XAUUSD Multi-Timeframe Trend AnalyzerOverview
The "XAUUSD Multi-Timeframe Trend Analyzer" is an advanced script designed to provide a comprehensive analysis of the XAUUSD (Gold/US Dollar) trend across multiple timeframes simultaneously. By combining several key technical indicators, this tool helps traders quickly assess the market direction and trend strength for M15, M30, H1, H4, and D1 timeframes.
Multi-Timeframe Analysis: Displays the trend direction and strength across M15, M30, H1, H4, and D1 timeframes, allowing for a complete overview in a single glance.
Comprehensive Indicator Blend: Utilizes six popular technical indicators to determine the trend—Moving Averages, RSI, MACD, Bollinger Bands, DMI, and Parabolic SAR.
Trend Strength Scoring: Provides a numerical trend strength score (from -6 to 6) based on the alignment of the indicators, with positive values indicating uptrends and negative values for downtrends.
Visual Table Display: Displays results in a color-coded table (green for uptrend, red for downtrend, yellow for neutral) with a strength score for each timeframe, helping traders quickly assess market conditions.
How It Works
This script calculates the overall trend and its strength for each selected timeframe by analyzing six widely-used technical indicators:
Moving Averages (MA): The script uses a Fast and a Slow Moving Average. When the Fast MA crosses above the Slow MA, it indicates an uptrend. When the Fast MA crosses below, it signals a downtrend.
Relative Strength Index (RSI): The RSI is used to assess momentum. An RSI value above 50 suggests bullish momentum, while a value below 50 suggests bearish momentum.
Moving Average Convergence Divergence (MACD): MACD measures momentum and trend direction. When the MACD line crosses above the signal line, it signals bullish momentum; when it crosses below, it signals bearish momentum.
Bollinger Bands: These measure price volatility. When the price is above the middle Bollinger Band, the script considers the trend to be bullish, and when it's below, bearish.
Directional Movement Index (DMI): The DMI compares positive directional movement (DI+) and negative directional movement (DI-). A stronger DI+ over DI- signals an uptrend and vice versa.
Parabolic SAR: This indicator is used for determining potential trend reversals and setting stop-loss levels. If the price is above the Parabolic SAR, it indicates an uptrend, and if below, a downtrend.
Trend Strength Calculation
The script calculates a trend strength score for each timeframe:
Each indicator adds or subtracts 1 to the score based on whether it aligns with an uptrend or a downtrend.
A score of 6 indicates a Strong Uptrend, with all indicators aligned bullishly.
A score of -6 indicates a Strong Downtrend, with all indicators aligned bearishly.
Intermediate scores (e.g., 2 or -2) indicate Weak Uptrend or Weak Downtrend, suggesting that not all indicators are in agreement.
A score between 1 and -1 indicates a Neutral trend, suggesting uncertainty in the market.
How to Use
Assess Trend Direction and Strength: The table provides an easy-to-read summary of the trend and its strength on different timeframes. Look for timeframes where the strength is high (either 6 for a strong uptrend or -6 for a strong downtrend) to confirm the market’s overall direction.
Use in Conjunction with Other Strategies: This indicator is designed to provide a comprehensive view of the market. Traders should combine it with other strategies, such as price action analysis or candlestick patterns, to further confirm their trades.
Trend Reversal or Continuation: A weak trend (e.g., a strength of 2 or -2) could signal a possible reversal or a trend that has lost momentum. Strong trends (with a strength of 6 or -6) indicate higher confidence in trend continuation.
Multiple Timeframe Confirmation: Look for alignment across multiple timeframes to confirm the strength and direction of the trend before entering trades. For example, if M15, M30, and H1 are all showing a strong uptrend, it suggests a higher probability of the trend continuing.
Customization Options
- Adjustable Indicators: Users can modify the length and parameters of the Moving Averages, RSI, MACD, Bollinger Bands, DMI, and Parabolic SAR to suit their trading style.
- Flexible Timeframes: You can toggle between different timeframes (M15, M30, H1, H4, D1) to focus on the intervals most relevant to your strategy.
Ideal For
- Traders looking for a detailed, multi-timeframe trend analysis tool for XAUUSD.
- Traders who rely on trend-following strategies and need confirmation across multiple timeframes.
- Those who prefer a multi-indicator approach to avoid false signals and improve the accuracy of their trades.
Disclaimer
This indicator is for informational and educational purposes only. It is recommended to combine this with proper risk management strategies and your own analysis. Past performance does not guarantee future results. Always perform your own due diligence before making trading decisions.
חפש סקריפטים עבור "RSI MACD"
Mars Signals - SSL Trend AnalyzerIntroduction
The "Mars Signals - Precision Trend Analyzer with SSL Baseline & Price Action Zones" is a comprehensive technical analysis tool designed for traders seeking to enhance their market analysis and trading strategies. This indicator integrates multiple advanced trading concepts, including dynamic moving averages, trend detection algorithms, momentum indicators, volume analysis, higher timeframe confirmation, candlestick pattern recognition, and precise price action zones. By combining these elements, the indicator aims to provide clear and actionable buy and sell signals, helping traders to make informed decisions in various market conditions.
Core Components and Functionality
1.Dynamic Baseline Calculation
Moving Average Types: The indicator allows users to select from a variety of moving average types for the baseline calculation, including Simple Moving Average (SMA), Exponential Moving Average (EMA), Hull Moving Average (HMA), Weighted Moving Average (WMA), Double EMA (DEMA), Triple EMA (TEMA), Least Squares Moving Average (LSMA), Triangular Moving Average (TMA), Kijun (from Ichimoku Kinko Hyo), and McGinley's Dynamic.
Baseline Length: Users can customize the length of the moving average, providing flexibility to adjust the sensitivity of the baseline to market movements.
Signal Line Generation: The indicator computes a dynamic signal line based on the relationship between the close price and the moving averages of the high and low prices. This signal line adapts to market volatility and trend changes.
2.SSL Baseline Integration
SSL Baseline: In addition to the primary baseline, the indicator incorporates an SSL (Semaphore Signal Level) Baseline, which further refines trend detection by considering the highs and lows over a specified period.
Dual Confirmation: The combination of the primary baseline and the SSL baseline enhances the reliability of the trend signals by requiring agreement between both baselines before generating a signal.
3.Momentum and Trend Filters
Relative Strength Index (RSI): The indicator uses the RSI to assess the momentum of price movements, filtering out signals that occur during overbought or oversold conditions.
Moving Average Convergence Divergence (MACD): The MACD is employed to identify the direction and strength of the trend, adding another layer of confirmation to the signals.
Average Directional Index (ADX): The ADX measures the strength of the trend, ensuring that signals are generated only when the market shows significant directional movement.
4.Volume Analysis
Volume Filter: An optional volume filter compares the current volume to its moving average, allowing traders to focus on signals that occur during periods of higher market activity.
5.Higher Timeframe Confirmation
Multi-Timeframe Analysis: The indicator can incorporate data from a higher timeframe, comparing the current price to the higher timeframe's baseline and signal line. This feature helps traders align their trades with the broader market trend.
6.Candlestick Pattern Recognition
Bullish Patterns: The indicator detects bullish patterns such as Bullish Engulfing, Piercing Line, Hammer, and Doji.
Bearish Patterns: It also identifies bearish patterns like Bearish Engulfing, Dark Cloud Cover, Shooting Star, and Doji.
Pattern Prioritization: The patterns are prioritized to highlight the most significant formations, which can serve as additional confirmation for trade entries and exits.
7.Price Action Zones
Support and Resistance Levels: The indicator automatically identifies pivot highs and lows to establish dynamic support and resistance levels.
Zone Visualization: It draws shaded rectangles on the chart to represent these zones, providing a clear visual aid for potential reversal or breakout areas.
ATR-Based Zone Width: The zones' thickness is dynamically calculated using the Average True Range (ATR), adjusting to the current market volatility.
Background Coloring: The chart background changes color when the price is above the maximum resistance or below the minimum support, alerting traders to significant price movements.
Interpreting the Signals
1.Buy Signals
Conditions:
Price crosses above the signal line.
RSI is below 70 (not overbought).
MACD line is above the signal line (indicating bullish momentum).
ADX is above the user-defined threshold (default is 20), confirming a strong trend.
(Optional) Volume is above its moving average if the volume filter is enabled.
(Optional) Price is above the higher timeframe baseline and signal line if the higher timeframe filter is enabled.
(Optional) A bullish candlestick pattern is detected if the candlestick pattern filter is enabled.
Visual Indicators:
An upward-pointing label with the text "BUY" appears below the price bar.
The baseline and SSL baseline lines turn to colors indicating bullish conditions.
2.Sell Signals
Conditions:
Price crosses below the signal line.
RSI is above 30 (not oversold).
MACD line is below the signal line (indicating bearish momentum).
ADX is above the user-defined threshold, confirming a strong trend.
(Optional) Volume is above its moving average if the volume filter is enabled.
(Optional) Price is below the higher timeframe baseline and signal line if the higher timeframe filter is enabled.
(Optional) A bearish candlestick pattern is detected if the candlestick pattern filter is enabled.
Visual Indicators:
A downward-pointing label with the text "SELL" appears above the price bar.
The baseline and SSL baseline lines turn to colors indicating bearish conditions.
3.Support and Resistance Zones
Interpretation:
Resistance Zones: Represent areas where the price may face selling pressure. A break above these zones can signal a strong bullish move.
Support Zones: Represent areas where the price may find buying interest. A break below these zones can signal a strong bearish move.
Background Color:
The background turns red when the price is above the maximum resistance, indicating potential overextension.
The background turns green when the price is below the minimum support, indicating potential undervaluation.
Effective Usage Strategies
1.Customization
Adjusting Baseline and SSL Settings: Traders should experiment with different moving average types and lengths to match their trading style and the specific characteristics of the asset being analyzed.
Filtering Parameters: Modify RSI, MACD, and ADX settings to fine-tune the sensitivity of the signals.
Volume and Higher Timeframe Filters: Enable these filters to add robustness to the signals, especially in volatile markets or when trading higher timeframes.
2.Combining with Other Analysis
Fundamental Analysis: Use the indicator in conjunction with fundamental insights to validate technical signals.
Risk Management: Always apply proper risk management techniques, such as setting stop-loss and take-profit levels based on the support and resistance zones provided by the indicator.
3.Backtesting
Historical Analysis: Utilize the indicator's settings to backtest trading strategies on historical data, helping to identify the most effective configurations before applying them in live trading.
4.Monitoring Market Conditions
Volatility Awareness: Pay attention to the ATR and ADX readings to understand market volatility and trend strength, adjusting strategies accordingly.
Event Considerations: Be cautious around major economic announcements or events that may impact market behavior beyond technical indications.
Indicator Inputs and Customization Options
Baseline Type and Length: Select from multiple moving average types and specify the period length.
ADX Settings: Adjust the length, smoothing, and threshold for trend strength confirmation.
Volume Filter: Enable or disable the volume confirmation filter.
Higher Timeframe Filter: Choose to incorporate higher timeframe analysis and specify the desired timeframe.
Candlestick Patterns: Enable or disable the detection of candlestick patterns for additional signal confirmation.
SSL Baseline Type and Length: Customize the SSL baseline settings separately from the primary baseline.
Price Action Zones Settings:
Zone Thickness: Adjust the visual thickness of the support and resistance zones.
Lookback Period: Define how far back the indicator looks for pivot points.
ATR Multiplier for Zone Width: Set the multiplier for ATR to determine the dynamic width of the zones.
Maximum Number of Zones: Limit the number of support and resistance zones displayed.
Pivot Bars: Customize the number of bars to the left and right used for identifying pivot highs and lows.
Conclusion
The "Mars Signals - Precision Trend Analyzer with SSL Baseline & Price Action Zones" is a versatile and powerful tool that amalgamates essential technical analysis techniques into a single, user-friendly indicator. By providing clear visual signals and incorporating multiple layers of confirmation, it assists traders in identifying high-probability trading opportunities. Whether you are a day trader, swing trader, or long-term investor, this indicator can be tailored to suit your trading style and enhance your decision-making process.
To maximize the benefits of this indicator:
Understand Each Component: Familiarize yourself with how each part of the indicator contributes to the overall signal generation.
Customize Thoughtfully: Adjust the settings based on the asset class, market conditions, and your risk tolerance.
Practice Diligently: Use demo accounts or paper trading to practice and refine your strategy before deploying it in live markets.
Stay Informed: Continuously educate yourself on technical analysis and market dynamics to make the most informed decisions.
Disclaimer
Trading financial markets involves risk, and past performance is not indicative of future results. This indicator is a tool to aid in analysis and should not be the sole basis for any trading decision. Always conduct your own research and consider consulting with a licensed financial advisor.
Uptrick : HMA Adaptive Trend and Volatility BandsThis proprietary trading indicator, named "Uptrick: HMA Adaptive Trend and Volatility Bands," offers a sophisticated blend of trend detection and volatility measurement for financial markets. Designed to overlay directly on the price chart, it leverages a variety of technical analysis tools to provide clear visual signals and comprehensive market insights.
Key Features:
Hull Moving Average (HMA) with Volatility Bands:
HMA Calculation: Utilizes the Hull Moving Average (HMA) for smooth trend identification, applied to the average price of high and low (hl2).
Adaptive Volatility Bands: Incorporates bands around the HMA based on a responsive standard deviation adjusted by an Exponential Moving Average (EMA). These bands dynamically expand and contract with market volatility.
Parameters:
Length: Configurable period for the HMA and standard deviation (default 14).
Multiplier: Determines the width of the bands (default 2.0).
MACD (Moving Average Convergence Divergence):
MACD Calculation: Includes fast and slow EMA periods with a signal line to detect trend direction and strength.
Histogram: Difference between MACD line and signal line to visualize momentum.
Parameters:
Fast Length: Short-term EMA period (default 6).
Slow Length: Long-term EMA period (default 13).
Signal Length: Signal line EMA period (default 5).
Relative Strength Index (RSI):
RSI Calculation: Measures the speed and change of price movements to identify overbought or oversold conditions.
Parameter:
RSI Length: Period for RSI calculation (default 10).
Average True Range (ATR):
ATR Calculation: Evaluates market volatility by considering the true range over a specified period.
Parameter:
ATR Length: Period for ATR calculation (default 7).
Volume and Liquidity Analysis:
Volume: Directly incorporated into the indicator to gauge market activity.
Liquidity: Assessed using the HMA of volume to determine the ease of trade execution.
Parameter:
Liquidity Length: Period for HMA of volume calculation (default 14).
Trend Identification:
Uptrend Conditions: A combination of positive MACD histogram, RSI above 50, ATR above its HMA, and volume exceeding liquidity.
Downtrend Conditions: Negative MACD histogram, RSI below 50, ATR above its HMA, and volume exceeding liquidity.
Visual Cues: Color-coded background (green for uptrend, red for downtrend) with corresponding labels on the price chart to indicate trend shifts.
Additional Moving Averages and Bollinger Bands:
SMA (Simple Moving Average): Includes 50 and 200-period SMAs for long-term trend analysis.
EMA (Exponential Moving Average): Includes a 20-period EMA for short-term trend analysis.
Bollinger Bands: Standard deviation bands around a 20-period SMA to measure market volatility and identify potential breakout points.
Information Table:
Real-Time Data Display: An optional table that provides current values for key metrics such as price, volume, liquidity, ATR, RSI, MACD histogram, SMAs, EMA, Buy+Sell Pressure, ATH, Global liquidity, Distance from ATH and Bollinger Bands, offering traders a comprehensive snapshot of market conditions.
Visualization:
Upper and Lower Bands: Clearly plotted with distinct colors (blue for upper, red for lower) to highlight volatility boundaries.
Trend Labels: Automatic annotations on the chart to signal uptrend and downtrend conditions.
Background Highlighting: Subtle shading to visually emphasize prevailing trend conditions.
This indicator is designed for traders seeking an advanced tool to detect trends, measure volatility, and make informed trading decisions based on comprehensive technical analysis. By integrating multiple technical indicators and providing clear visual signals, it aims to enhance trading accuracy and market insight.
CulturaTrading IndicadorThe CULTURATRADING INDICATOR refines trading signals by integrating advanced analysis techniques across RSI, MACD, and ADX indicators. Here's a deep dive into its functionalities:
RSI Analysis:
Buying Signal Identification: The RSI component is calibrated not just to flag potential reversal points but to identify strong momentum. An RSI exceeding 60 is not merely an overbought signal; it indicates a robust buying momentum when it turns blue, aligning with CULTURATRADING STRATEGY's criteria for a potential long position.
Level 55 Significance: This level acts as a transitional threshold. When the RSI retreats below this point, it suggests a weakening momentum, prompting a reassessment of open positions.
Oversold Condition & Action: An RSI dipping below 40 signals an oversold condition, turning red, and aligning with a potential for a next long signal. staying alert when RSI stay over 40 level again and over on RSI Moving Average Following the idea CULTURATRADING STRATEGY.
Moving Average on RSI (MA RSI):
The inclusion of a Moving Average on the RSI serves as a trend filter. When the RSI is above the MA RSI, it underscores the strength of the current trend; conversely, if the RSI falls below the MA RSI, it calls for close all RSI long trade.
Volatility Histogram:
Color Coding & Market Response: The histogram changes colors based on market volatility and trend strength. Blue indicates a bullish trend continuation, where traders might consider entering long or holding positions. Rose suggests a market shift where traders should be vigilant, potentially taking profits from long or opening shorts positions. Grey denotes low volatility, signaling a period of market indecision where entering new trades may carry higher risk. staying out
Stop-Loss Placement: The histogram assists in identifying optimal stop-loss levels, providing visual cues for setting them just beyond the recent volatility extremes to protect against market whipsaws.
ADX Trend Strength Layer:
This layer offers a visual representation of the trend's strength. A rising ADX above the 25 level with a slope on the MACD line indicates a strong trend and defining directionality to trade (long if it close blue or short if its close rose), reinforcing the confidence in following the trend.
Usage & Importance:
While the CULTURATRADING STRATEGY provides a robust framework for trade execution, the CULTURATRADING INDICATOR is crucial for visualizing and confirming the signals it generates. It simplifies the complex interplay of various technical signals into a coherent visual format, aiding traders in making informed decisions.
The combination of RSI, MA RSI, and the volatility histogram offers a tri-layered approach to market analysis, enabling traders to discern between strong trends, pullbacks, and consolidations.
By integrating these elements, the CULTURATRADING INDICATOR serves as an indispensable tool for traders utilizing the CULTURATRADING STRATEGY, providing clarity and enhancing decision-making efficacy.
Disclaimer:
This indicator is designed for educational purposes to provide a visual aid in market analysis. Traders are advised to use it as part of a comprehensive risk-managed strategy. It is not intended as financial advice.
Choose Symbol, Mode with Hull,Stochatic Mom,EMA,MACD,RSI,TableThis Pine Script code is a comprehensive indicator for the TradingView platform, offering a variety of technical analysis tools. Below is an English introduction to its features and purposes:
Introduction:
This indicator is designed for traders on TradingView and provides a multi-functional analysis toolset. It includes different charting modes (Heikin-Ashi, Linear, and Normal), a Hull Moving Average (Hull), Stochastic Momentum, RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), EMA (Exponential Moving Average), Bollinger Bands, and a summary table displaying key metrics.
Key Features:
Charting Modes:
Users can choose between "Heikin-Ashi," "Linear," or "Normal" modes to visualize price data in different ways.
Hull Moving Average:
The script incorporates the Hull Moving Average for trend analysis, highlighting potential buy and sell signals.
Stochastic Momentum:
Stochastic Momentum, with customizable parameters (K, D, and Smooth), is included to identify overbought and oversold conditions.
RSI (Relative Strength Index):
RSI is calculated and displayed, aiding in identifying potential trend reversals or exhaustion points.
MACD (Moving Average Convergence Divergence):
The MACD indicator is included, along with a histogram, to highlight changes in momentum and potential crossovers.
RSI Momentum:
RSI Momentum is calculated, providing additional insights into momentum changes.
Exponential Moving Averages (EMA):
The script calculates and displays three EMAs (Exponential Moving Averages) with customizable periods.
Bollinger Bands:
Bollinger Bands are incorporated, offering insights into volatility and potential price reversals.
Summary Table:
A table is displayed on the chart summarizing key metrics, including Stochastic MoM, RSI, MACD, RSI EMA, Hull percentage change, and EMA values.
Customization:
Users have the option to customize various parameters, including chart modes, lengths of moving averages, Stochastic parameters, and more.
Usage:
The indicator aims to provide a comprehensive view of price action and potential trend changes. Traders can use it for technical analysis and decision-making.
Important Note:
This script is provided for educational purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
Fusion: Machine Learning SuiteThe Fusion: Machine Learning Suite combines multiple technical analysis dimensions and harnesses the predictive power of machine learning, seamlessly integrating a diverse array of classic and novel indicators to deliver precision, adaptability, and innovation.
Features and Capabilities
Multidimensional Analysis: Fusion: MLS integrates various technical analysis dimensions to offer a more comprehensive perspective.
Machine Learning Integration: Utilizing ML algorithms, Fusion: MLS offers adaptability to market changes.
Custom Indicators: Including dimensions like "Moon Lander", "Cap Line" and "Z-Pack" the indicator expands the scope of traditional technical analysis methods.
Tailored Customization: With customization options, Fusion: MLS allows traders to configure the tool to suit their specific strategies and market focus.
In the following sections, we'll explore the features and settings of Fusion: MLS in detail, providing insights into how it can be utilized.
Major Features and Settings
The indicator consists of several core components and settings, each designed to provide specific functionalities and insights. Here's an in-depth look:
Machine Learning Component
Distance Classifier: A Strategic Approach to Market Analysis
In the world of trading and investment, the ability to classify and predict price movements is paramount. Machine learning offers powerful tools for this purpose.
The Fusion: MLS indicator among others incorporates an Approximate Nearest Neighbors (ANN)* algorithm, a machine learning classification technique, and allows the selection of various distance functions .
This flexibility sets Fusion: MLS apart from existing solutions. The available distance functions include:
Euclidean: Standard distance metric, commonly used as a default.
Chebyshev: Also known as maximum value distance.
Manhattan: Sum of absolute differences.
Minkowski: Generalized metric that includes Euclidean and Manhattan as special cases.
Mahalanobis: Measures distance between points in a correlated space.
Lorentzian: Known for its robustness to outliers and noise.
*For a deeper understanding of the Approximate Nearest Neighbors (ANN) algorithm, traders are encouraged to refer to the relevant articles that can be found in the public domain.
Alternative scoring system
Fusion: MLS also includes a custom scoring alternative based on directional price action.
"Combined: Directional" and "Alpha: Directional" scoring types represent our own directional change algorithm, simple yet effective in displaying trend direction changes early on. They are visualized by color changes when scoring becomes below or above zero.
Changes in scoring quickly reflect shifts in buyer and seller sentiment.
Traders may choose signals by Color Change in the indicator settings to get alerts when scoring color shifts, not waiting until the histogram crosses the zero level.
Application in Trading
Machine learning classification has become an integral part of modern trading, offering innovative ways to analyze and interpret financial data.
Many algorithmic trading systems leverage ML classification to automate trading decisions. By continuously learning from real-time data, these systems can adapt to changing market conditions and execute trades with increased efficiency and accuracy.
ML classification allows for the development of tailored trading strategies as traders can select specific algorithms, dimensions, and filters that align with their trading style, goals, and the particular market they are operating.
We have integrated ML classification with traditional trading tools, such as moving averages and technical indicators. This fusion creates a more robust analysis framework, combining the strengths of classical techniques with the adaptability of machine learning.
Whether used independently or in conjunction with other tools, ML classification represents a significant advancement in trading technology, opening new avenues for exploration, innovation, and success in the financial world.
ML: Weighting System
The Fusion: MLS indicator introduces a unique weighting system that allows traders to customize the influence of various technical indicators in the machine learning process. This feature is not only innovative but also provides a level of control and adaptability that sets it apart from other indicators.
Customizable Weights
The weighting system allows users to assign specific weights to different indicators such as Moon Lander, RSI, MACD, Money Flow, Bollinger Bands, Cap Line, Z-Pack, Squeeze Momentum*, and MA Crossover. These weights can be adjusted manually, providing the ability to emphasize or de-emphasize specific indicators based on the trader's strategy or market conditions.
*Note, we determined via testing that the popular "Squeeze" indicator can actually be well replicated by simply using inputs of 15 & 199 in the bedrock indicator - MACD ; while we employed the standard "Squeeze" formula (developed by J. Carter ) in Fusion: MLS, traders are hereby made aware of our research findings regarding such.
The weighting system's importance lies in its ability to provide a more nuanced and personalized analysis. By adjusting the weights of different indicators a trader focusing on momentum strategies might assign higher weights to the Squeeze Momentum and MA Crossover indicators, while a trader looking for volatility might emphasize RSI and Bollinger Bands.
The ability to customize weights adds a layer of complexity and adaptability that is rare in standard machine-learning indicators.
Custom Indicators: Moon Lander
The "Moon Lander" is not just a catchy name; it's a robust feature inspired by principles from aerospace engineering and offers a unique perspective on trading analysis. Here's a conceptual overview:
Fast EMA and Kalman Matrix
"Moon Lander" incorporates both a Fast Exponential Moving Average (EMA) and a Kalman Matrix in its design. These two elements are combined to create a histogram, providing a specific approach to data analysis.
The Kalman Matrix, or Kalman Filter, is a mathematical concept used for estimating variables that can be measured indirectly and contain noise or uncertainty. It's a standard tool in machine learning and control systems, known for its ability to provide optimal estimates based on observed data.
Kalman Filter: A Navigational Tool
The Kalman filter, an essential part of "Moon Lander," is a mathematical concept known for its applications in navigation and control systems used by NASA in the apollo program :
Guidance in Uncertainty: Just as the Kalman filter helped guide complex aerospace missions through uncertain paths, it assists traders in navigating the often unpredictable financial markets.
Filtering Noise: In trading, the Kalman filter serves to filter out market noise, allowing traders to focus on the underlying trends.
Predictive Capabilities: Its ability to predict future states makes it a valuable tool for forecasting market movements and trend directions.
Custom Indicators: Cap Line and Z-Pack
Fusion: MLS integrates our additional proprietary custom indicators that have been published on TradingView earlier:
Cap Line: Delve into the specific functionalities and applications of our proprietary "Cap Line" indicator in the published description on TradingView.
Z-Pack: Explore the analytical perspectives, focused on the z-score methodology, and custom "Z-Pack" indicator by reviewing the published description on TradingView.
Buy/Sell Signal Generation Algorithms
Fusion: MLS offers various options for generating buy/sell signals, tailored to different trading strategies and perspectives:
Fusion: Allows traders to select any number of dimensions to receive buy/sell signals from, offering customized signal generation.
ML: Utilizes the machine learning ANN distance for signal generation.
Color Change: Generates signals by selected scoring type color change.
Displayed Dimension, Alpha Dimension: Generate signals based on specific selected dimensions.
These algorithms provide flexibility in determining buy/sell signals, catering to different trading styles and market conditions.
Filters
Filters are used to refine and selectively include or exclude signals based on specific criteria. Rather than generating signals, these filters act as gatekeepers, ensuring that only the signals meeting certain conditions are considered. Here's an overview of the filters used:
Dynamic State Predictor (DSP)
The DSP employs the Kalman Matrix to evaluate existing signals by comparing the fast and slow-moving averages, both processed through the Kalman Matrix. Based on the relationship between these averages, the DSP may exclude specific signals, depending on whether they align with upward or downward trends.
Average Directional Index (ADX)
The ADX filter evaluates the strength of existing trends and filters out signals that do not meet the specified ADX threshold and length, focusing on significant market movements.
Feature Engineering: RSI
Applies a filter to the existing signals, clearing out those that do not meet the criteria for RSI overbought or oversold threshold condition.
Feature Engineering: MACD
Assesses existing signals to identify changes in the strength, direction, momentum, and duration of a trend, filtering out those that do not align with MACD trend direction.
The Visual Component
The machine learning component is an internal component. However, the indicator also offers an equally important and useful visual component. It is a graphical representation of the multiple technical analysis dimensions, that can be combined in various ways (where the name "Fusion" comes from), allowing traders to visualize the underlying data and its analysis.
Displayed Dimension: Visualization and Normalization
The Fusion: MLS indicator offers a "Displayed Dimension" feature that visualizes various dimensions as a histogram. These dimensions may include RSI, MAs, BBs, MACD, etc.
RSI Dimension on the image + ML signals
Normalization: Each dimension is normalized. If any dimension has extreme values, a Fisher transformation is applied to bring them within a reasonable range.
Combined Dimension: When selecting the "Combined" option , the normalized values of the selected dimensions are combined using techniques such as standardization, normalization, or winsorization. This flexibility enables tailored visualization and analysis.
Alpha Dimension: Enhancing Analysis
The "Alpha Dimension" feature allows traders to select an additional dimension alongside the Displayed Dimension. This facilitates a combined analysis, enhancing the depth of insights.
Theme Selection
Fusion: MLS offers various themes such as "Sailfish", "Iceberg", "Moon", "Perl", "Candy" and "Monochrome" Traders can select a theme that resonates with their preference, enhancing visual appeal. There is also a "Custom" theme available that allows the user to choose the colors of the theme.
Customizing Fusion: MLS for Various Markets and Strategies
Fusion: MLS is designed with customization in mind. Traders can tailor the indicator to suit various markets and trading strategies. Selecting specific dimensions allows it to align with individual trading goals.
Selecting Dimensions: Choose the dimensions that resonate with your trading approach, whether focusing on trend-following, momentum, or other strategies.
Adjusting Parameters: Fine-tune the parameters of each dimension, including custom ones like "Moon Lander," to suit specific market conditions.
Theme Customization: Select a theme that aligns with your visual preferences, enhancing your chart's readability and appeal.
Utilizing Research: Leverage the underlying algorithms and research, such as machine learning classification by ANN and the Kalman filter, to deepen your understanding and application of Fusion: MLS.
Alerts
The indicator includes an alerting system that notifies traders when new buy or sell signals are detected.
Disclaimer
The information provided herein is intended for informational purposes only and should not be construed as investment advice, endorsement, nor a recommendation to buy or sell any financial instruments. Fusion: MLS is a technical analysis tool, and like all tools, it should be used with caution and in conjunction with other forms of analysis.
Traders and investors are encouraged to consult with a licensed financial professional and conduct their own research before making any trading or investment decisions. Past performance of the Fusion: MLS indicator or any trading strategy does not guarantee future results, and all trading involves risk. Users of Fusion: MLS should understand the underlying algorithms and assumptions and consider their individual risk tolerance and investment goals when using this tool.
Three Golden By Moonalert =========================
English
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Three Golden By Moonalert
(Green Bar) BUY = All three conditions are agree uptrend.
1 candlestick is on the middle line of Bollinger Bands
2 RSI is more than 50
3 MACD cross up Zero Line
(Red Bar) SELL = All three conditions are agree downtrend
1 candlestick is under the middle line of Bollinger Bands
2 RSI is less than 50
3 MACD cross down Zero Line
(Yello Bar) Wait and see = some candition are agree uptrend or downtrend
Basic logic is
Green = Buy
Red = Sell
Yello = wait and see
Working Good for TF Daily.
=========================
THAI
=========================
เขียว = ซื้อ ( Bollinger bands , Rsi , Macd บอกขึ้นทั้งหมด )
เเดง = ขาย ( Bollinger bands , Rsi , Macd บอกลงทั้งหมด )
เหลือง = นั่งนิ่งๆ ( Bollinger bands , Rsi , Macd บอกขั้นหรือลงบางตัว )
สามารถปรับMACD ระหว่าง
Cross Signal กับ Cross Zeroได้ เเนะนำอย่างหลัง
สามารถปรับ EMA 20 50 200 เปิดปิดได้ที่ตั้งค่า
MACRS {Lite}This is the open-source stripped down version of the full-featured RSI-MACD indicator (MACRS), with the ADO and the option to filter out weekend price action removed.
The main oscillator is the RSI modulated by the MACD (default). The RSI mode can be disabled to revert to a normal MACD oscillator for the main oscillator.
When the main oscillator (thicker line) is > 0, it is green; and if it is < 0, it is red.
The MACD can be re-scaled and whenever its value > 100, a background fill between the oscillator and the zeroline appear to indicates overbought condition; and < -100 indicates oversold condition. The user can tweak the scaling factor to optimize this for a given chart and timeframe.
A (thick transparent light blue) volume oscillator is also provided. An increase in volume trend provides confirmation of (or solidifies) the movements in the main oscillator over that period. A falling volume oscillator trend raises doubts on the main oscillator trend, and hints of the possibility of a counter-trend (also look at the secondary ADO oscillator for clues).
The novel aspects and principles of this indicator and this source code are the property of © cybernetwork.
This indicator and script is free for the TV community to use.
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
3CRGANG - Histogram (Basic)This indicator provides traders with a unified view of momentum by combining multiple classic oscillators into a single histogram. By aggregating momentum signals into one visual output, it simplifies trend analysis, helping traders identify momentum shifts without managing multiple indicators separately.
What It Does
The 3CRGANG - Histogram (Basic) calculates a momentum-based histogram using a user-selected oscillator (e.g., RSI, MACD, MFI, RVI, Stochastic, Stochastic RSI, or TMASlope). The histogram is plotted with color-coded bars to indicate bullish, bearish, or neutral momentum, alongside predefined alert levels and a trend status table for quick reference.
Why It’s Useful
This script addresses the challenge of monitoring multiple momentum indicators by consolidating them into a single histogram. Each oscillator measures momentum differently (e.g., RSI tracks price strength, MACD focuses on moving average convergence, MFI incorporates volume), but the script normalizes these signals into a unified output. This reduces chart clutter and provides a clear, actionable signal for identifying trend direction, making it easier for traders to focus on key momentum shifts across various market conditions.
How It Works
The script follows these steps to generate the histogram:
Oscillator Selection: Traders choose one oscillator to base the histogram on. For example: RSI measures the speed and change of price movements, MACD tracks the relationship between two exponential moving averages, and MFI combines price and volume to measure buying/selling pressure. The choice of oscillator affects the histogram’s sensitivity to price movements.
Fast Oscillator Calculation: A fast-moving oscillator is computed using the selected method over a user-defined period (default: 8 bars). For instance, RSI calculates the relative strength of price gains versus losses, while MACD computes the difference between short and long EMAs. The result is normalized to a range centered around zero.
Histogram Plotting: The oscillator’s output is adjusted by a modification factor (default: 1) for sensitivity tuning and plotted as a histogram. Positive values indicate bullish momentum, negative values indicate bearish momentum, and values near zero suggest a lack of clear trend.
Color Coding: Bars are colored based on momentum and price direction: green for bullish momentum (price moving upward, histogram value typically positive), red for bearish momentum (price moving downward, histogram value typically negative), and grey for neutral momentum (ranging conditions or unclear trend).
Alert Levels: Predefined buy and sell levels are plotted as dotted lines to mark significant momentum thresholds. For most oscillators, levels are set at 20 (buy) and -20 (sell), representing overbought/oversold conditions based on historical performance. For TMASlope, levels are adjusted to 0.04 and -0.04, as it measures the slope of a triangular moving average relative to the average true range (ATR).
Trend Table: A table in the top-right corner displays the current timeframe’s trend status ("Buy Only," "Sell Only," or "Ranging") based on the histogram value, price direction, and alert levels, along with the histogram’s numerical value.
Underlying Concepts
The script is built on the concept of momentum aggregation, aiming to capture short-term price dynamics while filtering noise. By using a fast-moving oscillator, it emphasizes recent price action, and the histogram format provides a visual summary of momentum strength. The alert levels are derived from typical overbought/oversold thresholds for each oscillator, adjusted to ensure consistency across different methods. The trend table adds a layer of interpretation, helping traders quickly assess whether the momentum aligns with the broader trend.
Use Case
Trending Markets: In a bullish trend, green bars above the buy alert level (e.g., 20) indicate strong upward momentum, suggesting potential long entries. In a bearish trend, red bars below the sell alert level (e.g., -20) suggest short opportunities.
Ranging Markets: Grey bars or values between alert levels indicate a lack of clear momentum, prompting caution or scalping strategies.
Confirmation Tool: Use the histogram to confirm price action signals, such as breakouts or reversals, by ensuring momentum aligns with the direction of the move. For example, a breakout with green bars above the buy level may signal a stronger trend.
Settings
Choose Type: Select the oscillator to use (default: RSI - CLASSIC).
Source: Choose between Close or HL2 price data (default: Close).
Histogram Length: Set the period for oscillator calculation (options: 5, 8, 13; default: 8).
Modification Factor: Adjust the sensitivity of the histogram (default: 1).
Notes
The script supports classic oscillators only and operates on the current timeframe.
If volume data is unavailable for your ticker, MFI calculations may not work; select another oscillator to continue plotting.
Disclaimer
This indicator is a tool for analyzing market trends and does not guarantee trading success. Trading involves risk, and past performance is not indicative of future results. Always use proper risk management.
Bayesian TrendEnglish Description (primary)
1. Overview
This script implements a Naive Bayesian classifier to estimate the probability of an upcoming bullish, bearish, or neutral move. It combines multiple indicators—RSI, MACD histogram, EMA price difference in ATR units, ATR level vs. its average, and Volume vs. its average—to calculate likelihoods for each market direction. Each indicator is “binned” (categorized into discrete zones) and assigned conditional probabilities for bullish/bearish/neutral scenarios. The script then normalizes these probabilities and paints bars in green if bullish is most likely, red if bearish is most likely, or blue if neutral is most likely. A small table is also displayed in the top-right corner of the chart, showing real-time probabilities.
2. How it works
Indicator Calculations: The script calculates RSI, MACD (line and histogram), EMA, ATR, and Volume metrics.
Binning: Each metric is converted into a discrete category (e.g., low, medium, high). For example, RSI < 30 is binned as “low,” while RSI > 70 is binned as “high.”
Conditional Probabilities: User-defined tables specify the conditional probabilities of each bin under three hypotheses (Up, Down, Neutral).
Naive Bayesian Formula: The script multiplies the relevant conditional probabilities, normalizes them, and derives the final probabilities (Up, Down, or Neutral).
Visualization:
Bar Colors: Bars are green when the Up probability exceeds 50%, red for Down, and blue otherwise.
Table: Displays numeric probabilities of Up, Down, and Neutral in percentage terms.
3. How to use it
Add the script to your chart.
Observe the colored bars:
Green suggests a higher probability for bullish movement.
Red suggests a higher probability for bearish movement.
Blue indicates a higher probability of sideways or uncertain conditions.
Check the table in the top-right corner to see exact probabilities (Up/Down/Neutral).
Use the input settings to adjust thresholds (RSI, MACD, Volume, etc.), define alert conditions (e.g., when Up probability crosses 50%), and decide whether to trigger alerts on bar close or in real-time.
4. Originality and usefulness
Originality: This script uniquely applies a Naive Bayesian approach to a blend of classic and volume-based indicators. It demonstrates how different indicator “zones” can be combined to produce probabilistic insights.
Usefulness: Traders can interpret the probability breakdown to gauge the script’s bias. Unlike single indicators, this approach synthesizes several signals, potentially offering a more holistic perspective on market conditions.
5. Limitations
The conditional probabilities are manually assigned and may not reflect actual market behavior across all instruments or timeframes.
Results depend on the user’s choice of thresholds and indicator settings.
Like any indicator, past performance does not guarantee future results. Always confirm signals with additional analysis.
6. Disclaimer
This script is intended for educational and informational purposes only. It does not constitute financial advice. Trading involves significant risk, and you should make decisions based on your own analysis. Neither the script’s author nor TradingView is liable for any financial losses.
Русское описание (Russian translation, optional)
Этот индикатор реализует наивный Байесовский классификатор для оценки вероятности предстоящего роста (Up), падения (Down) или бокового движения (Neutral). Он комбинирует несколько индикаторов—RSI, гистограмму MACD, разницу цены и EMA в единицах ATR, уровень ATR относительно своего среднего значения и объём относительно своего среднего—чтобы вычислить вероятности для каждого направления рынка. Каждый индикатор делится на «зоны» (low, mid, high), которым приписаны условные вероятности для бычьего/медвежьего/нейтрального исхода. Скрипт нормирует эти вероятности и раскрашивает бары в зелёный, красный или синий цвет в зависимости от того, какая вероятность выше. Также в правом верхнем углу отображается таблица с текущими значениями вероятностей.
Confluence StrategyOverview of Confluence Strategy
The Confluence Strategy in trading refers to the combination of multiple technical indicators, support/resistance levels, and chart patterns to identify high-probability trading opportunities. The idea is that when several indicators agree on a price movement, the likelihood of that movement being successful increases.
Key Components
Technical Indicators:
Moving Averages (MA): Commonly used to determine the trend direction. Look for crossovers (e.g., the 50-day MA crossing above the 200-day MA).
Relative Strength Index (RSI): Helps identify overbought or oversold conditions. A reading above 70 may indicate overbought conditions, while below 30 suggests oversold.
MACD (Moving Average Convergence Divergence): Useful for spotting changes in momentum. Look for MACD crossovers and divergence from price.
Support and Resistance Levels:
Identify key levels where price has historically reversed. These can be drawn from previous highs/lows, Fibonacci retracement levels, or psychological price levels.
Chart Patterns:
Patterns like head and shoulders, double tops/bottoms, or flags can indicate potential reversals or continuations in price.
Strategy Implementation
Set Up Your Chart:
Add the desired indicators (e.g., MA, RSI, MACD) to your TradingView chart.
Mark significant support and resistance levels.
Identify Confluence Points:
Look for situations where multiple indicators align. For instance, if the price is near a support level, the RSI is below 30, and the MACD shows bullish divergence, this may signal a buying opportunity.
Entry and Exit Points:
Entry: Place a trade when your confluence conditions are met. Use limit orders for better prices.
Exit: Set profit targets based on resistance levels or use trailing stops. Consider the risk-reward ratio to ensure your trades are favorable.
Risk Management:
Always implement stop-loss orders to protect against unexpected market moves. Position size should reflect your risk tolerance.
Example of a Confluence Trade
Setup:
Price approaches a strong support level.
RSI shows oversold conditions (below 30).
The 50-day MA is about to cross above the 200-day MA (bullish crossover).
Action:
Enter a long position as the conditions align.
Set a stop loss just below the support level and a take profit at the next resistance level.
Conclusion
The Confluence Strategy can significantly enhance trading accuracy by ensuring that multiple indicators support a trade decision. Traders on TradingView can customize their indicators and charts to fit their personal trading styles, making it a flexible approach to technical analysis.
Swiss Knife [MERT]Introduction
The Swiss Knife indicator is a comprehensive trading tool designed to provide a multi-dimensional analysis of the market. By integrating a wide array of technical indicators across multiple timeframes, it offers traders a holistic view of market sentiment, momentum, and potential reversal points. This indicator is particularly useful for traders looking to combine trend analysis, momentum indicators, volume data, and price action into a single, easy-to-read format.
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Key Features
Multi-Timeframe Analysis : Evaluates indicators on Daily , 4-Hour , 1-Hour , and 15-Minute timeframes.
Comprehensive Indicator Suite : Incorporates MACD , Awesome Oscillator (AO) , Parabolic SAR , SuperTrend , DPO , RSI , Stochastic Oscillator , Bollinger Bands , Ichimoku Cloud , Chande Momentum Oscillator (CMO) , Donchian Channels , ADX , volume-based momentum indicators, Fractals , and divergence detection.
Market Sentiment Scoring : Aggregates signals from multiple indicators to provide an overall sentiment score.
Visual Aids : Displays EMA lines, trendlines, divergence signals, and a sentiment table directly on the chart.
Super Trend Reversal Signals : Identifies potential market reversal points by assessing the momentum of automated trading bots.
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Explanation of Each Indicator
Moving Average Convergence Divergence (MACD)
- Purpose : Measures the relationship between two moving averages of price.
- Interpretation : A positive histogram suggests bullish momentum; a negative histogram indicates bearish momentum.
Awesome Oscillator (AO)
- Purpose : Gauges market momentum by comparing recent market movements to historic ones.
- Interpretation : Above zero indicates bullish momentum; below zero indicates bearish momentum.
Parabolic SAR (SAR)
- Purpose : Identifies potential reversal points in price direction.
- Interpretation : Dots below price suggest an uptrend; dots above price suggest a downtrend.
SuperTrend
- Purpose : Determines the prevailing market trend.
- Interpretation : Provides buy or sell signals based on price movements relative to the SuperTrend line.
Detrended Price Oscillator (DPO)
- Purpose : Removes trend from price to identify cycles.
- Interpretation : Values above zero suggest price is above the moving average; values below zero indicate it is below.
Relative Strength Index (RSI)
- Purpose : Measures the speed and change of price movements.
- Interpretation : Values above 50 indicate bullish momentum; values below 50 indicate bearish momentum.
Stochastic Oscillator
- Purpose : Compares a particular closing price to a range of its prices over a certain period.
- Interpretation : Values above 50 indicate bullish conditions; values below 50 indicate bearish conditions.
Bollinger Bands (BB)
- Purpose : Measures market volatility and provides relative price levels.
- Interpretation : Price above the middle band suggests bullishness; below the middle band suggests bearishness.
Ichimoku Cloud
- Purpose : Provides support and resistance levels, trend direction, and momentum.
- Interpretation : Bullish signals when price is above the cloud; bearish signals when price is below the cloud.
Chande Momentum Oscillator (CMO)
- Purpose : Measures momentum on both up and down days.
- Interpretation : Values above 50 indicate strong upward momentum; values below -50 indicate strong downward momentum.
Donchian Channels
- Purpose : Identifies volatility and potential breakouts.
- Interpretation : Price above the upper band suggests bullish breakout; below the lower band suggests bearish breakout.
Average Directional Index (ADX)
- Purpose : Measures the strength of a trend.
- Interpretation : DI+ above DI- indicates bullish trend; DI- above DI+ indicates bearish trend.
Volume Momentum Indicators (VolMom, CumVolMom, POCMom)
- Purpose : Analyze volume to assess buying and selling pressure.
- Interpretation : Positive values suggest bullish volume momentum; negative values indicate bearish volume momentum.
Fractals
- Purpose : Identify potential reversal points in the market.
- Interpretation : Up fractals may indicate a future downtrend; down fractals may indicate a future uptrend.
Divergence Detection
- Purpose : Identifies divergences between price and various indicators (RSI, MACD, Stochastic, OBV, MFI, A/D Line).
- Interpretation : Bullish divergences suggest potential upward reversal; bearish divergences suggest potential downward reversal.
- Note : This functionality utilizes the library from Divergence Indicator .
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Coloring Scheme
Background Color
- Purpose : Reflects the overall market sentiment by combining sentiment scores from all indicators across different timeframes.
- Interpretation :
- Green Shades : Indicate bullish market sentiment.
- Red Shades : Indicate bearish market sentiment.
- Intensity : The strength of the color corresponds to the strength of the sentiment score.
Sentiment Table
- Purpose : Displays the status of each indicator across different timeframes.
- Interpretation :
- Green Cell : The indicator suggests a bullish signal.
- Red Cell : The indicator suggests a bearish signal.
- Percentage Score : Indicates the overall bullish or bearish sentiment on that timeframe.
Exponential Moving Averages (EMAs)
- Purpose : Provide dynamic support and resistance levels.
- Colors :
- EMA 10 : Lime
- EMA 20 : Yellow
- EMA 50 : Orange
- EMA 100 : Red
- EMA 200 : Purple
Trendlines
- Purpose : Visual representation of support and resistance levels based on pivot points.
- Interpretation :
- Upward Trendlines : Colored green , indicating support levels.
- Downward Trendlines : Colored red , indicating resistance levels.
- Note : Trendlines are drawn using the library from Simple Trendlines .
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Utility of Market Sentiment
The indicator aggregates signals from multiple technical indicators across various timeframes to compute an overall market sentiment score . This comprehensive approach helps traders understand the prevailing market conditions by:
Confirming Trends : Multiple indicators pointing in the same direction can confirm the strength of a trend.
Identifying Reversals : Divergences and fractals can signal potential turning points.
Timeframe Alignment : Aligning signals across different timeframes can enhance the probability of successful trades.
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Divergences
Divergence occurs when the price of an asset moves in the opposite direction of a technical indicator, suggesting a potential reversal.
- Bullish Divergence : Price makes a lower low, but the indicator makes a higher low.
- Bearish Divergence : Price makes a higher high, but the indicator makes a lower high.
The indicator detects divergences for:
RSI
MACD
Stochastic Oscillator
On-Balance Volume (OBV)
Money Flow Index (MFI)
Accumulation/Distribution Line (A/D Line)
By identifying these divergences, traders can spot early signs of trend reversals and adjust their strategies accordingly.
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Trendlines
Trendlines are essential tools for identifying support and resistance levels. The indicator automatically draws trendlines based on pivot points:
- Upward Trendlines (Support) : Connect higher lows, indicating an uptrend.
- Downward Trendlines (Resistance) : Connect lower highs, indicating a downtrend.
These trendlines help traders visualize the trend direction and potential breakout or reversal points.
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Super Trend Reversals (ST Reversal)
The core idea behind the Super Trend Reversals indicator is to assess the momentum of automated trading bots (often referred to as 'Supertrend bots') that enter the market during critical turning points. Specifically, the indicator is tuned to identify when the market is nearing bottoms or peaks, just before it shifts direction based on the triggered Supertrend signals. This approach helps traders:
Engage Early : Enter the market as reversal momentum builds up.
Optimize Entries and Exits : Enter under favorable conditions and exit before momentum wanes.
By capturing these reversal points, traders can enhance their trading performance.
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Conclusion
The Swiss Knife indicator serves as a versatile tool that combines multiple technical analysis methods into a single, comprehensive indicator. By assessing various aspects of the market—including trend direction, momentum, volume, and price action—it provides traders with valuable insights to make informed trading decisions.
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Citations
- Divergence Detection Library : Divergence Indicator by DevLucem
- Trendline Drawing Library : Simple Trendlines by HoanGhetti
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Note : This indicator is intended for informational purposes and should be used in conjunction with other analysis techniques. Always perform due diligence before making trading decisions.
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OneThingToRuleThemAll [v1.4]This script was created because I wanted to be able to display a contextual chart of commonly used indicators for scalping and swing traders, with the ability to control the visual representation on the charts as their cross-overs, cross-unders, or changes of state happen in real time. Additionally, I wanted the ability to control how or when they are displayed. While looking through other community projects, I found they lacked the ability to full customize the output controls and values used for these indicators.
The script leverages standard RSI/MACD/VWAP/MVWAP/EMA calculations to help a trader visually make more informed decisions on entering or exiting a trade, depending on their understanding on what the indicators represent. Paired with a table directly on the chart, it allows a trader to quickly reference values to make more informed decisions without having to look away from the price action or look through multiple indicator outputs.
The main functionality of the indicator is controlled within the settings directly on the chart. There a user can enable the visual representations, or disable, and configure how they are displayed on the charts by altering their values or style types.
Users have the ability to enable/disable visual representations of:
The indicator chart
RSI Cross-over and RSI Reversals
MACD Uptrends and Downtrends
VWAP Cross-overs and Cross-unders
VWAP Line
MVWAP Cross-overs and Cross-unders
MVWAP Line
EMA Cross-overs and Cross-unders
EMA Line
Some traders like to use these visual indications as thresholds to enter or exit trades. Its best to find out which ones work the best with the security you are trying to trade. Personally, I use the table as a reference in conjunction with the RSI chart indicators to help me decide a logical trailing stop if I am scalping. Some users might like the track EMA200 crossovers, and have visual representations on the chart for when that happens. However, users may use the other indicators in other methods, and this script provides the ability to be able to configure those both visually and by value.
The pine script code is open source and itself is fairly straightforward, it is mostly written to provide the ultimate level of control the the user of the various indicators. Please reach out to me directly if you would like a further understanding of the code and an explanation on anything that may be unclear.
Enjoy :)
-dead1.
McClellan Indicators (Oscillator, Summation Index w/ RSI & MACD)Four indicators in one based on the McClellan Oscillator for both the NYSE and Nasdaq exchanges. Designed to be used in conjunction with each other- plot the Oscillator (Osc), Summation Index (MSI), and RSI/MACD of the MSI on both your SPX and Nasdaq chart. Select the exchange and indicator within the settings. These tools are secondary- but when the signals are combined with the action of the index and stocks can be helpful in identifying market turns and trend strength.
McClellan Oscillator--
The Osc is a market breadth tool that uses a fast and slow EMA based on the difference between advancing and declining stocks on the exchange. Used primarily to identify breadth thrusts, divergences, and extremes (oversold/overbought). Plot horizontal levels to see when the market internals are extremely overbought or oversold, and take note of when the Osc is declining while the market is advancing or vice versa.
McClellan Summation Index--
For intermediate trends the MSI is a running total of the Osc which can be used to confirm the strength of a trend, and spot potential reversals. A 10 period ema is included on this indicator, where crossovers can aid in spotting the change in trend of market internals, and divergences can identify when market internals are not in line with the trend. Shading is applied for when the internals are in a bullish or bearish trend.
Two additional indicators are the RSI and MACD of the Summation Index. An overbought or oversold MSI RSI generally indicates a strong trend in the market internals, however you may want to take note when the RSI stalls and begins to "hook" in the opposite direction. This indicator has signals to show when the market internals may be turning and to be on lookout for trend change.
Similarly- the MACD of the MSI identifies the strength of the trend, and crossovers can be used to help spot reversals. Shading is included in this indicator to spot the bullish/bearish trend of internals.
OBV with Volume/Momentum DivergenceCredits go to vyperphi696 and LazyBear for the original OBV with Divergence script.
This indicator has the new option to check for momentum divergence, which I have done by adding RSI and MACD data.
Hence the indicator allows combined testing of volume and momentum divergence. This feature aims to improve trend reversal detection by reducing false positives.
In summary, 3 divergence categories are shown by default as lines:
Volume + RSI + MACD (dark green/red)
Volume + RSI / Volume + MACD (light green/red)
Volume (gray)
Line colors can be adjusted via plot settings. Therefore it is also possible to distinguish Volume + RSI and Volume + MACD divergence if necessary.
Lastly, I edited the indicator scaling mechanism when changing from one timeframe to another; the transitions are smoother now. This only applies when auto-scaling is off.
Easy Backtester PROWHAT IS EasyBacktester ?
EasyBacktester is a tool that helps you backtest trading strategies built by yourself with an included strategy builder and a multitude of options.
From within the parameters of the tool, you can specifically pick your entry settings across 12 most common indicators, such as "RSI", "MACD", "Moving Averages" etc... Then you can immediately visualise your setup's Stop-loss & Take Profit, your expected Profits & Loss and a lot of other statistics for your entry strategy. Once you are satisfied with your entry strategy, you are given a set of tools to optimize your setup using stop-loss rules, take profits rules, partial profits, trailing-stops, entry timing...
WHY IS THIS TOOL DIFFERENT ?
EasyBacktester is a backtesting engine with no coding skills required. TradingView allows for "Strategy Scripting" using PineScript, which is not an option for non-coder audience. EasyBacktester fills this gap and allow non-coders to get an idea of how their trading strategies may perform using mouse clicks only.
Some similar attempts have been made on TradingView, allowing some limited options, but none have the same capabilities EasyBacktester offers, for instance, as of April 2022 these features have not been seen in any other TradingView tools:
- partial take profits
- leverage simulation
- a multitude of trailing stop-loss possibilities including trail triggers and trail parameters
- visualisation of entries including stop-loss, take profits, partial take profits, and trailing stops. One can now visualize such complex setups.
- visualisation of Profits & Loss
- time in trade
- wait strategy after a signal: for example, when RSI is oversold, "WAIT until price retraces 100% of the original signal" amongst other possibilities
QUICK START GUIDE:
STEP 1: DEFINE YOUR SIGNAL STRATEGY
From the settings of the tool, find the "SIGNALS STRATEGY" section.
Select a type of entry you wish to simulate, for example "LONGs", and activate the checkbox right before "Simulate".
Right below, you will find 4 signal builder for you to play with and pick your strategy accordingly.
For example, to simulate a signal when RSI is oversold, follow these steps:
- On the 1st multiple choice box, select "RSI"
- On the 2nd multiple choice box, select "is below..."
- On the 3rd multiple choice box, select "OverSold level"
Don't forget to activate this rule by checking the checkbox in front of it.
After this first step, one should immediately see the chart affected with some plots. The dots represents the signal entry defined by the rule we just created, and the red/green boxes visually represent trades that could have been taken with this signal which, in this example, occurs "when RSI is below oversold level". Note that all specific parameters for RSI including its specific "oversold level" is customisable at the end of the tools settings along with all other indicators settings.
STEP 2: STATISTICS
By default, the "APPEARANCE" section only plots potential entry signals (materialized by dots) and actual entry boxes (materialized by red/green boxes).
But the user can easily add other precious statistics to the chart, and obviously the most important one for backtesting: Profits & Loss (P&L).
In the "STATISTICS" section please check the "P&L" box to see appear a chart of the simulated P&L for our example. You should immediately see a new graph below the chart representing the evolution of the P&L after each entry.
Other statistics are available to the user, including: Equity, Number of Trades, Time in Position, Number of trades Won, Number of trades Lost, Number of trades Stopped.
Play around with those to see them plotted on your chart.
STEP 3: OPTIMIZE YOUR ENTRY
Under the "ENTRY STRATEGY" section, one can pick how to enter AFTER the signal, which provides the user with an extensive flexibility to pick its timing.
Here there are a various set of choices offered, ranging from the default "Market Order at Next Candle Open", to "Limit Order: at signal's candle open" or even "Stop-Buy: at break of last candle high". As its name suggests, this option allows you to actually wait before randomly enter in trade.
It is important to also note that the user can totally prevent entry if the conditions are not filled after a customizable number of candles represented in "Max bars to wait for entry" (default being 1, meaning the engine will wait the condition to be filled during only 1 candle)
STEP 4: MANAGE YOUR RISK
Under the "RISK MANAGEMENT" section, the user is given a series of options to set the amount (s)he would like to risk.
This is extremely important to set, and is the result of a combination of customizable options including:
- the Initial Capital of the account
- the amount to risk per trade, and HOW to risk it: some fixed % the initial equity or adjust the stop-loss to the desired risk ?
- use of leverage or not
- initial stop-loss, as well as minimum and maximum
- trailing stop-loss: what should trigger the trailing ? and by how much should the engine trail ?
STEP 5: HAVE AN EXIT PLAN
Under the "EXIT STRATEGY", the user can define how to exit the trade.
For instance, here again a lot of options are given:
- Take Profit: exit at some level of profits defined by a multiple of the stop-loss, or a multiple of the ATR, or some % or points
- Partial Profit taking before exit
- Panic close position after some time spent on the trade
STEP 6: FURTHER OPTIMIZATIONS
Under the sections "Commissions" & "Calendar & Sessions", one can simulate real trading conditions by including commissions fees as well as filtering actual dates and trading sessions. These sections are straightforward for any user to use.
SETP 7: INDICATORS SETTINGS
Since EasyBacktester uses a predefined set of indicators to get started, those indicators are also customizable in the last section of the settings. Here, one can easily customize RSI periodicity, MACD lengths, Moving averages types & lengths, ATR, etc...
STEP 8: GOING FURTHER
This is only a start to give users an overview of how various options affect their trading performance. But of course, each trader has its special recipe and specific detailed setup that is not possible to embed in a single tool. For advanced simulation, EasyBacktester provides plug & play connectors for advanced users. Namely, there are 3 connectors:
- signal connector
- trail trigger connector
- exit connector
Each of these connectors are an opportunity to customize the engine signals, trail trigger and exit choices with the user's own options. This case does require a little bit of coding, but it can easily be implemented by copy-pasting existing resources or with a slight help of a professional. In fact, the only conditions to build a proper connector is to export a plot with the numbers 1 (for signals), 2 (for trigger trails) and 3 (for exits). Here is an example of custom SIGNAL connector compatible with EasyBacktester, to produce a signal when last RSI was below 30 and current RSI reads above 30:
============================================================
//@version=5
indicator("My custom RSI signal")
// when previous RSI 14 was below 30 and current RSI 14 is above 30, set "custom_signal" to 1, otherwise set "custom_signal" to 0
custom_signal = ta.rsi(close, 14) < 30 and ta.rsi(close, 14) > 30 ? 1 : 0
// Export a plot of "custom_signal", but do not display it
plot(custom_signal, title="my signal", display=display.none)
============================================================
Once this indicator has been built, the user only needs to connect it with EasyBacktester as follow:
1. Open a desired chart, and add both EasyBacktester indicator as well as the custom "My custom signal" we just created above.
2. Open EasyBacktester's settings, and in the first option, there is "Connect signals source" which by default is set to "close". In the multiple choices, find your custom signal which should be named something like "My custom RSI signal: my signal", generally speaking the name is built like this " : ".
3. Now the custom code is connected to EasyBacktester, but we need to indicate the engine we actually want to use it as custom signal.
4. Under the "SIGNALS STRATEGY" section, where we generally build signals rules, there is special rule for this specific connection named "Use external source as entry signal". Just check the checkbox to activate it and see how the chart took our custom signal into consideration.
That's it for the overview of EasyBacktester. Thank you for reading and happy trading :)
Panel RSI MACD DMI//RSI
//--Default length : 14
//--RSI > 70 : Background is RED
//--RSI < 30 : Background is GREEN
//--RSI Between 30 and 70 : Background is BLUE
//MACD
//--Default: 12,26,9
//--MACD cross above Zero Line / Signal Line : Background is GREEN
//--MACD cross below Zero Line / Signal Line : Background is RED
//--Others condition : Background is BLUE
//DMI
//--Default: 14, 14
//--ADX > 20 : Text is GREEN
//--ADX < 20 : Text is RED
//--DI+ > DI- : Background is BLUE
//--DI- > DI+ : Background is YELLOW
My BTST RSI MACD X-BODYMy trial of script base on EMA , RSI and MACD to filter the best Candle which has likely hood of uptrend in a practice of " Buy Today Sell Tomorrow".
Blue candle : 40 to 70 % RSI
Orange candle > 80 % RSI
Yellow candle < 30 % RSI - buy and can hold
Lime candle buy above MACD line.
Also i added the label when 2 EMAs and SMA cross each other in solid body candle.
Buy on Blue, Lime.
Buy and hold on Yellow candle.
Good luck!
{INDYAN} Ichimoku for IntradayIts based on ichimoku, i removed cloud and mod some changes for better use in intraday trading. It can be used in stocks and index as well.Not tested on MCX and Forex.
Just watch at crossover of tenkan and kijun that RSI macd is above 60 and zero or not? If all three parameters meet go for long... exit when again another cross happen or rsi go below 40.for sell side watch for rsi below 40 and macd below zero line while crossover of kijun tenkan.
Better to use it with RSI+Macd Fast to get more accurate results.
#for better confirmation look value of VWAP and decide accordingly
Do back test before using it.
Happy Trading
Love INDyAN
#change line color as per ur wish but value should be same as it was default entered.
{INDYAN} RSI + MACDModded RSI and MACD for intraday use. If rsi above 60 and macd is above zero line then go for buy and if rsi is below 40 and macd below zero line then go for sell side. use it in small timeframe i.e. 3 minute or less.
better for scalp trading
Happy Trading
Love INDYAN
#It can be used best with INDYAN Go With Trend
Multi momentum indicatorScript contains couple momentum oscillators all in one pane
List of indicators:
RSI
Stochastic RSI
MACD
CCI
WaveTrend by LazyBear
MFI
Default active indicators are RSI and Stochastic RSI
Other indicators are disabled by default
RSI, StochRSI and MFI are modified to be bounded to range from 100 to -100. That's why overbought is 40 and 60 instead 70 and 80 while oversold -40 and -60 instead 30 and 20.
MACD and CCI as they are not bounded to 100 or 200 range, they are limited to 100 - -100 by default when activated (extras are simply hidden) but there is an option to show full indicator.
In settings there are couple more options like show crosses or show only histogram.
Default source for all indicators is close (except WaveTrend and MFI which use hlc3) and it could be changed but for all indicators.
There is an option for 2nd RSI which can be set for any timeframe and background calculated by Fibonacci levels.
MACD and RSI divergence by Rexio v2Hi everyone!
I wrote this indicator for intraday trading and it cannot be use only by itself you need to at least draw some S/R lines to make it useful. It is based at MACD histogram and gives signal when it sees divergence on MACD/RSI/MACD's Histogram (or all at once - settings) when macd's histogram switchs trend. Im using it to playing with a trend most of the time looking for hidden divergence at higher time frame and after that looking for regular divergence at lower time frame.
Im not a computer programist nor professional trader so it is only for educational purposes only.
