Dynamic Volume RSI (DVRSI) [QuantAlgo]Introducing the Dynamic Volume RSI (DVRSI) by QuantAlgo 📈✨
Elevate your trading and investing strategies with the Dynamic Volume RSI (DVRSI) , a powerful tool designed to provide clear insights into market momentum and trend shifts. This indicator is ideal for traders and investors who want to stay ahead of the curve by using volume-responsive calculations and adaptive smoothing techniques to enhance signal clarity and reliability.
🌟 Key Features:
🛠 Customizable RSI Settings: Tailor the indicator to your strategy by adjusting the RSI length and price source. Whether you’re focused on short-term trades or long-term investments, DVRSI adapts to your needs.
🌊 Adaptive Smoothing: Enable adaptive smoothing to filter out market noise and ensure cleaner signals in volatile or choppy market conditions.
🎨 Dynamic Color-Coding: Easily identify bullish and bearish trends with color-coded candles and RSI plots, offering clear visual cues to track market direction.
⚖️ Volume-Responsive Adjustments: The DVRSI reacts to volume changes, giving greater significance to high-volume price moves and improving the accuracy of trend detection.
🔔 Custom Alerts: Stay informed with alerts for key RSI crossovers and trend changes, allowing you to act quickly on emerging opportunities.
📈 How to Use:
✅ Add the Indicator: Set up the DVRSI by adding it to your chart and customizing the RSI length, price source, and smoothing options to fit your specific strategy.
👀 Monitor Visual Cues: Watch for trend shifts through the color-coded plot and candles, signaling changes in momentum as the RSI crosses key levels.
🔔 Set Alerts: Configure alerts for critical RSI crossovers, such as the 50 line, ensuring you stay on top of potential market reversals and opportunities.
🔍 How It Works:
The Dynamic Volume RSI (DVRSI) is a unique indicator designed to provide more accurate and responsive signals by incorporating both price movement and volume sensitivity into the RSI framework. It begins by calculating the traditional RSI values based on a user-defined length and price source, but unlike standard RSI tools, the DVRSI applies volume-weighted adjustments to reflect the strength of market participation.
The indicator dynamically adjusts its sensitivity by factoring in volume to the RSI calculation, which means that price moves backed by higher volumes carry more weight, making the signal more reliable. This method helps identify stronger trends and reduces the risk of false signals in low-volume environments. To further enhance accuracy, the DVRSI offers an adaptive smoothing option that allows users to reduce noise during periods of market volatility. This adaptive smoothing function responds to market conditions, providing a cleaner signal by reducing erratic movements or price spikes that could lead to misleading signals.
Additionally, the DVRSI uses dynamic color-coding to visually represent the strength of bullish or bearish trends. The candles and RSI plots change color based on the RSI values crossing critical thresholds, such as the 50 level, offering an intuitive way to recognize trend shifts. Traders can also configure alerts for specific RSI crossovers (e.g., above 50 or below 40), ensuring that they stay informed of potential trend reversals and significant market shifts in real-time.
The combination of volume sensitivity, adaptive smoothing, and dynamic trend visualization makes the DVRSI a robust and versatile tool for traders and investors looking to fine-tune their market analysis. By incorporating both price and volume data, this indicator delivers more precise signals, helping users make informed decisions with greater confidence.
Disclaimer:
The Dynamic Volume RSI is designed to enhance your market analysis but should not be used as a sole decision-making tool. Always consider multiple factors before making any trading or investment decisions. Past performance is not indicative of future results.
Trend
Dynamic Touch Trendlines [QuantVue]The Dynamic Touch Trendlines (DTT) indicator automatically draws and manages trendlines on your chart, helping traders identify key support and resistance levels.
What sets the DTT indicator apart from other trendline indicators is its ability to let traders customize the number of touches required to validate a trendline. This flexibility allows you to fine-tune the indicator for different markets or trading styles, ensuring only strong trendlines with the specified number of touches are considered valid.
This indicator features both uptrend lines (drawn from pivot lows) and downtrend lines (drawn from pivot highs), making it suitable for detecting bullish and bearish trends.
An uptrend line connects three (default setting) or more significant lows, showing where price has historically found support. Traders often look for price to bounce off this line during pullbacks in an uptrend.
When price breaks below an uptrend line, it suggests a weakening of the bullish trend. This could mean that buyers are losing strength, and the market may be transitioning into a bearish phase, providing a potential opportunity for traders to enter short positions or exit long positions.
Conversely, a downtrend line connects three (default setting) or more significant highs, indicating potential resistance in a downtrend. Price action below this line can signal continued bearish momentum.
When price breaks above a downtrend line, it indicates a potential reversal of the bearish trend. This can signal the end of selling pressure and the beginning of a new bullish phase, offering traders a potential opportunity to enter long positions.
Key settings:
Minimum Touches: This sets the number of price touches required to validate a trendline. Increasing the minimum touches filters out weaker trends, ensuring that only more reliable trendlines are drawn.
Buffer: The buffer is used to account for minor price overshoots or near misses relative to the trendline. It creates a margin around the trendline, allowing price to come close to the line—whether it overshoots slightly or falls just short—and still count as a valid touch. This helps ensure that small price fluctuations or market noise don’t prevent valid trendline touches from being recognized, making the trendlines more reliable.
Trendline Break Source: Allows traders to define how a trendline is considered broken—either based on the close of the price bar or the wicks (highs and lows) of the price action.
The DTT indicator also features alerts whenever a new trendline is detected or an existing trendline is broken!
Dynamic ConfluenceThe Dynamic MA Confluence Indicator is a powerful tool designed to simplify your trading experience by automatically identifying the most influential moving average (MA) lengths on your chart. Whether you're using Simple Moving Averages (SMA) or Exponential Moving Averages (EMA), this indicator helps you pinpoint the MA length that holds the greatest confluence, allowing you to make informed trading decisions with ease.
How It Works:
This indicator analyzes a wide range of moving averages, from short-term to long-term, to determine which ones are closest to each other. By setting a "Proximity Percentage," you can control how close these MAs need to be to be considered as having confluence. The indicator then calculates the average of these close MAs to establish a dynamic support or resistance level on your chart.
Why Use This Indicator?
Automatic Optimization: Unsure of which MA length to apply? The indicator automatically highlights the MA length with the most confluence, giving you a clear edge in identifying significant market levels.
Adaptability: Choose between SMA and EMA to suit your trading strategy and market conditions.
Enhanced Decision-Making: By focusing on the MA length with the greatest influence, you can better anticipate market movements and adjust your strategies accordingly.
Customizable Sensitivity: Adjust the Proximity Percentage to fine-tune the indicator's sensitivity, ensuring it aligns with your trading preferences.
Key Feature:
Current Key Confluence MA Length: Displayed in an optional table, this feature shows the MA length that currently has the most impact on the confluence level, providing you with actionable insights at a glance.
Whether you're a seasoned trader or just starting, the Dynamic MA Confluence Indicator offers a streamlined approach to understanding market dynamics, helping you trade smarter and with more confidence. This presentation text is designed to clearly communicate the purpose, functionality, and benefits of the indicator, making it easy for users to understand its value and how it can enhance their trading strategies.
The Dynamic MA Confluence Indicator is a tool designed to assist traders in analyzing market trends. It should not be considered as financial advice or a guarantee of future performance. Trading involves significant risk, and it is possible to lose more than your initial investment. Users should conduct their own research and consider their financial situation before making trading decisions. Always consult with a financial advisor if you are unsure about any trading strategies or decisions. This disclaimer is intended to remind users of the inherent risks in trading and the importance of conducting their own due diligence.
Periodical Trend [BigBeluga]The Periodical Trend indicator is designed to provide a detailed analysis of market trends and volatility. It utilizes a combination of Moving Averages and volatility measures to plot trend line, highlight potential trend reversals, and indicate mean reversion opportunities. The indicator offers customizable display options, allowing traders to adjust for sensitivity, volatility bands, and price deviation visibility.
🔵 KEY FEATURES
● Periodical Trend Analysis
Uses (high + volatility) or (low - volatility) as the foundation for trend analysis with a set period.
// Condition to update the AVG array based on the selected mode
if mode == "Normal"
? bar_index == 122
: bar_index % period == 0
AVG.push(close) // Add the close price to the AVG array
// Update AVG array based on the period and price comparison
if bar_index % period == 0
if close > AVG.last() // If the current close is greater than the last stored value in AVG
AVG.push(low - vlt) // Add the low price minus volatility to the array
if close < AVG.last() // If the current close is lower than the last stored value in AVG
AVG.push(high + vlt) // Add the high price plus volatility to the array
Provides adjustable sensitivity modes ("Normal" and "Sensitive") for different market conditions.
Trend direction is visualized with dynamic color coding based on the relationship between the trend line and price.
● Volatility Bands
Displays upper and lower volatility bands derived from a moving average of price volatility (high-low).
The bands help identify potential breakout zones, overbought, or oversold conditions.
Users can toggle the visibility of the bands to suit their trading style.
● Mean Reversion Signals
Detects mean reversion opportunities when price deviates significantly from the trend line.
Includes both regular and strong mean reversion signals, marked directly on the chart.
Signals are based on oscillator crossovers, offering potential entry and exit points.
● Price Deviation Oscillator
Plots an oscillator that measures the deviation of price from the average trend line.
The oscillator is normalized using standard deviation, highlighting extreme price deviations.
Traders can choose to display the oscillator for in-depth analysis of price behavior relative to the trend.
● Dynamic Trend Coloring
The indicator colors the background on the direction of the trend.
Green indicates bullish trends, while blue indicates bearish trends.
The trend colors adapt dynamically to market conditions, providing clear visual cues for traders.
🔵 HOW TO USE
● Trend Analysis
The trend line represents the current market direction. A green trend line suggests a bullish trend, while a blue trend line indicates a bearish trend.
Use the trend line in conjunction with volatility bands to confirm potential breakouts or areas of consolidation.
● Volatility Bands
Volatility bands offer insight into potential overbought or oversold conditions.
Price exceeding these bands can signal a strong trend continuation or a possible reversal.
● Mean Reversion Strategies
Look for mean reversion signals (regular and strong) when price shows signs of reverting to the trend line after significant deviation.
Regular signals are represented by small dots, while strong signals are represented by larger circles.
These signals can be used as entry or exit points, depending on the market context.
● Price Deviation Analysis
The oscillator provides a detailed view of price deviations from the trend line.
A positive oscillator value indicates that the price is above the trend, while a negative value suggests it is below.
Use the oscillator to identify potential overbought or oversold conditions within the trend.
🔵 USER INPUTS
● Period
Defines the length of the period used for calculating the trend line. A higher period smooths out the trend, while a shorter period makes the trend line more sensitive to price changes.
● Mode
Choose between "Normal" and "Sensitive" modes for trend detection. The "Sensitive" mode responds more quickly to price changes, while the "Normal" mode offers smoother trend lines.
● Volatility Bands
Toggle the display of upper and lower volatility bands. These bands help identify potential areas of price exhaustion or continuation.
● Price Deviation
Toggle the display of the price deviation oscillator. This oscillator shows the deviation of the current price from the trend line and highlights extreme conditions.
● Mean Reversion Signals
Toggle the display of mean reversion signals. These signals highlight potential reversal points when the price deviates significantly from the trend.
● Strong Mean Reversion Signals
Toggle the display of stronger mean reversion signals, which occur at more extreme deviations from the trend.
● Width
Adjust the thickness of the trend line for better visibility on the chart.
🔵 CONCLUSION
The Periodical Trend indicator combines trend analysis, volatility bands, and mean reversion signals to provide traders with a comprehensive tool for market analysis. By offering customizable display options and dynamic trend coloring, this indicator can adapt to different trading styles and market conditions. Whether you are a trend follower or a mean reversion trader, the Periodical Trend indicator helps identify key market opportunities and potential reversals.
For optimal results, it is recommended to use this indicator alongside other technical analysis tools and within the context of a well-structured trading strategy.
Multi Adaptive Moving Average (MAMA)The Multi Adaptive Moving Average (MAMA) indicator is an advanced tool for technical analysis, designed to provide traders with a detailed understanding of market trends and potential future price movements. This indicator utilizes multiple Simple Moving Averages (SMAs) and forecasting techniques to enhance decision-making processes.
Simple Moving Averages (SMAs):
Short MA (20-period): This moving average is highly responsive to price changes, making it ideal for capturing short-term trends. It helps traders identify quick market shifts and potential entry or exit points.
Mid MA (50-period): This average strikes a balance between short- and long-term trends, offering insights into the market's intermediate direction. It aids in confirming the sustainability of short-term trends.
Long MA (100-period): By smoothing out price data over a longer period, this moving average is useful for identifying long-term trends and filtering out short-term volatility.
Very Long MA (200-period): Often considered a critical indicator for determining the overall market trend, this average helps confirm the direction and strength of long-term movements.
Forecasting:
Flat Forecast: This approach assumes that prices will remain constant in the near future, which is particularly useful in markets trading sideways without a clear trend direction.
Linear Regression Forecast: This method uses historical data to project future price movements, offering a dynamic forecast based on existing trends. It helps traders anticipate potential price changes and plan their strategies accordingly.
Advantages:
Comprehensive Trend Analysis: By incorporating four different SMAs, the indicator provides a layered view of market trends across various timeframes. This enables traders to identify potential trend reversals and continuations with greater accuracy.
Predictive Insights: The forecasting feature offers traders a forward-looking perspective, enabling them to anticipate market movements and adjust their trading strategies proactively. This can be especially advantageous in volatile markets.
Customization: The MAMA indicator is highly customizable, allowing traders to adjust parameters such as the source of price data and the inclusion of the current unclosed candle. This flexibility ensures that the indicator can be tailored to fit different trading styles and market conditions.
Visual Clarity: The use of distinct colors for each SMA and their forecasts enhances visual interpretation, making it easier for traders to quickly assess market conditions and make informed decisions. The inclusion of a legend further aids in distinguishing between the different moving averages and their respective forecasts.
How to Use:
Trend Confirmation: Use the alignment of the SMAs to confirm market trends. For example, when the Short MA crosses above the Mid and Long MAs, it may indicate a bullish trend, while the opposite could suggest a bearish trend.
Entry and Exit Points: Look for crossovers between the SMAs as potential signals for entering or exiting trades. The forecasts can help in timing these decisions by providing an expectation of future price movements.
Risk Management: Utilize the Very Long MA to set stop-loss and take-profit levels, as it reflects the long-term trend and can help in avoiding trades against the prevailing market direction.
The MAMA indicator is intended to support technical analysis and should not be used as the sole basis for making trading decisions. Financial markets are inherently uncertain, and past performance does not guarantee future results. Traders should use this tool in conjunction with other analytical methods and consider their risk tolerance and investment objectives. It is advisable to conduct thorough research and consult with a financial advisor before making significant trading decisions. Always be aware of the risks involved in trading and invest only what you can afford to lose.
Displacement [QuantVue]Displacement refers to a significant and forceful price movement that indicates a potential shift in market sentiment or trend. Displacement is characterized by a strong push in price action, often seen after a period of consolidation or within a trending market. It is a key concept used to identify the strength of a move and to confirm the direction of the market.
The "Displacement" indicator does this by focusing on identifying strong, directional price movements by combining candlestick analysis with volatility (ATR).
Displacement often appears as a group of candles that are all positioned in the same direction, these candles typically have large bodies and short wicks.
How the indicator works:
Body Size Requirement: Ensures that only candles with a significant body size (relative to their total range) are considered, helping to identify strong market moves.
Consecutive Candle Analysis: Identifies shifts in market sentiment by requiring a series of consecutive bullish or bearish candles to confirm a potential change in trend.
ATR-Based Analysis: Uses the Average True Range (ATR) to gauge market volatility and filter out minor price fluctuations, focusing on substantial movements.
Once all of the requirements are met a triangle is plotted above or below the bar.
Trend Signals with TP & SL [UAlgo] StrategyThe "Trend Signals with TP & SL Strategy" is a trading strategy designed to capture trend continuation signals while incorporating sophisticated risk management techniques. This strategy is tailored for traders who wish to capitalize on trending market conditions with precise entry and exit points, automatically calculating Take Profit (TP) and Stop Loss (SL) levels based on either Average True Range (ATR) or percentage values. The strategy aims to enhance trade management by preventing multiple simultaneous positions and dynamically adapting to changing market conditions.
This strategy is highly configurable, allowing traders to adjust sensitivity, the ATR calculation method, and the cloud moving average length. Additionally, the strategy can display buy and sell signals directly on the chart, along with visual representation of entry points, stop losses, and take profits. It also features a cloud-based trend analysis using a MACD-driven color fill that indicates the strength and direction of the trend.
🔶 Key Features
Configurable Trend Continuation Signals:
Source Selection: The strategy uses the midpoint of the high-low range as the default source, but it is adjustable.
Sensitivity: The sensitivity of the trend signals can be adjusted using a multiplier, ranging from 0.5 to 5.
ATR Calculation: The strategy allows users to choose between two ATR calculation methods for better adaptability to different market conditions.
Cloud Moving Average: Traders can adjust the cloud moving average length, which is used in conjunction with MACD to provide a visual trend indication.
Take Profit & Stop Loss Management:
ATR-Based or Percent-Based: The strategy offers flexibility in setting TP and SL levels, allowing traders to choose between ATR-based multipliers or fixed percentage values.
Dynamic Adjustment: TP and SL levels are dynamically adjusted according to the selected method, ensuring trades are managed based on real-time market conditions.
Prevention of Multiple Positions:
Single Position Control: To reduce risk and enhance strategy reliability, the strategy includes an option to prevent multiple positions from being opened simultaneously.
Visual Trade Indicators:
Buy/Sell Signals: Clearly displays buy and sell signals on the chart for easy interpretation.
Entry, SL, and TP Lines: Draws lines for entry price, stop loss, and take profit directly on the chart, helping traders to monitor trades visually.
Trend Cloud: A color-filled cloud based on MACD and the cloud moving average provides a visual cue of the trend’s direction and strength.
Performance Summary Table:
In-Chart Statistics: A table in the top right of the chart displays key performance metrics, including total trades, wins, losses, and win rate percentage, offering a quick overview of the strategy’s effectiveness.
🔶 Interpreting the Indicator
Trend Signals: The strategy identifies trend continuation signals based on price action relative to an ATR-based threshold. A buy signal is generated when the price crosses above a key level, indicating an uptrend. Conversely, a sell signal occurs when the price crosses below a level, signaling a downtrend.
Cloud Visualization: The cloud, derived from MACD and moving averages, changes color to reflect the current trend. A positive cloud in aqua suggests an uptrend, while a red cloud indicates a downtrend. The transparency of the cloud offers further nuance, with more solid colors denoting stronger trends.
Entry and Exit Management: Once a trend signal is generated, the strategy automatically sets TP and SL levels based on your chosen method (ATR or percentage). The stop loss and take profit lines will appear on the chart, showing where the strategy will exit the trade. If the price reaches either the SL or TP, the trade is closed, and the respective line is deleted from the chart.
Performance Metrics: The strategy’s performance is tracked in real-time with an in-chart table. This table provides essential information about the number of trades executed, the win/loss ratio, and the overall win rate. This information helps traders assess the strategy's effectiveness and make necessary adjustments.
This strategy is designed for those who seek to engage with trending markets, offering robust tools for entry, exit, and overall trade management. By understanding and leveraging these features, traders can potentially improve their trading outcomes and risk management.
🔷 Related Script
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Change in State of Delivery (CISD) [LuxAlgo]The Change In State Of Delivery (CISD) indicator detects and displays Change in State Of Delivery, a concept related to market structures.
Users can choose between two different CISD detection methods. Various filtering options are also included to filter out less significant CISDs.
🔶 USAGE
A Change in State of Delivery (CISD) is a concept closely related to market structures, where price breaks a level of interest, confirming trends and their continuations from the resulting breakouts.
Unlike more traditional market structures which rely on swing points, CISDs rely on a persistent sequence of candles, using the sequence extremes as breakout levels.
CISDs are detected as follows:
Bullish: The price closes above the opening price of the first candle in a sequence of bearish candles (or its own opening price if it's the only candle).
Bearish: The price closes below the opening price of the first candle in a sequence of bullish candles (or its own opening price if it's the only candle).
If a newly detected CISD aligns with the indicator's current established trend, this confirms a trend continuation (represented with a dashed line).
On the other hand, if a newly detected CISD is in the opposite direction to the detected trend it can confirm a trend reversal (represented with a solid line).
🔹 Liquidity Sweep Detection Method
Using Liquidity Sweeps to update CISD breakout levels allows us to obtain less frequent and more relevant levels that are less sensitive to noisy price variations.
Sweeps are obtained from detected Swing Points , with a higher Swing Length allowing us to obtain longer-term swing levels and potentially more detected sweeps from a specific level over time.
Note: The 'Swing Length' setting is only applicable on the Liquidity Sweep Detection Method and will only change the Liquidity levels.
A Liquidity Sweep is valid when the price reaches an important liquidity level , after which the price closes below/above this level.
Bullish scenario: The price goes below a previous unbroken Swing Low but closes above.
Bearish scenario: The price goes above a previous unbroken Swing High but closes below.
After a Liquidity Sweep has been detected, the last level of importance acts as support/resistance . Breaking this level in the other direction changes the state of delivery .
Users must keep observing the price and significant levels, as highlighted by the white rectangle in the above example.
🔹 CISD Filtering
Users can adjust the following two settings:
Minimum CISD Duration: The minimum length of the 'CISD' line
Maximum Swing Validity: The maximum length of the 'CISD' line; potential CISD lines that aren't broken are deleted when exceeding the limit.
The chart can get cluttered when the Minimum CISD Duration is low. Users could focus on a switch in trend (first solid line CISD ), where the following dashed CISD lines can be seen as extra opportunities/confirmations.
🔶 DETAIL
🔹 Using Different Timeframes
When an important liquidity level (Previous Swing high/low, FVG, etc.) is reached on the higher timeframe, the user can move to a lower timeframe to check whether there is a CISD .
Above example:
The high of the last candle breaches a liquidity level (previous Swing High). The opening price of the last candle acts as a trigger/confirmation level.
A confirmed CISD is seen in a lower timeframe, just after this Liquidity Sweep. This could be an early opportunity.
Later, a confirmed CISD on the higher timeframe is established.
🔶 SETTINGS
Detection Method: Classic or Liquidity Sweep
Swing Length: Period used for the swing detection, with higher values returning longer-term Swing Levels.
Minimum CISD Duration: The minimum length of the CISD line
Maximum Swing Validity: The maximum length of the CISD line; potential CISD lines that aren't broken are deleted when exceeding the limit.
Radius Trend [ChartPrime]RADIUS TREND
⯁ OVERVIEW
The Radius Trend [ ChartPrime ] indicator is an innovative technical analysis tool designed to visualize market trends using a dynamic, radius-based approach. By incorporating adaptive bands that adjust based on price action and volatility, this indicator provides traders with a unique perspective on trend direction, strength, and potential reversal points.
The Radius Trend concept involves creating a dynamic trend line that adjusts its angle and position based on market movements, similar to a radius sweeping across a chart. This approach allows for a more fluid and adaptive trend analysis compared to traditional linear trend lines.
◆ KEY FEATURES
Dynamic Trend Band: Calculates and plots a main trend band that adapts to market conditions.
Radius-Based Adjustment: Uses a step-based radius approach to adjust the trend band angle.
// Apply step angle to trend lines
if bar_index % n == 0 and trend
multi1 := 0
multi2 += step
band += distance1 * multi2
if bar_index % n == 0 and not trend
multi1 += step
multi2 := 0
band -= distance1 * multi1
Volatility-Adjusted Calculations: Incorporates price range volatility for more accurate band placement.
Trend Direction Visualization: Provides clear color-coding to distinguish between uptrends and downtrends.
Flexible Parameters: Allows users to adjust the radius step and initial distance for customized analysis.
◆ USAGE
Trend Identification: Use the color and direction of the main band to determine the current market trend.
Trend Strength Analysis: Observe the angle and consistency of the band for insights into trend strength.
Reversal Detection: Watch for price crossing the main band or crossing a dashed band as a potential trend reversal signal.
Volatility Assessment: The distance between price and bands can provide insights into market volatility.
⯁ USER INPUTS
Radius Step: Controls the rate of angle adjustment for the trend band (default: 0.15, step: 0.001).
Start Points Distance: Sets the initial distance multiplier for band calculations (default: 2, step: 0.1).
The Radius Trend indicator offers traders a unique and dynamic approach to trend analysis. By combining radius-based trend adjustments with volatility-sensitive calculations, it provides a fluid representation of market trends. This indicator is particularly useful for traders looking to identify trend persistence, potential reversal points, and adaptive support/resistance levels across various market conditions and timeframes.
Gaussian Kernel Smoothing EMAGaussian Kernel Smoothing EMA
The Gaussian Kernel Smoothing EMA integrates the exponential moving average with kernel smoothing techniques to refine the trend tool. Kernel smoothing is a non-parametric technique used to estimate a smooth curve from a set of data points. It is particularly useful in reducing noise and capturing the underlying structure of data. The smoothed value at each point is calculated as a weighted average of neighboring points, with the weights determined by a kernel function.
The Gaussian kernel is a popular choice in kernel smoothing due to its properties of being smooth, symmetric, and having infinite support. This function gives higher weights to data points closer to the target point and lower weights to those further away, resulting in a smooth and continuous estimate. Since price isn't normally distributed a logarithmic transformation is performed to remove most of its skewness to be able to fit the Gaussian kernel.
This indicator also has a bandwidth, which in kernel smoothing controls the width of the window over which the smoothing is performed. It determines how much influence nearby data points have on the smoothed value. In this indicator, the bandwidth is dynamically adjusted based on the standard deviation of the log-transformed prices so that the smoothing adapts to the underlying variability and potential volatility.
Bandwidth Factor: The bandwidth factor in this indicator is used to adjust the degree of the smoothing applied to the MA. In kernel smoothing, Bandwidth controls the width of the window over which the smoothing is applied. It determines how many data points around a central point are considered when calculating a smooth value. A smaller bandwidth results in less smoothing, while a larger bandwidth smooths out more noise, leading to a broader, more general trend.
Polynomial Regression Keltner Channel [ChartPrime]Polynomial Regression Keltner Channel
⯁ OVERVIEW
The Polynomial Regression Keltner Channel [ ChartPrime ] indicator is an advanced technical analysis tool that combines polynomial regression with dynamic Keltner Channels. This indicator provides traders with a sophisticated method for trend analysis, volatility assessment, and identifying potential overbought and oversold conditions.
◆ KEY FEATURES
Polynomial Regression: Uses polynomial regression for trend analysis and channel basis calculation.
Dynamic Keltner Channels: Implements Keltner Channels with adaptive volatility-based bands.
Overbought/Oversold Detection: Provides visual cues for potential overbought and oversold market conditions.
Trend Identification: Offers clear trend direction signals and change indicators.
Multiple Band Levels: Displays four levels of upper and lower bands for detailed market structure analysis.
Customizable Visualization: Allows toggling of additional indicator lines and signals for enhanced chart analysis.
◆ FUNCTIONALITY DETAILS
⬥ Polynomial Regression Calculation:
Implements a custom polynomial regression function for trend analysis.
Serves as the basis for the Keltner Channel, providing a smoothed centerline.
//@function Calculates polynomial regression
//@param src (series float) Source price series
//@param length (int) Lookback period
//@returns (float) Polynomial regression value for the current bar
polynomial_regression(src, length) =>
sumX = 0.0
sumY = 0.0
sumXY = 0.0
sumX2 = 0.0
sumX3 = 0.0
sumX4 = 0.0
sumX2Y = 0.0
n = float(length)
for i = 0 to n - 1
x = float(i)
y = src
sumX += x
sumY += y
sumXY += x * y
sumX2 += x * x
sumX3 += x * x * x
sumX4 += x * x * x * x
sumX2Y += x * x * y
slope = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX)
intercept = (sumY - slope * sumX) / n
n - 1 * slope + intercept
⬥ Dynamic Keltner Channel Bands:
Calculates ATR-based volatility for dynamic band width adjustment.
Uses a base multiplier and adaptive volatility factor for flexible band calculation.
Generates four levels of upper and lower bands for detailed market structure analysis.
atr = ta.atr(length)
atr_sma = ta.sma(atr, 10)
// Calculate Keltner Channel Bands
dynamicMultiplier = (1 + (atr / atr_sma)) * baseATRMultiplier
volatility_basis = (1 + (atr / atr_sma)) * dynamicMultiplier * atr
⬥ Overbought/Oversold Indicator line and Trend Line:
Calculates an OB/OS value based on the price position relative to the innermost bands.
Provides visual representation through color gradients and optional signal markers.
Determines trend direction based on the polynomial regression line movement.
Generates signals for trend changes, overbought/oversold conditions, and band crossovers.
◆ USAGE
Trend Analysis: Use the color and direction of the basis line to identify overall trend direction.
Volatility Assessment: The width and expansion/contraction of the bands indicate market volatility.
Support/Resistance Levels: Multiple band levels can serve as potential support and resistance areas.
Overbought/Oversold Trading: Utilize OB/OS signals for potential reversal or pullback trades.
Breakout Detection: Monitor price crossovers of the outermost bands for potential breakout trades.
⯁ USER INPUTS
Length: Sets the lookback period for calculations (default: 100).
Source: Defines the price data used for calculations (default: HLC3).
Base ATR Multiplier: Adjusts the base width of the Keltner Channels (default: 0.1).
Indicator Lines: Toggle to show additional indicator lines and signals (default: false).
⯁ TECHNICAL NOTES
Implements a custom polynomial regression function for efficient trend calculation.
Uses dynamic ATR-based volatility adjustment for adaptive channel width.
Employs color gradients and opacity levels for intuitive visual representation of market conditions.
Utilizes Pine Script's plotchar function for efficient rendering of signals and heatmaps.
The Polynomial Regression Keltner Channel indicator offers traders a sophisticated tool for trend analysis, volatility assessment, and trade signal generation. By combining polynomial regression with dynamic Keltner Channels, it provides a comprehensive view of market structure and potential trading opportunities. The indicator's adaptability to different market conditions and its customizable nature make it suitable for various trading styles and timeframes.
Bias Finder [UAlgo]The "Bias Finder " indicator is a tool designed to help traders identify market bias and trends effectively. This indicator leverages smoothed Heikin Ashi candles and oscillators to provide a clear visual representation of market trends and potential reversals. By utilizing higher timeframes and smoothing techniques, the indicator aims to filter out market noise and offer a more reliable signal for trading decisions.
🔶 Key Features
Heikin Ashi Candles: The indicator uses Heikin Ashi candles, a special type of candlestick that incorporates information from the previous candle to potentially provide smoother visuals and highlight potential trend direction.
Oscillator: The indicator calculates an oscillator based on the difference between the smoothed opening and closing prices of a higher timeframe. This oscillator helps visualize the strength of the bias.
Light Teal: Strong bullish trend.
Dark Teal: Weakening bullish trend.
Light Red: Strong bearish trend.
Dark Red: Weakening bearish trend.
Standard Deviation: The indicator can optionally display upper and lower standard deviation bands based on the Heikin Ashi high and low prices. These bands can help identify potential breakout areas.
Oscillator Period: Adjust the sensitivity of the oscillator.
Higher Timeframe: Select a timeframe for the Heikin Ashi candles and oscillator calculations (must be equal to or greater than the chart's timeframe).
Display Options: Choose whether to display Heikin Ashi candles, market bias fill, standard deviation bands, and HA candle colors based on the bias.
Alerts: Enable/disable specific alerts and customize their messages.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
LOWESS (Locally Weighted Scatterplot Smoothing) [ChartPrime]LOWESS (Locally Weighted Scatterplot Smoothing)
⯁ OVERVIEW
The LOWESS (Locally Weighted Scatterplot Smoothing) [ ChartPrime ] indicator is an advanced technical analysis tool that combines LOWESS smoothing with a Modified Adaptive Gaussian Moving Average. This indicator provides traders with a sophisticated method for trend analysis, pivot point identification, and breakout detection.
◆ KEY FEATURES
LOWESS Smoothing: Implements Locally Weighted Scatterplot Smoothing for trend analysis.
Modified Adaptive Gaussian Moving Average: Incorporates a volatility-adapted Gaussian MA for enhanced trend detection.
Pivot Point Identification: Detects and visualizes significant pivot highs and lows.
Breakout Detection: Tracks and optionally displays the count of consecutive breakouts.
Gaussian Scatterplot: Offers a unique visualization of price movements using randomly colored points.
Customizable Parameters: Allows users to adjust calculation length, pivot detection, and visualization options.
◆ FUNCTIONALITY DETAILS
⬥ LOWESS Calculation:
Utilizes a weighted local regression to smooth price data.
Adapts to local trends, reducing noise while preserving important price movements.
⬥ Modified Adaptive Gaussian Moving Average:
Combines Gaussian weighting with volatility adaptation using ATR and standard deviation.
Smooths the Gaussian MA using LOWESS for enhanced trend visualization.
⬥ Pivot Point Detection and Visualization:
Identifies pivot highs and lows using customizable left and right bar counts.
Draws lines and labels to mark broke pivot points on the chart.
⬥ Breakout Tracking:
Monitors price crossovers of pivot lines to detect breakouts.
Optionally displays and updates the count of consecutive breakouts.
◆ USAGE
Trend Analysis: Use the color and direction of the smoothed Gaussian MA line to identify overall trend direction.
Breakout Trading: Monitor breakouts from pivot levels and their persistence using the breakout count feature.
Volatility Assessment: The spread of the Gaussian scatterplot can provide insights into market volatility.
⯁ USER INPUTS
Length: Sets the lookback period for LOWESS and Gaussian MA calculations (default: 30).
Pivot Length: Determines the number of bars to the left for pivot calculation (default: 5).
Count Breaks: Toggle to show the count of consecutive breakouts (default: false).
Gaussian Scatterplot: Toggle to display the Gaussian MA as a scatterplot (default: true).
⯁ TECHNICAL NOTES
Implements a custom LOWESS function for efficient local regression smoothing.
Uses a modified Gaussian MA calculation that adapts to market volatility.
Employs Pine Script's line and label drawing capabilities for clear pivot point visualization.
Utilizes random color generation for the Gaussian scatterplot to enhance visual distinction between different time periods.
The LOWESS (Locally Weighted Scatterplot Smoothing) indicator offers traders a sophisticated tool for trend analysis and breakout detection. By combining advanced smoothing techniques with pivot point analysis, it provides a comprehensive view of market dynamics. The indicator's adaptability to different market conditions and its customizable nature make it suitable for various trading styles and timeframes.
Brooks Always In [KintsugiTrading]Brooks Always In
Overview:
The "Brooks Always In Indicator" by KintsugiTrading is a tool designed for traders who follow price action methodologies inspired by Al Brooks. This indicator identifies key bar patterns and breakouts, plots an Exponential Moving Average (EMA), and highlights consecutive bullish and bearish bars. It is intended to assist traders in making informed decisions based on price action dynamics.
Features:
Consecutive Bar Patterns:
Identifies and highlights consecutive bullish and bearish bars.
Differentiates between bars that are above/below the EMA and those that are not.
Customizable EMA:
Option to display an Exponential Moving Average (EMA) with user-defined length and offset.
The EMA can be smoothed using various methods such as SMA, EMA, SMMA (RMA), WMA, and VWMA.
Breakout Patterns:
Recognizes bullish and bearish breakout bars and outside bars.
Tracks inside bars and prior bar conditions to better understand the market context.
Customizable Display:
Users can display or hide the EMA, consecutive bar patterns, and consecutive bars relative to the moving average.
How to Use:
Customize Settings:
First, I like to navigate to the top right corner of the chart (bolt icon), and change both the bull and bear body color to match the background (white/black) - this helps the user visualize the indicator far better.
Next, Toggle to display EMA, consecutive bar patterns, and consecutive bars relative to the moving average using the provided input options.
Adjust the EMA length, source, and offset as per your trading strategy.
Select the smoothing method and length for the EMA if desired.
Analyze Key Patterns:
Observe the highlighted bars on the chart to identify consecutive bullish and bearish patterns.
Use the plotted EMA to gauge the general trend and analyze the relationship between price bars and the moving average.
Informed Decision Making:
Utilize the identified bar patterns and breakouts to make informed trading decisions, such as identifying potential entry and exit points based on price action dynamics.
Good luck with your trading!
Trend LinesThis script, titled "Trend Lines," is designed to detect and plot significant trend lines on a TradingView chart, based on pivot points. It highlights both uptrend and downtrend lines using different colors and allows customization of line styles, including color and thickness. Here's a breakdown of how the script works:
Inputs
Left Bars (lb) and Right Bars (rb): These inputs determine the number of bars to the left and right of a pivot point used to identify significant highs and lows.
Show Pivot Points: A boolean input to display markers at detected pivot points on the chart.
Show Old Line as Dashed: A boolean input to display older trend lines as dashed for visual distinction.
Uptrend Line Color (ucolor) and Downtrend Line Color (dcolor): Color inputs to customize the appearance of uptrend and downtrend lines.
Uptrend Line Thickness (uthickness) and Downtrend Line Thickness (dthickness): Inputs to adjust the thickness of the trend lines.
Calculations
Pivot Highs and Lows: The script calculates potential pivot highs and lows by looking at lb bars to the left and rb bars to the right. If a bar's high is the highest (or low is the lowest) within this window, it is considered a pivot point.
Trend Lines: The script connects the most recent and previous pivot highs to form downtrend lines, and the most recent and previous pivot lows to form uptrend lines. These lines are drawn with the specified color and thickness.
Angles: The angle of each trend line is calculated to determine whether the trend is strengthening or weakening. If the trend changes significantly, the line's extension is adjusted accordingly.
Plotting
Pivot Point Markers: If Show Pivot Points is enabled, markers labeled "H" for highs and "L" for lows are plotted at the pivot points.
Trend Lines: The script draws lines between pivot points, coloring them according to the trend direction (uptrend or downtrend). If Show Old Line as Dashed is enabled, the script sets older lines to a dashed style to indicate they are no longer the most recent trend lines.
This script is useful for traders who want to visually identify key support and resistance levels based on historical price action, helping them to make more informed trading decisions. The customization options allow traders to tailor the appearance of the trend lines to suit their personal preferences or charting style.
OrderBlock Trend (CISD)OrderBlock Trend (CISD) Indicator
Overview:
The "OrderBlock Trend (CISD)" AKA: change in state of delivery by ICT inner circle trader this indicator is designed to help traders identify and visualize market trends based on higher timeframe candle behavior. This script leverages the concept of order blocks, which are price levels where significant buying or selling activity has occurred, to signal potential trend reversals or continuations. By analyzing bullish and bearish order blocks on a higher timeframe, the indicator provides visual cues and statistical insights into the market's current trend dynamics.
Key Features:
Higher Timeframe Analysis: The indicator uses a higher timeframe (e.g., Daily) to assess the trend direction based on the open and close prices of candles. This approach helps in identifying more significant and reliable trend changes, filtering out noise from lower timeframes.
Bullish and Bearish Order Blocks: The script detects the first bullish or bearish candle on the selected higher timeframe and uses these candles as reference points (order blocks) to determine the trend direction. A bullish trend is indicated when the current price is above the last bearish order block's open price, and a bearish trend is indicated when the price is below the last bullish order block's open price.
Visual Trend Indication: The indicator visually represents the trend using background colors and plot shapes:
A green background and a square shape above the bars indicate a bullish trend.
A red background and a square shape above the bars indicate a bearish trend.
Candle Count and Statistics: The script keeps track of the number of up and down candles during bullish and bearish trends, providing percentages of up and down candles in each trend. This data is displayed in a table, giving traders a quick overview of market sentiment during each trend phase.
User Customization: The higher timeframe can be adjusted according to the trader's preference, allowing flexibility in trend analysis based on different time horizons.
Concepts and Calculations:
The "OrderBlock Trend (CISD)" indicator is based on the concept of order blocks, a key area where institutional traders are believed to place large orders, creating significant support or resistance levels. By identifying these blocks on a higher timeframe, the indicator aims to highlight potential trend reversals or continuations. The use of higher timeframe data helps filter out minor fluctuations and focus on more meaningful price movements.
The candle count and percentage calculations provide additional context, allowing traders to understand the proportion of bullish or bearish candles within each trend. This information can be useful for assessing the strength and consistency of a trend.
How to Use:
Select the Higher Timeframe: Choose the higher timeframe (e.g., Daily) that best suits your trading strategy. The default setting is "D" (Daily), but it can be adjusted to other timeframes as needed.
Interpret the Trend Signals:
A green background indicates a bullish trend, while a red background indicates a bearish trend. The corresponding square shapes above the bars reinforce these signals.
Use the information on the proportion of up and down candles during each trend to gauge the trend's strength and consistency.
Trading Decisions: The indicator can be used in conjunction with other technical analysis tools and indicators to make informed trading decisions. It is particularly useful for identifying trend reversals and potential entry or exit points based on the behavior of higher timeframe order blocks.
Customization and Optimization: Experiment with different higher timeframes and settings to optimize the indicator for your specific trading style and preferences.
Conclusion:
The "OrderBlock Trend (CISD)" indicator offers a comprehensive approach to trend analysis, combining the power of higher timeframe order blocks with clear visual cues and statistical insights. By understanding the underlying concepts and utilizing the provided features, traders can enhance their trend detection and decision-making processes in the markets.
Disclaimer:
This indicator is intended for educational purposes and should be used in conjunction with other analysis methods. Always perform your own research and risk management before making trading decisions.
Some known bugs when you switch to lower timeframe while using daily timeframe data it didn't use the daily candle close to establish the trend change but your current time frame If some of you know how to fix it that would be great if you help me to I would try my best to fix this in the future :) credit to ChatGPT 4o
Multi-Regression StrategyIntroducing the "Multi-Regression Strategy" (MRS) , an advanced technical analysis tool designed to provide flexible and robust market analysis across various financial instruments.
This strategy offers users the ability to select from multiple regression techniques and risk management measures, allowing for customized analysis tailored to specific market conditions and trading styles.
Core Components:
Regression Techniques:
Users can choose one of three regression methods:
1 - Linear Regression: Provides a straightforward trend line, suitable for steady markets.
2 - Ridge Regression: Offers a more stable trend estimation in volatile markets by introducing a regularization parameter (lambda).
3 - LOESS (Locally Estimated Scatterplot Smoothing): Adapts to non-linear trends, useful for complex market behaviors.
Each regression method calculates a trend line that serves as the basis for trading decisions.
Risk Management Measures:
The strategy includes nine different volatility and trend strength measures. Users select one to define the trading bands:
1 - ATR (Average True Range)
2 - Standard Deviation
3 - Bollinger Bands Width
4 - Keltner Channel Width
5 - Chaikin Volatility
6 - Historical Volatility
7 - Ulcer Index
8 - ATRP (ATR Percentage)
9 - KAMA Efficiency Ratio
The chosen measure determines the width of the bands around the regression line, adapting to market volatility.
How It Works:
Regression Calculation:
The selected regression method (Linear, Ridge, or LOESS) calculates the main trend line.
For Ridge Regression, users can adjust the lambda parameter for regularization.
LOESS allows customization of the point span, adaptiveness, and exponent for local weighting.
Risk Band Calculation:
The chosen risk measure is calculated and normalized.
A user-defined risk multiplier is applied to adjust the sensitivity.
Upper and lower bounds are created around the regression line based on this risk measure.
Trading Signals:
Long entries are triggered when the price crosses above the regression line.
Short entries occur when the price crosses below the regression line.
Optional stop-loss and take-profit mechanisms use the calculated risk bands.
Customization and Flexibility:
Users can switch between regression methods to adapt to different market trends (linear, regularized, or non-linear).
The choice of risk measure allows adaptation to various market volatility conditions.
Adjustable parameters (e.g., regression length, risk multiplier) enable fine-tuning of the strategy.
Unique Aspects:
Comprehensive Regression Options:
Unlike many indicators that rely on a single regression method, MRS offers three distinct techniques, each suitable for different market conditions.
Diverse Risk Measures: The strategy incorporates a wide range of volatility and trend strength measures, going beyond traditional indicators to provide a more nuanced view of market dynamics.
Unified Framework:
By combining advanced regression techniques with various risk measures, MRS offers a cohesive approach to trend identification and risk management.
Adaptability:
The strategy can be easily adjusted to suit different trading styles, timeframes, and market conditions through its various input options.
How to Use:
Select a regression method based on your analysis of the current market trend (linear, need for regularization, or non-linear).
Choose a risk measure that aligns with your trading style and the market's current volatility characteristics.
Adjust the length parameter to match your preferred timeframe for analysis.
Fine-tune the risk multiplier to set the desired sensitivity of the trading bands.
Optionally enable stop-loss and take-profit mechanisms using the calculated risk bands.
Monitor the regression line for potential trend changes and the risk bands for entry/exit signals.
By offering this level of customization within a unified framework, the Multi-Regression Strategy provides traders with a powerful tool for market analysis and trading decision support. It combines the robustness of regression analysis with the adaptability of various risk measures, allowing for a more comprehensive and flexible approach to technical trading.
Crab Harmonic Pattern [TradingFinder] Harmonic Chart patterns🔵 Introduction
The Crab pattern is recognized as a reversal pattern in technical analysis, utilizing Fibonacci numbers and percentages for chart analysis. This pattern can predict suitable price reversal areas on charts using Fibonacci ratios.
The structure of the Crab pattern can manifest in both bullish and bearish forms on the chart. By analyzing this structure, traders can identify points where the price direction changes, which are essential for making informed trading decisions.
The pattern's structure is visually represented on charts as shown below. To gain a deeper understanding of the Crab pattern's functionality, it is beneficial to become familiar with its various harmonic forms.
🟣 Types of Crab Patterns
The Crab pattern is categorized into two types based on its structure: bullish and bearish. The bullish Crab is denoted by the letter M, while the bearish Crab is indicated by the letter W in technical analysis.
Typically, a bullish Crab pattern signals a potential price increase, whereas a bearish Crab pattern suggests a potential price decrease on the chart.
The direction of price movement depends significantly on the price's position within the chart. By identifying whether the pattern is bullish or bearish, traders can determine the likely direction of the price reversal.
Bullish Crab :
Bearish Crab :
🔵 How to Use
When trading using the Crab pattern, crucial parameters include the end time of the correction and the point at which the chart reaches its peak. Generally, the best time to buy is when the chart nears the end of its correction, and the best time to sell is when it approaches the peak price.
As we discussed, the end of the price correction and the time to reach the peak are measured using Fibonacci ratios. By analyzing these levels, traders can estimate the end of the correction in the chart waves and select a buying position for their stock or asset upon reaching that ratio.
🟣 Bullish Crab Pattern
In this pattern, the stock price is expected to rise at the pattern's completion, transitioning into an upward trend. The bullish Crab pattern usually begins with an upward trend, followed by a price correction, after which the stock resumes its upward movement.
If a deeper correction occurs, the price will change direction at some point on the chart and rise again towards its target price. Price corrections play a critical role in this pattern, as it aims to identify entry and exit points using Fibonacci ratios, allowing traders to make purchases at the end of the corrections.
When the price movement lines are connected on the chart, the bullish Crab pattern resembles the letter M.
🟣 Bearish Crab Pattern
In this pattern, the stock price is expected to decline at the pattern's completion, leading to a strong downward trend. The bearish Crab pattern typically starts with a price correction in a downward trend and, after several fluctuations, reaches a peak where the direction changes downward, resulting in a significant price drop.
This pattern uses Fibonacci ratios to identify points where the price movement is likely to change direction, enabling traders to exit their positions at the chart's peak. When the price movement lines are connected on the chart, the bearish Crab pattern resembles the letter W.
🔵 Setting
🟣 Logical Setting
ZigZag Pivot Period : You can adjust the period so that the harmonic patterns are adjusted according to the pivot period you want. This factor is the most important parameter in pattern recognition.
Show Valid Format : If this parameter is on "On" mode, only patterns will be displayed that they have exact format and no noise can be seen in them. If "Off" is, the patterns displayed that maybe are noisy and do not exactly correspond to the original pattern.
Show Formation Last Pivot Confirm : if Turned on, you can see this ability of patterns when their last pivot is formed. If this feature is off, it will see the patterns as soon as they are formed. The advantage of this option being clear is less formation of fielded patterns, and it is accompanied by the latest pattern seeing and a sharp reduction in reward to risk.
Period of Formation Last Pivot : Using this parameter you can determine that the last pivot is based on Pivot period.
🟣 Genaral Setting
Show : Enter "On" to display the template and "Off" to not display the template.
Color : Enter the desired color to draw the pattern in this parameter.
LineWidth : You can enter the number 1 or numbers higher than one to adjust the thickness of the drawing lines. This number must be an integer and increases with increasing thickness.
LabelSize : You can adjust the size of the labels by using the "size.auto", "size.tiny", "size.smal", "size.normal", "size.large" or "size.huge" entries.
🟣 Alert Setting
Alert : On / Off
Message Frequency : This string parameter defines the announcement frequency. Choices include: "All" (activates the alert every time the function is called), "Once Per Bar" (activates the alert only on the first call within the bar), and "Once Per Bar Close" (the alert is activated only by a call at the last script execution of the real-time bar upon closing). The default setting is "Once per Bar".
Show Alert Time by Time Zone : The date, hour, and minute you receive in alert messages can be based on any time zone you choose. For example, if you want New York time, you should enter "UTC-4". This input is set to the time zone "UTC" by default.
CofG Oscillator w/ Added Normalizations/TransformationsThis indicator is a unique study in normalization/transformation techniques, which are applied to the CG (center of gravity) Oscillator, a popular oscillator made by John Ehlers.
The idea to transform the data from this oscillator originated from observing the original indicator, which exhibited numerous whips. Curious about the potential outcomes, I began experimenting with various normalization/transformation methods and discovered a plethora of interesting results.
The indicator offers 10 different types of normalization/transformation, each with its own set of benefits and drawbacks. My personal favorites are the Quantile Transformation , which converts the dataset into one that is mostly normally distributed, and the Z-Score , which I have found tends to provide better signaling than the original indicator.
I've also included the option of showing the mean, median, and mode of the data over the period specified by the transformation period. Using this will allow you to gather additional insights into how these transformations effect the distribution of the data series.
I've also included some notes on what each transformation does, how it is useful, where it fails, and what I've found to be the best inputs for it (though I'd encourage you to play around with it yourself).
Types of Normalization/Transformation:
1. Z-Score
Overview: Standardizes the data by subtracting the mean and dividing by the standard deviation.
Benefits: Centers the data around 0 with a standard deviation of 1, reducing the impact of outliers.
Disadvantages: Works best on data that is normally distributed
Notes: Best used with a mid-longer transformation period.
2. Min-Max
Overview: Scales the data to fit within a specified range, typically 0 to 1.
Benefits: Simple and fast to compute, preserves the relationships among data points.
Disadvantages: Sensitive to outliers, which can skew the normalization.
Notes: Best used with mid-longer transformation period.
3. Decimal Scaling
Overview: Normalizes data by moving the decimal point of values.
Benefits: Simple and straightforward, useful for data with varying scales.
Disadvantages: Not commonly used, less intuitive, less advantageous.
Notes: Best used with a mid-longer transformation period.
4. Mean Normalization
Overview: Subtracts the mean and divides by the range (max - min).
Benefits: Centers data around 0, making it easier to compare different datasets.
Disadvantages: Can be affected by outliers, which influence the range.
Notes: Best used with a mid-longer transformation period.
5. Log Transformation
Overview: Applies the logarithm function to compress the data range.
Benefits: Reduces skewness, making the data more normally distributed.
Disadvantages: Only applicable to positive data, breaks on zero and negative values.
Notes: Works with varied transformation period.
6. Max Abs Scaler
Overview: Scales each feature by its maximum absolute value.
Benefits: Retains sparsity and is robust to large outliers.
Disadvantages: Only shifts data to the range , which might not always be desirable.
Notes: Best used with a mid-longer transformation period.
7. Robust Scaler
Overview: Uses the median and the interquartile range for scaling.
Benefits: Robust to outliers, does not shift data as much as other methods.
Disadvantages: May not perform well with small datasets.
Notes: Best used with a longer transformation period.
8. Feature Scaling to Unit Norm
Overview: Scales data such that the norm (magnitude) of each feature is 1.
Benefits: Useful for models that rely on the magnitude of feature vectors.
Disadvantages: Sensitive to outliers, which can disproportionately affect the norm. Not normally used in this context, though it provides some interesting transformations.
Notes: Best used with a shorter transformation period.
9. Logistic Function
Overview: Applies the logistic function to squash data into the range .
Benefits: Smoothly compresses extreme values, handling skewed distributions well.
Disadvantages: May not preserve the relative distances between data points as effectively.
Notes: Best used with a shorter transformation period. This feature is actually two layered, we first put it through the mean normalization to ensure that it's generally centered around 0.
10. Quantile Transformation
Overview: Maps data to a uniform or normal distribution using quantiles.
Benefits: Makes data follow a specified distribution, useful for non-linear scaling.
Disadvantages: Can distort relationships between features, computationally expensive.
Notes: Best used with a very long transformation period.
Conclusion
Feel free to explore these normalization/transformation techniques to see how they impact the performance of the CG Oscillator. Each method offers unique insights and benefits, making this study a valuable tool for traders, especially those with a passion for data analysis.
Advanced ADX [CryptoSea]The Advanced ADX Analysis is a sophisticated tool designed to enhance market analysis through detailed ADX calculations. This tool is built for traders who seek to identify market trends, strength, and potential reversals with higher accuracy. By leveraging the Average Directional Index (ADX), Directional Indicator Plus (DI+), and Directional Indicator Minus (DI-), this indicator offers a comprehensive view of market dynamics.
New Overlay Feature: This script uses the new 'force overlay' feature which lets you plot on the chart as well as plotting in an oscillator pane at the same time.
force_overlay=true
Key Features
Comprehensive ADX Tracking: Tracks ADX values along with DI+ and DI- to provide a complete view of market trend strength and direction. The ADX measures the strength of the trend, while DI+ and DI- indicate the trend direction. This combined analysis helps traders identify strong and weak trends with precision.
Trend Duration Monitoring: Monitors the duration of strong and weak trends, offering insights into trend persistence and potential reversals. By keeping track of how long the ADX has been above or below a certain threshold, traders can gauge the sustainability of the current trend.
Customizable Alerts: Features multiple alert options for strong trends, weak trends, and DI crossovers, ensuring traders are notified of significant market events. These alerts can be tailored to notify traders when certain conditions are met, such as when the ADX crosses a threshold or when DI+ crosses DI-.
Adaptive Display Options: Includes customizable background color settings and extended statistics display for in-depth market analysis. Users can choose to highlight strong or weak trends on the chart background, making it easier to visualize market conditions at a glance.
In the example below, we have a bullish scenario play out where the DI+ has been above the DI- for 11 candles and our dashboard shows the average is 10.48 candles. With the ADX above its threshold this would be a bullish signal.
This ended up in a 20%+ move to the upside. The dashboard will help point out things to consider when looking to exit the position, the DI+ getting close to the max DI+ duration would be a sign that momentum is weakening and that price may cool off or even reverse.
How it Works
ADX Calculation: Computes the ADX, DI+, and DI- values using a user-defined period. The ADX is derived from the smoothed average of the absolute difference between DI+ and DI-. This calculation helps determine the strength of a trend without considering its direction.
Trend Duration Analysis: Tracks and calculates the duration of strong and weak trends, as well as DI+ and DI- durations. This analysis provides a detailed view of how long a trend has been in place, helping traders assess the reliability of the trend.
Alert System: Provides a robust alert system that triggers notifications for strong trends, weak trends, and DI crossovers. The alerts are based on specific conditions such as the duration of the trend or the crossover of directional indicators, ensuring traders are informed about critical market movements.
Visual Enhancements: Utilizes color gradients and background settings to visually represent trend strength and duration. This feature enhances the visual analysis of trends, making it easier for traders to identify significant market changes at a glance.
In the example below, we see the ADX weakening after we have just had a move up, if you are looking to get into this position you want to see the ADX growing with either the DI+ or DI- breaking their average durations.
As you can see below, although the ADX manages to move above the threshold, there are no DI+/- breaks which is shown by price moving sideways. Not something most traders would be interested in.
Application
Strategic Decision-Making: Assists traders in making informed decisions by providing detailed analysis of ADX movements and trend durations. By understanding the strength and direction of trends, traders can better time their entries and exits.
Trend Confirmation: Reinforces trading strategies by confirming potential reversals and trend strength through ADX and DI analysis. This confirmation helps traders validate their trading signals, reducing the risk of false signals.
Customized Analysis: Adapts to various trading styles with extensive input settings that control the display and sensitivity of trend data. Traders can customize the indicator to suit their specific needs, making it a versatile tool for different trading strategies.
The Advanced ADX Analysis by is an invaluable addition to a trader's toolkit, offering depth and precision in market trend analysis to navigate complex market conditions effectively. With its comprehensive tracking, alert system, and customizable display options, this indicator provides traders with the tools they need to stay ahead of the market.
SOL & BTC EMA with BTC/SOL Price Difference % and BTC Dom EMAThis script is designed to provide traders with a comprehensive analysis of Solana (SOL) and Bitcoin (BTC) by incorporating Exponential Moving Averages (EMAs) and price difference percentages. It also includes the BTC Dominance EMA to offer insights into the overall market dominance of Bitcoin.
Features:
SOL EMA: Plots the Exponential Moving Average (EMA) for Solana (SOL) based on a customizable period length.
BTC EMA: Plots the Exponential Moving Average (EMA) for Bitcoin (BTC) based on a customizable period length.
BTC Dominance EMA: Plots the Exponential Moving Average (EMA) for BTC Dominance, which helps in understanding Bitcoin's market share relative to other cryptocurrencies.
BTC/SOL Price Difference %: Calculates and plots the percentage difference between BTC and SOL prices, adjusted for their respective EMAs. This helps in identifying relative strength or weakness between the two assets.
Background Highlight: Colors the background to visually indicate whether the BTC/SOL price difference percentage is positive (green) or negative (red), aiding in quick decision-making.
Inputs:
SOL Ticker: Symbol for Solana (default: BINANCE
).
BTC Ticker: Symbol for Bitcoin (default: BINANCE
).
BTC Dominance Ticker: Symbol for Bitcoin Dominance (default: CRYPTOCAP
.D).
EMA Length: The length of the EMA (default: 20 periods).
Usage:
This script is intended for traders looking to analyze the relationship between SOL and BTC, using EMAs to smooth out price data and highlight trends. The BTC/SOL price difference percentage can help traders identify potential trading opportunities based on the relative movements of SOL and BTC.
Note: Leverage trading involves significant risk and may not be suitable for all investors. Ensure you have a good understanding of the market conditions and employ proper risk management techniques.
Moving Average Z-Score Suite [BackQuant]Moving Average Z-Score Suite
1. What is this indicator
The Moving Average Z-Score Suite is a versatile indicator designed to help traders identify and capitalize on market trends by utilizing a variety of moving averages. This indicator transforms selected moving averages into a Z-Score oscillator, providing clear signals for potential buy and sell opportunities. The indicator includes options to choose from eleven different moving average types, each offering unique benefits and characteristics. It also provides additional features such as standard deviation levels, extreme levels, and divergence detection, enhancing its utility in various market conditions.
2. What is a Z-Score
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values. It is measured in terms of standard deviations from the mean. For instance, a Z-Score of 1.0 means the value is one standard deviation above the mean, while a Z-Score of -1.0 indicates it is one standard deviation below the mean. In the context of financial markets, Z-Scores can be used to identify overbought or oversold conditions by determining how far a particular value (such as a moving average) deviates from its historical mean.
3. What moving averages can be used
The Moving Average Z-Score Suite allows users to select from the following eleven moving averages:
Simple Moving Average (SMA)
Hull Moving Average (HMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Double Exponential Moving Average (DEMA)
Running Moving Average (RMA)
Linear Regression Curve (LINREG) (This script can be found standalone )
Triple Exponential Moving Average (TEMA)
Arnaud Legoux Moving Average (ALMA)
Kalman Hull Moving Average (KHMA)
T3 Moving Average
Each of these moving averages has distinct properties and reacts differently to price changes, allowing traders to select the one that best fits their trading style and market conditions.
4. Why Turning a Moving Average into a Z-Score is Innovative and Its Benefits
Transforming a moving average into a Z-Score is an innovative approach because it normalizes the moving average values, making them more comparable across different periods and instruments. This normalization process helps in identifying extreme price movements and mean-reversion opportunities more effectively. By converting the moving average into a Z-Score, traders can better gauge the relative strength or weakness of a trend and detect potential reversals. This method enhances the traditional moving average analysis by adding a statistical perspective, providing clearer and more objective trading signals.
5. How It Can Be Used in the Context of a Trading System
In a trading system, it can be used to generate buy and sell signals based on the Z-Score values. When the Z-Score crosses above zero, it indicates a potential buying opportunity, suggesting that the price is above its mean and possibly trending upward. Conversely, a Z-Score crossing below zero signals a potential selling opportunity, indicating that the price is below its mean and might be trending downward. Additionally, the indicator's ability to show standard deviation levels and extreme levels helps traders set profit targets and stop-loss levels, improving risk management and trade planning.
6. How It Can Be Used for Trend Following
For trend-following strategies, it can be particularly useful. The Z-Score oscillator helps traders identify the strength and direction of a trend. By monitoring the Z-Score and its rate of change, traders can confirm the persistence of a trend and make informed decisions to enter or exit trades. The indicator's divergence detection feature further enhances trend-following by identifying potential reversals before they occur, allowing traders to capitalize on trend shifts. By providing a clear and quantifiable measure of trend strength, this indicator supports disciplined and systematic trend-following strategies.
No backtests for this indicator due to the many options and ways it can be used,
Enjoy
Multiple Non-Linear Regression [ChartPrime]This indicator is designed to perform multiple non-linear regression analysis using four independent variables: close, open, high, and low prices. Here's a breakdown of its components and functionalities:
Inputs:
Users can adjust several parameters:
Normalization Data Length: Length of data used for normalization.
Learning Rate: Rate at which the algorithm learns from errors.
Smooth?: Option to smooth the output.
Smooth Length: Length of smoothing if enabled.
Define start coefficients: Initial coefficients for the regression equation.
Data Normalization:
The script normalizes input data to a range between 0 and 1 using the highest and lowest values within a specified length.
Non-linear Regression:
It calculates the regression equation using the input coefficients and normalized data. The equation used is a weighted sum of the independent variables, with coefficients adjusted iteratively using gradient descent to minimize errors.
Error Calculation:
The script computes the error between the actual and predicted values.
Gradient Descent: The coefficients are updated iteratively using gradient descent to minimize the error.
// Compute the predicted values using the non-linear regression function
predictedValues = nonLinearRegression(x_1, x_2, x_3, x_4, b1, b2, b3, b4)
// Compute the error
error = errorModule(initial_val, predictedValues)
// Update the coefficients using gradient descent
b1 := b1 - (learningRate * (error * x_1))
b2 := b2 - (learningRate * (error * x_2))
b3 := b3 - (learningRate * (error * x_3))
b4 := b4 - (learningRate * (error * x_4))
Visualization:
Plotting of normalized input data (close, open, high, low).
The indicator provides visualization of normalized data values (close, open, high, low) in the form of circular markers on the chart, allowing users to easily observe the relative positions of these values in relation to each other and the regression line.
Plotting of the regression line.
Color gradient on the regression line based on its value and bar colors.
Display of normalized input data and predicted value in a table.
Signals for crossovers with a midline (0.5).
Interpretation:
Users can interpret the regression line and its crossovers with the midline (0.5) as signals for potential buy or sell opportunities.
This indicator helps users analyze the relationship between multiple variables and make trading decisions based on the regression analysis. Adjusting the coefficients and parameters can fine-tune the model's performance according to specific market conditions.
