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TASC 2023.06 Stochastic Distance Oscillator█ OVERVIEW This script implements the stochastic distance oscillator (SDO) , a momentum indicator introduced by Vitali Apirine in an article featured in TASC's June 2023 edition of Traders' Tips . The SDO is a variation of the classic stochastic oscillator and is designed to identify overbought and oversold levels, as well as detect bull and bear trend changes. █ CONCEPTS Unlike the classic stochastic oscillator, which compares an asset's price to its past price range, the SDO measures the size of the current distance relative to the maximum-minimum distance range over a set number of periods. The current distance is defined as the distance between the current price and the price n periods ago. The readings of the SDO can be used to identify the following states of the asset price: Uptrend state: the oscillator crosses over 50 from a non-uptrend state. Downtrend state: the oscillator crosses under -50 from a non-downtrend state. Overbought state: the oscillator is in an uptrend and crosses -50 for the first time. Oversold state: the oscillator is in a downtrend and crosses 50 for the first time. Trend continuity: the oscillator crosses 0 in the direction of the current trend. The script indicates these five conditions using on-chart signals and background coloring. █ CALCULATIONS The SDO is calculated as follows: 1. Calculate the distance between the current price and the price n periods ago, as well as the maximum and minimum distances for the selected lookback period. The author recommends using one of two values of n , 14 or 40 bars. 2. Calculate the time series % D that represents the relation between the asset's current distance and its distance range over a loockback period: % D = (Abs(current distance) − Abs(minimum distance)) / (Abs(maximum distance) − Abs(minimum distance)) * 100 3. Use the calculated % D to obtain the SDO: If the closing price is above the close n periods ago, SDO = % D If the closing price is below the close n periods ago, SDO = −% D If the closing price equals the close n periods ago or the current distance equals the minimum distance, SDO = 0 4. Smooth the SDO using an exponential moving average (​EMA). The author recommends using an EMA in the range from 3 to 6 . Adjustable input parameters include the number of periods n , the lookback period for calculating % D , the smoothing EMA length, and the overbought/oversold threshold level.
אינדיקטור Pine Script®
מאת ‎PineCodersTASC‎
3
TASC 2023.05 Cong Adaptive Moving Average█ OVERVIEW TASC's May 2023 edition of Traders' Tips features an article titled "An Adaptive Moving Average For Swing Trading" by Scott Cong. The article presents a new adaptive moving average (​AMA) that adjusts its parameters automatically based on market volatility. The ​AMA tracks price closely during trending movements and remains flat during congestion areas. █ CONCEPTS Conventional moving averages (MAs) use a fixed lookback period, which may lead to limited performance in constantly changing market conditions. Perry ​Kaufman's adaptive moving average , first described in his 1995 book Smarter Trading, is a great example of how an ​AMA can self-adjust to adapt to changing environments. Scott Cong draws inspiration from ​Kaufman's approach and proposes a new way to calculate the ​AMA smoothing factor. █ CALCULATIONS Following Perry Kaufman's approach, Scott Cong's ​AMA is calculated progressively as: AMA = α * Close + (1 − α) * AMA(1), where: Close = Close of the current bar AMA(1) = ​AMA value of the previous bar α = Smoothing factor between 0 and 1, defined by the lookback period The smoothing factor determines the performance of ​AMA. In Cong's approach, it is calculated as: α = Result / Effort, where: Result = Highest price of the n period − Lowest price of the n period Effort = Sum(​TR, n ), where ​TR stands for Wilder’s true range values of individual bars of the n period n = Lookback period As the price range is always no greater than the total journey, α is ensured to be between 0 and 1.
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5
TASC 2023.04 Undersampled Double MA█ OVERVIEW TASC's April 2023 edition of Traders' Tips features an article entitled "Just Ignore Them: Undersampling The Data As A Smoothing Technique" by John ​Ehlers, which explores a method for reducing noise through data sampling. This script implements the article's proposed Undersampled Double MA indicator. █ CONCEPTS The conventional approach to reducing the impact of noise in the market data on trading rules is to use smoothing filters like moving averages. However, John ​Ehlers suggests that changing the sample rate of the datastream is a simple and effective way to smooth the data and reduce noise. Specifically, he argues that undersampling the data removes high-frequency components contributing to noisiness. Notably, the elimination of these components produces less lag than that of conventional smoothing filters. The Undersampled Double MA indicator implemented in this script represents a practical application of smoothing with undersampling. It samples the data at a given rate (for instance, once every five bars, as suggested by ​Ehlers), then processes the resulting data using a Hann window filter with two different periods, producing two smooth data streams that traders can use in the same way as the combination of two conventional moving averages of different lengths (i.e., fast and slow MAs). █ CALCULATIONS The script samples data once every N th bar (by default, N = 5) and smooths the undersampled data with 6-period and 12-period Hann filters, which it plots on the main chart. Users can adjust the sampling period and the periods of each Hann filter to their liking from the inputs in the script settings.
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1
TASC 2023.03 Every Little Bit Helps█ OVERVIEW TASC's February 2023 edition of Traders' Tips includes an article titled "Every Little Bit Helps: Averaging The Open And Close To Reduce Noise" by John​ Ehlers​. This code implements the numerical example from this article. █ CONCEPTS Using theories from digital signal processing as a starting point, John ​Ehlers argues that using the average of the open and close as the source time series of an indicator instead of using only the closing price can often lead to noise reduction in the output. This effect especially applies when there is no gap between the current bar's opening and the previous bar's closing prices. This trick can reduce noise in many common indicators such as the ​​RSI, ​​MACD, and ​​Stochastic. █ CALCULATIONS Following the example presented in the original publication, this script illustrates the proposed strategy using the ​ ​Relative Strength Index (​RSI) as a test indicator. It plots two series: ​RSI calculated using only closing prices as its source. ​RSI of the same length as the first, but calculated using the average of open and close prices as its source, i.e. (open+close)/2 . This script demonstrates that using the average of open and close as the calculation source results in a smoother indicator. To visually emphasize the advantage of this proposed trick, the script's color scheme is sensitive to both the ​RSI value and the difference between the two ​RSI data streams.
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TASC 2023.01 TRAdj EMA█ OVERVIEW TASC's January 2023 edition of Traders' Tips includes an article titled "True Range Adjusted ​Exponential ​Moving ​Average (TRadj ​EMA)" by Vitali Apirine. This code implements the indicator presented in that publication. █ CONCEPTS The True Range Adjusted ​Exponential ​Moving Average (TRAdj ​EMA) is a trend-following indicator that considers volatility for a quicker response to price changes. It is an ​ ​EMA ​ that modulates its weighting factor based on the true range (TR) of the asset. In a trading strategy, traders can use a TRAdj ​EMA in tandem with ​​an ​EMA of the same length to identify an asset's trend, or they can use it alongside ​another TRAdj ​EMA of a different length to identify turning points and filter price movements. █ CALCULATIONS The following steps are performed to calculate the indicator: • Calculate the asset's TR according to the standard definition proposed by J. Welles Wilder, Jr. • Define the ​true range adjustment factor as: ​ ​ ​ ​ ​ ​ ​ ​ TRAdj =( Current TR − Minimum TR) ⁄ ( Maximum TR − Minimum TR ), where Maximum TR and Minimum TR are the maximum and minimum TR values over the specified lookback period . • Calculate the TRAdj ​EMA in the same manner as a traditional ​​EMA, but with a weighting factor defined as: ​ ​ ​ ​ ​ ​ ​ ​ 2*(1 + TRAdj * Multiplier ) ⁄ ( ​EMA length + 1), where Multiplier is an input parameter that ranges from 5 to 10. For comparison, a traditional ​EMA uses a weighting factor of 2 ⁄ ( ​EMA length + 1).
אינדיקטור Pine Script®
מאת ‎PineCodersTASC‎
8
TASC 2022.12 Short-Term Continuation And Reversal Signals█ OVERVIEW TASC's December 2022 edition Traders' Tips includes an article by Barbara Star titled "Short-Term Continuation And Reversal Signals". This is the code that implements the concepts presented in this publication. █ CONCEPTS The article takes two classic indicators, the Commodity Channel Index (​CCI) and the Directional Movement Indicator (DMI), makes changes to the traditional ways of visualizing their readings, and uses them together to generate potential signals. The author first discusses the benefits of converting the ​DMI indicator to an oscillator format by subtracting the ​−​DI from the ​+​DI, which is then displayed as a histogram. Next, the author shows how the use of an on-chart visual framework (i.e., choosing the line style and color, coloring price bars, etc.) can help traders interpret the signals produced the considered pair of indicators. █ CALCULATIONS The article offers the following signals based on the readings of the ​DMI and ​CCI pair, suitable for several types of trades: • Short-term trend change signals: A ​DMI oscillator above zero indicates that prices are in an uptrend. A DMI oscillator below the zero line and falling means that selling pressure is dominating and price is trending down. The sign of the ​DMI oscillator is indicated by the color of the price bars (which correlates with the color of the ​DMI histogram). Namely, green, red and grey price bars correspond to the DMI oscillator above, below and equal to zero . Colored price bars and the ​DMI oscillator make it easy for trend traders to recognize changes in short-term trends. • Trend continuation signals: Blue circles appear near the bottom of the oscillator chart border when the ​​DMI is above the zero line and the price is above its simple moving average in an uptrend . Dark red circles appear near the top of the chart in a downtrend when the ​DMI oscillator is below its zero line and below the 18-period moving average. Trend continuation signals are useful for those looking to add to existing positions, as well as for traders waiting for a pullback after a trend has started. • Reversal signals: The ​CCI signals a reversal to the downside when it breaks out of its +100 and then returns at some point, crossing below the +100 level. This is indicated by a magenta-colored diamond shape near the top the chart. The ​CCI signals a reversal to the upside when it moves below its −100 level and then at some point comes back to cross above the −100 level. This is indicated by a yellow diamond near the bottom of the chart. Reversal signals offer short-term rallies for countertrend traders as well as for swing traders looking for longer-term moves using the interplay between continuation and reversal signals.
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4
TASC 2022.11 Phasor Analysis█ OVERVIEW TASC's November 2022 edition Traders' Tips includes an article by John ​Ehlers titled "Recurring Phase Of Cycle Analysis". This is the code that implements the ​ phasor analysis indicator presented in this publication. █ CONCEPTS The article explores the use of phasor analysis to identify market trends. An ordinary rotating phasor diagram is a two-dimensional vector, anchored to the origin, whose rotation rate corresponds to the cycle period in the price data stream. Similarly, Ehlers' phasor is a representation of angular phase rotation along the course of time. Its angle reflects the current phase of the cycle. Angles -180, -90, +90 and +180 degrees correspond to the beginning, valley, peak and end of the cycle, respectively. If the observed cycle is very long, the market can be considered trending . In his article, John ​Ehlers defined trending behavior to occur when the derived instantaneous cycle period value is greater that 60 bars. The author also introduced guidelines for long and short entries in a trending state. Depending on the tuning of the indicator period input, a long entry position may occur when the phasor angle is around the approximate vicinity of −90 degrees, while a short entry position may occur when the phasor angle will be around the approximate vicinity of +90 degrees. Applying these definitive guidelines, the author proposed a state variable that is indicated by +1 for a trending long position, 0 for cycling, and −1 for a trending short position (or out). The phasor angle, the cycle period, and the state variable are made available with three selectable display modes provided for this TradingView indicator. █ CALCULATIONS The calculations are carried out as follows. First, the price data stream is correlated with cosine and sine of a fixed cycle period. This produces two new data streams that correspond to the projections of the frequency domain phasor diagram to the horizontal (so-called real ) and vertical (so-called imaginary ) axis respectively. The wavelength of the cycle period input should be set for the midrange vicinities of the phasor to coincide with the peaks and valleys of the charted price data. Secondly, the phase angle of the phasor is easily computed as the arctangent of the ratio of the imaginary component to the real component. The difference between the current phasor values and its last is then employed to calculate a derived instantaneous period and market state. This computation is then repeated successively for each individual bar over the entire duration of the data set.
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מאת ‎PineCodersTASC‎
8
TASC 2022.10 RS VA EMA█ OVERVIEW TASC's October 2022 edition Traders' Tips includes the second part of the "​Relative Strength Moving Averages" article series authored by Vitali Apirine. This is the code that implements the ​Relative Strength Volume-Adjusted ​Exponential Moving Average (​RS VA ​EMA) presented in this publication. █ CONCEPTS In his article series, the author argues that the relative strength of price, ​volume, and ​volatility can potentially be used to filter price movements and define turning points. In particular, the ​RS VA ​​EMA indicator is designed to account for the ​relative strength of ​volume. Like the traditional ​ exponential moving average (​EMA) , it is a lagging trend-following indicator. The difference is that it responds more quickly. In a trading strategy, ​​RS VA ​EMA is suggested to be used in combination with ​​EMA of the same length to determine the overall trend or in combination with ​RS VA ​EMA of a different length to identify turning points and filter price movements. █ CALCULATIONS The calculation of ​RS VA ​EMA is based on the concept of ​volume strength (VS). By definition, VS measures the difference between "positive" and "negative" ​volume flow. ​Volume is indicated as "positive" when the close is higher than the previous close and "negative" when the close is below the previous close. The following steps are used in the calculation process: • Calculate the ​volume strength (VS) of a given length. • Multiply VS by a predefined multiplier and calculate the ​EMA of the resulting time series. The values of 10,10,10 are the typical input settings for ​RS VA ​EMA, where the first parameter is the length of the moving average, the second is the length of VS, and the third is the ​volume strength multiplier.
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2
TASC 2022.09 LRAdj EMA█ OVERVIEW TASC's September 2022 edition of Traders' Tips includes an article by Vitali Apirine titled "The Linear Regression-Adjusted Exponential Moving Average". This script implements the titular indicator presented in this article. █ CONCEPT The Linear Regression-Adjusted ​Exponential Moving Average (LRAdj ​EMA) is a new tool that combines a ​ linear regression indicator with exponential moving averages . First, the indicator accounts for the ​linear regression deviation, that is, the distance between the price and the ​linear regression indicator. Subsequently, an ​exponential moving average (​EMA) smooths the price data and and provides an indication of the current direction. As part of a trading system, LRAdj ​EMA can be used in conjunction with an ​exponential moving average of the same length to identify the overall trend. Alternatively, using LRAdj EMAs of different lengths together can help identify turning points. █ CALCULATION The script uses the following input parameters: ​EMA Length ​LR Lookback Period Multiplier The calculation of LRAdj ​EMA is carried out as follows: Current LRAdj ​EMA = Prior LRAdj ​EMA + MLTP × (1+ LRAdj × Multiplier ) × ( Price − Prior LRAdj ​EMA ), where MLTP is a weighting multiplier defined as MLTP = 2 ⁄ ( ​EMA Length + 1), and LRAdj is the ​linear regression adjustment (LRAdj) multiplier: LRAdj = (Abs( Current ​LR ​Dist )−Abs( Minimum ​LR ​Dist )) ⁄ (Abs( Maximum ​LR ​Dist )−Abs( Minimum ​LR ​Dist )) When calculating the LRAdj multiplier, the absolute values of the following quantities are used: Current ​LR ​Dist is the distance between the current close and the ​linear regression indicator with a length determined by the ​LR Lookback Period parameter, Minimum ​LR ​Dist is the minimum distance between the close and the ​linear regression indicator for the LR lookback period , Maximum ​LR ​Dist is the maximum distance between the close and the ​linear regression indicator for the LR lookback period .
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מאת ‎PineCodersTASC‎
1
TASC 2022.08 Trading The Fear Index█ OVERVIEW TASC's August 2022 edition of Traders' Tips includes an article by Markos Katsanos titled "Trading The Fear Index". This script implements a trading strategy called the “daily long/short trading system for ​volatility ETFs” presented in this article. █ CONCEPTS This long-term strategy aims to capitalize on stock market volatility by using exchange-traded funds (ETFs or ETNs) linked to the VIX index. The strategy rules (see below) are based on a combination of the movement of the ​Cboe VIX index, the readings of the stochastic oscillator applied to the SPY ​ETF relative to the ​​VIX, and a custom indicator presented in the article and called the correlation trend . Thus, they are not based on the price movement of the traded ​ETF itself, but rather on the movement of the ​VIX and of the S&P 500 index. This allows the strategy to capture most of the spikes in ​volatility while profiting from the long-term time decay of the traded ETFs. █ STRATEGY RULES Long rules Rising ​volatility: The ​VIX should rise by more than 50% in the last 6 days. Trend: The correlation trend of the ​VIX should be 0.8 or higher and also higher than yesterday's value. ​VIX-​SPY relative position: The 25-day and 10-day ​VIX ​stochastics should be above the 25-day and 10-day ​SPY ​stochastics respectively. In addition, the 10-day ​stochastic of the ​VIX should be above its yesterday's value. Long positions are closed if the 10-day ​stochastic of the ​SPY rises above the 10-day ​​stochastic of the ​VIX or falls below the yesterday's value. Short rules Declining ​volatility: The ​VIX should drop over 20% in the last 6 days and should be down during the last 3 days. ​VIX threshold: The ​VIX should spend less than 35% of time below 15. ​VIX-​SPY relative position: The 10-day ​VIX ​stochastic should be below the 10-day ​SPY ​stochastic. In addition, the 10-day ​SPY ​stochastic should be higher than the yesterday's value. Long positions are closed if the first two Long rules are triggered (Rising ​volatility and Trend). The script allows you to display the readings of the indicators used in the strategy rules in the form of oscillator time series (as in the preview chart) and/or in the form of a table.
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מאת ‎PineCodersTASC‎
TASC 2022.07 Pairs Rotation With Ehlers Loops█ OVERVIEW TASC's July 2022 edition of Traders' Tips includes an article by John Ehlers titled "Pairs Rotation With ​Ehlers Loops". This is the code that implements the Ehlers Loops applied to pairs rotation trading. █ CONCEPTS John Ehlers developed Ehlers loops as a tool to visualize the performance of one data stream versus another. Initially, he used this tool to chart price versus volume. However, Ehlers loops proved to be suitable for determining the timing of the pairs rotation strategy . This strategy works by having a long position in only one of two securities, depending on which one is considered stronger at a given time. When the prices of two securities (filtered and scaled with a standard deviation for consistent presentation) are plotted against each other, the curvature and direction of rotation on the chart can help guide decisions on long positions. For example, when plotting a stock versus a referenced symbol, a vertical upward movement while rotating clockwise is a sign of going long the stock. Similarly, a horizontal movement to the right while rotating counterclockwise is the sign to go long the reference. A higher probability of a reversal is expected when the price moves more than one or two standard deviations. █ CALCULATIONS The script uses the following steps to calculate the Ehlers Loops: The price data of both securities in the pair are individually filtered using identical high-pass and SuperSmoother filters. This results in two band-limited data streams, having a nominally zero mean. The input parameters Low-Pass Period and High-Pass Period control the filter bandwidth and thus can modify the shape of the Ehlers Loops. Subsequently, the filtered data streams are scaled in terms of standard deviation by dividing each of them by their root-mean-square (​RMS) values. These data streams are plotted as zero-mean oscillators. Finally, the scaled data streams are displayed one against another for the selected time interval (defined by the input parameter Loop Segments ). In the resulting scatterplot, the thicker line corresponds to the later data points. The fluctuations of the filtered price data of the chart symbol are plotted along the y -axis, and the price changes of the referenced symbol are shown along the x -axis.
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4
TASC 2022.06 Ehlers Loops█ OVERVIEW TASC's June 2022 edition Traders' Tips includes an article by John ​Ehlers titled "Ehlers Loops. Part 1". This is the code implementing the price-volume Ehlers Loops he introduced in the publication. █ CONCEPTS John ​Ehlers developed Ehlers loops as a tool to visualize the performance of one data stream versus another, both filtered and scaled. In this article, the author applies his concept to exploit and/or dispel the dogmatic principles of reliable price-volume relationships. The script offers two different ways to visualize ​Ehlers Loops: Oscillators (default option) In this implementation, filtered and scaled ​volume is plotted along with filtered and scaled price as zero-mean oscillators. Observation of the relative direction of ​volume and price oscillators can be discretionarily used to interpret and predict market conditions. For example, it is generally assumed that an increase in ​volume and an increase in price define a ​bullish condition. Similarly, decreasing ​volume and increasing price are generally considered ​bearish. A decrease in ​volume and a decrease in price is considered a ​bullish condition. The increase in ​volume and decrease in price is often thought to be ​​bearish. Scatterplot This Crocker-style visualization displays filtered and scaled price against filtered and scaled ​volume for the selected timespan. Fluctuations in ​volume are plotted along the x -axis, while price changes along the y -axis. This way of visualizing the ​Ehlers Loop allows you to analyze the curvature and directional path of the price in relation to ​volume, offering a different comparative perspective. The boundaries of the price and ​volume scale on the ​Ehlers Loop Crocker-chart are presented in standard deviations. Deviations can be used to predict possible future price or ​volume fluctuations. The expected probability of potential reversals is 68%, 95% and 99.7% at one, two and three standard deviations, respectively. █ CALCULATIONS The following steps are used to build an ​Ehlers Loop:  • Both price and ​volume are filtered to be band-limited signals. This is done by applying the high-pass Butterworth filter in combination with the low-pass SuperSmooth filter.   The cutoff wavelengths of the high-pass and low-pass filters are defined by the input parameters HPPeriod and LPPeriod , respectively.   These values change the appearance of the ​Ehlers Loops and can be customized to your trading style.  • The filtered price and ​volume time series are then scaled in terms of standard deviation by dividing each by their root-mean-square values.  • The resultant price and ​volume data are plotted as zero-mean oscillators or as a scatterplot.
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11
TASC 2022.05 Relative Strength Exponential Moving Average█ OVERVIEW TASC's May 2022 edition Traders' Tips includes the "Relative Strength Moving Averages" article authored by Vitali Apirine. This is the code implementing the Relative Strength Exponential Moving Average (​RS EMA) indicator introduced in this publication. █ CONCEPTS ​RS EMA is an adaptive trend-following indicator with reduced lag characteristics. By design, this was made possible by harnessing the relative strength of price. It operates in a similar fashion to a traditional EMA, but it has an improved response to price fluctuations. In a trading strategy, ​RS EMA can be used in conjunction with an EMA of the same length to identify the overall trend (see the preview chart). Alternatively, ​RS EMAs with different lengths can define turning points and filter price movements. ​RS EMA is an adaptive trend-following indicator with reduced lag characteristics. By design, this was made possible by harnessing the relative strength of price. It operates in a similar fashion to a traditional EMA, but it has an improved response to price fluctuations. █ CALCULATIONS The following steps are used in the calculation process: • Calculate the relative strength (​RS) of a given length. • Multiply ​RS by a chosen coefficient (multiplier) to adapt the EMA filtering the original time series. Calculate the EMA of the resulting time series. The author recommends ​RS EMA(10,10,10) as typical settings, where the first parameter is the EMA length, the second parameter is the ​RS length, and the third parameter is the ​RS multiplier. Other values may be substituted depending on your trading style and goals.
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4
TASC 2022.04 S&P500 Hybrid Seasonal System█  OVERVIEW TASC's April 2022 edition of Traders' Tips includes the "Sell In May? Stock Market Seasonality" article authored by Markos Katsanos. This is the code implementing the "Hybrid ​Seasonal System" from the article. █  CONCEPTS In his article, Markos Katsanos takes an updated look at the "Sell in May" adage by reviewing recent historical data for ​seasonal equity market tendencies. The author explores the development of a trading strategy (a set of buy and sell rules) based on this research. He starts from the enhanced buy & hold system featured in his July 2021 TASC article, and adds additional technical conditions. These include volatility conditions ( VIX and ATR ) plus the "Volume Flow Indicator" (VFI), which is a custom money flow indicator that Katsanos introduced in his June 2004 TASC article. He provides an example of a trading system that others can test for themselves and modify as they see fit. The author notes that the system could likely be improved further by adding money management conditions (such as a stop-loss), or by adding more technical conditions not considered in the ​scope of this article. █  CALCULATIONS The entry and exit rules that constitute the trading system are defined below. The critical values of ​VIX, ​ATR and VFI (specified below) used in the calculations were determined by optimization for a daily chart of the SPY ETF . By default, the strategy only allows long entries. However, the script offers the possibility to initiate short entries upon exiting long trades through the "Long Only" toggle in the script's inputs. Long Entry Rules ​• Seasonal: The ​seasonal trade is initiated on the first business day October at the open. • Volatility: In case of high ​volatility, that is if the ​VIX is above 60% or the 15-day ​​ATR was above 90% over the past 25 days, the ​seasonal trade is deferred until later in the month or year, when the ​volatility subsides. Exit/Short Entry Rules • Seasonal: The exit/short signal is triggered on the first business day of August at the open. • Volatility: The exit/short signal is triggered if ​VIX is above 120 % (i.e. 2 times the corresponding threshold parameter). • Money flow (VFI): The exit/short signal is triggered if the VFI crosses under a critical value (-20) while its 10-day moving average is pointing down. Join TradingView!
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2
TASC 2022.03 Relative Strength Volatility-Adjusted EMA█  OVERVIEW TASC's March 2022 edition of Traders' Tips includes the "Relative Strength Moving Averages - Part 3: The Relative Strength Volatility-Adjusted Exponential Moving Average" article authored by Vitali ​Apirine. This is the code that implements the "RS VolatAdj EMA" from the article. █  CONCEPTS In a three-part article series, Vitaly Apirine examines ways to filter price movements and define turning points by applying the Relative Strength concept to exponential moving averages . The resulting indicator is more responsive and is intended to account for the relative strength of volatility . █  CALCULATIONS The calculation process uses the following steps: Select an appropriate volatility index (in our case it is VIX ). Calculate up day volatility (UV) smoothed by a 10-day EMA. Calculate down day volatility (DV) smoothed by a 10-day EMA. Take the absolute value of the difference between UV and DV and divide by the sum of UV and DV. This is the Volatility Strength we need. Calculate a MLTP constant - the weighting multiplier for an exponential moving average. Combine Volatility Strength and MLTP to create an exponential moving average on current price data. Join TradingView!
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TASC 2022.02 Ehlers' Elegant Oscillator█ OVERVIEW TASC's February 2022 edition of Traders' Tips includes the "Inverse ​Fisher Transform Redux — An Elegant Oscillator" article authored by John ​Ehlers. This is the code implementing the "Elegant Oscillator" from the article. █ CONCEPTS By applying the inverse Fisher transform to a waveform with a nominal Gaussian probability distribution using root mean square ( RMS ) scaling and smoothing the result, John ​Ehlers creates an oscillator that swings between -1 and 1. █ CALCULATIONS The calculation process uses the following steps:  • Compute the 2-bar difference of closing prices.  • Calculate the root mean square (​RMS) of the differences.  • Scale the differences using the computed ​RMS.  • Apply the inverse Fisher transform to the scaled values.  • Smooth the transformed data with the SuperSmoother filter. Join TradingView!
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18
TASC 2022.01 Improved RSI w/Hann█ OVERVIEW TASC's January 2022 edition Traders' Tips includes the "(Yet Another Improved) RSI Enhanced With Hann Windowing" article authored by John Ehlers. Once again John Ehlers revolutionizes the RSI indicator. This is TradingView's Pine Script code for the indicator. █ CONCEPTS By employing a Hann windowed finite impulse response filter ( FIR ), John Ehlers has enhanced the "Relative Strength Indicator" ( RSI ) to provide an improved oscillator with exceptional smoothness. █ NOTES Calculations The method of calculations using "closes up" and "closes down" from Welles Wilder's RSI described in his 1978 book is still inherent to Ehlers enhanced formula. However, a finite impulse response (FIR) Hann windowing technique is employed following the closes up/down calculations instead of the original Wilder infinite impulse response averaging filter. The resulting oscillator waveform is confined between +/-1.0 with a 0.0 centerline regardless of chart interval, as opposed to Wilder's original formulation, which was confined between 0 and 100 with a centerline of 50. On any given trading timeframe, the value of Ehlers' enhanced RSI found above the centerline typically represents an overvalued region, while undervalued regions are typically found below the centerline. Background The original RSI indicator was designed by J. Welles Wilder and presented in his "New Concepts in Technical Trading Systems" book published in 1978. Join TradingView!
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מאת ‎PineCodersTASC‎
6
TASC 2021.12 Directional Movement w/Hann█  OVERVIEW Presented here is code for the "Directional Movement w/Hann" indicator originally conceived by John ​Ehlers. The code is also published in the December 2021 issue of Trader's Tips by Technical Analysis of Stocks & ​Commodities (TASC) magazine. Ehlers continues here his exploration of the application of Hann windowing to conventional trading indicators. █  FEATURES The rolling length can be modified in the script's inputs, as well as the width of the line. █  NOTES Calculations The calculation starts with the classic definition of PlusDM and MinusDM. These directional movements are summed in an exponential moving average (EMA). Then, this EMA is further smoothed in a finite impulse response (FIR) filter using Hann window coefficients over the calculation period. Background The DMI and ADX indicators were designed by J. Welles Wilder and presented in his "New Concepts in Technical Trading Systems" book published in 1978. Join TradingView!
אינדיקטור Pine Script®
מאת ‎PineCodersTASC‎
7
TASC 2021.11 MADH Moving Average Difference, Hann█  OVERVIEW Presented here is code for the "Moving Average Difference, Hann" indicator originally conceived by John ​Ehlers. The code is also published in the November 2021 issue of Trader's Tips by Technical Analysis of Stocks & ​Commodities (TASC) magazine. █  CONCEPTS By employing a Hann windowed finite impulse response filter (FIR), John ​Ehlers has enhanced the Moving Average Difference (MAD) to provide an oscillator with exceptional smoothness. ​Of notable mention, the wave form of MADH resembles ​Ehlers' "Reverse EMA" Indicator, formerly revealed in the September 2017 issue of TASC. Many variations of the "Reverse EMA" were published in TradingView's Public Library. █  FEATURES Three values in the script's "Settings/Inputs" provide control over the oscillators behavior:  • The price source  • A "Short Length" with a default of 8, to manage the lower band edge of the oscillator  • The "Dominant Cycle", originally set at 27, which appears to be a placeholder for an adaptive control mechanism Two coloring options are provided for the line's fill:  • "ZeroCross", the default, uses the line's position above/below the zero level. This is the mode used in the top version of MADH on this chart.  • "Momentum" uses the line's up/down state, as shown in the bottom version of the indicator on the chart. █  NOTES Calculations The source price is used in two independent Hann windowed FIR filters having two different periods (lengths) of historical observation for calculation, one being a "Short Length" and the other termed "Dominant Cycle". These are then passed to a "rate of change" calculation and then returned by the reusable function. The secret ​sauce is that a "windowed Hann FIR filter" is superior tp a generic SMA filter, and that ultimately reveals Ehlers' clever enhancement. We'll have to wait and see what ingenuities ​Ehlers has next to unleash. Stay tuned... The `madh()` function code was optimized for computational efficiency in Pine, differing visibly from ​Ehlers' original formula, but yielding the same results as ​Ehlers' version. Background This indicator has a sibling indicator discussed in the "The MAD Indicator, Enhanced" article by ​Ehlers. MADH is an evolutionary update from the prior MAD indicator code published in the October 2021 issue of TASC. Sibling Indicators  • Moving Average Difference (MAD)  • Cycle/Trend Analytics Related Information  • Cycle/Trend Analytics And The MAD Indicator  • The Reverse ​EMA Indicator  • Hann Window  • ROC Join TradingView!
אינדיקטור Pine Script®
מאת ‎PineCodersTASC‎
7
TASC 2021.10 - MAD Moving Average DifferencePresented here is code for the "Moving Average Difference" indicator originally conceived by John Ehlers, also referred to as MAD. This is one of TradingView's first code releases published in the October 2021 issue of Trader's Tips by Technical Analysis of Stocks & Commodities (TASC) magazine. This indicator has a companion indicator that is discussed in the article entitled Cycle/Trend Analytics And The MAD Indicator , authored by John Ehlers. He's providing an innovative double dose of indicator code for the month of October 2021. John Ehlers generally describes it as a "thinking man's" MACD . MAD has similar, yet distinct, intended operation. For those of you familiar with the MACD indicator operation, you will find MACD adjustments having defaults of 12 and 26, while MAD has comparable adjustments with defaults of 8 and 23. These are intended for adjustment, same as any other oscillator. The MAD indicator can be basically described as two simple moving averages applied within a "rate of change" (ROC) calculation. Further Related Information  • SMA  • ROC Join TradingView!
אינדיקטור Pine Script®
מאת ‎PineCodersTASC‎
3
TASC 2021.10 - Cycle/Trend AnalyticsPresented here is code for the "Cycle/Trend Analytics" indicator originally conceived by John Ehlers​. This is another one of TradingView's first code releases published in the October 2021 issue of Trader's Tips by Technical Analysis of Stocks & Commodities (TASC) magazine. This indicator, referred to as "CTA" in later explanations, has a companion indicator that is discussed in the article entitled MAD Moving Average Difference , authored by John Ehlers​. He's providing an innovative double dose of indicator code for the month of October 2021. Modes of Operation ​CTA has two modes defined as "trend" and "cycle". Ehlers' intention from what can be gathered from the article is to portray "the strength of the trend" in trend mode on real data. Cycle mode exhibits the response of the bank of calculations when a hypothetical ​sine ​wave is utilized as price. When cycle mode is chosen, two other lines will be displayed that are not shown in trend mode. A more detailed explanation of the indicator's technical functionality and intention can be found in the original Cycle/Trend Analytics And The MAD Indicator article, which requires a subscription. Computational Functionality The ​​CTA indicator only has one adjustment in the indicator "Settings" for choice of modes. The default mode of operation is "trend". Trend mode applies raw price data to the bank of plots, while the cycle mode employs a sinusoidal oscillator set to a cycle period of 30 bars. These are passed to multiple ​SMAs, which are then subtracted from the original source data. The result is a fascinating display of plots embellished with vivid array of gradient color on real data or the hypothetical ​sine ​wave. Related Information  • SMA  • color.rgb() Join TradingView
אינדיקטור Pine Script®
מאת ‎PineCodersTASC‎
2