HTF Oscillators RSI/ROC/MFI/CCI/AO - Dynamic SmoothingThe Interplay of Time Frames: A Balanced View
Navigating the markets often involves interpreting trends from multiple angles. The HTF Oscillators with Dynamic Smoothing indicator enables you to do just that. This tool provides the option to integrate smoothed oscillator readings from Higher Time Frames (HTF) into lower time frame charts, such as a 1-minute chart. By doing so, the indicator offers a balanced viewpoint that bridges the gap between micro and macro perspectives, helping you make informed decisions without losing sight of the broader market context.
Features
Multi-Oscillator Support
Choose from a range of popular oscillators like the Relative Strength Index (RSI), Rate of Change (ROC), Money Flow Index (MFI), Commodity Channel Index (CCI), and Awesome Oscillator (AO). These oscillators are commonly used as foundational building blocks in trading strategy scripts by traders worldwide. Switch effortlessly between them, depending on your trading strategy and requirements. To maintain consistency and a familiar user experience, our script adopts the same visual aesthetics that you'll find in Pine Script indicators on TradingView: a sleek purple line for the oscillator and a transparent band filling. These visual elements are not only pleasing to the eye but also widely appreciated by the trading community.
Dynamic Smoothing
The unique dynamic smoothing feature calculates a smoothing factor based on the ratio of minutes between the Higher Time Frame (HTF) and your current time frame. This provides a sleek and responsive oscillator line that still holds the weight of the longer trend. One of the significant advantages of this feature is user experience; when you change your time frame, the HTF-values in your settings will remain consistent. This ensures that you can easily switch between different time frames without losing the insights provided by your selected HTF.
Visual Aids
Visual cues are an essential part of any trading strategy. The indicator not only plots signals to mark overbought and oversold conditions based on the dynamically smoothed oscillator but also provides you with the flexibility to customize your visual experience. You have the option to toggle on/off the display of these signals depending on your specific needs. Additionally, bands can be displayed at overbought and oversold levels, along with a reference middle line. If you switch between different oscillators (available in the parameter settings), remember to manually adjust the bands in the input settings to ensure signals matches with the type of oscillator to your liking.
User-Friendly Settings
We've grouped related settings together, making it easier for you to find what you're looking for. Adjust the oscillator type, length of bars, smoothing settings, and more with just a few clicks.
Information Table
A standout feature of this indicator is the real-time information table, which displays the values of all selected oscillators based on your specified Higher Time Frame (HTF) settings. This can be particularly useful for traders who depend on multiple indicators for their decision-making process. The data presented in the table is synchronized with the HTF options you've configured in the input settings, allowing for a more efficient and quick scan of values from higher time frames.
Educational Corner: The Power of the Information Table and Customization
The table incorporated into this indicator isn't just eye-candy; it's a practical tool designed to elevate your trading strategy. It dynamically displays real-time values of various oscillators for the HTF you've chosen. This is an exemplary use of TradingView's scripting capabilities to blend multiple indicators into a single visual panel, streamlining your analysis and decision-making process.
But here's the best part: You're not limited to what we've created. With some basic understanding of TradingView's scripting language, Pine Script, you can easily adapt this table to include different indicators that suit your unique trading style. The logic in the script is modular and can serve as a foundation for your own customized trading dashboard. So, go ahead, get creative and explore new combinations of indicators that will help you excel in your trading endeavors!
You no longer have to toggle between different charts or indicators to get the information you need; it's all there in one neatly organized table. We encourage you to tap into this feature and make it your own, empowering your trading like never before.
By doing so, you not only gain a more comprehensive toolset, but you also engage more deeply with your trading strategy, understanding its nuances and, ultimately, making more informed decisions.
Conclusion
The HTF Oscillators with Dynamic Smoothing is a versatile and powerful tool that brings together the best of both worlds: the perspective of higher time frames and the granularity of shorter ones. Its feature-rich setting options and real-time information table make it a potential useful addition to your trading toolkit.
Remember, while this indicator offers a comprehensive and smarter way to look at the markets, it is not a foolproof method for predicting market movements. Always use it in conjunction with other analysis methods and risk management strategies.
Oscillators
Velocity Acceleration Indicator [CC]The Velocity Acceleration Indicator was created by Scott Cong (Stocks and Commodities Sep 2023, pgs 8-15). This is another personal variation of his formula designed to capture the overall velocity acceleration of the underlying stock by applying the velocity formula to the original indicator to find the acceleration of the underlying velocity. I changed a few things around and managed actually to get less lag and quicker signals for this version, so make sure you compare the Velocity Indicator script that I published yesterday. This indicator is also visually similar to a typical stochastic indicator but uses a different underlying calculation. This works well as a momentum indicator, and the values are completely unbounded, so the best ways to determine bullish or bearish trends is either by using a crossover or crossunder between the indicator and the midline or to buy or sell the indicator when it reaches a high or low point and starts to fall or rise respectively. I used the zero line for my default version to help determine the bullish or bearish trends. I have also included multiple colors to differentiate between very strong signals and normal signals, so very strong signals are darker in color, and normal signals use lighter colors. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish! I will have some more new scripts in the next week or so.
Velocity Indicator [CC]The Velocity Indicator was created by Scott Cong (Stocks and Commodities Sep 2023, pgs 8-15). This is my variation of his formula designed to capture the overall velocity of the underlying stock by applying the typical velocity formula. This indicator is visually similar to a typical stochastic indicator but uses a different underlying calculation. This works well as a momentum indicator, and the values are completely unbounded, so the best ways to determine bullish or bearish trends is either by using a crossover or crossunder between the indicator and the midline or to buy or sell the indicator when it reaches a high or low point and starts to fall or rise respectively. For my default version, I used the zero line to help determine the bullish or bearish trends. I have also included multiple colors to differentiate between very strong signals and normal signals, so very strong signals are darker in color, and normal signals use lighter colors. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish! I will have some more new scripts in the next week or so.
L&S Volatility Index Refurbished█ Introduction
This is my second version of the L&S Volatility Index, hence the name "Refurbished".
The first version can be found at this link:
The reason I released a separate version is because I rewrote the source code from scratch with the aim of both improving the indicator and staying as close as possible to the original concept.
I feel that the first version was somewhat exotic and polluted in relation to the indicator originally described by the authors.
In short, the main idea remains the same, however, the way of presenting the result has been changed, reiterating what was said.
█ CONCEPTS
The L&S Volatility Index measures the volatility of price in relation to a moving average.
The indicator was originally described by Brazilian traders Alexandre Wolwacz (Stormer) and Fábio Figueiredo (Vlad) from L&S Educação Financeira.
Basically, this indicator can be used in two ways:
1. In a mean reversion strategy, when there is an unusual distance from it;
2. In a trend following strategy, when the price is in an acceptable region.
As an indicator of volatility, the greatest utility is shown in first case.
This is because it allows identifying abnormal prices, extremely stretched in relation to an average, including market crashes.
How the calculation is done:
First, the distance of the price from a given average in percentage terms is measured.
Then, the historical average volatility is obtained.
Finally the indicator is calculated through the ratio between the distance and the historical volatility.
According to the description proposed by the creators, when the L&S Volatility Index is above 30 it means that the price is "stretched".
The closer to 100 the more stretched.
When it reaches 0, it means the price is on average.
█ What to look for
Basically, you should look at non-standard prices.
How to identify it?
When the oscillator is outside the Dynamic Zone and/or the Fixed Zone (above 30), it is because the price is stretched.
Nothing on the market is guaranteed.
As with the RSI, it is not because the RSI is overbought or oversold that the price will necessarily go down or up.
It is critical to know when NOT to buy, NOT to sell or NOT to do anything.
It is always important to consider the context.
█ Improvements
The following improvements have been implemented.
It should be noted that these improvements can be disabled, thus using the indicator in the "purest" version, the same as the one conceived by the creators.
Resources:
1. Customization of limits and zones:
2. Customization of the timeframe, which can be different from the current one.
3. Repaint option (prints the indicator in real time even if the bar has not yet closed. This produces more signals).
4. Customization of price inputs. This affects the calculation.
5. Customization of the reference moving average (the moving average used to calculate the price distance).
6. Customization of the historical volatility calculation strategy.
- Accumulated ATR: calculates the historical volatility based on the accumulated ATR.
- Returns: calculates the historical volatility based on the returns of the source.
Both forms of volatility calculation have their specific utilities and applications.
Therefore, it is worthwhile to have both approaches available, and one should not necessarily replace the other.
Each method has its advantages and may be more appropriate in different contexts.
The first approach, using the accumulated ATR, can be useful when you want to take into account the implied volatility of prices over time,
reflecting broader price movements and higher impact events. It can be especially relevant in scenarios where unexpected events can drastically affect prices.
The second approach, using the standard deviation of returns, is more common and traditionally used to measure historical volatility.
It considers the variability of prices relative to their average, providing a more general measure of market volatility.
Therefore, both forms of calculation have their merits and can be useful depending on the context and specific analysis needs.
Having both options available gives users flexibility in choosing the most appropriate volatility measure for the situation at hand.
* When choosing "Accumulated ATR", if the indicator becomes difficult to see, there are 3 possibilities:
a) manually adjust the Fixed Zone value;
b) disable the Fixed Zone and use only the Dynamic Zone;
c) normalize the indicator.
7. Signal line (a moving average of the oscillator).
8. Option to normalize the indicator or not.
9. Colors to facilitate direction interpretation.
Since the L&S is a volatility indicator, it does not show whether the price is rising or falling.
This can sometimes confuse the user.
That said, the idea here is to show certain colors where the price is relative to the average, making it easier to analyze.
10. Alert messages for automations.
Short Term IndeXThe Short-Term Index (STIX) is a simple market indicator designed to assess short-term overbought or oversold conditions in the stock market. Leveraging a combination of advancing and declining issues, STIX provides valuable insights into market sentiment and potential reversals. To enhance its interpretability and reveal the underlying trend with greater clarity, STIX has been refined through a Heiken-Ashi transformation, ensuring a smoother representation of market dynamics.
Calculation and Methodology:
stix = ta.ema(adv / (adv + dec) * 100, len)
STIX is calculated by dividing the difference between the sum of advancing issues (ADV) by the total number of issues traded (ADV + DEC). This quotient is multiplied by 100 to express the result as a percentage. The STIX index ranges from 0 to 100, where extreme values indicate potential overbought (mainly above 60) or oversold (mainly below 40) market conditions.
Heiken-Ashi Transformation:
By applying a Heiken-Ashi transformation to STIX, the indicator gains improved visual clarity and noise reduction. This transformation enhances the ability to identify trend shifts and potential reversal points, making it an even more valuable tool for traders and investors.
Utility and Use Cases:
-The Short-Term Index (STIX) offers a range of practical applications-
1. Overbought/Oversold Conditions: STIX provides a clear indication of short-term overbought or oversold conditions, helping traders anticipate potential market reversals.
2. Reversal Points: STIX can help pinpoint potential reversal points in short-term market trends, providing traders with opportunities to enter or exit positions.
3. Trend Analysis: By observing STIX values over time, traders can assess the strength and sustainability of short-term trends, aiding in trend-following strategies.
The Short-Term Index (STIX), enhanced by its Heiken-Ashi transformation, equips traders and investors with a tool for assessing short-term market conditions, confirming price movements, and identifying potential reversal points. Its robust methodology and refined presentation contribute to a more comprehensive understanding of short-term market dynamics, enabling traders to make well-informed trading decisions.
See Also:
- Other Market Breadth Indicators-
Trig-Log Scaled Momentum OscillatorTaylor Series Approximations for Trigonometry:
1. The indicator starts by calculating sine and cosine values of the close price using Taylor Series approximations. These approximations use polynomial terms to estimate the values of these trigonometric functions.
Mathematical Component Formation:
2. The calculated sine and cosine values are then multiplied together. This gives us the primary mathematical component, termed as the 'trigComponent'.
Smoothing Process:
3. To ensure that our indicator is less susceptible to market noise and more reactive to genuine price movements, this 'trigComponent' undergoes a smoothing process using a simple moving average (SMA). The length of this SMA is defined by the user.
Logarithmic Transformation:
4. With our smoothed value, we apply a natural logarithm approximation. Again, this approximation is based on the Taylor expansion. This step ensures that all resultant values are positive and offers a different scale to interpret the smoothed component.
Dynamic Scaling:
5. To make our indicator more readable and comparable over different periods, the logarithmically transformed values are scaled between a range. This range is determined by the highest and lowest values of the transformed component over the user-defined 'lookback' period.
ROC (Rate of Change) Direction:
6. The direction of change in our scaled value is determined. This offers a quick insight into whether our mathematical component is increasing or decreasing compared to the previous value.
Visualization:
7. Finally, the indicator plots the dynamically scaled and smoothed mathematical component on the chart. The color of the plotted line depends on its direction (increasing or decreasing) and its boundary values.
MACD HTF - Dynamic SmoothingEnhancing Your 1-Minute Trades with Dynamic HTF MACD Smoothing
Ever found yourself glued to a 1-minute chart, trying to catch every minor price movement, yet feeling like you're missing the bigger picture? Picture this: a solid MACD line on that chart, dynamically smoothed from a higher timeframe (HTF). This tool offers two significant benefits over other existing HTF MACD indicators:
User-Friendly Interface: No need to manually adjust input parameters every time you switch to a different timeframe.
Smooth Charting: Say goodbye to the zigzag lines that often result from plotting higher time frame resolutions on a lower time frame.
Understanding the MACD
The Moving Average Convergence Divergence (MACD) is one of the most widely used and trusted technical indicators in the trading community. Invented by Gerald Appel in the late 1970s, the MACD helps traders understand the relationship between two moving averages of a security's price. It consists of the MACD line (difference between a 12-period and 26-period Exponential Moving Average) and the Signal line (9-period EMA of the MACD line). When the MACD line crosses above the Signal line, it's viewed as a bullish signal, and vice versa. The difference between the two lines is represented as a histogram, providing insights into potential buy or sell opportunities.
Features of the Dynamic HTF MACD Smoothing Script
Time Frame Flexibility: Choose a higher timeframe to derive MACD values and apply dynamic smoothing to your current timeframe.
Multiple Moving Averages: The script supports various MA types like EMA, SMA, DEMA, TEMA, WMA and HMA.
Alerts: Get real-time alerts for MACD crossover and crossunder.
Customizability: From the type of moving average to its length, customize as per your strategy.
Visual Indicators: Clearly plots signals when MACD crossover or crossunder occurs for potential entries.
At last
A massive shoutout to all the wizards and generous contributors in the community! You inspire innovations and new tools, paving the path forward. Here's to a community where we learn and build together. Cheers to collective growth!
AI SuperTrend Clustering Oscillator [LuxAlgo]The AI SuperTrend Clustering Oscillator is an oscillator returning the most bullish/average/bearish centroids given by multiple instances of the difference between SuperTrend indicators.
This script is an extension of our previously posted SuperTrend AI indicator that makes use of k-means clustering. If you want to learn more about it see:
🔶 USAGE
The AI SuperTrend Clustering Oscillator is made of 3 distinct components, a bullish output (always the highest), a bearish output (always the lowest), and a "consensus" output always within the two others.
The general trend is given by the consensus output, with a value above 0 indicating an uptrend and under 0 indicating a downtrend. Using a higher minimum factor will weigh results toward longer-term trends, while lowering the maximum factor will weigh results toward shorter-term trends.
Strong trends are indicated when the bullish/bearish outputs are indicating an opposite sentiment. A strong bullish trend would for example be indicated when the bearish output is above 0, while a strong bearish trend would be indicated when the bullish output is below 0.
When the consensus output is indicating a specific trend direction, an opposite indication from the bullish/bearish output can highlight a potential reversal or retracement.
🔶 DETAILS
The indicator construction is based on finding three clusters from the difference between the closing price and various SuperTrend using different factors. The centroid of each cluster is then returned. This operation is done over all historical bars.
The highest cluster will be composed of the differences between the price and SuperTrends that are the highest, thus creating a more bullish group. The lowest cluster will be composed of the differences between the price and SuperTrends that are the lowest, thus creating a more bearish group.
The consensus cluster is composed of the differences between the price and SuperTrends that are not significant enough to be part of the other clusters.
🔶 SETTINGS
ATR Length: ATR period used for the calculation of the SuperTrends.
Factor Range: Determine the minimum and maximum factor values for the calculation of the SuperTrends.
Step: Increments of the factor range.
Smooth: Degree of smoothness of each output from the indicator.
🔹 Optimization
This group of settings affects the runtime performances of the script.
Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
Historical Bars Calculation: Calculation window of the script (in bars).
TaLib RSI (ta-lib uses SMA)If you've ever been confused because Ta-Lib RSI differs from TradingView's RSI...
Look no further than here which instead of using the Rolling Moving Average, will instead use the Simple Moving Average
Gaussian Average Rate Oscillator
Within the ALMA calculation, the Gaussian function is applied to each price data point within the specified window. The idea is to give more weight to data points that are closer to the center and reduce the weight for points that are farther away.
The strategy calculates and compares two different Rate of Change (ROC) indicators: one based on the Arnaud Legoux Moving Average (ALMA) and the other based on a smoothed Exponential Moving Average (EMA). The primary goal of this strategy is to identify potential buy and sell signals based on the relationship between these ROC indicators.
Here's how the strategy logic works
Calculating the ROC Indicators:
The script first calculates the ROC (Rate of Change) of the smoothed ALMA and the smoothed EMA. The smoothed ALMA is calculated using a specified window size and is then smoothed further with a specified smoothing period. The smoothed EMA is calculated using a specified EMA length and is also smoothed with the same smoothing period.
Comparing ROCs:
The script compares the calculated ROC values of the smoothed ALMA and smoothed EMA.
The color of the histogram bars representing the ROC of the smoothed ALMA depends on its relationship with the ROC of the smoothed EMA. Green indicates that the ROC of ALMA is higher, red indicates that it's lower, and black indicates equality.
Similarly, the color of the histogram bars representing the ROC of the smoothed EMA is determined based on its relationship with the ROC of the smoothed ALMA, they are simply inversed so that they match.
With the default color scheme, green bars indicate the Gaussian average is outperforming the EMA within the breadth and red bars mean it's underperforming. This is regardless of the rate of average price changes.
Generating Trade Signals:
Based on the comparison of the ROC values, the strategy identifies potential crossover points and trends. Buy signals could occur when the ROC of the smoothed ALMA crosses above the ROC of the smoothed EMA. Sell signals could occur when the ROC of the smoothed ALMA crosses below the ROC of the smoothed EMA.
Additional Information:
The script also plots a zero rate line at the zero level to provide a reference point for interpreting the ROC values.
In summary, the strategy attempts to capture potential buy and sell signals by analyzing the relationships between the ROC values of the smoothed ALMA and the smoothed EMA. These signals can provide insights into potential trends and momentum shifts in the price data.
Golden Transform The Golden Transform Oscillator contains multiple technical indicators and conditions for making buy and sell decisions. Here's a breakdown of its components and what it's trying to achieve:
Strategy Setup:
The GT is designed to be plotted on the chart without overlaying other indicators.
Rate of Change (ROC) Calculation:
The Rate of Change (ROC) indicator is calculated with a specified period ("Rate of Change Length").
The ROC measures the percentage change in price over the specified period.
Hull Modified TRIX Calculation:
The Hull Modified TRIX indicator is calculated with a specified period ("Hull TRIX Length").
The Hull MA (Moving Average) formula, a modified WMA, is used to calculate a modified TRIX indicator, which is a momentum oscillator.
Hull MA Calculation:
A Hull Moving Average (Hull MA) is calculated as an entry filter.
Fisher Transform Calculation:
The Fisher Transform indicator is calculated to serve as a preemptive exit filter.
It involves mathematical transformations of price data to create an oscillator that can help identify potential reversals. The Fisher Transform is further smoothed using a Hull Moving Average (HMA).
Conditions and Signals:
Long conditions are determined based on crossovers between ROC and TRIX, as well as price relative the the MA. Short conditions are inversed.
Exit Conditions:
Exit conditions are defined for both long and short positions.
For long positions, the strategy exits if ROC crosses under TRIX, or if the smoothed Fisher Transform crosses above a threshold and declines. Once again, short conditions are the inverse.
Visualization and Plotting:
The script uses background colors for entry and shapes for exits to highlight different levels and conditions for the ROC/TRIX correlation.
It plots the Fisher Transform values and a lag trigger on the chart.
Overall, this script is a complex algorithm that combines multiple technical indicators and conditions to generate trading signals and manage positions in the financial markets. It aims to identify potential entry and exit points based on the interplay of the mentioned indicators and conditions.
OBV Oscillator Volume FilterOBV Oscillator Volume Filter
Introduction
The On-Balance Volume (OBV) is a widely-used technical indicator that aims to relate price and volume in trading. Price and volume are two of the most basic and yet crucial concepts in price movement. Together, they can reveal a lot about the instruments trends and the market's sentiment. This On Balance Volume (OBV) Oscillator incorporates enhanced features like a volume filter using a rolling window to detect outliers in accumulated volume, making it an advanced and more refined version of the standard OBV.
Interpreting the OBV Indicator
The primary function of the OBV is to accumulate volume. In simpler terms:
When the market closes higher than the previous candle, all of that candle's volume is considered 'up-volume'.
Conversely, when the market closes lower than the previous day, all of that candle's volume is considered 'down-volume'.
A rising OBV suggests that volume is being accumulated, indicating bullish market sentiment. A declining OBV, on the other hand, points to a bearish sentiment.
Features of the Script
1. Moving Averages Selection:
The script provides users with the option to select among six types of moving averages (EMA, DEMA, TEMA, SMA, WMA, HMA) to calculate the OBV. This feature offers flexibility and enables traders to choose an MA type they're most comfortable with or find the most effective.
2. Smoothing Option:
To reduce the inherent noise in the indicator, there's an option to apply smoothing. It uses a Simple Moving Average (SMA) to produce a clearer signal, making it easier for traders to interpret and respond to. If you don't want to use smoothing, just simply change the input length of smoothing to 1 in the settings.
3. Outlier Detection:
One of the standout features is the use of a rolling window to detect volume outliers. This ensures that the OBV only reacts to significant volume changes and isn't overly influenced by random spikes or drops. The volume filter is calculated based on a % of the highest OBV volume of X number of bars back. Users can adjust the time (# bars) and the sensitivity (%) of the volume filter. A longer timeperiode (# bars) and a higher % (sensitivity) in the settings result to less signals presented by the indicator.
4. Divergence Detection:
The script automatically highlights both regular and hidden divergences on the chart. Divergences can be a powerful signal of potential price reversals. This feature aids traders in spotting potential buy or sell opportunities based on divergences between price and OBV.
Regular Bullish Divergence: When the price makes lower lows, but the OBV makes higher lows.
Hidden Bullish Divergence: When the price makes higher lows, but the OBV makes lower lows.
Regular Bearish Divergence: When the price makes higher highs, but the OBV makes lower highs.
Hidden Bearish Divergence: When the price makes lower highs, but the OBV makes higher highs.
5. Alerts for Trend Reversals:
The script incorporates alerts that notify traders when the OBV indicates potential trend reversals. This feature can be instrumental in catching early entries or exits.
Disclaimer
It's crucial to understand that no single indicator should be used in isolation. To increase the probability of making accurate market predictions, always use the OBV Oscillator in conjunction with other indicators and tools. Remember that all trading involves risk, and it's possible to lose your invested capital. Always seek advice from a financial advisor before making any trading decisions. By enhancing the OBV with features like the volume filter, multiple MA types, smoothing, and divergence detection, this script becomes a potent tool in a trader's arsenal. Use it wisely, and always ensure to maintain proper risk management.
Enhanced Smoothed RSIThe "Enhanced Smoothed RSI Factor" indicator is a robust technical analysis tool designed to assist traders in identifying potential trends and reversals. This indicator combines elements of the Relative Strength Index (RSI) with a smoothed factor, enhancing its reliability and responsiveness. By visualizing the Enhanced Smoothed RSI Factor alongside the standard RSI and their associated upper and lower bands, traders gain insights into potential overbought and oversold conditions, facilitating more informed trading decisions.
How to Use:
Inputs Configuration : Adjust the indicator's parameters according to your trading preferences. Modify the source data (source) to suit the price data you want to analyze. Set the RSI period (rsiPeriod) for RSI calculations, the moving average period (movingAvgPeriod) for the bands, and the smoothing factor (factor) for enhanced responsiveness.
Enhanced Smoothed RSI Factor : The indicator calculates the Enhanced Smoothed RSI Factor by applying an exponential moving average (EMA) to the RSI values. This factor reflects changes in price momentum.
Comparison with Standard RSI : Observe the Enhanced Smoothed RSI Factor and the standard RSI side by side on your chart. While the standard RSI offers insights into price momentum, the Enhanced Smoothed RSI Factor adds an extra layer of smoothing for potentially clearer trend indications.
Bands and Bar Coloring : The indicator plots upper and lower bands, which are derived from weighted and simple moving averages of the Enhanced Smoothed RSI Factor. The color of the bars changes based on the position of the Enhanced Smoothed RSI Factor relative to the bands. Green bars indicate values above the upper band, red bars indicate values below the lower band, and gray bars indicate values within the bands.
Overbought and Oversold Levels : The indicator provides horizontal lines at levels 140 and 80. When the Enhanced Smoothed RSI Factor crosses above 140, it suggests a potential bullish trend, while crossing below 80 suggests a potential bearish trend. Additionally, levels 200 and 180 indicate overbought conditions, and levels 100 and 80 indicate oversold conditions.
Additional Insights : The indicator's upper and lower bands provide valuable insights into potential trend reversals. When the Enhanced Smoothed RSI Factor crosses above the upper band, it may signal an overextended bullish trend. Conversely, a crossover below the lower band may indicate an overextended bearish trend.
Important Considerations :
This indicator is most effective when used in conjunction with other technical analysis tools and strategies.
It's recommended to avoid making trading decisions solely based on the Enhanced Smoothed RSI Factor. Combine it with other indicators, chart patterns, and fundamental analysis.
Adjust the overbought and oversold levels to align with your trading strategy and the specific market conditions.
Please remember that trading involves risks, and the indicator's signals are not guaranteed. Always conduct thorough research and consider using a practice account before implementing any trading strategy.
Gaussian Detrended ReversionThis strategy, titled "Gaussian Detrended Reversion Strategy," aims to identify potential price reversals using the customized Gaussian Detrended Price Oscillator (GDPO) in combination with smoothed price cycles.
Key Elements of the Strategy:
GDPO Calculation: The strategy first calculates the Detrended Price Oscillator (DPO) by comparing the close price to an Exponential Moving Average (EMA) of a specified period. This calculation helps identify short-term price cycles by detrending the price data.
Gaussian Smoothing: The DPO values are then smoothed using the Arnaud Legoux Moving Average (ALMA), applying a Gaussian smoothing technique. This smoothed version of the DPO is intended to filter out noise and provide a clearer picture of price trends.
Entry and Exit Conditions: The strategy defines conditions for both long and short entry points as well as exit points. It looks for specific crossover events between the smoothed GDPO and its lagged version. The strategy enters a long position when the smoothed GDPO crosses above the lag and is negative, and exits the long position when the smoothed GDPO crosses below the lag or the zero line. Similarly, the strategy enters a short position when the smoothed GDPO crosses below the lag and is positive, and exits the short position when the smoothed GDPO crosses above the lag or the zero line.
Visualization: The smoothed GDPO and its lag are plotted on the chart using distinct colors. The zero line is also displayed as a reference point. Additionally, the chart background changes color when the strategy enters a long or short position. Cross markers are also plotted at the crossover points as exit cues.
Overall, this strategy aims to capture potential price reversals using the GDPO and Gaussian smoothing, with specific entry and exit conditions to guide trading decisions.
Relative Strength Volume ComparisonThe Relative Strength Volume Comparison is a powerful tool that can help traders identify the current trend based on volume pressure and potential reversals.
This oscillator is made of two lines and the overbought and oversold levels. Each of these two lines is a relative-strength formula that contains both the famous RSI and CCI formulas, smoothed by a Hull moving average.
The two lines are different for input. The colored line is based just on price and changes color based on the relation with the other line. The second line uses as input an average of three different popular volume indicators: The OBV, the Accumulation/Distribution, and the PVT.
Thanks to this tool, which uses 6 different formulas combined, traders can:
- Identify the current trend direction, based on the color of the area fill and the first colored line
- Identify potential reversal areas thanks to the overbought and oversold levels, customizable in the input section alongside the length and smoothing parameters.
EMA X Oscillator
This indicator combines elements of the Exponential Moving Average (EMA) crossover and Rate of Change (ROC), generating a solid simple tool for technical analysis.
Overall, this script creates an oscillator by calculating the Rate of Change between two Exponential Moving Averages (Fast and Slow) based on the chosen smoothing methods and lengths. The oscillator helps identify potential trends. It offers customization options for the types of smoothing and other parameters, making it versatile for various strategies.
Realized Profit & Loss [BigBeluga]The Realized Loss & Profit indicator aims to find potential dips and tops in price by utilizing the security function syminfo.basecurrency + "_LOSSESADDRESSES".
The primary objective of this indicator is to present an average, favorable buying/selling opportunity based on the number of people currently in profit or loss.
The script takes into consideration the syminfo.basecurrency, so it should automatically adapt to the current coin.
🔶 USAGE
Users have the option to enable the display of either Loss or Profit, depending on their preferred visualization.
Examples of displaying Losses:
Example of displaying Profits:
🔶 CONCEPTS
The concept aims to assign a score to the data in the ticker representing the realized losses. This score will provide users with an average of buying/selling points that are better to the typical investor.
🔶 SETTINGS
Users have complete control over the script settings.
🔹 Calculation
• Profit: Display people in profit on an average of the selected length.
• Loss: Display people in loss on an average of the selected length.
🔹 Candle coloring
• True: Color the candle when data is above the threshold.
• False: Do not color the candle.
🔹 Levels
- Set the level of a specific threshold.
• Low: Low losses (green).
• Normal: Low normal (yellow).
• Medium: Low medium (orange).
• High: Low high (red).
🔹 Z-score Length: Length of the z-score moving window.
🔹 Threshold: Filter out non-significant values.
🔹 Histogram width: Width of the histogram.
🔹 Colors: Modify the colors of the displayed data.
🔶 LIMITATIONS
• Since the ticker from which we obtain data works only on the daily timeframe, we are
restricted to displaying data solely from the 1D timeframe.
• If the coin does not have any realized loss data, we can't use this script.
Simple Angle MesurmentThis Pine Script indicator allows you to accurately measure the angle of any given source line on a chart. The angle is calculated based on the slope of the line, providing insights into the direction and steepness of the line's movement. By utilizing mathematical calculations and trigonometric functions, the indicator helps traders and analysts assess trends and make informed decisions.
QQE Weighted Oscillator [LuxAlgo]The QQE (Quantitative Qualitative Estimation) Weighted Oscillator improves on its original version by weighting the RSI based on the indications given by the trailing stop, requiring more effort in order for a cross with the trailing stop to occur.
🔶 USAGE
The QQE Weighted Oscillator is comprised of a smoothed RSI oscillator and a trailing stop derived from this same RSI. The oscillator can be used to indicate whether the market is overbought/oversold as well as an early indication of trend reversals thanks to the leading nature of the RSI.
Using higher Factor values will return a longer-term trailing stop.
Like with a regular RSI divergence can be indicative of a reversal.
Further weighting will control how much "effort" is required for the trailing stop to cross the RSI. For example. For example, an RSI above the trailing stop will require a higher degree of negative price variations in order for a potential cross to occur when using higher weights.
This can cause higher weightings to return more cyclical and smoother results.
🔶 SETTINGS
Length: Length of the RSI oscillator.
Factor: Multiplicative factor used for the trailing stop calculation.
Smooth: Degree of smoothness of the RSI oscillator.
Weight: Degree of weighting used for the RSI calculation.
Machine Learning Momentum Index (MLMI) [Zeiierman]█ Overview
The Machine Learning Momentum Index (MLMI) represents the next step in oscillator trading. By blending traditional momentum analysis with machine learning, MLMI delivers a potent and dynamic tool that aligns with the complexities of modern financial landscapes. Offering traders an adaptive way to understand and act on market momentum and trends, this oscillator provides real-time insights into market momentum and prevailing trends.
█ How It Works:
Momentum Analysis: MLMI employs a dual-layer analysis, utilizing quick and slow weighted moving averages (WMA) of the Relative Strength Index (RSI) to gauge the market's momentum and direction.
Machine Learning Integration: Through the k-Nearest Neighbors (k-NN) algorithm, MLMI intelligently examines historical data to make more accurate momentum predictions, adapting to the intricate patterns of the market.
MLMI's precise calculation involves:
Weighted Moving Averages: Calculations of quick (5-period) and slow (20-period) WMAs of the RSI to track short-term and long-term momentum.
k-Nearest Neighbors Algorithm: Distances between current parameters and previous data are measured, and the nearest neighbors are used for predictive modeling.
Trend Analysis: Recognition of prevailing trends through the relationship between quick and slow-moving averages.
█ How to use
The Machine Learning Momentum Index (MLMI) can be utilized in much the same way as traditional trend and momentum oscillators, providing key insights into market direction and strength. What sets MLMI apart is its integration of artificial intelligence, allowing it to adapt dynamically to market changes and offer a more nuanced and responsive analysis.
Identifying Trend Direction and Strength: The MLMI serves as a tool to recognize market trends, signaling whether the momentum is upward or downward. It also provides insights into the intensity of the momentum, helping traders understand both the direction and strength of prevailing market trends.
Identifying Consolidation Areas: When the MLMI Prediction line and the WMA of the MLMI Prediction line become flat/oscillate around the mid-level, it's a strong sign that the market is in a consolidation phase. This insight from the MLMI allows traders to recognize periods of market indecision.
Recognizing Overbought or Oversold Conditions: By identifying levels where the market may be overbought or oversold, MLMI offers insights into potential price corrections or reversals.
█ Settings
Prediction Data (k)
This parameter controls the number of neighbors to consider while making a prediction using the k-Nearest Neighbors (k-NN) algorithm. By modifying the value of k, you can change how sensitive the prediction is to local fluctuations in the data.
A smaller value of k will make the prediction more sensitive to local variations and can lead to a more erratic prediction line.
A larger value of k will consider more neighbors, thus making the prediction more stable but potentially less responsive to sudden changes.
Trend length
This parameter controls the length of the trend used in computing the momentum. This length refers to the number of periods over which the momentum is calculated, affecting how quickly the indicator reacts to changes in the underlying price movements.
A shorter trend length (smaller momentumWindow) will make the indicator more responsive to short-term price changes, potentially generating more signals but at the risk of more false alarms.
A longer trend length (larger momentumWindow) will make the indicator smoother and less responsive to short-term noise, but it may lag in reacting to significant price changes.
Please note that the Machine Learning Momentum Index (MLMI) might not be effective on higher timeframes, such as daily or above. This limitation arises because there may not be enough data at these timeframes to provide accurate momentum and trend analysis. To overcome this challenge and make the most of what MLMI has to offer, it's recommended to use the indicator on lower timeframes.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Information Entropy OscillatorHello Traders
This Trading Indicator / script is my interpritation of the use of shannons entropy in Trading, hope you find this usefull !!!
Information Entropy Oscillator :
In Physics, entropy is a concept and a measurable physical property that is most commonly associated with the state of disorder, randomness or uncertainty of a system. In the Thermodynamic field Entropy also describes how much energy is not available to do work, The more disordered a system and higher the entropy, the less of a system's energy is available to do work. This last definition is central to the idea of this trading idea, Briefly this is because the lower the information Entropy the “more predictable” is price movement which is characterized by a two states process up(h), and down(d) - (green and red candles), thus the more predictable a up or down move, Given the definition this also means more “energy” which can be thought of as the systems “predictive power” is available to do work, where work in this case to predict the likelihood of a trend continuation.
In Information Theory, the entropy of a random variable (A statistical term that describes either a discrete or continuous event with a respective (discrete or continuous) probability, where the latter is expressed via a CDF - cumulative distribution function) is the average level of "information", "surprise", or "uncertainty" inherent to the variable's possible outcomes. note : this is the definition for Entropy that this script is built upon
Formual Derivation :
Interpretations of Information Entropy Values (Polar approach)
when , …
H(x) = 0 Max-Information gain (purity of knowledge available)
H(x) = 1 No INformation gain, When both states probabilities are equal, i.e. H = T = 0.5, the function yields maximum uncertainty and therefore maximum entropy. This reflects
When Information gain is nearing 0, thus low, the script attempts to predict the proceeding trend direction, for example when entropy is low and all bars preceding the real market / time bars have all been positive and the real time bar closes as a red candle (close < yesterday's open) the script takes this as a high information gain signal, “predicting” a Bearish trend.
The Script Also comes with a Information Entropy heat map to plot entropy (inspired by Oppenheimer and Barbie lol), to see this turn off all candle plots, plots in the Chart settings, under the symbol header .
Normalized Adaptive Trend Lines [MAMA and FAMA]These indicators was originally developed by John F. Ehlers (Stocks & Commodities V. 19:10: MESA Adaptive Moving Averages). Everget wrote the initial functions for these in pine script. I have simply normalized the indicators and chosen to use the Laplace transformation instead of the hilbert transformation
How the Indicator Works:
The indicator employs a series of complex calculations, but we'll break it down into key steps to understand its functionality:
LaplaceTransform: Calculates the Laplace distribution for the given src input. The Laplace distribution is a continuous probability distribution, also known as the double exponential distribution. I use this because of the assymetrical return profile
MESA Period: The indicator calculates a MESA period, which represents the dominant cycle length in the price data. This period is continuously adjusted to adapt to market changes.
InPhase and Quadrature Components: The InPhase and Quadrature components are derived from the Hilbert Transform output. These components represent different aspects of the price's cyclical behavior.
Homodyne Discriminator: The Homodyne Discriminator is a phase-sensitive technique used to determine the phase and amplitude of a signal. It helps in detecting trend changes.
Alpha Calculation: Alpha represents the adaptive factor that adjusts the sensitivity of the indicator. It is based on the MESA period and the phase of the InPhase component. Alpha helps in dynamically adjusting the indicator's responsiveness to changes in market conditions.
MAMA and FAMA Calculation: The MAMA and FAMA values are calculated using the adaptive factor (alpha) and the input price data. These values are essentially adaptive moving averages that aim to capture the current trend more effectively than traditional moving averages.
But Omar, why would anyone want to use this?
The MAMA and FAMA lines offer benefits:
The indicator offers a distinct advantage over conventional moving averages due to its adaptive nature, which allows it to adjust to changing market conditions. This adaptability ensures that investors can stay on the right side of the trend, as the indicator becomes more responsive during trending periods and less sensitive in choppy or sideways markets.
One of the key strengths of this indicator lies in its ability to identify trends effectively by combining the MESA and MAMA techniques. By doing so, it efficiently filters out market noise, making it highly valuable for trend-following strategies. Investors can rely on this feature to gain clearer insights into the prevailing trends and make well-informed trading decisions.
This indicator is primarily suppoest to be used on the big timeframes to see which trend is prevailing, however I am not against someone using it on a timeframe below the 1D, just be careful if you are using this for modern portfolio theory, this is not suppoest to be a mid-term component, but rather a long term component that works well with proper use of detrended fluctuation analysis.
Dont hesitate to ask me if you have any questions
Again, I want to give credit to Everget and ChartPrime!
Vortex Cross w/MA ConfirmationThis script is a trading strategy that combines the Vortex Indicator and a Moving Average (MA) to generate potential entry signals for long and short positions.
1. Vortex Indicator:
The Vortex Indicator consists of two lines: Vortex Positive (VIP) and Vortex Negative (VIM). It is designed to identify trend direction and measure the strength of a trend.
2. Moving Average (MA):
The script uses a chosen type of Moving Average (SMA, EMA, SMMA, WMA, or VWMA) to smooth the price data. The smoothed line is referred to as the "Smoothing Line."
3. Determine Long and Short Conditions:
The script looks for potential long entry signals when VIP crosses above VIM, highlighting each crossover on the chart, and the closing price is above the Smoothing Line. It searches for short entry signals when VIM crosses above VIP, with the closing price is below the Smoothing Line. When the long or short conditions are met, the strategy enters either a long or short position accordingly.
Potential Usage:
The strategy can be utilized in trending markets, where the Vortex Indicator helps identify trend direction and strength, and the Moving Average smooths the price data to filter out some noise. It aims to capture trends and ride them while avoiding false signals during choppy or sideways markets.
