Wpr — Indicators and Strategies

Dual Timeframe Williams Percent Range This is a dual timeframe Williams Percent Range indicator. Function: The idea behind this indicator is for trader to see what the Williams %r is doing on higher timeframes without the need to change the chart. I added the "Smoothing" function to take the jagged lines out of the higher timeframe. It has a better flow this way. If we choose the 4H and the Daily timeframes for example. In this bullish situation I wait for the Daily WPR to cross above the -50 mid line. Then the faster 4H WPR will eventually hit the bottom and begin to rise again back into the trend. This is the "Reset" of the 4H WPR and when the 4H WPR crosses up above the -50 mid line again it means price should begin to rise on the chart. I added the option to change the colour when the signal lines cross the -50. It is good to use a fast time frame so you can see the WPR hitting the bottom in an uptrend, but not too fast. The Heiken Ashi candle sticks are a very good addition to this system. You can also use a colour changing 200 EMA if you run the "1H/Daily" in the WPR. Or the 50 EMA if you run the Daily 4H. This system could be used on lower timeframes too but I have not tested it there. The Dual WPR indicator, the colour changing 50 EMA and Heiken Ashi have been optimised for the 4H/Daily. If you want to set alerts the the faster WPR line crossing the -50 is good, on candle close. This way you will only need one alert per chart. If you get an alert on the EURUSD 4H that the 4H WPR has crossed up then look to see what what the Daily WPR is doing. If it is also above the -50 mid line then EURUSD is probably trending up. Thank you to TradingView for supplying the Williams %r template. I hope this helps some other traders out there. I combined the Supertrend and the Coloured EMA in the main screen into one indicator. This is my first indicator published :-) Have fun out there and good luck. Eddie T.

End-pointed SSA of Williams %R [Loxx]End-pointed SSA of Williams %R is an indicator that runes Williams %R SSA calculation through a Singular Spectrum Analysis (SSA) algorithm to derive a smoother final output. The reduction in noise from the traditional Williams %R is significant. What is Williams %R? Williams %R , also known as the Williams Percent Range, is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. The Williams %R may be used to find entry and exit points in the market. The indicator is very similar to the Stochastic oscillator and is used in the same way. It was developed by Larry Williams and it compares a stock’s closing price to the high-low range over a specific period, typically 14 days or periods. What is Singular Spectrum Analysis ( SSA )? Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability. For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required. "Caterpillar" SSA The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of: Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique); Singular Value Decomposition of the trajectory matrix; Reconstruction of the original time series based on a number of selected eigenvectors. This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing. The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed. Included: Bar coloring [*Alerts [*Signals [*Loxx's Expanded Source Types Related Williams %R Indicators Williams %R on Chart w/ Dynamic Zones Williams %R w/ Bollinger Bands Intermediate Williams %R w/ Discontinued Signal Lines Related SSA Indicators End-pointed SSA of FDASMA End-pointed SSA of Normalized Price Oscillator

Rsi/W%R/Stoch/Mfi: HTF overlay mini-plots Overlay mini-plots for various indicators. Shows current timeframe; and option to plot 2x higher timeframes (i.e. 15min and 60min on the 5min chart above). The idea is to de-clutter chart when you just want real-time snippets for an indicator. Useful for gauging overbought/oversold, across timeframes, at a glance. ~~Indicators~~ ~RSI: Relative strength index ~W%R: Williams percent range ~Stochastic ~MFI: Money flow index ~~Inputs~~ ~indicator length (NB default is set to 12, NOT the standard 14) ~choose 2x HTFs, show/hide HTF plots ~choose number of bars to show (current timeframe only; HTF plots show only 6 bars) ~horizontal position: offset (bars); shift plots right or left. Can be negative ~vertical position: top/middle/bottom ~other formatting options (color, line thickness, show/hide labels, 70/30 lines, 80/20 lines) ~~tips~~ ~should be relatively easy to add further indicators, so long as they are 0-100 based; by editing lines 9 and 11 ~change the vertical compression of the plots by playing around with the numbers (+100, -400, etc) in lines 24 and 25

CFB-Adaptive, Williams %R w/ Dynamic Zones [Loxx]CFB-Adaptive, Williams %R w/ Dynamic Zones is a Jurik-Composite-Fractal-Behavior-Adaptive Williams % Range indicator with Dynamic Zones. These additions to the WPR calculation reduce noise and return a signal that is more viable than WPR alone. What is Williams %R? Williams %R , also known as the Williams Percent Range, is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. The Williams %R may be used to find entry and exit points in the market. The indicator is very similar to the Stochastic oscillator and is used in the same way. It was developed by Larry Williams and it compares a stock’s closing price to the high-low range over a specific period, typically 14 days or periods. What is Composite Fractal Behavior ( CFB )? All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets. Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration. Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action. The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit! This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic. CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis. What is Jurik Volty used in the Juirk Filter? One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive. What is the Jurik Moving Average? Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines. Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun. What are Dynamic Zones? As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl" Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator. One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem. Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system. An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader. To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition: Find V such that: For dynamic zone buy: P{X <= V}=P1 For dynamic zone sell: P{X >= V}=P2 where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone. The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory. Calculating the Dynamic Zones The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone. For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone. In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone. Included: Bar coloring 3 signal variations w/ alerts Divergences w/ alerts Loxx's Expanded Source Types

Williams %R on Chart w/ Dynamic Zones [Loxx]Williams %R on Chart w/ Dynamic Zones is a Williams %R indicator but instead of being an oscillator it appears on chart. The WPR calculation used here leverages T3 moving average for its calculation. In addition, the WPR is bound by Dynamic Zones. What is Williams %R? Williams %R , also known as the Williams Percent Range, is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. The Williams %R may be used to find entry and exit points in the market. The indicator is very similar to the Stochastic oscillator and is used in the same way. It was developed by Larry Williams and it compares a stock’s closing price to the high-low range over a specific period, typically 14 days or periods. What is T3 moving average? Developed by Tim Tillson, the T3 Moving Average is considered superior to traditional moving averages as it is smoother, more responsive and thus performs better in ranging market conditions as well. What are Dynamic Zones? As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl" Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator. One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem. Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system. An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader. To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition: Find V such that: For dynamic zone buy: P{X <= V}=P1 For dynamic zone sell: P{X >= V}=P2 where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone. The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory. Calculating the Dynamic Zones The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone. For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone. In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone. Included Bar coloring Channels fill Loxx's Expanded Source Types 35+ moving average types

Williams %R w/ Bollinger Bands [Loxx]Williams %R w/ Bollinger Bands is a Williams %R indicator with Bollinger bands. The Bollinger bands are used to determine when breakouts/breakdowns occur. What is Williams %R? Williams %R , also known as the Williams Percent Range, is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. The Williams %R may be used to find entry and exit points in the market. The indicator is very similar to the Stochastic oscillator and is used in the same way. It was developed by Larry Williams and it compares a stock’s closing price to the high-low range over a specific period, typically 14 days or periods. Included: bar coloring signals alerts

Phase Accumulation, Smoothed Williams %R Histogram [Loxx]Phase Accumulation, Smoothed Williams %R Histogram is a Williams %R indicator using dynamic inputs from Ehlers Phase Accumulation Dominant Cycle Period Algorithm. This indicator includes alerts and signals and is in a smoothed histogram form. The version of Phase Accumulation in this indicator is a modified form of of Ehlers algorithm to allow for better smoothing and cycle length selection. What is Williams %R? Williams %R , also known as the Williams Percent Range, is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. The Williams %R may be used to find entry and exit points in the market. The indicator is very similar to the Stochastic oscillator and is used in the same way. It was developed by Larry Williams and it compares a stock’s closing price to the high-low range over a specific period, typically 14 days or periods. What is Phase Accumulation? The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample. The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio. Included: -Toggle on/off bar coloring -Toggle on/off signals -Alerts long/short -Loxx's Expanded Source Types Library

William %R Scalper for Gold with tortle WPR William %R Scalper with Tortle WPR is small update from WPR Scalper tool. It is used with small time frame : 5 , 3 or 1 minute. it uses tree different William %R indicators : one for fast move with 9 period WPR9 and one slow with 54 period WPR54 pLus tortle WPR 255. You can find buy zone when WPR255< -90 WPR9 < -90 and WPR54 < -80 and sell zone when WPR 255 > -20, WPR9 > -10 AND WPR54 > -20. Tortle WPR (255) provide price trend

Complex Oscillator [-W-]Eng. Tradingview in the free version has a limitation - you can only use three indicators on the chart. Complex Oscillator indicator combines several indicators in one, it is: - RSI - Stochastic - WPR (%R) - Volumes The first three are chosen because their values are in the range |0-100| and one scale can be used for them. The volumes are added because I personally feel sorry to allocate one of the three available places for them. =) It is much more convenient to use them together with some other indicator. Volumes also in the range 0-100, that is, they will not show the real numerical value, but only the value relative to the previous volumes. You can display all the indicators at once or only a few of them. The chart above shows the same indicator in three different variations. If you know any other standard indicators with values in the range |0-100|, write in the comments, I will add to this indicator. Rus. Tradingview в бесплатной версии имеет ограничение - вы можете использовать только три индикатора на графике. Индикатор Complex Oscillator объединяет несколько индикаторов в одном, это: - RSI - Stochastic - WPR (%R) - Volumes Первые три выбраны из-за того, что их значения лежат в диапазоне |0-100| и для них можно использовать одну шкалу. Объёмы добавлены, потому что лично мне жалко выделять для них одно из трёх доступных мест. =) Намного удобнее использовать их вместе с каким-нибудь другим индикатором. Объёмы относительные, тоже лежат в диапазоне 0-100, то есть реальное численное значение они не покажут, а только величину относительно предыдущих объёмов. Вы можете вывести показания сразу всех индикаторов или только нескольких из них. На графике выше представлен один и тот же индикатор в трёх разных вариациях. Если вы знаете ещё какие-нибудь стандартные индикаторы со значениями в интервале |0-100|, напишите в комментариях, я добавлю в этот индикатор.