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Colored Directional Movement and Bollinger Band's Cloud by DGTThis study combines Bollinger Bands, one of the most popular technical analysis indicators on the market, and Directional Movement (DMI), which is another quite valuable technical analysis indicator. Bollinger Bands used in conjunction with Directional Movement (DMI) may help getting a better understanding of the ever changing landscape of the market and perform more advanced technical analysis Here are details of the concept applied 1- Plots Bollinger Band’s (BB) Cloud colored based on Bollinger Band Width (BBW) Indicator’s value Definition Bollinger Bands (created by John Bollinger ) are a way to measure volatility . As volatility increases, the wider the bands become and similarly as volatility decreases, the gap between bands narrows Bollinger Bands, in widely used approach, consist of a band of three lines. Likewise common usage In this study a band of five lines is implemented The line in the middle is a Simple Moving Average (SMA) set to a period of 20 bars (the most popular usage). The SMA then serves as a base for the Upper and Lower Bands. The Upper and Lower Bands are used as a way to measure volatility by observing the relationship between the Bands and price. the Upper and Lower Bands in this study are set to two and three standard deviations (widely used form is only two standard deviations) away from the SMA (The Middle Line), hence there are two Upper Bands and two Lower Bands. The background between two Upper Bands is filled with a green color and the background between two Lower Bands is filled with a red color. In this we have obtained Bollinger Band’s (BB) Clouds (Upper Cloud and Lower Cloud) Additionally the intensity of the color of the background is calculated with Bollinger Bands Width ( BBW ), which is a technical analysis indicator derived from the standard Bollinger Bands indicator. Bollinger Bands Width, quantitatively measures the width between the Upper and Lower Bands. In this study the intensity of the color of the background is increased if BBW value is greater than %25 What to look for Price Actions : Prices are almost always within the bands especially at this study the bands of three standard deviations away from the SMA. Price touching or breaking the BB Clouds could be considered as buying or selling opportunity. However this is not always the case, there are exceptions such as Walking the Bands. “Walking the Bands” can occur in either a strong uptrend or a strong downtrend. During a strong trend, there may be repeated instances of price touching or breaking through the BB Clouds. Each time that this occurs, it is not a signal, it is a result of the overall strength of the move. In this study in order to get a better understanding of the trend and add ability to perform some advanced technical analysis Directional Movement Indicator (DMI) is added to be used in conjunction with Bollinger Bands. Cycling Between Expansion and Contraction : One of the most well-known theories in regards to Bollinger Bands is that volatility typically fluctuates between periods of expansion (Bands Widening : surge in volatility and price breaks through the BB Cloud) and contraction (Bands Narrowing : low volatility and price is moving relatively sideways). Using Bollinger Bands in conjunction with Bollinger Bands Width may help identifying beginning of a new directional trend which can result in some nice buying or selling signals. Of course the trader should always use caution 2- Plots Colored Directional Movement Line Definition Directional Movement (DMI) (created by J. Welles Wilder ) is actually a collection of three separate indicators combined into one. Directional Movement consists of the Average Directional Index (ADX) , Plus Directional Indicator (+D I) and Minus Directional Indicator (-D I) . ADX's purposes is to define whether or not there is a trend present. It does not take direction into account at all. The other two indicators (+DI and -DI) are used to compliment the ADX. They serve the purpose of determining trend direction. By combining all three, a technical analyst has a way of determining and measuring a trend's strength as well as its direction. This study combines all three lines in a single colored shapes series plotted on the top of the price chart indicating the trend strength with different colors and its direction with triangle up and down shapes. What to look for Trend Strength : Analyzing trend strength is the most basic use for the DMI. Wilder believed that a DMI reading above 25 indicated a strong trend, while a reading below 20 indicated a weak or non-existent trend Crosses : DI Crossovers are the significant trading signal generated by the DMI With this study A Strong Trend is assumed when ADX >= 25 Bullish Trend is defined as (+D I > -DI ) and (ADX >= 25), which is plotted as green triangle up shape on top of the price chart Bearish Trend is defined as (+D I < -DI ) and (ADX >= 25), which is plotted as red triangle down shape on top of the price chart Week Trend is assumed when 17< ADX < 25, which is plotted as black triangles up or down shape, depending on +DI-DI values, on top of the price chart Non-Existent Trend is assumed when ADX < 17, which is plotted as yellow triangles up or down shape, depending on +DI-DI values, on top of the price chart Additionally intensity of the colors used in all cases above are defined by comparing ADX’s current value with its previous value Summary of the Study: Even more simplified and visually enhanced DMI drawing comparing to its classical usage (may require a bit practice to get used to it) As said previously, to get a better understanding of the trend and add ability to perform some advanced technical analysis Directional Movement Indicator (DMI) is used in conjunction with Bollinger Bands. PS: Analysis and tests are performed with high volatile Cryptocurrency Market Source of References : definitions provided herein are gathered from TradingView’s knowledgebase/library Disclaimer: The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd tradingview user liable for any possible claim for damages arising from any decision you make based on use of the script
Pine Script™ 인디케이터
dgtrd의
17
Distance Oscillator - evoThis shows the distance to a moving average of your choice as histogram, you can select your moving average at input "Oscillator Source". You need to have a plot on your chart (like EMA or anything else) so you can connect this indicator to it. I used Ichimoku Cloud's 20 period Conversion line (blue line) as example on the chart. You can look for divergence on the histogram, that works because most moving averages follow price, they do not lead price. Which means if the distance gets smaller but the trend still continues, it may be a loss of momentum and often a sign for a reversal or pause. I applied a moving average of the histogram, you can use this to wait for a cross to confirm divergence or can be useful to smooth signals a bit. Of course you have to play around with it a little and see what works best for you, I have not tested all settings and timeframes.
Pine Script™ 인디케이터
EvoCrypto의
2
Minkowski Distance Factor Adaptive Period MACDHi, this script comes from the idea that Ricardo Santos' Minkovski Distance Function is transferred to the period as a factor. Minkowski distance is used as a percentage factor with the help of Relative Strength Index function. Minkowski Distance Function Script : And thus an adaptive MACD was created. This script can give much better results in more optimized larger periods. I leave the decision to determine the periods and weights. I used the weights of 9,12,26 and periods created with multiplied by factor. Regards.
Pine Script™ 인디케이터
Noldo의
3
Distance to MASummary: Calculate the distance of the price to a moving average. Also be able to identify if the average distance is decreasing or increasing based on signal line. Details Length: The moving average length to measure against. Source: The price input source use to measure the distance from. Signal Length: The average of distance between Source and MA. If the average is increasing the color of the signal line will be green and if it is decreasing then it will be red.
Pine Script™ 인디케이터
imzeeshan의
14
SpreadTrade - Distance (ps4 ver. 2)This script implements a rebrushed distance-based pair trading strategy. In this strategy, normally they trade the difference between the prices of two instruments. This difference is also called spread. Here, however we’ll trade the difference between two time frames of one instrument. And that's the main trick. Common procedure consists of the following steps: 1. Select two CORRELATED stocks. Here we'll use the same instrument in different TFs. 2. Generate the spread by calculating the difference between the prices/instruments. For distance based pair trading, we need to (rescale the data first and then) check the distance between them. 3. Define the logic to trade the spread and generate the trading signals. In this example we’ll calculate the rolling mean and rolling standard deviation of the spread. Whenever the spread goes above a rolling mean by one standard deviation, we’ll short the spread expecting the mean reversion behavior to hold true. And whenever the spread goes below its rolling mean by one standard deviation, we’ll go long on the spread. Mind that the meaning of the orange and blue signals depends on whether tf variable is smaller or larger than the built-in timeframe.multiplier variable, i.e. tf of the chart. For details see analyticsprofile.com Donate: PayPal (paypal.me)
Pine Script™ 인디케이터
capissimo의
9
Volume Weighted DistanceThis script holds several useful functions from statistics and machine learning (ML) and takes measurement of a volume weighted distance in order to identify local trends. It attempts at applying ML techniques to time series processing, shows how different distance measures behave and gives you an arsenal of tools for your endeavors. Tested with BTCUSD. REM: oddly enough, many people forget that the scripts in PS are generally just STUDIES, i.e. exercises, experiments, trials, and do not embody a final solution. Please treat them as intended ;))
Pine Script™ 인디케이터
capissimo의
2
Closed Form Distance VolatilityIntroduction Calculating distances in signal processing/statistics/time-series analysis imply measuring the distance between two probability distribution, i am not really familiar with distances but since some formulas are in closed form they can be easily used for volatility estimation. This volatility indicator will use three methods originally made to measure the distance of gaussian copulas, using those methods for volatility estimation is fairly easy and provide a different approach to statistical dispersion. The indicator have a length parameter and a method parameter to select the method used for volatility estimation, i describe each methods below. Hellinger Method Each method will use the rolling sum of the low price and the rolling sum of the high price instead of probability distributions. The Hellinger method have many application from the measurement of distances to the use as a cost function for neural networks. Its closed form is defined as the square root of 1 - a^0.25b^0.25/(0.5a + 0.5b)^0.5 where a and b are both positive series. In our indicator a is the rolling sum of the high price and b the rolling sum of the low price. This method give a classic estimation of volatility. Bhattacharyya Method The Bhattacharyya method is another method who use a natural logarithm, this method can visually filter small volatility variation. It is defined as 0.5 * log((0.5a+0.5b)/√(ab)) . Wasserstein Method This method was originally using a trimmed mean for its calculation. The original method is defined as the square of the trimmed mean of a + b - 2√(a^0.5ba^0.5) , a median has been used instead of a trimmed mean for efficiency sake, both central tendency estimators are robust to outliers. Conclusion I showed that closed form formulas for distance calculation could be derived into volatility estimators with different properties. They could be used with series in a range of (0,1) to provide a smoothing variable for exponential smoothing.
Pine Script™ 인디케이터
alexgrover의
2
Distance Weighted Moving AverageAdopted to Pine from systemtradersuccess.com They wrote that this average is designed to be a robust version of a moving average to reduce the impact of outliers, but I dont see a significant difference comparing it with SMA. So, I published it for the educational purposes. To learn more about the robust filters and averages google Hampel Filter, Interquartile Range Filter and Recursive Median Filter (or any other filter that is based on quartiles). Good luck!
Pine Script™ 인디케이터
everget의
1