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".
โ 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
โ 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 ฯ 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 Kaufmanโs 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 Ehlersโs โSuper Smootherโ which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers Smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers Smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility .
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume . Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
Requirements
Inputs
Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Outputs
Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator
Confirmation 1: GKD-C Confirmation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest strategy
Additional features will be added in future releases.
Cari dalam skrip untuk "swing trading"
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 Kaufmanโs 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 Ehlersโs โSuper Smootherโ 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 stockโs 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 dayโs 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 โ 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 ฮธ.
ฮธavg(var ;M) + (1 โ ฮธ) avg (var ;N) = 2ฮธvar/(M+1-(M-1)L) + 2(1-ฮธ)var/(M+1-(M-1)L)
Solving for ฮธ 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 ฮธ.
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.
Unicorn QuantDeeply customizable trading algorithm with instant backtesting. It emulates real trading and displays all the actions it takes on the chart. For example, it shows when to enter or partially close a position, move Stop-Loss to breakeven, etc. The user can replicate these actions in their trading terminal in real time. The algorithm uses up to three Take-Profit levels, and a Stop-Loss level that can move in a trade to protect the floating profit.
The script can send real-time alerts to the userโs Email and to the cell phone via notifications in the TradingView app.
The indicator is designed to be used on all timeframes, including lower ones for intraday trading and scalping.
HOW TO USE
Set the Stop-Loss and up to three Take-Profit levels. Choose the rules for moving the Stop-Loss level in a trade. Adjust the sensitivity of the trading signals. And check the backtest result in the Instant Backtesting dashboard. If the performance of the strategy satisfies you, proceed with the forward testing or live trading.
When using this script, please, keep in mind that past results do not necessarily reflect future results and there are many factors that influence trading results.
FEATURES
Trading Signals
The feature calculates Buy and Sell signals for trend or swing trading. The user can change the Sensitivity parameter to control the frequency of the signals. This allows them to be adjusted for different markets and timeframes.
Position Manager
To make the Position Manager setup as easy as possible, the algorithm calculates Stop-Loss and Take-Profit levels in Average True Range (ATR) units. They are self-adjusting for any market and timeframe, since they account for its average volatility .
You don't have to worry about what market you are trading - Forex, Stocks, Crypto, etc. With the self-adjusting Stop-Loss and Take-Profit, you can find settings that work for one market and use the same numerical values as a starting point for a completely different market.
Instant Backtesting
After changing any settings, you can immediately see the performance of the strategy on the Instant Backtesting panel. Two metrics are displayed there - the percentage of profitable trades and the total return. This information, as well as the historical trades shown on the chart, will help you quickly and easily evaluate the settings.
SETTINGS
TRADING SIGNALS
Sensitivity - controls the sensitivity of the trading signals algorithm. It determines the frequency of the trading signals. The higher the value of this parameter, the less trading signals you get and the longer trends the algorithm tries to catch. The lower the sensitivity value, the more signals you receive. This can be useful if you want to profit from small price movements.
POSITION MANAGER
SL - sets the Stop-Loss level measured in ATR units.
TP1, TP2, TP3 - set the Take-Profit levels measured in the ATR units.
Close % at TP1, Close % at TP2, Close % at TP3 - set portions of the open position (as a percentage of the initial order size) to close at each of the TP levels.
At TP1 move SL to, At TP2 move SL to - set the rules for moving the Stop-Loss level in an open trade to protect the floating profit.
Show Open Position Dashboard - turns on/off a dashboard that shows the current Stop-Loss and Take-Profit levels for the open position.
BACKTESTING
Use Starting Date - turns on/off the starting date for the strategy and backtests. When off, all available historical data is used.
Starting Date - sets the starting date for the strategy and backtests.
Show Instant Backtesting Dashboard - turns on/off a dashboard that shows the current strategy performance: the percentage of profitable trades and total return.
Leverage - sets the leverage that the strategy uses.
Auto Fibonacci Retracement - Real-Time (Expo)โ Fibonacci retracement is a popular technical analysis method to draw support and resistance levels. The Fibonacci levels are calculated between 2 swing points (high/low) and divided by the key Fibonacci coefficients equal to 23.6%, 38.2%, 50%, 61.8%, and 100%. The percentage represents how much of a prior move the price has retraced.
โ Our Auto Fibonacci Retracement indicator analyzes the market in real-time and draws Fibonacci levels automatically for you on the chart. Real-time fib levels use the current swing points, which gives you a huge advantage when using them in your trading. You can always be sure that the levels are calculated from the correct swing high and low, regardless of the current trend. The algorithm has a trend filter and shifts the swing points if there is a trend change.
The user can set the preferred swing move to scalping, trend trading, or swing trading. This way, you can use our automatic fib indicator to do any trading. The auto fib works on any market and timeframe and displays the most important levels in real-time for you.
โ This Auto Fib Retracement indicator for TradingView is powerful since it does the job for you in real-time. Apply it to the chart, set the swing move to fit your trading style, and leave it on the chart. The indicator does the rest for you. The auto Fibonacci indicator calculates and plots the levels for you in any market and timeframe. In addition, it even changes the swing points based on the current trend direction, allowing traders to get the correct Fibonacci levels in every trend.
โ How does the Auto Fib Draw the levels?
The algorithm analyzes the recent price action and examines the current trend; based on the trend direction, two significant swings (high and low) are identified, and Fibonacci levels will then be plotted automatically on the chart. If the algorithm has identified an uptrend, it will calculate the Fibonacci levels from the swing low and up to the swing high. Similarly, if the algorithm has identified a downtrend, it will calculate the Fibonacci levels from the swing high and down to the swing low.
โ HOW TO USE
The levels allow for a quick and easy understanding of the current Fibonacci levels and help traders anticipate and react when the price levels are tested. In addition, the levels are often used for entries to determine stop-loss levels and to set profit targets. It's also common for traders to use Fibonacci levels to identify resistance and support levels.
Traders can set alerts when the levels are tested.
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Disclaimer
Copyright by Zeiierman.
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Moving Average Filters Add-on w/ Expanded Source Types [Loxx]Moving Average Filters Add-on w/ Expanded Source Types is a conglomeration of specialized and traditional moving averages that will be used in most of indicators that I publish moving forward. There are 39 moving averages included in this indicator as well as expanded source types including traditional Heiken Ashi and Better Heiken Ashi candles. You can read about the expanded source types clicking here . About half of these moving averages are closed source on other trading platforms. This indicator serves as a reference point for future public/private, open/closed source indicators that I publish to TradingView. Information about these moving averages was gleaned from various forex and trading forums and platforms as well as TASC publications and other assorted research publications.
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Included moving averages
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.
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.
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.
Hull Moving Average - HMA
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.
IE/2 - Early T3 by Tim Tilson
The IE/2 is a Moving Average that uses Linear Regression slope in its calculation to help with smoothing. It's a worthy Moving Average on it's own, even though it is the precursor and very early version of the famous "T3 Indicator".
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.
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 it's 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 price 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.
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 weight 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.
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 tomorrows price.
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 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 a 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 Ehlersโs โSuper Smootherโ which consists of a 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.
The TMA and Sine Weighted Moving Average Filter are almost identical at times.
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, it's 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.
Volume Weighted EMA - VEMA
Utilizing tick volume in MT4 (or real volume in MT5), this EMA will use the Volume reading in its decision to plot its moves. The more Volume it detects on a move, the more authority (confirmation) it has. And this EMA uses those Volume readings to plot its movements.
Studies show that tick volume and real volume have a very strong correlation, so using this filter in MT4 or MT5 produces very similar results and readings.
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.
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What are Heiken Ashi "better" candles?
The "better formula" was proposed in an article/memo by BNP-Paribas (In Warrants & Zertifikate, No. 8, August 2004 (a monthly German magazine published by BNP Paribas, Frankfurt), there is an article by Sebastian Schmidt about further development (smoothing) of Heikin-Ashi chart.)
They proposed to use the following:
(Open+Close)/2+(((Close-Open)/( High-Low ))*ABS((Close-Open)/2))
instead of using :
haClose = (O+H+L+C)/4
According to that document the HA representation using their proposed formula is better than the traditional formula.
What are traditional Heiken-Ashi candles?
The Heikin-Ashi technique averages price data to create a Japanese candlestick chart that filters out market noise.
Heikin-Ashi charts, developed by Munehisa Homma in the 1700s, share some characteristics with standard candlestick charts but differ based on the values used to create each candle. Instead of using the open, high, low, and close like standard candlestick charts, the Heikin-Ashi technique uses a modified formula based on two-period averages. This gives the chart a smoother appearance, making it easier to spots trends and reversals, but also obscures gaps and some price data.
Expanded generic source types:
Close = close
Open = open
High = high
Low = low
Median = hl2
Typical = hlc3
Weighted = hlcc4
Average = ohlc4
Average Median Body = (open+close)/2
Trend Biased = (see code, too complex to explain here)
Trend Biased (extreme) = (see code, too complex to explain here)
Included:
-Toggle bar color on/off
-Toggle signal line on/off
TPRC - Time-based Price Range Channel [Free]You define a time range (hours and minutes) and based on this, the indicator draws the price range (high / low) as a channel in your chart - projected into the future and, if desired, also for past days. You are completely free to choose the time range and NOT limited to trading sessions.
In addition, further lines are drawn below / above the price range channel at a distance that you can define (based on the price range).
These lines can serve as target levels, support and resistance lines.
What functions does this free version of the indicator offer?
Selection of the time range for which a price range is to be determined and based on this a price range channel is to be created
Display of 3 additional lines above / below the price range channel
Distance between the lines: height of the price range
Display of the price range channels for the past 3 days as well as for the current day.
Lines are shown in gray
For the past days, only those lines are displayed that are required due to the distance to the price. This will make your chart cleaner.
(Details about the premium version can be found on TradingView: )
How can this indicator be used?
The time-based price range channel and the additional lines can serve as support and resistance lines.
Whether you are enthusiastic about scalping, swing trading or another type of trading,โฆ โTPRC - Time-based Price Range Channelโ could therefore support you. Try it out. I want to invite you to experiment and thereby adapt โTPRCโ to your own way of trading.
Due to the free choice with regard to the time span, for example โopening range (break-out)โ strategies and the like are conceivable. Much has been written or published as a video on the subjects of "Price Range Trading", "Range Trading", "Opening Range Breakout Trading" and the like. Research on this is recommended to every interested trader. I would be happy to provide a list of interesting articles on this topic - just send me a short message.
Due to the implementation and the functions, the focus is definitely on intraday trading strategies.
For which timeframe is this indicator intended?
This indicator was developed for Chart Time Intervals between 1 and 120 minutes, whereby the following Chart Time Intervals have proven themselves and successfully withstand tests: 1, 2, 5, 10, 15, 30, 60, 90
What do I need to consider?
It may be advisable to add further indicators and an analysis of the market structure in order to confirm the signals issued by the indicator. Please note that when you make adjustments to any strategy, you always carry out particularly detailed tests.
Will this indicator be further developed and will I receive free updates?
All my indicators are of course constantly updated and, if possible and with the aim of the indicator justifiable, supplemented by user requests.
An example of the use of this indicator (here with the premium version)
#revision: dv699
RobocanThis script is equipped with
๐ต Robo 2
It offers strategic trading entry and exit points. Truly unique tool for technical analysis for the financial market as it includes calculation of specific metrics like MACD, ATR and RSI.
๐ต Bull & Bear
The signal can be a fairly valuable tool. Momentum is one of those aspects of the market that is crucial to understanding price movements, yet it is so hard to get a solid grip on. It can be used in some instances to generate quality signals but much like with any signal generating indicator, it should be used with caution.
When indicator gives you " Bull " signal , short term momentum is now rising faster than the long term momentum. This can present a bullish buying opportunity.
When indicator gives you "Bear " signal, short term momentum is now falling faster then the long term momentum. This can present a bearish selling opportunity.
๐ต Robo's Cloud
The indicator inspired from Ichimoku CLoud, it uses an unique formula to generate clouds on its own system!
" BUY or ENTER "when the price breaks the Cloud in the direction of the breakout (UP ) and the cloud turns to green colour. Stay in the market until the cloud turns to red colour. Let's assume that You are a swing trader and use 1D candles as long as The candle is above the "green " cloud , you should continue with a trend! No need to hurry to sell until you see the " red " cloud.
๐ต Super Robo
It can perform greatly in a bull and bear market
It's unique algorithm find profitable coins based on "Early Bird + Buy 2 + Volume "gives you ENTRY and EXIT ideas
It works perfectly on the 1W - 3D - 1D charts
๐ต Hell & Moon
When the โMoon or Hell โcloses below top of the closing price, a Moon - Buy signal is generated
It works perfectly on the 1W - 1D - 3H charts
๐ต Early Bird Signals
Being an early bird rather than a night owl will naturally lead you to become more successful in trading. There is no secret magic formula to success; this is something you must accept. Trading success is the result of a โsimpleโ list made up of four things: hard work, timing, persistence , and a good dose of Early Bird signals.
it provides high risk & high reward opportunities.
Dont use more than 3 Robo signals at the same time on the chart. Why?
Example, Robo 2 already included 3 different indicators in the formula.
Robo 2 : Truly unique tool for technical analysis for the financial market as it includes calculation of specific metrics like SAR + MACD + Price Movement that gives you ENTRY and EXIT ideas ( Buy 2 & Sell 2 )
If you use more than 3 robo signals, you try to use around " 10 - 12 " different indicators at the same time!
DON'T DO IT!
To get maximum results from your robo advisors, follow the advice below ;
A ) 3 robo signals
B ) 3 robo signals + 1 side strategy
A or B + Pick one bonus below
Dynamic Support Resistance,
Fibonacci Levels
Pivot Support Resistance
Robo signals :
Robo 1
Robo 2
Super EngineeringRobo
Robo 3
Robo 4
Bull & Bear
Hell & Moon
Early Bird
EngineeringRobo's cloud
Ultimate MA crossover strategy
Side strategies :
McGinley Dynamic
Bollinger Bands Strategy
MA 20 & MA 50
MA 50 & MA 200
EMA Trendlines
Robo ( 2 + 3 ) shows you that if the signals are covering each other. So, It is good to keep open it when you use Robo 2 and Robo 3 at the same time.
If you are following any signals, you should always wait for the candle close before buying or selling.
The signal can come and go anytime during the live candle. ALL indicators do that, that is not considered repainting.
Repainting is when a signal appears, the candle is closed, and when you refresh the chart it disappeared. It is logical that until the candle is closed the signal is not decided yet, hence the alert setup as Once per bar Close.
Deluxe never repaints! Yes, you heard it right: you will never have to worry about signal changing after the candle is closed.
________________________________________________________________________Timeframes_____________________________________________________________________
Our recommendations to get the best results:
Swing Trading Crypto : Use 1D Time Frame Candles
Swing Trading Stocks : Use 1W Time Frame Candles
Swing Trading Commodities : Use 1W Time Frame Candles
Day Trading Crypto : Use 3H Time Frame Candles
Day Trading Stocks : Use 1D Time Frame Candles
Day Trading Commodities : Use 1D Time Frame Candles
Not recommended any other time frames.
It gives you all the tools and information you need for day-to-day trading and investing, while also keeping a great buy and sell signals! No excuse to lose in any financial market anymore! Try now!
How can you add the algorithm into your chart?
1. Login to TradingView.com
2. From the homepage, click on โChartโ in the top navigation bar
3. Select โIndicatorsโ on the top-center-middle panel
4. In the indicator library, type "Robocan "
5. Use the website link below to obtain access to this indicator
INTRADAY/SWING TRADING - 3 EMASEstimados/as inversores:
Diagrameฬ este indicador para hacer tradings de corto o muy corto plazo.
Es un indicador que a simple vista ayuda al usuario a entrar en posiciones de Compra o de Venta.
Este indicador es un sistema de 3 EMAS. La primera, la de color verde es una EMA de 4 periodos. La segunda, la de color amarillo es una EMA de 9 periodos. Y por uฬltimo, la de color rojo es una EMA de 18 periodos.
Por otro lado tiene senฬales de Compra y de Venta las cuales tienen una alta eficacia y eficiencia.
Las senฬales de BUY (Compra) se dan cuando la EMA verde cruza al alza a la EMA roja. Las senฬales de SELL (Venta) se dan cuando la EMA roja cruza a la baja a la EMA verde.
En algunas ocasiones, estos cruces se pueden producir muy raฬpido generando unas falsas entradas en compra o en venta seguฬn corresponda.
Para subsanar esto, es importante que se utilice este sistema de BUY y SELL con las columnas de color verde o rojo seguฬn corresponda seguฬn se ve el graฬfico.
El fondo de color verde se da cuando la EMA verde y la EMA amarilla se encuentran por encima de la EMA roja. Sin embargo, cuando la EMA roja se encuentra por encima de la EMA verde y de la EMA amarilla el fondo es de color rojo.
Es importante remarcar que si la EMA verde estaฬ por encima de la EMA roja pero la EMA amarilla se encuentra por debajo de la EMA roja, en el graฬfico no se va a ver ninguฬn color de fondo. Por otro lado, cuando la EMA verde este por debajo de la EMA roja, pero la EMA amarilla todaviฬa se encuentre por encima de la EMA roja, tampoco va a poder verse ninguฬn tipo de color de fondo.
En resumidas cuentas:
COMPRA-BUY -> Cuando aparezca la senฬal de BUY y ademaฬs, esta senฬal se complemente con un fondo de color VERDE, entonces debemos entrar en LONG. Para cerrar la operacioฬn, de manera ganadora, tenemos que esperar a que desaparezca el color de fondo VERDE.
VENTA-SELL -> Cuando aparezca la senฬal de SELL y ademaฬs, esta senฬal se complemente con un fondo de color ROJO, entonces, debemos entrar en SHORT. Para cerrar la operacioฬn, de manera ganadora, tenemos que esperar a que desparezca el color de fondo ROJO.
RECOMENDACIOฬN: Siempre tener presente que cada inversor tiene una aversioฬn al riesgo distinta. Por favor, cada uno que use este indicador, primero haga una gestioฬn de riesgo y utilice SIEMPRE Stop Loss luego de abrir una posicioฬn ya sea estipulando que el precio va a subir o a bajar, es decir, entrando en LONG o en SHORT.
Espero que este indicador les sirva.
Saludos a todos.
DEAR INVESTORS:
I plotted this indicator for short or very short term trading.
It is an indicator that at a glance helps the user to enter Buy or Sell positions.
This indicator is a 3 EMAS system. The first, the green one, is a 4-period EMA . The second one, the one in yellow, is a 9-period EMA . And finally, the one in red is an EMA of 18 periods.
On the other hand, it has Buy and Sell signals which are highly effective and efficient.
The BUY signals are given when the green EMA crosses higher than the red EMA . SELL (Sell) signals are given when the red EMA crosses down to the green EMA .
On some occasions, these crosses can occur very quickly, generating false tickets for purchase or sale as appropriate.
To correct this, it is important that this system of BUY and SELL is used with the green or red columns as appropriate as the graph is seen.
The green colored background occurs when the green EMA and the yellow EMA are above the red EMA . However, when the red EMA is above the green EMA and the yellow EMA the bottom is red.
It is important to note that if the green EMA is above the red EMA but the yellow EMA is below the red EMA , no background color will be seen on the chart. On the other hand, when the green EMA is below the red EMA , but the yellow EMA is still above the red EMA , you will not be able to see any kind of background color either.
In short:
BUY-BUY -> When the BUY signal appears and this signal is complemented by a GREEN background, then we must enter LONG. To close the operation, in a winning way, we have to wait for the GREEN background color to disappear.
VENTA-SELL -> When the SELL signal appears and also this signal is complemented with a RED background, then, we must enter SHORT. To close the operation, in a winning way, we have to wait for the RED background color to disappear.
RECOMMENDATION: Always keep in mind that each investor has a different aversion to risk. Please, everyone who uses this indicator, first do a risk management and ALWAYS use Stop Loss after opening a position either by stipulating that the price is going to rise or fall, that is, entering LONG or SHORT.
I hope this indicator helps you.
Greetings to all.
[blackcat] L2 Ehlers Fisherized Deviation Scaled OscillatorLevel: 2
Background
John F. Ehlers introuced Fisherized Deviation Scaled Oscillator in Oct, 2018.
Function
In โProbabilityโProbably A Good Thing To Know,โ John Ehlers introduces a procedure for measuring an indicatorโs probability distribution to determine if it can be used as part of a reversion-to-the-mean trading strategy. Dr. Ehlers demonstrates this method with several of his existing indicators and presents a new indicator that he calls a deviation-scaled oscillator with Fisher transform. It charts the probability density of an oscillator to evaluate its applicability to swing trading.
Key Signal
FisherFilt --> Ehlers Fisherized Deviation Scaled Oscillator fast line
Trigger --> Ehlers Fisherized Deviation Scaled Oscillator slow line
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 91th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
[blackcat] L2 Swing Oscillator Swing MeterLevel: 2
Background
Swing trading is a type of trading aimed at making short to medium term profits from a trading pair over a period of a few days to several weeks. Swing traders mainly use technical analysis to look for trading opportunities. In addition to analyzing price trends and patterns, these traders can also use fundamental analysis.
Function
L2 Swing Oscillator Swing Meter is an oscillator based on breakouts. Another important feature of it is the swing meter, which confirms the top or bottom's confidence level with different color candles. The higher of the candles stack up, the higher confidence level is indicated.
Key Signal
absolutebot ---> absolute bottom with very high confidence level
ltbot ---> long term bottom with high confidence level
mtbot ---> middle term bottom with moderate confidence level
stbot ---> short term bottom with low confidence level
absolutetop ---> absolute top with very high confidence level
lttop ---> long term top with high confidence level
mttop ---> middle term top with moderate confidence level
sttop ---> short term top with low confidence level
fastline ---> oscillator fast line
slowline ---> oscillator slow line
Pros and Cons
Pros:
1. reconfigurable swing oscillator based on breakouts
2. swing meter can confirm/validate the bottom and top signal
Cons:
1. not appliable with trading pairs without volume information
2. small time frame may not trigger swing meter function
Remarks
This is a simple but very comprehensive technical indicator
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Stock Analysis SoftwareStock Analysis Software is a full trading setup and style that is meant for swing trading stocks, but can also be used for Forex, cryptocurrencies, indices and commodities. Whatever your choice of trading style (Intraday, Scalping, Swing trading, Investing) or trading instrument is (FX, Futures, Cryptos, Stocks) I can tailor it for you specifically. For example if you want to use it for trading Forex intraday I will show you how to use it for that.
The software consists of 11 indicators, 7 are custom and 4 are common and well known indicators available on Tradingview. The system itself is part software and part learning my specific pattern finding techniques. There is no one without the other. This is a complete system
This trading system is something I have developed over the last 10 years through extensive research and development and is now available on this platform.
The indicators used are mostly screening for trend breakouts, support and resistance, specific candle patterns, overextended, volume spikes and more.
This is a system that can be taught easily if one is motivated to learn.
The setup includes a video guide and a live one-on-one full breakdown on how to use it to your benefit.
Trade Crusher: Swing and Day Trade IndicatorHow to use the indicator
Add to favorites/apply to chart.
The indicator can be used for both Swing trade and Intra-day trading.
Swing trading:
--Use with background colors
--Input: 30 or 36
--Time frame: Daily or Weekly
--Buy only when background is aqua
--Sell only when background is red
--Use with bars or candles (use candles without borders to avoid confusion). I suggest to just use bars.
--Place buy orders above the 1st or 2nd blue bar after black bars. The background must be aqua.
--Ignore yellow bars with aqua background. They are shake out bars at the beginning or a trend and warnings of reversal
towards the end of trend.
--Place sell orders bellow the 1st or 2nd yellow bar after black bars. The background must be red.
--Ignore blue bars with red background (same as above).
--Black bars are nothing: Pullbacks/Chop
Day Trading:
--DO NOT use background colors. Un-click.
--Input: 10
--Time frame: 5 minutes
--Use with bars or candles (use candles without borders to avoid confusion). I suggest to just use bars.
--Place buy orders above the 1st or 2nd blue bar after black bars.
--Place sell orders bellow the 1st or 2nd yellow bar after black bars.
--Utilize some sort of scanner that can identify stocks with heavy pre-market volume (news, earnings, etc)
***
Use stop losses however you normally do. Take profits however you normally do.
I do not suggest using with other indicators as you may just paralyze your brain, however, if you find something that works, drop a comment.
Best of luck
TrendShikari NTS - StudyTrendShikari NTS is a Nifty Index, Swing trading system with great profitability. This is the STUDY file for you to generate E-mail / SMS signal alerts (based on your TV plan) and to see crisp and clear graphical Daily trade level plotting. For seeing backtest results and next day trading levels in advance use the STRATEGY file from indicator library. Access to this system will be limited. See my profile status field to see how you can gain access.
Salient Features
1. Daily Bar System. System analyzes a Daily chart of NIFTY to give signals with average holding period of 5 days.
2. Automatic Long and Short signal generation. No need to draw waves / lines and other fancy stuff on your charts to analyze NIFTY any more.
3. Backtester Results Available - Thanks to TradingView, backtest results for previous years (from 1990) are available right in the charting platform for NIFTY.
Having a good trading system is one thing and trading it to make money is a whole different ball game. One thing you must always do if you want to mimic the backtest results in live trading is to follow the rules mentioned below as if your life depends on it.
Trading Rules
1. Each day the system gives you a Long and Short trading level. You go Long on NIFTY when the Daily Long level is breached and you go Short on NIFTY when the Daily Short Level is breached.
2. Trade using Nifty Options, In the Money calls, one strike below the nearest strike price for going Long using Call Option or one strike above the nearest strike price for going Short using Put Option.
3. Preset exit and entry orders of appropriate option contracts every day at market open. To set the levels see the difference in Nifty spot price and the trading levels given by system and then multiply it with 0.8 to give an approximate order trigger price in both directions for the corresponding option contracts.
4. Book profit when Nifty moves significantly along signal direction. Every time NIFTY moves 100 points in your direction you exit the current option contract and enter a trade in the next strike price in the same direction.
5. Rollover before expiry. Its important that you rollover (ideally one day before the expiry day) your Option contact positions by exiting the current month contract and take a new position in the next month contract of the same type and strike price of the current month contract.
6. Trade only Nifty using this system. Also Daily chart has to be used for trading. System parameters have been tested and optimized for Nifty Index Daily patterns only and hence is likely to give stated results with Nifty Daily chart only.
7. Trade all signals. Don't pick and choose or add your own or someone else's analysis to filter the signals. Take confidence from the objective backtest results and not any subjective interpretations.
8. Trade with only that amount of money you can afford to loose. Initial capital that you need to have to trade one lot of NIFTY Option using this system should be at least INR 150000. You need only INR 7500 - 15000 to open a position and the rest is the margin of safety you need to have in your trading account to account for drawdowns in trading. You can add the capital in a staggered need to basis to your trading account. But make sure you have the initial capital mentioned above at your disposal, if need be.
As always your thoughts and inputs are welcome. Happy Trading !!!
Multi Stoch + VWAP Heatmap + Histogram + ScalpingThis indicator was developed by referencing various indicators from many contributors. I apologize that I cannot identify all the original authors due to the numerous sources referenced. Thank you to everyone who contributed to the trading community.
Important Notice: Please use this indicator with sufficient caution and proper risk management. I do not assume any responsibility for any losses incurred from using this indicator. Trade at your own risk.
Alternative version:
Acknowledgment & Disclaimer:
This indicator incorporates ideas and concepts from numerous community indicators. I sincerely apologize for not being able to properly credit all the original creators due to the extensive references used. My heartfelt gratitude goes out to all the talented developers in the trading community.
Risk Warning: Please exercise extreme caution when using this indicator. All trading involves substantial risk of loss, and I accept no liability for any financial losses that may result from the use of this indicator. Always implement proper risk management and trade responsibly.
Multi Stoch + VWAP Heatmap + Histogram + Scalping Usage Guide
๐ง Basic Settings
Parameter Settings (Recommended for XAU/USD)
Fast Stoch Length: 5 # Ultra-short term trend
Medium Stoch Length: 14 # Short term trend
Slow Stoch Length: 21 # Medium term trend
%K Smoothing: 2 # High sensitivity setting
%D Smoothing: 2 # High sensitivity setting
Overbought Level: 75 # Sell zone
Oversold Level: 25 # Buy zone
๐ Reading the Chart
1. Histogram (Background Bar Chart)
Green tones: Strong uptrend
Red tones: Strong downtrend
Gray: Trendless/neutral
2. Line Display
Blue lines: Ultra-short term Stochastic (K1/D1)
Orange lines: Short term Stochastic (K2/D2)
Purple lines: Medium term Stochastic (K3/D3)
Yellow line: VWAP (normalized)
3. Horizontal Lines
Upper line (75): Sell zone
Center line (50): Neutral line
Lower line (25): Buy zone
๐ฏ Signal Types and Meanings
Scalping Signals (โ marks)
Green โ (bottom): ๐ Scalp buy entry
RSI(7) < 25 + K1 < 30 combination
VWAP bounce targeting
Red โ (top): ๐ Scalp sell entry
RSI(7) > 75 + K1 > 70 combination
VWAP rejection targeting
Main Trend Signals
โฒ (large, green): ๐ช Strong buy signal - Multiple conditions aligned
โผ (large, red): ๐ช Strong sell signal - Multiple conditions aligned
โณ (medium, green): ๐ Normal buy signal
โฝ (medium, orange): ๐ Normal sell signal
Warning/Reversal Signals
โผ (pink): โ ๏ธ Sell warning - Trend reversal caution
โณ (teal): โ ๏ธ Buy warning - Trend reversal caution
Cross Signals (โ marks, positioned up/down)
Green โ (bottom): Histogram crosses above VWAP
Red โ (top): Histogram crosses below VWAP
๐ Practical Usage
Scalping Strategy (1-5 minute charts recommended)
Entry: Enter on green โ or red โ signals
Take Profit: At opposite zone or next โ signal
Stop Loss: Around 10-15 pips (for gold)
Time Session: London-NY overlap optimal
Swing Trading Strategy (15min-1hour charts)
Entry: Strong โฒโผ signals
Take Profit: Opposite warning signals (โผโณ)
Stop Loss: VWAP reverse break or 30-50 pips
Day Trading Strategy (5-15 minute charts)
Trend Confirmation: Histogram color
Entry: โณโฝ signals
Take Profit: Opposite zone reached
Stop Loss: 20-30 pips
โก XAU/USD Specific Usage
Session-Based Strategy
Tokyo Session (9-15 JST): Wait and see, small scalps
London Session (16-24 JST): Main trading
NY Session (22-6 JST): Most active, all signals valid
Major News Events
Pre-announcement: Reduce positions
Post-announcement: Trend following with โ signals
๐ Filter Functions
ATR Filter
Small price movements filtered as noise
Signals only on significant price moves
Time Filter
Strong signals only during high volatility sessions
Weaker signals during low volatility periods
Consecutive Signal Prevention
Duplicate signals within 2 bars filtered out
Prevents noise trading
โ๏ธ Settings Customization
For Aggressive Trading
Signal Cooldown: 1 # More frequent signals
ATR Length: 5 # More sensitive filter
For Conservative Trading
Signal Cooldown: 5 # Relaxed signals
ATR Length: 20 # Stricter filter
Overbought: 80 # More extreme levels
Oversold: 20
๐ฑ Recommended Alert Settings
Strong Buy/Sell Signal: Priority โ
โ
โ
Scalping Buy/Sell Signal: Priority โ
โ
โ
Reverse Warning: Priority โ
โ
โ
(for position management)
โ ๏ธ Important Notes
Scalping requires quick decision-making
Multiple timeframe confirmation recommended
Exercise caution during major news events
Risk management is top priority
This indicator is a versatile multi-functional tool suitable for short to medium-term trading strategies!
๐ Trading Examples
Scalping Example
Wait for green โ at oversold level (below 30)
Enter long position immediately
Target: 50 level or red โ signal
Stop: Below recent swing low
Day Trading Example
Histogram turns green (bullish trend)
Wait for โณ buy signal near support
Target: Overbought level (75)
Exit: Warning signal โผ appears
Risk Management Rules
Never risk more than 2% per trade
Use proper position sizing
Set stops before entry
Take partial profits at key levels
This comprehensive guide will help you maximize the potential of this advanced multi-timeframe indicator!
P/E Rating by The Noiseless TraderP/E Rating by The Noiseless Trader
This script analyzes a symbolโs Price-to-Earnings (P/E) ratio, using Diluted EPS (TTM) fundamentals directly from TradingView.
The script calculates the Price-to-Earnings ratio (P/E) using Diluted EPS (TTM) fundamentals. It then identifies:
PE High โ the highest valuation point over a long historical range.
PE Low โ the lowest valuation point over the same period.
PE Median โ the midpoint between the two extremes, offering a fair-value benchmark.
PE (Int) โ an additional intermediate low to track more recent undervaluation points.
These levels are plotted directly on the chart as horizontal references, with markers showing the exact bars/dates when the extremes occurred. Candles corresponding to those days are also highlighted for context.
Bars corresponding to these extremes are highlighted (red = PE High, green = PE Low).
How it helps
Provides a historical valuation framework that complements technical analysis.
Helps identify whether current price action is happening near overvalued or undervalued zones.
Adds a long-term perspective to support swing trading and investing decisions.
Offers a simple visual map of how far the market has moved from โcheapโ to โexpensive.โ
This tool is best suited for long-term investors and swing traders who want to merge fundamentals with technical setups.
This indicator is intended as an educational aid for students of The Noiseless Trader. It bridges the gap between fundamental valuation (earnings multiples) and technical execution, allowing learners to apply classroom concepts in real-time market conditions.
VWAP with period (rajib127)VWAP with Adjustable Period (rajib127)
This advanced VWAP (Volume Weighted Average Price) indicator offers enhanced functionality with customizable anchor periods and multiple standard deviation bands.
Key Features:
Adjustable Anchor Period: Unlike standard VWAP that resets daily, this indicator allows you to set custom anchor timeframes (Daily, Weekly, Monthly) to match your trading strategy
Multiple Deviation Bands: Display up to 3 sets of bands with customizable multipliers for better support/resistance identification
Dual Calculation Modes: Choose between Standard Deviation or Percentage-based band calculations
Flexible Price Sources: Select from 7 different price calculation methods (Typical, Close, High, Low, Median, Weighted, Open)
Timeframe Visibility Control: Option to hide VWAP on higher timeframes (Daily and above) for cleaner charts
Visual Enhancements: Color-coded bands with fill areas and real-time value display table
Trading Applications:
Identify dynamic support and resistance levels
Spot mean reversion opportunities when price deviates from bands
Use different anchor periods for swing trading vs day trading strategies
Combine with other indicators for confluence-based entries
Unique Advantage:
The ability to adjust the VWAP reset period makes this indicator versatile for various trading styles - from intraday scalping with hourly resets to swing trading with weekly anchors.
Perfect for traders who want more control over their VWAP analysis beyond the standard daily reset limitation.
Vwapbot (VWAP + Ut Bot Alerts)Vwapbot (VWAP + Ut Bot Alerts) - Complete Guide
This Pine Script indicator combines two powerful trading tools: Volume Weighted Average Price (VWAP) and the UT Bot trend-following system. Here's a comprehensive breakdown:
What This Indicator Does
The indicator provides:
1. VWAP calculation with deviation bands
2. UT Bot trend signals with trailing stops
3. Combined confluence alerts when both indicators align
4. Visual information table showing current market conditions
Core Components
1. VWAP (Volume Weighted Average Price)
What it is: VWAP calculates the average price weighted by volume, giving more importance to high-volume periods.
Settings:
โข VWAP Source: Price used for calculation (default: hlc3 - average of high, low, close)
โข VWAP Anchor: Reset period (Session/Week/Month/Quarter/Year)
Usage:
โข Price above VWAP = bullish bias
โข Price below VWAP = bearish bias
โข VWAP acts as dynamic support/resistance
2. VWAP Deviation Bands
What they show: Statistical boundaries around VWAP based on price volatility
Settings:
โข Standard Deviation Multiplier: How far the bands extend (default: 1.0)
โข Show Bands: Toggle visibility
Usage:
โข Gray dashed lines: 1 standard deviation bands (normal price range)
โข Red dotted lines: 2 standard deviation bands (extreme price levels)
โข Price touching outer bands may indicate reversal opportunities
3. UT Bot (Ultimate Trend Bot)
What it does: Creates a trailing stop system that follows trends and signals reversals
Settings:
โข Key Value: Sensitivity multiplier (1.0 = balanced, lower = more sensitive)
โข ATR Period: Lookback period for volatility calculation (default: 10)
How it works:
โข Uses ATR (Average True Range) to calculate dynamic support/resistance levels
โข Green line = uptrend (trailing stop below price)
โข Red line = downtrend (trailing stop above price)
4. UT Bot Alerts are integrated to the logic of Volume Profile i,e VWAP, the UT Bot Stop trailing line plot its data and change trends obtaining it's logic from the VWAP and Standard Deviation bands, thus it differs in it's logic of UT Bot alerts from other indicators.
Visual Elements
On-Chart Displays:
1. Blue line: VWAP
2. Gray lines: 1st deviation bands
3. Red lines: 2nd deviation bands
4. Green/Red thick line: UT Bot trailing stop
5. Green triangles up: Buy signals
6. Red triangles down: Sell signals
7. Background color: Light green (bullish) / Light red (bearish)
Information Table (Top Right):
โข VWAP: Current VWAP value
โข UT Bot: Current trailing stop level
โข Trend: Bullish/Bearish status
โข Price vs VWAP: Above/Below comparison
โข Deviation: Percentage distance from VWAP
โข Volume: Current bar volume
Trading Signals
Basic Signals:
1. UT Bot Buy: Green triangle when trend turns bullish
2. UT Bot Sell: Red triangle when trend turns bearish
3. VWAP Cross Above: Price crosses above VWAP
4. VWAP Cross Below: Price crosses below VWAP
Confluence Signals :
1. Bullish Confluence: UT Bot buy signal + Price above VWAP
2. Bearish Confluence: UT Bot sell signal + Price below VWAP
How to Use This Indicator
For Trend Following:
1. Enter long when you get a bullish confluence signal
2. Enter short when you get a bearish confluence signal
3. Exit when the UT Bot trend changes color
For Mean Reversion:
1. Look for reversals when price hits the 2nd deviation bands
2. Confirm with UT Bot signals
3. Target return to VWAP
For Support/Resistance:
1. Use VWAP as dynamic support in uptrends, resistance in downtrends
2. Watch for bounces at deviation bands
3. Confirm direction with UT Bot trend color
Best Practices
Timeframes:
โข Intraday: Use Session VWAP anchor
โข Swing trading: Use Weekly/Monthly anchors
โข Position trading: Use Monthly/Quarterly anchors
Risk Management:
โข Stop loss: Below/above the UT Bot trailing stop
โข Position sizing: Smaller positions when price is at extreme deviation bands
โข Confluence: Wait for both VWAP and UT Bot alignment for strongest signals
Market Conditions:
โข Trending markets: Focus on UT Bot signals and VWAP direction bias
โข Ranging markets: Use deviation bands for entry/exit points
โข High volume periods: VWAP becomes more significant
Alert System
The indicator provides 6 types of alerts:
1. UT Bot buy/sell signals
2. VWAP crossover alerts
3. Confluence alerts (most important)
Set up alerts for confluence signals to catch the highest probability setups when both indicators align.
This indicator works best when combined with proper risk management and used in conjunction with market structure analysis. The confluence signals provide the highest probability entries, while the individual components help with market.
Advice from the publisher:
For using with Indices e.g NIFTY 50, BANKNIFTY etc. use parameters:
UT BOT Key Value : 1
UT BOT ATR Period : 10
Standard Deviation Multiplier : 1 {Default}
For using with commodities e.g NATURALGAS, CRUDEOIL etc. use parameters:
UT BOT Key Value : 2
UT BOT ATR Period : 7
Standard Deviation Multiplier : 1 {Default}
TRADE ORBIT: Stochastic Multi-Filtered Signals + Trend Tools๐ TRADE ORBIT: Stochastic Multi-Filtered Signals + Trend Tools
๐น Overview
This indicator combines dual stochastic filters with trend-following tools (BB, VWAP, SMAs, EMAs) to generate high-probability buy/sell signals. It helps traders align momentum reversals with the overall trend direction, improving accuracy in both intraday and swing trading.
๐น How It Works
Slow Stochastic (50,3,3) Background Filter
โ
Green = Trend bullish (%K > %D, both > 25)
โ
Red = Trend bearish (%K < %D, both < 75)
โ
Blue = Overbought (both > 80)
โ
Black = Oversold (both < 20)
Fast Stochastic (5,3,3) Signal Filter
โ
Buy Signal โ Background is Green or Blue + Fast %K crosses above %D below 25
โ
Sell Signal โ Background is Red or Black + Fast %K crosses below %D above 75
Additional Trend Tools
๐ Bollinger Bands (20,2) โ Detect volatility & targets
๐ VWAP โ Institutional volume-weighted levels
๐ SMA 9 โ Short-term trend filter
๐ SMA 34 (High, Low, Close) โ Multi-price smoothing for strong S/R zones
๐ EMA 30 & EMA 123 โ Trend confirmation
๐น Trading Logic
๐ข Buy signals appear when momentum turns bullish in alignment with the slow stochastic background.
๐ด Sell signals appear when momentum turns bearish in alignment with the background filter.
๐ฏ Use Bollinger Bands & VWAP as natural targets and support/resistance.
๐ Use SMA/EMA ribbons to confirm trend direction before entering trades.
๐น Best Use Cases
โ
Intraday Trading โ Catch reversals from oversold/overbought zones.
โ
Swing Trading โ Enter when momentum aligns with trend filters.
โ
Trend Confirmation โ Use EMAs & SMAs to filter false signals.
โ ๏ธ Disclaimer: This script is for educational purposes only and not financial advice. Always combine with your own analysis and risk management.
Gravity Trend Line with ยฑ10% Bands_QianYu๐ Law of Gravity in Stock Trading โ by Hu Liyang (่ก็ซ้ณ)โoften called the โGodfather of Asian Stock Marketsโ
โฆ Conceptual Origin
The โLaw of Gravityโ was developed by Mr. Hu Liyang, drawing an analogy between the gravitational pull in physics and the relationship between stock prices and moving averages. It is a medium-term mean reversion theory that helps traders identify rebound opportunities when prices deviate too far from their trend lines.
๐ Indicator Summary: Gravity Trend Line with ยฑ10% Bands
๐ง How It's Calculated:
Gravity Trend Line = Average of SMA(30) and SMA(70)
Represents the fair value zone or center of gravity for price over a medium-term period.
Upper Band = Gravity Line + 10%
Lower Band = Gravity Line - 10%
A shaded zone shows the space between the upper and lower bands โ your "gravity channel."
๐งญHow to Use It for Swing Trading (1H and 4H Charts)
1. Trend Bias Filter
If price is consistently above the Gravity Line, the trend bias is bullish.
If price is below the Gravity Line, the bias is bearish.
Use this to align your trades with the prevailing direction on 4H (macro view) and fine-tune entries on 1H.
2.Trade Entry Zones
Long Setup (buy):
Look for price near or just below the lower band (oversold zone).
Combine with bullish candles or reversal indicators (e.g., MACD bullish crossover, RSI < 30 turning up).
Confirmation: price reclaims the lower band or moves toward gravity line.
Short Setup (sell):
Look for price near or just above the upper band (overbought zone).
Combine with bearish confirmation (e.g., MACD bearish crossover, RSI > 70 turning down).
Confirmation: price starts rejecting from upper band toward gravity line.
3. Take Profit / Exit Zones
Partial TP: At the Gravity Line (mean reversion level).
Final TP: At opposite band (if price has strong momentum).
Alternatively, exit on crossback below gravity line after a long, or above it after a short.
4. Avoiding Traps
Avoid entering trades in the middle of the band (around the Gravity Line) unless there's strong breakout confirmation.
Use 4H for trend context, and 1H for entry precision.
Avoid trading against the broader gravity slope:
If gravity line is clearly sloping up, favor longs.
If sloping down, favor shorts.
๐ Example Strategy Workflow:
Timeframe:
Use 4H for directional bias
Use 1H for entries and exits
Example Long Setup (1H Chart):
Price dips below lower band while 4H trend is up.
Bullish candle forms or RSI/MACD confirms momentum shift.
Entry: price closes back above the lower band.
TP1: near gravity line.
TP2: near upper band.
Or, exit when gain hits +8% to +15%, depending on risk appetite.
๐ Final Notes:
This is a mean-reversion + trend confirmation tool โ best used with additional confluence (candlestick patterns, volume, divergence).
It works well in ranging to gently trending markets โ not ideal for sharp breakouts unless combined with breakout filters.
This indicator is for educational and reference purposes only.
It is not intended to be a recommendation or signal to buy or sell any security.
Use at your own discretion. Always perform your own due diligence before trading.
Tide Tracker ZonesTide Tracker Zones โ Advanced Trend & Pullback Visualizer
Overview
Tide Tracker Zones is a sophisticated trading tool designed for traders who require clarity, precision, and actionable insights in real time. The indicator converts price action into dynamic trend zones, allowing users to instantly recognize market direction, potential reversals, and low-risk entry opportunities. By visualizing the market in this way, traders can focus on execution rather than deciphering complex charts.
Unlike static indicators, Tide Tracker Zones adapts to market volatility, providing a clear picture of bullish and bearish pressure across multiple timeframes. Its visual design, including color-coded trend zones, a prominent guide line, and carefully placed signals, ensures that market behavior is easy to interpret, making it suitable for scalping, swing trading, and longer-term strategies alike.
How It Works
The indicator relies on dynamic upper and lower bands derived from recent price ranges and a configurable multiplier. These bands expand during volatile periods and contract when price action stabilizes, creating flexible zones that reflect the dominant market tide.
A guide line tracks the active band, serving as a continuous reference for trend direction. Unlike traditional moving averages, the guide line does not clutter the chart but instead provides a subtle, intuitive indication of whether the market is in a bullish or bearish phase. Background shading reinforces this trend visually, highlighting bullish zones in one color and bearish zones in another, so the prevailing market flow is immediately clear.
The system continuously evaluates price relative to the bands to determine trend direction and detect potential reversals. When price crosses a band and flips the trend, the guide line updates, and signals are generated, providing traders with actionable information without overwhelming the chart.
Signals and Pullbacks
Tide Tracker Zones offers visual cues that make entry points more obvious and less speculative. Trend reversal arrows are plotted when the market changes direction: BUY arrows indicate a shift from bearish to bullish, and SELL arrows indicate a shift from bullish to bearish.
The indicator also highlights first pullbacks within an active trend. These pullback dots mark low-risk opportunities to enter a trend in progress, filtered to ensure that only the most relevant signals are displayed. The system uses ATR-based spacing to place arrows and dots vertically on the chart, preventing visual clutter and ensuring readability even during periods of high volatility.
Color-coded zones enhance situational awareness. Bullish zones are displayed in a customizable orange, while bearish zones are shown in green. Transparency is dynamically adjusted to maintain chart clarity while still providing a clear indication of trend strength.
Strategy Integration
Tide Tracker Zones can be used effectively for both trend-following and pullback strategies. Traders may enter positions in the direction of the guide line and colored zone, using trend reversal arrows for confirmation. First pullback dots offer tactical entries with reduced risk, allowing traders to enter a trend after a brief retracement.
Stop-loss levels can be placed just beyond the opposing trend zone, while take-profit targets may be determined using the width of the bands to account for market volatility. The indicator adapts seamlessly across multiple timeframes. Higher timeframes provide context and filter noise, while lower timeframes allow traders to refine entry timing. This makes it a versatile tool for scalping, swing trading, or longer-term positions.
Advanced Techniques
For traders seeking greater precision, Tide Tracker Zones can be combined with volume or momentum indicators to validate signals. Observing the sequence of trend arrows and pullback dots allows users to develop a systematic approach to entries and exits. Monitoring the width and behavior of the bands over time can also provide insights into periods of expanding or contracting volatility, helping traders anticipate market shifts.
Adjustments to the spread length and multiplier allow the indicator to be tuned for different assets and market conditions. By understanding the interaction between the guide line, trend zones, and pullback signals, traders can create a robust framework for decision-making, reducing guesswork and improving consistency.
Why Use Tide Tracker Zones
Tide Tracker Zones provides instant clarity and actionable insight in any market. Its dynamic zones and guide line give a clear visual understanding of trend direction, while trend reversal arrows and pullback dots highlight potential entry points. Unlike traditional indicators, it adapts to volatility and changing conditions, making it reliable across multiple asset classes and timeframes.
By combining trend detection, pullback analysis, and intuitive visual guidance, Tide Tracker Zones equips traders with a complete framework for disciplined, confident trading, transforming complex price action into a visual map of opportunity.
Malama's Quantum Swing Modulator# Multi-Indicator Swing Analysis with Probability Scoring
## What Makes This Script Original
This script combines pivot point detection with a **weighted scoring system** that dynamically adjusts indicator weights based on market regime (trending vs. ranging). Unlike standard multi-indicator approaches that use fixed weightings, this implementation uses ADX to detect market conditions and automatically rebalances the influence of RSI, MFI, and price deviation components accordingly.
## Core Methodology
**Dynamic Weight Allocation System:**
- **Trending Markets (ADX > 25):** Prioritizes momentum (50% weight) with reduced oscillator influence (20% each for RSI/MFI)
- **Ranging Markets (ADX < 25):** Emphasizes mean reversion signals (40% each for RSI/MFI) with no momentum bias
- **Price Wave Component:** Uses EMA deviation normalized by ATR to measure distance from central tendency
**Pivot-Based Level Analysis:**
- Detects swing highs/lows using configurable left/right lookback periods
- Maintains the most recent pivot levels as key reference points
- Calculates proximity scores based on current price distance from these levels
**Volume Confirmation Logic:**
- Defines "volume entanglement" when current volume exceeds SMA by user-defined factor
- Integrates volume confirmation into confidence scoring rather than signal generation
## Technical Implementation Details
**Scoring Algorithm:**
The script calculates separate bullish and bearish "superposition" scores using:
```
Bullish Score = (RSI_bull ร weight) + (MFI_bull ร weight) + (price_wave ร weight ร position_filter) + (momentum ร weight)
```
Where:
- RSI_bull = 100 - RSI (inverted for oversold bias)
- MFI_bull = 100 - MFI (inverted for oversold bias)
- Position_filter = Only applies when price is below EMA for bullish signals
- Momentum component = Only active in trending markets
**Confidence Calculation:**
Base confidence starts at 25% and increases based on:
- Market regime alignment (trending/ranging appropriate conditions)
- Volume confirmation presence
- Oscillator extreme readings (RSI < 30 or > 70 in ranging markets)
- Price position relative to wave function (EMA)
**Probability Output:**
Final probability = (Base Score ร 0.6) + (Proximity Score ร 0.4)
This balances indicator confluence with proximity to identified levels.
## Key Differentiators
**vs. Standard Multi-Indicator Scripts:** Uses regime-based dynamic weighting instead of fixed combinations
**vs. Simple Pivot Indicators:** Adds quantified probability and confidence scoring to pivot levels
**vs. Basic Oscillator Combinations:** Incorporates market structure analysis through ADX regime detection
## Visual Components
**Wave Function Display:** EMA with ATR-based uncertainty bands for trend context
**Pivot Markers:** Clear visualization of detected swing highs and lows
**Analysis Table:** Real-time probability, confidence, and action recommendations for current pivot levels
## Practical Application
The dynamic weighting system helps avoid common pitfalls of multi-indicator analysis:
- Reduces oscillator noise during strong trends by emphasizing momentum
- Increases mean reversion sensitivity during sideways markets
- Provides quantified probability rather than subjective signal interpretation
## Important Limitations
- Requires sufficient historical data for pivot detection and volume calculations
- Probability scores are based on current market regime and may change as conditions evolve
- The scoring system is designed for confluence analysis, not standalone trading decisions
- Past probability accuracy does not guarantee future performance
## Technical Requirements
- Works on all timeframes but requires adequate lookback history
- Volume data required for entanglement calculations
- Best suited for liquid instruments where volume patterns are meaningful
This approach provides a systematic framework for evaluating swing trading opportunities while acknowledging the probabilistic nature of technical analysis.
SigmoidCycle Oscillator [LuminoAlgo]Purpose:
The SineCycle Oscillator measures price momentum using sigmoid function mathematics (S-curve transformation) borrowed from neural network theory. It generates an oscillator that fluctuates around 1.0, identifying momentum shifts and potential reversal points.
Mathematical Foundation:
This indicator applies the sigmoid logistic function concept: y = 1/(1+e^-x) , which creates an S-shaped curve. In financial markets context, this transformation:
- Maps price changes to a bounded range (-1 to +1)
- Provides non-linear sensitivity (high near zero, low at extremes)
- Naturally filters outliers without lag penalty
Calculation Process:
1. Statistical Normalization: Price deviations are measured from a moving average baseline and scaled by recent volatility (standard deviation over N periods)
2. Sigmoid Transformation: Normalized values undergo S-curve transformation, which weights small movements linearly but compresses large movements logarithmically
3. Dual Timeframe Analysis:
โข Short window: User-defined period (N)
โข Long window: Double period (2N)
โข Ratio calculation: Short sigmoid average รท Long sigmoid average
4. Volatility-Weighted Smoothing: Final values use exponential smoothing where the smoothing factor adjusts based on the coefficient of variation (volatility/mean ratio)
What Makes This Different:
Unlike linear momentum oscillators (RSI, Stochastic) that use fixed mathematical relationships, the sigmoid transformation creates variable sensitivity zones. This mimics how professional traders mentally weight price movements.
Trading Application:
Signal Types:
- Momentum: Green (>1.0) = bullish, Red (<1.0) = bearish
- Reversals: 1.0 line crosses with volume confirmation
- Divergence: Price makes new high/low, oscillator doesn't
- Exhaustion: Extended readings (>1.2 or <0.8) suggest overextension
Optimal Conditions:
- Works best: Trending markets with clear swings
- Avoid: Low volume, ranging markets under 1% daily movement
- Timeframes: 4H and above for reliability
Parameter Guidelines:
- Length 8-10: Day trading (expect more whipsaws)
- Length 14-20: Swing trading (balanced signals)
- Length 25-30: Position trading (fewer, stronger signals)
Limitations:
- Lag increases with higher length settings
- Can give false signals during news-driven spikes
- Requires additional confirmation in choppy markets
Trading Framework:
Based on momentum persistence theory - assumes trends continue until sigmoid curve flattens (indicating momentum exhaustion). The mathematical model captures both mean reversion (extreme readings) and trend following (mid-range readings) characteristics.
Coloured Multi Moving Averages by Fin VirajColoured Multi Moving Averages by Fin Viraj
๐ Overview
A comprehensive and clean moving averages indicator that supports 9 different types of moving averages with dynamic visual feedback. Perfect for both short-term trading and long-term analysis.
โจ Key Features
๐ Multiple Moving Average Types
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
VWMA (Volume Weighted Moving Average)
HMA (Hull Moving Average)
DEMA (Double Exponential Moving Average)
TEMA (Triple Exponential Moving Average)
ZLEMA (Zero Lag Exponential Moving Average)
RMA (Rolling Moving Average/Wilder's Smoothing)
๐จ Dynamic Visual Feedback
Smart Color Change: MA1 automatically changes color based on price position (green when price above, red when price below)
Candle Highlighting: Candles change color when price crosses MA1
Individual Visibility Controls: Show/hide each MA independently
Customizable Styling: Adjust colors and line thickness
โ๏ธ Flexible Configuration
3 Independent MAs: Each can be set to different types and periods
Default Settings: 9-period, 21-period, and 50-period (commonly used in trading)
Easy Customization: Quick dropdown selection for MA types
Clean Interface: Organized settings groups for easy navigation
๐ How to Use
For Day Trading:
Set MA1 to HMA(9) for quick signals
Set MA2 to EMA(21) for medium-term trend
Set MA3 to SMA(50) for overall trend direction
For Swing Trading:
Set MA1 to EMA(9) for entry signals
Set MA2 to EMA(21) for trend confirmation
Set MA3 to EMA(50) for major trend
For Long-term Investing:
Use SMA(20), SMA(50), SMA(200) combination
Focus on price position relative to MA1 color changes
๐จ Alert System
Price crossing above MA1
Price crossing below MA1
Customizable alert messages with ticker symbols
๐ฏ Perfect For:
โ
Trend identification
โ
Entry and exit timing
โ
Multi-timeframe analysis
โ
Support and resistance levels
โ
Clean chart analysis without clutter
๐ก Pro Tips:
Color Coding: Use the dynamic MA1 color change to quickly identify trend direction
MA Hierarchy: Arrange faster MA above slower MA for better trend visualization
Candle Colors: Enable candle color change for immediate visual confirmation of MA1 crosses
Mix & Match: Experiment with different MA combinations (e.g., HMA + EMA + SMA)
๐ง Default Configuration:
MA1: EMA(9) - Fast trend detection
MA2: EMA(21) - Medium-term trend
MA3: EMA(50) - Long-term trend
Dynamic colors enabled for instant visual feedback
No complex tables, no cluttered signals - just clean, professional moving averages with smart visual enhancements.
Compatible with all markets: Stocks, Forex, Crypto, Commodities, and Indices.
