Fourier Transformed & Kalman Filtered EMA Crossover [Mattes]The Fourier Transformed & Kalman Filtered EMA Crossover (FTKF EMAC) is a trend-following indicator that leverages Fourier Transform approximation, Kalman Filtration, and two Exponential Moving Averages (EMAs) of different lengths to provide accurate and smooth market trend signals. By combining these three components, it captures the underlying market cycles, reduces noise, and produces actionable insights, making it suitable for detecting both emerging trends and confirming existing ones.
TECHNICALITIES:
>>> The Fourier Transform approximation is designed to identify dominant cyclical patterns in price action by focusing on key frequencies, while filtering out noise and less significant movements. It emphasizes the most meaningful price cycles, enabling the indicator to isolate important trends while ignoring minor fluctuations. This cyclical awareness adds an extra layer of depth to trend detection, allowing the EMAs to work with a cleaner and more reliable data set.
>>> The Kalman Filter adds dynamic noise reduction, adjusting its predictions of future price trends based on past and current data. As new price data comes in, the filter recalibrates itself to ensure that the price action remains smooth and devoid of erratic movements. This real-time adjustment is key to minimizing lag while avoiding false signals, which ensures that the EMAs react to more accurate and stable market data. The Kalman Filter’s ability to smooth price data without losing sensitivity to trend changes complements the Fourier approximation, ensuring a high level of precision in volatile and stable market environments.
>>> The EMA Crossover involves using two EMAs: a shorter EMA that reacts quickly to price movements and a longer EMA that responds more slowly. The shorter EMA is responsible for capturing immediate market shifts, detecting potential bullish or bearish trends. The longer EMA smooths out price fluctuations and provides trend confirmation, working with the shorter EMA to ensure the signals are reliable. When the shorter EMA crosses above the longer EMA, it indicates a bullish trend, likewise when it goes below the longer EMA, it signals a bearish trend. This setup provides a clear way to track market direction, with color-coded signals (green for bullish, red for bearish) for visual clarity. The flexibility of adjusting the EMA periods allows traders to fine-tune the indicator to their preferred timeframe and strategy, making it adaptable to different market conditions.
|-> A key technical aspect is that the first EMA should always be shorter than the second one. If the first EMA is longer than the second, the tool’s effectiveness is compromised because the faster EMA is designed to signal long conditions, while the longer one is made for signaling a bearish trend. Reversing their roles would lead to delayed or confused signals, reducing the indicator’s ability to detect trend shifts early and making it less efficient in volatile markets. This is the only key weakness of the indicator, failure to submit to this rule will result in confusion.
>>> These components work together like a clock to create a comprehensive and effective trend-following system. The Fourier approximation highlights key cyclical movements, the Kalman Filter refines these movements by removing noise, and the EMAs interpret the filtered data to generate actionable trend signals. Each component enhances the next, ensuring that the final output is both responsive and reliable, with minimal false signals or lag. creating an indicator using widespread concepts which haven't been combined before.
Summary
This indicator combines Fourier Transform approximation, Kalman Filtration, and two EMAs of different lengths to deliver accurate and timely trend-following signals. The Fourier approximation identifies dominant market cycles, while the Kalman Filter dynamically removes noise and refines the price data in real time. The two EMAs then use this filtered data to generate buy and sell signals based on their crossovers. The shorter EMA reacts quickly to price changes, while the longer EMA provides smoother trend confirmation. The components work in synergy to capture trends with minimal false signals or lag, ensuring traders can act promptly on market shifts. Customizable EMA periods make the tool adaptable to different market conditions, enhancing its versatility for various trading strategies.
To use the indicator, traders should adjust the EMA lengths based on their timeframe and strategy, ensuring that the shorter EMA remains shorter than the longer EMA to preserve the tool’s responsiveness. The color-coded signals offer visual clarity, making it easy to identify potential entry and exit points. This confluence of Fourier, Kalman, and EMA methodologies provides a smooth, highly effective trend-following tool that excels in both trending and ranging markets.

# 考夫曼自適應移動平均線(KAMA)

Adaptive MA Scalping StrategyAdaptive MA Scalping Strategy
The Adaptive MA Scalping Strategy is an innovative trading approach that merges the strengths of the Kaufman's Adaptive Moving Average (KAMA) with the Moving Average Convergence Divergence (MACD) histogram. This combination results in a momentum-adaptive moving average that dynamically adjusts to market conditions, providing traders with timely and reliable signals.
How It Works
Kaufman's Adaptive Moving Average (KAMA): Unlike traditional moving averages, KAMA adjusts its sensitivity based on market volatility. It becomes more responsive during trending markets and less sensitive during periods of consolidation, effectively filtering out market noise.
MACD Histogram Integration: The strategy incorporates the MACD histogram, a momentum indicator that measures the difference between a fast and a slow exponential moving average (EMA). By adding the MACD histogram values to the KAMA, the strategy creates a new line—the momentum-adaptive moving average (MOMA)—which captures both trend direction and momentum.
Signal Generation:
Long Entry: The strategy enters a long position when the closing price crosses above the MOMA. This indicates a potential upward momentum shift.
Exit Position: The position is closed when the closing price crosses below the MOMA, signaling a potential decline in momentum.
Cloud Calculation Detail
The MOMA is calculated by adding the MACD histogram value to the KAMA of the price. This addition effectively adjusts the KAMA based on the momentum indicated by the MACD histogram. When momentum is strong, the MACD histogram will have higher values, causing the MOMA to adjust accordingly and provide earlier entry or exit signals.
Performance on Stocks
This strategy has demonstrated excellent performance on stocks when applied to the 1-hour timeframe. Its adaptive nature allows it to respond swiftly to market changes, capturing profitable trends while minimizing the impact of false signals caused by market noise. The combination of KAMA's adaptability and MACD's momentum detection makes it particularly effective in volatile market conditions commonly seen in stock trading.
Key Parameters
KAMA Length (malen): Determines the sensitivity of the KAMA. A length of 100 is used to balance responsiveness with noise reduction.
MACD Fast Length (fast): Sets the period for the fast EMA in the MACD calculation. A value of 24 helps in capturing short-term momentum changes.
MACD Slow Length (slow): Sets the period for the slow EMA in the MACD calculation. A value of 52 smooths out longer-term trends.
MACD Signal Length (signal): Determines the period for the signal line in the MACD calculation. An 18-period signal line is used for timely crossovers.
Advantages of the Strategy
Adaptive to Market Conditions: By adjusting to both volatility and momentum, the strategy remains effective across different market phases.
Enhanced Signal Accuracy: The fusion of KAMA and MACD reduces false signals, improving the accuracy of trade entries and exits.
Simplicity in Execution: With straightforward entry and exit rules based on price crossovers, the strategy is user-friendly for traders at all experience levels

KAMA Cloud STIndicator:
Description:
The KAMA Cloud indicator is a sophisticated trading tool designed to provide traders with insights into market trends and their intensity. This indicator is built on the Kaufman Adaptive Moving Average (KAMA), which dynamically adjusts its sensitivity to filter out market noise and respond to significant price movements. The KAMA Cloud leverages multiple KAMAs to gauge trend direction and strength, offering a visual representation that is easy to interpret.
How It Works:
The KAMA Cloud uses twenty different KAMA calculations, each set to a distinct lookback period ranging from 5 to 100. These KAMAs are calculated using the average of the open, high, low, and close prices (OHLC4), ensuring a balanced view of price action. The relative positioning of these KAMAs helps determine the direction of the market trend and its momentum.
By measuring the cumulative relative distance between these KAMAs, the indicator effectively assesses the overall trend strength, akin to how the Average True Range (ATR) measures market volatility. This cumulative measure helps in identifying the trend’s robustness and potential sustainability.
The visualization component of the KAMA Cloud is particularly insightful. It plots a 'cloud' formed between the base KAMA (set at a 100-period lookback) and an adjusted KAMA that incorporates the cumulative relative distance scaled up. This cloud changes color based on the trend direction — green for upward trends and red for downward trends, providing a clear, visual representation of market conditions.
How the Strategy Works:
The KAMA Cloud ST strategy employs multiple KAMA calculations with varying lengths to capture the nuances of market trends. It measures the relative distances between these KAMAs to determine the trend's direction and strength, much like the original indicator. The strategy enhances decision-making by plotting a 'cloud' formed between the base KAMA (set to a 100-period lookback) and an adjusted KAMA that scales according to the cumulative relative distance of all KAMAs.
Key Components of the Strategy:
Multiple KAMA Layers: The strategy calculates KAMAs for periods ranging from 5 to 100 to analyze short to long-term market trends.
Dynamic Cloud: The cloud visually represents the trend’s strength and direction, updating in real-time as the market evolves.
Signal Generation: Trade signals are generated based on the orientation of the cloud relative to a smoothed version of the upper KAMA boundary. Long positions are initiated when the market trend is upward, and the current cloud value is above its smoothed average. Conversely, positions are closed when the trend reverses, indicated by the cloud falling below the smoothed average.
Suggested Usage:
Market: Stocks, not cryptocurrency
Timeframe: 1 Hour
Indicator:

KAMA CloudDescription:
The KAMA Cloud indicator is a sophisticated trading tool designed to provide traders with insights into market trends and their intensity. This indicator is built on the Kaufman Adaptive Moving Average (KAMA), which dynamically adjusts its sensitivity to filter out market noise and respond to significant price movements. The KAMA Cloud leverages multiple KAMAs to gauge trend direction and strength, offering a visual representation that is easy to interpret.
How It Works:
The KAMA Cloud uses twenty different KAMA calculations, each set to a distinct lookback period ranging from 5 to 100. These KAMAs are calculated using the average of the open, high, low, and close prices (OHLC4), ensuring a balanced view of price action. The relative positioning of these KAMAs helps determine the direction of the market trend and its momentum.
By measuring the cumulative relative distance between these KAMAs, the indicator effectively assesses the overall trend strength, akin to how the Average True Range (ATR) measures market volatility. This cumulative measure helps in identifying the trend’s robustness and potential sustainability.
The visualization component of the KAMA Cloud is particularly insightful. It plots a 'cloud' formed between the base KAMA (set at a 100-period lookback) and an adjusted KAMA that incorporates the cumulative relative distance scaled up. This cloud changes color based on the trend direction — green for upward trends and red for downward trends, providing a clear, visual representation of market conditions.
Benefits:
Dynamic Sensitivity: By adapting to the market's volatility, KAMA provides more reliable signals than traditional moving averages.
Trend Clarity: The color-coded cloud visually enhances the perception of the trend’s direction and strength, making it easier for traders to decide on their trading strategy.
Versatility: Suitable for various asset classes, including stocks, forex, commodities, and cryptocurrencies, across different timeframes.
Decision Support: Helps traders understand not just the direction but the strength of trends, aiding in more informed decision-making regarding entries, exits, and risk management.
Usage:
The KAMA Cloud is ideal for traders who need a robust trend-following tool that adjusts according to market dynamics. It can be used as a standalone indicator or in conjunction with other technical analysis tools to enhance trading strategies. Look for the cloud’s color shifts as potential signals for trend reversals or continuations, and consider the cloud’s thickness as an indication of trend strength.
Whether you are a day trader, swing trader, or long-term investor, the KAMA Cloud offers a unique approach to understanding market trends, helping you navigate the complexities of various market conditions with confidence.

No Lag SupertrendNo Lag Supertrend indicator improves upon the original supertrend by incorporating calculation methods that enhance responsiveness and accuracy. Traditional supertrend indicators often suffer from lag, which can delay signals and affect trading decisions. No Lag Supertrend addresses this issue through the use of KAMA (Kaufman’s Adaptive Moving Average) and Hull ATR (Average True Range) calculations.
Goals of No Lag Supertrend:
- Lag reduction: one of the main issues with traditional supertrend indicators is their lag, which can result in delayed entry and exit signals. By integrating KAMA and Hull ATR, the no lag supertrend minimizes this delay, providing more timely signals.
- Market Noise Filtering: The combined use of KAMA and Hull ATR effectively filters out market noise, ensuring that signals are based on significant price movements rather than minor fluctuations.
- Consistency Across Different Market Conditions: The adaptive nature of KAMA and the smooth responsiveness of Hull ATR ensure that the No Lag Supertrend performs consistently across various market conditions, from trending to volatile markets.
Credits: This code is based on the TradingView supertrend but improved the ATR calculations.

Kaufman Adaptive Moving Average (KAMA) Strategy [TradeDots]"The Kaufman Adaptive Moving Average (KAMA) Strategy" is a trend-following system that leverages the adaptive qualities of the Kaufman Adaptive Moving Average (KAMA). This strategy is distinguished by its ability to adjust dynamically to market volatility, enhancing trading accuracy by minimizing the effects of false and delayed signals often associated with the Simple Moving Average (SMA).
HOW IT WORKS
This strategy is centered around use of the Kaufman Adaptive Moving Average (KAMA) indicator, which refines the principles of the Exponential Moving Average (EMA) with a superior smoothing technique.
KAMA distinguishes itself by its responsiveness to changes in market prices through an "Efficiency Ratio (ER)." This ratio is computed by dividing the recent absolute net price change by the cumulative sum of the absolute price changes over a specified period. The resulting ER value ranges between 0 and 1, where 0 indicates high market noise and 1 reflects stronger market momentum.
Using ER, we could get the smoothing constant (SC) for the moving average derived using the following formula:
fastest = 2/(fastma_length + 1)
slowest = 2/(slowma_length + 1)
SC = math.pow((ER * (fastest-slowest) + slowest), 2)
The KAMA line is then calculated by applying the SC to the difference between the current price and the previous KAMA.
APPLICATION
For entering long positions, this strategy initializes when there is a sequence of 10 consecutive rising KAMA lines. Conversely, a sequence of 10 consecutive falling KAMA lines triggers sell orders for long positions. The same logic applies inversely for short positions.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
Users are advised to adjust and personalize this trading strategy to better match their individual trading preferences and style.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.

TASC 2024.01 Gap Momentum System█ OVERVIEW
TASC's January 2024 edition of Traders' Tips features an article titled “Gap Momentum” by Perry J. Kaufman. The article discusses how a trader might create a momentum strategy based on opening gap data. This script implements the Gap Momentum system presented therein.
█ CONCEPTS
In the article, Perry J. Kaufman introduces Gap Momentum as a cumulative series constructed in the same way as On-Balance Volume (OBV) , but using gap openings (today’s open minus yesterday’s close).
To smoothen the resulting time series (i.e., obtain the " signal line "), the author applies a simple moving average . Subsequently, he proposes the following two trading rules for a long-only trading system:
• Enter a long position when the signal line is moving higher.
• Exit when the signal line is moving lower.
█ CALCULATIONS
The calculation of Gap Momentum involves the following steps:
1. Calculate the ratio of the sum of positive gaps over the past N days to the sum of negative gaps (absolute values) over the same time period.
2. Add the resulting gap ratio to the cumulative time series. This time series is the Gap Momentum.
3. Keep moving forward, as in an N-day moving average.

Kaufman Efficiency Ratio (KER)The Kaufman Efficiency Ratio (also known as the Efficiency Ratio or ER) is a technical indicator used in technical analysis to measure the efficiency of a financial instrument's price movement. It was developed by Perry J. Kaufman and is designed to help traders and analysts identify the trendiness or choppiness of a market.
The Kaufman Efficiency Ratio is calculated using the following formula:
ER = (Change in Price over N periods) / (Sum of the absolute price changes over N periods)
Here's how the formula works:
"Change in Price over N periods" is the net price change over a specified number of periods (usually days or bars). It's calculated by subtracting the closing price of N periods ago from the current closing price.
"Sum of the absolute price changes over N periods" is the sum of the absolute values of price changes (i.e., ignoring the direction) over the same N periods.
The resulting Efficiency Ratio (ER) value will fall within the range of 0 to 1, with 1 indicating a perfectly trending market and 0 indicating a perfectly choppy or range-bound market. In other words, the closer the ER is to 1, the stronger and more efficient the trend is perceived to be.

Volume-Weighted Kaufman's Adaptive Moving AverageThe Volume-Weighted Kaufman's Adaptive Moving Average (VW-KAMA) is a technical indicator that combines the Volume-Weighted Moving Average (VWMA) and the Kaufman's Adaptive Moving Average (KAMA) to create a more responsive and adaptable moving average.
Advantages:
Volume-Weighted: It takes into account the volume of trades, giving more weight to periods with higher trading volume, which can help filter out periods of low activity.
Adaptive: The indicator adjusts its smoothing constant based on market conditions, becoming more sensitive in trending markets and less sensitive in choppy or sideways markets.
Versatility: VW-KAMA can be used for various purposes, including trend identification, trend following, and determining potential reversal points and act as dynamic support and resistance level.

twisted SMA strategy [4h] Hello
I would like to introduce a very simple strategy that uses a combination of 3 simple moving averages ( SMA 4 , SMA 9 , SMA 18 )
this is a classic combination showing the most probable trend directions
Crosses were marked on the basis of the color of the candles (bulish cross - blue / bearish cross - maroon)
ma 100 was used to determine the main trend, which is one of the most popular 4-hour candles
We define main trend while price crosses SMA100 ( for bullish trend I use green candle color )
The long position strategy was created in combination of 3 moving averages with Kaufman's adaptive moving average by alexgrover
The strategy is very accurate and is easy to use indicators
the strategy uses only Buy (Long) signals in a combination of crossovers of the SMA 4, SMA 9, SMA 18 and the Kaufman Adaptive Moving Average.
As a signal to close a long position, only the opposite signal of the intersection of 3 different moving averages is used
the current strategy is recommended for higher time zones (4h +) due to the strength of the closing candles, which translates into signal strength
works fascinatingly well for long-term bullish market assets (for example 4h Apple, Tesla charts)
Enjoy and trade safe ;)

kama
█ Description
An adaptive indicator could be defined as market conditions following indicator, in summary, the parameter of the indicator would be adjusted to fit its optimum value to the current price action. KAMA, Kaufman's Adaptive Moving Average, an adaptive trendline indicator developed by Perry J. Kaufman, with the notion of using the fastest trend possible based on the smallest calculation period for the existing market conditions, by applying an exponential smoothing formula to vary the speed of the trend (changing smoothing constant each period), as cited from Trading Systems and Methods p.g. 780 (Perry J. Kaufman). In this indicator, the proposed notion is on the Efficiency Ratio within the computation of KAMA, which will use a Dominant Cycle instead, an adaptive filter developed by John F. Ehlers, on determining the n periods, aiming to achieve an optimum lookback period, with respect to the original Efficiency Ratio calculation period of less than 14, and 8 to 10 is preferable.
█ Kaufman's Adaptive Moving Average
kama_ = kama + smoothing_constant * (price - kama )
where:
price = current price (source)
smoothing_constant = (efficiency_ratio * (fastest - slowest) + slowest)^2
fastest = 2/(fastest length + 1)
slowest = 2/(slowest length + 1)
efficiency_ratio = price - price /sum(abs(src - src , int(dominant_cycle))
█ Feature
The indicator will have a specified default parameter of: length = 14; fast_length = 2; slow_length = 30; hp_period = 48; source = ohlc4
KAMA trendline i.e. output value if price above the trendline and trendline indicates with green color, consider to buy/long position
while, if the price is below the trendline and the trendline indicates red color, consider to sell/short position
Hysteresis Band
Bar Color
other example

Cong Adaptive Moving AverageDr. Scott Cong's new adaptation of an adaptive moving average (AMA), featured in TASC March 2023.
It adjusts its parameters automatically according to the volatility of market, tracking price closely in trending movement, staying flat in congestion areas.
Perry Kaufman’s adaptive moving average, first described in his 1995 book Smarter Trading, is a great example of how an AMA can self-adjust to adapt to changing environments. This indicator presents a new scheme for an adaptive moving average that is responsive, smooth, and robust.

Another New Adaptive Moving Average [CC]The New Adaptive Moving Average was created by Scott Cong (Stocks and Commodities Mar 2023) and this is a companion indicator to my previous script . This indicator still works off of the same concept as before with effort vs results but this indicator takes a slightly different approach and instead defines results as the absolute difference between the closing price and a closing price x bars ago. As you can see in my chart example, this indicator works great to stay with the current trend and provides either a stop loss or take profit target depending on which direction you are going in. As always, I use darker colors to show stronger signals and lighter colors to show normal signals. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicator scripts you would like to see me publish!

A New Adaptive Moving Average [CC]The New Adaptive Moving Average was created by Scott Cong (Stocks and Commodities Mar 2023) and his idea was to focus on the Adaptive Moving Average created by Perry Kaufman and to try to improve it by introducing a concept of effort vs results. In this case the effort would be the total range of the underlying price action since each bar is essentially a war of the bulls vs the bears. The result would be the total range of the close so we are looking for the highest close and lowest close in that same time period. This gives us an alpha that we can use to plug into the Kaufman Adaptive Moving Average algorithm which gives us a brand new indicator that can hug the price just enough to allow us to ride the stock up or down. I have color coded it to be darker colors when it is a strong signal and lighter colors when it is a normal signal. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you would like to see me publish!

VHF Adaptive Linear Regression KAMAIntroduction
Heyo, in this indicator I decided to add VHF adaptivness, linear regression and smoothing to a KAMA in order to squeeze all out of it.
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. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.
VHF:
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Linear Regression Curve:
A line that best fits the prices specified over a user-defined time period.
This is very good to eliminate bad crosses of KAMA and the pric.
Usage
You can use this indicator on every timeframe I think. I mostly tested it on 1 min, 5 min and 15 min.
Signals
Enter Long -> crossover(close, kama) and crossover(kama, kama )
Enter Short -> crossunder(close, kama) and crossunder(kama, kama )
Thanks for checking this out!
--
Credits to
▪️@cheatcountry – Hann Window Smoohing
▪️@loxx – VHF and T3
▪️@LucF – Gradient

ER-Adaptive ATR Limit Channels w/ States [Loxx]As simple as it gets, channels based on high, low and ATR distances, Shows possible short term support / resistance or can be used as a take profit/stop-loss in some trading systems. It does this by comparing high/low values of price to multiplied by a multiple of ATR to determine when the trend changes. States are included to change the sensitivity to trend changes. 1 is very sensitive, 3 is least sensitive.
This uses Loxx's Expanded Source Types. You can read about them here:
What is ER 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

Hull Kaufman SuperTrend Cloud (HKST Cloud)TLDR: This is a long only trend following system that uses highest and lowest values of three trend following indicators to form a "cloud". Enter when the candle high crosses above the highest band. Close if the low or close crosses below the lowest band.
3 indicators
1. Kaufman Adaptive moving Average - set at 20
2. Hull Moving Average (of the Kaufman Adaptive moving average) - set at 20
3. SuperTrend - I believe this is set at 5 periods and 3*atr but this can be changed
Cloud
the upper band is the highest of the 3 indicators
the lower band is the lowest of the 3 indicators
Entry and Exit:
Enter when the high crosses above the upper band of the cloud.
(This means you will only get a long signal if the high was previously below the upper band of the cloud and then crosses over.)
Exit when the low crosses below the lower band of the cloud .
(This means that this rule will only close if the low was previously above the upper band and then crosses under)
The "Exit" may not trigger if the low (the wick) never gets above the band. In this case the long order will close if the closing price is below the cloud.
Logic
Kaufamn is the best moving average i have found at responding to changes in volatility. This means it moves up or down quickly during expansive moves but becoming very flat during relative choppiness. However, getting flat causes the the Kaufman AMA to trigger a lot of false signals when volatility is transitioning from high to low. This is why the Hull moving average (with its rounded turns) keeps a lot of the false continuations contained because usually prices need to decisively move higher in order to over come the Hull moving average which continues to increase/decrease during the candles after an expansive move.
The super trend places a stagnant floor and ceiling which acts as a great stoploss or trigger as price action attempts to trend in a certain direction. The super trend allows for the user to adjust the likely hood of this cloud indicator changing from bullish to bearish based on the volatility of the asset that is being analyzed.
individually all of these are great. Together the trader can use this cloud to create a trend following or anlysis system that captures the bulk of moves.
Not my best explanation but this indicator is actually pretty simple.
Hope this helps. Happy Trades
-Snarky Puppy

JFD-Adaptive, GKYZ-Filtered KAMA [Loxx]JFD-Adaptive, GKYZ-Filtered KAMA is a Kaufman Adaptive Moving Average with the option to make it Jurik Fractal Dimension Adaptive. This also includes a Garman-Klass-Yang-Zhang Historical Volatility Filter to reduce noise.
What is 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. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.
What is Jurik Fractal Dimension?
There is a weak and a strong way to measure the random quality of a time series.
The weak way is to use the random walk index ( RWI ). You can download it from the Omega web site. It makes the assumption that the market is moving randomly with an average distance D per move and proposes an amount the market should have changed over N bars of time. If the market has traveled less, then the action is considered random, otherwise it's considered trending.
The problem with this method is that taking the average distance is valid for a Normal (Gaussian) distribution of price activity. However, price action is rarely Normal, with large price jumps occuring much more frequently than a Normal distribution would expect. Consequently, big jumps throw the RWI way off, producing invalid results.
The strong way is to not make any assumption regarding the distribution of price changes and, instead, measure the fractal dimension of the time series. Fractal Dimension requires a lot of data to be accurate. If you are trading 30 minute bars, use a multi-chart where this indicator is running on 5 minute bars and you are trading on 30 minute bars.
What is Garman-Klass-Yang-Zhang Historical Volatility?
Yang and Zhang derived an extension to the Garman Klass historical volatility estimator that allows for opening jumps. It assumes Brownian motion with zero drift. This is currently the preferred version of open-high-low-close volatility estimator for zero drift and has an efficiency of 8 times the classic close-to-close estimator. Note that when the drift is nonzero, but instead relative large to the volatility , this estimator will tend to overestimate the volatility . The Garman-Klass-Yang-Zhang Historical Volatility calculation is as follows:
GKYZHV = sqrt((Z/n) * sum((log(open(k)/close( k-1 )))^2 + (0.5*(log(high(k)/low(k)))^2) - (2*log(2) - 1)*(log(close(k)/open(2:end)))^2))
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring

STD-Filtered, Adaptive Exponential Hull Moving Average [Loxx]STD-Filtered, Adaptive Exponential Hull Moving Average is a Kaufman Efficiency Ratio Adaptive Hull Moving Average that uses EMA instead of WMA for its computation. I've also added standard deviation stepping to further smooth the signal. Using EMA instead of WMA turns the Hull into what's called the AEHMA. You can read more about the EHMA here: eceweb1.rutgers.edu
What is the traditional Hull Moving Average?
The Hull Moving Average (HMA) attempts to minimize the lag of a traditional moving average while retaining the smoothness of the moving average line. Developed by Alan Hull in 2005, this indicator makes use of weighted moving averages to prioritize more recent values and greatly reduce lag. The resulting average is more responsive and well-suited for identifying entry points.
What is Kaufman's Efficiency Ratio?
The Efficiency Ratio (ER) was first presented by Perry Kaufman in his 1995 book ‘Smarter Trading‘. It is calculated by dividing the price change over a period by the absolute sum of the price movements that occurred to achieve that change. The resulting ratio ranges between 0 and 1 with higher values representing a more efficient or trending market.
The value of the ER ranges between 0 and 1. It has the value of 1 when prices move in the same direction for the full time over which the indicator is calculated, e.g. n bars period. It has a value of 0 when prices are unchanged over the n periods. When prices move in wide swings within the interval, the sum of the denominator becomes very large compared to the numerator and ER approaches zero.
Some uses for ER:
A qualifier for a trend following trade; a trend is considered “persistent” only when RE is above a certain value, e.g. 0.3 or 0.4 .
A filter to screen out choppy stocks/markets, where breakouts are frequently “fakeouts”.
In an adaptive trading system, helping to determine whether to apply a trend following algorithm or a mean reversion algorithm.
It is used in the calculation of Kaufman’s Adaptive Moving Average (KAMA).
How to calculate the Hull Adaptive Moving Average (HAMA)
Find Signal to Noise ratio (SNR)
Normalize SNR from 0 to 1
Calculate adaptive alphas
Apply EMAs
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types

Adaptive Deviation [Loxx]Adaptive Deviation is an educational/conceptual indicator that is a new spin on the regular old standard 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.
The green line is the Adaptive Deviation, the white line is regular Standard Deviation. This concept will be used in future indicators to further reduce noise and adapt to price volatility.
Included
Loxx's Expanded Source Types

Adaptive Rebound Line (ARL)The Adaptive Rebound Line (ARL) focuses on the rebound of price action according to the trend.
While it does not focus on showing the trend, it does help in anticipating price rebounds.
It achieves this by adapting quickly and by reducing lag.
It is recommended to use this with a trend-identifying indicator.
It was inspired by the Hull Moving Average and the KAMA.
Additional indicator show in the chart is Tide Finder Plus .

ER-Adaptive ATR [Loxx]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
You can use this indicator the same way you'd use the standard ATR.

Efficiency-Ratio-Adaptive EMA [Loxx]Efficiency ratio was invented by Perry Kaufman as a measure of volatility and as a way of making some calculations adaptive. In his adaptive moving average he uses 3 periods for calculation which makes it a bit "cryptic" and, by all means, not so simple to use. This version is simplifying the whole thing without an intention to clone the KAMA indicator--but with the intention to use the efficiency ratio for adapting the average calculations and to use only two parameters for that:
period
price
Included:
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