D-BoT Alpha 'Short' SMA and RSI StrategyDostlar selamlar,
İşte son derece basit ama etkili ve hızlı, HTF de çok iyi sonuçlar veren bir strateji daha, hepinize bol kazançlar dilerim ...
Nedir, Nasıl Çalışır:
Strateji, iki ana girdiye dayanır: SMA ve RSI. SMA hesaplama aralığı 200 olarak, RSI ise 14 olarak ayarlanmıştır. Bu değerler, kullanıcı tercihlerine veya geriye dönük test sonuçlarına göre ayarlanabilir.
Strateji, iki koşul karşılandığında bir short sinyali oluşturur: RSI değeri, belirlenen bir giriş seviyesini (burada 51 olarak belirlenmiş) aşar ve kapanış fiyatı SMA değerinin altındadır.
Strateji, kısa pozisyonu üç durumda kapatır: Kapanış fiyatı, takip eden durdurma seviyesinden (pozisyon açıldığından beri en düşük kapanış olarak belirlenmiştir) büyükse, RSI değeri belirlenen bir durdurma seviyesini (bu durumda 54) aşarsa veya RSI değeri belirli bir kar al seviyesinin (bu durumda 32) altına düşerse.
Güçlü Yönleri:
İki farklı gösterge (SMA ve RSI) kullanımı, yalnızca birini kullanmaktan daha sağlam bir sinyal sağlayabilir.
Strateji, karları korumaya ve fiyat dalgalanmalarında kayıpları sınırlamaya yardımcı olabilecek bir iz süren durdurma seviyesi içerir.
Script oldukça anlaşılır ve değiştirmesi nispeten kolaydır.
Zayıf Yönleri:
Strateji, hacim, oynaklık veya daha geniş piyasa eğilimleri gibi diğer potansiyel önemli faktörleri göz önünde bulundurmaz.
RSI seviyeleri ve SMA süresi için belirli parametreler sabittir ve tüm piyasa koşulları veya zaman aralıkları için optimal olmayabilir.
Strateji oldukça basittir. Trade maliyetini (kayma veya komisyonlar gibi) hesaba katmaz, bu da trade performansını önemli ölçüde etkileyebilir.
Bu Stratejiyle Nasıl İşlem Yapılır:
Strateji, short işlemler için tasarlanmıştır. RSI, 51'in üzerine çıktığında ve kapanış fiyatı 200 periyotluk SMA'nın altında olduğunda işleme girer. RSI, 54'ün üzerine çıktığında veya 32'nin altına düştüğünde veya fiyat, pozisyon açıldığından beri en düşük kapanış fiyatının üzerine çıktığında işlemi kapatır.
Lütfen Dikkat, bu strateji veya herhangi bir strateji izole bir şekilde kullanılmamalıdır. Tüm bu çalışmalar eğitsel amaçlıdır. Yatırım tavsiyesi içermez.
This script defines a trading strategy based on Simple Moving Average (SMA) and the Relative Strength Index (RSI) indicators. Here's an overview of how it works, along with its strengths and weaknesses, and how to trade using this strategy:
How it works:
The strategy involves two key inputs: SMA and RSI. The SMA length is set to 200, and the RSI length is set to 14. These values can be adjusted based on user preferences or back-testing results.
The strategy generates a short signal when two conditions are met: The RSI value crosses over a defined entry level (set at 51 here), and the closing price is below the SMA value.
When a short signal is generated, the strategy opens a short position.
The strategy closes the short position under three conditions: If the close price is greater than the trailing stop (which is set as the lowest close since the position opened), if the RSI value exceeds a defined stop level (54 in this case), or if the RSI value drops below a certain take-profit level (32 in this case).
Strengths:
The use of two different indicators (SMA and RSI) can provide a more robust signal than using just one.
The strategy includes a trailing stop, which can help to protect profits and limit losses as the price fluctuates.
The script is straightforward and relatively easy to understand and modify.
Weaknesses:
The strategy doesn't consider other potentially important factors, such as volume, volatility, or broader market trends.
The specific parameters for the RSI levels and SMA length are hard-coded, and may not be optimal for all market conditions or timeframes.
The strategy is very simplistic. It doesn't take into account the cost of trading (like slippage or commissions), which can significantly impact trading performance.
How to trade with this strategy:
The strategy is designed for short trades. It enters a trade when the RSI crosses above 51 and the closing price is below the 200-period SMA. It will exit the trade when the RSI goes above 54 or falls below 32, or when the price rises above the lowest closing price since the position was opened.
Please note, this strategy or any strategy should not be used in isolation. It's important to consider other aspects of trading such as risk management, capital allocation, and combining different strategies to diversify. Back-testing the strategy on historical data and demo trading before going live is also a recommended practice.
Cerca negli script per "relative strength"
Advanced Currency Strength Meter# Advanced Currency Strength Meter (ACSM)
The Advanced Currency Strength Meter (ACSM) is a scientifically-based indicator that measures relative currency strength using established academic methodologies from international finance and behavioral economics. This indicator provides traders with a comprehensive view of currency market dynamics through multiple analytical frameworks.
### Theoretical Foundation
#### 1. Purchasing Power Parity (PPP) Theory
Based on Cassel's (1918) seminal work and refined by Froot & Rogoff (1995), PPP suggests that exchange rates should reflect relative price levels between countries. The ACSM momentum component captures deviations from long-term equilibrium relationships, providing insights into currency misalignments.
#### 2. Uncovered Interest Rate Parity (UIP) and Carry Trade Theory
Building on Fama (1984) and Lustig et al. (2007), the indicator incorporates volatility-adjusted momentum to capture carry trade flows and interest rate differentials that drive currency strength. This approach helps identify currencies benefiting from interest rate differentials.
#### 3. Behavioral Finance and Currency Momentum
Following Burnside et al. (2011) and Menkhoff et al. (2012), the model recognizes that currency markets exhibit persistent momentum effects due to behavioral biases and institutional flows. The indicator captures these momentum patterns for trading opportunities.
#### 4. Portfolio Balance Theory
Based on Branson & Henderson (1985), the relative strength matrix captures how portfolio rebalancing affects currency cross-rates and creates trading opportunities between different currency pairs.
### Technical Implementation
#### Core Methodologies:
- **Z-Score Normalization**: Following Sharpe (1994), provides statistical significance testing without arbitrary scaling
- **Momentum Analysis**: Uses return-based metrics (Jegadeesh & Titman, 1993) for trend identification
- **Volatility Adjustment**: Implements Average True Range methodology (Wilder, 1978) for risk-adjusted strength
- **Composite Scoring**: Equal-weight methodology to avoid overfitting and maintain robustness
- **Correlation Analysis**: Risk management framework based on Markowitz (1952) portfolio theory
#### Key Features:
- **Multi-Source Data Integration**: Supports OANDA, Futures, and CFD data sources
- **Scientific Methodology**: No arbitrary scaling or curve-fitting; all calculations based on established statistical methods
- **Comprehensive Dashboard**: Clean, professional table showing currency strengths and best trading pairs
- **Alert System**: Automated notifications for strong/weak currency conditions and extreme values
- **Best Pair Identification**: Algorithmic detection of highest-potential trading opportunities
### Practical Applications
#### For Swing Traders:
- Identify currencies in strong uptrends or downtrends
- Select optimal currency pairs based on relative strength divergence
- Time entries based on momentum convergence/divergence
#### For Day Traders:
- Use with real-time futures data for intraday opportunities
- Monitor currency correlations for risk management
- Detect early reversal signals through extreme value alerts
#### For Portfolio Managers:
- Multi-currency exposure analysis
- Risk management through correlation monitoring
- Strategic currency allocation decisions
### Visual Design
The indicator features a clean, professional dashboard that displays:
- **Currency Strength Values**: Each major currency (EUR, GBP, JPY, CHF, AUD, CAD, NZD, USD) with color-coded strength values
- **Best Trading Pairs**: Filtered list of highest-potential currency pairs with BUY/SELL signals
- **Market Analysis**: Real-time identification of strongest and weakest currencies
- **Potential Score**: Quantitative measure of trading opportunity strength
### Data Sources and Latency
The indicator supports multiple data sources to accommodate different trading needs:
- **OANDA (Delayed)**: Free data with 15-20 minute delay, suitable for swing trading
- **Futures (Real-time)**: CME currency futures for real-time analysis
- **CFDs**: Alternative real-time data source option
### Mathematical Framework
#### Strength Calculation:
Momentum = (Price - Price ) / Price * 100
Z-Score = (Price - Mean) / Standard Deviation
Volatility-Adjusted = Momentum / ATR-based Volatility
Composite = 0.5 * Momentum + 0.3 * Z-Score + 0.2 * Volatility-Adjusted
#### USD Strength Derivation:
USD strength is calculated as the weighted average of all USD-based pairs, providing a true baseline for relative strength comparison.
### Performance Considerations
The indicator is optimized for:
- **Computational Efficiency**: Uses Pine Script v6 best practices
- **Memory Management**: Appropriate lookback periods and array handling
- **Visual Clarity**: Clean table design optimized for both light and dark themes
- **Alert Reliability**: Robust signal generation with statistical significance testing
### Limitations and Risk Disclosure
- Model performance may vary during extreme market stress (Black Swan events)
- Requires stable data feeds for accurate calculations
- Not optimized for high-frequency scalping strategies
- Central bank interventions may temporarily distort signals
- Performance assumes normal market conditions with behavioral adjustments
### Academic References
- Branson, W. H., & Henderson, D. W. (1985). "The Specification and Influence of Asset Markets"
- Burnside, C., Eichenbaum, M., & Rebelo, S. (2011). "Carry Trade and Momentum in Currency Markets"
- Cassel, G. (1918). "Abnormal Deviations in International Exchanges"
- Fama, E. F. (1984). "Forward and Spot Exchange Rates"
- Froot, K. A., & Rogoff, K. (1995). "Perspectives on PPP and Long-Run Real Exchange Rates"
- Jegadeesh, N., & Titman, S. (1993). "Returns to Buying Winners and Selling Losers"
- Lustig, H., Roussanov, N., & Verdelhan, A. (2007). "Common Risk Factors in Currency Markets"
- Markowitz, H. (1952). "Portfolio Selection"
- Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012). "Carry Trades and Global FX Volatility"
- Sharpe, W. F. (1994). "The Sharpe Ratio"
- Wilder, J. W. (1978). "New Concepts in Technical Trading Systems"
### Usage Instructions
1. **Setup**: Add the indicator to your chart and select your preferred data source
2. **Currency Selection**: Choose which currencies to analyze (default: all major currencies)
3. **Methodology**: Select calculation method (Composite recommended for most users)
4. **Monitoring**: Watch the dashboard for strength changes and best pair opportunities
5. **Alerts**: Set up notifications for strong/weak currency conditions
Adiyogi Trend🟢🔴 “Adiyogi” Trend — Market Alignment Visualizer
“Adiyogi” Trend is a powerful, non-intrusive trend detection system built for traders who seek clarity, discipline, and alignment with true market flow. Inspired by the meditative stillness of Adiyogi and the need for mindful, high-probability decisions, this tool offers a clean and intuitive visual guide to trending environments — without cluttering the chart or pushing forced trades.
This is not a buy/sell signal generator. Instead, it is designed as a background confirmation engine that helps you stay on the right side of the market by identifying moments of true directional strength.
🧠 Core Logic
The “Adiyogi” Trend indicator highlights the background of your chart in green or red when multiple layers of strength and structure align — including momentum, market positioning, and relative force. Only when these internal components agree does the system activate a directional state.
It’s built on three foundational energies of trend confirmation:
Strength of movement
Structure in price action
Conviction in momentum
By combining these into one visual background, the indicator filters out indecision and helps you stay focused during real trend phases — whether you're day trading, swing trading, or holding longer-term positions.
📌 Core Concepts Behind the Tool
The indicator integrates three essential market filters—each confirming a different dimension of trend strength:
ADX (Average Directional Index) – Measures trend momentum.
You’ve chosen a very responsive setting (ADX Length = 2), which helps catch the earliest possible signs of momentum emergence.
The threshold is ADX ≥ 22, ensuring that weak or sideways markets are filtered out.
SuperTrend (10,1) – Captures short-term trend direction.
This setup follows price closely and reacts quickly to reversals, making it ideal for fast-moving assets or intraday strategies.
SuperTrend acts as the structural confirmation of directional bias.
RSI (Relative Strength Index) – Measures strength based on recent price closes.
You’ve configured RSI > 50 for bullish zones and < 50 for bearish—a neutral midpoint standard often used by professional traders.
This ensures that only trades in sync with momentum and recent strength are highlighted.
🌈 How It Visually Works
Background turns GREEN when:
ADX ≥ 22, indicating strong momentum
Price is above the 20 EMA and above SuperTrend (10,1)
RSI > 50, confirming recent strength
Background turns RED when:
ADX ≥ 22, indicating strong momentum
Price is below the 20 EMA and below SuperTrend (10,1)
RSI < 50, confirming recent weakness
The background remains neutral (transparent) when trend conditions are not clearly aligned—this is the tool's way of keeping you out of indecisive markets.
A label (BULL / BEAR) appears only when the bias flips from the previous one. This helps avoid repeated or redundant alerts, focusing your attention only when something changes.
📊 Practical Uses & Benefits
✅ Stay with the trend: Perfectly filters out choppy or sideways markets by only activating when conditions align across momentum, structure, and strength.
✅ Pre-trade confirmation: Use this tool to confirm trade setups from other indicators or price action patterns.
✅ Avoid noise: Prevent overtrading by focusing only on high-quality trend conditions.
✅ Visual clarity: Unlike arrows or plots that clutter the chart, this tool subtly highlights trend conditions in the background, preserving your price action view.
📍 Important Notes
This is not a buy/sell signal generator. It is a trend-confirmation system.
Use it in conjunction with your existing entry setups—such as breakouts, order blocks, retests, or candlestick patterns.
The tool helps you stay in sync with the dominant direction, especially when combining multiple timeframes.
Can be used on any market (stocks, forex, crypto, indices) and on any timeframe.
Advanced Trend and Volatility Indicator with Alerts by ZaimonThis script presents a comprehensive analytical tool that integrates multiple technical indicators to provide a holistic view of market trends and volatility. By uniquely combining Moving Averages (MA), Relative Strength Index (RSI), Stochastic Oscillator, Bollinger Bands, and Average True Range (ATR), it offers nuanced insights into price movements and helps identify potential trading opportunities.
---
### **Key Features and Integration:**
1. **Moving Averages (MA20 & MA50):**
- **Trend Identification:**
- **Methodology:** Calculates two Simple Moving Averages—MA20 (short-term) and MA50 (long-term).
- **Bullish Trend:** When MA20 crosses above MA50, indicating upward momentum.
- **Bearish Trend:** When MA20 crosses below MA50, signaling downward momentum.
- **Golden Cross & Death Cross Alerts:**
- **Golden Cross:** MA20 crossing above MA50 generates a bullish alert and visual symbol.
- **Death Cross:** MA20 crossing below MA50 triggers a bearish alert and visual symbol.
- **Integration:**
- Serves as the foundational trend indicator, influencing interpretations of other indicators within the script.
2. **Relative Strength Index (RSI):**
- **Momentum Measurement:**
- **Methodology:** Calculates RSI to assess the speed and change of price movements over a 14-period length.
- **Overbought/Oversold Conditions:** Customizable thresholds set at 70 (overbought) and 30 (oversold).
- **Alerts:**
- Generates alerts when RSI crosses above or below the specified thresholds.
- **Integration:**
- Confirms trend strength identified by MAs.
- Overbought/Oversold signals can precede potential trend reversals, especially when aligned with MA crossovers.
3. **Stochastic Oscillator:**
- **Momentum and Reversal Signals:**
- **Methodology:** Uses %K and %D lines to evaluate price momentum relative to high-low range over recent periods.
- **Bullish Signal:** %K crossing above %D in oversold territory (below 20).
- **Bearish Signal:** %K crossing below %D in overbought territory (above 80).
- **Alerts:**
- Provides alerts on bullish and bearish crossovers in extreme regions.
- **Integration:**
- Enhances RSI signals by providing additional momentum confirmation.
- When both RSI and Stochastic indicate overbought/oversold conditions, it strengthens the likelihood of a reversal.
4. **Bollinger Bands:**
- **Volatility Visualization:**
- **Methodology:** Plots upper and lower bands based on standard deviations from a moving average (BB Basis).
- **Dynamic Support/Resistance:** Prices touching or exceeding the bands may indicate potential reversals.
- **Integration:**
- Works with RSI and Stochastic to identify overextended price movements.
- Helps in assessing volatility alongside trend and momentum indicators.
5. **Average True Range (ATR):**
- **Volatility Assessment:**
- **Methodology:** Calculates ATR over a 14-period length to measure market volatility.
- **ATR Bands:** Plots upper and lower bands relative to the current price using an ATR multiplier.
- **Integration:**
- Assists in setting stop-loss and take-profit levels based on current volatility.
- Complements Bollinger Bands for a comprehensive volatility analysis.
6. **Information Table:**
- **Real-Time Data Display:**
- Shows current values of MA20, MA50, RSI, Stochastic %K and %D, BB Basis, ATR, and Trend Status.
- **Trend Status Indicator:**
- Displays "Bullish," "Bearish," or "Sideways" based on MA conditions.
- **Integration:**
- Provides a consolidated view for quick decision-making without analyzing individual indicators separately.
7. **Periodic Labels:**
- **Enhanced Visibility:**
- Adds labels every 50 bars showing RSI and Stochastic values.
- **Integration:**
- Helps track momentum changes over time and spot longer-term patterns.
---
### **How the Components Work Together:**
- **Synergistic Analysis:**
- **Trend Confirmation:** MA crossovers establish the primary trend, while RSI and Stochastic confirm momentum within that trend.
- **Volatility Context:** Bollinger Bands and ATR provide context on market volatility, refining entry and exit points suggested by trend and momentum indicators.
- **Signal Strength:** Concurrent signals from multiple indicators increase confidence in trading decisions.
---
### **Usage Guidelines:**
1. **Trend Analysis:**
- **Identify Trend Direction:**
- Observe MA20 and MA50 crossovers.
- Refer to the Trend Status in the information table.
- **Confirm with Momentum Indicators:**
- Ensure RSI and Stochastic support the identified trend.
2. **Entry and Exit Points:**
- **Overbought/Oversold Conditions:**
- Look for RSI and Stochastic reaching extreme levels.
- Consider entering positions when oversold in a bullish trend or overbought in a bearish trend.
- **Bollinger Band Interactions:**
- Use price interactions with Bollinger Bands to identify potential reversal zones.
3. **Risk Management:**
- **ATR-Based Levels:**
- Set stop-loss and take-profit levels using ATR bands to account for current volatility.
- **Adjusting to Volatility:**
- Modify position sizes and targets based on Bollinger Band width and ATR values.
4. **Alerts Setup:**
- **Customize Alert Thresholds:**
- Configure alerts for MA crossovers, RSI levels, and Stochastic crossovers according to your trading strategy.
- **Stay Informed:**
- Use alerts to monitor key events without constant chart observation.
---
### **Customization:**
- **Flexible Parameters:**
- All indicator lengths, thresholds, and settings are adjustable to suit different trading styles and timeframes.
- **Adjustable Visuals:**
- Modify plot colors, line styles, and label positions to enhance chart readability.
---
### **Originality and Value Addition:**
This script differentiates itself by:
- **Integrated Approach:**
- Seamlessly combining multiple indicators to provide a more comprehensive analysis than using each indicator separately.
- **Enhanced Visualization:**
- Utilizing plots, fills, labels, and an information table to present data intuitively.
- **User-Friendly Features:**
- Pre-configured alerts and real-time data displays reduce the need for manual monitoring.
By explaining how each component interacts and contributes to the overall analysis, the script adds substantial value to traders seeking a multi-faceted tool for market analysis.
---
### **Additional Notes:**
- **Learning Resource:**
- The script is well-commented, serving as an educational tool for those learning Pine Script and technical analysis integration.
- **Further Enhancements:**
- Opportunities exist to incorporate additional indicators like MACD or ADX, and to develop advanced alert logic, such as RSI or Stochastic divergences.
---
### **Disclaimer:**
- **Educational Purpose Only:**
- This script is provided for informational purposes and should not be construed as financial advice.
- **Risk Acknowledgment:**
- Trading involves significant risk; past performance is not indicative of future results.
- **Due Diligence:**
- Users should conduct their own analysis and consider consulting a financial professional before making trading decisions.
---
By providing detailed explanations of the methodologies and the synergistic use of multiple indicators, this script aligns with TradingView's guidelines for originality and usefulness. It offers traders a unique tool that enhances market analysis through the thoughtful integration of technical indicators.
Relative Currency StrengthThis indicator shows the relative strength of the majors and crosses compared to each other. So, if you are taking a EURUSD long, are you taking it because the Euro is strong or the USD is weak or both? How do you know? This indicator will show you how strong a current is compared to the other majors and crosses. So in the EURUSD example, you will know how strong the EUR is compared to NZD, AUD, JPY, CHF, GBP, CAD and USD and how strong the USD is compared to the NZD, AUD, JPY, CHF, EUR, GBP and CAD. You can then make an informed choice as to whether the trade makes sense.
Notice in the examples below how the indicator clearly shows how CHF was weak all day and GBP was strong in the morning but then collapsed in the afternoon.
The indicator functions by taking a set point in the day and comparing how price compares to it for the rest of the day. I set it to Europe open and then take context of how a currency is comparing to that price (verses the other currencies) over the course of the day.
You can use the indicator in 2 ways - you set a currency as a baseline and see how other currencies fluctuate about it or you can see how all the currencies strengths compare to each other.
If you have the full tradingview membership you can have 8 screens and see how each currency compares. if you set the indicator to automatic it will automatically default to the base currency that you compare to OANDA gold.
The general strength is useful as a general overview as to where strength and weakness is in the charts. It works by using gold as the baseline which is a reliable way to compare strengths.
REMEMBER, THIS GIVES SUMMARY DATA. USE IT TO GET MARKET CONTEXT IN ORDER TO IDENTIFY WHERE STRENGTH AND WEAKNESS IS - YOU CANT JUST TRADE FROM IT. It's extremely useful in fast moving markets to easily stay aware of what is happening.
VolumePrice Intensity AnalyzerVolumePrice Intensity Analyzer
The VolumePrice Intensity Analyzer is a Pine Script v6 indicator designed to measure market activity intensity through the trading value (Price * Volume, scaled to millions). It helps traders identify significant volume-price interactions, track trends, and gauge momentum by combining volume analysis with trend-following tools.
Features:
Volume-Based Analysis: Calculates Price * Volume in millions to highlight market activity levels.
Trend Identification: Plots 20-day and 50-day SMAs of the trading value to smooth fluctuations and reveal sustained trends.
Relative Strength: Displays the ratio of daily Price * Volume to the long-term SMA in a separate pane, helping traders assess activity intensity relative to historical averages.
Real-Time Metrics: A table shows the current Price * Volume and its ratio to the long SMA, updated continuously with bold text formatting (v6 feature).
Alerts: Triggers notifications for high trading values (when Price * Volume exceeds 1.5x the long SMA) and SMA crossovers (short SMA crossing above long SMA).
Visual Cues: Uses dynamic bar colors (teal for bullish, gray for bearish) and background highlights to mark significant market activity.
Customizable Inputs: Adjust SMA periods, scaling factor, and alert threshold via the settings panel, with tooltips for clarity (v6 feature).
Originality:
Unlike basic volume indicators, this tool combines Price * Volume with trend analysis (SMAs), relative strength (ratio plot), and actionable alerts. The real-time table and visual highlights provide a unique, at-a-glance view of market intensity, making it a valuable addition for volume and trend-focused traders.
Calculations:
Trading Value (P*V): (Close * Volume) * Scale Factor (default scale factor of 1e-6 converts to millions).
SMAs: 20-day and 50-day Simple Moving Averages of the trading value to identify short- and long-term trends.
Ratio: Daily Price * Volume divided by the 50-day SMA, plotted in a separate pane to show relative activity strength.
Bar Colors: Teal (RGB: 0, 132, 141) for bullish bars (close > open or close > previous close), gray for bearish or neutral bars.
Background Highlight: Light yellow (hex: #ffcb3b, 81% transparency) when Price * Volume exceeds the long SMA by the alert threshold.
Plotted Elements:
Short SMA P*V (M): Red line, 20-day SMA of Price*Volume in millions.
Long SMA P*V (M): Blue line, 50-day SMA of Price*Volume in millions.
Today P*V (M): Columns, daily Price*Volume in millions (teal/gray based on price action).
Daily V*P/Longer Term Average: Purple line in a separate pane, ratio of daily Price * Volume to the 50-day SMA.
Usage:
Spot High Activity: Look for Price * Volume columns exceeding the SMAs or spikes in the ratio plot to identify significant market moves.
Confirm Trends: Use SMA crossovers (e.g., short SMA crossing above long SMA) as bullish trend signals, or vice versa for bearish trends.
Monitor Intensity: The table provides real-time Price * Volume and ratio values, while background highlights signal high activity periods.
Versatility: Suitable for stocks, forex, crypto, or any market with volume data, across various timeframes.
How to Use:
Add the indicator to your chart.
Adjust inputs (SMA periods, scale factor, alert threshold) via the settings panel to match your trading style.
Watch for alerts, check the table for real-time metrics, and observe the ratio plot for relative strength signals.
Use the background highlights and bar colors to quickly spot significant market activity and price action.
This indicator leverages Pine Script v6 features like lazy evaluation for performance and advanced text formatting for better visuals, making it a powerful tool for traders focusing on volume, trends, and momentum.
Money Flow Index Trend Zone Strength [UAlgo]The "Money Flow Index Trend Zone Strength " indicator is designed to analyze and visualize the strength of market trends and OB/OS zones using the Money Flow Index (MFI). The MFI is a momentum indicator that incorporates both price and volume data, providing insights into the buying and selling pressure in the market. This script enhances the traditional MFI by introducing trend and zone strength analysis, helping traders identify potential trend reversals and continuation points.
🔶 Customizable Settings
Amplitude: Defines the range for the MFI Zone Strength calculation.
Wavelength: Period used for the MFI calculation and Stochastic calculations.
Smoothing Factor: Smoothing period for the Stochastic calculations.
Show Zone Strength: Enables/disables visualization of the MFI Zone Strength line.
Show Trend Strength: Enables/disables visualization of the MFI Trend Strength area.
Trend Strength Signal Length: Period used for the final smoothing of the Trend Strength indicator.
Trend Anchor: Selects the anchor point (0 or 50) for the Trend Strength Stochastic calculation.
Trend Transform MA Length: Moving Average length for the Trend Transform calculation.
🔶 Calculations
Zone Strength (Stochastic MFI):
The highest and lowest MFI values over a specified amplitude are used to normalize the MFI value:
MFI Highest: Highest MFI value over the amplitude period.
MFI Lowest: Lowest MFI value over the amplitude period.
MFI Zone Strength: (MFI Value - MFI Lowest) / (MFI Highest - MFI Lowest)
By normalizing and smoothing the MFI values, we aim to highlight the relative strength of different market zones.
Trend Strength:
The smoothed MFI zone strength values are further processed to calculate the trend strength:
EMA of MFI Zone Strength: Exponential Moving Average of the MFI Zone Strength over the wavelength period.
Stochastic of EMA: Stochastic calculation of the EMA values, smoothed with the same smoothing factor.
Purpose: The trend strength calculation provides insights into the underlying market trends. By using EMA and stochastic functions, we can filter out noise and better understand the overall market direction. This helps traders stay aligned with the prevailing trend and make more informed trading decisions.
🔶 Usage
Interpreting Zone Strength: The zone strength plot helps identify overbought and oversold conditions. A higher zone strength indicates potential overbought conditions, while a lower zone strength suggests oversold conditions, can suggest areas for entry/exit decisions.
Interpreting Trend Strength: The trend strength plot visualizes the underlying market trend, can help signal potential trend continuation or reversal based on the chosen anchor point.
Using the Trend Transform: The trend transform plot provides an additional layer of trend analysis, helping traders identify potential trend reversals and continuation points.
Combine the insights from the zone strength and trend strength plots with other technical analysis tools to make informed trading decisions. Look for confluence between different indicators to increase the reliability of your trades.
🔶 Disclaimer:
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Stochastic Zone Strength Trend [wbburgin](This script was originally invite-only, but I'd vastly prefer contributing to the TradingView community more than anything else, so I am making it public :) I'd much rather share my ideas with you all.)
The Stochastic Zone Strength Trend indicator is a very powerful momentum and trend indicator that 1) identifies trend direction and strength, 2) determines pullbacks and reversals (including oversold and overbought conditions), 3) identifies divergences, and 4) can filter out ranges. I have some examples below on how to use it to its full effectiveness. It is composed of two components: Stochastic Zone Strength and Stochastic Trend Strength.
Stochastic Zone Strength
At its most basic level, the stochastic Zone Strength plots the momentum of the price action of the instrument, and identifies bearish and bullish changes with a high degree of accuracy. Think of the stochastic Zone Strength as a much more robust equivalent of the RSI. Momentum-change thresholds are demonstrated by the "20" and "80" levels on the indicator (see below image).
Stochastic Trend Strength
The stochastic Trend Strength component of the script uses resistance in each candlestick to calculate the trend strength of the instrument. I'll go more into detail about the settings after my description of how to use the indicator, but there are two forms of the stochastic Trend Strength:
Anchored at 50 (directional stochastic Trend Strength):
The directional stochastic Trend Strength can be used similarly to the MACD difference or other histogram-like indicators : a rising plot indicates an upward trend, while a falling plot indicates a downward trend.
Anchored at 0 (nondirectional stochastic Trend Strength):
The nondirectional stochastic Trend Strength can be used similarly to the ADX or other non-directional indicators : a rising plot indicates increasing trend strength, and look at the stochastic Zone Strength component and your instrument to determine if this indicates increasing bullish strength or increasing bearish strength (see photo below):
(In the above photo, a bearish divergence indicated that the high Trend Strength predicted a strong downwards move, which was confirmed shortly after. Later, a bullish move upward by the Zone Strength while the Trend Strength was elevated predicated a strong upwards move, which was also confirmed. Note the period where the Trend Strength never reached above 80, which indicated a ranging period (and thus unprofitable to enter or exit)).
How to Use the Indicator
The above image is a good example on how to use the indicator to determine divergences and possible pivot points (lines and circles, respectively). I recommend using both the stochastic Zone Strength and the stochastic Trend Strength at the same time, as it can give you a robust picture of where momentum is in relation to the price action and its trajectory. Every color is changeable in the settings.
Settings
The Amplitude of the indicator is essentially the high-low lookback for both components.
The Wavelength of the indicator is how stretched-out you want the indicator to be: how many amplitudes do you want the indicator to process in one given bar.
A useful analogy that I use (and that I derived the names from) is from traditional physics. In wave motion, the Amplitude is the up-down sensitivity of the wave, and the Wavelength is the side-side stretch of the wave.
The Smoothing Factor of the settings is simply how smoothed you want the stochastic to be. It's not that important in most circumstances.
Trend Anchor was covered above (see my description of Trend Strength). The "Trend Transform MA Length" is the EMA length of the Trend Strength that you use to transform it into the directional oscillator. Think of the EMA being transformed onto the 50 line and then the Trend Strength being dragged relative to that.
Trend Transform MA Length is the EMA length you want to use for transforming the nondirectional Trend Strength (anchored at 0) into the directional Trend Strength (anchored at 50). I suggest this be the same as the wavelength.
Trend Plot Type can transform the Nondirectional Trend Strength into a line plot so that it doesn't murk up the background.
Finally, the colors are changeable on the bottom.
Explanation of Zone Strength
If you're knowledgeable in Pine Script, I encourage you to look at the code to try to understand the concept, as it's a little complicated. The theory behind my Zone Strength concept is that the wicks in every bar can be used create an index of bullish and bearish resistance, as a wick signifies that the price crossed above a threshold before returning to its origin. This distance metric is unique because most indicators/formulas for calculating relative strength use a displacement metric (such as close - open) instead of measuring how far the price actually moved (up and down) within a candlestick. This is what the Zone Strength concept represents - the hesitation within the bar that is not typically represented in typical momentum indicators.
In the script's code I have step by step explanations of how the formula is calculated and why it is calculated as such. I encourage you to play around with the amplitude and wavelength inputs as they can make the zone strength look very different and perform differently depending on your interests.
Enjoy!
Walker
Composite MomentumComposite Momentum Indicator - Enhancing Trading Insights with RSI & Williams %R
The Composite Momentum Indicator is a powerful technical tool that combines the Relative Strength Index (RSI) and Williams %R indicators from TradingView. This unique composite indicator offers enhanced insights into market momentum and provides traders with a comprehensive perspective on price movements. By leveraging the strengths of both RSI and Williams %R, the Composite Momentum Indicator offers distinct advantages over a simple RSI calculation.
1. Comprehensive Momentum Analysis:
The Composite Momentum Indicator integrates the RSI and Williams %R indicators to provide a comprehensive analysis of market momentum. It takes into account both the strength of recent price gains and losses (RSI) and the relationship between the current closing price and the highest-high and lowest-low price range (Williams %R). By combining these two momentum indicators, traders gain a more holistic view of market conditions.
2. Increased Accuracy:
While the RSI is widely used for measuring overbought and oversold conditions, it can sometimes generate false signals in certain market environments. The Composite Momentum Indicator addresses this limitation by incorporating the Williams %R, which focuses on the price range and can offer more accurate signals in volatile market conditions. This combination enhances the accuracy of momentum analysis, allowing traders to make more informed trading decisions.
3. Improved Timing of Reversals:
One of the key advantages of the Composite Momentum Indicator is its ability to provide improved timing for trend reversals. By incorporating both RSI and Williams %R, traders can identify potential turning points more effectively. The Composite Momentum Indicator offers an early warning system for identifying overbought and oversold conditions and potential trend shifts, helping traders seize opportunities with better timing.
4. Enhanced Divergence Analysis:
Divergence analysis is a popular technique among traders, and the Composite Momentum Indicator strengthens this analysis further. By comparing the RSI and Williams %R within the composite calculation, traders can identify divergences between the two indicators more easily. Divergence between the RSI and Williams %R can signal potential trend reversals or the weakening of an existing trend, providing valuable insights for traders.
5. Customizable Moving Average:
The Composite Momentum Indicator also features a customizable moving average (MA), allowing traders to further fine-tune their analysis. By incorporating the MA, traders can smooth out the composite momentum line and identify longer-term trends. This additional layer of customization enhances the versatility of the indicator, catering to various trading styles and timeframes.
The Composite Momentum Indicator, developed using the popular TradingView indicators RSI and Williams %R, offers a powerful tool for comprehensive momentum analysis. By combining the strengths of both indicators, traders can gain deeper insights into market conditions, improve accuracy, enhance timing for reversals, and leverage divergence analysis. With the added customization of the moving average, the Composite Momentum Indicator provides traders with a versatile and effective tool to make more informed trading decisions.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
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Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
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Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
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Crowding model ║ BullVision🔬 Overview
The Crypto Crowding Model Pro is a sophisticated analytical tool designed to visualize and quantify market conditions across multiple cryptocurrencies. By leveraging Relative Strength Index (RSI) and Z-score calculations, this indicator provides traders with an intuitive and detailed snapshot of current crypto market dynamics, highlighting areas of extreme momentum, crowded trades, and potential reversal points.
⚙️ Key Concepts
📊 RSI and Z-Score Analysis
RSI (Relative Strength Index) evaluates the momentum and strength of each cryptocurrency, identifying overbought or oversold conditions.
Z-Score Normalization measures each asset's current price deviation relative to its historical average, identifying statistically significant extremes.
🎯 Crowding Analytics
An integrated analytics panel provides real-time crowding metrics, quantifying market sentiment into four distinct categories:
🔥 FOMO (Fear of Missing Out): High momentum, potential exhaustion.
❄️ Fear: Low momentum, potential reversal or consolidation.
📈 Recovery: Moderate upward momentum after a downward trend.
💪 Strength: Stable bullish conditions with sustained momentum.
🖥️ Visual Scatter Plot
Assets are plotted on a dynamic scatter plot, positioning each cryptocurrency according to its RSI and Z-score.
Color coding, symbol shapes, and sizes help quickly identify main market segments (BTC, ETH, TOTAL, OTHERS) and individual asset conditions.
🧩 Quadrant Classification
Assets are categorized into four quadrants based on their momentum and deviation:
Overbought Extended: High RSI and positive Z-score.
Recovery Phase: Low RSI but positive Z-score.
Oversold Compressed: Low RSI and negative Z-score.
Strong Consolidation: High RSI but negative Z-score.
🔧 User Customization
🎨 Visual Settings
Bar Scale: Adjust the scatter plot visual scale.
Asset Visibility: Optionally display key market benchmarks (TOTAL, BTC, ETH, OTHERS).
Gradient Background: Enhances visual interpretation of asset clusters.
Crowding Analytics Panel: Toggle the analytics panel on/off.
📊 Indicator Parameters
RSI Length: Defines the calculation period for RSI.
Z-score Lookback: Historical lookback period for normalization.
Crowding Alert Threshold: Sets alert sensitivity for crowded market conditions.
🎯 Zone Settings
Quadrant Labels: Displays descriptive labels for each quadrant.
Danger Zones: Highlights extreme RSI levels indicative of heightened market risk.
📈 Visual Output
Dynamic Scatter Plot: Visualizes asset positioning clearly and intuitively.
Gradient and Grid: Professional gridlines and subtle gradient backgrounds assist visual assessment.
Danger Zone Highlights: Visually indicates RSI extremes to warn of potential market turning points.
Crowding Analytics Panel: Real-time summary of market sentiment and asset distribution.
🔍 Use Cases
This indicator is particularly beneficial for traders and analysts looking to:
Identify crowded trades and potential reversal points.
Quickly assess overall market sentiment and individual asset strength.
Integrate a robust momentum analysis into broader technical or fundamental strategies.
Enhance market timing and improve risk management decisions.
⚠️ Important Notes
This indicator does not provide explicit buy or sell signals.
It is intended solely for informational, analytical, and educational purposes.
Past performance and signals are not indicative of future market results.
Always combine with additional tools and analysis as part of comprehensive decision-making.
T-Virus Sentiment [hapharmonic]🧬 T-Virus Sentiment: Visualize the Market's DNA
Remember the iconic T-Virus vial from the first Resident Evil? That powerful, swirling helix of potential has always fascinated me. It sparked an idea: what if we could visualize the market's underlying health in a similar way? What if we could capture the "genetic code" of market sentiment and contain it within a dynamic, 3D indicator? This project is the result of that idea, brought to life with Pine Script.
The indicator's main goal is to measure the strength and direction of market sentiment by analyzing the "genetic code" of price action through a variety of trusted indicators. The result is displayed as a liquid level within a DNA helix, a bubble density representing buying pressure, and a T-Virus mascot that reflects the overall mood.
🧐 Core Concept: How It Works
The primary output of the indicator is the "Active %" gauge you see on the right side of the vial. This percentage represents the overall sentiment score, calculated as an average from 7 different technical analysis tools. Each tool is analyzed on every bar and assigned a score from 1 (strong bearish pressure) to 5 (strong bullish potential).
In this indicator, we re-imagine market dynamics through the lens of a viral outbreak. A strong bear market is like a virus taking hold, pulling all technical signals down into a state of weakness. Conversely, a powerful bull market is like an antiviral serum ; positive signals rise and spread toward the top of the vial, indicating that the system is being injected with strength.
This is not just another line on a chart. It's a comprehensive sentiment dashboard designed to give an immediate, at-a-glance understanding of the confluence between 7 classic technical indicators. The incredible 3D model of the vial itself was inspired by a design concept found here .
⚛️ The 4 Core Elements of T-Virus Sentiment
These four elements work in harmony to give a complete, multi-faceted picture of market sentiment. Each component tells a different part of the story.
The Virus Mascot: An instant emotional cue. This character provides the quickest possible read on the overall market mood, combining sentiment with volume pressure.
The Antiviral Serum Level: The main quantitative output. This is the liquid level in the DNA helix and the percentage gauge on the right, representing the average sentiment score from all 7 indicators.
Buy Pressure & Bubble Density: This visualizes volume flow. The density of bubbles represents the intensity of accumulation (buying) versus distribution (selling). It's the "power" behind the move.
The Signal Distribution: This shows the confluence (or dispersion) of sentiment. Are all signals bullish and clustered at the top, or are they scattered, indicating a conflicted market? The position of the indicator labels is crucial, as each is assigned to one of five distinct zones:
Base Bottom: The market is at its weakest. Signals here suggest strong bearish control and distribution.
Lower Zone: The market is still bearish, but signals may be showing early signs of accumulation or bottoming.
Neutral Core (Center): A state of balance or sideways consolidation. The market is waiting for a new direction.
Upper Zone: Bullish momentum is becoming clear. Signals are strengthening and showing bullish control.
Top Cap: The market is "heating up" with strong bullish sentiment, potentially nearing overbought conditions.
🐂🐻 The Virus Mascot: The At-a-Glance Indicator
This character acts as a shortcut to confirm market health. It combines the sentiment score with volume, preventing false confidence in a low-volume rally.
Its state is determined by a dual-check: the overall "Antiviral Serum Level" and the "Buy Pressure" must both be above 50%.
Green & Smiling: The 'all clear' signal. This means that not only is the overall technical sentiment bullish, but it's also being supported by real buying pressure. This is a sign of a healthy bull market.
Red & Angry: A warning sign. This appears if either the sentiment is weak, or a bullish sentiment is not being confirmed by buying volume. The latter could indicate a potential "bull trap" or an exhaustive move.
This mascot can be disabled from the settings page under "Virus Mascot Styling" if a cleaner look is preferred.
🫧 Bubble Density: Gauging Buy vs. Sell Pressure
The bubbles visualize the battle between buyers and sellers. There are two modes to control how this is calculated:
Mode 1: Visible Range (The 'Big Picture' View)
This default mode is best for getting a broad, contextual understanding of the current session. It dynamically analyzes the volume of every single candlestick currently visible on the screen to calculate the buy/sell pressure ratio. It answers the question: "Over the entire period I'm looking at, who is in control?" As you zoom in or out, the calculation adapts.
Mode 2: Custom Lookback (The 'Precision' View)
This mode is for traders who need to analyze short-term pressure. You can define a fixed number of recent bars to analyze, which is perfect for scalping or understanding the volume dynamics leading into a key level. It answers the question: "What is happening right now ?" In the example above, a lookback of 2 focuses only on the most recent action, clearly showing intense, immediate selling pressure (few bubbles) and a corresponding drop in the sentiment score to 29%.
ℹ️ Interactive Tooltips: Dive Deeper
We believe in transparency, not 'black box' indicators. This feature transforms the indicator from a visual aid into an active learning tool.
Simply hover the mouse over any indicator label (like EMA, OBV, etc.) to get a detailed tooltip. It will explain the specific data points and thresholds that signal met to be placed in its current zone. This helps build trust in the signals and allows users to fine-tune the indicator settings to better match their own trading style.
🎯 The Scoring Logic Breakdown
The "Antiviral Serum Level" gauge is the average score from 7 technical analysis tools. Each is graded on a 5-point scale (1=Strong Bearish to 5=Strong Bullish). Here’s a detailed, transparent look at how each "gene" is evaluated:
Relative Strength Index (RSI)
Measures momentum and overbought/oversold conditions.
Group 1 (Strong Bearish): RSI > 80 (Extreme Overbought)
Group 2 (Bearish): 70 < RSI ≤ 80 (Overbought)
Group 3 (Neutral): 30 ≤ RSI ≤ 70
Group 4 (Bullish): 20 ≤ RSI < 30 (Oversold)
Group 5 (Strong Bullish): RSI < 20 (Extreme Oversold)
Exponential Moving Averages (EMA)
Evaluates the trend's strength and structure based on the alignment of multiple EMAs (9, 21, 50, 100, 200, 250).
Group 1 (Strong Bearish): A perfect bearish sequence (9 < 21 < 50 < ...)
Group 2 (Bearish Transition): Early signs of a potential reversal (e.g., 9 > 21 but still below 50)
Group 3 (Neutral / Mixed): MAs are intertwined or showing a partial bullish sequence.
Group 4 (Bullish): A strong bullish sequence is forming (e.g., 9 > 21 > 50 > 100)
Group 5 (Strong Bullish): A perfect bullish sequence (9 > 21 > 50 > 100 > 200 > 250)
Moving Average Convergence Divergence (MACD)
Analyzes the relationship between two moving averages to gauge momentum.
Group 1 (Strong Bearish): MACD & Histogram are negative and momentum is falling.
Group 2 (Weakening Bearish): MACD is negative but the histogram is rising or positive.
Group 3 (Neutral / Crossover): A crossover event is occurring near the zero line.
Group 4 (Bullish): MACD & Histogram are positive.
Group 5 (Strong Bullish): MACD & Histogram are positive, rising strongly, and accelerating.
Average Directional Index (ADX)
Measures trend strength, not direction. The score is based on both ADX value and the dominance of DI+ vs DI-.
Group 1 (Bearish / No Trend): ADX < 20 and DI- is dominant.
Group 2 (Developing Bearish Trend): 20 ≤ ADX < 25 and DI- is dominant.
Group 3 (Neutral / Indecision): Trend is weak or DI+ and DI- are nearly equal.
Group 4 (Developing Bullish Trend): 25 ≤ ADX ≤ 40 and DI+ is dominant.
Group 5 (Strong Bullish Trend): ADX > 40 and DI+ is dominant.
Ichimoku Cloud (IKH)
A comprehensive indicator that defines support/resistance, momentum, and trend direction.
Group 1 (Strong Bearish): Price is below the Kumo, Tenkan < Kijun, and Chikou is below price.
Group 2 (Bearish): Price is inside or below the Kumo, with mixed secondary signals.
Group 3 (Neutral / Ranging): Price is inside the Kumo, often with a Tenkan/Kijun cross.
Group 4 (Bullish): Price is above the Kumo with strong primary signals.
Group 5 (Strong Bullish): All signals are aligned bullishly: price above Kumo, bullish Tenkan/Kijun cross, bullish future Kumo, and Chikou above price.
Bollinger Bands (BB)
Measures volatility and relative price levels.
Group 1 (Strong Bearish): Price is below the lower band.
Group 2 (Bearish Territory): Price is between the lower band and the basis line.
Group 3 (Neutral): Price is hovering around the basis line.
Group 4 (Bullish Territory): Price is between the basis line and the upper band.
Group 5 (Strong Bullish): Price is above the upper band.
On-Balance Volume (OBV)
Uses volume flow to predict price changes. The score is based on OBV's trend and its position relative to its moving average.
Group 1 (Strong Bearish): OBV is below its MA and falling.
Group 2 (Weakening Bearish): OBV is below its MA but showing signs of rising.
Group 3 (Neutral): OBV is very close to its MA.
Group 4 (Bullish): OBV is above its MA and rising.
Group 5 (Strong Bullish): OBV is above its MA, rising strongly, and showing signs of a volume spike.
🧭 How to Use the T-Virus Sentiment Indicator
IMPORTANT: This indicator is a sentiment dashboard , not a direct buy/sell signal generator. Its strength lies in showing confluence and providing a quick, holistic view of the market's technical health.
Confirmation Tool: Use the "Active %" gauge to confirm a trade setup from your primary strategy. For example, if you see a bullish chart pattern, a high and rising sentiment score can add confidence to your trade.
Momentum & Trend Gauge: A consistently high score (e.g., > 75%) suggests strong, established bullish momentum. A consistently low score (< 25%) suggests strong bearish control. A score hovering around 50% often indicates a ranging or indecisive market.
Divergence & Warning System: Pay attention to divergences. If the price is making new highs but the sentiment score is failing to follow or is actively decreasing, it could be an early warning sign that the underlying momentum is weakening.
⚙️ Settings & Customization
The indicator is highly customizable to fit any trading style.
Position & Anchor: Control where the vial appears on the chart.
Styling (Vial, Helix, etc.): Nearly every visual element can be color-customized.
Signals: This is where the real power is. All underlying indicator parameters (RSI length, MACD settings, etc.) can be fine-tuned to match a personal strategy. The text labels can also be disabled if the chart feels cluttered.
Enjoy visualizing the market's DNA with the T-Virus Sentiment indicator
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.
Hull Moving Average Adaptive RSI (Ehlers)Hull Moving Average Adaptive RSI (Ehlers)
The Hull Moving Average Adaptive RSI (Ehlers) is an enhanced trend-following indicator designed to provide a smooth and responsive view of price movement while incorporating an additional momentum-based analysis using the Adaptive RSI.
Principle and Advantages of the Hull Moving Average:
- The Hull Moving Average (HMA) is known for its ability to track price action with minimal lag while maintaining a smooth curve.
- Unlike traditional moving averages, the HMA significantly reduces noise and responds faster to market trends, making it highly effective for detecting trend direction and changes.
- It achieves this by applying a weighted moving average calculation that emphasizes recent price movements while smoothing out fluctuations.
Why the Adaptive RSI Was Added:
- The core HMA line remains the foundation of the indicator, but an additional analysis using the Adaptive RSI has been integrated to provide more meaningful insights into momentum shifts.
- The Adaptive RSI is a modified version of the traditional Relative Strength Index that dynamically adjusts its sensitivity based on market volatility.
- By incorporating the Adaptive RSI, the HMA visually represents whether momentum is strengthening or weakening, offering a complementary layer of analysis.
How the Adaptive RSI Influences the Indicator:
- High Adaptive RSI (above 65): The market may be overbought, or bullish momentum could be fading. The HMA turns shades of red, signaling a possible exhaustion phase or potential reversals.
- Neutral Adaptive RSI (around 50): The market is in a balanced state, meaning neither buyers nor sellers are in clear control. The HMA takes on grayish tones to indicate this consolidation.
- Low Adaptive RSI (below 35): The market may be oversold, or bearish momentum could be weakening. The HMA shifts to shades of blue, highlighting potential recovery zones or trend slowdowns.
Why This Combination is Powerful:
- While the HMA excels in tracking trends and reducing lag, it does not provide information about momentum strength on its own.
- The Adaptive RSI bridges this gap by adding a clear visual layer that helps traders assess whether a trend is likely to continue, consolidate, or reverse.
- This makes the indicator particularly useful for spotting trend exhaustion and confirming momentum shifts in real-time.
Best Use Cases:
- Works effectively on timeframes from 1 hour (1H) to 1 day (1D), making it suitable for swing trading and position trading.
- Particularly useful for trading indices (SPY), stocks, forex, and cryptocurrencies, where momentum shifts are frequent.
- Helps identify not just trend direction but also whether that trend is gaining or losing strength.
Recommended Complementary Indicators:
- Adaptive Trend Finder: Helps identify the dominant long-term trend.
- Williams Fractals Ultimate: Provides key reversal points to validate trend shifts.
- RVOL (Relative Volume): Confirms significant moves based on volume strength.
This enhanced HMA with Adaptive RSI provides a powerful, intuitive visual tool that makes trend analysis and momentum interpretation more effective and efficient.
This indicator is for educational and informational purposes only. It should not be considered financial advice or a guarantee of performance. Always conduct your own research and use proper risk management when trading. Past performance does not guarantee future results.
Quantum Momentum FusionPurpose of the Indicator
"Quantum Momentum Fusion" aims to combine the strengths of RSI (Relative Strength Index) and Williams %R to create a hybrid momentum indicator tailored for volatile markets like crypto:
RSI: Measures the strength of price changes, great for understanding trend stability but can sometimes lag.
Williams %R: Assesses the position of the price relative to the highest and lowest levels over a period, offering faster responses but sensitive to noise.
Combination: By blending these two indicators with a weighted average (default 50%-50%), we achieve both speed and reliability.
Additionally, we use the indicator’s own SMA (Simple Moving Average) crossovers to filter out noise and generate more meaningful signals. The goal is to craft a simple yet effective tool, especially for short-term trading like scalping.
How Signals Are Generated
The indicator produces signals as follows:
Calculations:
RSI: Standard 14-period RSI based on closing prices.
Williams %R: Calculated over 14 periods using the highest high and lowest low, then normalized to a 0-100 scale.
Quantum Fusion: A weighted average of RSI and Williams %R (e.g., 50% RSI + 50% Williams %R).
Fusion SMA: 5-period Simple Moving Average of Quantum Fusion.
Signal Conditions:
Overbought Signal (Red Background):
Quantum Fusion crosses below Fusion SMA (indicating weakening momentum).
And Quantum Fusion is above 70 (in the overbought zone).
This is a sell signal.
Oversold Signal (Green Background):
Quantum Fusion crosses above Fusion SMA (indicating strengthening momentum).
And Quantum Fusion is below 30 (in the oversold zone).
This is a buy signal.
Filtering:
The background only changes color during crossovers, reducing “fake” signals.
The 70 and 30 thresholds ensure signals trigger only in extreme conditions.
On the chart:
Purple line: Quantum Fusion.
Yellow line: Fusion SMA.
Red background: Sell signal (overbought confirmation).
Green background: Buy signal (oversold confirmation).
Overall Assessment
This indicator can be a fast-reacting tool for scalping. However:
Volatility Warning: Sudden crypto pumps/dumps can disrupt signals.
Confirmation: Pair it with price action (candlestick patterns) or another indicator (e.g., volume) for validation.
Timeframe: Works best on 1-5 minute charts.
Suggested Settings for Long Timeframes
Here’s a practical configuration for, say, a 4-hour chart:
RSI Period: 20
Williams %R Period: 20
RSI Weight: 60%
Williams %R Weight: 40% (automatically calculated as 100 - RSI Weight)
SMA Period: 15
Overbought Level: 75
Oversold Level: 25
Volume Delta Candles HTF [TradingFinder] LTF Volume Candles 🔵 Introduction
In financial markets, understanding the concepts of supply and demand and their impact on price movements is of paramount importance. Supply and demand, as fundamental pillars of economics, reflect the interaction between buyers and sellers.
When buyers' strength surpasses that of sellers, demand increases, and prices tend to rise. Conversely, when sellers dominate buyers, supply overtakes demand, causing prices to drop. These interactions play a crucial role in determining market trends, price reversal points, and trading decisions.
Volume Delta Candles offer traders a practical way to visualize trading activity within each candlestick. By integrating data from lower timeframes or live market feeds, these candles eliminate the need for standalone volume indicators.
They present the proportions of buying and selling volume as intuitive colored bars, making it easier to interpret market dynamics at a glance. Additionally, they encapsulate critical metrics like peak delta, lowest delta, and net delta, allowing traders to grasp the market's internal order flow with greater precision.
In financial markets, grasping the interplay between supply and demand and its influence on price movements is crucial for successful trading. These fundamental economic forces reflect the ongoing balance between buyers and sellers in the market.
When buyers exert greater strength than sellers, demand dominates, driving prices upward. Conversely, when sellers take control, supply surpasses demand, and prices decline. Understanding these dynamics is essential for identifying market trends, pinpointing reversal points, and making informed trading decisions.
Volume Delta Candles provide an innovative method for evaluating trading activity within individual candlesticks, offering a simplified view without relying on separate volume indicators. By leveraging lower timeframe or real-time data, this tool visualizes the distribution of buying and selling volumes within a candle through color-coded bars.
This visual representation enables traders to quickly assess market sentiment and understand the forces driving price action. Buyer and seller strength is a critical concept that focuses on the ratio of buying to selling volumes. This ratio not only provides insights into the market's current state but also serves as a leading indicator for detecting potential shifts in trends.
Traders often rely on volume analysis to identify significant supply and demand zones, guiding their entry and exit strategies. Delta Candles translate these complex metrics, such as Maximum Delta, Minimum Delta, and Final Delta, into an easy-to-read visual format using Japanese candlestick structures, making them an invaluable resource for analyzing order flows and market momentum.
By merging the principles of supply and demand with comprehensive volume analysis, tools like the indicator introduced here offer unparalleled clarity into market behavior. This indicator calculates the relative strength of supply and demand for each candlestick by analyzing the ratio of buyers to sellers.
🔵 How to Use
The presented indicator is a powerful tool for analyzing supply and demand strength in financial markets. It helps traders identify the strengths and weaknesses of buyers and sellers and utilize this information for better decision-making.
🟣 Analyzing the Highest Volume Trades on Candles
A unique feature of this indicator is the visualization of price levels with the highest trade volume for each candlestick. These levels are marked as black lines on the candles, indicating prices where most trades occurred. This information is invaluable for identifying key supply and demand zones, which often act as support or resistance levels.
🟣 Trend Confirmation
The indicator enables traders to confirm bullish or bearish trends by observing changes in buyer and seller strength. When buyer strength increases and demand surpasses supply, the likelihood of a bullish trend continuation grows. Conversely, decreasing buyer strength and increasing seller strength may signal a potential bearish trend reversal.
🟣 Adjusting Timeframes and Calculation Methods
Users can customize the indicator's candlestick timeframe to align with their trading strategy. Additionally, they can switch between moving average and current candle modes to achieve more precise market analysis.
This indicator, with its accurate and visual data display, is a practical and reliable tool for market analysts and traders. Using it can help traders make better decisions and identify optimal entry and exit points.
🔵 Settings
Lower Time Frame Volume : This setting determines which timeframe the indicator should use to identify the price levels with the highest trade volume. These levels, displayed as black lines on the candlesticks, indicate prices where the most trades occurred.
It is recommended that users align this timeframe with their primary chart’s timeframe.
As a general rule :
If the main chart’s timeframe is low (e.g., 1-minute or 5-minute), it is better to keep this setting at a similarly low timeframe.
As the main chart’s timeframe increases (e.g., daily or weekly), it is advisable to set this parameter to a higher timeframe for more aligned data analysis.
Cumulative Mode :
Current Candle : Strength is calculated only for the current candlestick.
EMA (Exponential Moving Average) : The strength is calculated using an exponential moving average, suitable for identifying longer-term trends.
Calculation Period : The default period for the exponential moving average (EMA) is set to 21. Users can modify this value for more precise analysis based on their specific requirements.
Ultra Data : This option enables users to view more detailed data from various market sources, such as Forex, Crypto, or Stocks. When activated, the indicator aggregates and displays volume data from multiple sources.
🟣 Table Settings
Show Info Table : This option determines whether the information table is displayed on the chart. When enabled, the table appears in a corner of the chart and provides details about the strength of buyers and sellers.
Table Size : Users can adjust the size of the text within the table to improve readability.
Table Position : This setting defines the table’s placement on the chart.
🔵 Conclusion
The indicator introduced in this article is designed as an advanced tool for analyzing supply and demand dynamics in financial markets. By leveraging buyer and seller strength ratios and visually highlighting price levels with the highest trade volume, it aids traders in identifying key market zones.
Key features, such as adjustable analysis timeframes, customizable calculation methods, and precise volume data display, allow users to tailor their analyses to market conditions.
This indicator is invaluable for analyzing support and resistance levels derived from trade volumes, enabling traders to make more accurate decisions about entering or exiting trades.
By utilizing real market data and displaying the highest trade volume lines directly on the chart, it provides a precise perspective on market behavior. These features make it suitable for both novice and professional traders aiming to enhance their analysis and trading strategies.
With this indicator, traders can gain a better understanding of supply and demand dynamics and operate more intelligently in financial markets. By combining volume data with visual analysis, this tool provides a solid foundation for effective decision-making and improved trading performance. Choosing this indicator is a significant step toward refining analysis and achieving success in complex financial markets.
Volumatic S/R Levels [BigBeluga]THE VOLUMATIC S/R LEVELS
The Volumatic S/R Levels [ BigBeluga ] is an advanced technical analysis tool designed to identify and visualize significant support and resistance levels based on volume and price action.
The core concept of this indicator is to highlight areas where large volume and significant price movements coincide. It does this by plotting horizontal lines at price levels where unusually large candles (in terms of price range) occur alongside high trading volume. These lines represent potential support and resistance levels that are likely to be more significant due to the increased market activity they represent.
⬤ Key Features
Dynamic S/R Level Identification: Automatically detects and displays support and resistance levels from high volume candles.
Volume-Weighted Visualization: Uses line color to see positive or negative volume and box size to represent the strength of each level
Positive and Negative Volume:
Box Size Based on Volume:
Adaptive Levels Color: Adjusts level color based on price above or below level
Real-time Level Extension: Extends identified levels to the right side of the chart for better visibility
Volume and Percentage Labels: Displays volume information and relative strength percentage for each level
Dashed Levels: Displays levels with which price have interact multiple times
Dashboard: Shows max and min level information for quick reference
⬤ How to Use
Identify Key Levels: Look for horizontal lines representing potential support and resistance areas
Assess Level Strength:
- Thicker boxes indicate stronger levels, on which price reacts more
Monitor Price Interactions: Watch how price reacts when approaching these levels for potential trade setups
Volume Confirmation: Use the volume boxes to confirm the significance of each level
Relative Strength Analysis: Check the percentage labels to understand each level's importance relative to others
Trend Analysis: Use the color of the levels (lime for bullish, orange for bearish) to understand the overall market sentiment at different price points
Quick Reference: Utilize the dashboard to see the strongest and weakest levels at a glance
⬤ Customization
Levels Strength: Adjust the minimum threshold for level strength identification (default: 2.4)
Levels Amount: Set the maximum number of levels to display on the chart (max: 20)
The Volumatic S/R Levels indicator provides traders with a sophisticated tool for identifying key price levels backed by significant volume. By visualizing these levels directly on the chart and providing detailed volume and relative strength information, it offers valuable insights into potential areas of support, resistance, and price reversal. The addition of a ranking system and dashboard further enhances the trader's ability to quickly assess the most significant levels. This indicator is particularly useful for traders focusing on volume analysis and those looking to enhance their understanding of market structure. As with all technical tools, it's recommended to use this indicator in conjunction with other forms of analysis for comprehensive trading decisions.
Market Cipher B by WeloTradesMarket Cipher B by WeloTrades: Detailed Script Description
//Overview//
"Market Cipher B by WeloTrades" is an advanced trading tool that combines multiple technical indicators to provide a comprehensive market analysis framework. By integrating WaveTrend, RSI, and MoneyFlow indicators, this script helps traders to better identify market trends, potential reversals, and trading opportunities. The script is designed to offer a holistic view of the market by combining the strengths of these individual indicators.
//Key Features and Originality//
WaveTrend Analysis:
WaveTrend Channel (WT1 and WT2): The core of this script is the WaveTrend indicator, which uses the smoothed average of typical price to identify overbought and oversold conditions. WT1 and WT2 are calculated to track market momentum and cyclical price movements.
Major Divergences (🐮/🐻): The script detects and highlights major bullish and bearish divergences automatically, providing traders with visual cues for potential reversals. This helps in making informed decisions based on divergence patterns.
Relative Strength Index (RSI):
RSI Levels: RSI is used to measure the speed and change of price movements, with specific levels indicating overbought and oversold conditions.
Customizable Levels: Users can configure the overbought and oversold thresholds, allowing for a tailored analysis based on individual trading strategies.
MoneyFlow Indicator:
Fast and Slow MoneyFlow: This indicator tracks the flow of capital into and out of the market, offering insights into the underlying market strength. It includes configurable periods and multipliers for both fast and slow MoneyFlow.
Vertical Positioning: The script allows users to adjust the vertical position of MoneyFlow plots to maintain a clear and uncluttered chart.
Stochastic RSI:
Stochastic RSI Levels: This combines the RSI and Stochastic indicators to provide a momentum oscillator that is sensitive to price changes. It is used to identify overbought and oversold conditions within a specified period.
Customizable Levels: Traders can set specific levels for more precise analysis.
//How It Works//
The script integrates these indicators through advanced algorithms, creating a synergistic effect that enhances market analysis. Here’s a detailed explanation of the underlying concepts and calculations:
WaveTrend Indicator:
Calculation: WaveTrend is based on the typical price (average of high, low, and close) smoothed over a specified channel length. WT1 and WT2 are derived from this typical price and further smoothed using the Average Channel Length. The difference between WT1 and WT2 indicates momentum, helping to identify cyclical market trends.
RSI (Relative Strength Index):
Calculation: RSI calculates the average gains and losses over a specified period to measure the speed and change of price movements. It oscillates between 0 and 100, with levels set to identify overbought (>70) and oversold (<30) conditions.
MoneyFlow Indicator:
Calculation: MoneyFlow is derived by multiplying price changes by volume and smoothing the results over specified periods. Fast MoneyFlow reacts quickly to price changes, while Slow MoneyFlow offers a broader view of capital movement trends.
Stochastic RSI:
Calculation: Stochastic RSI is computed by applying the Stochastic formula to RSI values, which highlights the RSI’s relative position within its range over a given period. This helps in identifying momentum shifts more precisely.
//How to Use the Script//
Display Settings:
Users can enable or disable various components like WaveTrend OB & OS levels, MoneyFlow plots, and divergence alerts through checkboxes.
Example: Turn on "Show Major Divergence" to see major bullish and bearish divergence signals directly on the chart.
Adjust Channel Settings:
Customize the data source, channel length, and smoothing periods in the "WaveTrend Channel SETTINGS" group.
Example: Set the "Channel Length" to 10 for a more responsive WaveTrend line or adjust the "Average Channel Length" to 21 for smoother trends.
Set Overbought & Oversold Levels:
Configure levels for WaveTrend, RSI, and Stochastic RSI in their respective settings groups.
Example: Set the WaveTrend Overbought Level to 60 and Oversold Level to -60 to define critical thresholds.
Money Flow Settings:
Adjust the periods and multipliers for Fast and Slow MoneyFlow indicators, and set their vertical positions for better visualization.
Example: Set the Fast Money Flow Period to 9 and Slow Money Flow Period to 12 to capture both short-term and long-term capital movements.
//Justification for Combining Indicators//
Enhanced Market Analysis:
Combining WaveTrend, RSI, and MoneyFlow provides a more comprehensive view of market conditions. Each indicator brings a unique perspective, making the analysis more robust.
WaveTrend identifies cyclical trends, RSI measures momentum, and MoneyFlow tracks capital movement. Together, they provide a multi-dimensional analysis of the market.
Improved Decision-Making:
By integrating these indicators, the script helps traders make more informed decisions. For example, a bullish divergence detected by WaveTrend might be validated by an RSI moving out of oversold territory and supported by increasing MoneyFlow.
Customization and Flexibility:
The script offers extensive customization options, allowing traders to tailor it to their specific needs and strategies. This flexibility makes it suitable for different trading styles and timeframes.
//Conclusion//
The indicator stands out due to its innovative combination of WaveTrend, RSI, and MoneyFlow indicators, offering a well-rounded tool for market analysis. By understanding how each component works and how they complement each other, traders can leverage this script to enhance their market analysis and trading strategies, making more informed and confident decisions.
Remember to always backtest the indicator first before implying it to your strategy.
Dynamo
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Overview
Dynamo is built to be the Swiss-knife for price-movement & strength detection, it aims to provide a holistic view of the current price across multiple dimensions. This is achieved by combining 3 very specific indicators(RSI, Stochastic & ADX) into a single view. Each of which serve a different purpose, and collectively provide a simple, yet powerful tool to gauge the true nature of price-action.
Background
Dynamo uses 3 technical analysis tools in conjunction to provide better insights into price movement, they are briefly explained below:
Relative Strength Index(RSI)
RSI is a popular indicator that is often used to measure the velocity of price change & the intensity of directional moves. RSI computes the relative strength of the current price by comparing the security’s bullish strength versus bearish strength for a given period, i.e. by comparing average gain to average loss.
It is a range bound(0-100) variable that generates a bullish reading if average gain is higher, and a bullish reading if average loss is higher. Values over 50 are generally considered bullish & values less than 50 indicate a bearish market. Values over 70 indicate an overbought condition, and values below 30 indicate oversold condition.
Stochastic
Stochastic is an indicator that aims to measure the momentum in the market, by comparing most recent closing price of the security to its price range for a given period. It is based on the assumption that price tends to close near the recent high in an up trend, and it closes near the recent low during a down trend.
It is also range bound(0-100), values over 80 indicate overbought condition and values below 20 indicate oversold condition.
Average Directional Index(ADX)
ADX is an indicator that can quantify trend strength, it is derived from two underlying indices, known as Directional Movement Index(DMI). +DMI represents strength of the up trend, and -DMI represents strength of the down trend, and ADX is the average of the two.
ADX is non-directional or trend-neutral, which means, it does not follow the direction of the price, instead ADX will rise only when there is a strong trend, it does not matter if it’s an up trend or a down trend. Typical ranges of ADX are 25-50 for a strong trend, anything below 25 is considered as no trend or weak trend. ADX can frequently shoot upto higher values, but it generally finds exhaustion levels around the 60-75 range.
About the script
All these indicators are very powerful tools, but just like any other indicator they have their limitations. Stochastic & ADX can generate false signals in volatile markets, meaning price wouldn’t always follow through with what’s being indicated. ADX may even fail to generate a signal in less volatile markets, simply because it is based on moving averages, it tends to react slower to price changes. RSI can also lose it’s effectiveness when markets are trending strong, as it can stay in the overbought or oversold ranges for an extended period of time.
Dynamo aims to provide the trader with a much broader perspective by bringing together these contrasting indicators into a single simplified view. When Stochastic becomes less reliable in highly volatile conditions, one can cross validate their deduction by looking at RSI patterns. When RSI gets stuck in overbought or oversold range, one can refer to ADX to get better picture about the current trend. Similarly, various combinations of rules & setups can be formulated to get a more deterministic view, when working with either of these indicators.
There many possible use cases for a tool like this, and it totally depends on how you want to use it. An obvious option is to use it to trigger signals only after it has been confirmed by two or more indicators, for example, RSI & Stochastic make a great combination for cross-over or cross-under strategies. Some of the other options include trend detection, strength detection, reversals or price rejection points, possible duration of a trend, and all of these can very easily be translated into effective entry and exit points for trades.
How to use it
Dynamo is an easy-to-use tool, just add it to your chart and you’re good to start with your market analysis. Output consists of three overlapping plots, each of which tackle price movement from a slightly different angle.
Stochastic: A momentum indicator that plots the current closing price in relation to the price-range over a given period of time.
Can be used to detect the direction of the price movement, potential reversals, or duration of an up/down move.
Plotted as grey coloured histograms in the background.
Relative Strength Index(RSI): RSI is also a momentum indicator that measures the velocity with which the price changes.
Can be used to detect the speed of the price movement, RSI divergences can be a nice way to detect directional changes.
Plotted as an aqua coloured line.
Average Directional Index(ADX): ADX is an indicator that is used to measure the strength of the current trend.
Can be used to measure how strong the price movement is, both up and down, or to establish long terms trends.
Plotted as an orange coloured line.
Features
Provides a well-rounded view of the market movement by amalgamating some of the best strength indicators, helping traders make better informed decisions with minimal effort.
Simplistic plots that aim to convey clean signals, as a result, reducing clutter on the chart, and hopefully in the trader's head too.
Combines different types of indicators into a single view, which leads to an optimised use of the precious screen real-estate.
Final Note
Dynamo is designed to be minimalistic in functionality and in appearance, as it is being built to be a general purpose tool that is not only beginner friendly, but can also be highly-configurable to meet the needs of pro traders.
Thresholds & default values for the indicators are only suggestions based on industry standards, they may not be an exact match for all markets & conditions. Hence, it is advisable for the user to test & adjust these values according their securities and trading styles.
The chart highlights one of many possible setups using this tool, and it can used to create various types of setups & strategies, but it is also worth noting that the usability & the effectiveness of this tool also depends on the user’s understanding & interpretation of the underlying indicators.
Lastly, this tool is only an indicator and should only be perceived that way. It does not guarantee anything, and the user should do their own research before committing to trades based on any indicator.
Divergence Cheat Sheet'Divergence Cheat Sheet' helps in understanding what to look for when identifying divergences between price and an indicator. The strength of a divergence can be strong, medium, or weak. Divergences are always most effective when references prior peaks and on higher time frames. The most common indicators to identify divergences with are the Relative Strength Index (RSI) and the Moving average convergence divergence (MACD).
Regular Bull Divergence: Indicates underlying strength. Bears are exhausted. Warning of a possible trend direction change from a downtrend to an uptrend.
Hidden Bull Divergence: Indicates underlying strength. Good entry or re-entry. This occurs during retracements in an uptrend. Nice to see during the price retest of previous lows. “Buy the dips."
Regular Bear Divergence: Indicates underlying weakness. The bulls are exhausted. Warning of a possible trend direction change from an uptrend to a downtrend.
Hidden Bear Divergence: Indicates underlying weakness. Found during retracements in a downtrend. Nice to see during price retests of previous highs. “Sell the rallies.”
Divergences can have different strengths.
Strong Bull Divergence
Price: Lower Low
Indicator: Higher Low
Medium Bull Divergence
Price: Equal Low
Indicator: Higher Low
Weak Bull Divergence
Price: Lower Low
Indicator: Equal Low
Hidden Bull Divergence
Price: Higher Low
Indicator: Higher Low
Strong Bear Divergence
Price: Higher High
Indicator: Lower High
Medium Bear Divergence
Price: Equal High
Indicator: Lower High
Weak Bear Divergence
Price: Higher High
Indicator: Equal High
Hidden Bull Divergence
Price: Lower High
Indicator: Higher High
RSI+Stoch Band Oscillator📈 RSI + Stochastic Band Oscillator
Overview:
The RSI + Stochastic Band Oscillator is a technical indicator that combines the strengths of both the Relative Strength Index (RSI) and the Stochastic Oscillator. Instead of using static thresholds, this indicator dynamically constructs upper and lower bands based on the RSI and Stochastic overbought/oversold zones. It then measures the relative position of the current price within this adaptive range, effectively producing a normalized oscillator.
Key Components:
RSI-Based Dynamic Bands:
Using RSI values and exponential moving averages of price changes, upper and lower dynamic bands are constructed.
These bands adjust based on overbought and oversold levels, offering a more responsive framework than fixed RSI thresholds.
Stochastic-Based Dynamic Bands:
Similarly, Stochastic %K and %D values are used to construct dynamic bands.
These adapt to overbought and oversold levels by recalculating potential high/low values within the lookback window.
Oscillator Calculation:
The oscillator (osc) is computed as the relative position of the current close within the combined upper and lower bands of both RSI and Stochastic.
This value is normalized between 0 and 100, allowing clear identification of extreme conditions.
Visual Features:
The oscillator is plotted as a line between 0 and 100.
Color-filled areas highlight when the oscillator enters extreme zones:
Above 100 with falling momentum: Red zone (potential reversal).
Below 0 with rising momentum: Green zone (potential reversal).
Additional trend conditions (falling/rising RSI, %K, and %D) are used to strengthen reversal signals by confirming momentum shifts.
Enhanced Fuzzy SMA Analyzer (Multi-Output Proxy) [FibonacciFlux]EFzSMA: Decode Trend Quality, Conviction & Risk Beyond Simple Averages
Stop Relying on Lagging Averages Alone. Gain a Multi-Dimensional Edge.
The Challenge: Simple Moving Averages (SMAs) tell you where the price was , but they fail to capture the true quality, conviction, and sustainability of a trend. Relying solely on price crossing an average often leads to chasing weak moves, getting caught in choppy markets, or missing critical signs of trend exhaustion. Advanced traders need a more sophisticated lens to navigate complex market dynamics.
The Solution: Enhanced Fuzzy SMA Analyzer (EFzSMA)
EFzSMA is engineered to address these limitations head-on. It moves beyond simple price-average comparisons by employing a sophisticated Fuzzy Inference System (FIS) that intelligently integrates multiple critical market factors:
Price deviation from the SMA ( adaptively normalized for market volatility)
Momentum (Rate of Change - ROC)
Market Sentiment/Overheat (Relative Strength Index - RSI)
Market Volatility Context (Average True Range - ATR, optional)
Volume Dynamics (Volume relative to its MA, optional)
Instead of just a line on a chart, EFzSMA delivers a multi-dimensional assessment designed to give you deeper insights and a quantifiable edge.
Why EFzSMA? Gain Deeper Market Insights
EFzSMA empowers you to make more informed decisions by providing insights that simple averages cannot:
Assess True Trend Quality, Not Just Location: Is the price above the SMA simply because of a temporary spike, or is it supported by strong momentum, confirming volume, and stable volatility? EFzSMA's core fuzzyTrendScore (-1 to +1) evaluates the health of the trend, helping you distinguish robust moves from noise.
Quantify Signal Conviction: How reliable is the current trend signal? The Conviction Proxy (0 to 1) measures the internal consistency among the different market factors analyzed by the FIS. High conviction suggests factors are aligned, boosting confidence in the trend signal. Low conviction warns of conflicting signals, uncertainty, or potential consolidation – acting as a powerful filter against chasing weak moves.
// Simplified Concept: Conviction reflects agreement vs. conflict among fuzzy inputs
bullStrength = strength_SB + strength_WB
bearStrength = strength_SBe + strength_WBe
dominantStrength = max(bullStrength, bearStrength)
conflictingStrength = min(bullStrength, bearStrength) + strength_N
convictionProxy := (dominantStrength - conflictingStrength) / (dominantStrength + conflictingStrength + 1e-10)
// Modifiers (Volatility/Volume) applied...
Anticipate Potential Reversals: Trends don't last forever. The Reversal Risk Proxy (0 to 1) synthesizes multiple warning signs – like extreme RSI readings, surging volatility, or diverging volume – into a single, actionable metric. High reversal risk flags conditions often associated with trend exhaustion, providing early warnings to protect profits or consider counter-trend opportunities.
Adapt to Changing Market Regimes: Markets shift between high and low volatility. EFzSMA's unique Adaptive Deviation Normalization adjusts how it perceives price deviations based on recent market behavior (percentile rank). This ensures more consistent analysis whether the market is quiet or chaotic.
// Core Idea: Normalize deviation by recent volatility (percentile)
diff_abs_percentile = ta.percentile_linear_interpolation(abs(raw_diff), normLookback, percRank) + 1e-10
normalized_diff := raw_diff / diff_abs_percentile
// Fuzzy sets for 'normalized_diff' are thus adaptive to volatility
Integrate Complexity, Output Clarity: EFzSMA distills complex, multi-factor analysis into clear, interpretable outputs, helping you cut through market noise and focus on what truly matters for your decision-making process.
Interpreting the Multi-Dimensional Output
The true power of EFzSMA lies in analyzing its outputs together:
A high Trend Score (+0.8) is significant, but its reliability is amplified by high Conviction (0.9) and low Reversal Risk (0.2) . This indicates a strong, well-supported trend.
Conversely, the same high Trend Score (+0.8) coupled with low Conviction (0.3) and high Reversal Risk (0.7) signals caution – the trend might look strong superficially, but internal factors suggest weakness or impending exhaustion.
Use these combined insights to:
Filter Entry Signals: Require minimum Trend Score and Conviction levels.
Manage Risk: Consider reducing exposure or tightening stops when Reversal Risk climbs significantly, especially if Conviction drops.
Time Exits: Use rising Reversal Risk and falling Conviction as potential signals to take profits.
Identify Regime Shifts: Monitor how the relationship between the outputs changes over time.
Core Technology (Briefly)
EFzSMA leverages a Mamdani-style Fuzzy Inference System. Crisp inputs (normalized deviation, ROC, RSI, ATR%, Vol Ratio) are mapped to linguistic fuzzy sets ("Low", "High", "Positive", etc.). A rules engine evaluates combinations (e.g., "IF Deviation is LargePositive AND Momentum is StrongPositive THEN Trend is StrongBullish"). Modifiers based on Volatility and Volume context adjust rule strengths. Finally, the system aggregates these and defuzzifies them into the Trend Score, Conviction Proxy, and Reversal Risk Proxy. The key is the system's ability to handle ambiguity and combine multiple, potentially conflicting factors in a nuanced way, much like human expert reasoning.
Customization
While designed with robust defaults, EFzSMA offers granular control:
Adjust SMA, ROC, RSI, ATR, Volume MA lengths.
Fine-tune Normalization parameters (lookback, percentile). Note: Fuzzy set definitions for deviation are tuned for the normalized range.
Configure Volatility and Volume thresholds for fuzzy sets. Tuning these is crucial for specific assets/timeframes.
Toggle visual elements (Proxies, BG Color, Risk Shapes, Volatility-based Transparency).
Recommended Use & Caveats
EFzSMA is a sophisticated analytical tool, not a standalone "buy/sell" signal generator.
Use it to complement your existing strategy and analysis.
Always validate signals with price action, market structure, and other confirming factors.
Thorough backtesting and forward testing are essential to understand its behavior and tune parameters for your specific instruments and timeframes.
Fuzzy logic parameters (membership functions, rules) are based on general heuristics and may require optimization for specific market niches.
Disclaimer
Trading involves substantial risk. EFzSMA is provided for informational and analytical purposes only and does not constitute financial advice. No guarantee of profit is made or implied. Past performance is not indicative of future results. Use rigorous risk management practices.
RSI/MFI Divergence Finder [idahodev]Monitoring RSI (Relative Strength Index) and MFI (Money Flow Index) divergences on a stock or index chart offers several benefits to traders and analysts. Let's break down the advantages:
Comprehensive Market View: Combining both indicators provides a more complete picture of market conditions, as they measure different aspects of price movement. RSI focuses on recent gains/losses relative to price change, while MFI incorporates volume data to assess money flow in and out of a security.
Enhanced Signal Accuracy: When divergences occur simultaneously in both RSI and MFI, it may be considered a stronger signal than if only one indicator showed divergence. This can potentially lead to more reliable trading decisions.
Identification of False Breakouts: Divergences between these indicators and price action can help identify false breakouts or misleading price movements that are not supported by underlying market strength or volume.
More Nuanced Market Understanding: By examining divergent behavior between money flow (MFI) and momentum (RSI), traders gain a more detailed comprehension of the interplay between these factors in shaping market trends.
Early Warning Signs: These divergences can act as early warning signs for potential trend reversals or changes in market sentiment, allowing traders to adjust their strategies proactively.
It's important to note that RSI/MFI divergences should be used as part of a broader trading strategy rather than solely relying on them for buy/sell signals. They can serve as valuable tools for confirming trends, identifying potential turning points, or warning against overbought/oversold conditions.
When using these indicators together, traders must be cautious of false signals, especially in choppy markets or during periods of high volatility. It's crucial to combine this analysis with other technical and fundamental factors before making trading decisions.
In summary, monitoring RSI/MFI divergences may offer a way to gain insights into the underlying strengths and weaknesses of market movements.
This utility differs from other in that it allows for a choke/threshold/sensitivity setting to help weed out noisy signals. This needs to be carefully adjusted per chart.
It also allows for tuning of the MFI smoothing length (number of bars on the current chart) as well as how many previous bars it will take into consideration when calculating RSI and MFI divergences. It will signal when it sees alignment forming between RSI and MFI divergences in a direction. You will likely need to tune this script's settings every few days or at least anytime there is a change in overall market behavior or sustained volatility.
Ultimately, the goal with this script is to provide an additional level of confirmation of weakness or strength. It should be combined with other indicators such as exhaustion, pivots, supply/demand, trendline breaks or tests, and structure changes, to name a few complementary tools or strategies. It's not meant to be a standalone buy/sell signal indicator!
Here are some settings for futures that may help you get started:
ES (4m chart)
RSI Length: 26
MFI Length: 8
MFI Smoothing Length: 32
Divergence Sensitivity: 124
Left Bars for Pivot: 10
Right Bars for Pivot: 1
NQ (4m chart)
RSI Length: 14
MFI Length: 14
MFI Smoothing Length: 21
Divergence Sensitivity: 400
Left Bars for Pivot: 21
Right Bars for Pivot: 1
YM (4m chart)
RSI Length: 14
MFI Length: 14
MFI Smoothing Length: 21
Divergence Sensitivity: 810
Left Bars for Pivot: 33
Right Bars for Pivot: 1
