Adjustable — Indicators and Strategies

Adjustable Quad VWAP Overview A powerful multi-timeframe VWAP indicator that displays four customizable rolling VWAPs with advanced features for comprehensive trend analysis and signal generation. Key Features 🔧 Four Adjustable VWAPs Fast VWAP: Default 7 days (1-365 adjustable) Slow VWAP: Default 30 days (1-365 adjustable) Medium VWAP: Default 90 days (1-365 adjustable) Long VWAP: Default 365 days (1-365 adjustable)

rema Library " REMA " Custom Regional Exponential Moving Average with enhanced sensitivity to recent price action Description: What Makes REMA Unique? REMA introduces a dual-region weighting system that intelligently balances short-term responsiveness with long-term trend context, solving the fundamental limitation of standard EMAs where longer periods necessarily sacrifice recent price sensitivity. Key Differences from Standard EMA: Adaptive Regional Weighting: Applies stronger exponential decay to recent price data while maintaining appropriate weighting for historical context. Maintains Responsiveness at Any Length: Unlike standard EMAs where longer periods become progressively less responsive, REMA preserves significant sensitivity to recent price action even at 100+ period lengths. Mathematically Sound Enhancement: Preserves the core mathematical integrity of exponential averaging while introducing region-specific weighting that better reflects how traders actually interpret price action. Value to TradingView Community: Improved Signal Timing: Detects reversals 1-3 bars earlier than traditional EMAs without increasing false signals. Better Multi-Timeframe Analysis: Provides more consistent behavior across different period settings, reducing conflicting signals between timeframes. Ideal for Modern Markets: Better handles today's high-volatility, algorithm-driven markets where traditional indicators often lag too much to be effective. Optimized for Both Trend and Reversal Trading: Simultaneously provides strong trend-following capabilities while remaining sensitive to legitimate reversal signals. Computation Efficiency: The fast implementation offers enhanced capabilities with minimal computational overhead, making it practical for real-time analysis. REMA fills a critical gap between lagging long-period EMAs and noisy short-period EMAs, giving traders a single, versatile tool that adapts to market conditions more effectively than standard technical indicators. Implementation: rema(src, length, recency_bias, transition_point) Regional Exponential Moving Average that maintains recent price sensitivity even with long lookback periods Parameters: src (float) : Input source series length (int) : Overall EMA period length recency_bias (float) : Weighting factor to increase sensitivity to recent prices (1.0-3.0 recommended) transition_point (float) : Percentage point (0.0-1.0) in the lookback period where weighting shifts from recent to historical Returns: Custom exponentially weighted moving average with regional bias rema_fast(src, length, recency_bias) Simplified Regional EMA that uses a recursive calculation method Parameters: src (float) : Input source series length (int) : Overall EMA period recency_bias (float) : Factor to increase sensitivity to recent price (1.0-3.0 recommended) Returns: Computationally efficient regional EMA

Oscillating Length Moving Averages ***CREDIT TO TradingView's TA Library*** (), Attempted to use "import TradingView/ta/4" to import the library, but for whatever reason some of the functions failed to work, while others had no issue, so I opted to just copy paste what I wanted to use. This moving average uses an oscillator to influence the length used during calculation. Extremely customizable/tunable with ability to change Max and Min length values, length multiplier, length multiple,4 different settings ,( Decline , <>Peak, >Decline , <>Peak, ><Trough step6:Select Final Filtering method step7:plot

Weight Gain 4000 - (Adjustable Volume Weighted MA) - [mutantdog]Short Version: This is a fairly self-contained system based upon a moving average crossover with several unique features. The most significant of these is the adjustable volume weighting system, allowing for transformations between standard and weighted versions of each included MA. With this feature it is possible to apply partial weighting which can help to improve responsiveness without dramatically altering shape. Included types are SMA, EMA, WMA, RMA, hSMA, DEMA and TEMA. Potentially more will be added in future (check updates below). In addition there are a selection of alternative 'weighted' inputs, a pair of Bollinger-style deviation bands, a separate price tracker and a bunch of alert presets. This can be used out-of-the-box or tweaked in multiple ways for unusual results. Default settings are a basic 8/21 EMA cross with partial volume weighting. Dev bands apply to MA2 and are based upon the type and the volume weighting. For standard Bollinger bands use SMA with length 20 and try adding a small amount of volume weighting. A more detailed breakdown of the functionality follows. Long Version: ADJUSTABLE VOLUME WEIGHTING In principle any moving average should have a volume weighted analogue, the standard VWMA is just an SMA with volume weighting for example. Actually, we can consider the SMA to be a special case where volume is a constant 1 per bar (the value is somewhat arbitrary, the important part is that it's constant). Similar principles apply to the 'elastic' EVWMA which is the volume weighted analogue of an RMA. In any case though, where we have standard and weighted variants it is possible to transform one into the other by gradually increasing or decreasing the weighting, which forms the basis of this system. This is not just a simple multiplier however, that would not work due to the relative proportions being the same when set at any non zero value. In order to create a meaningful transformation we need to use an exponent instead, eg: volume^x , where x is a variable determined in this case by the 'volume' parameter. When x=1, the full volume weighting applies and when x=0, the volume will be reduced to a constant 1. Values in between will result in the respective partial weighting, for example 0.5 will give the square root of the volume. The obvious question here though is why would you want to do this? To answer that really it is best to actually try it. The advantages that volume weighting can bring to a moving average can sometimes come at the cost of unwanted or erratic behaviour. While it can tend towards much closer price tracking which may be desirable, sometimes it needs moderating especially in markets with lower liquidity. Here the adjustability can be useful, in many cases i have found that adding a small amount of volume weighting to a chosen MA can help to improve its responsiveness without overpowering it. Another possible use case would be to have two instances of the same MA with the same length but different weightings, the extent to which these diverge from each other can be a useful indicator of trend strength. Other uses will become apparent with experimentation and can vary from one market to another. THE INCLUDED MODES At the time of publication, there are 7 included moving average types with plans to add more in future. For now here is a brief explainer of what's on offer (continuing to use x as shorthand for the volume parameter), starting with the two most common types. SMA: As mentioned above this is essentially a standard VWMA, calculated here as sma(source*volume^x,length)/sma(volume^x,length). In this case when x=0 then volume=1 and it reduces to a standard SMA. RMA: Again mentioned above, this is an EVWMA (where E stands for elastic) with constant weighting. Without going into detail, this method takes the 1/length factor of an RMA and replaces it with volume^x/sum(volume^x,length). In this case again we can see that when x=0 then volume=1 and the original 1/length factor is restored. EMA: This follows the same principle as the RMA where the standard 2/(length+1) factor is replaced with (2*volume^x)/(sum(volume^x,length)+volume^x). As with an RMA, when x=0 then volume=1 and this reduces back to the standard 2/(length+1). DEMA: Just a standard Double EMA using the above. TEMA: Likewise, a standard Triple EMA using the above. hSMA: This is the same as the SMA except it uses harmonic mean calculations instead of arithmetic. In most cases the differences are negligible however they can become more pronounced when volume weighting is introduced. Furthermore, an argument can be made that harmonic mean calculations are better suited to downtrends or bear markets, in principle at least. WMA: Probably the most contentious one included. Follows the same basic calculations as for the SMA except uses a WMA instead. Honestly, it makes little sense to combine both linear and volume weighting in this manner, included only for completeness and because it can easily be done. It may be the case that a superior composite could be created with some more complex calculations, in which case i may add that later. For now though this will do. An additional 'volume filter' option is included, which applies a basic filter to the volume prior to calculation. For types based around the SMA/VWMA system, the volume filter is a WMA-4, for types based around the RMA/EVWMA system the filter is a RMA-2. As and when i add more they will be listed in the updates at the bottom. WEIGHTED INPUTS The ohlc method of source calculations is really a leftover from a time when data was far more limited. Nevertheless it is still the method used in charting and for the most part is sufficient. Often the only important value is 'close' although sometimes 'high' and 'low' can be relevant also. Since we are volume weighting however, it can be useful to incorporate as much information as possible. To that end either 'hlc3' or 'hlcc4' tend to be the best of the defaults (in the case of 24/7 charting like crypto or intraday trading, 'ohlc4' should be avoided as it is effectively the same as a lagging version of 'hlcc4'). There are many other (infinitely many, in fact) possible combinations that can be created, i have included a few here. The premise is fairly straightforward, by subtracting one value from another, the remaining difference can act as a kind of weight. In a simple case consider 'hl2' as simply the midrange ((high+low)/2), instead of this using 'high+low-open' would give more weight to the value furthest from the open, providing a good estimate of the median. An even better estimate can be achieved by combining that with 'high+low-close' to give the included result 'hl-oc2'. Similarly, 'hlc3' can be considered the basic mean of the three significant values, an included weighted version 'hlc2-o2' combines a sum with subtraction of open to give an estimated mean that may be more accurate. Finally we can apply a similar principle to the close, by subtracting the other values, this one potentially gets more complex so the included 'cc-ohlc4' is really the simplest. The result here is an overbias of the close in relation to the open and the midrange, while in most cases not as useful it can provide an estimate for the next bar assuming that the trend continues. Of the three i've included, hlc2-o2 is in my opinion the most useful especially in this context, although it is perhaps best considered to be experimental in nature. For that reason, i've kept 'hlcc4' as the default for both MAs. Additionally included is an 'aux input' which is the standard TV source menu and, where possible, can be set as outputs of other indicators. THE SYSTEM This one is fairly obvious and straightforward. It's just a moving average crossover with additional deviation (bollinger) bands. Not a lot to explain here as it should be apparent how it works. Of the two, MA1 is considered to be the fast and MA2 is considered to be the slow. Both can be set with independent inputs, types and weighting. When MA1 is above, the colour of both is green and when it's below the colour of both is red. An additional gradient based fill is there and can be adjusted along with everything else in the visuals section at the bottom. Default alerts are available for crossover/crossunder conditions along with optional marker plots. MA2 has the option for deviation bands, these are calculated based upon the MA type used and volume weighted according to the main parameter. In the case of a unweighted SMA being used they will be standard Bollinger bands. An additional 'source direct' price tracker is included which can be used as the basis for an alert system for price crossings of bands or MAs, while taking advantage of the available weighted inputs. This is displayed as a stepped line on the chart so is also a good way to visualise the differences between input types. That just about covers it then. The likelihood is that you've used some sort of moving average cross system before and are probably still using one or more. If so, then perhaps the additional functionality here will be of benefit. Thanks for looking, I welcome any feedack

MVWAP Moving Volume Weighted Average Price (VWAP), with adjustable length and Timeframe.

Adjustable MA & Alternating Extremities [LuxAlgo]Returns a moving average allowing the user to control the amount of lag as well as the amplitude of its overshoots thanks to a parametric kernel. The indicator displays alternating extremities and aims to provide potential points where price might reverse. Due to user requests, we added the option to display the moving average as candles instead of a solid line. Settings Length: MA period, refers to the number of most recent data points to use for its calculation. Mult: Multiplicative factor for each extremity. As Smoothed Candles: Allows the user to show the MA as a series of candles instead of a solid line. Show Alternating Extremities : Determines whether to display the alternating extremities or not. Lag: Controls the amount of lag of the MA, with higher values returning a MA with more lag. Overshoot: Controls the amplitude of the overshoots returned by the MA, with higher values increasing the amplitude of the overshoots. Usage Moving averages using parametric kernels allows users to have more control over characteristics such as lag or smoothness; this can greatly benefit the analyst. A moving average with reduced lag can be used as a leading moving average in a MA crossover system, while lag will benefit moving averages used as slow MA in a crossover system. Increasing 'Lag' will increase smoothness while increasing 'overshoot' will reduce lag. The following indicator puts more emphasis on its alternating extremities, an upper extremity will be shown once the high price crosses the upper extremity, while a low extremity will be shown once the low price crosses the lower extremity. These can be interpreted like extremities of a band indicator. The MA using a length value of 200 with a multiplicative factor of 1. In general, extremities will effectively return points where price might potentially bounce in ranging markets while closing prices under trending markets will often be found above an upper extremity and under a lower extremity. Reducing the lag of the moving average allows the user to obtain a more timely estimate of the underlying trend in the price, with a better fit overall. This allows the user to obtain potentially pertinent extremities where price might reverse upon a break, even under trending markets. In the above chart, the price initially breaks the upper extremity, however, we can observe that the upper extremity eventually reaches back the price, goes above it, provides a resistance, and effectively indicates a reversal. Users can plot candles from the moving average, these are fairly similar to heikin-ashi candles in the sense that CandleOpen(t) ≠ CandleClose(t-1) , each point of the candle is calculated as follows for our indicator: Open = Average between MA(t-1) and MA(t-2) High = MA using the high price as input Low = MA using the low price as input Close = MA using the closing price as input Details Lag is defined as the effect of moving averages to reflect past price variations instead of new ones, lag can be observed by the user and is the main cause of false signals. Lag is proportional to the degree of filtering returned by the moving average. Overshooting is a common effect encountered in non-lagging moving averages, and is defined as the tendency of a moving average to exceed a maximum level (or minimum level, which can be defined as undershooting ) MA and rolling maximum/minimum, both using a length of 50 bars. While we can think of lag as a cost of smoothness, we can think of overshooting as a cost for reduced lag on some occasions. Explaining the kernel design behind our moving average requires understanding of the logic behind lag reduction in moving averages. This can prove to be complex for non informed users, but let's just focus on the simpler part; moving averages can be defined as a weighted sum between past prices and a set of coefficients (kernel). MA(t) = b(0)C(t) + b(1)C(t-1) + b(2)C(t-2) + ... + b(n-1)C(t-n-1) Where n is the period of the moving average. Lag is (non optimally) reduced by "underweighting" past prices - that is multiplying them by negative numbers. The kernel used in our moving average is based on a modified sinewave. A weighted sum making use of a sinewave as a kernel would return an oscillator centered at 0. We can divide this sinewave by an increasing linear function in order to obtain a kernel allowing us to obtain a low lag moving average instead of a centered oscillator. This is the main idea in the design of the kernel used by our moving average. The kernel equation of our moving average is: sin(2πx^α)(1 - x^β) With 1>x>0 , and where α controls the lag, while β controls the overshoot amplitude. Using this equation we can obtain the following kernels: Here only α is changed, while β is equal to 1. Values to the left would represent the coefficients for the most recent prices. Notice how the most significant coefficients are given to the oldest prices in the case where α increases. Higher overshoot would require more negative values, this is controlled by β Here only β is changed, while α is equal to 1. Notice how higher values return lower negative coefficients. This effectively increases the overshoots amplitude in our moving average. We can decrease α in order for these negative coefficients to underweight more recent values. Using α = 0 allows us to simplify the kernel equation to: 1 - x^β Using this kernel we can obtain more classical moving averages, this can be seen from the following results: Using β = 1 allows us to obtain a linearly decreasing kernel (the one of a WMA), while increasing allows the kernel to converge toward a rectangular kernel (the one of SMA).

SDev Adjusted Stochastic Description : This Stochastic variant will auto-adjust stochastic period based on volatility measured by standard deviation. The idea behind it are in highly volatile market, %K period will be reduced to account for recent price range, while in low volatility market, %K period will be increased to account less of the recent price range. This idea is based on one of medium article written by Sofien Kaabar with slight modification on the adjusting logic implementation. Any ideas to further improve this indicator are welcome :) Disclaimer: I always felt Pinescript is a very fast to type language with excellent visualization capabilities, so I've been using it as code-testing platform prior to actual coding in other platform. Having said that, these study scripts was built only to test/visualize an idea to see its viability and if it can be used to optimize existing strategy. While some of it are useful and most are useless, none of it should be use as main decision maker.

LWMA: Linear Weighted Moving Average Couldn't find searching for Linearly Weighted Moving Average (LWMA) in tradingview. Found one with the LWMA title, but it uses plain WMA calculation without the linearity which more heavily weights recent price data, which I need, so I try to made one. LWMAs are also quicker to react to price changes than SMA and EMA. If you want a moving average with less lag than an SMA, try a LWMA. It kind of also have a more clarity in defining the price trend and reversals. Trade signals usually based on crossovers, they can also indicate areas of potential support or resistance. But beware though, multiple false signals may also occur before a significant trend develops. Use a filter, some decent volatility oscillator might do the job. -- The formula for this Linearly Weighted Moving Average is: LWMA = (( P n∗ W 1)+( P n−1∗ W 2)+( P n−2∗ W 3)...) / ∑W P = Price for the period n = The most recent period, n-1 is the prior period, and n-2 is two periods prior W = The assigned weight to each period, with the highest weight going first and then descending linearly based on the number of periods being used. I hope I'm doing right translating it to Pine Script 4. Let me know if I miss something.

TEMA - Triple Moving Averages TEMA - Triple Moving Averages, adjustable

Automatic Support, Resistance, Fibonacci Levels This indicator plots absolute high and low values for up to five completely adjustable time periods (in months, weeks, days, hours, minutes) and optionally calculates the Fibonacci levels on the pair of absolutes of your choice, ascending or descending, and mimics the shading available in the built-in Fib charting tools (e.g. retracement). Here are a few screenshots of the same chart with various options selected. 3-Month, 4-Week, and 5-Day absolutes with 3-Monthly Fib plotted: The same chart with 4-Weekly Fib: The same chart with 5-Daily Fib: 5-Day, 12-Hour, 90-Minute absolutes with 12-Hourly Fib: Zoomed in, on a 30-minute interval, with 90-minute Fib: With descending ("inverted") 90-minute Fib: I started putting this together for Vazzyb, who was looking for a way to automate plotting horizontal support and resistance levels for monthly, weekly, and daily extremes, and then I added additional features as they occurred to me. Special thanks to Paaax, who suggested I add Fib levels. I am leaving the code open, so you may feel free to grab snippets you like and use them for your own purposes. Of particular interest may be my custom "calc_fib()" function, which accepts any series pair, as well as a Boolean indicating whether to invert, and returns an array with each of the major Fibonacci levels: . If anyone likes this enough to feel generous, please feel free: BTC 3KmFchJ18QvMzAJKDcFQXvyK9p1EHWQdhP BCH qqtrw64ptuwprk5vtj3z8qwkvh3v0jawxq7khqng7x ETH 0x9b51361A278910Ba3945C7519C9f0FA8a77df18d LTC MDeWWsP7XCG2zQuZ2dYALZXQ52u2qkc8fh P.S. If you want the time lengths to be as close to accurate as possible, don't forget to change the number of days per week when using for cryptocurrency!