Multiple Swing High/Low with SLExplanation:
Swing Highs and Lows:
The script detects swing highs and swing lows using ta.pivothigh() and ta.pivotlow() on a 3-minute basis.
Each swing high is drawn with a green line, and each swing low is drawn with a red line.
Stop-Loss (SL) Lines:
For each swing high, a stop-loss line is drawn 15 points below the swing high.
For each swing low, a stop-loss line is drawn 15 points above the swing low.
The SL line for swing highs is drawn in red, and the SL line for swing lows is drawn in blue.
Labels for Swing High/Low and SL:
Labels with text like "Swing High" or "Swing Low" are added at the swing points, and SL labels are added at the stop-loss levels.
These labels can be toggled on/off using the showSwingTags input.
Line Extension:
The line.set_x2() function ensures that the swing lines and SL lines are extended to the current bar as price moves.
Key Changes:
Removed Arrays: Instead of using arrays to store lines and labels, we now handle each line and label individually. This avoids the issue where complex types (line and label) were being stored in arrays, which Pine Script doesn't support directly.
Dynamic Creation: New lines and labels are dynamically created as new swings occur, and they stay on the chart until the script is removed or the chart is reloaded.
Trading
Multiple Swing High/Low with SLExplanation:
Swing Highs and Lows:
The script detects swing highs and swing lows using ta.pivothigh() and ta.pivotlow() on a 3-minute basis.
Each swing high is drawn with a green line, and each swing low is drawn with a red line.
Stop-Loss (SL) Lines:
For each swing high, a stop-loss line is drawn 15 points below the swing high.
For each swing low, a stop-loss line is drawn 15 points above the swing low.
The SL line for swing highs is drawn in red, and the SL line for swing lows is drawn in blue.
Labels for Swing High/Low and SL:
Labels with text like "Swing High" or "Swing Low" are added at the swing points, and SL labels are added at the stop-loss levels.
These labels can be toggled on/off using the showSwingTags input.
Line Extension:
The line.set_x2() function ensures that the swing lines and SL lines are extended to the current bar as price moves.
Key Changes:
Removed Arrays: Instead of using arrays to store lines and labels, we now handle each line and label individually. This avoids the issue where complex types (line and label) were being stored in arrays, which Pine Script doesn't support directly.
Dynamic Creation: New lines and labels are dynamically created as new swings occur, and they stay on the chart until the script is removed or the chart is reloaded.
Quantum RSI Signals Suite [QuantAlgo]Introducing Quantum RSI Signals Suite 🎯💫
The Quantum RSI Signals Suite by QuantAlgo is a sophisticated technical indicator that combines statistical z-score analysis with enhanced trend following to identify market trends and reversals. This premium system integrates normalized RSI readings with multi-timeframe statistical measurements to help traders and investors identify trend direction and potential reversals. By evaluating both RSI dynamics and directional trend analysis together, this tool enables users to make data-driven trading decisions with statistical validation.
🌊 Indicator Architecture
The Quantum RSI Signals Suite provides a unique framework for assessing market trends through a blend of normalized RSI and dynamic trend-weighted z-score calculations. Unlike traditional RSI indicators that use fixed overbought/oversold levels, this system incorporates statistical measurements and directional trend analysis to adjust sensitivity automatically. By combining normalized RSI values with adaptive z-score zones and trend following analysis, it evaluates both current market conditions and historical context, while the statistical parameters ensure stable yet responsive signals. This quantum approach allows users to identify trending conditions while remaining aware of statistical extremes, enhancing both trend-following and mean-reversion strategies.
📊 Technical Composition and Calculation
The Quantum RSI Signals Suite is composed of several technical components that create a dynamic trending system:
RSI Normalization: Utilizes scaled RSI values (-1 to 1) for balanced momentum representation
Z-Score Analysis: Computes statistical significance of RSI movements to determine dynamic zones
Trend Following Analysis: Analyzes historical z-score movements to identify persistent trends
Signal Amplification: Combines z-score with trend analysis for enhanced signal generation
📈 Key Indicators and Features
The Quantum RSI Signals Suite utilizes normalized RSI with customizable length and z-score parameters to adapt to different trading styles. Advanced calculations are applied to determine statistical significance levels, providing context-aware boundaries for trend identification. The trend following component evaluates historical z-score movements to validate signals and identify potential reversals.
The indicator incorporates multi-layered visualization with:
Color-coded histogram and trend representation (bullish/bearish)
Combined statistical and trend-based signals
Dynamic trend-weighted scoring system
Mean reversion signals with distinct markers (⤻/↷)
Gradient fills for better visual clarity
Programmable alerts for trend changes
⚡️ Practical Applications and Examples
✅ Add the Indicator: Add the indicator to your TradingView chart by clicking on the star icon to add it to your favorites ⭐️
👀 Monitor Signals: Watch the final score's position relative to the zero line to identify trend direction and potential reversals. The combined histogram and line visualization makes trend changes clearly visible.
🎯 Track Signals: Pay attention to the mean reversion markers that appear above and below the price chart:
→ Upward triangles (⤻) signal potential bullish reversals when final score crosses above zero
→ X crosses (↷) indicate potential bearish reversals when final score crosses below zero
🔔 Set Alerts: Configure alerts for trend changes in both bullish and bearish directions, ensuring you can act on significant technical developments promptly.
🌟 Summary and Tips
The Quantum RSI Signals Suite by QuantAlgo is a sophisticated technical tool, designed to support both trend following and mean reversion strategies across different market environments. By combining normalized RSI analysis with statistical z-score measurements and trend following analysis, it helps traders and investors identify significant trend changes while measuring statistical extremes, providing validated signals. The tool's adaptability through customizable RSI length, z-score parameters, and trend analysis settings makes it suitable for various trading timeframes and styles, allowing users to capture opportunities while maintaining awareness of statistical market conditions.
Key parameters to optimize for your trading or investing style:
RSI Length: Adjust for more or less sensitivity to price changes (default: 14)
Z-Score Length: Fine-tune the statistical window for signal stability (default: 20)
Trend Analysis Range: Balance historical context with current market conditions
Source Data: Customize price input for specialized strategies
EMA 3 CryptoEMA 3 Crypto Indicator
Definition
The EMA 3 Crypto Indicator is a specialized tool designed for cryptocurrency traders to track trends and identify potential trading opportunities with precision. Utilizing three Exponential Moving Averages (EMAs) with lengths of 13, 25, and 55, this indicator provides insights into short, medium, and long-term price movements. Its configuration is optimized for the unique characteristics of the crypto market, such as high volatility and 24/7 trading, while remaining adaptable for use in other financial markets.
Purpose and Benefits
This indicator empowers traders by:
- Capturing Volatility: Reacts swiftly to sudden price changes, a common feature in crypto trading.
- Highlighting Trends: Clearly distinguishes bullish and bearish trends through the alignment of the EMAs.
- Supporting Strategic Decisions: Serves as a dynamic support or resistance level, enabling better timing for entries and exits.
Each EMA offers a specific perspective:
- EMA 13: Tracks short-term price momentum, ideal for fast-paced markets.
- EMA 25: Provides a balanced view of trend strength and corrections.
- EMA 55: Focuses on the overall market direction, filtering out noise from smaller price fluctuations.
Why "EMA 3 Crypto" for Cryptocurrencies?
1. Handles High Volatility: Perfectly suited for the rapid price swings of crypto markets.
2. Continuous Tracking: Designed for 24/7 market activity, ensuring traders stay informed at all times.
3. Dynamic Adjustments: Adapts to both trending and consolidating markets with its multi-EMA framework.
Visual Design
To enhance usability, each EMA is color-coded and styled for quick recognition:
- EMA 13: Deepskyblue, representing short-term trends.
- EMA 25: Orange, for medium-term analysis.
- EMA 55: Violet, focusing on long-term market direction.
The color scheme ensures clarity and helps traders spot crossovers and trend shifts effortlessly.
Applications
- Trend Confirmation: Align your trades with the dominant trend by observing EMA slopes and crossovers.
- Reversal Detection: Use crossover patterns to anticipate potential trend reversals.
- Support and Resistance: Identify dynamic price levels where the market may bounce or reject.
- Trade Validation: Combine the EMA 3 Crypto with volume or momentum indicators for enhanced accuracy.
Limitations
While effective in trending markets, the EMA 3 Crypto Indicator may produce false signals in choppy or consolidating conditions. It’s recommended to use it alongside other technical tools, such as RSI or MACD, to validate signals.
Summary
The EMA 3 Crypto Indicator is a powerful, versatile tool tailored for the fast-paced world of cryptocurrencies. By combining three EMAs with distinct timeframes, it offers a layered perspective on market trends, helping traders make data-driven decisions. Its intuitive design and robust performance make it an essential addition to any crypto trader’s toolkit, while its adaptability ensures it can be used effectively in other markets as well.
This updated description ties directly to the name EMA 3 Crypto and emphasizes its primary focus on cryptocurrency trading. Let me know if you'd like to refine it further!
skX FVG Enhanced Indicator [1m,5m] skX FVG Indicator
Fair Value Gaps (FVGs) are particularly effective for scalping altcoins due to their tendency to fill price inefficiencies. These gaps occur during strong momentum moves where price leaves an 'empty' zone that often acts as a magnet for price to return to. In the volatile alt market, these gaps frequently present high-probability scalping opportunities.
Why FVGs Work in Alts:
• Quick price movements create more gaps
• Higher volatility increases gap frequency
• Institutional algorithms tend to fill these inefficiencies
• Works especially well in lower timeframes (1-5m)
Key Features:
✓ Automatic FVG detection with size filtering
✓ Smart timeframe adaptation (1m, 5m, Custom settings)
✓ Trend detection using 8/21/55 EMA system
✓ Dynamic TP/SL levels based on ATR
✓ Risk:Reward ratio automation
✓ Visual signals that stick to price levels
✓ Clear information display panel
✓ Built-in alerts system
How to Use:
1. Select your preferred timeframe (1m or 5m recommended)
2. Watch for triangle signals (▲ bullish, ▼ bearish)
3. Confirm with trend direction (shown in panel)
4. Use provided TP/SL levels for trade management
5. Set alerts for new FVG formations
Settings Explained:
• Auto Mode: Adjusts gap size to timeframe
• Custom Gap Size: Manual gap size control
• ATR Period: Volatility measurement window
• ATR Multiplier: Stop loss distance
• Risk:Reward: Take profit ratio
Best Practices:
• Use in conjunction with support/resistance
• Trade in direction of main trend
• Monitor higher timeframe structure
• Start with recommended settings
• Backtest before live trading
Note: This indicator works best in volatile market conditions and should be used as part of a complete trading strategy.
Good luck trading!
-skX
Adaptive Price Zone Oscillator [QuantAlgo]Adaptive Price Zone Oscillator 🎯📊
The Adaptive Price Zone (APZ) Oscillator by QuantAlgo is an advanced technical indicator designed to identify market trends and reversals through adaptive price zones based on volatility-adjusted bands. This sophisticated system combines typical price analysis with dynamic volatility measurements to help traders and investors identify trend direction, potential reversals, and market volatility conditions. By evaluating both price action and volatility together, this tool enables users to make informed trading decisions while adapting to changing market conditions.
💫 Dynamic Zone Architecture
The APZ Oscillator provides a unique framework for assessing market trends through a blend of smoothed typical prices and volatility-based calculations. Unlike traditional oscillators that use fixed parameters, this system incorporates dynamic volatility measurements to adjust sensitivity automatically, helping users determine whether price movements are significant relative to current market conditions. By combining smoothed price trends with adaptive volatility zones, it evaluates both directional movement and market volatility, while the smoothing parameters ensure stable yet responsive signals. This adaptive approach allows users to identify trending conditions while remaining aware of volatility expansions and contractions, enhancing both trend-following and mean-reversion strategies.
📊 Indicator Components & Mechanics
The APZ Oscillator is composed of several technical components that create a dynamic trending system:
Typical Price: Utilizes HLC3 (High, Low, Close average) as a balanced price representation
Volatility Measurement: Computes exponential moving average of price changes to determine dynamic zones
Smoothed Calculations: Applies additional smoothing to reduce noise while maintaining responsiveness
Trend Detection: Evaluates price position relative to adaptive zones to determine market direction
📈 Key Indicators and Features
The APZ Oscillator utilizes typical price with customizable length and threshold parameters to adapt to different trading styles. Volatility calculations are applied to determine zone boundaries, providing context-aware levels for trend identification. The trend detection component evaluates price action relative to the adaptive zones, helping validate trends and identify potential reversals.
The indicator also incorporates multi-layered visualization with:
Color-coded trend representation (bullish/bearish)
Clear trend state indicators (+1/-1)
Mean reversion signals with distinct markers
Gradient fills for better visual clarity
Programmable alerts for trend changes
⚡️ Practical Applications and Examples
✅ Add the Indicator : Add the indicator to your TradingView chart by clicking on the star icon to add it to your favorites ⭐️
👀 Monitor Trend State : Watch the oscillator's position relative to the zero line to identify trend direction and potential reversals. The step-line visualization with diamonds makes trend changes clearly visible.
🎯 Track Signals : Pay attention to the mean reversion markers that appear above and below the price chart:
→ Upward triangles (⤻) signal potential bullish reversals
→ X crosses (↷) indicate potential bearish reversals
🔔 Set Alerts : Configure alerts for trend changes in both bullish and bearish directions, ensuring you can act on significant technical developments promptly.
🌟 Summary and Tips
The Adaptive Price Zone Oscillator by QuantAlgo is a versatile technical tool, designed to support both trend following and mean reversion strategies across different market environments. By combining smoothed typical price analysis with dynamic volatility-based zones, it helps traders and investors identify significant trend changes while measuring market volatility, providing reliable technical signals. The tool's adaptability through customizable length, threshold, and smoothing parameters makes it suitable for various trading timeframes and styles, allowing users to capture opportunities while maintaining awareness of changing market conditions.
Key parameters to optimize for your trading style:
APZ Length: Adjust for more or less sensitivity to price changes
Threshold: Fine-tune the volatility multiplier for wider or narrower zones
Smoothing: Balance noise reduction with signal responsiveness
Bitcoin: Mayer MultipleMayer Multiple Indicator
The Mayer Multiple is a powerful tool designed to help traders assess market conditions and identify optimal buying or selling opportunities. It calculates the ratio between the current price and its 200-day simple moving average (SMA), visualizing key thresholds that indicate value zones, caution areas, and overheated markets.
Key Features:
Dynamic Market Zones: Clearly marked levels like "Smash Buy," "Boost DCA," and "Extreme Euphoria" to guide your trading decisions.
Customizable Input: Adjust the SMA length to fit your strategy.
Color-Coded Signals: Intuitive visualization of market sentiment for quick analysis.
Comprehensive Thresholds: Historical insights into price behavior with plotted reference levels based on probabilities.
This indicator is ideal for traders aiming to enhance their long-term strategies and improve decision-making in volatile markets. Use it to gain an edge in identifying potential turning points and managing risk effectively.
EMA Volatility Channel [QuantAlgo]EMA Volatility Channel 🌊📈
The EMA Volatility Channel by QuantAlgo is an advanced technical indicator designed to capture price volatility and trend dynamics through adaptive channels based on exponential moving averages. This sophisticated system combines EMA-based trend analysis with dynamic volatility-adjusted bands to help traders and investors identify trend direction, potential reversals, and market volatility conditions. By evaluating both price momentum and volatility together, this tool enables users to make informed trading decisions while adapting to changing market conditions.
💫 Dynamic Channel Architecture
The EMA Volatility Channel provides a unique framework for assessing market trends through a blend of exponential moving averages and volatility-based channel calculations. Unlike traditional channel indicators that use fixed-width bands, this system incorporates dynamic volatility measurements to adjust channel width automatically, helping users determine whether price movements are significant relative to current market conditions. By combining smooth EMA trends with adaptive volatility bands, it evaluates both directional movement and market volatility, while the smoothing parameters ensure stable yet responsive channel adjustments. This adaptive approach allows users to identify trending conditions while remaining aware of volatility expansions and contractions, enhancing both trend-following and reversal strategies.
📊 Indicator Components & Mechanics
The EMA Volatility Channel is composed of several technical components that create a dynamic channel system:
EMA Midline: Calculates a smoothed exponential moving average that serves as the channel's centerline, providing a clear reference for trend direction.
Volatility Measurement: Computes average price movement to determine dynamic channel width, adapting to changing market conditions automatically.
Smooth Band Calculation: Applies additional smoothing to the channel bands, reducing noise while maintaining responsiveness to significant price movements.
📈 Key Indicators and Features
The EMA Volatility Channel combines various technical tools to deliver a comprehensive analysis of market conditions.
The indicator utilizes exponential moving averages with customizable length and smoothing parameters to adapt to different trading styles. Volatility calculations are applied to determine channel width, providing context-aware boundaries for price movement. The trend detection component evaluates price action relative to the channel bands, helping validate trends and identify potential reversals.
The indicator incorporates multi-layered visualization with color-coded channels and bars to signal both trend direction and market position. These adaptive visual cues, combined with programmable alerts for channel breakouts, help traders and investors track both trend changes and volatility conditions, supporting both trend-following and mean-reversion strategies.
⚡️ Practical Applications and Examples
✅ Add the Indicator: Add the indicator to your TradingView chart by clicking on the star icon to add it to your favorites ⭐️
👀 Monitor Channel Position: Watch the price position relative to the channel bands to identify trend direction and potential reversals. When price moves outside the channel, consider potential trend changes or extreme conditions.
🔔 Set Alerts: Configure alerts for channel breakouts and trend changes, ensuring you can act on significant technical developments promptly.
🌟 Summary and Tips
The EMA Volatility Channel by QuantAlgo is a versatile technical tool, designed to support both trend following and volatility analysis across different market environments. By combining smooth EMA trends with dynamic volatility-based channels, it helps traders and investors identify significant price movements while measuring market volatility, providing reliable technical signals. The tool's adaptability across timeframes makes it suitable for both trend-following and reversal strategies, allowing users to capture opportunities while maintaining awareness of changing market conditions.
Adapted RSI w/ Multi-Asset Regime Detection v1.1The relative strength index (RSI) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of an asset's recent price changes to detect overbought or oversold conditions in the price of said asset.
In addition to identifying overbought and oversold assets, the RSI can also indicate whether your desired asset may be primed for a trend reversal or a corrective pullback in price. It can signal when to buy and sell.
The RSI will oscillate between 0 and 100. Traditionally, an RSI reading of 70 or above indicates an overbought condition. A reading of 30 or below indicates an oversold condition.
The RSI is one of the most popular technical indicators. I intend to offer a fresh spin.
Adapted RSI w/ Multi-Asset Regime Detection
Our Adapted RSI makes necessary improvements to the original Relative Strength Index (RSI) by combining multi-timeframe analysis with multi-asset monitoring and providing traders with an efficient way to analyse market-wide conditions across different timeframes and assets simultaneously. The indicator automatically detects market regimes and generates clear signals based on RSI levels, presenting this data in an organised, easy-to-read format through two dynamic tables. Simplicity is key, and having access to more RSI data at any given time, allows traders to prepare more effectively, especially when trading markets that "move" together.
How we calculate the RSI
First, the RSI identifies price changes between periods, calculating gains and losses from one look-back period to the next. This look-back period averages gains and losses over 14 periods, which in this case would be 14 days, and those gains/losses are calculated based on the daily closing price. For example:
Average Gain = Sum of Gains over the past 14 days / 14
Average Loss = Sum of Losses over the past 14 days / 14
Then we calculate the Relative Strength (RS):
RS = Average Gain / Average Loss
Finally, this is converted to the RSI value:
RSI = 100 - (100 / (1 + RS))
Key Features
Our multi-timeframe RSI indicator enhances traditional technical analysis by offering synchronised Daily, Weekly, and Monthly RSI readings with automatic regime detection. The multi-asset monitoring system allows tracking of up to 10 different assets simultaneously, with pre-configured major pairs that can be customised to any asset selection. The signal generation system provides clear market guidance through automatic regime detection and a five-level signal system, all presented through a sophisticated visual interface with dynamic RSI line colouring and customisable display options.
Quick Guide to Use it
Begin by adding the indicator to your chart and configuring your preferred assets in the "Asset Comparison" settings.
Position the two information tables according to your preference.
The main table displays RSI analysis across three timeframes for your current asset, while the asset table shows a comparative analysis of all monitored assets.
Signals are colour-coded for instant recognition, with green indicating bullish conditions and red for bearish conditions. Pay special attention to regime changes and signal transitions, using multi-timeframe confluence to identify stronger signals.
How it Works (Regime Detection & Signals)
When we say 'Regime', a regime is determined by a persistent trend or in this case momentum and by leveraging this for RSI, which is a momentum oscillator, our indicator employs a relatively simple regime detection system that classifies market conditions as either Bullish (RSI > 50) or Bearish (RSI < 50). Our benchmark between a trending bullish or bearish market is equal to 50. By leveraging a simple classification system helps determine the probability of trend continuation and the weight given to various signals. Whilst we could determine a Neutral regime for consolidating markets, we have employed a 'neutral' signal generation which will be further discussed below...
Signal generation occurs across five distinct levels:
Strong Buy (RSI < 15)
Buy (RSI < 30)
Neutral (RSI 30-70)
Sell (RSI > 70)
Strong Sell (RSI > 85)
Each level represents different market conditions and probability scenarios. For instance, extreme readings (Strong Buy/Sell) indicate the highest probability of mean reversion, while neutral readings suggest equilibrium conditions where traders should focus on the overall regime bias (Bullish/Bearish momentum).
This approach offers traders a new and fresh spin on a popular and well-known tool in technical analysis, allowing traders to make better and more informed decisions from the well presented information across multiple assets and timeframes. Experienced and beginner traders alike, I hope you enjoy this adaptation.
simple swing indicator-KTRNSE:NIFTY
1. Pivot High/Low as Lines:
Purpose: Identifies local peaks (pivot highs) and troughs (pivot lows) in price and draws horizontal lines at these levels.
How it Works:
A pivot high occurs when the price is higher than the surrounding bars (based on the pivotLength parameter).
A pivot low occurs when the price is lower than the surrounding bars.
These pivots are drawn as horizontal lines at the price level of the pivot.
Visualization:
Pivot High: A red horizontal line is drawn at the price level of the pivot high.
Pivot Low: A green horizontal line is drawn at the price level of the pivot low.
Example:
Imagine the price is trending up, and at some point, it forms a peak. The script identifies this peak as a pivot high and draws a red line at the price of that peak. Similarly, if the price forms a trough, the script will draw a green line at the low point.
2. Moving Averages (20-day and 50-day):
Purpose: Plots the 20-day and 50-day simple moving averages (SMA) on the chart.
How it Works:
The 20-day SMA smooths the closing price over the last 20 days.
The 50-day SMA smooths the closing price over the last 50 days.
These lines provide an overview of short-term and long-term price trends.
Visualization:
20-day SMA: A blue line showing the 20-day moving average.
50-day SMA: An orange line showing the 50-day moving average.
Example:
When the price is above both moving averages, it indicates an uptrend. If the price crosses below these averages, it might signal a downtrend.
3. Supertrend:
Purpose: The Supertrend is an indicator based on the Average True Range (ATR) and is used to track the market trend.
How it Works:
When the market is in an uptrend, the Supertrend line will be green.
When the market is in a downtrend, the Supertrend line will be red.
Visualization:
Uptrend: The Supertrend line will be plotted in green.
Downtrend: The Supertrend line will be plotted in red.
Example:
If the price is above the Supertrend, the market is considered to be in an uptrend, and if the price is below the Supertrend, the market is in a downtrend.
4. Momentum (Rate of Change):
Purpose: Measures the rate at which the price changes over a set period, showing if the momentum is positive or negative.
How it Works:
The Rate of Change (ROC) measures how much the price has changed over a certain number of periods (e.g., 14).
Positive ROC indicates upward momentum, and negative ROC indicates downward momentum.
Visualization:
Positive ROC: A purple line is plotted above the zero line.
Negative ROC: A purple line is plotted below the zero line.
Example:
If the ROC line is above zero, it means the price is increasing, suggesting bullish momentum. If the ROC is below zero, it indicates bearish momentum.
5. Volume:
Purpose: Displays the volume of traded assets, giving insight into the strength of price movements.
How it Works:
The script will color the volume bars based on whether the price closed higher or lower than the previous bar.
Green bars indicate bullish volume (closing price higher than the previous bar), and red bars indicate bearish volume (closing price lower than the previous bar).
Visualization:
Bullish Volume: Green volume bars when the price closes higher.
Bearish Volume: Red volume bars when the price closes lower.
Example:
If you see a green volume bar, it suggests that the market is participating in an uptrend, and the price has closed higher than the previous period. Red bars indicate a downtrend or selling pressure.
6. MACD (Moving Average Convergence Divergence):
Purpose: The MACD is a trend-following momentum indicator that shows the relationship between two moving averages of the price.
How it Works:
The MACD Line is the difference between the 12-period EMA (Exponential Moving Average) and the 26-period EMA.
The Signal Line is the 9-period EMA of the MACD Line.
The MACD Histogram shows the difference between the MACD line and the Signal line.
Visualization:
MACD Line: A blue line representing the difference between the 12-period and 26-period EMAs.
Signal Line: An orange line representing the 9-period EMA of the MACD line.
MACD Histogram: A red or green histogram that shows the difference between the MACD line and the Signal line.
Example:
When the MACD line crosses above the Signal line, it’s considered a bullish signal. When the MACD line crosses below the Signal line, it’s considered a bearish signal.
Full Chart Example:
Imagine you're looking at a price chart with all the indicators:
Pivot High/Low Lines are drawn as red and green horizontal lines.
20-day and 50-day SMAs are plotted as blue and orange lines, respectively.
Supertrend shows a green or red line indicating the trend.
Momentum (ROC) is shown as a purple line oscillating around zero.
Volume bars are green or red based on whether the close is higher or lower.
MACD appears as a blue line and orange line, with a red or green histogram showing the MACD vs. Signal line difference.
How the Indicators Work Together:
Trend Confirmation: If the price is above the Supertrend line and both SMAs are trending up, it indicates a strong bullish trend.
Momentum: If the ROC is positive and the MACD line is above the Signal line, it further confirms bullish momentum.
Volume: Increasing volume, especially with green bars, suggests that the trend is being supported by active participation.
By using these combined indicators, you can get a comprehensive view of the market's trend, momentum, and potential reversal points (via pivot highs and lows).
Simple Moving Average with Regime Detection by iGrey.TradingThis indicator helps traders identify market regimes using the powerful combination of 50 and 200 SMAs. It provides clear visual signals and detailed metrics for trend-following strategies.
Key Features:
- Dual SMA System (50/200) for regime identification
- Colour-coded candles for easy trend visualisation
- Metrics dashboard
Core Signals:
- Bullish Regime: Price < 200 SMA
- Bearish Regime: Price > 200 SMA
- Additional confirmation: 50 SMA Cross-over or Cross-under (golden cross or death cross)
Metrics Dashboard:
- Current Regime Status (Bull/Bear)
- SMA Distance (% from price to 50 SMA)
- Regime Distance (% from price to 200 SMA)
- Regime Duration (bars in current regime)
Usage Instructions:
1. Apply the indicator to your chart
2. Configure the SMA lengths if desired (default: 50/200)
3. Monitor the color-coded candles:
- Green: Bullish regime
- Red: Bearish regime
4. Use the metrics dashboard for detailed analysis
Settings Guide:
- Length: Short-term SMA period (default: 50)
- Source: Price calculation source (default: close)
- Regime Filter Length: Long-term SMA period (default: 200)
- Regime Filter Source: Price source for regime calculation (default: close)
Trading Tips:
- Use bullish regimes for long positions
- Use bearish regimes for capital preservation or short positions
- Consider regime duration for trend strength
- Monitor distance metrics for potential reversals
- Combine with other systems for confluence
#trend-following #moving average #regime #sma #momentum
Risk Management:
- Not a standalone trading system
- Should be used with proper position sizing
- Consider market conditions and volatility
- Always use stop losses
Best Practices:
- Monitor multiple timeframes
- Use with other confirmation tools
- Consider fundamental factors
Version: 1.0
Created by: iGREY.Trading
Release Notes
// v1.1 Allows table overlay customisation
// v1.2 Update to v6 pinescript
GP - SRSI ChannelGP - SRSI Channel Indicator
The GP - SRSI Channel is a channel indicator derived from the Stochastic RSI (SRSI) oscillator. It combines SRSI data from multiple timeframes to analyze minimum, maximum, and closing values, forming a channel based on these calculations. The goal is to identify overbought and oversold zones with color coding and highlight potential trading opportunities by indicating trend reversal points.
How It Works
SRSI Calculation: The indicator calculates the Stochastic RSI values using open, high, low, and close prices from the selected timeframes.
Channel Creation: Minimum and maximum values derived from these calculations are combined across multiple timeframes. The midpoint is calculated as the average of these values.
Color Coding: Zones within the channel are color-coded with a gradient from red to green based on the ratios. Green zones typically indicate selling opportunities, while red zones suggest buying opportunities.
Visual Elements:
The channel boundaries (min/max) are displayed as lines.
Overbought/oversold regions (95-100 and 0-5) are highlighted with shaded areas.
Additional explanatory labels are placed on key levels to guide users.
How to Use
Trading Strategy: This indicator can be used for both trend following and identifying reversal points. Selling opportunities can be evaluated when the channel reaches the upper green zone, while buying opportunities can be considered in the lower red zone.
Timeframe Selection: Users can analyze multiple timeframes simultaneously to gain a broader perspective.
Customization: RSI and Stochastic RSI parameters are adjustable, allowing users to tailor the indicator to their trading strategies.
Important Note
This indicator is for informational purposes only and should not be used as a sole basis for trading decisions. Please validate the results of the indicator with your own analysis.
EMA Hierarchy Score V.1.0
EMA Hierarchy Score V.1.0
Purpose
The EMA Hierarchy Score indicator assesses the relative positioning of multiple Exponential Moving Averages (EMAs) for a financial asset. This tool provides insights into trend strength by calculating ideal and non-ideal configurations of EMAs, allowing for effective interpretation when used alongside standard EMA charts.
Variables and Inputs
The indicator organizes a set of EMAs and other metrics into a hierarchy for scoring:
* Primary Variables (A–J):
A: Close price
B: Open price
C: Previous close price
D to J: EMAs of configurable periods (5, 9, 13, 21, 26, 52, 100).
* User Inputs:
* Customizable periods for each EMA, allowing users to adjust the indicator’s sensitivity.
* Customizable period and standard deviation multiplier for Bollinger Bands, enabling further control over the indicator’s analysis.
Mathematical Method
The EMA Hierarchy Score calculates how closely the current EMA structure aligns with an “ideal” configuration through a structured scoring system:
1- Hierarchy Scoring:
* Ideal Order: Defined as A > B > C > D > E > F > G > H > I > J, representing a strong upward trend where each EMA progressively increases.
* Non-Ideal Order: Defined as J > I > H > G > F > E > D > C > B > A, indicating a weak or downward trend where each EMA progressively decreases.
* Optimal Order: Calculated based on achieving maximum alignment with the ideal configuration for each EMA across the chosen period.
* Sub-Optimal Order: The least-aligned structure across the same period.
2- Score Calculation:
* The indicator calculates a score by comparing all EMA pairs in values. For each comparison, a score increment of +1 (ideal) or -1 (non-ideal) is applied.
* The final score reflects the EMA configuration’s deviation from the ideal order:
- Positive Score: Indicates closer alignment with the ideal structure.
- Negative Score: Indicates deviation toward a non-ideal structure.
3- Smoothed and Signal Lines:
* A smoothed score is created using a Simple Moving Average (SMA) of the raw hierarchy score.
* A signal line (an SMA of the smoothed score) further aids in tracking directional shifts in the score.
4- Trend Labels and Bollinger Bands:
* Trend Labels: Display "UP" or "DOWN" based on the smoothed score’s relationship to the signal line.
* Bollinger Bands: Plotted around a selected source (smoothedLine, signalLine, or score) to analyze score volatility and deviations from the mean. The period and standard deviation multiplier for Bollinger Bands are user-configurable.
Result Definition
The Ideal and Non-Ideal Scores represent the upper and lower bounds of achievable configurations, ensuring the score does not exceed these values.
1- Ideal and Non-Ideal Result:
* Calculated based on how closely the current EMA configuration follows the “ideal” ascending or descending order.
* Ideal Score: Defined as +165, representing perfect alignment with the ideal configuration.
* Non-Ideal Score: Defined as -165, indicating full alignment with the descending, non-ideal structure.
* The score is bounded by these values and will not go above or below this range.
2- Optimal and Sub-Optimal Scores:
* Optimal Score: The highest score over the selected scoring period, calculated with the same period as the Bollinger Bands. Using consistent periods reinforces the reliability of the score by aligning with the period already used to gauge volatility.
* Sub-Optimal Score: The lowest score over the same period, capturing points of minimal alignment with the ideal order.
Interpretation and Analysis
1- Use with EMA Charts:
* This indicator is designed to be used alongside EMA charts, as its results provide insights into the relative order of EMAs and their alignment with trend strength.
* The EMA Hierarchy Score interprets the underlying EMA structure, offering additional context on whether current trends are aligned with optimal or non-optimal EMA configurations.
2- Ideal and Non-Ideal Analysis:
* A positive EMA Hierarchy Score indicates an orderly, ideal upward trend, suggesting stronger alignment with the ideal structure.
* A negative score signals a potential downward trend or deviation from the ideal structure.
3 - Trend Indicators and Bands:
* Trend Labels: The "UP" and "DOWN" labels offer real-time feedback on trend direction shifts, based on the smoothed score and signal line relationship.
* Bollinger Bands: Visualize the range of score fluctuations, helping to identify breakout or breakdown points.
4 - Optimal and Sub-Optimal Scores:
* Use the Optimal Score to understand peak trend alignment and Sub-Optimal Score to spot potential reversal or correction zones.
* A consistently high score over time indicates trend stability, while variations may suggest instability.
Quick Reference Table
The table displayed at the top right provides an at-a-glance view of key metrics:
* Ideal and Non-Ideal Score: Fixed at ±165 to represent the calculated ideal and non-ideal configuration.
* Optimal and Sub-Optimal Scores: Show maximum and minimum scores over the scoring period, color-coded green for positive and red for negative values.
This concise table helps users quickly assess indicator values, reducing the need to interpret multiple chart lines and making it easier to understand overall trend strength.
Disclaimer
The EMA Hierarchy Score V.1.0 is a technical analysis tool designed to assist in understanding the alignment and strength of trends as defined by EMA configurations. This indicator does not constitute investment advice, nor does it make specific recommendations for buying or selling assets. Users should consult with a financial advisor before making any trading decisions, as past performance or technical signals do not guarantee future results. The developers of this indicator disclaim all liability for potential financial losses arising from reliance on this tool. Users assume full responsibility for interpreting and applying the indicator’s outputs in their investment decisions.
Anchored Average Trading PriceThis "Anchored Average Trading Price" indicator allows users to anchor the calculation of the average trading price to a specific candle. By selecting an anchor date and time, the indicator begins calculating the average trading price from that point forward. This tool is particularly helpful for traders who want to analyze the price action relative to a key event or a particular point in time on the chart.
Key Features:
1. Flexible Anchoring: The indicator lets you set an anchor time, which determines the specific candle from which the average trading price calculation starts.
2. Customizable Calculation Method: You have the option to choose the basis of the average calculation:
- Open Price
- Close Price
- Average Daily Traded Price (calculated as `(Open + High + Low + Close) / 4`)
3. Automatic Updating: Once the anchor is set, the indicator dynamically updates on each new candle to continuously reflect the average trading price since the anchor point.
Potential Uses and Functionality Expansions:
- Trend Analysis: By observing the average trading price over time, you can gauge market sentiment and track trends from a particular event or time in the market.
- Support and Resistance: Anchoring this indicator to major highs, lows, or significant events could help identify dynamic support and resistance levels as the market interacts with the average price line.
- Customization Options: Future updates could allow additional flexibility, such as:
- A reset feature for users to easily re-anchor without changing the timestamp.
- Additional price calculation methods, like VWAP (Volume Weighted Average Price) for volume-based insights.
- Alerts when price crosses above or below the anchored average, signaling potential entry or exit points.
AI x Meme Impulse Tracker [QuantraSystems]AI x Meme Impulse Tracker
Quantra Systems guarantees that the information created and published within this document and on the Tradingview platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors.
Important Note!
The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script.
Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance.
This ensures that the system is less likely to degrade in performance over time, compared to hyper-optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post-backtest.
Even More Important Note!!
The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing.
While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes.
Introduction
The AI x Meme Impulse Tracker is a cutting-edge, fast-acting rotational algorithm designed to capitalize on the strength of assets within pre-selected categories. Using a custom function built on top of the RSI Pulsar, the system measures momentum through impulses rather than traditional trend following methods. This allows for swifter reallocations based on short bursts of strength.
This system focuses on precision and agility - making it highly adaptable in volatile markets. The strategy is built around three independent asset categories - with allocations only made to the strongest asset in each - ensuring that capital movement (in particular between blockchains) is kept to a minimum for efficiency purposes while maintaining exposure to the highest performing tokens.
Legend
Token Inputs:
The Impulse Tracker is designed with dynamic asset selection - allowing traders to customize the inputs for each category. This feature enables flexible system management, as the number of active tokens within each category can be adjusted at any time. Whether the user chooses the default of 13 tokens per category, or fewer, the system will automatically recalibrate. This ensures that all calculations, from relative strength to individual performance assessments, adjust as required. Disabled tokens are treated by the system as if they don’t exist - seamlessly updating performance metrics and the Impulse Tracker’s allocation behavior to maintain the highest level of efficiency and accuracy.
System Equity Curve:
The Impulse Tracker plots both the rotational system’s equity and the Buy-and-Hold (or ‘HODL’) benchmark of Bitcoin for comparison. While the HODL approach allocates the entire portfolio to Bitcoin and functions as an index to compare to, the Impulse Tracker dynamically allocates based on strength impulses within the chosen tokens and categories. The system equity curve is representative of adding an equal capital split between the strongest assets of each category. The relative strength system does handle ‘ties’ of strength - in this situation multiple tokens from a single category can be included in the final equity curve, with the allocated weight to that category split between the tied assets.
TABLES:
Equity Stats:
This table is held in Quantra System's typical UI design language. It offers a comprehensive snapshot of the system’s performance, with key metrics organized to help traders quickly assess both short-term and cumulative results. The left side provides details on individual asset performance, while the right side presents a comparison of the system’s risk-adjusted metrics against a simple BTC Hodl strategy.
The leftmost column of the Equity Stats table showcases performance indicators for the system’s current allocations. This provides quick identification of the current strongest tokens, based on confirmed and non-repainting data as soon as the current opens and the last bar closes.
The right-hand side compares the performance differences between the system and Hodl profits, both on a cumulative basis and analyzing only the previous bar. The total number of position changes is also tracked in this table - an important metric when calculating total slippage and should be used to determine how ‘hands-on’ the strategy will be on the current timeframe.
The lower part of the table highlights a direct comparison of the AI x Memes Impulse strategy with buy-and-hold Bitcoin. The risk adjusted performance ratios, Sharpe, Sortino and Omega, are shown side by side, as well as the maximum drawdown experienced by both strategies within the set testing window.
Screener Table:
This table provides a detailed breakdown of the performance for each asset that has been the strongest in its category at some point and thus received an allocation. The table tracks several key metrics for each asset - including returns, volatility, Sharpe ratio, Sortino ratio, Omega ratio, and maximum drawdown. It also displays the signals for both current and previous periods, as well as the assets weight in the theoretical portfolio. Assets that have never received a signal are also included, giving traders an overview of which assets have contributed to the portfolio's performance and which have not played a role so far.
The position changes cell also offers important insights, as it shows the frequency of not just total position changes, but also rebalancing events.
Detailed Slippage Table:
The Detailed Slippage Table provides a comprehensive breakdown of the calculated slippage and fees incurred throughout the strategy’s operations. It contains several key metrics that give traders a granular view of the costs associated with executing the system:
Selected Slippage - Displays the current slippage rate, as defined in the input menu.
Removal Slippage - This accounts for any slippage or fees incurred when removing an allocation from a token.
Reallocation Slippage - Tracks the slippage or fees when reallocating capital to existing positions.
Addition Slippage - Measures the slippage or fees incurred when allocating capital to new tokens.
Final Slippage - Is the sum of all the individual slippage points and provides a quick view of the total slippage accounted for by the system.
The table is also divided into two columns:
Last Transaction Slippage + Fees - Displays any slippage or fees incurred based on position changes within the current bar.
Total Slippage + Fees - Shows the cumulative slippage and fees incurred since the portfolio’s selected start date.
Visual Customization:
Several customizable features are included within the input menu to enhance user experience. These include custom color palettes, both preloaded and user-selectable. This allows traders to personalize the visual appearance of the tables, ensuring clarity and consistency with their preferred interface themes and background coloring.
Additionally, users can adjust both the position and sizes of all the tables - enabling complete tailoring to the trader’s layout and specific viewing preferences and screen configurations. This level of customization ensures a more intuitive and flexible interaction with the system’s data.
Core Features and Methodologies
Advanced Risk Management - A Unique Filtering Approach:
The Equity Curve Activation Filter introduces an innovative way to dynamically manage capital allocation, aligning with periods of market trend strength. This filter is rooted in the understanding that markets move cyclically - altering between periods trending and mean-reverting periods. This cycle is especially pronounced in the crypto markets, where strong uptrends are often followed by prolonged periods of sideways movements or corrections as participants take profits and momentum fades.
The Cyclical Nature of Markets and Trend Following:
Financial markets do not trend indefinitely. Each uptrend or downtrend, whether over high and low timeframes, tends to culminate in a phase where momentum exhausts - leading to the sideways or corrective phases. This cycle results from the natural dynamics of market participants: during extended trends, more participants jump in, riding the momentum until profit taking causes the trend to slow down or reverse. This cyclical behavior occurs across all timeframes and in all markets - making it essential to adapt trading strategies in attempt to minimize losses during less favorable conditions.
In a trend following system, profitability often mirrors this cyclical pattern. Trend following strategies thrive when markets are moving directionally, capturing gains as price moves with strength in a single direction. However in phases where the market chops sideways, trend following strategies will usually experience drawdowns and reduced returns due to the impersistent nature of any trends. This fluctuation in trend following profitability can actually serve as one of the best coincident indicators of broader market regime change - when profitability begins to fade, it often signals a transition to drawn out unfavorable trend trading conditions.
The Equity Curve as a Market Signal
Within the Impulse Tracker, a continuous equity curve is calculated based upon the system's allocation to the strongest tokens. This equity curve effectively tracks the system’s performance under all market conditions. However, instead of solely relying on the direct performance of the selected tokens, the system applies additional filters to analyze the trend strength of this equity curve itself.
In the same way you only want to purchase an asset that is moving up in price, you only want to allocate capital to a strategy whose equity curve is trending upwards!
The Equity Curve Activation Filter consistently monitors the trend of this equity curve through various filter indicators, such as the “Wave Pendulum Trend”, the “Quasar QSM” and the “MAQSM” (an aggregate of multiple types of averages). These filters help determine whether the equity curve is trending upwards, signaling a favorable period for trend following. When the equity curve is in a positive trend, capital is allocated to the system as normal - allowing it to capture gains during favorable market conditions, Conversely, when the trend weakens and the equity curves begins to stagnate or decline, the activation filter shifts the system into a “cash” positions - temporarily halting allocations in order to prevent market exposure during choppy or mean reverting phases.
Timing Allocation With Market Conditions
This unique filtering approach ensures that the system is primarily active during periods when market trends are most supportive. By aligning capital allocations with the uptrend in trend following profitability, the system is designed to enter during periods of strong momentum and move to cash when momentum with the equity curve wanes. This approach reduces the risk of overtrading in less favorable conditions and preserves capital for the next favorable trend.
In essence the Equity Curve Allocation Filter serves as a dynamic risk management layer that leverages the cyclicality of trend following profitability in order to navigate shifting market phases.
Sensitivity and Signal Responsiveness:
The Quasar Sensitivity Setting allows users to fine-tune the system’s responsiveness to asset signals. High sensitivity settings lead to quicker position changes, making the system highly reactive to short term strength impulses. This is especially useful in fast moving markets where token strength can shift rapidly. The Sensitive setting might be more applicable to higher volatility or lower market cap assets - as the increased volatility increases the necessity of faster position cutting in order to front run the crowd. Of course - a balanced approach is ideal, as if the signals are too fast there will be too many whips and false signals. (And extra fees + slippage!)
The benefit of this script is because of the advanced slippage calculations, false signals are sufficiently punished (unlike systems without fees or slippage) - so it will become immediately apparent if the false signals have a significantly detrimental impact on the system’s equity curve.
Asset specific signals within each category are re-evaluated after the close of each bar to ensure that capital is always allocated to the highest performing asset. If a token’s momentum begins to fade the system swiftly reallocates to the next strongest asset within that category.
Category Filter - Allocates only to the Strongest Asset per group
One of the core innovations of the AI x Meme Impulse Tracker is the customizable Category Filter, which ensures that only the strongest-performing asset within each predefined group receives capital allocation. This approach not only increases the precision of asset selection but also allows traders to tailor the system to specific token narratives or categories. Sectors can include trending themes such as high-attention meme tokens, AI-driven tokens, or even categorize assets by blockchain ecosystems like Ethereum, Solana, or Base chain. This flexibility enables users to align their strategies with the latest market narratives or to optimize for specific groups, focusing on high-beta tokens within well defined sectors for a more targeted exposure. By keeping the focus on category leaders, the system avoids diluting its impact across underperforming assets, thereby maximizing capital efficiency and reducing unnecessary trading costs.
Dynamic Asset Reallocation:
Dynamic reallocation ensures that the system remains nimble and adapts to changing market conditions. Unlike slower systems, the Quasar method continually monitors for changes in asset strength and reallocates capital accordingly - ensuring that the system is always positioned in the highest performing assets within each category.
Position Changes and Slippage:
The Impulse Tracker places a strong emphasis on realistic simulation, prioritizing accuracy over inflated backtest results. This approach ensures that slippage is accounted for in a more aggressive manner than what may be experienced in real-world execution.
Each position change within the system - whether it’s buying, selling, reallocating, or rebalancing between assets - incurs slippage. Slippage is applied to both ends of every transaction: when a position is entered and exited, and when reallocating capital from one token to another. This dynamic behavior is further enhanced by a customizable slippage/fees input, allowing users to simulate realistic transaction costs based on their own market conditions and execution behaviors.
The slippage model works by applying a weighted slippage to the equity curve, taking into account the actual amount of capital being moved. Slippage is not applied in a blanket manner but rather in proportion to the allocation changes. For example, if the system reallocates from a single 100% position to two 50% allocations, slippage will be applied to the 50% removed from the first asset and the 50% added to the new asset, resulting in a 1x slippage multiplier.
This process becomes more granular when multiple assets are involved. For instance, if reallocating from two 50% positions to three 33% positions, slippage will be incurred on each of the changes, but at a reduced rate (⅔ x slippage), reflecting the smaller percentage of portfolio equity being moved. The slippage model accounts for all types of allocation shifts, whether increasing or decreasing the number of tokens held, providing a realistic assessment of system costs.
Here are some detailed examples to illustrate how slippage is calculated based on different scenarios:
100% → 50% / 50%: 1x slippage applied to both position changes (2 allocation changes).
50% / 50% → 33% / 33% / 33%: ⅔ x slippage multiplier applied across 3 allocation changes.
33% / 33% / 33% → 100%: 4/3 x slippage multiplier applied across 3 allocation changes.
In practice, not every position change will be rebalanced perfectly, leading to a lower number of transactions and lower costs in practice. Additionally, with the use of limit orders, a trader can easily reduce the costs of entering a position, as well as ensuring a competitive entry price.
By simulating slippage in this granular manner, the system captures the absolute maximum level of fees and slippage, in order to ensure that backtest results lean towards an underrepresentation - opposed to inflated results compared with practical execution.
A Special Note on Slippage
In the image above, the system has been applied to four different timeframes - 20h, 15h, 10h, and 5h - using identical settings and a selected slippage amount of 2%. By isolating a recent trend leg, we can illustrate an important concept: while the 15h timeframe is more profitable than the 20h timeframe, this difference stems from a core trading principle. Lower timeframes typically provide more data points and allow for quicker entries and exits in a robust system. This often results in reduced downside and compounding of gains.
However, slippage, fees, and execution constraints are limiting factors, especially in volatile, low-cap cryptocurrencies. Although lower timeframes can improve performance by increasing trade frequency, each trade incurs heavy slippage costs that accumulate - impacting the portfolio’s capital at a compounding rate. In this example, the chosen slippage rate of 2% per trade is designed to reflect the realistic trading costs, emphasizing how lower timeframe trading comes at the cost of increased slippage and fees
Finding the optimal balance between timeframe and slippage impact requires careful consideration of factors such as portfolio size, liquidity of selected tokens, execution speed, and the fee rate of the exchange you execute trades on.
Equity Curve and Performance Calculations
To provide a benchmark, the script also generates a Buy-and-Hold (or "HODL") equity curve that represents a complete allocation to Bitcoin. This allows users to easily compare the performance of the dynamic rotation system with that more traditional benchmark strategy.
The script tracks key performance metrics for both the dynamic portfolio and the HODL strategy, including:
Sharpe Ratio
The Sharpe Ratio is a key metric that evaluates a portfolio’s risk-adjusted return by comparing its ‘excess’ return to its volatility. Traditionally, the Sharpe Ratio measures returns relative to a risk-free rate. However, in our system’s calculation, we omit the risk-free rate and instead measure returns above a benchmark of 0%. This adjustment provides a more universal comparison, especially in the context of highly volatile assets like cryptocurrencies, where a traditional risk-free benchmark, such as the usual 3-month T-bills, is often irrelevant or too distant from the realities of the crypto market.
By using 0% as the baseline, we focus purely on the strategy's ability to generate raw returns in the face of market risk, which makes it easier to compare performance across different strategies or asset classes. In an environment like cryptocurrency, where volatility can be extreme, the importance of relative return against a highly volatile backdrop outweighs comparisons to a risk-free rate that bears little resemblance to the risk profile of digital assets.
Sortino Ratio
The Sortino Ratio improves upon the Sharpe Ratio by specifically targeting downside risk and leaves the upside potential untouched. In contrast to the Sharpe Ratio (which penalizes both upside and downside volatility), the Sortino Ratio focuses only on negative return deviations. This makes it a more suitable metric for evaluating strategies like the AI x Meme Impulse Tracker - that aim to minimize drawdowns without restricting upside capture. By measuring returns relative to a 0% baseline, the Sortino ratio provides a clearer assessment of how well the system generates gains while avoiding substantial losses in highly volatile markets like crypto.
Omega Ratio
The Omega Ratio is calculated as the ratio of gains to losses across all return thresholds, providing a more complete view of how the system balances upside and downside risk even compared to the Sortino Ratio. While it achieves a similar outcome to the Sortino Ratio by emphasizing the system's ability to capture gains while limiting losses, it is technically a mathematically superior method. However, we include both the Omega and Sortino ratios in our metric table, as the Sortino Ratio remains more widely recognized and commonly understood by traders and investors of all levels.
Usage Summary:
While the backtests in this description are generated as if a trader held a portfolio of just the strongest tokens, this was mainly designed as a method of logical verification and not a recommended investment strategy. In practice, this system can be used in multiple ways.
It can be used as above, or as a factor in forming part of a broader asset selection system, or even a method of filtering tokens by strength in order to inform a day trader which tokens might be optimal to look for long-only trading setups on an intrabar timeframe.
Final Summary:
The AI x Meme Impulse Tracker is a powerful algorithm that leverages a unique strength and impulse based approach to asset allocation within high beta token categories. Built with a robust risk management framework, the system’s Equity Curve Activation Filter dynamically manages capital exposure based on the cyclical nature of market trends, minimizing exposure during weaker phases.
With highly customizable settings, the Impulse Tracker enables precise capital allocation to only the strongest assets, informed by real-time metrics and rigorous slippage modeling in order to provide the best view of historical profitability. This adaptable design, coupled with advanced performance analytics, makes it a versatile tool for traders seeking an edge in fast moving and volatile crypto markets.
Cross-Asset Correlation Trend IndicatorCross-Asset Correlation Trend Indicator
This indicator uses correlations between the charted asset and ten others to calculate an overall trend prediction. Each ticker is configurable, and by analyzing the trend of each asset, the indicator predicts an average trend for the main asset on the chart. The strength of each asset's trend is weighted by its correlation to the charted asset, resulting in a single average trend signal. This can be a rather robust and effective signal, though it is often slow.
Functionality Overview :
The Cross-Asset Correlation Trend Indicator calculates the average trend of a charted asset based on the correlation and trend of up to ten other assets. Each asset is assigned a trend signal using a simple EMA crossover method (two customizable EMAs). If the shorter EMA crosses above the longer one, the asset trend is marked as positive; if it crosses below, the trend is negative. Each trend is then weighted by the correlation coefficient between that asset’s closing price and the charted asset’s closing price. The final output is an average weighted trend signal, which combines each trend with its respective correlation weight.
Input Parameters :
EMA 1 Length : Sets the period of the shorter EMA used to determine trends.
EMA 2 Length : Sets the period of the longer EMA used to determine trends.
Correlation Length : Defines the lookback period used for calculating the correlation between the charted asset and each of the other selected assets.
Asset Tickers : Each of the ten tickers is configurable, allowing you to set specific assets to analyze correlations with the charted asset.
Show Trend Table : Toggle to show or hide a table with each asset’s weighted trend. The table displays green, red, or white text for each weighted trend, indicating positive, negative, or neutral trends, respectively.
Table Position : Choose the position of the trend table on the chart.
Recommended Use :
As always, it’s essential to backtest the indicator thoroughly on your chosen asset and timeframe to ensure it aligns with your strategy. Feel free to modify the input parameters as needed—while the defaults work well for me, they may need adjustment to better suit your assets, timeframes, and trading style.
As always, I wish you the best of luck and immense fortune as you develop your systems. May this indicator help you make well-informed, profitable decisions!
Bullrun Profit Maximizer [QuantraSystems]Bullrun Profit Maximizer
Quantra Systems guarantees that the information created and published within this document and on the Tradingview platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors.
Important Note!
The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script.
Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance.
This ensures that the system is less likely to degrade in performance over time, compared to hyper optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post backtest.
Even More Important Note!!
The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing.
While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes.
Introduction
The "Adaptive Pairwise Momentum System" is not a prototype to the Bullrun Profit Maximizer (BPM) . The Bullrun Profit Maximizer is a fully re-engineered, higher frequency momentum system.
The Bullrun Profit Maximizer (BPM) uses a completely different filter logic and refines momentum calculations, specifically to support higher frequency trading on Crypto's Blue Chip assets. It correctly calculates fees and slippage by compounding them against System Profit before plotting the equity curve.
Unlike prior systems, this script utilizes a completely new filter logic and refined momentum calculation, specifically built to support higher frequency trading on blue-chip assets, while minimizing the impact of fees and slippage.
While the APMS focuses on Macro Trend Alignment, the BPM instead applies an equity curve based filter, allowing for targeted precision on the current asset’s trend without relying on broader market conditions. This approach delivers more responsive and asset specific signals, enhancing agility in today’s fast paced crypto markets.
The BPM dynamically optimizes capital allocation across up to four high performing assets, ensuring that the portfolio adapts swiftly to changing market conditions. The system logic consists of sophisticated quantitative methods, rapid momentum analysis and alpha cyclicality/seasonality optimizations. The overarching goal is to ensure that the portfolio is always invested in the highest performing asset based on dynamic market conditions, while at the same time managing risk through rapid asset filters and internal mechanisms like alpha cyclicality, volatility and beta analysis.
In addition to these core functionalities, the BPM comes with the typical Quantra Systems UI design, structured to reduce data clutter and provide users with only the most essential, impactful information. The BPM UI format delivers clear and easy to read signals. It enables rapid decision making in a high frequency environment without compromising on depth or accuracy.
Bespoke Logic Filtering with Equity Curve Precision
The BPM script utilizes a completely new methodology and focuses on intraday rotations of blue-chip crypto assets, while previously built systems were designed with a longer term focus in mind.
In response to the need for more precise signal generation, the BPM replaces the previous macro trend filter with a new, highly specific equity curve activation filter. This unique logic filter is driven solely by the performance trends of the asset currently held by the system. By analyzing the equity curve directly, this system can make more targeted, timely allocations based on asset specific momentum, allowing for quick adjustments that are more relevant to the held asset rather than general market conditions.
The benefits of this new, unique approach are twofold: first, it avoids premature allocation shifts based on broader macro movements, and second, it enables the system to adapt dynamically to the performance of each asset individually. This asset specific filtering allows traders to capitalize on localized strength within individual blue-chip cryptoassets without being affected by lags in the overall market trend.
High Frequency Momentum Calculation for Enhanced Flexibility
The BPM incorporates a newly designed momentum calculation that increases its suitability across lower timeframes. This new momentum indicator captures and processes more data points within a shorter window than ever before, rather than extending bar intervals and potentially losing high frequency detail. This creates a smooth, data rich featureset that is especially suited for blue-chip assets, where liquidity reduces slippage and fees, making higher frequency trading viable.
By retaining more data, this system captures subtle shifts in momentum more effectively than traditional approaches, offering higher resolution insights. These modifications result in a system capable of generating highly responsive signals on faster timeframes, empowering traders to act quickly in volatile markets.
User Interface and Enhanced Readability
The BPM also features a reimagined, streamlined user interface, making it easier than ever to monitor essential signals at a glance. The new layout minimizes extraneous data points in the tables, leaving only the most actionable information for traders. This cleaner presentation is purpose built to help traders identify the strongest asset in real time, with clear, color coded signals to facilitate swift decision making in fast moving markets.
Equity Stats Table : Designed for clarity, the stats table focuses on the current allocation’s performance metrics, emphasizing the most critical metrics without unnecessary clutter.
Color Coded Highlights : The interface includes the option to highlight both the current top performing asset, and historical allocations - with indicators of momentum shifts and performance metrics readily accessible.
Clear Signals : Visual cues are presented in an enhanced way to improve readability, including simplified line coloring, and improve visualization of the outperforming assets in the allocation table.
Dynamic Asset Reallocation
The BPM dynamically allocates capital to the strongest performing asset in a selected pool. This system incorporates a re-engineered, pairwise momentum measurement designed to operate at higher frequencies. The system evaluates each asset against others in real time, ensuring only the highest momentum asset receives allocation. This approach keeps the portfolio positioned for maximum efficiency, with an updated weighting logic that favors assets showing both strength and sustainability.
Position Changes and Slippage Calculation
Position changes are optimized for faster reallocation, with realistic slippage and fee calculations factored into each trade. The system’s structure minimizes the impact of these costs on blue-chip assets, allowing for more active management on short timeframes without incurring significant drag on performance.
A Special Note on Fees + Slippage
In the image above, the system has been applied to four different timeframes - 12h, 8h, 4h and 1h - using identical settings and a selected slippage and fees amount of 0.2%. In this stress test, we isolate the choppy downwards period from the previous Bitcoin all time high - set in March 2024, to the current date where Bitcoin is currently sitting at around the same level.
This illustrates an important concept: starting at the 12h, the system performed better as the timeframes decreased. In fact, only on the 4hr chart did the system equity curve make a new all time high alongside Bitcoin. It is worth noting that market phases that are “non-trending” are generally the least profitable periods to use a momentum/trend system - as most systems will get caught by false momentum and will “buy the top,” and then proceed to “sell the bottom.”
Lower timeframes typically offer more data points for the algorithm to compute over, and enable quicker entries and exits within a robust system, often reducing downside risk and compounding gains more effectively - in all market environments.
However, slippage, fees, and execution constraints are still limiting factors. Although blue-chip cryptocurrencies are more liquid and can be traded with lower fees compared to low cap assets, frequent trading on lower timeframes incurs cumulative slippage costs. With the BPM system set to a realistic slippage rate of 0.2% per trade, this example emphasizes how even lower fees impact performance as trade frequency increases.
Finding the optimal balance between timeframe and slippage impact requires careful consideration of factors such as portfolio size, liquidity of selected tokens, execution speed, and the fee rate of the exchange you execute trades on.
Number of Position Changes
Understanding the number of position changes in a strategy is critical to assessing its feasibility in real world trading. Frequent position changes can lead to increased costs due to slippage and fees. Monitoring the number of position changes provides insight into the system’s behavior - helping to evaluate how active the strategy is and whether it aligns with the trader's desired time input for position management.
Equity Curve and Performance Calculations
To provide a benchmark, the script also generates a Buy-and-Hold (or "HODL") equity curve that represents a 100% allocation to Bitcoin, the highest market cap cryptoasset. This allows users to easily compare the performance of the dynamic rotation system with that of a more traditional investment strategy.
The script tracks key performance metrics for both the dynamic portfolio and the HODL strategy, including:
Sharpe Ratio
The Sharpe Ratio is a key metric that evaluates a portfolio’s risk adjusted return by comparing its ‘excess’ return to its volatility. Traditionally, the Sharpe Ratio measures returns relative to a risk-free rate. However, in our system’s calculation, we omit the risk-free rate and instead measure returns above a benchmark of 0%. This adjustment provides a more universal comparison, especially in the context of highly volatile assets like cryptocurrencies, where a traditional risk-free benchmark, such as the usual 3-month T-bills, is often irrelevant or too distant from the realities of the crypto market.
By using 0% as the baseline, we focus purely on the strategy's ability to generate raw returns in the face of market risk, which makes it easier to compare performance across different strategies or asset classes. In an environment like cryptocurrency, where volatility can be extreme, the importance of relative return against a highly volatile backdrop outweighs comparisons to a risk-free rate that bears little resemblance to the risk profile of digital assets.
Sortino Ratio
The Sortino Ratio improves upon the Sharpe Ratio by specifically targeting downside risk and leaves the upside potential untouched. In contrast to the Sharpe Ratio (which penalizes both upside and downside volatility), the Sortino Ratio focuses only on negative return deviations. This makes it a more suitable metric for evaluating strategies like the Bullrun Profit Maximizer - that aim to minimize drawdowns without restricting upside capture. By measuring returns relative to a 0% baseline, the Sortino ratio provides a clearer assessment of how well the system generates gains while avoiding substantial losses in highly volatile markets like crypto.
Omega Ratio
The Omega Ratio is calculated as the ratio of gains to losses across all return thresholds, providing a more complete view of how the system balances upside and downside risk even compared to the Sortino Ratio. While it achieves a similar outcome to the Sortino Ratio by emphasizing the system's ability to capture gains while limiting losses, it is technically a mathematically superior method. However, we include both the Omega and Sortino ratios in our metric table, as the Sortino Ratio remains more widely recognized and commonly understood by traders and investors of all levels.
Usage Summary:
While the backtests in this description are generated as if a trader held a portfolio of just the strongest tokens, this was mainly designed as a method of logical verification and not a recommended investment strategy. In practice, this system can be used in multiple ways.
It can be used as above, or as a factor in forming part of a broader asset selection tool, or even a method of filtering tokens by strength in order to inform a day trader which tokens might be optimal to look at, for long-only trading setups on an intrabar timeframe.
Summary
The Bullrun Profit Maximizer is an advanced tool tailored for traders, offering the precision and agility required in today’s markets. With its asset specific equity curve filter, reworked momentum analysis, and streamlined user interface, this system is engineered to maximize gains and minimize risk during bullmarkets, with a strong focus on risk adjusted performance.
Its refined approach, focused on high resolution data processing and adaptive reallocation, makes it a powerful choice for traders looking to capture high quality trends on clue-chip assets, no matter the market’s pace.
Trend Titan Neutronstar [QuantraSystems]Trend Titan NEUTRONSTAR
Credits
The Trend Titan NEUTRONSTAR is a comprehensive aggregation of nearly 100 unique indicators and custom combinations, primarily developed from unique and public domain code.
We'd like to thank our TradingView community members: @IkKeOmar for allowing us to add his well-built "Normalized KAMA Oscillator" and "Adaptive Trend Lines " indicators to the aggregation, as well as @DojiEmoji for his valuable "Drift Study (Inspired by Monte Carlo Simulations with BM)".
Introduction
The Trend Titan NEUTRONSTAR is a robust trend following algorithm meticulously crafted to meet the demands of crypto investors. Designed with a multi layered aggregation approach, NEUTRONSTAR excels in navigating the unique volatility and rapid shifts of the cryptocurrency market. By stacking and refining a variety of carefully selected indicators, it combines their individual strengths while reducing the impact of noise or false signals. This "aggregation of aggregators" approach enables NEUTRONSTAR to produce a consistently reliable trend signal across assets and timeframes, making it an exceptional tool for investors focused on medium to long term market positioning.
NEUTRONSTAR ’s powerful trend following capabilities provide investors with straightforward, data driven analysis. It signals when tokens exhibit sustained upward momentum and systematically removes allocations from assets showing signs of weakness. This structure aids investors in recognizing peak market phases. In fact, one of NEUTRONSTAR ’s most valuable applications is its potential to help investors time exits near the peak of bull markets. This aims to maximize gains while mitigating exposure to downturns.
Ultimately, NEUTRONSTAR equips investors with a high precision, adaptable framework for strategic decision making. It offers robust support to identify strong trends, manage risk, and navigate the dynamic crypto market landscape.
With over a year of rigorous forward testing and live trading, NEUTRONSTAR demonstrates remarkable robustness and effectiveness, maintaining its performance without succumbing to overfitting. The system has been purposefully designed to avoid unnecessary optimization to past data, ensuring it can adapt as market conditions evolve. By focusing on aggregating valuable trend signals rather than tuning to historical performance, the NEUTRONSTAR serves as a reliable universal trend following system that aligns with the natural market cycles of growth and correction.
Core Methodology
The foundation of the NEUTRONSTAR lies in its multi aggregated structure, where five custom developed trend models are combined to capture the dominant market direction. Each of these aggregates has been carefully crafted with a specific trend signaling period in mind, allowing it to adapt seamlessly across various timeframes and asset classes. Here’s a breakdown of the key components:
FLARE - The original Quantra Signaling Matrix (QSM) model, best suited for timeframes above 12 hours. It forms the foundation of long term trend detection, providing stable signals.
FLAREV2 - A refined and more sophisticated model that performs well across both high and low timeframes, adding a layer of adaptability to the system.
NEBULA - An advanced model combining FLARE and FLAREV2. NEBULA brings the advantages of both components together, enhancing reliability and capturing smoother, more accurate trends.
SPARK - A high speed trend aggregator based on the QSM Universal model. It focuses on fast moving trends, providing early signals of potential shifts.
SUNBURST - A balanced aggregate that combines elements of SPARK and FLARE, confirming SPARK’s signals while minimizing false positives.
Each of these models contributes its own unique perspective on market movement. By layering fast, medium, and slower trend following signals, NEUTRONSTAR can confirm strong trends while filtering out shorter term noise. The result is a comprehensive tool that signals clear market direction with minimized false signals.
A Unique Approach to Trend Aggregation
One of the defining characteristics of NEUTRONSTAR is its deliberate choice to avoid perfectly time coherent indicators within its aggregation. In simpler terms, NEUTRONSTAR purposefully incorporates trend following indicators with slightly different signal periods, rather than synchronizing all components to a single signaling period. This choice brings significant benefits in terms of diversification, adaptability, and robustness of the overall trend signal.
When aggregating multiple trend following components, if all indicators were perfectly time coherent - meaning they responded to market changes in exactly the same way and over the time periods - the resulting signal would effectively be no different from a single trend following indicator. This uniformity would limit the system’s ability to capture a variety of market conditions, leaving it vulnerable to the same noise or false signals that any single indicator might encounter. Instead, NEUTRONSTAR leverages a balanced mix of indicators with varied timing: some fast, some slower, and some in the medium range. This choice allows the system to extract the unique strengths of each component, creating a combined signal that is stronger and more reliable than any single indicator.
By incorporating different signal periods, NEUTRONSTAR achieves what can be thought of as a form of edge accumulation. The fast components within NEUTRONSTAR , for example, are highly sensitive to quick shifts in market direction. These indicators excel at identifying early trend signals, enabling NEUTRONSTAR to react swiftly to emerging momentum. However, these fast indicators alone would be prone to reacting to market noise, potentially generating too many premature signals. This is where the medium term indicators come into play. These components operate with a slower reaction time, filtering out the short term fluctuations and confirming the direction of the trend established by the faster indicators. The combination of these varying signal speeds results in a balanced, adaptive response to market changes.
This approach also allows NEUTRONSTAR to adapt to different market regimes seamlessly. In fast moving, volatile markets, the faster indicators provide an early alert to potential trend shifts, while the slower components offer a stabilizing influence, preventing overreaction to temporary noise. Conversely, in steadier or trending markets, the medium and slower indicators sustain the trend signal, reducing the likelihood of premature exits. This flexible design enhances NEUTRONSTAR ’s ability to operate effectively across multiple asset classes and timeframes, from short term fluctuations to longer term market cycles.
The result is a powerful, multi-layered trend following tool that remains adaptive, capturing the benefits of both fast and medium paced reactions without becoming overly sensitive to short term noise. This unique aggregation methodology also supports NEUTRONSTAR ’s robustness, reducing the risk of overfitting to historical data and ensuring that the system can perform reliably in forward testing and live trading environments. The slightly staggered signal periods provide a greater degree of resilience, making NEUTRONSTAR a dependable choice for traders looking to capitalize on sustained trends while minimizing exposure during periods of market uncertainty.
In summary, the lack of perfect time coherence among NEUTRONSTAR ’s sub components is not a flaw - but a deliberate, robust design choice.
Risk Management through Market Mode Analysis
An essential part of NEUTRONSTAR is its ability to assess the market's underlying behavior and adapt accordingly. It employs a Market Mode Analysis mechanism that identifies when the market is either in a “Trending State” or a “Mean Reverting State.” When enough confidence is established that the market is trending, the system confirms and signals a “Trending State,” which is optimal for maintaining positions in the direction of the trend. Conversely, if there’s insufficient confidence, it labels the market as “Mean Reverting,” alerting traders to potentially avoid trend trades during likely sideways movement.
This distinction is particularly valuable in crypto, where asset prices often oscillate between aggressive trends and consolidation periods. The Market Mode Analysis keeps traders aligned with the broader market conditions, minimizing exposure during periods of potential whipsaws and maximizing gains during sustained trends.
Zero Overfitting: Design and Testing for Real World Resilience
Unlike many trend following indicators that rely heavily on backtesting and optimization, NEUTRONSTAR was built to perform well in forward testing and live trading without post design adjustments. Over a year of live market exposure has all but proven its robustness, with the system’s methodology focused on universal applicability and simplicity rather than curve fitting to past data. This approach ensures the aggregator remains effective across different market cycles and maintains relevance as new data unfolds.
By avoiding overfitting, NEUTRONSTAR is inherently more resistant to the common issue of strategy degradation over time, making it a valuable tool for traders seeking reliable market analysis you can trust for the long term.
Settings and Customization Options
To accommodate a range of trading styles and market conditions, NEUTRONSTAR includes adjustable settings that allow for fine tuning sensitivity and signal generation:
Calculation Method - Users can choose between calculating the NEUTRONSTAR score based on aggregated scores or by using the state of individual aggregates (long, neutral, short). The score method provides faster signals with slightly more noise, while the state based approach offers a smoother signal.
Sensitivity Threshold - This setting adjusts the system’s sensitivity, defining the width of the neutral zone. Higher thresholds reduce sensitivity, allowing for a broader range of volatility before triggering a trend reversal.
Market Regime Sensitivity - A sensitivity adjustment, ranging from 0 to 100, that affects the sensitivity of the sub components in market regime calculation.
These settings offer flexibility for users to tailor NEUTRONSTAR to their specific needs, whether for medium term investment strategies or shorter term trading setups.
Visualization and Legend
For intuitive usability, NEUTRONSTAR uses color coded bar overlays to indicate trend direction:
Green - indicates an uptrend.
Gray - signals a neutral or transition phase.
Purple - denotes a downtrend.
An optional background color can be enabled for market mode visualization, indicating the overall market state as either trending or mean reverting. This feature allows traders to assess trend direction and strength at a glance, simplifying decision making.
Additional Metrics Table
To support strategic decision making, NEUTRONSTAR includes an additional metrics table for in depth analysis:
Performance Ratios - Sharpe, Sortino, and Omega ratios assess the asset’s risk adjusted returns.
Volatility Insights - Provides an average volatility measure, valuable for understanding market stability.
Beta Measurement - Calculates asset beta against BTC, offering insight into asset volatility in the context of the broader market.
These metrics provide deeper insights into individual asset behavior, supporting more informed trend based allocations. The table is fully customizable, allowing traders to adjust the position and size for a seamless integration into their workspace.
Final Summary
The Trend Titan NEUTRONSTAR indicator is a powerful and resilient trend following system for crypto markets, built with a unique aggregation of high performance models to deliver dependable, noise reduced trend signals. Its robust design, free from overfitting, ensures adaptability across various assets and timeframes. With customizable sensitivity settings, intuitive color coded visualization, and an advanced risk metrics table, NEUTRONSTAR provides traders with a comprehensive tool for identifying and riding profitable trends, while safeguarding capital during unfavorable market phases.
Universal Trend and Valuation System [QuantAlgo]Universal Trend and Valuation System 📊🧬
The Universal Trend and Valuation System by QuantAlgo is an advanced indicator designed to assess asset valuation and trends across various timeframes and asset classes. This system integrates multiple advanced statistical indicators and techniques with Z-score calculations to help traders and investors identify overbought/sell and oversold/buy signals. By evaluating valuation and trend strength together, this tool empowers users to make data-driven decisions, whether they aim to follow trends, accumulate long-term positions, or identify turning points in mean-reverting markets.
💫 Conceptual Foundation and Innovation
The Universal Trend and Valuation System by QuantAlgo provides a unique framework for assessing market valuation and trend dynamics through a blend of Z-score analysis and trend-following algorithm. Unlike traditional indicators that only reflect price direction, this system incorporates multi-layered data to reveal the relative value of an asset, helping users determine whether it’s overvalued, undervalued, or approaching a trend reversal. By combining high quality trend-following tools, such as Dynamic Score Supertrend, DEMA RSI, and EWMA, it evaluates trend stability and momentum quality, while Z-scores of performance ratios like Sharpe, Sortino, and Omega standardize deviations from historical trends, enabling traders and investors to spot extreme conditions. This dual approach allows users to better identify accumulation (undervaluation) and distribution (overvaluation) phases, enhancing strategies like Dollar Cost Averaging (DCA) and overall timing for entries and exits.
📊 Technical Composition and Calculation
The Universal Trend-Following Valuation System is composed of several trend-following and valuation indicators that create a dynamic dual scoring model:
Risk-Adjusted Ratios (Sharpe, Sortino, Omega): These ratios assess trend quality by analyzing an asset’s risk-adjusted performance. Sharpe and Sortino provide insight into trend consistency and risk/reward, while Omega evaluates profitability potential, helping traders and investors assess how favorable a trend or an asset is relative to its associated risk.
Dynamic Z-Scores: Z-scores are applied to various metrics like Price, RSI, and RoC, helping to identify statistical deviations from the mean, which indicate potential extremes in valuation. By combining these Z-scores, the system produces a cumulative score that highlights when an asset may be overbought or oversold.
Aggregated Trend-Following Indicators: The model consolidates multiple high quality indicators to highlight probable trend shifts. This helps confirm the direction and strength of market moves, allowing users to spot reversals or entry points with greater clarity.
📈 Key Indicators and Features
The Universal Trend and Valuation System combines various technical and statistical tools to deliver a well-rounded analysis of market trends and valuation:
The indicator utilizes trend-following indicators like RSI with DEMA smoothing and Dynamic Score Supertrend to minimize market noise, providing clearer and more stable trend signals. Sharpe, Sortino, and Omega ratios are calculated to assess risk-adjusted performance and volatility, adding a layer of analysis for evaluating trend quality. Z-scores are applied to these ratios, as well as Price and Rate of Change (RoC), to detect deviations from historical trends, highlighting extreme valuation levels.
The system also incorporates multi-layered visualization with gradient color coding to signal valuation states across different market conditions. These adaptive visual cues, combined with threshold-based alerts for overbought and oversold zones, help traders and investors track probable trend reversals or continuations and identify accumulation or distribution zones, adding reliability to both trend-following and mean-reversion strategies.
⚡️ Practical Applications and Examples
✅ Add the Indicator: Add the Universal Trend-Following Valuation System to your favourites and to your chart.
👀 Monitor Trend Shifts and Valuation Levels: Watch the average Z score, trend probability state and gradient colors to identify overbought and oversold conditions. During undervaluation, consider using a DCA strategy to gradually accumulate positions (buy), while overvaluation may signal distribution or profit-taking phases (sell).
🔔 Set Alerts: Configure alerts for significant trend or valuation changes, ensuring you can act on market movements promptly, even when you’re not actively monitoring the charts.
🌟 Summary and Usage Tips
The Universal Trend and Valuation System by QuantAlgo is a highly adaptable tool, designed to support both trend-following and valuation analysis across different market environments. By combining valuation metrics with high quality trend-following indicators, it helps traders and investors identify the relative value of an asset based on historical norms, providing more reliable overbought/sell and oversold/buy signals. The tool’s flexibility across asset types and timeframes makes it ideal for both short-term trading and long-term investment strategies like DCA, allowing users to capture meaningful trends while minimizing noise.
Session Breaks [Market Mindset]Session Break Indicator
This powerful tool marks session breaks on your chart based on your chosen timeframe, helping you quickly spot key points in market sessions.
Customize it to fit your trading style with the following settings:
Resolution: Select the timeframe you want session start and end lines for.
Wait: Turn this on to delay new line creation until the bar closes, keeping your chart clean in real-time.
Styling: Adjust line width and color for optimal clarity.
Perfect for traders wanting a clear view of session transitions and opportunities!
Trading Sessions with Global HolidaysDescription:
This versatile Pine Script provides traders with a visual representation of major global trading sessions: London, New York, Sydney, Hong Kong, Tokyo, and Frankfurt. By highlighting these sessions on the chart, users can better identify optimal trading opportunities aligned with market activity.
Key Features:
Customizable Colors: Choose distinct colors for each trading session, with adjustable opacity to suit your visual preferences.
Holiday Awareness: The script takes into account significant holidays from various countries, ensuring that sessions are only highlighted when the markets are open.
Weekend Option: Users have the flexibility to display or hide weekend sessions, helping to tailor the analysis to specific trading strategies.
Session Activation: Easily enable or disable individual trading sessions based on your trading preferences, allowing for a customized experience.
Visual Clarity: With subtle color tones, the script maintains a clean and professional appearance, ensuring that the session highlights are noticeable without being overwhelming.
Perfect for: Day traders, swing traders, and anyone interested in global market dynamics. Whether you are a novice or an experienced trader, this tool enhances your ability to analyze market trends and make informed decisions.
Elevate your trading experience with this intuitive and informative Trading Sessions script!
Dynamic Score SMA [QuantAlgo]Dynamic Score SMA 📈🌊
The Dynamic Score SMA by QuantAlgo offers a powerful trend-following approach that combines the simplicity of the Simple Moving Average (SMA) with an innovative dynamic trend scoring technique . By continuously evaluating price movement relative to the SMA over a customizable window, this indicator adapts to varying market conditions, providing traders and investors with clearer, more adaptable trend signals. With this dynamic scoring approach, the Dynamic Score SMA helps identify trend shifts, allowing for more strategic decision-making.
🌟 Conceptual Foundation and Innovation
At the core of the Dynamic Score SMA is its dynamic trend score system , which assesses price movements by comparing them to the SMA over a series of historical data points. This technique goes beyond traditional SMA indicators by offering a dynamic, probabilistic evaluation of trend strength, delivering a more responsive and nuanced view of market direction. The integration of this scoring system enables traders and investors to navigate both trending and sideway markets with greater confidence and precision.
⚙️ Technical Composition and Calculation
The Dynamic Score SMA leverages the Simple Moving Average to establish a baseline trend, with customizable SMA length to control the indicator’s sensitivity. The dynamic trend scoring technique then evaluates price behavior relative to the SMA over a specified window, generating a trend score that reflects the current market bias.
When the score crosses the designated uptrend or downtrend thresholds, the indicator signals a potential trend shift. By adjusting the SMA length, window duration, and thresholds, users can refine the indicator’s responsiveness to match their preferred trading or investing strategy, making it suitable for both volatile and steady markets.
📈 Features and Practical Applications
Customizable SMA Length: Set the length of the SMA to control how sensitive the trend is to price changes. Longer lengths produce smoother trends, while shorter lengths increase responsiveness.
Window Length for Dynamic Scoring: Adjust the window length to determine how many data points are considered in the dynamic trend score calculation, allowing for more tailored analysis of recent versus long-term trends.
Uptrend/Downtrend Thresholds: Define thresholds for triggering trend signals. Higher thresholds reduce sensitivity, providing clearer signals in volatile markets, while lower thresholds capture shorter-term movements.
Bar and Background Coloring: Visual cues, including bar coloring and background fills, provide a quick reference for current trend direction, making it easier to monitor market conditions.
Trend Confirmation: The dynamic trend scoring system verifies trend strength, offering more reliable entry and exit points by filtering out potential false signals.
⚡️ How to Use
✅ Add the Indicator: Add the Dynamic Score SMA to your favourites, then apply it to your chart. Customize the SMA length, window size, and thresholds to match your trading or investing preferences.
👀 Monitor Trend Shifts: Observe the trend in relation to the SMA and watch for signals when the score crosses key thresholds. Bar and/or background coloring will help identify the current trend direction and any shifts in momentum.
🔔 Set Alerts: Configure alerts for significant trend crossovers and reversals, enabling you to act on market changes in real-time without needing constant chart observation.
💫 Summary and Usage Tips
The Dynamic Score SMA by QuantAlgo is a sophisticated trend-following indicator that combines the familiarity of the SMA with a dynamic trend scoring system, providing a more adaptable and probabilistic approach to trend analysis. By tailoring the SMA length, scoring window, and thresholds, traders and investors can fine-tune the indicator for both short-term adjustments and long-term trend following. For optimal use, adjust sensitivity based on market volatility, and rely on the visual cues for clear trend confirmation. Whether you’re navigating choppy markets or stable trends, the Dynamic Score SMA offers a refined approach to capturing market direction with enhanced precision.
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.
