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.
스크립트에서 "香港交易所技术指标分析（RSI、MACD、布林带）"에 대해 찾기
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.
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.
Three Golden By Moonalert =========================
English
=========================
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
Strategy Tester EMA-SMA-RSI-MACDOn Tradingview I never saw a custom adjustable strategy script yet, so this is it,
you can change different things and see if you'll get a good strategy or not
Settings:
First choose the source, you can choose out of:
close, open, high, low, ohlc4, hlc3, hl2
Then choose you strategy: Long & Short, Long only or Short only
Next, choose your entry "Buy/Long" (which is the "close Short position" when "Short"):
- (E)MA 1 > (E)MA 2 (Each can be made ema or sma)
- close above (E)MA 1
- RSI strategy
- macd > signal
- macd > 0
- signal > 0
Then choose your RSI values if needed (for example you want a trigger when EMA 1 > SMA 2
but only if RSI > 60, then change "IF RSI >" from 0 to 60
Next you can choose an extra argument
and even a second argument with Higher Time Frame settings
Under this you can change your (E)MA values as desired (HTF values, MACD and RSI length can be found lower)
All the same with the exit/close (or if "Short", this is your entry)
Again, change everything as you wish
Then comes the RSI length setting, MACD settings and HTF settings, followed by SL/TP settings
(you also can enable/disable SL/TP), and TIME settings (for example you want to know the profit only from this year)
Alerts are provided in next script
Have fun!
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.
Table Logic ExtractorTable Logic Extractor v2.0
Advanced multi-timeframe analysis with intelligent trade recommendations!
Overview:
This sophisticated indicator provides comprehensive market analysis through multiple technical indicators and timeframes. It combines EMA analysis, RSI momentum, MACD signals, Bollinger Bands, volume analysis, divergence detection, and intelligent trade recommendations with support/resistance distance calculations and trading style detection.
Key Features:
✅ Multi-Indicator Analysis - EMA, RSI, MACD, Bollinger Bands, Volume, ATR
✅ Multi-Timeframe Analysis - M1, M5, M15, M30 trend comparison
✅ Divergence Detection - Bullish and bearish divergence with strength calculation
✅ Support/Resistance Analysis - Distance calculations with Fibonacci levels
✅ Trading Style Detection - Trend, Range, Breakout, Scalping identification
✅ Intelligent Trade Signals - Style-based trade recommendations with confidence levels
✅ Risk Management - Stop Loss and Take Profit calculations
✅ Comprehensive Table - Real-time analysis with 14 different metrics
How It Works:
The indicator uses advanced analysis:
• Multi-Timeframe - M1, M5, M15, M30 trend analysis
• Style Detection - Automatic trading style identification
• S/R Analysis - Fibonacci-based support/resistance levels
• Weighted Scoring - EMA (2.0), RSI (1.5), MACD (1.5), BB (1.0), Volume (1.0)
• Intelligent Signals - Style-based trade recommendations
Trading Style Detection:
• TREND TRADING - Strong trend + aligned timeframes (Green)
• RANGE TRADING - Low volatility + sideways movement (Yellow)
• BREAKOUT TRADING - High volume + near levels (Orange)
• SCALPING - High volatility + quick moves (Red)
Information Table (14 Metrics):
Real-time display showing:
• ATR volatility with signal (HIGH/MED/LOW/NORMAL VOL)
• Divergence status with strength percentage
• S/R Distance with Fibonacci levels
• Stop Loss (2.0:1 ratio) and Take Profit 1 (1.5:1 ratio)
• Multi-Timeframe analysis (M1, M5, M15, M30)
• Scalping signals with confidence levels
• Current trend with strength percentage
• Intelligent trade recommendations
Trade Recommendations:
• TREND BUY/SELL - All timeframes aligned (High confidence)
• SHORT-TERM BUY/SELL - M5 signal only (Medium confidence)
• SCALPING BUY/SELL - M5 vs higher timeframes (Low confidence)
• WAIT - No clear signal (No confidence)
Support/Resistance Analysis:
• Fibonacci Levels: 23.6%, 38.2%, 50% retracements
• Distance Categories: Very Near (Red), Near (Orange), Medium (Yellow), Far (Green)
• ATR-based distance measurement
• Real-time proximity alerts
Scalping Detection:
Specialized signals based on:
• High volatility (ATR ratio > 1.5)
• Quick price moves (fast momentum)
• Volume confirmation (high volume spikes)
• RSI extremes (oversold/overbought)
Settings:
• EMA - Fast (9), Slow (21), Trend (50)
• RSI - Length (14), Overbought (70), Oversold (30)
• MACD - Fast (12), Slow (26), Signal (9)
• Bollinger Bands - Length (20), Multiplier (2.0)
• ATR - Length (14) for volatility measurement
• Volume Threshold - 1.5x average volume
• Divergence - Lookback (3), Threshold (0.5)
Best Practices:
🎯 Adapt strategy to detected trading style
📊 Use multi-timeframe analysis for confirmation
⚡ Monitor S/R distances for entry timing
🛡️ Always use calculated Stop Loss levels
🔍 Watch for divergence signals
📈 Follow intelligent trade recommendations
Pro Tips:
• Table provides all essential information in one place
• Trading style detection helps adapt your strategy
• S/R distance shows proximity to key levels
• Confidence levels indicate signal reliability
• Multi-timeframe alignment increases success rate
• Scalping signals work best in high volatility
Alerts:
• Trend Change Alert - "Trend changed across timeframes"
• Divergence Alert - "Divergence detected"
• Scalping Alert - "Scalping opportunity"
• Trade Signal Alert - "Trade recommendation available"
Version 2.0 Improvements:
• Advanced multi-timeframe analysis (M1, M5, M15, M30)
• Intelligent trading style detection
• Comprehensive support/resistance analysis
• Professional trade recommendations with confidence levels
• Scalping detection with specialized signals
• Risk management with calculated SL/TP levels
• 14-metric comprehensive information table
Created with ❤️ for the trading community
This indicator is free to use for both commercial and non-commercial purposes.
Complexity v3.2Complex Trend Analyzer v6.1 v3.2
Advanced multi-indicator trend analysis with dynamic timeframe adaptation!
Overview:
This sophisticated indicator combines multiple technical analysis tools for comprehensive trend analysis. It features EMA crossovers, RSI momentum, MACD signals, Bollinger Bands, volume analysis, divergence detection, and multi-timeframe analysis with dynamic parameter adaptation based on market volatility.
Key Features:
✅ Multi-Indicator Analysis - EMA, RSI, MACD, Bollinger Bands, Volume, ATR
✅ Divergence Detection - Bullish and bearish divergence with strength calculation
✅ Dynamic Timeframe Adaptation - Parameters adjust automatically based on timeframe
✅ Trend Tracking - Complete trend lifecycle with BUY/SELL/END signals
✅ Multi-Timeframe Analysis - M5, M15, M30 trend comparison
✅ Risk Management - Volatility filtering and warning system
✅ Visual Clarity - Clean labels, trend lines, and information table
How It Works:
The indicator uses a weighted scoring system:
• EMA (2.0) - Primary trend direction
• RSI (1.5) - Momentum confirmation
• MACD (1.5) - Trend momentum
• Bollinger Bands (1.0) - Volatility context
• Volume (1.0) - Volume confirmation
• Price Action (0.5 each) - Higher highs/lows
Signal Logic:
• BUY - Weighted score > threshold + filters passed
• SELL - Weighted score > threshold + filters passed
• END - Trend reversal conditions met
Visual Elements:
• 🟢 BUY - Green label with trend tracking
• 🔴 SELL - Red label with trend tracking
• ⚫ END - Gray label marking trend end
• × BUY - Green crosses for bullish divergence
• × SELL - Red crosses for bearish divergence
• ⚠️ - Warning signals for trend reversals
Information Table:
Real-time display showing:
• ATR volatility with signal (HIGH/MED/LOW/NORMAL VOL)
• Divergence status with strength percentage
• BUY/SELL signal count and overall signal
• Multi-Timeframe analysis (M5, M15, M30)
• Current trend with strength percentage
• Detailed trend strength analysis
Dynamic Adaptation:
Parameters automatically adjust based on timeframe:
• M1 - Fastest reaction (1.5-7.5 bars)
• M3 - Quick response (2-10 bars)
• M5 - Standard setting (3-15 bars)
• M15 - Slower, more reliable (4-20 bars)
Settings:
• EMA - Fast (9), Slow (21), Trend (50)
• RSI - Length (14), Overbought (70), Oversold (30)
• MACD - Fast (12), Slow (26), Signal (9)
• Bollinger Bands - Length (20), Multiplier (2.0)
• ATR - Length (14) for volatility measurement
• Volume Threshold - 1.5x average volume
Best Practices:
🎯 Works best in trending markets
📊 Use as overlay on main chart
⚡ Combine with price action analysis
🛡️ Always use proper risk management
🔍 Watch for divergence signals
⚠️ Pay attention to warning signals
Pro Tips:
• Green background = Strong uptrend, Red background = Strong downtrend
• Orange background = Risk zone (high volatility/RSI extremes)
• × marks indicate divergence opportunities
• ⚠️ warnings signal potential trend reversals
• Use multi-timeframe analysis for confirmation
• Monitor the information table for comprehensive market view
Alerts:
• BUY Alert - "BUY signal detected"
• SELL Alert - "SELL signal detected"
• Divergence Alert - "Divergence detected"
• Warning Alert - "Trend warning"
Version 3.2 Improvements:
• Enhanced multi-indicator analysis
• Improved divergence detection with strength calculation
• Advanced dynamic timeframe adaptation
• Comprehensive risk management system
• Professional visual presentation
• Weighted scoring system for better accuracy
Created with ❤️ for the trading community
This indicator is free to use for both commercial and non-commercial purposes.
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.
Modular Range-Trading Strategy (V9.2)# 模块化震荡行情策略 (V9.2)
# Modular Range-Trading Strategy (V9.2)
## 策略简介 | Strategy Overview
该策略基于布林带 (Bollinger Bands)、RSI、MACD、ADX 等经典指标的组合，通过多逻辑模块化结构识别震荡区间的价格反转机会，支持多空双向操作，并在相同逻辑下允许智能加仓，适用于震荡市场的回测和研究。
This strategy combines classic indicators such as Bollinger Bands, RSI, MACD, and ADX to identify price reversal opportunities within ranging markets. It features a modular multi-logic structure, allowing both long and short trades with intelligent pyramiding under the same logic. It is designed for backtesting and research in range-bound conditions.
---
## 功能特点 | Key Features
- **多逻辑结构**：支持多套震荡逻辑（动能确认均值回归、布林带极限反转等）。
- **加仓与仓位互斥**：同逻辑下可智能加仓，不同逻辑间自动互斥，避免冲突。
- **回测可调时间范围**：可自定义回测起止时间，精准评估策略表现。
- **指标可视化**：布林带、RSI、MACD 及动态 ATR 止损线实时绘图。
- **K线收盘确认信号**：通过 `barstate.isconfirmed` 控制信号，避免未收盘的虚假信号。
- **Multi-logic structure**: Supports multiple range-trading logics (e.g., momentum-based mean reversion, Bollinger Band reversals).
- **Pyramiding with mutual exclusion**: Allows intelligent pyramiding within the same logic while preventing conflicts between different logics.
- **Adjustable backtesting range**: Customizable start and end dates for accurate performance evaluation.
- **Visual indicators**: Real-time plotting of Bollinger Bands, RSI, MACD, and dynamic ATR stop lines.
- **Close-bar confirmation**: Uses `barstate.isconfirmed` to avoid false signals before bar close.
---
## 使用说明 | Usage
1. 将该脚本添加到 TradingView 图表。
2. 在参数中设置回测时间段和指标参数。
3. 仅用于学习与策略研究，请勿直接用于实盘交易。
1. Add this script to your TradingView chart.
2. Configure backtesting dates and indicator parameters as needed.
3. For educational and research purposes only. **Not for live trading.**
---
## ⚠️ 免责声明 | Disclaimer
本策略仅供学习和研究使用，不构成任何形式的投资建议。
作者不参与任何实盘交易、资金管理或收益分成，也不保证策略盈利能力。
严禁将本脚本用于任何非法集资、私募募资或与虚拟货币相关的金融违法活动。
使用本策略即表示您自行承担所有风险与法律责任。
This strategy is for educational and research purposes only and does not constitute investment advice.
The author does not participate in live trading, asset management, or profit sharing, nor guarantee profitability.
The use of this script in illegal fundraising, private placements, or cryptocurrency-related financial activities is strictly prohibited.
By using this strategy, you accept all risks and legal responsibilities.
---
Adaptive Fibonacci Pullback System -FibonacciFluxAdaptive Fibonacci Pullback System (AFPS) - FibonacciFlux
This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Original concepts by FibonacciFlux.
Abstract
The Adaptive Fibonacci Pullback System (AFPS) presents a sophisticated, institutional-grade algorithmic strategy engineered for high-probability trend pullback entries. Developed by FibonacciFlux, AFPS uniquely integrates a proprietary Multi-Fibonacci Supertrend engine (0.618, 1.618, 2.618 ratios) for harmonic volatility assessment, an Adaptive Moving Average (AMA) Channel providing dynamic market context, and a synergistic Multi-Timeframe (MTF) filter suite (RSI, MACD, Volume). This strategy transcends simple indicator combinations through its strict, multi-stage confluence validation logic. Historical simulations suggest that specific MTF filter configurations can yield exceptional performance metrics, potentially achieving Profit Factors exceeding 2.6 , indicative of institutional-level potential, while maintaining controlled risk under realistic trading parameters (managed equity risk, commission, slippage).
4 hourly MTF filtering
1. Introduction: Elevating Pullback Trading with Adaptive Confluence
Traditional pullback strategies often struggle with noise, false signals, and adapting to changing market dynamics. AFPS addresses these challenges by introducing a novel framework grounded in Fibonacci principles and adaptive logic. Instead of relying on static levels or single confirmations, AFPS seeks high-probability pullback entries within established trends by validating signals through a rigorous confluence of:
Harmonic Volatility Context: Understanding the trend's stability and potential turning points using the unique Multi-Fibonacci Supertrend.
Adaptive Market Structure: Assessing the prevailing trend regime via the AMA Channel.
Multi-Dimensional Confirmation: Filtering signals with lower-timeframe Momentum (RSI), Trend Alignment (MACD), and Market Conviction (Volume) using the MTF suite.
The objective is to achieve superior signal quality and adaptability, moving beyond conventional pullback methodologies.
2. Core Methodology: Synergistic Integration
AFPS's effectiveness stems from the engineered synergy between its core components:
2.1. Multi-Fibonacci Supertrend Engine: Utilizes specific Fibonacci ratios (0.618, 1.618, 2.618) applied to ATR, creating a multi-layered volatility envelope potentially resonant with market harmonics. The averaged and EMA-smoothed result (`smoothed_supertrend`) provides a robust, dynamic trend baseline and context filter.
// Key Components: Multi-Fibonacci Supertrend & Smoothing
average_supertrend = (supertrend1 + supertrend2 + supertrend3) / 3
smoothed_supertrend = ta.ema(average_supertrend, st_smooth_length)
2.2. Adaptive Moving Average (AMA) Channel: Provides dynamic market context. The `ama_midline` serves as a key filter in the entry logic, confirming the broader trend bias relative to adaptive price action. Extended Fibonacci levels derived from the channel width offer potential dynamic S/R zones.
// Key Component: AMA Midline
ama_midline = (ama_high_band + ama_low_band) / 2
2.3. Multi-Timeframe (MTF) Filter Suite: An optional but powerful validation layer (RSI, MACD, Volume) assessed on a lower timeframe. Acts as a **validation cascade** – signals must pass all enabled filters simultaneously.
2.4. High-Confluence Entry Logic: The core innovation. A pullback entry requires a specific sequence and validation:
Price interaction with `average_supertrend` and recovery above/below `smoothed_supertrend`.
Price confirmation relative to the `ama_midline`.
Simultaneous validation by all enabled MTF filters.
// Simplified Long Entry Logic Example (incorporates key elements)
long_entry_condition = enable_long_positions and
(low < average_supertrend and close > smoothed_supertrend) and // Pullback & Recovery
(close > ama_midline and close > ama_midline) and // AMA Confirmation
(rsi_filter_long_ok and macd_filter_long_ok and volume_filter_ok) // MTF Validation
This strict, multi-stage confluence significantly elevates signal quality compared to simpler pullback approaches.
1hourly filtering
3. Realistic Implementation and Performance Potential
AFPS is designed for practical application, incorporating realistic defaults and highlighting performance potential with crucial context:
3.1. Realistic Default Strategy Settings:
The script includes responsible default parameters:
strategy('Adaptive Fibonacci Pullback System - FibonacciFlux', shorttitle = "AFPS", ...,
initial_capital = 10000, // Accessible capital
default_qty_type = strategy.percent_of_equity, // Equity-based risk
default_qty_value = 4, // Default 4% equity risk per initial trade
commission_type = strategy.commission.percent,
commission_value = 0.03, // Realistic commission
slippage = 2, // Realistic slippage
pyramiding = 2 // Limited pyramiding allowed
)
Note: The default 4% risk (`default_qty_value = 4`) requires careful user assessment and adjustment based on individual risk tolerance.
3.2. Historical Performance Insights & Institutional Potential:
Backtesting provides insights into historical behavior under specific conditions (always specify Asset/Timeframe/Dates when sharing results):
Default Performance Example: With defaults, historical tests might show characteristics like Overall PF ~1.38, Max DD ~1.16%, with potential Long/Short performance variance (e.g., Long PF 1.6+, Short PF < 1).
Optimized MTF Filter Performance: Crucially, historical simulations demonstrate that meticulous configuration of the MTF filters (particularly RSI and potentially others depending on market) can significantly enhance performance. Under specific, optimized MTF filter settings combined with appropriate risk management (e.g., 7.5% risk), historical tests have indicated the potential to achieve **Profit Factors exceeding 2.6**, alongside controlled drawdowns (e.g., ~1.32%). This level of performance, if consistently achievable (which requires ongoing adaptation), aligns with metrics often sought in institutional trading environments.
Disclaimer Reminder: These results are strictly historical simulations. Past performance does not guarantee future results. Achieving high performance requires careful parameter tuning, adaptation to changing markets, and robust risk management.
3.3. Emphasizing Risk Management:
Effective use of AFPS mandates active risk management. Utilize the built-in Stop Loss, Take Profit, and Trailing Stop features. The `pyramiding = 2` setting requires particularly diligent oversight. Do not rely solely on default settings.
4. Conclusion: Advancing Trend Pullback Strategies
The Adaptive Fibonacci Pullback System (AFPS) offers a sophisticated, theoretically grounded, and highly adaptable framework for identifying and executing high-probability trend pullback trades. Its unique blend of Fibonacci resonance, adaptive context, and multi-dimensional MTF filtering represents a significant advancement over conventional methods. While requiring thoughtful implementation and risk management, AFPS provides discerning traders with a powerful tool potentially capable of achieving institutional-level performance characteristics under optimized conditions.
Acknowledgments
Developed by FibonacciFlux. Inspired by principles of Fibonacci analysis, adaptive averaging, and multi-timeframe confirmation techniques explored within the trading community.
Disclaimer
Trading involves substantial risk. AFPS is an analytical tool, not a guarantee of profit. Past performance is not indicative of future results. Market conditions change. Users are solely responsible for their decisions and risk management. Thorough testing is essential. Deploy at your own considered risk.
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.
---
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.
---
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 .
---
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 .
---
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.
---
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.
---
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.
---
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.
---
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.
---
Citations
- Divergence Detection Library : Divergence Indicator by DevLucem
- Trendline Drawing Library : Simple Trendlines by HoanGhetti
---
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.
---
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.
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
Forex scalper 2xEMA + SRSI + MACDThis is a forex scalping strategy designed for the most liquid pairs, like major forex pairs.
Its made of
1 EMA 50
1 EMA 100
Stochastic RSI
MACD
Rules
For long :close of the candle is above moving average 50, moving average 50> moving average 100, macd histogram is positive and cross over of stochastic rsi with the oversold level.
For short :close of the candle is below moving average 50, moving average 50 < moving average 100, macd histogram is negative and cross under of stochastic rsi with the overbought level.
Exit
For exit we have take profit and stop loss using fixed pip points.
For this example on EURUSD we use 20 pips for both tp and sl
IF you have any questions let me know !
{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.
Triple Confirmation StrategyTriple Confirmation Strategy (TCS)
This indicator combines three different technical tools to provide more reliable entry signals:
RSI + Moving Average crossover → momentum confirmation
MACD line & signal crossover → trend direction signal
OBV + EMA crossover → volume-based confirmation
A signal is valid only if all three conditions occur within a given number of bars (default: 5). Optionally, it can be set to trigger only when the third confirmation happens at the current bar.
✨ Features
BUY / SELL markers on the chart
Alertcondition support → alerts can be set instantly
Grouped settings (RSI, MACD, OBV, Logic)
Diagnostic overlay (WSCD-style): RSI, MACD, and OBV visualized on a normalized –100…100 scale for easier monitoring
🎯 Usage
Suitable for both intraday and swing trading with default settings.
Parameters are fully customizable (lookback periods, bar window, diagnostic overlay).
Signals should not be used as a standalone trading system but are most effective when combined with broader context and other forms of analysis.
