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
Cerca negli script per "美元未来10天走势预测"
STD-Filtered, Ultra Low Lag Moving Average [Loxx]STD-Filtered, Ultra Low Lag Moving Average is a FIR filter that smooths price using a low-pass filtering with weights derived from a normalized cardinal since function. This indicator attempts to reduce lag to an extreme degree. Try this on various time frames with various Type inputs, 0 is the default, so see where the sweet spot is for your trading style.
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.
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)
How this works, (easy mode)
1. Use a HA or HAB source type
2. The lower the Type value the smoother the moving average
3. Standard deviation stepping is added to further reduce noise
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Ehlers Linear Extrapolation Predictor [Loxx]Ehlers Linear Extrapolation Predictor is a new indicator by John Ehlers. The translation of this indicator into PineScript™ is a collaborative effort between @cheatcountry and I.
The following is an excerpt from "PREDICTION" , by John Ehlers
Niels Bohr said “Prediction is very difficult, especially if it’s about the future.”. Actually, prediction is pretty easy in the context of technical analysis. All you have to do is to assume the market will behave in the immediate future just as it has behaved in the immediate past. In this article we will explore several different techniques that put the philosophy into practice.
LINEAR EXTRAPOLATION
Linear extrapolation takes the philosophical approach quite literally. Linear extrapolation simply takes the difference of the last two bars and adds that difference to the value of the last bar to form the prediction for the next bar. The prediction is extended further into the future by taking the last predicted value as real data and repeating the process of adding the most recent difference to it. The process can be repeated over and over to extend the prediction even further.
Linear extrapolation is an FIR filter, meaning it depends only on the data input rather than on a previously computed value. Since the output of an FIR filter depends only on delayed input data, the resulting lag is somewhat like the delay of water coming out the end of a hose after it supplied at the input. Linear extrapolation has a negative group delay at the longer cycle periods of the spectrum, which means water comes out the end of the hose before it is applied at the input. Of course the analogy breaks down, but it is fun to think of it that way. As shown in Figure 1, the actual group delay varies across the spectrum. For frequency components less than .167 (i.e. a period of 6 bars) the group delay is negative, meaning the filter is predictive. However, the filter has a positive group delay for cycle components whose periods are shorter than 6 bars.
Figure 1
Here’s the practical ramification of the group delay: Suppose we are projecting the prediction 5 bars into the future. This is fine as long as the market is continued to trend up in the same direction. But, when we get a reversal, the prediction continues upward for 5 bars after the reversal. That is, the prediction fails just when you need it the most. An interesting phenomenon is that, regardless of how far the extrapolation extends into the future, the prediction will always cross the signal at the same spot along the time axis. The result is that the prediction will have an overshoot. The amplitude of the overshoot is a function of how far the extrapolation has been carried into the future.
But the overshoot gives us an opportunity to make a useful prediction at the cyclic turning point of band limited signals (i.e. oscillators having a zero mean). If we reduce the overshoot by reducing the gain of the prediction, we then also move the crossing of the prediction and the original signal into the future. Since the group delay varies across the spectrum, the effect will be less effective for the shorter cycles in the data. Nonetheless, the technique is effective for both discretionary trading and automated trading in the majority of cases.
EXPLORING THE CODE
Before we predict, we need to create a band limited indicator from which to make the prediction. I have selected a “roofing filter” consisting of a High Pass Filter followed by a Low Pass Filter. The tunable parameter of the High Pass Filter is HPPeriod. Think of it as a “stone wall filter” where cycle period components longer than HPPeriod are completely rejected and cycle period components shorter than HPPeriod are passed without attenuation. If HPPeriod is set to be a large number (e.g. 250) the indicator will tend to look more like a trending indicator. If HPPeriod is set to be a smaller number (e.g. 20) the indicator will look more like a cycling indicator. The Low Pass Filter is a Hann Windowed FIR filter whose tunable parameter is LPPeriod. Think of it as a “stone wall filter” where cycle period components shorter than LPPeriod are completely rejected and cycle period components longer than LPPeriod are passed without attenuation. The purpose of the Low Pass filter is to smooth the signal. Thus, the combination of these two filters forms a “roofing filter”, named Filt, that passes spectrum components between LPPeriod and HPPeriod.
Since working into the future is not allowed in EasyLanguage variables, we need to convert the Filt variable to the data array XX . The data array is first filled with real data out to “Length”. I selected Length = 10 simply to have a convenient starting point for the prediction. The next block of code is the prediction into the future. It is easiest to understand if we consider the case where count = 0. Then, in English, the next value of the data array is equal to the current value of the data array plus the difference between the current value and the previous value. That makes the prediction one bar into the future. The process is repeated for each value of count until predictions up to 10 bars in the future are contained in the data array. Next, the selected prediction is converted from the data array to the variable “Prediction”. Filt is plotted in Red and Prediction is plotted in yellow.
The Predict Extrapolation indicator is shown above for the Emini S&P Futures contract using the default input parameters. Filt is plotted in red and Predict is plotted in yellow. The crossings of the Predict and Filt lines provide reliable buy and sell timing signals. There is some overshoot for the shorter cycle periods, for example in February and March 2021, but the only effect is a late timing signal. Further reducing the gain and/or reducing the BarsFwd inputs would provide better timing signals during this period.
ADDITIONS
Loxx's Expanded source types:
Library for expanded source types:
Explanation for expanded source types:
Three different signal types: 1) Prediction/Filter crosses; 2) Prediction middle crosses; and, 3) Filter middle crosses.
Bar coloring to color trend.
Signals, both Long and Short.
Alerts, both Long and Short.
Weighted percentile nearest rankYo, posting it for the whole internet, took the whole day to find / to design the actual working solution for weighted percentile 'nearest rank' algorithm, almost no reliable info online and a lot of library-style/textbook-style solutions that don't provide on real world production level.
The principle:
0) initial data
data = 22, 33, 11, 44, 55
weights = 5 , 3 , 2 , 1 , 4
array(s) size = 5
1) sort data array, apply the sorting pattern to the weights array, resulting:
data = 11, 22, 33, 44, 55
weights = 2 , 5 , 3 , 1 , 4
2) get weights cumsum and sum:
weights = 2, 5, 3 , 1 , 4
weights_cum = 2, 7, 10, 11, 15
weights_sum = 15
3) say we wanna find 50th percentile, get a threshold value:
n = 50
thres = weights_sum / 100 * n
7.5 = 15 / 100 * 50
4) iterate through weights_cum until you find a value that >= the threshold:
for i = 0 to size - 1
2 >= 7.5 ? nah
7 >= 7.5 ? nah
10 >= 7.5 ? aye
5) take the iteration index that resulted "aye", and find the data value with the same index, that's gonna be the resulting percentile.
i = 2
data = 33
This one is not an approximation, not an estimator, it's the actual weighted percentile nearest rank as it is.
I tested the thing extensively and it works perfectly.
For the skeptics, check lines 40, 41, 69 in the code, you can comment/uncomment dem to switch for unit (1) weights, resulting in the usual non-weighted percentile nearest rank that ideally matches the TV's built-in function.
Shoutout for @wallneradam for the sorting function mane
...
Live Long and Prosper
HPI for crypto [ptt]The Herrick Payoff Index is designed to show the amount of money flowing into or out of a futures contract.
This indicator uses open interest (from Binance PERP like this BTCUSDTPERP_OI) from during its calculations, therefore, the pairs being analyzed must contain open interest data on Binance.
The indicator only works with USDT pairs! Like RVNUSDT, BTCUSDT... does not work with USD pairs!
The indicator works in two mode.
Index mode - when the values moving 0-100
In this case, if the value below 10, it shows the money is flowing out of the futures contract and near the local bottom. If the value above 90, it shows the money is flowing into the futures contract and near the local top.
(The two trigger can be modified, the default is low:10 and high:90)
Oscillator mode - when the values moving around the origo (0)
In this case, if the value above 0 (green), it shows the money is flowing into the futures contract, this is bullish
If the value below 0 (red), it shows the money is flowing out of the futures contract, this is bearish
scalping with market facilitationThis strategy is for scalping low timeframes for 10 pips. I have yet to see a strategy with this unique combo of indicators.
First we have volume indicator market facilitation, where we are looking for volume and mfi to be up, then we want the adx 5 to be above level 30 and above its ema period 3, then if these conditions are good we take shorts when ema 8 is below ema 100 and longs when ema8 is above ema 100 with parabolic sar in its propet place, also to verify trend we have obv over or under its ema of 55 and macd line over its signal line.
I have heikenashi bars on with the regular priceline showing so j see actual price levels, when i get a buy signal i set a buystop above the high of that bar and have a stoploss of 7.5 pips and a take profit of 10 pips, reverse for sells, i have to use metatrader to trade so i use this as my signals to trade.
Note this is not advice trade at your own risk no guarantees in anything in life, but i wanted to share this for it is helping me with my trades to be more strict and semi mechanical. I use it for forex time frames 1 3 5 15 mjn
DMI StrategyThis strategy is based on DMI indicator. It helps me to identify base or top of the script. I mostly use this script to trade in Nifty bank options, even when the signal comes in nifty . It can be used to trade in other scripts as well. Pivot points can also be used to take entry. Long entry is taken when DI+(11) goes below 10 and DI-(11) goes above 40 , whereas short entry is taken when DI-(11) goes below 10 and DI+(11) goes above 40.
For bank nifty , I take the trade in the strike price for which the current premium is nearby 300, with the SL of 20%. If premium goes below 10% I buy one more lot to average, but exit if the premium goes below 20% of the first entry. If the trade moves in the correct direction, we need to start trailing our stoploss or exit at the pre-defined target.
As this a strategy, there is one problem. While we are in the phase of "long", if again the "long" phase comes, it will not be shown on chart until a "short" phase has come, and vice versa. This has been resolved by creating an indicator instead of strategy with the name of "DMI Buy-sell on chart". Please go through that to get more entry points.
Please have a look at strategy tester to back test
DMI StrategyThis strategy is based on DMI indicator. It helps me to identify base or top of the script. I mostly use this script to trade in Nifty bank options, even when the signal comes in nifty. It can be used to trade in other scripts as well. Pivot points can also be used to take entry. Long entry is taken when DI+(11) goes below 10 and DI-(11) goes above 40, whereas short entry is taken when DI-(11) goes below 10 and DI+(11) goes above 40.
For bank nifty, I take the trade in the strike price for which the current premium is nearby 300, with the SL of 20%. If premium goes below 10% I buy one more lot to average, but exit if the premium goes below 20% of the first entry. If the trade moves in the correct direction, we need to start trailing our stoploss or exit at the pre-defined target.
Please have a look at strategy tester to back test.
Mark MinerviniHi everyone,
Just sharing a script that I made when I began to be interested in Mark Minervini, Wiliam O'Neil, Nicolas Darvas,.. trading style.
This script displays :
- 10 EMA (orange)
- 20 EMA (blue light)
- 50 SMA (blue)
-150 SMA (green)
-200 SMA (red)
- Shows when the stock is "extended" from EMA10 to high of the candle (Works in Daily, you can adjust the % to make it match with the stock's volatility)
(Shows a red area between price and 10 EMA)
- Shows when Mark Minervini's trend template is respected by highlighting green between 150 & 200 SMA.
I Although added bollinger bands and 5 EMA for very strong stocks. (I never use them)
Have fun
itrade buy/sellThe indicator was written based on several types of other indicators.
I took ema, rsi ema and an augmented version of qqe rsi.
The indicator checks for oversold or overbought on all of these indicators and, based on this, issues a buy or sell signal.
In the indicator, you can adjust the length of each point for yourself, so you can set rsi to 10 or 100, as it suits you.
The indicator works better on higher timeframes 4h-1w
But it can also be used on smaller timeframes, but the lower the timeframe, the higher the risk.
_________________________________________________________________________________________________________
Индикатор был написан на основе нескольких видов других индикаторов.
Я взял ema,rsi ema идополненую версию qqe rsi.
Индикатор проверяет перепроданость или перекупленость на этих всех индикаторах и изходя из этого выдаёт сигнал на покупку или продажу.
В индикаторе можно настроить длинну каждого пункта под себя,так вы можете поставить rsi на 10 или же на 100,как вам будет удобно.
Индикатор работает лучше на больших таймфреймах 4ч-1w
Но так же его можно использовать на более мелких таймфреймах,но чем ниже таймфрейм,тем выше риск.
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.
Zero-lag, 3-Pole Super Smoother [Loxx]Zero-lag, 3-Pole Super Smoother is an Ehlers 3-pole smoother with lag reduction
What is 3-pole Super Smoother?
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter. Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
Included:
-Color bars
-Loxx's Expanded Source Types
3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping [Loxx]3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping is an Ehlers 3-pole smoother with EMA deviations corrective stepping. This allows for greater response to volatility.
What is 3-pole Super Smoother?
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter. Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
What is EMA Deviation Corrected?
Dr. Alexander Uhl invented a method that he used to filter the moving average and to check for signals.
By definition, the Standard Deviation (SD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. The built-in MetaTrader 5 Standard Deviation can change that and can use one of the 4 basic types of averages for calculations. This version is not doing that. It is, instead, using the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we shall call it EMA deviation.
It is similar to Standard Deviation, but on a first glance you shall notice that it is "faster" than the Standard Deviation and that makes it useful when the speed of reaction to volatility is expected from any code or trading system.
Included:
-Color bars
-Loxx's Expanded Source Types
percentageLibLibrary "percentageLib"
: every thing need anout percentage
getPercentage(entry, exit)
: get percentage change of of two value
Parameters:
entry : : value of entry price
exit : : value of exit price
Returns: : negative or positive value
applyPercentageNoAddUp(price, percentage)
: apply percentage change on value decrease or increase
Parameters:
price : : value of price
percentage : : percentage change can be negative or positive
Returns: : return only positive value
applyPercentageAddUp(price, percentage)
: apply percentage change on value decrease or increase
Parameters:
price : : value of price
percentage : : percentage change can be negative or positive
Returns: : return only positive value
reversePercentage(percentage)
: get percentage (positive or negative) and return the percentage need to back to previous price
Parameters:
percentage : : percentage change can be negative or positive
Returns: : return positive/negative value
@example : reversePercentage(10) =>11.11111111111111111111111 , reversePercentage(10) =>9.0909090909090909
getReversePercentage(price, percentage)
: get two prices and return the percentage need to back to previous price
Parameters:
price : : value of price
percentage : : percentage change can be negative or positive
Returns: : return only positive value
@example : getReversePercentage(100,90) =>11.11111111111111111111111
multipeBarTotalPercentage()
Super Sniper - Screener - MTF- Multi Asset -Multi IndicatorSuper Sniper Screener with Multi Indicator EMA Crossover, Trend, ADX, MACD, SSL, CCI, STOCHASTIC, RSI, HMA, QQE, WAE. Trade Entry/Exit with multiple Indicator for Multiple Assets and Multiple Timeframe. if you are on a Higher / Lower Time frame and are looking for Entry/Exit based on a lower timeframe then this indicator is the best. The all in one screener for EMA Crossover, Trend, ADX, MACD, SSL, CCI, STOCHASTIC, RSI, HMA, QQE, WAE
What is Sniper Entry?
if you are on a Higher / Lower Time frame and are looking for Entry/Exit based on a lower timeframe then this indicator is the best.
About this indicator.
horizontal Lines are in pink (down) and blue(up) color grey (neutral)
10 horizontal lines are plotted each for a timeframe + Asset / Currency / Stock
Back ground represents UP/DOWN Trend based on EMA 200 with the current time frame.
RED-Dot and GREEN-Dot are buy sell signals of SSL Channel for current Time Frame.
You can choose only one type of indicator (EMA Crossover, Trend, ADX, MACD, SSL, CCI, STOCHASTIC, RSI, HMA, QQE, WAE) for all the 10-lines
Select Asset and Timeframe for Each Line on the Graph.
Based on the color of the line and changes on Multi Timeframe you can ENTER/EXIT a Trade on current Timeframe.
Hence a Sniper Entry with multiple Indicator Screener
Keltner Channel Width Oscillator (KingThies)Definition
The Keltner Channel Width oscillator is a technical analysis indicator derived originally from the same relationship the Bollinger Band Width indicator takes on Bollinger Bands.
Similar to the Bollinger Bands, Kelts measure volatility in relation to price, and factor in various range calculations to create three bands around the price of a given stock or digital asset. The Middle Line is typically a 20 Day Exponential Moving Average while the upper and lower bands highlight price at different range variations around its basis. Keltner Channel Width serve as a way to quantitatively measure the width between the Upper and Lower Bands and identify opportunities for entires and exits, based on the relative range price is experiencing that day.
Calculation
Kelt Channel Width = (Upper Band - Lower Band) / Middle Band
More on Keltner Channels
Keltner channel was first described by a Chicago grain trader called Chester W. Keltner in his 1960 book How to Make Money in Commodities. Though Keltner claimed no ownership of the original idea and simply called it the ten-day moving average trading rule, his name was applied by those who heard of this concept through his books.
Similarly to the Bollinger Bands, Keltner channel is a technical analysis tool based on three parallel lines. In fact, the Keltner indicator consists of a central moving average in addition to channel lines spread above and below it. The central line represents a 10-day simple moving average of what Chester W. Keltner called typical price. The typical price is defined as the average of the high, low and close. The distance between the central line and the upper, or lower line, is equivalent to the simple moving average of the preceding 10 days' trading ranges.
One way to interpret the Keltner Channel would be to consider the price breakouts outside of the channel. A trader would track price movement and consider any close above the upper line as a strong buy signal. Equivalently, any close below the lower line would be considered a strong sell signal. The trader would follow the trend emphasized by the indicator while complementing his analysis with the use of other indicators as well. However, the breakout method only works well when the market moves from a range-bound setting to an established trend. In a trend-less configuration, the Keltner Channel is better used as an overbought/oversold indicator. Thus, as the price breaks out below the lower band, a trader waits for the next close inside the Keltner Channel and considers this price behavior as an oversold situation indicating a potential buy signal. Similarly, as the price breaks out above the upper band, the trader waits for the next close inside the Keltner Channel and considers this price action as an overbought situation indicating a potential sell signal. By waiting for the price to close within the Channel, the trader avoids getting caught in a real upside or downside breakout.
Time FunctionsLibrary "TimeFunctions"
Utility functions to handle time in Pine Script
TimeframetoInt()
Returns an int that corresponds to a timeframe string:
"1" => 1
"5" => 5
"10" => 10
"15" => 15
"30" => 30
"60" => 60
"H1" => 60
"H4" => 240
"1D" => 1440
BarsSinceOpen()
Returns the number of bars that have passed since the opening of the New York Session.
Boom Hunter + Hull Suite + Volatility Oscillator StrategyTRADE CONDITIONS
Long entry:
Boom Hunter (leading indicator): Trigger line crosses over Quotient 2 line (white cross over red)
Hull Suite (trend confirmation): Price closed above hull suite line and hull suite is green (represented by horizontal line at -10 in strategy pane)
Volatility Oscillator (volatility confirmation): Volatility spike trigger line is above upper band (represented by horizontal line at -30 in strategy pane)
Short entry:
Boom Hunter (leading indicator): Trigger line crosses under Quotient 2 line (white cross under red)
Hull Suite (trend confirmation): Price closed below hull suite line and hull suite is red (represented by horizontal line at -10 in strategy pane)
Volatility Oscillator (volatility confirmation): Volatility spike trigger line is below lower band (represented by horizontal line at -30 in strategy pane)
Risk management:
Each trade risks 3% of account (configurable in settings)
SL size determined by swing low/high of previous X candles (configurable in settings) or 1 ATR if swing is less than 1 ATR
TP is calculated by Risk:Reward ratio (configurable in settings)
TIPS
Timeframe: I have found good results running on BTC/USDT 5M chart
Note: To help visual identification of trade entries and exits you may wish to add the Hull Suite and Volatility Oscillator to the chart separately. It was not possible to display them in a clear way within a single panel for the strategy. Make sure you set the settings of the auxiliary indicators to match what is in the settings of this indicator if you do decide to add them.
CREDITS
Boom Hunter Pro by veryfid
Hull Suite by InSilico
Volatility Oscillator by veryfid
Custom Multi-Timeframe IndicatorIt's a pretty simple example of a nice custom screener you can run for multiple timeframes. This is an RSI screener, but you can easily change the function for your own Indicator.
Screener displays:
Last value of the instrument
RSI value for up to 10 instruments / 4 timeframes.
In Parameters you can change:
4 timeframes
10 instruments
Parameters for RSI
Styling parameters for table
If the selected timeframe will be lower than the current one you will receive a warning message in a table.
Thanks to @MUQWISHI to help me code it.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
Nifty_Top10Movement of Nifty Top 10 Stocks
This script shows us the movement in Nifty Top 10 stocks based on price.
Divergence Macd+RSI Fast[RSU] -- No RepaintThis indicator combines the divergence of rsi and macd and displays it on the candlestick chart.
RSI:
1. When rsi is at a high point, once it falls by 1 k line, it will detect the divergence from the previous high point. This can quickly find the divergence that has taken effect and help you quickly capture the trend before a sharp decline or rise.
The difference between other RSI divergence indicators: the official divergence indicator is to detect the 5 and the k line, which may lead to a large amount of decline.
2. This indicator detects the previous high and the previous low of 5, 10, 20 lengths at the same time, instead of only detecting a fixed length, so that more deviations can be found.
MACD:
1. When MACD-diff line(orange color) is at a high point, once it falls by 1 k line, it will detect the divergence from the previous high point. This can quickly find the divergence that has taken effect and help you quickly capture the trend before a sharp decline or rise.
2. This indicator detects the previous high and the previous low of 5, 10, 20, 40 , 60 lengths at the same time, instead of only detecting a fixed length, so that more divergences can be found.
Notice:
Because it is a quick divergence detection, it is recommended to confirm that the divergence takes effect after the current k is completely closed first. I have identified this state in the indicator as "k not end".
Disadvantages and Risks:
Since it is a quick discovery, there will be error identification. Error divergences will recolor to grey.
Suggestion:
Use Alert catching divergence occurrences.
Please do not:
Don't go short in the uptrend, don't go long in the downtrend.
Top divergences that occur because of a strong uptrend are usually only temporary pullbacks. Bottom divergences in persistent declines are also temporary rallies. Do not attempt to trade such low-return trades.
It is recommended to use the divergence indicator when the stock price has made a new high and retraced, and once again made a new high, because this often leads to the end of the trend.
Divergence how to use:
1. After the previous candlestick was completely closed, a bottom divergence was found.
2. Open an long order at the beginning of the second bar, or as close to the bottom as possible (because the stop loss will be smaller).
3. Break the stop loss price below the previous low where the divergence occurred, which already means that the divergence is wrong.
OrderbugThis script demonstrate what I don't understand - according to documentation with order managements
The from_entry parameter should execute the exit on this specified trade.
The chart shows the right information, but the list of trades, in the Strategy tester panel doesn't make sense:
This is an extract of the CSV file I got from this script:
The enter1 is getting matched against 4 others entry names, each closing 30% of the position.
10,Exit Long,exit loss2
10,Entry Long,enter 3
===================
9,Exit Long,exit loss4
9,Entry Long,enter 3
==================
8,Exit Long,exit loss4
8,Entry Long,enter 2
==================
7,Exit Long,exit loss3
7,Entry Long,enter 2
===================
6,Exit Long,exit loss8
6,Entry Long,enter 2
==================
5,Exit Long,exit loss10
5,Entry Long,enter 2
==================
4,Exit Long,exit loss10
4,Entry Long,enter 1
===================
3,Exit Long,exit loss5
3,Entry Long,enter 1
===================
2,Exit Long,exit loss9
2,Entry Long,enter 1
===================
1,Exit Long,exit loss6
1,Entry Long,enter 1
Numbers RenkoRenko with Volume and Time in the box was developed by David Weis (Authority on Wyckoff method) and his student.
I like this style (I don't know what it is officially called) because it brings out the potential of Wyckoff method and Renko, and looks beautiful.
I can't find this style Indicator anywhere, so I made something like it, then I named "Numbers Renko" (数字 練行足 in Japanese).
Caution : This indicator only works exactly in Renko Chart.
////////// Numbers Renko General Settings //////////
Volume Divisor : To make good looking Volume Number.
ex) You set 100. When Volume is 0.056, 0.05 x 100 = 5.6. 6 is plotted in the box (Decimal are round off).
Show Only Large Renko Volume : show only Renko Volume which is larger than Average Renko Volume (it is calculated by user selected moving average, option below).
Show Renko Time : "Only Large Renko Time" show only Renko Time which is larger than Average Renko Time (it is calculated by user selected moving average, option below).
EMA period for calculation : This is used to calculate Average Renko Time and Average Renko Volume (These are used to decide Numbers colors and Candles colors). Default is EMA, You can choice SMA.
////////// Numbers Renko Coloring //////////
The Numbers in the box are color coded by compared the current Renko Volume with the Average Renko Volume.
If the current Renko Volume is 2 times larger than the ARV, Color2 will be used. If the current Renko Volume is 1.5 times larger than the ARV, Color1.5 will be used. Color1 If the current Renko Volume is larger than the ARV . Color0.5 is larger than half Athe RV and Color0 is less than or equal to half the ARV. Color1, Color1.5 and Color2 are Large Value, so only these colored Numbers are showed when use "Show Only ~ " option.
Default is Renko Volume based Color coding, You can choice Renko Time based Color coding. Therefore you can use two type coloring at the same time. ex) The Numbers Colors are Renko Volume based. Candle body, border and wick Colors are Renko Time based.
////////// Weis Wave Volume //////////
Show Effort vs Result : Weis Wave Volume divided by Wave Length.
ex) If 100 Up WWV is accumulated between 30 Up Renko Box, 100 / 30 = 3.33... will be 3.3 (Second decimal will be rounded off).
No Result Ratio : If current "Effort vs Result" is "No Result Ratio" times larger than Average Effort vs Result, Square Mark will be show. AEvsR is calculated by 5SMA.
ex) You set 1.5. If Current EvsR is 20 and AEvsR is 10, 20 > 10 x 1.5 then Square Mark will be show.
If the left and right arrows are in the same direction, the right arrow is omitted.
Show Comparison Marks : Show left side arrow by compare current value to previous previous value and show right side small arrow by compare current value to previous value.
ex) Current Up WWV is 17 and Previous Up WWV (previous previous value) is 12, left side arrow is Up. Previous Dn WWV is 20, right side small arrow is Dn.
Large Volume Ratio : If current WWV is "Large Volume Ratio" times larger than Average WWV, Large WWV color is used.
Sample layout
