Hinton Map█ HINTON MAP
This script displays a Hinton Map visualization of market data for user-defined tickers and timeframes. It uses color gradients to represent the magnitude and direction of price change, RSI, and a combination of both.
This is one example. You can modify and try other values as you wish, but do keep the incoming values between -1 and 1.
In the Example Usage:
Users can input up to 5 symbols and 5 timeframes. For each ticker/timeframe combination:
The box size represents the relative magnitude of the 2-bar percentage change.
The box fill color represents the direction and magnitude of the 2-bar percentage change.
The box border color and thickness represent the RSI deviation from 50.
The inner box color represents a combination of price change magnitude and RSI deviation from 50.
Hovering over each box displays a tooltip with the ticker, timeframe, percentage change, and RSI.
Inputs:
• Unit Size (bars):
The size of each Hinton unit in bars.
Type: int
Default Value: 10
• Border Width:
The base width of the inner box border.
Type: int
Default Value: 3
• Negative Hue (0-360):
The hue value for negative price changes (0-360).
Type: float
Default Value: 100
• Positive Hue (0-360):
The hue value for positive price changes (0-360).
Type: float
Default Value: 180
• Ticker 1-5:
The tickers to display on the Hinton map.
Type: string
Default Value: AAPL
• Timeframes (comma separated):
The timeframes to display on the Hinton map (comma-separated).
Type: string
Default Value: 1, 5, 60, 1D, 1W
(Fun Note: My Home town is named `Hinton`)
Indicators and strategies
Candle AnalysisImportant Setup Note
Optimize Your Viewing Experience
To ensure the Candle Analysis Indicator displays correctly and to prevent any default chart colors from interfering with the indicator's visuals, please adjust your chart settings:
Right-Click on the Chart and select "Settings".
Navigate to the "Symbol" tab.
Set transparent default candle colors:
- Body
-Borders
- Wick
By customizing these settings, you'll experience the full visual benefits of the indicator without any overlapping colors or distractions.
Elevate your trading strategy with the Candle Analysis Indicator—a powerful tool designed to give you a focused view of the market exactly when you need it. Whether you're honing in on specific historical periods or testing new strategies, this indicator provides the clarity and control you've been looking for.
Key Features:
🔹 Custom Date Range Selection
Tailored Analysis: Choose your own start and end dates to focus on the market periods that matter most to you.
Historical Insights: Dive deep into past market movements to uncover hidden trends and patterns.
🔹 Dynamic Backtesting Simulation
Interactive Playback: Enable backtesting to simulate how the market unfolded over time.
Strategy Testing: Watch candles appear at your chosen interval, allowing you to test and refine your trading strategies in real-time scenarios.
🔹 Enhanced Visual Clarity
Focused Visualization: Only candles within your specified date range are highlighted, eliminating distractions from irrelevant data.
Distinct Candle Styling: Bullish and bearish candles are displayed with unique colors and transparency, making it easy to spot market sentiment at a glance.
🔹 User-Friendly Interface
Easy Setup: Simple input options mean you can configure the indicator quickly without any technical hassle.
Versatile Application: Compatible with various timeframes—whether you're trading intraday, daily, or weekly.
Multi Fibonacci Supertrend with Signals【FIbonacciFlux】Multi Fibonacci Supertrend with Signals (MFSS)
Overview
The Multi Fibonacci Supertrend with Signals (MFSS) is an advanced technical analysis tool that combines multiple Supertrend indicators using Fibonacci ratios to identify trend directions and potential trading opportunities.
Key Features
1. Fibonacci-Based Supertrend Levels
* Factor 1 (Weak) : 0.618 - The golden ratio
* Factor 2 (Medium) : 1.618 - The Fibonacci ratio
* Factor 3 (Strong) : 2.618 - The extension ratio
2. Visual Components
* Multi-layered Trend Lines
* Different line weights for easy identification
* Progressive transparency from Factor 1 to Factor 3
* Color-coded trend directions (Green for bullish, Red for bearish)
* Dynamic Fill Areas
* Gradient fills between price and trend lines
* Visual representation of trend strength
* Automatic color adjustment based on trend direction
* Signal Indicators
* Clear BUY/SELL labels on chart
* Position-adaptive signal placement
* High-visibility color scheme
3. Signal Generation Logic
The system generates signals based on two key conditions:
* Primary Condition :
* BUY : Price crossunder Supertrend2 (Factor 1.618)
* SELL : Price crossover Supertrend2 (Factor 1.618)
* Confirmation Filter :
* Signals only trigger when Supertrend3 confirms the trend direction
* Reduces false signals in volatile markets
Technical Details
Input Parameters
* ATR Period : 10 (default)
* Customizable for different market conditions
* Affects sensitivity of all Supertrend levels
* Factor Settings :
* All factors are customizable
* Default values based on Fibonacci sequence
* Minimum value: 0.01
* Step size: 0.01
Alert System
* Built-in alert conditions
* Customizable alert messages
* Real-time notification support
Use Cases
* Trend Trading
* Identify strong trend directions
* Filter out weak signals
* Confirm trend continuations
* Risk Management
* Multiple trend levels for stop-loss placement
* Clear entry and exit signals
* Trend strength visualization
* Market Analysis
* Multi-timeframe analysis capability
* Trend strength assessment
* Market structure identification
Benefits
* Reliability
* Based on proven Supertrend algorithm
* Enhanced with Fibonacci mathematics
* Multiple confirmation levels
* Clarity
* Clear visual signals
* Easy-to-interpret interface
* Reduced noise in signal generation
* Flexibility
* Customizable parameters
* Adaptable to different markets
* Suitable for various trading styles
Performance Considerations
* Optimized code structure
* Efficient calculation methods
* Minimal resource usage
Installation and Usage
Setup
* Add indicator to chart
* Adjust parameters if needed
* Enable alerts as required
Best Practices
* Use with other confirmation tools
* Adjust factors based on market volatility
* Consider timeframe appropriateness
Backtesting Results and Strategy Performance
This indicator is specifically designed for pullback trading with optimized risk-reward ratios in trend-following strategies. Below are the detailed backtesting results from our proprietary strategy implementation:
BTCUSDT Performance (Binance)
* Test Period: Approximately 7 years
* Risk-Reward Ratio: 2:1
* Take Profit: 8%
* Stop Loss: 4%
Key Metrics (BTCUSDT):
* Net Profit: +2,579%
* Total Trades: 551
* Win Rate: 44.8%
* Profit Factor: 1.278
* Maximum Drawdown: 42.86%
ETHUSD Performance (Binance)
* Risk-Reward Ratio: 4.33:1
* Take Profit: 13%
* Stop Loss: 3%
Key Metrics (ETHUSD):
* Net Profit: +8,563%
* Total Trades: 581
* Win Rate: 32%
* Profit Factor: 1.32
* Maximum Drawdown: 55%
Strategy Highlights:
* Optimized for pullback trading in strong trends
* Focus on high risk-reward ratios
* Proven effectiveness in major cryptocurrency pairs
* Consistent performance across different market conditions
* Robust profit factor despite moderate win rates
Note: These results are from our proprietary strategy implementation and should be used as reference only. Individual results may vary based on market conditions and implementation.
Important Considerations:
* The strategy demonstrates strong profitability despite lower win rates, emphasizing the importance of proper risk-reward ratios
* Higher drawdowns are compensated by significant overall returns
* The system shows adaptability across different cryptocurrencies with consistent profit factors
* Results suggest optimal performance in volatile crypto markets
Real Trading Examples
BTCUSDT 4-Hour Chart Analysis
Example of pullback strategy implementation on Bitcoin, showing clear trend definition and entry points
ETHUSDT 4-Hour Chart Analysis
Ethereum chart demonstrating effective signal generation during strong trends
BTCUSDT Detailed Signal Example (15-Minute Scalping)
Close-up view of signal generation and trend confirmation process on 15-minute timeframe, demonstrating the indicator's effectiveness for scalping operations
Chart Analysis Notes:
* Green and red zones clearly indicate trend direction
* Multiple timeframe confirmation visible through different Supertrend levels
* Clear entry signals during pullbacks in established trends
* Precise stop-loss placement opportunities below support levels
Implementation Guidelines:
* Wait for main trend confirmation from Factor 3 (2.618)
* Enter trades on pullbacks to Factor 2 (1.618)
* Use Factor 1 (0.618) for fine-tuning entry points
* Place stops below the relevant Supertrend level
Footnotes:
* Charts provided are from Binance exchange, using both 4-hour and 15-minute timeframes
* Trading view screenshots captured during actual market conditions
* Indicators shown: Multi Fibonacci Supertrend with all three factors
* Time period: Recent market activity showing various market conditions
Important Notice:
These charts are for educational purposes only. Past performance does not guarantee future results. Always conduct your own analysis and risk management.
Disclaimer
This indicator is for informational purposes only. Past performance is not indicative of future results. Always conduct proper risk management and due diligence.
License
Open source under MIT License
Author's Note
Contributions and suggestions for improvement are welcome. Please feel free to fork and enhance.
VWAP2 --ClaireIndicator Release Notes
I am excited to introduce a powerful multi-timeframe Volume Weighted Average Price (VWAP) indicator. This tool helps traders analyze market trends and identify key support and resistance levels across various timeframes. Below are the main features and usage guidelines for this indicator:
Key Features
Open Price for Each Timeframe
The "Open" option represents the opening price for each specific timeframe, such as daily, weekly, monthly, etc.
Previous vs. Current Levels
Levels prefixed with 'P' (e.g., pwval) are calculated for the previous period, while those without 'P' (e.g., wval) represent the current period. For instance, pwval is the VWAP-calculated Value Area Low (VAL) for the previous week, whereas wval applies to the current week.
VWAP Calculation Standards
VWAP can be calculated using a standard deviation (S) or a percentage (P). The "Multiplier" indicates how many standard deviations are applied, with a default setting of S (standard deviation) and a multiplier of 1.
Data Source Default
The default data source for calculations is hlc3, which is the average of high, low, and close prices. This can be adjusted if needed.
Merge Function
The Merge option visually groups data that is closely aligned within a specified range, allowing for a clearer representation of critical price levels.
Viewing Recommendations
When analyzing higher dimensions, it is recommended to enable Quarter (Q) and Year (Y) settings to identify important price levels near the current price. For detailed attention, you can disable levels that are significantly distant from the current price.
Data Limitations
Free TradingView accounts can pull data from up to 20,000 candles. This means the indicator is most accurate and comprehensive on 1-hour and 4-hour timeframes, given these data constraints.
Usage Guidelines
Trend Analysis: Utilize VWAP and bands across different timeframes to identify market trend continuations or reversals.
Support and Resistance Identification: Use the calculated upper and lower bands as potential support or resistance levels to optimize entry and exit points in your trading.
Combined Application: It is recommended to use this indicator alongside other technical analysis tools to improve the accuracy of your analysis and the reliability of your trading decisions.
I believe this versatile and highly customizable VWAP indicator will become an essential part of your trading toolkit, helping you to better understand market dynamics and make more precise trading decisions.
Bharat Jhunjhunwala v2- Volume Color CodingPurpose of the Script
This script is designed for traders in India to visualize and analyze trading volume in the stock market. It colors the volume bars based on how the current trading volume compares to its average over the past 20 periods. This helps traders quickly identify unusual trading activity.
How It Works Step by Step
Setting Up the Indicator
Name & Display: The script is named "Bharat Jhunjhunwala- Volume Color Coding" and it displays below the main stock chart (overlay=false).
Volume Format: It formats the data specifically for volume (format=format.volume).
User Input
Volume Moving Average Period: Users can set how many periods (e.g., days) to consider for calculating the average volume. By default, it's set to 20 periods, and users can adjust this if needed.
Predefined Volume Ratios
The script uses fixed ratios to categorize the current volume:
Ultra High Volume: 3.0 times the average volume.
High Volume: 1.8 times the average volume.
Ultra Low Volume: Between 0.4 and 0.7 times the average volume.
Note: These ratios are hardcoded, meaning users cannot change them.
Calculating the Average Volume
The script calculates the Simple Moving Average (SMA) of the volume over the specified number of periods (default is 20). This average helps determine what "normal" volume looks like.
Determining Volume Levels
Ultra High Volume: If the current volume is 3 times or more the average, it's flagged as ultra high.
High Volume: If the current volume is 1.8 times or more but less than ultra high, it's flagged as high.
Ultra Low Volume: If the current volume is between 0.4 and 0.7 times the average, it's flagged as ultra low.
Normal Volume: Any volume that doesn't fit the above categories is considered normal.
Color Coding the Volume Bars
Based on the volume level determined:
Red: Ultra High Volume
Orange: High Volume
Blue: Ultra Low Volume
Green: Normal Volume
This color coding makes it easy to spot unusual trading activity at a glance.
Plotting on the Chart
Volume Bars: The script plots the actual trading volume as colored bars according to the categories above.
Volume Moving Average Line: It also plots the 20-period moving average of the volume as a green line. This line serves as a reference to compare current volumes against the average.
Why This is Useful
Quick Identification: Traders can instantly see when there's unusually high or low trading activity, which might indicate significant market moves or investor interest.
Trend Analysis: By comparing current volume to the moving average, traders can assess whether the current market behavior is stronger or weaker than usual.
Decision Making: Colored volume bars can help in making informed trading decisions, such as entering or exiting positions based on volume spikes or drops.
Customization
Adjusting the Moving Average: While the volume ratios are fixed, traders can change the number of periods used to calculate the moving average to better fit different trading strategies or time frames.
Summary
This script enhances the standard volume indicator by adding color-coded signals based on predefined volume thresholds relative to a moving average. It's a helpful tool for Indian stock traders to monitor and react to changes in trading volume effectively.
Multi OHLC4 VWAPsVWAP Indicators, which are often restricted to a single timeframe, this script incorporates a multi-timeframe analysis tool that includes Intraday, Weekly, Monthly, Quarterly, and Yearly VWAPs.
Key Features:
Multi-Timeframe Support: Choose from Intraday, Weekly, Monthly, Quarterly, or Yearly VWAP calculations, each tailored to professional trading strategies. The ability to switch between different timeframes enables comprehensive market analysis, catering to both short-term traders and long-term investors.
Advanced Rolling VWAP Options: The script supports both standard VWAP and Rolling VWAP modes, allowing users to reset VWAP calculations based on specific conditions (daily, weekly, monthly, etc.). This functionality enhances dynamic trend recognition.
Customizable Color Schemes: The slope direction of each VWAP line is visually represented with unique color schemes for each timeframe, enabling quick interpretation of price movement. Users can fully customize these colors to suit their trading preferences.
Enhanced Plotting Style: Uses a "line with breaks" style to emphasize trend changes, with line thickness increasing for longer timeframes. This visual hierarchy makes it easy to distinguish between short-term and long-term trends.
How It Helps Traders:
This script combines multiple VWAP calculations into a single overlay, enabling traders to identify support and resistance zones, detect trend shifts, and perform in-depth market analysis effortlessly. By using the OHLC/4 average price instead of the traditional close-only approach, the script offers a balanced perspective, reflecting both intraday volatility and long-term price trends.
Why Choose OHLC/4 VWAP Pro Multi? Many standard VWAP scripts focus only on aesthetics or a single timeframe analysis. This script brings a more comprehensive view to VWAP calculations by implementing a multi-timeframe strategy with customizable rolling features. It aims to fill a gap for traders looking for a more nuanced and professional tool, with the flexibility to adapt to diverse trading strategies.
Dynamic Score Supertrend [QuantAlgo]Dynamic Score Supertrend 📈🚀
The Dynamic Score Supertrend by QuantAlgo introduces a sophisticated trend-following tool that combines the well-known Supertrend indicator with an innovative dynamic trend scoring technique . By tracking market momentum through a scoring system that evaluates price behavior over a customizable window, this indicator adapts to changing market conditions. The result is a clearer, more adaptive tool that helps traders and investors detect and capitalize on trend shifts with greater precision.
💫 Conceptual Foundation and Innovation
At the core of the Dynamic Score Supertrend is the dynamic trend score system , which measures price movements relative to the Supertrend’s upper and lower bands. This scoring technique adds a layer of trend validation, assessing the strength of price trends over time. Unlike traditional Supertrend indicators that rely solely on ATR calculations, this system incorporates a scoring mechanism that provides more insight into trend direction, allowing traders and investors to navigate both trending and choppy markets with greater confidence.
✨ Technical Composition and Calculation
The Dynamic Score Supertrend utilizes the Average True Range (ATR) to calculate the upper and lower Supertrend bands. The dynamic trend scoring technique then compares the price to these bands over a customizable window, generating a trend score that reflects the current market direction.
When the score exceeds the uptrend or downtrend thresholds, it signals a possible shift in market direction. By adjusting the ATR settings and window length, the indicator becomes more adaptable to different market conditions, from steady trends to periods of higher volatility. This customization allows users to refine the Supertrend’s sensitivity and responsiveness based on their trading or investing style.
📈 Features and Practical Applications
Customizable ATR Settings: Adjust the ATR length and multiplier to control the sensitivity of the Supertrend bands. This allows the indicator to smooth out noise or react more quickly to price shifts, depending on market conditions.
Window Length for Dynamic Scoring: Modify the window length to adjust how many data points the scoring system considers, allowing you to tailor the indicator’s responsiveness to short-term or long-term trends.
Uptrend/Downtrend Thresholds: Set thresholds for identifying trend signals. Increase these thresholds for more reliable signals in choppy markets, or lower them for more aggressive entry points in trending markets.
Bar and Background Coloring: Visual cues such as bar coloring and background fills highlight the direction of the current trend, making it easier to spot potential reversals and trend shifts.
Trend Confirmation: The dynamic trend score system provides a clearer confirmation of trend strength, helping you identify strong, sustained movements while filtering out false signals.
⚡️ How to Use
✅ Add the Indicator: Add the Dynamic Score Supertrend to your favourites, then apply it to your chart. Adjust the ATR length, multiplier, and dynamic score settings to suit your trading or investing strategy.
👀 Monitor Trend Shifts: Track price movements relative to the Supertrend bands and use the dynamic trend score to confirm the strength of a trend. Bar and background colors make it easy to visualize key trend shifts.
🔔 Set Alerts: Configure alerts when the dynamic trend score crosses key thresholds, so you can act on significant trend changes without constantly monitoring the charts.
🌟 Summary and Usage Tips
The Dynamic Score Supertrend by QuantAlgo is a robust trend-following tool that combines the power of the Supertrend with an advanced dynamic scoring system. This approach provides more adaptable and reliable trend signals, helping traders and investors make informed decisions in trending markets. The customizable ATR settings and scoring thresholds make it versatile across various market conditions, allowing you to fine-tune the indicator for both short-term momentum and long-term trend following. To maximize its effectiveness, adjust the settings based on current market volatility and use the visual cues to confirm trend shifts. The Dynamic Score Supertrend offers a refined, probabilistic approach to trading and investing, making it a valuable addition to your toolkit.
Market Bias IndicatorOverview
This Pine Script™ code generates a "Market Sentiment Dashboard" on TradingView, providing a visual summary of market sentiment across multiple timeframes. This tool aids traders in making informed decisions by displaying real-time sentiment analysis based on Exponential Moving Averages (EMA).
Key Features
Panel Positioning:
Custom Placement: Traders can position the dashboard at the top, middle, or bottom of the chart and align it to the left, centre, or right, ensuring optimal integration with other chart elements.
Customizable Colours:
Sentiment Colours: Users can define colours for bullish, bearish, and neutral market conditions, enhancing the dashboard's readability.
Text Colour: Customizable text colour ensures clarity against various background colours.
Label Size:
Scalable Labels: Adjustable label sizes (from very small to very large) ensure readability across different screen sizes and resolutions.
Market Sentiment Calculation:
EMA-Based Sentiment: The dashboard calculates sentiment using a 9-period EMA. If the EMA is higher than two bars ago, the sentiment is bullish; if lower, it's bearish; otherwise, it's neutral.
Multiple Timeframes: Sentiment is calculated for several timeframes: 30 minute, 1 hour, 4 hour, 6 hour, 8 hour, 12 hour, 1 day, and 1 week. This broad analysis provides a comprehensive view of market conditions.
Dynamic Table:
Structured Display: The dashboard uses a table to organize and display sentiment data clearly.
Real-Time Updates: The table updates in real-time, providing traders with up-to-date market information.
How It Works
EMA Calculation: The script requests EMA(9) values for each specified timeframe and compares the current EMA with the EMA from two bars ago to determine market sentiment.
Colour Coding: Depending on the sentiment (Bullish, Bearish, or Neutral), the corresponding cell in the table is color-coded using predefined colours.
Table Display: The table displays the timeframe and corresponding sentiment, allowing traders to quickly assess market trends.
Benefits to Traders
Quick Assessment: Traders can quickly evaluate market sentiment across multiple timeframes without switching charts or manually calculating indicators.
Enhanced Visualization: The color-coded sentiment display makes it easy to identify trends at a glance.
Multi-Timeframe Analysis: Provides a broad view of short-term and long-term market trends, helping traders confirm trends and avoid false signals.
This dashboard enhances the overall trading experience by providing a comprehensive, customizable, and easy-to-read summary of market sentiment.
Usage Instructions
Add the Script to Your Chart: Apply the "Market Sentiment Dashboard" indicator to your TradingView chart.
Customize Settings: Adjust the panel position, colours, and label sizes to fit your preferences.
Interpret Sentiment: Use the color-coded table to quickly understand the market sentiment across different timeframes and make informed trading decisions.
Lot Size CalculatorThis Pine Script indicator, "Lot Size Calculator", is designed to help traders effectively manage their risk by calculating the optimal lot size for a given position based on account balance, risk percentage, and the distance to Stop Loss. The script also visually plots key price levels such as Entry Price, Stop Loss, and multiple Take Profit targets (1R, 2R, 3R).
Key Features:
Risk Management: Enter your account balance, risk percentage, entry price, and stop loss price to calculate the optimal lot size for your trade. The lot size is computed based on the risk amount you are willing to take.
Take Profit Levels: The script calculates and plots Take Profit levels for 1R, 2R, and 3R multiples of the risk, providing a structured approach to setting targets and managing rewards.
Visual Representation: The indicator plots horizontal lines on the chart for Entry Price, Stop Loss, and Take Profit levels. The Take Profit levels are styled as dotted lines for easy differentiation, and all lines extend infinitely in both directions for clarity.
Convenient Information Table: A table displayed in the top-right corner of the chart provides key information such as account balance, lot size, entry price, stop loss price, and risk details. The lot size value is highlighted for better visibility.
This tool is ideal for traders looking to maintain disciplined risk management and to visually identify key levels directly on the chart.
Universal Trend Following Strategy | RocheurUniversal All Assets Strategy by Rocheur
The Universal All Assets Strategy is a cutting-edge, trend-following algorithm designed to operate seamlessly across multiple asset classes, including equities, commodities, forex, and cryptocurrencies. This strategy leverages the power of eight unique indicators, offering traders robust, adaptive signals. Its dynamic logic, combined with a comprehensive risk management framework, allows for precision trading in a variety of market conditions.
Core Methodologies and Features
1. Eight Integrated Trend Indicators
At the heart of the Universal All Assets Strategy are eight sophisticated trend-following indicators, each designed to capture different facets of market behavior. These indicators work together to provide a multi-dimensional analysis of price trends, filtering out noise and reacting only to significant movements:
Directional Moving Averages : Tracks the primary market trend, offering a clear indication of long-term price direction, ideal for identifying sustained upward or downward movements.
Smoothed Moving Averages : Reduces short-term volatility and noise to reveal the underlying trend, enhancing signal clarity and helping traders avoid reacting to temporary price spikes.
RSI Loops : Utilizes the Relative Strength Index (RSI) to assess market momentum, using a unique for loop mechanism to smooth out data and enhance precision.
Supertrend Filters : This indicator dynamically adjusts to market volatility, closely following price action to detect significant breakouts or reversals. The Supertrend is a core component for identifying shifts in trend direction with minimal lag.
RVI for Loop : The Relative Volatility Index (RVI) measures the strength of market volatility. It is optimized with a for loop mechanism, which smooths out the data and improves directional cues, especially in choppy or sideways markets.
Hull for Loop : The Hull Moving Average is designed to minimize lag while offering a smooth, responsive trend line. The for loop mechanism further enhances this by making the Hull even more sensitive to trend shifts, ensuring faster reaction to market movements without generating excessive noise.
These indicators evaluate market conditions independently, assigning a score of 1 for bullish trends and -1 for bearish trends. The average score across all eight indicators is calculated for each time frame (or bar), and this score determines whether the strategy should enter, exit, or remain neutral in a trade.
2. Scoring and Signal Confirmation
The strategy’s confirmation system ensures that trades are initiated only when there is strong alignment across multiple indicators:
A Long Position (Buy) is initiated when the majority of indicators generate a bullish signal, i.e., the average score exceeds a predefined upper threshold.
A Short Position or Exit is triggered when the average score falls below a lower threshold, signaling a bearish trend or neutral market.
By using a majority-rule confirmation system, the strategy filters out weak signals, reducing the chances of reacting to market noise or false positives. This ensures that only robust trends—those supported by multiple indicators—trigger trades.
Adaptive Logic for All Asset Classes
The Universal All Assets Strategy stands out for its ability to adapt dynamically across different asset classes. Whether it’s applied to highly volatile assets like cryptocurrencies or more stable instruments like equities, the strategy fine-tunes its behavior to match the asset’s volatility profile and price behavior.
Volatility Filters : The system incorporates volatility-sensitive filters, such as the Average True Range (ATR) and standard deviation metrics, which dynamically adjust its sensitivity based on market conditions. This ensures the strategy remains responsive to significant price movements while filtering out inconsequential fluctuations.
This adaptability makes the Universal All Assets Strategy effective across diverse markets, providing consistent performance whether the market is trending, range-bound, or experiencing high volatility.
Customization and Flexibility
1. Directional Bias
The strategy offers traders the flexibility to set a customizable directional bias, allowing it to focus on:
Long-only trades during bullish markets.
Short-only trades during bear markets.
Bi-directional trades for those looking to capitalize on both uptrends and downtrends.
This bias can be fine-tuned based on market conditions, trader preference, or risk tolerance, without compromising the integrity of the overall signal-generation process.
2. Volatility Sensitivity
Traders can adjust the strategy’s volatility sensitivity through customizable settings. By modifying how the system reacts to volatility, traders can make the strategy more aggressive in high-volatility environments or more conservative in quieter markets, depending on their individual trading style.
Visual Representation of Component Behavior
One of the unique features of the strategy is its real-time visual representation of the eight indicators through a component table displayed on the chart. This table provides a clear overview of the current status of each indicator:
A score of 1 indicates a bullish signal.
A score of -1 indicates a bearish signal.
The table is updated at each time frame (bar), showing how each indicator is contributing to the overall trend decision. This real-time feedback allows traders to monitor the exact composition of the strategy’s signal, helping them better understand market dynamics.
Oscillator Visualization for Trend Detection
To complement the component table, the strategy includes a trend oscillator displayed beneath the price chart, offering a visual summary of the overall market direction:
Green bars represent bullish trends when the majority of indicators signal an uptrend.
Red bars represent bearish trends or a neutral (cash) position when the majority of indicators detect a downtrend.
This oscillator allows traders to quickly assess the market’s overall direction at a glance, without needing to analyze each individual indicator, providing a clear and immediate visual of the market trend.
Backtested and Forward-Tested for Real-World Conditions
The Universal All Assets Strategy has been thoroughly tested under real-world trading conditions, incorporating key factors like:
Slippage : Set at 20 ticks to represent real market fluctuations.
Order Size : Calculated as 10% of equity, ensuring appropriate risk exposure for realistic capital management.
Commission : A fee of 0.05% has been factored in to account for trading costs.
These settings ensure that the strategy’s performance metrics—such as the Sortino Ratio , Sharpe Ratio , Omega Ratio , and Profit Factor —are reflective of actual trading environments. The rigorous backtesting and forward-testing processes ensure that the strategy produces realistic results, making it compatible with the markets it is written for and demonstrating how the system would behave in live conditions. It also includes robust risk management tools to minimize drawdowns and preserve capital, making it suitable for both professional and retail traders.
Anti-Fragile Design and Realistic Expectations
The Universal All Assets Strategy is engineered to be anti-fragile, thriving in volatile markets by adjusting to turbulence rather than being damaged by it. This is a crucial feature that ensures the strategy remains effective even during times of significant market instability.
Moreover, the strategy is transparent about realistic expectations, acknowledging that no system can guarantee a 100% win rate and that past performance is not indicative of future results. This transparency fosters trust and provides traders with a realistic framework for long-term success, making it an ideal choice for traders looking to navigate complex market conditions with confidence.
Acknowledgment of External Code
Special credit goes to bii_vg, whose invite-only code was used with permission in the development of the Universal All Assets Strategy. Their contributions have been instrumental in refining certain aspects of this strategy, ensuring its robustness and adaptability across various markets.
Conclusion
The Universal All Assets Strategy by Rocheur offers traders a powerful, adaptable tool for capturing trends across a wide range of asset classes. Its eight-indicator confirmation system, combined with customizable settings and real-time visual representations, provides a comprehensive solution for traders seeking precision, flexibility, and consistency. Whether used in high-volatility markets or more stable environments, the strategy’s dynamic adaptability, transparent logic, and robust testing make it an excellent choice for traders aiming to maximize performance while managing risk effectively.
Oscillator Price Divergence & Trend Strategy (DPS) // AlgoFyreThe Oscillator Price Divergence & Trend Strategy (DPS) strategy combines price divergence and trend indicators for trend trading. It uses divergence conditions to identify entry points and a trend source for directional bias. The strategy incorporates risk management through dynamic position sizing based on a fixed risk amount. It allows for both long and short positions with customizable stop-loss and take-profit levels. The script includes visualization options for entry, stop-loss, and take-profit levels, enhancing trade analysis.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Divergence-Trend Combination
🔸Dynamic Position Sizing
🔸Customizable Risk Management
🔶 FUNCTIONALITY
🔸Indicators
🞘 Trend Indicator
🞘 Oscillator Source
🔸Conditions
🞘 Long Entry
🞘 Short Entry
🞘 Take Profit
🞘 Stop Loss
🔶 INSTRUCTIONS
🔸Adding the Strategy to the Chart
🔸Configuring the Strategy
🔸Backtesting and Practice
🔸Market Awareness
🔸Visual Customization
🔶 CONCLUSION
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🔶 ORIGINALITY The Divergence Trend Trading with Dynamic Position Sizing strategy uniquely combines price divergence indicators with trend analysis to optimize entry and exit points. Unlike static trading strategies, it employs dynamic position sizing based on a fixed risk amount, ensuring consistent risk management. This approach allows traders to adapt to varying market conditions by adjusting position sizes according to predefined risk parameters, enhancing both flexibility and control in trading decisions. The strategy's integration of customizable stop-loss and take-profit levels further refines its risk management capabilities, making it a robust tool for both trending and volatile markets.
🔸Divergence-Trend Combination By combining trend direction with divergence conditions, the strategy enhances the accuracy of entry signals, aligning trades with prevailing market trends.
🔸Dynamic Position Sizing This strategy calculates position sizes dynamically, based on a fixed risk amount, allowing traders to maintain consistent risk exposure across trades.
🔸Customizable Risk Management Traders can set flexible risk-reward ratios and adjust stop-loss and take-profit levels, tailoring the strategy to their risk tolerance and market conditions.
🔶 FUNCTIONALITY The Divergence Trend Trading with Dynamic Position Sizing strategy leverages a combination of trend indicators and price and oscillator divergences to identify optimal trading opportunities. This strategy is designed to capitalize on medium to long-term price movements and works best on h1, h4 or D1 timeframes. It allows traders to manage risk effectively while taking advantage of both long and short positions.
🔸Indicators 🞘 Trend Indicator: A long trend is used to determine market direction, ensuring trades align with prevailing trends.
Recommendation: We recommend using the Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre indicator with the following settings for trend detection. However, you can use any trend indicator that suits your trading style, e.g. an EMA 200.
🞘 Oscillator Source: The oscillator source is used for momentum price divergence identification. Any momentum oscillator can be used, e.g. RSI, Stochastic etc. A good oscillator is the Stochastic with the following settings:
🔸Conditions 🞘 Long Entry: A long entry condition is met if price closes above the trend AND selected divergence conditions are met, e.g. regular bullish divergence with a 10 bar lookback period with the divergence being below the 50 point mean. If the info table shows all 3 columns in the same color, the entry conditions are met and a position is opened.
🞘 Short Entry: A short entry condition is met if price closes below the trend AND selected divergence conditions are met, e.g. regular bearish divergence with a 10 bar lookback period with the divergence being above the 50 point mean.
🞘 Take Profit: Take Profit is determined by the Risk to Reward Ratio settings depending on the price distance between the entry price and the stop loss price, e.g. if stop loss is 1% away from entry and Risk Reward Ratio is 3:1 then Take Profit will be set at 3% from entry.
🞘 Stop Loss: Stop loss is a fixed level away from the trend source. For long positions, stop loss is set below the trend, and for short positions, above the trend.
🔶 INSTRUCTIONS The Divergence Trend Trading with Dynamic Position Sizing strategy can be set up by adding it to your TradingView chart and configuring parameters such as the oscillator source, trend source, and risk management settings. This strategy is designed to capitalize on short-term price movements by dynamically adjusting position sizes based on predefined risk parameters. Enhance the accuracy of signals by combining this strategy with additional indicators like trend-following or momentum-based tools. Adjust settings to better manage risk and optimize entry and exit points.
🔸Adding the Strategy to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Divergence Trend Trading with Dynamic Position Sizing // AlgoFyre" in the indicators list.
Click on the strategy to add it to your chart.
🔸Configuring the Strategy:
Open the strategy settings by clicking on the gear icon next to its name on the chart.
Oscillator Source: Select the source for the oscillator. An oscillator like Stochastic needs to be attached to the chart already in order to be used as an oscillator source to be selectable.
Trend Source: Choose the trend source to determine market direction. A trend indicator like Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre needs to be attached to the chart already in order to be used as a trend source to be selectable.
Stop Loss Percentage: Set the stop loss distance from the trend source as a percentage.
Risk/Reward Ratio: Define the desired risk/reward ratio for trades.
🔸Backtesting and Practice:
Backtest the strategy on historical data to understand how it performs in various market environments.
Practice using the strategy on a demo account before implementing it in live trading.
🔸Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The strategy reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Visual Customization Visualization Settings: Customize the display of entry price, take profit, and stop loss levels.
Color Settings: Switch to the AlgoFyre theme or set custom colors for bullish, bearish, and neutral states.
Table Settings: Enable or disable the information table and adjust its position.
🔶 CONCLUSION
The Divergence Trend Trading with Dynamic Position Sizing strategy provides a robust framework for capitalizing on short-term market trends by combining price divergence with dynamic position sizing. This strategy leverages divergence conditions to identify entry points and utilizes a trend source for directional bias, ensuring trades align with prevailing market conditions. By incorporating dynamic position sizing based on a fixed risk amount, traders can effectively manage risk and adapt to varying market conditions. The strategy's customizable stop-loss and take-profit levels further enhance its risk management capabilities, making it a versatile tool for both trending and volatile markets. With its strategic blend of technical indicators and risk management, the Divergence Trend Trading strategy offers traders a comprehensive approach to optimizing trade execution and maximizing potential returns.
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
TechniTrend: Candle Pattern Detector (CPD) v3TechniTrend: Candle Pattern Detector (CPD)
The "TechniTrend: Candle Pattern Detector (CPD)" is a powerful tool designed to enhance the analysis of candlestick patterns across financial charts to understand market behavior. This indicator detects a wide range of reversal and continuation patterns, providing traders with insights into potential market movements. It incorporates dynamic filtering and customizable settings for precision in pattern recognition, allowing users to tailor the detection criteria to different trading styles.
🔷 Key Features
Comprehensive Pattern Detection: Identifies numerous candlestick patterns, including bullish and bearish reversals, continuation setups, and indecision formations.
Dynamic Filtering Options: Filter patterns are based on trend conditions, moving average positioning, and additional criteria to increase signal accuracy.
Customizable Input Settings: Provides adjustable parameters, such as body ratios and shadow length requirements, enabling traders to fine-tune detection thresholds.
Real-Time Alerts: Generates alerts when patterns are detected, ensuring traders can respond swiftly to market opportunities.
Graphical Representation: Visualizes detected patterns on the chart using intuitive labels, colors, and markers, helping to identify key signals quickly.
Supported Patterns
The indicator covers a wide range of candlestick patterns.
❇️ 51 Candlestick Patterns
🟢 Bullish Reversal Candlestick Patterns:
Bullish engulfing - Hammer - Morning star - Piercing line - Three white soldiers - Inverted hammer - Three Inside Up - Bullish Harami - Tweezer Bottom - White Marubozu - Dragonfly Doji - Three Outside Up - Bullish Counterattack Line - Bullish Abandoned Baby - Bullish Tri-Star - Hammer Doji - Morning Star Doji
🔴 Bearish Reversal Candlestick Patterns:
Bearish engulfing - Shooting star - Evening star - Hanging man - Three black crows - Dark cloud cover - Hanging Man Doji - Three Inside Down - Bearish Harami - Tweezer Top - Black Marubozu - Three Outside Down - Bearish Counterattack Line - Gravestone Doji - Evening Star Doji - Bearish Abandoned Baby - Bearish Tri-Star
🟩 Bullish Continuation Candlestick Patterns:
Rising Three Methods - Bullish Kicker - Mat Hold Bullish - Three Line Strike - Upside Tasuki Gap - Rising Window
🟥 Bearish Continuation Candlestick Patterns:
Falling Three Methods - Bearish Kicker - Mat Hold Bearish - Three Line Strike Bearish - Downside Tasuki Gap - Falling Window - On Neck Bearish
🟡 Indecision Candlestick Patterns:
Doji - Long Legged Doji - Spinning top - High Wave
Usage Recommendations
Optimized for Any Market: Designed for stocks, forex, cryptocurrencies, and other assets.
Ideal for Multi-Timeframe Analysis: Use it across different timeframes for better market timing.
Customization Options
Pattern Detection Settings: Users can adjust parameters like body-to-range ratios, shadow length requirements, and gap conditions for accurate detection.
Moving Average Filtering: Choose separate moving averages for reversal and continuation patterns to filter out false signals.
Table Display: These tables display pattern counts, allowing traders to assess the frequency and significance of various candlestick formations quickly.
Alert Configurations: Set custom alerts for specific patterns to stay informed about potential trading opportunities.
Story of Candlestick Pattern:
Candlestick patterns have a rich history rooted in ancient Japanese trading practices dating back to the 17th century. They were first developed by rice traders to visualize price movements and detect patterns reflecting market psychology. The logic behind candlestick patterns lies in the emotions driving market participants—fear, greed, uncertainty, and hope—captured through the open, high, low, and close prices.
Each pattern tells a story about buyers' and sellers' behavior, illustrating shifts in sentiment that can signal reversals or continuations in the market trend. By recognizing these patterns, traders can anticipate potential price movements and make informed decisions. The longevity and continued relevance of candlestick analysis highlight its effectiveness in understanding market dynamics.
🔓 Unlock Access
Check out the Author's Instructions or Dm me to Access the full version of the candlestick analysis with TechniTrend: Candle Pattern Detector (CPD).
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.
KaracaticaKaracatica Indicator - Dynamic Trend Following.
The Karacatica Indicator is designed for traders looking for a comprehensive approach to trend trading by combining directional movements and Average True Range (ATR).
Key Features: ATR-Based Trend Detection: The indicator uses Average True Range (ATR) to measure market volatility and integrates with price action to capture strong trend movements.
Directional Indicators (DI's): Calculates DI's (Positive Directional Index Di+ and Negative Directional Index Di-) to compare buying and selling pressure. This allows for more accurate trend identification, highlighting when buyers or sellers dominate.
Signal Generation: Buy Signal: Generated when price action is bullish (close is above the previous period's close) and DI+ exceeds DI-, indicating that buyers are in control.
Sell Signal: Triggered when price action is bearish (close is below the previous period’s close) and DI- exceeds DI+, showing that sellers dominate the market.
Visual Signals: Green triangle (▲) indicating a buy opportunity, plotted below the bar.
Fuchsia triangle (▼) signaling a sell opportunity, plotted above the bar.
Customizable Inputs: The indicator allows users to adjust the ATR period, DI length, and ADX smoothing parameters, giving it the flexibility to suit different trading styles and timeframes.
Why should you use it?
This indicator simplifies the process of analyzing the combination market direction and trend strength. It is especially useful for traders who like strong directional movements and want clear, visually represented entry signals. The Karacatica Indicator can generate good buy or sell signals in trading and can be used on multiple assets and timeframes, making it adaptable to different market conditions.
Settings Overview: ATR Period: Sets the period for calculating ATR, used to determine market volatility.
DI Length: The length of the lookback period for DI+ and DI- calculations.
ADX Smoothing: Smooths the ADX (Average Directional Index) to reduce noise.
Feel free to experiment with this indicator, share feedback, and adapt it to your trading strategy. Good trading!
Statistical ArbitrageThe Statistical Arbitrage Strategy, also known as pairs trading, is a quantitative trading method that capitalizes on price discrepancies between two correlated assets. The strategy assumes that over time, the prices of these two assets will revert to their historical relationship. The core idea is to take advantage of mean reversion, a principle suggesting that asset prices will revert to their long-term average after deviating significantly.
Strategy Mechanics:
1. Selection of Correlated Assets:
• The strategy focuses on two historically correlated assets (e.g., equity index futures like Dow Jones Mini and S&P 500 Mini). These assets tend to move in the same direction due to similar underlying fundamentals, such as overall market conditions. By tracking their relative prices, the strategy seeks to exploit temporary mispricings.
2. Spread Calculation:
• The spread is the difference between the prices of the two assets. This spread represents the relationship between the assets and serves as the basis for determining when to enter or exit trades.
3. Mean and Standard Deviation:
• The historical average (mean) of the spread is calculated using a Simple Moving Average (SMA) over a chosen period. The strategy also computes the standard deviation (volatility) of the spread, which measures how far the spread has deviated from the mean over time. This allows the strategy to define statistically significant price deviations.
4. Entry Signal (Mean Reversion):
• A buy signal is triggered when the spread falls below the mean by a multiple (e.g., two) of the standard deviation. This indicates that one asset is temporarily undervalued relative to the other, and the strategy expects the spread to revert to its mean, generating profits as the prices converge.
5. Exit Signal:
• The strategy exits the trade when the spread reverts to the mean. At this point, the mispricing has been corrected, and the profit from the mean reversion is realized.
Academic Support:
Statistical arbitrage has been widely studied in finance and economics. Gatev, Goetzmann, and Rouwenhorst’s (2006) landmark study on pairs trading demonstrated that this strategy could generate excess returns in equity markets. Their research found that by focusing on historically correlated stocks, traders could identify pricing anomalies and profit from their eventual correction.
Additionally, Avellaneda and Lee (2010) explored statistical arbitrage in different asset classes and found that exploiting deviations in price relationships can offer a robust, market-neutral trading strategy. In these studies, the strategy’s success hinges on the stability of the relationship between the assets and the timely execution of trades when deviations occur.
Risks of Statistical Arbitrage:
1. Correlation Breakdown:
• One of the primary risks is the breakdown of correlation between the two assets. Statistical arbitrage assumes that the historical relationship between the assets will hold in the future. However, market conditions, company fundamentals, or external shocks (e.g., macroeconomic changes) can cause these assets to deviate permanently, leading to potential losses.
• For instance, if two equity indices historically move together but experience divergent economic conditions or policy changes, their prices may no longer revert to the expected mean.
2. Execution Risk:
• This strategy relies on efficient execution and tight spreads. In volatile or illiquid markets, the actual price at which trades are executed may differ significantly from expected prices, leading to slippage and reduced profits.
3. Market Risk:
• Although statistical arbitrage is designed to be market-neutral (i.e., not dependent on the overall market direction), it is not entirely risk-free. Systematic market shocks, such as financial crises or sudden shifts in market sentiment, can affect both assets simultaneously, causing the spread to widen rather than revert to the mean.
4. Model Risk:
• The assumptions underlying the strategy, particularly regarding mean reversion, may not always hold true. The model assumes that asset prices will return to their historical averages within a certain timeframe, but the timing and magnitude of mean reversion can be uncertain. Misestimating this timeframe can lead to extended drawdowns or unrealized losses.
5. Overfitting:
• Over-reliance on historical data to fine-tune the strategy parameters (e.g., the lookback period or standard deviation thresholds) may result in overfitting. This means that the strategy works well on past data but fails to perform in live markets due to changing conditions.
Conclusion:
The Statistical Arbitrage Strategy offers a systematic and quantitative approach to trading that capitalizes on temporary price inefficiencies between correlated assets. It has been proven to generate returns in academic studies and is widely used by hedge funds and institutional traders for its market-neutral characteristics. However, traders must be aware of the inherent risks, including correlation breakdown, execution risks, and the potential for prolonged deviations from the mean. Effective risk management, diversification, and constant monitoring are essential for successfully implementing this strategy in live markets.
$TUBR: 7-25-99 Moving Average7, 25, and 99 Period Moving Averages
This indicator plots three moving averages: the 7-period, 25-period, and 99-period Simple Moving Averages (SMA). These moving averages are widely used to smooth out price action and help traders identify trends over different time frames. Let's break down the significance of these specific moving averages from both supply and demand perspectives and a price action perspective.
1. Supply and Demand Perspective:
- 7-period Moving Average (Short-Term) :
The 7-period moving average represents the short-term sentiment in the market. It captures the rapid fluctuations in price and is heavily influenced by recent supply and demand changes. Traders often look to the 7-period SMA for immediate price momentum, with price moving above or below this line signaling short-term strength or weakness.
- Bullish Supply/Demand : When price is above the 7-period SMA, it suggests that buyers are currently in control and demand is higher than supply. Conversely, price falling below this line indicates that supply is overpowering demand, leading to a short-term downtrend.
Is current price > average price in past 7 candles (depending on timeframe)? This will tell you how aggressive buyers are in short term.
- Key Supply/Demand Zones : The 7-period SMA often acts as dynamic support or resistance in a trending market, where traders might use it to enter or exit positions based on how price interacts with this level.
- 25-period Moving Average (Medium-Term) :
The 25-period SMA smooths out more of the noise compared to the 7-period, providing a more stable indication of intermediate trends. This moving average is often used to gauge the market's supply and demand balance over a broader timeframe than the short-term 7-period SMA.
- Supply/Demand Balance : The 25-period SMA reflects the medium-term equilibrium between supply and demand. A crossover between the price and the 25-period SMA may indicate a shift in this balance. When price sustains above the 25-period SMA, it shows that demand is strong enough to maintain an upward trend. Conversely, if the price stays below it, supply is likely exceeding demand.
Is current price > average price in past 25 candles (depending on timeframe)? This will tell you how aggressive buyers are in mid term.
- Momentum Shift : Crossovers between the 7-period and 25-period SMAs can indicate momentum shifts between short-term and medium-term demand. For example, if the 7-period crosses above the 25-period, it often signifies growing short-term demand relative to the medium-term trend, signaling potential buy opportunities. What this crossover means is that if 7MA > 25MA that means in past 7 candles average price is more than past 25 candles.
- 99-period Moving Average (Long-Term):
The 99-period SMA represents the long-term trend and reflects the market's supply and demand over an extended period. This moving average filters out short-term fluctuations and highlights the market's overall trajectory.
- Long-Term Supply/Demand Dynamics : The 99-period SMA is slower to react to changes in supply and demand, providing a more stable view of the market's overall trend. Price staying above this line shows sustained demand dominance, while price consistently staying below reflects ongoing supply pressure.
Is current price > average price in past 99 candles (depending on timeframe)? This will tell you how aggressive buyers are in long term.
- Market Trend Confirmation : When both the 7-period and 25-period SMAs are above the 99-period SMA, it signals a strong bullish trend with demand outweighing supply across all timeframes. If all three SMAs are below the 99-period SMA, it points to a bear market where supply is overpowering demand in both the short and long term.
2. Price Action Perspective :
- 7-period Moving Average (Short-Term Trends):
The 7-period moving average closely tracks price action, making it highly responsive to quick shifts in price. Traders often use it to confirm short-term reversals or continuations in price action. In an uptrend, price typically stays above the 7-period SMA, whereas in a downtrend, price stays below it.
- Short-Term Price Reversals : Crossovers between the price and the 7-period SMA often indicate short-term reversals. When price breaks above the 7-period SMA after staying below it, it suggests a potential bullish reversal. Conversely, a price breakdown below the 7-period SMA could signal a bearish reversal.
- 25-period Moving Average (Medium-Term Trends) :
The 25-period SMA helps identify the medium-term price action trend. It balances short-term volatility and longer-term stability, providing insight into the more persistent trend. Price pullbacks to the 25-period SMA during an uptrend can act as a buying opportunity for trend traders, while pullbacks during a downtrend may offer shorting opportunities.
- Pullback and Continuation: In trending markets, price often retraces to the 25-period SMA before continuing in the direction of the trend. For instance, if the price is in a bullish trend, traders may look for support at the 25-period SMA for potential continuation trades.
- 99-period Moving Average (Long-Term Trend and Market Sentiment ):
The 99-period SMA is the most critical for identifying the overall market trend. Price consistently trading above the 99-period SMA indicates long-term bullish momentum, while price staying below the 99-period SMA suggests bearish sentiment.
- Trend Confirmation : Price action above the 99-period SMA confirms long-term upward momentum, while price action below it confirms a downtrend. The space between the shorter moving averages (7 and 25) and the 99-period SMA gives a sense of the strength or weakness of the trend. Larger gaps between the 7 and 99 SMAs suggest strong bullish momentum, while close proximity indicates consolidation or potential reversals.
- Price Action in Trending Markets : Traders often use the 99-period SMA as a dynamic support/resistance level. In strong trends, price tends to stay on one side of the 99-period SMA for extended periods, with breaks above or below signaling major changes in market sentiment.
Why These Numbers Matter:
7-Period MA : The 7-period moving average is a popular choice among short-term traders who want to capture quick momentum changes. It helps visualize immediate market sentiment and is often used in conjunction with price action to time entries or exits.
- 25-Period MA: The 25-period MA is a key indicator for swing traders. It balances sensitivity and stability, providing a clearer picture of the intermediate trend. It helps traders stay in trades longer by filtering out short-term noise, while still being reactive enough to detect reversals.
- 99-Period MA : The 99-period moving average provides a broad view of the market's direction, filtering out much of the short- and medium-term noise. It is crucial for identifying long-term trends and assessing whether the market is bullish or bearish overall. It acts as a key reference point for longer-term trend followers, helping them stay with the broader market sentiment.
Conclusion:
From a supply and demand perspective, the 7, 25, and 99-period moving averages help traders visualize shifts in the balance between buyers and sellers over different time horizons. The price action interaction with these moving averages provides valuable insight into short-term momentum, intermediate trends, and long-term market sentiment. Using these three MAs together gives a more comprehensive understanding of market conditions, helping traders align their strategies with prevailing trends across various timeframes.
------------- RULE BASED SYSTEM ---------------
Overview of the Rule-Based System:
This system will use the following moving averages:
7-period MA: Represents short-term price action.
25-period MA: Represents medium-term price action.
99-period MA: Represents long-term price action.
1. Trend Identification Rules:
Bullish Trend:
The 7-period MA is above the 25-period MA, and the 25-period MA is above the 99-period MA.
This structure shows that short, medium, and long-term trends are aligned in an upward direction, indicating strong bullish momentum.
Bearish Trend:
The 7-period MA is below the 25-period MA, and the 25-period MA is below the 99-period MA.
This suggests that the market is in a downtrend, with bearish momentum dominating across timeframes.
Neutral/Consolidation:
The 7-period MA and 25-period MA are flat or crossing frequently with the 99-period MA, and they are close to each other.
This indicates a sideways or consolidating market where there’s no strong trend direction.
2. Entry Rules:
Bullish Entry (Buy Signals):
Primary Buy Signal:
The price crosses above the 7-period MA, AND the 7-period MA is above the 25-period MA, AND the 25-period MA is above the 99-period MA.
This indicates the start of a new upward trend, with alignment across the short, medium, and long-term trends.
Pullback Buy Signal (for trend continuation):
The price pulls back to the 25-period MA, and the 7-period MA remains above the 25-period MA.
This indica
tes that the pullback is a temporary correction in an uptrend, and buyers may re-enter the market as price approaches the 25-period MA.
You can further confirm the signal by waiting for price action (e.g., bullish candlestick patterns) at the 25-period MA level.
Breakout Buy Signal:
The price crosses above the 99-period MA, and the 7-period and 25-period MAs are also both above the 99-period MA.
This confirms a strong bullish breakout after consolidation or a long-term downtrend.
Bearish Entry (Sell Signals):
Primary Sell Signal:
The price crosses below the 7-period MA, AND the 7-period MA is below the 25-period MA, AND the 25-period MA is below the 99-period MA.
This indicates the start of a new downtrend with alignment across the short, medium, and long-term trends.
Pullback Sell Signal (for trend continuation):
The price pulls back to the 25-period MA, and the 7-period MA remains below the 25-period MA.
This indicates that the pullback is a temporary retracement in a downtrend, providing an opportunity to sell as price meets resistance at the 25-period MA.
Breakdown Sell Signal:
The price breaks below the 99-period MA, and the 7-period and 25-period MAs are also below the 99-period MA.
This confirms a strong bearish breakdown after consolidation or a long-term uptrend reversal.
3. Exit Rules:
Bullish Exit (for long positions):
Short-Term Exit:
The price closes below the 7-period MA, and the 7-period MA starts crossing below the 25-period MA.
This indicates weakening momentum in the uptrend, suggesting an exit from the long position.
Stop-Loss Trigger:
The price falls below the 99-period MA, signaling the breakdown of the long-term trend.
This can act as a final exit signal to minimize losses if the long-term uptrend is invalidated.
Bearish Exit (for short positions):
Short-Term Exit:
The price closes above the 7-period MA, and the 7-period MA starts crossing above the 25-period MA.
This indicates a potential weakening of the downtrend and signals an exit from the short position.
Stop-Loss Trigger:
The price breaks above the 99-period MA, invalidating the bearish trend.
This signals that the market may be reversing to the upside, and exiting short positions would be prudent.
Swing Breakout Sequence [LuxAlgo]The Swing Breakout Sequence tool enables traders to identify a directional price action scalping sequence comprising two unsuccessful breakouts in the same direction, with the expectation of a third.
🔶 USAGE
This sequence looks for pressure on one side of a swing zone.
The market tried to break out of the zone twice but failed. This led to a pullback into the zone after each attempt. Once a reversal inside the zone is identified, the sequence is complete. It is expected that the market will move from the final reversal within the zone to the final breakout attempt outside the zone.
The sequence of price action is as follows:
Point 1: Breakout attempt out of the swing zone
Point 2: Pullback into the zone
Point 3: Breakout attempt out of Point 1
Point 4: Pullback into the zone, tapping into Point 2 liquidity
Point 5: Reversal structure with Point 4 in the form of a double top or double bottom
This sequence assumes traders will be caught off-guard when they try to capitalize on the initial breakout at Point 1, which is likely to result in a loss. If the breakout at Point 3 fails, all traders will be caught out and switch positions.
If there is enough pressure in the swing zone to cause a reversal at Point 5, the trapped traders could be the start of the next breakout attempt.
🔹 Sequence Detection
Traders can define sequence behavior and adjust detection with three parameters from the Settings panel.
Disabling Points 4 and 5 will detect the most uncompleted sequences.
🔹 Showing/Hiding Elements
Traders can change the look of sequences by showing or hiding their parts using the Style settings.
🔶 SETTINGS
Swing Length: Number of candles to confirm a swing high or swing low. A higher number detects larger swings.
Internal Length: Number of candles to confirm a internal high or internal low. A lower number detects smaller swings. It must be the same size or smaller than the swing length.
🔹 Detection
Point 4 Beyond Point 2: It only detects sequences where Point 4 is beyond Point 2.
Show Point 5: Enable/disable Point 5 detection.
Require Equal H/L at Point 5: Enable/Disable double top/bottom detection at Point 5 within a given threshold. A bigger value detects more sequences.
🔹 Style
Show Sequence Path: Enable/disable a line between sequence points.
Show Boxes: Enable/disable colored boxes for each sequence.
Show Lines: Enable/disable horizontal lines from each point of the sequence.
Default Color: Define the color or enable/disable auto color.
TrendVizPro (BETA)The provided script is a Pine Script code designed for TradingView that creates a sophisticated technical indicator known as “TrendVizPro (BETA).” This script performs advanced trend analysis using various tools, including candle patterns, RSI (Relative Strength Index), simple moving averages (SMA), previous-day price levels, and multi-timeframe analysis.
Key Features:
Candle Style Selection: Users can choose between traditional candlesticks or Heiken Ashi candlesticks for better visualization of trends.
Trend Identification:
Uptrend, Downtrend, and Neutral Trend conditions are determined using smoothed Heiken Ashi candles and the relationship between short and long SMAs.
The script highlights trends using customizable colors (green for uptrend, red for downtrend, white for neutral).
RSI Calculation:
Calculates the RSI and indicates overbought/oversold market conditions with visual signals.
Customizable RSI lengths, overbought/oversold levels, and associated colors.
Price Targeting System:
Automatically calculates potential price targets based on historical volatility, which can be overridden manually.
Upper and lower target price lines can be plotted, showing where the price might move based on historical data or user-defined percentages.
Multi-Timeframe Analysis:
A table is displayed that shows the RSI, trend, and condition (overbought, oversold, or neutral) across various timeframes (3m, 5m, 15m, 30m, 1h, 2h, 4h, Daily).
The table adapts dynamically based on the data for each timeframe.
Previous Day’s High, Low, and Average:
Plots lines representing the previous day’s high, low, and average price levels.
The midpoint between these values is also plotted for additional context.
Trading Signals:
Long and short trading signals are generated based on the trend’s strength and direction.
Exit signals are plotted to indicate potential points to exit trades.
How to Use:
Input Settings:
Candle Style: Select “Traditional Candle” or “Super Trend Heiken Ashi Candle” to choose how price data is visualized.
Trend Colors: Customize the colors for different trend conditions (Uptrend, Neutral, Downtrend).
RSI Settings: Adjust the RSI length, overbought/oversold levels, and corresponding signal colors.
Price Target: Toggle the autopilot mode to use historical data to calculate potential price targets, or manually input a percentage for custom target prices.
Table and Signal Visibility: Decide whether to display the multi-timeframe analysis table, open price, previous day levels, and various trading signals (long, short, exit).
Analyzing the Chart:
When applied to a chart, the indicator plots different price levels (open price, previous day levels, target prices) using lines.
The current trend is displayed via candle colors, and uptrend/downtrend signals are shown on the chart using arrows (long or short positions).
The multi-timeframe table provides a quick overview of trend and RSI conditions for different timeframes.
Signal Use:
Long Signals: Indicated by green arrows below bars, suggesting a strong uptrend.
Short Signals: Indicated by red arrows above bars, signaling a strong downtrend.
Exit Signals: Marked with X symbols, indicating when to consider exiting a long or short position.
Trend Entry and Exit:
Trend Entry/Exit Lines: When activated, orange lines mark optimal trend entry points, and blue lines show potential trend exit points.
Customizable Visuals:
The background color and plot styles (dashed lines, solid lines, labels) are customizable to make the chart more visually distinct and easy to interpret.
Advanced Use Cases:
Multi-Timeframe Traders: Use the multi-timeframe analysis table to check how trends and RSI values behave across different intervals, helping to identify key support/resistance levels or trend continuation points.
Intraday Trading: The script is highly effective for day traders, as it allows visualization of important intraday levels, such as previous highs/lows and current trend conditions.
Swing Trading: Swing traders can leverage the autopilot price target feature to identify optimal exit points based on historical price behavior.
Conclusion:
This indicator is a comprehensive tool designed for traders seeking to automate their trend and signal analysis. With flexible settings, it can cater to multiple trading styles, from scalping to swing trading, all within the TradingView platform.
Daily Engulfing Pattern DetectorThis indicator identifies bullish and bearish engulfing patterns on daily timeframes.
A bullish engulfing pattern occurs when a green candle completely engulfs the previous red candle,
taking out its low and closing above both its open and close prices. This suggests a potential trend reversal from bearish to bullish.
A bearish engulfing pattern occurs when a red candle completely engulfs the previous green candle,
taking out its high and closing below both its open and close prices. This suggests a potential trend reversal from bullish to bearish.
Features:
- Works on daily timeframe by default (customizable)
- Displays visual markers: green triangles for bullish patterns, red triangles for bearish patterns
- Includes built-in alerts for both pattern types
Set up alerts by right-clicking the indicator and selecting "Create Alert"
REBUX - 1m NY Opening Session Stock Trader w/alerts & SignalsREBUX - 1m NY Opening Session Stock Trader w/alerts & Signals
This closed-source indicator is designed to trade the volatile New York opening session on a 1-minute chart, offering traders a unique approach to scalping high-probability opportunities. What sets this script apart is its ability to dynamically detect key price ranges in real-time, then apply a percentage-based offset for precise trade entries, along with an automatic take-profit calculation based on the session’s volatility.
How It Works: The script monitors the first few minutes of the New York session to define a price range based on the session high and low. It then calculates entry levels using customizable percentage offsets. When the price crosses above or below these levels, the script triggers alerts for potential buy (LONG) or sell (SHORT) entries. Additionally, the script implements a take-profit level based on a percentage of the detected price range, automatically adjusting as volatility changes.
Key features include:
Customizable timing: Traders can configure the number of minutes after the session open to define the price range and when to stop trading for the day.
Dynamic price offsets: Entry points are calculated based on percentage offsets from the session’s range, ensuring flexibility in volatile markets.
Visual aids and alerts: The script plots visual labels on the chart for LONG and SHORT signals, and provides take-profit exit points, helping traders make informed decisions.
Originality and Usefulness: Unlike many open-source scalping scripts that rely on static strategies or traditional indicators, this script uses a session-specific approach, adapting to real-time price action and volatility. Its focus on the high-impact New York open and integration of automatic TP calculations make it an effective and unique tool for day traders who need to react quickly to market movements.
This script is particularly useful for traders who want to capitalize on the sharp price movements that occur at the NY session open, providing actionable alerts and visual signals to streamline the trading process.
RiskMosaic | SandiB V2Risk On/Off System
This indicator acts as a comprehensive framework that integrates a diverse range of indicators—spanning liquidity, sentiment, market volatility, and macroeconomic factors—to construct a holistic view of risk.
By blending these varied components, the system identifies shifts in risk-on and risk-off environments, providing a complete and dynamic assessment of global market conditions.
This allows for more informed decision-making by capturing both localized and broad market influences in real time, enabling proactive risk management and the ability to adapt to rapidly changing conditions.
Composition :
4 different categories - each one equal weight
-> Mix of Global & U.S Liquidity
-> Mix of different macro factors
-> Mix of Crypto and Commodities
-> Mix of Volatility & Risk Indicators
Colors description:
- Green = strong = full risk on sentiment/environment
- Red = weak = full risk off sentiment/environment
- Blue = recovery = medium risk on sentiment/environment
- Purple = contraction = medium risk of sentiment/environment
-> Colors are based on oscillator line:
- crossing over 0 or 0.4 = green
- crossing under 0 or -0.5 = red
- crossing over -0.35 = blue
- crossing under 0.35 = purple
Value at Risk [OmegaTools]The "Value at Risk" (VaR) indicator is a powerful financial risk management tool that helps traders estimate the potential losses in a portfolio over a specified period of time, given a certain level of confidence. VaR is widely used by financial institutions, traders, and risk managers to assess the probability of portfolio losses in both normal and volatile market conditions. This TradingView script implements a comprehensive VaR calculation using several models, allowing users to visualize different risk scenarios and adjust their trading strategies accordingly.
Concept of Value at Risk
Value at Risk (VaR) is a statistical technique used to measure the likelihood of losses in a portfolio or financial asset due to market risks. In essence, it answers the question: "What is the maximum potential loss that could occur in a given portfolio over a specific time horizon, with a certain confidence level?" For instance, if a portfolio has a one-day 95% VaR of $10,000, it means that there is a 95% chance the portfolio will not lose more than $10,000 in a single day. Conversely, there is a 5% chance of losing more than $10,000. VaR is a key risk management tool for portfolio managers and traders because it quantifies potential losses in monetary terms, allowing for better-informed decision-making.
There are several ways to calculate VaR, and this indicator script incorporates three of the most commonly used models:
Historical VaR: This approach uses historical returns to estimate potential losses. It is based purely on past price data, assuming that the past distribution of returns is indicative of future risks.
Variance-Covariance VaR: This model assumes that asset returns follow a normal distribution and that the risk can be summarized using the mean and standard deviation of past returns. It is a parametric method that is widely used in financial risk management.
Exponentially Weighted Moving Average (EWMA) VaR: In this model, recent data points are given more weight than older data. This dynamic approach allows the VaR estimation to react more quickly to changes in market volatility, which is particularly useful during periods of market stress. This model uses the Exponential Weighted Moving Average Volatility Model.
How the Script Works
The script starts by offering users a set of customizable input settings. The first input allows the user to choose between two main calculation modes: "All" or "OCT" (Only Current Timeframe). In the "All" mode, the script calculates VaR using all available methodologies—Historical, Variance-Covariance, and EWMA—providing a comprehensive risk overview. The "OCT" mode narrows the calculation to the current timeframe, which can be particularly useful for intraday traders who need a more focused view of risk.
The next input is the lookback window, which defines the number of historical periods used to calculate VaR. Commonly used lookback periods include 21 days (approximately one month), 63 days (about three months), and 252 days (roughly one year), with the script supporting up to 504 days for more extended historical analysis. A longer lookback period provides a more comprehensive picture of risk but may be less responsive to recent market conditions.
The confidence level is another important setting in the script. This represents the probability that the loss will not exceed the VaR estimate. Standard confidence levels are 90%, 95%, and 99%. A higher confidence level results in a more conservative risk estimate, meaning that the calculated VaR will reflect a more extreme loss scenario.
In addition to these core settings, the script allows users to customize the visual appearance of the indicator. For example, traders can choose different colors for "Bullish" (Risk On), "Bearish" (Risk Off), and "Neutral" phases, as well as colors for highlighting "Breaks" in the data, where returns exceed the calculated VaR. These visual cues make it easy to identify periods of heightened risk at a glance.
The actual VaR calculation is broken down into several models, starting with the Historical VaR calculation. This is done by computing the logarithmic returns of the asset's closing prices and then using linear interpolation to determine the percentile corresponding to the desired confidence level. This percentile represents the potential loss in the asset over the lookback period.
Next, the script calculates Variance-Covariance VaR using the mean and standard deviation of the historical returns. The standard deviation is multiplied by a z-score corresponding to the chosen confidence level (e.g., 1.645 for 95% confidence), and the resulting value is subtracted from the mean return to arrive at the VaR estimate.
The EWMA VaR model uses the EWMA for the sigma parameter, the standard deviation, obtaining a specific dynamic in the volatility. It is particularly useful in volatile markets where recent price behavior is more indicative of future risk than older data.
For traders interested in intraday risk management, the script provides several methods to adjust VaR calculations for lower timeframes. By using intraday returns and scaling them according to the chosen timeframe, the script provides a dynamic view of risk throughout the trading day. This is especially important for short-term traders who need to manage their exposure during high-volatility periods within the same day. The script also incorporates an EWMA model for intraday data, which gives greater weight to the most recent intraday price movements.
In addition to calculating VaR, the script also attempts to detect periods where the asset's returns exceed the estimated VaR threshold, referred to as "Breaks." When the returns breach the VaR limit, the script highlights these instances on the chart, allowing traders to quickly identify periods of extreme risk. The script also calculates the average of these breaks and displays it for comparison, helping traders understand how frequently these high-risk periods occur.
The script further visualizes the risk scenario using a risk phase classification system. Depending on the level of risk, the script categorizes the market as either "Risk On," "Risk Off," or "Risk Neutral." In "Risk On" mode, the market is considered bullish, and the indicator displays a green background. In "Risk Off" mode, the market is bearish, and the background turns red. If the market is neither strongly bullish nor bearish, the background turns neutral, signaling a balanced risk environment.
Traders can customize whether they want to see this risk phase background, along with toggling the display of the various VaR models, the intraday methods, and the break signals. This flexibility allows traders to tailor the indicator to their specific needs, whether they are day traders looking for quick intraday insights or longer-term investors focused on historical risk analysis.
The "Risk On" and "Risk Off" phases calculated by this Value at Risk (VaR) script introduce a novel approach to market risk assessment, offering traders an advanced toolset to gauge market sentiment and potential risk levels dynamically. These risk phases are built on a combination of traditional VaR methodologies and proprietary logic to create a more responsive and intuitive way to manage exposure in both normal and volatile market conditions. This method of classifying market conditions into "Risk On," "Risk Off," or "Risk Neutral" is not something that has been traditionally associated with VaR, making it a groundbreaking addition to this indicator.
How the "Risk On" and "Risk Off" Phases Are Calculated
In typical VaR implementations, the focus is on calculating the potential losses at a given confidence level without providing an overall market outlook. This script, however, introduces a unique risk classification system that takes the output of various VaR models and translates it into actionable signals for traders, marking whether the market is in a Risk On, Risk Off, or Risk Neutral phase.
The Risk On and Risk Off phases are primarily determined by comparing the current returns of the asset to the average VaR calculated across several different methods, including Historical VaR, Variance-Covariance VaR, and EWMA VaR. Here's how the process works:
1. Threshold Setting and Effect Calculation: The script first computes the average VaR using the selected models. It then checks whether the current returns (expressed as a negative value to signify loss) exceed the average VaR value. If the current returns surpass the calculated VaR threshold, this indicates that the actual market risk is higher than expected, signaling a potential shift in market conditions.
2. Break Analysis: In addition to monitoring whether returns exceed the average VaR, the script counts the number of instances within the lookback period where this breach occurs. This is referred to as the "break effect." For each period in the lookback window, the script checks whether the returns surpass the calculated VaR threshold and increments a counter. The percentage of periods where this breach occurs is then calculated as the "effect" or break percentage.
3. Dual Effect Check (if "Double" Risk Scenario is selected): When the user chooses the "Double" risk scenario mode, the script performs two layers of analysis. First, it calculates the effect of returns exceeding the VaR threshold for the current timeframe. Then, it calculates the effect for the lower intraday timeframe as well. Both effects are compared to the user-defined confidence level (e.g., 95%). If both effects exceed the confidence level, the market is deemed to be in a high-risk situation, thus triggering a Risk Off phase. If both effects fall below the confidence level, the market is classified as Risk On.
4. Risk Phases Determination: The final risk phase is determined by analyzing these effects in relation to the confidence level:
- Risk On: If the calculated effect of breaks is lower than the confidence level (e.g., fewer than 5% of periods show returns exceeding the VaR threshold for a 95% confidence level), the market is considered to be in a relatively safe state, and the script signals a "Risk On" phase. This is indicative of bullish conditions where the potential for extreme loss is minimal.
- Risk Off: If the break effect exceeds the confidence level (e.g., more than 5% of periods show returns breaching the VaR threshold), the market is deemed to be in a high-risk state, and the script signals a "Risk Off" phase. This indicates bearish market conditions where the likelihood of significant losses is higher.
- Risk Neutral: If the break effect hovers near the confidence level or if there is no clear trend indicating a shift toward either extreme, the market is classified as "Risk Neutral." In this phase, neither bulls nor bears are dominant, and traders should remain cautious.
The phase color that the script uses helps visualize these risk phases. The background will turn green in Risk On conditions, red in Risk Off conditions, and gray in Risk Neutral phases, providing immediate visual feedback on market risk. In addition to this, when the "Double" risk scenario is selected, the background will only turn green or red if both the current and intraday timeframes confirm the respective risk phase. This double-checking process ensures that traders are only given a strong signal when both longer-term and short-term risks align, reducing the likelihood of false signals.
A New Way of Using Value at Risk
This innovative Risk On/Risk Off classification, based on the interaction between VaR thresholds and market returns, represents a significant departure from the traditional use of Value at Risk as a pure risk measurement tool. Typically, VaR is employed as a backward-looking measure of risk, providing a static estimate of potential losses over a given timeframe with no immediate actionable feedback on current market conditions. This script, however, dynamically interprets VaR results to create a forward-looking, real-time signal that informs traders whether they are operating in a favorable (Risk On) or unfavorable (Risk Off) environment.
By incorporating the "break effect" analysis and allowing users to view the VaR breaches as a percentage of past occurrences, the script adds a predictive element that can be used to time market entries and exits more effectively. This **dual-layer risk analysis**, particularly when using the "Double" scenario mode, adds further granularity by considering both current timeframe and intraday risks. Traders can therefore make more informed decisions not just based on historical risk data, but on how the market is behaving in real-time relative to those risk benchmarks.
This approach transforms the VaR indicator from a risk monitoring tool into a decision-making system that helps identify favorable trading opportunities while alerting users to potential market downturns. It provides a more holistic view of market conditions by combining both statistical risk measurement and intuitive phase-based market analysis. This level of integration between VaR methodologies and real-time signal generation has not been widely seen in the world of trading indicators, marking this script as a cutting-edge tool for risk management and market sentiment analysis.
I would like to express my sincere gratitude to @skewedzeta for his invaluable contribution to the final script. From generating fresh ideas to applying his expertise in reviewing the formula, his support has been instrumental in refining the outcome.
