RSI Forecast [Titans_Invest]RSI Forecast
Introducing one of the most impressive RSI indicators ever created – arguably the best on TradingView, and potentially the best in the world.
RSI Forecast is a visionary evolution of the classic RSI, merging powerful customization with groundbreaking predictive capabilities. While preserving the core principles of traditional RSI, it takes analysis to the next level by allowing users to anticipate potential future RSI movements.
Real-Time RSI Forecasting:
For the first time ever, an RSI indicator integrates linear regression using the least squares method to accurately forecast the future behavior of the RSI. This innovation empowers traders to stay one step ahead of the market with forward-looking insight.
Highly Customizable:
Easily adapt the indicator to your personal trading style. Fine-tune a variety of parameters to generate signals perfectly aligned with your strategy.
Innovative, Unique, and Powerful:
This is the world’s first RSI Forecast to apply this predictive approach using least squares linear regression. A truly elite-level tool designed for traders who want a real edge in the market.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an RSI time series like this:
Time →
RSI →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public RSI with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
RSI Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🥇 This is the world’s first RSI indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________
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🔮 Linear Regression: PineScript Technical Parameters 🔮
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Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
• Overbought: When the RSI is above 70, indicating that the asset may be overbought.
• Oversold: When the RSI is below 30, indicating that the asset may be oversold.
• Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
📈 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
📉 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : RSI Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Komut dosyalarını "东方财富的AI短线宝坑爹" için ara
GranDoc - Week, Day, Month, and Session Separator5Indicator Name: GranDoc's - Week, Day, Month, and Session Separator
Version: Pine Script v5
Author: Jonpaul Nnamdi Opara (GranDoc )
Description
The "GranDoc - Week, Day, Month, and Session Separator" is a highly customizable TradingView indicator designed to enhance chart analysis by visually marking critical time-based transitions. Developed by Jonpaul Nnamdi Opara, this tool plots vertical lines with labels or background highlights to denote the start and end of weeks, days, months, and major trading sessions (Frankfurt, London, NY Morning, NY Afternoon, Sydney, and Tokyo). Traders can tailor colors, line styles, widths, transparency, and session times to align with their strategies and timezones.
Ideal for forex, stocks, futures, and crypto traders, this indicator simplifies the identification of key market periods—such as session openings/closings or new weeks—that often signal increased volatility or trend shifts. It’s optimized for intraday timeframes for session separators but supports all timeframes for week, day, and month markers, making it a versatile addition to any trader’s toolkit.
Features
Week Separators: Marks Monday starts with customizable lines and "Week Start" labels.
Day Separators: Highlights daily openings with lines and "Day Start" labels.
Month Separators: Indicates new months with lines and "Month Start" labels.
Session Separators: Plots lines and labels for major trading sessions’ start and end:
Frankfurt (default: 07:00–15:00 UTC)
London (default: 08:00–16:00 UTC)
NY Morning (default: 13:00–16:00 UTC)
NY Afternoon (default: 16:00–21:00 UTC)
Sydney (default: 22:00–06:00 UTC)
Tokyo (default: 00:00–08:00 UTC)
Timezone Support: Adjusts session times with a UTC offset (±12 hours).
Display Flexibility : Toggle between labeled vertical lines or background highlights.
Customization: Fine-tune colors, line styles (solid, dashed, dotted), widths, and transparency.
Background Mode: Highlights periods with translucent backgrounds for cleaner charts.
[ i]Labeled Lines: Each line includes descriptive labels (e.g., "London Open", "Tokyo Closed") when not in background mode.
How to Use
Add to Chart:
Copy the script into TradingView’s Pine Editor.
Click "Add to Chart" to apply the indicator.
Customize Settings:
Open settings via double-click or the "Settings" gear icon.
Timezone Offset: Set your UTC offset (e.g., -5 for EST) to align sessions.
Toggles: Enable/disable week, day, month, or session separators.
Appearance: Adjust colors, line styles, widths, and transparency for each separator.
Session Times: Modify start/end hours and minutes if defaults don’t suit your market.
Background Mode: Enable "Show as Background" for colored backgrounds instead of lines, and tweak "Session Background Transparency."
Labels: Labeled lines (e.g., "Sydney Open") appear automatically unless background mode is active.
Chart Compatibility:
Session separators require intraday timeframes (e.g., 1-minute to 4-hour).
Week, day, and month separators work across all timeframes.
Confirm your chart’s timezone aligns with your analysis.
Analyze:
Use separators to pinpoint session transitions, daily openings, or weekly shifts for trade planning.
Labels make it easy to spot key periods on busy charts.
Pair with indicators like RSI, volume, or support/resistance for deeper insights.
Example Use Cases
Forex Trading: Highlight London and NY session opens/closes for high-liquidity entries.
Day Trading: Reset strategies at daily separators and monitor intraday volatility.
Swing Trading: Use week/month separators to track longer-term trends.
Session Focus: Isolate sessions like Tokyo for regional market analysis.
Chart Clarity: Background mode declutters charts while marking key times.
Notes
Session separators are disabled on daily+ timeframes to prevent clutter.
Verify timezone offset for accurate session alignment.
Background mode suits lower timeframes for readability.
Labels are visible only when background mode is disabled.
Feedback
Share your thoughts or suggestions to make this indicator even better! Reach out via TradingView or connect with the author for insights. Happy trading!
About the Author
Dr. Jonpaul Nnamdi Opara, a PhD graduate from Ehime University, Japan, is a researcher and developer specializing in AI and machine learning. His work on automated landslide mapping and defect detection, published in journals like GEOMATE, showcases his precision-driven approach. With the "GranDoc" indicator, Jonpaul brings intuitive, data-driven clarity to financial markets, reflecting his expertise in creating impactful tools.
Tetris with Auto-PlayThis indicator is implemented in Pine Script™ v6 and serves as a demonstration of TradingView's capabilities. The core concept is to simulate a classic Tetris game by creating a grid-based environment and managing game state entirely within Pine Script.
Key Technical Aspects:
Grid Representation:
The script defines a custom grid structure using a user-defined type that holds the grid’s dimensions and a one-dimensional array to simulate a two-dimensional board. This structure is used to track occupied cells, clear full rows, and determine stack height.
Piece Management:
A second custom type is used to represent the state of a tetromino piece, including its type, rotation, and position. The code includes functions to calculate the block offsets for each tetromino based on its rotation state.
Collision Detection and Piece Locking:
Dedicated functions check for collisions against the grid borders and existing blocks. When a collision is detected during a downward move, the piece is locked into the grid, and any complete lines are cleared.
AIgo-Driven Placement:
The script incorporates a simple heuristic to determine the best placement for the next tetromino. It simulates different rotations and horizontal positions, evaluating each based on aggregated column height, cleared lines, holes, and bumpiness. This decision-making process is encapsulated in an AI-like function that returns the optimal rotation and placement.
Rendering Using Tables:
The visual representation is managed via TradingView’s table objects. The game board is rendered with a bordered layout, while a separate preview table displays the next piece and the current score. Each cell is updated with text and background colors that correspond to the state of the game.
Execution Flow and Timing:
The main execution loop handles real-time updates by dropping pieces at set intervals and checking for game-over conditions. The code leverages persistent variables and time comparisons to control game speed and manage transitions between piece drops.
Executing:
Add the indicator to the chart
It starts playing itself till game over
There are no parameters to change in this version but the grid in the code directly
p.s. Sadly we have no interactive buttons in the current pinescript versions to play ourself, but its about the possibilitys what we could do ;-)
Maybe in a future version there is more possible, if i find time to enhance and expand the idea
Have fun :-)
Machine Learning Trendlines Cluster [LuxAlgo]The ML Trendlines Cluster indicator allows traders to automatically identify trendlines using a machine learning algorithm based on k-means clustering and linear regression, highlighting trendlines from clustered prices.
For trader's convenience, trendlines can be filtered based on their slope, allowing them to filter out trendlines that are too horizontal, or instead keep them depending on the user-selected settings.
🔶 USAGE
Traders only need to set the number of trendlines (clusters) they want the tool to detect and the algorithm will do the rest.
By default the tool is set to detect 4 clusters over the last 500 bars, in the image above it is set to detect 10 clusters over the same period.
This approach only focuses on drawing trendlines from prices that share a common trading range, offering a unique perspective to traditional trendlines. Trendlines with a significant slope can highlight higher dispersion within its cluster.
🔹 Trendline Slope Filtering
Traders can filter trendlines by their slope to display only steep or flat trendlines relative to a user-defined threshold.
The image above shows the three different configurations of this feature:
Filtering disabled
Filter slopes above threshold
Filter slopes below threshold
🔶 DETAILS
K-means clustering is a popular machine-learning algorithm that finds observations in a data set that are similar to each other and places them in a group.
The process starts by randomly assigning each data point to an initial group and calculating the centroid for each. A centroid is the center of the group. K-means clustering forms the groups in such a way that the variances between the data points and the centroid of the cluster are minimized.
The trendlines are displayed according to the linear regression function calculated for each cluster.
🔶 SETTINGS
Window Size: Maximum number of bars to get data from
Clusters: Maximum number of clusters (trendlines) to detect
🔹 Optimization
Maximum Iteration Steps: Maximum loop iterations for cluster computation
🔹 Slope Filter
Threshold Multiplier: Multiplier applied to a volatility measure, higher multiplier equals higher threshold
Filter Slopes: Enable/Disable Trendline Slope Filtering, select to filter trendlines with slopes ABOVE or BELOW the threshold
🔹 Style
Upper Zone: Color to display in the top zone
Lower Zone: Color to display in the bottom zone
Lines: Style for the lines
Size: Line size
aivance_Multi-Index Performance Comparison# Multi-Index Performance Comparison
This indicator allows traders and investors to easily compare the performance of multiple global market and sector indexes from a user-defined start date. All indexes are normalized to 100% at the specified start date, making relative performance comparisons straightforward.
## Features:
- Customizable start date for performance comparison
- Toggleable global market indexes (S&P 500, MSCI World, DAX, Nasdaq 100, EURO STOXX 50, Japan, Hong Kong)
- Toggleable sector indexes (Materials, Health Care, Financial, Technology, AI & Robotics)
- Clear visualization with distinct colors for each index
- Reference line at 100% for easy benchmark comparison
## How to Use:
1. Set your desired start date for normalizing performance
2. Toggle indexes on/off under the "Inputs" tab
3. Compare relative performance across different markets and sectors
Perfect for identifying relative strength, sector rotation, or global market correlations over your specific timeframe of interest.
Zen ABR### **Average Bar Range (ABR) Levels** – A Simple Yet Powerful Scalping Tool
The **ABR Levels** indicator dynamically calculates the **average bar range** and plots three key levels on your chart:
✅ **0.5x ABR** – Half the average bar range
✅ **1x ABR** – The full average bar range
✅ **2x ABR** – Double the average bar range
This provides an **instant volatility gauge**, helping traders adjust position size, stop losses, and targets **based on current market conditions** rather than arbitrary numbers.
### **How It Works**
- The indicator calculates **the average range of the last 8 bars** (default setting, adjustable).
- The **ABR levels update in real time**, appearing in the **top-right corner of your chart** for easy reference.
- Works across **any instrument and timeframe** – great for scalpers and intraday traders.
### **Why Use ABR Levels?**
🚀 **Avoid oversized losses** – Trade dynamically instead of using fixed stops and targets.
📉 **Recognize shifts in market conditions** – Identify when volatility is expanding or contracting.
🎯 **Refine your entries & exits** – Use ABR levels to **scale** your positions intelligently.
### **Pro Tip:**
Never enter a trade **aiming for half-R** profits. If a trade goes badly, **you might manage it to break even or half-R**, but aiming for tiny wins will **wreck your long-term edge**.
🔹 **This script is open-source!** If you're not on TradingView, you can copy the code into any AI tool to generate it for your platform. 🚀
Smart Adaptive Signal SystemSmart Adaptive Signal System
Description: The Smart Adaptive Signal System is a sophisticated indicator that generates intelligent buy/sell signals by dynamically adapting to market conditions. It predicts target prices based on momentum and volatility, providing more accurate and reliable trading opportunities.
How It Works:
Dynamic Signal Generation: The system predicts target prices by considering factors such as volatility and momentum. This allows it to react instantly to trend changes and market fluctuations.
Adaptive Thresholds: Buy and sell signals are triggered with adaptive thresholds, adjusting according to market volatility. This ensures flexibility in the face of sudden market changes.
Trend-Based Reset: Users can choose to reset threshold values based on a time interval or trend change. This feature helps the system re-adapt to current market conditions for greater accuracy.
Target Price Prediction: Target prices are calculated using momentum and volatility, helping the system predict future price movements.
How to Use:
Buy/Sell Signals: The indicator generates buy and sell signals based on market conditions. Look for a "down arrow" for a buy signal and an "up arrow" for a sell signal on the chart.
Target Price Lines: Along with buy and sell signals, the system draws target price lines. This helps you visualize potential future price levels.
Flexible Settings: Users can customize analysis periods, minimum change percentages, and other parameters to fit their needs.
Features:
Dynamic buy and sell signals
Target price predictions
Volatility and momentum-based analysis
User-friendly and flexible settings
Trend-based adaptive resetting
Alerts: The Smart Adaptive Signal System responds quickly to sudden market changes, but always use it in conjunction with other indicators like support and resistance levels. Signal accuracy may vary depending on market conditions.
Machine Learning SupertrendThe Machine Learning Supertrend is an advanced trend-following indicator that enhances the traditional Supertrend with Gaussian Process Regression (GPR) and kernel-based learning. Unlike conventional methods that rely purely on historical ATR values, this indicator integrates machine learning techniques to dynamically estimate volatility and forecast future price movements, resulting in a more adaptive and robust trend detection system.
At the core of this indicator lies Gaussian Process Regression (GPR), which utilizes a Radial Basis Function (RBF) kernel to model price distributions and anticipate future trends. Instead of simply looking at past price action, it constructs a kernel matrix, enabling a probabilistic approach to price forecasting. This allows the indicator to not only detect current trends but also project potential trend reversals with greater accuracy.
By applying machine learning to ATR estimation, the ML Supertrend dynamically adjusts its thresholds based on predicted values rather than a fixed multiplier. This makes the trend signals more responsive to market conditions, reducing false signals and minimizing whipsaws often seen with traditional Supertrend indicators. The upper and lower bands are no longer static but evolve based on the underlying price structure, improving the reliability of trend shifts.
When the price crosses these adaptive levels, the indicator detects a trend change and plots it accordingly. Green signifies a bullish trend, while red indicates a bearish one. Alerts can also be triggered when the trend shifts, allowing traders to react quickly to potential reversals.
What makes this approach powerful is its ability to adapt to different market conditions. Traditional ATR-based methods use fixed parameters that might not always be optimal, whereas this ML-driven Supertrend continuously refines its estimations based on real-time data. The result is a more intelligent, less lagging, and highly adaptive trend-following tool.
This indicator is particularly useful for traders looking to enhance trend-following strategies with AI-driven insights. It reduces noise, improves signal reliability, and even offers a degree of trend forecasting, making it ideal for those who want a more advanced and dynamic alternative to standard Supertrend indicators.
This indicator is provided for educational and informational purposes only. It does not constitute financial advice, and past performance is not indicative of future results. Trading involves risk, and users should conduct their own research and use proper risk management before making investment decisions.
Johnny's Volatility-Driven Trend Identifier w/ Reversal SignalsJohnny's Volatility-Driven Trend Identifier w/ Reversal Signals is designed to identify high-probability trend shifts and reversals by incorporating volatility, momentum, and impulse-based filtering. It is specifically built for traders who want to capture strong trend movements while minimizing false signals caused by low volatility noise.
By leveraging Rate of Change (ROC), Relative Strength Index (RSI), and Average True Range (ATR)-based volatility detection, the indicator dynamically adapts to market conditions. It highlights breakout trends, reversals, and early signs of momentum shifts using strategically placed labels and color-coded trend visualization.
Inspiration taken from Top G indicator .
What This Indicator Does
The Volatility-Driven Trend Identifier works by:
Measuring Market Extremes & Momentum:
Uses ROC normalization with standard deviation to identify impulse moves in price action.
Implements RSI filtering to determine overbought/oversold conditions that validate trend strength.
Utilizes ATR-based volatility tracking to ensure signals only appear when meaningful market movements are occurring.
Identifying Key Trend Events:
Power Peak (🔥): Marks a confirmed strong downtrend, ideal for shorting opportunities.
Surge (🚀): Indicates a confirmed strong uptrend, signaling a potential long entry.
Soft Surge (↗): Highlights a mild bullish reentry or early uptrend formation.
Soft Peak (↘): Shows a mild bearish reentry or early downtrend formation.
Providing Adaptive Filtering for Reliable Signals:
Filters out weak trends with a volatility check, ensuring signals appear only in strong market conditions.
Implements multi-level confirmation by combining trend strength metrics, preventing false breakouts.
Uses gradient-based visualization to color-code market sentiment for quick interpretation.
What This Indicator Signals
Breakouts & Impulse Moves: 🚀🔥
The Surge (🚀) and Power Peak (🔥) labels indicate confirmed momentum breakouts, where the trend has been validated by a combination of ROC impulse, RSI confirmation, and ATR volatility filtering.
These signals suggest that the market is entering a strong trend, and traders can align their entries accordingly.
Early Trend Formation & Reentries: ↗ ↘
The Soft Surge (↗) and Soft Peak (↘) labels indicate areas where a trend might be forming, but is not yet fully confirmed.
These signals help traders anticipate potential entries before the trend gains full strength.
Volatility-Adaptive Trend Filtering: 📊
Since the indicator only activates in volatile conditions, it avoids the pitfalls of low-range choppy markets where false signals frequently occur.
ATR-driven adaptive windowing allows the indicator to dynamically adjust its sensitivity based on real-time volatility conditions.
How to Use This Indicator
1. Identifying High-Probability Entries
Bullish Entries (Long Trades)
Look for 🚀 Surge signals in an uptrend.
Confirm with RSI (should be above 50 for momentum).
Ensure volatility is increasing to validate the breakout.
Use ↗ Soft Surge signals for early entries before the trend fully confirms.
Bearish Entries (Short Trades)
Look for 🔥 Power Peak signals in a downtrend.
RSI should be below 50, indicating downward momentum.
Volatility should be rising, ensuring market momentum is strong.
Use ↘ Soft Peak signals for early entries before a full bearish confirmation.
2. Avoiding False Signals
Ignore signals when the market is ranging (low ATR).
Check RSI and ROC alignment to ensure trend confirmation.
Use additional confluences (e.g., price action, support/resistance levels, moving averages) for enhanced accuracy.
3. Trend Confirmation & Filtering
The stronger the trend, the higher the likelihood that Surge (🚀) and Power Peak (🔥) signals will continue in their direction.
Soft Surge (↗) and Soft Peak (↘) act as early warning signals before major breakouts occur.
What Makes This a Machine Learning-Inspired Moving Average?
While this indicator is not a direct implementation of machine learning (as Pine Script lacks AI/ML capabilities), it mimics machine learning principles by adapting dynamically to market conditions using the following techniques:
Adaptive Trend Selection:
It does not rely on fixed moving averages but instead adapts dynamically based on volatility expansion and momentum detection.
ATR-based filtering adjusts the indicator’s sensitivity to real-time conditions.
Multi-Factor Confirmation (Feature Engineering Equivalent in ML):
Combines ROC, RSI, and ATR in a structured way, similar to how ML models use multiple inputs to filter and classify data.
Implements conditional trend recognition, ensuring that only valid signals pass through the filter.
Noise Reduction with Data Smoothing:
The algorithm avoids false signals by incorporating trend intensity thresholds, much like how ML models remove outliers to refine predictions.
Adaptive filtering ensures that low-volatility environments do not produce misleading signals.
Why Use This Indicator?
✔ Reduces False Signals: Multi-factor validation ensures only high-confidence signals are triggered.
✔ Works in All Market Conditions: Volatility-adaptive nature allows the indicator to perform well in both trending and ranging markets.
✔ Great for Swing & Intraday Trading: It helps spot momentum shifts early and allows traders to catch major market moves before they fully develop.
✔ Visually Intuitive: Color-coded trends and clear signal markers make it easy to interpret.
Price in BTC (x1000)I'm not a coder. I just knocked this together with AI
Shows how the current asset performed relative to BTC (COINBASE:BTCUSD) on the current timeframe
Works with assets priced in USD, USDT and USDC but you can easily add more
Had to multiply the price by 1000 to mitigate leading zeros and improve compatibility with low-denomination assets (e.g. PEPE)
MAs and crossovers included
Feel free to use it however you want
TOL LANGIT ATR v7 - AI EnhancedThe TOL LANGIT ATR v7 is an adaptive technical indicator designed to identify market trends, support and resistance levels, and breakout points. It uses the Average True Range (ATR) and volatility to dynamically adjust trend bands, with visual markers for buy and sell signals. The indicator also highlights key support (blue) and resistance (orange) levels, and alerts users when these levels are broken. It’s perfect for trend following, breakout trading, and reversal strategies, and includes easy-to-set alerts for key market changes.
Market Cycles
The Market Cycles indicator transforms market price data into a stochastic wave, offering a unique perspective on market cycles. The wave is bounded between positive and negative values, providing clear visual cues for potential bullish and bearish trends. When the wave turns green, it signals a bullish cycle, while red indicates a bearish cycle.
Designed to show clarity and precision, this tool helps identify market momentum and cyclical behavior in an intuitive way. Ideal for fine-tuning entries or analyzing broader trends, this indicator aims to enhance the decision-making process with simplicity and elegance.
Inside Bar Breakout/Fakeout with AI Scenarios [Yosiet]Inside Bar Breakout/Fakeout Indicator with Scenarios
The Indicator is a powerful tool for traders looking to identify potential breakout and fakeout opportunities based on inside bar patterns. This indicator combines multiple technical analysis concepts to provide a comprehensive view of market behavior, helping traders make more informed decisions.
Key Features
Inside bar detection with filtering
Breakout and fakeout identification
Three distinct scenario detections
Customizable moving average calculations
Flexible visualization options
Alert conditions for various events
How It Works
The indicator identifies inside bars and filters them based on a maximum number of consecutive inside bars. It then detects breakouts and fakeouts using user-defined parameters. The script also calculates moving averages to determine trend direction.
Three specific scenarios are detected:
Strong breakout followed by a strong reversal
Weak breakout with multiple doji/weak candles
Strong breakout without reversal
These scenarios are visually represented on the chart, allowing traders to quickly identify potential trading opportunities.
How to Use
Apply the indicator to your chart
Adjust the input parameters to suit your trading style
Look for inside bar patterns and subsequent breakouts/fakeouts
Pay attention to the three scenario markers for additional context
Use the alert conditions to stay informed of potential opportunities
EMD Oscillator (Zeiierman)█ Overview
The Empirical Mode Decomposition (EMD) Oscillator is an advanced indicator designed to analyze market trends and cycles with high precision. It breaks down complex price data into simpler parts called Intrinsic Mode Functions (IMFs), allowing traders to see underlying patterns and trends that aren’t visible with traditional indicators. The result is a dynamic oscillator that provides insights into overbought and oversold conditions, as well as trend direction and strength. This indicator is suitable for all types of traders, from beginners to advanced, looking to gain deeper insights into market behavior.
█ How It Works
The core of this indicator is the Empirical Mode Decomposition (EMD) process, a method typically used in signal processing and advanced scientific fields. It works by breaking down price data into various “layers,” each representing different frequencies in the market’s movement. Imagine peeling layers off an onion: each layer (or IMF) reveals a different aspect of the price action.
⚪ Data Decomposition (Sifting): The indicator “sifts” through historical price data to detect natural oscillations within it. Each oscillation (or IMF) highlights a unique rhythm in price behavior, from rapid fluctuations to broader, slower trends.
⚪ Adaptive Signal Reconstruction: The EMD Oscillator allows traders to select specific IMFs for a custom signal reconstruction. This reconstructed signal provides a composite view of market behavior, showing both short-term cycles and long-term trends based on which IMFs are included.
⚪ Normalization: To make the oscillator easy to interpret, the reconstructed signal is scaled between -1 and 1. This normalization lets traders quickly spot overbought and oversold conditions, as well as trend direction, without worrying about the raw magnitude of price changes.
The indicator adapts to changing market conditions, making it effective for identifying real-time market cycles and potential turning points.
█ Key Calculations: The Math Behind the EMD Oscillator
The EMD Oscillator’s advanced nature lies in its high-level mathematical operations:
⚪ Intrinsic Mode Functions (IMFs)
IMFs are extracted from the data and act as the building blocks of this indicator. Each IMF is a unique oscillation within the price data, similar to how a band might be divided into treble, mid, and bass frequencies. In the EMD Oscillator:
Higher-Frequency IMFs: Represent short-term market “noise” and quick fluctuations.
Lower-Frequency IMFs: Capture broader market trends, showing more stable and long-term patterns.
⚪ Sifting Process: The Heart of EMD
The sifting process isolates each IMF by repeatedly separating and refining the data. Think of this as filtering water through finer and finer mesh sieves until only the clearest parts remain. Mathematically, it involves:
Extrema Detection: Finding all peaks and troughs (local maxima and minima) in the data.
Envelope Calculation: Smoothing these peaks and troughs into upper and lower envelopes using cubic spline interpolation (a method for creating smooth curves between data points).
Mean Removal: Calculating the average between these envelopes and subtracting it from the data to isolate one IMF. This process repeats until the IMF criteria are met, resulting in a clean oscillation without trend influences.
⚪ Spline Interpolation
The cubic spline interpolation is an advanced mathematical technique that allows smooth curves between points, which is essential for creating the upper and lower envelopes around each IMF. This interpolation solves a tridiagonal matrix (a specialized mathematical problem) to ensure that the envelopes align smoothly with the data’s natural oscillations.
To give a relatable example: imagine drawing a smooth line that passes through each peak and trough of a mountain range on a map. Spline interpolation ensures that line is as smooth and close to reality as possible. Achieving this in Pine Script is technically demanding and demonstrates a high level of mathematical coding.
⚪ Amplitude Normalization
To make the oscillator more readable, the final signal is scaled by its maximum amplitude. This amplitude normalization brings the oscillator into a range of -1 to 1, creating consistent signals regardless of price level or volatility.
█ Comparison with Other Signal Processing Methods
Unlike standard technical indicators that often rely on fixed parameters or pre-defined mathematical functions, the EMD adapts to the data itself, capturing natural cycles and irregularities in real-time. For example, if the market becomes more volatile, EMD adjusts automatically to reflect this without requiring parameter changes from the trader. In this way, it behaves more like a “smart” indicator, intuitively adapting to the market, unlike most traditional methods. EMD’s adaptive approach is akin to AI’s ability to learn from data, making it both resilient and robust in non-linear markets. This makes it a great alternative to methods that struggle in volatile environments, such as fixed-parameter oscillators or moving averages.
█ How to Use
Identify Market Cycles and Trends: Use the EMD Oscillator to spot market cycles that represent phases of buying or selling pressure. The smoothed version of the oscillator can help highlight broader trends, while the main oscillator reveals immediate cycles.
Spot Overbought and Oversold Levels: When the oscillator approaches +1 or -1, it may indicate that the market is overbought or oversold, signaling potential entry or exit points.
Confirm Divergences: If the price movement diverges from the oscillator's direction, it may indicate a potential reversal. For example, if prices make higher highs while the oscillator makes lower highs, it could be a sign of weakening trend strength.
█ Settings
Window Length (N): Defines the number of historical bars used for EMD analysis. A larger window captures more data but may slow down performance.
Number of IMFs (M): Sets how many IMFs to extract. Higher values allow for a more detailed decomposition, isolating smaller cycles within the data.
Amplitude Window (L): Controls the length of the window used for amplitude calculation, affecting the smoothness of the normalized oscillator.
Extraction Range (IMF Start and End): Allows you to select which IMFs to include in the reconstructed signal. Starting with lower IMFs captures faster cycles, while ending with higher IMFs includes slower, trend-based components.
Sifting Stopping Criterion (S-number): Sets how precisely each IMF should be refined. Higher values yield more accurate IMFs but take longer to compute.
Max Sifting Iterations (num_siftings): Limits the number of sifting iterations for each IMF extraction, balancing between performance and accuracy.
Source: The price data used for the analysis, such as close or open prices. This determines which price movements are decomposed by the indicator.
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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!
Backside Bubble ScalpingFrom LIHKG
Pine from Perplexity AI
以下是Backside Bubble Scalping策略的使用說明，旨在幫助交易者理解如何在美股交易中應用這一策略。
使用說明：Backside Bubble Scalping 策略
1. 前提條件
交易時間：此策略適用於香港時間晚上9:30 PM至12:00 AM。
圖表類型：使用1分鐘圖表進行交易。
2. 策略概述
Backside Bubble Scalping策略包含兩種主要的設置：尖backside和鈍backside。這些設置通常在10:00 PM至12:00 AM之間出現。
3. 指標設定
VWAP（粉紅色）：成交量加權平均價格，用於識別市場趨勢。
9 EMA（綠色）：9期指數移動平均線，用於捕捉短期價格變化。
4. 識別 Backside 設置
尖backside
特徵：
當市場趨勢為純紅色下跌，並形成尖尖的V形底部。
入場條件：
當價格突破9 EMA並經過小幅盤整後，進場做多。
鈍backside
特徵：
在混合顏色的趨勢中，形成鈍鈍的V形底部。
入場條件：
在盤整期間進場做多。
5. 止損和止盈設置
止損位置：
尖backside：設置在9 EMA上方的盤整範圍底部加上0.2。
鈍backside：設置在V底部的最低點加上0.2。
止盈位置：
尖backside：當價格跌破VWAP或出現一根K線沒有跟隨時出場。
鈍backside：當一根K線的三分之二身體向下突破9 EMA時出場。
6. 操作步驟
監控市場動態：在指定的交易時間內，觀察VWAP和9 EMA的變化。
識別入場信號：根據尖backside或鈍backside的條件進行判斷，確定何時進場。
設置止損和止盈：根據上述條件設置止損和止盈位，以管理風險。
執行交易：根據信號執行交易，並持續監控市場情況以調整策略。
7. 注意事項
避免在VWAP附近進行交易，以減少失敗風險。
如果出現影線（wick bar），建議不要進行交易，因為這可能表示該設置失敗。
Machine Learning Signal FilterIntroducing the "Machine Learning Signal Filter," an innovative trading indicator designed to leverage the power of machine learning to enhance trading strategies. This tool combines advanced data processing capabilities with user-friendly customization options, offering traders a sophisticated yet accessible means to optimize their market analysis and decision-making processes. Importantly, this indicator does not repaint, ensuring that signals remain consistent and reliable after they are generated.
Machine Learning Integration
The "Machine Learning Signal Filter" employs machine learning algorithms to analyze historical price data and identify patterns that may not be immediately apparent through traditional technical analysis. By utilizing techniques such as regression analysis and neural networks, the indicator continuously learns from new data, refining its predictive capabilities over time. This dynamic adaptability allows the indicator to adjust to changing market conditions, potentially improving the accuracy of trading signals.
Key Features and Benefits
Dynamic Signal Generation: The indicator uses machine learning to generate buy and sell signals based on complex data patterns. This approach enables it to adapt to evolving market trends, offering traders timely and relevant insights. Crucially, the indicator does not repaint, providing reliable signals that traders can trust.
Customizable Parameters: Users can fine-tune the indicator to suit their specific trading styles by adjusting settings such as the temporal synchronization and neural pulse rate. This flexibility ensures that the indicator can be tailored to different market environments.
Visual Clarity and Usability: The indicator provides clear visual cues on the chart, including color-coded signals and optional display of signal curves. Users can also customize the table's position and text size, enhancing readability and ease of use.
Comprehensive Performance Metrics: The indicator includes a detailed metrics table that displays key performance indicators such as return rates, trade counts, and win/loss ratios. This feature helps traders assess the effectiveness of their strategies and make data-driven decisions.
How It Works
The core of the "Machine Learning Signal Filter" is its ability to process and learn from large datasets. By applying machine learning models, the indicator identifies potential trading opportunities based on historical data patterns. It uses regression techniques to predict future price movements and neural networks to enhance pattern recognition. As new data is introduced, the indicator refines its algorithms, improving its accuracy and reliability over time.
Use Cases
Trend Following: Ideal for traders seeking to capitalize on market trends, the indicator helps identify the direction and strength of price movements.
Scalping: With its ability to provide quick signals, the indicator is suitable for scalpers aiming for rapid profits in volatile markets.
Risk Management: By offering insights into trade performance, the indicator aids in managing risk and optimizing trade setups.
In summary, the "Machine Learning Signal Filter" is a powerful tool that combines the analytical strength of machine learning with the practical needs of traders. Its ability to adapt and provide actionable insights makes it an invaluable asset for navigating the complexities of financial markets.
The "Machine Learning Signal Filter" is a tool designed to assist traders by providing insights based on historical data and machine learning techniques. It does not guarantee profitable trades and should be used as part of a comprehensive trading strategy. Users are encouraged to conduct their own research and consider their financial situation before making trading decisions. Trading involves significant risk, and it is possible to lose more than the initial investment. Always trade responsibly and be aware of the risks involved.
CVD with Moving Average (Trend Colors) [SYNC & TRADE]Yesterday I wrote a simple and easy code for the indicator "Cumulative Delta Volume with a moving average" using AI.
Introduction:
Delta is the difference between buys and sells. If there are more purchases, the delta is positive, if there are more sales, the delta is negative. We look at each candle separately on a particular time frame, which does not give us an overall picture over time.
Cumulative volume delta is in many ways an extension of volume delta, but it covers longer periods of time and provides different trading signals. Like the volume delta indicator, the Cumulative Volume Delta (CVD) indicator measures the relationship between buying and selling pressure, but does not focus on one specific candle (or other chart element), but rather gives a picture over time.
What did you want to get?
I have often seen that they tried to attach RSI and the Ichimoku cloud to the cumulative delta of volume, but I have never seen a cumulative delta of volume with a moving average. A moving average that takes data from the cumulative volume delta will be different from the moving average of the underlying asset. It has been noted that often at the intersection of the cumulative volume delta and the moving average, this is a more accurate signal to buy or sell than the same intersections for the underlying asset.
Initially, 5 moving averages were made with values of 21, 55, 89, 144 and 233, but I realized that this overloads the chart. It is easier to change the length of the moving average depending on the time frame you are using than to overload the chart. The final version with one moving SMA, EMA, RMA, WMA, HMA.
The logic for applying a moving average to a cumulative volume delta:
You choose a moving average, just like you would on your underlying asset. Use the moving average you like and the period you are used to working with. Each TF has its own settings.
What we see on the graph:
This is not an oscillator, but an adapted version for a candlestick chart (line only). Using it, you can clearly see where the market is moving based on the cumulative volume delta. The cool thing is that you can include your moving average applied to the cumulative volume delta. Thanks to this, you can see a trend movement, a return to the moving average to continue the trend.
Opportunities not lost:
The most interesting thing is that it remains possible to observe the divergence of the asset and the cumulative delta of the volume. This gives a great advantage. Those who have not worked with divergence do not rush into it right away. There may be 3 peaks in divergence (as with oversold/overbought), but it works many times more clearly than RSI and MACD.
Here's a good example on the daily chart. The moment we were all waiting for 75,000. The cumulative Delta Volume fell with each peak, while the price chart (tops) were approximately level.
Usually they throw (allow to buy) without volume for sales (delta down, price up) in order to merge at a more interesting price. And they also drain without the volume of purchases for a squeeze (price down / delta up) and again I buy back at a more interesting price. There are more complex estimation options; you can read about the divergence of the cumulative delta of the CVD volume. I just recommend doing a backtest.
Recommendations:
One more moment. Use the indicator on the stock exchange, where there is the most money, by turnover and by asset. Choose Binance, not Bybit. Those. choose the BTC asset, for example, but on the Binance exchange. Not futures, but spot.
The greater the turnover on the exchange for an asset, and the fewer opportunities to enter with leverage, the less volatile the price and the more beautiful and accurate the chart.
Works on all assets. There is a subscription limit (the number of calculated bars) that has little effect on anything. Can be applied to any asset where there is volume (not SPX, but ES1, not MOEX, but MX1!).
Перевод на русский.
Вчера написал с помощью AI простой и легкий код индикатора "Кумулятивная Дельта Объема со скользящей средней".
Введение:
Дельта (Delta) — это разница между покупками и продажами. Если покупок больше — дельта положительная, если больше продаж — дельта отрицательная. Мы смотрим на каждую свечу отдельно на том или ином таймфрейме, что не дает нам общей картины во времени.
Кумулятивная дельта объема — во многом продолжение дельты объёмов, но она включает более длительные периоды времени и дает другие торговые сигналы. Как и индикатор дельты объёма, индикатор кумулятивной дельты объема (Cumulative Volume Delta, CVD) измеряет связь между давлением покупателей и продавцов, но при этом не фокусируется на одной конкретной свече (или другом элементе графика), а дает картину во времени.
Что хотел получить?
Часто видел, что к кумулятивной детьте объема пытались прикрепить RSI и облако ишимоку, но никогда не видел кумулятивную дельту объема со скользящей средней. Скользящая средняя которая берет данные от кумулятивной дельты объема будет отличатся от скользящей средней основного актива. Было замечено, что часто в местах пересечения кумулятивной дельты объема и скользящей средней - это более точный сигнал к покупке или продаже, чем такие же пересечения по основному активу.
Изначально было сделанно 5 скользящих со значениями 21, 55, 89, 144 и 233, но я понял, что это перегружает график. Проще менять длину скользящей средней от используемого таймфрейма, чем перегружать график. Финальный вариант с одной скользящей SMA, EMA, RMA, WMA, HMA.
Логика применения скользящей средней к кумулятивной дельте объема:
Вы выбираете скользящую среднюю, так же как и на основном активе. Применяйте ту скользящую среднюю, которая вам нравится и период, с которым привыкли работать. На каждом TF свои настройки.
Что мы видим на графике:
Это не осциллятор, а адаптированная версия к свечному графику (только линия). С помощью него вы можете наглядно посмотреть куда движется рынок по кумулятивной дельте объема. Самое интересное, что вы можете включить свою скользящую среднюю, применимую к кумулятивной дельте объема. Благодаря этому вы можете видеть трендовое движение, возврат к средней скользящей для продолжения тренда.
Не потерянные возможности:
Самое интересное, что осталась возможность наблюдать за дивергенцией актива и кумулятивной дельтой объема. Это дает большое преимущество. Те кто не работал с дивергенцией не бросайтесь на нее сразу. Может быть и 3 пика в дивергенции (как с перепроданностью / перекупленностью), но работает в разы четче чем RSI и MACD.
Вот хороший пример на дневном графике. Момент когда мы все ждали 75000. Кумулятивная Дельта Объема падала с каждым пиком, в то время как ценовой график (вершины) были примерно на уровне.
Обычно закидывают (разрешают покупать) без объема на продажи (дельта вниз цена вверх), чтобы слить по более интересной цене. И также сливают без объема покупок для сквиза (цена вниз / дельта вверх) и опять откупаю по более интересной цене. Существуют более сложные варианты оценки, можете почитать про дивергенцию кумулятивной дельты объема CVD. Только рекомендую сделать бэктест.
Рекомендации:
Еще момент. Используйте индикатор, на бирже, там где больше всего денег, по обороту и по активу. Выбирайте не Bybit, а Binance. Т.е. выбираете актив BTC, к примеру, но на бирже Binance. Не фьючерс, а спот.
Чем более большие обороты на бирже, по активу, и меньше возможностей заходить с плечами, тем менее волатильная цена и более красивый и точный график.
Работает на всех активах. Есть ограничение по подписке (количество рассчитываемых баров) мало влияет на что. Можно применить к любому активу где есть объем (не SPX, а ES1, не MOEX, а MX1!).
Support/Resistance v2 (ML) KmeanKmean with Standard Deviation Channel
1. Description of Kmean
Kmean (or K-means) is a popular clustering algorithm used to divide data into K groups based on their similarity. In the context of financial markets, Kmean can be applied to find the average price values over a specific period, allowing the identification of major trends and levels of support and resistance.
2. Application in Trading
In trading, Kmean is used to smooth out the price series and determine long-term trends. This helps traders make more informed decisions by avoiding noise and short-term fluctuations. Kmean can serve as a baseline around which other analytical tools, such as channels and bands, are constructed.
3. Description of Standard Deviation (stdev)
Standard deviation (stdev) is a statistical measure that indicates how much the values of data deviate from their mean value. In finance, standard deviation is often used to assess price volatility. A high standard deviation indicates strong price fluctuations, while a low standard deviation indicates stable movements.
4. Combining Kmean and Standard Deviation to Predict Short-Term Price Behavior
Combining Kmean and standard deviation creates a powerful tool for analyzing market conditions. Kmean shows the average price trend, while the standard deviation channels demonstrate the boundaries within which the price can fluctuate. This combination helps traders to:
Identify support and resistance levels.
Predict potential price reversals.
Assess risks and set stop-losses and take-profits.
Should you have any questions about code, please reach me at Tradingview directly.
Hope you find this script helpful!
Crypto Narratives: Relative StrengthThis indicator offers a unique perspective on the crypto market by focusing on the relative strength of different narratives. It aggregates RSI data from multiple tokens associated with each narrative, providing a comprehensive view of the sentiment and momentum behind these themes. You can use it to take profit, find W bottoms or M tops to enter and exit narratives. and generally see what hot at the moment with lots of pretty colours.
This indicator tracks the relative strength of various crypto narratives using the Relative Strength Index (RSI) of representative tokens. It allows users to gauge the momentum and sentiment behind different themes in the cryptocurrency market.
Functionality:
The indicator calculates the average RSI values for the current leading tokens associated with ten different crypto narratives:
- AI (Artificial Intelligence)
- Ordinals
- DeFi (Decentralized Finance)
- Memes
- Gaming
- Level 1 (Layer 1 Protocols)
- Sol Betas (Solana Ecosystem)
- Storage/DePin
- RWA (Real-World Assets)
- ReStaking
he average RSI values for each narrative are calculated by summing the RSI values of the associated tokens and dividing by the number of tokens. The indicator plots the 3-period simple moving average (SMA) of each narrative's RSI using different colors and line styles.
Users can customize the RSI length, line width, and label offset through the input options. If the "Show Labels" option is enabled, the indicator displays labels for each narrative's RSI value on the most recent bar.
The indicator also includes horizontal lines representing overbought and oversold levels, which can be adjusted through the input options. Alerts are triggered when a narrative's RSI crosses above the overbought level or below the oversold level. The alerts include the narrative name, RSI value, and a suggestion to consider selling or buying.
Machine Learning: Multiple Logistic Regression
Multiple Logistic Regression Indicator
The Logistic Regression Indicator for TradingView is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach to decision-making in financial markets.
How It Works:
Technical Indicators:
The script uses multiple technical indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend as input variables for the logistic regression model.
These indicators are normalized to create categorical variables, providing a consistent scale for the model.
Logistic Regression:
The logistic regression function is applied to the normalized input variables (x1 to x6) with user-defined coefficients (b0 to b6).
The logistic regression model predicts the probability of a binary outcome, with values closer to 1 indicating a bullish signal and values closer to 0 indicating a bearish signal.
Loss Function (Cross-Entropy Loss):
The cross-entropy loss function is calculated to quantify the difference between the predicted probability and the actual outcome.
The goal is to minimize this loss, which essentially measures the model's accuracy.
// Error Function (cross-entropy loss)
loss(y, p) =>
-y * math.log(p) - (1 - y) * math.log(1 - p)
// y - depended variable
// p - multiple logistic regression
Gradient Descent:
Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the weights of the logistic regression model.
The script iteratively updates the weights (b1 to b6) based on the negative gradient of the loss function with respect to each weight.
// Adjusting model weights using gradient descent
b1 -= lr * (p + loss) * x1
b2 -= lr * (p + loss) * x2
b3 -= lr * (p + loss) * x3
b4 -= lr * (p + loss) * x4
b5 -= lr * (p + loss) * x5
b6 -= lr * (p + loss) * x6
// lr - learning rate or step of learning
// p - multiple logistic regression
// x_n - variables
Learning Rate:
The learning rate (lr) determines the step size in the weight adjustment process. It prevents the algorithm from overshooting the minimum of the loss function.
Users can set the learning rate to control the speed and stability of the optimization process.
Visualization:
The script visualizes the output of the logistic regression model by coloring the SMA.
Arrows are plotted at crossover and crossunder points, indicating potential buy and sell signals.
Lables are showing logistic regression values from 1 to 0 above and below bars
Table Display:
A table is displayed on the chart, providing real-time information about the input variables, their values, and the learned coefficients.
This allows traders to monitor the model's interpretation of the technical indicators and observe how the coefficients change over time.
How to Use:
Parameter Adjustment:
Users can adjust the length of technical indicators (rsi_length, cci_length, etc.) and the Z score length based on their preference and market characteristics.
Set the initial values for the regression coefficients (b0 to b6) and the learning rate (lr) according to your trading strategy.
Signal Interpretation:
Buy signals are indicated by an upward arrow (▲), and sell signals are indicated by a downward arrow (▼).
The color-coded SMA provides a visual representation of the logistic regression output by color.
Table Information:
Monitor the table for real-time information on the input variables, their values, and the learned coefficients.
Keep an eye on the learning rate to ensure a balance between model adjustment speed and stability.
Backtesting and Validation:
Before using the script in live trading, conduct thorough backtesting to evaluate its performance under different market conditions.
Validate the model against historical data to ensure its reliability.
Machine Learning: STDEV Oscillator [YinYangAlgorithms]This Indicator aims to fill a gap within traditional Standard Deviation Analysis. Rather than its usual applications, this Indicator focuses on applying Standard Deviation within an Oscillator and likewise applying a Machine Learning approach to it. By doing so, we may hope to achieve an Adaptive Oscillator which can help display when the price is deviating from its standard movement. This Indicator may help display both when the price is Overbought or Underbought, and likewise, where the price may face Support and Resistance. The reason for this is that rather than simply plotting a Machine Learning Standard Deviation (STDEV), we instead create a High and a Low variant of STDEV, and then use its Highest and Lowest values calculated within another Deviation to create Deviation Zones. These zones may help to display these Support and Resistance locations; and likewise may help to show if the price is Overbought or Oversold based on its placement within these zones. This Oscillator may also help display Momentum when the High and/or Low STDEV crosses the midline (0). Lastly, this Oscillator may also be useful for seeing the spacing between the High and Low of the STDEV; large spacing may represent volatility within the STDEV which may be helpful for seeing when there is Momentum in the form of volatility.
Tutorial:
Above is an example of how this Indicator looks on BTC/USDT 1 Day. As you may see, when the price has parabolic movement, so does the STDEV. This is due to this price movement deviating from the mean of the data. Therefore when these parabolic movements occur, we create the Deviation Zones accordingly, in hopes that it may help to project future Support and Resistance locations as well as helping to display when the price is Overbought and Oversold.
If we zoom in a little bit, you may notice that the Support Zone (Blue) is smaller than the Resistance Zone (Orange). This is simply because during the last Bull Market there was more parabolic price deviation than there was during the Bear Market. You may see this if you refer to their values; the Resistance Zone goes to ~18k whereas the Support Zone is ~10.5k. This is completely normal and the way it is supposed to work. Due to the nature of how STDEV works, this Oscillator doesn’t use a 1:1 ratio and instead can develop and expand as exponential price action occurs.
The Neutral (0) line may also act as a Support and Resistance location. In the example above we can see how when the STDEV is below it, it acts as Resistance; and when it’s above it, it acts as Support.
This Neutral line may also provide us with insight as towards the momentum within the market and when it has shifted. When the STDEV is below the Neutral line, the market may be considered Bearish. When the STDEV is above the Neutral line, the market may be considered Bullish.
The Red Line represents the STDEV’s High and the Green Line represents the STDEV’s Low. When the STDEV’s High and Low get tight and close together, this may represent there is currently Low Volatility in the market. Low Volatility may cause consolidation to occur, however it also leaves room for expansion.
However, when the STDEV’s High and Low are quite spaced apart, this may represent High levels of Volatility in the market. This may mean the market is more prone to parabolic movements and expansion.
We will conclude our Tutorial here. Hopefully this has given you some insight into how applying Machine Learning to a High and Low STDEV then creating Deviation Zones based on it may help project when the Momentum of the Market is Bullish or Bearish; likewise when the price is Overbought or Oversold; and lastly where the price may face Support and Resistance in the form of STDEV.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Backtest Strategy Optimizer AdapterBacktest Strategy Optimizer Adapter
With this library, you will be able to run one or multiple backtests with different variables (combinations). For example, you can run 100 backtests of Supertrend at once with an increment factor of 0.1. This way, you can easily fetch the most profitable settings and apply them to your strategy.
To get a better understanding of the code, you can check the code below.
Single backtest results
= backtest.results(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Add backtest results to a table
backtest.table(initial_capital, profit_and_loss, open_balance, winrate, entries, exits, wins, losses, backtest_table_position, backtest_table_margin, backtest_table_transparency, backtest_table_cell_color, backtest_table_title_cell_color, backtest_table_text_color)
Backtest result without chart labels
= backtest.run(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result profit
profit = backtest.profit(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result winrate
winrate = backtest.winrate(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Start Date
You can set the start date either by using a timestamp or a number that refers to the number of bars back.
Stop Loss / Take Profit Issue
Unfortunately, I did not manage to achieve 100% accuracy for the take profit and stop loss. The original TradingView backtest can stop at the correct position within a bar using the strategy.exit stop and limit variables. However, it seems unachievable with a crossunder/crossover function in PineScript unless it is calculated on every tick (which would make the backtesting results invalid). So far, I have not found a workaround, and I would be grateful if someone could solve this issue, if it is even possible. If you have any solutions or fixes, please let me know!
Multiple Backtest Results / Optimizer
You can run multiple backtests in a single strategy or indicator, but there are certain requirements for placing the correct code in the right way. To view examples of running multiple backtests, you can refer to the links provided in the updates I posted below. In the samples I have also explained how you can auto-generate code for your backtest strategy.
Machine Learning: Anchored Gaussian Process Regression [LuxAlgo]Machine Learning: Anchored Gaussian Process Regression is an anchored version of Machine Learning: Gaussian Process Regression .
It implements Gaussian Process Regression (GPR), a popular machine-learning method capable of estimating underlying trends in prices as well as forecasting them. Users can set a Training Window by choosing 2 points. GPR will be calculated for the data between these 2 points.
Do remember that forecasting trends in the market is challenging, do not use this tool as a standalone for your trading decisions.
🔶 USAGE
When adding the indicator to the chart, users will be prompted to select a starting and ending point for the calculations, click on your chart to select those points.
Start & end point are named 'Anchor 1' & 'Anchor 2', the Training Window is located between these 2 points. Once both points are positioned, the Training Window is set, whereafter the Gaussian Process Regression (GPR) is calculated using data between both Anchors .
The blue line is the GPR fit, the red line is the GPR prediction, derived from data between the Training Window .
Two user settings controlling the trend estimate are available, Smooth and Sigma.
Smooth determines the smoothness of our estimate, with higher values returning smoother results suitable for longer-term trend estimates.
Sigma controls the amplitude of the forecast, with values closer to 0 returning results with a higher amplitude.
One of the advantages of the anchoring process is the ability for the user to evaluate the accuracy of forecasts and further understand how settings affect their accuracy.
The publication also shows the mean average (faint silver line), which indicates the average of the prices within the calculation window (between the anchors). This can be used as a reference point for the forecast, seeing how it deviates from the training window average.
🔶 DETAILS
🔹 Limited Training Window
The Training Window is limited due to matrix.new() limitations in size.
When the 2 points are too far from each other (as in the latter example), the line will end at the maximum limit, without giving a size error.
The red forecasted line is always given priority.
🔹 Positioning Anchors
Typically Anchor 1 is located further in history than Anchor 2 , however, placing Anchor 2 before Anchor 1 is perfectly possibly, and won't give issues.
🔶 SETTINGS
Anchor 1 / Anchor 2: both points will form the Training Window .
Forecasting Length: Forecasting horizon, determines how many bars in the 'future' are forecasted.
Smooth: Controls the degree of smoothness of the model fit.
Sigma: Noise variance. Controls the amplitude of the forecast, lower values will make it more sensitive to outliers.
