Smooth Slow RSIThis is a smoothed rsi indicator. That means that the input for the rsi calculations is smoothed. This results in faster and smoother responses from the rsi! On top of that I have incorporated an advanced formula for rsi that will amplify weak signals. This means you can use longer times with accuracy. For instance if you are on the 3 minute and you want a 30 minute rsi you can just simply multiply your length by 10 and you will get real results. Normally if you do this you wont get the right output. I hope you find this release useful! Here's a recap on how rsi generally works incase this is your first time:
The relative strength index is a technical indicator used in the analysis of financial markets. It is intended to chart the current and historical strength or weakness of a stock or market based on the closing prices of a recent trading period. The indicator should not be confused with relative strength.
Cari dalam skrip untuk "accuracy"
Volume percentrank[TV1]Volume percentrank
Volume normalized by percentile.
The indicator calculates the percentile of the trading volume . The volume in the base asset or quote asset can be selected as data. To calculate the volume of a quoted asset, the closing price or another standard method for calculating the price of a bar can be used.
A feature of percentile calculation with a small data sample length is low accuracy. Despite the fact that the script allows you to calculate a percentile with a length of 1, using a percentile length less than 100 is not recommended.
The percentile calculation method does not allow correctly calculating the percentile at the beginning of the chart due to the lack of all data in the selection, therefore, when the date of the first bar changes (this happens on small timeframes if the TradingView subscription does not allow you to see all historical data), the indicator will be repainted up to the bar number equal to the percentile sample length.
Huge values of the percentile length may cause a script error. If the indicator doesn't work, just make the percentile length smaller.
Объем, нормализованный по процентилью.
Индикатор вычисляет процентиль объема торгов. В качестве данных может быть выбран объем в базовом(base) активе или котировочном(quote) активе. Для расчета объема в котировочном активе может использоваться цена закрытия либо другой стандартный метод расчета цены бара.
Особенностью расчета процентиля при малой длине выборки данных является малая точность. Не смотря на то, что скрипт позволяет вычиcлить процентиль с длинной 1, использовать длину процентиля меньше 100 не рекомендуется.
Метод расчета процентиля не позволяет корректно рассчитать процентиль в начале графика из-за отсутствия всех данных в выборке, поэтому при изменении даты первого бара (это происходит на малых таймфреймах, если подписка TradingView не позволяет видеть все исторические данные) индикатор подвержен перерисовке вплоть до номера бара равного длине выборки процентиля.
Большие значения длины процентиля могут приводить к ошибке скрипта. Если индикатор не работает, просто сделайте длину процентиля меньше.
Volatility Inverse Correlation CandleThis is an educational tool that can help you find direct or inverse relations between two assets.
In this case I am using VIX and SPX .
The way it works is the next one :
So I am looking at the current open value of VIX in comparison with the previous close ( if it either above or below) and after on the SPX I am looking into the history and see for example which type of candle we had in respect with the opening value from VIX .
So for example, lets imagine that today is monday, and the weekly open value from VIX was higher than previous friday close value. Now I am going to see with the inverse correlation , if based on this idea, the current weekly candle from SPX finished in a bear candle.
The same can be applied for the bearish situation, so if we had an open from VIX lower than previous close, we are looking to check the SPX bull candle accuracy.
At the same time, for a different type of calculation I have added an internal lookup into heikin ashi values.
If you have any questions please let me know !
Intrabar VWAPIf your chart timeframe is 1 hour, then each candle show you the OHLC over an hour.
The OHLC price information is rather course grained and does not include the volume.
What if you could split each 1h candle into smaller candles and calculate the Volume Weighted Average Price (VWAP) on those ?
That is exactly what this indicator does. It virtually splits your chart's candles into 1 minute candles and calculates the VWAP on those to give you a better aggregated price per candle, which includes the volume information too.
Known Limitation:
The intra-bar timeframe is 1 minute for simplicity and highest accuracy. I can make this configurable you have a good case.
STD/Clutter-Filtered, Variety FIR Filters [Loxx]STD/Clutter-Filtered, Variety FIR Filters is a FIR filter explorer. The following FIR Digital Filters are included.
Rectangular - simple moving average
Hanning
Hamming
Blackman
Blackman/Harris
Linear weighted
Triangular
There are 10s of windowing functions like the ones listed above. This indicator will be updated over time as I create more windowing functions in Pine.
Uniform/Rectangular Window
The uniform window (also called the rectangular window) is a time window with unity amplitude for all time samples and has the same effect as not applying a window.
Use this window when leakage is not a concern, such as observing an entire transient signal.
The uniform window has a rectangular shape and does not attenuate any portion of the time record. It weights all parts of the time record equally. Because the uniform window does not force the signal to appear periodic in the time record, it is generally used only with functions that are already periodic within a time record, such as transients and bursts.
The uniform window is sometimes called a transient or boxcar window.
For sine waves that are exactly periodic within a time record, using the uniform window allows you to measure the amplitude exactly (to within hardware specifications) from the Spectrum trace.
Hanning Window
The Hanning window attenuates the input signal at both ends of the time record to zero. This forces the signal to appear periodic. The Hanning window offers good frequency resolution at the expense of some amplitude accuracy.
This window is typically used for broadband signals such as random noise. This window should not be used for burst or chirp source types or other strictly periodic signals. The Hanning window is sometimes called the Hann window or random window.
Hamming Window
Computers can't do computations with an infinite number of data points, so all signals are "cut off" at either end. This causes the ripple on either side of the peak that you see. The hamming window reduces this ripple, giving you a more accurate idea of the original signal's frequency spectrum.
Blackman
The Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. It is close to optimal, only slightly worse than a Kaiser window.
Blackman-Harris
This is the original "Minimum 4-sample Blackman-Harris" window, as given in the classic window paper by Fredric Harris "On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform", Proceedings of the IEEE, vol 66, no. 1, pp. 51-83, January 1978. The maximum side-lobe level is -92.00974072 dB.
Linear Weighted
A Weighted Moving Average puts more weight on recent data and less on past data. This is done by multiplying each bar’s price by a weighting factor. Because of its unique calculation, WMA will follow prices more closely than a corresponding Simple Moving Average.
Triangular Weighted
Triangular windowing is known for very smooth results. The weights in the triangular moving average are adding more weight to central values of the averaged data. Hence the coefficients are specifically distributed. Some of the examples that can give a clear picture of the coefficients progression:
period 1 : 1
period 2 : 1 1
period 3 : 1 2 1
period 4 : 1 2 2 1
period 5 : 1 2 3 2 1
period 6 : 1 2 3 3 2 1
period 7 : 1 2 3 4 3 2 1
period 8 : 1 2 3 4 4 3 2 1
Read here to read about how each of these filters compare with each other: Windowing
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 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.
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
Related Indicators
STD/Clutter-Filtered, Kaiser Window FIR Digital Filter
STD- and Clutter-Filtered, Non-Lag Moving Average
Clutter-Filtered, D-Lag Reducer, Spec. Ops FIR Filter
STD-Filtered, Ultra Low Lag Moving Average
SGX Nifty OHLC for Nifty 50 IndexSGX Nifty OHLC for Nifty 50 Index
What is this Indicator?
• This indicator calculates the OHLC levels of SGX Nifty.
How does SGX Nifty impact NIFTY and the Indian Market?
• Helps in predicting NIFTY50 Index behavior.
• The closing price of today's 9.14 am (IST) SGX Nifty will be the Open of today's Nifty50 Open. This helps to determine the opening Gap of Nifty50.
• SGX Nifty OHLC levels can act as support and resistance in Nifty50.
Who to use?
• Beneficial for Day Traders, who trade in NIFTY Index.
What timeframe to use?
• Use 1 minute for better accuracy.
• Other timeframes will also work.
Important Note
• Use 1 min timeframe for accurate OHLC.
• In other timeframes OHLC will have negligible difference, it won't be huge.
• This indicator will appear only on NIFTY Index and Futures chart.
• To hide the warning label go to the indicator Menu.
SUPER RSI [Gabbo]RSI revolutionizes the classic RSI by allowing you to modify its behavior based on different chart types and dynamic multi-source calculations.
It’s designed for traders who want greater precision and adaptability in momentum analysis across various market conditions.
Whether you want to apply the RSI on alternative candles like Heikin Ashi, Renko, or even combine multiple data sources, this tool provides maximum flexibility.
🔷 Key Features
🟩Customizable Chart Inputs
Apply RSI calculations not only on traditional candles but also on alternative bar types like Heikin Ashi, Kagi, Line Break, Point & Figure, and Renko for a deeper understanding of trend strength.
🟩Multi-Source Aggregation
Blend multiple sources together to create a more stable and refined RSI signal. Combine 2, 3, 4, or even 5 different sources into a single input.
🟩Dynamic RSI and Bands
Unlock advanced options to dynamically adjust the RSI itself and its surrounding bands based on real-time price action.
🔷 Technical Details and Customizable Inputs
1️⃣ Bar Type Selection:
Choose the type of chart structure used for RSI calculation:
Candles (classic)
Heikin Ashi
Kagi
Line Break
Point & Figure
Renko
2️⃣ Use Different Source???
Activate multi-source RSI by combining multiple elements:
2 sources : (Source 1 + Source 2) ÷ 2
3 sources : (Source 1 + Source 2 + Source 3) ÷ 3
4 sources : (Source 1 + Source 2 + Source 3 + Source 4) ÷ 4
5 sources : (Source 1 + Source 2 + Source 3 + Source 4 + Source 5) ÷ 5
3️⃣ Use Dynamic RSI???
Enable a dynamic RSI calculation that adjusts in real-time to market behavior for greater responsiveness.
4️⃣ Use Dynamic Band???
Enable dynamic bands that adapt to price action rather than relying on fixed static thresholds.
🔍 How to Use Dynamic RSI Source Pro
📈 Choose Your Candle Type
Select the bar format that best matches your strategy needs—classic candles, Heikin Ashi, Renko, and more.
🧩 Customize Your Data Source
Activate multi-source input to create smoother, more reliable RSI signals.
⚡ Unlock Dynamic Adaptation
Enable dynamic RSI and bands to adjust automatically to live price movements and enhance signal accuracy.
☄️ With Dynamic RSI Source Pro, you can elevate your RSI analysis by applying it dynamically across multiple candle types and sources, giving you a new level of control and precision.
Kase Peak Oscillator w/ Divergences [Loxx]Kase Peak Oscillator is unique among first derivative or "rate-of-change" indicators in that it statistically evaluates over fifty trend lengths and automatically adapts to both cycle length and volatility. In addition, it replaces the crude linear mathematics of old with logarithmic and exponential models that better reflect the true nature of the market. Kase Peak Oscillator is unique in that it can be applied across multiple time frames and different commodities.
As a hybrid indicator, the Peak Oscillator also generates a trend signal via the crossing of the histogram through the zero line. In addition, the red/green histogram line indicates when the oscillator has reached an extreme condition. When the oscillator reaches this peak and then turns, it means that most of the time the market will turn either at the present extreme, or (more likely) at the following extreme.
This is both a reversal and breakout/breakdown indicator. Crosses above/below zero line can be used for breakouts/breakdowns, while the thick green/red bars can be used to detect reversals
The indicator consists of three indicators:
The PeakOscillator itself is rendered as a gray histogram.
Max is a red/green solid line within the histogram signifying a market extreme.
Yellow line is max peak value of two (by default, you can change this with the deviations input settings) standard deviations of the Peak Oscillator value
White line is the min peak value of two (by default, you can change this with the deviations input settings) standard deviations of the PeakOscillator value
The PeakOscillator is used two ways:
Divergence: Kase Peak Oscillator may be used to generate traditional divergence signals. The difference between it and traditional divergence indicators lies in its accuracy.
PeakOut: The second use is to look for a Peak Out. A Peak Out occurs when the histogram breaks beyond the PeakOut line and then pulls back. A Peak Out through the maximum line will be displayed magenta. A Peak Out, which only extends through the Peak Min line is called a local Peak Out, and is less significant than a normal Peak Out signal. These local Peak Outs are to be relied upon more heavily during sideways or corrective markets. Peak Outs may be based on either the maximum line or the minimum line. Maximum Peak Outs, however, are rarer and thus more significant than minimum Peak Outs. The magnitude of the price move may be greater following the maximum Peak Out, but the likelihood of the break in trend is essentially the same. Thus, our research indicates that we should react equally to a Peak Out in a trendy market and a Peak Min in a choppy or corrective market.
Included:
Bar coloring
Alerts
R-squared Adaptive T3 w/ DSL [Loxx]R-squared Adaptive T3 w/ DSL is the following T3 indicator but with Discontinued Signal Lines added to reduce noise and thereby increase signal accuracy. This adaptation makes this indicator lower TF scalp friendly.
What is R-squared Adaptive?
One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement.
The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average .
R-squared is used here to derive a T3 factor used to modify price before passing price through a six-pole non-linear Kalman filter.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included:
Bar coloring
Signals
Alerts
EMA and FEMA Signla/DSL smoothing
Loxx's Expanded Source Types
Swing Trading SPX CorrelationThis is a long timeframe script designed to benefit from the correlation with the Percentage of stocks Above 200 moving average from SPX
At the same time with this percentage we are creating a weighted moving average to smooth its accuracy.
The rules are simple :
If the moving average is increasing its a long signal/short exit
If the moving average is decreased its a short signal/long exit.
Curently the strategy has been adapted for long only entries.
If you have any questions let me know !
Moving Average Filters Add-on w/ Expanded Source Types [Loxx]Moving Average Filters Add-on w/ Expanded Source Types is a conglomeration of specialized and traditional moving averages that will be used in most of indicators that I publish moving forward. There are 39 moving averages included in this indicator as well as expanded source types including traditional Heiken Ashi and Better Heiken Ashi candles. You can read about the expanded source types clicking here . About half of these moving averages are closed source on other trading platforms. This indicator serves as a reference point for future public/private, open/closed source indicators that I publish to TradingView. Information about these moving averages was gleaned from various forex and trading forums and platforms as well as TASC publications and other assorted research publications.
________________________________________________________________
Included moving averages
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA, it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA.
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average (DEMA) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA. It's also considered a leading indicator compared to the EMA, and is best utilized whenever smoothness and speed of reaction to market changes are required.
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA (Simple Moving Average). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA.
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Hull Moving Average - HMA
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points.
IE/2 - Early T3 by Tim Tilson
The IE/2 is a Moving Average that uses Linear Regression slope in its calculation to help with smoothing. It's a worthy Moving Average on it's own, even though it is the precursor and very early version of the famous "T3 Indicator".
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA (Simple Moving Average) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and it's smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA (Least Squares Moving Average)
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA. Although it's similar to the Simple Moving Average, the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track price better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average. The Linear Weighted Moving Average calculates the average by assigning different weight to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrows price.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA.
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average (SMA), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen a an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA (Smoothed Moving Average). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a a Two pole Butterworth filter combined with a 2-bar SMA (Simple Moving Average) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA. They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
The TMA and Sine Weighted Moving Average Filter are almost identical at times.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, it's signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers.
Volume Weighted EMA - VEMA
Utilizing tick volume in MT4 (or real volume in MT5), this EMA will use the Volume reading in its decision to plot its moves. The more Volume it detects on a move, the more authority (confirmation) it has. And this EMA uses those Volume readings to plot its movements.
Studies show that tick volume and real volume have a very strong correlation, so using this filter in MT4 or MT5 produces very similar results and readings.
Zero Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers, as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA, this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
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What are Heiken Ashi "better" candles?
The "better formula" was proposed in an article/memo by BNP-Paribas (In Warrants & Zertifikate, No. 8, August 2004 (a monthly German magazine published by BNP Paribas, Frankfurt), there is an article by Sebastian Schmidt about further development (smoothing) of Heikin-Ashi chart.)
They proposed to use the following:
(Open+Close)/2+(((Close-Open)/( High-Low ))*ABS((Close-Open)/2))
instead of using :
haClose = (O+H+L+C)/4
According to that document the HA representation using their proposed formula is better than the traditional formula.
What are traditional Heiken-Ashi candles?
The Heikin-Ashi technique averages price data to create a Japanese candlestick chart that filters out market noise.
Heikin-Ashi charts, developed by Munehisa Homma in the 1700s, share some characteristics with standard candlestick charts but differ based on the values used to create each candle. Instead of using the open, high, low, and close like standard candlestick charts, the Heikin-Ashi technique uses a modified formula based on two-period averages. This gives the chart a smoother appearance, making it easier to spots trends and reversals, but also obscures gaps and some price data.
Expanded generic source types:
Close = close
Open = open
High = high
Low = low
Median = hl2
Typical = hlc3
Weighted = hlcc4
Average = ohlc4
Average Median Body = (open+close)/2
Trend Biased = (see code, too complex to explain here)
Trend Biased (extreme) = (see code, too complex to explain here)
Included:
-Toggle bar color on/off
-Toggle signal line on/off
XBT Average Electricity CostXBT Average Electricity Cost
Cambridge Bitcoin Electricity Consumption Index (CBECI) - Bitcoin's global electricity consumption in TwH.
Note: Uses MONTHLY averages of raw data from CBECI. TV script run-time is too slow with Daily/Weekly data here.
This requires manual updating once a month for ongoing accuracy.
Source
Adaptive, Relative Strength EMA (RSEMA) [Loxx]TASC's May 2022 edition Traders' Tipsl includes the "Relative Strength Moving Averages" article authored by Vitali Apirine. This is the code implementing the Relative Strength Exponential Moving Average (RS EMA) indicator introduced in this publication.
This indicator adds onto Vitali Apirine's work by including three different types of momentum used to calculate RSEMA as well as fixed and adaptive cycle calculations to be used as dynamic inputs to calculate momentum. The purpose of these additional calculation methods is to attempt to filter out noice and track trends by using different methods and inputs to calculation momentum.
Momentum methods
-Wilder relative strength
-Chande momentum
-Momentum component of Jurik's RSX RSI
Cycle calculation methods
-Fixed
-Vertical horizontal filter
-Ehlers' Autocorrelation Dominant Cycle
What is Wilder relative strength?
The Relative Strength Index (RSI), developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Traditionally the RSI is considered overbought when above 70 and oversold when below 30.
What is Chande momentum?
Chande Momentum was designed specifically to track the movement and momentum of a security. It calculates the difference between the sum of both recent gains and recent losses, then dividing the result by the sum of all price movement over the same period.
What is the momentum component of Jurik's RSX RSI?
RSI is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurk RSX retains all the useful features of RSI , but with one important exception: the noise is gone with no added lag. For our purposes here, we derive momentum minus the lag.
Vertical horizontal filter?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX in the Directional Movement System. Trend indicators can then be employed in trending markets and momentum indicators in ranging markets.
What is autocorrelation?
Ehlers Autocorrelation is used in the calculation of dominant cycle length to be injected into standard technical analysis tools to improve TA accuracy. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods.
As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Ehlers Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars.
Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation.
Happy trading!
Naked Intrabar POCThis indicator with an unfortunate and very non PC sounding name approximates (!) the intrabar point of control (POC) either from time or volume at price.
Due to pine limitations, bin size and the sample lower time frame selection will have at least some effect on the accuracy of the approximation. The trade off is between accuracy and historical availability, however bar replay can be used to view prior historical states beyond what is visible from the current real time bar.
In order for all intrabar POC circles to be visible, you will need to manually set the visual order of the indicator by bringing it to the front.
Since the POC represents a price point around which the highest market participation occurred, the exposed global variable intrabar_poc may (or may not) be interesting as an alternative to ohlc based source input.
BlackMEX - Production CostBitcoin's Value as determined by Joules of energy input only
Calculations per Medium article EV = (Energy-in) / (Supply Growth Rate) * (Fiat Factor)
Historic Energy Efficiency data can only be entered monthly due to processing speed constraints of below data load and should be considred an estimate only.
Energy Efficiency Data requires manual updating. Currently accurate as of 28 December 2019
Bitcoin Production Cost
Cambridge Bitcoin Electricity Consumption Index (CBECI) - Bitcoin's global electricity consumption in TwH.
NB: Uses MONTHLY averages of raw data from CBECI. TV script run-time is too slow with Daily/Weekly data here.
This requires manual updating once a month for ongoing accuracy.
Crab Range FinderFinds the "crab range" of current price action.
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The lines corresponds are follows:
Green line represents the average high price
Orange line represents the median price
Yellow line represents the average low price
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By default, the last 5 bars are used when calculating the price lines, this value can be changed in settings as needed to tune for accuracy.
Stochastic OB/OS Zones HeatmapThe code is based on the Stochastic RSI Heatmap, but uses a normal Stochastic instead the Stochastic RSI when calculating "k" for more accuracy. Credit for the idea goes to Indicator-Jones.
The heatmap starts from the oversold (20) / overbought (80) levels respectively. The more oversold / overbought the price, the more intense the color (blue / fuchsia)
[CP]Pivot Boss Multi Timeframe CPR Inception with MACD and EMAINTRODUCTION:
This indicator combines multi-timeframe CPR bands with MACD Momentum and EMA trend, all projected on the candlestick chart through a novel visualization.
If you have seen my other indicators on TradingView, you would know that I use floor pivots a lot and “Secrets of a Pivot Boss” is my favorite book. While using floor pivots, time and again I have noticed an interesting price behavior,
Trending moves in price typically start from around the Central Pivot Range (CPR). The CPR could be from ANY timeframe. These moves can easily be caught using simple momentum and trend indicators like MACD and EMA crossovers.
Yes, it is that simple. Follow along to understand how to use this indicator.
INDICATOR SETTINGS:
RANGEBOUND MACD AND EMA MARKINGS:
TradingView limits the max number of labels that can be shown on a chart to 500. Therefore, if you go far back enough, you won't see any markings for the MACD or EMA setups. If you are looking to test the efficacy of this indicator in the past, change the start and end dates to your desired timeframe and then select the ‘Mark MACD and EMA Setups in Range?’ option.
MULTI TIMEFRAME CENTRAL PIVOT RANGE:
Here you can select CPRs and their bands from which timeframes are shown on the chart. I will share my favorite settings later in this description.
CPR CONFIGURATION:
Show CPR Labels: CPRs markings can carry labels, so that you don’t confuse between which line is what. Use this setting to toggle them On/Off.
Show Next Time Period Pivots: Check this option if you want to see the CPR of the next time period. This is typically done to figure out the ’Two Day CPR Relationship’ . Read the book, “Secrets of a Pivot Boss”, to understand more.
EMA TREND:
Show EMA on the Chart: EMAs will be plotted on the chart. Standard stuff.
Mark EMA Crossovers on Chart: EMA crossovers will be marked on the chart in diamond shapes. If you are using EMA crossovers, I recommend setting this option to True.
Rest of the EMA settings are fairly obvious.
MACD MOMENTUM:
Projecting MACD parameters directly on the candlesticks is surely going to give you a new perspective about price action and MACD.
Also, in order to better understand the MACD projections on the chart, you can add a standard MACD indicator on the chart with default settings to figure out what my indicator is actually showing you.
Marking MACD Crossovers on Chart: Marks the MACD signal crossovers on the chart. This visualization was a game changer for me.
Show MACD Histogram on Chart: Projects the complete MACD Histogram in a novel fashion (Try it!). You will be able to visually see the ebbs and flow of momentum in the charts.
Mark MACD Histogram Peaks on Chart: Marks only the MACD peaks instead of the complete histogram. Peaks are a great way to enter an ongoing trend and to play an intraday rangebound market.
Rest of the settings are just the standard settings that you will find in a typical MACD indicator.
ALERTS:
Not shown in the settings panel, but I have added alerts for EMA and MACD Crossovers so that you don’t have to sit in front of the charts or constantly check the price all day long.
If you don’t know how to set alerts in TradingView, then please Google it.
INDICATOR USAGE EXAMPLES:
This indicator can be used in intraday as well as in higher timeframes.
There are quite a few variations possible, I personally prefer to use the EMA crossovers in intraday (5m) and MACD on Daily timeframes.
This is just a matter of personal preference, some people might prefer using EMAs only or MACD only in all timeframes.
Here are my personal settings for the intraday 5-minute timeframe:
Turn on all the CPR pivots starting from Yearly all the way to Daily. You can turn on 6 hourly and 4 hourly as well if you want.
Hourly CPR is mostly used when the price is in a strong trend and you missed the entry and don’t know when to enter. Price will typically experience pullbacks towards the Hourly CPR, before resuming in the direction of the trend. That is your chance to hop onto the bandwagon.
For Intraday, I keep the Bands off. Just a personal preference here.
You can turn ON the Show CPR Labels , if you want.
Turn ON both the options in the EMA TREND section. You would want to see the EMA crossovers marked on the chart as well as the EMAs themselves, as the distance between the two EMAs will give you an idea about the strength of the trend.
Keep rest of the settings in the EMA section as default (you can change the colors if you wish). I keep the same EMAs as the ones kept in the MACD indicator. I like to keep things simple.
In the MACD MOMENTUM section, turn ON Mark MACD Histogram Peaks on Chart and all the other options turned OFF. Leave the other settings as default. By the way, these are the default settings of the standard MACD Indicator.
You can set up EMA Bullcross and Bearcross alarms if you like.
Before checking out the examples, remember one super simple rule:
SOME OF THE BEST TRENDING MOVES IN THE MARKET, BE IT INTRADAY OR OTHERWISE, ORIGINATE IN THE VICINITY OF A LARGER TIMEFRAME PIVOT/CPR.
Look for price settling above/below a pivot, and then a move away from the pivot in any direction is typically a trending move.
You can use hourly pivots or MACD Histogram peaks marked on the chart to enter an existing trend, or add to your positions.
Let’s have a look at a few recent intraday examples from the Crypto, Indian, and US equity markets.
I have added my comments in the charts to make you easily understand what is going on.
Understand that both, moving average crossover and MACD, will give out a lot of signals (chop) every day. But almost 70% of them are going to be fake signals. It is the signals that you get when the price is near a Pivot, that tend to convert into gorgeous trending moves that last.
BTC 5m Charts
NIFTY Futures 5m Charts (good intraday trends are hard to find here, as the market is very efficient)
TSLA 5m Charts
Some important points for using this indicator in higher timeframes:
For higher timeframes, my personal preference is to go with the MACD indicator. I personally find MACD to be lethal on daily and weekly timeframes, if you know how to use it well.
The default settings of the indicator are the settings I use for both, Daily and Weekly, timeframes. Additionally, I turn off the CPR labels.
In theory large trending moves still have a big probability to start near an important pivot level, however, in larger timeframes, trending moves can start from anywhere. They need not start in the vicinity of any important pivot (but they often do!).
Weekly pivots can act as great pullback levels when the price is in strong momentum, when trading on the daily timeframe.
Quarterly Pivots act as great pullback levels when the price is in strong momentum, when trading on the weekly timeframe.
BTC Weekly Chart
BTC Daily Chart
Nifty Weekly Chart
Nifty Daily Chart
NASDAQ Weekly Chart
NASDAQ Daily Chart
FINAL WORDS:
Please understand that I have Cherry Picked the examples to showcase the capability of the indicator and its usage.
DO NOT conflate the accuracy of examples with the accuracy of this indicator.
Biggest catch is the fact that this indicator, like every other indicator out there, will have whipsaws. Some I have also marked in the example charts.
You need to come up with your own technique to avoid whipsaws, one technique I have shared here…… big moves typically start near pivots.
Work on avoiding whipsaws and finding you own edge in the markets.
If you really want to learn how to use Pivots, read the book ’Secrets of a Pivot Boss’ . This book can change your life.
ALMA cross signal by hk4jerry<< ALMA CROSS signal >>
*NONE REPAINT STRATEGY*
--As a result of testing for a month, using alma does not result in repainting--
--ALMA 크로스 결과는 한달간의 테스트 결과, 리페인팅되지 않습니다--
(ENGLISH description O)
==NOTE==
1. MA 크로스 지표는 잘못된 신호들이 자주 등장합니다. 정확성을 더 높일수 있는 방법은 없을까 고민을 해봤습니다. 더 낮은 가격에 매수하고, 더 높은 가격에서 매도하는 것이 중요했습니다. 우리가 흔히 저점, 고점을 알아내기 위한 지표이자, 선행지표인 RSI를 추가하는 방법을 연구했습니다.
2. 예를 들어, MA 크로스 매수 신호가 발생했을때, rsi값이 50이면 가격이 더 떨어질 가능성이 큽니다. 하지만, rsi값이 30이하인 경우에만 매수 신호가 발생한다면, 그 가격이 저점일 확률이 매우 높아지는 원리 입니다.
3. 신호는 확률입니다. 트레이딩에 100%는 없습니다. 그 확률을 높이는 것은 리스크 관리 입니다. 분할 매수 관점으로 포지션을 잡으시거나, 단기 매매로 가져가시는걸 추천드립니다.
==rsi ma source 설정==
1. 'rsi ma' 값의 소스입니다.
2. 'rsi 길이' 는 값이 클수록 더욱 정확한 시그널이 발생합니다.
3. EMA 길이가 짧을수록 더 많은 시그널이 발생합니다. 그러나, 정확도는 떨어집니다.
==rsi ma 설정==
1. rsi를 source로한 EMA입니다.
2. rsi와 유사한 성격을 가집니다.
3. 'rsi ma' 값이 30이하이면 과매도, 70이상이면 과매수 입니다.
4. ' rsi ma long value' 이 30이면 매수 신호가 rsi ma 값이 30 이하인 경우에만 발생함을 의미 합니다.
5. "rsi ma short value' 가 70이면 매도 신호가 rsi ma 값이 70 이상인 경우에만 발생함을 의미 합니다.
==rsi 설정==
1. 실제 rsi(14,close) 값을 의미합니다.
2. rsi ma value와 비슷한 기능입니다.
3. rsi 길이가 14이므로, 값은 40~50 사이가 적당합니다.
4. 30 또는 70으로 설정할 시, 신호가 거의 발생하지 않습니다.
(ENG)
==NOTE==
1. MA cross indicator often shows false signals. I was wondering if there is a way to increase the accuracy further. It was important to buy at a lower price and sell at a higher price. We studied how to add RSI, which is a leading indicator and an indicator to find lows and highs, often.
2. For example, when a buy MA cross signal occurs, if the rsi value is 50, the price is more likely to fall. However, if a buy signal occurs only when the rsi value is below 30, the probability that the price is at the bottom is very high.
3. A signal is a probability. There is no 100% in trading. Increasing that probability is risk management. It is recommended to hold a position from the perspective of a split buy or take it as a short-term trade.
==rsi ma source option==
1. The source of the 'rsi ma' value.
2. The larger the 'rsi length' value, the more accurate the signal is generated.
3. Shorter EMA lengths produce more signals. However, the accuracy is reduced.
==rsi ma options==
1. EMA with rsi as the source.
2. It has similar characteristics to rsi.
3. If the 'rsi ma' value is below 30, it is oversold, and if it is above 70, it is overbought.
4. If 'rsi ma long value' is 30, it means that a buy signal will only occur when the rsi ma value is less than or equal to 30.
5. If "rsi ma short value' is 70, it means that a sell signal will only occur when the rsi ma value is above 70.
==rsi option==
1. It means the actual rsi(14,close) value.
2. This function is similar to rsi ma value.
3. Since the rsi length is 14, a value between 40 and 50 is appropriate.
4. When set to 30 or 70, almost no signal is generated.
EDMA Scalping Strategy (Exponentially Deviating Moving Average)This strategy uses crossover of Exponentially Deviating Moving Average (MZ EDMA ) along with Exponential Moving Average for trades entry/exits. Exponentially Deviating Moving Average (MZ EDMA ) is derived from Exponential Moving Average to predict better exit in top reversal case.
EDMA Philosophy
EDMA is calculated in following steps:
In first step, Exponentially expanding moving line is calculated with same code as of EMA but with different smoothness (1 instead of 2).
In 2nd step, Exponentially contracting moving line is calculated using 1st calculated line as source input and also using same code as of EMA but with different smoothness (1 instead of 2).
In 3rd step, Hull Moving Average with 2/3 of EDMA length is calculated using final line as source input. This final HMA will be equal to Exponentially Deviating Moving Average.
EDMA Defaults
Currently default EDMA and EMA length is set to 20 period which I've found better for higher timeframes but this can be adjusted according to user's timeframe. I would soon add Multi Timeframe option in script too. Chikou filter's period is set to 25.
Additional Features
EMA Band: EMA band is shown on chart to better visualize EMA cross with EDMA .
Dynamic Coloring: Chikou Filter library is used for derivation of dynamic coloring of EDMA and its band.
Trade Confirmation with Chikou Filter: Trend filteration from Chikou filter library is used as an option to enhance trades signals accuracy.
Strategy Default Test Settings
For backtesting purpose, following settings are used:
Initial capital=10000 USD
Default quantity value = 5 % of total capital
Commission value = 0.1 %
Pyramiding isn't included.
Backtesting data never assures that the same results would occur in future and also above settings use very less of total portfolio for trades, which in a way results less maximum drawdown along with less total profit on initial capital too. For example, increasing default quantity value will definity increase maximum drawdown value. The other way is also to use fix contracts in backtesting but it all depends on users general practice. Best option is to explore backtesting results with manually modified settings on different charts, before trusting them for other uses in future.
Usage and In-Detail Backtesting
This strategy has built-in option to enable trade confirmations with Chikou filter which will reduce the total number of trades increasing profit factor.
Symmetrically Weighted Moving Average (SWMA) on input source, may risk repainting in real-time data. Better option is to run a trade on bar close or simply left this optin unchecked.
I've set Chikou filter unchecked to increase number of trades (greater than 100) on higher timeframe (12H) and this can be changed according to your precision requirement and timeframe.
Timeframes lower than 4H usually have more noise. So its better to use higher EDMA and EMA length on lower timeframes which will decrease total number of offsetting trades increasing average total number of bars within a single trade.
Original "Exponentially Deviating Moving Average (MZ EDMA )" Indicator can be found here.
Exponentially Deviating Moving Average (MZ EDMA)Exponentially Deviating Moving Average (MZ EDMA) is derived from Exponential Moving Average to predict better exit in top reversal case.
EDMA Philosophy
EDMA is calculated in following steps:
In first step, Exponentially expanding moving line is calculated with same code as of EMA but with different smoothness (1 instead of 2).
In 2nd step, Exponentially contracting moving line is calculated using 1st calculated line as source input and also using same code as of EMA but with different smoothness (1 instead of 2).
In 3rd step, Hull Moving Average with 3/2 of EDMA length is calculated using final line as source input. This final HMA will be equal to Exponentially Deviating Moving Average.
EDMA Advantages
EDMA's main advantage is that in case of top price reversal it deviates from conventional EMA of 2*Length. This benefits in using EDMA for EMA cross with quick signals avoiding unnecessary crossovers. EDMA's deviation in case of top reversal can be seen as below:
EDMA presents better smoothened curve which acts as better Support and resistance. EDMA coparison with conventional EMA of 2*length of EDMA is as follows.
Additional Features
EMA Band: EMA band is shown on chart to better visualize EMA cross with EDMA.
Dynamic Coloring: Chikou Filter library is used for derivation of dynamic coloring of EDMA and its band.
Alerts: Alerts are provided of all trade signals. Weak buy/sell would trigger if EMA of 2*EDMA_length crosses EDMA. Strong buy/sell would trigger if EMA of same length as of EDMA crosses EDMA.
Trade Confirmation with Chikou Filter: Trend filteration from Chikou filter library is used as an option to enhance trades signals accuracy.
Defaults
Currently default EDMA and EMA1 length is set to 20 period which I've found better for higher timeframes but this can be adjusted according to user's timeframe. I would soon add Multi Timeframe option in script too. Chikou filter's period is set to 25.
Pi Cycle Top Ribbon [Mamo]This is a modification on the original Pi Cycle Top Indicator by Philip Swift.
It consists of 2 moving averages with one of them being multiplied by a chosen number. When the lower moving average crosses the higher (with multiple) moving average, the bull market top is indicated.
The original indicator showed bull market tops within a 3 day accuracy. This version shows the exact tops on the exact day for 2013 and 2017.
There are 7 different perfect solution shown as a band in this modified indicator. Each solution is a color pair and can be viewed separately by turning each combination off or on in the settings.
[CP]Pivot Boss Floor Pivots with ATR Dilation and Dynamic LevelsINTRODUCTION:
Compared to all the Pivot Indicators available on Trading View Public Library, this Floor Pivots Indicator differentiates itself in two major original ways:
Dilates the Pivot Support/Resistance Levels into Support/Resistance Bands based on volatility
Displays the S/R Levels Dynamically , that is, only those levels will be shown that are close enough to the price resulting in much cleaner looking charts.
There were a few features whose logic I had figured out, but I could not implement them due Pine Script’s Limitation (they should really work on increasing Pine Script’s capacity instead of adding more and more features to the language in order to make it look ‘better’):
Showing multiple timeframe pivots at the same time (not possible due to Pine Script’s limitation on the ‘Max Number of Outputs’ )
Automatic Detection of highly profitable Double Hot Pivot Zones (DPZ), also due to the ‘Max Number of Outputs’ limit
GENERAL USER INPUTS:
Most of the settings are self-explanatory, however, a few of them need some explanation:
Show Floor Pivots Dynamically – This will turn ON the dynamic pivot levels, please note that this function will work ONLY IN INTRADAY timeframes.
Dynamic Pivot ATR Period – Period over which the ATR value is calculated to show the pivots dynamically.
ATR Threshold for Dynamic Floor Pivots – Simply put, the indicator will start displaying Pivot Levels if they fall within the 2*ATR distance (default value) of the price. You can increase this number if the volatility increases and vice-versa.
Use ATR to Dilate Intraday Pivot Levels – This will turn ON Floor Pivot Dilation, turning pivot ‘lines’ into ‘bands’ .
ATR Dilation Factor – This number decides the width of the Pivot bands. Larger this number, thicker the bands. Typically, high volatility stocks will require a higher number.
ATR Period – Same as Dynamic Pivot ATR Period, but for Pivot Level Dilation.
INDICATOR USAGE EXAMPLES:
This indicator works great in conjunction with my Pivot Boss Candlestick Scanner indicator.
There are a lot of optimizations I have done in the code, although it looks trivial at first glance, but it's fairly complex.
Feel free to use it and modify it as you wish.
Here are a few examples where the indicator has shown great entries and exits, with the default settings:
NIFTY 5m Chart
Reliance 5m Chart
Tesla 5m Chart
Bitcoin-USDT 15m Chart
FINAL WORDS:
Please understand that I have Cherry Picked the examples to showcase the capability of the indicator and its usage.
DO NOT conflate the accuracy of examples with the accuracy of this indicator.
Once you start using floor pivots, you will realize that a lot of days simply don’t give any high probability setups and you will simply sit out of the market and do nothing (which is a good thing).
If you really want to learn how to use Pivots, read the book ’Secrets of a Pivot Boss’ . This book can change your life.
