RSI Crossover Signal Companion - Alerts + Visuals🔷 RSI Crossover Signal Companion — Alerts + Visuals
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of recent price movements. It helps traders identify overbought or oversold conditions, possible trend reversals, and momentum strength.
This utility builds on TradingView’s classic Relative Strength Index (RSI) by adding real-time alerts and triangle markers when the RSI crosses its own moving average — a common technique for early momentum detection.
It is designed as a lightweight, visual companion tool for traders using RSI/MA crossover logic in manual or semi-automated strategies.
🔍 Features
✅ Preserves the full original RSI layout, including:
• Gradient fill and overbought/oversold zones
• Standard RSI input settings (length, source, etc.)
• MA smoothing options with user-defined type and length
🔺 Adds visual triangle markers:
🔼 Up triangle when RSI crosses above its MA
🔽 Down triangle when RSI crosses below its MA
📢 Built-in alerts for RSI/MA crosses:
“RSI Crossed Above MA”
“RSI Crossed Below MA”
📈 How to Use
This script is ideal for:
• Spotting early momentum shifts
• Confirming entries or exits in other systems (price action, trendlines, breakouts)
• Building alert-based automation (webhooks, bots, etc.)
Popular use cases:
• Combine with trend indicators like MA200 or MA12
• Use in confluence with price structure and divergence
• Validate breakout moves with momentum confirmation
⚙️ Customization
RSI length, MA length, MA type, and source are fully adjustable
Triangle marker size, shape, and color can be edited under Style
Alerts are pre-built and ready for use
Cari dalam skrip untuk "signal"
Buy and Sell Pressure Signals (Clean)This script identifies strong buy and sell pressure based purely on candlestick structure — no indicators, no clutter. It highlights key reversal or momentum candles using minimal visuals:
🔼 Green Triangle (Buy Pressure): A bullish candle with a large body and small upper/lower wicks, indicating strong upward momentum and buyer control.
🔽 Red Triangle (Sell Pressure): A bearish candle with a large body and small wicks, showing strong downward momentum and seller dominance.
Designed for traders who prefer clean, price-action-based signals without text labels or distracting overlays. Ideal for scalping, trend confirmation, or identifying exhaustion zones.
Equal High/Low (EQH/EQL) [AlgoAlpha]OVERVIEW
This script detects and visualizes Equal High (EQH) and Equal Low (EQL) zones—key liquidity areas where price has previously stalled or reversed. These levels often attract institutional interest due to the liquidity buildup around them. The indicator is built to highlight such zones using dynamic thresholding, overbought/oversold RSI filtering, and adaptive mitigation logic to manage zone relevance over time.
CONCEPTS
Equal Highs/Lows are price points where the market has repeatedly failed to break past a certain high or low, hinting at areas where stop orders and pending interest may be concentrated. These areas are often prime targets for liquidity grabs or reversals. By combining this with RSI filtering, the script avoids false signals during neutral conditions and instead focuses on zones where market pressure is more directional.
FEATURES
Detection Logic: The script identifies EQH and EQL zones by comparing the similarity between recent highs or lows with a dynamic volatility threshold. The `tolerance` input allows users to control how strict this comparison is.
RSI Filtering: If enabled, it only creates zones when RSI is significantly overbought or oversold (based on the `state_thresh` input). This helps ensure zones form only in meaningful market conditions.
Zone Display: Bullish (EQL) zones are shown in grey, while bearish (EQH) zones are in blue. Two horizontal lines mark the zone using wick and body extremes, and a filled area visualizes the zone between them.
Zone Management: Zones automatically extend with price until they’re invalidated. You can choose whether a zone is removed based on wick or body sweeps and whether it requires one or two candle confirmations. Zones also expire after a customizable number of bars.
Alerts: Four alert conditions are built in—when a new EQH/EQL is formed and when one is mitigated—making it easy to integrate into alert-based workflows.
USAGE
Equal highs/lows can be used as liquidity markers, either as entry points or as take-profit targets.
This tool is ideal for liquidity-based strategies and helps traders map out possible reversal or sweep zones that often precede aggressive moves.
Global M2 YoY % Increase signalThe script produces a signal each time the global M2 increases more than 2.5%. This usually coincides with bitcoin prices pumps, except when it is late in the business cycle or the bitcoin price / halving cycle.
It leverages dylanleclair Global M2 YoY % change, with several modifications:
adding a 10 week lead at the YoY Change plot for better visibility, so that the bitcoin pump moreless coincides with the YoY change.
signal increases > 2.5 in Global M2 at the point at which they occur with a green triangle up.
Stochastics + CM Williams VixFix (Simple Buy Signal)📈 Stochastics + CM Williams VixFix (Simple Buy Signal)
This indicator combines two powerful tools to detect potential bottoming opportunities:
✅ Stochastics: Looks for momentum reversals. A signal is triggered when both %K and %D are below the oversold threshold (default: 20), suggesting the asset is deeply oversold.
✅ CM Williams Vix Fix: A volatility-based fear detector. When it spikes above its dynamic threshold, it indicates potential panic selling — often preceding a market bounce.
💡 Buy Signal is generated when:
%K and %D are both below 20
VixFix shows a volatility spike (green condition)
Use this script to identify high-probability reversal setups, especially during market corrections or panic phases.
Auto TrendLines [TradingFinder] Support Resistance Signal Alerts🔵 Introduction
The trendline is one of the most essential tools in technical analysis, widely used in financial markets such as Forex, cryptocurrency, and stocks. A trendline is a straight line that connects swing highs or swing lows and visually indicates the market’s trend direction.
Traders use trendlines to identify price structure, the strength of buyers and sellers, dynamic support and resistance zones, and optimal entry and exit points.
In technical analysis, trendlines are typically classified into three categories: uptrend lines (drawn by connecting higher lows), downtrend lines (formed by connecting lower highs), and sideways trends (moving horizontally). A valid trendline usually requires at least three confirmed touchpoints to be considered reliable for trading decisions.
Trendlines can serve as the foundation for a variety of trading strategies, such as the trendline bounce strategy, valid breakout setups, and confluence-based analysis with other tools like candlestick patterns, divergences, moving averages, and Fibonacci levels.
Additionally, trendlines are categorized into internal and external, and further into major and minor levels, each serving unique roles in market structure analysis.
🔵 How to Use
Trendlines are a key component in technical analysis, used to identify market direction, define dynamic support and resistance zones, highlight strategic entry and exit points, and manage risk. For a trendline to be reliable, it must be drawn based on structural principles—not by simply connecting two arbitrary points.
🟣 Selecting Pivot Types Based on Trend Direction
The first step is to determine the market trend: uptrend, downtrend, or sideways.
Then, choose pivot points that match the trend type :
In an uptrend, trendlines are drawn by connecting low pivots, especially higher lows.
In a downtrend, trendlines are formed by connecting high pivots, specifically lower highs.
It is crucial to connect pivots of the same type and structure to ensure the trendline is valid and analytically sound.
🟣 Pivot Classification
This indicator automatically classifies pivot points into two categories :
Major Pivots :
MLL : Major Lower Low
MHL : Major Higher Low
MHH : Major Higher High
MLH : Major Lower High
These define the primary structure of the market and are typically used in broader structural analysis.
Minor Pivots :
mLL: minor Lower Low
mHL: minor Higher Low
mHH: minor Higher High
mLH: minor Lower High
These are used for drawing more precise trendlines within corrective waves or internal price movements.
Example : In a downtrend, drawing a trendline from an MHH to an mHH creates structural inconsistency and introduces noise. Instead, connect points like MHL to MHL or mLH to mLH for a valid trendline.
🟣 Drawing High-Precision Trendlines
To ensure a reliable trendline :
Use pivots of the same classification (Major with Major or Minor with Minor).
Ensure at least three valid contact points (three touches = structural confirmation).
Draw through candles with the least deviation (choose wicks or bodies based on confluence).
Preferably draw from right to left for better alignment with current market behavior.
Use parallel lines to turn a single trendline into a trendline zone, if needed.
🟣 Using Trendlines for Trade Entries
Bounce Entry: When price approaches the trendline and shows signs of reversal (e.g., a reversal candle, divergence, or support/resistance), enter in the direction of the trend with a logical stop-loss.
Breakout Entry: When price breaks through the trendline with strong momentum and a confirmation (such as a retest or break of structure), consider trading in the direction of the breakout.
🟣 Trendline-Based Risk Management
For bounce entries, the stop-loss is placed below the trendline or the last pivot low (in an uptrend).
For breakout entries, the stop-loss is set behind the breakout candle or the last structural level.
A broken trendline can also act as an exit signal from a trade.
🟣 Combining Trendlines with Other Tools (Confluence)
Trendlines gain much more strength when used alongside other analytical tools :
Horizontal support and resistance levels
Moving averages (such as EMA 50 or EMA 200)
Fibonacci retracement zones
Candlestick patterns (e.g., Engulfing, Pin Bar)
RSI or MACD divergences
Market structure breaks (BoS / ChoCH)
🔵 Settings
Pivot Period : This defines how sensitive the pivot detection is. A higher number means the algorithm will identify more significant pivot points, resulting in longer-term trendlines.
Alerts
Alert :
Enable or disable the entire alert system
Set a custom alert name
Choose how often alerts trigger (every time, once per bar, or on bar close)
Select the time zone for alert timestamps (e.g., UTC)
Each trendline type supports two alert types :
Break Alert : Triggered when price breaks the trendline
React Alert : Triggered when price reacts or bounces off the trendline
These alerts can be independently enabled or disabled for all trendline categories (Major/Minor, Internal/External, Up/Down).
Display :
For each of the eight trendline types, you can control :
Whether to show or hide the line
Whether to delete the previous line when a new one is drawn
Color, line style (solid, dashed, dotted), extension direction (e.g., right only), and width
Major lines are typically thicker and more opaque, while minor lines appear thinner and more transparent.
All settings are designed to give the user full control over the appearance, behavior, and alert system of the indicator, without requiring manual drawing or adjustments.
🔵 Conclusion
A trendline is more than just a line on the chart—it is a structural, strategic, and flexible tool in technical analysis that can serve as the foundation for understanding price behavior and making trading decisions. Whether in trending markets or during corrections, trendlines help traders identify market direction, key zones, and high-potential entry and exit points with precision.
The accuracy and effectiveness of a trendline depend on using structurally valid pivot points and adhering to proper market logic, rather than relying on guesswork or personal bias.
This indicator is built to solve that exact problem. It automatically detects and draws multiple types of trendlines based on actual price structure, separating them into Major/Minor and Internal/External categories, and respecting professional analytical principles such as pivot type, trend direction, and structural location.
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
CCI with Zero Signal by Edwin KCCI with Zero Signal by Edwin K is a custom Commodity Channel Index (CCI) indicator designed for traders to analyze market trends and momentum more effectively. It combines the CCI calculation with a visually distinct histogram and color-coded candlestick bars for enhanced clarity and decision-making.
Key Features:
CCI Line:
Plots the CCI line based on the specified length (default: 21).
Helps identify overbought or oversold conditions, momentum shifts, and trend reversals.
Zero Signal Line:
A horizontal line at 0 serves as a reference point to distinguish between bullish and bearish momentum.
Histogram:
Displays a histogram that reflects the CCI's values.
Histogram bars change colors dynamically based on their relation to the zero line and the trend's direction.
Green/Lime: Positive momentum (above zero).
Red/Maroon: Negative momentum (below zero).
Candlestick Coloring:
Automatically paints candlesticks based on the histogram's color.
Provides an intuitive visual cue for momentum shifts directly on the price chart.
Use Cases:
Trend Confirmation: Use the histogram and candlestick colors to confirm the strength and direction of trends.
Momentum Shifts: Identify transitions between bullish and bearish momentum when the CCI crosses the zero line.
Entry and Exit Points: Combine this indicator with other tools to pinpoint optimal trade entries and exits.
This indicator offers a user-friendly yet powerful visualization of the CCI, making it an excellent tool for traders aiming to enhance their technical analysis.
ThinkTech AI SignalsThink Tech AI Strategy
The Think Tech AI Strategy provides a structured approach to trading by integrating liquidity-based entries, ATR volatility thresholds, and dynamic risk management. This strategy generates buy and sell signals while automatically calculating take profit and stop loss levels, boasting a 64% win rate based on historical data.
Usage
The strategy can be used to identify key breakout and retest opportunities. Liquidity-based zones act as potential accumulation and distribution areas and may serve as future support or resistance levels. Buy and sell zones are identified using liquidity zones and ATR-based filters. Risk management is built-in, automatically calculating take profit and stop loss levels using ATR multipliers. Volume and trend filtering options help confirm directional bias using a 50 EMA and RSI filter. The strategy also allows for session-based trading, limiting trades to key market hours for higher probability setups.
Settings
The risk/reward ratio can be adjusted to define the desired stop loss and take profit calculations. The ATR length and threshold determine ATR-based breakout conditions for dynamic entries. Liquidity period settings allow for customized analysis of price structure for support and resistance zones. Additional trend and RSI filters can be enabled to refine trade signals based on moving averages and momentum conditions. A session filter is included to restrict trade signals to specific market hours.
Style
The strategy includes options to display liquidity lines, showing key support and resistance areas. The first 15-minute candle breakout zones can also be visualized to highlight critical market structure points. A win/loss statistics table is included to track trade performance directly on the chart.
This strategy is intended for descriptive analysis and should be used alongside other confluence factors. Optimize your trading process with Think Tech AI today!
Cumulative Volume Delta with SignalThis premium-grade technical indicator provides deep insights into market sentiment by tracking the difference between buying and selling pressure through volume analysis. SCVD offers a sophisticated approach to volume profile analysis, helping traders identify potential trend reversals and momentum shifts before price action confirms them.
Key Features:
Real-Time Volume Delta Analysis: Visualizes buying vs. selling pressure through color-coded candles
Smart Signal Line Integration: EMA-based signal line helps identify trend changes and trading opportunities
Multi-Timeframe Capabilities: Automatically selects optimal lower timeframes for precision or can be customized
Daily Reset Option: Anchor period functionality for intraday delta analysis
Professional Visualization: Clean, color-coded display with zero reference line
Trading Applications:
Identify divergences between price action and volume delta for potential reversals
Spot accumulation/distribution patterns through delta behavior
Use signal line crossovers for entry/exit timing
Confirm trend strength by analyzing delta momentum
Detect potential false breakouts through volume confirmation
Perfect for day traders, swing traders, and institutional investors who rely on volume analysis for decision-making. This indicator combines sophisticated volume delta metrics with an intuitive interface to provide actionable trading insights across all markets and timeframes.
ZenAlgo - QZenAlgo - Q
Description
ZenAlgo - Q is an oscillator based on the QQE (Quantitative Qualitative Estimation) method. This version incorporates refinements for additional visualization and interpretation options. It is designed to help traders observe momentum changes and divergence patterns in price movements.
Key Features
QQE-Based Calculation : Derived from the open-source QQE script by Glaz (Metastock Version of QQE), with modifications for alternative visualization.
Dual RSI-Based Analysis : Uses two RSI calculations to provide additional context on price movements.
Adaptive Trend Bands : Adjust dynamically based on the market conditions.
Divergence Identification : Highlights potential differences between price action and oscillator movement.
Dynamic Color Coding : Displays histogram bars to illustrate shifts in oscillator values.
Configurable Alerts : Enables notifications for specific oscillator conditions.
How It Works
The indicator calculates a smoothed RSI-based oscillator that tracks the relative strength of price movement. It applies an exponential moving average (EMA) smoothing to reduce noise while maintaining responsiveness.
Two adaptive bands are calculated using a variation of the QQE method, which helps define dynamic overbought and oversold conditions.
The histogram bars shift in color based on the position of the oscillator relative to the bands. Lighter shades indicate weaker momentum, while stronger momentum is represented by more saturated colors.
The script also includes a secondary RSI component, which provides an additional layer of analysis. This secondary RSI helps refine momentum trends by smoothing out short-term fluctuations.
Divergence identification is built-in, highlighting where price action deviates from oscillator readings. Bullish divergence occurs when price forms a lower low while the oscillator forms a higher low, and bearish divergence is identified when price forms a higher high while the oscillator forms a lower high.
The indicator does not generate buy or sell signals but instead provides contextual information that can be used alongside other trading strategies.
Use Cases
Trend Observation : Traders can use the histogram to observe whether momentum is strengthening or weakening over time. A shift in color can indicate a potential change in trend strength.
Divergence Analysis : By comparing oscillator divergence with price movement, traders can identify situations where price action may be losing momentum. Divergences do not guarantee reversals but can serve as an early warning to re-evaluate positions.
Momentum Tracking : The dual RSI structure allows users to monitor both short-term and long-term momentum. When both RSI components are aligned, it suggests a more stable trend, while divergence between them may indicate potential consolidation or trend shifts.
Supplementary Analysis : This indicator is best used as a supporting tool alongside volume-based or trend-following indicators. It helps visualize underlying price behavior but should not be used in isolation for decision-making.
Market Context Interpretation : The combination of adaptive bands and histogram visualization allows traders to assess how recent price action compares to historical movement, helping to place current conditions in a broader market context.
Attribution
This script is an adaptation of the open-source QQE script originally developed by Glaz. We acknowledge and appreciate the original author's work, which served as a foundation for our modifications.
Disclaimer
This indicator is intended for informational purposes only. It should not be interpreted as financial advice. Always conduct independent research and risk management before making trading decisions.
E9 MM Nuke signalScript identifies wickless candles on a specified higher timeframe and plots them on a lower timeframe (If desired), such as 15 minutes. It includes options to adjust the margin for error (e.g. 5 tick wick), higher timeframe, and toggle the volume filter with period adjustment.
Wickless candles signal strong market sentiment shifts, indicating areas of significant buying or selling pressure. These areas can become key levels of support or resistance, making them crucial to monitor for potential price revisits.
Why Price Revisits Wickless Areas
Manipulators often create artificial wickless candles to deceive traders. However, genuine market movements can also produce wickless candles, indicating a strong consensus among market participants. In either case, the price is likely to revisit these areas as traders and investors react to the perceived market sentiment shift.
Key Features:
Margin Input:
Description: Allows users to specify the margin in 0.01 tick increments to account for small wicks due to spread issues.
Example: A margin of 0.05 ticks means the script will consider candles wickless if the high is within 0.05 ticks of the open and the low is within 0.05 ticks of the open.
Volume Filter:
Description: Users can enable or disable a volume filter to consider only candles with a volume greater than the average volume over a specified period.
Default: Enabled by default.
Volume Period Input: Users can specify the period for calculating the average volume (e.g., 9 periods).
Higher Timeframe Input:
Description: Allows users to select the higher timeframe on which to identify wickless candles.
Options: H4 ("240"), Daily ("D"), Weekly ("W"), Monthly ("M").
Plotting:
Bearish Wickless Candles: Plotted with a red circle and a "🐻" emoji above the bar.
Bullish Wickless Candles: Plotted with a green circle and a "🐂" emoji below the bar.
Drawdown Visualisation█ OVERVIEW
The Drawdown Visualisation indicator calculates and displays the instrument’s drawdown (in percent) relative to its all‐time high (ATH) from a user‐defined start date. It provides customisable options for label appearance, threshold lines (0%, –50%, –100%), and can plot historic drawdown levels via pivot detection.
█ USAGE
This indicator should be used with the Percentage Retracement from ATH indicator.
█ KEY FEATURES
Custom Date Settings — Use a custom start date so that only specified price action is considered.
Retracement Level Calculation — Determines ATH and computes multiple retracement levels using percentages from 0% to –100%.
Visual Signals and Customisation — Plots configurable horizontal lines and labels that display retracement percentages and prices.
Time Filtering — Bases calculations on data from the desired time period.
Historic Drawdowns — Display historical drawdowns
█ PURPOSE
Assist traders in visualising the depth of price retracements from recent or historical peaks.
Identify critical zones where the market may find support or resistance after reaching an ATH.
Facilitate more informed entry and exit decisions by clearly demarcating retracement levels on the chart.
█ IDEAL USERS
Swing Traders — Looking to exploit pullbacks following strong upward moves.
Technical Analysts — Interested in pinpointing key retracement levels as potential reversal or continuation points.
Price Action Traders — Focused on the nuances of market peaks and subsequent corrections.
Strategy Developers — Keen to backtest and refine approaches centred on retracement dynamics.
VWMA with Buy/Sell Signalshe VWMA (Volume Weighted Moving Average) is a technical indicator that averages prices over a specified period while giving more weight to periods with higher trading volumes. This makes the VWMA more sensitive to price movements during high-volume trading compared to a simple moving average (SMA).
Adding Buy/Sell Signals to a VWMA-based script involves identifying trends or crossover points that indicate potential entry (Buy) or exit (Sell) opportunities.
Core Features of the Script:
VWMA Calculation:
Uses the typical price ((High + Low + Close) / 3) or closing price for computation.
Weighting is based on the volume traded in each period.
Machine Learning Moving Average [LuxAlgo]The Machine Learning Moving Average (MLMA) is a responsive moving average making use of the weighting function obtained Gaussian Process Regression method. Characteristic such as responsiveness and smoothness can be adjusted by the user from the settings.
The moving average also includes bands, used to highlight possible reversals.
🔶 USAGE
The Machine Learning Moving Average smooths out noisy variations from the price, directly estimating the underlying trend in the price.
A higher "Window" setting will return a longer-term moving average while increasing the "Forecast" setting will affect the responsiveness and smoothness of the moving average, with higher positive values returning a more responsive moving average and negative values returning a smoother but less responsive moving average.
Do note that an excessively high "Forecast" setting will result in overshoots, with the moving average having a poor fit with the price.
The moving average color is determined according to the estimated trend direction based on the bands described below, shifting to blue (default) in an uptrend and fushia (default) in downtrends.
The upper and lower extremities represent the range within which price movements likely fluctuate.
Signals are generated when the price crosses above or below the band extremities, with turning points being highlighted by colored circles on the chart.
🔶 SETTINGS
Window: Calculation period of the moving average. Higher values yield a smoother average, emphasizing long-term trends and filtering out short-term fluctuations.
Forecast: Sets the projection horizon for Gaussian Process Regression. Higher values create a more responsive moving average but will result in more overshoots, potentially worsening the fit with the price. Negative values will result in a smoother moving average.
Sigma: Controls the standard deviation of the Gaussian kernel, influencing weight distribution. Higher Sigma values return a longer-term moving average.
Multiplicative Factor: Adjusts the upper and lower extremity bounds, with higher values widening the bands and lowering the amount of returned turning points.
🔶 RELATED SCRIPTS
Machine-Learning-Gaussian-Process-Regression
SuperTrend-AI-Clustering
