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Oscillator Workbench — Chart [LucF]█  OVERVIEW This indicator uses an on-chart visual framework to help traders with the interpretation of any oscillator's behavior. The advantage of using this tool is that you do not need to know all the ins and outs of a particular oscillator such as RSI, CCI, Stochastic, etc. Your choice of oscillator and settings in this indicator will change its visuals, which allows you to evaluate different configurations in the context of how the workbench models oscillator behavior. My hope is that by using the workbench, you may come up with an oscillator selection and settings that produce visual cues you find useful in your trading. The workbench works on any symbol and timeframe. It uses the same presentation engine as my Delta ​Volume Channels indicator; those already familiar with it will feel right at home here. █  CONCEPTS Oscillators An oscillator is any signal that moves up and down a centerline. The centerline value is often zero or 50. Because the range of oscillator values is different than that of the symbol prices we look at on our charts, it is usually impossible to display an oscillator on the chart, so we typically put oscillators in a separate pane where they live in their own space. Each oscillator has its own profile and properties that dictate its behavior and interpretation. Oscillators can be bounded , meaning their values oscillate between fixed values such as 0 to 100 or +1 to -1, or unbounded when their maximum and minimum values are undefined. Oscillator weight How do you display an oscillator's value on a chart showing prices when both values are not on the same scale? The method I use here converts the oscillator's value into a percentage that is used to weigh a reference line. The weight of the oscillator is calculated by maintaining its highest and lowest value above and below its centerline since the beginning of the chart's history. The oscillator's relative position in either of those spaces is then converted to a percentage, yielding a positive or negative value depending on whether the oscillator is above or below its centerline. This method works equally well with bounded and unbounded oscillators. Oscillator Channel The ​​oscillator channel is the space between two moving averages: the reference line and a weighted version of that line. The reference line is a moving average of a type, source and length which you select. The weighted line uses the same settings, but it averages the oscillator-weighted price source. The weight applied to the source of the reference line can also include the relative size of the bar's ​volume in relation to previous bars. The effect of this is that the oscillator's weight on bars with higher total ​volume will carry greater weight than those with lesser ​volume. The ​oscillator channel can be in one of four states, each having its corresponding color:  • Bull (teal): The weighted line is above the reference line.  • Strong bull (lime): The bull condition is fulfilled and the bar's close is above the reference line and both the reference and the weighted lines are rising.  • Bear (maroon): The weighted line is below the reference line.  • Strong bear (pink): The bear condition is fulfilled and the bar's close is below the reference line and both the reference and the weighted lines are falling. Divergences In the context of this indicator, a divergence is any bar where the slope of the reference line does not match that of the weighted line. No directional bias is assigned to divergences when they occur. You can also choose to define divergences as differences in polarity between the oscillator's slope and the polarity of close-to-close values. This indicator's divergences are designed to identify transition levels. They have no polarity; their ​bullish/​bearish bias is determined by the behavior of price relative to the divergence channel after the divergence channel is built. Divergence Channel The divergence channel is the space between two levels (by default, the bar's low and high ) saved when divergences occur. When price has breached a channel and a new divergence occurs, a new channel is created. Until that new channel is breached, bars where additional divergences occur will expand the channel's levels if the bar's price points are outside the channel. Price breaches of the divergence channel will change its state. Divergence channels can be in one of five different states:  • Bull (teal): Price has breached the channel to the upside.  • Strong bull (lime): The bull condition is fulfilled and the ​oscillator channel is in the strong bull state.  • Bear (maroon): Price has breached the channel to the downside.  • Strong bear (pink): The bear condition is fulfilled and the ​oscillator channel is in the strong bear state.  • Neutral (gray): The channel has not been breached. █  HOW TO USE THE INDICATOR Load the indicator on an active chart (see here if you don't know how). The default configuration displays:  • The Divergence channel's levels.  • Bar colors using the state of the ​oscillator channel. The default settings use:  • RSI as the oscillator, using the close source and a length of 20 bars.  • An Arnaud-Legoux moving average on the close and a length of 20 bars as the reference line.  • The weighted version of the reference line uses only the oscillator's weight, i.e., without the relative ​volume's weight.   The weighted line is capped to three standard deviations of the reference.  • The divergence channel's levels are determined using the high and low of the bars where divergences occur.   Breaches of the channel require a bar's low to move above the top of the channel, and the bar's high to move below the channel's bottom. No markers appear on the chart; if you want to create alerts from this script, you will need first to define the conditions that will trigger the markers, then create the alert, which will trigger on those same conditions. To learn more about how to use this indicator, you must understand the concepts it uses and the information it displays, which requires reading this description. There are no videos to explain it. █  FEATURES The script's inputs are divided in five sections: "Oscillator", "Oscillator channel", "Divergence channel", "Bar Coloring" and "Marker/Alert Conditions". Oscillator This is where you configure the oscillator you want to study. Thirty oscillators are available to choose from, but you can also use an oscillator from another indicator that is on your chart, if you want. When you select an external indicator's plot as the oscillator, you must also specify the value of its centerline. Oscillator Channel Here, you control the visibility and colors of the reference line, its weighted version, and the oscillator channel between them. You also specify what type of moving average you want to use as a reference line, its source and its length. This acts as the ​oscillator channel's baseline. The weighted line is also a moving average of the same type and length as the reference line, except that it will be calculated from the weighted version of the source used in the reference line. By default, the weighted line is capped to three standard deviations of the reference line. You can change that value, and also elect to cap using a multiple of ATR instead. The cap provides a mechanism to control how far the weighted line swings from the reference line. This section is also where you can enable the relative ​volume component of the weight. Divergence Channel This is where you control the appearance of the divergence channel and the key price values used in determining the channel's levels and breaching conditions. These choices have an impact on the behavior of the channel. More generous level prices like the default low and high selection will produce more conservative channels, as will the default choice for breach prices. In this section, you can also enable a mode where an attempt is made to estimate the channel's bias before price breaches the channel. When it is enabled, successive increases/decreases of the channel's top and bottom levels are counted as new divergences occur. When one count is greater than the other, a bull/bear bias is inferred from it. You can also change the detection mode of divergences, and choose to display a mark above or below bars where divergences occur. Bar Coloring You specify here:  • The method used to color chart bars, if you choose to do so.  • If you want to hollow out the bodies of bars where ​volume has not increased since the last bar. Marker/Alert Conditions Here, you specify the conditions that will trigger up or down markers. The trigger conditions can include a combination of state transitions of the ​oscillator and the divergence channels. The triggering conditions can be filtered using a variety of conditions. Configuring the marker conditions is necessary before creating an alert from this script, as the alert will use the marker conditions to trigger. Realtime values will repaint, as is usually the case with oscillators, but markers only appear on bar closes, so they will not repaint. Keep in mind, when looking at markers on historical bars, that they are positioned on the bar when it closes — NOT when it opens. Raw values The raw values calculated by this script can be inspected using the Data Window, including the oscillator's value and the weights. █  INTERPRETATION Except when mentioned otherwise, this section's charts use the indicator's default settings, with different visual components turned on or off. The aim of the ​ oscillator channel is to provide a visual representation of an oscillator's general behavior. The simplest characteristic of the channel is its bull/bear state, determined by whether the weighted line is above or below the reference line. One can then distinguish between its bull and strong bull states, as transitions from strong bull to bull states will generally happen when trends are losing steam. While one should not infer a reversal from such transitions, they can be a good place to tighten stops. Only time will tell if a reversal will occur. One or more divergences will often occur before reversals. This shows the oscillator channel, with the reference line and the thicker, weighted line: The nature of the divergence channel 's design makes it particularly adept at identifying consolidation areas if its settings are kept on the conservative side. The divergence channel will also reveal transition areas. A gray divergence channel should usually be considered a no-trade zone. More adventurous traders can use the oscillator ​ channel to orient their trade entries if they accept the risk of trading in a neutral divergence channel, which by definition will not have been breached by price. This show only the divergence channels: This chart shows divergence channels and their levels, and colors bars on divergences and on the state of the oscillator channel, which is not visible on the chart: If your charts are already busy with other stuff you want to hold on to, you could consider using only the chart bar coloring component of this indicator. Here we only color bars using the combined state of the oscillator and divergence channel, and we do not color the bodies of bars where ​volume has not increased. Note that my chart's settings do not color the candle bodies: At its simplest, one way to use this indicator would be to look for overlaps of the strong bull/bear colors in both the ​oscillator channel and a divergence channel, as these identify points where price is breaching the divergence channel when the oscillator's state is consistent with the direction of the breach. Tip One way to use the Workbench is to combine it with my Delta ​Volume Channels indicator. If both indicators use the same MA as a reference line, you can display its delta ​volume channel instead of the oscillator channel. This chart shows such a setup. The Workbench displays its divergence levels, the weighted reference line using the default ​RSI oscillator, and colors bars on divergences. The ​DV Channels indicator only displays its delta ​volume channel, which uses the same MA as the workbench for its baseline. This way you can ascertain the ​volume delta situation in contrast with the visuals of the Workbench: █  LIMITATIONS  • For some of the oscillators, assumptions are made concerning their different parameters when they are more complex than just a source and length.   See the `oscCalc()` function in this indicator's code for all the details, and ask me in a comment if you can't find the information you need.  • When an oscillator using ​volume is selected and no ​volume information is available for the chart's symbol, an error will occur.  • The method I use to convert an oscillator's value into a percentage is fragile in the early history of datasets   because of the nascent expression of the oscillator's range during those early bars. █  NOTES Working with this workbench This indicator is called a workbench for a reason; it is designed for traders interested in exploring its behavior with different oscillators and settings, in the hope they can come up with a setup that suits their trading methodology. I cannot tell you which setup is the best because its setup should be compatible with your trading methodology, which may require faster or slower transitions, thus different configurations of the settings affecting the calculations of the divergence channels. For Pine Script™ Coders  • This script uses the new overload of the fill() function which now makes it possible to do vertical gradients in Pine. I use it for both channels displayed by this script.  • I use the new arguments for plot() 's `display` parameter to control where the script plots some of its values,   namely those I only want to appear in the script's status line and in the Data Window.  • I used my ta library for some of the oscillator calculations and helper functions.  • I also used TradingView's ta library for other oscillator calculations.  • I wrote my script using the revised recommendations in the Style Guide from the Pine v5 User Manual.
Indicatore Pine Script®
di LucF
81
Candlestick Pattern Criteria and Analysis Indicator█  OVERVIEW Define, then locate the presence of a candle that fits a specific criteria. Run a basic calculation on what happens after such a candle occurs. Here, I’m not giving you an edge, but I’m giving you a clear way to find one. IMPORTANT NOTE: PLEASE READ: THE INDICATOR WILL ALWAYS INITIALLY LOAD WITH A RUNTIME ERROR. WHEN INITIALLY LOADED THERE NO CRITERIA SELECTED. If you do not select a criteria or run a search for a criteria that doesn’t exist, you will get a runtime error. If you want to force the chart to load anyway, enable the debug panel at the bottom of the settings menu. Who this is for: - People who want to engage in TradingView for tedious and challenging data analysis related to candlestick measurement and occurrence rate and signal bar relationships with subsequent bars. People who don’t know but want to figure out what a strong bullish bar or a strong bearish bar is. Who this is not for: - People who want to be told by an indicator what is good or bad or buy or sell. Also, not for people that don’t have any clear idea on what they think is a strong bullish bar or a strong bearish bar and aren’t willing to put in the work. Recommendation: Use on the candle resolution that accurately reflects your typical holding period. If you typically hold a trade for 3 weeks, use 3W candles. If you hold a trade for 3 minutes, use 3m candles. Tldr; Read the tool tips and everything above this line. Let me know any issues that arise or questions you have. █  CONCEPTS Many trading styles indicate that a certain candle construct implies a bearish or bullish future for price. That said, it is also common to add to that idea that the context matters. Of course, this is how you end up with all manner of candlestick patterns accounting for thousands of pages of literature. No matter the context though, we can distill a discretionary trader's decision to take a trade based on one very basic premise: “A trader decides to take a trade on the basis of the rightmost candle's construction and what he/she believes that candle construct implies about the future price.” This indicator vets that trader’s theory in the most basic way possible. It finds the instances of any candle construction and takes a look at what happens on the next bar. This current bar is our “Signal Bar.” █  GUIDE I said that we vet the theory in the most basic way possible. But, in truth, this indicator is very complex as a result of there being thousands of ways to define a ‘strong’ candle. And you get to define things on a very granular level with this indicator. Features: 1. Candle Highlighting When the user’s criteria is met, the candle is highlighted on the chart. The following candle is highlighted based on whether it breaks out, breaks down, or is an inside bar. 2. User-Defined Criteria Criteria that you define include: Candle Type: Bull bars, Bear bars, or both Candle Attributes Average Size based on Standard Deviation or Average of all potential bars in price history Search within a specific price range Search within a specific time range Clarify time range using defined sessions and with or without weekends 3. Strike Lines on Candle Often you want to know how price reacts when it gets back to a certain candle. Also it might be true that candle types cluster in a price region. This can be identified visually by adding lines that extend right on candles that fit the criteria. 4. User-Defined Context Labeled “Alternative Criteria,” this facet of the script allows the user to take the context provided from another indicator and import it into the indicator to use as a overriding criteria. To account for the fact that the external indicator must be imported as a float value, true (criteria of external indicator is met) must be imported as 1 and false (criteria of external indicator is not met) as 0. Basically a binary Boolean. This can be used to create context, such as in the case of a traditional fractal, or can be used to pair with other signals. If you know how to code in Pinescript, you can save a copy and simply add your own code to the section indicated in the code and set your bull and bear variables accordingly and the code should compile just fine with no further editing needed. Included with the script to maximize out-of-the-box functionality, there is preloaded as alternative criteria a code snippet. The criteria is met on the bull side when the current candle close breaks out above the prior candle high. The bear criteria is met when the close breaks below the prior candle. When Alternate Criteria is run by itself, this is the only criteria set and bars are highlighted when it is true. You can qualify these candles by adding additional attributes that you think would fit well. Using Alternative Criteria, you are essentially setting a filter for the rest of the criteria. 5. Extensive Read Out in the Data Window (right side bar pop out window). As you can see in the thumbnail, there is pasted a copy of the Data Window Dialogue. I am doubtful I can get the thumbnail to load up perfectly aligned. Its hard to get all these data points in here. It may be better suited for a table at this point. Let me know what you think. The primary, but not exclusive, purpose of what is in the Data Window is to talk about how often your criteria happens and what happens on the next bar. There are a lot of pieces to this. Red = Values pertaining to the size of the current bar only Blue = Values pertaining or related to the total number of signals Green = Values pertaining to the signal bars themselves, including their measurements Purple = Values pertaining to bullish bars that happen after the signal bar Fuchsia = Values pertaining to bearish bars that happen after the signal bar Lime = Last four rows which are your percentage occurrence vs total signals percentages The best way I can explain how to understand parts you don’t understand otherwise in the data window is search the title of the row in the code using ‘ctrl+f’ and look at it and see if it makes more sense. █ [b}Available Candle Attributes Candle attributes can be used in any combination. They include: [*}Bodies [*}High/Low Range [*}Upper Wick [*}Lower Wick [*}Average Size [*}Alternative Criteria Criteria will evaluate each attribute independently. If none is set for a particular attribute it is bypassed. Criteria Quantity can be in Ticks, Points, or Percentage. For percentage keep in mind if using anything involving the candle range will not work well with percentage. Criteria Operators are “Greater Than,” “Less Than,” and “Threshold.” Threshold means within a range of two numbers. █ Problems with this methodology and opportunities for future development: #1 This kind of work is hard. If you know what you’re doing you might be able to find success changing out the inputs for loops and logging results in arrays or matrices, but to manually go through and test various criteria is a lot of work. However, it is rewarding. At the time of publication in early Oct 2022, you will quickly find that you get MUCH more follow through on bear bars than bull bars. That should be obvious because we’re in the middle of a bear market, but you can still work with the parameters and contextual inputs to determine what maximizes your probability. I’ve found configurations that yield 70% probability across the full series of bars. That’s an edge. That means that 70% of the time, when this criteria is met, the next bar puts you in profit. #2 The script is VERY heavy. Takes an eternity to load. But, give it a break, it’s doing a heck of a lot! There is 10 unique arrays in here and a loop that is a bit heavy but gives us the debug window. #3 If you don’t have a clear idea its hard to know where to start. There are a lot of levers to pull on in this script. Knowing which ones are useful and meaningful is very challenging. Combine that with long load times… its not great. #4 Your brain is the only thing that can optimize your results because the criteria come from your mind. Machine learning would be much more useful here, but for now, you are the machine. Learn. #5 You can’t save your settings. So, when you find a good combo, you’ll have to write it down elsewhere for future reference. It would be nice if we could save templates on custom indicators like we can on some of the built in drawing tools, but I’ve had no success in that. So, I recommend screenshotting your settings and saving them in Notion.so or some other solid record keeping database. Then you can go back and retrieve those settings. #6 no way to export these results into conditions that can be copy/pasted into another script. Copy/Paste of labels or tables would be the best feature ever at this point. Because you could take the criteria and put it in a label, copy it and drop it into another strategy script or something. But… men can dream. █ Opportunities to PineCoders Learn: 1. In this script I’m importing libraries, showing some of my libraries functionality. Hopefully that gives you some ideas on how to use them too. The price displacement library (which I love!) Creative and conventional ways of using debug() how to display arrays and matrices on charts I didn’t call in the library that holds the backtesting function. But, also demonstrating, you can always pull the library up and just copy/paste the function out of there and into your script. That’s fine to do a lot of the time. 2. I am using REALLY complicated logic in this script (at least for me). I included extensive descriptions of this ? : logic in the text of the script. I also did my best to bracket () my logic groups to demonstrate how they fit together, both for you and my future self. 3. The breakout, built-in, “alternative criteria” is actually a small bit of genius built in there if you want to take the time to understand that block of code and think about some of the larger implications of the method deployed. As always, a big thank you to TradingView and the Pinescript community, the Pinescript pros who have mentored me, and all of you who I am privileged to help in their Pinescripting journey. "Those who stay will become champions" - Bo Schembechler
Indicatore Pine Script®
di kurtsmock
6
Tradesharpe Session BiasThis script is designed for traders who want help defining their session bias it is for people who trade in sessions which will most likely be 1 4h candle. The way I trade using Price action to get my daily bias, to either look for sells or buys or both I look at the previous daily candle close and previous 4hr candle close before analyzing the structure on the lower time frames to get my session bias of bullish/bearish. so this indicator compares the daily and 4hr candles to develop a bias for example previous daily bullish + Previous 4hr Bullish = BULLISH BIAS previous daily Bearish + Previous 4hr Bearish = BEARISH BIAS if Daily bullish 4hr bearish = MIXED SESSION if daily bearish 4hr bullish = MIXED SESSION MIXED SESSION = Can argue both buys and sells BEARISH SESSION = Best to look for Sells only based on my trading style BULLISH SESSION = Best to look for Buys only based on my trading style
Indicatore Pine Script®
di TradeSharpe99
8
Divergence Cheat Sheet'Divergence Cheat Sheet' helps in understanding what to look for when identifying divergences between price and an indicator. The strength of a divergence can be strong, medium, or weak. Divergences are always most effective when references prior peaks and on higher time frames. The most common indicators to identify divergences with are the Relative Strength Index (RSI) and the Moving average convergence divergence (MACD). Regular Bull Divergence: Indicates underlying strength. Bears are exhausted. Warning of a possible trend direction change from a downtrend to an uptrend. Hidden Bull Divergence: Indicates underlying strength. Good entry or re-entry. This occurs during retracements in an uptrend. Nice to see during the price retest of previous lows. “Buy the dips." Regular Bear Divergence: Indicates underlying weakness. The bulls are exhausted. Warning of a possible trend direction change from an uptrend to a downtrend. Hidden Bear Divergence: Indicates underlying weakness. Found during retracements in a downtrend. Nice to see during price retests of previous highs. “Sell the rallies.” Divergences can have different strengths. Strong Bull Divergence Price: Lower Low Indicator: Higher Low Medium Bull Divergence Price: Equal Low Indicator: Higher Low Weak Bull Divergence Price: Lower Low Indicator: Equal Low Hidden Bull Divergence Price: Higher Low Indicator: Higher Low Strong Bear Divergence Price: Higher High Indicator: Lower High Medium Bear Divergence Price: Equal High Indicator: Lower High Weak Bear Divergence Price: Higher High Indicator: Equal High Hidden Bull Divergence Price: Lower High Indicator: Higher High
Indicatore Pine Script®
di TanHef
7
Possible RSI [Loxx]Possible RSI is a normalized, variety second-pass normalized, Variety RSI with Dynamic Zones and optionl High-Pass IIR digital filtering of source price input. This indicator includes 7 types of RSI. High-Pass Fitler (optional) The Ehlers Highpass Filter is a technical analysis tool developed by John F. Ehlers. Based on aerospace analog filters, this filter aims at reducing noise from price data. Ehlers Highpass Filter eliminates wave components with periods longer than a certain value. This reduces lag and makes the oscialltor zero mean. This turns the RSI output into something more similar to Stochasitc RSI where it repsonds to price very quickly. First Normalization Pass RSI (Relative Strength Index) is already normalized. Hence, making a normalized RSI seems like a nonsense... if it was not for the "flattening" property of RSI. RSI tends to be flatter and flatter as we increase the calculating period--to the extent that it becomes unusable for levels trading if we increase calculating periods anywhere over the broadly recommended period 8 for RSI. In order to make that (calculating period) have less impact to significant levels usage of RSI trading style in this version a sort of a "raw stochastic" (min/max) normalization is applied. Second-Pass Variety Normalization Pass There are three options to choose from: 1. Gaussian (Fisher Transform), this is the default: The Fisher Transform is a function created by John F. Ehlers that converts prices into a Gaussian normal distribution. The normaliztion helps highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. 2. Softmax: The softmax function, also known as softargmax: or normalized exponential function,  converts a vector of K real numbers into a probability distribution of K possible outcomes. It is a generalization of the logistic function to multiple dimensions, and used in multinomial logistic regression. The softmax function is often used as the last activation function of a neural network to normalize the output of a network to a probability distribution over predicted output classes, based on Luce's choice axiom. 3. Regular Normalization (devaitions about the mean): Converts a vector of K real numbers into a probability distribution of K possible outcomes without using log sigmoidal transformation as is done with Softmax. This is basically Softmax without the last step. 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. 7 Types of RSI See here to understand which RSI types are included: Included: Bar coloring 4 signal types Alerts Loxx's Expanded Source Types Loxx's Variety RSI Loxx's Dynamic Zones
Indicatore Pine Script®
di loxx
1
Koncorde PlusKONCORDE IS ONLY INTENDED TO BE APPLIED TO ASSETS WHERE VOLUME DATA IS PROVIDED. This indicator is made up of 6 indicators: 4 trend (RSI, MFI, BB, Stochastic) and 2 volume. The 2's for volume are the PVI (positive volume index) and the NVI (negative volume index). These two indicators are the interesting ones as they are programmed to proportionally attribute the volume traded between the strong hands (sharks) and the weak hands (minnows). As for what time period to use, the bigger the better, since after all what we are doing is data analysis and therefore the more data, the better. When strong hands (blue histogram) are below zero, they are said to be selling while when they are above zero, they are said to be buying. The same goes for weak hands (green histogram). Meaning of each zone: Blue histogram: strong hand (sharks). If it is positive it indicates accumulation and if it is negative distribution. Green histogram: weak hand (minnows). If it is positive it indicates buy and if it is negative it indicates sale. Brown histogram: Indicates the trend and depends on previous values ​​of weak hands and trend indicators (RSI, MFI, BB, Stochastic). Red line: It is an average that smoothes the trend indicated by the brown histogram (default is the EMA). Crossing Pattern The pattern gives us a bullish entry signal when the trend (brown histogram) crosses above the average (red line) and is positioned bearish when the trend crosses below the average. Zero Pattern When the price trend (brown histogram) tends to zero, it means that there will be a change in its trend. This pattern is for trading in a bullish position. Spring Pattern When a cross between the average (red line) and the trend (brown histogram) has already occurred, and in addition the weak hands are above the price trend, that "spring on the mountain" is formed that gives us to understand that the upward trend will be more than evident. Mirror Pattern This pattern occurs when there is panic in the market and weak hands are selling (below zero). If at that moment the strong hands are buyers, the price tends to level off to begin the rise later. This pattern is compatible with the Crossover Pattern, having more guarantees of success. If just after finishing the mirror pattern, the Crossover Pattern plus the Spring Pattern appears, then we have a good chance of winning. Bear Hug Pattern This pattern is for bearish positions only. It is the opposite figure to the mirror pattern. That is, we have strong hands clearly selling and weak hands clearly buying and above the price trend (brown histogram). It is the figure where you can see that the strong hands are distributing the assets to the weak hands. Harpoon Pattern If when the mirror pattern occurs, the red line crosses the blue histogram, a very strong bullish entry signal is produced. Add an exit signal which occurs when we are in a spring pattern but the big hands start selling, mostly coinciding with the start of the bear hug pattern. General rules for operating the Mirror Pattern: a) Wait for the green histogram to start recovery, rise to positive values; if possible, until it crosses from bottom to top the brown line (brown histogram) and/or red average . b) The blue histogram should be consistently positive. If it turns and goes towards negative values ​​it can indicate a failed pattern at that same point. c) Locate the low of the lower candle within the pattern and place the Stop Loss just below it for reference. d) If we are not sure (we almost never will be) that there will be a turn or if it could finally be a bearish continuation we can use the SL to go short . Additional: A panel with performance statistics of the analyzed asset was added. Added an indicator that shows the cumulative delta volume in the form of triangles at the top of the chart. Added of user @DonovanWall PS: Unofficial version, I was guided by the description of the BLAI5 author's website www.blai5.net DISCLAIMER: For educational and entertainment purposes only. Nothing in this content should be interpreted as financial advice or a recommendation to buy or sell any sort of security or investment including all types of cryptos. DYOR, TYOB.
Indicatore Pine Script®
di OskarGallard
26
Yasir Hameed Advance RSI IndicatorRELATIVE STRENGTH INDEX ( RSI ) This is a tool that is widely used Especially for Overbought and Oversold systems, but I have made some changes in this indicator, How to use it...! I have set it as the default setting - RSI Length: 7 - Overbought: 70 - Oversold: 30 What is unique about this tool? we can see 3 conditions: 1) RSI Overbought / Oversold with Bullish Engulfing / Bearish Engulfing 2) RSI Overbought / Oversold with Hammer and Shooting Star 3) RSI Overbought / Oversold with 2 Bullish Bars / 2 Bearish Bars 4) RSI Overbought / Oversold with All Patterns at the same time When the RSI reaches its Oversold line, the code will wait for Bullish Engulfing pattren, when oversold and Bullish engulfing matched, This indicator will generate a buy signal when the condition is met, and same as for Bear market, When the RSI reaches its Overbought line, the code will wait for Bearish Engulfing pattren, This indicator will generate a sell/exit signal when the condition is met, 2nd condition is that a Hammer candle will be waited for when RSI touches the Overbought line, for Bullish Move and Shooting Star candle will be waited for when RSI touches the Overbought line, for Bullish Move, for Bearish Move 3rd Condition is also the same as Condition 1 and Condition 2, When the RSI reaches its Oversold line, the code will wait for 2 Bullish Bars, when oversold and 2 Bullish Bars matched then this indicator will generate a buy signal, and same as for Bear market, When the RSI reaches its Overbought line, the code will wait for 2 Bearish Bars, when overbought and 2 Bearish Bars matched then this indicator will generate a Sell signal, 4th Condition is that we can use All Conditions at the same time, - Bullish Engulfing / Bearish Engulfing - Hammer and Shooting Star - 2 Bullish Bars / 2 Bearish Bars
Indicatore Pine Script®
di Yasir_Hameed
7
TriexDev - SuperBuySellTrend (PLUS+)Minimal but powerful. Have been using this for myself, so thought it would be nice to share publicly. Of course no script is correct 100% of the time, but this is one of if not the best in my basic tools. (This is the expanded/PLUS version) Github Link for latest/most detailed + tidier documentation Base Indicator - Script Link TriexDev - SuperBuySellTrend (SBST+) TradingView Trend Indicator --- SBST Plus+ Using the "plus" version is optional, if you only want the buy/sell signals - use the "base" version. ## What are vector candles? Vector Candles (inspired to add from TradersReality/MT4) are candles that are colour coded to indicate higher volumes, and likely flip points / direction changes, or confirmations. These are based off of PVSRA (Price, Volume, Support, Resistance Analysis). You can also override the currency that this runs off of, including multiple ones - however adding more may slow things down. PVSRA - From MT4 source: Situation "Climax" Bars with volume >= 200% of the average volume of the 10 previous chart TFs, and bars where the product of candle spread x candle volume is >= the highest for the 10 previous chart time TFs. Default Colours: Bull bars are green and bear bars are red. Situation "Volume Rising Above Average" Bars with volume >= 150% of the average volume of the 10 previous chart TFs. Default Colours: Bull bars are blue and bear are blue-violet. A blue or purple bar can mean the chart has reached a top or bottom. High volume bars during a movement can indicate a big movement is coming - or a top/bottom if bulls/bears are unable to break that point - or the volume direction has flipped. This can also just be a healthy short term movement in the opposite direction - but at times sets obvious trend shifts. ## Volume Tracking You can shift-click any candle to get the volume of that candle (in the pair token/stock), if you click and drag - you will see the volume for that range. ## Bollinger Bands Bollinger Bands can be enabled in the settings via the toggle. Bollinger Bands are designed to discover opportunities that give investors a higher probability of properly identifying when an asset is oversold (bottom lines) or overbought (top lines). >There are three lines that compose Bollinger Bands: A simple moving average (middle band) and an upper and lower band. >The upper and lower bands are typically 2 standard deviations +/- from a 20-day simple moving average, but they can be modified. --- Base Indicator ## What is ATR? The average true range (ATR) is a technical analysis indicator, which 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 - the current low; - the absolute value of the current high - the previous close; - and the absolute value of the current low - the previous close. The ATR is then a moving average, generally using 10/14 days, of the true ranges. ## What does this indicator do? Uses the ATR and multipliers to help you predict price volatility, ranges and trend direction. > The buy and sell signals are generated when the indicator starts plotting either on top of the closing price or below the closing price. A buy signal is generated when the ‘Supertrend’ closes above the price and a sell signal is generated when it closes below the closing price. > It also suggests that the trend is shifting from descending mode to ascending mode. Contrary to this, when a ‘Supertrend’ closes above the price, it generates a sell signal as the colour of the indicator changes into red. > A ‘Supertrend’ indicator can be used on equities, futures or forex, or even crypto markets and also on daily, weekly and hourly charts as well, but generally, it will be less effective in a sideways-moving market. Thanks to KivancOzbilgic who made the original SuperTrend Indicator this was based off --- ## Usage Notes Two indicators will appear, the default ATR multipliers are already set for what I believe to be perfect for this particular (double indicator) strategy. If you want to break it yourself (I couldn't find anything that tested more accurately myself), you can do so in the settings once you have added the indicator. Basic rundown: - A single Buy/Sell indicator in the dim colour; may be setting a direction change, or just healthy movement. - When the brighter Buy/Sell indicator appears; it often means that a change in direction (uptrend or downtrend) is confirmed. --- You can see here, there was a (brighter) green indicator which flipped down then up into a (brighter) red sell indicator which set the downtrend. At the end it looks like it may be starting to break the downtrend - as the price is hitting the trend line. (Would watch for whether it holds above or drops below at that point) Another example, showing how sometimes it can still be correct but take some time to play out - with some arrow indicators. Typically I would also look at oscillators, RSI and other things to confirm - but here it held above the trend lines nicely, so it appeared to be rather obvious. It's worth paying attention to the trend lines and where the candles are sitting. Once you understand/get a feel for the basics of how it works - it can become a very useful tool in your trading arsenal. Also works for traditional markets & commodities etc in the same way / using the same ATR multipliers, however of course crypto generally has bigger moves. --- You can use this and other indicators to confirm likeliness of a direction change prior to the brighter/confirmation one appearing - but just going by the 2nd(brighter) indicators, I have found it to be surprisingly accurate. Tends to work well on virtually all timeframes, but personally prefer to use it on 5min,15min,1hr, 4hr, daily, weekly. Will still work for shorter/other timeframes, but may be more accurate on mid ones. --- This will likely be updated as I go / find useful additions that don't convolute things. The base indicator may be updated with some limited / toggle-able features in future also.
Indicatore Pine Script®
di Xeirt
4
VHF-Adaptive, Digital Kahler Variety RSI w/ Dynamic Zones [Loxx]VHF-Adaptive, Digital Kahler Variety RSI w/ Dynamic Zones is an RSI indicator with adaptive inputs, Digital Kahler filtering, and Dynamic Zones. This indicator uses a Vertical Horizontal Filter for calculating the adaptive period inputs and allows the user to select from 7 different types of RSI. What is VHF Adaptive Cycle? Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length. What is Digital Kahler? From Philipp Kahler's article for www.traders-mag.com, August 2008. "A Classic Indicator in a New Suit: Digital Stochastic" Digital Indicators Whenever you study the development of trading systems in particular, you will be struck in an extremely unpleasant way by the seemingly unmotivated indentations and changes in direction of each indicator. An experienced trader can recognise many false signals of the indicator on the basis of his solid background; a stupid trading system usually falls into any trap offered by the unclear indicator course. This is what motivated me to improve even further this and other indicators with the help of a relatively simple procedure. The goal of this development is to be able to use this indicator in a trading system with as few additional conditions as possible. Discretionary traders will likewise be happy about this clear course, which is not nerve-racking and makes concentrating on the essential elements of trading possible. How Is It Done? The digital stochastic is a child of the original indicator. We owe a debt of gratitude to George Lane for his idea to design an indicator which describes the position of the current price within the high-low range of the historical price movement. My contribution to this indicator is the changed pattern which improves the quality of the signal without generating too long delays in giving signals. The trick used to generate this “digital” behavior of the indicator. It can be used with most oscillators like RSI or CCI . First of all, the original is looked at. The indicator always moves between 0 and 100. The precise position of the indicator or its course relative to the trigger line are of no interest to me, I would just like to know whether the indicator is quoted below or above the value 50. This is tantamount to the question of whether the market is just trading above or below the middle of the high-low range of the past few days. If the market trades in the upper half of its high-low range, then the digital stochastic is given the value 1; if the original stochastic is below 50, then the value –1 is given. This leads to a sequence of 1/-1 values – the digital core of the new indicator. These values are subsequently smoothed by means of a short exponential moving average . This way minor false signals are eliminated and the indicator is given its typical form. 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 4 signal types Alerts Loxx's Expanded Source Types Loxx's Moving Averages Loxx's Variety RSI Loxx's Dynamic Zones
Indicatore Pine Script®
di loxx
1
CFB-Adaptive Velocity Histogram [Loxx]CFB-Adaptive Velocity Histogram is a velocity indicator with One-More-Moving-Average Adaptive Smoothing of input source value and Jurik's Composite-Fractal-Behavior-Adaptive Price-Trend-Period input with Dynamic Zones. All Juirk smoothing allows for both single and double Jurik smoothing passes. Velocity is adjusted to pips but there is no input value for the user. This indicator is tuned for Forex but can be used on any time series data. 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
Indicatore Pine Script®
di loxx
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
Indicatore Pine Script®
di loxx
1
Engulfing Candle Marker (Full Candle)A simple indicator that marks FULL CANDLE engulfing candles... current high > previous high current low < previous low bullish: close > open (triangle below) bearish: close < open (triangle above) A lot of the engulfing indicators available are body only. I created a FULL CANDLE indicator because that's what I prefer using. Can be useful for spotting both reversal and continuation price action. Always have a complete trading plan and backtest it for yourself, engulfing candles in themselves to not guarantee a profit. You are more powerful than you know...keep expanding!
Indicatore Pine Script®
di TradingHeroes
4
R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs. [Loxx]The full name of this indicator is R-Squared Adaptive Fisher Transform w/ Dynamic Zones and Divergences. This is an R-squared adaptive Fisher transform with adjustable dynamic zones, signals, alerts, and divergences. What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. What is R-squared Adaptive? One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement. The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average . R-squared is used here to derive an r-squared value that is then modified by a user input "factor" 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 4 signal variations w/ alerts Divergences w/ alerts Loxx's Expanded Source Types
Indicatore Pine Script®
di loxx
2
STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones [Loxx]STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones is a standard deviation filtered R-squared Adaptive T3 moving average with dynamic zones. What is the T3 moving average? Better Moving Averages Tim Tillson November 1, 1998 Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years. Introduction "Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done." This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced. "Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator. Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus. We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes: It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values. It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits. The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade! Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how: Two Interesting Moving Averages We will examine two benchmark moving averages based on Linear Regression analysis. In both cases, a Linear Regression line of length n is fitted to price data. I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that 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. We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA . Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present. However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line. These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot. A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2. Filter Techniques Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag. There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by: L' = L(time series) + L(time series - L(time series)) That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to: 2L-L(L) This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994). In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator. We will use these two techniques to construct our better moving average, after we explore the first one a little more closely. Fixing Overshoot An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2. Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once. This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible. The solution to the overshoot problem is to recall what we are doing with twicing: DEMA (n) = EMA (n) + EMA (time series - EMA (n)) The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block: EMA (n) + EMA (time series - EMA (n))*.7; This is algebraically the same as: EMA (n)*1.7-EMA( EMA (n))*.7; I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is: GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v, Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data: T3(n) = GD ( GD ( GD (n))) In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves. What is R-squared Adaptive? One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement. The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average . R-squared is used here to derive a T3 factor used to modify price before passing price through a six-pole non-linear Kalman filter. 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 Signals Alerts Loxx's Expanded Source Types
Indicatore Pine Script®
di loxx
Variety RSI w/ Dynamic Zones [Loxx]Variety RSI w/ Dynamic Zones is an indicator with 7 different RSI types with Dynamic Zones. This indicator has signal crossing options for signal, middle, and all Dynamic Zone levels. What is RSI? 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 is displayed as an oscillator (a line graph) on a scale of zero to 100. The indicator was developed by J. Welles Wilder Jr. and introduced in his seminal 1978 book, New Concepts in Technical Trading Systems. The RSI can do more than point to overbought and oversold securities. It can also indicate securities that may be 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. 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 RSI source pre-smoothing options Bar coloring 4 types of signal crossing options Alerts Loxx's Expanded Source Types Loxx's RSI Variety RSI types
Indicatore Pine Script®
di loxx
2
Natural Market Mirror (NMM) and NMAs w/ Dynamic Zones [Loxx]Natural Market Mirror (NMM) and NMAs w/ Dynamic Zones is a very complex indicator derived from Sloman's Ocean Theory. This indicator contains 3 core outputs and those outputs, depending on the one you select to be used to crate a long/short signal, will be highlighted and bound by Dynamic Zones. Pre-smoothing of source input is available, you only need to increase the period length to greater than 1. The smoothing algorithm used here it's Ehlers Two-pole Super Smoother. This indicator should be used as you would use the popular QQE, the difference being this indicator is multi-level momentum adaptive, and QQE is fixed RSI-based. This indicator is multilayer adaptive. The three core indicators calculations are as follows: NMM = Natural Market Mirror, solid line NMF = Natural Moving Average Fast, dashed line (white when off) NMA = Natural Moving Average Regular, dashed line (yellow when off) Whichever one you select to be used as the signal output base, that line with increased in width and change color to match the price inputted trend. The Dynamic Zones will then readjust around that selected output and form a new bounding zone for signal output. What is the Ocean Natural Market Mirror? Created by Jim Sloman, the NMA is a momentum indicator that automatically adjusts to volatility without being programed to do so. For more info, read his guide "Ocean Theory, an Introduction" What is the Ocean Natural Moving Average? Also created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility. 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 types of signal output options Alerts Loxx's Expanded Source Types
Indicatore Pine Script®
di loxx
4
Dynamic Zone of Bollinger Band Stops Line [Loxx]Dynamic Zone of Bollinger Band Stops Line is a Bollinger Band indicator with Dynamic Zones. This indicator serves as both a trend indicator and a dynamic stop-loss indicator. What are Bollinger Bands? 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. Bollinger Bands were developed and copyrighted by famous technical trader John Bollinger, designed to discover opportunities that give investors a higher probability of properly identifying when an asset is oversold or overbought. 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 Signals Alerts 3 types of signal smoothing
Indicatore Pine Script®
di loxx
1
Dynamic Zones of On Chart Stochastic [Loxx]Dynamic Zones of On Chart Stochastic is a Stochastic indicator that sits on top of the chart instead of below as an oscillator. Dynamic zone levels are included to find breakouts/breakdowns and reversals. What is the Stochastic Oscillator? A stochastic oscillator is a momentum indicator comparing a particular closing price of a security to a range of its prices over a certain period of time. The sensitivity of the oscillator to market movements is reducible by adjusting that time period or by taking a moving average of the result. It is used to generate overbought and oversold trading signals, utilizing a 0–100 bounded range of values. 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 Signals Alerts 4 types of signal smoothing
Indicatore Pine Script®
di loxx
Fisher Transform w/ Dynamic Zones [Loxx]What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. 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 3 signal types Bar coloring Alerts Channels fill Loxx's Expanded Source Types
Indicatore Pine Script®
di loxx
1
Dynamic Zone Range on PDFMA [Loxx]Dynamic Zone Range on PDFMA is a Probability Density Function Moving Average oscillator with Dynamic Zones. What is Probability Density Function? Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. 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 4 signal types Bar coloring Alerts Channels fill
Indicatore Pine Script®
di loxx
1
Dynamic Zone Range on OMA [Loxx]Dynamic Zone Range on OMA is an One More Moving Average oscillator with Dynamic Zones. What is the One More Moving Average (OMA)? The usual story goes something like this : which is the best moving average? Everyone that ever started to do any kind of technical analysis was pulled into this "game". Comparing, testing, looking for new ones, testing ... The idea of this one is simple: it should not be itself, but it should be a kind of a chameleon - it should "imitate" as much other moving averages as it can. So the need for zillion different moving averages would diminish. And it should have some extra, of course: The extras: it has to be smooth it has to be able to "change speed" without length change it has to be able to adapt or not (since it has to "imitate" the non-adaptive as well as the adaptive ones) The steps: Smoothing - compared are the simple moving average (that is the basis and the first step of this indicator - a smoothed simple moving average with as little lag added as it is possible and as close to the original as it is possible) Speed 1 and non-adaptive are the reference for this basic setup. Speed changing - same chart only added one more average with "speeds" 2 and 3 (for comparison purposes only here) Finally - adapting : same chart with SMA compared to one more average with speed 1 but adaptive (so this parameters would make it a "smoothed adaptive simple average") Adapting part is a modified Kaufman adapting way and this part (the adapting part) may be a subject for changes in the future (it is giving satisfactory results, but if or when I find a better way, it will be implemented here) Some comparisons for different speed settings (all the comparisons are without adaptive turned on, and are approximate. Approximation comes from a fact that it is impossible to get exactly the same values from only one way of calculation, and frankly, I even did not try to get those same values). speed 0.5 - T3 (0.618 Tilson) speed 2.5 - T3 (0.618 Fulks/Matulich) speed 1 - SMA , harmonic mean speed 2 - LWMA speed 7 - very similar to Hull and TEMA speed 8 - very similar to LSMA and Linear regression value Parameters: Length - length (period) for averaging Source - price to use for averaging Speed - desired speed (i limited to -1.5 on the lower side but it even does not need that limit - some interesting results with speeds that are less than 0 can be achieved) Adaptive - does it adapt or not Variety Moving Averages w/ Dynamic Zones contains 33 source types and 35+ moving averages with double dynamic zones levels. 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 4 signal types Bar coloring Alerts Channels fill
Indicatore Pine Script®
di loxx
2
Variety Moving Averages w/ Dynamic Zones [Loxx]Variety Moving Averages w/ Dynamic Zones contains 33 source types and 35+ moving averages with double dynamic zones levels. 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 Alerts Channels fill Loxx's Expanded Source Types 35+ moving average types
Indicatore Pine Script®
di loxx
2
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
Indicatore Pine Script®
di loxx
6