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taLibrary "ta" █  OVERVIEW This library holds technical ​analysis functions calculating values for which no Pine built-in exists. Look first. Then leap. █  FUNCTIONS cagr(entryTime, entryPrice, exitTime, exitPrice) It calculates the "Compound Annual Growth Rate" between two points in time. The ​CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two instruments. Because it annualizes values, the function requires a minimum of one day between the two end points (annualizing returns over smaller periods of times doesn't produce very meaningful figures).   Parameters:      entryTime : The starting timestamp.      entryPrice : The starting point's price.      exitTime : The ending timestamp.      exitPrice : The ending point's price.   Returns: ​CAGR in % (50 is 50%). Returns `na` if there is not >=1D between `entryTime` and `exitTime`, or until the two time points have not been reached by the script. █  v2, Mar. 8, 2022 Added functions `allTimeHigh()` and `allTimeLow()` to find the highest or lowest value of a source from the first historical bar to the current bar. These functions will not look ahead; they will only return new highs/lows on the bar where they occur. allTimeHigh(​src) Tracks the highest value of `src` from the first historical bar to the current bar.   Parameters:      src : (series int/float) Series to track. Optional. The default is `high`.   Returns: (float) The highest value tracked. allTimeLow(​src) Tracks the lowest value of `src` from the first historical bar to the current bar.   Parameters:      src : (series int/float) Series to track. Optional. The default is `low`.   Returns: (float) The lowest value tracked. █  v3, Sept. 27, 2022 This version includes the following new functions: aroon(length)   Calculates the values of the Aroon indicator.   Parameters:      length (simple int) : (simple int) Number of bars (length).   Returns: ( [float, float ]) A tuple of the Aroon-Up and Aroon-Down values. coppock(source, longLength, shortLength, smoothLength)   Calculates the value of the Coppock Curve indicator.   Parameters:      source (float) : (series int/float) Series of values to process.      longLength (simple int) : (simple int) Number of bars for the fast ROC value (length).      shortLength (simple int) : (simple int) Number of bars for the slow ROC value (length).      smoothLength (simple int) : (simple int) Number of bars for the weigted moving average value (length).   Returns: (float) The oscillator value. dema(source, length)   Calculates the value of the Double Exponential Moving Average (DEMA).   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The double exponentially weighted moving average of the `source`. d​ema2(​src, length)   An alternate ​Double Exponential ​Moving Average (D​ema) function to `d​ema()`, which allows a "series float" length argument.   Parameters:      ​src : (series int/float) Series of values to process.      length : (series int/float) Length for the smoothing parameter calculation.   Returns: (float) The double exponentially ​weighted moving average of the `​src`. dm(length)   Calculates the value of the "Demarker" indicator.   Parameters:      length (simple int) : (simple int) Number of bars (length).   Returns: (float) The oscillator value. donchian(length)   Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.   Parameters:      length (int) : (series int) Number of bars (length).   Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively. ​ema2(​src, length)   An alternate ​ema function to the `ta.​ema()` built-in, which allows a "series float" length argument.   Parameters:      ​src : (series int/float) Series of values to process.      length : (series int/float) Number of bars (length).   Returns: (float) The exponentially ​weighted moving average of the `​src`. eom(length, div)   Calculates the value of the Ease of Movement indicator.   Parameters:      length (simple int) : (simple int) Number of bars (length).      div (simple int) : (simple int) Divisor used for normalzing values. Optional. The default is 10000.   Returns: (float) The oscillator value. frama(source, length)   The Fractal Adaptive Moving Average (FRAMA), developed by John Ehlers, is an adaptive moving average that dynamically adjusts its lookback period based on fractal geometry.   Parameters:      source (float) : (series int/float) Series of values to process.      length (int) : (series int) Number of bars (length).   Returns: (float) The fractal adaptive moving average of the `source`. ft(source, length)   Calculates the value of the Fisher Transform indicator.   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Number of bars (length).   Returns: (float) The oscillator value. ht(source)   Calculates the value of the Hilbert Transform indicator.   Parameters:      source (float) : (series int/float) Series of values to process.   Returns: (float) The oscillator value. ichimoku(conLength, baseLength, senkouLength)   Calculates values of the Ichimoku Cloud indicator, including tenkan, kijun, senkouSpan1, senkouSpan2, and chikou. NOTE: offsets forward or backward can be done using the `offset` argument in `plot()`.   Parameters:      conLength (int) : (series int) Length for the Conversion Line (Tenkan). The default is 9 periods, which returns the mid-point of the 9 period Donchian Channel.      baseLength (int) : (series int) Length for the Base Line (Kijun-sen). The default is 26 periods, which returns the mid-point of the 26 period Donchian Channel.      senkouLength (int) : (series int) Length for the Senkou Span 2 (Leading Span B). The default is 52 periods, which returns the mid-point of the 52 period Donchian Channel.   Returns: ( [float, float, float, float, float ]) A tuple of the Tenkan, Kijun, Senkou Span 1, Senkou Span 2, and Chikou Span values. NOTE: by default, the senkouSpan1 and senkouSpan2 should be plotted 26 periods in the future, and the Chikou Span plotted 26 days in the past. ift(source)   Calculates the value of the Inverse Fisher Transform indicator.   Parameters:      source (float) : (series int/float) Series of values to process.   Returns: (float) The oscillator value. kvo(fastLen, slowLen, trigLen)   Calculates the values of the Klinger Volume Oscillator.   Parameters:      fastLen (simple int) : (simple int) Length for the fast moving average smoothing parameter calculation.      slowLen (simple int) : (simple int) Length for the slow moving average smoothing parameter calculation.      trigLen (simple int) : (simple int) Length for the trigger moving average smoothing parameter calculation.   Returns: ( [float, float ]) A tuple of the KVO value, and the trigger value. pzo(length)   Calculates the value of the Price Zone Oscillator.   Parameters:      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The oscillator value. rms(source, length)   Calculates the Root Mean Square of the `source` over the `length`.   Parameters:      source (float) : (series int/float) Series of values to process.      length (int) : (series int) Number of bars (length).   Returns: (float) The RMS value. rwi(length)   Calculates the values of the Random Walk Index.   Parameters:      length (simple int) : (simple int) Lookback and ATR smoothing parameter length.   Returns: ( [float, float ]) A tuple of the `rwiHigh` and `rwiLow` values. stc(source, fast, slow, cycle, d1, d2)   Calculates the value of the Schaff Trend Cycle indicator.   Parameters:      source (float) : (series int/float) Series of values to process.      fast (simple int) : (simple int) Length for the MACD fast smoothing parameter calculation.      slow (simple int) : (simple int) Length for the MACD slow smoothing parameter calculation.      cycle (simple int) : (simple int) Number of bars for the Stochastic values (length).      d1 (simple int) : (simple int) Length for the initial %D smoothing parameter calculation.      d2 (simple int) : (simple int) Length for the final %D smoothing parameter calculation.   Returns: (float) The oscillator value. stochFull(periodK, smoothK, periodD)   Calculates the %K and %D values of the Full Stochastic indicator.   Parameters:      periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).      smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).      periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).   Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values. stochRsi(lengthRsi, periodK, smoothK, periodD, source)   Calculates the %K and %D values of the Stochastic RSI indicator.   Parameters:      lengthRsi (simple int) : (simple int) Length for the RSI smoothing parameter calculation.      periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).      smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).      periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).      source (float) : (series int/float) Series of values to process. Optional. The default is `close`.   Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values. supertrend(factor, atrLength, wicks)   Calculates the values of the SuperTrend indicator with the ability to take candle wicks into account, rather than only the closing price.   Parameters:      factor (float) : (series int/float) Multiplier for the ATR value.      atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.      wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is false.   Returns: ( [float, int ]) A tuple of the superTrend value and trend direction. szo(source, length)   Calculates the value of the Sentiment Zone Oscillator.   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The oscillator value. t3(source, length, vf)   Calculates the value of the Tilson Moving Average (T3).   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.      vf (simple float) : (simple float) Volume factor. Affects the responsiveness.   Returns: (float) The Tilson moving average of the `source`. t3Alt(source, length, vf)   An alternate Tilson Moving Average (T3) function to `t3()`, which allows a "series float" `length` argument.   Parameters:      source (float) : (series int/float) Series of values to process.      length (float) : (series int/float) Length for the smoothing parameter calculation.      vf (simple float) : (simple float) Volume factor. Affects the responsiveness.   Returns: (float) The Tilson moving average of the `source`. tema(source, length)   Calculates the value of the Triple Exponential Moving Average (TEMA).   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The triple exponentially weighted moving average of the `source`. tema2(source, length)   An alternate Triple Exponential Moving Average (TEMA) function to `tema()`, which allows a "series float" `length` argument.   Parameters:      source (float) : (series int/float) Series of values to process.      length (float) : (series int/float) Length for the smoothing parameter calculation.   Returns: (float) The triple exponentially weighted moving average of the `source`. trima(source, length)   Calculates the value of the Triangular Moving Average (TRIMA).   Parameters:      source (float) : (series int/float) Series of values to process.      length (int) : (series int) Number of bars (length).   Returns: (float) The triangular moving average of the `source`. trima2(​src, length)   An alternate Triangular ​Moving Average (TRIMA) function to `trima()`, which allows a "series int" length argument.   Parameters:      ​src : (series int/float) Series of values to process.      length : (series int) Number of bars (length).   Returns: (float) The triangular moving average of the `​src`. trix(source, length, signalLength, exponential)   Calculates the values of the TRIX indicator.   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.      signalLength (simple int) : (simple int) Length for smoothing the signal line.      exponential (simple bool) : (simple bool) Condition to determine whether exponential or simple smoothing is used. Optional. The default is `true` (exponential smoothing).   Returns: ( [float, float, float ]) A tuple of the TRIX value, the signal value, and the histogram. uo(fastLen, midLen, slowLen)   Calculates the value of the Ultimate Oscillator.   Parameters:      fastLen (simple int) : (series int) Number of bars for the fast smoothing average (length).      midLen (simple int) : (series int) Number of bars for the middle smoothing average (length).      slowLen (simple int) : (series int) Number of bars for the slow smoothing average (length).   Returns: (float) The oscillator value. vhf(source, length)   Calculates the value of the Vertical Horizontal Filter.   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Number of bars (length).   Returns: (float) The oscillator value. vi(length)   Calculates the values of the Vortex Indicator.   Parameters:      length (simple int) : (simple int) Number of bars (length).   Returns: ( [float, float ]) A tuple of the viPlus and viMinus values. vzo(length)   Calculates the value of the Volume Zone Oscillator.   Parameters:      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The oscillator value. williamsFractal(period)   Detects Williams Fractals.   Parameters:      period (int) : (series int) Number of bars (length).   Returns: ( [bool, bool ]) A tuple of an up fractal and down fractal. Variables are true when detected. wpo(length)   Calculates the value of the Wave Period Oscillator.   Parameters:      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The oscillator value. █  v7, Nov. 2, 2023 This version includes the following new and updated functions: atr2(length)   An alternate ATR function to the `ta.atr()` built-in, which allows a "series float" `length` argument.   Parameters:      length (float) : (series int/float) Length for the smoothing parameter calculation.   Returns: (float) The ATR value. changePercent(newValue, oldValue)   Calculates the percentage difference between two distinct values.   Parameters:      newValue (float) : (series int/float) The current value.      oldValue (float) : (series int/float) The previous value.   Returns: (float) The percentage change from the `oldValue` to the `newValue`. donchian(length)   Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.   Parameters:      length (int) : (series int) Number of bars (length).   Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively. highestSince(cond, source)   Tracks the highest value of a series since the last occurrence of a condition.   Parameters:      cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the highest `source`.      source (float) : (series int/float) Series of values to process. Optional. The default is `high`.   Returns: (float) The highest `source` value since the last time the `cond` was `true`. lowestSince(cond, source)   Tracks the lowest value of a series since the last occurrence of a condition.   Parameters:      cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the lowest `source`.      source (float) : (series int/float) Series of values to process. Optional. The default is `low`.   Returns: (float) The lowest `source` value since the last time the `cond` was `true`. relativeVolume(length, anchorTimeframe, isCumulative, adjustRealtime)   Calculates the volume since the last change in the time value from the `anchorTimeframe`, the historical average volume using bars from past periods that have the same relative time offset as the current bar from the start of its period, and the ratio of these volumes. The volume values are cumulative by default, but can be adjusted to non-accumulated with the `isCumulative` parameter.   Parameters:      length (simple int) : (simple int) The number of periods to use for the historical average calculation.      anchorTimeframe (simple string) : (simple string) The anchor timeframe used in the calculation. Optional. Default is "D".      isCumulative (simple bool) : (simple bool) If `true`, the volume values will be accumulated since the start of the last `anchorTimeframe`. If `false`, values will be used without accumulation. Optional. The default is `true`.      adjustRealtime (simple bool) : (simple bool) If `true`, estimates the cumulative value on unclosed bars based on the data since the last `anchor` condition. Optional. The default is `false`.   Returns: ( [float, float, float ]) A tuple of three float values. The first element is the current volume. The second is the average of volumes at equivalent time offsets from past anchors over the specified number of periods. The third is the ratio of the current volume to the historical average volume. rma2(source, length)   An alternate RMA function to the `ta.rma()` built-in, which allows a "series float" `length` argument.   Parameters:      source (float) : (series int/float) Series of values to process.      length (float) : (series int/float) Length for the smoothing parameter calculation.   Returns: (float) The rolling moving average of the `source`. supertrend2(factor, atrLength, wicks)   An alternate SuperTrend function to `supertrend()`, which allows a "series float" `atrLength` argument.   Parameters:      factor (float) : (series int/float) Multiplier for the ATR value.      atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.      wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is `false`.   Returns: ( [float, int ]) A tuple of the superTrend value and trend direction. vStop(source, atrLength, atrFactor)   Calculates an ATR-based stop value that trails behind the `source`. Can serve as a possible stop-loss guide and trend identifier.   Parameters:      source (float) : (series int/float) Series of values that the stop trails behind.      atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.      atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.   Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool". vStop2(source, atrLength, atrFactor)   An alternate Volatility Stop function to `vStop()`, which allows a "series float" `atrLength` argument.   Parameters:      source (float) : (series int/float) Series of values that the stop trails behind.      atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.      atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.   Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool". Removed Functions: allTimeHigh(src)   Tracks the highest value of `src` from the first historical bar to the current bar. allTimeLow(src)   Tracks the lowest value of `src` from the first historical bar to the current bar. trima2(src, length)   An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a "series int" length argument.
Perpustakaan Pine Script®
oleh TradingView
25
Why EMA Isn't What You Think It IsMany new traders adopt the Exponential Moving Average (EMA) believing it's simply a "better Simple Moving Average (SMA)". This common misconception leads to fundamental misunderstandings about how EMA works and when to use it. EMA and SMA differ at their core. SMA use a window of finite number of data points, giving equal weight to each data point in the calculation period. This makes SMA a Finite Impulse Response (FIR) filter in signal processing terms. Remember that FIR means that "all that we need is the 'period' number of data points" to calculate the filter value. Anything beyond the given period is not relevant to FIR filters – much like how a security camera with 14-day storage automatically overwrites older footage, making last month's activity completely invisible regardless of how important it might have been. EMA, however, is an Infinite Impulse Response (IIR) filter. It uses ALL historical data, with each past price having a diminishing - but never zero - influence on the calculated value. This creates an EMA response that extends infinitely into the past—not just for the last N periods. IIR filters cannot be precise if we give them only a 'period' number of data to work on - they will be off-target significantly due to lack of context, like trying to understand Game of Thrones by watching only the final season and wondering why everyone's so upset about that dragon lady going full pyromaniac. If we only consider a number of data points equal to the EMA's period, we are capturing no more than 86.5% of the total weight of the EMA calculation. Relying on he period window alone (the warm-up period) will provide only 1 - (1 / e^2) weights, which is approximately 1−0.1353 = 0.8647 = 86.5%. That's like claiming you've read a book when you've skipped the first few chapters – technically, you got most of it, but you probably miss some crucial early context. ▶️ What is period in EMA used for? What does a period parameter really mean for EMA? When we select a 15-period EMA, we're not selecting a window of 15 data points as with an SMA. Instead, we are using that number to calculate a decay factor (α) that determines how quickly older data loses influence in EMA result. Every trader knows EMA calculation: α = 1 / (1+period) – or at least every trader claims to know this while secretly checking the formula when they need it. Thinking in terms of "period" seriously restricts EMA. The α parameter can be - should be! - any value between 0.0 and 1.0, offering infinite tuning possibilities of the indicator. When we limit ourselves to whole-number periods that we use in FIR indicators, we can only access a small subset of possible IIR calculations – it's like having access to the entire RGB color spectrum with 16.7 million possible colors but stubbornly sticking to the 8 basic crayons in a child's first art set because the coloring book only mentioned those by name. For example: Period 10 → alpha = 0.1818 Period 11 → alpha = 0.1667 What about wanting an alpha of 0.17, which might yield superior returns in your strategy that uses EMA? No whole-number period can provide this! Direct α parameterization offers more precision, much like how an analog tuner lets you find the perfect radio frequency while digital presets force you to choose only from predetermined stations, potentially missing the clearest signal sitting right between channels. Sidenote: the choice of α = 1 / (1+period) is just a convention from 1970s, probably started by J. Welles Wilder, who popularized the use of the 14-day EMA. It was designed to create an approximate equivalence between EMA and SMA over the same number of periods, even thought SMA needs a period window (as it is FIR filter) and EMA doesn't. In reality, the decay factor α in EMA should be allowed any valye between 0.0 and 1.0, not just some discrete values derived from an integer-based period! Algorithmic systems should find the best α decay for EMA directly, allowing the system to fine-tune at will and not through conversion of integer period to float α decay – though this might put a few traditionalist traders into early retirement. Well, to prevent that, most traditionalist implementations of EMA only use period and no alpha at all. Heaven forbid we disturb people who print their charts on paper, draw trendlines with rulers, and insist the market "feels different" since computers do algotrading! ▶️ Calculating EMAs Efficiently The standard textbook formula for EMA is: EMA = CurrentPrice × alpha + PreviousEMA × (1 - alpha) But did you know that a more efficient version exists, once you apply a tiny bit of high school algebra: EMA = alpha × (CurrentPrice - PreviousEMA) + PreviousEMA The first one requires three operations: 2 multiplications + 1 addition. The second one also requires three ops: 1 multiplication + 1 addition + 1 subtraction. That's pathetic, you say? Not worth implementing? In most computational models, multiplications cost much more than additions/subtractions – much like how ordering dessert costs more than asking for a water refill at restaurants. Relative CPU cost of float operations : Addition/Subtraction: ~1 cycle Multiplication: ~5 cycles (depending on precision and architecture) Now you see the difference? 2 * 5 + 1 = 11 against 5 + 1 + 1 = 7. That is ≈ 36.36% efficiency gain just by swapping formulas around! And making your high school math teacher proud enough to finally put your test on the refrigerator. ▶️ The Warmup Problem: how to start the EMA sequence right How do we calculate the first EMA value when there's no previous EMA available? Let's see some possible options used throughout the history: Start with zero : EMA(0) = 0. This creates stupidly large distortion until enough bars pass for the horrible effect to diminish – like starting a trading account with zero balance but backdating a year of missed trades, then watching your balance struggle to climb out of a phantom debt for months. Start with first price : EMA(0) = first price. This is better than starting with zero, but still causes initial distortion that will be extra-bad if the first price is an outlier – like forming your entire opinion of a stock based solely on its IPO day price, then wondering why your model is tanking for weeks afterward. Use SMA for warmup : This is the tradition from the pencil-and-paper era of technical analysis – when calculators were luxury items and "algorithmic trading" meant your broker had neat handwriting. We first calculate an SMA over the initial period, then kickstart the EMA with this average value. It's widely used due to tradition, not merit, creating a mathematical Frankenstein that uses an FIR filter (SMA) during the initial period before abruptly switching to an IIR filter (EMA). This methodology is so aesthetically offensive (abrupt kink on the transition from SMA to EMA) that charting platforms hide these early values entirely, pretending EMA simply doesn't exist until the warmup period passes – the technical analysis equivalent of sweeping dust under the rug. Use WMA for warmup : This one was never popular because it is harder to calculate with a pencil - compared to using simple SMA for warmup. Weighted Moving Average provides a much better approximation of a starting value as its linear descending profile is much closer to the EMA's decay profile. These methods all share one problem: they produce inaccurate initial values that traders often hide or discard, much like how hedge funds conveniently report awesome performance "since strategy inception" only after their disastrous first quarter has been surgically removed from the track record. ▶️ A Better Way to start EMA: Decaying compensation Think of it this way: An ideal EMA uses an infinite history of prices, but we only have data starting from a specific point. This creates a problem - our EMA starts with an incorrect assumption that all previous prices were all zero, all close, or all average – like trying to write someone's biography but only having information about their life since last Tuesday. But there is a better way. It requires more than high school math comprehension and is more computationally intensive, but is mathematically correct and numerically stable. This approach involves compensating calculated EMA values for the "phantom data" that would have existed before our first price point. Here's how phantom data compensation works: We start our normal EMA calculation: EMA_today = EMA_yesterday + α × (Price_today - EMA_yesterday) But we add a correction factor that adjusts for the missing history: Correction = 1 at the start Correction = Correction × (1-α) after each calculation We then apply this correction: True_EMA = Raw_EMA / (1-Correction) This correction factor starts at 1 (full compensation effect) and gets exponentially smaller with each new price bar. After enough data points, the correction becomes so small (i.e., below 0.0000000001) that we can stop applying it as it is no longer relevant. Let's see how this works in practice: For the first price bar: Raw_EMA = 0 Correction = 1 True_EMA = Price (since 0 ÷ (1-1) is undefined, we use the first price) For the second price bar: Raw_EMA = α × (Price_2 - 0) + 0 = α × Price_2 Correction = 1 × (1-α) = (1-α) True_EMA = α × Price_2 ÷ (1-(1-α)) = Price_2 For the third price bar: Raw_EMA updates using the standard formula Correction = (1-α) × (1-α) = (1-α)² True_EMA = Raw_EMA ÷ (1-(1-α)²) With each new price, the correction factor shrinks exponentially. After about -log₁₀(1e-10)/log₁₀(1-α) bars, the correction becomes negligible, and our EMA calculation matches what we would get if we had infinite historical data. This approach provides accurate EMA values from the very first calculation. There's no need to use SMA for warmup or discard early values before output converges - EMA is mathematically correct from first value, ready to party without the awkward warmup phase. Here is Pine Script 6 implementation of EMA that can take alpha parameter directly (or period if desired), returns valid values from the start, is resilient to dirty input values, uses decaying compensator instead of SMA, and uses the least amount of computational cycles possible. // Enhanced EMA function with proper initialization and efficient calculation ema(series float source, simple int period=0, simple float alpha=0)=> // Input validation - one of alpha or period must be provided if alpha<=0 and period<=0 runtime.error("Alpha or period must be provided") // Calculate alpha from period if alpha not directly specified float a = alpha > 0 ? alpha : 2.0 / math.max(period, 1) // Initialize variables for EMA calculation var float ema = na // Stores raw EMA value var float result = na // Stores final corrected EMA var float e = 1.0 // Decay compensation factor var bool warmup = true // Flag for warmup phase if not na(source) if na(ema) // First value case - initialize EMA to zero // (we'll correct this immediately with the compensation) ema := 0 result := source else // Standard EMA calculation (optimized formula) ema := a * (source - ema) + ema if warmup // During warmup phase, apply decay compensation e *= (1-a) // Update decay factor float c = 1.0 / (1.0 - e) // Calculate correction multiplier result := c * ema // Apply correction // Stop warmup phase when correction becomes negligible if e <= 1e-10 warmup := false else // After warmup, EMA operates without correction result := ema result // Return the properly compensated EMA value ▶️ CONCLUSION EMA isn't just a "better SMA"—it is a fundamentally different tool, like how a submarine differs from a sailboat – both float, but the similarities end there. EMA responds to inputs differently, weighs historical data differently, and requires different initialization techniques. By understanding these differences, traders can make more informed decisions about when and how to use EMA in trading strategies. And as EMA is embedded in so many other complex and compound indicators and strategies, if system uses tainted and inferior EMA calculatiomn, it is doing a disservice to all derivative indicators too – like building a skyscraper on a foundation of Jell-O. The next time you add an EMA to your chart, remember: you're not just looking at a "faster moving average." You're using an INFINITE IMPULSE RESPONSE filter that carries the echo of all previous price actions, properly weighted to help make better trading decisions. EMA done right might significantly improve the quality of all signals, strategies, and trades that rely on EMA somewhere deep in its algorithmic bowels – proving once again that math skills are indeed useful after high school, no matter what your guidance counselor told you.
Indikator Pine Script®
oleh mihakralj
Moving_AveragesLibrary "Moving_Averages" This library contains majority important moving average functions with int series support. Which means that they can be used with variable length input. For conventional use, please use tradingview built-in ta functions for moving averages as they are more precise. I'll use functions in this library for my other scripts with dynamic length inputs. ema(src, len)   Exponential Moving Average (EMA)   Parameters:      src : Source      len : Period   Returns: Exponential Moving Average with Series Int Support (EMA) alma(src, len, a_offset, a_sigma)   Arnaud Legoux Moving Average (ALMA)   Parameters:      src : Source      len : Period      a_offset : Arnaud Legoux offset      a_sigma : Arnaud Legoux sigma   Returns: Arnaud Legoux Moving Average (ALMA) covwema(src, len)   Coefficient of Variation Weighted Exponential Moving Average (COVWEMA)   Parameters:      src : Source      len : Period   Returns: Coefficient of Variation Weighted Exponential Moving Average (COVWEMA) covwma(src, len)   Coefficient of Variation Weighted Moving Average (COVWMA)   Parameters:      src : Source      len : Period   Returns: Coefficient of Variation Weighted Moving Average (COVWMA) dema(src, len)   DEMA - Double Exponential Moving Average   Parameters:      src : Source      len : Period   Returns: DEMA - Double Exponential Moving Average edsma(src, len, ssfLength, ssfPoles)   EDSMA - Ehlers Deviation Scaled Moving Average   Parameters:      src : Source      len : Period      ssfLength : EDSMA - Super Smoother Filter Length      ssfPoles : EDSMA - Super Smoother Filter Poles   Returns: Ehlers Deviation Scaled Moving Average (EDSMA) eframa(src, len, FC, SC)   Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA)   Parameters:      src : Source      len : Period      FC : Lower Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average      SC : Upper Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average   Returns: Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA) ehma(src, len)   EHMA - Exponential Hull Moving Average   Parameters:      src : Source      len : Period   Returns: Exponential Hull Moving Average (EHMA) etma(src, len)   Exponential Triangular Moving Average (ETMA)   Parameters:      src : Source      len : Period   Returns: Exponential Triangular Moving Average (ETMA) frama(src, len)   Fractal Adaptive Moving Average (FRAMA)   Parameters:      src : Source      len : Period   Returns: Fractal Adaptive Moving Average (FRAMA) hma(src, len)   HMA - Hull Moving Average   Parameters:      src : Source      len : Period   Returns: Hull Moving Average (HMA) jma(src, len, jurik_phase, jurik_power)   Jurik Moving Average - JMA   Parameters:      src : Source      len : Period      jurik_phase : Jurik (JMA) Only - Phase      jurik_power : Jurik (JMA) Only - Power   Returns: Jurik Moving Average (JMA) kama(src, len, k_fastLength, k_slowLength)   Kaufman's Adaptive Moving Average (KAMA)   Parameters:      src : Source      len : Period      k_fastLength : Number of periods for the fastest exponential moving average      k_slowLength : Number of periods for the slowest exponential moving average   Returns: Kaufman's Adaptive Moving Average (KAMA) kijun(_high, _low, len, kidiv)   Kijun v2   Parameters:      _high : High value of bar      _low : Low value of bar      len : Period      kidiv : Kijun MOD Divider   Returns: Kijun v2 lsma(src, len, offset)   LSMA/LRC - Least Squares Moving Average / Linear Regression Curve   Parameters:      src : Source      len : Period      offset : Offset   Returns: Least Squares Moving Average (LSMA)/ Linear Regression Curve (LRC) mf(src, len, beta, feedback, z)   MF - Modular Filter   Parameters:      src : Source      len : Period      beta : Modular Filter, General Filter Only - Beta      feedback : Modular Filter Only - Feedback      z : Modular Filter Only - Feedback Weighting   Returns: Modular Filter (MF) rma(src, len)   RMA - RSI Moving average   Parameters:      src : Source      len : Period   Returns: RSI Moving average (RMA) sma(src, len)   SMA - Simple Moving Average   Parameters:      src : Source      len : Period   Returns: Simple Moving Average (SMA) smma(src, len)   Smoothed Moving Average (SMMA)   Parameters:      src : Source      len : Period   Returns: Smoothed Moving Average (SMMA) stma(src, len)   Simple Triangular Moving Average (STMA)   Parameters:      src : Source      len : Period   Returns: Simple Triangular Moving Average (STMA) tema(src, len)   TEMA - Triple Exponential Moving Average   Parameters:      src : Source      len : Period   Returns: Triple Exponential Moving Average (TEMA) thma(src, len)   THMA - Triple Hull Moving Average   Parameters:      src : Source      len : Period   Returns: Triple Hull Moving Average (THMA) vama(src, len, volatility_lookback)   VAMA - Volatility Adjusted Moving Average   Parameters:      src : Source      len : Period      volatility_lookback : Volatility lookback length   Returns: Volatility Adjusted Moving Average (VAMA) vidya(src, len)   Variable Index Dynamic Average (VIDYA)   Parameters:      src : Source      len : Period   Returns: Variable Index Dynamic Average (VIDYA) vwma(src, len)   Volume-Weighted Moving Average (VWMA)   Parameters:      src : Source      len : Period   Returns: Volume-Weighted Moving Average (VWMA) wma(src, len)   WMA - Weighted Moving Average   Parameters:      src : Source      len : Period   Returns: Weighted Moving Average (WMA) zema(src, len)   Zero-Lag Exponential Moving Average (ZEMA)   Parameters:      src : Source      len : Period   Returns: Zero-Lag Exponential Moving Average (ZEMA) zsma(src, len)   Zero-Lag Simple Moving Average (ZSMA)   Parameters:      src : Source      len : Period   Returns: Zero-Lag Simple Moving Average (ZSMA) evwma(src, len)   EVWMA - Elastic Volume Weighted Moving Average   Parameters:      src : Source      len : Period   Returns: Elastic Volume Weighted Moving Average (EVWMA) tt3(src, len, a1_t3)   Tillson T3   Parameters:      src : Source      len : Period      a1_t3 : Tillson T3 Volume Factor   Returns: Tillson T3 gma(src, len)   GMA - Geometric Moving Average   Parameters:      src : Source      len : Period   Returns: Geometric Moving Average (GMA) wwma(src, len)   WWMA - Welles Wilder Moving Average   Parameters:      src : Source      len : Period   Returns: Welles Wilder Moving Average (WWMA) ama(src, _high, _low, len, ama_f_length, ama_s_length)   AMA - Adjusted Moving Average   Parameters:      src : Source      _high : High value of bar      _low : Low value of bar      len : Period      ama_f_length : Fast EMA Length      ama_s_length : Slow EMA Length   Returns: Adjusted Moving Average (AMA) cma(src, len)   Corrective Moving average (CMA)   Parameters:      src : Source      len : Period   Returns: Corrective Moving average (CMA) gmma(src, len)   Geometric Mean Moving Average (GMMA)   Parameters:      src : Source      len : Period   Returns: Geometric Mean Moving Average (GMMA) ealf(src, len, LAPercLen_, FPerc_)   Ehler's Adaptive Laguerre filter (EALF)   Parameters:      src : Source      len : Period      LAPercLen_ : Median Length      FPerc_ : Median Percentage   Returns: Ehler's Adaptive Laguerre filter (EALF) elf(src, len, LAPercLen_, FPerc_)   ELF - Ehler's Laguerre filter   Parameters:      src : Source      len : Period      LAPercLen_ : Median Length      FPerc_ : Median Percentage   Returns: Ehler's Laguerre Filter (ELF) edma(src, len)   Exponentially Deviating Moving Average (MZ EDMA)   Parameters:      src : Source      len : Period   Returns: Exponentially Deviating Moving Average (MZ EDMA) pnr(src, len, rank_inter_Perc_)   PNR - percentile nearest rank   Parameters:      src : Source      len : Period      rank_inter_Perc_ : Rank and Interpolation Percentage   Returns: Percentile Nearest Rank (PNR) pli(src, len, rank_inter_Perc_)   PLI - Percentile Linear Interpolation   Parameters:      src : Source      len : Period      rank_inter_Perc_ : Rank and Interpolation Percentage   Returns: Percentile Linear Interpolation (PLI) rema(src, len)   Range EMA (REMA)   Parameters:      src : Source      len : Period   Returns: Range EMA (REMA) sw_ma(src, len)   Sine-Weighted Moving Average (SW-MA)   Parameters:      src : Source      len : Period   Returns: Sine-Weighted Moving Average (SW-MA) vwap(src, len)   Volume Weighted Average Price (VWAP)   Parameters:      src : Source      len : Period   Returns: Volume Weighted Average Price (VWAP) mama(src, len)   MAMA - MESA Adaptive Moving Average   Parameters:      src : Source      len : Period   Returns: MESA Adaptive Moving Average (MAMA) fama(src, len)   FAMA - Following Adaptive Moving Average   Parameters:      src : Source      len : Period   Returns: Following Adaptive Moving Average (FAMA) hkama(src, len)   HKAMA - Hilbert based Kaufman's Adaptive Moving Average   Parameters:      src : Source      len : Period   Returns: Hilbert based Kaufman's Adaptive Moving Average (HKAMA)
Perpustakaan Pine Script®
oleh MightyZinger
Demonstration of how history length affects all EMA valuesI saw some discussion of this so I whipped up an example to prove the that effect of history length on EMA values is pronounced, even for bars much further than the EMA length from the first candle of the chart. This chart has two 89-bar EMAs of the close: a green one and a red one. However, for the red one, the first 89 bars of the graph are considered to have a close of "0", which is exactly whatTradingView's EMA calculation uses for bars before the start of the graph. This is because unlike other moving averages, which reference the price of previous bars, the EMA references the EMA of previous bars. Therefore, bars closer to the beginning of the chart, where TradingView can't calculate an EMA because there is no previous EMA and therefore uses 0, will return substantially different values for the EMA() function that the same cart would with more history. The further a bar is back in history, the less influence it has. However, every single historical bar has some influence on the EMA of every later bar. To allow you to see this for yourself, this script contains the following inputs which you can change to see the effect: -EMA period (default 89) -Number of bars to ignore for EMA2 (default 89) -decimal precision to show differences in. By making this a large number you can see that, although the effects diminish, history length affects all EMA values for the char. -label spacing (increase this if you have a long history and run into TV's 50-label limit)
Indikator Pine Script®
oleh ApopheniaPays
Mean Reversion Cloud (Ornstein-Uhlenbeck) // AlgoFyreThe Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator detects mean-reversion opportunities by applying the Ornstein-Uhlenbeck process. It calculates a dynamic mean using an Exponential Weighted Moving Average, surrounded by volatility bands, signaling potential buy/sell points when prices deviate. TABLE OF CONTENTS 🔶 ORIGINALITY 🔸Adaptive Mean Calculation 🔸Volatility-Based Cloud 🔸Speed of Reversion (θ) 🔶 FUNCTIONALITY 🔸Dynamic Mean and Volatility Bands 🞘 How it works 🞘 How to calculate 🞘 Code extract 🔸Visualization via Table and Plotshapes 🞘 Table Overview 🞘 Plotshapes Explanation 🞘 Code extract 🔶 INSTRUCTIONS 🔸Step-by-Step Guidelines 🞘 Setting Up the Indicator 🞘 Understanding What to Look For on the Chart 🞘 Possible Entry Signals 🞘 Possible Take Profit Strategies 🞘 Possible Stop-Loss Levels 🞘 Additional Tips 🔸Customize settings 🔶 CONCLUSION ▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅ 🔶 ORIGINALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) is a unique indicator that applies the Ornstein-Uhlenbeck stochastic process to identify mean-reverting behavior in asset prices. Unlike traditional moving average-based indicators, this model uses an Exponentially Weighted Moving Average (EWMA) to calculate the long-term mean, dynamically adjusting to recent price movements while still considering all historical data. It also incorporates volatility bands, providing a "cloud" that visually highlights overbought or oversold conditions. By calculating the speed of mean reversion (θ) through the autocorrelation of log returns, this indicator offers traders a more nuanced and mathematically robust tool for identifying mean-reversion opportunities. These innovations make it especially useful for markets that exhibit range-bound characteristics, offering timely buy and sell signals based on statistical deviations from the mean. 🔸Adaptive Mean Calculation Traditional MA indicators use fixed lengths, which can lead to lagging signals or over-sensitivity in volatile markets. The Mean Reversion Cloud uses an Exponentially Weighted Moving Average (EWMA), which adapts to price movements by dynamically adjusting its calculation, offering a more responsive mean. 🔸Volatility-Based Cloud Unlike simple moving averages that only plot a single line, the Mean Reversion Cloud surrounds the dynamic mean with volatility bands. These bands, based on standard deviations, provide traders with a visual cue of when prices are statistically likely to revert, highlighting potential reversal zones. 🔸Speed of Reversion (θ) The indicator goes beyond price averages by calculating the speed at which the price reverts to the mean (θ), using the autocorrelation of log returns. This gives traders an additional tool for estimating the likelihood and timing of mean reversion, making the signals more reliable in practice. 🔶 FUNCTIONALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator is designed to detect potential mean-reversion opportunities in asset prices by applying the Ornstein-Uhlenbeck stochastic process. It calculates a dynamic mean through the Exponentially Weighted Moving Average (EWMA) and plots volatility bands based on the standard deviation of the asset's price over a specified period. These bands create a "cloud" that represents expected price fluctuations, helping traders to identify overbought or oversold conditions. By calculating the speed of reversion (θ) from the autocorrelation of log returns, the indicator offers a more refined way of assessing how quickly prices may revert to the mean. Additionally, the inclusion of volatility provides a comprehensive view of market conditions, allowing for more accurate buy and sell signals. Let's dive into the details: 🔸Dynamic Mean and Volatility Bands The dynamic mean (μ) is calculated using the EWMA, giving more weight to recent prices but considering all historical data. This process closely resembles the Ornstein-Uhlenbeck (OU) process, which models the tendency of a stochastic variable (such as price) to revert to its mean over time. Volatility bands are plotted around the mean using standard deviation, forming the "cloud" that signals overbought or oversold conditions. The cloud adapts dynamically to price fluctuations and market volatility, making it a versatile tool for mean-reversion strategies. 🞘 How it works Step one: Calculate the dynamic mean (μ) The Ornstein-Uhlenbeck process describes how a variable, such as an asset's price, tends to revert to a long-term mean while subject to random fluctuations. In this indicator, the EWMA is used to compute the dynamic mean (μ), mimicking the mean-reverting behavior of the OU process. Use the EWMA formula to compute a weighted mean that adjusts to recent price movements. Assign exponentially decreasing weights to older data while giving more emphasis to current prices. Step two: Plot volatility bands Calculate the standard deviation of the price over a user-defined period to determine market volatility. Position the upper and lower bands around the mean by adding and subtracting a multiple of the standard deviation. 🞘 How to calculate Exponential Weighted Moving Average (EWMA) The EWMA dynamically adjusts to recent price movements: mu_t = lambda * mu_{t-1} + (1 - lambda) * P_t Where mu_t is the mean at time t, lambda is the decay factor, and P_t is the price at time t. The higher the decay factor, the more weight is given to recent data. Autocorrelation (ρ) and Standard Deviation (σ) To measure mean reversion speed and volatility: rho = correlation(log(close), log(close ), length) Where rho is the autocorrelation of log returns over a specified period. To calculate volatility: sigma = stdev(close, length) Where sigma is the standard deviation of the asset's closing price over a specified length. Upper and Lower Bands The upper and lower bands are calculated as follows: upper_band = mu + (threshold * sigma) lower_band = mu - (threshold * sigma) Where threshold is a multiplier for the standard deviation, usually set to 2. These bands represent the range within which the price is expected to fluctuate, based on current volatility and the mean. 🞘 Code extract // Calculate Returns returns = math.log(close / close ) // Calculate Long-Term Mean (μ) using EWMA over the entire dataset var float ewma_mu = na // Initialize ewma_mu as 'na' ewma_mu := na(ewma_mu ) ? close : decay_factor * ewma_mu + (1 - decay_factor) * close mu = ewma_mu // Calculate Autocorrelation at Lag 1 rho1 = ta.correlation(returns, returns , corr_length) // Ensure rho1 is within valid range to avoid errors rho1 := na(rho1) or rho1 <= 0 ? 0.0001 : rho1 // Calculate Speed of Mean Reversion (θ) theta = -math.log(rho1) // Calculate Volatility (σ) sigma = ta.stdev(close, corr_length) // Calculate Upper and Lower Bands upper_band = mu + threshold * sigma lower_band = mu - threshold * sigma 🔸Visualization via Table and Plotshapes The table shows key statistics such as the current value of the dynamic mean (μ), the number of times the price has crossed the upper or lower bands, and the consecutive number of bars that the price has remained in an overbought or oversold state. Plotshapes (diamonds) are used to signal buy and sell opportunities. A green diamond below the price suggests a buy signal when the price crosses below the lower band, and a red diamond above the price indicates a sell signal when the price crosses above the upper band. The table and plotshapes provide a comprehensive visualization, combining both statistical and actionable information to aid decision-making. 🞘 Code extract // Reset consecutive_bars when price crosses the mean var consecutive_bars = 0 if (close < mu and close >= mu) or (close > mu and close <= mu) consecutive_bars := 0 else if math.abs(deviation) > 0 consecutive_bars := math.min(consecutive_bars + 1, dev_length) transparency = math.max(0, math.min(100, 100 - (consecutive_bars * 100 / dev_length))) 🔶 INSTRUCTIONS The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator can be set up by adding it to your TradingView chart and configuring parameters such as the decay factor, autocorrelation length, and volatility threshold to suit current market conditions. Look for price crossovers and deviations from the calculated mean for potential entry signals. Use the upper and lower bands as dynamic support/resistance levels for setting take profit and stop-loss orders. Combining this indicator with additional trend-following or momentum-based indicators can improve signal accuracy. Adjust settings for better mean-reversion detection and risk management. 🔸Step-by-Step Guidelines 🞘 Setting Up the Indicator Adding the Indicator to the Chart: Go to your TradingView chart. Click on the "Indicators" button at the top. Search for "Mean Reversion Cloud (Ornstein-Uhlenbeck)" in the indicators list. Click on the indicator to add it to your chart. Configuring the Indicator: Open the indicator settings by clicking on the gear icon next to its name on the chart. Decay Factor: Adjust the decay factor (λ) to control the responsiveness of the mean calculation. A higher value prioritizes recent data. Autocorrelation Length: Set the autocorrelation length (θ) for calculating the speed of mean reversion. Longer lengths consider more historical data. Threshold: Define the number of standard deviations for the upper and lower bands to determine how far price must deviate to trigger a signal. Chart Setup: Select the appropriate timeframe (e.g., 1-hour, daily) based on your trading strategy. Consider using other indicators such as RSI or MACD to confirm buy and sell signals. 🞘 Understanding What to Look For on the Chart Indicator Behavior: Observe how the price interacts with the dynamic mean and volatility bands. The price staying within the bands suggests mean-reverting behavior, while crossing the bands signals potential entry points. The indicator calculates overbought/oversold conditions based on deviation from the mean, highlighted by color-coded cloud areas on the chart. Crossovers and Deviation: Look for crossovers between the price and the mean (μ) or the bands. A bullish crossover occurs when the price crosses below the lower band, signaling a potential buying opportunity. A bearish crossover occurs when the price crosses above the upper band, suggesting a potential sell signal. Deviations from the mean indicate market extremes. A large deviation indicates that the price is far from the mean, suggesting a potential reversal. Slope and Direction: Pay attention to the slope of the mean (μ). A rising slope suggests bullish market conditions, while a declining slope signals a bearish market. The steepness of the slope can indicate the strength of the mean-reversion trend. 🞘 Possible Entry Signals Bullish Entry: Crossover Entry: Enter a long position when the price crosses below the lower band with a positive deviation from the mean. Confirmation Entry: Use additional indicators like RSI (above 50) or increasing volume to confirm the bullish signal. Bearish Entry: Crossover Entry: Enter a short position when the price crosses above the upper band with a negative deviation from the mean. Confirmation Entry: Look for RSI (below 50) or decreasing volume to confirm the bearish signal. Deviation Confirmation: Enter trades when the deviation from the mean is significant, indicating that the price has strayed far from its expected value and is likely to revert. 🞘 Possible Take Profit Strategies Static Take Profit Levels: Set predefined take profit levels based on historical volatility, using the upper and lower bands as guides. Place take profit orders near recent support/resistance levels, ensuring you're capitalizing on the mean-reversion behavior. Trailing Stop Loss: Use a trailing stop based on a percentage of the price deviation from the mean to lock in profits as the trend progresses. Adjust the trailing stop dynamically along the calculated bands to protect profits as the price returns to the mean. Deviation-Based Exits: Exit when the deviation from the mean starts to decrease, signaling that the price is returning to its equilibrium. 🞘 Possible Stop-Loss Levels Initial Stop Loss: Place an initial stop loss outside the lower band (for long positions) or above the upper band (for short positions) to protect against excessive deviations. Use a volatility-based buffer to avoid getting stopped out during normal price fluctuations. Dynamic Stop Loss: Move the stop loss closer to the mean as the price converges back towards equilibrium, reducing risk. Adjust the stop loss dynamically along the bands to account for sudden market movements. 🞘 Additional Tips Combine with Other Indicators: Enhance your strategy by combining the Mean Reversion Cloud with momentum indicators like MACD, RSI, or Bollinger Bands to confirm market conditions. Backtesting and Practice: Backtest the indicator on historical data to understand how it performs in various market environments. Practice using the indicator on a demo account before implementing it in live trading. Market Awareness: Keep an eye on market news and events that might cause extreme price movements. The indicator reacts to price data and might not account for news-driven events that can cause large deviations. 🔸Customize settings 🞘 Decay Factor (λ): Defines the weight assigned to recent price data in the calculation of the mean. A value closer to 1 places more emphasis on recent prices, while lower values create a smoother, more lagging mean. 🞘 Autocorrelation Length (θ): Sets the period for calculating the speed of mean reversion and volatility. Longer lengths capture more historical data, providing smoother calculations, while shorter lengths make the indicator more responsive. 🞘 Threshold (σ): Specifies the number of standard deviations used to create the upper and lower bands. Higher thresholds widen the bands, producing fewer signals, while lower thresholds tighten the bands for more frequent signals. 🞘 Max Gradient Length (γ): Determines the maximum number of consecutive bars for calculating the deviation gradient. This setting impacts the transparency of the plotted bands based on the length of deviation from the mean. 🔶 CONCLUSION The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator offers a sophisticated approach to identifying mean-reversion opportunities by applying the Ornstein-Uhlenbeck stochastic process. This dynamic indicator calculates a responsive mean using an Exponentially Weighted Moving Average (EWMA) and plots volatility-based bands to highlight overbought and oversold conditions. By incorporating advanced statistical measures like autocorrelation and standard deviation, traders can better assess market extremes and potential reversals. The indicator’s ability to adapt to price behavior makes it a versatile tool for traders focused on both short-term price deviations and longer-term mean-reversion strategies. With its unique blend of statistical rigor and visual clarity, the Mean Reversion Cloud provides an invaluable tool for understanding and capitalizing on market inefficiencies.
Indikator Pine Script®
oleh AlgoFyre
Moving Averages ProxyLibrary "MovingAveragesProxy" Moving Averages Proxy - Library of all moving averages spread out in different libraries rvwap(_src, fixedTfInput, minsInput, hoursInput, daysInput, minBarsInput)   Calculates the Rolling VWAP (customized VWAP developed by the team of TradingView)   Parameters:      _src : (float) Source. Default: close      fixedTfInput : (bool) Use a fixed time period. Default: false      minsInput : (int) Minutes. Default: 0      hoursInput : (int) Hours. Default: 0      daysInput : (int) Days. Default: 1      minBarsInput : (int) Bars. Default: 10   Returns: (float) Rolling VWAP correlationMa(src, len, factor)   Correlation Moving Average   Parameters:      src : (float) Source. Default: close      len : (int) Length      factor : (float) Factor. Default: 1.7   Returns: (float) Correlation Moving Average regma(src, len, lambda)   Regularized Exponential Moving Average   Parameters:      src : (float) Source. Default: close      len : (int) Length      lambda : (float) Lambda. Default: 0.5   Returns: (float) Regularized Exponential Moving Average repma(src, len)   Repulsion Moving Average   Parameters:      src : (float) Source. Default: close      len : (int) Length   Returns: (float) Repulsion Moving Average epma(src, length, offset)   End Point Moving Average   Parameters:      src : (float) Source. Default: close      length : (int) Length      offset : (float) Offset. Default: 4   Returns: (float) End Point Moving Average lc_lsma(src, length)   1LC-LSMA (1 line code lsma with 3 functions)   Parameters:      src : (float) Source. Default: close      length : (int) Length   Returns: (float) 1LC-LSMA Moving Average aarma(src, length)   Adaptive Autonomous Recursive Moving Average   Parameters:      src : (float) Source. Default: close      length : (int) Length   Returns: (float) Adaptive Autonomous Recursive Moving Average alsma(src, length)   Adaptive Least Squares   Parameters:      src : (float) Source. Default: close      length : (int) Length   Returns: (float) Adaptive Least Squares ahma(src, length)   Ahrens Moving Average   Parameters:      src : (float) Source. Default: close      length : (int) Length   Returns: (float) Ahrens Moving Average adema(src)   Ahrens Moving Average   Parameters:      src : (float) Source. Default: close   Returns: (float) Moving Average autol(src, lenDev)   Auto-Line   Parameters:      src : (float) Source. Default: close      lenDev : (int) Length for standard deviation   Returns: (float) Auto-Line fibowma(src, length)   Fibonacci Weighted Moving Average   Parameters:      src : (float) Source. Default: close      length : (int) Length   Returns: (float) Moving Average fisherlsma(src, length)   Fisher Least Squares Moving Average   Parameters:      src : (float) Source. Default: close      length : (int) Length   Returns: (float) Moving Average leoma(src, length)   Leo Moving Average   Parameters:      src : (float) Source. Default: close      length : (int) Length   Returns: (float) Moving Average linwma(src, period, weight)   Linear Weighted Moving Average   Parameters:      src : (float) Source. Default: close      period : (int) Length      weight : (int) Weight   Returns: (float) Moving Average mcma(src, length)   McNicholl Moving Average   Parameters:      src : (float) Source. Default: close      length : (int) Length   Returns: (float) Moving Average srwma(src, length)   Square Root Weighted Moving Average   Parameters:      src : (float) Source. Default: close      length : (int) Length   Returns: (float) Moving Average EDSMA(src, len)   Ehlers Dynamic Smoothed Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: EDSMA smoothing. dema(x, t)   Double Exponential Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: DEMA smoothing. tema(src, len)   Triple Exponential Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: TEMA smoothing. smma(src, len)   Smoothed Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: SMMA smoothing. hullma(src, len)   Hull Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: Hull smoothing. frama(x, t)   Fractal Reactive Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: FRAMA smoothing. kama(x, t)   Kaufman's Adaptive Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: KAMA smoothing. vama(src, len)   Volatility Adjusted Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: VAMA smoothing. donchian(len)   Donchian Calculation.   Parameters:      len : Lookback length to use.   Returns: Average of the highest price and the lowest price for the specified look-back period. Jurik(src, len)   Jurik Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: JMA smoothing. xema(src, len)   Optimized Exponential Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: XEMA smoothing. ehma(src, len)   EHMA - Exponential Hull Moving Average   Parameters:      src : Source      len : Period   Returns: Exponential Hull Moving Average (EHMA) covwema(src, len)   Coefficient of Variation Weighted Exponential Moving Average (COVWEMA)   Parameters:      src : Source      len : Period   Returns: Coefficient of Variation Weighted Exponential Moving Average (COVWEMA) covwma(src, len)   Coefficient of Variation Weighted Moving Average (COVWMA)   Parameters:      src : Source      len : Period   Returns: Coefficient of Variation Weighted Moving Average (COVWMA) eframa(src, len, FC, SC)   Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA)   Parameters:      src : Source      len : Period      FC : Lower Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average      SC : Upper Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average   Returns: Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA) etma(src, len)   Exponential Triangular Moving Average (ETMA)   Parameters:      src : Source      len : Period   Returns: Exponential Triangular Moving Average (ETMA) rma(src, len)   RMA - RSI Moving average   Parameters:      src : Source      len : Period   Returns: RSI Moving average (RMA) thma(src, len)   THMA - Triple Hull Moving Average   Parameters:      src : Source      len : Period   Returns: Triple Hull Moving Average (THMA) vidya(src, len)   Variable Index Dynamic Average (VIDYA)   Parameters:      src : Source      len : Period   Returns: Variable Index Dynamic Average (VIDYA) zsma(src, len)   Zero-Lag Simple Moving Average (ZSMA)   Parameters:      src : Source      len : Period   Returns: Zero-Lag Simple Moving Average (ZSMA) zema(src, len)   Zero-Lag Exponential Moving Average (ZEMA)   Parameters:      src : Source      len : Period   Returns: Zero-Lag Exponential Moving Average (ZEMA) evwma(src, len)   EVWMA - Elastic Volume Weighted Moving Average   Parameters:      src : Source      len : Period   Returns: Elastic Volume Weighted Moving Average (EVWMA) tt3(src, len, a1_t3)   Tillson T3   Parameters:      src : Source      len : Period      a1_t3 : Tillson T3 Volume Factor   Returns: Tillson T3 gma(src, len)   GMA - Geometric Moving Average   Parameters:      src : Source      len : Period   Returns: Geometric Moving Average (GMA) wwma(src, len)   WWMA - Welles Wilder Moving Average   Parameters:      src : Source      len : Period   Returns: Welles Wilder Moving Average (WWMA) cma(src, len)   Corrective Moving average (CMA)   Parameters:      src : Source      len : Period   Returns: Corrective Moving average (CMA) edma(src, len)   Exponentially Deviating Moving Average (MZ EDMA)   Parameters:      src : Source      len : Period   Returns: Exponentially Deviating Moving Average (MZ EDMA) rema(src, len)   Range EMA (REMA)   Parameters:      src : Source      len : Period   Returns: Range EMA (REMA) sw_ma(src, len)   Sine-Weighted Moving Average (SW-MA)   Parameters:      src : Source      len : Period   Returns: Sine-Weighted Moving Average (SW-MA) mama(src, len)   MAMA - MESA Adaptive Moving Average   Parameters:      src : Source      len : Period   Returns: MESA Adaptive Moving Average (MAMA) fama(src, len)   FAMA - Following Adaptive Moving Average   Parameters:      src : Source      len : Period   Returns: Following Adaptive Moving Average (FAMA) hkama(src, len)   HKAMA - Hilbert based Kaufman's Adaptive Moving Average   Parameters:      src : Source      len : Period   Returns: Hilbert based Kaufman's Adaptive Moving Average (HKAMA) getMovingAverage(type, src, len, lsmaOffset, inputAlmaOffset, inputAlmaSigma, FC, SC, a1_t3, fixedTfInput, daysInput, hoursInput, minsInput, minBarsInput, lambda, volumeWeighted, gamma_aarma, smooth, linweight, volatility_lookback, jurik_phase, jurik_power)   Abstract proxy function that invokes the calculation of a moving average according to type   Parameters:      type : (string) Type of moving average      src : (float) Source of series (close, high, low, etc.)      len : (int) Period of loopback to calculate the average      lsmaOffset : (int) Offset for Least Squares MA      inputAlmaOffset : (float) Offset for ALMA      inputAlmaSigma : (float) Sigma for ALMA      FC : (int) Lower Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average      SC : (int) Upper Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average      a1_t3 : (float) Tillson T3 Volume Factor      fixedTfInput : (bool) Use a fixed time period in Rolling VWAP      daysInput : (int) Days in Rolling VWAP      hoursInput : (int) Hours in Rolling VWAP      minsInput : (int) Minutrs in Rolling VWAP      minBarsInput : (int) Bars in Rolling VWAP      lambda : (float) Regularization Constant in Regularized EMA      volumeWeighted : (bool) Apply volume weighted calculation in selected moving average      gamma_aarma : (float) Gamma for Adaptive Autonomous Recursive Moving Average      smooth : (float) Smooth for Adaptive Least Squares      linweight : (float) Weight for Volume Weighted Moving Average      volatility_lookback : (int) Loopback for Volatility Adjusted Moving Average      jurik_phase : (int) Phase for Jurik Moving Average      jurik_power : (int) Power for Jurik Moving Average   Returns: (float) Moving average
Perpustakaan Pine Script®
oleh andre_007
14
Volatility Signal-to-Noise Ratio🙏🏻 this is VSNR: the most effective and simple volatility regime detector & automatic volatility threshold scaler that somehow no1 ever talks about. This is simply an inverse of the coefficient of variation of absolute returns, but properly constructed taking into account temporal information, and made online via recursive math with algocomplexity O(1) both in expanding and moving windows modes. How do the available alternatives differ (while some’re just worse)? Mainstream quant stat tests like Durbin-Watson, Dickey-Fuller etc: default implementations are ALL not time aware. They measure different kinds of regime, which is less (if at all) relevant for actual trading context. Mix of different math, high algocomplexity. The closest one is MMI by financialhacker, but his approach is also not time aware, and has a higher algocomplexity anyways. Best alternative to mine, but pls modify it to use a time-weighted median. Fractal dimension & its derivatives by John Ehlers: again not time aware, very low info gain, relies on bar sizes (high and lows), which don’t always exist unlike changes between datapoints. But it’s a geometric tool in essence, so this is fundamental. Let it watch your back if you already use it. Hurst exponent: much higher algocomplexity, mix of parametric and non-parametric math inside. An invention, not a math entity. Again, not time aware. Also measures different kinds of regime. How to set it up: Given my other tools, I choose length so that it will match the amount of data that your trading method or study uses multiplied by ~ 4-5. E.g if you use some kind of bands to trade volatility and you calculate them over moving window 64, put VSNR on 256. However it depends mathematically on many things, so for your methods you may instead need multipliers of 1 or ~ 16. Additionally if you wanna use all data to estimate SNR, put 0 into length input. How to use for regime detection: First we define: MR bias: mean reversion bias meaning volatility shorts would work better, fading levels would work better Momo bias: momentum bias meaning volatility longs would work better, trading breakouts of levels would work better. The study plots 3 horizontal thresholds for VSNR, just check its location: Above upper level: significant Momo bias Above 1 : Momo bias Below 1 : MR bias Below lower level: significant MR bias Take a look at the screenshots, 2 completely different volatility regimes are spotted by VSNR, while an ADF does not show different regime: ^^ CBOT:ZN1! ^^ INDEX:BTCUSD How to use as automatic volatility threshold scaler Copy the code from the script, and use VSNR as a multiplier for your volatility threshold. E.g you use a regression channel and fade/push upper and lower thresholds which are RMSEs multiples. Inside the code, multiply RMSE by VSNR, now you’re adaptive. ^^ The same logic as when MM bots widen spreads with vola goes wild. How it works: Returns follow Laplace distro -> logically abs returns follow exponential distro , cuz laplace = double exponential. Exponential distro has a natural coefficient of variation = 1 -> signal to noise ratio defined as mean/stdev = 1 as well. The same can be said for Student t distro with parameter v = 4. So 1 is our main threshold. We can add additional thresholds by discovering SNRs of Student t with v = 3 and v = 5 (+- 1 from baseline v = 4). These have lighter & heavier tails each favoring mean reversion or momentum more. I computed the SNR values you see in the code with mpmath python module, with precision 256 decimals, so you can trust it I put it on my momma. Then I use exponential smoothing with properly defined alphas (one matches cumulative WMA and another minimizes error with WMA in moving window mode) to estimate SNR of abs returns. … Lightweight huh? ∞
Indikator Pine Script®
oleh gorx1
4
TA█ TA Library 📊 OVERVIEW TA is a Pine Script technical analysis library. This library provides 25+ moving averages and smoothing filters , from classic SMA/EMA to Kalman Filters and adaptive algorithms, implemented based on academic research. 🎯 Core Features Academic Based - Algorithms follow original papers and formulas Performance Optimized - Pre-calculated constants for faster response Unified Interface - Consistent function design Research Based - Integrates technical analysis research 🎯 CONCEPTS Library Design Philosophy This technical analysis library focuses on providing: Academic Foundation Algorithms based on published research papers and academic standards Implementations that follow original mathematical formulations Clear documentation with research references Developer Experience Unified interface design for consistent usage patterns Pre-calculated constants for optimal performance Comprehensive function collection to reduce development time Single import statement for immediate access to all functions Each indicator encapsulated as a simple function call - one line of code simplifies complexity Technical Excellence 25+ carefully implemented moving averages and filters Support for advanced algorithms like Kalman Filter and MAMA/FAMA Optimized code structure for maintainability and reliability Regular updates incorporating latest research developments 🚀 USING THIS LIBRARY Import Library //@version=6 import DCAUT/TA/1 as dta indicator("Advanced Technical Analysis", overlay=true) Basic Usage Example // Classic moving average combination ema20 = ta.ema(close, 20) kama20 = dta.kama(close, 20) plot(ema20, "EMA20", color.red, 2) plot(kama20, "KAMA20", color.green, 2) Advanced Trading System // Adaptive moving average system kama = dta.kama(close, 20, 2, 30) = dta.mamaFama(close, 0.5, 0.05) // Trend confirmation and entry signals bullTrend = kama > kama and mamaValue > famaValue bearTrend = kama < kama and mamaValue < famaValue longSignal = ta.crossover(close, kama) and bullTrend shortSignal = ta.crossunder(close, kama) and bearTrend plot(kama, "KAMA", color.blue, 3) plot(mamaValue, "MAMA", color.orange, 2) plot(famaValue, "FAMA", color.purple, 2) plotshape(longSignal, "Buy", shape.triangleup, location.belowbar, color.green) plotshape(shortSignal, "Sell", shape.triangledown, location.abovebar, color.red) 📋 FUNCTIONS REFERENCE ewma(source, alpha) Calculates the Exponentially Weighted Moving Average with dynamic alpha parameter. Parameters: source (series float) : Series of values to process. alpha (series float) : The smoothing parameter of the filter. Returns: (float) The exponentially weighted moving average value. dema(source, length) Calculates the Double Exponential Moving Average (DEMA) of a given data series. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Double Exponential Moving Average value. tema(source, length) Calculates the Triple Exponential Moving Average (TEMA) of a given data series. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Triple Exponential Moving Average value. zlema(source, length) Calculates the Zero-Lag Exponential Moving Average (ZLEMA) of a given data series. This indicator attempts to eliminate the lag inherent in all moving averages. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Zero-Lag Exponential Moving Average value. tma(source, length) Calculates the Triangular Moving Average (TMA) of a given data series. TMA is a double-smoothed simple moving average that reduces noise. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Triangular Moving Average value. frama(source, length) Calculates the Fractal Adaptive Moving Average (FRAMA) of a given data series. FRAMA adapts its smoothing factor based on fractal geometry to reduce lag. Developed by John Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Fractal Adaptive Moving Average value. kama(source, length, fastLength, slowLength) Calculates Kaufman's Adaptive Moving Average (KAMA) of a given data series. KAMA adjusts its smoothing based on market efficiency ratio. Developed by Perry J. Kaufman. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the efficiency calculation. fastLength (simple int) : Fast EMA length for smoothing calculation. Optional. Default is 2. slowLength (simple int) : Slow EMA length for smoothing calculation. Optional. Default is 30. Returns: (float) The calculated Kaufman's Adaptive Moving Average value. t3(source, length, volumeFactor) Calculates the Tilson Moving Average (T3) of a given data series. T3 is a triple-smoothed exponential moving average with improved lag characteristics. Developed by Tim Tillson. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. volumeFactor (simple float) : Volume factor affecting responsiveness. Optional. Default is 0.7. Returns: (float) The calculated Tilson Moving Average value. ultimateSmoother(source, length) Calculates the Ultimate Smoother of a given data series. Uses advanced filtering techniques to reduce noise while maintaining responsiveness. Based on digital signal processing principles by John Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the smoothing calculation. Returns: (float) The calculated Ultimate Smoother value. kalmanFilter(source, processNoise, measurementNoise) Calculates the Kalman Filter of a given data series. Optimal estimation algorithm that estimates true value from noisy observations. Based on the Kalman Filter algorithm developed by Rudolf Kalman (1960). Parameters: source (series float) : Series of values to process. processNoise (simple float) : Process noise variance (Q). Controls adaptation speed. Optional. Default is 0.05. measurementNoise (simple float) : Measurement noise variance (R). Controls smoothing. Optional. Default is 1.0. Returns: (float) The calculated Kalman Filter value. mcginleyDynamic(source, length) Calculates the McGinley Dynamic of a given data series. McGinley Dynamic is an adaptive moving average that adjusts to market speed changes. Developed by John R. McGinley Jr. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the dynamic calculation. Returns: (float) The calculated McGinley Dynamic value. mama(source, fastLimit, slowLimit) Calculates the Mesa Adaptive Moving Average (MAMA) of a given data series. MAMA uses Hilbert Transform Discriminator to adapt to market cycles dynamically. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5. slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05. Returns: (float) The calculated Mesa Adaptive Moving Average value. fama(source, fastLimit, slowLimit) Calculates the Following Adaptive Moving Average (FAMA) of a given data series. FAMA follows MAMA with reduced responsiveness for crossover signals. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5. slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05. Returns: (float) The calculated Following Adaptive Moving Average value. mamaFama(source, fastLimit, slowLimit) Calculates Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA). Parameters: source (series float) : Series of values to process. fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5. slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05. Returns: ( ) Tuple containing values. laguerreFilter(source, length, gamma, order) Calculates the standard N-order Laguerre Filter of a given data series. Standard Laguerre Filter uses uniform weighting across all polynomial terms. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Length for UltimateSmoother preprocessing. gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.8. order (simple int) : The order of the Laguerre filter (1-10). Higher order increases lag. Optional. Default is 8. Returns: (float) The calculated standard Laguerre Filter value. laguerreBinomialFilter(source, length, gamma) Calculates the Laguerre Binomial Filter of a given data series. Uses 6-pole feedback with binomial weighting coefficients. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Length for UltimateSmoother preprocessing. gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.5. Returns: (float) The calculated Laguerre Binomial Filter value. superSmoother(source, length) Calculates the Super Smoother of a given data series. SuperSmoother is a second-order Butterworth filter from aerospace technology. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Period for the filter calculation. Returns: (float) The calculated Super Smoother value. rangeFilter(source, length, multiplier) Calculates the Range Filter of a given data series. Range Filter reduces noise by filtering price movements within a dynamic range. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the average range calculation. multiplier (simple float) : Multiplier for the smooth range. Higher values increase filtering. Optional. Default is 2.618. Returns: ( ) Tuple containing filtered value, trend direction, upper band, and lower band. qqe(source, rsiLength, rsiSmooth, qqeFactor) Calculates the Quantitative Qualitative Estimation (QQE) of a given data series. QQE is an improved RSI that reduces noise and provides smoother signals. Developed by Igor Livshin. Parameters: source (series float) : Series of values to process. rsiLength (simple int) : Number of bars for the RSI calculation. Optional. Default is 14. rsiSmooth (simple int) : Number of bars for smoothing the RSI. Optional. Default is 5. qqeFactor (simple float) : QQE factor for volatility band width. Optional. Default is 4.236. Returns: ( ) Tuple containing smoothed RSI and QQE trend line. sslChannel(source, length) Calculates the Semaphore Signal Level (SSL) Channel of a given data series. SSL Channel provides clear trend signals using moving averages of high and low prices. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: ( ) Tuple containing SSL Up and SSL Down lines. ma(source, length, maType) Calculates a Moving Average based on the specified type. Universal interface supporting all moving average algorithms. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. maType (simple MaType) : Type of moving average to calculate. Optional. Default is SMA. Returns: (float) The calculated moving average value based on the specified type. atr(length, maType) Calculates the Average True Range (ATR) using the specified moving average type. Developed by J. Welles Wilder Jr. Parameters: length (simple int) : Number of bars for the ATR calculation. maType (simple MaType) : Type of moving average to use for smoothing. Optional. Default is RMA. Returns: (float) The calculated Average True Range value. macd(source, fastLength, slowLength, signalLength, maType, signalMaType) Calculates the Moving Average Convergence Divergence (MACD) with customizable MA types. Developed by Gerald Appel. Parameters: source (series float) : Series of values to process. fastLength (simple int) : Period for the fast moving average. slowLength (simple int) : Period for the slow moving average. signalLength (simple int) : Period for the signal line moving average. maType (simple MaType) : Type of moving average for main MACD calculation. Optional. Default is EMA. signalMaType (simple MaType) : Type of moving average for signal line calculation. Optional. Default is EMA. Returns: ( ) Tuple containing MACD line, signal line, and histogram values. dmao(source, fastLength, slowLength, maType) Calculates the Dual Moving Average Oscillator (DMAO) of a given data series. Uses the same algorithm as the Percentage Price Oscillator (PPO), but can be applied to any data series. Parameters: source (series float) : Series of values to process. fastLength (simple int) : Period for the fast moving average. slowLength (simple int) : Period for the slow moving average. maType (simple MaType) : Type of moving average to use for both calculations. Optional. Default is EMA. Returns: (float) The calculated Dual Moving Average Oscillator value as a percentage. continuationIndex(source, length, gamma, order) Calculates the Continuation Index of a given data series. The index represents the Inverse Fisher Transform of the normalized difference between an UltimateSmoother and an N-order Laguerre filter. Developed by John F. Ehlers, published in TASC 2025.09. Parameters: source (series float) : Series of values to process. length (simple int) : The calculation length. gamma (simple float) : Controls the phase response of the Laguerre filter. Optional. Default is 0.8. order (simple int) : The order of the Laguerre filter (1-10). Optional. Default is 8. Returns: (float) The calculated Continuation Index value. 📚 RELEASE NOTES v1.0 (2025.09.24) ✅ 25+ technical analysis functions ✅ Complete adaptive moving average series (KAMA, FRAMA, MAMA/FAMA) ✅ Advanced signal processing filters (Kalman, Laguerre, SuperSmoother, UltimateSmoother) ✅ Performance optimized with pre-calculated constants and efficient algorithms ✅ Unified function interface design following TradingView best practices ✅ Comprehensive moving average collection (DEMA, TEMA, ZLEMA, T3, etc.) ✅ Volatility and trend detection tools (QQE, SSL Channel, Range Filter) ✅ Continuation Index - Latest research from TASC 2025.09 ✅ MACD and ATR calculations supporting multiple moving average types ✅ Dual Moving Average Oscillator (DMAO) for arbitrary data series analysis
Perpustakaan Pine Script®
oleh DCAUT
GWAP (Gamma Weighted Average Price)Gamma Weighted Average Price (GWAP) Indicator The Gamma Weighted Average Price (GWAP) is a dynamic financial indicator that applies exponentially decaying weights to historical prices to calculate a weighted average. The method leverages the exponential decay function, controlled by a gamma factor, to prioritize recent price data while gradually diminishing the influence of older observations. This approach builds upon techniques commonly found in time-series analysis, including Exponentially Weighted Moving Averages (EWMA), which are extensively used in financial modeling (Campbell, Lo & MacKinlay, 1997). Theoretical Context and Justification The gamma-weighted approach follows principles similar to those in Exponentially Weighted Moving Averages (EWMA), often used in volatility modeling, where weights decay exponentially over time. The exponential decay model can improve signal responsiveness compared to simple moving averages (Hyndman & Athanasopoulos, 2018). This design helps capture recent market dynamics without ignoring past trends, a common requirement in high-frequency trading systems (Bandi & Russell, 2006). Practical Applications 1. Trend Detection: The GWAP can help identify bullish and bearish trends: • When the price is above GWAP, the market exhibits bullish momentum. • Conversely, when the price is below GWAP, bearish momentum prevails. 2. Volatility Filtering: Because of the gamma weighting mechanism, GWAP reduces the noise commonly seen in volatile markets, making it a useful tool for traders looking to smooth price fluctuations while retaining actionable signals. 3. Crossovers for Trade Signals: Similar to moving average strategies, traders can use price crossovers with the GWAP as trade signals: • Buy Signal: When the price crosses above the GWAP. • Sell Signal: When the price crosses below the GWAP. 4. Adaptive Gamma Weighting: The gamma factor allows for further customization. • Higher gamma values (>1) place greater emphasis on older data, suitable for long-term trend analysis. • Lower gamma values (<1) heavily weight recent price movements, ideal for fast-moving markets. Example Use Case A trader analyzing the S&P 500 may use a gamma factor of 0.92 with a 14-period GWAP to detect shifts in market sentiment during periods of heightened volatility. When the index price crosses above the GWAP, this could signal a potential recovery, prompting a buy entry. Conversely, when the price moves below the GWAP during a correction, it may suggest a short-selling opportunity. Scientific References • Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press. • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts. • Bandi, F. M., & Russell, J. R. (2006). Microstructure Noise, Realized Variance, and Optimal Sampling. Econometrica.
Indikator Pine Script®
oleh EdgeTools
Multiple Timeframe continuity with Crossover Alerts█  OVERVIEW This Indicator calculates the EMA 9/20 and the RSI with its SMA on multiple timeframes and indicates their crossings. In addition this script alerts the user when crossings appear. █  USAGE Use the checkboxes to activate different timeframes. With the dropdown menu you can select the timeframe in minutes. Furthermroie use the checkboxes to activate different crossovers. At the end of the settings you can find the same options for the RSI. You can also let the script indicate only the overlapping of both indicator crossovers by using the combination option. █  KNOWLEDGE EMA: The ema function returns the exponentially weighted moving average. In ema weighting factors decrease exponentially. It calculates by using a formula: EMA = alpha * source + (1 - alpha) * EMA , where alpha = 2 / (length + 1). SMA: The sma function returns the moving average, that is the sum of last y values of x, divided by y. RSI: The RSI is classified as a momentum oscillator, measuring the velocity and magnitude of price movements. Momentum is the rate of the rise or fall in price. The RSI computes momentum as the ratio of higher closes to lower closes: stocks which have had more or stronger positive changes have a higher RSI than stocks which have had more or stronger negative changes. RMA: Moving average used in RSI. It is the exponentially weighted moving average with alpha = 1 / length. (Source: TradingView PineScript reference & en.wikipedia.org) █  Credits Thanks to @KhanPhelan with his EMA 9/20 trading idea Credits to TradingView for their RSI function █  Disclaimer This is my first Script, any feedback is welcome.
Indikator Pine Script®
oleh Bobo3003
EVaR Indicator and Position SizingThe Problem: Financial markets consistently show "fat-tailed" distributions where extreme events occur with higher frequency than predicted by normal distributions (Gaussian or even log-normal). These fat tails manifest in sudden price crashes, volatility spikes, and black swan events that traditional risk measures like volatility can underestimate. Standard deviation and conventional VaR calculations assume normally distributed returns, leaving traders vulnerable to severe drawdowns during market stress. Cryptocurrencies and volatile instruments display particularly pronounced fat-tailed behavior, with extreme moves occurring 5-10 times more frequently than normal distribution models would predict. This reality demands a more sophisticated approach to risk measurement and position sizing. The Solution: Entropic Value at Risk (EVAR) EVaR addresses these limitations by incorporating principles from statistical mechanics and information theory through Tsallis entropy. This advanced approach captures the non-linear dependencies and power-law distributions characteristic of real financial markets. Entropy is more adaptive than standard deviations and volatility measures. I was inspired to create this indicator after reading the paper " The End of Mean-Variance? Tsallis Entropy Revolutionises Portfolio Optimisation in Cryptocurrencies " by by Sana Gaied Chortane and Kamel Naoui. Key advantages of EVAR over traditional risk measures: Superior tail risk capture: More accurately quantifies the probability of extreme market moves Adaptability to market regimes: Self-calibrates to changing volatility environments Non-parametric flexibility: Makes less assumptions about the underlying return distribution Forward-looking risk assessment: Better anticipates potential market changes (just look at the charts :) Mathematically, EVAR is defined as: EVAR_α(X) = inf_{z>0} {z * log(1/α * M_X(1/z))} Where the moment-generating function is calculated using q-exponentials rather than conventional exponentials, allowing precise modeling of fat-tailed behavior. Technical Implementation This indicator implements EVAR through a q-exponential approach from Tsallis statistics: Returns Calculation: Price returns are calculated over the lookback period Moment Generating Function: Approximated using q-exponentials to account for fat tails EVAR Computation: Derived from the MGF and confidence parameter Normalization: Scaled to for intuitive visualization Position Sizing: Inversely modulated based on normalized EVAR The q-parameter controls tail sensitivity—higher values (1.5-2.0) increase the weighting of extreme events in the calculation, making the model more conservative during potentially turbulent conditions. Indicator Components 1. EVAR Risk Visualization Dynamic EVAR Plot: Color-coded from red to green normalized risk measurement (0-1) Risk Thresholds: Reference lines at 0.3, 0.5, and 0.7 delineating risk zones 2. Position Sizing Matrix Risk Assessment: Current risk level and raw EVAR value Position Recommendations: Percentage allocation, dollar value, and quantity Stop Parameters: Mathematically derived stop price with percentage distance Drawdown Projection: Maximum theoretical loss if stop is triggered Interpretation and Application The normalized EVAR reading provides a probabilistic risk assessment: < 0.3: Low risk environment with minimal tail concerns 0.3-0.5: Moderate risk with standard tail behavior 0.5-0.7: Elevated risk with increased probability of significant moves > 0.7: High risk environment with substantial tail risk present Position sizing is automatically calculated using an inverse relationship to EVAR, contracting during high-risk periods and expanding during low-risk conditions. This is a counter-cyclical approach that ensures consistent risk exposure across varying market regimes, especially when the market is hyped or overheated. Parameter Optimization For optimal risk assessment across market conditions: Lookback Period: Determines the historical window for risk calculation Q Parameter: Controls tail sensitivity (higher values increase conservatism) Confidence Level: Sets the statistical threshold for risk assessment For cryptocurrencies and highly volatile instruments, a q-parameter between 1.5-2.0 typically provides the most accurate risk assessment because it helps capturing the fat-tailed behavior characteristic of these markets. You can also increase the q-parameter for more conservative approaches. Practical Applications Adaptive Risk Management: Quantify and respond to changing tail risk conditions Volatility-Normalized Positioning: Maintain consistent exposure across market regimes Black Swan Detection: Early identification of potential extreme market conditions Portfolio Construction: Apply consistent risk-based sizing across diverse instruments This indicator is my own approach to entropy-based risk measures as an alterative to volatility and standard deviations and it helps with fat-tailed markets. Enjoy!
Indikator Pine Script®
oleh HenriqueCentieiro
34
[EG] MA ATR ChannelsGreetings - the aim of this indicator was to code a single indicator with a selectable moving average, so I could examine price relationships to MA's and Average True Range (ATR) bollinger type bands. You can obviously approach this tool in so many different ways so I am going to share first an overview of moving averages and a short overview of how I use this this indicator. Simple ( SMA ) – A simple average of the past N (length) prices. Just add the price data for each N (bar) and divide the total by N. Exponential ( EMA ) – An exponential moving average with a greater weight for recent prices. The weighting is exponential. An N-period EMA takes more than N data points into account and gradually dilutes past data’s effect. Double Exponential ( DEMA ) - Same as an EMA , the Double exponential moving average , or DEMA , is a measure of a security's trending average price that gives the even more weight to recent price data. Aimed to help reduce lag. Triple Exponential ( TEMA ) - Same as an EMA , the Triple exponential moving average , or TEMA , is a measure of a security's trending average price that gives the even more weight to recent price data than EMA or DEMA . Aimed to help reduce lag. Weighted ( WMA ) – An average of the past N prices with a linear weighting, again giving greater weight to more recent prices. Hull ( HMA ) - The Hull Moving Average (developed by Alan Hull) has the purpose of reducing lag, increasing responsiveness while at the same time eliminating noise. It emphasises recent prices over older ones, resulting in a fast-acting yet smooth moving average that can be used to identify the prevailing market trend. Wilder's (RMA) - Wilder's smoothing is a type of exponential moving average . It takes one parameter, the period n, and price. Larger values for n will have a greater smoothing effect on the input data but will also create more lag. It is equivalent to a 2n-1 Exponential Moving Average . For example, a 10 period Wilder's smoothing is the same as a 19 period exponential moving average . Symmetrically Weighted ( SWMA ) - Weight distribution starts from median of given period and it's reduced linearly to the sides so the ending and starting point of period have the least weight. It's smooth and fast but reacts late to trend changes on higher lengths (lookback). Arnaud Legoux ( ALMA ) - Arnaud Legoux Moving Average removes small price fluctuations and enhances trend via applying a moving average twice, once from left to right, and once from right to left and combines both. At the end of this process the phase shift (price lag) commonly associated with moving averages is significantly reduced. Volume-Weighted ( VWMA ) - A Volume-Weighted Moving Average gives a different weight to each closing price and this weight depends on the volume of that period. For example, the closing price of a day with high volume will have a greater weight on the moving average value. Volume Weighted Average Price ( VWAP ) - Though not necessarily a MA - Volume-weighted average price ( VWAP ) is a ratio of the cumulative share price to the cumulative volume traded over a given time period and so I thought would be useful as an ATR tool. The VWAP is calculated using the opening price for each day and adjusting in real time right up until the close of the session. Thus, the calculation uses intraday data only. So what is Average True Range ? Average True Range is a measure of volatility . It's an area that represents roughly how much you can expect a security to change in price over a time period. Average true range is usually calculated by applying Wilders Smoothing to True Range. If you want regular ATR - use RMA as the input for the ATR. The ATR is then divided into periods based on derivatives of Phi (3.14) and Fibs (0.618, 1.618 etc.) You will notice price bounces off the lines. Look for patterns. The indicator - consisting of 3 parts: Price/Fast MA - this is an MA anywhere between 3-20 periods that is reflective of very recent price action. It is red when price is below - and green when above. Recommendations : SMA , EMA , WMA , HMA Trend/Medium MA - this is a slower MA that you could set anywhere between 30 - 100 periods that is reflective of overall bull/bear market trend depending on both it's direction and whether the Price MA / price is lower or higher. Recommendations: EMA , WMA , VWMA , RMA, ALMA Average True Range - this is a way to measure and visualise range the price may be capable of in - if it is towards or below the 2.1 multiplier - a bull reversal is more likely and vice versea. The multi's are set to factors of Pi and Fibonacci ratio's. Green channel means bullish, red channel means bearish. Gold means sign of a likely reversal. If the PMA enters the channel - it is likely the reversal is cancelled for a short period more. Recommendations : RMA, EMA , VWMA , ALMA , SWMA , VWAP How I use it : First of all - Consider longs when channel is green - or going to bounce on a support line - and consider shorts based on the opposite. This is not a buy/sell indicator - this is a MAP to PRICE to give reference and meaning to price movements across multiple time frames - very useful when using with a volume indicator and an RSI. I personally use it on the 3m chart but change the TFM to 5 for 15m data. If you wish to see any other more exotic or interesting MA's added please feel free to request them in the comments ! And thanks for checking out my first indicator
Indikator Pine Script®
oleh eggman94
2
[AIO] Multi Collection Moving Averages 140 MA TypesAll In One Multi Collection Moving Averages. Since signing up 2 years ago, I have been collecting various Сollections. I decided to get it into a decent shape and make it one of the biggest collections on TV, and maybe the entire internet. And now I'm sharing my collection with you. 140 Different Types of Moving Averages are waiting for you. Specifically : " AARMA | Adaptive Autonomous Recursive Moving Average ADMA | Adjusted Moving Average ADXMA | Average Directional Moving Average ADXVMA | Average Directional Volatility Moving Average AHMA | Ahrens Moving Average ALF | Ehler Adaptive Laguerre Filter ALMA | Arnaud Legoux Moving Average ALSMA | Adaptive Least Squares ALXMA | Alexander Moving Average AMA | Adaptive Moving Average ARI | Unknown ARSI | Adaptive RSI Moving Average AUF | Auto Filter AUTL | Auto-Line BAMA | Bryant Adaptive Moving Average BFMA | Blackman Filter Moving Average CMA | Corrected Moving Average CORMA | Correlation Moving Average COVEMA | Coefficient of Variation Weighted Exponential Moving Average COVNA | Coefficient of Variation Weighted Moving Average CTI | Coral Trend Indicator DEC | Ehlers Simple Decycler DEMA | Double EMA Moving Average DEVS | Ehlers - Deviation Scaled Moving Average DONEMA | Donchian Extremum Moving Average DONMA | Donchian Moving Average DSEMA | Double Smoothed Exponential Moving Average DSWF | Damped Sine Wave Weighted Filter DWMA | Double Weighted Moving Average E2PBF | Ehlers 2-Pole Butterworth Filter E2SSF | Ehlers 2-Pole Super Smoother Filter E3PBF | Ehlers 3-Pole Butterworth Filter E3SSF | Ehlers 3-Pole Super Smoother Filter EDMA | Exponentially Deviating Moving Average (MZ EDMA) EDSMA | Ehlers Dynamic Smoothed Moving Average EEO | Ehlers Modified Elliptic Filter Optimum EFRAMA | Ehlers Modified Fractal Adaptive Moving Average EHMA | Exponential Hull Moving Average EIT | Ehlers Instantaneous Trendline ELF | Ehler Laguerre filter EMA | Exponential Moving Average EMARSI | EMARSI EPF | Edge Preserving Filter EPMA | End Point Moving Average EREA | Ehlers Reverse Exponential Moving Average ESSF | Ehlers Super Smoother Filter 2-pole ETMA | Exponential Triangular Moving Average EVMA | Elastic Volume Weighted Moving Average FAMA | Following Adaptive Moving Average FEMA | Fast Exponential Moving Average FIBWMA | Fibonacci Weighted Moving Average FLSMA | Fisher Least Squares Moving Average FRAMA | Ehlers - Fractal Adaptive Moving Average FX | Fibonacci X Level GAUS | Ehlers - Gaussian Filter GHL | Gann High Low GMA | Gaussian Moving Average GMMA | Geometric Mean Moving Average HCF | Hybrid Convolution Filter HEMA | Holt Exponential Moving Average HKAMA | Hilbert based Kaufman Adaptive Moving Average HMA | Harmonic Moving Average HSMA | Hirashima Sugita Moving Average HULL | Hull Moving Average HULLT | Hull Triple Moving Average HWMA | Henderson Weighted Moving Average IE2 | Early T3 by Tim Tilson IIRF | Infinite Impulse Response Filter ILRS | Integral of Linear Regression Slope JMA | Jurik Moving Average KA | Unknown KAMA | Kaufman Adaptive Moving Average & Apirine Adaptive MA KIJUN | KIJUN KIJUN2 | Kijun v2 LAG | Ehlers - Laguerre Filter LCLSMA | 1LC-LSMA (1 line code lsma with 3 functions) LEMA | Leader Exponential Moving Average LLMA | Low-Lag Moving Average LMA | Leo Moving Average LP | Unknown LRL | Linear Regression Line LSMA | Least Squares Moving Average / Linear Regression Curve LTB | Unknown LWMA | Linear Weighted Moving Average MAMA | MAMA - MESA Adaptive Moving Average MAVW | Mavilim Weighted Moving Average MCGD | McGinley Dynamic Moving Average MF | Modular Filter MID | Median Moving Average / Percentile Nearest Rank MNMA | McNicholl Moving Average MTMA | Unknown MVSMA | Minimum Variance SMA NLMA | Non-lag Moving Average NWMA | Dürschner 3rd Generation Moving Average (New WMA) PKF | Parametric Kalman Filter PWMA | Parabolic Weighted Moving Average QEMA | Quadruple Exponential Moving Average QMA | Quick Moving Average REMA | Regularized Exponential Moving Average REPMA | Repulsion Moving Average RGEMA | Range Exponential Moving Average RMA | Welles Wilders Smoothing Moving Average RMF | Recursive Median Filter RMTA | Recursive Moving Trend Average RSMA | Relative Strength Moving Average - based on RSI RSRMA | Right Sided Ricker MA RWMA | Regressively Weighted Moving Average SAMA | Slope Adaptive Moving Average SFMA | Smoother Filter Moving Average SMA | Simple Moving Average SSB | Senkou Span B SSF | Ehlers - Super Smoother Filter P2 SSMA | Super Smooth Moving Average STMA | Unknown SWMA | Self-Weighted Moving Average SW_MA | Sine-Weighted Moving Average TEMA | Triple Exponential Moving Average THMA | Triple Exponential Hull Moving Average TL | Unknown TMA | Triangular Moving Average TPBF | Three-pole Ehlers Butterworth TRAMA | Trend Regularity Adaptive Moving Average TSF | True Strength Force TT3 | Tilson (3rd Degree) Moving Average VAMA | Volatility Adjusted Moving Average VAMAF | Volume Adjusted Moving Average Function VAR | Vector Autoregression Moving Average VBMA | Variable Moving Average VHMA | Vertical Horizontal Moving Average VIDYA | Variable Index Dynamic Average VMA | Volume Moving Average VSO | Unknown VWMA | Volume Weighted Moving Average WCD | Unknown WMA | Weighted Moving Average XEMA | Optimized Exponential Moving Average ZEMA | Zero Lag Moving Average ZLDEMA | Zero-Lag Double Exponential Moving Average ZLEMA | Ehlers - Zero Lag Exponential Moving Average ZLTEMA | Zero-Lag Triple Exponential Moving Average ZSMA | Zero-Lag Simple Moving Average " Don't forget that you can use any Moving Average not only for the chart but also for any of your indicators without affecting the code as in my example. But remember that some MAs are not designed to work with anything other than a chart. All MA and Code lists are sorted strictly alphabetically by short name (A-Z). Each MA has its own number (ID) by which you can display the Moving Average you need. Next to the ID selection there are tooltips with short names and their numbers. Use them. The panel below will help you to read the Name of the selected MA. Because of the size of the collection I think this is the optimal and most convenient use. Correct me if this is not the case. Unknown - Some MAs I collected so long ago that I lost the full real name and couldn't find the authors. If you recognize them, please let me know. I have deliberately simplified all MAs to input just Source and Length. Because the collection is so large, it would be quite inconvenient and difficult to customize all MA functions (multipliers, offset, etc.). If you need or like any MA you will still have to take it from my collection for your code. I tried to leave the basic MA settings inside function in first strings. I have tried to list most of the authors, but since the bulk of the collection was created a long time ago and was not intended for public publication I could not find all of them. Some of the features were created from scratch or may have been slightly modified, so please be careful. If you would like to improve this collection, please write to me in PM. Also Credits, Likes, Awards, Loves and Thanks to :     @alexgrover     @allanster     @andre_007     @auroagwei     @blackcat1402     @bsharpe     @cheatcountry     @CrackingCryptocurrency     @Duyck     @ErwinBeckers     @everget     @glaz     @gotbeatz26107     @HPotter     @io72signals     @JacobAmos     @JoshuaMcGowan     @KivancOzbilgic     @LazyBear     @loxx     @LuxAlgo     @MightyZinger     @nemozny     @NGBaltic     @peacefulLizard50262     @RicardoSantos     @StalexBot     @ThiagoSchmitz     @TradingView     — 𝐀𝐧𝐝 𝐎𝐭𝐡𝐞𝐫𝐬 ! So just a Big Thank You to everyone who has ever and anywhere shared their codes.
Indikator Pine Script®
oleh HALDRO
9
Fast EMA above Slow EMA with MACD (by Coinrule)An exponential moving average ( EMA ) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average . An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average ( SMA ), which applies an equal weight to all observations in the period. Moving average convergence divergence ( MACD ) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average ( EMA ) from the 12-period EMA . The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the coin when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence ( MACD ) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls. The Strategy enters and closes the trade when the following conditions are met: LONG The MACD histogram turns bullish EMA8 is greater than EMA26 EXIT Price increases 3% trailing Price decreases 1% trailing This strategy is back-tested from 1 January 2022 to simulate how the strategy would work in a bear market and provides good returns. Pairs that produce very strong results include AXSUSDT on the 5-minute timeframe. This short timeframe means that this strategy opens and closes trades regularly. Additionally, the trailing stop loss and take profit conditions can also be changed to match your needs. The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
Strategi Pine Script®
oleh Coinrule
oussamacryptoWhat Is an Exponential Moving Average (EMA)? An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average (SMA), which applies an equal weight to all observations in the period.
Strategi Pine Script®
oleh xoussamachoubix
Indicators Combination Framework v3 IND [DTU]Hello All, This script is a framework to analyze and see the results by combine selected indicators for (long, short, longexit, shortexit) conditions. I was designed this for beginners and users to facilitate to see effects of the technical indicators combinations on the chart WITH NO CODE You can improve your strategies according the results of this system by connecting the framework to a strategy framework/template such as Pinecoder, Benson, daveatt or custom. This is enhanced version of my previous indicator "Indicators & Conditions Test Framework " Currently there are 93 indicators (23 newly added) connected over library. You can also import an External Indicator or add Custom indicator (In the source) It is possible to change it from Indicator to strategy (simple one) by just remarking strategy parts in the source code and see real time profit of your combinations Feel free to change or use it in your source Special thanks goes to Pine wizards: Trading view (built-in Indicators), @Rodrigo, @midtownsk8rguy, @Lazybear, @Daveatt and others for their open source codes and contributions SIMPLE USAGE 1. SETTING: Show Alerts= True (To see your entries and Exists) 2. Define your Indicators (ex: INDICATOR1: ema(close,14), INDICATOR2: ema(close,21), INDICATOR3: ema(close,200) 3. Define Your Combinations for long & Short Conditions a. For Long: (INDICATOR1 crossover INDICATOR2) AND (INDICATOR3 < close) b. For Short: (INDICATOR1 crossunder INDICATOR2) AND (INDICATOR3 > close) 4. Select Strategy/template (Import strategy to chart) that you export your signals from the list 5. Analyze the best profit by changing Indicators values SOME INDICATORS DETAILS Each Indicator includes: - Factorization : Converting the selected indicator to Double, triple Quadruple such as EMA to DEMA, TEMA QEMA - Log : Simple or log10 can be used for calculation on function entries - Plot Type : You can overlay the indicator on the chart (such ema) or you can use stochastic/Percentrank approach to display in the variable hlines range - Extended Parametes : You can use default parameters or you can use extended (P1,P2) parameters regarding to indicator type and your choice - Color : You can define indicator color and line properties - Smooth : you can enable swma smooth - indicators : you can select one of the 93 function like ema(),rsi().. to define your indicator - Source : you can select from already defined indicators (IND1-4), External Indicator (EXT), Custom Indicator (CUST), and other sources (close, open...) CONDITION DETAILS - There are are 4 type of conditions, long entry, short entry, long exit, short exit. - Each condition are built up from 4 combinations that joined with "AND" & "OR" operators - You can see the results by enabling show alerts check box - If you only wants to enter long entry and long exit, just fill these conditions - If "close on opposite" checkbox selected on settings, long entry will be closed on short entry and vice versa COMBINATIONS DETAILS - There are 4 combinations that joined with "AND" & "OR" operators for each condition - combinations are built up from compare 1st entry with 2nd one by using operator - 1st and 2nd entries includes already defined indicators (IND1-5), External Indicator (EXT), Custom Indicator (CUST), and other sources (close, open...) - Operators are comparison values such as >,<, crossover,... - 2nd entry include "VALUE" parameter that will use to compare 1st indicator with value area - If 2nd indicator selected different than "VALUE", value are will mean previous value of the selection. (ex: value area= 2, 2nd entry=close, means close ) - Selecting "NONE" for the 1st entry will disable calculation of current and following combinations JOINS DETAILS - Each combination will join wiht the following one with the JOIN (AND, OR) operator (if the following one is not equal "NONE") CUSTOM INDICATOR - Custom Indicator defines harcoded in the source code. - You can call it with "CUST" in the Indicator definition source or combination entries source - You can change or implement your custom indicator by updating the source code EXTERNAL INDICATOR - You can import an external indicator by selecting it from the ext source. - External Indicator should be already imported to the chart and it have an plot function to output its signal EXPORTING SIGNAL - You can export your result to an already defined strategy template such as Pine coders, Benson, Daveatt Strategy templates - Or you can define your custom export for other future strategy templates ALERTS - By enabling show alerts checkbox, you can see long entry exits on the bottom, and short entry exits aon the top of the chart ADDITIONAL INFO - You can see all off the inputs descriptions in the tooltips. (You can also see the previous version for details) - Availability to set start, end dates - Minimize repainting by using security function options (Secure, Semi Secure, Repaint) - Availability of use timeframes - Version 3 INDICATORS LIST (More to be added): ▼▼▼ OVERLAY INDICATORS ▼▼▼ alma(src,len,offset=0.85,sigma=6).-------Arnaud Legoux Moving Average ama(src,len,fast=14,slow=100).-----------Adjusted Moving Average accdist().-------------------------------Accumulation/distribution index. cma(src,len).----------------------------Corrective Moving average dema(src,len).---------------------------Double EMA (Same as EMA with 2 factor) ema(src,len).----------------------------Exponential Moving Average gmma(src,len).---------------------------Geometric Mean Moving Average highest(src,len).------------------------Highest value for a given number of bars back. hl2ma(src,len).--------------------------higest lowest moving average hma(src,len).----------------------------Hull Moving Average. lagAdapt(src,len,perclen=5,fperc=50).----Ehlers Adaptive Laguerre filter lagAdaptV(src,len,perclen=5,fperc=50).---Ehlers Adaptive Laguerre filter variation laguerre(src,len).-----------------------Ehlers Laguerre filter lesrcp(src,len).-------------------------lowest exponential esrcpanding moving line lexp(src,len).---------------------------lowest exponential expanding moving line linreg(src,len,loffset=1).---------------Linear regression lowest(src,len).-------------------------Lovest value for a given number of bars back. mcginley(src, len.-----------------------McGinley Dynamic adjusts for market speed shifts, which sets it apart from other moving averages, in addition to providing clear moving average lines percntl(src,len).------------------------percentile nearest rank. Calculates percentile using method of Nearest Rank. percntli(src,len).-----------------------percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks. previous(src,len).-----------------------Previous n (len) value of the source pivothigh(src,BarsLeft=len,BarsRight=2).-Previous pivot high. src=src, BarsLeft=len, BarsRight=p1=2 pivotlow(src,BarsLeft=len,BarsRight=2).--Previous pivot low. src=src, BarsLeft=len, BarsRight=p1=2 rema(src,len).---------------------------Range EMA (REMA) rma(src,len).----------------------------Moving average used in RSI. It is the exponentially weighted moving average with alpha = 1 / length. sar(start=len, inc=0.02, max=0.02).------Parabolic SAR (parabolic stop and reverse) is a method to find potential reversals in the market price direction of traded goods.start=len, inc=p1, max=p2. ex: sar(0.02, 0.02, 0.02) sma(src,len).----------------------------Smoothed Moving Average smma(src,len).---------------------------Smoothed Moving Average super2(src,len).-------------------------Ehlers super smoother, 2 pole super3(src,len).-------------------------Ehlers super smoother, 3 pole supertrend(src,len,period=3).------------Supertrend indicator swma(src,len).---------------------------Sine-Weighted Moving Average tema(src,len).---------------------------Triple EMA (Same as EMA with 3 factor) tma(src,len).----------------------------Triangular Moving Average vida(src,len).---------------------------Variable Index Dynamic Average vwma(src,len).---------------------------Volume Weigted Moving Average volstop(src,len,atrfactor=2).------------Volatility Stop is a technical indicator that is used by traders to help place effective stop-losses. atrfactor=p1 wma(src,len).----------------------------Weigted Moving Average vwap(src_).------------------------------Volume Weighted Average Price (VWAP) is used to measure the average price weighted by volume ▼▼▼ NON OVERLAY INDICATORS ▼▼ adx(dilen=len, adxlen=14, adxtype=0).----adx. The Average Directional Index (ADX) is a used to determine the strength of a trend. len=>dilen, p1=adxlen (default=14), p2=adxtype 0:ADX, 1:+DI, 2:-DI (def:0) angle(src,len).--------------------------angle of the series (Use its Input as another indicator output) aroon(len,dir=0).------------------------aroon indicator. Aroons major function is to identify new trends as they happen.p1 = dir: 0=mid (default), 1=upper, 2=lower atr(src,len).----------------------------average true range. RMA of true range. awesome(fast=len=5,slow=34,type=0).------Awesome Oscilator is an indicator used to measure market momentum. defaults : fast=len= 5, p1=slow=34, p2=type: 0=Awesome, 1=difference bbr(src,len,mult=1).---------------------bollinger %% bbw(src,len,mult=2).---------------------Bollinger Bands Width. The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band. cci(src,len).----------------------------commodity channel index cctbbo(src,len).-------------------------CCT Bollinger Band Oscilator change(src,len).-------------------------A.K.A. Momentum. Difference between current value and previous, source - source . is most commonly referred to as a rate and measures the acceleration of the price and/or volume of a security cmf(len=20).-----------------------------Chaikin Money Flow Indicator used to measure Money Flow Volume over a set period of time. Default use is len=20 cmo(src,len).----------------------------Chande Momentum Oscillator. Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period. cog(src,len).----------------------------The cog (center of gravity) is an indicator based on statistics and the Fibonacci golden ratio. copcurve(src,len).-----------------------Coppock Curve. was originally developed by Edwin Sedge Coppock (Barrons Magazine, October 1962). correl(src,len).-------------------------Correlation coefficient. Describes the degree to which two series tend to deviate from their ta.sma values. count(src,len).--------------------------green avg - red avg cti(src,len).----------------------------Ehler s Correlation Trend Indicator by dev(src,len).----------------------------ta.dev() Measure of difference between the series and its ta.sma dpo(len).--------------------------------Detrended Price OScilator is used to remove trend from price. efi(len).--------------------------------Elders Force Index (EFI) measures the power behind a price movement using price and volume. eom(len=14,div=10000).-------------------Ease of Movement.It is designed to measure the relationship between price and volume.p1 = div: 10000= (default) falling(src,len).------------------------ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output) fisher(len).-----------------------------Fisher Transform is a technical indicator that converts price to Gaussian normal distribution and signals when prices move significantly by referencing recent price data histvol(len).----------------------------Historical volatility is a statistical measure used to analyze the general dispersion of security or market index returns for a specified period of time. kcr(src,len,mult=2).---------------------Keltner Channels Range kcw(src,len,mult=2).---------------------ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel. klinger(type=len).-----------------------Klinger oscillator aims to identify money flow’s long-term trend. type=len: 0:Oscilator 1:signal macd(src,len).---------------------------MACD (Moving Average Convergence/Divergence) mfi(src,len).----------------------------Money Flow Index s a tool used for measuring buying and selling pressure msi(len=10).-----------------------------Mass Index (def=10) is used to examine the differences between high and low stock prices over a specific period of time nvi().-----------------------------------Negative Volume Index obv().-----------------------------------On Balance Volume pvi().-----------------------------------Positive Volume Index pvt().-----------------------------------Price Volume Trend ranges(src,upper=len, lower=-5).---------ranges of the source. src=src, upper=len, v1:lower=upper . returns: -1 source=upper otherwise 0 rising(src,len).-------------------------ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output) roc(src,len).----------------------------Rate of Change rsi(src,len).----------------------------Relative strength Index rvi(src,len).----------------------------The Relative Volatility Index (RVI) is calculated much like the RSI, although it uses high and low price standard deviation instead of the RSI’s method of absolute change in price. smi_osc(src,len,fast=5, slow=34).--------smi Oscillator smi_sig(src,len,fast=5, slow=34).--------smi Signal stc(src,len,fast=23,slow=50).------------Schaff Trend Cycle (STC) detects up and down trends long before the MACD. Code imported from stdev(src,len).--------------------------Standart deviation trix(src,len) .--------------------------the rate of change of a triple exponentially smoothed moving average. tsi(src,len).----------------------------The True Strength Index indicator is a momentum oscillator designed to detect, confirm or visualize the strength of a trend. ultimateOsc(len.-------------------------Ultimate Oscillator indicator (UO) indicator is a technical analysis tool used to measure momentum across three varying timeframes variance(src,len).-----------------------ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta.sma), and it informally measures how far a set of numbers are spread out from their mean. willprc(src,len).------------------------Williams %R wad().-----------------------------------Williams Accumulation/Distribution. wvad().----------------------------------Williams Variable Accumulation/Distribution. HISTORY v3.01 ADD: 23 new indicators added to indicators list from the library. Current Total number of Indicators are 93. (to be continued to adding) ADD: 2 more Parameters (P1,P2) for indicator calculation added. Par:(Use Defaults) uses only indicator(Source, Length) with library's default parameters. Par:(Use Extra Parameters P1,P2) use indicator(Source,Length,p1,p2) with additional parameters if indicator needs. ADD: log calculation (simple, log10) option added on indicator function entries ADD: New Output Signals added for compatibility on exporting condition signals to different Strategy templates. ADD: Alerts Added according to conditions results UPD: Indicator source inputs now display with indicators descriptions UPD: Most off the source code rearranged and some functions moved to the new library. Now system work like a little bit frontend/backend UPD: Performance improvement made on factorization and other source code UPD: Input GUI rearranged UPD: Tooltips corrected REM: Extended indicators removed UPD: IND1-IND4 added to indicator data source. Now it is possible to create new indicators with the previously defined indicators value. ex: IND1=ema(close,14) and IND2=rsi(IND1,20) means IND2=rsi(ema(close,14),20) UPD: Custom Indicator (CUST) added to indicator data source and Combination Indicator source. UPD: Volume added to indicator data source and Combination Indicator source. REM: Custom indicators removed and only one custom indicator left REM: Plot Type "Org. Range (-1,1)" removed UPD: angle, rising, falling type operators moved to indicator library
Indikator Pine Script®
oleh dturkuler
1
Indicator Functions with Factor and HeikinAshiHello all, This indicator returns below selected indicators values with entered parameters. Also you can add factorization, functions candles, function HeikinAshi and more to the plot. VERSION: Version 1: returns series only source and Length with already defined default values Version 2: returns series with source, Length, p1 and p2 parameters according to the indicator definition (ex: ) PARAMETERS p1 p2 for defining multi arguments (See indicators list) indicator input value usable with verison=V2 selected.. ex: for alma( src , len ,offset=0.85,sigma=6), set source=source, len=length, p1=0.85 an p2=6 FACTOR: Add double triple, Quadruple factors to selected indicator (like converting EMA to 2-DEMA, 3-TEMA, 4-QEMA...) 1-Original 2-Double 3-Triple 4-Quadruple LOG Log: Use log, log10 on function entries PLOTTING: PType: Plotting type of the function on the screen Original :use original values Org. Range (-1,1): usable for indicators between range -1 and 1 Stochastic: Convert indicator values by using stochastic calculation between -1 & 1. (use AT/% length to better view) PercentRank: Convert indicator values by using Percent Rank calculation between -1 & 1. (use AT/% length to better view) ST/%: length for plotting Type for stochastic and Percent Rank options Smooth: Use SWMA for smoothing the function DISPLAY TYPES Plot Candles: Display the selected indicator as candle by implementing values Plot Ind: Display result of indicator with selected source HeikinAshi: Display Selected indicator candles with Heikin Ashi calculation INDICATOR LIST: hide = 'DONT DISPLAY', //Dont display & calculate the indicator. (For my framework usage) alma = 'alma( src , len ,offset=0.85,sigma=6)', // Arnaud Legoux Moving Average ama = 'ama( src , len ,fast=14,slow=100)', //Adjusted Moving Average acdst = 'accdist()', // Accumulation/distribution index. cma = 'cma( src , len )', //Corrective Moving average dema = 'dema( src , len )', // Double EMA (Same as EMA with 2 factor) ema = 'ema( src , len )', // Exponential Moving Average gmma = 'gmma( src , len )', //Geometric Mean Moving Average hghst = 'highest( src , len )', //Highest value for a given number of bars back. hl2ma = 'hl2ma( src , len )', //higest lowest moving average hma = 'hma( src , len )', // Hull Moving Average . lgAdt = 'lagAdapt( src , len ,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter lgAdV = 'lagAdaptV( src , len ,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter variation lguer = 'laguerre( src , len )', //Ehler's Laguerre filter lsrcp = 'lesrcp( src , len )', //lowest exponential esrcpanding moving line lexp = 'lexp( src , len )', //lowest exponential expanding moving line linrg = 'linreg( src , len ,loffset=1)', // Linear regression lowst = 'lowest( src , len )', //Lovest value for a given number of bars back. pcnl = 'percntl( src , len )', //percentile nearest rank. Calculates percentile using method of Nearest Rank. pcnli = 'percntli( src , len )', //percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks. rema = 'rema( src , len )', //Range EMA (REMA) rma = 'rma( src , len )', //Moving average used in RSI . It is the exponentially weighted moving average with alpha = 1 / length. sma = 'sma( src , len )', // Smoothed Moving Average smma = 'smma( src , len )', // Smoothed Moving Average supr2 = 'super2( src , len )', //Ehler's super smoother, 2 pole supr3 = 'super3( src , len )', //Ehler's super smoother, 3 pole strnd = 'supertrend( src , len ,period=3)', //Supertrend indicator swma = 'swma( src , len )', //Sine-Weighted Moving Average tema = 'tema( src , len )', // Triple EMA (Same as EMA with 3 factor) tma = 'tma( src , len )', //Triangular Moving Average vida = 'vida( src , len )', // Variable Index Dynamic Average vwma = 'vwma( src , len )', // Volume Weigted Moving Average wma = 'wma( src , len )', //Weigted Moving Average angle = 'angle( src , len )', //angle of the series (Use its Input as another indicator output) atr = 'atr( src , len )', // average true range . RMA of true range. bbr = 'bbr( src , len ,mult=1)', // bollinger %% bbw = 'bbw( src , len ,mult=2)', // Bollinger Bands Width . The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band. cci = 'cci( src , len )', // commodity channel index cctbb = 'cctbbo( src , len )', // CCT Bollinger Band Oscilator chng = 'change( src , len )', //Difference between current value and previous, source - source. cmo = 'cmo( src , len )', // Chande Momentum Oscillator . Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period. cog = 'cog( src , len )', //The cog (center of gravity ) is an indicator based on statistics and the Fibonacci golden ratio. cpcrv = 'copcurve( src , len )', // Coppock Curve. was originally developed by Edwin "Sedge" Coppock (Barron's Magazine, October 1962). corrl = 'correl( src , len )', // Correlation coefficient . Describes the degree to which two series tend to deviate from their ta. sma values. count = 'count( src , len )', //green avg - red avg dev = 'dev( src , len )', //ta.dev() Measure of difference between the series and it's ta. sma fall = 'falling( src , len )', //ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output) kcr = 'kcr( src , len ,mult=2)', // Keltner Channels Range kcw = 'kcw( src , len ,mult=2)', //ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel. macd = 'macd( src , len )', // macd mfi = 'mfi( src , len )', // Money Flow Index nvi = 'nvi()', // Negative Volume Index obv = 'obv()', // On Balance Volume pvi = 'pvi()', // Positive Volume Index pvt = 'pvt()', // Price Volume Trend rise = 'rising( src , len )', //ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output) roc = 'roc( src , len )', // Rate of Change rsi = 'rsi( src , len )', // Relative strength Index smosc = 'smi_osc( src , len ,fast=5, slow=34)', //smi Oscillator smsig = 'smi_sig( src , len ,fast=5, slow=34)', //smi Signal stdev = 'stdev( src , len )', //Standart deviation trix = 'trix( src , len )' , //the rate of change of a triple exponentially smoothed moving average . tsi = 'tsi( src , len )', //True Strength Index vari = 'variance( src , len )', //ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta. sma ), and it informally measures how far a set of numbers are spread out from their mean. wilpc = 'willprc( src , len )', // Williams %R wad = 'wad()', // Williams Accumulation/Distribution . wvad = 'wvad()' //Williams Variable Accumulation/Distribution I will update the indicator list when I will update the library Thanks to tradingview, @RodrigoKazuma for their open source indicators
Indikator Pine Script®
oleh dturkuler
1
lib_Indicators_v2_DTULibrary "lib_Indicators_v2_DTU" This library functions returns included Moving averages, indicators with factorization, functions candles, function heikinashi and more. Created it to feed as backend of my indicator/strategy "Indicators & Combinations Framework Advanced v2 " that will be released ASAP. This is replacement of my previous indicator (lib_indicators_DT) I will add an indicator example which will use this indicator named as "lib_indicators_v2_DTU example" to help the usage of this library Additionally library will be updated with more indicators in the future NOTES: Indicator functions returns only one series :-( plotcandle function returns candle series INDICATOR LIST: hide = 'DONT DISPLAY', //Dont display & calculate the indicator. (For my framework usage) alma = 'alma(src,len,offset=0.85,sigma=6)', //Arnaud Legoux Moving Average ama = 'ama(src,len,fast=14,slow=100)', //Adjusted Moving Average acdst = 'accdist()', //Accumulation/distribution index. cma = 'cma(src,len)', //Corrective Moving average dema = 'dema(src,len)', //Double EMA (Same as EMA with 2 factor) ema = 'ema(src,len)', //Exponential Moving Average gmma = 'gmma(src,len)', //Geometric Mean Moving Average hghst = 'highest(src,len)', //Highest value for a given number of bars back. hl2ma = 'hl2ma(src,len)', //higest lowest moving average hma = 'hma(src,len)', //Hull Moving Average. lgAdt = 'lagAdapt(src,len,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter lgAdV = 'lagAdaptV(src,len,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter variation lguer = 'laguerre(src,len)', //Ehler's Laguerre filter lsrcp = 'lesrcp(src,len)', //lowest exponential esrcpanding moving line lexp = 'lexp(src,len)', //lowest exponential expanding moving line linrg = 'linreg(src,len,loffset=1)', //Linear regression lowst = 'lowest(src,len)', //Lovest value for a given number of bars back. pcnl = 'percntl(src,len)', //percentile nearest rank. Calculates percentile using method of Nearest Rank. pcnli = 'percntli(src,len)', //percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks. rema = 'rema(src,len)', //Range EMA (REMA) rma = 'rma(src,len)', //Moving average used in RSI. It is the exponentially weighted moving average with alpha = 1 / length. sma = 'sma(src,len)', //Smoothed Moving Average smma = 'smma(src,len)', //Smoothed Moving Average supr2 = 'super2(src,len)', //Ehler's super smoother, 2 pole supr3 = 'super3(src,len)', //Ehler's super smoother, 3 pole strnd = 'supertrend(src,len,period=3)', //Supertrend indicator swma = 'swma(src,len)', //Sine-Weighted Moving Average tema = 'tema(src,len)', //Triple EMA (Same as EMA with 3 factor) tma = 'tma(src,len)', //Triangular Moving Average vida = 'vida(src,len)', //Variable Index Dynamic Average vwma = 'vwma(src,len)', //Volume Weigted Moving Average wma = 'wma(src,len)', //Weigted Moving Average angle = 'angle(src,len)', //angle of the series (Use its Input as another indicator output) atr = 'atr(src,len)', //average true range. RMA of true range. bbr = 'bbr(src,len,mult=1)', //bollinger %% bbw = 'bbw(src,len,mult=2)', //Bollinger Bands Width. The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band. cci = 'cci(src,len)', //commodity channel index cctbb = 'cctbbo(src,len)', //CCT Bollinger Band Oscilator chng = 'change(src,len)', //Difference between current value and previous, source - source . cmo = 'cmo(src,len)', //Chande Momentum Oscillator. Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period. cog = 'cog(src,len)', //The cog (center of gravity) is an indicator based on statistics and the Fibonacci golden ratio. cpcrv = 'copcurve(src,len)', //Coppock Curve. was originally developed by Edwin "Sedge" Coppock (Barron's Magazine, October 1962). corrl = 'correl(src,len)', //Correlation coefficient. Describes the degree to which two series tend to deviate from their ta.sma values. count = 'count(src,len)', //green avg - red avg dev = 'dev(src,len)', //ta.dev() Measure of difference between the series and it's ta.sma fall = 'falling(src,len)', //ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output) kcr = 'kcr(src,len,mult=2)', //Keltner Channels Range kcw = 'kcw(src,len,mult=2)', //ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel. macd = 'macd(src,len)', //macd mfi = 'mfi(src,len)', //Money Flow Index nvi = 'nvi()', //Negative Volume Index obv = 'obv()', //On Balance Volume pvi = 'pvi()', //Positive Volume Index pvt = 'pvt()', //Price Volume Trend rise = 'rising(src,len)', //ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output) roc = 'roc(src,len)', //Rate of Change rsi = 'rsi(src,len)', //Relative strength Index smosc = 'smi_osc(src,len,fast=5, slow=34)', //smi Oscillator smsig = 'smi_sig(src,len,fast=5, slow=34)', //smi Signal stdev = 'stdev(src,len)', //Standart deviation trix = 'trix(src,len)' , //the rate of change of a triple exponentially smoothed moving average. tsi = 'tsi(src,len)', //True Strength Index vari = 'variance(src,len)', //ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta.sma), and it informally measures how far a set of numbers are spread out from their mean. wilpc = 'willprc(src,len)', //Williams %R wad = 'wad()', //Williams Accumulation/Distribution. wvad = 'wvad()' //Williams Variable Accumulation/Distribution. } f_func(string, float, simple, float, float, float, simple) f_func Return selected indicator value with different parameters. New version. Use extra parameters for available indicators   Parameters:      string : FuncType_ indicator from the indicator list      float : src_ close, open, high, low,hl2, hlc3, ohlc4 or any      simple : int length_ indicator length      float : p1 extra parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      float : p2 extra parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      float : p3 extra parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators   Returns: float Return calculated indicator value fn_heikin(float, float, float, float) fn_heikin Return given src data (open, high,low,close) as heikin ashi candle values   Parameters:      float : o_ open value      float : h_ high value      float : l_ low value      float : c_ close value   Returns: float heikin ashi open, high,low,close vlues that will be used with plotcandle fn_plotFunction(float, string, simple, bool) fn_plotFunction Return input src data with different plotting options   Parameters:      float : src_ indicator src_data or any other series.....      string : plotingType Ploting type of the function on the screen      simple : int stochlen_ length for plotingType for stochastic and PercentRank options      bool : plotSWMA Use SWMA for smoothing Ploting   Returns: float fn_funcPlotV2(string, float, simple, float, float, float, simple, string, simple, bool, bool) fn_funcPlotV2 Return selected indicator value with different parameters. New version. Use extra parameters fora available indicators   Parameters:      string : FuncType_ indicator from the indicator list      float : src_data_ close, open, high, low,hl2, hlc3, ohlc4 or any      simple : int length_ indicator length      float : p1 extra parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      float : p2 extra parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      float : p3 extra parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators      string : plotingType Ploting type of the function on the screen      simple : int stochlen_ length for plotingType for stochastic and PercentRank options      bool : plotSWMA Use SWMA for smoothing Ploting      bool : log_ Use log on function entries   Returns: float Return calculated indicator value fn_factor(string, float, simple, float, float, float, simple, simple, string, simple, bool, bool) fn_factor Return selected indicator's factorization with given arguments   Parameters:      string : FuncType_ indicator from the indicator list      float : src_data_ close, open, high, low,hl2, hlc3, ohlc4 or any      simple : int length_ indicator length      float : p1 parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      float : p2 parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      float : p3 parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators      simple : int fact_ Add double triple, Quatr factor to selected indicator (like converting EMA to 2-DEMA, 3-TEMA, 4-QEMA...)      string : plotingType Ploting type of the function on the screen      simple : int stochlen_ length for plotingType for stochastic and PercentRank options      bool : plotSWMA Use SWMA for smoothing Ploting      bool : log_ Use log on function entries   Returns: float Return result of the function fn_plotCandles(string, simple, float, float, float, simple, string, simple, bool, bool, bool) fn_plotCandles Return selected indicator's candle values with different parameters also heikinashi is available   Parameters:      string : FuncType_ indicator from the indicator list      simple : int length_ indicator length      float : p1 parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      float : p2 parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      float : p3 parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)      simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators      string : plotingType Ploting type of the function on the screen      simple : int stochlen_ length for plotingType for stochastic and PercentRank options      bool : plotSWMA Use SWMA for smoothing Ploting      bool : log_ Use log on function entries      bool : plotheikin_ Use Heikin Ashi on Plot   Returns: float
Perpustakaan Pine Script®
oleh dturkuler
1
Multiple EMAAn exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average (SMA), which applies an equal weight to all observations in the period. The EMA is a moving average that places a greater weight and significance on the most recent data points. Like all moving averages, this technical indicator is used to produce buy and sell signals based on crossovers and divergences from the historical average. Traders often use several different EMA lengths, such as 10-day, 50-day, and 200-day moving averages. Points to remember: Exponential moving averages are more sensitive to the recent price EMA can signal good trades, but it can also keep you out of bad trades EMA offers dynamic support and resistance levels, which is good for trailing Stop Loss The EMA slope shape has hidden secrets The rules for the EMA trading strategy can be modified to fit your own trading needs. We don’t claim this to be hard rules, but they are good on their own to make for a great trading strategy. Make sure you first test out the EMA strategy on a paper trading account before you risk any of your hard-earned money
Indikator Pine Script®
oleh salemselva
1
Uptrick: Logarithmic Crypto Bands Description : Introduction The `Uptrick: Logarithmic Crypto Bands` indicator introduces an innovative approach to technical analysis tailored specifically for the cryptocurrency markets. By leveraging logarithmic transformations combined with dynamic exponential bands, this indicator offers a sophisticated method for identifying critical support and resistance levels, assessing market trends, and evaluating volatility. Its unique approach stands out from traditional indicators by addressing the specific challenges of high volatility and erratic price movements inherent in cryptocurrency trading. Originality and Usefulness ** 1. Unique Logarithmic Transformation: ** - Innovation : Unlike traditional indicators that often use raw price data, the Uptrick: Logarithmic Crypto Bands applies a logarithmic transformation to the closing prices: logPrice = math.log(close). This approach is original because it reduces the impact of extreme price fluctuations, providing a smoother and more stable price series. This transformation addresses a common issue in cryptocurrency markets where large price swings can obscure true market trends. - Advantage : The logarithmic transformation compresses the price range, which allows traders to better identify long-term trends and reduce the noise caused by outlier price movements. This results in a more reliable basis for analysis and enhances the ability to detect meaningful market patterns. **2. Dynamic Exponential Bands :** - Innovation : The indicator employs exponential calculations to derive dynamic support and resistance levels based on a central base line : baseLine * math.pow(multiplier, n). Unlike static bands that remain fixed regardless of market conditions, these bands adjust dynamically according to market volatility. - Advantage : The dynamic nature of the bands provides a more responsive and adaptive tool for traders. As market volatility changes, the bands widen or narrow accordingly, offering a more accurate reflection of potential support and resistance levels. This adaptability improves the tool's effectiveness in varying market conditions compared to static or traditional bands. Detailed Description and Substantiation **1. Logarithmic Price Calculation :** - Code : ` logPrice = math.log(close) - Description : This calculation converts the closing price into its logarithmic value. By compressing the price range, it minimizes the distortion caused by extreme price movements, which can be particularly pronounced in the volatile cryptocurrency markets. - Purpose : To provide a stabilized price series that facilitates more accurate trend analysis and reduces the influence of erratic price fluctuations. **2. Moving Averages of Logarithmic Prices :** - ** Long-Term Moving Average :** - Code : maLongLogPrice = ta.sma(logPrice, longLength) longLength = 2000 - ** Description : A simple moving average of the logarithmic price over a long period. This average helps filter out short-term noise and provides insight into the long-term market trend. - Purpose : To offer a perspective on the overall market direction, making it easier to identify enduring trends and distinguish them from short-term price movements. - Short-Term Moving Average : - Code : maShortLogPrice = ta.sma(logPrice, shortLength) shortLength = 900 - Description : A simple moving average of the logarithmic price over a shorter period. This component captures more immediate price trends and potential reversal points. - Purpose : To detect short-term trends and changes in market direction, allowing traders to make timely trading decisions based on recent price action. **3. Base Line Calculation :** - Code : baseLine = math.exp(maShortLogPrice) - Description : Converts the short-term moving average of the logarithmic price back to the original price scale. This base line serves as the central reference point for calculating the surrounding bands. - Purpose : To establish a benchmark level from which the exponential bands are calculated, providing a central reference for assessing potential support and resistance levels. **4. Band Calculation and Plotting :** - ** Code :** - Band 1: plot(baseLine * math.pow(multiplier, 1), color=color.new(color.yellow, 20), linewidth=1, title="Band 1") - Band 2: plot(baseLine * math.pow(multiplier, 2), color=color.new(color.yellow, 20), linewidth=1, title="Band 2") - Band 3: plot(baseLine * math.pow(multiplier, 3), color=color.new(color.yellow, 20), linewidth=1, title="Band 3") - Band 4: plot(baseLine * math.pow(multiplier, 4), color=color.new(color.yellow, 20), linewidth=1, title="Band 4") - Band 5: plot(baseLine * math.pow(multiplier, 5), color=color.new(color.yellow, 10), linewidth=1, title="Band 5") - Band 6: plot(baseLine * math.pow(multiplier, 6), color=color.new(color.yellow, 0), linewidth=1, title="Band 6") - * Multiplier : Set at 1.3, adjusts the spacing between bands to accommodate varying levels of market volatility. - Description : Bands are plotted at exponential intervals from the base line. Each band represents a potential support or resistance level, with the spacing between them increasing exponentially. The color opacity of each band indicates its level of significance, with closer bands being more relevant for immediate trading decisions. ** How to Use the Indicator :** **1. Identifying Support and Resistance Levels :** - Support Levels : The lower bands, closer to the base line, can act as potential support levels. When the price approaches these bands from above, they may indicate areas where the price could stabilize or reverse direction. - Resistance Levels : The upper bands, further from the base line, serve as resistance levels. When the price nears these bands from below, they can act as barriers to price movement, potentially leading to reversals or stalls. **2. Confirming Trends :** - Uptrend Confirmation : When the price consistently remains above the base line and moves towards higher bands, it signals a strong bullish trend. This confirmation helps traders capitalize on upward price movements. - Downtrend Confirmation : When the price stays below the base line and approaches lower bands, it indicates a bearish trend. This confirmation assists traders in acting on downward price movements. 3. Analyzing Volatility : - Wide Bands : Wider spacing between bands reflects higher market volatility. This indicates a more turbulent trading environment, where price movements are less predictable. Traders may need to adjust their strategies to handle increased volatility. - Narrow Bands : Narrower bands suggest lower volatility and a more stable market environment. This can result in more predictable price movements and clearer trading signals. **4. Entry and Exit Points :** - Entry Points : Consider buying when the price bounces off the base line or a band, which could signal support in an uptrend. - Exit Points : Evaluate selling or taking profits when the price nears upper bands or shows signs of reversal at these levels. This approach helps in locking in gains or minimizing losses during a downtrend. **Chart Example:** Here you can see how the price reacted getting closer to this level. All green circles show a bounce-off. So just from looking at the chart we can see a potential bounce again pretty soon. ** Disclosure :** - ** Performance Claims :** The `Uptrick: Logarithmic Crypto Bands` indicator is designed to assist traders in analyzing price levels and trends. It is important to understand that this tool provides historical data analysis and does not guarantee future performance. The features and benefits described are based on historical market behavior and should not be seen as a prediction of future results. Traders should use this indicator as part of a broader trading strategy and consider other factors before making trading decisions.
Indikator Pine Script®
oleh Uptrick
Machine Learning: SuperTrend Strategy TP/SL [YinYangAlgorithms]The SuperTrend is a very useful Indicator to display when trends have shifted based on the Average True Range (ATR). Its underlying ideology is to calculate the ATR using a fixed length and then multiply it by a factor to calculate the SuperTrend +/-. When the close crosses the SuperTrend it changes direction. This Strategy features the Traditional SuperTrend Calculations with Machine Learning (ML) and Take Profit / Stop Loss applied to it. Using ML on the SuperTrend allows for the ability to sort data from previous SuperTrend calculations. We can filter the data so only previous SuperTrends that follow the same direction and are within the distance bounds of our k-Nearest Neighbour (KNN) will be added and then averaged. This average can either be achieved using a Mean or with an Exponential calculation which puts added weight on the initial source. Take Profits and Stop Losses are then added to the ML SuperTrend so it may capitalize on Momentum changes meanwhile remaining in the Trend during consolidation. By applying Machine Learning logic and adding a Take Profit and Stop Loss to the Traditional SuperTrend, we may enhance its underlying calculations with potential to withhold the trend better. The main purpose of this Strategy is to minimize losses and false trend changes while maximizing gains. This may be achieved by quick reversals of trends where strategic small losses are taken before a large trend occurs with hopes of potentially occurring large gain. Due to this logic, the Win/Loss ratio of this Strategy may be quite poor as it may take many small marginal losses where there is consolidation. However, it may also take large gains and capitalize on strong momentum movements. Tutorial: In this example above, we can get an idea of what the default settings may achieve when there is momentum. It focuses on attempting to hit the Trailing Take Profit which moves in accord with the SuperTrend just with a multiplier added. When momentum occurs it helps push the SuperTrend within it, which on its own may act as a smaller Trailing Take Profit of its own accord. We’ve highlighted some key points from the last example to better emphasize how it works. As you can see, the White Circle is where profit was taken from the ML SuperTrend simply from it attempting to switch to a Bullish (Buy) Trend. However, that was rejected almost immediately and we went back to our Bearish (Sell) Trend that ended up resulting in our Take Profit being hit (Yellow Circle). This Strategy aims to not only capitalize on the small profits from SuperTrend to SuperTrend but to also capitalize when the Momentum is so strong that the price moves X% away from the SuperTrend and is able to hit the Take Profit location. This Take Profit addition to this Strategy is crucial as momentum may change state shortly after such drastic price movements; and if we were to simply wait for it to come back to the SuperTrend, we may lose out on lots of potential profit. If you refer to the Yellow Circle in this example, you’ll notice what was talked about in the Summary/Overview above. During periods of consolidation when there is little momentum and price movement and we don’t have any Stop Loss activated, you may see ‘Signal Flashing’. Signal Flashing is when there are Buy and Sell signals that keep switching back and forth. During this time you may be taking small losses. This is a normal part of this Strategy. When a signal has finally been confirmed by Momentum, is when this Strategy shines and may produce the profit you desire. You may be wondering, what causes these jagged like patterns in the SuperTrend? It's due to the ML logic, and it may be a little confusing, but essentially what is happening is the Fast Moving SuperTrend and the Slow Moving SuperTrend are creating KNN Min and Max distances that are extreme due to (usually) parabolic movement. This causes fewer values to be added to and averaged within the ML and causes less smooth and more exponential drastic movements. This is completely normal, and one of the perks of using k-Nearest Neighbor for ML calculations. If you don’t know, the Min and Max Distance allowed is derived from the most recent(0 index of data array) to KNN Length. So only SuperTrend values that exhibit distances within these Min/Max will be allowed into the average. Since the KNN ML logic can cause these exponential movements in the SuperTrend, they likewise affect its Take Profit. The Take Profit may benefit from this movement like displayed in the example above which helped it claim profit before then exhibiting upwards movement. By default our Stop Loss Multiplier is kept quite low at 0.0000025. Keeping it low may help to reduce some Signal Flashing while not taking extra losses more so than not using it at all. However, if we increase it even more to say 0.005 like is shown in the example above. It can really help the trend keep momentum. Please note, although previous results don’t imply future results, at 0.0000025 Stop Loss we are currently exhibiting 69.27% profit while at 0.005 Stop Loss we are exhibiting 33.54% profit. This just goes to show that although there may be less Signal Flashing, it may not result in more profit. We will conclude our Tutorial here. Hopefully this has given you some insight as to how Machine Learning, combined with Trailing Take Profit and Stop Loss may have positive effects on the SuperTrend when turned into a Strategy. Settings: SuperTrend: ATR Length: ATR Length used to create the Original Supertrend. Factor: Multiplier used to create the Original Supertrend. Stop Loss Multiplier: 0 = Don't use Stop Loss. Stop loss can be useful for helping to prevent false signals but also may result in more loss when hit and less profit when switching trends. Take Profit Multiplier: Take Profits can be useful within the Supertrend Strategy to stop the price reverting all the way to the Stop Loss once it's been profitable. Machine Learning: Only Factor Same Trend Direction: Very useful for ensuring that data used in KNN is not manipulated by different SuperTrend Directional data. Please note, it doesn't affect KNN Exponential. Rationalized Source Type: Should we Rationalize only a specific source, All or None? Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None? Machine Learning Smoothing Type: How should we smooth our Fast and Slow ML Datas to be used in our KNN Distance calculation? SMA, EMA or VWMA? KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using. Machine Learning Length: How far back is our Machine Learning going to keep data for. k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for? Fast ML Data Length: What is our Fast ML Length?? This is used with our Slow Length to create our KNN Distance. Slow ML Data Length: What is our Slow ML Length?? This is used with our Fast Length to create our KNN Distance. If you have any questions, comments, ideas or concerns please don't hesitate to contact us. HAPPY TRADING!
Strategi Pine Script®
oleh YinYangAlgorithms
11