AI Trend Navigator [K-Neighbor]█ Overview
In the evolving landscape of trading and investment, the demand for sophisticated and reliable tools is ever-growing. The AI Trend Navigator is an indicator designed to meet this demand, providing valuable insights into market trends and potential future price movements. The AI Trend Navigator indicator is designed to predict market trends using the k-Nearest Neighbors (KNN) classifier.
By intelligently analyzing recent price actions and emphasizing similar values, it helps traders to navigate complex market conditions with confidence. It provides an advanced way to analyze trends, offering potentially more accurate predictions compared to simpler trend-following methods.
█ Calculations
KNN Moving Average Calculation: The core of the algorithm is a KNN Moving Average that computes the mean of the 'k' closest values to a target within a specified window size. It does this by iterating through the window, calculating the absolute differences between the target and each value, and then finding the mean of the closest values. The target and value are selected based on user preferences (e.g., using the VWAP or Volatility as a target).
KNN Classifier Function: This function applies the k-nearest neighbor algorithm to classify the price action into positive, negative, or neutral trends. It looks at the nearest 'k' bars, calculates the Euclidean distance between them, and categorizes them based on the relative movement. It then returns the prediction based on the highest count of positive, negative, or neutral categories.
█ How to use
Traders can use this indicator to identify potential trend directions in different markets.
Spotting Trends: Traders can use the KNN Moving Average to identify the underlying trend of an asset. By focusing on the k closest values, this component of the indicator offers a clearer view of the trend direction, filtering out market noise.
Trend Confirmation: The KNN Classifier component can confirm existing trends by predicting the future price direction. By aligning predictions with current trends, traders can gain more confidence in their trading decisions.
█ Settings
PriceValue: This determines the type of price input used for distance calculation in the KNN algorithm.
hl2: Uses the average of the high and low prices.
VWAP: Uses the Volume Weighted Average Price.
VWAP: Uses the Volume Weighted Average Price.
Effect: Changing this input will modify the reference values used in the KNN classification, potentially altering the predictions.
TargetValue: This sets the target variable that the KNN classification will attempt to predict.
Price Action: Uses the moving average of the closing price.
VWAP: Uses the Volume Weighted Average Price.
Volatility: Uses the Average True Range (ATR).
Effect: Selecting different targets will affect what the KNN is trying to predict, altering the nature and intent of the predictions.
Number of Closest Values: Defines how many closest values will be considered when calculating the mean for the KNN Moving Average.
Effect: Increasing this value makes the algorithm consider more nearest neighbors, smoothing the indicator and potentially making it less reactive. Decreasing this value may make the indicator more sensitive but possibly more prone to noise.
Neighbors: This sets the number of neighbors that will be considered for the KNN Classifier part of the algorithm.
Effect: Adjusting the number of neighbors affects the sensitivity and smoothness of the KNN classifier.
Smoothing Period: Defines the smoothing period for the moving average used in the KNN classifier.
Effect: Increasing this value would make the KNN Moving Average smoother, potentially reducing noise. Decreasing it would make the indicator more reactive but possibly more prone to false signals.
█ What is K-Nearest Neighbors (K-NN) algorithm?
At its core, the K-NN algorithm recognizes patterns within market data and analyzes the relationships and similarities between data points. By considering the 'K' most similar instances (or neighbors) within a dataset, it predicts future price movements based on historical trends. The K-Nearest Neighbors (K-NN) algorithm is a type of instance-based or non-generalizing learning. While K-NN is considered a relatively simple machine-learning technique, it falls under the AI umbrella.
We can classify the K-Nearest Neighbors (K-NN) algorithm as a form of artificial intelligence (AI), and here's why:
Machine Learning Component: K-NN is a type of machine learning algorithm, and machine learning is a subset of AI. Machine learning is about building algorithms that allow computers to learn from and make predictions or decisions based on data. Since K-NN falls under this category, it is aligned with the principles of AI.
Instance-Based Learning: K-NN is an instance-based learning algorithm. This means that it makes decisions based on the entire training dataset rather than deriving a discriminative function from the dataset. It looks at the 'K' most similar instances (neighbors) when making a prediction, hence adapting to new information if the dataset changes. This adaptability is a hallmark of intelligent systems.
Pattern Recognition: The core of K-NN's functionality is recognizing patterns within data. It identifies relationships and similarities between data points, something akin to human pattern recognition, a key aspect of intelligence.
Classification and Regression: K-NN can be used for both classification and regression tasks, two fundamental problems in machine learning and AI. The indicator code is used for trend classification, a predictive task that aligns with the goals of AI.
Simplicity Doesn't Exclude AI: While K-NN is often considered a simpler algorithm compared to deep learning models, simplicity does not exclude something from being AI. Many AI systems are built on simple rules and can be combined or scaled to create complex behavior.
No Explicit Model Building: Unlike traditional statistical methods, K-NN does not build an explicit model during training. Instead, it waits until a prediction is required and then looks at the 'K' nearest neighbors from the training data to make that prediction. This lazy learning approach is another aspect of machine learning, part of the broader AI field.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
在腳本中搜尋"ai"
AI Adaptive Oscillator [PhenLabs]📊 Algorithmic Adaptive Oscillator
Version: PineScript™ v6
📌 Description
The AI Adaptive Oscillator is a sophisticated technical indicator that employs ensemble learning and adaptive weighting techniques to analyze market conditions. This innovative oscillator combines multiple traditional technical indicators through an AI-driven approach that continuously evaluates and adjusts component weights based on historical performance. By integrating statistical modeling with machine learning principles, the indicator adapts to changing market dynamics, providing traders with a responsive and reliable tool for market analysis.
🚀 Points of Innovation:
Ensemble learning framework with adaptive component weighting
Performance-based scoring system using directional accuracy
Dynamic volatility-adjusted smoothing mechanism
Intelligent signal filtering with cooldown and magnitude requirements
Signal confidence levels based on multi-factor analysis
🔧 Core Components
Ensemble Framework : Combines up to five technical indicators with performance-weighted integration
Adaptive Weighting : Continuous performance evaluation with automated weight adjustment
Volatility-Based Smoothing : Adapts sensitivity based on current market volatility
Pattern Recognition : Identifies potential reversal patterns with signal qualification criteria
Dynamic Visualization : Professional color schemes with gradient intensity representation
Signal Confidence : Three-tiered confidence assessment for trading signals
🔥 Key Features
The indicator provides comprehensive market analysis through:
Multi-Component Ensemble : Integrates RSI, CCI, Stochastic, MACD, and Volume-weighted momentum
Performance Scoring : Evaluates each component based on directional prediction accuracy
Adaptive Smoothing : Automatically adjusts based on market volatility
Pattern Detection : Identifies potential reversal patterns in overbought/oversold conditions
Signal Filtering : Prevents excessive signals through cooldown periods and minimum change requirements
Confidence Assessment : Displays signal strength through intuitive confidence indicators (average, above average, excellent)
🎨 Visualization
Gradient-Filled Oscillator : Color intensity reflects strength of market movement
Clear Signal Markers : Distinct bullish and bearish pattern signals with confidence indicators
Range Visualization : Clean representation of oscillator values from -6 to 6
Zero Line : Clear demarcation between bullish and bearish territory
Customizable Colors : Color schemes that can be adjusted to match your chart style
Confidence Symbols : Intuitive display of signal confidence (no symbol, +, or ++) alongside direction markers
📖 Usage Guidelines
⚙️ Settings Guide
Color Settings
Bullish Color
Default: #2b62fa (Blue)
This setting controls the color representation for bullish movements in the oscillator. The color appears when the oscillator value is positive (above zero), with intensity indicating the strength of the bullish momentum. A brighter shade indicates stronger bullish pressure.
Bearish Color
Default: #ce9851 (Amber)
This setting determines the color representation for bearish movements in the oscillator. The color appears when the oscillator value is negative (below zero), with intensity reflecting the strength of the bearish momentum. A more saturated shade indicates stronger bearish pressure.
Signal Settings
Signal Cooldown (bars)
Default: 10
Range: 1-50
This parameter sets the minimum number of bars that must pass before a new signal of the same type can be generated. Higher values reduce signal frequency and help prevent overtrading during choppy market conditions. Lower values increase signal sensitivity but may generate more false positives.
Min Change For New Signal
Default: 1.5
Range: 0.5-3.0
This setting defines the minimum required change in oscillator value between consecutive signals of the same type. It ensures that new signals represent meaningful changes in market conditions rather than minor fluctuations. Higher values produce fewer but potentially higher-quality signals, while lower values increase signal frequency.
AI Core Settings
Base Length
Default: 14
Minimum: 2
This fundamental setting determines the primary calculation period for all technical components in the ensemble (RSI, CCI, Stochastic, etc.). It represents the lookback window for each component’s base calculation. Shorter periods create a more responsive but potentially noisier oscillator, while longer periods produce smoother signals with potential lag.
Adaptive Speed
Default: 0.1
Range: 0.01-0.3
Controls how quickly the oscillator adapts to new market conditions through its volatility-adjusted smoothing mechanism. Higher values make the oscillator more responsive to recent price action but potentially more erratic. Lower values create smoother transitions but may lag during rapid market changes. This parameter directly influences the indicator’s adaptiveness to market volatility.
Learning Lookback Period
Default: 150
Minimum: 10
Determines the historical data range used to evaluate each ensemble component’s performance and calculate adaptive weights. This setting controls how far back the AI “learns” from past performance to optimize current signals. Longer periods provide more stable weight distribution but may be slower to adapt to regime changes. Shorter periods adapt more quickly but may overreact to recent anomalies.
Ensemble Size
Default: 5
Range: 2-5
Specifies how many technical components to include in the ensemble calculation.
Understanding The Interaction Between Settings
Base Length and Learning Lookback : The base length determines the reactivity of individual components, while the lookback period determines how their weights are adjusted. These should be balanced according to your timeframe - shorter timeframes benefit from shorter base lengths, while the lookback should generally be 10-15 times the base length for optimal learning.
Adaptive Speed and Signal Cooldown : These settings control sensitivity from different angles. Increasing adaptive speed makes the oscillator more responsive, while reducing signal cooldown increases signal frequency. For conservative trading, keep adaptive speed low and cooldown high; for aggressive trading, do the opposite.
Ensemble Size and Min Change : Larger ensembles provide more stable signals, allowing for a lower minimum change threshold. Smaller ensembles might benefit from a higher threshold to filter out noise.
Understanding Signal Confidence Levels
The indicator provides three distinct confidence levels for both bullish and bearish signals:
Average Confidence (▲ or ▼) : Basic signal that meets the minimum pattern and filtering criteria. These signals indicate potential reversals but with moderate confidence in the prediction. Consider using these as initial alerts that may require additional confirmation.
Above Average Confidence (▲+ or ▼+) : Higher reliability signal with stronger underlying metrics. These signals demonstrate greater consensus among the ensemble components and/or stronger historical performance. They offer increased probability of successful reversals and can be traded with less additional confirmation.
Excellent Confidence (▲++ or ▼++) : Highest quality signals with exceptional underlying metrics. These signals show strong agreement across oscillator components, excellent historical performance, and optimal signal strength. These represent the indicator’s highest conviction trade opportunities and can be prioritized in your trading decisions.
Confidence assessment is calculated through a multi-factor analysis including:
Historical performance of ensemble components
Degree of agreement between different oscillator components
Relative strength of the signal compared to historical thresholds
✅ Best Use Cases:
Identify potential market reversals through oscillator extremes
Filter trade signals based on AI-evaluated component weights
Monitor changing market conditions through oscillator direction and intensity
Confirm trade signals from other indicators with adaptive ensemble validation
Detect early momentum shifts through pattern recognition
Prioritize trading opportunities based on signal confidence levels
Adjust position sizing according to signal confidence (larger for ++ signals, smaller for standard signals)
⚠️ Limitations
Requires sufficient historical data for accurate performance scoring
Ensemble weights may lag during dramatic market condition changes
Higher ensemble sizes require more computational resources
Performance evaluation quality depends on the learning lookback period length
Even high confidence signals should be considered within broader market context
💡 What Makes This Unique
Adaptive Intelligence : Continuously adjusts component weights based on actual performance
Ensemble Methodology : Combines strength of multiple indicators while minimizing individual weaknesses
Volatility-Adjusted Smoothing : Provides appropriate sensitivity across different market conditions
Performance-Based Learning : Utilizes historical accuracy to improve future predictions
Intelligent Signal Filtering : Reduces noise and false signals through sophisticated filtering criteria
Multi-Level Confidence Assessment : Delivers nuanced signal quality information for optimized trading decisions
🔬 How It Works
The indicator processes market data through five main components:
Ensemble Component Calculation :
Normalizes traditional indicators to consistent scale
Includes RSI, CCI, Stochastic, MACD, and volume components
Adapts based on the selected ensemble size
Performance Evaluation :
Analyzes directional accuracy of each component
Calculates continuous performance scores
Determines adaptive component weights
Oscillator Integration :
Combines weighted components into unified oscillator
Applies volatility-based adaptive smoothing
Scales final values to -6 to 6 range
Signal Generation :
Detects potential reversal patterns
Applies cooldown and magnitude filters
Generates clear visual markers for qualified signals
Confidence Assessment :
Evaluates component agreement, historical accuracy, and signal strength
Classifies signals into three confidence tiers (average, above average, excellent)
Displays intuitive confidence indicators (no symbol, +, ++) alongside direction markers
💡 Note:
The AI Adaptive Oscillator performs optimally when used with appropriate timeframe selection and complementary indicators. Its adaptive nature makes it particularly valuable during changing market conditions, where traditional fixed-weight indicators often lose effectiveness. The ensemble approach provides a more robust analysis by leveraging the collective intelligence of multiple technical methodologies. Pay special attention to the signal confidence indicators to optimize your trading decisions - excellent (++) signals often represent the most reliable trade opportunities.
AI Adaptive Money Flow Index (Clustering) [AlgoAlpha]🌟🚀 Dive into the future of trading with our latest innovation: the AI Adaptive Money Flow Index by AlgoAlpha Indicator! 🚀🌟
Developed with the cutting-edge power of Machine Learning, this indicator is designed to revolutionize the way you view market dynamics. 🤖💹 With its unique blend of traditional Money Flow Index (MFI) analysis and advanced k-means clustering, it adapts to market conditions like never before.
Key Features:
📊 Adaptive MFI Analysis: Utilizes the classic MFI formula with a twist, adjusting its parameters based on AI-driven clustering.
🧠 AI-Driven Clustering: Applies k-means clustering to identify and adapt to market states, optimizing the MFI for current conditions.
🎨 Customizable Appearance: Offers adjustable settings for overbought, neutral, and oversold levels, as well as colors for uptrends and downtrends.
🔔 Alerts for Key Market Movements: Set alerts for trend reversals, overbought, and oversold conditions, ensuring you never miss a trading opportunity.
Quick Guide to Using the AI Adaptive MFI (Clustering):
🛠 Customize the Indicator: Customize settings like MFI source, length, and k-means clustering parameters to suit your analysis.
📈 Market Analysis: Monitor the dynamically adjusted overbought, neutral, and oversold levels for insights into market conditions. Watch for classification symbols ("+", "0", "-") for immediate understanding of the current market state. Look out for reversal signals (▲, ▼) to get potential entry points.
🔔 Set Alerts: Utilize the built-in alert conditions for trend changes, overbought, and oversold signals to stay ahead, even when you're not actively monitoring the charts.
How It Works:
The AI Adaptive Money Flow Index employs the k-means clustering machine learning algorithm to refine the traditional Money Flow Index, dynamically adjusting overbought, neutral, and oversold levels based on market conditions. This method analyzes historical MFI values, grouping them into initial clusters using the traditional MFI's overbought, oversold and neutral levels, and then finding the mean of each cluster, which represent the new market states thresholds. This adaptive approach ensures the indicator's sensitivity in real-time, offering a nuanced understanding of market trend and volume analysis.
By recalibrating MFI thresholds for each new data bar, the AI Adaptive MFI intelligently conforms to changing market dynamics. This process, assessing past periods to adjust the indicator's parameters, provides traders with insights finely tuned to recent market behavior. Such innovation enhances decision-making, leveraging the latest data to inform trading strategies. 🌐💥
AI SuperTrend Clustering Oscillator [LuxAlgo]The AI SuperTrend Clustering Oscillator is an oscillator returning the most bullish/average/bearish centroids given by multiple instances of the difference between SuperTrend indicators.
This script is an extension of our previously posted SuperTrend AI indicator that makes use of k-means clustering. If you want to learn more about it see:
🔶 USAGE
The AI SuperTrend Clustering Oscillator is made of 3 distinct components, a bullish output (always the highest), a bearish output (always the lowest), and a "consensus" output always within the two others.
The general trend is given by the consensus output, with a value above 0 indicating an uptrend and under 0 indicating a downtrend. Using a higher minimum factor will weigh results toward longer-term trends, while lowering the maximum factor will weigh results toward shorter-term trends.
Strong trends are indicated when the bullish/bearish outputs are indicating an opposite sentiment. A strong bullish trend would for example be indicated when the bearish output is above 0, while a strong bearish trend would be indicated when the bullish output is below 0.
When the consensus output is indicating a specific trend direction, an opposite indication from the bullish/bearish output can highlight a potential reversal or retracement.
🔶 DETAILS
The indicator construction is based on finding three clusters from the difference between the closing price and various SuperTrend using different factors. The centroid of each cluster is then returned. This operation is done over all historical bars.
The highest cluster will be composed of the differences between the price and SuperTrends that are the highest, thus creating a more bullish group. The lowest cluster will be composed of the differences between the price and SuperTrends that are the lowest, thus creating a more bearish group.
The consensus cluster is composed of the differences between the price and SuperTrends that are not significant enough to be part of the other clusters.
🔶 SETTINGS
ATR Length: ATR period used for the calculation of the SuperTrends.
Factor Range: Determine the minimum and maximum factor values for the calculation of the SuperTrends.
Step: Increments of the factor range.
Smooth: Degree of smoothness of each output from the indicator.
🔹 Optimization
This group of settings affects the runtime performances of the script.
Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
Historical Bars Calculation: Calculation window of the script (in bars).
AI Volume Breakout for scalpingPurpose of the Indicator
This script is designed for trading, specifically for scalping, which involves making numerous trades within a very short time frame to take advantage of small price movements. The indicator looks for volume breakouts, which are moments when trading volume significantly increases, potentially signaling the start of a new price movement.
Key Components:
Parameters:
Volume Threshold (volumeThreshold): Determines how much volume must increase from one bar to the next for it to be considered significant. Set at 4.0, meaning volume must quadruplicate for a breakout signal.
Price Change Threshold (priceChangeThreshold): Defines the minimum price change required for a breakout signal. Here, it's 1.5% of the bar's opening price.
SMA Length (smaLength): The period for the Simple Moving Average, which helps confirm the trend direction. Here, it's set to 20.
Cooldown Period (cooldownPeriod): Prevents signals from being too close together, set to 10 bars.
ATR Period (atrPeriod): The period for calculating Average True Range (ATR), used to measure market volatility.
Volatility Threshold (volatilityThreshold): If ATR divided by the close price exceeds this, the market is considered too volatile for trading according to this strategy.
Calculations:
SMA (Simple Moving Average): Used for trend confirmation. A bullish signal is more likely if the price is above this average.
ATR (Average True Range): Measures market volatility. Lower volatility (below the threshold) is preferred for this strategy.
Signal Generation:
The indicator checks if:
Volume has increased significantly (volumeDelta > 0 and volume / volume >= volumeThreshold).
There's enough price change (math.abs(priceDelta / open) >= priceChangeThreshold).
The market isn't too volatile (lowVolatility).
The trend supports the direction of the price change (trendUp for bullish, trendDown for bearish).
If all these conditions are met, it predicts:
1 (Bullish) if conditions suggest buying.
0 (Bearish) if conditions suggest selling.
Cooldown Mechanism:
After a signal, the script waits for a number of bars (cooldownPeriod) before considering another signal to avoid over-trading.
Visual Feedback:
Labels are placed on the chart:
Green label for bullish breakouts below the low price.
Red label for bearish breakouts above the high price.
How to Use:
Entry Points: Look for the labels on your chart to decide when to enter trades.
Risk Management: Since this is for scalping, ensure each trade has tight stop-losses to manage risk due to the quick, small movements.
Market Conditions: This strategy might work best in markets with consistent volume and price changes but not extreme volatility.
Caveats:
This isn't real AI; it's a heuristic based on volume and price. Actual AI would involve machine learning algorithms trained on historical data.
Always backtest any strategy, and consider how it behaves in different market conditions, not just the ones it was designed for.
AI Channels (Clustering) [LuxAlgo]The AI Channels indicator is constructed based on rolling K-means clustering, a common machine learning method used for clustering analysis. These channels allow users to determine the direction of the underlying trends in the price.
We also included an option to display the indicator as a trailing stop from within the settings.
🔶 USAGE
Each channel extremity allows users to determine the current trend direction. Price breaking over the upper extremity suggesting an uptrend, and price breaking below the lower extremity suggesting a downtrend. Using a higher Window Size value will return longer-term indications.
The "Clusters" setting allows users to control how easy it is for the price to break an extremity, with higher values returning extremities further away from the price.
The "Denoise Channels" is enabled by default and allows to see less noisy extremities that are more coherent with the detected trend.
Users who wish to have more focus on a detected trend can display the indicator as a trailing stop.
🔹 Centroid Dispersion Areas
Each extremity is made of one area. The width of each area indicates how spread values within a cluster are around their centroids. A wider area would suggest that prices within a cluster are more spread out around their centroid, as such one could say that it is indicative of the volatility of a cluster.
Wider areas around a specific extremity can indicate a larger and more spread-out amount of prices within the associated cluster. In practice price entering an area has a higher chance to break an associated extremity.
🔶 DETAILS
The indicator performs K-means clustering over the most recent Window Size prices, finding a number of user-specified clusters. See here to find more information on cluster detection.
The channel extremities are returned as the centroid of the lowest, average, and highest price clusters.
K-means clustering can be computationally expensive and as such we allow users to determine the maximum number of iterations used to find the centroids as well as the number of most historical bars to perform the indicator calculation. Do note that increasing the calculation window of the indicator as well as the number of clusters will return slower results.
🔶 SETTINGS
Window Size: Amount of most recent prices to use for the calculation of the indicator.
Clusters": Amount of clusters detected for the calculation of the indicator.
Denoise Channels: When enabled, return less noisy channels extremities, disabling this setting will return the exact centroids at each time but will produce less regular extremities.
As Trailing Stop: Display the indicator as a trailing stop.
🔹 Optimization
This group of settings affects the runtime performance of the script.
Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
Historical Bars Calculation: Calculation window of the script (in bars).
AI Momentum [YinYang]Overview:
AI Momentum is a kernel function based momentum Indicator. It uses Rational Quadratics to help smooth out the Moving Averages, this may give them a more accurate result. This Indicator has 2 main uses, first it displays ‘Zones’ that help you visualize the potential movement areas and when the price is out of bounds (Overvalued or Undervalued). Secondly it creates signals that display the momentum of the current trend.
The Zones are composed of the Highest Highs and Lowest lows turned into a Rational Quadratic over varying lengths. These create our Rational High and Low zones. There is however a second zone. The second zone is composed of the avg of the Inner High and Inner Low zones (yellow line) and the Rational Quadratic of the current Close. This helps to create a second zone that is within the High and Low bounds that may represent momentum changes within these zones. When the Rationalized Close crosses above the High and Low Zone Average it may signify a bullish momentum change and vice versa when it crosses below.
There are 3 different signals created to display momentum:
Bullish and Bearish Momentum. These signals display when there is current bullish or bearish momentum happening within the trend. When the momentum changes there will likely be a lull where there are neither Bullish or Bearish momentum signals. These signals may be useful to help visualize when the momentum has started and stopped for both the bulls and the bears. Bullish Momentum is calculated by checking if the Rational Quadratic Close > Rational Quadratic of the Highest OHLC4 smoothed over a VWMA. The Bearish Momentum is calculated by checking the opposite.
Overly Bullish and Bearish Momentum. These signals occur when the bar has Bullish or Bearish Momentum and also has an Rationalized RSI greater or less than a certain level. Bullish is >= 57 and Bearish is <= 43. There is also the option to ‘Factor Volume’ into these signals. This means, the Overly Bullish and Bearish Signals will only occur when the Rationalized Volume > VWMA Rationalized Volume as well as the previously mentioned factors above. This can be useful for removing ‘clutter’ as volume may dictate when these momentum changes will occur, but it can also remove some of the useful signals and you may miss the swing too if the volume just was low. Overly Bullish and Bearish Momentum may dictate when a momentum change will occur. Remember, they are OVERLY Bullish and Bearish, meaning there is a chance a correction may occur around these signals.
Bull and Bear Crosses. These signals occur when the Rationalized Close crosses the Gaussian Close that is 2 bars back. These signals may show when there is a strong change in momentum, but be careful as more often than not they’re predicting that the momentum may change in the opposite direction.
Tutorial:
As we can see in the example above, generally what happens is we get the regular Bullish or Bearish momentum, followed by the Rationalized Close crossing the Zone average and finally the Overly Bullish or Bearish signals. This is normally the order of operations but isn’t always how it happens as sometimes momentum changes don’t make it that far; also the Rationalized Close and Zone Average don’t follow any of the same math as the Signals which can result in differing appearances. The Bull and Bear Crosses are also quite sporadic in appearance and don’t generally follow any sort of order of operations. However, they may occur as a Predictor between Bullish and Bearish momentum, signifying the beginning of the momentum change.
The Bull and Bear crosses may be a Predictor of momentum change. They generally happen when there is no Bullish or Bearish momentum happening; and this helps to add strength to their prediction. When they occur during momentum (orange circle) there is a less likely chance that it will happen, and may instead signify the exact opposite; it may help predict a large spike in momentum in the direction of the Bullish or Bearish momentum. In the case of the orange circle, there is currently Bearish Momentum and therefore the Bull Cross may help predict a large momentum movement is about to occur in favor of the Bears.
We have disabled signals here to properly display and talk about the zones. As you can see, Rationalizing the Highest Highs and Lowest Lows over 2 different lengths creates inner and outer bounds that help to predict where parabolic movement and momentum may move to. Our Inner and Outer zones are great for seeing potential Support and Resistance locations.
The secondary zone, which can cross over and change from Green to Red is also a very important zone. Let's zoom in and talk about it specifically.
The Middle Zone Crosses may help deduce where parabolic movement and strong momentum changes may occur. Generally what may happen is when the cross occurs, you will see parabolic movement to the High / Low zones. This may be the Inner zone but can sometimes be the outer zone too. The hard part is sometimes it can be a Fakeout, like displayed with the Blue Circle. The Cross doesn’t mean it may move to the opposing side, sometimes it may just be predicting Parabolic movement in a general sense.
When we turn the Momentum Signals back on, we can see where the Fakeout occurred that it not only almost hit the Inner Low Zone but it also exhibited 2 Overly Bearish Signals. Remember, Overly bearish signals mean a momentum change in favor of the Bulls may occur soon and overly Bullish signals mean a momentum change in favor of the Bears may occur soon.
You may be wondering, well what does “may occur soon” mean and how do we tell?
The purpose of the momentum signals is not only to let you know when Momentum has occurred and when it is still prevalent. It also matters A LOT when it has STOPPED!
In this example above, we look at when the Overly Bullish and Bearish Momentum has STOPPED. As you can see, when the Overly Bullish or Bearish Momentum stopped may be a strong predictor of potential momentum change in the opposing direction.
We will conclude our Tutorial here, hopefully this Indicator has been helpful for showing you where momentum is occurring and help predict how far it may move. We have been dabbling with and are planning on releasing a Strategy based on this Indicator shortly.
Settings:
1. Momentum:
Show Signals: Sometimes it can be difficult to visualize the zones with signals enabled.
Factor Volume: Factor Volume only applies to Overly Bullish and Bearish Signals. It's when the Volume is > VWMA Volume over the Smoothing Length.
Zone Inside Length: The Zone Inside is the Inner zone of the High and Low. This is the length used to create it.
Zone Outside Length: The Zone Outside is the Outer zone of the High and Low. This is the length used to create it.
Smoothing length: Smoothing length is the length used to smooth out our Bullish and Bearish signals, along with our Overly Bullish and Overly Bearish Signals.
2. Kernel Settings:
Lookback Window: The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50.
Relative Weighting: Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25.
Start Regression at Bar: Bar index on which to start regression. The first bars of a chart are often highly volatile, and omission of these initial bars often leads to a better overall fit. Recommended range: 5-25.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
AI SwingAI Swing is an indicator that spot overbought over oversold situation.
This indicator is specialized for the forex market (but it can be used on the crypto market)
Please choose the type of indicator you want to use and choose the chart time accordingly :
AI Swing : use the daily chart
AI Intraday : use the 1 hour chart
AI Scalping : use the 15 minute chart
AI Swing est un indicateur qui met en évidence les périodes de sur achat et de sur vente
L'indicateur est spécialisé pour le forex (mais il peut être utilisé sur le marché crypto)
Choisissez le type d'indicateur que vous voulez utiliser et choisissez le temps du graphique approprié :
AI Swing : Utiliser le graphique en journalier
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SuperTrend AI (Clustering) [LuxAlgo]The SuperTrend AI indicator is a novel take on bridging the gap between the K-means clustering machine learning method & technical indicators. In this case, we apply K-Means clustering to the famous SuperTrend indicator.
🔶 USAGE
Users can interpret the SuperTrend AI trailing stop similarly to the regular SuperTrend indicator. Using higher minimum/maximum factors will return longer-term signals.
The displayed performance metrics displayed on each signal allow for a deeper interpretation of the indicator. Whereas higher values could indicate a higher potential for the market to be heading in the direction of the trend when compared to signals with lower values such as 1 or 0 potentially indicating retracements.
In the image above, we can notice more clear examples of the performance metrics on signals indicating trends, however, these performance metrics cannot perform or predict every signal reliably.
We can see in the image above that the trailing stop and its adaptive moving average can also act as support & resistance. Using higher values of the performance memory setting allows users to obtain a longer-term adaptive moving average of the returned trailing stop.
🔶 DETAILS
🔹 K-Means Clustering
When observing data points within a specific space, we can sometimes observe that some are closer to each other, forming groups, or "Clusters". At first sight, identifying those clusters and finding their associated data points can seem easy but doing so mathematically can be more challenging. This is where cluster analysis comes into play, where we seek to group data points into various clusters such that data points within one cluster are closer to each other. This is a common branch of AI/machine learning.
Various methods exist to find clusters within data, with the one used in this script being K-Means Clustering , a simple iterative unsupervised clustering method that finds a user-set amount of clusters.
A naive form of the K-Means algorithm would perform the following steps in order to find K clusters:
(1) Determine the amount (K) of clusters to detect.
(2) Initiate our K centroids (cluster centers) with random values.
(3) Loop over the data points, and determine which is the closest centroid from each data point, then associate that data point with the centroid.
(4) Update centroids by taking the average of the data points associated with a specific centroid.
Repeat steps 3 to 4 until convergence, that is until the centroids no longer change.
To explain how K-Means works graphically let's take the example of a one-dimensional dataset (which is the dimension used in our script) with two apparent clusters:
This is of course a simple scenario, as K will generally be higher, as well the amount of data points. Do note that this method can be very sensitive to the initialization of the centroids, this is why it is generally run multiple times, keeping the run returning the best centroids.
🔹 Adaptive SuperTrend Factor Using K-Means
The proposed indicator rationale is based on the following hypothesis:
Given multiple instances of an indicator using different settings, the optimal setting choice at time t is given by the best-performing instance with setting s(t) .
Performing the calculation of the indicator using the best setting at time t would return an indicator whose characteristics adapt based on its performance. However, what if the setting of the best-performing instance and second best-performing instance of the indicator have a high degree of disparity without a high difference in performance?
Even though this specific case is rare its however not uncommon to see that performance can be similar for a group of specific settings (this could be observed in a parameter optimization heatmap), then filtering out desirable settings to only use the best-performing one can seem too strict. We can as such reformulate our first hypothesis:
Given multiple instances of an indicator using different settings, an optimal setting choice at time t is given by the average of the best-performing instances with settings s(t) .
Finding this group of best-performing instances could be done using the previously described K-Means clustering method, assuming three groups of interest (K = 3) defined as worst performing, average performing, and best performing.
We first obtain an analog of performance P(t, factor) described as:
P(t, factor) = P(t-1, factor) + α * (∆C(t) × S(t-1, factor) - P(t-1, factor))
where 1 > α > 0, which is the performance memory determining the degree to which older inputs affect the current output. C(t) is the closing price, and S(t, factor) is the SuperTrend signal generating function with multiplicative factor factor .
We run this performance function for multiple factor settings and perform K-Means clustering on the multiple obtained performances to obtain the best-performing cluster. We initiate our centroids using quartiles of the obtained performances for faster centroids convergence.
The average of the factors associated with the best-performing cluster is then used to obtain the final factor setting, which is used to compute the final SuperTrend output.
Do note that we give the liberty for the user to get the final factor from the best, average, or worst cluster for experimental purposes.
🔶 SETTINGS
ATR Length: ATR period used for the calculation of the SuperTrends.
Factor Range: Determine the minimum and maximum factor values for the calculation of the SuperTrends.
Step: Increments of the factor range.
Performance Memory: Determine the degree to which older inputs affect the current output, with higher values returning longer-term performance measurements.
From Cluster: Determine which cluster is used to obtain the final factor.
🔹 Optimization
This group of settings affects the runtime performances of the script.
Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
Historical Bars Calculation: Calculation window of the script (in bars).
AI Volume SignalsAI Volume Signals
The AI Volume Signals indicator detects significant volume spikes and combines them with trend direction and candle color to generate buy and sell signals. This script utilizes an Exponential Moving Average (EMA) of volume to detect abnormal volume spikes, which could indicate strong market activity. It also filters signals based on the trend direction determined by a 50-period EMA of the price.
Key Features:
Volume Spike Detection: The indicator detects when the current volume exceeds the EMA of volume by a user-defined multiplier, signaling an unusual increase in market activity.
Trend Direction Filter: The 50-period EMA of the price is used to determine the market trend. Buy signals are generated when the price is above the EMA (uptrend), and sell signals occur when the price is below the EMA (downtrend).
Candle Color Filter: The indicator only generates a buy signal when the current candle is bullish (green), and a sell signal when the current candle is bearish (red).
Optional Volume EMA Line: A customizable option allows users to toggle the visibility of the Volume EMA line on the chart. By default, the line is hidden, but can be enabled in the settings.
Signals:
Buy Signal: Generated when a volume spike occurs, the trend is upward, and the current candle is bullish.
Sell Signal: Generated when a volume spike occurs, the trend is downward, and the current candle is bearish.
Alerts:
Buy Alert: Alerts the user when a buy signal is triggered.
Sell Alert: Alerts the user when a sell signal is triggered.
Visualization:
Buy Signal: A green label appears below the bar when the buy conditions are met.
Sell Signal: A red label appears above the bar when the sell conditions are met.
Volume EMA: A line representing the EMA of the volume is plotted on the chart for reference. The visibility of this line can be toggled in the settings.
This indicator can help traders identify potential entry points based on increased volume activity while considering trend direction and candlestick patterns.
Adaptive AI Predictor (v2) by OberlunarAdaptive AI Predictor by Oberlunar
This script is designed to dynamically adapt to market changes, leveraging a neural network-inspired model to identify reliable trading signals. It analyzes price variations, processes patterns in the market, and provides clear buy and sell signals based on dynamic force calculations.
The script goes beyond simple indicators by incorporating adaptive learning principles. It tracks the success of its signals over time, calculating both the average and median forces behind winning trades. These insights allow the script to continuously refine its performance, ensuring it remains responsive to evolving market conditions.
Clear signals are displayed on the chart, showing the strength of the signal and its median historical success.
Configuration Parameters
Number of Nodes: : This parameter controls the number of nodes through which the data is processed. A higher number of nodes can improve the model’s ability to represent complex dynamics, but may also increase bias and a low capacity of generalization.
Input Scaling: Determines how much the input signal (percentage price change) is amplified before being processed. If the value is too low, the system may not react sufficiently to price changes; if too high, it might become too sensitive to market noise.
Scaling: Controls the strength of interactions between internal nodes. A higher value makes interactions between the neurons (nodes) stronger, but might also lead to instability in the signals.
Leak Rate: This parameter determines how fast information is "forgotten" within the system. A higher value means the model "forgets" previous information more quickly, making it more responsive to recent changes.
Sparsity: Controls the density of connections between internal nodes. A higher value increases the likelihood that a connection between nodes is "active." This affects the system’s ability to model complex dynamics and can also influence computational speed.
Signal Threshold: Sets the limit beyond which the predicted signal is considered significant. A value too low could generate too frequent and noisy signals, while a value too high might reduce useful signals.
History Length: Determines how much historical data is considered for training the system. A higher value uses more historical data but could slow down computations.
Learning Rate: Controls the speed at which the system updates its internal weights. A value too high might cause oscillations in the results, while one too low might slow down the adaptation process.
Exponential Decay Factor: Defines how quickly the weights adapt based on errors. A higher value reduces the impact of older weights, allowing the model to adapt faster to recent changes.
How It Works
Input Signal: The system observes the percentage price change between two consecutive bars (current close vs. previous close).
State Update: The states of the nodes are updated based on the input signal and internal interactions between the neurons. The update is influenced by the leak rate, which determines how fast nodes "forget" previous information.
Weight Training: Weights are trained to minimize the error between the system’s prediction and the observed price change. The system uses exponential regression to update the weights efficiently.
Signal Generation: Buy (BUY) and sell (SELL) signals are generated based on an analysis of the overall values of the nodes' states. If the overall strength (average of the nodes' states) exceeds a certain threshold, a buy signal is generated. If it's lower than a negative threshold, a sell signal is triggered.
Visualization and Signals
Signals on the Chart: Buy and sell signals are displayed on the chart with specific labels, indicating the signal's strength and median successful strength previously adopted . The strength is based on the distance from the threshold. The stronger the signal, the more intense the label color.
Debug Table: A debug table shows details about the input weights, node states, and the success of buy/sell signals, allowing you to monitor the system's behavior in real-time.
Simple Capital Management: The system calculates the position size based on available capital and updates the current capital after each trade. The profit or loss is displayed as a percentage of the initial capital.
How to Use It
Initial Configuration: Customize the configuration parameters based on your trading strategy and style. If you’re a more conservative trader, you might prefer higher thresholds and lower scaling.
Monitor Signals: Follow the buy and sell signals generated on the chart. Each signal is accompanied by its strength (percentage), which will help you decide how aggressively to position.
Very simple Position Management: When a buy signal is emitted, you can open a buy position, and when a sell signal is emitted, you can close the position. The system automatically calculates the profit or loss for each trade.
Adapting to Market Conditions: Adjust the parameters based on market volatility and your risk tolerance. If the market is highly volatile, you might want to increase sensitivity to signals or reduce the number of nodes for faster responsiveness.
With this system, you can leverage dynamic predictive signals based on a combination of historical data and continuous adaptation, improving your trading decisions.
To obtain good results remember to fine-tune by a model reparametrization.
Crypto Index Creator (MEMES & AI Supercycle Dominance, etc)This indicator aims to help to create any INDEX desired including but not limited to its Market Cap and Dominance on the crypto market.
This script was inspired originally by Murad's "Memecoins Dominance" but then I extended it to AI and can be extended to anything in fact, so you can create any index!
I made each token entry editable so that the script can survive the evolution of time as likely projects and INDEXES are going to change a lot, so that you can add/modify your own indices of preference if not listed by default and in order to make it future proof.
You can play with the settings, can compare to BTC, ETC, SOL, etc. for helping in your studies
You also have the option to check the info of each symbol on a table available on the settings, in order to help you figure out if there are any errors and also help you to easily check how the symbols are performing individually
Notes:
- Many projects are not like MEMECOINS that have fixed supply, normally VC projects have a very variable circulating supply, so you might want to update the info of the circulating supply for your projects to make it more accurate if you desire.
- For this script there is a limit of 32 Symbols, due to tradingview own limits, yet you can always "add" multiple projects per line as long as their circulating supply is the same.
- You might want to edit/sort the tickers of the top3, top5 and top10 if they follow bellow those top ranks, but this is not necessary if you don't care about Top 3-10 specific calculations.
- My default "indices" were made of token selections of mine as of November 2024, those defaults indices/tickers I might or might not update them eventually but you are free to adapt/modify the tickers in the settings as history evolves, and you can leave your own indexes on the comment section of this post for others to use
- As you might not be able to create/store multiple different indexes at the same time, you might want to add this indicator multiple times on your screen and then modify the tickers of each instance of this indicator, by that you can have multiple indexes.
AI Strat ATR Dinamico + ADX + Trend Adaptivo (No Repaint)Below is a fully self-contained, English-language description of every input, function, and logical block inside the “AI Strat ATR Dinamico + ADX + Trend Adaptivo (No Repaint)” indicator. You can copy and paste this into TradingView’s “Description” field when you publish, without exposing any Pine code.
---
## Indicator Name and Purpose
**Name (Short Title):**
AI Strat Adaptive v3 (NoRepaint)
**Overview:**
This indicator combines multiple technical tools—RSI, EMA, ATR (with a dynamic multiplier), ADX/DI, and an “AI‐style” scoring mechanism—to generate trend-filtered and reversal signals. It also optionally confirms signals on a higher timeframe, dynamically adjusts its sensitivity based on volatility, and plots intrabar stop‐loss (SL) and take‐profit (TP) levels derived from ATR. Special care has been taken to ensure that no signals “repaint” (i.e., once drawn on a closed bar, they never disappear or shift).
---
## 1. Main Inputs
All of the inputs appear in the Settings dialog for the published indicator. Below is a detailed explanation of each input, grouped by logical category.
### A. RSI & EMA Base Parameters
1. **RSI Length (Base)**
* **Input type:** Integer (default 14)
* **Description:** Number of bars used to calculate the Relative Strength Index (RSI). A shorter RSI reacts more quickly to price changes; a longer RSI is smoother.
2. **RSI Overbought Threshold**
* **Input type:** Integer (default 60)
* **Description:** If the RSI value rises above this level, it contributes a “sell” signal component. You can adjust this (e.g., 70) to make your system more conservative.
3. **RSI Oversold Threshold**
* **Input type:** Integer (default 40)
* **Description:** If the RSI falls below this level, it contributes a “buy” signal component. Raising this threshold (e.g., 50) makes the strategy more aggressive in seeking reversals.
4. **EMA Length (Base)**
* **Input type:** Integer (default 20)
* **Description:** Number of bars for the Exponential Moving Average (EMA). A shorter EMA will produce more frequent crossovers, a longer EMA is smoother.
### B. ATR & Volatility Filter Parameters
5. **ATR Length (Base)**
* **Input type:** Integer (default 14)
* **Description:** Number of bars to calculate Average True Range (ATR). The ATR is used both for measuring volatility and for dynamic SL/TP levels.
6. **ATR SMA Length**
* **Input type:** Integer (default 50)
* **Description:** Number of bars to compute a Simple Moving Average of the ATR itself. This gives a baseline of “normal” volatility. If ATR rises significantly above this SMA, the indicator treats the market as “high volatility.”
7. **ATR Multiplier Base**
* **Input type:** Float (default 1.2, step 0.1)
* **Description:** Base multiplier for ATR when filtering for volatility. The actual threshold is computed as `ATR_SMA × (ATR_Multiplier Base) × sqrt(current_ATR / ATR_SMA)`. In other words, the multiplier becomes larger if volatility is rising, and smaller if volatility is falling.
8. **Disable Volatility Filter**
* **Input type:** Boolean (default false)
* **Description:** If enabled (true), the indicator will ignore any volatility‐based filtering, using signals regardless of ATR behavior. If disabled (false), signals only fire when ATR > (ATR\_SMA × dynamic multiplier).
### C. Price-Change & “AI Score” Parameters
9. **Price Change Period (bars)**
* **Input type:** Integer (default 3)
* **Description:** The number of bars back to measure percentage price change. Used to ensure that a “trend” signal is accompanied by a sufficiently positive (for longs) or negative (for shorts) price movement over this many bars.
10. **Base AI Score Threshold**
* **Input type:** Float (default 0.1)
* **Description:** The indicator computes a composite “AI-style” score by combining the RSI signal (overbought/oversold) and an EMA crossover signal. Only if the absolute value of that composite score exceeds this threshold will a trend signal be eligible. Raising it makes signals rarer but (potentially) higher-conviction.
### D. SMA “ICT” Trend Filter Parameters
11. **ICT SMA Long Length (Base)**
* **Input type:** Integer (default 50)
* **Description:** Number of bars for the “long” Simple Moving Average (SMA) used in the internal trend filter. Typically, price must be above this SMA (and ADX must be strong) to confirm an uptrend, or below it (and ADX strong) to confirm a downtrend.
12. **ICT SMA Short1 Length (Base)**
* **Input type:** Integer (default 10)
* **Description:** Secondary “fast” SMA used both for reversal logic (e.g., price crossing above it can count as a bullish reversal) and part of the internal trend confirmation.
13. **ICT SMA Short2 Length (Base)**
* **Input type:** Integer (default 20)
* **Description:** A second “medium” SMA used for reversal triggers (e.g., crossovers or crossunders alongside RSI conditions).
### E. ADX & DI Parameters
14. **Base ADX Length**
* **Input type:** Integer (default 14)
* **Description:** Number of bars for the ADX (Average Directional Index) moving averages, which measure trend strength. The same length is used for +DI and –DI smoothing.
15. **Base ADX Threshold**
* **Input type:** Float (default 25.0, step 0.5)
* **Description:** If ADX > this threshold and +DI > –DI, we consider an uptrend; if ADX > this threshold and –DI > +DI, we consider a downtrend. Raising this value demands stronger trends to qualify.
### F. Sensitivity & Cooldown
16. **Sensitivity (0–1)**
* **Input type:** Float between 0.0 and 1.0 (default 0.5)
* **Description:** A general “mixture” parameter used internally to weight how aggressively the indicator leans into trend versus reversal. In practice, the code uses it to fine-tune exact thresholds for switching between trend and reversal conditions. You can leave it at 0.5 unless you want to bias more heavily toward either regime.
17. **Base Cooldown Bars Between Signals**
* **Input type:** Integer (default 5, min 0)
* **Description:** Once a long or short signal fires, the indicator will wait at least this many bars before allowing a new signal in the same direction. Prevents “signal flipping” on each bar. A higher number forces fewer, more spaced-out entries.
18. **Trend Confirmation Bars**
* **Input type:** Integer (default 3, min 1)
* **Description:** After the directional filters (+DI/–DI cross, price vs. SMA), the indicator still requires that price remains on the same side of the long SMA for at least this many consecutive bars before confirming “trend up” or “trend down.” Larger values smooth out false breakouts but may lag signals.
### G. Higher Timeframe Confirmation
19. **Use Higher Timeframe Confirmation**
* **Input type:** Boolean (default true)
* **Description:** If true, the indicator will request a block of values (SMA, +DI, –DI, ADX) from a higher timeframe (default 60 minutes) and require that the higher timeframe is also in agreement (strong uptrend or strong downtrend) before confirming your current-timeframe trend. This helps filter out lower-timeframe noise.
20. **Higher Timeframe (TF) for Confirmation**
* **Input type:** Timeframe (default “60”)
* **Description:** The chart timeframe (e.g., 5, 15, 60 minutes) whose trend conditions must also be true. It’s sent through a `request.security(..., lookahead=barmerge.lookahead_off)` call so that it never “paints ahead.”
### H. Dynamic TP/SL Parameters
21. **TP as ATR Multiple**
* **Input type:** Float (default 2.0, step 0.1)
* **Description:** When a trade is open, the “take-profit” price is determined by looking at the highest high (for longs) or lowest low (for shorts) observed since entry, and then plotting a cross (“X”) at that level when the trend finally flips. This is purely for display. However, separate from that, this parameter can be adapted if you want a strictly ATR–based TP. In the “Minimal” version, TP is ≈ (highest high) once trend inverts, but you could rewrite it to use `entry_price + ATR×TP_Multiplier`.
22. **SL as ATR Multiple**
* **Input type:** Float (default 1.0, step 0.1)
* **Description:** While in a trade, a trailing SL line is plotted each bar. Its value is always `entry_price ± (ATR × SL_Multiplier)`. When the trend inverts, the SL no longer updates, and you see it on the chart.
### I. Display and Mode Options
23. **Show Debug Lines**
* **Input type:** Boolean (default true)
* **Description:** When enabled, the indicator will plot all intermediate lines—ATR SMA, ATR Threshold, +DI, –DI, ADX (current and HTF), HTF SMA, etc.—so that you can diagnose exactly what’s happening. Turn this off to hide all debug information and only see entry/exit shapes.
24. **Enable Scalping Mode**
* **Input type:** Boolean (default false)
* **Description:** If true, many of the “base” parameters are halved (e.g., RSI length becomes 7 instead of 14, ATR length becomes 7 instead of 14, ADX length becomes 7, etc.), and the ADX threshold is multiplied by 0.8. This makes all oscillators and moving averages more reactive, suited for very short-term (scalping) setups.
---
## 2. Core Calculation Blocks
Below is a high-level description of each logical block (in code order), translated from Pine into conceptual steps.
### A. Adjust Inputs if “Scalping Mode” Is On
If **Scalping Mode** = true, then:
* `RSI_Length` becomes `max(1, round(Base_RSI_Length / 2))`
* `EMA_Length` becomes `max(1, round(Base_EMA_Length / 2))`
* `ATR_Length` becomes `max(1, round(Base_ATR_Length / 2))`
* `Price_Change_Period` becomes `max(1, round(Base_Price_Change_Period / 2))`
* `SMA_Long_Length`, `SMA_Short1_Length`, and `SMA_Short2_Length` are each halved (minimum 1).
* `ADX_Length` = `max(1, round(Base_ADX_Length / 2))`
* `ADX_Threshold` = `Base_ADX_Threshold × 0.8`
* `Cooldown_Bars` = `max(0, round(Base_Cooldown_Bars / 2))`
Otherwise, all adjusted lengths = their base values.
### B. RSI, EMA & “AI Score” on Current Timeframe
1. **Compute RSI:**
* Uses the (possibly adjusted) `RSI_Length`.
* Denote this as `RSI_Value`.
2. **Compute ATR & Its SMA:**
* `ATR_Value` = `ta.atr(ATR_Length)`.
* `ATR_SMA` = `ta.sma(ATR_Value, ATR_SMA_Length)`.
* Then define `Volatility_Increase` = (`ATR_Value > ATR_SMA`).
* If the volatility has increased, the weighting of RSI vs. EMA changes.
3. **Compute Weights:**
* If `Volatility_Increase == true`, then:
* `RSI_Weight = 0.7`
* `EMA_Weight = 0.3`
* Otherwise:
* `RSI_Weight = 0.3`
* `EMA_Weight = 0.7`
4. **RSI Signal Component (`RSI_Sig`):**
* If `RSI_Value > RSI_Overbought`, then `RSI_Sig = –1`.
* Else if `RSI_Value < RSI_Oversold`, then `RSI_Sig = +1`.
* Otherwise, `RSI_Sig = 0`.
5. **EMA Value & Signal Component (`EMA_Sig`):**
* `EMA_Value` = `ta.ema(close, EMA_Length)`.
* `EMA_Sig = +1` if the current close crosses **above** the EMA; `EMA_Sig = –1` if the current close crosses **below** the EMA; else `0`.
6. **Compute Raw “AI Score”:**
$$
Raw\_AI = (RSI\_Sig \times RSI\_Weight)\;+\;(EMA\_Sig \times EMA\_Weight)
$$
Then,
$$
AI\_Score = \frac{Raw\_AI}{(RSI\_Weight + EMA\_Weight)}
$$
(This normalization ensures the score always ranges between –1 and +1 if both weights sum to 1.)
### C. Dynamic ATR Multiplier & Volatility Filter
1. **Volatility Factor:**
$$
Volatility\_Factor = \frac{ATR\_Value}{ATR\_SMA}
$$
2. **Dynamic ATR Multiplier:**
$$
ATR\_Multiplier = ATR\_Multiplier\_Base \times \sqrt{Volatility\_Factor}
$$
3. **High Volatility Condition (`High_Volatility`):**
* If `Disable_Volatility_Filter == true`, then treat `High_Volatility = true` always.
* Else, `High_Volatility = (ATR_Value > ATR_SMA × ATR_Multiplier)`.
### D. Price Change Percentage
* **Compute Price Change:**
$$
Price\_Change = \frac{(Close - Close )}{Close } \times 100
$$
* This is the percent return from `Price_Change_Period` bars ago to now.
* For a valid long‐trend signal, we require `Price_Change > 0`; for a short trend, `Price_Change < 0`.
### E. Local SMAs for Trend/Reversal Filters
* `SMA_Close_Long` = `ta.sma(close, SMA_Long_Length)`.
* `SMA_Close_Short1` = `ta.sma(close, SMA_Short1_Length)`.
* `SMA_Close_Short2` = `ta.sma(close, SMA_Short2_Length)`.
These three SMAs help define the “local trend” and reversal breakout points:
* **Primary Trend Filter:**
* Price must be above `SMA_Close_Long` for an uptrend filter, or below `SMA_Close_Long` for a downtrend filter.
* **Reversal Filter:**
* A bullish reversal is detected if **(RSI < Oversold AND close crosses above EMA)** OR **(RSI < Oversold AND close crosses above SMA\_Close\_Short1)**.
* A bearish reversal is detected if **(RSI > Overbought AND close crosses below EMA)** OR **(RSI > Overbought AND close crosses below SMA\_Close\_Short1)**.
### F. Manual +DI, –DI & ADX on Current Timeframe
Instead of relying on the built-in `ta.adx`, the script calculates DI and ADX manually. This makes it easier to replicate the exact logic on a higher timeframe via `request.security`. The steps are:
1. **Directional Movement (DM) Components:**
* `Up_Move` = `high – high `
* `Down_Move` = `low – low`
* `Plus_DM` = `Up_Move` if (`Up_Move > Down_Move` AND `Up_Move > 0`), else `0`
* `Minus_DM` = `Down_Move` if (`Down_Move > Up_Move` AND `Down_Move > 0`), else `0`
2. **True Range (TR) Components:**
* `TR1` = `high – low`
* `TR2` = `abs(high – close )`
* `TR3` = `abs(low – close )`
* `True_Range` = `max(TR1, TR2, TR3)`
3. **Smoothed Averages (RMA):**
* `Sm_TR` = `ta.rma(True_Range, ADX_Length)`
* `Sm_Plus` = `ta.rma(Plus_DM, ADX_Length)`
* `Sm_Minus`= `ta.rma(Minus_DM, ADX_Length)`
4. **Compute DI%:**
$$
Plus\_DI = \frac{Sm\_Plus}{Sm\_TR} \times 100,\quad
Minus\_DI = \frac{Sm\_Minus}{Sm\_TR} \times 100
$$
5. **DX and ADX:**
$$
DX = \frac{|Plus\_DI - Minus\_DI|}{Plus\_DI + Minus\_DI} \times 100,\quad
ADX = ta.rma(DX, ADX_Length)
$$
These values are referred to as `(plus_di, minus_di, adx_val)` for the current timeframe.
---
## 3. Higher Timeframe (HTF) Confirmation Function
If **Use Higher Timeframe Confirmation** is enabled, the script calls a single helper (Pine) function `f_htf` with two parameters: the ADX length and the SMA length (both taken from the “base” or “scaled” values). Internally, `f_htf` simply reruns the manual DI/ADX logic (same as above) on the higher timeframe’s bar data, and also includes that timeframe’s closing price and its SMA for trend comparison.
* **Request.Security Call:**
```
= request.security(
syminfo.tickerid,
higher_tf,
f_htf(adx_length, sma_long_len),
lookahead=barmerge.lookahead_off
)
```
* `lookahead=barmerge.lookahead_off` ensures that no HTF value “paints” early; you always see only confirmed HTF bars.
* The returned tuple provides:
1. `ht_close` = HTF closing price
2. `ht_sma` = HTF SMA of length `sma_long_len`
3. `ht_pdi` = HTF +DI percentage
4. `ht_mdi` = HTF –DI percentage
5. `ht_adx` = HTF ADX value
---
## 4. Trend & Reversal Filters (Current & HTF)
### A. Current-Timeframe Trend Filter
1. **Uptrend\_Basic (Current TF)**
$$
(plus\_di > minus\_di)\;\land\;(adx\_val > ADX\_Threshold)\;\land\;(close > SMA\_Close\_Long)
$$
2. **Downtrend\_Basic (Current TF)**
$$
(minus\_di > plus\_di)\;\land\;(adx\_val > ADX\_Threshold)\;\land\;(close < SMA\_Close\_Long)
$$
3. **Trend Confirmation by Bars:**
* `Bars_Since_Below` = number of bars since `close <= SMA_Close_Long`.
* `Bars_Since_Above` = number of bars since `close >= SMA_Close_Long`.
* If `Uptrend_Basic == true` AND `Bars_Since_Below ≥ Trend_Confirmation_Bars` → mark `Uptrend_Confirm = true`.
* If `Downtrend_Basic == true` AND `Bars_Since_Above ≥ Trend_Confirmation_Bars` → mark `Downtrend_Confirm = true`.
### B. Reversal Filters (Current TF)
1. **Bullish Reversal (`Rev_Bullish`):**
* If `(RSI < RSI_Oversold AND close crosses above EMA_Value)` OR
`(RSI < RSI_Oversold AND close crosses above SMA_Close_Short1)`
→ then `Rev_Bullish = true`.
2. **Bearish Reversal (`Rev_Bearish`):**
* If `(RSI > RSI_Overbought AND close crosses below EMA_Value)` OR
`(RSI > RSI_Overbought AND close crosses below SMA_Close_Short1)`
→ then `Rev_Bearish = true`.
### C. Higher-Timeframe Trend Filter (HTF)
1. **HTF Uptrend (`HT_Uptrend`):**
$$
(ht\_pdi > ht\_mdi)\;\land\;(ht\_adx > ADX\_Threshold)\;\land\;(ht\_close > ht\_sma)
$$
2. **HTF Downtrend (`HT_Downtrend`):**
$$
(ht\_mdi > ht\_pdi)\;\land\;(ht\_adx > ADX\_Threshold)\;\land\;(ht\_close < ht\_sma)
$$
3. **Combine Current & HTF:**
* If **Use\_HTF\_Confirmation == true**, then:
* `Uptrend_Confirm := Uptrend_Confirm AND HT_Uptrend`
* `Downtrend_Confirm := Downtrend_Confirm AND HT_Downtrend`
* Otherwise, just use the current timeframe’s `Uptrend_Confirm` and `Downtrend_Confirm`.
4. **Define `CurrentTrend` (Integer):**
* `CurrentTrend = +1` if `Uptrend_Confirm == true`.
* `CurrentTrend = –1` if `Downtrend_Confirm == true`.
* Otherwise, `CurrentTrend = 0`.
5. **Reset “One Trade Per Trend”:**
* There is a persistent variable `LastTradeTrend`.
* Every time `CurrentTrend` flips (i.e., `CurrentTrend != CurrentTrend `), the code sets `LastTradeTrend := 0`.
* That allows one new entry once the detected trend has changed.
---
## 5. One‐Time “Cooldown” Logic
* **`LastSignalBar`**
* A persistent integer (initially undefined).
* After each confirmed long or short entry, `LastSignalBar` is set to the bar index where that signal fired.
* **`Bars_Since_Signal`**
* If `LastSignalBar` is undefined, treat as a very large number (so that initial signals are always allowed).
* Otherwise, `Bars_Since_Signal = bar_index – LastSignalBar`.
* **Cooldown Check:**
* A new long (or short) can only be generated if `(Bars_Since_Signal > Signal_Cooldown)`.
* This prevents multiple signals in rapid succession.
---
## 6. Entry Conditions (No Repaint)
All of the conditions below are calculated “intrabar,” but the script only actually registers a **signal** on **bar close** (`barstate.isconfirmed`) so that signals never repaint.
### A. Trend‐Based “Raw” Conditions
1. **Trend\_Long\_Raw:**
$$
(AI\_Score > AI\_Score\_Threshold)\;\land\;Uptrend\_Confirm\;\land\;High\_Volatility\;\land\;(Price\_Change > 0)
$$
2. **Trend\_Short\_Raw:**
$$
(AI\_Score < -AI\_Score\_Threshold)\;\land\;Downtrend\_Confirm\;\land\;High\_Volatility\;\land\;(Price\_Change < 0)
$$
### B. Reversal “Raw” Conditions
1. **Rev\_Long\_Raw:**
$$
Rev\_Bullish\;\land\;(CurrentTrend \neq +1)
$$
2. **Rev\_Short\_Raw:**
$$
Rev\_Bearish\;\land\;(CurrentTrend \neq -1)
$$
### C. Combine Raw Signals
* `Raw_Long = Trend_Long_Raw OR Rev_Long_Raw`.
* `Raw_Short = Trend_Short_Raw OR Rev_Short_Raw`.
### D. Confirmed Long/Short Signal Flags
On each new bar **close** (`barstate.isconfirmed == true`):
* **Long\_Signal\_Confirmed** can fire if:
1. `Raw_Long == true`
2. `LastTradeTrend != +1` (we haven’t already taken a long in this same trend)
3. `Bars_Since_Signal > Signal_Cooldown`
If all three hold, then on this bar close the code sets:
* `Long_Signal = true`
* `LastTradeTrend := +1`
* `LastSignalBar := bar_index`
Otherwise, `Long_Signal := false` on this bar.
* **Short\_Signal\_Confirmed** works the same way but with `Raw_Short`, `LastTradeTrend != -1`, etc.
If triggered, it sets `Short_Signal = true`, `LastTradeTrend := -1`, and `LastSignalBar := bar_index`. Otherwise `Short_Signal := false`.
* **Important:** If the bar is still forming (`else` branch of `barstate.isconfirmed`), then both `Long_Signal` and `Short_Signal` are forced to `false`. This guarantees that no shape or alert appears until the bar actually closes.
---
## 7. Plotting Entry/Exit Shapes
1. **Trend Long Signal (Triangle Up)**
* Condition: `Long_Signal == true` **AND** `Trend_Long_Raw == true`.
* Appearance: A small, semi-transparent lime green triangle drawn **below** the bar.
2. **Trend Short Signal (Triangle Down)**
* Condition: `Short_Signal == true` **AND** `Trend_Short_Raw == true`.
* Appearance: A small, semi-transparent maroon triangle drawn **above** the bar.
3. **Reversal Long Signal (Circle)**
* Condition: `Long_Signal == true` **AND** `Rev_Long_Raw == true`.
* Appearance: A tiny, more transparent green circle drawn **below** the bar.
4. **Reversal Short Signal (Circle)**
* Condition: `Short_Signal == true` **AND** `Rev_Short_Raw == true`.
* Appearance: A tiny, more transparent red circle drawn **above** the bar.
Since `Long_Signal` and `Short_Signal` only ever become true at bar close, these shapes are never repainted or removed once drawn.
---
## 8. Unified Alert Message
* As soon as a new bar closes with either `Long_Signal` or `Short_Signal == true`, an alert message is sent:
* If `Long_Signal`, then `alert_msg = "action=BUY"`.
* If `Short_Signal`, then `alert_msg = "action=SELL"`.
* If neither, `alert_msg = ""` (no alert).
* The code calls `alert(alert_msg, freq=alert.freq_once_per_bar)` only if `barstate.isconfirmed` and `alert_msg` is non‐empty. This ensures exactly one alert per confirmed bar, no intrabar pops.
---
## 9. Dynamic TP/SL Logic (Minimal Implementation)
Once a long or short position is “open,” the script tracks these variables:
1. **Persistent Flags and Prices** (all persist between bars until reset):
* `InLong` (Boolean)
* `InShort` (Boolean)
* `Long_Max` (Float)
* `Short_Min` (Float)
* `Entry_Price` (Float)
2. **On Bar Close:**
* If `Long_Signal == true` →
* Set `InLong := true`,
* `Entry_Price := close` of that bar,
* `Long_Max := high ` (last bar’s high, so that we’re not using “future” data).
* If `Short_Signal == true` →
* Set `InShort := true`,
* `Entry_Price := close`,
* `Short_Min := low `.
3. **While `InLong == true`:**
* Continuously update `Long_Max = max(Long_Max, current high)` on each bar (intrabar, but finalized each close).
* Compute a dynamic SL:
$$
SL_{Long} = Entry\_Price - (ATR \times SL\_ATR\_Multiplier).
$$
* If **current trend** flips to non-uptrend (`CurrentTrend != +1`), mark `ExitLong = true`.
* Then the routine plots `TP_Long = Long_Max` as a cross (“X”) at that level.
* Set `InLong := false` so that no further changes to `Long_Max` or `Entry_Price` happen on future bars.
4. **While `InShort == true`:**
* Continuously update `Short_Min = min(Short_Min, current low)`.
* Compute a dynamic SL:
$$
SL_{Short} = Entry\_Price + (ATR \times SL\_ATR\_Multiplier).
$$
* If trend flips to non-downtrend (`CurrentTrend != –1`), mark `ExitShort = true`.
* Then the routine plots `TP_Short = Short_Min`.
* Set `InShort := false` to freeze those values.
5. **Plotting TP/SL if “Show Debug” is On:**
* **TP Shapes:**
* When `ExitLong == true`, plot a solid lime “X” at `TP_Long` (highest high).
* When `ExitShort == true`, plot a solid maroon “X” at `TP_Short` (lowest low).
* **SL Lines:**
* If still `InLong`, draw a thin red line at `SL_Long` on each bar.
* If still `InShort`, draw a thin green line at `SL_Short`.
Thus, your charts visually show the highest‐high take-profit cross for longs, the lowest-low take-profit cross for shorts, and a continuously updating trailing SL until the trend flips. Because all of this is triggered on confirmed bars, nothing “jumps around” after the fact.
---
## 10. Debug‐Only Plot Lines (When Enabled)
When **Show Debug Lines** = true, the indicator will also plot:
1. **ATR SMA (Orange):**
* The simple moving average of ATR over `ATR_SMA_Length`.
2. **ATR Threshold (Yellow):**
* `ATR_SMA × ATR_Multiplier` (the dynamically scaled threshold).
3. **+DI & –DI (Current TF):**
* +DI plotted as a green line, –DI plotted as a red line (opacity \~70%).
4. **ADX (Current TF, Blue):**
* A blue line for the present timeframe’s ADX.
5. **ADX Threshold (Gray):**
* A horizontal gray line showing `ADX_Threshold`.
6. **+DI & –DI (HTF, Darker Colors):**
* If HTF confirmation is on, “HTF +DI” is a greener but more transparent line; “HTF –DI” is a redder but more transparent line.
7. **ADX (HTF, Blue but Transparent):**
* HTF ADX plotted in blue (high transparency).
8. **HTF SMA (Orange, Transparent):**
* The higher timeframe’s SMA (same length as `SMA_Long_Length`), drawn in fainter orange.
9. **Volatility Zone Fill (Yellow Tinted Area):**
* Fills the area between `ATR_SMA` and `ATR_SMA × ATR_Multiplier`.
* Indicates “normal” versus “high‐volatility” regimes.
These debug lines are purely visual aids. Disable them if you want a cleaner chart.
---
## 11. Putting It All Together — Step-By-Step Flow
1. **Read Inputs** (RSI lengths, EMA length, ATR settings, etc.).
2. **Optionally Halve All Lengths** if “Scalping Mode” is checked.
3. **Calculate Current TF Indicators:**
* RSI, ATR, ATR\_SMA, EMA, price change, various SMAs, DI/ADX.
4. **Compute “AI Score”** (weighted sum of RSI and EMA signals).
5. **Compute Dynamic ATR Multiplier** and decide if “High Volatility” is true.
6. **Compute Raw Trend/Reversal Conditions** on the current timeframe (without triggering yet).
7. **Fetch HTF Values** in one `request.security` call (SMAs, DI/ADX).
8. **Combine Current & HTF Trend Filters** to confirm `Uptrend_Confirm` or `Downtrend_Confirm`.
9. **Check Reversal Conditions** (price crossing EMA or SMA short, in overbought/oversold zones).
10. **Enforce “One Trade Per Trend”** (clear `LastTradeTrend` whenever `CurrentTrend` flips).
11. **Enforce Cooldown** (must wait at least `Signal_Cooldown` bars since the prior signal).
12. **On Bar Close:**
* If `Raw_Long` AND not already in a long trend AND cooldown met, then fire `Long_Signal`.
* Else if `Raw_Short` AND not already in a short trend AND cooldown met, then fire `Short_Signal`.
* Otherwise, no new signal on this bar.
13. **Plot Long/Short Entry Shapes** according to whether it was a Trend signal or a Reversal signal.
14. **Send Alert** (“action=BUY” or “action=SELL”) exactly once per confirmed bar.
15. **If New Long/Short Signal, Set `InLong`/`InShort`, Record Entry Price, Initialize `Long_Max`/`Short_Min`.**
16. **While `InLong` is true:** Update `Long_Max = max(previous Long_Max, current high)`. Compute `SL_Long`. If the current trend flips (no longer uptrend), set `ExitLong = true`, plot a “TP X,” and close the position logic.
17. **While `InShort` is true:** Similarly update `Short_Min`, compute `SL_Short`, and if trend flips, set `ExitShort = true`, plot a “TP X,” and close the position logic.
18. **Optionally Display Debug Lines** (ATR SMA, ATR threshold, DI/ADX, HTF DI/ADX, etc.).
---
## 12. How to Use in TradingView Community
When you publish this indicator to the TradingView community—choosing “Protected” or “Invite-only” visibility—you can paste the above description into the “Description” field. Users will see exactly what each input does, how signals are generated, and what the various plotted lines represent, **without ever seeing the script source**. In this way, the code itself remains hidden but the logic is fully documented.
1. **Go to “Create New Indicator”** on TradingView.
2. **Paste Your Pine Code** (the full indicator script) in the Pine editor and save it.
3. **Set Visibility = Protected** (or Invite-only).
4. **In the “Description” Text Box, paste the entirety of this document** (steps 1–11).
5. **Click “Publish Script.”**
Users who view your indicator will see its name (“AI Strat Adaptive v3 (NoRepaint)”), a list of all inputs (with default values), and the detailed English description above. They can then load it on any chart, adjust inputs, and see the plotted signals, TP/SL lines, and optional debug overlays—without accessing the underlying Pine code.
---
### Summary of Key Points
* **RSI, EMA, ATR, DI/ADX, and “AI Score”** work together to define “trend vs. reversal.”
* **Dynamic volatility filter** uses ATR and ATR\_SMA to adapt the weighting of RSI vs. EMA and decide whether “volatility is high enough” to permit a trend trade.
* **One trade per detected trend** and a **cooldown period** prevent over‐trading.
* **Higher timeframe confirmation** (optional) further filters out noise.
* **No-repaint logic**:
* All signals only appear at bar close (`barstate.isconfirmed`).
* HTF values are fetched with `lookahead=barmerge.lookahead_off`.
* **Entry shapes** (triangles and circles) clearly mark trend vs. reversal entries.
* **Dynamic TP/SL**: highest‐high (or lowest‐low) since entry is used as TP, ATR×multiplier as SL.
* **Debug mode** (optional) shows every intermediate line for full transparency.
Use this description verbatim (or adapt it slightly for your personal style) when publishing. That way, your community sees exactly how each component works—inputs, functions, filters—while the Pine source code remains private.
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.
AI Moving Average (Expo)█ Overview
The AI Moving Average indicator is a trading tool that uses an AI-based K-nearest neighbors (KNN) algorithm to analyze and interpret patterns in price data. It combines the logic of a traditional moving average with artificial intelligence, creating an adaptive and robust indicator that can identify strong trends and key market levels.
█ How It Works
The algorithm collects data points and applies a KNN-weighted approach to classify price movement as either bullish or bearish. For each data point, the algorithm checks if the price is above or below the calculated moving average. If the price is above the moving average, it's labeled as bullish (1), and if it's below, it's labeled as bearish (0). The K-Nearest Neighbors (KNN) is an instance-based learning algorithm used in classification and regression tasks. It works on a principle of voting, where a new data point is classified based on the majority label of its 'k' nearest neighbors.
The algorithm's use of a KNN-weighted approach adds a layer of intelligence to the traditional moving average analysis. By considering not just the price relative to a moving average but also taking into account the relationships and similarities between different data points, it offers a nuanced and robust classification of price movements.
This combination of data collection, labeling, and KNN-weighted classification turns the AI Moving Average (Expo) Indicator into a dynamic tool that can adapt to changing market conditions, making it suitable for various trading strategies and market environments.
█ How to Use
Dynamic Trend Recognition
The color-coded moving average line helps traders quickly identify market trends. Green represents bullish, red for bearish, and blue for neutrality.
Trend Strength
By adjusting certain settings within the AI Moving Average (Expo) Indicator, such as using a higher 'k' value and increasing the number of data points, traders can gain real-time insights into strong trends. A higher 'k' value makes the prediction model more resilient to noise, emphasizing pronounced trends, while more data points provide a comprehensive view of the market direction. Together, these adjustments enable the indicator to display only robust trends on the chart, allowing traders to focus exclusively on significant market movements and strong trends.
Key SR Levels
Traders can utilize the indicator to identify key support and resistance levels that are derived from the prevailing trend movement. The derived support and resistance levels are not just based on historical data but are dynamically adjusted with the current trend, making them highly responsive to market changes.
█ Settings
k (Neighbors): Number of neighbors in the KNN algorithm. Increasing 'k' makes predictions more resilient to noise but may decrease sensitivity to local variations.
n (DataPoints): Number of data points considered in AI analysis. This affects how the AI interprets patterns in the price data.
maType (Select MA): Type of moving average applied. Options allow for different smoothing techniques to emphasize or dampen aspects of price movement.
length: Length of the moving average. A greater length creates a smoother curve but might lag recent price changes.
dataToClassify: Source data for classifying price as bullish or bearish. It can be adjusted to consider different aspects of price information
dataForMovingAverage: Source data for calculating the moving average. Different selections may emphasize different aspects of price movement.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
AI Smart Liquidity Signal 🚀🚀 AI Smart Liquidity Signal
🔐 إشارات تداول ذكية مبنية على اختراقات السيولة الحقيقية + فلاتر دقيقة تؤكد الإشارات بفعالية.
✅ فلترة الاتجاه العام + الاتجاه الذكي
✅ تأكيد عبر RSI و MACD
✅ مناطق دعم ومقاومة ديناميكية ومتعددة الفريمات
✅ أهداف Take Profit و Stop Loss محسوبة تلقائيًا عبر ATR
⛔️ بدون إعادة رسم – إشارات موثوقة ومجربة
🔎 نسبة دقة تفوق 95% على البيانات التاريخية
💬 للحصول على صلاحية الاستخدام أو الدعم:
@AISmartliquiditySignalBot
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🚀 AI Smart Liquidity Signal
🔐 Real-time smart signals based on true liquidity breakouts with advanced filters.
✅ General + Smart Trend filters
✅ RSI & MACD confirmation
✅ Dynamic and Multi-Timeframe Support/Resistance
✅ Auto-calculated Take Profit & Stop Loss via ATR
⛔️ No repaint – trusted & verified signals
🔎 Historical accuracy above 95%
💬 For access or support:
@AISmartliquiditySignalBot
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📌 ملاحظة / Disclaimer:
هذا المؤشر مخصص لأغراض تعليمية ولا يُعد نصيحة مالية. التداول على مسؤوليتك الخاصة.
This script is for educational purposes only and does not constitute financial advice. Trade at your own risk.
AI Trend Momentum SniperThe AI Trend Momentum Sniper is a powerful technical analysis tool designed for day trading. This strategy combines multiple momentum and trend indicators to identify high-probability entry and exit points. The indicator utilizes a combination of Supertrend, MACD, RSI, ATR (Average True Range), and On-Balance Volume (OBV) to generate real-time signals for buy and sell opportunities.
Key Features:
Supertrend for detecting market direction (bullish or bearish).
MACD for momentum confirmation, highlighting changes in market momentum.
RSI to filter out overbought/oversold conditions and ensure high-quality trades.
ATR as a volatility filter to adjust for changing market conditions.
OBV (On-Balance Volume) to confirm volume strength and trend validity.
Dynamic Stop-Loss & Take-Profit based on ATR to manage risk and lock profits.
This indicator is tailored for intraday traders looking for quick market moves, especially in volatile and high liquidity assets like Bitcoin (BTC) and Ethereum (ETH). It helps traders capture short-term trends with efficient risk management tools.
How to Apply:
Set Your Chart: Apply the AI Trend Momentum Sniper to a 5-minute (M5) or 15-minute (M15) chart for optimal performance.
Buy Signal: When the indicator generates a green arrow below the bar, it indicates a buy signal based on positive trend and momentum alignment.
Sell Signal: A red arrow above the bar signals a sell condition when the trend and momentum shift bearish.
Stop-Loss and Take-Profit: The indicator automatically calculates dynamic stop-loss and take-profit levels based on the ATR value for each trade, ensuring proper risk management.
Alerts: Set up custom alerts for buy or sell signals, and get notified instantly when opportunities arise.
Best Markets for Use:
BTC/USDT, ETH/USDT – High liquidity and volatility.
Major altcoins with sufficient volume.
Avoid using it on low-liquidity assets where price action may become erratic.
Timeframes:
This indicator is best suited for lower timeframes (5-minute to 15-minute charts) to capture quick price movements in trending markets.
AI InfinityAI Infinity – Multidimensional Market Analysis
Overview
The AI Infinity indicator combines multiple analysis tools into a single solution. Alongside dynamic candle coloring based on MACD and Stochastic signals, it features Alligator lines, several RSI lines (including glow effects), and optionally enabled EMAs (20/50, 100, and 200). Every module is individually configurable, allowing traders to tailor the indicator to their personal style and strategy.
Important Note (Disclaimer)
This indicator is provided for educational and informational purposes only.
It does not constitute financial or investment advice and offers no guarantee of profit.
Each trader is responsible for their own trading decisions.
Past performance does not guarantee future results.
Please review the settings thoroughly and adjust them to your personal risk profile; consider supplementary analyses or professional guidance where appropriate.
Functionality & Components
1. Candle Coloring (MACD & Stochastic)
Objective: Provide an immediate visual snapshot of the market’s condition.
Details:
MACD Signal: Used to identify bullish and bearish momentum.
Stochastic: Detects overbought and oversold zones.
Color Modes: Offers both a simple (two-color) mode and a gradient mode.
2. Alligator Lines
Objective: Assist with trend analysis and determining the market’s current phase.
Details:
Dynamic SMMA Lines (Jaw, Teeth, Lips) that adjust based on volatility and market conditions.
Multiple Lengths: Each element uses a separate smoothing period (13, 8, 5).
Transparency: You can show or hide each line independently.
3. RSI Lines & Glow Effects
Objective: Display the RSI values directly on the price chart so critical levels (e.g., 20, 50, 80) remain visible at a glance.
Details:
RSI Scaling: The RSI is plotted in the chart window, eliminating the need to switch panels.
Dynamic Transparency: A pulse effect indicates when the RSI is near critical thresholds.
Glow Mode: Choose between “Direct Glow” or “Dynamic Transparency” (based on ATR distance).
Custom RSI Length: Freely adjustable (default is 14).
4. Optional EMAs (20/50, 100, 200)
Objective: Utilize moving averages for trend assessment and identifying potential support/resistance areas.
Details:
20/50 EMA: Select which one to display via a dropdown menu.
100 EMA & 200 EMA: Independently enabled.
Color Logic: Automatically green (price > EMA) or red (price < EMA). Each EMA’s up/down color is customizable.
Configuration Options
Candle Coloring:
Choose between Gradient or Simple mode.
Adjust the color scheme for bullish/bearish candles.
Transparency is dynamically based on candle body size and Stochastic state.
Alligator Lines:
Toggle each line (Jaw/Teeth/Lips) on or off.
Select individual colors for each line.
RSI Section:
RSI Length can be set as desired.
RSI lines (0, 20, 50, 80, 100) with user-defined colors and transparency (pulse effect).
Additional lines (e.g., RSI 40/60) are also available.
Glow Effects:
Switch between “Dynamic Transparency” (ATR-based) and “Direct Glow”.
Independently applied to the RSI 100 and RSI 0 lines.
EMAs (20/50, 100, 200):
Activate each one as needed.
Each EMA’s up/down color can be customized.
Example Use Cases
Trend Identification:
Enable Alligator lines to gauge general trend direction through SMMA signals.
Timing:
Watch the Candle Colors to spot potential overbought or oversold conditions.
Fine-Tuning:
Utilize the RSI lines to closely monitor important thresholds (50 as a trend barometer, 80/20 as possible reversal zones).
Filtering:
Enable a 50 EMA to quickly see if the market is trading above (bullish) or below (bearish) it.
AI Crypto Signals BTCUSD 15m Ultimate ScriptBYBIT:BTCUSD
Hello everyone! Sky First Capital in partnership with AI Crypto Signals is proud to introduce the AI Crypto Signals 15M BTCUSD Ultimate Script . This script works well on the 15M, 30M, 45M and 1HR chart using traditional candles. This means no false data or inaccurate entry/exit points such as with the ones using HA candles.
The script is based upon an initial strategy developed by user Bunghole here on TradingView, but we have optimized it, back-tested it with ideal settings, and added alerts that you can use to connect with your trading bot such as Alertatron, Cornix, etc. This script uses BB (Bollinger Bands) and RSI (Relative Strength Index) as indicators for signals.
Back-testing data for the 15M chart from 7/1/2021 to 10/15/2021 showed a 51.19% profit.
Back-testing data for the 1HR chart from 7/1/2021 to 10/15/2021 showed a 191% profit.
This script does not repaint.
Ideal use is to enter and exit at the close of the candle and take-profit/stop-loss once per candle.
This script has Entry/Exit/Take-Profit/Stop-Loss alerts.
We offer consulting and training services if you need help on using this script or getting it configured with an automated trading system.
We offer a 24 hour free trial of the script, send us a message to request access.
AI-Powered Breakout with Advanced FeaturesDescription
This script is designed to detect breakout moments in financial markets using a combination of traditional breakout detection methods and adaptive moving averages. By leveraging elements of artificial intelligence, the script provides a more dynamic and responsive approach to identifying potential entry and exit points in trading.
Usefulness
This script stands out by integrating a traditional breakout finder with an adaptive moving average component. The adaptive moving average adjusts dynamically based on the differences between fast and slow exponential moving averages (EMAs), offering a more flexible and responsive detection of support and resistance levels. This combination aims to reduce false signals and enhance the reliability of breakout detections, making it a valuable tool for traders seeking to capture market movements more effectively.
Features
1. Breakout Detection: Utilizes pivot highs and lows to identify significant breakout points over a user-defined period. This method helps in capturing the essential support and resistance levels that are critical in breakout trading.
2. AI Machine Learning Component - Adaptive Moving Average: Implements an adaptive moving average using two exponential moving averages (EMAs). adaptiveMA is dynamically adjusted based on the difference between a fast average and a slow average.
3. Buy/Sell Signals: The script generates buy and sell signals when bullish and bearish breakouts occur, respectively. These signals are visually represented on the chart, helping traders to quickly identify potential trading opportunities.
4. Visualization: Draws horizontal lines at identified breakout levels and plots shapes (arrows) on the chart to indicate buy/sell signals. This makes it easy for traders to see where significant breakout points are and where to consider entering or exiting trades.
Underlying Concepts
1. Breakout Finder Logic: The script uses pivot points (highs and lows) to detect breakout levels. It stores these pivot points in arrays and monitors them for persistence, ensuring that the detected breakouts are significant and reliable.
2. Adaptive Moving Average (AMA): The AMA is a key component that enhances the script's responsiveness. By calculating the differences between fast and slow EMAs, the AMA adapts to changing market conditions, providing a more accurate measure of trends and potential reversals.
How to Use
• Adjustable Parameters: The script includes several user-adjustable parameters:
o Lookback Length: Defines the period over which the script calculates the highest high and lowest low for breakout detection.
o Multiplier for Adaptive MA: Adjusts the sensitivity of the adaptive moving average.
o Period for Pivots: Sets the period for detecting pivot highs and lows.
o Max Breakout Length: Specifies the maximum length for breakout consideration.
o Threshold Rate: Determines the threshold rate for breakout validation.
o Minimum Number of Tests: Sets the minimum number of tests required to validate a breakout.
o Colors and Line Style: Customize the colors and line styles for breakout levels.
Interpreting Signals
o Green Arrows: Indicate a bullish breakout signal, suggesting a potential buy opportunity.
o Red Arrows: Indicate a bearish breakout signal, suggesting a potential sell opportunity.
o Horizontal Lines: Show the breakout levels, helping to visualize support and resistance areas.
By combining traditional breakout detection with advanced adaptive moving averages, this script aims to provide traders with a robust tool for identifying and capitalizing on market breakouts.
Credits
Parts of this script were inspired and adapted from the "Breakout Finder" script by LonesomeTheBlue. Significant improvements include the integration of the adaptive moving average component and enhancements to the breakout detection logic.
AI-Based Indicator V.1.01This is a Strategy based on Artificial Intelligence (AI) algorithms which can be used as a decision support system. In this version I use Heikin Ashi chart and reduce input parameters.
How to use:
1- Select the Heikin Ashi chart.
2- The default values of T for BTCUSD in "30m chart" is 0.12. It can be changed to achieve the best performance for BTCUSD or other tickers in arbitrary time frames.
3. When the background is green buy, and when the background is red sell.
AI-Based Indicator V.1This is an indicator based on Artificial Intelligence (AI) algorithms which can be used (alone or along with other indicators) as a decision support system.
How to use:
1- The default values of Input 1, Input 2, R, and T for BTCUSD are “Close”, “Close”, 4320, and 0.15 respectively. They can be changed to achieve the best performance for BTCUSD or other tickers.
2- Use one of the time frames 4H to 15m.
3. When the background is green buy, and when the background is red sell.
PowerHouse SwiftEdge AI v2.10 with Custom Filters & AI AnalysisPowerHouse SwiftEdge AI v2.10 with Custom Filters & AI Analysis
Overview
PowerHouse SwiftEdge AI v2.10 is an advanced TradingView Pine Script indicator designed to identify high-probability trading setups by combining pivot-based structure analysis, multi-timeframe trend detection, and adaptive AI-driven signal filtering. The script integrates Change of Character (CHoCH) and Break of Structure (BOS) signals with customizable momentum, volume, breakout, and trend filters to enhance trade precision. Additionally, it offers an optional AI Market Analysis module that predicts future price trends across multiple timeframes, providing traders with a comprehensive market outlook.
The script is highly customizable, allowing users to tailor inputs to their trading style, whether for scalping, swing trading, or long-term strategies. It is suitable for all asset classes, including stocks, forex, crypto, and commodities, and performs optimally on timeframes ranging from 1-minute to daily charts.
Key Features
Pivot-Based Signal Generation:
Identifies pivot highs and lows to detect CHoCH (reversal patterns) and BOS (continuation patterns).
Signals are plotted as "Buy" or "Sell" labels with optional "Get Ready" pre-signals to prepare traders for potential setups.
Take-profit (TP) levels are automatically calculated based on user-defined points, with optional TP box visualization.
Multi-Timeframe Trend Analysis:
Analyzes trends across seven timeframes (1M, 5M, 15M, 30M, 1H, 4H, D) using EMA and VWAP to determine bullish, bearish, or neutral conditions.
Displays a futuristic AI-Trend Matrix dashboard showing trend direction, strength, and confidence levels for quick decision-making.
Customizable Signal Filters:
Momentum Filter: Ensures signals align with significant price changes, adjusted dynamically using ATR-based volatility.
Higher Timeframe Trend Filter: Requires signals to align with the trend of a user-selected higher timeframe (e.g., 1H).
Lower Timeframe Trend Filter: Prevents signals that conflict with the trend of a user-selected lower timeframe (e.g., 5M).
Volume Filter: Optionally requires above-average volume to confirm signals.
Breakout Filter: Optionally requires price to break previous highs/lows for signal validation.
Repeated Signal Restriction: Prevents consecutive signals in the same trend direction until the trend changes on a user-defined timeframe.
AI-Driven Adaptivity:
Incorporates Cumulative Volume Delta (CVD) to assess buying/selling pressure and classify market volatility (Low, Medium, High).
Uses ATR to dynamically adjust momentum thresholds, ensuring signals adapt to current market conditions.
Optional AI Market Analysis module predicts trends across multiple timeframes by combining trend, momentum, and volatility scores.
Visual Elements:
Plots CHoCH and BOS levels as horizontal lines with distinct colors (aqua for CHoCH sell, lime for CHoCH buy, fuchsia for BOS sell, teal for BOS buy).
Draws dynamic support and resistance trendlines based on short and long-term price action, colored by trend strength.
Displays TP levels and pivot highs/lows for easy reference.
How It Works
The script combines several technical analysis concepts to create a robust trading system:
Market Structure Analysis:
Pivot highs and lows are identified using a user-defined lookback period (Pivot Length).
CHoCH occurs when price crosses below a pivot high (bearish reversal) or above a pivot low (bullish reversal).
BOS occurs when price breaks a previous pivot low (bearish continuation) or pivot high (bullish continuation).
Trend and Momentum Integration:
Trends are determined by comparing price to EMA and VWAP on multiple timeframes.
Momentum is calculated as the percentage price change, with thresholds adjusted by ATR to account for volatility.
"Get Ready" signals appear when momentum approaches the threshold, preparing traders for potential CHoCH or BOS signals.
Signal Filtering:
Filters ensure signals align with user-defined criteria (e.g., trend direction, volume, breakouts).
The Restrict Repeated Signals option prevents over-signaling by requiring a trend change on a specified timeframe before generating a new signal in the same direction.
AI Market Analysis:
The optional AI module calculates a score for each timeframe based on trend direction, momentum, and volatility (ATR compared to its SMA).
Scores are translated into predictions (▲ for bullish, ▼ for bearish, — for neutral), displayed in a dedicated table.
CVD and Volatility Context:
CVD tracks buying vs. selling pressure by accumulating volume based on price direction.
Volatility is classified using CVD magnitude, influencing the script’s visual cues and signal sensitivity.
Why This Combination?
The integration of pivot-based structure analysis, multi-timeframe trend filtering, and AI-driven adaptivity addresses common trading challenges:
Precision: CHoCH and BOS signals focus on key market turning points, reducing noise from minor price fluctuations.
Context: Multi-timeframe analysis ensures trades align with broader market trends, improving win rates.
Adaptivity: ATR and CVD adjustments make the script responsive to changing market conditions, avoiding static thresholds that fail in volatile or quiet markets.
Customization: Extensive input options allow traders to adapt the script to their preferred markets, timeframes, and risk profiles.
Predictive Insight: The AI Market Analysis module provides forward-looking trend predictions, helping traders anticipate market moves.
This combination creates a self-contained system that balances responsiveness with reliability, making it suitable for both novice and experienced traders.
How to Use
Add to Chart:
Apply the indicator to your TradingView chart for any asset and timeframe.
Recommended timeframes: 5M to 1H for scalping/day trading, 4H to D for swing trading.
Configure Inputs:
Pivot Length: Adjust (default 5) to control sensitivity to pivot highs/lows. Lower values for faster signals, higher for stronger confirmations.
Momentum Threshold: Set the minimum price change (default 0.01%) for signals. Increase for stricter conditions.
Take Profit Points: Define TP distance (default 10 points). Adjust based on asset volatility.
Signal Filters: Enable/disable filters (momentum, trend, volume, breakout) to match your strategy.
Higher/Lower Timeframe: Select timeframes for trend alignment (e.g., 1H for higher, 5M for lower).
AI Market Analysis: Enable for predictive trend insights across timeframes.
Get Ready Signals: Enable to see pre-signals for potential setups.
Interpret Signals:
Buy/Sell Labels: Act on green "Buy" or red "Sell" labels, confirming with TP levels and trend direction.
Get Ready Labels: Yellow "Get Ready BUY" or orange "Get Ready SELL" indicate potential setups; prepare but wait for confirmation.
CHoCH/BOS Lines: Use aqua/lime (CHoCH) and fuchsia/teal (BOS) lines as key support/resistance levels.
AI-Trend Matrix: Check the top-right dashboard for trend strength (%), confidence (%), and timeframe-specific trends.
AI Market Analysis Table: If enabled, view predictions (▲/▼/—) for each timeframe to anticipate market direction.
Trading Tips:
Combine signals with other indicators (e.g., RSI, MACD) for additional confirmation.
Use higher timeframe trend alignment for higher-probability trades.
Adjust TP and signal distance based on asset volatility and trading style.
Monitor the AI-Trend Matrix for trend strength; values above 50% or below -50% indicate strong directional bias.
Originality
PowerHouse SwiftEdge AI v2.10 stands out due to its unique blend of:
Adaptive Signal Generation: ATR-based momentum thresholds and CVD-driven volatility context ensure signals remain relevant across market conditions.
Multi-Timeframe Synergy: The script’s ability to filter signals based on both higher and lower timeframe trends provides a rare balance of precision and context.
AI-Powered Insights: The AI Market Analysis module offers predictive capabilities not commonly found in traditional indicators, simulating institutional-grade analysis.
Visual Clarity: The futuristic dashboard and color-coded trendlines make complex data accessible, enhancing usability for all trader levels.
Unlike standalone pivot or trend indicators, this script integrates multiple layers of analysis into a cohesive system, reducing false signals and providing actionable insights without requiring external tools or research.
Limitations
False Signals: No indicator is foolproof; signals may fail in choppy or low-volume markets. Use filters to mitigate.
Timeframe Sensitivity: Performance varies by timeframe and asset. Test settings thoroughly.
AI Predictions: The AI Market Analysis is based on historical data and simplified scoring; it’s not a guaranteed forecast.
Resource Usage: Enabling all filters and AI analysis may slow performance on lower-end devices.
