle line confirms the uptrend.
5-Minute Scalping Strategy: RSI not in overbought territory, and MACD showing bullish momentum confirms continuation.
Scenario 2: Identifying Reversals
Bollinger Bands: Price touching or moving outside the lower band suggests oversold conditions.
Linear Regression Channels: Price at the lower channel line indicates potential support.
5-Minute Scalping Strategy: RSI in oversold territory, and MACD showing a bullish crossover indicates a reversal.
Scenario 3: Volatility Breakouts
Bollinger Bands: Bands contracting indicates low volatility and potential breakout.
Linear Regression Channels: Price moving away from the middle line signals potential breakout direction.
Scalping Strategy: MACD and RSI confirming the breakout direction for entry.
Input Parameters:
Define settings for Bollinger Bands, Linear Regression Channels, and the scalping strategy.
Allow users to customize lengths, multipliers, and colors.
Bollinger Bands Calculation:
Calculate the basis (SMA) and standard deviation.
Derive the upper and lower bands from the basis and standard deviation.
Linear Regression Channel Calculation:
Compute the slope, average, and intercept of the linear regression line.
Calculate deviations to plot upper and lower channel lines.
5-Minute Scalping Strategy:
Calculate RSI, MACD, and SMA for short-term trend analysis.
Define buy and sell conditions based on these indicators.
Plotting and Alerts:
Plot Bollinger Bands and Linear Regression Channels on the chart.
Plot buy and sell signals with shapes.
Set alerts for key conditions like exiting the regression channel bounds and trend switches.
Conclusion
By combining Bollinger Bands, Linear Regression Channels, and a 5-minute scalping strategy, this indicator offers a robust tool for traders. Bollinger Bands provide volatility insights, Linear Regression Channels highlight trend direction and potential reversals, and the scalping strategy offers precise entry and exit points. Together, these tools can enhance a trader's ability to make informed decisions in various market conditions.
Liquidity Grab Zones | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Liquidity Grab Zones Indicator! This indicator finds liquidity grabs in the current ticker and renders buyside & sellside liquidity grab zones. The retests and breakout of the zones are labeled, and you can set up alerts to get notified. For more information, please check the "HOW DOES IT WORK" section.
Features of the new Liquidity Grab Zones Indicator :
Renders Buyside & Sellside Liquidity Grab Zones
Retests & Breaks
Inverse Zones After Broken Feature
Alerts For All Features
Customizable Algorithm
Customizable Styles
🚩UNIQUENESS
Liquidity grabs can be useful when determining candles that have executed a lot of market orders, so you can plann your trades accordingly. This indicator lets you customize the pivot length and the wick-body ratio for liquidity grabs, provide retest & breakout labels, with customized styling and alerts.
📌 HOW DOES IT WORK ?
Liquidity grabs occur when one of the latest pivots has a false breakout. Then, if the wick to body ratio of the bar is higher than 0.5 (can be changed from the settings) a zone is plotted.
These zones usually indicate areas of high market interest where price action may reverse or accelerate. Identifying these zones can provide traders with critical levels for entering or exiting trades. A breakout of these zones generally mean strong movements are inbound, while failing breakouts make these zones act like support / resistance zones.
The indicator also reverses the type of the zone after an invalidation (can be turned off from the settings). This feature helps traders identify potential reversals more accurately.
The zone width is set to the area from the wick to the body of the candlestick, which can be seen here :
⚙️SETTINGS
1. General Configuration
Pivot Length -> This setting determines the range of the pivots. This means a candle has to have the highest / lowest wick of the previous X bars and the next X bars to become a high / low pivot.
Wick-Body Ratio -> After a pivot has a false breakout, the wick-body ratio of the latest candle is tested. The resulting ratio must be higher than this setting for it to be considered as a liquidity grab.
Zone Invalidation -> Select between Wick & Close price for Liquidity Grab Zone Invalidation.
Use these customizable settings to fine-tune the indicator according to your trading strategy and preferences.
Dynamic Price Oscillator (Zeiierman)█ Overview
The Dynamic Price Oscillator (DPO) by Zeiierman is designed to gauge the momentum and volatility of asset prices in trading markets. By integrating elements of traditional oscillators with volatility adjustments and Bollinger Bands, the DPO offers a unique approach to understanding market dynamics. This indicator is particularly useful for identifying overbought and oversold conditions, capturing price trends, and detecting potential reversal points.
█ How It Works
The DPO operates by calculating the difference between the current closing price and a moving average of the closing price, adjusted for volatility using the True Range method. This difference is then smoothed over a user-defined period to create the oscillator. Additionally, Bollinger Bands are applied to the oscillator itself, providing visual cues for volatility and potential breakout signals.
█ How to Use
⚪ Trend Confirmation
The DPO can serve as a confirmation tool for existing trends. Traders might look for the oscillator to maintain above or below its mean line to confirm bullish or bearish trends, respectively. A consistent direction in the oscillator's movement alongside price trend can provide additional confidence in the strength and sustainability of the trend.
⚪ Overbought/Oversold Conditions
With the application of Bollinger Bands directly on the oscillator, the DPO can highlight overbought or oversold conditions in a unique manner. When the oscillator moves outside the Bollinger Bands, it signifies an extreme condition.
⚪ Volatility Breakouts
The width of the Bollinger Bands on the oscillator reflects market volatility. Sudden expansions in the bands can indicate a breakout from a consolidation phase, which traders can use to enter trades in the direction of the breakout. Conversely, a contraction suggests a quieter market, which might be a signal for traders to wait or to look for range-bound strategies.
⚪ Momentum Trading
Momentum traders can use the DPO to spot moments when the market momentum is picking up. A sharp move of the oscillator towards either direction, especially when crossing the Bollinger Bands, can indicate the start of a strong price movement.
⚪ Mean Reversion
The DPO is also useful for mean reversion strategies, especially considering its volatility adjustment feature. When the oscillator touches or breaches the Bollinger Bands, it indicates a deviation from the normal price range. Traders might look for opportunities to enter trades anticipating a reversion to the mean.
⚪ Divergence Trading
Divergences between the oscillator and price action can be a powerful signal for reversals. For instance, if the price makes a new high but the oscillator fails to make a corresponding high, it may indicate weakening momentum and a potential reversal. Traders can use these divergence signals to initiate counter-trend moves.
█ Settings
Length: Determines the lookback period for the oscillator and Bollinger Bands calculation. Increasing this value smooths the oscillator and widens the Bollinger Bands, leading to fewer, more significant signals. Decreasing this value makes the oscillator more sensitive to recent price changes, offering more frequent signals but with increased noise.
Smoothing Factor: Adjusts the degree of smoothing applied to the oscillator's calculation. A higher smoothing factor reduces noise, offering clearer trend identification at the cost of signal timeliness. Conversely, a lower smoothing factor increases the oscillator's responsiveness to price movements, which may be useful for short-term trading but at the risk of false signals.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Liquidity Trendline With Signals [BigBeluga]The Liquidity Trendline is an indicator designed to identify potential breakouts by utilizing pivot points. These pivotal moments can trigger significant market reactions, either by breaking out or by serving as breakout and retest signals.
🔶 FEATURES
The indicator contains the following features:
Period of the calculation
Padding (spacing between the 2 lines)
Signal for breakouts
🔶 USAGE
As shown in the example, breakouts can be powerful points to see reversions in the market and can lead to a lot of volatility in the market.
When a trendline is broken, a signal will be plotted; the user can disable/enable those signals.
A trendline is formed when 2 consecutive pivot points are found, each of them lower or higher than the previous one. this is the anchor point for our trend line that we will use to spot rejection or breakouts
The delay in the creation of those trend lines will be the period input used to find the pivot point on the chart.
Another good example is using these trendlines as simple retests.
Prices bouncing on top of them will suggest a possible continuation of the current trend.
We can filter out stronger breakouts by looking at how many times the price has rejected the trendline, more rejections will result in more liquidity once the price breaks it.
Signals are plotted on the chart for every breakout that happens.
Another good utility is simply using them as retest once the price breaks those levels and holding above/below them, indicating a possible support or resistance area used for confluence
Here is another good example of how we can correctly spot price deviating from our trendline and spotting powerful continuation in price.
As said before we can filter out bad and good breakouts simply by looking at how many times rejected from those levels.
More rejection will result in a stronger reaction
🔶 CONCLUSION
This script is as simple as that and can be used in a few ways to spot reversals, price continuation, or even sentiment in price (bullish or bearish).
Smart Money Concepts (SMC) [LuxAlgo]This all-in-one indicator displays real-time market structure (internal & swing BOS / CHoCH), order blocks, premium & discount zones, equal highs & lows, and much more...allowing traders to automatically mark up their charts with widely used price action methodologies. Following the release of our Fair Value Gap script, we received numerous requests from our community to release more features in the same category.
"Smart Money Concepts" (SMC) is a fairly new yet widely used term amongst price action traders looking to more accurately navigate liquidity & find more optimal points of interest in the market. Trying to determine where institutional market participants have orders placed (buy or sell side liquidity) can be a very reasonable approach to finding more practical entries & exits based on price action.
The indicator includes alerts for the presence of swing structures and many other relevant conditions.
Features
This indicator includes many features relevant to SMC, these are highlighted below:
Full internal & swing market structure labeling in real-time
Break of Structure (BOS)
Change of Character (CHoCH)
Order Blocks (bullish & bearish)
Equal Highs & Lows
Fair Value Gap Detection
Previous Highs & Lows
Premium & Discount Zones as a range
Options to style the indicator to more easily display these concepts
Settings
Mode: Allows the user to select Historical (default) or Present, which displays only recent data on the chart.
Style: Allows the user to select different styling for the entire indicator between Colored (default) and Monochrome.
Color Candles: Plots candles based on the internal & swing structures from within the indicator on the chart.
Internal Structure: Displays the internal structure labels & dashed lines to represent them. (BOS & CHoCH).
Confluence Filter: Filter non-significant internal structure breakouts.
Swing Structure: Displays the swing structure labels & solid lines on the chart (larger BOS & CHoCH labels).
Swing Points: Displays swing points labels on chart such as HH, HL, LH, LL.
Internal Order Blocks: Enables Internal Order Blocks & allows the user to select how many most recent Internal Order Blocks appear on the chart.
Swing Order Blocks: Enables Swing Order Blocks & allows the user to select how many most recent Swing Order Blocks appear on the chart.
Equal Highs & Lows: Displays EQH/EQL labels on chart for detecting equal highs & lows.
Bars Confirmation: Allows the user to select how many bars are needed to confirm an EQH/EQL symbol on chart.
Fair Value Gaps: Displays boxes to highlight imbalance areas on the chart.
Auto Threshold: Filter out non-significant fair value gaps.
Timeframe: Allows the user to select the timeframe for the Fair Value Gap detection.
Extend FVG: Allows the user to choose how many bars to extend the Fair Value Gap boxes on the chart.
Highs & Lows MTF: Allows the user to display previous highs & lows from daily, weekly, & monthly timeframes as significant levels.
Premium/Discount Zones: Allows the user to display Premium, Discount, and Equilibrium zones on the chart
Usage
Users can see automatic CHoCH and BOS labels to highlight breakouts of market structure, which allows to determine the market trend. In the chart below we can see the internal structure which displays more frequent labels within larger structures. We can also see equal highs & lows (EQH/EQL) labels plotted alongside the internal structure to frequently give indications of potential reversals.
In the chart below we can see the swing market structure labels. These are also labeled as BOS and CHoCH but with a solid line & larger text to show larger market structure breakouts & trend reversals. Users can be mindful of these larger structure labels while trading internal structures as displayed in the previous chart.
Order blocks highlight areas where institutional market participants open positions, one can use order blocks to determine confirmation entries or potential targets as we can expect there is a large amount of liquidity at these order blocks. In the chart below we can see 2 potential trade setups with confirmation entries. The path outlined in red would be a potential short entry targeting the blue order block below, and the path outlined in green would be a potential long entry, targeting the red order blocks above.
As we can see in the chart below, the bullish confirmation entry played out in this scenario with the green path outlined in hindsight. As price breaks though the order blocks above, the indicator will consider them mitigated causing them to disappear, and as per the logic of these order blocks they will always display 5 (by default) on the chart so we can now see more actionable levels.
The Smart Money Concepts indicator has many other features and here we can see how they can also help a user find potential levels for price action trading. In the screenshot below we can see a trade setup using the Previous Monthly High, Strong High, and a Swing Order Block as a stop loss. Accompanied by the Premium from the Discount/Premium zones feature being used as a potential entry. A potential take profit level for this trade setup that a user could easily identify would be the 50% mark labeled with the Fair Value Gap & the Equilibrium all displayed automatically by the indicator.
Conclusion
This indicator highlights all relevant components of Smart Money Concepts which can be a very useful interpretation of market structure, liquidity, & more simply put, price action. The term was coined & popularized primarily within the forex community & by ICT while making its way to become a part of many traders' analysis. These concepts, with or without this indicator do not guarantee a trader to be trading within the presence of institutional or "bank-level" liquidity, there is no supporting data regarding the validity of these teachings.
MA total distance on chartNOTE:
The name I used for this indicator was created by me and I’m not sure if it has been used or created by any other trader/creator in the past or not!
Motivation to create:
One of the most important uses of “moving averages” is indicating the trend! There are different ways you can distinguish trend by using moving averages and one of the most popular type of it is comparing closing price to a MA. In this case if close is higher than the MA, trend is bullish and if close is lower than MA, it’s bearish. This method is really useful and I see great results in my long-term back-tests, especially SMA-100 in 1H chart filter so many fake signals in many different indicator-based strategies (Personal experience). There are so many problems with using indicators that sometimes have difficult solutions but one of them is fake breakout!
Looking at the top picture, you’ll get a breakout has happened but trend did not change!
A super bearish trend is obviously visible in the chart and we know a small break out might be a fake one, but what if we have an indicator make conditions of a trend change a little harder?
Introduction:
I was careful about how I used moving averages and I got that I will take not only the last candle close price into consideration, so in these kind of false breakouts I will not fall into trap of them, On the contrary, I find a good opportunity to enter the market opposite of the MA break! (In this case short trade). I calculate the total distance of last 40 candles and divide them to 40, to get the average distance, to each a mathematical score for power of our trend comparing to the MA!
Number are just default you can change them.
In the picture below you can see how well it filtered the false breakout.
As it is obvious, Timeframe, MA length, MA source and MA type are editable.
Since I do not tested this indicator enough (for me enough means more than 5000 trades and 10 years) I can’t suggest any settings as the best one.
The distance length, which means number of candles that their distance to MA is considered in our calculations, the distance source and also smoothing of the MATD is editable too.
And without editing it will look like something like this!
Turtle Trade Channels Indicator TUTCILegendary trade system which proved that great traders can be made, not born.
Turtle Trade Experiment made 80% annual return for 4 years and made 150 million $
Turtle Trade trend following system is a complete opposite to the "buy low and sell high" approach.
This trend following system was taught to a group of average and normal individuals, and almost everyone turned into a profitable trader.
They used the basis logic of well known DONCHIAN CHANNELS which developed by Richard Donchian.
The main rule is "Trade an 20-day breakout and take profits when an 10-day high or low is breached ". Examples:
Buy a 20-day breakout and close the trade when price action reaches a 10-day low.
Go short a 20-day breakout and close the trade when price action reaches a 10-day high.
In this indicator,
The red line is the trading line which indicates the trend directio n:
Price bars over the trend line indicates uptrend
Price bars under the trend line means downtrend
The dotted blue line is the exit line.
Original system is:
Go long when the price High is equal to or above previous 20 day Highest price.
Go short when the price Low is equal to or below previous 20 day Lowest price.
Exit long positions when the price touches the exit line
Exit short positions when the price touches the exit line
Recommended initial stop-loss is ATR * 2 from the opening price.
Default system parameters were 20,10 and 55,20.
Original Turtle Rules:
To trade exactly like the turtles did, you need to set up two indicators representing the main and the failsafe system.
Set up the main indicator with EntryPeriod = 20 and ExitPeriod = 10 (A.k.a S1)
Set up the failsafe indicator with EntryPeriod = 55 and ExitPeriod = 20 using a different color. (A.k.a S2)
The entry strategy using S1 is as follows
Buy 20-day breakouts using S1 only if last signaled trade was a loss.
Sell 20-day breakouts using S1 only if last signaled trade was a loss.
If last signaled trade by S1 was a win, you shouldn't trade -Irregardless of the direction or if you traded last signal it or not-
The entry strategy using S2 is as follows:
Buy 55-day breakouts only if you ignored last S1 signal and the market is rallying without you
Sell 55-day breakouts only if you ignored last S1 signal and the market is pluging without you
You can Highlight the chart with provided trade signals:
Green background color when Long
Red background color when Short
No background color when flat
WARNING: TURTLE TRADE STOP or ADDING more UNITS RULES ARE NOT INCLUDED.
Author: Kıvanç Özbilgiç
Also you can show or hide trade signals with the button on the settings menu
CBG Key Numbers v6Here is my opening range, key numbers indicator. It takes the Opening Range (5 minutes by default) and then plots the opening range and up to 7 extensions of that range above and below.
It's amazing how the OR is stamped up on the rest of the day's price movements.
2 strategies (at least) are to play the OR range breakout and to fade when price hits an extreme range.
You have total control over how you set up the various lines and colors.
If you start overlaying the trading day with the OR and it's extensions, you will see amazing patterns become clear. For example, the pump and reverse. This is where price pumps right out of the opening and then reverses later in the morning.
I have the opening price set to big circles as this is one of the most important reference points during the day.
Important: For some reason, the 9:30 am time Opening acts differently for equities and futures . For equities, you can set the time values to 0930. But for futures , to capture the Open at 9:30, you have to set the time values to start at 0830. I haven't been able to find a better solution but setting the times manually works. Make sure to set all the time values on the Options screen.
There is one more setting of interest. It is called IB Target Amount. This is a number above and below the opening range that I have observed price to hit whenever there's a breakout. This will allow you to predict a price target on breakouts. For SPY , I have found that price usually breaks out to at least 50 cents. On ES futures , it's 6 dollars. This can help you lock in 10% and 20% when trading options and is a great tool. That's why I have it so prominent in red. You will also see price return to this level during the day and act as support or resistance.
Please disregard the red and green shaded rectangles. They are my own support and resistance zones and TV wouldn't let me hide them from the picture. :-)
I mostly use this on a 5 minute chart but any timeframe will work.
Turtle Trade Channels by KıvanÇ fr3762his trend following system was designed by Dennis Gartman and Bill Eckhart, and relies on breakouts of historical highs and lows to take and close trades: it is the complete opposite to the "buy low and sell high" approach. This trend following system was taught to a group of average and normal individuals, and almost everyone turned into a profitable trader.
The main rule is "Trade an N-day breakout and take profits when an M-day high or low is breached (N must me above M)". Examples:
Buy a 10-day breakout and close the trade when price action reaches a 5-day low.
Go short a 20-day breakout and close the trade when price action reaches a 10-day high.
In this indicator, the red line is the trading line, and the dotted blue line is the exit line. Original system is:
Go long when the trading line crosses below close price
Go short when the trading line rosses above close price
Exit long positions when the price touches the exit line
Exit short positions when the price touches the exit line
Recommended initial stop-loss is ATR * 2 from the opening price. Default system parameters were 20,10 and 55,20.
Original Turtle Rules:
To trade exactly like the turtles did, you need to set up two indicators representing the main and the failsafe system.
Set up the main indicator with TradePeriod = 20 and StopPeriod = 10 (A.k.a S1)
Set up the failsafe indicator with TradePeriod = 55 and StopPeriod = 20 using a different color. (A.k.a S2)
The entry strategy using S1 is as follows
Buy 20-day breakouts using S1 only if last signaled trade was a loss.
Sell 20-day breakouts using S1 only if last signaled trade was a loss.
If last signaled trade by S1 was a win, you shouldn't trade -Irregardless of the direction or if you traded last signal it or not-
The entry strategy using S2 is as follows:
Buy 55-day breakouts only if you ignored last S1 signal and the market is rallying without you
Sell 55-day breakouts only if you ignored last S1 signal and the market is pluging without you
The turtles had a progressive position sizing approach that boosted their winnings. Once a trading decision has been made you should...
Developers: Dennis Gartman and Bill Eckhart
İndikatörü geliştiren: Dennis Gartman and Bill Eckhart
Multi-Strategy Trading Screener SummaryI only combined famous scripts, all thanks to wonderful scripts and community out there .
ThankYou !
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Core Architecture
Multi-Symbol Analysis: Tracks up to 5 configurable tickers simultaneously
Multi-Timeframe Support: Each symbol can use different timeframes
Real-Time Dashboard: Color-coded table displaying all signals and analysis
Trend Validation: All signals include trend alignment confirmation
Integrated Trading Strategies
1. Breaker Blocks (Order Blocks)
Detects institutional order blocks using swing analysis
Tracks when blocks are broken and become "breaker blocks"
Monitors retests of broken levels
Shows trend alignment (✓ aligned, ⚠️ misaligned)
2. Chandelier Exit
ATR-based trend-following exit system
Provides BUY/SELL signals based on dynamic stop levels
Uses configurable ATR multiplier and lookback period
3. Smart Money Breakout
Channel breakout detection with volatility normalization
Identifies accumulation/distribution phases
Generates persistent BUY/SELL signals on breakouts
4. Trendline Breakout
Dynamic trendline detection using pivot highs/lows
Calculates trendline slopes and breakout points
Provides BUY signals on upward breaks, SELL on downward breaks
Dashboard Columns Explained
Symbol: Ticker being analyzed
Trend: Overall SuperTrend direction (🟢 UP / 🔴 DOWN / ⚪ FLAT)
Timeframe: Analysis timeframe with clock icon
Breaker Block: Type (Bullish/Bearish) with trend alignment indicator
Status: Price position relative to breaker block (Inside/Approaching/Far)
Retests: Number of times the broken level was retested (indicates level strength)
Volume: Volume associated with the order block formation
Chandelier: BUY/SELL signals from Chandelier Exit strategy
Smart Money: BUY/SELL signals from breakout detection
Trendline: BUY/SELL signals from trendline breakouts
Key Features
No HOLD States: All signals show definitive BUY (🟢) or SELL (🔴) only
Persistent Signals: Signals remain active until opposite conditions trigger
Color Coding: Visual distinction between bullish (green) and bearish (red) signals
Trend Alignment: Enhanced accuracy through trend confirmation logic
This screener provides a comprehensive view of market conditions across multiple strategies, helping identify high-probability trading opportunities when signals align.
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
Extreme Zone Volume ProfileExtreme Zone Volume Profile (EZVP)
Originality & Innovation
The Extreme Zone Volume Profile (EZVP) revolutionizes traditional volume profile analysis by applying statistical zone classification to volume distribution. Unlike standard volume profiles that display raw volume data, EZVP segments the price range into statistically meaningful zones based on percentile thresholds, allowing traders to instantly identify where volume concentration suggests strong support/resistance versus areas of potential breakout.
Technical Methodology
Core Algorithm:
Distributes volume across user-defined bins (20-200) over a lookback period
Calculates volume-weighted price levels for each bin
Applies percentile-based zone classification to the price range (not volume ranking)
Zone B (extreme zones): Outer percentile tails representing potential rejection areas
Zone A (significant zones): Secondary percentile bands indicating strong interest levels
Center Zone: Bulk trading range where most price discovery occurs
Mathematical Foundation:
The script uses price-range percentiles rather than volume percentiles. If the total price range is divided into 100%, Zone B captures the extreme price tails (default 2.5% each end ≈ 2 standard deviations), Zone A captures the next significant bands (default 14% each ≈ 1 standard deviation), leaving the center for normal distribution trading.
Key Calculations:
POC (Point of Control): Price level with maximum volume accumulation
Volume-weighted mean price: Total volume × price / total volume
Median price: Geometric center of the price range
Rightward-projected bars: Volume bars extend forward from current time to avoid historical chart clutter
Trading Applications
Zone Interpretation:
Zone B (Red/Green): Extreme price levels where volume suggests strong rejection potential. Price reaching these zones often indicates overextension and possible reversal points.
Zone A (Orange/Teal): Significant support/resistance areas with substantial volume interest. These levels often act as intermediate targets or consolidation zones.
Center (Gray): Fair value area where most trading occurs. Price tends to return to this range during normal market conditions.
Strategic Usage:
Reversal Trading: Look for rejection signals when price enters Zone B areas
Breakout Confirmation: Volume expansion beyond Zone B boundaries suggests genuine breakouts
Support/Resistance: Zone A boundaries often provide reliable entry/exit levels
Mean Reversion: Price tends to gravitate toward the volume-weighted mean and POC lines
Unique Value Proposition
EZVP addresses three key limitations of traditional volume profiles:
Visual Clarity: Standard profiles can be cluttered and difficult to interpret quickly. EZVP's color-coded zones provide instant visual feedback about price significance.
Statistical Framework: Rather than relying on subjective interpretation of volume nodes, EZVP applies objective percentile-based classification, making support/resistance identification more systematic.
Forward-Looking Display: Rightward-projecting bars keep historical price action clean while maintaining current market structure visibility.
Configuration Guide
Lookback Period (10-1000): Controls the historical depth of volume calculation. Shorter periods for intraday scalping, longer for swing trading.
Number of Bins (20-200): Resolution of volume distribution. Higher values provide more granular analysis but may create noise on lower timeframes.
Zone Percentages:
Zone B: Extreme threshold (default 2.5% = ~2σ statistical significance)
Zone A: Significant threshold (default 14% = ~1σ statistical significance)
Visual Controls: Toggle individual elements (POC, median, mean, zone lines) to customize display complexity for your trading style.
Technical Requirements
Pine Script v6 compatible
Maximum bars back: 5000 (ensures sufficient historical data)
Maximum boxes: 500 (supports high-resolution bin counts)
Maximum lines: 50 (accommodates all zone and reference lines)
This indicator synthesizes volume profile theory with statistical zone analysis, providing a quantitative framework for identifying high-probability support/resistance levels based on volume distribution patterns rather than arbitrary price levels.
Globex Trap w/ percentage [SLICKRICK]Globex Trap w/ Percentage
Overview
The Globex Trap w/ Percentage indicator is a powerful tool designed to help traders identify high-probability trading opportunities by analyzing price action during the Globex (overnight) session and regular trading hours. By combining Globex session ranges with Supply & Demand zones, this indicator highlights potential "trap" areas where significant price reactions may occur. Additionally, it calculates the Globex session range as a percentage of the daily Average True Range (ATR), providing valuable context for assessing market volatility.
This indicator is ideal for traders in futures markets or other instruments traded during Globex sessions, offering a visual and analytical edge for spotting key price levels and potential reversals or breakouts.
Key Features
Globex Session Tracking:
Visualizes the high and low of the Globex session (default: 3:00 PM to 6:30 AM PST) with customizable time settings.
Displays a semi-transparent box to mark the Globex range, with labels for "Globex High" and "Globex Low."
Calculates the Globex range as a percentage of the daily ATR, displayed as a label for quick reference.
Supply & Demand Zones:
Identifies Supply & Demand zones during regular trading hours (default: 6:00 AM to 8:00 AM PST) with customizable time settings.
Draws semi-transparent boxes to highlight these zones, aiding in the identification of key support and resistance areas.
Trap Area Identification:
Highlights potential trap zones where Globex ranges and Supply & Demand zones overlap, indicating areas where price may reverse or consolidate due to trapped traders.
Customizable Settings:
Adjust Globex and Supply & Demand session times to suit your trading preferences.
Toggle visibility of Globex and Supply & Demand zones independently.
Customize box colors for better chart readability.
Set the lookback period (default: 10 days) to control how many historical zones are displayed.
Configure the ATR length (default: 14) for the percentage calculation.
PST Timezone Default:
All times are based on Pacific Standard Time (PST) by default, ensuring accurate session tracking for users in this timezone or those aligning with U.S. West Coast market hours.
Recommended Usage
Timeframes: Best used on 1-hour charts or lower (e.g., 15-minute, 5-minute) for precise entry and exit points.
Markets: Optimized for futures (e.g., ES, NQ, CL) and other instruments traded during Globex sessions.
Historical Data: Ensure at least 10 days of historical data for optimal visualization of zones.
Strategy Integration: Use the indicator to identify potential reversals or breakouts at Globex highs/lows or Supply & Demand zones. The ATR percentage provides context for whether the Globex range is significant relative to typical daily volatility.
How It Works
Globex Session:
Tracks the high and low prices during the user-defined Globex session (default: 3:00 PM to 6:30 AM PST).
When the session ends, a box is drawn from the start to the end of the session, capturing the high and low prices.
Labels are placed at the midpoint of the session, showing "Globex High," "Globex Low," and the range as a percentage of the daily ATR (e.g., "75.23% of Daily ATR").
Supply & Demand Zones:
Tracks the high and low prices during the user-defined regular trading hours (default: 6:00 AM to 8:00 AM PST).
Draws a box to mark these zones, which often act as key support or resistance levels.
ATR Percentage:
Calculates the Globex range (high minus low) and divides it by the daily ATR to express it as a percentage.
This metric helps traders gauge whether the overnight price movement is significant compared to the instrument’s typical volatility.
Time Handling:
Uses PST (UTC-8) for all time calculations, ensuring accurate session timing for users aligning with this timezone.
Properly handles overnight sessions that cross midnight, ensuring seamless tracking.
Input Settings
Globex Session Settings:
Show Globex Session: Enable/disable Globex session visualization (default: true).
Globex Start/End Time: Set the start and end times for the Globex session (default: 3:00 PM to 6:30 AM PST).
Globex Box Color: Customize the color of the Globex session box (default: semi-transparent gray).
Supply & Demand Zone Settings:
Show Supply & Demand Zone: Enable/disable zone visualization (default: true).
Zone Start/End Time: Set the start and end times for Supply & Demand zones (default: 6:00 AM to 8:00 AM PST).
Zone Box Color: Customize the color of the zone box (default: semi-transparent aqua).
General Settings:
Days to Look Back: Number of historical days to display zones (default: 10).
ATR Length: Period for calculating the daily ATR (default: 14).
Notes
All times are in Pacific Standard Time (PST). Adjust the start and end times if your market operates in a different timezone or if you prefer different session windows.
The indicator is optimized for instruments with active Globex sessions, such as futures. Results may vary for non-24/5 markets.
A typo in the label "Globe Low" (should be "Globex Low") will be corrected in future updates.
Ensure your TradingView chart is set to display sufficient historical data to view the full lookback period.
Why Use This Indicator?
The Globex Trap w/ Percentage indicator provides a unique combination of session-based range analysis, Supply & Demand zone identification, and volatility context via the ATR percentage. Whether you’re a day trader, swing trader, or scalper, this tool helps you:
Pinpoint key price levels where institutional traders may act.
Assess the significance of overnight price movements relative to daily volatility.
Identify potential trap zones for high-probability setups.
Customize the indicator to fit your trading style and market preferences.
TBBT (Two Bar Break Through)📘 TBBT (Two Bar Break Through)
Overview
TBBT is a simple breakout indicator that generates signals when the current bar breaks above or below the previous bar’s high/low.
Signals are marked in **green (60% transparent)** on the chart for both buy and sell conditions.
- Buy → Current high > Previous high and candle closes bullish
- Sell → Current low < Previous low and candle closes bearish
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Key Features
1. Straightforward breakout logic
• Detects upward breakouts of the previous bar’s high.
• Detects downward breakouts of the previous bar’s low.
2. Visuals
• Green labels (60% transparency).
• “Buy” label plotted below breakout bars.
• “Sell” label plotted above breakdown bars.
3. Alerts
• Alerts available for both Buy and Sell conditions.
• Custom alert messages included.
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How to Use
• Add TBBT to your chart.
• Watch for “Buy” or “Sell” signals to identify momentum shifts.
• Combine with other filters (trend, volume, higher timeframe analysis) for more reliable setups.
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👉 In short:
**TBBT highlights simple two-bar breakout signals** with clean green markers for both directions.
Polynomial Regression HeatmapPolynomial Regression Heatmap – Advanced Trend & Volatility Visualizer
Overview
The Polynomial Regression Heatmap is a sophisticated trading tool designed for traders who require a clear and precise understanding of market trends and volatility. By applying a second-degree polynomial regression to price data, the indicator generates a smooth trend curve, augmented with adaptive volatility bands and a dynamic heatmap. This framework allows users to instantly recognize trend direction, potential reversals, and areas of market strength or weakness, translating complex price action into a visually intuitive map.
Unlike static trend indicators, the Polynomial Regression Heatmap adapts to changing market conditions. Its visual design—including color-coded candles, regression bands, optional polynomial channels, and breakout markers—ensures that price behavior is easy to interpret. This makes it suitable for scalping, swing trading, and longer-term strategies across multiple asset classes.
How It Works
The core of the indicator relies on fitting a second-degree polynomial to a defined lookback period of price data. This regression curve captures the non-linear nature of market movements, revealing the true trajectory of price beyond the distortions of noise or short-term volatility.
Adaptive upper and lower bands are constructed using ATR-based scaling, surrounding the regression line to reflect periods of high and low volatility. When price moves toward or beyond these bands, it signals areas of potential overextension or support/resistance.
The heatmap colors each candle based on its relative position within the bands. Green shades indicate proximity to the upper band, red shades indicate proximity to the lower band, and neutral tones represent mid-range positioning. This continuous gradient visualization provides immediate feedback on trend strength, market balance, and potential turning points.
Optional polynomial channels can be overlaid around the regression curve. These three-line channels are based on regression residuals and a fixed width multiplier, offering additional reference points for analyzing price deviations, trend continuation, and reversion zones.
Signals and Breakouts
The Polynomial Regression Heatmap includes statistical pivot-based signals to highlight actionable price movements:
Buy Signals – A triangular marker appears below the candle when a pivot low occurs below the lower regression band.
Sell Signals – A triangular marker appears above the candle when a pivot high occurs above the upper regression band.
These markers identify significant deviations from the regression curve while accounting for volatility, providing high-quality visual cues for potential entry points.
The indicator ensures clarity by spacing markers vertically using ATR-based calculations, preventing overlap during periods of high volatility. Users can rely on these signals in combination with heatmap intensity and regression slope for contextual confirmation.
Interpretation
Trend Analysis :
The slope of the polynomial regression line represents trend direction. A rising curve indicates bullish bias, a falling curve indicates bearish bias, and a flat curve indicates consolidation.
Steeper slopes suggest stronger momentum, while gradual slopes indicate more moderate trend conditions.
Volatility Assessment :
Band width provides an instant visual measure of market volatility. Narrow bands correspond to low volatility and potential consolidation, whereas wide bands indicate higher volatility and significant price swings.
Heatmap Coloring :
Candle colors visually represent price position within the bands. This allows traders to quickly identify zones of bullish or bearish pressure without performing complex calculations.
Channel Analysis (Optional) :
The polynomial channel defines zones for evaluating potential overextensions or retracements. Price interacting with these lines may suggest areas where mean-reversion or trend continuation is likely.
Breakout Signals :
Buy and Sell markers highlight pivot points relative to the regression and volatility bands. These are statistical signals, not arbitrary triggers, and should be interpreted in context with trend slope, band width, and heatmap intensity.
Strategy Integration
The Polynomial Regression Heatmap supports multiple trading approaches:
Trend Following – Enter trades in the direction of the regression slope while using the heatmap for momentum confirmation.
Pullback Entries – Use breakouts or deviations from the regression bands as low-risk entry points during trend continuation.
Mean Reversion – Price reaching outer channel boundaries can indicate potential reversal or retracement opportunities.
Multi-Timeframe Alignment – Overlay on higher and lower timeframes to filter noise and improve entry timing.
Stop-loss levels can be set just beyond the opposing regression band, while take-profit targets can be informed by the distance between the bands or the curvature of the polynomial line.
Advanced Techniques
For traders seeking greater precision:
Combine the Polynomial Regression Heatmap with volume, momentum, or volatility indicators to validate signals.
Observe the width and slope of the regression bands over time to anticipate expanding or contracting volatility.
Track sequences of breakout signals in conjunction with heatmap intensity for systematic trade management.
Adjusting regression length allows customization for different assets or timeframes, balancing responsiveness and smoothing. The combination of polynomial curve, adaptive bands, heatmap, and optional channels provides a comprehensive statistical framework for informed decision-making.
Inputs and Customization
Regression Length – Determines the number of bars used for polynomial fitting. Shorter lengths increase responsiveness; longer lengths improve smoothing.
Show Bands – Toggle visibility of the ATR-based regression bands.
Show Channel – Enable or disable the polynomial channel overlay.
Color Settings – Customize bullish, bearish, neutral, and accent colors for clarity and visual preference.
All other internal parameters are fixed to ensure consistent statistical behavior and minimize potential misconfiguration.
Why Use Polynomial Regression Heatmap
The Polynomial Regression Heatmap transforms complex price action into a clear, actionable visual framework. By combining non-linear trend mapping, adaptive volatility bands, heatmap visualization, and breakout signals, it provides a multi-dimensional perspective that is both quantitative and intuitive.
This indicator allows traders to focus on execution, interpret market structure at a glance, and evaluate trend strength, overextensions, and potential reversals in real time. Its design is compatible with scalping, swing trading, and long-term strategies, providing a robust tool for disciplined, data-driven trading.
Ludvig Indicator PROThe Ludvig Indicator is designed to identify high-probability breakout setups by combining trend, volume, volatility, and relative strength filters. It helps you enter stocks (or ETFs/crypto) when institutional money is likely flowing in, while avoiding false breakouts and weak trends.
🔑 Core Features
Zero-Lag EMA (ZLEMA)
Faster, less lagging trend detection compared to traditional EMAs.
Used as the basis for dynamic ATR bands.
ATR Volatility Bands
Adaptive bands based on the Average True Range (ATR).
Define the zone where price must close outside to confirm trend strength.
Breakout Confirmation
Requires price to close above recent highs (lookback configurable).
Ensures signals are “true breakouts,” not just noise around moving averages.
Volume Filter (Relative Volume)
Validates breakouts with significantly higher volume than average.
Prevents low-liquidity signals from triggering.
Trend Strength (ADX)
Built-in ADX calculation ensures only strong, trending moves are considered.
Default filter: ADX ≥ 18 (configurable).
Relative Strength vs. Benchmark
Compares the asset’s momentum against a benchmark (default: SPY).
Only signals when the asset is outperforming the benchmark.
Useful for sector rotation and picking leaders instead of laggards.
Alerts & Signals
Breakout entries are marked with small green triangles.
Built-in alerts for automated notifications (TradingView alerts).
EMA21/SMA21 + ATR Bands SuiteThe EMA/SMA + ATR Bands Suite is a powerful technical overlay built around one of the most universally respected zones in trading: the 21-period moving average. By combining both the EMA21 and SMA21 into a unified framework, this tool defines the short-term mean with greater clarity and reliability, offering a more complete picture of trend structure, directional bias, and price equilibrium. These two moving averages serve as the central anchor — and from them, the script dynamically calculates adaptive ATR bands that expand and contract with market volatility. Whether you trade breakouts, pullbacks, or reversion setups, the 21 midline combined with ATR extensions offers a powerful lens for real-time market interpretation — adaptable to any timeframe or asset.
🔍 What's Inside?
✅ EMA21 + SMA21 Full Plots and Reduced-History Segments using arrays:
Enable full plots or segmented lines for the most recent candles only with automatic color coding. The reduced-history plots are perfect for reducing clutter on your chart.
✅ ATR Bands (2.5x & 5x):
Adaptive ATR-based volatility envelopes plotted around the midline (EMA21 + SMA21) to indicate:
🔸Potential reversion zones.
🔸Trend continuation breakouts.
🔸Dynamic support/resistance levels.
🔸 Expanding or contracting volatility states
🔸 Trend-aware color changes — yellow when both bands are rising, purple when falling, and gray when direction is mixed
✅ Dual MA Fills (EMA21/SMA21):
Visually track when short-term momentum shifts using a fill between EMA21 and SMA21
✅ EMA5 & EMA200 Labels:
Display anchored labels with rounded values + % difference from price, helping you track short-term + macro trends in real-time.
✅ Intelligent Bar Coloring
Bars are automatically colored based on both price direction and position relative to the EMA/SMA. This provides instant visual feedback on trend strength and structural alignment — no need to second-guess the market tone.
✅ Dynamic Close Line Tools:
Track recent price action with flexible close-following lines
✅ RSI Overlay on Candles:
Optional RSI + RSI SMA displayed above the current bar, with automatic color logic.
🎯 Use Cases
➖Trend Traders can identify when price is stacked bullishly across moving averages and breaking above ATR zones.
➖Mean Reversion Traders can fade extremes at 2.5x or 5x ATR zones.
➖Scalpers get immediate trend insight from colored bar overlays and close-following lines.
➖Swing Traders can combine multi-timeframe EMAs with volatility thresholds for higher confluence.
📌 Final Note:
As powerful as this script can be, no single indicator should be used in isolation. For best results, combine it with price action analysis, higher-timeframe context, and complementary tools like trendlines, moving averages, or support/resistance levels. Use it as part of a well-rounded trading approach to confirm setups — not to define them alone.
VSA - The Volume HUDVSA Volume HUD: Your At-a-Glance Volume Dashboard
Tired of cluttered charts with multiple indicators taking up screen space?
The VSA Volume HUD is a clean, powerful, and fully customisable Heads-Up Display that puts all the critical volume and price action data you need into one compact box, right on your chart.
Designed for traders who rely on Volume Spread Analysis (VSA), this tool helps you instantly gauge the strength, conviction, and context behind every price move as it happens.
Key Features
This indicator isn't just about showing the current volume; it provides a comprehensive, real-time analysis of the market's activity.
Real-time VSA Dashboard: A persistent on-screen table that updates with every tick, giving you instant feedback without needing to look away from the price. The HUD is fully draggable (hold Ctrl/Cmd + click and drag) to place it anywhere you like.
Essential Volume Metrics:
Current Volume: Displayed in a clean, abbreviated format (e.g., 1.25M for millions, 54.3K for thousands).
% Change (vs. Previous Bar): Instantly see if volume is expanding or contracting.
Vs Short-Term Average: Compare the current bar's volume to a moving average to spot unusual spikes.
Volume Velocity: Measures the rate of change in volume over a short period, helping you spot acceleration or deceleration in market interest.
Relative Volume (RVOL): See how the current volume compares to the average for that specific time of day, perfect for identifying abnormally high or low activity.
Price Action & Volatility Context:
Range vs. ATR: Quickly determine if the current bar's volatility is expanding or contracting compared to the recent average.
Price vs. VWAP: See how far the current price has deviated from the session's Volume-Weighted Average Price, a key level for institutional traders.
Deep Customization is Key
Tailor the HUD to perfectly match your trading style and chart aesthetic.
Display & Layout:
Compact Mode: Remove the metric labels for a sleek, minimalist view that saves screen space.
Bar Meters: Enable optional visual bars next to key metrics for a quick, graphical representation of strength.
Total Control: Toggle every single metric on or off to build the exact dashboard you need. Adjust text size, position, and background opacity with ease.
Smart Coloring & Visual Alerts:
Advanced VSA Coloring: This isn't just about up/down candles. The script intelligently colors volume based on confluence. It highlights increasing volume on a strong up-bar (bullish confirmation) or increasing volume on a down-bar (potential climax or distribution), giving you a deeper VSA context.
High Volume Highlight: Make standout bars impossible to miss! The entire HUD background can change color automatically when volume surges past a custom threshold (e.g., over 150% of the average), instantly drawing your attention to critical moments.
Full Color Customization: Change every color to match your chart's theme, including separate colors for bullish/bearish moves, the background, and the border.
How to Use It
The VSA Volume HUD is a powerful confirmation tool. Use it to:
Confirm Breakouts: Look for a spike in Volume vs. Average and RVOL as price breaks a key level.
Spot Exhaustion: Notice high volume on a narrow-range candle after a long trend, visible through the Range/ATR metric.
Gauge Conviction: Use the Advanced Coloring to see if volume is supporting the price move (e.g., green volume on a green candle) or diverging from it.
