Mad Trading Scientist - Guppy MMA with Bollinger Bands📘 Indicator Name:
Guppy MMA with Bollinger Bands
🔍 What This Indicator Does:
This TradingView indicator combines Guppy Multiple Moving Averages (GMMA) with Bollinger Bands to help you identify trend direction and volatility zones, ideal for spotting pullback entries within trending markets.
🔵 1. Guppy Multiple Moving Averages (GMMA):
✅ Short-Term EMAs (Blue) — represent trader sentiment:
EMA 3, 5, 8, 10, 12, 15
✅ Long-Term EMAs (Red) — represent investor sentiment:
EMA 30, 35, 40, 45, 50, 60
Usage:
When blue (short) EMAs are above red (long) EMAs and spreading → Strong uptrend
When blue EMAs cross below red EMAs → Potential downtrend
⚫ 2. Bollinger Bands (Volatility Envelopes):
Length: 300 (captures the longer-term price range)
Basis: 300-period SMA
Upper & Lower Bands:
±1 Standard Deviation (light gray zone)
±2 Standard Deviations (dark gray zone)
Fill Zones:
Highlights standard deviation ranges
Emphasizes extreme vs. normal price moves
Usage:
Price touching ±2 SD bands signals potential exhaustion
Price reverting to the mean suggests pullback or re-entry opportunity
💡 Important Note: Use With Momentum Filter
✅ For superior accuracy, this indicator should be combined with your invite-only momentum filter on TradingView.
This filter helps confirm whether the trend has underlying strength or is losing momentum, increasing the probability of successful entries and exits.
🕒 Recommended Timeframe:
📆 1-Hour Chart (60m)
This setup is optimized for short- to medium-term swing trading, where Guppy structures and Bollinger reversion work best.
🔧 Practical Strategy Example:
Long Trade Setup:
Short EMAs are above long EMAs (strong uptrend)
Price pulls back to the lower 1 or 2 SD band
Momentum filter confirms bullish strength
Short Trade Setup:
Short EMAs are below long EMAs (strong downtrend)
Price rises to the upper 1 or 2 SD band
Momentum filter confirms bearish strength
Cari dalam skrip untuk "市值60亿的股票"
Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework
The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike.
## Theoretical Foundation
### Historical Context of Bear Market Prediction
Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models.
Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data.
### Methodological Framework
The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment.
The mathematical foundation follows the general form:
Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100
Where:
- Wi = Category weight (i = 1,2,3,4)
- Ci = Normalized category score
- Categories: Macroeconomic, Technical, Sentiment, Breadth
## Component Analysis
### 1. Macroeconomic Risk Factors
#### Yield Curve Analysis
The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations.
Implementation:
- 2Y-10Y Spread: Captures market expectations of monetary policy trajectory
- 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time
Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure.
#### Credit Risk Premium Assessment
High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress.
Threshold Calibration:
- Normal conditions: < 350 basis points
- Elevated risk: 350-500 basis points
- Severe stress: > 500 basis points
#### Currency and Commodity Stress Indicators
The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress.
### 2. Technical Analysis Factors
#### Multi-Timeframe Moving Average Analysis
The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules.
Implementation:
- Price relative to 50-day and 200-day simple moving averages
- Moving average convergence/divergence analysis
- Multi-timeframe MACD assessment (daily and weekly)
#### Momentum and Volatility Analysis
The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement.
### 3. Market Sentiment Factors
#### Volatility Index Analysis
The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average.
Calibration:
- Low volatility: VIX < 20
- Elevated concern: VIX 20-25
- High fear: VIX > 25
- Panic conditions: VIX > 30
#### Put-Call Ratio Analysis
Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets.
### 4. Market Breadth Factors
#### Advance-Decline Analysis
Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns.
Components:
- Daily advance-decline ratio
- Advance-decline line momentum
- McClellan Oscillator (Ema19 - Ema39 of A-D difference)
#### New Highs-New Lows Analysis
The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990).
## Dynamic Threshold Methodology
The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions.
Dynamic Threshold Calculation:
- Warning Level: Base threshold ± (Volatility × 1.0)
- Danger Level: Base threshold ± (Volatility × 1.5)
- Bounds: ±10-20 points from base threshold
## Professional Implementation
### Institutional Usage Patterns
Professional risk managers typically employ multi-factor bear market models in several contexts:
#### 1. Portfolio Risk Management
- Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70%
- Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached
- Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods
#### 2. Risk Budgeting
- Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations
- Stress Testing: Using probability levels to calibrate stress test scenarios
- Capital Requirements: Adjusting regulatory capital based on systemic risk assessment
#### 3. Client Communication
- Risk Reporting: Quantifying market risk for client presentations
- Investment Committee Decisions: Providing objective risk metrics for strategic decisions
- Performance Attribution: Explaining defensive positioning during market stress
### Implementation Framework
Professional traders typically implement such models through:
#### Signal Hierarchy:
1. Probability < 30%: Normal risk positioning
2. Probability 30-50%: Increased hedging, reduced leverage
3. Probability 50-70%: Defensive positioning, cash building
4. Probability > 70%: Maximum defensive posture, short exposure consideration
#### Risk Management Integration:
- Position Sizing: Inverse relationship between probability and position size
- Stop-Loss Adjustment: Tighter stops during elevated risk periods
- Correlation Monitoring: Increased attention to cross-asset correlations
## Strengths and Advantages
### 1. Comprehensive Coverage
The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes.
### 2. Dynamic Adaptability
The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes.
### 3. Real-Time Processing
Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management.
### 4. Transparency and Interpretability
The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals.
### 5. Historical Validation
Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power.
## Limitations and Weaknesses
### 1. Data Dependencies
The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions.
### 2. Regime Change Sensitivity
Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007).
### 3. False Signal Risk
Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods.
### 4. Currency and Geographic Bias
The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets.
### 5. Correlation Breakdown
During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002).
## References
Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851.
Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.
Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24.
Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192.
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.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26.
Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49.
Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261.
Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research.
Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720.
Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Magdon-Ismail, M., & Atiya, A. F. (2004). Maximum drawdown. Risk, 17(10), 99-102.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.
Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46.
Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871-908.
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of Financial and Quantitative Analysis, 25(1), 113-125.
Zweig, M. E. (1986). Winning on Wall Street. Warner Books.
5:30 AM IST Close + Offset Lines + TablesDescription:
This script captures the 5:30 AM IST close price and plots it on the chart along with dynamic offset levels above and below (±5, ±20, ±40, ±60, ±80 points). It also displays these levels in neatly organized tables at the top-right and bottom-right corners for quick reference.
🔹 Timezone: Asia/Kolkata (IST)
🔹 Useful for: Intraday traders who reference early morning levels
🔹 Visual aids:
Orange line for 5:30 AM close
Green lines for points above
Red lines for points below
Tables summarizing all levels
This tool helps identify key early-morning reference zones that can act as support/resistance or breakout targets.
Realtime ATR-Based Stop Loss Numerical OverlayRealtime ATR-Based Stop Loss Numerical Overlay
A simple, effective tool for dynamic risk management based on ATR (Average True Range) without adding cluttered and distracting lines all over your chart.
📌 Description
This script plots a real-time stop loss level using the Average True Range (ATR) on your chart, helping you set consistent, volatility-based stops. It supports both:
✅ Current chart timeframe
✅ Custom fixed timeframe inputs (1m, 5m, 15m, 1h, etc.)
The stop level is calculated as:
Stop = ATR × Multiplier
and updates in real-time. An overlay table displays on the bottom-right of your chart with the calculated stop value in a clean, simple way.
⚙️ Settings
ATR Timeframe Source:
Choose between using the current chart's timeframe or a fixed one (e.g. 5, 15, 60, D, etc).
ATR Length:
Period used to calculate the ATR (default is 14).
Stop Loss Multiplier:
Multiplies the ATR value to define your stop (e.g., 1.5 × ATR).
Wait for Timeframe Closes:
If enabled, the ATR value waits for the selected timeframe’s candle to close before updating. If unselected, it will update in real time.
🛠️ How to Use
Add this script to your chart from your indicators list.
Configure your desired timeframe, ATR length, and multiplier in the settings panel.
Use the value shown in the table overlay as your suggested stop loss distance from entry.
Adjust your position sizing accordingly to fit your risk tolerance.
This tool is especially useful for traders looking for adaptive risk management that evolves with market volatility — whether scalping intraday or swing trading.
💡 Pro Tip
The ATR stop can also be used to dynamically trail your stop behind price movement.
Dual Stochastic Enhanced (with Presets giua64)Script Title: Dual Stochastic Enhanced (with Presets giua64)
Overview:
This indicator enhances the traditional Dual Stochastic strategy, aiming to provide more filtered and potentially reliable trading signals. By integrating dynamic overbought/oversold levels via Bollinger Bands on the slow stochastic, a trend filter based on a moving average, momentum confirmation via RSI, and user-friendly selectable presets, "Dual Stochastic Enhanced" seeks to offer a more robust approach to identifying potential entry points.
Key Features:
Dual Stochastics: Utilizes a slow stochastic (configurable, e.g., 14 periods) as a context filter and a fast stochastic (configurable, e.g., 5 periods) as a signal trigger.
Bollinger Bands on Slow Stochastic: Instead of fixed overbought/oversold levels (80/20), Bollinger Bands are applied to the %K line of the slow stochastic. This creates dynamic zones that adapt to the stochastic's own volatility.
Trend Filter: A moving average (configurable type and length, e.g., EMA 100 as seen in the example chart for general context) on the price helps filter signals, allowing only trades aligned with the prevailing trend.
RSI Confirmation: An RSI oscillator (configurable length, e.g., 14 periods) is used to confirm momentum. Signals require the RSI to cross certain thresholds to validate the strength of the move.
User Presets: Includes presets for "Scalping," "Intraday," and "Swing trading," which quickly set all key parameters to suit different styles and timeframes. A "Custom" option is also available for full manual configuration.
Clear Visual Signals: Long (green) and Short (red) arrows appear on the chart when all entry conditions are met.
Active Zone Highlighting: The background of the indicator panel changes color (green or red) when "active zone" conditions (a combination of stochastics, trend, and RSI) are favorable.
Information Panel: A table in the top-right corner of the indicator panel displays the current status of the selected preset, trend filter, RSI value, and stochastic levels.
Signal Logic:
A LONG signal is generated when:
The fast stochastic %K crosses above its %D line.
The slow stochastic %K line is below its lower Bollinger Band (dynamic oversold condition).
The fast stochastic %K line is also in a low area (e.g., <25) to confirm the trigger is not premature.
The closing price is above the trend moving average (uptrend).
The RSI is above its long confirmation level (e.g., >40), indicating sufficient bullish momentum.
A SHORT signal is generated when:
The fast stochastic %K crosses below its %D line.
The slow stochastic %K line is above its upper Bollinger Band (dynamic overbought condition).
The fast stochastic %K line is also in a high area (e.g., >75).
The closing price is below the trend moving average (downtrend).
The RSI is below its short confirmation level (e.g., <60), indicating sufficient bearish momentum.
How to Use:
Select a Preset suitable for your trading style and the timeframe you are analyzing (e.g., Scalping for M1-M15, Intraday for M5-H1, Swing for H4-D1).
Alternatively, choose "Custom" and manually adjust all parameters (stochastic lengths, smoothing, Bollinger Bands, Moving Average, RSI, confirmation thresholds).
Observe the Information Panel for a quick understanding of the current conditions.
Evaluate the arrow signals, always considering the broader market context, price action, and any other confluences (supports/resistances, chart patterns).
The background highlighting can help quickly identify periods where conditions are aligned for potential trades.
Disclaimer:
This script is provided for educational and informational purposes only. Trading involves significant risk, and past performance is not indicative of future results. Always thoroughly test any strategy or indicator on historical data and on a demo account before risking real capital. The author assumes no responsibility for any losses incurred from the use of this script.
Author: giua64
Adaptive Multi-TF Indicator Table with Presets giua64📌 Script Name:
Adaptive Multi-Timeframe Indicator Table with Presets — giua64
📄 Description:
This script displays an adaptive multi-timeframe dashboard that summarizes the signals of three key technical indicators:
Moving Averages (MAs), Relative Strength Index (RSI), and MACD.
It provides a fast and visually intuitive overview of market conditions across five timeframes (5m, 15m, 30m, 1h, 4h), helping traders quickly identify potential directional biases (e.g., bullish, bearish, or neutral) based on either predefined presets or fully manual settings.
🧰 Preset Configurations:
You can choose between four trading styles, each with optimized indicator parameters:
Scalping
• MAs: 5 / 10 (Fast), 20 / 50 (Slow)
• RSI: 7 periods | Overbought: 70 | Oversold: 30
• MACD: 5 / 13 | Signal: 3
Intraday
• MAs: 9 / 21 (Fast), 50 / 100 (Slow)
• RSI: 14 periods | Overbought: 60 | Oversold: 40
• MACD: 12 / 26 | Signal: 9
Swing
• MAs: 10 / 20 (Fast), 50 / 200 (Slow)
• RSI: 14 periods | Overbought: 65 | Oversold: 35
• MACD: 12 / 26 | Signal: 9
Manual
• Full custom control over all indicator settings.
🛠️ All settings can be customized manually from the options panel, including the exact MA periods, RSI thresholds, and MACD structure.
🧠 How It Works:
For each timeframe, the script evaluates:
MA crossover status (two levels):
The first symbol refers to the crossover of the fast MAs
The second symbol refers to the crossover of the slow MAs
🟢 = Bullish crossover
🔴 = Bearish crossover
➖ = Flat or no clear signal
RSI Direction:
↑ = RSI above upper threshold (potential overbought)
↓ = RSI below lower threshold (potential oversold)
→ = RSI in neutral range
MACD Line vs Signal Line:
↑ = MACD line is above signal line (bullish)
↓ = MACD line is below signal line (bearish)
→ = Flat or neutral signal
Each signal is assigned a numerical score. These are aggregated per timeframe to compute a combined score that reflects the directional bias for that specific time window.
🧠 Adaptive Logic by Asset:
This script is designed to be universally compatible across all asset types — including forex, crypto, stocks, indices, and commodities.
Thanks to its multi-timeframe nature and flexible indicator presets, the script automatically adjusts its behavior based on the asset selected, ensuring relevant analysis without requiring manual recalibration.
🧾 Summary Table Output:
At the bottom of the dashboard, a combined sentiment is displayed for:
3TF → 5m, 15m, 30m
4TF → Adds 1h
5TF → Adds 4h
Each row shows:
Signal → LONG / SHORT / NEUTRAL
Confidence (%) → Based on score aggregation and signal consistency
📌 Customization Options:
Table Position: Left, Right, or Center
Text Size: Small, Normal, or Large
Full Manual Configuration: All MA, RSI, and MACD parameters can be adjusted as needed
⚠️ Disclaimer:
This script is for educational and analytical purposes only.
It does not constitute financial advice or guarantee any trading results.
Always do your own research and apply responsible risk management.
Wick Spike 50% Detector (15m & 1h)This script identifies candles with significant upper or lower wicks (spikes) based on a percentage of the total candle range. It helps spot potential reversals, exhaustion moves, or liquidity grabs — especially useful in volatile markets.
📍 Key Features:
15-Minute Timeframe:
Red Triangle Above: Candle range ≥ 0.35% and upper wick ≥ 50% of the range.
Green Triangle Below: Candle range ≥ 0.30% and lower wick ≥ 50% of the range.
1-Hour Timeframe:
Red Circle Above: Candle range ≥ 0.50% and upper wick ≥ 50%.
Green Circle Below: Candle range ≥ 0.50% and lower wick ≥ 50%.
📢 Alerts:
Alerts trigger when the 50% spike condition is met — within the last 60 seconds before candle close — ensuring timely notifications.
🎯 Designed to assist traders in identifying spike-driven opportunities and refining entry/exit strategies.
Triple Stochastic Confluence by AtallaTriple Stochastic Confluence by Atalla - Indicator Summary
Overview
The "Triple Stochastic Confluence by Atalla" is a technical indicator for TradingView that identifies potential trading opportunities using the confluence of three Stochastic oscillators with different timeframes. The indicator focuses exclusively on the %D lines (signal lines) of the Stochastics.
Key Components
Three Stochastic Oscillators
Short-term Stochastic: Period 9, %K Smoothing 1, %D Period 3
Medium-term Stochastic: Period 14, %K Smoothing 1, %D Period 3
Long-term Stochastic: Period 60, %K Smoothing 1, %D Period 10
Visual Display
White lines for the first two Stochastics (%D lines)
Yellow line for the third (long-term) Stochastic (%D line)
Background color changes to highlight trading opportunities:
Yellow background: Bullish signal
Red background: Bearish signal
Trading Signals Logic
Bullish Signal (Yellow Background)
A bullish signal occurs when any Stochastic %D line is in the oversold zone (≤25%) while at least one of the other %D lines is in the overbought zone (≥75%).
Bearish Signal (Red Background)
A bearish signal occurs when any Stochastic %D line is in the overbought zone (≥75%) while at least one of the other %D lines is in the oversold zone (≤25%).
Configurable Parameters
Stochastic periods and smoothing values
Overbought level (default: 75%)
Oversold level (default: 25%)
Alert Conditions
The indicator includes alert conditions for both bullish and bearish confluence signals, allowing users to set up automated notifications for trading opportunities.
Trading Philosophy
This indicator leverages the concept of momentum divergence across different timeframes. When oscillators at different timeframes show opposing extreme readings (one in oversold and another in overbought), it may indicate a potential reversal point in the market. The indicator's strength lies in identifying these confluences automatically and providing clear visual signals.
Stoch Quad Oscillator📘 Stoch Quad Oscillator – User Guide
✅ Purpose
The Stoch Quad Oscillator is a multi-timeframe stochastic oscillator tool that helps traders detect oversold and overbought conditions, momentum shifts, and quad rotation signals using four distinct stochastic configurations. It includes visual cues, customizable parameters, and background highlights to improve decision-making during trend reversals or momentum surges.
🛠️ Inputs & Parameters
⏱ Timeframe
Timeframe for Stochastic Calculation: Defines which chart timeframe to use for stochastic calculations (default is "1" minute). This enables multi-timeframe analysis while on a lower timeframe chart.
📈 Stochastic Parameters
Four different stochastic configurations are used:
Label %K Length %D Smoothing Notes
K9 D3 9 3 Fastest, short-term view
K14 D3 14 3 Moderately short-term
K40 D4 40 4 Medium-term trend view
K60 D10 60 10 Long-term strength
Smoothing Type: Choose between SMA or EMA to control how smoothed the %D line is.
🎯 Levels
Overbought Level: Default 80
Oversold Level: Default 20
These are used to indicate overextended price conditions on any of the stochastic plots.
🔄 Quad Rotation Detection Settings
When enabled, the script detects synchronized oversold/overbought conditions with strong momentum using all 4 stochastic readings.
Enable Quad Rotation: Toggles detection on or off
Slope Calculation Bars: Number of bars used to calculate slope of %D lines
Slope Threshold: Minimum slope strength for signal (higher = stronger confirmation)
Oversold Quad Level: Total of all four stochastic values that define a quad oversold zone
Overbought Quad Level: Total of all four stochastic values that define a quad overbought zone
Oversold Quad Highlight Color: Background color when oversold quad is triggered
Overbought Quad Highlight Color: Background color when overbought quad is triggered
Slope Averaging Method: Either Simple Average or Weighted Average (puts more weight on higher timeframes)
Max Signal Bar Window: Defines how recent the signal must be to be considered valid
📊 Plots & Visual Elements
📉 Stochastic %D Lines
Each stochastic is plotted separately:
K9 D3 – Red
K14 D3 – Orange
K40 D4 – Fuchsia
K60 D10 – Silver
These help visualize short to long-term momentum simultaneously.
📏 Horizontal Reference Lines
Overbought Line (80) – Red
Oversold Line (20) – Green
These help you identify threshold breaches visually.
🌈 Background Highlighting
The indicator provides background highlights to mark potential signal zones:
✅ All Oversold or Overbought Conditions
When all four stochastics are either above overbought or below oversold:
Bright Red if all are overbought
Bright Green if all are oversold
🚨 Quad Rotation Signal Zones (if enabled)
Triggered when:
The combined sum of all four stochastic levels is extremely low/high (below/above oversoldQuadLevel or overboughtQuadLevel)
The average slope of the 4 %D lines is sharply positive (> slopeThreshold)
Highlights:
Custom Red Tint = Strong overbought quad signal
Custom Green Tint = Strong oversold quad signal
These zones can indicate momentum shifts or reversal potential when used with price action or other tools.
⚠️ Limitations & Considerations
This indicator does not provide trade signals. It visualizes conditions and potential setups.
It is best used in confluence with price action, support/resistance levels, and other indicators.
False positives may occur in ranging markets. Reduce reliance on slope thresholds during low volatility.
Quad signals rely on slope strength, which may lag slightly behind sudden reversals.
🧠 Tips for Use
Combine with volume, MACD, or PSAR to confirm direction before entry.
Watch for divergences between price and any of the stochastics.
Use on higher timeframes (e.g., 5m–30m) to filter for swing trading setups; use shorter TFs (1m–5m) for scalping signals.
Adjust oversoldQuadLevel and overboughtQuadLevel based on market conditions (e.g., in trending vs ranging markets).
Trailing Cumulative Volume DeltaShort Description:
A dynamic volume delta indicator that calculates a trailing sum of net buying/selling pressure over a user-defined number of recent bars, offering a more adaptive view of order flow momentum compared to fixed-anchor CVD.
Overview:
The Trailing Cumulative Volume Delta (TCVD) indicator provides a powerful way to analyze market sentiment by tracking the net difference between buying and selling volume. Unlike traditional Cumulative Volume Delta (CVD) indicators that typically reset at fixed intervals (e.g., daily, weekly), the TCVD calculates a rolling sum of volume delta over a specified number of recent bars. This "trailing" approach offers a more fluid and responsive measure of recent order flow dynamics.
How it Works:
Per-Bar Delta Calculation: For each bar on your chart, the indicator first calculates the net Volume Delta. This is done by looking at a finer, user-configurable Lower Timeframe (e.g., 1-minute data for a 15-minute chart bar) to determine the aggressive buying vs. selling volume within that bar.
Trailing Sum: The indicator then sums these individual per-bar net deltas over a user-defined Trailing Bars lookback period. For example, if "Trailing Bars" is set to 20, the TCVD value will represent the cumulative net delta of the last 20 bars.
Visualization:
The TCVD is plotted in a "MACD-Columns-Style" in a separate pane.
Teal: When the TCVD value is increasing (suggesting growing net buying pressure or diminishing net selling pressure over the trailing period).
Red: When the TCVD value is decreasing (suggesting growing net selling pressure or diminishing net buying pressure over the trailing period).
White: When it is returning to the mean.
How to Interpret and Use TCVD:
Trend Strength & Momentum:
A rising TCVD suggests that, on average over the trailing period, buying pressure is dominant or strengthening. This can confirm bullish price action or indicate underlying strength.
A falling TCVD suggests that selling pressure is dominant or strengthening, potentially confirming bearish price action or indicating weakness.
Divergences:
Unlike other Divergences, the CVD has two different types of Divergences: a) Absorption and b) Exhaustion. You only want to trade the Absorption pattern.
Zero Line Crossovers:
TCVD crossing above the zero line can indicate a shift towards net positive buying pressure over the lookback period.
TCVD crossing below the zero line can indicate a shift towards net positive selling pressure.
Confirmation: Use TCVD to confirm breakouts or breakdowns. A price breakout accompanied by a strongly rising TCVD is generally more reliable.
Key Settings:
Trailing Bars: (Default: 10)
Determines the number of recent bars to include in the cumulative delta sum.
Shorter periods make the TCVD more responsive to immediate changes.
Longer periods provide a smoother, longer-term view of order flow.
Use custom timeframe: (Checkbox, Default: false)
Allows you to override the automatic selection of the lower timeframe for delta calculation.
Timeframe for Delta Calculation: (Default: "1" - 1 minute)
Specifies the lower timeframe data used to calculate the volume delta for each individual chart bar.
Choosing a very fine timeframe (e.g., seconds) can provide high precision but may be limited by data availability or processing load.
If "Use custom timeframe" is unchecked, the script attempts to choose a sensible default based on your chart's timeframe (e.g., "1S" for second charts, "1" for intraday, "5" for daily, "60" for weekly+).
Examples:
Confirming Breakout Strength:
Price breaks out above a significant resistance level.
If the TCVD is also sharply rising and has perhaps crossed above its zero line, it provides confirmation that strong buying interest is fueling the breakout, increasing confidence in its validity.
Important Notes:
This indicator requires reliable volume data from your broker/data feed to function correctly. If your chart does not have volume, or if the volume data is unreliable, the TCVD will not be accurate.
Like all indicators, TCVD is best used as part of a comprehensive trading strategy, in conjunction with price action analysis and other indicators or tools.
Experiment with the Trailing Bars and Timeframe for Delta Calculation settings to find what best suits your trading style, the asset you are analyzing, and the chart timeframe you are using.
Feel free to modify this, add your personal touch, or include specific screenshots when you publish!
Customizable Order Flow DashboardOrder Flow Dashboard – Indicator Summary
This TradingView indicator displays a real-time dashboard showing the candle direction (Bullish, Bearish) and countdown timers for three user-selected timeframes. It helps traders quickly assess multi-timeframe alignment during live sessions.
Features:
Custom Timeframes – Select any 3 timeframes (e.g. 1m, 5m, 1H)
Candle Trend Detection – Bullish (green), Bearish (red), or Neutral (gray)
Countdown Timer – Displays time remaining until the current candle closes in MM:SS format
Clean Labels – Automatically formats timeframes like “60” into “1H”
Table Display – Dashboard appears in the top-right corner of the chart
How to Use:
Add the script to your chart.
Open settings and select your preferred timeframes.
Monitor the table to view candle direction and time remaining for each selected timeframe.
Use Case:
Ideal for traders who want fast visual confirmation of trend direction across multiple timeframes to support entry and exit decisions.
Elliott Wave Noise FilterElliott Wave Noise Filter
Overview
The Elliott Wave Noise Filter is a specialized indicator for TradingView, designed to solve one of the biggest challenges in Elliott Wave analysis on lower timeframes: the identification of market noise. By combining multiple advanced filtering techniques, this indicator helps distinguish meaningful price action from random fluctuations.
The Problem
On lower timeframes—especially below 15 minutes—Elliott Wave analysis is significantly impacted by excessive market noise. This noise can lead to misinterpretation of wave structures, making it difficult to execute reliable trading decisions.
The Solution
The Elliott Wave Noise Filter utilizes four powerful methods to detect and filter noise:
ATR-Based Volatility Analysis: Identifies price movements too small to be structurally meaningful
Volume Confirmation: Filters out price moves that occur with insufficient volume
Trend Strength Measurement (ADX): Detects periods of weak trend activity, where noise tends to dominate
Fractal Pattern Recognition: Marks significant turning points that could be relevant for Elliott Wave analysis
Features
Visual Indicators
Background Coloring: Red indicates noise; green signifies a clear signal
Hull Moving Average: Smooths price action and highlights the prevailing trend
Fractal Markers: Triangles mark significant highs and lows
Status Panel: Displays current noise status and ADX value
Customization Options
ATR Period: Adjust the lookback period for ATR calculations
Noise Threshold: Defines the percentage of ATR below which a movement is considered noise
Volume Filter: Can be enabled or disabled
Volume Threshold: Sets the ratio to average volume for a move to be deemed significant
Hull MA Display and Length: Configure the moving average settings
ADX Parameters: Adjust trend strength sensitivity
Use Cases
For Elliott Wave Analysis
Eliminate noise to identify cleaner wave structures
Use fractal markers as potential wave endpoints
Reference the Hull MA for determining the broader trend
For General Trading
Identify high-noise periods to avoid low-quality setups
Spot clearer market phases for better entries
Assess price action quality through visual cues
Multi-Timeframe Approach
Apply the indicator across different timeframes for a comprehensive view
Prefer trading when both higher and lower timeframes align with consistent signals
Optimal Settings
For Very Short Timeframes (1–5 minutes)
Higher Noise Threshold (0.4–0.5)
Longer ATR Period (20–30)
Higher Volume Threshold (1.0–1.2)
For Medium Timeframes (15–60 minutes)
Medium Noise Threshold (0.2–0.3)
Standard ATR Period (14)
Standard Volume Threshold (0.8)
For Higher Timeframes (4h and above)
Lower Noise Threshold (0.1–0.2)
Shorter ATR Period (10)
Lower Volume Threshold (0.6–0.7)
Conclusion
The Elliott Wave Noise Filter is an essential tool for any Elliott Wave analyst or trader working on lower timeframes. By reducing noise and emphasizing significant market movements, it enables more precise analysis and potentially more profitable trading decisions.
Note: As with any technical indicator, the Elliott Wave Noise Filter should be used as part of a broader trading strategy and not as a standalone signal for trade execution.
Context MTF [Th16rry]Context MTF
A multi-timeframe trend context indicator that overlays an Exponential Moving Average (EMA) and a Weighted Moving Average (WMA) whose look-back periods adapt automatically to your chart’s timeframe. Inspired by Mike Bellafore and Brian Shannon (Multi timeframe analysis)
🔍 Overview
Context MTF helps you quickly gauge the prevailing trend and its strength by plotting two complementary moving averages in a single view:
* EMA (solid line) for smooth, responsive trend direction
* WMA (dotted line) for emphasis on recent price action
By automatically selecting period lengths that reflect meaningful market cycles, Context MTF provides intuitive context at a glance:
| Timeframe | Period | Market Cycle Represented |
| :--------: | :----: | :----------------------: |
| Daily (D) | 63 | Quarterly trend |
| Weekly (W) | 52 | Yearly trend |
| 1H (60) | 126 | Monthly trend |
| 15m (15) | 130 | Weekly trend |
| 5m (5) | 78 | Last 24 hours |
⚙️ How It Works
1. Automatic Period Selection
The script detects your chart’s timeframe and applies the appropriate look-back for both EMA and WMA.
2. Solid vs. Dotted
* EMA is drawn as a continuous solid line.
* WMA is rendered as a dotted line of the same color, highlighting short-term momentum within the broader trend.
3. Visual Trend Context
* Widening Gap : Indicates strengthening trend momentum.
* Convergence/Overlap : Suggests a market in consolidation or range.
🎯 Benefits
* Multi-Timeframe Context in a single pane—no need to switch charts.
* Instant trend strength assessment by comparing EMA vs. WMA divergence.
* Clear identification of range conditions when averages align.
* Fully automated period adjustment —set and forget.
⚙️ Settings
* Color : Shared color for both lines (default blue).
* Line Width : Adjustable via script inputs (default 2).
* Dotted WMA : Simulated using built-in dotted line styling for precise rendering.
Use Context MTF to enhance trend-based strategies, confirm breakout momentum, or filter ranging markets. Ideal for swing traders, day traders, and anyone who values clear, time-aligned trend information on every timeframe.
Stochastic RSI with MTF TableShort Description of the Script
The provided Pine Script indicator, titled "Stochastic RSI with MTF Table," calculates and displays the Stochastic RSI for the current timeframe and multiple other timeframes (5m, 15m, 30m, 60m, 240m, and daily). The Stochastic RSI is a momentum indicator that blends the Relative Strength Index (RSI) and Stochastic Oscillator to identify overbought and oversold conditions, as well as potential trend reversals via K and D line crossovers.
Key features of the script include:
Inputs: Customizable parameters such as K smoothing (default 3), D smoothing (default 3), RSI length (default 14), Stochastic length (default 14), source price (default close), and overbought/oversold levels (default 80/20).
MTF Table: A table displays the Stochastic RSI status for each timeframe:
"OB" (overbought) if K > 80, "OS" (oversold) if K < 20, or "N" (neutral) otherwise.
Crossovers: "K↑D" for bullish (K crosses above D) and "K↓D" for bearish (K crosses below D).
Visualization: Plots the K and D lines for the current timeframe, with horizontal lines at 80 (overbought), 50 (middle), and 20 (oversold), plus a background fill for clarity.
Table Position: Configurable to appear in one of four chart corners (default: top-right).
This indicator helps traders assess momentum across multiple timeframes simultaneously, aiding in the identification of trend strength and potential entry/exit points.
Trading Strategy with 50EMA and 200EMA for Highest Winning Rate
To create a strategy with the best probability of a high winning rate using the Stochastic RSI MTF indicator alongside the 50-period Exponential Moving Average (50EMA) and 200-period Exponential Moving Average (200EMA), we can combine trend identification with momentum-based entry timing. The 50EMA and 200EMA are widely used to determine medium- and long-term trends, while the Stochastic RSI MTF table provides multi-timeframe momentum signals. Here’s the strategy:
1. Determine the Overall Trend
Bullish Trend: The 50EMA is above the 200EMA on the current timeframe (e.g., daily or 60m chart). This suggests an uptrend, often associated with a "Golden Cross."
Bearish Trend: The 50EMA is below the 200EMA on the current timeframe. This indicates a downtrend, often linked to a "Death Cross."
Implementation: Plot the 50EMA and 200EMA on your chart and visually confirm their relative positions.
2. Identify Entry Signals Using the Stochastic RSI MTF Table
In a Bullish Trend (50EMA > 200EMA):
Look for timeframes in the MTF table showing:
Oversold (OS): K < 20, indicating a potential pullback in the uptrend where price may rebound.
Bullish Crossover (K↑D): K crosses above D, signaling rising momentum and a potential entry point.
Example: If the 60m and 240m timeframes show "OS" or "K↑D," this could be a buy signal.
In a Bearish Trend (50EMA < 200EMA):
Look for timeframes in the MTF table showing:
Overbought (OB): K > 80, suggesting a rally in the downtrend where price may reverse downward.
Bearish Crossover (K↓D): K crosses below D, indicating declining momentum and a potential short entry.
Example: If the 30m and daily timeframes show "OB" or "K↓D," this could be a sell/short signal.
Current Timeframe Check: Use the plotted K and D lines on your trading timeframe for precise entry timing (e.g., confirm a K↑D crossover on a 60m chart for a long trade).
3. Confirm Signals Across Multiple Timeframes
Strengthen the Signal: A higher winning rate is more likely when multiple timeframes align with the trend and signal. For instance:
Bullish trend + "OS" or "K↑D" on 60m, 240m, and daily = strong buy signal.
Bearish trend + "OB" or "K↓D" on 15m, 60m, and 240m = strong sell signal.
Prioritize Higher Timeframes: Signals from the 240m or daily timeframe carry more weight due to their indication of broader trends, increasing reliability.
4. Set Stop-Loss and Take-Profit Levels
Long Trades (Bullish):
Stop-Loss: Place below the most recent swing low or below the 50EMA, whichever is closer, to protect against trend reversals.
Take-Profit: Target a key resistance level or use a risk-reward ratio (e.g., 2:1 or 3:1) based on the stop-loss distance.
Short Trades (Bearish):
Stop-Loss: Place above the most recent swing high or above the 50EMA, whichever is closer.
Take-Profit: Target a key support level or apply a similar risk-reward ratio.
Trailing Stop Option: As the trend progresses, trail the stop below the 50EMA (for longs) or above it (for shorts) to lock in profits.
5. Risk Management
Position Sizing: Risk no more than 1-2% of your trading capital per trade to minimize losses from false signals.
Volatility Consideration: Adjust stop-loss distances and position sizes based on the asset’s volatility (e.g., wider stops for volatile stocks or crypto).
Avoid Overtrading: Wait for clear alignment between the EMA trend and MTF signals to avoid low-probability setups.
Example Scenario
Chart: 60-minute timeframe.
Trend: 50EMA > 200EMA (bullish).
MTF Table: 60m shows "OS," 240m shows "K↑D," and daily is "N."
Action: Enter a long position when the 60m K line crosses above D, confirming the table signal.
Stop-Loss: Below the recent 60m swing low (e.g., 2% below entry).
Take-Profit: At the next resistance level or a 3:1 reward-to-risk ratio.
Outcome: High probability of success due to trend alignment and multi-timeframe confirmation.
Why This Strategy Works
Trend Following: Trading in the direction of the 50EMA/200EMA trend reduces the risk of fighting the market’s momentum.
Momentum Timing: The Stochastic RSI MTF table pinpoints pullbacks or reversals within the trend, improving entry timing.
Multi-Timeframe Confirmation: Alignment across timeframes filters out noise, increasing the win rate.
Risk Control: Defined stop-loss and position sizing protect against inevitable losses.
Caveats
No strategy guarantees a 100% win rate; false signals can occur, especially in choppy markets.
Test this strategy on historical data or a demo account to verify its effectiveness for your asset and timeframe.
This approach leverages the strengths of both trend-following (EMA) and momentum (Stochastic RSI) tools, aiming for a high-probability, disciplined trading system.
Hybrid Swing/Day Alert System - PLATINUM EditionThis indicator is a complete trading assistant designed for crypto swing and day traders, built to identify high-probability long and short setups based on a multi-confirmation system.
Strategy Logic
The system scans and confirms entries only when 6 major confluences align:
1. EMA Trend: Price is above or below the EMA 9, 21, and 200 (bullish or bearish trend).
2. RSI Zone: RSI(14) is between 40-60 (ideal reversal zone).
3. Volume Confirmation: Volume is declining on pullback and then spikes.
4. Accumulation/Distribution: A/D line rising (for longs) or falling (for shorts).
5. Fibonacci Pullback Zone: Automatic detection of swing high/low and checks if price is inside the golden zone (0.5-0.618).
Built-In Alerts
- Long Setup Confirmed - Short Setup Confirmed - Setup Forming: Monitor
Conclusion
This script is ideal for disciplined traders who value confluence-based entries, risk/reward logic, and trend-aligned trades. Perfect for semi-automated trading via alerts or manual execution.6. Candle Pattern: Bullish (hammer, doji, engulfing) or Bearish (rejection wick, engulfing, doji).
Visual Features
- Long Entry: Green square
- Short Entry: Red triangle
- Pre-Signal Alert: Blue circle (confluence forming)
- Dynamic Table: Displays all 6 confirmations in real time
- Fibonacci Zones: Auto-plotted long/short retracement zones
- Customizable: Turn on/off alerts, overlays, and direction filters
Best Use Cases
- 4H/Daily: Trend confirmation
- 1H: Entry execution
- 15min: Scalping (use cautiously)
- Works great with BTC, ETH, SOL, XAU, and meme coins
Adaptive Volume-Weighted RSI (AVW-RSI)Concept Summary
The AVW-RSI is a modified version of the Relative Strength Index (RSI), where each price change is weighted by the relative trading volume for that period. This means periods of high volume (typically driven by institutions or “big money”) have a greater influence on the RSI calculation than periods of low volume.
Why AVW-RSI Helps Traders
Avoids Weak Signals During Low Volume
Standard RSI may show overbought/oversold zones even during low-volume periods (e.g., during lunch hours or after news).
AVW-RSI gives less weight to these periods, avoiding misleading signals.
Amplifies Strong Momentum Moves
If RSI is rising during high volume, it's more likely driven by institutional buying—AVW-RSI reflects that stronger by weighting the RSI component.
Filters Out Retail Noise
By prioritizing high-volume candles, it naturally discounts fakeouts caused by thin markets or retail-heavy moves.
Highlights Institutional Entry/Exit
Useful for spotting hidden accumulation/distribution that classic RSI would miss.
How It Works (Calculation Logic)
Traditional RSI Formula Recap
RSI = 100 - (100 / (1 + RS))
RS = Average Gain / Average Loss (over N periods)
Modified Step – Apply Volume Weight
For each period
Gain_t = max(Close_t - Close_{t-1}, 0)
Loss_t = max(Close_{t-1} - Close_t, 0)
Weight_t = Volume_t / AvgVolume(N)
WeightedGain_t = Gain_t * Weight_t
WeightedLoss_t = Loss_t * Weight_t
Weighted RSI
AvgWeightedGain = SMA(WeightedGain, N)
AvgWeightedLoss = SMA(WeightedLoss, N)
RS = AvgWeightedGain / AvgWeightedLoss
AVW-RSI = 100 - (100 / (1 + RS))
Visual Features on Chart
Line Color Gradient
Color gets darker as volume weight increases, signaling stronger conviction.
Overbought/Oversold Zones
Traditional: 70/30
Suggested AVW-RSI zones: Use dynamic thresholds based on historical volatility (e.g., 80/20 for high-volume coins).
Volume Spike Flags
Mark RSI turning points that occurred during volume spikes with a special dot/symbol.
Trading Strategies with AVW-RSI
1. Weighted RSI Divergence
Regular RSI divergence becomes more powerful when volume is high.
AVW-RSI divergence with volume spike is a strong signal of reversal.
2. Trend Confirmation
RSI crossing above 50 during rising volume is a good entry signal.
RSI crossing below 50 with high volume is a strong exit or short trigger.
3. Breakout Validation
Price breaking resistance + AVW-RSI > 60 with volume = Confirmed breakout.
Price breaking but AVW-RSI < 50 or on low volume = Potential fakeout.
Example Use Case
Stock XYZ is approaching a resistance zone. A trader sees:
Standard RSI: 65 → suggests strength.
Volume is 3x the average.
AVW-RSI: 78 → signals strong momentum with institutional backing.
The trader enters confidently, knowing this isn't just low-volume hype.
Limitations / Tips
Works best on liquid assets (Forex majors, large-cap stocks, BTC/ETH).
Should be used alongside price action and volume analysis—not standalone.
Periods of extremely high volume (news events) might need smoothing to avoid spikes.
On Balance Volume W DivergenceOBV With Divergence Indicator
A comprehensive On Balance Volume (OBV) indicator enhanced with divergence detection capabilities.
Core Features:
Classic OBV calculation with volume-based price movement tracking
Advanced divergence detection system
Multiple smoothing options for OBV
Bollinger Bands integration
Technical Components:
Volume-based price movement analysis
Pivot point detection for divergence
Customizable lookback periods
Adjustable divergence range parameters
Customization Options:
Multiple Moving Average types (SMA, EMA, SMMA, WMA, VWMA)
Bollinger Bands with adjustable standard deviation
Divergence sensitivity settings
Visual customization for signals and alerts
The indicator combines traditional OBV analysis with modern divergence detection, offering traders a powerful tool for identifying potential trend reversals and market momentum shifts.
Key Parameters:
- Pivot Lookback Right/Left: 5 (default)
- Divergence Range: 5-60 bars
- MA Length: 14 (default)
- BB StdDev: 2.0 (default)
Alert System:
- Bullish divergence alerts
- Bearish divergence alerts
- Customizable alert messages
Note: The indicator requires volume data to function properly and will display an error if volume data is not available.
Candle Rating (1–5)This “Candle Rating (1–5)” indicator measures where each bar’s close sits within its own high-low range and assigns a simple strength score:
Range Calculation
It computes the candle’s total range (high − low) and finds the close’s position as a percentage of that range (0 = close at low, 1 = close at high).
Five-Point Rating
1 (Strong Buy): Close in the top 20% of the range
2 (Moderate Buy): 60–80%
3 (Neutral): 40–60%
4 (Moderate Sell): 20–40%
5 (Strong Sell): Bottom 20%
Visual Feedback
It plots the numeric rating above each bar (colored green → red), giving you an at-a-glance read of candle momentum and potential reversal strength across any timeframe.
CAN INDICATORCAN Moving Averages Indicator - Feature Guide
1. Multiple Moving Averages (20 MAs)
- Supports up to 20 individual moving averages
- Each MA can be independently configured:
- Enable/Disable toggle
- Length (period) setting
- Type selection (SMA, EMA, DEMA, VWMA, RMA, WMA)
- Color customization
- Individual timeframe settings when global timeframe is disabled
Pre-configured MA Settings:
1. MA1-8: SMA type
- Lengths: 20, 50, 100, 200, 365, 489, 600, 1460
2. MA9-20: EMA type
- Lengths: 30, 60, 120, 240, 300, 400, 500, 700, 800, 900, 1000, 2000
2. Global Timeframe Settings
Location: Global Settings group
Features:
- Use Global Timeframe: Toggle to use one timeframe for all MAs
- Global Timeframe: Select the timeframe to apply globally
3. Label Display Options
Location: Main Inputs section
Controls:
- Show MA Type: Display MA type (SMA, EMA, etc.)
- Show MA Length: Display period length
- Show Resolution: Display timeframe
- Label Offset: Adjust label position
4. Cross Alerts System
Location: Cross Alerts group
Features:
1. Price Crosses:
- Alerts when price crosses any selected MA
- Select MA to monitor (1-20)
- Triggers on crossover/crossunder
2. MA Crosses:
- Alerts when one MA crosses another
- Select fast MA (1-20)
- Select slow MA (1-20)
- Triggers on crossover/crossunder
5. Relative Strength (RS) Analysis
Location: Relative Strength group
Features:
- Select any MA to monitor (1-20)
- Compares MA to its own average
- Adjustable RS Length (default 14)
- Visual feedback via background color:
- Green: MA above its average (uptrend)
- Red: MA below its average (downtrend)
- Customizable colors and transparency
6. Moving Average Types Available
1. **SMA** (Simple Moving Average)
- Equal weight to all prices
2. **EMA** (Exponential Moving Average)
- More weight to recent prices
3. **DEMA** (Double Exponential Moving Average)
- Reduced lag compared to EMA
4. **VWMA** (Volume Weighted Moving Average)
- Incorporates volume data
5. **RMA** (Running Moving Average)
- Smoother than EMA
6. **WMA** (Weighted Moving Average)
- Linear weight distribution
Usage Tips
1. **For Trend Following:**
- Enable longer-period MAs (MA4-MA8)
- Use cross alerts between long-term MAs
- Monitor RS for trend strength
2. **For Short-term Trading:**
- Focus on shorter-period MAs (MA1-MA3, MA9-MA11)
- Enable price cross alerts
- Use multiple timeframe analysis
3. **For Multiple Timeframe Analysis:**
- Disable global timeframe
- Set different timeframes for each MA
- Compare MA relationships across timeframes
4. **For Performance:**
- Disable unused MAs
- Limit active alerts to necessary pairs
- Use RS selectively on key MAs
Overnight Bias: Net Long/Short with PercentOvernight bias can assist with NY session gap fades or gap and go trading once the NY session is open.
Some general gap rules are:
1. Gap Direction Aligned with Overnight Bias
Rule: If the NY session gaps up and the overnight bias is Net Long (e.g., >60% of bars above the overnight open), favor longs.
Confirmation: Look for price to hold above overnight open or VWAP.
Invalidation: If price re-enters the overnight range, reassess.
2. Gap Opposing Overnight Bias (Contrarian Setup)
Rule: If the NY opens opposite the overnight bias, expect potential gap fill or reversal.
Trade Bias: Look for retracement back toward the overnight open or VWAP.
Example: Overnight was Net Long, but NY gaps down → wait for reclaim of VWAP to go long, else fade strength.
3. Gap Into Prior Day Value Area (VAH to VAL)
Rule: If the NY session gaps into the prior day value area:
It implies mean reversion behavior.
Expect price to rotate toward the POC (point of control).
Trade Bias: Fade toward POC if overnight bias is balanced or opposite the gap direction.
4. Gap Outside Prior Day Value Area
Rule: A gap above VAH or below VAL suggests potential breakout or new trend day.
Trade Bias: If overnight bias aligns (e.g., gap above VAH + Net Long overnight), consider trend continuation.
Invalidation: If price breaks back inside the prior day value area, watch for failed breakout → fade trade possible.
5. Gap Above Prior Day High / Below Prior Day Low
Rule: This is a true breakout gap.
Above Prior High + Net Long Bias: Look for continuation.
Below Prior Low + Net Short Bias: Look for sell pressure continuation.
Trade Bias: Use pullbacks to the prior high/low or overnight open for continuation setups.
6. Gap Within Prior Day Range
Rule: If the NY open is within the prior day’s high and low, expect chop or balanced conditions.
Trade Bias: Use overnight VWAP and prior POC as decision zones. Be cautious unless a breakout occurs.
7. Failed Gap and Re-entry into Prior Day Range
Rule: If price gaps above prior high but re-enters the prior range, it's a failed breakout.
Trade Bias: Look for a fade back to VAH or POC.
Confirmation: Watch for breakdown below overnight VWAP or failure to hold overnight open.
8. Gap + Overnight VWAP Divergence
Rule: If price gaps opposite the direction of VWAP (e.g., VWAP rising, gap down), wait for confirmation.
Trade Bias: Be cautious with early trades. Bias may flip if VWAP is reclaimed.
9. Gap + Overnight Open Test
Rule: If price opens with a gap and then retests the overnight open, that level becomes a decision zone.
Trade Bias:
Hold above = trend continuation.
Rejection = gap fill or reversal.
10. Unfilled Gap = Trend Bias
Rule: If the gap remains unfilled for the first 30–60 minutes, it increases the odds of a trend day.
Trade Bias: Trade pullbacks in the direction of the gap and overnight bias.
Should anyone have suggestion to add please do so.
Guppy Multiple Moving Average (GMMA)The GMMA Momentum Indicator plots 12 EMAs on your chart, divided into two groups:
Short-term EMAs (6 lines, default periods: 3, 5, 8, 10, 12, 15): Represent short-term trader sentiment and momentum.
Long-term EMAs (6 lines, default periods: 30, 35, 40, 45, 50, 60): Reflect long-term investor behavior and broader market trends.
By analyzing the interaction between these two groups, the indicator identifies:
Bullish and bearish trends based on the relative positions of the short- and long-term EMAs.
Momentum strength through the spread or convergence of the EMAs.
Potential reversals or breakouts via compression signals.
This PineScript version enhances the traditional GMMA by adding visual cues like background colors, bearish signals, and compression detection, making it ideal for swing traders seeking clear, actionable insights.
The GMMA Momentum Indicator provides several key features:
1. Trend Identification
Bullish Trend: When the short-term EMAs (green lines) are above the long-term EMAs (blue lines) and spreading apart, it signals strong upward momentum. The chart background turns light green to highlight this condition.
Bearish Trend: When the short-term EMAs cross below the long-term EMAs and converge, it indicates downward momentum. The background turns light red, and an orange downward triangle appears above the bar to mark a new bearish signal.
2. Momentum Analysis
The spread between the short-term EMAs reflects the strength of short-term momentum. A wide spread suggests strong momentum, while a tight grouping indicates weakening momentum or consolidation. Similarly, the long-term EMAs act as dynamic support or resistance, guiding traders on the broader trend.
3. Compression Detection
Compression occurs when both the short-term and long-term EMAs converge, signaling low volatility and a potential breakout or reversal. A yellow upward triangle appears below the bar when compression is detected, alerting traders to watch for price action.
4. Visual Cues
Green short-term EMAs: Show short-term trader activity.
Blue long-term EMAs: Represent long-term investor sentiment.
Background colors: Light green for bullish trends, light red for bearish trends, and transparent for neutral conditions.
Orange downward triangles: Mark new bearish trends.
Yellow upward triangles: Indicate compression, hinting at potential breakouts.
How to Use the GMMA Momentum Indicator for Swing Trading
Swing trading involves capturing price moves over days to weeks, and the GMMA Momentum Indicator is an excellent tool for this strategy. Here’s how to use it effectively:
1. Identifying Trade Entries
Buy Opportunities:
Look for a bullish trend (green background) where the short-term EMAs are above the long-term EMAs and spreading apart, indicating strong momentum.
A compression signal (yellow triangle) followed by a breakout above resistance or a bullish candlestick pattern can confirm an entry.
Example: On a daily chart, if the short-term EMAs cross above the long-term EMAs and the background turns green, consider entering a long position, especially if volume supports the move.
Sell Opportunities:
Watch for a bearish signal (orange downward triangle) or a bearish trend (red background) where the short-term EMAs cross below the long-term EMAs.
Example: If the short-term EMAs collapse below the long-term EMAs and an orange triangle appears, it may signal a shorting opportunity or a time to exit longs.
2. Managing Trades
Use the long-term EMAs as dynamic support (in uptrends) or resistance (in downtrends) to set stop-loss levels or trail stops.
Monitor the spread of the short-term EMAs. A widening spread suggests the trend is strong, while convergence may indicate it’s time to take profits or tighten stops.
3. Anticipating Reversals
Compression signals (yellow triangles) highlight periods of low volatility, often preceding significant price moves. Combine these with price action (e.g., breakouts or reversals) or other indicators (e.g., RSI or volume) for confirmation.
Example: If a compression signal appears near a key support level and the price breaks upward, it could signal the start of a new bullish swing.
4. Best Practices
Timeframes: The indicator works well on daily or 4-hour charts for swing trading, but you can adjust the EMA periods for shorter (e.g., 1-hour) or longer (e.g., weekly) timeframes.
Confirmation: Combine the GMMA with other tools like support/resistance levels, candlestick patterns, or oscillators (e.g., MACD) to reduce false signals.
Risk Management: Always use proper position sizing and stop-losses, as EMAs are lagging indicators and may produce delayed signals in choppy markets.
X HL RangeOverview:
The X Range indicator is a multi-timeframe visualization tool designed to display the high and low price ranges of previous candles from higher timeframes (HTFs) directly on a lower timeframe chart. It helps traders identify significant price zones and potential support/resistance levels by visually representing the price range of up to three previous candles for each selected timeframe.
Key Features:
Multi-Timeframe Support: The indicator supports three configurable higher timeframes (default: 60 min, 15 min, 5 min) which can be independently toggled on or off.
Custom Candle Range Display: For each enabled timeframe, users can choose to display the range of the most recent 1, 2, or 3 completed candles.
Dynamic Box Drawing: Price ranges are highlighted using rectangular boxes that extend across the chart to show where the highs and lows of each selected HTF candle occurred.
Custom Styling: Each timeframe's boxes can be individually styled with user-defined background and border colors to suit visual preferences or chart themes.
Efficient Redrawing: Boxes update in real-time as new higher timeframe candles complete, and previous boxes are removed to prevent chart clutter.
Use Case:
This indicator is particularly useful for intraday traders who want to align entries and exits with higher timeframe levels. By visualizing previous HTF ranges on a lower timeframe chart, traders gain contextual awareness of where price is likely to react or consolidate, aiding in decision-making for breakouts, reversals, or trend continuation setups.
MA Crossover with Adaptive Trend Strength📘 MA Crossover with Adaptive Trend Strength —
📌 Overview
This TradingView indicator plots two moving averages (Fast & Slow) with user-selected types (T3, EMA, SMA, HMA), visual crossovers, and dynamically calculates an adaptive trend strength score using Z-scores of multiple features. Optional higher timeframe (HTF) confirmation is supported. A color-filled region between the MAs visually indicates momentum direction.
⚙️ Inputs & Controls
📈 Moving Average Settings
Fast MA Length: Length of the fast-moving average (default: 9).
Slow MA Length: Length of the slow-moving average (default: 21).
MA Type: Type of moving average used (T3, EMA, SMA, HMA).
Source: Input data source (default: close).
T3 Volume Factor: Only used when T3 is selected, controls smoothing (range: 0–1).
🎨 Visual Controls
Bullish Fill Color: Fill color when Fast MA is above Slow MA.
Bearish Fill Color: Fill color when Fast MA is below Slow MA.
Show Gradient Fill: Enable or disable the colored area between Fast & Slow MAs.
Trend Label Position: Choose where the trend strength label appears (top or bottom).
Label Update Interval: Number of bars between label updates (reduces clutter).
⏱ Multi-Timeframe Support
Higher Timeframe: Timeframe used for confirmation (default: 60 min).
Use HTF Confirmation: Enables filtering of trend score by higher timeframe trend direction.
📊 Lookback Configuration
Auto Lookback Based on Timeframe: Dynamically adapts scoring lookback period per chart timeframe.
Manual Lookback: Manual fallback lookback length when auto is off.
🧮 MA Calculation Options
T3 MA: Custom T3 function with exponential moving averages and volume factor.
EMA/SMA: Built-in Pine functions (ta.ema, ta.sma).
HMA: Hull Moving Average using WMA calculations.
📉 Trend Strength Calculation
🧠 Z-Score Inputs
Distance between MAs (zDist)
Slope of the Fast MA (zSlope)
Volume (zVol)
ATR (zATR)
📏 Choppiness & Adaptive Weighting
A Choppiness Index (based on ATR & price range) reduces score impact in sideways markets.
Dynamically adjusts Z-score weights:
W1: Distance
W2: Slope
W3: Volume
W4: ATR
🔁 HTF Confirmation
Optionally multiplies the trend score by the direction of the higher timeframe trend to filter noise.
🟩 Plot & Visual Elements
📊 MA Lines
Plots Fast and Slow MA lines in colors based on selected MA type.
🌈 Gradient Fill
Fills the area between Fast and Slow MAs with opacity proportional to their difference.
Colors based on bullish/bearish condition.
🏷️ Trend Strength Label
Updates every n bars (Label Update Interval).
Shows:
Trend Classification: Weak, Moderate, Strong
Numerical Score
Label position (top or bottom) is configurable.
🔔 Crossover Signals
Bullish Crossover ("B"): Fast MA crosses above Slow MA.
Bearish Crossover ("S"): Fast MA crosses below Slow MA.
Labels are plotted at crossover points.
Old labels are removed after a threshold (100) to reduce chart clutter.
📋 Score Summary Table
A table showing:
Max Score within the lookback period
Min Score
HTF Confirmation Status (ON / OFF)
Updates on the same user-defined interval as the trend label.
🚨 Alerts
Condition Description
Bullish MA Cross Fast MA crosses above Slow MA
Bearish MA Cross Fast MA crosses below Slow MA
These are provided via alertcondition() for use in alert creation.
📌 Customization Tips
Turn off the gradient fill for a cleaner chart.
Use HTF confirmation to reduce false positives in ranging markets.
Adjust label update frequency to prevent visual clutter on faster timeframes.
Use T3 MA with volume factor for smoother signals in volatile markets.
