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STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt [Loxx]STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt is a normalized Cardinal Sine Filter Kernel Weighted Fir Filter that uses Ehler's FIR filter calculation instead of the general FIR filter calculation. This indicator has Kalman Velocity lag reduction, a standard deviation filter, a clutter filter, and a kernel noise filter. When calculating the Kernels, the both sides are calculated, then smoothed, then sliced to just the Right side of the Kernel weights. Lastly, blackman windowing is used for our purposes here. You can read about blackman windowing here: Blackman window Advantages of Blackman Window over Hamming Window Method for designing FIR Filter The Kernel amplitudes are shown below with their corresponding values in yellow: This indicator is intended to be used with Heikin-Ashi source inputs, specially HAB Median. You can read about this here: Moving Average Filters Add-on w/ Expanded Source Types What is a Finite Impulse Response Filter? In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero. FIR filters can be discrete-time or continuous-time, and digital or analog. A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed. An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight. Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response. Ehlers FIR Filter Ehlers Filter (EF) was authored, not surprisingly, by John Ehlers. Read all about them here: Ehlers Filters What is Normalized Cardinal Sine? The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms. In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by sinc x = sinx / x In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by sinc x = sin(pi * x) / (pi * x) What is a Clutter Filter? For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal. What is a Dual Element Lag Reducer? Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array: 2 * coeff - coeff The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing . Included Bar coloring Loxx's Expanded Source Types Signals Alerts
Pine Script® 인디케이터
loxx의
2
STD- and Clutter-Filtered, Non-Lag Moving Average [Loxx]STD- and Clutter-Filtered, Non-Lag Moving Average is a Weighted Moving Average with a minimal lag using a damping cosine wave as the line of weight coefficients. The indicator has two filters. They are static (in points) and dynamic (expressed as a decimal). They allow cutting the price noise giving a stepped shape to the Moving Average. Moreover, there is the possibility to highlight the trend direction by color. This also includes a standard deviation and clutter filter. This filter is a FIR filter. What is a Generic or Direct Form FIR Filter? In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero. FIR filters can be discrete-time or continuous-time, and digital or analog. A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed. An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight. What is a Clutter Filter? For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal. What is a Dual Element Lag Reducer? Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array: 2 * coeff - coeff The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing . Included Bar coloring Loxx's Expanded Source Types Signals Alerts
Pine Script® 인디케이터
loxx의
3
Clutter-Filtered, D-Lag Reducer, Spec. Ops FIR Filter [Loxx]Clutter-Filtered, D-Lag Reducer, Spec. Ops FIR Filter is a FIR filter moving average with extreme lag reduction and noise elimination technology. This is a special instance of a static weight FIR filter designed specifically for Forex trading. This is not only a useful indictor, but also a demonstration of how one would create their own moving average using FIR filtering weights. This moving average has static period and weighting inputs. You can change the lag reduction and the clutter filtering but you can't change the weights or the numbers of bars the weights are applied to in history. Plot of weighting coefficients used in this indicator These coefficients were derived from a smoothed cardinal sine weighed SMA on EURUSD in Matlab. You can see the coefficients in the code. What is Normalized Cardinal Sine? The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms. In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by sinc x = sinx / x In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by sinc x = sin(pi * x) / (pi * x) What is a Generic or Direct Form FIR Filter? In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero. FIR filters can be discrete-time or continuous-time, and digital or analog. A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed. An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight. Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response. What is a Clutter Filter? For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal. What is a Dual Element Lag Reducer? Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array: 2 * coeff - coeff The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing . Things to note Due to the computational demands of this indicator, there is a bars back input modifier that controls how many bars back the indicator is calculated on. Because of this, the first few bars of the indicator will sometimes appear crazy, just ignore this as it doesn't effect the calculation. Related Indicators STD-Filtered, Ultra Low Lag Moving Average Included Bar coloring Loxx's Expanded Source Types Signals Alerts
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loxx의
4
Simple LevelsSImple levels is a clean way to automatically plot important daily levels including: Yesterday's High Yesterday's Low 50% level between Prior High/Low Today's Open Premarket Low Premarket High This Daily Levels indicator is unique in its ability to: -Plot all of the daily level PLUS premarket high/low levels (extended hours must be turned ON) -Can hide past days levels, only plotting levels on the current day, to keep chart cleaner -Can extend line levels right or fullscreen -Plots the level price at each level on the chart -Can show/hide price levels labels -Can add supplemental premarket levels plot to show levels being formed during the premarket time period -Coded with line.new vs plot so dashed lines are available as a style -Automatically hides the indicator if the timeframe selected is Daily or greater
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simplemmxm의
BTMM|TDIThis is the trader's dynamic index inspired by Steve Mauro's BTMM strategy. In addition to the RSI, Trendline, Baseline, Volatility Bands I have also included additional trend biases that are painted in the background to provide more confluence when the markets break out in either direction. For convenience, a position size calculator is included for all users to quickly calculate lot sizes on forex pairs with difference account balance currencies. The calculator works accurately on forex pairs. DO NOT USE for crypto or indices as some brokers have unique contract sizes that could not be fully incorporated into the tool. There is also data table that displays historical values of the RSI, Trendline, Baseline, and an EMA vs Price scoring procedure that covers the current candle (t0) and up to 3 candles back. The table is meant to provide a snapshot view of either bullish or bearish dominance that can be deciphered with a quick glance.
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The_Trading_Jedi의
12
Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]Helme-Nikias Weighted Burg AR-SE Extra. of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm, but unlike the usual WB algo, this one uses Helme-Nikias weighting. This method is commonly used in speech modeling and speech prediction engines. This is a linear method of forecasting data. You'll notice that this method uses a different weighting calculation vs Weighted Burg method. This new weighting is the following: w = math.pow(array.get(x, i - 1), 2), the squared lag of the source parameter and w += math.pow(array.get(x, i), 2), the sum of the squared source parameter This take place of the rectangular, hamming and parabolic weighting used in the Weighted Burg method Also, this method includes Levinson–Durbin algorithm. as was already discussed previously in the following indicator: Levinson-Durbin Autocorrelation Extrapolation of Price What is Helme-Nikias Weighted Burg Autoregressive Spectral Estimate Extrapolation of price? In this paper a new stable modification of the weighted Burg technique for autoregressive (AR) spectral estimation is introduced based on data-adaptive weights that are proportional to the common power of the forward and backward AR process realizations. It is shown that AR spectra of short length sinusoidal signals generated by the new approach do not exhibit phase dependence or line-splitting. Further, it is demonstrated that improvements in resolution may be so obtained relative to other weighted Burg algorithms. The method suggested here is shown to resolve two closely-spaced peaks of dynamic range 24 dB whereas the modified Burg schemes employing rectangular, Hamming or "optimum" parabolic windows fail. Data inputs Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar LastBar - bar where to start the prediction PastBars - how many bars back to model LPOrder - order of linear prediction model; 0 to 1 FutBars - how many bars you want to forward predict Things to know Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages. This indicator repaints Further reading A high-resolution modified Burg algorithm for spectral estimation Related Indicators Levinson-Durbin Autocorrelation Extrapolation of Price Weighted Burg AR Spectral Estimate Extrapolation of Price
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loxx의
Point of Control V2 The genesis of this project was to create a POC library that would be available to deliver volume profile information via pine to other scripts of indicators and strategies. This is a republish of an invite only script to open access This is the indicator version of the library function. A few points of significance: - Allows the choice of reset of the study period, day/week or bars. This is simple enough to expand to other conditions - Bar count resets starting from the beginning of the data set (bar index =0) vs bars back from the end of the data set - A 'period' in this context is the time between resets - the start of the POC (eg. start of Day or Week) until it resets (for example at the beginning of a next day or week) - Automates the determination of the increment level rather than the user specifying ticks or price brackets - Does not allow for setting the # of rows and then calculating the implied price increment levels - When a period is complete it is often useful to look back at the POCs of historical periods, or extend them forward. - This script will find the historical POCs around the current price and display them rather than extend all the historical POC lines to the right - This script also looks across all the period POCs and identifies the master POC or what I call the Grand POC, and also the next 3 runner up POCs This indicator is also available as a library. BINANCE:BTCUSDT NSE:NIFTY OANDA:XAUUSD NASDAQ:AAPL TVC:USOIL
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JohnBaron의
30
PointofControlLibrary "PointofControl" POC_f() The genesis of this project was to create a POC library that would be available to deliver volume profile information via pine to other scripts of indicators and strategies. This is the indicator version of the library function. A few things that would be unique with the built in - it allows you to choose the kind of reset of the period, day/week or bars. This is simple enough to expand to other conditions - it resets on bar count starting from the beginning of the data set (bar index =0) vs bars back from the end of the data set - A 'period' in this context is the time between resets - the start of the POC until it resets (for example at the beginning of a new day or week) - it will calculate an increment level rather than the user specifying ticks or price brackets - it does not allow for setting the # of rows and then calculating the implied price levels - When a period is complete it is often useful to look back at the POCs of historical periods, or extend them forward. - This script will find the historical POCs around the current price and display them rather than extend all the historical POC lines to the right - This script also looks across all the period POCs and identifies the master POC or what I call the Grand POC, and also the next 3 runner up POCs There is a matching indicator to this library
Pine Script® 라이브러리
JohnBaron의
7
EPS & SalesHi everyone, I just adapted a little utility script to visualise EPS % increase (quarters vs Year -1) and sales. I used the code from @ARUN_SAXENA and modified it to fix what I saw as issues. (Using base 3M instead of 1M + request.earnings(syminfo.tickerid, earnings.actual, ignore_invalid_symbol=true) instead of request.financial(syminfo.tickerid, "EARNINGS_PER_SHARE", "FQ") Data will differ from MarketSmith because they use sometimes actual EPS sometimes standard, but think we can at least trust what we see in term of % The tool is far from being perfect !
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Fred6724의
277
Trigonometric compare close vs obvTrigonometric compare This is copy and mod of a script from alexgrower which did this great trigonometric math. As there was this idea floating around from some unicorn doing it instead of close also with the ta.obv, why not compare them. from a first idea: green=bullish trend red=baserish trend blue=deciding and acceleration zone or maybe SL hunting of whales Plot1: trigonometrics for obv Plot2: trigonometrics for close Plot3: trigonometrics for obv-close what to trade or how to trade no idea, just hat do post the basic idea of this compare. have fun
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djmad의
Candle Strength IndicatorThe candle strength indicator depicts the average strength of the price action by evaluating bullish vs bearish candles. The scale is relative to price fluctuation and the size of the candles for the particular ticker / market, so there are no significant levels. A cross on the zero line would generally indicate a change in trend / sentiment. This indicator may be useful as a filter for entries and use in confluence with other indicators.
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PtGambler의
2
Historical US Bond Yield CurvePreface: I'm just the bartender serving today's freshly blended concoction; I'd like to send a massive THANK YOU to all the coders and PineWizards for the locally-sourced ingredients. I am simply a code editor, not a code author. Many thanks to these original authors! Source 1 (Aug 8, 2019): Source 2 (Aug 11, 2019): About the Indicator: The term yield curve refers to the yields of U.S. treasury bills, notes, and bonds in order from shortest to longest maturity date. The yield curve describes the shapes of the term structures of interest rates and their respective terms to maturity in years. The slope of the yield curve tells us how the bond market expects short-term interest rates to move in the future based on bond traders' expectations about economic activity and inflation. The best use of the yield curve is to get a sense of the economy's direction rather than to try to make an exact prediction. This indicator plots the U.S. yield curve as maturity (x-axis/time) vs yield (y-axis/price) in addition to historical yield curves and advanced data tickers . The visual array of historical yield curves helps investors visualize shifts in the yield curve that are useful when identifying & forecasting economic conditions. The bond market can help predict the direction of the economy which can be useful in crafting your investment strategy. An inverted 10y/2y yield curve for durations longer than 5 consecutive trading days signals an almost certain recession on the horizon. An inversion happens when short-term bonds pay better than longer-term bonds. There is Federal Reserve Board data that suggests the 10y3m may be a better predictor of recessions. Features: Advanced dual data ticker that performs curve & important spread analysis, plus additional hover info. Advanced yield curve data labels with additional hover info. Customizable historical curves and color theme. ‼ IMPORTANT: Hover over labels/tables for advanced information. Chart asset and timeframe may affect the yield curve results; I have found consistently accurate results using BINANCE:BTCUSDT on 1d timeframe. Historical curve lookbacks will have an effect on whether the curve analysis says the curve is bull/bear steepening/flattening, so please use appropriate lookbacks. ⚠ DISCLAIMER: Not financial advice. Not a trading system. DYOR. I am not affiliated with the original authors, TradingView, Binance, or the Federal Reserve Board. About the Editor: I am a former FINRA Registered Representative, inventor/patent holder, futures trader, and hobby PineScripter.
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BarefootJoey의
Goertzel Cycle Period [Loxx]Goertzel Cycle Period is an indicator that uses Goertzel algorithm to extract the cycle period of ticker's price input to then be injected into advanced, adaptive indicators and technical analysis algorithms. The following information is extracted from: "MESA vs Goertzel-DFT, 2003 by Dennis Meyers" Background MESA which stands for Maximum Entropy Spectral Analysis is a widely used mathematical technique designed to find the frequencies present in data. MESA was developed by J.P Burg for his Ph.D dissertation at Stanford University in 1975. The use of the MESA technique for stocks has been written about in many articles and has been popularized as a trading technique by John Ehlers. The Fourier Transform is a mathematical technique named after the famed French mathematician Jean Baptiste Joseph Fourier 1768-1830. In its digital form, namely the discrete-time Fourier Transform (DFT) series, is a widely used mathematical technique to find the frequencies of discrete time sampled data. The use of the DFT has been written about in many articles in this magazine (see references section). Today, both MESA and DFT are widely used in science and engineering in digital signal processing. The application of MESA and Fourier mathematical techniques are prevalent in our everyday life from everything from television to cell phones to wireless internet to satellite communications. MESA Advantages & Disadvantage MESA is a mathematical technique that calculates the frequencies of a time series from the autoregressive coefficients of the time series. We have all heard of regression. The simplest regression is the straight line regression of price against time where price(t) = a+b*t and where a and b are calculated such that the square of the distance between price and the best fit straight line is minimized (also called least squares fitting). With autoregression we attempt to predict tomorrows price by a linear combination of M past prices. One of the major advantages of MESA is that the frequency examined is not constrained to multiples of 1/N (1/N is equal to the DFT frequency spacing and N is equal to the number of sample points). For instance with the DFT and N data points we can only look a frequencies of 1/N, 2/N, Ö.., 0.5. With MESA we can examine any frequency band within that range and any frequency spacing between i/N and (i+1)/N . For example, if we had 100 bars of price data, we might be interested in looking for all cycles between 3 bars per cycle and 30 bars/ cycle only and with a frequency spacing of 0.5 bars/cycle. DFT would examine all bars per cycle of between 2 and 50 with a frequency spacing constrained to 1/100. Another of the major advantages of MESA is that the dominant spectral (frequency) peaks of the price series, if they exist, can be identified with fewer samples than the DFT technique. For instance if we had a 10 bar price period and a high signal to noise ratio we could accurately identify this period with 40 data samples using the MESA technique. This same resolution might take 128 samples for the DFT. One major disadvantage of the MESA technique is that with low signal to noise ratios, that is below 6db (signal amplitude/noise amplitude < 2), the ability of MESA to find the dominant frequency peaks is severely diminished.(see Kay, Ref 10, p 437). With noisy price series this disadvantage can become a real problem. Another disadvantage of MESA is that when the dominant frequencies are found another procedure has to be used to get the amplitude and phases of these found frequencies. This two stage process can make MESA much slower than the DFT and FFT . The FFT stands for Fast Fourier Transform. The Fast Fourier Transform(FFT) is a computationally efficient algorithm which is a designed to rapidly evaluate the DFT. We will show in examples below the comparisons between the DFT & MESA using constructed signals with various noise levels. DFT Advantages and Disadvantages. The mathematical technique called the DFT takes a discrete time series(price) of N equally spaced samples and transforms or converts this time series through a mathematical operation into set of N complex numbers defined in what is called the frequency domain. Why would we what to do that? Well it turns out that we can do all kinds of neat analysis tricks in the frequency domain which are just to hard to do, computationally wise, with the original price series in the time domain. If we make the assumption that the price series we are examining is made up of signals of various frequencies plus noise, than in the frequency domain we can easily filter out the frequencies we have no interest in and minimize the noise in the data. We could then transform the resultant back into the time domain and produce a filtered price series that hopefully would be easier to trade. The advantages of the DFT and itís fast computation algorithm the FFT, are that it is extremely fast in calculating the frequencies of the input price series. In addition it can determine frequency peaks for very noisy price series even when the signal amplitude is less than the noise amplitude. One of the disadvantages of the FFT is that straight line, parabolic trends and edge effects in the price series can distort the frequency spectrum. In addition, end effects in the price series can distort the frequency spectrum. Another disadvantage of the FFT is that it needs a lot more data than MESA for spectral resolution. However this disadvantage has largely been nullified by the speed of today's computers. Goertzel algorithm attempts to resolve these problems... What is the Goertzel algorithm? The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of the individual terms of the discrete Fourier transform (DFT). It is useful in certain practical applications, such as recognition of dual-tone multi-frequency signaling (DTMF) tones produced by the push buttons of the keypad of a traditional analog telephone. The algorithm was first described by Gerald Goertzel in 1958. Like the DFT, the Goertzel algorithm analyses one selectable frequency component from a discrete signal. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. For covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms, but for computing a small number of selected frequency components, it is more numerically efficient. The simple structure of the Goertzel algorithm makes it well suited to small processors and embedded applications. The main calculation in the Goertzel algorithm has the form of a digital filter, and for this reason the algorithm is often called a Goertzel filter Where is Goertzel algorithm used? This package contains the advanced mathematical technique called the Goertzel algorithm for discrete Fourier transforms. This mathematical technique is currently used in today's space-age satellite and communication applications and is applied here to stock and futures trading. While the mathematical technique called the Goertzel algorithm is unknown to many, this algorithm is used everyday without even knowing it. When you press a cell phone button have you ever wondered how the telephone company knows what button tone you pushed? The answer is the Goertzel algorithm. This algorithm is built into tiny integrated circuits and immediately detects which of the 12 button tones(frequencies) you pushed. Future Additions: Bartels test for cycle significance, testing output cycles for utility Hodrick Prescott Detrending, smoothing Zero-Lag Regression Detrending, smoothing High-pass or Double WMA filtering of source input price data References: 1. Burg, J. P., ëMaximum Entropy Spectral Analysisî, Ph.D. dissertation, Stanford University, Stanford, CA. May 1975. 2. Kay, Steven M., ìModern Spectral Estimationî, Prentice Hall, 1988 3. Marple, Lawrence S. Jr., ìDigital Spectral Analysis With Applicationsî, Prentice Hall, 1987 4. Press, William H., et al, ìNumerical Receipts in C++: the Art of Scientific Computingî, Cambridge Press, 2002. 5. Oppenheim, A, Schafer, R. and Buck, J., ìDiscrete Time Signal Processingî, Prentice Hall, 1996, pp663-634 6. Proakis, J. and Manolakis, D. ìDigital Signal Processing-Principles, Algorithms and Applicationsî, Prentice Hall, 1996., pp480-481 7. Goertzel, G., ìAn Algorithm for he evaluation of finite trigonometric seriesî American Math Month, Vol 65, 1958 pp34-35.
Pine Script® 인디케이터
loxx의
15
Metals:Backwardation/ContangoMETALS: Gold , Silver , Copper ( GC , SI, HG) Quickly visualize carrying charge market vs backwardized market by comparing the price of the next 2 years of futures contracts. Carrying charge (contract prices increasing into the future) = normal, representing the costs of carrying/storage of a commodity. When this is flipped to Backwardation (contract prices decreasing into the future): its a bullish sign: Buyers want this commodity, and they want it NOW. Note: indicator does not map to time axis in the same way as price; it simply plots the progression of contract months out into the future; left to right; so timeframe DOESN'T MATTER for this plot There's likely some more efficient way to write this; e.g. when plotting for Gold ( GC ); 21 of the security requests are redundant; but they are still made; and can make this slower to load TO UPDATE(once a year will do): in REQUEST CONTRACTS section, delete old contracts (top) and add new ones (bottom). Then in PLOTTING section, Delete old contract labels (bottom); add new contract labels (top); adjust the X in 'bar_index-(X+_historical)' numbers accordingly This is one of three similar indicators: Meats | Metals | Grains -If you want to build from this; to work on other commodities ; be aware that Tradingview limits the number of contract calls to 40 (hence the 3 seperate indicators) Tips: -Right click and reset chart if you can't see the plot; or if you have trouble with the scaling. -Right click and add to new scale if you prefer this not to overlay directly on price. Or move to new pane below. --Added historical input: input days back in time; to see the historical shape of the Futures curve via selecting 'days back' snapshot updated 15th June 2022 © twingall
Pine Script® 인디케이터
twingall의
5
Grains:Backwardation/ContangoGRAINS: Wheat , Soybeans , Corn (ZW, ZS, ZC ) Quickly visualize carrying charge market vs backwardized market by comparing the price of the next 2 years of futures contracts. Carrying charge (contract prices increasing into the future) = normal, representing the costs of carrying/storage of a commodity. When this is flipped to Backwardation (contract prices decreasing into the future): its a bullish sign: Buyers want this commodity, and they want it NOW. The above chart shows a nice example of backwardation. Note: indicator does not map to time axis in the same way as price; it simply plots the progression of contract months out into the future; left to right; so timeframe DOESN'T MATTER for this plot There's likely some more efficient way to write this; e.g. when plotting for Wheat (ZW); 15 of the security requests are redundant; but they are still made; and can make this slower to load TO UPDATE(once a year will do): in REQUEST CONTRACTS section, delete old contracts (top) and add new ones (bottom). Then in PLOTTING section, Delete old contract labels (bottom); add new contract labels (top); adjust the X in 'bar_index-(X+_historical)' numbers accordingly This is one of three similar indicators: Meats | Metals | Grains -If you want to build from this; to work on other commodities ; be aware that Tradingview limits the number of contract calls to 40 (hence the 3 seperate indicators) Tips: -Right click and reset chart if you can't see the plot; or if you have trouble with the scaling. -Right click and add to new scale if you prefer this not to overlay directly on price. Or move to new pane below. --Added historical input: input days back in time; to see the historical shape of the Futures curve via selecting 'days back' snapshot updated 15th June 2022 © twingall
Pine Script® 인디케이터
twingall의
2
Meats: Backwardation/CantangoMEATS: Live Cattle , Feeder Cattle, Lean Hogs (LE, GF , HE) Quickly visualize carrying charge market vs backwardized market by comparing the price of the next 2 years of futures contracts. Carrying charge (contract prices increasing into the future) = normal, representing the costs of carrying/storage of a commodity. When this is flipped to Backwardation (contract prices decreasing into the future): its a bullish sign: Buyers want this commodity, and they want it NOW. Note: indicator does NOT map to time axis in the same way as price; it simply plots the progression of contract months out into the future; left to right; so timeframe DOESN'T MATTER for this plot There's likely some more efficient way to write this; e.g. when plotting for Live Cattle (LE); 8 of the security requests are redundant; but they are still made; and can make this slower to load TO UPDATE(once a year will do): in REQUEST CONTRACTS section, delete old contracts (top) and add new ones (bottom). Then in PLOTTING section, Delete old contract labels (bottom); add new contract labels (top); adjust the X in 'bar_index-(X+_historical)' numbers accordingly This is one of three similar indicators: Meats | Metals | Grains -If you want to build from this; to work on other commodities ; be aware that Tradingview limits the number of contract calls to 40 (hence the 3 seperate indicators) Tips: -Right click and reset chart if you can't see the plot; or if you have trouble with the scaling. -Right click and add to new scale if you prefer this not to overlay directly on price. Or move to new pane below. --Added historical input: input days back in time; to see the historical shape of the Futures curve via selecting 'days back' snapshot updated 15th June 2022 © twingall
Pine Script® 인디케이터
twingall의
Bjorgum Double Tap█   OVERVIEW Double Tap is a pattern recognition script aimed at detecting Double Tops and Double Bottoms. Double Tap can be applied to the broker emulator to observe historical results, run as a trading bot for live trade alerts in real time with entry signals, take profit, and stop orders, or to simply detect patterns. █   CONCEPTS How Is A Pattern Defined? Doubles are technical formations that are both reversal patterns and breakout patterns. These formations typically have a distinctive “M” or a “W” shape with price action breaking beyond the neckline formed by the center of the pattern. They can be recognized when a ​pivot fails to break when tested for a second time and the retracement that follows breaks beyond the key level opposite. This can trap entrants that were playing in the direction of the prior trend. Entries are made on the breakout with a target projected beyond the neckline equal to the height of the pattern. Pattern Recognition Patterns are recognized through the use of ​zig-zag; a method of filtering price action by connecting swing highs and lows in an alternating fashion to establish trend, ​support and resistance, or derive shapes from price action. The script looks for the highest or lowest point in a given number of bars and updates a list with the values as they form. If the levels are exceeded, the values are updated. If the direction changes and a new significant point is made, a new point is added to the list and the process starts again. Meanwhile, we scan the list of values looking for the distinctive shape to form as previously described. █   STRATEGY RESULTS Back Testing Historical back testing is the most common method to test a strategy due in part to the general ease of gathering quick results. The underlying theory is that any strategy that worked well in the past is likely to work well in the future, and conversely, any strategy that performed poorly in the past is likely to perform poorly in the future. It is easy to poke holes in this theory, however, as for one to accept it as gospel, one would have to assume that future results will match what has come to pass. The randomness of markets may see to it otherwise, so it is important to scrutinize results. Some commonly used methods are to compare to other markets or benchmarks, perform statistical analysis on the results over many iterations and on differing datasets, walk-forward testing, out-of-sample analysis, or a variety of other techniques. There are many ways to interpret the results, so it is important to do research and gain knowledge in the field prior to taking meaningful conclusions from them. 👉  In short, it would be naive to place trust in one good backtest and expect positive results to continue. For this reason, results have been omitted from this publication. Repainting Repainting is simply the difference in behaviour of a strategy in real time vs the results calculated on the historical dataset. The strategy, by default, will wait for confirmed signals and is thus designed to not repaint. Waiting for bar close for entires aligns results in the real time data feed to those calculated on historical bars, which contain far less data. By doing this we align the behaviour of the strategy on the 2 data types, which brings significance to the calculated results. To override this behaviour and introduce repainting one can select "Recalculate on every tick" from the properties tab. It is important to note that by doing this alerts may not align with results seen in the strategy tester when the chart is reloaded, and thus to do so is to forgo backtesting and restricts a strategy to forward testing only. 👉  It is possible to use this script as an indicator as opposed to a full strategy by disabling "Use Strategy" in the "Inputs" tab. Basic alerts for detection will be sent when patterns are detected as opposed to complex order syntax. For alerts mid-bar enable "Recalculate on every tick" , and for confirmed signals ensure it is disabled. █   EXIT ORDERS Limit and Stop Orders By default, the strategy will place a stop loss at the invalidation point of the pattern. This point is beyond the pattern high in the case of Double Tops, or beneath the pattern low in the case of Double Bottoms. The target or take profit point is an equal-legs measurement, or 100% of the pattern height in the direction of the pattern bias. Both the stop and the limit level can be adjusted from the user menu as a percentage of the pattern height. Trailing Stops Optional from the menu is the implementation of an ATR based trailing stop. The trailing stop is designed to begin when the target projection is reached. From there, the script looks back a user-defined number of bars for the highest or lowest point +/- the ATR value. For tighter stops the user can look back a lesser number of bars, or decrease the ATR multiple. When using either Alertatron or Trading Connector, each change in the trail value will trigger an alert to update the stop order on the exchange to reflect the new trail price. This reduces latency and slippage that can occur when relying on alerts only as real exchange orders fill faster and remain in place in the event of a disruption in communication between your strategy and the exchange, which ensures a higher level of safety. 👉  It is important to note that in the case the trailing stop is enabled, limit orders are excluded from the exit criteria. Rather, the point in time that the limit value is exceeded is the point that the trail begins. As such, this method will exit by stop loss only. █   ALERTS Five Built-in 3rd Party Destinations The following are five options for delivering alerts from Double Tap to live trade execution via third party ​API solutions or chat bots to share your trades on social media. These destinations can be selected from the input menu and alert syntax will automatically configure in alerts appropriately to manage trades. Custom JSON JSON, or JavaScript Object Notation, is a readable format for structuring data. It is used primarily to transmit data between a server and a web application. In regards to this script, this may be a custom intermediary web application designed to catch alerts and interface with an exchange ​API. The JSON message is a trade map for an application to read equipped with where its been, where its going, targets, stops, quantity; a full diagnostic of the current state and its previous state. A web application could be configured to follow the messages sent in this format and conduct trades in sync with alerts running on the TV server. Below is an example of a rendered JSON alert: { "passphrase": "1234", "time": "2022-05-01T17:50:05Z", "ticker": "ETHUSDTPERP", "plot": { "stop_price": 2600.15, "limit_price": 3100.45 }, "strategy": { "position_size": 0.1, "order_action": "buy", "market_position": "long", "market_position_size": 0, "prev_market_position": "flat", "prev_market_position_size": 0 } } Trading Connector Trading Connector is a third party fully autonomous Chrome extension designed to catch alert webhooks from TradingView and interface with MT4/MT5 to execute live trades from your machine. Alerts to Trading Connector are simple; just select the destination from the input drop down menu, set your ticker in the "TC Ticker" box in the "Alert Strings" section and enter your URL in the alert window when configuring your alert. Alertatron Alertatron is an automated algo platform for cryptocurrency trading that is designed to automate your trading strategies. Although the platform is currently restricted to crypto, it offers a versatile interface with high flexibility syntax for complex market orders and conditions. To direct alerts to Alertatron, select the platform from the 3rd party drop down, configure your ​API key in the ”Alertatron Key” box and add your URL in the alert message box when making alerts. 3 Commas 3 Commas is an easy and quick to use click-and-go third party crypto ​API solution. Alerts are simple without overly complex syntax. Messages are simply pasted into alerts and executed as alerts are triggered. There are 4 boxes at the bottom of the "Inputs" tab where the appropriate messages to be placed. These messages can be copied from 3 Commas after the bots are set up and pasted directly into the settings menu. Remember to select 3 Commas as a destination from the third party drop down and place the appropriate URL in the alert message window. Discord Some may wish to share their trades with their friends in a Discord chat via webhook chat bot. Messages are configured to notify of the pattern type with targets and stop values. A bot can be configured through the integration menu in a Discord chat to which you have appropriate access. Select Discord from the 3rd party drop down menu and place your chat bot URL in the alert message window when configuring alerts. 👉  For further information regarding alert setup, refer to the platform specific instructions given by the chosen third party provider. █   IMPORTANT NOTES Setting Alerts For alert messages to be properly delivered on order fills it is necessary to place the following placeholder in the alert message box when creating an alert. {{strategy.order.alert_message}} This placeholder will auto-populate the alert message with the appropriate syntax that is designated for the 3rd party selected in the user menu. Order Sizing and Commissions The values that are sent in alert messages are populated from live metrics calculated by the strategy. This means that the actual values in the "Properties" tab are used and must be set by the user. The initial capital, order size, commission, etc. are all used in the calculations, so it is important to set these prior to executing live trades. Be sure to set the commission to the values used by the exchange as well. 👉  It is important to understand that the calculations on the account size take place from the beginning of the price history of the strategy. This means that if historical results have inflated or depleted the account size from the beginning of trade history until now, the values sent in alerts will reflect the calculated size based on the inputs in the "Properties" tab. To start fresh, the user must set the date in the "Inputs" tab to the current date as to remove trades from the trade history. Failure to follow this instruction can result in an unexpected order size being sent in the alert. █   FOR PINECODERS  • With the recent introduction of matrices in Pine, the script utilizes a matrix to track ​pivot points with the bars they occurred on, while tracking if that ​pivot has been traded against to prevent duplicate detections after a trade is exited.  • Alert messages are populated with placeholders ; capability that previously was only possible in alertcondition() , but has recently been extended to `strategy.*()` functions for use in the `alert_message` argument. This allows delivery of live trade values to populate in strategy alert messages.  • New arguments have been added to strategy.exit() , which allow differentiated messages to be sent based on whether the exit occurred at the stop or the limit. The new arguments used in this script are `alert_profit` and `alert_loss` to send messages to Discord
Pine Script™ 전략
Bjorgum의
78
RSI vs BITFINEX BTC Longs/Shorts Margin Ratio Percentage RankThis indicator plots the RSI of the current token with a color based on percentage rank of the RSI of BITFINEX:BTCUSDLONGS divided by BITFINEX:BTCUSDSHORTS, with a plot of the moving average of the RSI. It can optionally plot the RSI in white and the ratio RSI in color, or the ratio as background color. It can also plot the raw ratio rather than the percentage rank if selected. I was interested in the ratio of BITFINEX:BTCUSDLONGS to BITFINEX:BTCUSDSHORTS as a measure of market sentiment and how that sentiment would magnify RSI changes. The volatility of the BTCUSDLONGS : BTCUSDSHORTS ratio was too low to get a good read, using a percent rank of the RSI of the ratio made the results more visible. This indicator should be used on a BTC chart.
Pine Script® 인디케이터
EltAlt의
1