Clenow MomentumClenow Momentum Method
The Clenow Momentum Method, developed by Andreas Clenow, is a systematic, quantitative trading strategy focused on capturing medium- to long-term price trends in financial markets. Popularized through Clenow’s book, Stocks on the Move: Beating the Market with Hedge Fund Momentum Strategies, the method leverages momentum—an empirically observed phenomenon where assets that have performed well in the recent past tend to continue performing well in the near future.
Theoretical Foundation
Momentum investing is grounded in behavioral finance and market inefficiencies. Investors often exhibit herding behavior, underreact to new information, or chase trends, causing prices to trend beyond fundamental values. Clenow’s method builds on academic research, such as Jegadeesh and Titman (1993), which demonstrated that stocks with high returns over 3–12 months outperform those with low returns over similar periods.
Clenow’s approach specifically uses **annualized momentum**, calculated as the rate of return over a lookback period (typically 90 days), annualized to reflect a yearly percentage. The formula is:
Momentum=(((Close N periods agoCurrent Close)^N252)−1)×100
- Current Close: The most recent closing price.
- Close N periods ago: The closing price N periods back (e.g., 90 days).
- N: Lookback period (commonly 90 days).
- 252: Approximate trading days in a year for annualization.
This metric ranks stocks by their momentum, prioritizing those with the strongest upward trends. Clenow’s method also incorporates risk management, diversification, and volatility adjustments to enhance robustness.
Methodology
The Clenow Momentum Method involves the following steps:
1. Universe Selection:
- A broad universe of liquid stocks is chosen, often from major indices (e.g., S&P 500, Nasdaq 100) or global exchanges.
- Filters should exclude illiquid stocks (e.g., low average daily volume) or those with extreme volatility.
2. Momentum Calculation:
- Stocks are ranked based on their annualized momentum over a lookback period (typically 90 days, though 60–120 days can be common tests).
- The top-ranked stocks (e.g., top 10–20%) are selected for the portfolio.
3. Volatility Adjustment (Optional):
- Clenow sometimes adjusts momentum scores by volatility (e.g., dividing by the standard deviation of returns) to favor stocks with smoother trends.
- This reduces exposure to erratic price movements.
4. Portfolio Construction:
- A diversified portfolio of 10–25 stocks is constructed, with equal or volatility-weighted allocations.
- Position sizes are often adjusted based on risk (e.g., 1% of capital per position).
5. Rebalancing:
- The portfolio is rebalanced periodically (e.g., weekly or monthly) to maintain exposure to high-momentum stocks.
- Stocks falling below a momentum threshold are replaced with higher-ranked candidates.
6. Risk Management:
- Stop-losses or trailing stops may be applied to limit downside risk.
- Diversification across sectors reduces concentration risk.
Implementation in TradingView
Key features include:
- Customizable Lookback: Users can adjust the lookback period in pinescript (e.g., 90 days) to align with Clenow’s methodology.
- Visual Cues: Background colors (green for positive, red for negative momentum) and a zero line help identify trend strength.
- Integration with Screeners: TradingView’s stock screener can filter high-momentum stocks, which can then be analyzed with the custom indicator.
Strengths
1. Simplicity: The method is straightforward, relying on a single metric (momentum) that’s easy to calculate and interpret.
2. Empirical Support: Backed by decades of academic research and real-world hedge fund performance.
3. Adaptability: Applicable to stocks, ETFs, or other asset classes, with flexible lookback periods.
4. Risk Management: Diversification and periodic rebalancing reduce idiosyncratic risk.
5. TradingView Integration: Pine Script implementation enables real-time visualization, enhancing decision-making for stocks like NVDA or SPY.
Limitations
1. Mean Reversion Risk: Momentum can reverse sharply in bear markets or during sector rotations, leading to drawdowns.
2. Transaction Costs: Frequent rebalancing increases trading costs, especially for retail traders with high commissions. This is not as prevalent with commission free trading becoming more available.
3. Overfitting Risk: Over-optimizing lookback periods or filters can reduce out-of-sample performance.
4. Market Conditions: Underperforms in low-momentum or highly volatile markets.
Practical Applications
The Clenow Momentum Method is ideal for:
Retail Traders: Use TradingView’s screener to identify high-momentum stocks, then apply the Pine Script indicator to confirm trends.
Portfolio Managers: Build diversified momentum portfolios, rebalancing monthly to capture trends.
Swing Traders: Combine with volume filters to target short-term breakouts in high-momentum stocks.
Cross-Platform Workflow: Integrate with Python scanners to rank stocks, then visualize on TradingView for trade execution.
Comparison to Other Strategies
Vs. Minervini’s VCP: Clenow’s method is purely quantitative, while Minervini’s Volatility Contraction Pattern (your April 11, 2025 query) combines momentum with chart patterns. Clenow is more systematic but less discretionary.
Vs. Mean Reversion: Momentum bets on trend continuation, unlike mean reversion strategies that target oversold conditions.
Vs. Value Investing: Momentum outperforms in bull markets but may lag value strategies in recovery phases.
Conclusion
The Clenow Momentum Method is a robust, evidence-based strategy that capitalizes on price trends while managing risk through diversification and rebalancing. Its simplicity and adaptability make it accessible to retail traders, especially when implemented on platforms like TradingView with custom Pine Script indicators. Traders must be mindful of transaction costs, mean reversion risks, and market conditions. By combining Clenow’s momentum with volume filters and alerts, you can optimize its application for swing or position trading.
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Bullish and Bearish Breakout Alert for Gold Futures PullbackBelow is a Pine Script (version 6) for TradingView that includes both bullish and bearish breakout conditions for my intraday trading strategy on micro gold futures (MGC). The strategy focuses on scalping two-legged pullbacks to the 20 EMA or key levels with breakout confirmation, tailored for the Apex Trader Funding $300K challenge. The script accounts for the Daily Sentiment Index (DSI) at 87 (overbought, favoring pullbacks). It generates alerts for placing stop-limit orders for 175 MGC contracts, ensuring compliance with Apex’s rules ($7,500 trailing threshold, $20,000 profit target, 4:59 PM ET close).
Script Requirements
Version: Pine Script v6 (latest for TradingView, April 2025).
Purpose:
Bullish: Alert when price breaks above a rejection candle’s high after a two-legged pullback to the 20 EMA in a bullish trend (price above 20 EMA, VWAP, higher highs/lows).
Bearish: Alert when price breaks below a rejection candle’s low after a two-legged pullback to the 20 EMA in a bearish trend (price below 20 EMA, VWAP, lower highs/lows).
Context: 5-minute MGC chart, U.S. session (8:30 AM–12:00 PM ET), avoiding overbought breakouts above $3,450 (DSI 87).
Output: Alerts for stop-limit orders (e.g., “Buy: Stop=$3,377, Limit=$3,377.10” or “Sell: Stop=$3,447, Limit=$3,446.90”), quantity 175 MGC.
Apex Compliance: 175-contract limit, stop-losses, one-directional news trading, close by 4:59 PM ET.
How to Use the Script in TradingView
1. Add Script:
Open TradingView (tradingview.com).
Go to “Pine Editor” (bottom panel).
Copy the script from the content.
Click “Add to Chart” to apply to your MGC 5-minute chart .
2. Configure Chart:
Symbol: MGC (Micro Gold Futures, CME, via Tradovate/Apex data feed).
Timeframe: 5-minute (entries), 15-minute (trend confirmation, manually check).
Indicators: Script plots 20 EMA and VWAP; add RSI (14) and volume manually if needed .
3. Set Alerts:
Click the “Alert” icon (bell).
Add two alerts:
Bullish Breakout: Condition = “Bullish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Bearish Breakout: Condition = “Bearish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Customize messages (default provided) and set notifications (e.g., TradingView app, SMS).
Example: Bullish alert at $3,377 prompts “Stop=$3,377, Limit=$3,377.10, Quantity=175 MGC” .
4. Execute Orders:
Bullish:
Alert triggers (e.g., stop $3,377, limit $3,377.10).
In TradingView’s “Order Panel,” select “Stop-Limit,” set:
Stop Price: $3,377.
Limit Price: $3,377.10.
Quantity: 175 MGC.
Direction: Buy.
Confirm via Tradovate.
Add bracket order (OCO):
Stop-loss: Sell 175 at $3,376.20 (8 ticks, $1,400 risk).
Take-profit: Sell 87 at $3,378 (1:1), 88 at $3,379 (2:1) .
Bearish:
Alert triggers (e.g., stop $3,447, limit $3,446.90).
Select “Stop-Limit,” set:
Stop Price: $3,447.
Limit Price: $3,446.90.
Quantity: 175 MGC.
Direction: Sell.
Confirm via Tradovate.
Add bracket order:
Stop-loss: Buy 175 at $3,447.80 (8 ticks, $1,400 risk).
Take-profit: Buy 87 at $3,446 (1:1), 88 at $3,445 (2:1) .
5. Monitor:
Green triangles (bullish) or red triangles (bearish) confirm signals.
Avoid bullish entries above $3,450 (DSI 87, overbought) or bearish entries below $3,296 (support) .
Close trades by 4:59 PM ET (set 4:50 PM alert) .
Momentum + Keltner Stochastic Combo)The Momentum-Keltner-Stochastic Combination Strategy: A Technical Analysis and Empirical Validation
This study presents an advanced algorithmic trading strategy that implements a hybrid approach between momentum-based price dynamics and relative positioning within a volatility-adjusted Keltner Channel framework. The strategy utilizes an innovative "Keltner Stochastic" concept as its primary decision-making factor for market entries and exits, while implementing a dynamic capital allocation model with risk-based stop-loss mechanisms. Empirical testing demonstrates the strategy's potential for generating alpha in various market conditions through the combination of trend-following momentum principles and mean-reversion elements within defined volatility thresholds.
1. Introduction
Financial market trading increasingly relies on the integration of various technical indicators for identifying optimal trading opportunities (Lo et al., 2000). While individual indicators are often compromised by market noise, combinations of complementary approaches have shown superior performance in detecting significant market movements (Murphy, 1999; Kaufman, 2013). This research introduces a novel algorithmic strategy that synthesizes momentum principles with volatility-adjusted envelope analysis through Keltner Channels.
2. Theoretical Foundation
2.1 Momentum Component
The momentum component of the strategy builds upon the seminal work of Jegadeesh and Titman (1993), who demonstrated that stocks which performed well (poorly) over a 3 to 12-month period continue to perform well (poorly) over subsequent months. As Moskowitz et al. (2012) further established, this time-series momentum effect persists across various asset classes and time frames. The present strategy implements a short-term momentum lookback period (7 bars) to identify the prevailing price direction, consistent with findings by Chan et al. (2000) that shorter-term momentum signals can be effective in algorithmic trading systems.
2.2 Keltner Channels
Keltner Channels, as formalized by Chester Keltner (1960) and later modified by Linda Bradford Raschke, represent a volatility-based envelope system that plots bands at a specified distance from a central exponential moving average (Keltner, 1960; Raschke & Connors, 1996). Unlike traditional Bollinger Bands that use standard deviation, Keltner Channels typically employ Average True Range (ATR) to establish the bands' distance from the central line, providing a smoother volatility measure as established by Wilder (1978).
2.3 Stochastic Oscillator Principles
The strategy incorporates a modified stochastic oscillator approach, conceptually similar to Lane's Stochastic (Lane, 1984), but applied to a price's position within Keltner Channels rather than standard price ranges. This creates what we term "Keltner Stochastic," measuring the relative position of price within the volatility-adjusted channel as a percentage value.
3. Strategy Methodology
3.1 Entry and Exit Conditions
The strategy employs a contrarian approach within the channel framework:
Long Entry Condition:
Close price > Close price periods ago (momentum filter)
KeltnerStochastic < threshold (oversold within channel)
Short Entry Condition:
Close price < Close price periods ago (momentum filter)
KeltnerStochastic > threshold (overbought within channel)
Exit Conditions:
Exit long positions when KeltnerStochastic > threshold
Exit short positions when KeltnerStochastic < threshold
This methodology aligns with research by Brock et al. (1992) on the effectiveness of trading range breakouts with confirmation filters.
3.2 Risk Management
Stop-loss mechanisms are implemented using fixed price movements (1185 index points), providing definitive risk boundaries per trade. This approach is consistent with findings by Sweeney (1988) that fixed stop-loss systems can enhance risk-adjusted returns when properly calibrated.
3.3 Dynamic Position Sizing
The strategy implements an equity-based position sizing algorithm that increases or decreases contract size based on cumulative performance:
$ContractSize = \min(baseContracts + \lfloor\frac{\max(profitLoss, 0)}{equityStep}\rfloor - \lfloor\frac{|\min(profitLoss, 0)|}{equityStep}\rfloor, maxContracts)$
This adaptive approach follows modern portfolio theory principles (Markowitz, 1952) and Kelly criterion concepts (Kelly, 1956), scaling exposure proportionally to account equity.
4. Empirical Performance Analysis
Using historical data across multiple market regimes, the strategy demonstrates several key performance characteristics:
Enhanced performance during trending markets with moderate volatility
Reduced drawdowns during choppy market conditions through the dual-filter approach
Optimal performance when the threshold parameter is calibrated to market-specific characteristics (Pardo, 2008)
5. Strategy Limitations and Future Research
While effective in many market conditions, this strategy faces challenges during:
Rapid volatility expansion events where stop-loss mechanisms may be inadequate
Prolonged sideways markets with insufficient momentum
Markets with structural changes in volatility profiles
Future research should explore:
Adaptive threshold parameters based on regime detection
Integration with additional confirmatory indicators
Machine learning approaches to optimize parameter selection across different market environments (Cavalcante et al., 2016)
References
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (2000). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.
Kaufman, P. J. (2013). Trading systems and methods (5th ed.). John Wiley & Sons.
Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35(4), 917-926.
Keltner, C. W. (1960). How to make money in commodities. The Keltner Statistical Service.
Lane, G. C. (1984). Lane's stochastics. Technical Analysis of Stocks & Commodities, 2(3), 87-90.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Pardo, R. (2008). The evaluation and optimization of trading strategies (2nd ed.). John Wiley & Sons.
Raschke, L. B., & Connors, L. A. (1996). Street smarts: High probability short-term trading strategies. M. Gordon Publishing Group.
Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285-300.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
MACD Bullish Cross Alert📘 Indicator Description – MACD Bullish Cross Alert
This indicator is designed to detect bullish momentum shifts using the classic MACD (Moving Average Convergence Divergence) crossover strategy.
Key Features:
Calculates the MACD Line and Signal Line using customizable inputs (default: 12, 26, 9).
Triggers an alert when the MACD Line (blue) crosses above the Signal Line (orange).
Helps identify early bullish trend reversals or momentum entry points.
Ideal for swing traders, position traders, and crypto investors using the weekly timeframe.
How to Use:
Add to any chart and set the timeframe to 1W (weekly).
Create an alert using the built-in MACD Bullish Crossover condition.
Combine with price action, volume, or RSI for higher conviction entries.
Use Cases:
Spotting early entry points after long downtrends.
Confirming a trend reversal in high timeframes.
Generating high-probability entries in trend-following systems.
MACD [AlchimistOfCrypto]🌠 MACD Optimized with Python – Decoding the Chaos of Markets 🌠
Category: Trend Analysis 📈
"Like the dynamic systems studied in chaos theory, financial markets appear unpredictable at first glance. Yet, as Edward Lorenz demonstrated, even in apparent chaos reside harmonious mathematical structures. The MACD (Moving Average Convergence Divergence) represents this quest for order within disorder—a mathematical formulation that extracts coherent signals from price noise. By combining moving averages of different periods, this indicator reveals hidden cycles and precise moments when market energy shifts, like a pendulum obeying the immutable laws of physics."
📊 Technical Overview
The MACD Optimized with Python is a revolutionary take on the classic Moving Average Convergence Divergence indicator. Powered by Python-driven optimizations 🐍, it adapts to specific timeframes, delivering razor-sharp signals for traders seeking to navigate the market’s chaos with precision.
⚙️ How It Works
- Python-Optimized Parameters 🔧: Unlike the standard MACD (12,26,9), our version uses mathematically tailored parameters for each timeframe:
- 1H: 11/38/27
- 4H: 9/98/27
- 1D: 45/90/29
- 1W: 9/16/3
- 2W: 5/20/5
- Intuitive Visuals 🎨:
- Crossovers marked by colored dots 🟢🔴 for clear entry/exit signals.
- Histogram with a color gradient 🌈 to show direction and momentum intensity.
- Customizable Signals 🎯: Choose to display long, short, or both signals to match your trading style.
🚀 How to Use This Indicator
1. Select Your Timeframe ⏰: Choose the timeframe aligned with your trading horizon (1H, 4H, 1D, 1W, or 2W).
2. Spot Crossovers 🔍: Watch for the MACD line (green) crossing the signal line (red) to identify potential trend changes.
3. Confirm with Divergence ✅: Combine crossovers with price-MACD divergence for high-probability trend reversal signals.
📅 Release Notes
Unlock the hidden order of markets with this Python-optimized MACD. Stay tuned for future enhancements! ✨
🏷️ Tags
#Trading #TechnicalAnalysis #MACD #TrendAnalysis #Python #MultiTimeframe #Divergence #Momentum #TradingStrategy #RiskManagement #Forex #Stocks #Crypto #ChaosTheory #OptimizedTrading
Change of Character FanChange of Character Fan
Overview
The Change of Character Fan is designed to help traders detect shifts (changes of character) in market direction and sentiment before they become fully visible through traditional candlestick analysis. Instead of relying solely on the shape or close of candlesticks, this indicator offers a direct, real-time look at the internal price action occurring within a single bar. This visibility into intrabar dynamics can potentially allow traders to enter or exit trades earlier, minimize false signals, and reduce their dependence on multiple lower-timeframe charts.
How it Works:
The indicator plots a "fan" consisting of five distinct slope lines within the current bar. Each line represents the internal trend of price movement based on user-defined lower timeframe data intervals.
By default, these intervals are set to 3, 5, 8, 13, and 21 samples from 1-second timeframe data.
Each line only appears when it has collected the minimum required number of intrabar data points.
The fan lines use a progressive opacity scale (lighter to darker), visually highlighting the confidence level or probability of directional continuation within the current bar.
At the open of every new bar, the fan disappears completely and gradually reappears as new data is gathered, ensuring clarity and eliminating outdated signals.
Understanding the Mathematics: Linear Regression Model
This indicator is built around the concept of a linear regression model. Linear regression is a statistical technique used to model and analyze relationships between variables—in this case, time (independent variable) and price (dependent variable).
How Linear Regression Works:
Linear regression fits a straight line (called a "line of best fit") through a set of data points, minimizing the overall distance between each point and the line itself.
Mathematically, this is achieved by minimizing the squared differences (errors) between the observed values (actual prices) and the predicted values (prices on the line).
The linear model used here can be expressed in the form:
y = mx + b
where:
𝑦
y is the predicted price,
𝑥
x represents time (each data sample interval),
𝑚
m is the slope of the line, representing the direction and velocity of the trend,
𝑏
b is the intercept (the theoretical price when x=0).
Why a Linear Model is Beneficial in this Indicator:
Simplicity and Reliability: Linear regression is simple, robust, and widely accepted as a baseline predictive model. It requires minimal computational resources, providing instant updates in real-time trading conditions.
Immediate Directional Feedback: The slope derived from linear regression immediately communicates the directional tendency of recent price action. A positive slope indicates upward pressure, and a negative slope signals downward pressure.
Noise Reduction: Even when price fluctuations are noisy or erratic, linear regression summarizes overall direction clearly, making it easier to detect genuine directional shifts (change of character) rather than random price noise.
Intrabar Analysis: Traditional candlestick analysis relies on fully formed candles, potentially delaying signals. By using linear regression on very short-term (intrabar) data, traders can detect shifts in momentum more quickly, providing an earlier signal than conventional candle patterns alone.
Practical Application:
This indicator helps traders to visually identify:
Early Trend Reversals: Intrabar analysis reveals momentum shifts potentially signaling reversals before they become obvious on conventional candles.
Momentum Continuations: Confidence is gained when all lines in the fan are clearly pointing in the same direction, indicating strong intrabar conviction.
Reduced False Signals: Traditional candlestick signals (e.g., hammer candles) sometimes produce false signals due to intrabar noise. By looking directly into intrabar dynamics, traders gain better context on whether candle patterns reflect genuine directional change or merely noise.
Important Requirements and Recommendations:
Subscription Requirements:
A TradingView subscription that supports sub-minute data (e.g., 1-second or 5-second resolution) is strongly recommended.
If your subscription doesn't include this data granularity, you must use a 1-minute lower timeframe, significantly reducing responsiveness. In this scenario, it's best suited for a 15-minute or higher chart, adjusting intervals to shorter periods.
Live Data Essential:
Real-time market data subscription is essential for the accuracy and effectiveness of this indicator.
Using delayed data reduces responsiveness and weakens the indicator's primary advantage.
Recommended Settings for Different Chart Timeframes:
1-minute chart: Use 1-second lower timeframe intervals (default intervals: 3, 5, 8, 13, 21).
5-minute chart: Adjust to a 5- or 10-second lower timeframe, possibly reducing intervals to shorter periods (e.g., 3, 5, 8, 10, 12).
15-minute or higher charts: Adjust lower timeframe to 1-minute if granular data is unavailable, with reduced interval lengths to maintain responsiveness.
Conclusion:
The Change of Character Fan empowers traders with early insight into directional shifts within each candle, significantly enhancing reaction speed, signal accuracy, and reducing dependency on multiple charts. Built on robust linear regression mathematics, it combines clarity, responsiveness, and ease-of-use in a powerful intrabar analysis tool.
Trade smarter, see sooner, and react faster.
Multi Timeframe Altered Money Flow Index by CoffeeShopCryptoMoney Flow Index is a long used tool in trading markets, understanding to where money is moving and most importantly when its going there.
One of the biggest challenges was the when part. Because seeing it on your current trading chart timeframe is easy but it gets difficult if youre attempting a top-down-analysis of market structure vs price performance.
The new formula presented by @CoffeeshopCrypto is a key solution to this timeframe analysis issue. Seems like I may have solved the "glitch-In-The-Matrix".
The issue was always setting a secondary MFI on your chart and telling the system you wanted to watch the 1 hour MFI from a 5 minute chart.
To do this you need to wait for 12 candles to close on your 5 minute chart before you can get a 1hour MFI value. The move may have already happend and you may be too late. If there was only a better faster way to see the changing values of the High Timeframe Money Flow Index in real time without changing chart times and losing place......oh wait.....there is one now!
This tool allows you to tell it what timeframe you are looking at,
and what you want to compare it to.
It runs the calculation in the background automatically to give you the real time values of your High Timeframe chart setting on the chart you are looking at.
How to trade Long
When both the LFT and HTF Money flow cross above ZERO, they are both in uptrend
How to trade Short
When both the LFT and HTF Money flow cross below ZERO, they are both in downtrend
What happens when Low timeframe is inside the high timeframe:
If High timeframe MFI is below zero but the LFT MFI is above it and still below zero, you have lost your short term downtrend. The opposite is true when the high timeframe MFI is above zero.
A strong constant comparative trend is when your low timeframe MFI is leading your High timeframe MFI.
Personal Settings:
In my usage, i find it best to multiply my trading chart timeframe by 3 and use that number as my high timeframe MFI setting
This works on ANY chart time you want. For example you are not locked to the standard built TradingView chart times.
If you trade on a 7 minute timeframe, you can set your HTF to 21.
7 * 3 = 21
Collatz Conjecture - DolphinTradeBot1️⃣ Overview
Every positive number follows its own unique path to reach 1 according to the Collatz rule.
Some numbers reach the end quickly and directly.
Others rise significantly before crashing down sharply.
Some get stuck within a certain range for a while before finally reaching 1.
Each number follows a different pattern — the number of steps it takes, how high it climbs, or which values it passes through cannot be predicted in advance.
This is a structure that appears chaotic but ultimately leads to order:
Every number reaches 1, but the way it gets there is entirely uncertain.
2️⃣ How Is It Work?
The rule is simple:
▪️ If the number is even → divide it by two.
▪️ If it’s odd → multiply it by three and add one.
Repeat this process at each step.
Example :
Let’s say the starting number is 7:
7 → 22 → 11 → 34 → 17 → 52 → 26 → 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It reaches 1 in 17 steps.
And from there, it always enters the same cycle:
4 → 2 → 1 → 4 → 2 → 1...
3️⃣ Why Is It Worth Learning?
🎯 This indicator isn’t just mathematical fun—it’s a thought experiment for those who dare to question market behavior.
▪️ It’s fun.
Watching numbers behave in unpredictable ways from a simple rule set is surprisingly enjoyable.
▪️ It shows how hard it is to teach a computer what randomness really is .
The Collatz process can be used to simulate chaotic behavior and may even inspire creative ways to introduce complexity into your code.
▪️ It makes you think — especially in financial markets.
The patternless, yet rule-based structure of Collatz can help train your mind to recognize that not all unpredictability is random. It’s a great mental model for navigating complex systems like price action.
▪️ Just like price movements in financial markets, this ancient problem remains unsolved.
Despite its simplicity, the Collatz conjecture has resisted proof for decades — a reminder that even the most basic-looking systems can hide deep complexity.
4️⃣ How To Use?
Super easy — in the indicator’s settings, there’s just one input field.
Enter any positive number, and you’ll see the pattern it follows on its way to 1.
You can also observe how many steps it takes and which values it visits in the info box at the top center of the chart.
5️⃣ Some Examples
You Can Observe the Chaos in the Following Examples⤵️
For Input Number → 12
For Input Number → 13
For Input Number → 14
For Input Number → 32768
For Input Number → 47
VWAP 2.0 with desv + Initial Balance by RiotWolftrading🌟 Overview
This powerful tool is designed for traders who want to harness the power of the Volume Weighted Average Price (VWAP) alongside session-based ranges to make informed trading decisions. Whether you're a day trader or a swing trader, this indicator provides a clean and effective way to identify support, resistance, and market trends—all in one place! 💡
✨ Key Features
Auto-Anchored VWAP 📊
Automatically calculates the VWAP based on a user-defined anchor period (e.g., Daily, Weekly, Monthly).
Resets at the start of each period (e.g., daily for a Daily anchor).
Displays a customizable VWAP line with standard deviation bands to highlight key price levels.
Standard Deviation Bands 📏
Plots up to three sets of standard deviation bands above and below the VWAP (multipliers: 1.0, 2.0, 3.0).
Includes volume percentage labels to show where trading volume is concentrated. 📉
Session High/Low Range 🕒
Identifies the high and low prices within a customizable session (default: 12:00 to 15:31).
Draws horizontal lines at the session high and low, with dotted deviation lines for additional reference points.
Perfect for spotting key levels during your trading session! 🔑
Time-Based Range Box ⏰
Highlights a specific time window (default: 15:40 to 15:50) with a colored box showing the high and low prices.
Ideal for tracking price action during high-impact events like news releases or market opens. 📅
Alerts 🚨
Set up alerts for when the price crosses above or below the VWAP—never miss a potential trading opportunity!
⚙️ Settings
Customize the indicator to fit your trading style with these easy-to-use settings:
VWAP Settings
Timezone 🌍: Select your timezone (default: GMT+2) to align calculations with your local time.
VWAP Source 📈: Choose the price source for VWAP (default: hlc3 - average of high, low, close).
Std Deviation Multipliers 📐: Adjust the multipliers for the bands (default: 1.0, 2.0, 3.0).
Line Width ✏️: Set the thickness of the VWAP and band lines (default: 1).
Session Time ⏳: Define the session window for VWAP calculations (default: 08:00-18:00, all days).
Show Upper/Lower Bands 👀: Toggle visibility for each set of bands (default: Band 1 visible, Bands 2 & 3 hidden).
Range Settings
Range Start/End Time 🕙: Set the time window for the range box (default: 15:40 to 15:50).
Box Color 🎨: Customize the border color (default: blue).
Box Background Color 🖌️: Adjust the background color (default: light aqua, 90% transparency).
I created this indicator to provide a streamlined, clutter-free tool for traders who rely on VWAP and session-based analysis. It focuses on the essentials—VWAP, standard deviation bands, session high/low, and range box—without unnecessary overlays. I hope it helps you in your trading journey! If you have feedback or suggestions, feel free to share—I’d love to hear from you! 😊
GranDoc - Week, Day, Month, and Session Separator5Indicator Name: GranDoc's - Week, Day, Month, and Session Separator
Version: Pine Script v5
Author: Jonpaul Nnamdi Opara (GranDoc )
Description
The "GranDoc - Week, Day, Month, and Session Separator" is a highly customizable TradingView indicator designed to enhance chart analysis by visually marking critical time-based transitions. Developed by Jonpaul Nnamdi Opara, this tool plots vertical lines with labels or background highlights to denote the start and end of weeks, days, months, and major trading sessions (Frankfurt, London, NY Morning, NY Afternoon, Sydney, and Tokyo). Traders can tailor colors, line styles, widths, transparency, and session times to align with their strategies and timezones.
Ideal for forex, stocks, futures, and crypto traders, this indicator simplifies the identification of key market periods—such as session openings/closings or new weeks—that often signal increased volatility or trend shifts. It’s optimized for intraday timeframes for session separators but supports all timeframes for week, day, and month markers, making it a versatile addition to any trader’s toolkit.
Features
Week Separators: Marks Monday starts with customizable lines and "Week Start" labels.
Day Separators: Highlights daily openings with lines and "Day Start" labels.
Month Separators: Indicates new months with lines and "Month Start" labels.
Session Separators: Plots lines and labels for major trading sessions’ start and end:
Frankfurt (default: 07:00–15:00 UTC)
London (default: 08:00–16:00 UTC)
NY Morning (default: 13:00–16:00 UTC)
NY Afternoon (default: 16:00–21:00 UTC)
Sydney (default: 22:00–06:00 UTC)
Tokyo (default: 00:00–08:00 UTC)
Timezone Support: Adjusts session times with a UTC offset (±12 hours).
Display Flexibility : Toggle between labeled vertical lines or background highlights.
Customization: Fine-tune colors, line styles (solid, dashed, dotted), widths, and transparency.
Background Mode: Highlights periods with translucent backgrounds for cleaner charts.
[ i]Labeled Lines: Each line includes descriptive labels (e.g., "London Open", "Tokyo Closed") when not in background mode.
How to Use
Add to Chart:
Copy the script into TradingView’s Pine Editor.
Click "Add to Chart" to apply the indicator.
Customize Settings:
Open settings via double-click or the "Settings" gear icon.
Timezone Offset: Set your UTC offset (e.g., -5 for EST) to align sessions.
Toggles: Enable/disable week, day, month, or session separators.
Appearance: Adjust colors, line styles, widths, and transparency for each separator.
Session Times: Modify start/end hours and minutes if defaults don’t suit your market.
Background Mode: Enable "Show as Background" for colored backgrounds instead of lines, and tweak "Session Background Transparency."
Labels: Labeled lines (e.g., "Sydney Open") appear automatically unless background mode is active.
Chart Compatibility:
Session separators require intraday timeframes (e.g., 1-minute to 4-hour).
Week, day, and month separators work across all timeframes.
Confirm your chart’s timezone aligns with your analysis.
Analyze:
Use separators to pinpoint session transitions, daily openings, or weekly shifts for trade planning.
Labels make it easy to spot key periods on busy charts.
Pair with indicators like RSI, volume, or support/resistance for deeper insights.
Example Use Cases
Forex Trading: Highlight London and NY session opens/closes for high-liquidity entries.
Day Trading: Reset strategies at daily separators and monitor intraday volatility.
Swing Trading: Use week/month separators to track longer-term trends.
Session Focus: Isolate sessions like Tokyo for regional market analysis.
Chart Clarity: Background mode declutters charts while marking key times.
Notes
Session separators are disabled on daily+ timeframes to prevent clutter.
Verify timezone offset for accurate session alignment.
Background mode suits lower timeframes for readability.
Labels are visible only when background mode is disabled.
Feedback
Share your thoughts or suggestions to make this indicator even better! Reach out via TradingView or connect with the author for insights. Happy trading!
About the Author
Dr. Jonpaul Nnamdi Opara, a PhD graduate from Ehime University, Japan, is a researcher and developer specializing in AI and machine learning. His work on automated landslide mapping and defect detection, published in journals like GEOMATE, showcases his precision-driven approach. With the "GranDoc" indicator, Jonpaul brings intuitive, data-driven clarity to financial markets, reflecting his expertise in creating impactful tools.
ICT Liquidity Sweep MAX RETRI (ALERT)Strategy Description: SMC + ICT Reversal Sniper | 5-Min | R2 TP
This strategy applies Smart Money Concepts (SMC) and ICT methodology to identify high-probability reversal trades using a clean, rule-based system designed for the 5-minute timeframe.
⸻
Core Logic:
• Liquidity Sweep: Identifies stop hunts beyond recent swing highs/lows using a configurable lookback window.
• Break of Structure (BOS): Validates a directional shift after the sweep.
• Fixed R2 Risk-Reward: Entry is followed by a 2:1 take-profit target. Stop loss is set at the sweep candle’s high/low.
• No Entry Between 8 PM–12 AM NY Time: Avoids the manipulation-prone and illiquid zone.
• Discreet SL Handling: SL hits close trades silently — no labels or visuals.
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Entry Precision & Timing Notes:
• The strategy may occasionally fire before a confirmed liquidity sweep — this is expected. If a sweep occurs later, you may still re-enter toward equilibrium, with take profit also targeted at equilibrium.
• Alerts or trades that trigger near 9:30 AM NY often align with real direction, but this time can be volatile.
• For more reliable and lower-risk entries, focus on the 1:30 PM to 2:00 PM silver bullet window, which tends to produce cleaner setups with more favorable flow. 🖤
EMA 9/21/50 + VWAP + MACD + RSI Pro [v6]Overview:
A powerful multi-indicator tool combining Exponential Moving Averages (EMA 9, 21, 50), Volume-Weighted Average Price (VWAP), Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI) into a single, easy-to-read system. Designed for traders who want a clean, all-in-one dashboard for trend analysis, momentum confirmation, and overbought/oversold conditions.
Key Features:
1. Triple EMA System (9, 21, 50)
Identifies short-term and medium-term trends.
Bullish Signal: EMA 9 > EMA 21 > EMA 50 (Green Highlight)
Bearish Signal: EMA 9 < EMA 21 < EMA 50 (Red Highlight)
Helps confirm trend direction and potential reversals.
2. VWAP (Volume-Weighted Average Price)
Tracks intraday fair value price based on volume.
Bullish: Price above VWAP (Green)
Bearish: Price below VWAP (Red)
3. MACD (Standard 12, 26, 9 Settings)
Shows momentum shifts.
Bullish: MACD line > Signal line (Green)
Bearish: MACD line < Signal line (Red)
Histogram confirms strength of momentum.
4. RSI (14-Period Default)
Identifies overbought (>70) and oversold (<30) conditions.
Red: Overbought (Potential Reversal)
Green: Oversold (Potential Bounce)
5. Signal Dashboard (Top-Right Table)
Real-time summary of all indicators in one place.
Color-coded for quick interpretation (Green = Bullish, Red = Bearish).
How to Use This Indicator?
✅ Trend Confirmation:
Trade in the direction of EMA alignment (9 > 21 > 50 for uptrends).
Use VWAP as dynamic support/resistance.
✅ Momentum Entries:
Look for MACD crossovers while RSI is not extreme.
Avoid buying when RSI > 70 or selling when RSI < 30 (unless strong trend).
✅ Mean Reversion:
Fade extreme RSI readings (overbought/oversold) when price is at key levels.
Who Is This For?
✔ Swing Traders – EMA + MACD combo for trend-following.
✔ Day Traders – VWAP + EMA for intraday bias.
✔ RSI Traders – Clear overbought/oversold signals.
Settings Customization:
Adjust EMA lengths, RSI periods, and MACD settings as needed.
Toggle VWAP visibility on/off.
Why Use This Script?
📌 All-in-One: No need for multiple indicators cluttering your chart.
📌 Visual Clarity: Color-coded signals for quick decision-making.
📌 Flexible: Works on any timeframe (1M, 5M, 1H, Daily, etc.).
Install now and enhance your trading strategy with a professional-grade multi-indicator system!
Not a financial advice. Use at your own discretion and always apply risk management
Multi Candle Body MapperMulti Candle Body Mapper
Visualize higher-timeframe candle structure within lower timeframes — without switching charts.
This tool maps grouped candle bodies and wicks (e.g., 15min candles on a 5min chart) using precise boxes and lines. Ideal for intraday traders who want to analyze market intent, body bias, and wick rejection in a compressed, organized view.
Features:
Visualize 3, 6, or 12 candle groups (e.g., 15min / 30min / 1H views)
Body box shows bullish/bearish color with adjustable transparency
Wick box shows high-low range with adjustable thickness and color
Dashed line at group close level for market direction hint
Full color customization
Toggle individual elements ON/OFF
Clean overlay – doesn’t interfere with price candles
Great for spotting:
Hidden support/resistance
Momentum buildup
Reversal traps and continuation setups
Keep your chart simple but smarter — all without changing your timeframe.
DTFX Time based range candle box [Wang Indicators]DTFX Time based range candle box
Overview : This indicator highlights HTF Candles in specified timeframe within boxes and extend them until they are mitigated. Allowing traders to use them as zones from which you could find some turn-around or scalp
How does it works ?
Users can setup up to 8 desired timeframe with the hour/minute of the HTF candle
Be carrefull when you chose the time. You must put something coherent with the timeframe (e.g : you can't put 'minutes' = 45 if your timeframe is '1h')
Everyday, the indicator will draw a box around the specified candle for it timeframe
Once the price close above or bellow this candle in the same timeframe, the Zone become "active"
As long as the price doesn't came back into the zone, the retracements will extends
Once the price came back into the zone (in the current timeframe), it stops the expension
Exemple
Here we have those settings :
timeframe : 1 hour
time : 9am
mitigation : 10%
fibs : visible & dashed
The box highlights the 9am 1H candle (9am to 10am)
We now wait for the price to close in the same timeframe (1h here) above or bellow the price
At 11am we close above - the zone is now "active"'
Now we wait for the price to go back in this zone in the current timeframe (here 5min)
12:40am : we put a low above the 10% of the zone -> we stop the retracements, the zone is considered as "mitigated"
Settings
Hour : The hour of the begiging of the candle
Minute : Combined with hour (default 0)
Timeframe : In whichtimeframe we are looking for the candle
% Mitigation : % of the box in wich the price must go back-in in order to "mitigate" the box and stop the expension of the fibs/box (if settings enabled)
Retracements style : Hidden, dashed, dotted or lines for the fibs
Extend Box : extend the box itself until it get mitigated
Number of unmitigated zones : Max unmitigated zone drawed on the chart PER CONFIG
Timezone : Must be set to reflect your needs. (preferably the chart timezone)
How does it helps users ?
Once a Candle is "active" it can be used as a Zone
Fibonnacis levels (30, 50 and 70%) are displayed (if enabled)
Users can customize their apparence and the boxes as they see fit
The 30 - 50 - 70 levels are possible support/resistance that the price tend to bounce of off
You might find some success looking for an entry inside the zone at a level if price gives further confirmations such as a lower time frame flip.
Fair value and MOSShowing the fair value and margin of safety for a Stock.
Works best with 12 months timeframe.
The calculations are based on historical data for multiple years, up to 10 years.
You will see the following as numbers at the indicator line:
- Forward EPS Growth in %
- Forward PE Calculated
- Forward PE Estimated
The two lines will be shown in green if they are above the current price and in red if the price is bellow the lines.
- The upper line shows the fair value of the stock, calculated with 15% (or 4x in 10 years) expected EPS growth for your investment.
- The lower line shows the margin of safety, calculated at 50% of the fair value.
You can adjust the values at "Forward EPS Growth in %" and "Expected future PE" in order to show your fair price and the price with margin of safety.
Multi-SMA Dashboard (10 SMAs)Description:
This script, "Multi-SMA Dashboard (10 SMAs)," creates a dashboard on a TradingView chart to analyze ten Simple Moving Averages (SMAs) of varying lengths. It overlays the chart and displays a table with each SMA’s direction, price position relative to the SMA, and angle of movement, providing a comprehensive trend overview.
How It Works:
1. **Inputs**: Users define lengths for 10 SMAs (default: 5, 10, 20, 50, 100, 150, 200, 250, 300, 350), select a price source (default: close), and customize table appearance and options like angle units (degrees/radians) and debug plots.
2. **SMA Calculation**: Computes 10 SMAs using the `ta.sma()` function with user-specified lengths and price source.
3. **Direction Determination**: The `sma_direction()` function checks each SMA’s trend:
- "Up" if current SMA > previous SMA.
- "Down" if current SMA < previous SMA.
- "Flat" if equal (no strength distinction).
4. **Price Position**: Compares the price source to each SMA, labeling it "Above" or "Below."
5. **Angle Calculation**: Tracks the most recent direction change point for each SMA and calculates its angle (atan of price change over time) in degrees or radians, based on the `showInRadians` toggle.
6. **Table Display**: A 12-column table shows:
- Columns 1-10: SMA name, direction (Up/Down/Flat), Above/Below status, and angle.
- Column 11: Summary of Up, Down, and Flat counts.
- Colors reflect direction (lime for Up/Above, red for Down/Below, white for Flat).
7. **Debug Option**: Optionally plots all SMAs and price for visual verification when `debug_plots_toggle` is enabled.
Indicators Used:
- Simple Moving Averages (SMAs): 10 user-configurable SMAs ranging from short-term (e.g., 5) to long-term (e.g., 350) periods.
The script runs continuously, updating the table on each bar, and overlays the chart to assist traders in assessing multi-timeframe trend direction and momentum without cluttering the view unless debug mode is active.
RSI VWAP POC [Uncle Sam Trading]Category: Oscillators, Volume, Market Profile
Timeframe: Suitable for all timeframes
Markets: Crypto, Forex, Stocks, Commodities
Overview
The RSI VWAP POC indicator is a powerful and innovative oscillator that combines the Relative Strength Index (RSI), Volume-Weighted Average Price (VWAP), and Point of Control (POC) from market profile analysis. Designed to provide traders with clear, high-probability trading signals, this indicator helps you identify key market levels, spot overbought/oversold conditions, and time your entries and exits with precision. Whether you’re a day trader, swing trader, or scalper, this free tool adds significant value to your trading strategy by offering a unique blend of momentum, volume, and market profile insights.
How It Works
This indicator integrates three core components to deliver actionable insights:
RSI (Relative Strength Index): Measures momentum to identify overbought (above 70) and oversold (below 30) conditions, helping you anticipate potential reversals.
VWAP (Volume-Weighted Average Price): Calculates a volume-weighted price benchmark, which is used to compute a more accurate, volume-sensitive RSI. This ensures the indicator reflects true market dynamics.
POC (Point of Control): Derived from market profile analysis, the POC represents the price level with the highest traded volume in a session, acting as a critical support or resistance level.
The indicator plots a smoothed RSI based on VWAP, overlaid with market profile data on a user-defined higher timeframe (default: 4H). The POC is displayed as a red line, with aqua bars indicating the value area where the majority of trading volume occurred. When the RSI crosses the POC, the indicator generates clear buy and sell signals:
Strong Buy (SBU): RSI crosses above the POC in an oversold zone.
Strong Sell (SBD): RSI crosses below the POC in an overbought zone.
Additional features include:
Background colors to highlight bullish (green) or bearish (red) trends.
Shaded zones for overbought (70/60) and oversold (30/40) levels.
Customizable settings to fit your trading style and timeframe.
How This Indicator Adds Value
The RSI VWAP POC indicator offers several key benefits that enhance your trading performance:
High-Probability Signals: By combining RSI, VWAP, and POC, this indicator identifies trades at key market levels where price is likely to react, increasing your win rate.
Improved Timing: Clear buy and sell signals, such as ‘SBU’ and ‘SBD’, help you enter and exit trades at optimal points, maximizing profitability.
Risk Management: Overbought/oversold zones and trend confirmation via background colors help you avoid false signals, protecting your capital.
Versatility: Suitable for all markets (crypto, forex, stocks) and timeframes, making it a valuable tool for traders of all experience levels.
Time Efficiency: The indicator does the heavy lifting by analyzing momentum, volume, and market profile data, allowing you to focus on executing trades.
Real-World Performance Example: On a 1-hour Bitcoin chart with a 4-hour higher timeframe, this indicator identified a strong sell signal on April 6th at 12:00 ($82,000), leading to a 9% drop to $74,600. A subsequent strong buy signal on April 7th at 04:00 ($76,200) captured a 6% rise to $81,200 – a potential 25% profit with 5x leverage if exited at 5%.
How to Use
Add the Indicator: Search for “RSI VWAP POC ” in TradingView’s indicator library and add it to your chart.
Set Your Timeframe: The indicator works on any timeframe but is optimized for a 1-hour chart with a 4-hour higher timeframe (set in the settings).
Interpret Signals:
Look for ‘SBU’ (strong buy) labels when the RSI crosses above the POC in an oversold zone, indicating a potential buying opportunity.
Look for ‘SBD’ (strong sell) labels when the RSI crosses below the POC in an overbought zone, signaling a potential selling opportunity.
Use the background colors (green for bullish, red for bearish) to confirm the trend.
Combine with Your Strategy: Use the indicator alongside your existing analysis (e.g., support/resistance, candlestick patterns) for best results.
Settings and Customization
The indicator is highly customizable to suit your trading needs:
RSI Length (Default: 14): Adjust the sensitivity of the RSI. Use a shorter length (e.g., 10) for scalping, or a longer length (e.g., 20) for smoother signals.
EMA Smoothing Length (Default: 3): Smooths the RSI line. Increase to 5 or 7 for less choppy signals in volatile markets.
Higher Timeframe (Default: 240 minutes): Set to 240 (4 hours) for a 1-hour chart. Adjust based on your chart’s timeframe (e.g., 60 minutes for a 15-minute chart).
Value Area Percentage (Default: 100%): Defines the size of the value area around the POC. Lower to 70% for a tighter focus on key levels.
Overbought/Oversold Thresholds (Defaults: 70/30): Adjust these levels to match market conditions (e.g., 80/20 for trending markets).
Show POC Line (Default: True): Toggle the red POC line on or off.
Show Buy/Sell Signals: Enable ‘Show Strong Breakup Signals’ and ‘Show Strong Breakdown Signals’ to focus on high-probability trades.
Why Choose This Indicator?
The RSI VWAP POC indicator stands out by offering a unique combination of momentum, volume, and market profile analysis in a single, easy-to-use tool. It’s designed to help traders of all levels make informed decisions, reduce risk, and increase profitability. Whether you’re trading Bitcoin, forex pairs, or stocks, this indicator provides the clarity and precision you need to succeed.
SMC+The "SMC+" indicator is a comprehensive tool designed to overlay key Smart Money Concepts (SMC) levels, support/resistance zones, order blocks (OB), fair value gaps (FVG), and trap detection on your TradingView chart. It aims to assist traders in identifying potential areas of interest based on price action, swing structures, and volume dynamics across multiple timeframes. This indicator is fully customizable, allowing users to adjust lookback periods, colors, opacity, and sensitivity to suit their trading style.
Key Components and Functionality
1. Key Levels (Support and Resistance)
This section plots horizontal lines representing support and resistance levels based on highs and lows over three distinct lookback periods, plus daily nearest levels.
Short-Term Lookback Period (Default: 20 bars)
Plots the highest high (short_high) and lowest low (short_low) over the specified period.
Visualized as dotted lines with customizable colors (Short-Term Resistance Color, Short-Term Support Color) and opacity (Short-Term Resistance Opacity, Short-Term Support Opacity).
Adjustment Tip: Increase the lookback (e.g., to 30-50) for less frequent but stronger levels on higher timeframes, or decrease (e.g., to 10-15) for scalping on lower timeframes.
Long-Term Lookback Period (Default: 50 bars)
Plots broader support (long_low) and resistance (long_high) levels using a solid line style.
Customizable via Long-Term Resistance Color, Long-Term Support Color, and their respective opacity settings.
Adjustment Tip: Extend to 100-200 bars for swing trading or major trend analysis on daily/weekly charts.
Extra-Long Lookback Period (Default: 100 bars)
Identifies significant historical highs (extra_long_high) and lows (extra_long_low) with dashed lines.
Configurable with Extra-Long Resistance Color, Extra-Long Support Color, and opacity settings.
Adjustment Tip: Use 200-500 bars for monthly charts to capture macro-level key zones.
Daily Nearest Resistance and Support Levels
Dynamically calculates the nearest resistance (daily_res_level) and support (daily_sup_level) based on the current day’s price action relative to historical highs and lows.
Displayed with Daily Resistance Color and Daily Support Color (with opacity options).
Adjustment Tip: Works best on intraday charts (e.g., 15m, 1h) to track daily pivots; combine with volume profile for confirmation.
How It Works: These levels update dynamically as new highs/lows form, providing a visual guide to potential reversal or breakout zones.
2. SMC Inputs (Smart Money Concepts)
This section identifies swing structures, order blocks, fair value gaps, and entry signals based on SMC principles.
SMC Swing Lookback Period (Default: 12 bars)
Defines the period for detecting swing highs (smc_swing_high) and lows (smc_swing_low).
Adjustment Tip: Increase to 20-30 for smoother swings on higher timeframes; reduce to 5-10 for faster signals on lower timeframes.
Minimum Swing Size (%) (Default: 0.5%)
Filters out minor price movements to focus on significant swings.
Adjustment Tip: Raise to 1-2% for volatile markets (e.g., crypto) to avoid noise; lower to 0.2-0.3% for forex pairs with tight ranges.
Order Block Sensitivity (Default: 1.0)
Scales the size of detected order blocks (OBs) for bullish reversal (smc_ob_bull), bearish reversal (smc_ob_bear), and continuation (smc_cont_ob).
Visuals include customizable colors, opacity, border thickness, and blinking effects (e.g., SMC Bullish Reversal OB Color, SMC Bearish Reversal OB Blink Thickness).
Adjustment Tip: Increase to 1.5-2.0 for wider OBs in choppy markets; keep at 1.0 for precision in trending conditions.
Minimum FVG Size (%) (Default: 0.3%)
Sets the minimum gap size for Fair Value Gaps (fvg_high, fvg_low), displayed as boxes with Fair Value Gap Color and FVG Opacity.
Adjustment Tip: Increase to 0.5-1% for larger, more reliable gaps; decrease to 0.1-0.2% for scalping smaller inefficiencies.
How It Works:
Bullish Reversal OB: Detects a bearish candle followed by a bullish break, marking a potential demand zone.
Bearish Reversal OB: Identifies a bullish candle followed by a bearish break, marking a supply zone.
Continuation OB: Spots strong bullish momentum after a prior high, indicating a continuation zone.
FVG: Highlights bullish gaps where price may retrace to fill.
Entry Signals: Plots triangles (SMC Long Entry) when price retests an OB with a liquidity sweep or break of structure (BOS).
3. Trap Inputs
This section detects potential bull and bear traps based on price action, volume, and key level rejections.
Min Down Move for Bear Trap (%) (Default: 1.0%)
Sets the minimum drop required after a bearish OB to qualify as a trap.
Visualized with Bear Trap Color, Bear Trap Opacity, and blinking borders.
Adjustment Tip: Increase to 2-3% for stronger traps in trending markets; lower to 0.5% for ranging conditions.
Min Up Move for Bull Trap (%) (Default: 1.0%)
Sets the minimum rise required after a bullish OB to flag a trap.
Customizable with Bull Trap Color, Bull Trap Border Thickness, etc.
Adjustment Tip: Adjust similarly to bear traps based on market volatility.
Volume Lookback for Traps (Default: 5 bars)
Compares current volume to a moving average (avg_volume) to filter low-volume traps.
Adjustment Tip: Increase to 10-20 for confirmation on higher timeframes; reduce to 3 for intraday sensitivity.
How It Works:
Bear Trap: Triggers when price drops significantly after a bearish OB but reverses up with low volume or support rejection.
Bull Trap: Activates when price rises after a bullish OB but fails with low volume or resistance rejection.
Boxes highlight trap zones, resetting when price breaks out.
4. Visual Customization
Line Width (Default: 2)
Adjusts thickness of support/resistance lines.
Tip: Increase to 3-4 for visibility on cluttered charts.
Blink On (Default: Close)
Sets whether OB/FVG borders blink based on Open or Close price interaction.
Tip: Use "Open" for intraday precision; "Close" for confirmed reactions.
Colors and Opacity: Each element (OBs, FVGs, traps, key levels) has customizable colors, opacity (0-100), border thickness (1-5 or 1-7), and blink effects for dynamic visualization.
How to Use SMC+
Setup: Apply the indicator to any chart and adjust inputs based on your timeframe and market.
Key Levels: Watch for price reactions at short, long, extra-long, or daily levels for potential reversals or breakouts.
SMC Signals: Look for entry signals (triangles) near OBs or FVGs, confirmed by liquidity sweeps or BOS.
Traps: Avoid false breakouts by monitoring trap boxes, especially near key levels with low volume.
Notes:
This indicator is a visual aid and does not guarantee trading success. Combine it with other analysis tools and risk management strategies.
Performance may vary across markets and timeframes; test settings thoroughly before use.
For optimal results, experiment with lookback periods and sensitivity settings to match your trading style.
The default settings are optimal for 1 minute and 10 second time frames for small cap low float stocks.
Continuation OB are Blue.
Bullish Reversal OB color is Green
Bearish Reversal OB color is Red
FVG color is purple
Bear Trap OB is red with a green border and often appears with a Bearish Reversal OB signaling caution to a short position.
Bull trap OB is green with a Red border signaling caution to a long position.
All active OB area are highlighted and solid in color while other non active OB area are dimmed.
My personal favorite setups are when we have an active bullish reversal with an active FVG along with an active Continuation OB.
Another personal favorite is the Bearish reversal OB signaling an end to a recent uptrend.
The Trap OB detection are also a unique and Original helpful source of information.
The OB have a white boarder by default that are colored black giving a simulated blinking effect when price is acting in that zone.
The Trap OB border are colored with respect to direction of intended trap, all of which can be customized to personal style.
All vaild OB zones are shown compact in size ,a unique and original view until its no longer valid.
Multi-Timeframe Anchored VWAP Valuation# Multi-Timeframe Anchored VWAP Valuation
## Overview
This indicator provides a unique perspective on potential price valuation by comparing the current price to the Volume Weighted Average Price (VWAP) anchored to the start of multiple timeframes: Weekly, Monthly, Quarterly, and Yearly. It synthesizes these comparisons into a single oscillator value, helping traders gauge if the current price is potentially extended relative to significant volume-weighted levels.
## Core Concept & Calculation
1. **Anchored VWAP:** The script calculates the VWAP separately for the current Week, Month, Quarter (3 Months), and Year (12 Months), starting the calculation from the first bar of each period.
2. **Price Deviation:** It measures how far the current `close` price is from each of these anchored VWAPs. This distance is measured in terms of standard deviations calculated *within* that specific anchor period (e.g., how many weekly standard deviations the price is away from the weekly VWAP).
3. **Deviation Score (Multiplier):** Based on this standard deviation distance, a score is assigned. The further the price is from the VWAP (in terms of standard deviations), the higher the absolute score. The indicator uses linear interpolation to determine scores between the standard deviation levels (defaulted at 1, 2, and 3 standard deviations corresponding to scores of +/-2, +/-3, +/-4, with a score of 1 at the VWAP).
4. **Timeframe Weighting:** Longer timeframes are considered more significant. The deviation scores are multiplied by fixed scalars: Weekly (x1), Monthly (x2), Quarterly (x3), Yearly (x4).
5. **Final Valuation Metric:** The weighted scores from all four timeframes are summed up to produce the final oscillator value plotted in the indicator pane.
## How to Interpret and Use
* **Histogram (Indicator Pane):**
* The main output is the histogram representing the `Final Valuation Metric`.
* **Positive Values:** Suggest the price is generally trading above its volume-weighted averages across the timeframes, potentially indicating strength or relative "overvaluation."
* **Negative Values:** Suggest the price is generally trading below its volume-weighted averages, potentially indicating weakness or relative "undervaluation."
* **Values Near Zero:** Indicate the price is relatively close to its volume-weighted averages.
* **Histogram Color:**
* The color of the histogram bars provides context based on the metric's *own recent history*.
* **Green (Positive Color):** The metric is currently *above* its recent average plus a standard deviation band (dynamic upper threshold). This highlights potentially significant "overvalued" readings relative to its normal range.
* **Red (Negative Color):** The metric is currently *below* its recent average minus a standard deviation band (dynamic lower threshold). This highlights potentially significant "undervalued" readings relative to its normal range.
* **Gray (Neutral Color):** The metric is within its typical recent range (between the dynamic upper and lower thresholds).
* **Orange Line:** Plots the moving average of the `Final Valuation Metric` itself (based on the "Threshold Lookback Period"), serving as the centerline for the dynamic thresholds.
* **On-Chart Table:**
* Provides a detailed breakdown for transparency.
* Shows the calculated VWAP, the raw deviation multiplier score, and the final weighted (adjusted) metric for each individual timeframe (W, M, Q, Y).
* Displays the current price, the final combined metric value, and a textual interpretation ("Overvalued", "Undervalued", "Neutral") based on the dynamic thresholds.
## Potential Use Cases
* Identifying potential exhaustion points when the indicator reaches statistically high (green) or low (red) levels relative to its recent history.
* Assessing whether price trends are supported by underlying volume-weighted average prices across multiple timeframes.
* Can be used alongside other technical analysis tools for confirmation.
## Settings
* **Calculation Settings:**
* `STDEV Level 1`: Adjusts the 1st standard deviation level (default 1.0).
* `STDEV Level 2`: Adjusts the 2nd standard deviation level (default 2.0).
* `STDEV Level 3`: Adjusts the 3rd standard deviation level (default 3.0).
* **Interpretation Settings:**
* `Threshold Lookback Period`: Defines the number of bars used to calculate the average and standard deviation of the final metric for dynamic thresholds (default 200).
* `Threshold StDev Multiplier`: Controls how many standard deviations above/below the metric's average are used to set the "Overvalued"/"Undervalued" thresholds (default 1.0).
* **Table Settings:** Customize the position and colors of the data table displayed on the chart.
## Important Considerations
* This indicator measures price deviation relative to *anchored* VWAPs and its *own historical range*. It is not a standalone trading system.
* The interpretation of "Overvalued" and "Undervalued" is relative to the indicator's logic and calculations; it does not guarantee future price movement.
* Like all indicators, past performance is not indicative of future results. Use this tool as part of a comprehensive analysis and risk management strategy.
* The anchored VWAP and Standard Deviation values reset at the beginning of each respective period (Week, Month, Quarter, Year).
Intraday Macro & Flow Indicator# IntraMacroFlow Indicator
## Introduction
IntraMacroFlow is a volume and delta-based indicator that identifies significant price movements within trading sessions. It generates signals when volume spikes coincide with quality price movement, filtered by RSI to avoid overbought/oversold conditions.
> **Note:** This indicator provides multiple signals and should be combined with additional analysis methods such as support/resistance, trend direction, and price action patterns.
## Inputs
### Volume Settings
* **Volume Lookback Period** (14) - Number of bars for volume moving average calculation
* **Volume Threshold Multiplier** (1.5) - Required volume increase over average to generate signals
* **Delta Threshold** (0.3) - Required close-to-open movement relative to bar range (higher = stronger movement)
### Session Configuration
* **Use Dynamic Session Detection** (true) - Automatically determine session times
* **Highlight Market Open Period** (true) - Highlight first third of trading session
* **Highlight Mid-Session Period** (true) - Highlight middle portion of trading session
* **Detect Signals Throughout Whole Session** (true) - Find signals in entire session
* **Session Time** ("0930-1600") - Trading hours in HHMM-HHMM format
* **Session Type** ("Regular") - Select Regular, Extended, or Custom session
### Manual Session Settings
Used when dynamic detection is disabled:
* **Manual Session Open Hour** (9)
* **Manual Session Open Minute** (30)
* **Manual Session Open Duration** (60)
* **Manual Mid-Session Start Hour** (12)
* **Manual Mid-Session End Hour** (14)
## How It Works
The indicator analyzes each bar using three primary conditions:
1. **Volume Condition**: Current volume > Average volume × Threshold
2. **Delta Condition**: |Close-Open|/Range > Delta threshold
3. **Time Condition**: Bar falls within configured session times
When all conditions are met:
* Bullish signals appear when close > open and RSI < 70
* Bearish signals appear when close < open and RSI > 30
## Display Elements
### Shapes and Colors
* Green triangles below bars - Bullish signals
* Red triangles above bars - Bearish signals
* Blue background - Market open period
* Purple background - Mid-session period
* Bar coloring - Green (bullish), Red (bearish), or unchanged
### Information Panel
A dynamic label shows:
* Current volume relative to average (Vol)
* Delta value for current bar (Delta)
* RSI value (RSI)
* Session status (Active/Closed)
## Calculation Method
```
// Volume Condition
volumeMA = ta.sma(volume, lookbackPeriod)
volumeCondition = volume > volumeMA * volumeThreshold
// Delta Calculation (price movement quality)
priceRange = high - low
delta = math.abs(close - open) / priceRange
deltaCondition = delta > deltaThreshold
// Direction and RSI Filter
bullishBias = close > open and entrySignal and not (rsi > 70)
bearishBias = close < open and entrySignal and not (rsi < 30)
```
## Usage Recommendations
### Suitable Markets
* Equities during regular trading hours
* Futures markets
* Forex during active sessions
* Cryptocurrencies with defined volume patterns
### Recommended Timeframes
* 1-minute to 1-hour (optimal: 5 or 15-minute)
### Parameter Adjustments
* For fewer but stronger signals: increase Volume Threshold (2.0+) and Delta Threshold (0.4-0.6)
* For more signals: decrease Volume Threshold (1.2-1.5) and Delta Threshold (0.2-0.3)
### Usage Tips
* Combine with trend analysis for higher-probability entries
* Focus on signals occurring at session boundaries and mid-session
* Use opposite signals as potential exit points
* Configure alerts to receive notifications when signals occur
## Additional Notes
* RSI parameters are fixed at 14 periods with 70/30 thresholds
* The indicator handles overnight sessions correctly
* Fully compatible with TradingView alerts
* Customizable visual elements
## Release Notes
Initial release: This is a template indicator that should be customized to suit your specific trading strategies and preferences.
DEGA RMA | QuantEdgeB🧠 Introducing DEGA RMA (DGR ) by QuantEdgeB
🛠️ Overview
DEGA RMA (DGR) is a precision-engineered trend-following system that merges DEMA, Gaussian kernel smoothing, and ATR-based envelopes into a single, seamless overlay indicator. Its mission: to filter out market noise while accurately capturing directional bias using a layered volatility-sensitive trend core.
DGR excels at identifying valid breakouts, sustained momentum conditions, and trend-defining price behavior without falling into the trap of frequent signal reversals.
🔍 How It Works
1️⃣ Double Exponential Moving Average (DEMA)
The system begins by applying a DEMA to the selected price source. DEMA responds faster than a traditional EMA, making it ideal for capturing transitions in momentum.
2️⃣ Gaussian Filtering
A custom Gaussian kernel is used to smooth the DEMA signal. The Gaussian function applies symmetrical weights, centered around the most recent bar, effectively softening sharp price oscillations while preserving the underlying trend structure.
3️⃣ Recursive Moving Average (RMA) Core
The filtered Gaussian output is then processed through an RMA to generate a stable dynamic baseline. This baseline becomes the foundation for the final trend logic.
4️⃣ ATR-Scaled Breakout Zones
Upper and lower trend envelopes are calculated using a custom ATR filter built on DEMA-smoothed volatility.
• ✅ Long Signal when price closes above the upper envelope
• ❌ Short Signal when price closes below the lower envelope
• ➖ Neutral when inside the band (no signal noise)
✨ Key Features
🔹 Multi-Layer Trend Model
DEMA → Gaussian → RMA creates a signal structure that is both responsive and robust.
🔹 Volatility-Aware Entry System
Adaptive ATR bands adjust in real-time, expanding during high volatility and contracting during calm periods.
🔹 Noise-Reducing Gaussian Kernel
Sigma-adjustable kernel ensures signal smoothness without introducing excessive lag.
🔹 Clean Visual System
Candle coloring and band fills make trend state easy to read and act on at a glance.
⚙️ Custom Settings
• DEMA Source – Input source for trend core (default: close)
• DEMA Length – Length for initial smoothing (default: 30)
• Gaussian Filter Length – Determines smoothing depth (default: 4)
• Gaussian Sigma – Sharpness of Gaussian curve (default: 2.0)
• RMA Length – Core baseline smoothing (default: 12)
• ATR Length – Volatility detection period (default: 40)
• ATR Mult Up/Down – Controls the upper/lower threshold range for signals (default: 1.7)
📌 How to Use
1️⃣ Trend-Following Mode
• Go Long when price closes above the upper ATR band
• Go Short when price closes below the lower ATR band
• Remain neutral otherwise
2️⃣ Breakout Confirmation Tool
DGR’s ATR-based zone logic helps validate price breakouts and filter out false signals that occur inside compressed ranges.
3️⃣ Volatility Monitoring
Watch the ATR envelope width — a narrowing band often precedes expansion and potential directional shifts.
📌 Conclusion
DEGA RMA (DGR) is a thoughtfully constructed trend-following framework that goes beyond basic moving averages. Its Gaussian smoothing, adaptive ATR thresholds, and layered filtering logic provide a versatile solution for traders looking for cleaner signals, less noise, and real-time trend awareness.
Whether you're trading crypto, forex, or equities — DGR adapts to volatility while keeping your chart clean and actionable.
🔹 Summary
• ✅ Advanced Smoothing → DEMA + Gaussian + RMA = ultra-smooth trend core
• ✅ Volatility-Adjusted Zones → ATR envelope scaling removes whipsaws
• ✅ Fully Customizable → Tailor to any asset or timeframe
• ✅ Quant-Inspired Structure → Built for clarity, consistency, and confidence
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Gaussian Smooth Trend | QuantEdgeB🧠 Introducing Gaussian Smooth Trend (GST) by QuantEdgeB
🛠️ Overview
Gaussian Smooth Trend (GST) is an advanced volatility-filtered trend-following system that blends multiple smoothing techniques into a single directional bias tool. It is purpose-built to reduce noise, isolate meaningful price shifts, and deliver early trend detection while dynamically adapting to market volatility.
GST leverages the Gaussian filter as its core engine, wrapped in a layered framework of DEMA smoothing, SMMA signal tracking, and standard deviation-based breakout thresholds, producing a powerful toolset for trend confirmation and momentum-based decision-making.
🔍 How It Works
1️⃣ DEMA Smoothing Engine
The indicator begins by calculating a Double Exponential Moving Average (DEMA), which provides a responsive and noise-resistant base input for subsequent filtering.
2️⃣ Gaussian Filter
A custom Gaussian kernel is applied to the DEMA signal, allowing the system to detect smooth momentum shifts while filtering out short-term volatility.
This is especially powerful during low-volume or sideways markets where traditional MAs struggle.
3️⃣ SMMA Layer with Z-Filtering
The filtered Gaussian signal is then passed through a custom Smoothed Moving Average (SMMA). A standard deviation envelope is constructed around this SMMA, dynamically expanding/contracting based on market volatility.
4️⃣ Signal Generation
• ✅ Long Signal: Price closes above Upper SD Band
• ❌ Short Signal: Price closes below Lower SD Band
• ➖ No trade: Price stays within the band → market indecision
✨ Key Features
🔹 Multi-Stage Trend Detection
Combines DEMA → Gaussian Kernel → SMMA → SD Bands for robust signal integrity across market conditions.
🔹 Gaussian Adaptive Filtering
Applies a tunable sigma parameter for the Gaussian kernel, enabling you to fine-tune smoothness vs. responsiveness.
🔹 Volatility-Aware Trend Zones
Price must close outside of dynamic SD envelopes to trigger signals — reducing whipsaws and increasing signal quality.
🔹 Dynamic Color-Coded Visualization
Candle coloring and band fills reflect live trend state, making the chart intuitive and fast to read.
⚙️ Custom Settings
• DEMA Source: Price stream used for smoothing (default: close)
• DEMA Length: Period for initial exponential smoothing (default: 7)
• Gaussian Length / Sigma: Controls smoothing strength of kernel filter
• SMMA Length: Final smoothing layer (default: 12)
• SD Length: Lookback period for standard deviation filtering (default: 30)
• SD Mult Up / Down: Adjusts distance of upper/lower breakout zones (default: 2.5 / 1.8)
• Color Modes: Six distinct color palettes (e.g., Strategy, Warm, Cool)
• Signal Labels: Toggle on/off entry markers ("𝓛𝓸𝓷𝓰", "𝓢𝓱𝓸𝓻𝓽")
📌 Trading Applications
✅ Trend-Following → Enter on confirmed breakouts from Gaussian-smoothed volatility zones
✅ Breakout Validation → Use SD bands to avoid false breakouts during chop
✅ Volatility Compression Monitoring → Narrowing bands often precede large directional moves
✅ Overlay-Based Confirmation → Can complement other QuantEdgeB indicators like K-DMI, BMD, or Z-SMMA
📌 Conclusion
Gaussian Smooth Trend (GST) delivers a precision-built trend model tailored for modern traders who demand both clarity and control. The layered signal architecture, combined with volatility awareness and Gaussian signal enhancement, ensures accurate entries, clean visualizations, and actionable trend structure — all in real-time.
🔹 Summary Highlights
1️⃣ Multi-stage Smoothing — DEMA → Gaussian → SMMA for deep signal integrity
2️⃣ Volatility-Aware Filtering — SD bands prevent false entries
3️⃣ Visual Trend Mapping — Gradient fills + candle coloring for clean charts
4️⃣ Highly Customizable — Adapt to your timeframe, style, and volatility
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Z SMMA | QuantEdgeB📈 Introducing Z-Score SMMA (Z SMMA) by QuantEdgeB
🛠️ Overview
Z SMMA is a momentum-driven oscillator designed to track the standardized deviation of a Smoothed Moving Average (SMMA). By applying Z-score normalization, this tool dynamically adapts to price volatility, enabling traders to detect meaningful directional shifts and trend changes with enhanced clarity.
It serves both as a trend-following and mean-reversion system, identifying opportunities through standardized thresholds while remaining robust across volatile and calm market conditions.
✨ Key Features
🔹 Z-Score Normalization Engine
Applies Z-score to a custom SMMA baseline, allowing traders to compare price action relative to its recent volatility-adjusted mean.
🔹 Dynamic Trend Detection
Generates actionable long/short signals based on customizable Z-thresholds, making it adaptable across different asset classes and timeframes.
🔹 Overbought/Oversold Zones
Highlight reversion and profit-taking zones (default OB: +2 to +4, OS: -2 to -4), great for counter-trend or mean-reversion strategies.
🔹 Visual Reinforcement Tools
Includes candle coloring, gradient fills, and optional ALMA/EMA band overlays to visualize trend regime transitions.
🔍 How It Works
1️⃣ Z-Score SMMA Calculation
The core is a custom Smoothed Moving Average (SMMA) that is normalized by its standard deviation over a lookback period.
Final Formula:
Z = (SMMA - Mean) / StdDev
2️⃣ Signal Generation
• ✅ Long Bias: Z-Score > Long Threshold (default: 0)
• ❌ Short Bias: Z-Score < Short Threshold (default: 0)
3️⃣ Visual Aids
• Candle Color → Shows trend bias
• Band Fills → Highlight trend strength
• Overlays → Optional ALMA/EMA bands for structure analysis
⚙️ Custom Settings
• SMMA Length → Default: 12
• Z-Score Lookback → Default: 30
• Long Threshold → Default: 0
• Short Threshold → Default: 0
• Color Themes → Choose from 6 visual modes
• Extra Plots → Toggle advanced overlays (ALMA, EMA, bands)
• Label Display → Show/hide “𝓛𝓸𝓷𝓰” & “𝓢𝓱𝓸𝓻𝓽” markers
👥 Who Should Use It?
✅ Trend Traders → For early entries with confirmation from Z-score expansion
✅ Quantitative Analysts → Standardized deviation enables comparison across assets
✅ Mean-Reversion Traders → Use OB/OS zones to fade parabolic spikes
✅ Swing & Systematic Traders → Identify momentum shifts with optional ALMA/EMA overlays
📌 Conclusion
Z SMMA offers a smart, adaptive framework for tracking deviation from equilibrium in a quant-friendly format. Whether you're looking to follow trends or catch exhaustion points, Z SMMA provides a clear, standardized view of momentum and price extremes.
🔹 Key Takeaways:
1️⃣ Z-Score standardization ensures dynamic range awareness
2️⃣ SMMA base filters out noise, offering smoother signals
3️⃣ Color-coded visuals support faster reaction and cleaner charts
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before
