Steel Step Assistant: Trend Visualizer + Market Flow 1.0This is a market flow signal indicator. Flow with the market and you will find yourself in good hands.
This indicator simply gives you a signal of the RIGHT time to follow a market trend/direction. The indicator is designed with Steel Step strategy rules for determining directions.
It calculates and provides the most market direction signals within a particular period of time.
It also gives a relatively accurate signal of trend reversals. Being an indicator, it is prone to a certain extent of inaccuracy. It is programmed to provide an accurate market direction/flow to the best of its abilities.
Always remember that the Steel Step strategy does not rely on indicators to trade.
The trend visualizer is an ordinary table that shows you trends in different time frames.
This indicator can be used on all charts and markets; crypto, commodities, forex, stock, indices, etc.
It is suitable for intra-day traders.
One way of using this is to enhance your information gathering on trends in order to understand the market structure or direction better.
This indicator helps educate users on the market structure. Users can quickly break down the market into layers, analyze the layers and connect them all to understand the market as a whole. After users understand the market, users need to decide and choose a specific trend they want to trade. The basic idea is to flow with the market.
This indicator can be combined with EW theory to understand the market structure easily.
When I understand the whole market structure, it boosts my trading performance to the maximum.
According to the Steel Step strategy, this indicator is designed to show the trend "one layer" above "the current TF layer". This method has been tested to enhance accuracy. This may sound confusing to some of you. You can find educational materials about the layer logic from my Steel Step strategy.
Find the instructions on how to view signals below.
***SIGNAL GUIDE***
To view signals/set signal alerts:
- To view 15min signals, use 3min chart
- To view 1H signals, use 15min chart
A second version to include more time frame layers and trends will be published soon. Look forward to it!
Please comment below or message me if you have any questions. Enjoy!
*Nobody should use this indicator as a confirmation signal for entry/exit for your trades. Please message me on how to use this indicator correctly. This indicator was designed to be used in conjunction with my Steel Step strategy, hence the name.
ابحث في النصوص البرمجية عن "trendline"
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.
Swing Counter [theEccentricTrader]█ OVERVIEW
This indicator counts the number of confirmed swing high and swing low scenarios on any given candlestick chart and displays the statistics in a table, which can be repositioned and resized at the user's discretion.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a high price equal to or above the price it opened.
• A red candle is one that closes with a low price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices (Basic)
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Peak and Trough Prices (Advanced)
• The advanced peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the highest preceding green candle high price, depending on which is higher.
• The advanced trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the lowest preceding red candle low price, depending on which is lower.
Green and Red Peaks and Troughs
• A green peak is one that derives its price from the green candle/s that constitute the swing high.
• A red peak is one that derives its price from the red candle that completes the swing high.
• A green trough is one that derives its price from the green candle that completes the swing low.
• A red trough is one that derives its price from the red candle/s that constitute the swing low.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Upper Trends
• A return line uptrend is formed when the current peak price is higher than the preceding peak price.
• A downtrend is formed when the current peak price is lower than the preceding peak price.
• A double-top is formed when the current peak price is equal to the preceding peak price.
Lower Trends
• An uptrend is formed when the current trough price is higher than the preceding trough price.
• A return line downtrend is formed when the current trough price is lower than the preceding trough price.
• A double-bottom is formed when the current trough price is equal to the preceding trough price.
█ FEATURES
Inputs
• Start Date
• End Date
• Position
• Text Size
• Show Sample Period
• Show Plots
• Show Lines
Table
The table is colour coded, consists of three columns and nine rows. Blue cells denote neutral scenarios, green cells denote return line uptrend and uptrend scenarios, and red cells denote downtrend and return line downtrend scenarios.
The swing scenarios are listed in the first column with their corresponding total counts to the right, in the second column. The last row in column one, row nine, displays the sample period which can be adjusted or hidden via indicator settings.
Rows three and four in the third column of the table display the total higher peaks and higher troughs as percentages of total peaks and troughs, respectively. Rows five and six in the third column display the total lower peaks and lower troughs as percentages of total peaks and troughs, respectively. And rows seven and eight display the total double-top peaks and double-bottom troughs as percentages of total peaks and troughs, respectively.
Plots
I have added plots as a visual aid to the swing scenarios listed in the table. Green up-arrows with ‘HP’ denote higher peaks, while green up-arrows with ‘HT’ denote higher troughs. Red down-arrows with ‘LP’ denote higher peaks, while red down-arrows with ‘LT’ denote lower troughs. Similarly, blue diamonds with ‘DT’ denote double-top peaks and blue diamonds with ‘DB’ denote double-bottom troughs. These plots can be hidden via indicator settings.
Lines
I have also added green and red trendlines as a further visual aid to the swing scenarios listed in the table. Green lines denote return line uptrends (higher peaks) and uptrends (higher troughs), while red lines denote downtrends (lower peaks) and return line downtrends (lower troughs). These lines can be hidden via indicator settings.
█ HOW TO USE
This indicator is intended for research purposes and strategy development. I hope it will be useful in helping to gain a better understanding of the underlying dynamics at play on any given market and timeframe. It can, for example, give you an idea of any inherent biases such as a greater proportion of higher peaks to lower peaks. Or a greater proportion of higher troughs to lower troughs. Such information can be very useful when conducting top down analysis across multiple timeframes, or considering entry and exit methods.
What I find most fascinating about this logic, is that the number of swing highs and swing lows will always find equilibrium on each new complete wave cycle. If for example the chart begins with a swing high and ends with a swing low there will be an equal number of swing highs to swing lows. If the chart starts with a swing high and ends with a swing high there will be a difference of one between the two total values until another swing low is formed to complete the wave cycle sequence that began at start of the chart. Almost as if it was a fundamental truth of price action, although quite common sensical in many respects. As they say, what goes up must come down.
The objective logic for swing highs and swing lows I hope will form somewhat of a foundational building block for traders, researchers and developers alike. Not only does it facilitate the objective study of swing highs and swing lows it also facilitates that of ranges, trends, double trends, multi-part trends and patterns. The logic can also be used for objective anchor points. Concepts I will introduce and develop further in future publications.
█ LIMITATIONS
Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green. You can avoid this problem by utilising the sample period filter.
The green and red candle calculations are based solely on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with. Alternatively, you can replace the scenarios with your own logic to account for the gap anomalies, if you are feeling up to the challenge.
The sample size will be limited to your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000. If upgrading is currently not an option, you can always keep a rolling tally of the statistics in an excel spreadsheet or something of the like.
█ NOTES
I feel it important to address the mention of advanced peak and trough price logic. While I have introduced the concept, I have not included the logic in my script for a number of reasons. The most pertinent of which being the amount of extra work I would have to do to include it in a public release versus the actual difference it would make to the statistics. Based on my experience, there are actually only a small number of cases where the advanced peak and trough prices are different from the basic peak and trough prices. And with adequate multi-timeframe analysis any high or low prices that are not captured using basic peak and trough price logic on any given time frame, will no doubt be captured on a higher timeframe. See the example below on the 1H FOREXCOM:USDJPY chart (Figure 1), where the basic peak price logic denoted by the indicator plot does not capture what would be the advanced peak price, but on the 2H FOREXCOM:USDJPY chart (Figure 2), the basic peak logic does capture the advanced peak price from the 1H timeframe.
Figure 1.
Figure 2.
█ RAMBLINGS
“Never was there an age that placed economic interests higher than does our own. Never was the need of a scientific foundation for economic affairs felt more generally or more acutely. And never was the ability of practical men to utilize the achievements of science, in all fields of human activity, greater than in our day. If practical men, therefore, rely wholly on their own experience, and disregard our science in its present state of development, it cannot be due to a lack of serious interest or ability on their part. Nor can their disregard be the result of a haughty rejection of the deeper insight a true science would give into the circumstances and relationships determining the outcome of their activity. The cause of such remarkable indifference must not be sought elsewhere than in the present state of our science itself, in the sterility of all past endeavours to find its empirical foundations.” (Menger, 1871, p.45).
█ BIBLIOGRAPHY
Menger, C. (1871) Principles of Economics. Reprint, Auburn, Alabama: Ludwig Von Mises Institute: 2007.
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
Rainbow Collection - VioletMoving averages come in all shapes and types. The most basic type is the simple moving average which is simply the sum divided by the quantity. Therefore, the simple moving average is the sum of the values divided by their number.
In technical analysis, you generally use moving averages to understand the underlying trend and to find trading signals. In the case of the Violet indicator, we are using a Hull moving average which is a special variation based on different weights to minimize lag.
The Violet indicator is therefore used as follows:
* A bullish signal is generated whenever the close price surpasses the 20-period Hull moving average while the previous close prices from periods were all below their respective Hull moving average of the period.
*A bearish signal is generated whenever the close price breaks the 20-period Hull moving average while the previous close prices from periods were all above their respective Hull moving average of the period.
The aim of the Violet indicator is to capture reversals as early as possible through a combination of lagged conditions based on the Fibonacci sequence.
Steel Step Assistant: Trend VisualizerSpecial thanks to Turicumo and Psychil for helping me write the code, both from my group.
Disclaimer: Nobody should use this indicator as a confirmation signal for entry/exit for your trades. Please message me on how to use this indicator correctly. This indicator was designed to be used in conjunction with my Steel Step strategy, hence the name.
This indicator simply gives a quick outlook of the market.
This indicator is an ordinary table that shows you the trends.
The default settings produce directions that are very similar to what I use for my strategy. You can change the settings as desired.
This indicator can be used on all charts and markets; crypto, commodities, forex, stock, indices, etc.
It is suitable for intra-day traders, as well as HTF traders.
One way of using this is to enhance your information gathering on trends in order to understand the market structure or direction better.
This indicator educates users on the market structure. Users can quickly break down the market into layers, analyze the layers and connect them all to understand the market as a whole. After users understand the market, users need to decide and choose a specific trend they want to trade. The basic idea is to flow with the market.
This indicator can be combined with EW theory to understand the market structure easily.
When I understand the whole market structure, it boosts my trading performance to the maximum.
Please comment below or message me if you have any questions. Enjoy!
Shorting when Bollinger Band Above Price with RSI (by Coinrule)The Bollinger Bands are among the most famous and widely used indicators. A Bollinger Band is a technical analysis tool defined by a set of trendlines plotted two standard deviations (positively and negatively) away from a simple moving average ( SMA ) of a security's price, but which can be adjusted to user preferences. They can suggest when an asset is oversold or overbought in the short term, thus providing the best time for buying and selling it.
The relative strength index ( RSI ) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security. The RSI can do more than point to overbought and oversold securities. It can also indicate securities primed for a trend reversal or corrective pullback in price. It can signal when to buy and sell. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
The short order is placed on assets that present strong momentum when it's more likely that it is about to reverse. The rule strategy places and closes the order when the following conditions are met:
ENTRY
The closing price is greater than the upper standard deviation of the Bollinger Bands
The RSI is less than 70.
EXIT
The trade is closed when the RSI is less than 70
The lower standard deviation of the Bollinger Band is less than the closing price.
This strategy was backtested from the beginning of 2022 to capture how this strategy would perform in a bear market.
The strategy assumes each order to trade 70% of the available capital to make the results more realistic. A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange by volume.
Faytterro Market Structerethis indicator creates the market structure with a little delay but perfectly. each zigzag is always drawn from highest to lowest. It also signals when the market structure is broken. signals fade over time.
The table above shows the percentage distance of the price from the last high and the last low.
zigzags are painted green when making higher peaks, while lower peaks are considered downtrends and are painted red. In fact, the indicator is quite simple to understand and use.
"length" is used to change the frequency of the signal.
"go to past" is used to see historical data.
Please review the examples:
AutoBF by Tren10xBroadening Formation is a powerful technical analysis tool that is characterized by two converging trendlines that widen over time. This pattern typically signals a period of volatility and uncertainty in the market and can indicate a potential reversal in trend direction.
This script uses advanced algorithms to automatically detect and plot broadening formations on your chart, making it easy to identify these patterns and potentially profit from them, all while saving you time from drawing them yourself. With customizable settings, this indicator is a must-have tool for any trader looking to take advantage of this powerful chart pattern.
Features:
● Automatically detects and plots broadening formations on any chart within TradingView
● Customizable settings for greater flexibility and control
● Choose to draw your broadening formation from the outside bar to the "Previous Candle" or "Compound Candle" aka to the previous lowest/highest candle within the outside bar.
● Clear visual display of broadening formations and easy identification
● Compatible with all markets and timeframes, from stocks and forex to cryptocurrencies and commodities
● Designed for both novice and experienced traders, with user-friendly interface and comprehensive documentation
● By default, the year will look back 75 years, the quarter will look back 20 years, the month will look back 7 years, the week will look back 3 years, and the day will look back 90 days. However, you now have the ability to change these at your will.
● Added the ability to enable Broadening Formations on the 6 Month, 2 Month, 2 Week, and 2 Day charts.
● ALERTS! Receive timely notifications when the price breaches or activates a broadening formation.
All Timeframes available:
● Year
● 6 Month
● Quarter
● 2 Month
● Month
● 2 Week
● Week
● 2 Day
● Day
tinyurl.com
Predicting future outcomes is impossible. Nobody knows what the future will bring. With this Broadening Formation Indicator, you will have the edge you need to identify potentially profitable trading opportunities and make more informed decisions in the markets.
Regards,
Tren10x
Disclaimer: It is essential to note that returns on investments are not guaranteed, and investors should exercise prudence in conducting thorough due diligence before making any investment decisions
I would like to express my gratitude to my wife for her meticulous testing and insightful contributions throughout the course of this project. Additionally, I extend my appreciation to the esteemed Alpha Pack Group, whose exceptional acumen and investment expertise have been instrumental in the success of this endeavor.
Broadening Formations [TFO]This indicator highlights deviations from broadening formations (or megaphone patterns). Deviations from broadening ranges can often foreshadow reversals, especially in consolidation phases. These deviations are highlighted via trendlines that change color when tested, and also have the option to be alerted.
These broadening formations are heavily used with "The Strat" and can add confluence when looking for reversals within higher timeframe points of interest.
GKD-B Baseline [Loxx]Giga Kaleidoscope Baseline is a Baseline module included in Loxx's "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 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 (such as "Baseline" shown on the chart above)
3. Confirmation 1 - a technical indicator used to identify trend. This should agree with the "Baseline"
4. Confirmation 2 - a technical indicator used to identify trend. This filters/verifies the trend identified by "Baseline" and "Confirmation 1"
5. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown.
6. Exit - a technical indicator used to determine when trend is exhausted.
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 module (Confirmation 1/2, Numbers 3 and 4 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 5 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 6 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-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 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 as shown on the chart above
Volatility/Volume: Jurik Volty
Confirmation 1: Vortex
Confirmation 2: 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 understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Now that you have a general understanding of the NNFX algorithm and the GKD trading system. let's go over what's inside the GKD-B Baseline itself.
GKD Baseline Special Features and Notable Inputs
GKD Baseline v1.0 includes 63 different moving averages:
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:
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.
Exotic Triggers
This version of Baseline allows the user to select from exotic or source triggers. An exotic trigger determines trend by either slope or some other mechanism that is special to each moving average. A source trigger is one of 32 different source types from Loxx's Exotic Source Types. You can read about these source types here:
Volatility Goldie Locks Zone
This volatility filter is the standard first pass filter that is used for all NNFX systems despite the additional volatility/volume filter used in step 5. For this filter, price must fall into a range of maximum and minimum values calculated using multiples of volatility. Unlike the standard NNFX systems, this version of volatility filtering is separated from the core Baseline and uses it's own moving average with Loxx's Exotic Source Types. The green and red dots at the top of the chart denote whether a candle qualifies for a either or long or short respectively. The green and red triangles at the bottom of the chart denote whether the trigger has crossed up or down and qualifies inside the Goldie Locks zone. White coloring of the Goldie Locks Zone mean line is where volatility is too low to trade.
Volatility Types Included
v1.0 Included Volatility
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility .
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility .
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility .
One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility . That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator consists of using the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e. it assumes that the underlying asset follows a GBM process with zero drift. Therefore the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1))
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by θ.
θavg(var ;M) + (1 − θ) avg (var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L)
Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg (var; N) against avg (var; M) - avg (var; N) and using the resulting beta estimate as θ.
Average True Range
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.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Additional features will be added in future releases.
This indicator is only available to ALGX Trading VIP group members . You can see the Author's Instructions below to get more information on how to get access.
Cosmic Rays LiteCosmic Rays Lite ( CR Lite ) draws dynamic non-repainting trendlines and helps
⭐ see small and large trends
⭐ predict reversals with high accuracy
⭐ spot bullish and bearish breakouts
⭐ time the end of breakouts
👀 HOW IT WORKS
Cosmic Rays Lite compresses 14 moving averages of varying type and length into 7 plots and filters those down into the closest support and resistance lines. When the price moves above final resistance or below final support, additional "ripple" lines are appended using aggregate standard deviation multiples. The visibility of each line is toggled depending on its stability and its proximity to the price.
📗 HOW TO USE IT
Cosmic Rays Lite is designed specifically to be visible only during the most relevant times so as not to clutter your main setup. Likewise you will know to pay attention when a certain Cosmic Rays Lite level is shown. The main strategies are:
long when the price reverses off of the support line and short when the price reverses off of the resistance line
long when the price breaks above a support or resistance line and short when it breaks below
long when it reaches a support ripple line and short when the price reaches a resistance ripple line
use in combination with other indicators to spot chart patterns such as triangles (see chart)
Trend Line Adam Moradi v1 (Tutorial Content)
The Pine Script strategy that plots pivot points and trend lines on a chart. The strategy allows the user to specify the period for calculating pivot points and the number of pivot points to be used for generating trend lines. The user can also specify different colors for the up and down trend lines.
The script starts by defining the input parameters for the strategy and then calculates the pivot high and pivot low values using the pivothigh() and pivotlow() functions. It then stores the pivot points in two arrays called trend_top_values and trend_bottom_values. The script also has two arrays called trend_top_position and trend_bottom_position which store the positions of the pivot points.
The script then defines a function called add_to_array() which takes in three arguments: apointer1, apointer2, and val. This function adds val to the beginning of the array pointed to by apointer1, and adds bar_index to the beginning of the array pointed to by apointer2. It then removes the last element from both arrays.
The script then checks if a pivot high or pivot low value has been calculated, and if so, it adds the value and its position to the appropriate arrays using the add_to_array() function.
Next, the script defines two arrays called bottom_lines and top_lines which will be used to store trend lines. It also defines a variable called starttime which is set to the current time.
The script then enters a loop to calculate and plot the trend lines. It first deletes any existing trend lines from the chart. It then enters two nested loops which iterate over the pivot points stored in the trend_bottom_values and trend_top_values arrays. For each pair of pivot points, the script calculates the slope of the line connecting them and checks if the line is a valid trend line by iterating over the price bars between the two pivot points and checking if the line is above or below the close price of each bar. If the line is found to be a valid trend line, it is plotted on the chart using the line.new() function.
Finally, the script colors the trend lines using the colors specified by the user.
Tutorial Content
'PivotPointNumber' is an input parameter for the script that specifies the number of pivot points to consider when calculating the trend lines. The value of 'PivotPointNumber' is set by the user when they configure the script. It is used to determine the size of the arrays that store the values and positions of the pivot points, as well as the number of pivot points to loop through when calculating the trend lines.
'up_trend_color' is an input parameter for the script that specifies the color to use for drawing the trend lines that are determined to be upward trends. The value of 'up_trend_color' is set by the user when they configure the script and is passed to the color parameter of the line.new() function when drawing the upward trend lines. It determines the visual appearance of the upward trend lines on the chart.
'down_trend_color' is an input parameter for the script that specifies the color to use for drawing the trend lines that are determined to be downward trends. The value of 'down_trend_color' is set by the user when they configure the script and is passed to the color parameter of the line.new() function when drawing the downward trend lines. It determines the visual appearance of the downward trend lines on the chart.
'pivothigh' is a variable in the script that stores the value of the pivot high point. It is calculated using the pivothigh() function, which returns the highest high over a specified number of bars. The value of 'pivothigh' is used in the calculation of the trend lines.
'pivotlow' is a variable in the script that stores the value of the pivot low point. It is calculated using the pivotlow() function, which returns the lowest low over a specified number of bars. The value of 'pivotlow' is used in the calculation of the trend lines.
'trend_top_values' is an array in the script that stores the values of the pivot points that are determined to be at the top of the trend. These are the pivot points that are used to calculate the upward trend lines.
'trend_top_position' is an array in the script that stores the positions (i.e., bar indices) of the pivot points that are stored in the 'trend_top_values' array. These positions correspond to the locations of the pivot points on the chart.
'trend_bottom_values' is an array in the script that stores the values of the pivot points that are determined to be at the bottom of the trend. These are the pivot points that are used to calculate the downward trend lines.
'trend_bottom_position' is an array in the script that stores the positions (i.e., bar indices) of the pivot points that are stored in the 'trend_bottom_values' array. These positions correspond to the locations of the pivot points on the chart.
apointer1 and apointer2 are variables used in the add_to_array() function, which is defined in the script. They are both pointers to arrays, meaning that they hold the memory addresses of the arrays rather than the arrays themselves. They are used to manipulate the arrays by adding new elements to the beginning of the arrays and removing elements from the end of the arrays.
apointer1 is a pointer to an array of floating-point values, while apointer2 is a pointer to an array of integers. The specific arrays that they point to depend on the arguments passed to the add_to_array() function when it is called. For example, if add_to_array(trend_top_values, trend_top_posisiton, pivothigh) is called, then apointer1 would point to the tval array and apointer2 would point to the tpos array.
'bottom_lines' (short for "Bottom Lines") is an array in the script that stores the line objects for the downward trend lines that are drawn on the chart. Each element of the array corresponds to a different trend line.
'top_lines' (short for "Top Lines") is an array in the script that stores the line objects for the upward trend lines that are drawn on the chart. Each element of the array corresponds to a different trend line.
Both 'bottom_lines' and 'top_lines' are arrays of type "line", which is a data type in PineScript that represents a line drawn on a chart. The line objects are created using the line.new() function and are used to draw the trend lines on the chart. The variables are used to store the line objects so that they can be manipulated and deleted later in the script.
Loops
maxline is a variable in the script that specifies the maximum number of trend lines that can be drawn on the chart. It is used to determine the size of the bottom_lines and top_lines arrays, which store the line objects for the trend lines.
The value of maxline is set to 3 at the beginning of the script, meaning that at most 3 trend lines can be drawn on the chart at a time. This value can be changed by the user if desired by modifying the assignment statement "maxline = 3".
'count_line_low' (short for "Count Line Low") is a variable in the script that keeps track of the number of downward trend lines that have been drawn on the chart. It is used to ensure that the maximum number of trend lines (as specified by the maxline variable) is not exceeded.
'count_line_high' (short for "Count Line High") is a variable in the script that keeps track of the number of upward trend lines that have been drawn on the chart. It is used to ensure that the maximum number of trend lines (as specified by the maxline variable) is not exceeded.
Both 'count_line_low' and 'count_line_high' are initialized to 0 at the beginning of the script and are incremented each time a new trend line is drawn. If either variable exceeds the value of maxline, then no more trend lines are drawn.
'pivot1', 'up_val1', 'up_val2', up1, and up2 are variables used in the loop that calculates the downward trend lines in the script. They are used to store intermediate values during the calculation process.
'pivot1' is a loop variable that is used to iterate through the pivot points (stored in the trend_bottom_values and trend_bottom_position arrays) that are being considered for use in the trend line calculation.
'up_val1' and 'up_val2' are variables that store the values of the pivot points that are used to calculate the downward trend line.
up1 and up2 are variables that store the positions (i.e., bar indices) of the pivot points that are stored in 'up_val1' and 'up_val2', respectively. These positions correspond to the locations of the pivot points on the chart.
'value1' and 'value2' are variables that are used to store the values of the pivot points that are being compared in the loop that calculates the trend lines in the script. They are used to determine whether a trend line can be drawn between the two pivot points.
For example, if 'value1' is the value of a pivot point at the top of the trend and 'value2' is the value of a pivot point at the bottom of the trend, then a trend line can be drawn between the two points if 'value1' is greater than 'value2'. The values of 'value1' and 'value2' are used in the calculation of the slope and intercept of the trend line.
'position1' and 'position2' are variables that are used to store the positions (i.e., bar indices) of the pivot points that are being compared in the loop that calculates the trend lines in the script. They are used to determine the distance between the pivot points, which is necessary for calculating the slope of the trend line.
For example, if 'position1' is the position of a pivot point at the top of the trend and 'position2' is the position of a pivot point at the bottom of the trend, then the distance between the two points is given by 'position1' - 'position2'. This distance is used in the calculation of the slope of the trend line.
'different', 'high_line', 'low_location', 'low_value', and 'valid' are variables that are used in the loop that calculates the downward trend lines in the script. They are used to store intermediate values during the calculation process.
'different' is a variable that stores the slope of the downward trend line being calculated. It is calculated as the difference in value between the two pivot points (stored in up_val1 and up_val2) divided by the distance between the pivot points (calculated using their positions, stored in up1 and up2).
'high_line' is a variable that stores the current value of the trend line being calculated at a given point in the loop. It is initialized to the value of the second pivot point (stored in up_val2) and is updated on each iteration of the loop using the value of different.
'low_location' is a variable that stores the position (i.e., bar_index) on the chart of the point where the trend line being calculated first touches the low price. It is initialized to the position of the second pivot point (stored in up2) and is updated on each iteration of the loop if the trend line touches a lower low.
'low_value' is a variable that stores the value of the trend line at the point where it first touches the low price. It is initialized to the value of the second pivot point (stored in up_val2) and is updated on each iteration of the loop if the trend line touches a lower low.
'valid' is a Boolean variable that is used to indicate whether the trend line being calculated is valid. It is initialized to true and is set to false if the trend line does not pass through all the lows between the pivot points. If valid is still true after the loop has completed, then the trend line is considered valid and is drawn on the chart.
d_value1, d_value2, d_position1, and d_position2 are variables that are used in the loop that calculates the upward trend lines in the script. They are used to store intermediate values during the calculation process.
d_value1 and d_value2 are variables that store the values of the pivot points that are used to calculate the upward trend line.
d_position1 and d_position2 are variables that store the positions (i.e., bar indices) of the pivot points that are stored in d_value1 and d_value2, respectively. These positions correspond to the locations of the pivot points on the chart.
The variables d_value1, d_value2, d_position1, and d_position2 have the same function as the variables uv1, uv2, up1, and up2, respectively, but for the calculation of the upward trend lines rather than the downward trend lines. They are used in a similar way to store intermediate values during the calculation process.
thank you.
Honeybridge WickFill & Momentum Shift IndicatorAs the creator of this script, I am proud to introduce the "Honeybridge WickFill & Momentum Shift Indicator," a powerful tool for traders looking to capitalize on market opportunities using these two proven strategies.
WICK FILLS
First, let's take a closer look at the WickFill method. In financial markets, candlestick charts are a popular way to visualize the price movement of a security, derivative, or currency over a specific time period. Each candlestick on the chart is composed of a real body and shadows, with the top of the upper shadow representing the highest price paid during the time period, and the bottom of the lower shadow representing the lowest price paid.
The WickFill method involves placing market orders at the closed price indicated by the candlestick. This means that if a trader using this method sees a long upper wick on a bullish candlestick chart, they may place a buy market order at the closed price of the candlestick with the take profit target at the candlestick high price (the highest price paid during the time period), in the belief that the price is likely to rise. Similarly, if they see a long lower wick on a bearish candle, they may place a sell market order at the closed price of the candlestick with the take profit target at the candlestick low price (the lowest price paid during the time period), in the belief that the price is likely to fall.
The idea behind the WickFill method is that the wicks of candlestick chart patterns can provide valuable information about the price action of a security and the sentiment of market participants. By placing market orders at the prices indicated by the candlesticks close, traders using this method hope to capitalize on potential price movements and maximize their returns.
The image below highlights two Wick Fill opportunities. A signal will be provided at the candle close that says: 'Sell WF' or 'Buy WF'.
--------------
MOMENTUM SHIFT REVERSAL
Now, let's turn to the Momentum Shift method. As the name suggests, this method involves identifying changes in the momentum of the price of a security. Traders who use this method are looking to capitalize on shifts in the strength or direction of the price momentum.
There are various ways to trade based on momentum shifts. For example, a trader may look for a security whose price is trending strongly in one direction and then look for a momentum shift that signals a change in the trend. They may then enter a trade in the direction of the new trend, hoping to ride the momentum of the price movement. Alternatively, a trader may look for a security whose price is moving in a range and then look for a momentum shift that signals a breakout from the range. They may then enter a trade in the direction of the breakout, hoping to capitalize on the momentum of the price movement.
--------------
WICK REJECTION REVERSALS
A second Momentum Shift method is the concept of wick rejection reversal trading opportunities. This is a powerful technique for traders looking to capitalize on market opportunities.
So, what exactly is a wick rejection reversal opportunity? Essentially, it is a situation in which the price of a security rejects a wick (or shadow or tail) of a candlestick chart pattern and then reverses direction. For example, if the price is trending upwards and then encounters resistance at a certain level, it may create a long upper wick on a candlestick chart. If the price then falls back below the level of resistance and continues trending downwards, this could be considered a wick rejection reversal opportunity.
Traders can use the Indicator and signals provided to identify wick rejection reversal opportunities by defining certain conditions. For example, the code includes conditions for identifying a "sell reversal" based on the presence of a green candle with a long upper wick, and an RSI value above a certain threshold. This type of setup may indicate that the price is rejecting the resistance represented by the long upper wick and is likely to continue trending downwards.
Similarly, the code includes conditions for identifying a "buy reversal" based on the presence of a red candle with a long lower wick, and an RSI value below a certain threshold. This type of setup may indicate that the price is rejecting the support represented by the long lower wick and is likely to continue trending upwards.
Traders can find wick rejection reversal opportunities particularly beneficial for several reasons. First, these opportunities can provide clear entry and exit points for trades, which can help traders manage risk and maximize their returns. By identifying a specific level of resistance or support that has been rejected by the price, traders can have a clear idea of where to place their orders and where to set their stop-losses.
Second, wick rejection reversal opportunities can be a reliable indicator of market sentiment and direction. By considering the wicks of candlestick chart patterns, traders can gain a deeper understanding of the forces at work in the market and how market participants are reacting to them. This can help traders make more informed decisions about when to enter or exit trades.
Finally, wick rejection reversal opportunities can be found in a variety of market conditions and across different financial instruments. Whether the market is trending, range-bound, or volatile, traders can use this indicator provided to identify wick rejection reversal opportunities and capitalize on them.
In conclusion, wick rejection reversal opportunities are a valuable technique for traders looking to capitalize on market opportunities and improve their returns. By using the indicator provided and considering the wicks of candlestick chart patterns, traders can identify clear entry and exit points, understand market sentiment, and trade across different market conditions and instruments.
The image below depicts two sell opportunities, the top left is a Momentum Shift example and the top right is a Wick Rejection example. A signal will be shown on the chart at the candle close that says: 'Sell R' or 'Buy R'.
--------------
CONCLUSION
So why might traders find the WickFill and Momentum Shift methods useful? There are several reasons. First, these methods can help traders identify potential trade opportunities that may not be immediately apparent from a simple analysis of price trends or chart patterns. By considering the wicks and momentum of a security's price movement, traders can gain a more nuanced understanding of the forces at work in the market and make more informed decisions about when to enter or exit trades.
Second, the WickFill and Momentum Shift methods can be used in conjunction with other technical analysis tools and techniques. For example, traders may use moving averages, oscillators, and trendlines to help confirm the presence of a WickFill or Momentum Shift opportunity. This can help traders increase the reliability and profitability of their trades.
Finally, the WickFill and Momentum Shift methods can be applied to a wide range of financial instruments, including stocks, forex, futures, and more. This versatility makes them useful for traders with diverse investment portfolios and strategies.
Overall, the WickFill and Momentum Shift methods are powerful tools for traders looking to capitalize on market opportunities and improve their returns. By considering the wicks and momentum of a security's price movement, traders can find profitable trading opportunities.
------------
FINAL COMMENT
Just like any other indicator or strategy out there, please consider the timeframe and asset that you are using this indicator with. Higher timeframe price action is more reliable than lower timeframe price action. For example, the 4H and Daily timeframes will provide more reliable signals than the 5m timeframe. With regards to assets, the indicator works extremely well with Forex pairs and Commodities, such as Gold.
I hope you enjoy the indicator.
Cosmic Channel LiteCosmic Channel Lite ( CC Lite) draws dynamic non-repainting trendlines and helps
⭐ know when a breakout is about to begin
⭐ predict the position and timing of the next swing reversal
⭐ predict sudden changes in volatility
⭐ recognize whether the price is in bearish or bullish territory
👀 HOW IT WORKS
Cosmic Channel Lite draws a dynamic channel consisting of a support line, basis line and resistance line. These are calculated by applying the Reduced Median Method to groups of moving averages of different type over several periods each, effectively taking 20 data points and reducing them to 3. In between, 6 internal levels are left to give context inside the channel with stable levels, the extremes of which help highlight the SR lines (see chart). The basis line color is determined by its smoothed angle with positive angles in green and negative in purple. The aim of this indicator is to provide a consistent and generic price context that works out-of-the-box and accordingly the settings have been stripped to the bare minimum with no need to continually adjust them.
📗 HOW TO USE IT
The Cosmic Channel Lite plots are meant to be used as a guide for entering and exiting positions and setting stop-loss and take profit levels. The indicator is deemed effective for any particular timeframe as long as the price stays within the maximum bounds of the indicator's plots. For this reason it is recommended to use Cosmic Channel Lite in a multi-chart layout where each chart has a different timeframe. The 5 primary strategies are:
long when the price reverses off of the support line and short when the price reverses off of the resistance line
long when the support line is highlighted and short when the resistance line is highlighted
long when the price breaks above the resistance line and short when the price breaks below the support line
long when the price moves above the basis line after being below it for a prolonged period and visa-versa (short when the price moves below the basis line)
long/short in the direction the price takes after a stable level ends
🔔 SMART ALERTS
Get notified at the most critical times by settings just one alert. Simply select CC Lite and Any alert() function call as the conditions when creating an alert and you will be tipped-off on bar-close as follows:
R─ (resistance line is highlighted)
S─ (support line is highlighted)
For example, an alert such as CC Lite 6h R─ would mean that during the last 6-hour bar the resistance line has been highlighted. The highlight lasts at least 15 bars from the first highlight bar regardless of price action.
True Range Adjusted Exponential Moving Average [CC]The True Range Adjusted Exponential Moving Average was created by Vitali Apirine (Stocks and Commodities Jan 2023 pgs 22-27) and this is the latest indicator in his EMA variation series. He has been tweaking the traditional EMA formula using various methods and this indicator of course uses the True Range indicator. The way that this indicator works is that it uses a stochastic of the True Range vs its highest and lowest values over a fixed length to create a multiple which increases as the True Range rises to its highest level and decreases as the True Range falls. This in turn will adjust the Ema to rise or fall depending on the underlying True Range. As with all of my indicators, I have color coded it to turn green when it detects a buy signal or turn red when it detects a sell signal. Darker colors mean it is a very strong signal and let me know if you find any settings that work well overall vs the default settings.
Let me know if you would like me to publish any other scripts that you recommend!
Theory Affinity TrendlinesThis indicator is perfect for traders who want to identify trend lines on a chart. It draws higher low uptrends and lower high downtrends, making it easy to see where the trend is going. You can also customize the settings to fit your needs, making it the perfect tool for your trading arsenal.
With this new tool, you can easily customize your experience to get the most out of your trading and analysis. With options like max lines, strength multiplier, pivot plots/text, and more, you can easily create the perfect trading analysis environment.
So why wait? Try it out today!
Leave feedback and let me know what you think.
// ############################################################################################## Input Descriptions
Pivot Left ----------------- look left n bars
Pivot Right ---------------- look right n bars
Strength ------------------- Pivot multiplier (Higher = Wider Trend lines)
Max Lines ------------------ Number of lines for each Uptrend and Downtrend
Structure Text ------------ Show HH, LL, etc. on chart
Structure Markers -------- Dots at the Pivot Highs and Lows
Plots ------------------------ Draw a line at Pivot Highs and Lows
Last Up Width ------------- Width of the current Uptrend line
Historical Up Width ------ Width of previous Uptrend lines
Last Down Width --------- Width of the current Downtrend lines
Historical Down Width --- Width of previous Downtrend lines
Line Offset ---------------- Shift trend lines right or left
* Lines may or may not "repaint". For use to identify trends that are more than likely already established and to identify trend line breaks.
Immediate Trend - VHXIMMEDIATE TREND - VULNERABLE_HUMAN_X
This indicator is used to identify the immediate trend in the market.
When a Short Term High (STH) is engulfed and closed above, we consider that as a bullish trend.
And Similarly, when a Short Term Low (STL) is engulfed and closed below, we consider that as a bullish trend.
STH - A candle that is higher than the one candle towards it's left and one candle towards it's right.
STL - A candle that is lower than the one candle towards it's left and one candle towards it's right.
HOW TO USE:
1. Do not take trades purely based on the immediate trend showcased by the indicator. Rather, use them as confluence with your trading strategy.
2. When you are expecting price to reverse at your point of interest (Denamd/Supply zone), this indicator can help you predict the reversal by showcasing the current trend.
3. Using this indicator you can travel the trend as long as there is a change of trend predicted by this indicator.
Trend line & pivot level
This script can plot pivot levels and trend lines that are haven't broke out.
In the setting, left and right means how to get the pivot. the pivot will be gotten based on the left candles and the right candles. boxes will be auto deleted after the box was broke.
Important: This is just a beta version, if you find some bug with using this script. Don't hesitate to contact me.
What the future version will have? Might be pattern scanner, multi trend line, levels in different time frame, break out alert, or better key level algorithm. Depends on when I have free time.
Cosmic GravityCosmic Gravity draws dynamic non-repainting trendlines and helps
⭐ know when to scalp
⭐ predict the position and timing of the next major reversal
⭐ predict sudden changes in volatility
⭐ recognize if the trend is bearish or bullish
👀 HOW IT WORKS
Cosmic Gravity draws a dynamic channel consisting of a basis line and several support and resistance levels for low/medium/high volatility situations, as defined by the Inner Channel and 2 Outer Channel plots respectively. The script achieves this by reducing a large number of select moving averages, their multiples, and other trend levels into a single basis line and deriving the remaining plots off of it using ATR and probability-constant multiples. The basis line color is determined by its smoothed vector similar to how our Cosmic Vector indicator paints its plot. The aim of this indicator is to provide a consistent and generic price context that works out-of-the-box; accordingly a single static average period is used throughout and the settings have been stripped to the bare minimum with no need to ever update them.
📗 HOW TO USE IT
Cosmic Gravity's channel levels are meant to be used as a guide for entering and exiting positions and setting stop-loss and take profit levels. The indicator is deemed effective for any particular timeframe as long as the price stays within the maximum bounds of the indicator's plots. For this reason it is recommended to use Cosmic Gravity in a multi-chart layout where each chart has a different timeframe. The 5 primary strategies are:
long when the price reverses off of an Outer Channel support level and short when the price reverses off of an Outer Channel resistance level
long when the price crosses above the basis line after being below it for a prolonged period and vice-versa (short when the price trend moves below the basis line)
long when the basis line color turns blue after being pink for a prolonged period and visa-versa (short when the basis line color turns pink)
long/short in the direction the price takes when it goes outside the Magnetic Gravity channel when this channel is in a tight squeeze
scalp as the price bounces between the Inner Channel levels (do this only while the price is contained inside the Inner Channel )
🔔 SMART ALERTS
Get notified at the most critical times with a single alert. Simply select Cosmic Gravity - Any alert() function call as the condition when creating an alert and you will be tipped-off on bar-close as follows:
RR↘ (price close crossed below Outer Channel R6 plot)
RR↗ (price high crossed above Outer Channel R6 plot)
R└ (price low entered R channel from above)
R┘ (price high exited R channel from above)
R┐ (price high exited R channel from below)
R┌ (price high entered R channel from below)
B↘ (price high crossed below Basis plot)
B↗ (price low crossed above Basis plot)
B╮ ( Basis vector turned negative)
B╯ ( Basis vector turned positive)
S└ (price low entered S channel from above)
S┘ (price low exited S channel from above)
S┐ (price low exited S channel from below)
S┌ (price high entered S channel from below)
SS↘ (price low crossed below Outer Channel S6 plot)
SS↗ (price close crossed above Outer Channel S6 plot)
For example, an alert such as Cosmic Gravity 6H R┐ B↘ means that during the last 6-hour bar the price exited the R channel from below and also crossed below the basis line.
🚩 DISCLAIMER
The information we create and publish here is not prohibited, doesn't constitute investment advice, and isn't created solely for qualified investors.
Converging Pullbacks and PeaksMulti Timeframe Converging Lines Indicator. Using the highest/lowest Values at 2 different lengths. Convergence created by taking the highest/lowest value and subtracting/adding the # of barssince the highest/lowest bar was set multiplied by the price multiplied by the float. Curves are created from averaging out the emas of the center lines of the extremeties.
Helps show trendlines automatically most of the time but can be tweaked by changing the floats or Fast/Slow lengths to you liking.
TradersCustomLibraryLibrary "TradersCustomLibrary"
TODO: add library description here
SelectOptimalTimeframeTrendlineSettings()
calculateShortStopLoss()
calculateLongStopLoss()
werdygerTrend()
trendLines()
stoch()
timeToString()
Aarika RSIHello traders, purpose of creating this indicator is simply trying to analyse the trend of any symbol.
This indicator can be used on any script like Indices, Stocks, Future, Currency & Crypto.
This RSI version is much simpler to identify the trend of the script than that of traditional RSI trendline. Rather than showing a line, this RSI indicates bars for better and clear visibility of RSI levels.
This is a modified version of © ParkF. I have modified it to simplest possible manner.
How to trade:
RSI level 80, I consider this as extreme-bought which means high chance if bear market from this point on any given timeframe. Whereas 20 is considered as extreme-sold and have a chance to go higher from the current level.
I recommend you to study this RSI before putting it into practice.
Always start with small target and then go for big one by trailing your profit. This is not a Holy Grail indicator which always gives profit but if you practice this indicator with consistency, your portfolio may give good returns.
Use proper money management for any trade. Go for paper trade and observe how this indicator behaves and once satisfied then only take real trade.
Disclaimer: Please make sure you study this indicator on different timeframes because inserted set of data may act differently on different scripts and may vary from timeframe to timeframe.
We advice you to use this indicator for trend-analysis and study purpose only. Author/publisher of this indicator is not responsible for your profit or loss if you use this indicator for trading purpose one way or another.
N.B.: We do not recommend using HeikinAshi charting for this particular indicator as the data inputs may behave differently than expected. If you have any query, you may comment below.
