Z-Score Heikin-Ashi TransformedThe Z-Score Heikin-Ashi Transformed (𝘡 𝘏-𝘈) indicator is a powerful technical tool that combines the principles of Z-Score and Heikin Ashi to provide traders with a smoothed representation of price movements and a standardized measure of market volatility.
The 𝘡 𝘏-𝘈 indicator applies the Z-Score calculation to price data and then transforms the resulting Z-Scores using the Heikin Ashi technique. Understanding the individual components of Z-Score and Heikin Ashi will provide a foundation for comprehending the methodology and unique features of this indicator.
Z-Score:
Z-Score is a statistical measure that quantifies the distance between a data point and the mean, relative to the standard deviation. It provides a standardized value that allows traders to compare different data points on a common scale. In the context of the 𝘡 𝘏-𝘈 indicator, Z-Score is calculated based on price data, enabling the identification of extreme price movements and the assessment of their significance.
Heikin Ashi:
Heikin Ashi is a popular charting technique that aims to filter out market noise and provide a smoother representation of price trends. It involves calculating each candlestick based on the average of the previous candle's open, close, high, and low prices. This approach results in a chart that reduces the impact of short-term price fluctuations and reveals the underlying trend more clearly.
Methodology:
The 𝘡 𝘏-𝘈 indicator starts by calculating the Z-Score of the price data, which provides a standardized measure of how far each price point deviates from the mean. Next, the resulting Z-Scores are transformed using the Heikin Ashi technique. Each Z-Score value is modified according to the Heikin Ashi formula, which incorporates the average of the previous Heikin Ashi candle's open and close prices. This transformation smooths out the Z-Score values and reduces the impact of short-term price fluctuations, providing a clearer view of market trends.
This tool enables traders to identify significant price movements and assess their relative strength compared to historical data. Positive transformed Z-Scores indicate that prices are above the average, suggesting potential overbought conditions, while negative transformed Z-Scores indicate prices below the average, suggesting potential oversold conditions. Traders can utilize this information to identify potential reversals, confirm trend strength, and generate trading signals.
Utility:
The indicator offers valuable insights into price volatility and trend analysis. By combining the standardized measure of Z-Score with the smoothing effect of Heikin Ashi, traders can make more informed trading decisions and improve their understanding of market dynamics. 𝘡 𝘏-𝘈 can be used in various trading strategies, including identifying overbought or oversold conditions, confirming trend reversals, and establishing entry and exit points.
Note that the 𝘡 𝘏-𝘈 should be used in conjunction with other technical indicators and analysis tools to validate signals and avoid false positives. Additionally, traders are encouraged to conduct thorough backtesting and experimentation with different parameter settings to optimize the effectiveness of the indicator for their specific trading approach.
Key Features:
Optional Reversion Doritos
Adjustable Reversion Threshold
2 Adjustable EMAs
Example Charts:
See Also:
On Balance Volume Heikin-Ashi Transformed
Pengayun
RSI Trend Transform [wbburgin]The RSI Trend Transform indicator is a dual-concept indicator that transforms volume data and price data into two different RSI values, which can then be used together to determine trend strength and momentum. The volume RSI does not use any price data in its calculation - it is purely a transform from nondirectional volume into a directional indicator.
The RSI for all three RSI values (price, volume,combined average) can be plotted as either stochastic or normal. The RSI calculation is adapted for use on volume, which is why the normal ta.rsi() function is not used for the price RSI calculation; both use the same formula for indicator consistency.
How to Use the Indicator
In the examples below, the Price RSI is plotted in yellow and the Volume RSI is plotted in red (length = 200, which is why the indicator is large in these examples). The indicator can be used on any timeframe and any asset, provided volume data is provided by the vendor to TradingView.
Identifying Bullish Trends
A rising volume RSI with a rising price RSI signifies a bullish trend. Example 1:
Example 2:
You can use the combined RSI (the average of the volume RSI and the price RSI) to help with the identification of these trends:
Identifying Bearish Trends
A falling volume RSI with a falling price RSI signifies a bearish trend:
Example 2:
Settings
Source is the source of the price RSI, the volume RSI will by default use volume in its calculations. If you have other indicators on-chart, you could even use the ATR, a volatility indicator, or any nondirectional or directional indicator and transform it into the "price" RSI.
Length is both the length of the RSI and the stochastic.
The next three rows are for each RSI you can plot on the indicator: price RSI, volume RSI, and combined RSI (average of price and volume). The first checkbox plots/removes them from the chart, you can subsequently choose the type of RSI (regular or stochastic), the color of the plot, and the length of the EMA smoothing applied afterward to the plot.
Upper Band and Lower Band refer to the overbought and oversold lines, respectively.
A note about the combined RSI- you will be unable to spot divergences if the combined RSI is the only plot on the indicator, so I encourage you to use the combined RSI as a way to confirm the overall trend if you notice the price RSI and the volume RSI and trending similarly.
RSI + ADX + MACDINDICADOR COMBINADO DE RSI + ADX
Aprovecha las ventajas de cada indicador en uno solo.
Teniendo en un solo indicador el momentum de cada tendencia y la fuerza relativa con sus puntos de sobre compra y sobre venta.
También al poder analizar divergencias en el indicador oscilador RSI y poder crear estrategias de entrada con el ADX
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RSI + ADX COMBINED INDICATOR
Take advantage of each indicator in one.
Having in a single indicator the momentum of each trend and the relative strength with its points of overbought and oversold.
Also by being able to analyze divergences in the RSI oscillator indicator and being able to create entry strategies with the ADX
DERECHOS DEL CREADOR PARA: Dvd_trading
Momentum Oscillator, Divergences & Signals [TrendAlpha]The "Momentum, Real Time Divergences & Signals " indicator is designed to provide traders with insights into market momentum, identify potential divergences, and generate buy and sell signals. It offers a comprehensive set of features to assist traders in making informed trading decisions.
The indicator starts by calculating the momentum oscillator based on user-defined parameters.
- Traders can adjust the "Length" parameter to customize the sensitivity of the oscillator. The default value is set to 7, but it can be modified according to individual preferences.
- The "Source" parameter allows traders to select the input source for the oscillator calculation, with the default being the closing price of the asset.
- Traders have the option to display divergence lines by switching on the "Show Lines" parameter. This feature helps identify potential divergences between the oscillator and the price.
The oscillator is calculated using a two-step process. First, a smoothing function is applied to the source data using the "sma" (simple moving average) function. Then, the rate of change is computed over the specified length using the "mom" (momentum) function. Positive oscillator values indicate upward momentum, while negative values indicate downward momentum.
The indicator also generates buy and sell signals by identifying bullish and bearish divergences. A bullish divergence occurs when the oscillator is negative and crosses above zero, while a bearish divergence occurs when the oscillator is positive and crosses below zero. The indicator checks for specific conditions to confirm the divergences, such as comparing the current oscillator value with the previous value and validating the corresponding price action.
When a bullish or bearish divergence is detected, the indicator plots circles to highlight these signals on the chart. A green circle indicates a bullish signal, suggesting a potential buying opportunity, while a red circle indicates a bearish signal, suggesting a potential selling opportunity. In addition to circles, the indicator also displays labels to provide further clarity on the signals. A "Buy" label is shown for bullish signals, and a "Sell" label is shown for bearish signals.
To visually represent the divergences, the indicator plots lines connecting the corresponding points on the oscillator. A green line is drawn for bullish divergences, while a red line is drawn for bearish divergences. Traders can easily observe the divergence patterns and their relationships with the price action, aiding them in making trading decisions.
- The indicator also includes alert conditions for both bullish and bearish divergences. Traders can set up alerts to receive notifications when potential divergences occur, allowing them to take timely action.
Rough AverageThe Rough Average indicator is a unique technical tool that calculates a modified average to provide insights into market conditions. It incorporates a combination of mathematical operations and existing indicators to offer traders a different perspective on price movements.
The Rough Average indicator aims to capture market dynamics through a specific calculation method. It utilizes two main components: a check for the approximate scale of the price and a profile calculation based on the Relative Strength Index (RSI) of the closing price.
Methodology:
Approximate Scale: The indicator determines the approximate scale of the price by analyzing the magnitude of the closing price. This step involves a mathematical process that identifies the power of 10 that best represents the scale. This function reduces overall lag and gives a better smoothing to the output of the calculation
Profile Calculation: The indicator calculates a profile value by summing the absolute values of the RSI of the closing price over a specified period. The RSI provides insights into the strength or weakness of price movements. The profile calculation considers a range of prices based on the determined scale.
Indicator Calculation:
The Rough Average is derived by applying the Exponential Moving Average (EMA) to the calculated profile. The EMA is a smoothing technique that emphasizes recent price data. The resulting value represents the modified average of the indicator.
Utility:
The Rough Average indicator offers traders an alternative perspective on market conditions. By utilizing a modified average calculation, it can reveal potential trends, reversals, or periods of market strength or weakness. Traders can use the Rough Average to complement their analysis and identify possible trading opportunities.
It is important to note that the effectiveness of the Rough Average indicator may vary depending on the specific market and trading strategy. It is recommended to combine its analysis with other technical indicators and conduct thorough testing before making trading decisions.
Key Features:
Customizable OB\OS Levels
Bar coloring methods: Trend, Reversions, Extremities
Example Charts:
Banana RSIBanana RSI is not just ap-PEAL-ing to the eyes!
This simple little indicator provides a New Approach to determining Overbought and Oversold levels, as well as taking advantage of a non-typical smoothing method for this type of indicator.
Banana RSI uses a Cumulative High and Low Average to draw the upper, lower, and midline.
The High and Low Averages use the data only from above or below the Cumulative Average to calculate their respective line.
In simpler terms:
The High average is an average of every value ABOVE the full average.
The Low average is an average of every value BELOW the full average.
This creates an automated method to determine overbought and oversold territory based on the charts historical movement.
Since every chart can be different, these levels change with the chart.
Banana RSI also uses a linear regression smoothing method , by taking advantage of the built-in Least Squares Moving Average, we are able to view a better reacting/less-lagging moving average.
Included are 2 Length-Adjustable LSMA lines to use however needed.
Using the Regression Lines along with the High & Low Averages provides a new view on the classic RSI indicator.
Enjoy!
Divergence Screener [Mr_Zed]Divergence Screener
This script allows you to scan multiple assets and timeframes for bullish and bearish divergences based on the RSI (Relative Strength Index) indicator.
Features
Supports up to 40 different assets and timeframes for simultaneous scanning.
Customizable settings for RSI period and source.
Adjustable lookback periods for identifying pivot highs and pivot lows.
Flexible range limits for filtering divergences based on the number of bars since the last pivot point.
Alerts for bullish and bearish divergences on confirmed bars.
Ta StrategyHello guys
This script follows traditional technical indicators
MACD, ADX, RSI and pivot points
If the price is above the resistance and the MACD has crossover ,and the RSI 14 is above 50
ADX is higher than 20, and DI+ is higher than DI-. This is a buy signal and vice versa for a sell signal
The script moves the stop loss to the entry price after the first target is reached
You can specify the quantity you want to sell when the price reaches the first target
There are also options like if you want the script to entry long or short, or both
you can reverse the strategy if it does not work well
If you want to inquire about any details, please let me know in the comments
RSI Dot Party - All Lengths From 1 To 120The RSI Dot Party indicator displays all RSI lengths from 1 to 120 as different colored dots on the chart.
🔶 Purpose
Show the reversal point of price action to time entries and exits.
🔶 USAGE
When a dot displays it is a indication of the reversal of the price/trend. The larger the dot the more likely it is to reverse.
The Default settings generates dots for extreme cases where the RSI is over = 90 or under = 10 for every RSI length in the range of 1-120.
Example if the RSI of length 1 or 2 or 3 or 4 or ... or 15 or 16 or 17 or ... or 80 or 81 or 82 or ... if any of does RSI crosses a boundary a dot is shown.
A boundary is the over/under the RSI oscillates in.
Customize the settings until the dots match up with the high and lows of past price action.
🔶 SETTINGS
🔹 Source
Source 1: Is the First Source RSI is calculated from
Source 2: Is the Second Source RSI is calculated from
🔹 Meta Settings
Hours back to draw: To speed up the script calculate it only draws a set number of hours back, default is 300 hours back in time to draw then it cuts off.
Show Dots: Show or disable dots
Show Bar Color: Color the bars for each RSI incident
Filter Cross: Filters and only shows dots when the RSI crosses above or bellow a boundary. If not all candles above or bellow the boundaries will display a dot.
Dots Location Absolute: Instead of showing the dots above or bellow the candle, the dots will show up on the top and bottom of the window.
🔹 7 RSI Groups
There are a total of 7 RSI colors.
Range Very Tiny: Default Color Green
Range Tiny: Default Color Purple
Range Small: Default Color Yellow
Range Normal: Default Color Red
Range Large: Default Color Blue
Range Huge: Default Color Dark Purple
Range Very Huge: Default Color White
🔹 RSI Group Settings
Hi/Low Color: Change the Color of that group.
Start/End: The Start and End range of this RSI color. Example if start = 5 and end = 10 the RSI of 5,6,7,8,9,10 will be displayed on the chart for that color, if any of does RSI goes above or bellow the boundary a dot is displayed on that candle.
Delay: The RSI needs to be above or bellow a boundary for x number of candles before displaying a dot. For example if delay = 2 and the RSI is over = 70 for 2 candles then it will display a dot.
Under/Over: Boundaries that indicate when to draw a dot, if over = 70 and RSI crosses above 70 a dot is displayed.
🔹 Show
Section that allows you to disable RSI grounds you dont want to see, this also removes them from the alert signal generated.
Show Low: Show or disable Low RSI dots
Show High: Show or disable High RSI dots
🔶 ALERTS
Alert for all New RSIs Dots Created in real time
The alert generated depends on what groups are showing or not, if the green group is disabled for example the alert will not be generated.
🔶 Warning
When a dot shows up it can continue moving. For example if a purple dot shows itself above a 15 minute candle, if that candle/price continue to extend up the dot will move up with it.
Dots can also disappear occasionally if the RSI moves in and out of a boundary within that candles life span.
🔶 Community
I hope you guys find this useful, if you have any questions or feature requests leave me a comment! Take care :D
Open interest flow / quantifytools- Overview
Open interest flow detects inflows (positions opening) and outflows (positions closing) using open interest and estimates delta (net buyers/sellers) for the flows. Users are able to choose any open interest source available on Tradingview, by default set to BTCUSDT OI fetched from Binance. Using historical open interest flows, bands depicting typical magnitude of flows are formed for benchmarking intensity of flows. On the inflow side, +1 represents average inflows while +2 represents 2x above average inflows, a level considered an extreme. In a vice versa manner, -1 represents average outflows while -2 represents 2x above average outflows. Extreme inflows indicate aggressive position opening, in other words exuberance. Extreme outflows on the other hand indicate forced exiting of positions, in other words liquidations.
- Concept
Open interest flow is calculated using position of OI source relative to its moving average (by default set to SMA 10), referred to as relative open interest from hereon. When relative OI is positive (open interest is above its moving average), new positions are considered to enter the market. When relative OI is negative (open interest is below its moving average), existing positions are considered to exit the market. Open interest delta (side opening/closing positions, either net buyers/sellers) is calculated using relative price in a similar fashion to relative OI, but using close of viewed symbol as source. Price is considered to be up when relative price is positive, down when relative price is negative. Using relative OI and relative price in tandem, the following assumptions are applied:
Price up, open interest up = longs entering market
Price down, open interest up = shorts entering market
Price down, open interest down = longs exiting market
Price up, open interest down = shorts exiting market
Bands depicting magnitude of open interest flows are calculated using average turning points in relative OI. +1 and -1 represent levels where flows on average turn towards mean rather than continue to increase/decrease. These levels are then multiplied up to +2 and -2, representing two times larger deviations from the normal. When inflows are above 1, positions opening have reached a point where flows historically turn down. Therefore, anything above 1 would be abnormal amount of open interest entering, an extreme stretch being at 2 or above. Same logic applies to outflows, but in a vice versa manner (below -1 abnormal, extreme at -2)
Flow bursts further refine indications of aggressive inflows/outflows by taking into account change in open interest flows. Burst indications are activated when open interest is above its average turning point, coupled with a sufficient increase/decrease in flows simultaneously. Bursts are essentially a filtered version of abnormal flows and therefore a more reliable indication of exuberance/liquidations. Burst sensitivity can be adjusted via input menu, available in 5 settings. 1 sets OI burst requirements to loosest (more signals, more noise) while 5 sets OI burst requirements to strictest (less signals, less noise). Exact criteria applied to bursts can be viewed via input menu tooltip.
- Features
Users can opt for OI source auto-select for CRYPTO/USDT pairs. When auto-select is enabled and another chart is opened, corresponding open interest source is automatically selected as long as requirements mentioned above are met.
Open interest flows can be visualized as chart color, available separately for flow states and flow bursts.
Relative price line and flow guidelines (reminders for flow interpretation) can be enabled via input menu. All colors are customizable.
- Alerts
Available alerts are the following:
- Abnormal long inflows/outflows
- Abnormal short inflows/outflows
- Abnormal inflows/outflows from either side
- Aggressive longs/shorts (flow burst up)
- Liquidated longs/shorts (flow burst down)
- Aggressive or liquidated longs/shorts
- Practical guide
Open interest as a standalone data point does not reveal which side is likely opening/exiting positions and how extreme the participant behavior is. Using the additional data provided by open interest flows, moments of greed and fear can be detected. Smart money does not short into dips and buy into rips. When buyers or sellers have participated in a large move and continue to show interest even when efforts are not rewarded at an already overextended price, participants are asking for trouble.
Similar events can be observed when extreme outflows take place, indicating forced exits such as stop-losses triggering. When enough participants are forced out, price is likely to take the path of least resistance which is to the opposite direction.
Autocorrelation OscillatorReleasing the autocorrelation oscillator.
NOTE! Please be sure to read the description. This is a theoretical indicator and its important to understand the theory behind its use.
About the indicator:
Before getting into the indicator and its functionality, its important to discuss the theoretical underpinnings of the indicator.
The autocorrelation oscillator operates on two theories of market behaviour that go hand in hand. Those theories are the market efficiency theory and the random walk theory (or hypothesis ).
Market efficiency theory: The market efficiency theory or "Efficient Market Hypothesis (EMH)" postulates that all available information is reflected in a ticker's price almost instantaneously and thus it is impossible for an investor or trader to get ahead of the market because we cannot respond to the speed that the market responds. Of course, there are many holes in this theory, the most notable being that the market is a function of humans. Absent humans and their technological integrations into the market, the market would cease to react at all. But that's besides the point. This is a widely accepted theory and one in which I can mathematically observe through statistical tests. The truth behind this theory is the market is efficient for responding to evolving economic and financial information, likely owning to huge amounts of computer and algorithmic integration into trading, and thus the market is more efficient than the average person is capable (absent computerized algorithms and integration) of ascertaining nuanced financial and economic circumstances. By the time we the people can appraise information, the market has already acted on it. And that is the main premise of the EMH.
The next theory is the Random Walk Theory or Hypothesis (RWH). This builds on the EMH and essentially postulates that the market reacts so quickly to price in current circumstances that it is too random for people to truly exploit and benefit from.
The result of these two theories is two-fold and can be summarized as such:
a) The market behaves in a chaotic fashion that is seemingly random and is incapable of being predicted effectively; and
b) The market is more efficient than a person in incorporating key fundamental information, contributing to the high degree of seemingly random behaviour.
So, how does this help us?
It is said, because of the EMH and the RWH, the only way to truly exploit the market for profit is by:
a) Buying and holding and investing under the bias that stocks will eventually rise in value; or
b) For short term trading, exploiting the pricing anomalies within the data.
So how do we exploit pricing anomalies within the data?
Well, in my own research on market efficiency and behaviour, I have identified many ways of figuring out some anomalies. One of the most effective ways is by looking at simple correlation of lagged values, or autocorrelation for short.
What is autocorrelation and how to use it in relation to EMH and RWH?
Autocorrelation refers to the correlative relationship among the values in a series. Put simply, its the relationship of the same variable over time. For example, if we wanted to look at the auto-correlation of a ticker's high price, we would take, say, 5 to 7 previous high prices and correlate them with the current high price in a series dataset. If the EMH and RWH are true, the correlation among all the variables should have an average less than 0.5 or greater than -0.5. This would indicate true randomness in the dataset and thus an efficient market.
However, if the average of all of the sum's of these correlations are greater than or equal to 0.5 or less than or equal to -0.5, that indicates there is a high degree of autocorrelation and thus the EMH ad RWH is being invalidated as the market is not operating efficiently. This is an anomaly and this anomaly can be exploited.
So how do we exploit it?
Well, when the EMH and RWH hypothesis is being invalidated, we can expect what I coin as a "Regression to Chaos" i.e. the market will revert back to an efficient equilibrium state. So if we have a high correlation of the lagged variables and a strong uptrend or downtrend correlation, we can expect an inefficient market to correct back to an efficient market (i.e. have a reversal from the current trend).
So how does the indicator work?
The indicator measures the lagged correlation of the previous 5 highs and lows of a ticker. A high correlation among all of the highs and lows that exceeds 0.8 would be an invalidation of the EMH and RWH and thus signal a correction to come (i.e. a Regression to Chaos).
The indicator will display this by changing colour. Red for a bearish reversal and green for a bullish. Let's take a look below using the ticker MSFT:
Above we can see the indicator identifying observed inefficiencies within the MSFT ticker on the 1 minute timeframe. The green vertical lines correspond to potential bullish reversals as a result of bearish inefficiencies, the red correspond to bearish reversals as a result of bullish inefficiencies.
You can see these lead to reversals within the ticker.
Components of the indicator:
In the chart above we see the following that are being indicated by arrows:
Red Arrows: Show the identified inefficiencies. Red for bullish inefficiencies (i.e. bearish reversal), green for bearish inefficiencies (i.e. bullish reversal)
Yellow Arrow: The lagged variable chart. This will display the current correlation among all the lagged variables the indicator is assessing.
Teal arrow: Displays the current strength of the trend by correlating the trend to time. A strong negative value (i.e. a value less than or equal to -0.5) indicates a strong downtrend, a strong positive value indicates the inverse.
You can unselect the data-tables in the settings menu if you just want to view the correlation line itself. This part of the indicator is customizable. You can also define the lookback period; however, it is strongly recommended to leave it at 14 as this maintains the use of this indicator as an oscillator.
And that is the indicator! Let me know your comments, questions and feedback below.
Safe trades everyone!
RSI Momentum TrendThe "RSI Momentum Trend" indicator is a valuable tool for traders seeking to identify momentum trends.
By utilizing the Relative Strength Index (RSI) and customizable momentum thresholds, this indicator helps traders spot potential bullish and bearish signals.
you can adjust input parameters such as the RSI period, positive and negative momentum thresholds, and visual settings to align with their trading strategies.
The indicator calculates the RSI and evaluates two momentum conditions: positive and negative.
The positive condition considers the previous RSI value, current RSI value, and positive change in the 5-period exponential moving average (EMA) of the closing price.
The negative condition looks at the current RSI value and negative change in the 5-period EMA.
Once a momentum condition is met, the indicator visually represents the signal on the chart.
The "RSI Momentum Trend" indicator provides you with a quick and effective way to identify momentum trends using RSI calculations.
By incorporating visual cues and customizable parameters, it assists traders in making informed decisions about potential market movements.
RSI Fractal Energy with Signal LineHere is my second script.
Introducing the RSI Fractal Energy Indicator.
This incorporates the Relative-Strength Index and Fractal Energy as the name implies.
This will help the trader identify:
1. Trend Strength: The higher the value of the indicator can indicate the strength of the trend and vice versa.
2. Reversal points: If the indicator is showing weakness and the market is making higher highs and lower lows this can indicate a reversal is possible.
3. Overbought and Oversold conditions: This indicator is currently set to 30(Oversold) and 70(Overbought), but this can be changed in the source code.
I also added a signal line to provide bullish/bearish crossovers.
I use this indicator on the 1 hr chart, but it can be used on any time frame.
Please let me know if you have any questions, comments, or concerns. Always open to learning more.
I will also provide updates as I continue to use my indicators.
Happy trading!
Relative Trend Index (RTI) by Zeiierman█ Overview
The Relative Trend Index (RTI) developed by Zeiierman is an innovative technical analysis tool designed to measure the strength and direction of the market trend. Unlike some traditional indicators, the RTI boasts a distinctive ability to adapt and respond to market volatility, while still minimizing the effects of minor, short-term market fluctuations.
The Relative Trend Index blends trend-following and mean-reverting characteristics, paired with a customizable and intuitive approach to trend strength, and its sensitivity to price action makes this indicator stand out.
█ Benefits of using this RTI instead of RSI
The Relative Strength Index (RSI) and the Relative Trend Index (RTI) are both powerful technical indicators, each with its own unique strengths.
However, there are key differences that make the RTI arguably more sophisticated and precise, especially when it comes to identifying trends and overbought/oversold (OB/OS) areas.
The RSI is a momentum oscillator that measures the speed and change of price movements and is typically used to identify overbought and oversold conditions in a market. However, its primary limitation lies in its tendency to produce false signals during extended trending periods.
On the other hand, the RTI is designed specifically to identify and adapt to market trends. Instead of solely focusing on price changes, the RTI measures the relative positioning of the current closing price within its recent range, providing a more comprehensive view of market conditions.
The RTI's adaptable nature is particularly valuable. The user-adjustable sensitivity percentage allows traders to fine-tune the indicator's responsiveness, making it more resilient to sudden market fluctuations and noise that could otherwise produce false signals. This feature is advantageous in various market conditions, from trending to choppy and sideways-moving markets.
Furthermore, the RTI's unique method of defining OB/OS zones takes into account the prevailing trend, which can provide a more precise reflection of the market's condition.
While the RSI is an invaluable tool in many traders' toolkits, the RTI's unique approach to trend identification, adaptability, and enhanced definition of OB/OS zones can provide traders with a more nuanced understanding of market conditions and potential trading opportunities. This makes the RTI an especially powerful tool for those seeking to ride long-term trends and avoid false signals.
█ Calculations
In summary, while simple enough, the math behind the RTI indicator is quite powerful. It combines the quantification of price volatility with the flexibility to adjust the trend sensitivity. It provides a normalized output that can be interpreted consistently across various trading scenarios.
The math behind the Relative Trend Index (RTI) indicator is rooted in some fundamental statistical concepts: Standard Deviation and Percentiles.
Standard Deviation: The Standard Deviation is a measure of dispersion or variability in a dataset. It quantifies the degree to which each data point deviates from the mean (or average) of the data set. In this script, the standard deviation is computed on the 'close' prices over a specified number of periods. This provides a measure of the volatility in the price over that period. The higher the standard deviation, the more volatile the price has been.
Percentiles: The percentile is a measure used in statistics indicating the value below which a given percentage of observations in a group falls. After calculating the upper and lower trends for the last 'length' periods and sorting these values, the script uses the 'Sensitivity ' parameter to extract percentiles from these sorted arrays. This is a powerful concept because it allows us to adjust the sensitivity of our signals. By choosing different percentiles (controlled through the 'Sensitivity' parameter), we can decide whether we want to react only to extreme events (high percentiles) or be more reactive and consider smaller deviations from the norm as significant (lower percentiles).
Finally, the script calculates the Relative Trend Index value, which is essentially a normalized measure indicating where the current price falls between the upper and lower trend values. This simple ratio is incredibly powerful as it provides a standardized measure that can be used across different securities and market conditions to identify potential trading signals.
Core Components
Trend Data Count: This parameter denotes the number of data points used in the RTI's calculation, determining the trend length. A higher count captures a more extended market view (long-term trend), providing smoother results that are more resistant to sudden market changes. In contrast, a lower count focuses on more recent data (short-term trend), yielding faster responses to market changes, albeit at the cost of increased susceptibility to market noise.
Trend Sensitivity Percentage: This parameter is employed to select the indices within the trend arrays used for upper and lower trend definitions. By adjusting this value, users can affect the sensitivity of the trend, with higher percentages leading to a less sensitive trend.
█ How to use
The RTI plots a line that revolves around a mid-point of 50. When the RTI is above 50, it implies that the market trend is bullish (upward), and when it's below 50, it indicates a bearish (downward) trend. Furthermore, the farther the RTI deviates from the 50 line, the stronger the trend is perceived to be.
Bullish
Bearish
The RTI includes user-defined Overbought and Oversold levels. These thresholds suggest potential trading opportunities when they are crossed, serving as a cue for traders to possibly buy or sell. This gives the RTI an additional use case as a mean-reversion tool, in addition to being a trend-following indicator.
In short
Trend Confirmation and Reversals: If the percentage trend value is consistently closer to the upper level, it can indicate a strong uptrend. Similarly, if it's closer to the lower level, a downtrend may be in play. If the percentage trend line begins to move away from one trend line towards the other, it could suggest a potential trend reversal.
Identifying Overbought and Oversold Conditions: When the percentage trend value reaches the upper trend line (signified by a value of 1), it suggests an overbought condition - i.e., the price has been pushed up, perhaps too far, and could be due for a pullback, or indicating a strong positive trend. Conversely, when the percentage trend value hits the lower trend line (a value of 0), it indicates an oversold condition - the price may have been driven down and could be set to rebound, or indicate a strong negative trend. Traders often use these overbought and oversold signals as contrarian indicators, considering them potential signs to sell (in overbought conditions) or buy (in oversold conditions). If the RTI line remains overbought or oversold for an extended period, it indicates a strong trend in that direction.
█ Settings
One key feature of the RTI is its configurability. It allows users to set the trend data length and trend sensitivity.
The trend data length represents the number of data points used in the trend calculation. A longer trend data length will reflect a more long-term trend, whereas a shorter trend data length will capture short-term movements.
Trend sensitivity refers to the threshold for determining what constitutes a significant trend. High sensitivity levels will deem fewer price movements as significant, hence making the trend less sensitive. Conversely, low sensitivity levels will deem more price movements as significant, hence making the trend more sensitive.
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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!
Correlation for Major Markets This indicator plots the correlation of major markets as an indicator. The major markets covered are the following:
DXY
GC
CL
ES
RTY
ZN
The chart shows all the correlations and cross-correlations of the above instruments plotted together. The user can go in the settings and choose what correlation to see, or if multiple correlations, choose to plot the indicator a second time.
Revolution SMA-EMA DivergenceThis is an MACD inspired indicator and it analyzes the difference between the SMA and EMA using the same time period. Unlike the MACD, it can give you a better understanding of the overall trend. Values above 0 is bullish and below 0 bearish. It consists of two cycles: Black histogram - the long-term cycle and orange histogram - the short-term cycle, as well as timing signal (red line).
RSI MTF [Market Yogi]The Multi-Time Frame RSI with Money Flow Index and Average is a powerful trading indicator designed to help traders identify overbought and oversold conditions across multiple time frames. It combines the Relative Strength Index (RSI) with the Money Flow Index (MFI) and provides an average value for better accuracy.
The Relative Strength Index (RSI) is a popular momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100 and is used to identify overbought and oversold conditions in an asset. By incorporating the RSI across multiple time frames, this indicator offers a broader perspective on market sentiment.
In addition to the RSI, this indicator also includes the Money Flow Index (MFI). The MFI is a volume-based oscillator that measures the inflow and outflow of money into an asset. It takes into account both price and volume, providing insights into the strength and direction of buying and selling pressure.
By combining the RSI and MFI across multiple time frames, traders gain a comprehensive understanding of market dynamics. The indicator allows for comparing the RSI and MFI values across different time frames, enabling traders to identify divergences and potential trend reversals.
Furthermore, this indicator provides an average value of the multi-time frame RSI, offering a consolidated signal that helps filter out noise and enhance the accuracy of trading decisions.
Key Features:
1. Multi-Time Frame RSI: Combines the RSI across different time frames to provide a comprehensive view of market sentiment.
2. Money Flow Index (MFI): Incorporates the MFI to gauge buying and selling pressure based on both price and volume.
3. Average Calculation: Computes the average value of the multi-time frame RSI to generate a consolidated trading signal.
4. Divergence Detection: Enables traders to spot divergences between the RSI and MFI values, indicating potential trend reversals.
5. Overbought and Oversold Levels: Highlights overbought and oversold levels on the RSI, aiding in timing entry and exit points.
The Multi-Time Frame RSI with Money Flow Index and Average is a versatile tool that can be applied to various trading strategies, including trend following, swing trading, and mean reversion. Traders can adjust the time frame settings to suit their preferences and trading style.
Note: It's important to use this indicator in conjunction with other technical analysis tools and indicators to validate signals and make informed trading decisions.
Heikin Ashi ROC Percentile Strategy**User Guide for the "Heikin Ashi ROC Percentile Strategy"**
This strategy, "Heikin Ashi ROC Percentile Strategy", is designed to provide an easy-to-use framework for trading based on the Heikin Ashi Rate of Change (ROC) and its percentiles.
Here's how you can use it:
1. **Setting the Start Date**: You can set the start date for the strategy in the user inputs at the top of the script. The variable `startDate` defines the point from which the script begins executing trades. Simply input the desired date in the format "YYYY MM DD". For example, to start the strategy from March 3, 2023, you would enter `startDate = timestamp("2023 03 03")`.
2. **Adjusting the Midline, Lookback Period, and Stop Loss Level**: The `zerohLine`, `rocLength`, and `stopLossLevel` inputs allow you to adjust the baseline for ROC, the lookback period for the SMA and ROC, and the level at which the strategy stops the loss, respectively. By tweaking these parameters, you can fine-tune the strategy to better suit your trading style or the particular characteristics of the asset you are trading.
3. **Understanding the Trade Conditions**: The script defines conditions for entering and exiting long and short positions based on crossovers and crossunders of the ROC and the upper and lower "kill lines". These lines are defined as certain percentiles of the ROC's highest and lowest values over a specified lookback period. When the ROC crosses above the lower kill line, the script enters a long position; when it crosses below the upper kill line, it exits the position. Similarly, when the ROC crosses below the upper kill line, the script enters a short position; when it crosses above the lower kill line, it exits the position.
In my testing, this strategy performed best on a day and hour basis. However, I encourage you to experiment with different timeframes and settings to see how the strategy performs under various conditions. Remember, there's no one-size-fits-all approach to trading; what works best will depend on your specific circumstances, goals, and risk tolerance.
If you find other useful applications for this strategy, please let me know in the comments. Your feedback is invaluable in helping to refine and improve this tool. Happy trading!
Trailing Stop with RSI - Momentum-Based StrategyTrailing Stop with RSI - Momentum-Based Strategy
Description:
The Trailing Stop with RSI strategy combines momentum analysis and trailing stop functionality to help traders identify potential entry and exit points in their trading decisions. This strategy is suitable for various markets and timeframes.
Key Features:
Momentum Analysis: The strategy incorporates momentum indicators to identify potential buying and selling opportunities based on momentum shifts in the price.
Trailing Stop Functionality: The strategy utilizes a trailing stop to protect profits and dynamically adjust the stop loss level as the trade moves in the desired direction.
RSI Confirmation: The Relative Strength Index (RSI) is included to provide additional confirmation for trade entries by considering overbought and oversold conditions.
How to Use:
Entry Conditions: Long positions are triggered when positive momentum is detected, and the RSI confirms an oversold condition. Short positions are triggered when negative momentum is detected, and the RSI confirms an overbought condition.
Trailing Stop Activation: Once a position is opened, the trailing stop is activated when the specified profit level (as a percentage) is reached.
Trailing Stop Level: The trailing stop maintains a stop loss level at a specified distance (as a percentage) from the highest profit achieved since opening the position.
Exit Conditions: The trailing stop will trigger an exit and close all positions when the trailing stop level is breached.
Markets and Conditions:
This strategy can be applied to various markets, including stocks, forex, cryptocurrencies, and commodities. It can be used in trending and ranging market conditions, making it versatile for different market environments.
Important Considerations:
Adjust Parameters: Traders can modify the length of the momentum and RSI indicators to suit their preferred timeframe and trading style.
Risk Management: It is recommended to consider appropriate position sizing, risk-to-reward ratios, and overall risk management practices when using this strategy.
Backtesting and Optimization: Traders are encouraged to backtest the strategy on historical data and optimize the parameters to find the best settings for their chosen market and timeframe.
By incorporating momentum analysis, trailing stop functionality, and RSI confirmation, this strategy aims to provide traders with a systematic approach to capturing profitable trades while managing risk effectively.
Linear Correlation Coefficient W/ MAs and Significance TestsThis Linear CC takes into account the log-normal distribution of stock prices and performs Pearson correlation on that data set. It also smoothens the results into an easy to read oscillator, and performs a two-tail t-test on the correlation coefficient data (with a = 0.05) to determine the significance of the coefficients. Significant results are shown in a solid yellow color while insignificant results are shown in a dark yellow color (you can eyeball this with a normal LCC by looking at results around -0.5 to +0.5).
Two MAs are provided as well for a quick trend analysis. You can reduce the lookback period, but it defaults to 31 for the sake of statistical standards.
Futures All List / Sell SignalAs of May 2023, there are more than 180 usdt perpetual coins on the binance futures exchange. These coins are included in the indicator in lists of 40. They are sorted instantly in the table from largest to smallest. The sorting style can be changed in the indicator settings. This indicator collects RSI and TSI values at desired values. The result has a maximum value of 600. A value of 600 signals that the price will decrease or remain stable for a certain period of time. Generally, a short can be expected from the closest point to 600. If 3 separate lists are selected by using 3 of these indicators, 120 coins can be analyzed at the same time. Available in all time zones. Examine it in a 3-minute timeframe. The line inside the indicator draws the instantaneous values of the relevant coin.
RSI-MFI Machine Learning [ Manhattan distance ]The RSI-MFI Machine Learning Indicator is a technical analysis tool that combines the Relative Strength Index (RSI) and Money Flow Index (MFI) indicators with the Manhattan distance metric.
It aims to provide insights into potential trade setups by leveraging machine learning principles and calculating distances between current and historical data points.
The indicator starts by calculating the RSI and MFI values based on the specified periods for each indicator.
The RSI measures the strength and speed of price movements, while the MFI evaluates the inflow and outflow of money in the market.
By combining these two indicators, the indicator captures both price momentum and money flow dynamics.
To apply machine learning principles , the indicator utilizes the Manhattan distance metric to quantify the similarity or dissimilarity between different data points.
The Manhattan distance is calculated by taking the absolute differences between corresponding RSI and MFI values of the current point and historical points.
Next, the indicator determines the nearest neighbors based on the calculated Manhattan distances.
The number of nearest neighbors is determined by the square root of the specified count of neighbors.
By identifying similar patterns and behaviors in the historical data, the indicator aims to uncover potential trade opportunities.
Trade signals are generated based on the calculated distances. The indicator compares each distance with the maximum distance encountered so far.
If a new maximum distance is found, it updates the value and considers the corresponding direction as a potential trade signal. The trade signals are stored in an array for further analysis.
Furthermore, the indicator considers the price action and a calculated regression line to differentiate between long and short trade signals.
Long trade signals are identified when the closing price is above the regression line, indicating a potentially bullish setup.
Short trade signals are identified when the closing price is below the regression line, indicating a potentially bearish setup.
The RSI-MFI Machine Learning Indicator visualizes the regression line on the price chart and labels the bars accordingly. It highlights the regression line with different colors based on the trade signals, making it easier for traders to identify potential entry or exit points.
Traders can use the RSI-MFI Machine Learning Indicator as a tool to analyze price movements, evaluate market conditions based on RSI and MFI, leverage machine learning concepts to find similar patterns, and make informed trading decisions.
Bensler COT OscillatorI tried to replicate the indicator I think Jason Shapiro from Crowded Market Report has kind of alluded to on his interviews and YouTube channel. I think I made the default colors on my indicator match Shapiro's. It's best if used in parallel with the indicator CoT-Buschi which is a nice COT indicator that I based my oscillator off of. That way you can see the effect of the oscillator and decide if you like how the time period affects the output. I am a total noob so just in case you think I know what I'm talking about or doing, I don't.
