UTC Day SeparatorsGlobally consistent back-tests: When you anchor indicators (VWAP, ADR, supply/demand boxes) to daily boundaries, basing them on UTC avoids daylight-saving mismatches between exchanges.
Quick regime inspection: You can eyeball overnight gaps or Asia/Europe/US session overlaps by seeing how price behaves relative to successive UTC days.
Chart cleanliness: Because the line is dotted and low-contrast, it gives a subtle reference grid without overwhelming candles or other plots.
Penunjuk dan strategi
New York Midnight Day SeparatorThis Pine Script indicator draws vertical separator lines on the chart at midnight in the New York timezone (Eastern Time). The lines mark the start of each new trading day from Monday to Friday, helping traders visually distinguish daily sessions based on New York market time. The separator lines are rendered as slightly transparent gray lines spanning the full price range of each midnight candle, providing a clean and unobtrusive visual aid for session tracking.
Liquidity mark-out indicator(by Lumiere)This indicator marks out every High that has a bullish candle followed by a bearish one, vice versa for lows.
Once the price reaches the marked-out liquidity, the line is removed automatically.
This indicator only shows the current liquidity of the time frame you are at.
(To get it look like the picture just chance the length to 30-50)
Key Features of the Liquidity Mark-Out Indicator:
🔹 Identifies Liquidity Zones – Marks highs and lows based on candlestick patterns.
🔹 Customizable Settings – Toggle highs/lows visibility 🎚️, adjust line colors 🎨, and set line length (bars) 📏.
🔹 Smart Clean-Up – Automatically removes swept levels (when price breaks through) for a clean chart 🧹.
🔹 Pattern-Based Detection –
Highs: Detects two-candle reversal patterns (🟢 bullish close → 🔴 bearish close).
Lows: Detects two-candle reversal patterns (🔴 bearish close → 🟢 bullish close).
🔹 Dynamic Lines – Projects liquidity levels forward (adjustable length) to track key zones 📈.
Perfect For Traders Looking To:
✅ Spot potential liquidity grabs 🎯
✅ Identify key support/resistance levels 🛑
✅ Clean up their chart from outdated levels 🖥️
Session Status Table📌 Session Status Table
Session Status Table is an indicator that displays the real-time status of the four major trading sessions:
* 🇯🇵 Asia (Tokyo)
* 🇬🇧 London
* 🇺🇸 New York AM
* 🇺🇸 New York PM
It shows which sessions are currently open, how much time remains until they open or close, and optionally sends alerts in advance.
🧩 Features:
* Real-time session table — shows the status of each session on the chart.
* Color-coded statuses:
* 🟢 Green – Session is open
* 🔴 Red – Session is closed
* ⚪ Gray – Weekend
* Countdown timers until session open or close.
* User alerts — receive a notification a custom number of minutes before a session starts.
⚙️ Customization:
* Table position — fully configurable.
* Session colors — customizable for open, closed, and weekend states.
* Session labels — customizable with icons.
* Notifications:
* Enabled through TradingView's Alerts panel.
* User-defined lead time before session opens.
🕒 Time Zones:
All times are calculated in UTC to ensure consistency across different markets and regions, avoiding discrepancies from time zones and daylight saving time.
🚨 How to enable alerts:
1. Open the "Alerts" panel in TradingView.
2. Click "Create Alert".
3. In the condition dropdown, choose "Session Status Table".
4. Set to any alert() trigger.
5. Save — you'll be notified a set number of minutes before each session begins.
ℹ️ Technical Notes:
* Built with Pine Script version 6.
* Logically divided into clear sections: inputs, session calculations, table rendering, and alerts.
* Optimized for performance and reliability on all timeframes.
Ideal for traders who use session activity in their strategies — especially in Forex, crypto, and futures markets.
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
AWR R & LR Oscillator with plots & tableHello trading viewers !
I'm glad to share with you one of my favorite indicator. It's the aggregate of many things. It is partly based on an indicator designed by gentleman goat. Many thanks to him.
1. Oscillator and Correlation Calculations
Overview and Functionality: This part of the indicator computes up to 10 Pearson correlation coefficients between a chosen source (typically the close price, though this is user-configurable) and the bar index over various periods. Starting with an initial period defined by the startPeriod parameter and increasing by a set increment (periodIncrement), each correlation coefficient is calculated using the built-in ta.correlation function over successive ranges. These coefficients are stored in an array, and the indicator calculates their average (avgPR) to provide a complete view of the market trend strength.
Display Features: Each individual coefficient, as well as the overall average, is plotted on the chart using a specific color. Horizontal lines (both dashed and solid) are drawn at levels 0, ±0.8, and ±1, serving as visual thresholds. Additionally, conditional fills in red or blue highlight when values exceed these thresholds, helping the user quickly identify potential extreme conditions (such as overbought or oversold situations).
2. Visual Signals and Automated Alerts
Graphical Signal Enhancements: To reinforce the analysis, the indicator uses graphical elements like emojis and shape markers. For example:
If all 10 curves drop below -0.79, a 🌋 emoji appears at the bottom of the chart;
When curves 2 through 10 are below -0.79, a ⛰️ emoji is displayed below the bar, potentially serving as a buy signal accompanied by an alert condition;
Likewise, symmetrical conditions for correlations exceeding 0.79 produce corresponding emojis (🤿 and 🏖️) at the top or bottom of the chart.
Alerts and Notifications: Using these visual triggers, several alertcondition statements are defined within the script. This allows users to set up TradingView alerts and receive real-time notifications whenever the market reaches these predefined critical zones identified by the multi-period analysis.
3. Regression Channel Analysis
Principles and Calculations: In addition to the oscillator, the indicator implements an analysis of regression channels. For each of the 8 configurable channels, the user can set a range of periods (for example, min1 to max1, etc.). The function calc_regression_channel iterates through the defined period range to find the optimal period that maximizes a statistical measure derived from a regression parameter calculated by the function r(p). Once this optimal period is identified, the indicator computes two key points (A and B) which define the main regression line, and then creates a channel based on the calculated deviation (an RMSE multiplied by a user-defined factor).
The regression channels are not displayed on the chart but are used to plot shapes & fullfilled a table.
Blue shapes are plotted when 6th channel or 7th channel are lower than 3 deviations
Yellow shapes are plotted when 6th channel or 7th channel are higher than 3 deviations
4. Scores, Conditions, and the Summary Table
Scoring System: The indicator goes further by assigning scores across multiple analytical categories, such as:
1. BigPear Score
What It Represents: This score is based on a longer-term moving average of the Pearson correlation values (SMA 100 of the average of the 10 curves of correlation of Pearson). The BigPear category is designed to capture where this longer-term average falls within specific ranges.
Conditions: The script defines nine boolean conditions (labeled BigPear1up through BigPear9up for the “up” direction).
Here's the rules :
BigPear1up = (bigsma_avgPR <= 0.5 and bigsma_avgPR > 0.25)
BigPear2up = (bigsma_avgPR <= 0.25 and bigsma_avgPR > 0)
BigPear3up = (bigsma_avgPR <= 0 and bigsma_avgPR > -0.25)
BigPear4up = (bigsma_avgPR <= -0.25 and bigsma_avgPR > -0.5)
BigPear5up = (bigsma_avgPR <= -0.5 and bigsma_avgPR > -0.65)
BigPear6up = (bigsma_avgPR <= -0.65 and bigsma_avgPR > -0.7)
BigPear7up = (bigsma_avgPR <= -0.7 and bigsma_avgPR > -0.75)
BigPear8up = (bigsma_avgPR <= -0.75 and bigsma_avgPR > -0.8)
BigPear9up = (bigsma_avgPR <= -0.8)
Conditions: The script defines nine boolean conditions (labeled BigPear1down through BigPear9down for the “down” direction).
BigPear1down = (bigsma_avgPR >= -0.5 and bigsma_avgPR < -0.25)
BigPear2down = (bigsma_avgPR >= -0.25 and bigsma_avgPR < 0)
BigPear3down = (bigsma_avgPR >= 0 and bigsma_avgPR < 0.25)
BigPear4down = (bigsma_avgPR >= 0.25 and bigsma_avgPR < 0.5)
BigPear5down = (bigsma_avgPR >= 0.5 and bigsma_avgPR < 0.65)
BigPear6down = (bigsma_avgPR >= 0.65 and bigsma_avgPR < 0.7)
BigPear7down = (bigsma_avgPR >= 0.7 and bigsma_avgPR < 0.75)
BigPear8down = (bigsma_avgPR >= 0.75 and bigsma_avgPR < 0.8)
BigPear9down = (bigsma_avgPR >= 0.8)
Weighting:
If BigPear1up is true, 1 point is added; if BigPear2up is true, 2 points are added; and so on up to 9 points from BigPear9up.
Total Score:
The positive score (posScoreBigPear) is the sum of these weighted conditions.
Similarly, there is a negative score (negScoreBigPear) that is calculated using a mirrored set of conditions (named BigPear1down to BigPear9down), each contributing a negative weight (from -1 to -9).
In essence, the BigPear score tells you—in a weighted cumulative way—where the longer-term correlation average falls relative to predefined thresholds.
2. Pear Score
What It Represents: This category uses the immediate average of the Pearson correlations (avgPR) rather than a longer-term smoothed version. It reflects a more current picture of the market’s correlation behavior.
How It’s Calculated:
Conditions: There are nine conditions defined for the “up” scenario (named Pear1up through Pear9up), which partition the range of avgPR into intervals. For instance:
Pear1up = (avgPR > -0.2 and avgPR <= 0)
Pear2up = (avgPR > -0.4 and avgPR <= -0.2)
Pear3up = (avgPR > -0.5 and avgPR <= -0.4)
Pear4up = (avgPR > -0.6 and avgPR <= -0.5)
Pear5up = (avgPR > -0.65 and avgPR <= -0.6)
Pear6up = (avgPR > -0.7 and avgPR <= -0.65)
Pear7up = (avgPR > -0.75 and avgPR <= -0.7)
Pear8up = (avgPR > -0.8 and avgPR <= -0.75)
Pear9up = (avgPR > -1 and avgPR <= -0.8)
There are nine conditions defined for the “down” scenario (named Pear1down through Pear9down), which partition the range of avgPR into intervals. For instance:
Pear1down = (avgPR >= 0 and avgPR < 0.2)
Pear2down = (avgPR >= 0.2 and avgPR < 0.4)
Pear3down = (avgPR >= 0.4 and avgPR < 0.5)
Pear4down = (avgPR >= 0.5 and avgPR < 0.6)
Pear5down = (avgPR >= 0.6 and avgPR < 0.65)
Pear6down = (avgPR >= 0.65 and avgPR < 0.7)
Pear7down = (avgPR >= 0.7 and avgPR < 0.75)
Pear8down = (avgPR >= 0.75 and avgPR < 0.8)
Pear9down = (avgPR >= 0.8 and avgPR <= 1)
Weighting:
Each condition has an associated weight, such as 0.9 for Pear1up, 1.9 for Pear2up, and so on, up to 9 for Pear9up.
Sum up :
Pear1up = 0.9
Pear2up = 1.9
Pear3up = 2.9
Pear4up = 3.9
Pear5up = 4.99
Pear6up = 6
Pear7up = 7
Pear8up = 8
Pear9up = 9
Total Score:
The positive score (posScorePear) is the sum of these values for each condition that returns true.
A corresponding negative score (negScorePear) is calculated using conditions for when avgPR falls on the positive side, with similar weights in the negative direction.
This score quantifies the current correlation reading by translating its relative level into a numeric score through a weighted sum.
3. Trendpear Score
What It Represents: The Trendpear score is more dynamic as it compares the current avgPR with its short-term moving average (sma_avgPR / 14 periods ) and also considers its relationship with an even longer moving average (bigsma_avgPR / 100 periods). It is meant to capture the trend or momentum in the correlation behavior.
How It’s Calculated:
Conditions: Nine conditions (from Trendpear1up to Trendpear9up) are defined to check:
Whether avgPR is below, equal to, or above sma_avgPR by different margins;
Whether it is trending upward (i.e., it is higher than its previous value).
Here are the rules
Trendpear1up = (avgPR <= sma_avgPR -0.2) and (avgPR >= avgPR )
Trendpear2up = (avgPR > sma_avgPR -0.2) and (avgPR <= sma_avgPR -0.07) and (avgPR >= avgPR )
Trendpear3up = (avgPR > sma_avgPR -0.07) and (avgPR <= sma_avgPR -0.03) and (avgPR >= avgPR )
Trendpear4up = (avgPR > sma_avgPR -0.03) and (avgPR <= sma_avgPR -0.02) and (avgPR >= avgPR )
Trendpear5up = (avgPR > sma_avgPR -0.02) and (avgPR <= sma_avgPR -0.01) and (avgPR >= avgPR )
Trendpear6up = (avgPR > sma_avgPR -0.01) and (avgPR <= sma_avgPR -0.001) and (avgPR >= avgPR )
Trendpear7up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR <= bigsma_avgPR)
Trendpear8up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR -0.03)
Trendpear9up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR)
Weighting:
The weights here are not linear. For example, the lightest condition may add 0.1 point, whereas the most extreme condition (e.g., when avgPR is not only above the moving average but also reaches a high proportion relative to bigsma_avgPR) might add as much as 90 points.
Trendpear1up = 0.1
Trendpear2up = 0.2
Trendpear3up = 0.3
Trendpear4up = 0.4
Trendpear5up = 0.5
Trendpear6up = 0.69
Trendpear7up = 7
Trendpear8up = 8.9
Trendpear9up = 90
Total Score:
The positive score (posScoreTrendpear) is the sum of the weights from all conditions that are satisfied.
A negative counterpart (negScoreTrendpear) exists similarly for when the trend indicates a downward bias.
Trendpear integrates both the level and the direction of change in the correlations, giving a strong numeric indication when the market starts to diverge from its short-term average.
4. Deviation Score
What It Represents: The “Écart” score quantifies how far the asset’s price deviates from the boundaries defined by the regression channels. This metric can indicate if the price is excessively deviating—which might signal an eventual reversion—or confirming a breakout.
How It’s Calculated:
Conditions: For each channel (with at least seven channels contributing to the scoring from the provided code), there are three levels of deviation:
First tier (EcartXup): Checks if the price is below the upper boundary but above a second boundary.
Second tier (EcartXup2): Checks if the price has dropped further, between a lower and a more extreme boundary.
Third tier (EcartXup3): Checks if the price is below the most extreme limit.
Weighting:
Each tier within a channel has a very small weight for the lowest severities (for example, 0.0001 for the first tier, 0.0002 for the second, 0.0003 for the third) with weights increasing with the channel index.
First channel : 0.0001 to 0.0003 (very short term)
Second channel : 0.001 to 0.003 (short term)
Third channel : 0.01 to 0.03 (short mid term)
4th channel : 0.1 to 0.3 ( mid term)
5th channel: 1 to 3 (long mid term)
6th channel : 10 to 30 (long term)
7th channel : 100 to 300 (very long term)
Total Score:
The overall positive score (posScoreEcart) is the sum of all the weights for conditions met among the first, second, and third tiers.
The corresponding negative score (negScoreEcart) is calculated similarly (using conditions when the price is above the channel boundaries), with the weights being the same in magnitude but negative in sign.
This layered scoring method allows the indicator to reflect both minor and major deviations in a gradated and cumulative manner.
Example :
Score + = 321.0001
Score - = -0.111
The asset price is really overextended in long term view, not for mid term & short term expect the in the very short term.
Score + = 0.0033
Score - = -1.11
The asset price is really extended in short term view, not for mid term (even a bit underextended) & long term is neutral
5. Slope Score
What It Represents: The Slope score captures the trend direction and steepness of the regression channels. It reflects whether the regression line (and hence the underlying trend) is sloping upward or downward.
How It’s Calculated:
Conditions:
if the slope has a uptrend = 1
if the slope has a downtrend = -1
Weighting:
First channel : 0.0001 to 0.0003 (very short term)
Second channel : 0.001 to 0.003 (short term)
Third channel : 0.01 to 0.03 (short mid term)
4th channel : 0.1 to 0.3 ( mid term)
5th channel: 1 to 3 (long mid term)
6th channel : 10 to 30 (long term)
7th channel : 100 to 300 (very long term)
The positive slope conditions incrementally add weights from 0.0001 for the smallest positive slopes to 100 for the largest among the seven checks. And negative for the downward slopes.
The positive score (posScoreSlope) is the sum of all the weights from the upward slope conditions that are met.
The negative score (negScoreSlope) sums the negative weights when downward conditions are met.
Example :
Score + = 111
Score - = -0.1111
Trend is up for longterm & down for mid & short term
The slope score therefore emphasizes both the magnitude and the direction of the trend as indicated by the regression channels, with an intentional asymmetry that flags strong downtrends more aggressively.
Summary
For each category—BigPear, Pear, Trendpear, Écart, and Slope—the indicator evaluates a defined set of conditions. Each condition is a binary test (true/false) based on different thresholds or comparisons (for example, comparing the current value to a moving average or a channel boundary). When a condition is true, its assigned weight is added to the cumulative score for that category. These individual scores, both positive and negative, are then displayed in a table, making it easy for the trader to see at a glance where the market stands according to each analytical dimension.
This comprehensive, weighted approach allows the indicator to encapsulate several layers of market information into a single set of scores, aiding in the identification of potential trading opportunities or market reversals.
5. Practical Use and Application
How to Use the Indicator:
Interpreting the Signals:
On your chart, observe the following components:
The individual correlation curves and their average, plotted with visual thresholds;
Visual markers (such as emojis and shape markers) that signal potential oversold or overbought conditions
The summary table that aggregates the scores from each category, offering a quick glance at the market’s state.
Trading Alerts and Decisions: Set your TradingView alerts through the alertcondition functions provided by the indicator. This way, you receive immediate notifications when critical conditions are met, allowing you to react as soon as the market reaches key levels. This tool is especially beneficial for advanced traders who want to combine multiple technical dimensions to optimize entry and exit points with a confluence of signals.
Conclusion and Additional Insights
In summary, this advanced indicator innovatively combines multi-scale Pearson correlation analysis (via multiple linear regressions) with robust regression channel analysis. It offers a deep and nuanced view of market dynamics by delivering clear visual signals and a comprehensive numerical summary through a built-in score table.
Combine this indicator with other tools (e.g., oscillators, moving averages, volume indicators) to enhance overall strategy robustness.
Watchlist AlertThis “Watchlist Alert” indicator is to help traders monitor multiple symbols and notify them whenever a specified target price is reached. Upon loading the script, you can define up to ten ticker symbols along with their individual price targets. The script stores these pairs in a persistent map so that, on each new bar, it retrieves the previous and current close prices for every symbol in your watchlist. If a symbol’s price crosses above or below its target, the script sends an alert (using your chosen alert frequency) and records the timestamp of that event.
Visually, the indicator displays a small table at the top center of your chart. For each watched symbol, it shows four columns: the symbol name, its latest close price (in the chart’s timeframe), the target price you set, and the last time an alert was emitted (formatted as MM.dd HH:mm:ss). By comparing the previous close to the current close and checking against the stored “lastAlertTime,” the script ensures that you receive exactly one alert per crossing event per bar.
In short, the key features are:
Input up to ten symbols with their corresponding float price targets.
Automatically check each symbol’s previous and current close values every bar.
Trigger a single alert when price crosses a target—either upward or downward.
Maintain a map of last alert timestamps to prevent duplicate notifications.
Display a real-time table listing each symbol’s current price, target, and last alert time.
Whenever you need to keep tabs on multiple instruments across different timeframes without manually tracking price levels, simply add this indicator to your chart. It runs in the background and pushes alerts as soon as any watched symbol touches its defined threshold.
SPX500 Quick Drop & Rise AlertsSimple script thats been adjusted for 1 minute trading on spx500.
It will show you and signal to you:
dropThreshold: how much the price must rise or fall (in percent) to trigger a signal. Default is 0.05 → 5%.
lookbackBars: how many bars back to compare against. Default is 1 (i.e., compare the current close to the previous bar’s close).
Theirs a few ways to use this, you might want to use your MA 238 as a reference point. Use it as a target or a level to bounce or reject from. Then use this indicator to help show you where the market energy is flowing.
Do some backtesting and see what you see. Only use it for New York open times would probably be best.
Youll have to change your mentality depending on if the market is trending / ranging ect of course.
Options Risk Manager v2.2.0 - Priority 7 CompleteScript Description for TradingView Publication
Options Risk Manager v2.2.0 - Priority 7 Complete
What does this script do?
Options Risk Manager v2.2.0 is a comprehensive position management system designed specifically for options traders. The indicator calculates precise stop loss levels, risk/reward targets, and position sizing based on user-defined risk parameters. It provides real-time profit/loss tracking, options Greeks monitoring, and automated alert systems for critical price levels.
The script displays entry points, stop losses, and profit targets directly on the chart while continuously calculating position metrics including dollar risk, account exposure, and probability of success. Version 2.2.0 introduces Priority 7 advanced alerts with dynamic risk warnings and multi-condition notifications.
How does it do it?
The script performs several key calculations:
1. Risk-Based Stop Loss Calculation - Determines stop loss levels based on percentage of entry price, automatically adjusting for calls versus puts. Put positions place stops above entry, while calls place stops below.
2. Position Sizing Algorithm - Calculates optimal contract quantities using account size, risk
percentage, and stop distance to ensure consistent risk per trade regardless of underlying price.
3. Options-Specific P&L Tracking - Incorporates Delta, Gamma, Vega, and Theta to provide accurate profit/loss calculations for options positions, including time decay effects.
4. Three-Phase Trade Management - Implements systematic position management through Entry
Phase (initial risk), Profit Phase (approaching target), and Trailing Phase (EMA-based exit
management).
5. Multi-Level Alert System - Monitors price action, Greeks thresholds, time decay acceleration, and account risk levels to generate context-aware notifications.
How to use it?
Initial Setup:
1. Apply indicator to any optionable security
2. Toggle "In Position" ON when entering a trade
3. Set Direction (Call/Put) and Side (Long/Short)
4. Enter the underlying price at position entry
5. Specify number of contracts and risk percentage
Position Management:
Blue line shows entry price
Red line indicates stop loss level
Orange line displays risk/reward target
Purple EMA line activates after target hit
Monitor real-time P&L in trade panels
Alert Configuration:
Enable Advanced Alerts in settings
Set profit/loss notification thresholds
Configure Greek-based warnings
Activate time decay alerts for expiration
Risk Parameters:
Risk % determines stop distance from entry
Account Value sets position sizing limits
Contract Multiplier (standard = 100)
R:R Ratio defines profit targets
What makes it unique?
Options Risk Manager addresses the specific challenges of options trading that generic indicators miss. The script accounts for the inverse relationship in put options (profiting from price declines), incorporates Greeks for accurate P&L calculations, and provides options-specific limit orders for TradeStation integration.
The three-phase management system removes emotional decision-making by defining clear rules for position management. Phase transitions occur automatically based on price action, shifting from initial risk management to profit protection to trend-following modes.
Version 2.2.0's Priority 7 alert system provides intelligent notifications that include live metrics, risk warnings, and market context rather than simple price crosses.
Key Features Summary
Options-Specific Calculations - Proper handling of calls/puts with inverse relationships
Risk-Based Position Sizing - Consistent risk regardless of underlying price
Greeks Integration - Delta, Gamma, Vega, Theta for accurate tracking
Phase Management System - Systematic three-stage position handling
Advanced Alert System - Context-aware notifications with metrics
TradeStation Integration - Option limit orders for execution
Visual Risk Display - Clear chart overlays for all levels
Probability Calculator - Win/loss probability with expected value
Multi-Account Support - Scales from small to large accounts
Important Notes
This indicator requires manual input of option prices and Greeks (available from your broker's option chain). It functions as a risk management overlay and does not generate entry signals. The calculations assume standard options contracts of 100 shares.
Designed for TradeStation platform with full functionality. Basic features available on other platforms
without options data integration. Always verify calculations with your broker's risk system before placing
trades.
Daily Open Line (9:30-16:00)This indicator automatically plots a horizontal line at each day's opening price during regular trading hours (9:30 AM to 4:00 PM, US Eastern Time).
The line starts exactly at the opening bar of the day and ends at the close (16:00).
Each day, a new line is drawn, making it easy to visualize and reference the daily open price throughout the session.
Useful for intraday traders to identify key support/resistance and monitor price action relative to the open.
You can customize the color, line width, and whether to display the open price label.
EMA Cross CounterSure! Here's a professional English presentation for your EMA200 script, which you can use on TradingView, your website, or in a video description. Let me know if you want a more casual or more technical version.
📈 EMA200 Script Presentation (English)
Title:
🔵 EMA200 Strategy – Powerful Trend Filter for Smart Traders
Description:
Welcome to the EMA200 Script, your essential tool for identifying trend direction and improving your trading decisions!
The EMA200 (Exponential Moving Average over 200 periods) is one of the most reliable indicators used by professional traders to determine the overall market trend. This script is designed to help you:
✅ Identify Bullish or Bearish Conditions – Know whether the market is trending up or down based on price action relative to the EMA200.
✅ Filter Your Entries – Avoid counter-trend trades and focus only on high-probability setups aligned with the dominant trend.
✅ Enhance Any Strategy – Use the EMA200 as a standalone trend filter or combine it with your existing indicators for better precision.
Holy Grail Setup with Confidence OpacityVersion 1
Triggers Raschke's Holy Grail set up. More translucent=less confidence, more opaque=more confidence.
Uses Raschke's default parameters
20 EMA + ADX > 30 + pullback and reversal
ADX stands for Average Directional Index, a technical indicator developed by Welles Wilder to quantify trend strength — not direction, just strength.
It's a core component of Linda Raschke’s Holy Grail strategy, where the goal is to only trade pullbacks during strong trends.
ADX ranges from 0 to 100:
Below 20: Weak or no trend (range-bound market)
25–30 and above: Strong trend
50+: Very strong trend (often near trend exhaustion)
In the Holy Grail setup, Raschke recommends only taking trades when ADX > 30, to ensure that:
The market is trending
Pullbacks are more likely to resolve in the direction of the trend
AWR Optimized LR GraphHello Trading Viewers !
Drawing linear regression channels at the best place and for many periods can be time consuming.
In the library, I've found some indicators that draw 1 or 2 but based on fixed number of bars or a duration...
Not always relevant, that's why I decide to create this indicator.
It calculates 8 linear regression channels according to 8 differents configurable periods.
Each time, the indicator will calculate for each specified duration range the best linear regression line & channel (2 standard regressions) for that period and then plot it on the graph.
You can settle how many linear regression channels you want to display.
For period, defaults configurations (number of candles studied) are :
Period 1
min1 = 33
max1 = 66
Period 2
min2 = 67
max2 = 128
Period 3
min3 = 129
max3 = 255
Period 4
min4 = 256
max4 = 510
Period 5
min5 = 511
max5 = 1020
Period 6
min6 = 1021
max6 = 2040
Period 7
min7 = 2041
max7 = 3500
Period 8
min8 = 3501
max8 = 4999
This default settings provide short-term, mid term, long term and a very long-term view.
You have to go back on the chart to display the channels that start on previous period that are currently not on the screen.
You can set a specific color for each linear regression channels.
The linear regression line is based on the least squares method, meaning: it calculates along each period the gap between a linear & the price & squarred it. Then it defines the linear in order to have always the least distance between price and the linear.
The more the price deviates from its regression line, the more statistically likely it is to return to its regression line.
Application of Regression Lines in Trading
Regression lines are widely used in trading and financial analysis to understand market trends and make informed predictions. Here are some key applications:
1. Trend Identification – Traders use regression lines to visualize the general direction of a stock or asset price, helping to confirm an upward or downward trend.
2. Price Predictions – Linear regression models assist in estimating future price movements based on historical data, allowing traders to anticipate changes.
3. Risk Assessment – By analyzing the slope and variation of a regression line, traders can gauge market volatility and potential risks.
4. Support and Resistance Levels – Regression channels help traders identify support and resistance zones, providing insight into optimal entry and exit points in a trend.
5. You can also use the short period linear regression channels vs the long period linear regression channels to identify important pivot points.
Advanced Volume Exhaustion & Absorption IndicatorNew Features:
1. Enhanced Exhaustion Settings
Choice of MA types (SMA, EMA, WMA, VWMA)
Adjustable ROC period and threshold
Minimum bars between signals to avoid clustering
Customizable price MA period
2. Advanced Absorption Settings
Separate lookback period from exhaustion
Option to use standard deviation or simple multiplier
Adjustable minimum bars between signals
Choice of MA types
3. Filtering Options
Volume Filter: Only show signals above a certain volume percentile
Trend Filter: Align signals with the overall trend
Volatility Filter: Filter based on ATR to focus on significant moves
4. Display Customization
Custom colors for all signal types
Adjustable label sizes
Background transparency control
Optional info table showing current metrics
5. Alert Management
Toggle alerts for each signal type
Alert repeat delay to prevent spam
Recommended Settings:
For ES1! (S&P 500 E-mini):
Volume Exhaustion:
- Lookback Period: 30
- Volume Threshold: 0.35
- ROC Period: 5
- ROC Threshold: -25%
- MA Type: EMA
- Price MA Period: 8
- Min Bars Since Last: 8
Volume Absorption:
- Lookback Period: 40
- Volume Multiplier: 2.5
- Price Tolerance: 0.15%
- Use StdDev: Yes
- StdDev Multiplier: 2.0
- Min Bars Since Last: 5
Filters:
- Volume Filter: ON (Min Percentile: 30)
- Trend Filter: OFF
- Volatility Filter: ON (ATR Length: 14, Multiplier: 0.8)
For NQ1! (Nasdaq 100 E-mini):
Volume Exhaustion:
- Lookback Period: 25
- Volume Threshold: 0.30
- ROC Period: 4
- ROC Threshold: -30%
- MA Type: EMA
- Price MA Period: 6
- Min Bars Since Last: 6
Volume Absorption:
- Lookback Period: 35
- Volume Multiplier: 2.8
- Price Tolerance: 0.20%
- Use StdDev: Yes
- StdDev Multiplier: 2.2
- Min Bars Since Last: 4
Filters:
- Volume Filter: ON (Min Percentile: 25)
- Trend Filter: OFF
- Volatility Filter: ON (ATR Length: 14, Multiplier: 1.0)
Key Differences Between ES1! and NQ1! Settings:
NQ1! is more volatile, so:
Slightly shorter lookback periods
Higher ROC thresholds (more negative)
Higher absorption multipliers
More tolerance for price movement
ES1! is more stable, so:
Longer lookback periods
Tighter price tolerances
More conservative multipliers
Longer minimum bars between signals
These settings are optimized for the 5-minute timeframe but can be adjusted for other timeframes by proportionally scaling the lookback periods.
QTN | Money CirculatingQTN | Money Circulating | VWAP-based turnover Multi Time Frames
This indicator visualizes real money flow in a stock by calculating the turnover (trading value) using volume multiplied by VWAP across daily, weekly, and monthly timeframes. It applies EMA smoothing to provide a clearer trend of money circulating in the market.
Features:
• VWAP-based turnover calculation for more accurate money flow measurement.
• EMA smoothing with customizable period.
• Table display of daily, weekly, and monthly turnover values in millions (M) for quick reference.
• Clean, color-coded visualization for easy interpretation.
Usage:
Ideal for traders and investors who want to gauge market participation intensity and detect shifts in trading momentum across multiple timeframes.
________________________________________________
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any security. Trading involves risk, and users should conduct their own research or consult with a financial advisor before making investment decisions. The author is not responsible for any trading losses.
Cumulative Intraday Volume with Long/Short LabelsThis indicator calculates a running total of volume for each trading day, then shows on the price chart when that total crosses levels you choose. Every day at 6:00 PM Eastern Time, the total goes back to zero so it always reflects only the current day’s activity. From that moment on, each time a new candle appears the indicator looks at whether the candle closed higher than it opened or lower. If it closed higher, the candle’s volume is added to the running total; if it closed lower, the same volume amount is subtracted. As a result, the total becomes positive when buyers have dominated so far today and negative when sellers have dominated.
Because futures markets close at 6 PM ET, the running total resets exactly then, mirroring the way most intraday traders think in terms of a single session. Throughout the day, you will see this running total move up or down according to whether more volume is happening on green or red candles. Once the total goes above a number you specify (for example, one hundred thousand contracts), the indicator will place a small “Long” label at that candle on the main price chart to let you know buying pressure has reached that level. Similarly, once the total goes below a negative number you choose (for example, minus one hundred thousand), a “Short” label will appear at that candle to signal that selling pressure has reached your chosen threshold. You can set these threshold numbers to whatever makes sense for your trading style or the market you follow.
Because raw volume alone never turns negative, this design uses candle direction as a sign. Green candles (where the close is higher than the open) add volume, and red candles (where the close is lower than the open) subtract volume. Summing those signed volume values tells you in a single number whether buying or selling has been stronger so far today. That number resets every evening, so it does not carry over any buying or selling from previous sessions.
Once you have this indicator on your chart, you simply watch the “summed volume” line as it moves throughout the day. If it climbs past your long threshold, you know buyers are firmly in control and a long entry might make sense. If it falls past your short threshold, you know sellers are firmly in control and a short entry might make sense. In quieter markets or times of low volume, you might use a smaller threshold so that even modest buying or selling pressure will trigger a label. During very active periods, a larger threshold will prevent too many signals when volume spikes frequently.
This approach is straightforward but can be surprisingly powerful. It does not rely on complex formulas or hidden statistical measures. Instead, it simply adds and subtracts daily volume based on candle color, then alerts you when that total reaches levels you care about. Over several years of historical testing, this formula has shown an ability to highlight moments when intraday sentiment shifts decisively from buyers to sellers or vice versa. Because the indicator resets every day at 6 PM, it always reflects only today’s sentiment and remains easy to interpret without carrying over past data. You can use it on any intraday timeframe, but it works especially well on five-minute or fifteen-minute charts for futures contracts.
If you want a clear gauge of whether buyers or sellers are dominating in real time, and you prefer a rule-based method rather than a complex model, this indicator gives you exactly that. It shows net buying or selling pressure at a glance, resets each session like most intraday traders do, and marks the moments when that pressure crosses the levels you decide are important. By combining a daily reset with signed volume, you get a single number that tells you precisely what the crowd is doing at any given moment, without any of the guesswork or hidden calculations that more complicated indicators often carry.
Vix_Fix Enhanced MTF [Cometreon]The VIX Fix Enhanced is designed to detect market bottoms and spikes in volatility, helping traders anticipate major reversals with precision. Unlike standard VIX Fix tools, this version allows you to control the standard deviation logic, switch between chart styles, customize visual outputs, and set up advanced alerts — all with no repainting.
🧠 Logic and Calculation
This indicator is based on Larry Williams' VIX Fix and integrates features derived from community requests/advice, such as inverse VIX logic.
It calculates volatility spikes using a customizable standard deviation of the lows and compares it to a moving high to identify potential reversal points.
All moving average logic is based on Cometreon's proprietary library, ensuring accurate and optimized calculations on all 15 moving average types.
🔷 New Features and Improvements
🟩 Custom Visual Styles
Choose how you want your VIX data displayed:
Line
Step Line
Histogram
Area
Column
You can also flip the orientation (bottom-up or top-down), change the source ticker, and tailor the display to match your charting preferences.
🟩 Multi-MA Standard Deviation Calculation
Customize the standard deviation formula by selecting from 15 different moving averages:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
RMA (Smoothed Moving Average)
HMA (Hull Moving Average)
JMA (Jurik Moving Average)
DEMA (Double Exponential Moving Average)
TEMA (Triple Exponential Moving Average)
LSMA (Least Squares Moving Average)
VWMA (Volume-Weighted Moving Average)
SMMA (Smoothed Moving Average)
KAMA (Kaufman’s Adaptive Moving Average)
ALMA (Arnaud Legoux Moving Average)
FRAMA (Fractal Adaptive Moving Average)
VIDYA (Variable Index Dynamic Average)
This gives you fine control over how volatility is measured and allows tuning the sensitivity for different market conditions.
🟩 Full Control Over Percentile and Deviation Conditions
You can enable or disable lines for standard deviation and percentile conditions, and define whether you want to trigger on over or under levels — adapting the indicator to your exact logic and style.
🟩 Chart Type Selection
You're no longer limited to candlestick charts! Now you can use Vix_Fix with different chart formats, including:
Candlestick
Heikin Ashi
Renko
Kagi
Line Break
Point & Figure
🟩 Multi-Timeframe Compatibility Without Repainting
Use a different timeframe from your chart with confidence. Signals remain stable and do not repaint. Perfect for spotting long-term reversal setups on lower timeframes.
🟩 Alert System Ready
Configure alerts directly from the indicator’s panel when conditions for over/under signals are met. Stay informed without needing to monitor the chart constantly.
🔷 Technical Details and Customizable Inputs
This indicator includes full control over the logic and appearance:
1️⃣ Length Deviation High - Adjusts the lookback period used to calculate the high deviation level of the VIX logic. Shorter values make it more reactive; longer values smooth out the signal.
2️⃣ Ticker - Choose a different chart type for the calculation, including Heikin Ashi, Renko, Kagi, Line Break, and Point & Figure.
3️⃣ Style VIX - Change the visual style (Line, Histogram, Column, etc.), adjust line width, and optionally invert the display (bottom-to-top).
📌 Fill zones for deviation and percentile are active only in Line and Step Line modes
4️⃣ Use Standard Deviation Up / Down - Enable the overbought and oversold zone logic based on upper and lower standard deviation bands.
5️⃣ Different Type MA (for StdDev) - Choose from 15 different moving averages to define the calculation method for standard deviation (SMA, EMA, HMA, JMA, etc.), with dedicated parameters like Phase, Sigma, and Offset for optimized responsiveness.
6️⃣ BB Length & Multiplier - Adjust the period and multiplier for the standard deviation bands, similar to how Bollinger Bands work.
7️⃣ Show StdDev Up / Down Line - Enable or disable the visibility of upper and lower standard deviation boundaries.
8️⃣ Use Percentile & Length High - Activate the percentile-based logic to detect extreme values in historical volatility using a customizable lookback length.
9️⃣ Highest % / Lowest % - Set the high and low percentile thresholds (e.g., 85 for high, 99 for low) that will be used to trigger over/under signals.
🔟 Show High / Low Percentile Line - Toggle the visual display of the percentile boundaries directly on the chart for clearer signal reference.
1️⃣1️⃣ Ticker Settings – Customize parameters for special chart types such as Renko, Heikin Ashi, Kagi, Line Break, and Point & Figure, adjusting reversal, number of lines, ATR length, etc.
1️⃣2️⃣ Timeframe – Enables using SuperTrend on a higher timeframe.
1️⃣3️⃣ Wait for Timeframe Closes -
✅ Enabled – Displays Vix_Fix smoothly with interruptions.
❌ Disabled – Displays Vix_Fix smoothly without interruptions.
☄️ If you find this indicator useful, leave a Boost to support its development!
Every feedback helps to continuously improve the tool, offering an even more effective trading experience. Share your thoughts in the comments! 🚀🔥
HD Pivot PointsThe Pivot Points Indicator is a classic yet powerful tool used in technical analysis to identify potential support and resistance levels in the market. Originally developed by floor traders to determine key price levels throughout the trading day, it has since become a staple for traders across all markets—from forex to stocks and commodities.
Pucci Trend EMA-SMA Crossover with TolerancePucci Trend EMA-SMA Crossover with Tolerance
This indicator helps identify market trends and generates trading signals based on the crossover between an Exponential Moving Average (EMA) and a Simple Moving Average (SMA) with an adjustable tolerance threshold. The signals work as follows:
Buy Signal (B) -> Triggers when the EMA crosses above the SMA, exceeding a user-defined tolerance (in basis points). Optionally, a price filter can require the high or low to be below the EMA for confirmation.
Sell Signal (S) -> Triggers when the SMA crosses above the EMA, exceeding the tolerance. The optional price filter may require the high or low to be above the EMA.
The tolerance helps reduce false signals by requiring a minimum distance between the moving averages before confirming a crossover. The price filter adds an extra confirmation layer by checking if price action respects the EMA level.
Important Notes:
1º No profitability guarantee: This tool is for analysis only and may generate losses.
2º "As Is" disclaimer: Provided without warranties or responsibility for trading outcomes.
3º Use Stop Loss: Users must determine their own risk management.
4º Parameter adjustment needed: Optimal MA periods and tolerance vary by timeframe.
5º Filter impact varies: Enabling/disabling the price filter may improve or worsen performance.
DCI### 📌 **DCI – Direction Correlation Index**
#### 🔹 **What It Is**
The **Direction Correlation Index (DCI)** is a tool for measuring how closely a group of up to 10 symbols move together in both *trend correlation* and *short-term direction*. It helps identify whether a group of assets is acting in unison or moving independently.
---
#### ⚙️ **How It Works**
DCI outputs three key metrics:
1. **Average Correlation**
* Measures the average of all pairwise correlations between the selected symbols.
* Prices are first standardized using a z-score (based on simple moving average and standard deviation over a user-defined lookback period).
* Correlation is calculated using Pearson’s method for all 45 symbol pairs.
* Result ranges from:
* `+1.00` = strong positive correlation
* `0.00` = no correlation
* `-1.00` = strong inverse correlation
2. **Direction Agreement %**
* Checks whether each symbol is moving up or down compared to its previous bar.
* Calculates the percentage of symbols moving in the same direction.
* For example: if 7 of 10 symbols are moving up and 3 are moving down, the direction agreement is 70%.
3. **Strong Correlation Count**
* Counts how many of the 45 symbol pairs have an absolute correlation above `0.7`.
* Helps highlight how many pairs are currently highly correlated.
---
#### 📈 **How to Use It**
1. **Select Symbols**
* In the **Settings**, you can input up to 10 custom symbols. These can be stocks, indices, forex pairs, crypto, or any tradable asset.
2. **Adjust the Lookback Period**
* Defines how many bars back are used to calculate z-scores and correlations.
* Default is `12`. Use shorter periods for faster response; longer periods for smoother, slower data.
3. **Interpret the Table (Plotted on Chart)**
* **Avg Corr**: Tells you how much the group is co-moving. High correlation often reflects unified market behavior.
* **Dir Agr %**: Shows directional sync. High values mean most instruments are trending the same way in the current bar.
* **> 0.7**: The number of pairs currently strongly correlated (|corr| > 0.7).
---
#### 🧠 **Practical Usage Tips**
* Use DCI to monitor **sector alignment**, **portfolio behavior**, or **market group momentum**.
* Confirm trend strength by checking if high correlation aligns with a strong direction agreement.
* Low correlation + mixed direction can signal **choppy or indecisive markets**.
* High correlation + strong direction = **trend confirmation** across your selected instruments.
- Made with DeepSeek
FVG Trailing Stop [LuxAlgo]The FVG Trailing Stop indicator tracks unmitigated Fair Value Gaps (FVG) data to produce a Trailing Stop indicator able to determine if the market is uptrending or downtrending easily.
🔶 USAGE
The FVG Trailing Stop is intended to identify trend directions through its position relative to the closing price:
Bullish: Price is located above the Trailing Stop, indicating that all Bearish FVGs have been mitigated and the trend is anticipated to continue upwards.
Bearish State: Price is located below the Trailing Stop, indicating that all Bullish FVGs have been mitigated and the trend is anticipated to continue downwards.
The Trailing Stop originates from two extremities obtained from the average of respective unmitigated FVGs. The specific directional average is also displayed as a more transparent secondary line, however, the trailing stop is derived from this value and a new trend will not be detected until the opposite directional average is crossed.
Price reaching the Trailing Stop is caused by retracements and can lead to the following scenarios:
Outcome 1: The directional average is crossed next, indicating a new trend direction.
Outcome 2: The directional average is held as support or resistance, leading to a new impulse and a continuation of the trend.
🔹 Reset on Cross
While price crossing the Trailing Stop should be considered as a sign of an upcoming trend change; it is possible for the price to still evolve outside it.
As a solution, we have included the "Reset on Cross" feature, which (as the name suggests) hides and resets the Trailing Stop each time it is crossed, leading to a "Neutral" state.
This opens the opportunity for the Trailing Stop to be displayed again once the price moves again in the direction of the pre-established trend. A trader might use this to accumulate positions within a specific trend.
🔶 DETAILS
The script uses a typical identification method for FVGs. Once identified, the script collects the point of the FVG farthest from the current price when formed.
For Upwards FVGs this is the bottom of the FVG.
For Downwards FVGs this is the top of the FVG.
The data is managed only to use the last input lookback of FVGs. If an FVG is mitigated, it frees up a spot in the memory for a new FVG, however, if the lookback is full, the oldest will be deleted.
From there, it uses a "trailing" logic only to move the Trailing Stop in one direction until the trailing stop resets or the direction flips.
The extremities used to calculate the Trailing Stop are created from 2 calculation steps, the first step involves taking the raw average of the FVG mitigation levels, and the second step applies a simple moving average (SMA) smoothing of the precedent-obtained averages.
🔶 SETTINGS
Unmitigated FVG Lookback: Sets the maximum number of Unmitigated FVGs that the script will use.
Smoothing Length: Sets the smoothing length for the Trailing Stop to reduce erratic results.
Reset on Cross: When enabled, hide and reset the Trailing Stop until the price starts moving in the pre-established trend direction again.
Average Candle SizeMeasures the average candle size in ticks across a given number of candles. This can be set simply in the indicator settings. Then it displays the average candle size in the lower left hand corner of the screen.
BackToBasic XEMAคำอธิบายการทำงานของอินดิเคเตอร์ "BackToBasic XEMA"
BackToBasic XEMA เป็นอินดิเคเตอร์ที่ใช้หลักการเปรียบเทียบ สองเส้นวิเคราะห์แนวโน้มราคาที่มีความไวต่างกัน
เมื่อเส้นที่ตอบสนองต่อราคาไวกว่า ตัดขึ้นเหนือเส้นที่ตอบสนองช้ากว่า → แสดงสัญญาณ Buy
ในทางกลับกัน หากตัดลงต่ำกว่า → แสดงสัญญาณ Sell
อินดิเคเตอร์นี้มีระบบพิเศษที่เรียกว่า เส้นติดตามผลอัตโนมัติ (Trail Line)
เมื่อราคาเคลื่อนไปในทิศทางที่ถูกต้องตามสัญญาณเกินระยะที่กำหนด (เช่น 2500 จุด)
จะมีเส้นแนวนอนลากตามระดับราคา เพื่อใช้เป็นแนวพิจารณาการปิดกำไรโดยอัตโนมัติ (หรือเชิงกลยุทธ์)
คุณสามารถปรับระยะห่างของสัญลักษณ์ Buy/Sell, เปิด/ปิดเส้นติดตาม และเลือกว่าจะใช้เส้นไหนเป็นฐานได้
🧠 HowBackToBasic XEMA Indicator Works
BackToBasic XEMA is an indicator based on comparing two trend-tracking lines with different sensitivities.
When the faster-reacting line crosses above the slower one → a Buy signal is shown.
Conversely, when it crosses below → a Sell signal appears.
It also features a dynamic horizontal trailing line, which only activates when the price has moved in the right direction by a certain amount (e.g., 2500 points).
This line extends horizontally from the latest calculated level and can be used as a reference for trailing stops or visual exit management.
Users can customize the symbol distance, toggle the trailing line, and choose which reference line to use for trailing.
