Steel Step Assistant: Trend VisualizerSpecial thanks to Turicumo and Psychil for helping me write the code, both from my group.
Disclaimer: Nobody should use this indicator as a confirmation signal for entry/exit for your trades. Please message me on how to use this indicator correctly. This indicator was designed to be used in conjunction with my Steel Step strategy, hence the name.
This indicator simply gives a quick outlook of the market.
This indicator is an ordinary table that shows you the trends.
The default settings produce directions that are very similar to what I use for my strategy. You can change the settings as desired.
This indicator can be used on all charts and markets; crypto, commodities, forex, stock, indices, etc.
It is suitable for intra-day traders, as well as HTF traders.
One way of using this is to enhance your information gathering on trends in order to understand the market structure or direction better.
This indicator educates users on the market structure. Users can quickly break down the market into layers, analyze the layers and connect them all to understand the market as a whole. After users understand the market, users need to decide and choose a specific trend they want to trade. The basic idea is to flow with the market.
This indicator can be combined with EW theory to understand the market structure easily.
When I understand the whole market structure, it boosts my trading performance to the maximum.
Please comment below or message me if you have any questions. Enjoy!
Tìm kiếm tập lệnh với "trendline"
Shorting when Bollinger Band Above Price with RSI (by Coinrule)The Bollinger Bands are among the most famous and widely used indicators. A Bollinger Band is a technical analysis tool defined by a set of trendlines plotted two standard deviations (positively and negatively) away from a simple moving average ( SMA ) of a security's price, but which can be adjusted to user preferences. They can suggest when an asset is oversold or overbought in the short term, thus providing the best time for buying and selling it.
The relative strength index ( RSI ) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security. The RSI can do more than point to overbought and oversold securities. It can also indicate securities primed for a trend reversal or corrective pullback in price. It can signal when to buy and sell. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
The short order is placed on assets that present strong momentum when it's more likely that it is about to reverse. The rule strategy places and closes the order when the following conditions are met:
ENTRY
The closing price is greater than the upper standard deviation of the Bollinger Bands
The RSI is less than 70.
EXIT
The trade is closed when the RSI is less than 70
The lower standard deviation of the Bollinger Band is less than the closing price.
This strategy was backtested from the beginning of 2022 to capture how this strategy would perform in a bear market.
The strategy assumes each order to trade 70% of the available capital to make the results more realistic. A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange by volume.
Faytterro Market Structerethis indicator creates the market structure with a little delay but perfectly. each zigzag is always drawn from highest to lowest. It also signals when the market structure is broken. signals fade over time.
The table above shows the percentage distance of the price from the last high and the last low.
zigzags are painted green when making higher peaks, while lower peaks are considered downtrends and are painted red. In fact, the indicator is quite simple to understand and use.
"length" is used to change the frequency of the signal.
"go to past" is used to see historical data.
Please review the examples:
AutoBF by Tren10xBroadening Formation is a powerful technical analysis tool that is characterized by two converging trendlines that widen over time. This pattern typically signals a period of volatility and uncertainty in the market and can indicate a potential reversal in trend direction.
This script uses advanced algorithms to automatically detect and plot broadening formations on your chart, making it easy to identify these patterns and potentially profit from them, all while saving you time from drawing them yourself. With customizable settings, this indicator is a must-have tool for any trader looking to take advantage of this powerful chart pattern.
Features:
● Automatically detects and plots broadening formations on any chart within TradingView
● Customizable settings for greater flexibility and control
● Choose to draw your broadening formation from the outside bar to the "Previous Candle" or "Compound Candle" aka to the previous lowest/highest candle within the outside bar.
● Clear visual display of broadening formations and easy identification
● Compatible with all markets and timeframes, from stocks and forex to cryptocurrencies and commodities
● Designed for both novice and experienced traders, with user-friendly interface and comprehensive documentation
● By default, the year will look back 75 years, the quarter will look back 20 years, the month will look back 7 years, the week will look back 3 years, and the day will look back 90 days. However, you now have the ability to change these at your will.
● Added the ability to enable Broadening Formations on the 6 Month, 2 Month, 2 Week, and 2 Day charts.
● ALERTS! Receive timely notifications when the price breaches or activates a broadening formation.
All Timeframes available:
● Year
● 6 Month
● Quarter
● 2 Month
● Month
● 2 Week
● Week
● 2 Day
● Day
tinyurl.com
Predicting future outcomes is impossible. Nobody knows what the future will bring. With this Broadening Formation Indicator, you will have the edge you need to identify potentially profitable trading opportunities and make more informed decisions in the markets.
Regards,
Tren10x
Disclaimer: It is essential to note that returns on investments are not guaranteed, and investors should exercise prudence in conducting thorough due diligence before making any investment decisions
I would like to express my gratitude to my wife for her meticulous testing and insightful contributions throughout the course of this project. Additionally, I extend my appreciation to the esteemed Alpha Pack Group, whose exceptional acumen and investment expertise have been instrumental in the success of this endeavor.
Broadening Formations [TFO]This indicator highlights deviations from broadening formations (or megaphone patterns). Deviations from broadening ranges can often foreshadow reversals, especially in consolidation phases. These deviations are highlighted via trendlines that change color when tested, and also have the option to be alerted.
These broadening formations are heavily used with "The Strat" and can add confluence when looking for reversals within higher timeframe points of interest.
GKD-B Baseline [Loxx]Giga Kaleidoscope Baseline is a Baseline module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is an NNFX algorithmic trading strategy?
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend (such as "Baseline" shown on the chart above)
3. Confirmation 1 - a technical indicator used to identify trend. This should agree with the "Baseline"
4. Confirmation 2 - a technical indicator used to identify trend. This filters/verifies the trend identified by "Baseline" and "Confirmation 1"
5. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown.
6. Exit - a technical indicator used to determine when trend is exhausted.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 module (Confirmation 1/2, Numbers 3 and 4 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 5 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 6 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Jurik Volty
Confirmation 1: Vortex
Confirmation 2: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Now that you have a general understanding of the NNFX algorithm and the GKD trading system. let's go over what's inside the GKD-B Baseline itself.
GKD Baseline Special Features and Notable Inputs
GKD Baseline v1.0 includes 63 different moving averages:
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Instantaneous Trendline
Kalman Filter
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Adaptive Moving Average - AMA
Description. The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility. It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average (DEMA), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average (EMA) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA. This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA (Exponential Moving Average) that is due to that fact (that he used it) sometimes called Wilder's EMA. This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA, but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
T3 is basically an EMA on steroids, You can read about T3 here:
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Kalman Filter
Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies. This means that the filter was originally designed to work with noisy data. Also, it is able to work with incomplete data. Another advantage is that it is designed for and applied in dynamic systems; our price chart belongs to such systems. This version is true to the original design of the trade-ready Kalman Filter where velocity is the triggering mechanism.
Kalman Filter is a more accurate smoothing/prediction algorithm than the moving average because it is adaptive: it accounts for estimation errors and tries to adjust its predictions from the information it learned in the previous stage. Theoretically, Kalman Filter consists of measurement and transition components.
Kaufman Adaptive Moving Average - KAMA
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA. The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers. The original idea behind this study (and several others created by John F. Ehlers) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA, a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility.
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
Exotic Triggers
This version of Baseline allows the user to select from exotic or source triggers. An exotic trigger determines trend by either slope or some other mechanism that is special to each moving average. A source trigger is one of 32 different source types from Loxx's Exotic Source Types. You can read about these source types here:
Volatility Goldie Locks Zone
This volatility filter is the standard first pass filter that is used for all NNFX systems despite the additional volatility/volume filter used in step 5. For this filter, price must fall into a range of maximum and minimum values calculated using multiples of volatility. Unlike the standard NNFX systems, this version of volatility filtering is separated from the core Baseline and uses it's own moving average with Loxx's Exotic Source Types. The green and red dots at the top of the chart denote whether a candle qualifies for a either or long or short respectively. The green and red triangles at the bottom of the chart denote whether the trigger has crossed up or down and qualifies inside the Goldie Locks zone. White coloring of the Goldie Locks Zone mean line is where volatility is too low to trade.
Volatility Types Included
v1.0 Included Volatility
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility .
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility .
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility .
One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility . That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator consists of using the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e. it assumes that the underlying asset follows a GBM process with zero drift. Therefore the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1))
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by θ.
θavg(var ;M) + (1 − θ) avg (var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L)
Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg (var; N) against avg (var; M) - avg (var; N) and using the resulting beta estimate as θ.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Additional features will be added in future releases.
This indicator is only available to ALGX Trading VIP group members . You can see the Author's Instructions below to get more information on how to get access.
Cosmic Rays LiteCosmic Rays Lite ( CR Lite ) draws dynamic non-repainting trendlines and helps
⭐ see small and large trends
⭐ predict reversals with high accuracy
⭐ spot bullish and bearish breakouts
⭐ time the end of breakouts
👀 HOW IT WORKS
Cosmic Rays Lite compresses 14 moving averages of varying type and length into 7 plots and filters those down into the closest support and resistance lines. When the price moves above final resistance or below final support, additional "ripple" lines are appended using aggregate standard deviation multiples. The visibility of each line is toggled depending on its stability and its proximity to the price.
📗 HOW TO USE IT
Cosmic Rays Lite is designed specifically to be visible only during the most relevant times so as not to clutter your main setup. Likewise you will know to pay attention when a certain Cosmic Rays Lite level is shown. The main strategies are:
long when the price reverses off of the support line and short when the price reverses off of the resistance line
long when the price breaks above a support or resistance line and short when it breaks below
long when it reaches a support ripple line and short when the price reaches a resistance ripple line
use in combination with other indicators to spot chart patterns such as triangles (see chart)
Trend Line Adam Moradi v1 (Tutorial Content)
The Pine Script strategy that plots pivot points and trend lines on a chart. The strategy allows the user to specify the period for calculating pivot points and the number of pivot points to be used for generating trend lines. The user can also specify different colors for the up and down trend lines.
The script starts by defining the input parameters for the strategy and then calculates the pivot high and pivot low values using the pivothigh() and pivotlow() functions. It then stores the pivot points in two arrays called trend_top_values and trend_bottom_values. The script also has two arrays called trend_top_position and trend_bottom_position which store the positions of the pivot points.
The script then defines a function called add_to_array() which takes in three arguments: apointer1, apointer2, and val. This function adds val to the beginning of the array pointed to by apointer1, and adds bar_index to the beginning of the array pointed to by apointer2. It then removes the last element from both arrays.
The script then checks if a pivot high or pivot low value has been calculated, and if so, it adds the value and its position to the appropriate arrays using the add_to_array() function.
Next, the script defines two arrays called bottom_lines and top_lines which will be used to store trend lines. It also defines a variable called starttime which is set to the current time.
The script then enters a loop to calculate and plot the trend lines. It first deletes any existing trend lines from the chart. It then enters two nested loops which iterate over the pivot points stored in the trend_bottom_values and trend_top_values arrays. For each pair of pivot points, the script calculates the slope of the line connecting them and checks if the line is a valid trend line by iterating over the price bars between the two pivot points and checking if the line is above or below the close price of each bar. If the line is found to be a valid trend line, it is plotted on the chart using the line.new() function.
Finally, the script colors the trend lines using the colors specified by the user.
Tutorial Content
'PivotPointNumber' is an input parameter for the script that specifies the number of pivot points to consider when calculating the trend lines. The value of 'PivotPointNumber' is set by the user when they configure the script. It is used to determine the size of the arrays that store the values and positions of the pivot points, as well as the number of pivot points to loop through when calculating the trend lines.
'up_trend_color' is an input parameter for the script that specifies the color to use for drawing the trend lines that are determined to be upward trends. The value of 'up_trend_color' is set by the user when they configure the script and is passed to the color parameter of the line.new() function when drawing the upward trend lines. It determines the visual appearance of the upward trend lines on the chart.
'down_trend_color' is an input parameter for the script that specifies the color to use for drawing the trend lines that are determined to be downward trends. The value of 'down_trend_color' is set by the user when they configure the script and is passed to the color parameter of the line.new() function when drawing the downward trend lines. It determines the visual appearance of the downward trend lines on the chart.
'pivothigh' is a variable in the script that stores the value of the pivot high point. It is calculated using the pivothigh() function, which returns the highest high over a specified number of bars. The value of 'pivothigh' is used in the calculation of the trend lines.
'pivotlow' is a variable in the script that stores the value of the pivot low point. It is calculated using the pivotlow() function, which returns the lowest low over a specified number of bars. The value of 'pivotlow' is used in the calculation of the trend lines.
'trend_top_values' is an array in the script that stores the values of the pivot points that are determined to be at the top of the trend. These are the pivot points that are used to calculate the upward trend lines.
'trend_top_position' is an array in the script that stores the positions (i.e., bar indices) of the pivot points that are stored in the 'trend_top_values' array. These positions correspond to the locations of the pivot points on the chart.
'trend_bottom_values' is an array in the script that stores the values of the pivot points that are determined to be at the bottom of the trend. These are the pivot points that are used to calculate the downward trend lines.
'trend_bottom_position' is an array in the script that stores the positions (i.e., bar indices) of the pivot points that are stored in the 'trend_bottom_values' array. These positions correspond to the locations of the pivot points on the chart.
apointer1 and apointer2 are variables used in the add_to_array() function, which is defined in the script. They are both pointers to arrays, meaning that they hold the memory addresses of the arrays rather than the arrays themselves. They are used to manipulate the arrays by adding new elements to the beginning of the arrays and removing elements from the end of the arrays.
apointer1 is a pointer to an array of floating-point values, while apointer2 is a pointer to an array of integers. The specific arrays that they point to depend on the arguments passed to the add_to_array() function when it is called. For example, if add_to_array(trend_top_values, trend_top_posisiton, pivothigh) is called, then apointer1 would point to the tval array and apointer2 would point to the tpos array.
'bottom_lines' (short for "Bottom Lines") is an array in the script that stores the line objects for the downward trend lines that are drawn on the chart. Each element of the array corresponds to a different trend line.
'top_lines' (short for "Top Lines") is an array in the script that stores the line objects for the upward trend lines that are drawn on the chart. Each element of the array corresponds to a different trend line.
Both 'bottom_lines' and 'top_lines' are arrays of type "line", which is a data type in PineScript that represents a line drawn on a chart. The line objects are created using the line.new() function and are used to draw the trend lines on the chart. The variables are used to store the line objects so that they can be manipulated and deleted later in the script.
Loops
maxline is a variable in the script that specifies the maximum number of trend lines that can be drawn on the chart. It is used to determine the size of the bottom_lines and top_lines arrays, which store the line objects for the trend lines.
The value of maxline is set to 3 at the beginning of the script, meaning that at most 3 trend lines can be drawn on the chart at a time. This value can be changed by the user if desired by modifying the assignment statement "maxline = 3".
'count_line_low' (short for "Count Line Low") is a variable in the script that keeps track of the number of downward trend lines that have been drawn on the chart. It is used to ensure that the maximum number of trend lines (as specified by the maxline variable) is not exceeded.
'count_line_high' (short for "Count Line High") is a variable in the script that keeps track of the number of upward trend lines that have been drawn on the chart. It is used to ensure that the maximum number of trend lines (as specified by the maxline variable) is not exceeded.
Both 'count_line_low' and 'count_line_high' are initialized to 0 at the beginning of the script and are incremented each time a new trend line is drawn. If either variable exceeds the value of maxline, then no more trend lines are drawn.
'pivot1', 'up_val1', 'up_val2', up1, and up2 are variables used in the loop that calculates the downward trend lines in the script. They are used to store intermediate values during the calculation process.
'pivot1' is a loop variable that is used to iterate through the pivot points (stored in the trend_bottom_values and trend_bottom_position arrays) that are being considered for use in the trend line calculation.
'up_val1' and 'up_val2' are variables that store the values of the pivot points that are used to calculate the downward trend line.
up1 and up2 are variables that store the positions (i.e., bar indices) of the pivot points that are stored in 'up_val1' and 'up_val2', respectively. These positions correspond to the locations of the pivot points on the chart.
'value1' and 'value2' are variables that are used to store the values of the pivot points that are being compared in the loop that calculates the trend lines in the script. They are used to determine whether a trend line can be drawn between the two pivot points.
For example, if 'value1' is the value of a pivot point at the top of the trend and 'value2' is the value of a pivot point at the bottom of the trend, then a trend line can be drawn between the two points if 'value1' is greater than 'value2'. The values of 'value1' and 'value2' are used in the calculation of the slope and intercept of the trend line.
'position1' and 'position2' are variables that are used to store the positions (i.e., bar indices) of the pivot points that are being compared in the loop that calculates the trend lines in the script. They are used to determine the distance between the pivot points, which is necessary for calculating the slope of the trend line.
For example, if 'position1' is the position of a pivot point at the top of the trend and 'position2' is the position of a pivot point at the bottom of the trend, then the distance between the two points is given by 'position1' - 'position2'. This distance is used in the calculation of the slope of the trend line.
'different', 'high_line', 'low_location', 'low_value', and 'valid' are variables that are used in the loop that calculates the downward trend lines in the script. They are used to store intermediate values during the calculation process.
'different' is a variable that stores the slope of the downward trend line being calculated. It is calculated as the difference in value between the two pivot points (stored in up_val1 and up_val2) divided by the distance between the pivot points (calculated using their positions, stored in up1 and up2).
'high_line' is a variable that stores the current value of the trend line being calculated at a given point in the loop. It is initialized to the value of the second pivot point (stored in up_val2) and is updated on each iteration of the loop using the value of different.
'low_location' is a variable that stores the position (i.e., bar_index) on the chart of the point where the trend line being calculated first touches the low price. It is initialized to the position of the second pivot point (stored in up2) and is updated on each iteration of the loop if the trend line touches a lower low.
'low_value' is a variable that stores the value of the trend line at the point where it first touches the low price. It is initialized to the value of the second pivot point (stored in up_val2) and is updated on each iteration of the loop if the trend line touches a lower low.
'valid' is a Boolean variable that is used to indicate whether the trend line being calculated is valid. It is initialized to true and is set to false if the trend line does not pass through all the lows between the pivot points. If valid is still true after the loop has completed, then the trend line is considered valid and is drawn on the chart.
d_value1, d_value2, d_position1, and d_position2 are variables that are used in the loop that calculates the upward trend lines in the script. They are used to store intermediate values during the calculation process.
d_value1 and d_value2 are variables that store the values of the pivot points that are used to calculate the upward trend line.
d_position1 and d_position2 are variables that store the positions (i.e., bar indices) of the pivot points that are stored in d_value1 and d_value2, respectively. These positions correspond to the locations of the pivot points on the chart.
The variables d_value1, d_value2, d_position1, and d_position2 have the same function as the variables uv1, uv2, up1, and up2, respectively, but for the calculation of the upward trend lines rather than the downward trend lines. They are used in a similar way to store intermediate values during the calculation process.
thank you.
Honeybridge WickFill & Momentum Shift IndicatorAs the creator of this script, I am proud to introduce the "Honeybridge WickFill & Momentum Shift Indicator," a powerful tool for traders looking to capitalize on market opportunities using these two proven strategies.
WICK FILLS
First, let's take a closer look at the WickFill method. In financial markets, candlestick charts are a popular way to visualize the price movement of a security, derivative, or currency over a specific time period. Each candlestick on the chart is composed of a real body and shadows, with the top of the upper shadow representing the highest price paid during the time period, and the bottom of the lower shadow representing the lowest price paid.
The WickFill method involves placing market orders at the closed price indicated by the candlestick. This means that if a trader using this method sees a long upper wick on a bullish candlestick chart, they may place a buy market order at the closed price of the candlestick with the take profit target at the candlestick high price (the highest price paid during the time period), in the belief that the price is likely to rise. Similarly, if they see a long lower wick on a bearish candle, they may place a sell market order at the closed price of the candlestick with the take profit target at the candlestick low price (the lowest price paid during the time period), in the belief that the price is likely to fall.
The idea behind the WickFill method is that the wicks of candlestick chart patterns can provide valuable information about the price action of a security and the sentiment of market participants. By placing market orders at the prices indicated by the candlesticks close, traders using this method hope to capitalize on potential price movements and maximize their returns.
The image below highlights two Wick Fill opportunities. A signal will be provided at the candle close that says: 'Sell WF' or 'Buy WF'.
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MOMENTUM SHIFT REVERSAL
Now, let's turn to the Momentum Shift method. As the name suggests, this method involves identifying changes in the momentum of the price of a security. Traders who use this method are looking to capitalize on shifts in the strength or direction of the price momentum.
There are various ways to trade based on momentum shifts. For example, a trader may look for a security whose price is trending strongly in one direction and then look for a momentum shift that signals a change in the trend. They may then enter a trade in the direction of the new trend, hoping to ride the momentum of the price movement. Alternatively, a trader may look for a security whose price is moving in a range and then look for a momentum shift that signals a breakout from the range. They may then enter a trade in the direction of the breakout, hoping to capitalize on the momentum of the price movement.
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WICK REJECTION REVERSALS
A second Momentum Shift method is the concept of wick rejection reversal trading opportunities. This is a powerful technique for traders looking to capitalize on market opportunities.
So, what exactly is a wick rejection reversal opportunity? Essentially, it is a situation in which the price of a security rejects a wick (or shadow or tail) of a candlestick chart pattern and then reverses direction. For example, if the price is trending upwards and then encounters resistance at a certain level, it may create a long upper wick on a candlestick chart. If the price then falls back below the level of resistance and continues trending downwards, this could be considered a wick rejection reversal opportunity.
Traders can use the Indicator and signals provided to identify wick rejection reversal opportunities by defining certain conditions. For example, the code includes conditions for identifying a "sell reversal" based on the presence of a green candle with a long upper wick, and an RSI value above a certain threshold. This type of setup may indicate that the price is rejecting the resistance represented by the long upper wick and is likely to continue trending downwards.
Similarly, the code includes conditions for identifying a "buy reversal" based on the presence of a red candle with a long lower wick, and an RSI value below a certain threshold. This type of setup may indicate that the price is rejecting the support represented by the long lower wick and is likely to continue trending upwards.
Traders can find wick rejection reversal opportunities particularly beneficial for several reasons. First, these opportunities can provide clear entry and exit points for trades, which can help traders manage risk and maximize their returns. By identifying a specific level of resistance or support that has been rejected by the price, traders can have a clear idea of where to place their orders and where to set their stop-losses.
Second, wick rejection reversal opportunities can be a reliable indicator of market sentiment and direction. By considering the wicks of candlestick chart patterns, traders can gain a deeper understanding of the forces at work in the market and how market participants are reacting to them. This can help traders make more informed decisions about when to enter or exit trades.
Finally, wick rejection reversal opportunities can be found in a variety of market conditions and across different financial instruments. Whether the market is trending, range-bound, or volatile, traders can use this indicator provided to identify wick rejection reversal opportunities and capitalize on them.
In conclusion, wick rejection reversal opportunities are a valuable technique for traders looking to capitalize on market opportunities and improve their returns. By using the indicator provided and considering the wicks of candlestick chart patterns, traders can identify clear entry and exit points, understand market sentiment, and trade across different market conditions and instruments.
The image below depicts two sell opportunities, the top left is a Momentum Shift example and the top right is a Wick Rejection example. A signal will be shown on the chart at the candle close that says: 'Sell R' or 'Buy R'.
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CONCLUSION
So why might traders find the WickFill and Momentum Shift methods useful? There are several reasons. First, these methods can help traders identify potential trade opportunities that may not be immediately apparent from a simple analysis of price trends or chart patterns. By considering the wicks and momentum of a security's price movement, traders can gain a more nuanced understanding of the forces at work in the market and make more informed decisions about when to enter or exit trades.
Second, the WickFill and Momentum Shift methods can be used in conjunction with other technical analysis tools and techniques. For example, traders may use moving averages, oscillators, and trendlines to help confirm the presence of a WickFill or Momentum Shift opportunity. This can help traders increase the reliability and profitability of their trades.
Finally, the WickFill and Momentum Shift methods can be applied to a wide range of financial instruments, including stocks, forex, futures, and more. This versatility makes them useful for traders with diverse investment portfolios and strategies.
Overall, the WickFill and Momentum Shift methods are powerful tools for traders looking to capitalize on market opportunities and improve their returns. By considering the wicks and momentum of a security's price movement, traders can find profitable trading opportunities.
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FINAL COMMENT
Just like any other indicator or strategy out there, please consider the timeframe and asset that you are using this indicator with. Higher timeframe price action is more reliable than lower timeframe price action. For example, the 4H and Daily timeframes will provide more reliable signals than the 5m timeframe. With regards to assets, the indicator works extremely well with Forex pairs and Commodities, such as Gold.
I hope you enjoy the indicator.
Cosmic Channel LiteCosmic Channel Lite ( CC Lite) draws dynamic non-repainting trendlines and helps
⭐ know when a breakout is about to begin
⭐ predict the position and timing of the next swing reversal
⭐ predict sudden changes in volatility
⭐ recognize whether the price is in bearish or bullish territory
👀 HOW IT WORKS
Cosmic Channel Lite draws a dynamic channel consisting of a support line, basis line and resistance line. These are calculated by applying the Reduced Median Method to groups of moving averages of different type over several periods each, effectively taking 20 data points and reducing them to 3. In between, 6 internal levels are left to give context inside the channel with stable levels, the extremes of which help highlight the SR lines (see chart). The basis line color is determined by its smoothed angle with positive angles in green and negative in purple. The aim of this indicator is to provide a consistent and generic price context that works out-of-the-box and accordingly the settings have been stripped to the bare minimum with no need to continually adjust them.
📗 HOW TO USE IT
The Cosmic Channel Lite plots are meant to be used as a guide for entering and exiting positions and setting stop-loss and take profit levels. The indicator is deemed effective for any particular timeframe as long as the price stays within the maximum bounds of the indicator's plots. For this reason it is recommended to use Cosmic Channel Lite in a multi-chart layout where each chart has a different timeframe. The 5 primary strategies are:
long when the price reverses off of the support line and short when the price reverses off of the resistance line
long when the support line is highlighted and short when the resistance line is highlighted
long when the price breaks above the resistance line and short when the price breaks below the support line
long when the price moves above the basis line after being below it for a prolonged period and visa-versa (short when the price moves below the basis line)
long/short in the direction the price takes after a stable level ends
🔔 SMART ALERTS
Get notified at the most critical times by settings just one alert. Simply select CC Lite and Any alert() function call as the conditions when creating an alert and you will be tipped-off on bar-close as follows:
R─ (resistance line is highlighted)
S─ (support line is highlighted)
For example, an alert such as CC Lite 6h R─ would mean that during the last 6-hour bar the resistance line has been highlighted. The highlight lasts at least 15 bars from the first highlight bar regardless of price action.
True Range Adjusted Exponential Moving Average [CC]The True Range Adjusted Exponential Moving Average was created by Vitali Apirine (Stocks and Commodities Jan 2023 pgs 22-27) and this is the latest indicator in his EMA variation series. He has been tweaking the traditional EMA formula using various methods and this indicator of course uses the True Range indicator. The way that this indicator works is that it uses a stochastic of the True Range vs its highest and lowest values over a fixed length to create a multiple which increases as the True Range rises to its highest level and decreases as the True Range falls. This in turn will adjust the Ema to rise or fall depending on the underlying True Range. As with all of my indicators, I have color coded it to turn green when it detects a buy signal or turn red when it detects a sell signal. Darker colors mean it is a very strong signal and let me know if you find any settings that work well overall vs the default settings.
Let me know if you would like me to publish any other scripts that you recommend!
Theory Affinity TrendlinesThis indicator is perfect for traders who want to identify trend lines on a chart. It draws higher low uptrends and lower high downtrends, making it easy to see where the trend is going. You can also customize the settings to fit your needs, making it the perfect tool for your trading arsenal.
With this new tool, you can easily customize your experience to get the most out of your trading and analysis. With options like max lines, strength multiplier, pivot plots/text, and more, you can easily create the perfect trading analysis environment.
So why wait? Try it out today!
Leave feedback and let me know what you think.
// ############################################################################################## Input Descriptions
Pivot Left ----------------- look left n bars
Pivot Right ---------------- look right n bars
Strength ------------------- Pivot multiplier (Higher = Wider Trend lines)
Max Lines ------------------ Number of lines for each Uptrend and Downtrend
Structure Text ------------ Show HH, LL, etc. on chart
Structure Markers -------- Dots at the Pivot Highs and Lows
Plots ------------------------ Draw a line at Pivot Highs and Lows
Last Up Width ------------- Width of the current Uptrend line
Historical Up Width ------ Width of previous Uptrend lines
Last Down Width --------- Width of the current Downtrend lines
Historical Down Width --- Width of previous Downtrend lines
Line Offset ---------------- Shift trend lines right or left
* Lines may or may not "repaint". For use to identify trends that are more than likely already established and to identify trend line breaks.
Immediate Trend - VHXIMMEDIATE TREND - VULNERABLE_HUMAN_X
This indicator is used to identify the immediate trend in the market.
When a Short Term High (STH) is engulfed and closed above, we consider that as a bullish trend.
And Similarly, when a Short Term Low (STL) is engulfed and closed below, we consider that as a bullish trend.
STH - A candle that is higher than the one candle towards it's left and one candle towards it's right.
STL - A candle that is lower than the one candle towards it's left and one candle towards it's right.
HOW TO USE:
1. Do not take trades purely based on the immediate trend showcased by the indicator. Rather, use them as confluence with your trading strategy.
2. When you are expecting price to reverse at your point of interest (Denamd/Supply zone), this indicator can help you predict the reversal by showcasing the current trend.
3. Using this indicator you can travel the trend as long as there is a change of trend predicted by this indicator.
Trend line & pivot level
This script can plot pivot levels and trend lines that are haven't broke out.
In the setting, left and right means how to get the pivot. the pivot will be gotten based on the left candles and the right candles. boxes will be auto deleted after the box was broke.
Important: This is just a beta version, if you find some bug with using this script. Don't hesitate to contact me.
What the future version will have? Might be pattern scanner, multi trend line, levels in different time frame, break out alert, or better key level algorithm. Depends on when I have free time.
Cosmic GravityCosmic Gravity draws dynamic non-repainting trendlines and helps
⭐ know when to scalp
⭐ predict the position and timing of the next major reversal
⭐ predict sudden changes in volatility
⭐ recognize if the trend is bearish or bullish
👀 HOW IT WORKS
Cosmic Gravity draws a dynamic channel consisting of a basis line and several support and resistance levels for low/medium/high volatility situations, as defined by the Inner Channel and 2 Outer Channel plots respectively. The script achieves this by reducing a large number of select moving averages, their multiples, and other trend levels into a single basis line and deriving the remaining plots off of it using ATR and probability-constant multiples. The basis line color is determined by its smoothed vector similar to how our Cosmic Vector indicator paints its plot. The aim of this indicator is to provide a consistent and generic price context that works out-of-the-box; accordingly a single static average period is used throughout and the settings have been stripped to the bare minimum with no need to ever update them.
📗 HOW TO USE IT
Cosmic Gravity's channel levels are meant to be used as a guide for entering and exiting positions and setting stop-loss and take profit levels. The indicator is deemed effective for any particular timeframe as long as the price stays within the maximum bounds of the indicator's plots. For this reason it is recommended to use Cosmic Gravity in a multi-chart layout where each chart has a different timeframe. The 5 primary strategies are:
long when the price reverses off of an Outer Channel support level and short when the price reverses off of an Outer Channel resistance level
long when the price crosses above the basis line after being below it for a prolonged period and vice-versa (short when the price trend moves below the basis line)
long when the basis line color turns blue after being pink for a prolonged period and visa-versa (short when the basis line color turns pink)
long/short in the direction the price takes when it goes outside the Magnetic Gravity channel when this channel is in a tight squeeze
scalp as the price bounces between the Inner Channel levels (do this only while the price is contained inside the Inner Channel )
🔔 SMART ALERTS
Get notified at the most critical times with a single alert. Simply select Cosmic Gravity - Any alert() function call as the condition when creating an alert and you will be tipped-off on bar-close as follows:
RR↘ (price close crossed below Outer Channel R6 plot)
RR↗ (price high crossed above Outer Channel R6 plot)
R└ (price low entered R channel from above)
R┘ (price high exited R channel from above)
R┐ (price high exited R channel from below)
R┌ (price high entered R channel from below)
B↘ (price high crossed below Basis plot)
B↗ (price low crossed above Basis plot)
B╮ ( Basis vector turned negative)
B╯ ( Basis vector turned positive)
S└ (price low entered S channel from above)
S┘ (price low exited S channel from above)
S┐ (price low exited S channel from below)
S┌ (price high entered S channel from below)
SS↘ (price low crossed below Outer Channel S6 plot)
SS↗ (price close crossed above Outer Channel S6 plot)
For example, an alert such as Cosmic Gravity 6H R┐ B↘ means that during the last 6-hour bar the price exited the R channel from below and also crossed below the basis line.
🚩 DISCLAIMER
The information we create and publish here is not prohibited, doesn't constitute investment advice, and isn't created solely for qualified investors.
Converging Pullbacks and PeaksMulti Timeframe Converging Lines Indicator. Using the highest/lowest Values at 2 different lengths. Convergence created by taking the highest/lowest value and subtracting/adding the # of barssince the highest/lowest bar was set multiplied by the price multiplied by the float. Curves are created from averaging out the emas of the center lines of the extremeties.
Helps show trendlines automatically most of the time but can be tweaked by changing the floats or Fast/Slow lengths to you liking.
TradersCustomLibraryLibrary "TradersCustomLibrary"
TODO: add library description here
SelectOptimalTimeframeTrendlineSettings()
calculateShortStopLoss()
calculateLongStopLoss()
werdygerTrend()
trendLines()
stoch()
timeToString()
Aarika RSIHello traders, purpose of creating this indicator is simply trying to analyse the trend of any symbol.
This indicator can be used on any script like Indices, Stocks, Future, Currency & Crypto.
This RSI version is much simpler to identify the trend of the script than that of traditional RSI trendline. Rather than showing a line, this RSI indicates bars for better and clear visibility of RSI levels.
This is a modified version of © ParkF. I have modified it to simplest possible manner.
How to trade:
RSI level 80, I consider this as extreme-bought which means high chance if bear market from this point on any given timeframe. Whereas 20 is considered as extreme-sold and have a chance to go higher from the current level.
I recommend you to study this RSI before putting it into practice.
Always start with small target and then go for big one by trailing your profit. This is not a Holy Grail indicator which always gives profit but if you practice this indicator with consistency, your portfolio may give good returns.
Use proper money management for any trade. Go for paper trade and observe how this indicator behaves and once satisfied then only take real trade.
Disclaimer: Please make sure you study this indicator on different timeframes because inserted set of data may act differently on different scripts and may vary from timeframe to timeframe.
We advice you to use this indicator for trend-analysis and study purpose only. Author/publisher of this indicator is not responsible for your profit or loss if you use this indicator for trading purpose one way or another.
N.B.: We do not recommend using HeikinAshi charting for this particular indicator as the data inputs may behave differently than expected. If you have any query, you may comment below.
All-in-One-BTFinancialsI like to share my ALL-IN-ONE script to help you understand trendlines, overbought/sold, unified EMA, Volume trades, Chopiness index and my favourite Fibonacci. It looks a bit messy but you will get used to it.
Kalman Gain Parameter MechanicsFrequently asked question is to explain how Gain parameter works in kalman funtion. This script serves as a visual representation of Gain parameter of Kalman function used in HMA-Kalman & Trendlines script. (The function creator's name was misspeled in that script as Kahlman)
To see better results set your Chart's timeframe to Daily.
ATR Trend Bands [Misu]█ This indicator shows an upper and lower band based on price action and ATR (Average True Range)
The average true range (ATR) is a market volatility indicator used in technical analysis.
█ Usages:
The purpose of this indicator is to identify changes in trends and price action.
It is mainly used to identify breaking points and trend reversals.
But it can also be used to show resistance or support levels.
█ Features:
> Buy & Sell Alerts
> Buy & Sell Labels
> Color Bars
> Show Bands
█ Parameters:
Length: Length is used to calculate ATR.
Atr Multiplier: A factor used to balance the impact of the ATR on the Trend Bands calculation.
double Bollinger BandsThis Bollinger Band indicator is a technical analysis tool defined by a set of trendlines plotted Four standard deviations (two positively and two negatively) away from a simple moving average (SMA) of a security's price, but which can be adjusted to user preference.
Step-MA Filtered Stochastic [Loxx]Step-MA Filtered Stochastic is a stochastic indicator with step moving average filtering. This smooths the signal by filtering out noise.
What is the Stochastic Indicator?
The stochastic oscillator, also known as stochastic indicator, is a popular trading indicator that is useful for predicting trend reversals. It also focuses on price momentum and can be used to identify overbought and oversold levels in shares, indices, currencies and many other investment assets.
The stochastic oscillator measures the momentum of price movements. Momentum is the rate of acceleration in price movement. The idea behind the stochastic indicator is that the momentum of an instrument’s price will often change before the price movement of the instrument actually changes direction. As a result, the indicator can be used to predict trend reversals.
The stochastic indicator can be used by experienced traders and those learning technical analysis. With the help of other technical analysis tools such as moving averages, trendlines and support and resistance levels, the stochastic oscillator can help to improve trading accuracy and identify profitable entry and exit points.
Included:
Bar coloring
3 signal variations w/ alerts
Loxx's Expanded Source Types
