GKD-C Variety Stepped, Variety Filter [Loxx]Giga Kaleidoscope GKD-C Variety Stepped, Variety Filter is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ Giga Kaleidoscope Modularized Trading System
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is the NNFX algorithmic trading strategy?
The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community.
The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management.
Here are the main components of the NNFX trading system:
1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines.
2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades.
3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages.
4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade.
5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level.
6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing.
Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels.
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Variety Stepped, Variety Filter as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
█ GKD-C Variety Stepped, Variety Filter
Variety Stepped, Variety Filter is an indicator that uses various types of stepping behavior to reduce false signals. This indicator includes 5+ volatility stepping types and 60+ moving averages.
Stepping calculations
First off, you can filter by both price and/or MA output. Both price and MA output can be filtered/stepped in their own way. You'll see two selectors in the input settings. Default is ATR ATR. Here's how stepping works in simple terms: if the price/MA output doesn't move by X deviations, then revert to the price/MA output one bar back.
ATR
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility .
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
See how this compares to Standard Devaition here:
Adaptive Deviation
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
See how this compares to ATR here:
ER-Adaptive ATR
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation ( SD ). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
For Pine Coders, this is equivalent of using ta.dev()
Included Filters
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Instantaneous Trendline
Kalman Filter
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Adaptive Moving Average - AMA
Description. The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility . It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average ( DEMA ), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average ( EMA ) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA . This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA ( Exponential Moving Average ) that is due to that fact (that he used it) sometimes called Wilder's EMA . This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average ). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average ( DEMA ), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average ( DEMA ), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA , but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
T3 is basically an EMA on steroids, You can read about T3 here:
T3 Striped
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Kalman Filter
Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies. This means that the filter was originally designed to work with noisy data. Also, it is able to work with incomplete data. Another advantage is that it is designed for and applied in dynamic systems; our price chart belongs to such systems. This version is true to the original design of the trade-ready Kalman Filter where velocity is the triggering mechanism.
Kalman Filter is a more accurate smoothing/prediction algorithm than the moving average because it is adaptive: it accounts for estimation errors and tries to adjust its predictions from the information it learned in the previous stage. Theoretically, Kalman Filter consists of measurement and transition components.
Kaufman Adaptive Moving Average - KAMA
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average ( KAMA ) is a moving average designed to account for market noise or volatility . KAMA will closely follow prices when the price swings are relatively small and the noise is low.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average ) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA . The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers . The original idea behind this study (and several others created by John F. Ehlers ) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA , a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers Smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers Smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility .
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume . Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
Requirements
Inputs
Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Outputs
Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator
Confirmation 1: GKD-C Confirmation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest strategy
Additional features will be added in future releases.
Cari dalam skrip untuk "relative strength"
Easy RSI by nnamWhat Does this Indicator Do?
The Easy RSI Indicator color codes candles based on their RSI Value vs. Open / Close (Red / Green). It plots the current price and current RSI value on the chart in real-time. Additionally, when the RSI Value is in an oversold or overbought condition, it plots that signal on the chart in real-time.
The initial candle color is the standard Red / Green Tradingview color, but a Gradient is added to the color which either darkens or lightens the color based on the RSI Value.
As seen in the screenshot below, the higher the RSI Value, the brighter the Green Color is. The lower the RSI Value, the brighter the Red Color is.
The current Price and current RSI Value are both plotted on the chart by default, but can be optionally switched off by the trader.
As seen in the screenshot below, the prices and RSI Values are easily seen while visually tracking the price in real-time.
RSI Overbought Values are plotted when the Overbought condition is triggered. The Default is RED for Overbought and GREEN for Oversold.
As seen in the screenshot below, with all three labels turned on under the input settings (these are ON by default) you can see the overbought condition, the current RSI Value, and current price all in one centralized area. Oversold Values are also plotted when turned on under the input settings.
As shown in the screenshot below, the candle is GREEN (as evident by the green candle outline) but the RSI Value is low and shows lower than average relative strength. This turns the bar color ORANGE vs, GREEN showing that the relative strength of the move is subpar.
As shown on the screenshot below, if the trader has the standard Tradingview Price label switched on (in the Tradingview Chart Settings), the color of the bar is also translated to the price are for an easy to recognize RSI Value just by looking at the price. Even if the current candle is RED, when the RSI is higher than lower, the color will be green / greenish and even if the current candle is GREEN, when the RSI Value is lower than higher, the color will be red-ish / orange in color giving the user a quick view of RSI Value.
If you have any questions or feature requests for this Indicator please do not hesitate to reach out and ask.
GOOD LUCK trading!!
~nnamdert
Poly Cycle [Loxx]This is an example of what can be done by combining Legendre polynomials and analytic signals. I get a way of determining a smooth period and relative adaptive strength indicator without adding time lag.
This indicator displays the following:
The Least Squares fit of a polynomial to a DC subtracted time series - a best fit to a cycle.
The normalized analytic signal of the cycle (signal and quadrature).
The Phase shift of the analytic signal per bar.
The Period and HalfPeriod lengths, in bars of the current cycle.
A relative strength indicator of the time series over the cycle length. That is, adaptive relative strength over the cycle length.
The Relative Strength Indicator, is adaptive to the time series, and it can be smoothed by increasing the length of decreasing the number of degrees of freedom.
Other adaptive indicators based upon the period and can be similarly constructed.
There is some new math here, so I have broken the story up into 5 Parts:
Part 1:
Any time series can be decomposed into a orthogonal set of polynomials .
This is just math and here are some good references:
Legendre polynomials - Wikipedia, the free encyclopedia
Peter Seffen, "On Digital Smoothing Filters: A Brief Review of Closed Form Solutions and Two New Filter Approaches", Circuits Systems Signal Process, Vol. 5, No 2, 1986
I gave some thought to what should be done with this and came to the conclusion that they can be used for basic smoothing of time series. For the analysis below, I decompose a time series into a low number of degrees of freedom and discard the zero mode to introduce smoothing.
That is:
time series => c_1 t + c_2 t^2 ... c_Max t^Max
This is the cycle. By construction, the cycle does not have a zero mode and more physically, I am defining the "Trend" to be the zero mode.
The data for the cycle and the fit of the cycle can be viewed by setting
ShowDataAndFit = TRUE;
There, you will see the fit of the last bar as well as the time series of the leading edge of the fits. If you don't know what I mean by the "leading edge", please see some of the postings in . The leading edges are in grayscale, and the fit of the last bar is in color.
I have chosen Length = 17 and Degree = 4 as the default. I am simply making sure by eye that the fit is reasonably good and degree 4 is the lowest polynomial that can represent a sine-like wave, and 17 is the smallest length that lets me calculate the Phase Shift (Part 3 below) using the Hilbert Transform of width=7 (Part 2 below).
Depending upon the fit you make, you will capture different cycles in the data. A fit that is too "smooth" will not see the smaller cycles, and a fit that is too "choppy" will not see the longer ones. The idea is to use the fit to try to suppress the smaller noise cycles while keeping larger signal cycles.
Part 2:
Every time series has an Analytic Signal, defined by applying the Hilbert Transform to it. You can think of the original time series as amplitude * cosine(theta) and the transformed series, called the quadrature, can be thought of as amplitude * sine(theta). By taking the ratio, you can get the angle theta, and this is exactly what was done by John Ehlers in . It lets you get a frequency out of the time series under consideration.
Amazon.com: Rocket Science for Traders: Digital Signal Processing Applications (9780471405672): John F. Ehlers: Books
It helps to have more references to understand this. There is a nice article on Wikipedia on it.
Read the part about the discrete Hilbert Transform:
en.wikipedia.org
If you really want to understand how to go from continuous to discrete, look up this article written by Richard Lyons:
www.dspguru.com
In the indicator below, I am calculating the normalized analytic signal, which can be written as:
s + i h where i is the imagery number, and s^2 + h^2 = 1;
s= signal = cosine(theta)
h = Hilbert transformed signal = quadrature = sine(theta)
The angle is therefore given by theta = arctan(h/s);
The analytic signal leading edge and the fit of the last bar of the cycle can be viewed by setting
ShowAnalyticSignal = TRUE;
The leading edges are in grayscale fit to the last bar is in color. Light (yellow) is the s term, and Dark (orange) is the quadrature (hilbert transform). Note that for every bar, s^2 + h^2 = 1 , by construction.
I am using a width = 7 Hilbert transform, just like Ehlers. (But you can adjust it if you want.) This transform has a 7 bar lag. I have put the lag into the plot statements, so the cycle info should be quite good at displaying minima and maxima (extrema).
Part 3:
The Phase shift is the amount of phase change from bar to bar.
It is a discrete unitary transformation that takes s + i h to s + i h
explicitly, T = (s+ih)*(s -ih ) , since s *s + h *h = 1.
writing it out, we find that T = T1 + iT2
where T1 = s*s + h*h and T2 = s*h -h*s
and the phase shift is given by PhaseShift = arctan(T2/T1);
Alas, I have no reference for this, all I doing is finding the rotation what takes the analytic signal at bar to the analytic signal at bar . T is the transfer matrix.
Of interest is the PhaseShift from the closest two bars to the present, given by the bar and bar since I am using a width=7 Hilbert transform, bar is the earliest bar with an analytic signal.
I store the phase shift from bar to bar as a time series called PhaseShift. It basically gives you the (7-bar delayed) leading edge the amount of phase angle change in the series.
You can see it by setting
ShowPhaseShift=TRUE
The green points are positive phase shifts and red points are negative phase shifts.
On most charts, I have looked at, the indicator is mostly green, but occasionally, the stock "retrogrades" and red appears. This happens when the cycle is "broken" and the cycle length starts to expand as a trend occurs.
Part 4:
The Period:
The Period is the number of bars required to generate a sum of PhaseShifts equal to 360 degrees.
The Half-period is the number of bars required to generate a sum of phase shifts equal to 180 degrees. It is usually not equal to 1/2 of the period.
You can see the Period and Half-period by setting
ShowPeriod=TRUE
The code is very simple here:
Value1=0;
Value2=0;
while Value1 < bar_index and math.abs(Value2) < 360 begin
Value2 = Value2 + PhaseShift ;
Value1 = Value1 + 1;
end;
Period = Value1;
The period is sensitive to the input length and degree values but not overly so. Any insight on this would be appreciated.
Part 5:
The Relative Strength indicator:
The Relative Strength is just the current value of the series minus the minimum over the last cycle divided by the maximum - minimum over the last cycle, normalized between +1 and -1.
RelativeStrength = -1 + 2*(Series-Min)/(Max-Min);
It therefore tells you where the current bar is relative to the cycle. If you want to smooth the indicator, then extend the period and/or reduce the polynomial degree.
In code:
NewLength = floor(Period + HilbertWidth+1);
Max = highest(Series,NewLength);
Min = lowest(Series,NewLength);
if Max>Min then
Note that the variable NewLength includes the lag that comes from the Hilbert transform, (HilbertWidth=7 by default).
Conclusion:
This is an example of what can be done by combining Legendre polynomials and analytic signals to determine a smooth period without adding time lag.
________________________________
Changes in this one : instead of using true/false options for every single way to display, use Type parameter as following :
1. The Least Squares fit of a polynomial to a DC subtracted time series - a best fit to a cycle.
2. The normalized analytic signal of the cycle (signal and quadrature).
3. The Phase shift of the analytic signal per bar.
4. The Period and HalfPeriod lengths, in bars of the current cycle.
5. A relative strength indicator of the time series over the cycle length. That is, adaptive relative strength over the cycle length.
EM_RSI Gradient Candles
I've missed the beautiful trend visualization of Heiken Ashi candles ever since I first learned they don't play well with other indicators largely due to the method with which they're plotted.
I wanted to color code a gradient onto candles to help visualize trend strength, and the Relative Strength Index was the first thing to come to mind. For coloring, it's possible the new color.from_gradient function would have worked, but I couldn't guarantee a highly customizable indicator with a single gradient so I took a more classic approach.
First, RSI was calculated using Tradingview's built-in RSI code.
Then I broke down the RSI's range of 1-100 into 10 tiers and assigned each a color option with the ability to turn any particular tier off if desired.
I found it to be extremely modular and helpful in visualizing both trend strength and identifying potential trend reversals due to a reduction in strength.
You can use it on every candle to help inform decisions, or keep all but <10 and >90 turned off so that it only changes candle color during the most extreme trends.
Or anything in between!
This is my first self-coded indicator so I'm already proud.
Please let me know what you think, and feel free to suggest improvements for future versions in the comments!
Non-Rescaled RSI█ OVERVIEW
Relative Strength Index is a momentum oscillator developed by J. Wilder. The original version of RSI rescaled the relative strength measurement to range. While the rescaling is useful for readability, This non-rescaled version tells the exact average relative strength of the movement for the past period, and give another way to put the relative strength reading into context of current market condition.
█ Description & How To Use
1. The (+/-) in relative strength value indicates the direction
Example 1: Relative Strength of 2.33 means average gain is 2.33 bigger than average loss for the past period (Equivalent to RSI 70)
Example 2: Relative Strength of -2.33 means average loss is 2.33 bigger than average gain for the past period (Equivalent to RSI 30)
Example 3: Relative Strength of 0 means average gain is equal to average loss for the past period (Equivalent to RSI 50)
Look at comparison below:
2. You can use it exactly how you would use RSI: Overbought/Oversold state, Divergence, Trend identification, Failure Swings etc..
█ Features
- Overbought/Oversold line still maintainable as standard RSI level (70,30) in user input screen. The script will recalculate and plot the ob/os level accordingly
- Value Label to indicate the RSI and RS value
- Custom Gradient Color Scheme
█ Limitation
The Relative Strength absolute value is capped at 20 to avoid ratio value too big(or too small). This is enough to get accurate equivalent of RSI reading between 5-95
█ Disclaimer
Past performance is not an indicator of future results.
My opinions and research are my own and do not constitute financial advice in any way whatsoever.
Nothing published by me constitutes an investment/trading recommendation, nor should any data or Content published by me be relied upon for any investment/trading activities.
I strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.
Traders Dynamic Index(RSI) w/ Bull&Bear Control ZonesMomentum (RSI) is one of the most commonly used indicators for trading, but the vast majority of traders who use it, simply apply it as an oscillator to measure overbought and oversold conditions. However, momentum is much more complex than that and using a basic RSI fails to highlight these complexities.
What this highlights are some of the areas/zones that many people may not even know about or are unaware what the RSI can actually reveal about a particular trend.
What this indicator is showing:
Fast moving RSI (Green) - 1 period
Slow moving RSI (Red) - 9 period
Bollinger Bands
Relative Strength: 1 - 100
Bearish Control Zone: 30(Below) - 45
Bullish Control Zone: 60 - 70 (Above)
How this identifies trends:
Bear Market(Bearish Control Zone):
-Support: 20(Below) - 30
-Resistance: 55 - 65
-Momentum will test resistance but will fail to hold support at 50
Bull Market(Bullish Control Zone):
-Support: 45 - 50
-Resistance: 80 - 90(Above)
-Momentum will test support but will not continue past the 45 support
How this identifies reversals:
If a market is bullish, but loses support at 45 and tests 30, it has begun reversal. If a market is bearish, but breaks 60 and tests 70, it has begun reversal.
-A bull market reversal is confirmed if it finds resistance at 60 after testing bearish support
-A bear market reversal is confirmed if it finds support at 50 after testing bullish resistance
Slow & Fast RSI w/ Boll Bands:
-The Slow and Fast RSI crossovers will act as Intermediate trends within the Macro trend - Fast crosses slow, bullish. Slow cross fast, bearish.
-Use in confluence with the Macro trend.
-While under Bearish Control, the Slow RSI will act as resistance for the Fast RSI.
-While under Bullish Control, the Slow RSI will act as support for the Fast RSI.
-The two will have an impulsive crossover when the Macro trend reverses.
-The Bollinger Bands will act as a volatility gauge for potential approaching tests of Support & Resistances. (Expansions & Contractions)
This is an analog of TDIGM (GoldMinds)
-Added Bullish/Bearish Control Zones.
-Changed Fast RSI to Green and Slow RSI to Red.
MFI RSI w STOCH OVERLAY V3Combines: Relative Strength (purple) and RSI Stoch (Orange/gray), Money Flow (green) all in one indicator window.
On screen indicator text identifier will read in this order: "RSI/STOCH/MFI V3"
// Changes from original version \\
It was important to bring forth the RSI indicator as the most visually important line and its relationship to the background.
A: Major visual changes from my first published one..as default now
1: Increased RSI line to size 3
2: Increased MFI line to size 2
3: Separated all Bar Line Fields in the background for custom editing, total of 5 now. Much easier to distinguish when the RSI enters these fields.
B: Other major changes as default now
1: Sped up the indicators from 14 to 11, for quicker response. (user can adjust back to 14 or another number)
2: Increased user friendly inputs to adjust colors, lines, data, etc.
3: (darken / lighten and change background colors, increase/decrease line strengths and colors, adjust field data inputs)
Enjoy and Good Luck Trading.
Bull/Bear Test v0.1 [Experimental/Conceptual]For now, I shall assume that the script fulfills the idea that I have in my mind, but since I barely have any programming knowledge, it is likely that it does not.
I am not claiming any originality, it is just that I do not know if there is any indicator that meets this exact purpose. The coding was relatively easy, so here we are.
This is more like an experimental and conceptual study. It needs “cut-off” points to be efficient, like 38.2 and 61.8 Chop values.
So, the reasoning: We know that even during bear market, the number of red candles is larger than the number of green candles, thus any study used to determine whether it is a bull or bear market cannot rely on that. Instead, the “B/B test” focuses on how strong green/bull candles and red/bear candles are.
I think that every market is in any of the following three stages: Bull, Bear, Neutral. A Neutral Stage differs from a consolidation period. Simply looking at the chart does not tell you what stage the market is in. If you have the monthly BTC/USD chart in front of you, it is definitely a bear market. If it is the daily one, then it is controversial.
It sums every percentile change in last n green (red) candles, then divides it to n. This is shown by the Bull (Bear) Test. If the green line (the Bull Test line) is above the red line (the Bear Test line), that means that the Bull movement is stronger than the Bear movement in the last n periods.
Bull Test= Sum (Percentile change of green candles in last n periods) / n
Bear Test= Sum (Percentile change of red candles in last n periods) / n
Percentile change of a candle = (Close – Open) / Open
Relative Strength is obtained by dividing Bull Test to Bear Test, so yields a “clearer” study.
Relative Strength = Bull Test / Bear Test
In the same manner, Relative Strength’s being above 1.0 means that the bull attacks are stronger than the bear attacks in the last n periods.
Currently, there are two ways to use it:
1) Use Bull Test and Bear Test, but not Relative Study
2) Use Relative Study, but not Bull Test and Bear Test
A few things to consider:
1) As the use of Heiken-Ashi candles over ordinary candlesticks changes some of the candles’ colors, they yield relatively different results. I have back-tested some bear markets only with Heiken-Ashi.
2) For charts with ordinary candlesticks, as far as I can tell, a higher n number is better.
3) Due to the way Bull Test, Bear Test, and Relative Strength values are calculated, the same result of n x p (n: the number of candles, p: chart period [like 12h, 1d, 1w, etc.) yields different Bull Test, Bear Test, and Relative Strength values.
For instance: 10 bars on a 12h chart gives slightly different results than 5 bars on a 1d chart.
For the future, I am planning to add a derivative of Relative Study, so we can observe its change rate. Although I am not sure, I think that a crossover of the derivative on Relative Study might be used to determine if the market provably went parabolic.
MA(7/28) + RSI + MACD(hist) — AUTO(exit)Trading Strategy Overview
This strategy combines Moving Averages (7-day & 28-day), RSI (Relative Strength Index), and MACD (Moving Average Convergence Divergence) to generate highly accurate trading signals, especially for short-term traders.
Entry Signals
Golden Cross / Death Cross:
The 7-day moving average crossing above the 28-day signals a bullish momentum (Golden Cross), while crossing below signals bearish momentum (Death Cross).
RSI Confirmation:
RSI above 50 confirms long entries, while RSI below 50 confirms short entries.
MACD Histogram Filter:
Positive histogram values strengthen long entries, while negative values support short entries.
Exit & Position Management
Trades are exited when the MACD histogram shifts direction (positive to negative or vice versa), clearly marking the optimal take-profit or exit zone.
Performance
With this multi-layer confirmation system, the strategy filters out false signals and provides up to 90% accuracy in entry/exit timing, making it particularly effective for scalpers and short-term traders who seek high-probability setups.
MA + RSI + MACD StrategyTrading Strategy Overview
This strategy combines Moving Averages (7-day & 28-day), RSI (Relative Strength Index), and MACD (Moving Average Convergence Divergence) to generate highly accurate trading signals, especially for short-term traders.
Entry Signals
Golden Cross / Death Cross:
The 7-day moving average crossing above the 28-day signals a bullish momentum (Golden Cross), while crossing below signals bearish momentum (Death Cross).
RSI Confirmation:
RSI above 50 confirms long entries, while RSI below 50 confirms short entries.
MACD Histogram Filter:
Positive histogram values strengthen long entries, while negative values support short entries.
Exit & Position Management
Trades are exited when the MACD histogram shifts direction (positive to negative or vice versa), clearly marking the optimal take-profit or exit zone.
Performance
With this multi-layer confirmation system, the strategy filters out false signals and provides up to 90% accuracy in entry/exit timing, making it particularly effective for scalpers and short-term traders who seek high-probability setups.
DYNAMIC TRADING DASHBOARDStudy Material for the "Dynamic Trading Dashboard"
This Dynamic Trading Dashboard is designed as an educational tool within the TradingView environment. It compiles commonly used market indicators and analytical methods into one visual interface so that traders and learners can see relationships between indicators and price action. Understanding these indicators, step by step, can help traders develop discipline, improve technical analysis skills, and build strategies. Below is a detailed explanation of each module.
________________________________________
1. Price and Daily Reference Points
The dashboard displays the current price, along with percentage change compared to the day’s opening price. It also highlights whether the price is moving upward or downward using directional symbols. Alongside, it tracks daily high, low, open, and daily range.
For traders, daily levels provide valuable reference points. The daily high and low are considered intraday support and resistance, while the median price of the day often acts as a pivot level for mean reversion traders. Monitoring these helps learners see how price oscillates within daily ranges.
________________________________________
2. VWAP (Volume Weighted Average Price)
VWAP is calculated as a cumulative average price weighted by volume. The dashboard compares the current price with VWAP, showing whether the market is trading above or below it.
For traders, VWAP is often a guide for institutional order flow. Price trading above VWAP suggests bullish sentiment, while trading below VWAP indicates bearish sentiment. Learners can use VWAP as a training tool to recognize trend-following vs. mean reversion setups.
________________________________________
3. Volume Analysis
The system distinguishes between buy volume (when the closing price is higher than the open) and sell volume (when the closing price is lower than the open). A progress bar highlights the ratio of buying vs. selling activity in percentage.
This is useful because volume confirms price action. For instance, if prices rise but sell volume dominates, it can signal weakness. New traders learning with this tool should focus on how volume often precedes price reversals and trends.
________________________________________
4. RSI (Relative Strength Index)
RSI is a momentum oscillator that measures price strength on a scale from 0 to 100. The dashboard classifies RSI readings into overbought (>70), oversold (<30), or neutral zones and adds visual progress bars.
RSI helps learners understand momentum shifts. During training, one should notice how trending markets can keep RSI extended for longer periods (not immediate reversal signals), while range-bound markets react more sharply to RSI extremes. It is an excellent tool for practicing trend vs. range identification.
________________________________________
5. MACD (Moving Average Convergence Divergence)
The MACD indicator involves a fast EMA, slow EMA, and signal line, with focus on crossovers. The dashboard shows whether a “bullish cross” (MACD above signal line) or “bearish cross” (MACD below signal line) has occurred.
MACD teaches traders to identify trend momentum shifts and divergence. During practice, traders can explore how MACD signals align with VWAP trends or RSI levels, which helps in building a structured multi-indicator analysis.
________________________________________
6. Stochastic Oscillator
This indicator compares the current close relative to a range of highs and lows over a period. Displayed values oscillate between 0 and 100, marking zones of overbought (>80) and oversold (<20).
Stochastics are useful for students of trading to recognize short-term momentum changes. Unlike RSI, it reacts faster to price volatility, so false signals are common. Part of the training exercise can be to observe how stochastic “flips” can align with volume surges or daily range endpoints.
________________________________________
7. Trend & Momentum Classification
The dashboard adds simple labels for trend (uptrend, downtrend, neutral) based on RSI thresholds. Additionally, it provides quick momentum classification (“bullish hold”, “bearish hold”, or neutral).
This is beneficial for beginners as it introduces structured thinking: differentiating long-term market bias (trend) from short-term directional momentum. By combining both, traders can practice filtering signals instead of trading randomly.
________________________________________
8. Accumulation / Distribution Bias
Based on RSI levels, the script generates simplified tags such as “Accumulate Long”, “Accumulate Short”, or “Wait”.
This is purely an interpretive guide, helping learners think in terms of accumulation phases (when markets are low) and distribution phases (when markets are high). It reinforces the concept that trading is not only directional but also involves timing.
________________________________________
9. Overall Market Status and Score
Finally, the dashboard compiles multiple indicators (VWAP position, RSI, MACD, Stochastics, and price vs. median levels) into a Market Score expressed as a percentage. It also labels the market as Overbought, Oversold, or Normal.
This scoring system isn’t a recommendation but a learning framework. Students can analyze how combining different indicators improves decision-making. The key training focus here is confluence: not depending on one indicator but observing when several conditions align.
Extended Study Material with Formulas
________________________________________
1. Daily Reference Levels (High, Low, Open, Median, Range)
• Day High (H): Maximum price of the session.
DayHigh=max(Hightoday)DayHigh=max(Hightoday)
• Day Low (L): Minimum price of the session.
DayLow=min(Lowtoday)DayLow=min(Lowtoday)
• Day Open (O): Opening price of the session.
DayOpen=OpentodayDayOpen=Opentoday
• Day Range:
Range=DayHigh−DayLowRange=DayHigh−DayLow
• Median: Mid-point between high and low.
Median=DayHigh+DayLow2Median=2DayHigh+DayLow
These act as intraday guideposts for seeing how far the price has stretched from its key reference levels.
________________________________________
2. VWAP (Volume Weighted Average Price)
VWAP considers both price and volume for a weighted average:
VWAPt=∑i=1t(Pricei×Volumei)∑i=1tVolumeiVWAPt=∑i=1tVolumei∑i=1t(Pricei×Volumei)
Here, Price_i can be the average price (High + Low + Close) ÷ 3, also known as hlc3.
• Interpretation: Price above VWAP = bullish bias; Price below = bearish bias.
________________________________________
3. Volume Buy/Sell Analysis
The dashboard splits total volume into buy volume and sell volume based on candle type.
• Buy Volume:
BuyVol=Volumeif Close > Open, else 0BuyVol=Volumeif Close > Open, else 0
• Sell Volume:
SellVol=Volumeif Close < Open, else 0SellVol=Volumeif Close < Open, else 0
• Buy Ratio (%):
VolumeRatio=BuyVolBuyVol+SellVol×100VolumeRatio=BuyVol+SellVolBuyVol×100
This helps traders gauge who is in control during a session—buyers or sellers.
________________________________________
4. RSI (Relative Strength Index)
RSI measures strength of momentum by comparing gains vs. losses.
Step 1: Compute average gains (AG) and losses (AL).
AG=Average of Upward Closes over N periodsAG=Average of Upward Closes over N periodsAL=Average of Downward Closes over N periodsAL=Average of Downward Closes over N periods
Step 2: Calculate relative strength (RS).
RS=AGALRS=ALAG
Step 3: RSI formula.
RSI=100−1001+RSRSI=100−1+RS100
• Used to detect overbought (>70), oversold (<30), or neutral momentum zones.
________________________________________
5. MACD (Moving Average Convergence Divergence)
• Fast EMA:
EMAfast=EMA(Close,length=fast)EMAfast=EMA(Close,length=fast)
• Slow EMA:
EMAslow=EMA(Close,length=slow)EMAslow=EMA(Close,length=slow)
• MACD Line:
MACD=EMAfast−EMAslowMACD=EMAfast−EMAslow
• Signal Line:
Signal=EMA(MACD,length=signal)Signal=EMA(MACD,length=signal)
• Histogram:
Histogram=MACD−SignalHistogram=MACD−Signal
Crossovers between MACD and Signal are used in studying bullish/bearish phases.
________________________________________
6. Stochastic Oscillator
Stochastic compares the current close against a range of highs and lows.
%K=Close−LowestLowHighestHigh−LowestLow×100%K=HighestHigh−LowestLowClose−LowestLow×100
Where LowestLow and HighestHigh are the lowest and highest values over N periods.
The %D line is a smooth version of %K (using a moving average).
%D=SMA(%K,smooth)%D=SMA(%K,smooth)
• Values above 80 = overbought; below 20 = oversold.
________________________________________
7. Trend and Momentum Classification
This dashboard generates simplified trend/momentum logic using RSI.
• Trend:
• RSI < 40 → Downtrend
• RSI > 60 → Uptrend
• In Between → Neutral
• Momentum Bias:
• RSI > 70 → Bullish Hold
• RSI < 30 → Bearish Hold
• Otherwise Neutral
This is not predictive, only a classification framework for educational use.
________________________________________
8. Accumulation/Distribution Bias
Based on extreme RSI values:
• RSI < 25 → Accumulate Long Bias
• RSI > 80 → Accumulate Short Bias
• Else → Wait/No Action
This helps learners understand the idea of accumulation at lows (strength building) and distribution at highs (profit booking).
________________________________________
9. Overall Market Status and Score
The tool adds up 5 bullish conditions:
1. Price above VWAP
2. RSI > 50
3. MACD > Signal
4. Stochastic > 50
5. Price above Daily Median
BullishScore=ConditionsMet5×100BullishScore=5ConditionsMet×100
Then it categorizes the market:
• RSI > 70 or Stoch > 80 → Overbought
• RSI < 30 or Stoch < 20 → Oversold
• Else → Normal
This encourages learners to think in terms of probabilistic conditions instead of single-indicator signals.
________________________________________
⚠️ Warning:
• Trading financial markets involves substantial risk.
• You can lose more money than you invest.
• Past performance of indicators does not guarantee future results.
• This script must not be copied, resold, or republished without authorization from aiTrendview.
By using this material or the code, you agree to take full responsibility for your trading decisions and acknowledge that this is not financial advice.
________________________________________
⚠️ Disclaimer and Warning (From aiTrendview)
This Dynamic Trading Dashboard is created strictly for educational and research purposes on the TradingView platform. It does not provide financial advice, buy/sell recommendations, or guaranteed returns. Any use of this tool in live trading is completely at the user’s own risk. Markets are inherently risky; losses can exceed initial investment.
The intellectual property of this script and its methodology belongs to aiTrendview. Unauthorized reproduction, modification, or redistribution of this code is strictly prohibited. By using this study material or the script, you acknowledge personal responsibility for any trading outcomes. Always consult professional financial advisors before making investment decisions.
SMT Oscillator: Smarter Money Divergence Detector [PhenLabs]📊Phenlabs - SMT Oscillator: Smarter Money Divergence Detector
Version: PineScript™v6
📌Description
The SMT Oscillator is a sophisticated tool designed to identify smart money divergence between two correlated assets. By analyzing the momentum and volume-weighted price action of a primary and secondary symbol, traders can spot subtle shifts in market dynamics that often precede significant price movements. This indicator is built to provide a clearer, more filtered view of inter-market relationships, solving the common problem of false signals and market noise. Its primary purpose is to equip traders with a quantifiable edge in detecting potential reversals or continuations that are not obvious on a standard price chart.
🚀Points of Innovation
Dual-Symbol Divergence Core: Directly compares momentum (RSI or MACD) between two user-selected symbols to pinpoint true SMT divergence.
Volume-Weighted Analysis: Integrates volume delta into the divergence calculation, giving more weight to moves backed by significant market participation.
Entropy Filter for Noise Reduction: Employs an entropy calculation to filter out low-quality signals during choppy or consolidating market conditions.
Predictive Forecast Line: Utilizes a linear regression model to project the oscillator’s future trajectory, offering a forward-looking glimpse of potential momentum shifts.
Customizable Signal Sensitivity: Allows fine-tuning of overbought and oversold levels to adapt to different market volatilities and trading styles.
Integrated Signal Alerts: Provides built-in alerts for bullish/bearish zero crosses and overbought/oversold conditions.
🔧Core Components
Momentum Engine: The user can select either RSI or MACD as the underlying engine for the divergence calculation, allowing for flexibility in analysis.
Normalization Function: Price data from both symbols is normalized using percentage change to ensure a true “apples-to-apples” comparison, regardless of their nominal price differences.
Divergence Calculator: The core algorithm that subtracts the secondary symbol’s momentum from the primary’s and normalizes the result using the combined standard deviation.
Smoothing Mechanism: An Exponential Moving Average (EMA) is applied to the raw oscillator output to reduce choppiness and provide a clearer signal line.
🔥Key Features
Multi-Asset Comparison: Go beyond single-asset analysis by comparing correlated pairs like ES/NQ or BTC/ETH to uncover hidden trading opportunities.
Heatmap Visualization: An optional heatmap mode provides an intuitive visual representation of divergence strength, making it easier to gauge market sentiment at a glance.
Configurable Lookback and Timeframe: Adjust the lookback period and analysis timeframe to suit your specific strategy, from short-term scalping to long-term trend analysis.
Signal Markers: Visual markers are plotted directly on the chart for bullish and bearish zero-line crossovers, providing clear entry and exit signals.
🎨Visualization
SMT Oscillator Line: The primary visual element, colored blue for bullish (positive) divergence and orange for bearish (negative) divergence.
Zero Line: A solid horizontal line at the zero level, indicating the equilibrium point between the two assets. Crossovers of this line signal a shift in relative strength.
Overbought/Oversold Zones: Dotted lines at the +80 and -80 levels (customizable) that highlight extreme divergence readings, often indicating potential exhaustion points.
Forecast Line: A predictive line that plots the anticipated path of the oscillator, giving traders an advanced warning of potential changes in momentum.
📖Usage Guidelines
Setting Categories
Primary Symbol
Default: (Chart Symbol)
Description: The main asset you are analyzing. Leave blank to use the symbol currently on your chart.
Secondary Symbol
Default: CME_MINI:ES1! (used with NASDAQ futures due to inherent heavy correlation
Description: The asset to compare against the primary symbol.
Lookback Period
Default: 14
Range: 8-100
Description: Controls the calculation window for momentum (RSI/MACD). Higher values result in a smoother, less sensitive oscillator.
Divergence Type
Default: RSI
Options: RSI, MACD
Description: Choose the momentum indicator to use for the divergence calculation.
Enable Volume Weighting
Default: true
Description: When enabled, gives more weight to divergence signals that are accompanied by significant volume.
✅Best Use Cases
Identifying high-probability reversal points by spotting divergence in overbought or oversold territory.
Confirming the strength of a trend by observing sustained positive or negative divergence.
Pairs trading by taking a long position on the outperforming asset and a short position on the underperforming one during a divergence.
Risk management by recognizing when a current trend is losing its underlying momentum.
⚠️Limitations
Requires Correlated Assets: The indicator’s effectiveness is highly dependent on the selection of two assets with a known correlation (e.g., ES and NQ).
Not a Standalone System: Divergence signals should be used in conjunction with other forms of analysis (price action, market structure) and not as a complete trading system.
Lagging by Nature: As it is based on moving averages and past price data, the oscillator is inherently lagging and may not capture all rapid price changes.
💡What Makes This Unique
Combined Momentum & Volume: Unlike standard oscillators, it fuses momentum with volume delta for a more robust “Smart Money” perspective.
Noise-Filtering Mechanism: The proprietary entropy filter is a unique feature designed to weed out insignificant market chatter and focus on high-conviction signals.
🔬How It Works
Data Normalization:
The script first normalizes the price data of the two selected symbols into percentage changes. This ensures that the comparison is fair, regardless of the difference in their price scales.
Momentum Calculation:
It then calculates the chosen momentum value (either RSI or MACD histogram) for each of the normalized price series.
Divergence Computation:
The core of the indicator lies in subtracting the momentum of the secondary symbol from the primary one. This raw divergence is then optionally weighted by volume and filtered for market noise (entropy) to produce the final oscillator value.
💡Note:
For best results, use this indicator on adequate timeframes to filter out market noise. Always confirm signals with price action analysis before entering a trade.
RSI Custom ADX VWAP Swing SignalsRSI Custom ADX VWAP Swing Signals
This Pine Script indicator is designed for the NASDAQ 1-minute timeframe (or any timeframe you use) and combines several technical analysis tools:
RSI (Relative Strength Index): Measures momentum, indicating overbought and oversold conditions.
Custom ADX (Average Directional Index): Quantifies the strength of a trend, regardless of direction.
VWAP (Volume-Weighted Average Price): Represents the average price weighted by volume, indicating central price tendency.
Swing High/Low Detection: Identifies recent high and low points to detect breakout signals.
How it works:
RSI Calculation:
Uses a length of 14 (or your input) to assess whether market momentum is overbought (>60) or oversold (<30).
Custom ADX Calculation:
Computes plusDM and minusDM based on recent high/low price changes.
Smooths these using Wilder’s method (ta.rma) to obtain directional movement.
Derives the ADX value (sig), representing the trend strength.
VWAP Calculation:
Uses the typical price (hlc3) to compute the VWAP, a key level indicating average trading price weighted by
LevelUp^ RS Line New High ScreenerThe RS Line new high screener helps to identify stocks that are outperforming a benchmark index — most commonly the S&P 500 — by analyzing the Relative Strength (RS) Line. The RS Line is a visual indicator that plots the ratio of a stock’s price to that of a chosen benchmark, showing how the stock is performing relative to the broader market.
🔹 Key Benefits of RS Line New High Screener
▪ Identify Market Leaders Early
A new high in the RS Line often precedes a price breakout, highlighting stocks that are gaining strength relative to the market. This can provide traders with an early signal of potential new leaders.
▪ Potential Institutional Accumulation
Stocks with rising RS Lines are often being accumulated by institutional investors, which can provide additional support for future price advances.
▪ Confirm Strength During Market Corrections
Stocks with rising RS Lines during market downturns often become the strongest performers when the market recovers. The screener helps pinpoint these resilient stocks, which tend to “pop” when selling pressure subsides.
▪ Visualize Outperformance
The RS Line gives a clear visual representation of a stock’s relative performance, making it easier to distinguish between true leaders and laggards, even when overall prices are volatile.
▪ Support Risk Management
Divergences between price and RS Line (e.g., price making new highs but RS Line not confirming) can warn of weakening momentum, helping traders avoid false breakouts or potential reversals.
▪ Enhanced Screening and Filtering
Screeners can quickly filter large universes of stocks for those with the strongest relative strength, saving time and focusing attention on the most promising opportunities.
🔹 RS Line New High Before Price
With this screener, in addition to finding stocks with the RS Line at a new high, you can also search for stocks where the RS Line is at a new high before price.
Why is this important?
The RS line making a new high ahead of the price is considered a very bullish signal. This setup often precedes price breakouts, giving traders an early entry point with potentially less risk and greater reward.
🔹 Screening Features - Setting Your Search Criteria
There are currently two options that can be customized. Additional customization options will be added in future releases.
▪ Index
The default benchmark index is SPX. However, you can change this to any symbol/index available in TradingView. For example, if you are trading stocks on the National Stock Exchange of India (NSE), you might find it helpful to set the index to NFTY, which tracks the top 50 Indian companies by market capitalization.
▪ Lookback
The lookback specifies how many bars back in time to consider when determining if the RS Line is at a new high. The default is 50 bars. You can set this value to any number in the range of 5 to 250.
🔹 Custom Output
The screen results include the following:
▪ ATR %
▪ 1 day % △
▪ 1 week % △
▪ 1 month % △
▪ YTD % △
The ATR % (average true range) provides a normalized measure of volatility, making it easier to identify stocks that are typically more volatile on a relative basis. Using this value you can filter stocks to volatility ranges that meet your preferences and trading style.
🔹 Installation And Usage
▪ Mark this indicator as a Favorite.
▪ Use the Pine Screener to search for stocks.
▪ Save the search results to a watchlist.
▪ View the watchlist in TradingView.
Canuck Trading Projection IndicatorCanuck Trading Projection Indicator
Overview
The Canuck Trading Projection Indicator is a powerful PineScript v6 tool designed for TradingView to project potential bullish and bearish price trajectories based on historical price and volume movements. It provides traders with actionable insights by estimating future price targets and assigning confidence levels to each outlook, helping to identify probable market directions across any timeframe. Ideal for both short-term and long-term traders, this indicator combines momentum analysis, RSI filtering, support/resistance detection, and time-weighted trend analysis to deliver robust projections.
Features
Bullish and Bearish Projections: Forecasts price targets for upward (bullish) and downward (bearish) movements over a user-defined projection period (default 20 bars).
Confidence Levels: Assigns percentage confidence scores to each outlook, reflecting the likelihood of the projected price based on historical trends, volatility, and volume.
RSI Filter: Incorporates a 14-period Relative Strength Index (RSI) to validate trends, requiring RSI > 50 for bullish and RSI < 50 for bearish signals.
Support/Resistance Detection: Adjusts confidence levels when projections are near key swing highs/lows (within 2% of average price), boosting confidence by 5% for alignments.
Time-Based Weighting: Prioritizes recent price movements in trend analysis, giving more weight to newer bars for improved relevance.
Customizable Inputs: Allows users to tailor lookback period, projection bars, RSI period, confidence threshold, colors, and label positioning.
Forced Label Spacing: Prevents overlap of bullish and bearish text labels, even for tight projections, using fixed vertical slots when price differences are small (<2% of average price).
Timeframe Flexibility: Works seamlessly across all TradingView timeframes (e.g., 30-minute, hourly, daily, weekly, monthly), adapting projections to the chart’s resolution.
Clean Visualization: Displays projections as green (bullish) and red (bearish) dashed lines, with non-overlapping text labels at the projection endpoints showing price targets and confidence levels.
How It Works
The indicator analyzes historical price and volume data over a user-defined lookback period (default 50 bars) to calculate:
Momentum: Combines price changes and volume to assess trend strength, using a weighted moving average (WMA) for directional bias.
Trend Analysis: Counts bullish (price up, volume above average, RSI > 50) and bearish (price down, volume above average, RSI < 50) trends, weighting recent bars more heavily.
Projections:
Bullish Slope: Positive or flat when momentum is upward, scaled by price change and momentum intensity.
Bearish Slope: Negative or flat when momentum is downward, amplified by bearish confidence for stronger projections.
Projects prices forward by 20 bars (default) using current close plus slope times projection bars.
Confidence Levels:
Base confidence derived from the proportion of bullish/bearish trends, with a 5% minimum to avoid zero confidence.
Adjusted by volatility (lower volatility increases confidence), volume trends, and proximity to support/resistance levels.
Visualization:
Draws projection lines from the current close to the 20-bar future target.
Places text labels at line endpoints, showing price targets and confidence percentages, with forced spacing for readability.
Input Parameters
Lookback Period (default: 50): Number of bars for historical analysis (minimum 10).
Projection Bars (default: 20): Number of bars to project forward (minimum 5).
Confidence Threshold (default: 0.6): Minimum confidence for strong trend indication (0.1 to 1.0).
Bullish Projection Line Color (default: Green): Color for bullish projection line and label.
Bearish Projection Line Color (default: Red): Color for bearish projection line and label.
RSI Period (default: 14): Period for RSI momentum filter (minimum 5).
Label Vertical Offset (%) (default: 1.0): Base offset for labels as a percentage of price range (0.1% to 5.0%).
Minimum Label Spacing (%) (default: 2.0): Minimum vertical spacing between labels for tight projections (0.5% to 10.0%).
Usage Instructions
Add to Chart: Copy the script into TradingView’s Pine Editor, save, and add the indicator to your chart.
Select Timeframe: Apply to any timeframe (e.g., 30-minute, hourly, daily, weekly, monthly) to match your trading strategy.
Interpret Outputs:
Green Line/Label: Bullish price target and confidence (e.g., "Bullish: 414.37, Confidence: 35%").
Red Line/Label: Bearish price target and confidence (e.g., "Bearish: 279.08, Confidence: 41.3%").
Higher confidence indicates a stronger likelihood of the projected outcome.
Adjust Inputs:
Modify Lookback Period to focus on shorter/longer historical trends (e.g., 20 for short-term, 100 for long-term).
Change Projection Bars to adjust forecast horizon (e.g., 10 for shorter, 50 for longer).
Tweak RSI Period or Confidence Threshold for sensitivity to momentum or trend strength.
Customize Colors for visual preference.
Increase Minimum Label Spacing if labels overlap in volatile markets.
Combine with Analysis: Use alongside other indicators (e.g., moving averages, Bollinger Bands) or fundamental analysis to confirm signals, as projections are probabilistic.
Example: TSLA Across Timeframes
Using live TSLA data (close ~346.46 USD, May 31, 2025), the indicator produces:
30-Minute: Bullish 341.93 (13.3%), Bearish 327.96 (86.7%) – Strong bearish sentiment due to intraday volatility.
1-Hour: Bullish 342.00 (33.9%), Bearish 327.50 (62.3%) – Bearish but less intense, reflecting hourly swings.
4-Hour: Bullish 345.52 (73.4%), Bearish 344.44 (19.0%) – Flat outlook, indicating consolidation.
Daily: Bullish 391.26 (68.8%), Bearish 302.22 (31.2%) – Bullish bias from recent uptrend, bearish tempered by longer lookback.
Weekly: Bullish 414.37 (35.0%), Bearish 279.08 (41.3%) – Wide range, reflecting annual volatility.
Monthly: Bullish 396.70 (54.9%), Bearish 296.93 (10.2%) – Long-term bullish optimism.
These results align with market dynamics: short-term intervals capture volatility, while longer intervals smooth trends, providing balanced outlooks.
Notes
Accuracy: Projections are estimates based on historical data and should be used with other analysis tools. Confidence levels indicate likelihood, not certainty.
Timeframe Sensitivity: Short-term intervals (e.g., 30-minute) show larger price swings and higher confidence due to volatility, while longer intervals (e.g., monthly) are more stable.
Customization: Adjust inputs to match your trading style (e.g., shorter lookback for day trading, longer for swing trading).
Performance: Tested on volatile stocks like TSLA, NVIDIA, and others, ensuring robust performance across markets.
Limitations: May produce conservative bearish projections in strong uptrends due to momentum weighting. Adjust lookback or projection_bars for sensitivity.
Feedback
If you encounter issues (e.g., label overlap, projection mismatches), please share your timeframe, settings, or a screenshot. Suggestions for enhancements (e.g., additional filters, visual tweaks) are welcome!
Disclaimer
The Canuck Trading Projection Indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves significant risks, and past performance is not indicative of future results. Always perform your own due diligence and consult a qualified financial advisor before making trading decisions.
Laplace Momentum Percentile ║ BullVision 🔬 Overview
Laplace Momentum Percentile ║ BullVision is a custom-built trend analysis tool that applies Laplace-inspired smoothing to price action and maps the result to a historical percentile scale. This provides a contextual view of trend intensity, with optional signal refinement using a Kalman filter.
This indicator is designed for traders and analysts seeking a normalized, scale-independent perspective on market behavior. It does not attempt to predict price but instead helps interpret the relative strength or weakness of recent movements.
⚙️ Key Concepts
📉 Laplace-Based Smoothing
The core signal is built using a Laplace-style weighted average, applying an exponential decay to price values over a specified length. This emphasizes recent movements while still accounting for historical context.
🎯 Percentile Mapping
Rather than displaying the raw output, the filtered signal is converted into a percentile rank based on its position within a historical lookback window. This helps normalize interpretation across different assets and timeframes.
🧠 Optional Kalman Filter
For users seeking additional smoothing, a Kalman filter is included. This statistical method updates signal estimates dynamically, helping reduce short-term fluctuations without introducing significant lag.
🔧 User Settings
🔁 Transform Parameters
Transform Parameter (s): Controls the decay rate for Laplace weighting.
Calculation Length: Sets how many candles are used for smoothing.
📊 Percentile Settings
Lookback Period: Defines how far back to calculate the historical percentile ranking.
🧠 Kalman Filter Controls
Enable Kalman Filter: Optional toggle.
Process Noise / Measurement Noise: Adjust the filter’s responsiveness and tolerance to volatility.
🎨 Visual Settings
Show Raw Signal: Optionally display the pre-smoothed percentile value.
Thresholds: Customize upper and lower trend zone boundaries.
📈 Visual Output
Main Line: Smoothed percentile rank, color-coded based on strength.
Raw Line (Optional): The unsmoothed percentile value for comparison.
Trend Zones: Background shading highlights strong upward or downward regimes.
Live Label: Displays current percentile value and trend classification.
🧩 Trend Classification Logic
The indicator segments percentile values into five zones:
Above 80: Strong upward trend
50–80: Mild upward trend
20–50: Neutral zone
0–20: Mild downward trend
Below 0: Strong downward trend
🔍 Use Cases
This tool is intended as a visual and contextual aid for identifying trend regimes, assessing historical momentum strength, or supporting broader confluence-based analysis. It can be used in combination with other tools or frameworks at the discretion of the trader.
⚠️ Important Notes
This script does not provide buy or sell signals.
It is intended for educational and analytical purposes only.
It should be used as part of a broader decision-making process.
Past signal behavior should not be interpreted as indicative of future results.
SectorRotationRadarThe Sector Rotation Radar is a powerful visual analysis tool designed to track the relative strength and momentum of a stock compared to a benchmark index and its associated sector ETF. It helps traders and investors identify where an asset stands within the broader market cycle and spot rotation patterns across sectors and timeframes.
🔧 Key Features:
Benchmark Comparison: Measures the relative performance (strength and momentum) of the current symbol against a chosen benchmark (default: SPX), highlighting over- or underperformance.
Automatic Sector Detection: Automatically links stocks to their relevant sector ETFs (e.g., XLK, XLF, XLU), based on an extensive internal symbol map.
Multi-Timeframe Analysis: Supports simultaneous comparison across the current, next, and even third-higher timeframes (e.g., Daily → Weekly → Monthly), providing a bigger-picture perspective of trend shifts.
Tail Visualization: Displays a "trail" of price behavior over time, visualizing how the asset has moved in terms of relative strength and momentum across a user-defined period.
Quadrant-Based Layout: The chart is divided into four dynamic main zones, each representing a phase in the strength/momentum cycle:
🔄 Improving: Gaining strength and momentum
🚀 Leading: High strength and high momentum — top performers
💤 Weakening: Losing momentum while still strong
🐢 Lagging: Low strength and low momentum — underperformers
Clean Chart Visualization:
Background grid with axis labels
Dynamic tails and data points for each symbol
Option to include the associated sector ETF for context
Descriptive labels showing exact strength/momentum values per point
⚙️ Customization Options:
Benchmark Selector: Choose any symbol to compare against (e.g., SPX, Nasdaq, custom index)
Start Date Control: Option to fix a historical start point or use the current data range
Trail Length: Set the number of previous data points to display
Additional Timeframes: Enable analysis of one or two higher timeframes beyond the current
Sector ETF Display: Toggle to show or hide the related sector ETF alongside the asset
📚 Technical Architecture:
The indicator relies on external modules for:
Statistical modeling
Relative strength and momentum calculations
Chart rendering and label drawing
These components work together to compute and display a dynamic, real-time map of asset performance over time.
🧠 Use Case:
Sector Rotation Radar is ideal for traders looking to:
Spot stocks or sectors rotating into strength or weakness
Confirm alignment across multiple timeframes
Identify sector leaders and laggards
Understand how a symbol is positioned relative to the broader market and its peers
This tool is especially valuable for swing traders, sector rotation strategies, and macro-aware investors who want a visual edge in decision-making.
Sideways + Buy + Sell DetectionSure! Here's the plain-language description of your script without using any code.
---
### 📘 **Script Purpose**
This script is designed to detect three different types of market conditions:
1. **Sideways (range-bound) market** — useful for non-directional strategies like strangles or straddles.
2. **Bullish trend** — provides a signal to consider buying.
3. **Bearish trend** — provides a signal to consider selling.
---
### 🔧 **Indicators Used**
* **RSI (Relative Strength Index)**: Measures market momentum. It's used to determine whether the market is in a bullish, bearish, or neutral zone.
* **ADX (Average Directional Index)** along with **DI+ and DI-**: Measures the strength and direction of a trend.
* **MACD (Moving Average Convergence Divergence)**: Confirms momentum and trend direction based on moving averages.
---
### 🟪 **Sideways Market Signal**
A sideways (non-trending) signal is shown when:
* RSI indicates the market is neither overbought nor oversold (in the middle range).
* ADX shows weak trend strength.
* The ADX value is lower than both DI+ and DI-, suggesting indecision or lack of clear trend.
A purple label appears below the bar when this condition is met.
---
### 🟩 **Buy Signal**
A buy signal is generated when:
* RSI shows strong upward momentum.
* ADX confirms there is a strong trend.
* MACD confirms bullish conditions with both the MACD and signal lines above zero and in the correct crossover direction.
A green label appears below the bar when these bullish conditions align.
---
### 🟥 **Sell Signal**
A sell signal appears when:
* RSI shows strong downward momentum.
* ADX confirms a strong trend.
* MACD confirms bearish conditions, with both MACD and signal lines below zero and in the correct crossover direction.
A red label appears — currently also plotted below the bar (which you may want to move above the bar for better clarity).
---
### ✅ **Use Case**
This script is suitable for:
* Deciding when to deploy **strangle/straddle** strategies in sideways markets.
* Identifying strong **bullish or bearish trends** for directional trades.
* Filtering out weak or indecisive conditions.
RSI Horizontal resistance levelsRSI Horizontal Resistance Levels
Purpose
This study automatically draws horizontal price rays every time the Relative Strength Index (RSI) trades inside a user‑defined band (default = 50 ± 1). The idea is to spotlight price levels that historically coincided with a specific RSI state—levels that often evolve into short‑term support or resistance as sentiment repeats.
How it works
Raw RSI – The script computes an un‑smoothed RSI of the closing price (rsiLength, default = 3).
Trigger zone – Whenever RSI falls within level ± tolerance (e.g., 49 – 51) the close price of that bar becomes a reference.
Horizontal ray – A ray (extend.right) is drawn from that close and continues indefinitely to the right, creating a live price level.
History management – Only the most recent N rays are kept (maxRays, default = 5); the oldest line is deleted automatically to avoid chart clutter.
Inputs
Name Default Description
RSI Length 3 Look‑back of the RSI calculation.
RSI Level 50 Center of the trigger zone. Common alternatives: 30, 70, custom mid‑lines.
Tolerance 1 Width of the RSI band on each side of RSI Level (set 0 for an exact hit).
Max Rays 5 Maximum number of active horizontal levels kept on screen.
Ray Color Yellow Visual color of the rays.
Ray Width 2 Thickness of the rays.
How to use it
Spot recurring reaction zones – Markets often hesitate near prices where momentum has flipped before. The rays reveal those spots automatically.
Combine with candlestick or volume cues – A level marked by this script plus a rejection wick, volume spike, or divergence can strengthen conviction.
Adapt the band –
50 ± tolerance → momentum balance line (trend pullbacks)
70 ± tolerance → overbought fade levels
30 ± tolerance → oversold bounce levels
Works on any asset or timeframe; shorter RSI periods highlight intraday rotations, longer periods capture swing pivots.
Tips & cautions
A drawn line is not a guarantee of future reversal—always validate with price action and risk management.
For high‑frequency strategies, consider lowering tolerance or increasing rsiLength to reduce noise.
You can add custom alerts on RSI crossing the band to receive push or email notifications.
Happy trading!
CyberCandle SwiftEdgeCyberCandle SwiftEdge
Overview
CyberCandle SwiftEdge is a cutting-edge, AI-inspired trading indicator designed for traders seeking precision and clarity in trend-following and swing trading. Powered by SwiftEdge, it combines Heikin Ashi candles, a gradient-colored Exponential Moving Average (EMA), and a Relative Strength Index (RSI) to deliver clear buy and sell signals. Featuring glowing visuals, dynamic signal icons, and a customizable RSI dashboard in the top-right corner, this script offers a futuristic interface for identifying high-probability trade setups on various timeframes (e.g., 1H, 4H).
What It Does
CyberCandle SwiftEdge integrates three powerful components to generate actionable trading signals:
Heikin Ashi Candles: Smooths price action to highlight trends, reducing market noise and making reversals easier to spot.
Gradient EMA: A 100-period EMA with dynamic color transitions (blue/cyan for uptrends, red/pink for downtrends) to confirm market direction.
RSI Dashboard: A neon-lit display showing RSI levels, indicating overbought (>70), oversold (<30), or neutral (30-70) conditions.
Buy and sell signals are marked with prominent, glowing icons (triangles and arrows) based on trend direction, momentum, and specific Heikin Ashi patterns. The script’s customizable parameters allow traders to tailor the strategy to their preferences, balancing signal frequency and precision.
How It Works
The strategy leverages the synergy of Heikin Ashi, EMA, and RSI to filter trades and highlight opportunities:
Trend Direction: The price must be above the EMA for buy signals (bullish trend) or below for sell signals (bearish trend). The EMA’s gradient color shifts based on its slope, visually reinforcing trend strength.
Momentum Confirmation: RSI must exceed a user-defined threshold (default: 50) for buy signals or fall below it for sell signals, ensuring momentum supports the trade.
Candle Patterns: Buy signals require a green Heikin Ashi candle (close > open), with the two prior candles having minimal upper wicks (≤5% of candle body) and being red (indicating a retracement). Sell signals require a red candle, minimal lower wicks, and two prior green candles.
RSI Dashboard: Positioned in the top-right corner, it features a glowing circle (red for overbought, green for oversold, blue for neutral), the current RSI value, and a status indicator (triangle for extremes, square for neutral). This provides instant momentum insights without cluttering the chart.
By combining Heikin Ashi’s trend clarity, EMA’s directional filter, and RSI’s momentum validation, CyberCandle SwiftEdge minimizes false signals and highlights trades with strong potential. Its vibrant, AI-like visuals make it easy to interpret at a glance.
How to Use It
Add to Chart: In TradingView, search for "CyberCandle SwiftEdge" and add it to your chart. Set the chart to Heikin Ashi candles for optimal compatibility.
Interpret Signals:
Buy Signal: Large green triangles and arrows appear below candles when the price is above the EMA, RSI is above the buy threshold (default: 50), and conditions for a bullish retracement are met. Consider entering a long position with a 1:2 risk/reward ratio.
Sell Signal: Large red triangles and arrows appear above candles when the price is below the EMA, RSI is below the sell threshold (default: 50), and conditions for a bearish retracement are met. Consider entering a short position.
RSI Dashboard: Monitor the top-right dashboard. A red circle (RSI > 70) suggests caution for buys, a green circle (RSI < 30) indicates potential buying opportunities, and a blue circle (RSI 30-70) signals neutrality.
Customize Parameters: Open the indicator’s settings to adjust:
EMA Length (default: 100): Increase (e.g., 200) for longer-term trends or decrease (e.g., 50) for shorter-term sensitivity.
RSI Length (default: 14): Adjust for more (e.g., 7) or less (e.g., 21) responsive momentum signals.
RSI Buy/Sell Thresholds (default: 50): Set higher (e.g., 55) for buys or lower (e.g., 45) for sells to require stronger momentum.
Wick Tolerance (default: 0.05): Increase (e.g., 0.1) to allow larger wicks, generating more signals, or decrease (e.g., 0.02) for stricter conditions.
Require Retracement (default: true): Disable to remove the two-candle retracement requirement, increasing signal frequency.
Trading: Use signals in conjunction with the RSI dashboard and market context. For example, avoid buy signals if the RSI dashboard is red (overbought). Always apply proper risk management, such as setting stop-losses based on recent lows/highs.
What Makes It Original
CyberCandle SwiftEdge stands out due to its futuristic, AI-inspired visual design and user-friendly customization:
Neon Aesthetics: Glowing Heikin Ashi candles, gradient EMA, and dynamic signal icons (triangles and arrows) with RSI-driven transparency create a high-tech, immersive experience.
RSI Dashboard: A compact, top-right display with a neon circle, RSI value, and adaptive status indicator (triangle/square) provides instant momentum insights without cluttering the chart.
Customizability: Users can fine-tune EMA length, RSI parameters, wick tolerance, and retracement requirements via TradingView’s settings, balancing signal frequency and precision.
Integrated Approach: The synergy of Heikin Ashi’s trend clarity, EMA’s directional strength, and RSI’s momentum validation offers a cohesive strategy that reduces false signals.
Why This Combination?
The script combines Heikin Ashi, EMA, and RSI for a complementary effect:
Heikin Ashi smooths price fluctuations, making it ideal for identifying sustained trends and retracements, which are critical for the strategy’s signal logic.
EMA provides a reliable trend filter, ensuring signals align with the broader market direction. Its gradient color enhances visual trend recognition.
RSI adds momentum context, confirming that signals occur during favorable conditions (e.g., RSI > 50 for buys). The dashboard makes RSI intuitive, even for non-technical users.
Together, these components create a balanced system that captures trend reversals after retracements, validated by momentum, with a visually engaging interface that simplifies decision-making.
Tips
Best used on volatile assets (e.g., BTC/USD, EUR/USD) and higher timeframes (1H, 4H) for clearer trends.
Experiment with parameters in the settings to match your trading style (e.g., increase wick tolerance for more signals).
Combine with other analysis (e.g., support/resistance) for higher-confidence trades.
Note
This indicator is for informational purposes and does not guarantee profits. Always backtest and use proper risk management before trading.
FX Rotation Dashboard – FX vs DXYFX Rotation Dashboard
This FX Rotation Dashboard indicator offers a clear, at-a-glance overview of the relative strength and momentum of major FX pairs when measured against the US Dollar Index (DXY). It helps identify which currency pairs are leading or lagging in performance and how their relative positioning is evolving over time.
🔎 Structure
Each row in the table represents a currency pair and provides the following data:
Symbol: The FX pair (e.g., EURJPY, GBPUSD, AUDNZD)
RS: The current relative strength ratio vs. DXY
MOM: The momentum of the RS – showing whether strength is accelerating or decelerating
Quadrant:
🔴 Lagging: Underperforming and losing momentum
🔵 Improving: Underperforming but gaining momentum
🟢 Leading: Outperforming with rising momentum
🟡 Weakening (not visible in current table): Outperforming but slowing down
Meaning: A descriptive label summarizing the quadrant status
🧭 Interpretation
Currency pairs in the Leading quadrant (e.g., EURJPY, GBPJPY, CHFJPY, CADJPY) are currently outperforming the USD with rising momentum – often indicating strength across both RS and MOM dimensions.
Pairs in the Improving quadrant (e.g., GBPUSD, EURUSD, AUDUSD) are showing positive momentum but still lagging in relative strength – potential early signs of rotation into leadership.
Pairs in the Lagging quadrant (e.g., USDCHF, USDJPY, AUDCHF) are underperforming and continue to weaken – often considered the least attractive at the current stage of the cycle.
🧠 Purpose
This FX Rotation visualization is particularly useful for:
Macro-level FX rotation analysis
Spotting emerging trends before they are fully priced in
Tracking performance of G10 and cross pairs against USD
It provides context for currency strength beyond individual price movements by placing them in a relative performance framework.
⚠️ Disclaimer: This indicator is for informational and analytical purposes only. It does not constitute investment advice or a recommendation to buy or sell any financial instrument. Always conduct your own research before making trading decisions.
Memecoin Screener | QuantumResearchMemecoin Screener | QuantumResearch
🚀 Overview
The Memecoin Screener is a specialized multi-asset relative strength tool designed to track, compare, and rank up to 10 different memecoins in real-time. Built for degens and serious meme investors alike, this screener goes beyond price action—analyzing inter-asset relative momentum using a proprietary ARSI-based strength scoring system.
Whether you're flipping $FARTCOIN or rotating between SEED_DONKEYDAN_MARKET_CAP:BONK , SEED_WANDERIN_JIMZIP900:WIF , or $BUTTHOLE, this tool will help you uncover which meme coin leads the pack—and which ones are fading into irrelevance.
🧩 1. Key Features
📊 Relative Strength Matrix
Each selected memecoin is compared against all others using ARSI. This creates a matrix of performance relationships between tokens, highlighting dominance and weakness.
🏆 Dynamic Ranking System
Every coin is scored based on its aggregate relative strength across the group, then ranked from strongest to weakest. The higher the score, the more dominant the token is across the pack.
🎯 Allocation Recommendations
Choose your allocation style—Aggressive, Mixed, or Conservative—and let the screener automatically assign exposure percentages to the top-ranked assets based on your risk profile.
Aggressive allocation
Mix allocation
Conservative allocation
🖥 Visual Screener Table
A clean, color-coded table tracks ✔︎ wins and ✘ losses in pairwise comparisons, shows total strength scores, ranks, and allocation recommendations—all at a glance.
🎨 Customizable Color Modes & UI Positioning
Choose from 8 stylish color palettes and 9 screen positions for the screener table. Tailor the visual layout to your trading workflow.
🧠 How It Works
1️⃣ Pairwise Strength Comparison
Each token is compared to every other token using the formula:
tokenX / tokenY → ARSI → strength score
2️⃣ Score Aggregation
The individual strength scores from all pairwise comparisons are summed to produce a final score for each token.
3️⃣ Ranking & Allocation
Scores are sorted and ranked. Based on the selected allocation mode, exposure is then recommended across the Top 3 coins only.
📈 Use Cases
🔍 Memecoin Rotation Strategy
Stay in the strongest trends and rotate out of weak ones using leaderboard-driven allocation.
⚔️ Long/Short Relative Plays
Go long the top-ranked coin and short the bottom-ranked one for a hedged memecoin momentum strategy.
📊 Group Sentiment Heatmap
Use the table to visually assess which assets are gaining or losing strength over time.
🎒 Position Sizing Guide
Let the allocation module assist you in determining where and how much to allocate, especially when flipping high-risk coins.
💡 Who Is This For?
✅ Degen Traders flipping microcaps and memes
✅ Solana memecoin fans tracking top performers
✅ Systematic traders looking for structured rotation
✅ Anyone seeking clarity in chaos during volatile market cycles
⚠️ Disclaimer
This tool is designed for informational purposes and does not constitute financial advice. Memecoins are volatile and highly speculative assets. Always perform your own due diligence and apply proper risk management.
Follow QuantumResearch for more alpha-driven tools that blend meme culture with advanced technical frameworks.
🧪 Meme smarter. Rotate faster. Survive longer.
RSI + ADX + ATR Combo Indicator: RSI + ADX + ATR Combo Filter
This indicator is a confluence filter tool that combines RSI, ADX, and ATR into a single, easy-to-read chart overlay. It is designed to help traders identify low-volatility, non-trending zones with balanced momentum—ideal for strategies that rely on breakouts or reversals.
🔍 Core Components:
RSI (Relative Strength Index)
Standard RSI with custom upper and lower bounds (default: 60 and 40).
Filters out extreme overbought/oversold regions and focuses on price consolidation zones.
ADX (Average Directional Index)
Measures trend strength.
When ADX is below a custom threshold (default: 20), it indicates a weak or range-bound trend.
ATR (Average True Range)
Represents volatility.
Low ATR values (default threshold: 2.5) are used to filter out high-volatility environments, helping refine entries.
🟣 Signal Logic:
A signal is highlighted with a background color when all three conditions are met:
RSI is between lower and upper bounds (e.g., 40 < RSI < 60) ✅
ADX is below the trend threshold (e.g., ADX < 20) ✅
ATR is below the volatility threshold (e.g., ATR < 2.5) ✅
These combined conditions suggest a low-volatility, low-trend strength, and balanced momentum zone—perfect for anticipating breakouts or strong directional moves.
