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[_ParkF]RSI (+ichimoku cloud)RSI
Typical RSI indicators were plotted with candles and expressed wick to resemble a candle chart,
and linear regression was added to predict changes in force intensity,
which allowed us to confirm support and resistance within linear regression .
In addition, divergence signal was marked as an additional basis for the price fluctuation point due to support and resistance .
In other words,
if the diversity signal appears together when the rsi candle is supported and resisted within linear regression ,
this is the basis for predicting that it is a point of change in the existing trend.
Finally, the period value and standard deviation of linear regression can be arbitrarily modified and used.
I hope it will help you with your trading.
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(+ichimoku cloud)
Clouds made of the preceding span 1 and the preceding span 2 of the balance table can predict the trend by displaying the current price balance ahead of the future.
In addition to the role of clouds in the above-described balance sheet, this indicator also shows the cloud band support and resistance of the current RSI value.
일반적인 RSI 지표를 캔들화 하였고 꼬리까지 포함하여 캔들 차트와 유사하게 표현 하고,
캔들화한 RSI 지표에 선형회귀(채널)를 추가 하여 RSI 지표 특유의 힘의 강도의 변화를 지지와 저항으로 확인할 수 있게 해봤습니다.
또한 다이버전스 신호를 추가하여 선형회귀(채널)로 인한 지지와 저항에 따른 가격 변동의 근거로 삼을 수 있습니다.
즉, 선형회귀(채널) 안에서 RSI 캔들이 지지와 저항을 받을 때 다이버전스 신호가 함께 나타난다면 이는 기존 추세의 변화 지점임을
예측해 볼 수 있는 근거가 됩니다.
마지막으로 선형회귀(채널)의 기간값과 표준편차는 임의로 수정하여 사용할 수 있습니다.
당신의 트레이딩에 도움이 되었으면 합니다.
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(+일목균형표의 구름)
일목균형표의 선행스팬1과 선행스팬2로 만들어진 구름은 현재 가격의 균형을 미래에 선행하여 표시하여 추세를 예측해볼 수 있습니다.
본 지표에서는 위에서 설명한 일목균형표의 구름의 역할과 더불어 현 RSI 값의 구름대 지지, 저항 또한 확인해볼 수 있습니다.
* I would like to express my gratitude to zdmre for revealing the linear regression source.
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Strategia
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Optimized Linear Regression ChannelReturn a linear regression channel with a window size within the range (min, max) such that the R-squared is maximized, this allows a better estimate of an underlying linear trend, a better detection of significant historical supports and resistance points, and avoid finding a good window size manually.
Settings
Min : Minimum window size value
Max : Maximum window size value
Mult : Multiplicative factor for the rmse, control the channel width.
Src : Source input of the indicator
Details
The indicator displays the specific window size that maximizes the R-squared at the bottom of the lower channel.
When optimizing we want to find parameters such that they maximize or minimize a certain function, here the r-squared. The R-squared is given by 1 minus the ratio between the sum of squares (SSE) of the linear regression and the sum of squares of the mean. We know that the mean will always produce an SSE greater or equal to the one of the linear regression, so the R-squared will always be in a (0,1) range. In the case our data has a linear trend, the linear regression will have a better fit, thus having a lower SSE than the SSE of the mean, has such the ratio between the linear regression SSE and the mean SSE will be low, 1 minus this ratio will return a greater result. A lower R-squared will tell you that your linear regression produces a fit similar to the one produced by the mean. The R-squared is also given by the square of the correlation coefficient between the dependent and independent variables.
In pinescript optimization can be done by running a function inside a loop, we run the function for each setting and keep the one that produces the maximum or minimum result, however, it is not possible to do that with most built-in functions, including the function of interest, correlation , as such we must recreate a rolling correlation function that can be used inside loops, such functions are generally loops-free, this means that they are not computed using a loop in the first place, fortunately, the rolling correlation function is simply based on moving averages and standard deviations, both can be computed without using a loop by using cumulative sums, this is what is done in the code.
Note that because the R-squared is based on the SSE of the linear regression, maximizing the R-squared also minimizes the linear regression SSE, another thing that is minimized is the horizontality of the fit.
In the example above we have a total window size of 27, the script will try to find the setting that maximizes the R-squared, we must avoid every data points before the volatile bearish candle, using any of these data points will produce a poor fit, we see that the script avoid it, thus running as expected. Another interesting thing is that the best R-squared is not always associated to the lowest window size.
Note that optimization does not fix core problems in a model, with the linear regression we assume that our data set posses a linear trend, if it's not the case, then no matter how many settings you use you will still have a model that is not adapted to your data. Wskaźnik
TF Segmented Linear RegressionFit a line at successive intervals, where the interval period is determined by a user-selected time frame, this allows the user to have an estimate of the intrinsic trend within various intervals.
Settings
Timeframe : Determine the period of the interval, if the timeframe is weekly then a new line will be fit at the start each weeks, by default "Daily"
Mult : Multiplication factor for the RMSE, determine the distance between the upper and lower extremities
Src : Input data for the indicator
Plot Extremities : Logical value, if true then the extremities of the channel are plotted, if false only the midline is plotted, true by default.
Usage
The timeframe setting should be higher than the current chart timeframe, note however that too large values of timeframe might return an error. Since the maximum number of lines that can be plotted is 54, using the extremities will only return 18 channels.
The indicator can be compared to the "regression trend" drawing tool
Main tf = 5 min with the indicator using a daily timeframe, the filled area is produced by the regression trend drawing tool using the same interval as the indicator, and coincide with it.
Main tf = 15 min with the indicator using a weekly timeframe, wider channel indicate that the values tend to be farther away from the fitted line.
A line with a significant slope indicates a strong trend, in that case, the width of the channel is determined by the amplitude of the retracements in the trend, with a narrower channel indicating a cleaner trend.
When the fitted line has a low slope value and the channel is wide, it means that there were two or more variations of opposite directions with large amplitudes within the interval, this also indicates that a linear model is not appropriate.
A slope approximately equal to 0 with a low channel width indicates a trendless market with cyclical variations of low amplitude in it.
Refrences
Determining the starting and ending points of the fitted line was done using a linear combination between the wma and sma
The wma and sma functions both use a series as period by making use of the Wma and Sum functions in the following script
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Efficient Trend Step ChannelIntroduction
The efficient trend-step indicator is a trend indicator that make use of the efficiency ratio in order to adapt to the market trend strength, this indicator originally aimed to remain static during ranging states while fitting the price only when large variations occur. The trend step indicator family unlike most moving averages has a boxy appearance and could therefore not be classified as smooth, this makes it an indicator relatively uninteresting to use as input for other non-trending indicators such as oscillators.
Today a channel indicator making use of the efficient trend-step is proposed, the indicator has an upper and a lower extremity who can be used for breakout or support and resistance methodologies, however we will see that the indicator is sometimes able to return accurate support and resistance levels.
The Indicator
The indicator has the same settings has the efficient trend step indicator, length control the period of the efficiency ratio, fast control the period of the rolling standard deviation used for trending states, slow control the period of the rolling standard deviation used for ranging states, fast should be lower than slow , if both are equal then the indicator is equal to the classical trend step indicator and length does no longer affect the indicator output. Lower values of fast/slow will make the indicator more reactive to small variations thus changing direction more often.
The color changes you can see on the indicator are changed depending on the prior direction took by the indicator output, if the indicator where higher than its precedent value, then the color will be blue until the indicator is lower than its precedent value. Those colors help you have an estimate of the current trend direction.
Channel Calculation And Role
The extremities made from the efficient trend step allow for more advanced trading rules, they can act as stop/target level and can also give a rough estimate of the current market volatility, with wider extremities indicating a more volatile market.
The extremities are made directly from the dev element used by the efficient trend-step, the upper extremity is made by summing the efficient trend step with the value of dev when the efficient trend step change, the lower extremity is made the same way but the value is subtracted instead.
Is it a weird choice ? It sure is strange to see such approach, the absolute rolling average error between the price and the efficient trend step could have been a logical measure but using dev instead is more efficient and also allow for a more adaptive approach which can benefit the support and resistance methodology, the last reason is because i didn't wanted to "denature" the trend-step signature of the indicator.
The figure above represent the measurement used for making the extremities (in green).
Since the previously described measure change only when the efficient trend step change, we can conclude that such measure is representative of a relatively large variation, since the efficient trend step aim to only change when a large variations appear.
We can see that the upper extremity acted as an accurate resistance in this upper variation of AMD,
Here as well, however like other bands indicators it is safer to take into account the current trend direction, a strong uptrend will have less difficulties crossing the upper extremity, therefore it might be better to rely on the support (lower extremity) on an up-trending market (indicator in blue), and on the resistance (upper extremity) on an down-trending market (indicator in orange).
The figure above show support and resistances signals, a cross represent a false signal, while green arrows represent correct ones with their respective direction.
Conclusion
The presented indicator add more possibilities to the interpretation of the efficient trend step, the extremities can act as stop/target level, however this use has to be controlled, and the level should be in accordance to your risk/reward ratio.
Showcasing another trend-step indicator was a real pleasure. Thanks for reading :)
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Price-Curve ChannelIntroduction
Although many will use lines in order to make support and resistances, others might use curves, this is logical since trends are not always linear. Therefore it was also important to take this into consideration, and when i published the price-line channel indicator, i already started a curved version of it. Therefore i propose this new indicator based on the recursive bands framework that allow to return curved support and resistances. The benefits of this indicator are : a totally stable approach, user friendly, and extremities able to converge faster toward the price.
The Indicator
The indicator is way faster than the price-line channel one, this is due to the fast convergence toward the price of the extremities. Length control the reactivity of the indicator, while mult is more related to the rate of convergence, values of mult lower than 1 will make the curve converge slower,
mult = .5
Higher values of mult will make the extremities converge faster toward the price.
mult = 2
Unlike the price-line channel indicator this one is directly "readjusted", this is due to the fact that the extremities are no longer linear, of course a "perfectly" curved version could come in an update, but for the moment it wasn't really a necessity.
Comparison With Price-Line
The fact that the extremities converge faster toward the price allow to possibly capture more tops/bottoms/retracements. However the extremities of both indicator have the same behavior regarding their accuracy, for example the upper extremity have a higher chance to detect a retracement when on a downtrend, while the lower extremity have higher chance to detect a retracement while on a up-trend.
On The Indicator Construction
The recursive bands framework is the core of the indicator, it is important to use it. The curved effect is given by multiplying the correction factor by the barssince function, therefore the correction factor is no longer constant which in return allow for a non linear output.
The size is divided by the square of length in order to keep a certain logic between the output and the length period.
Conclusion
The recursive bands framework prove again to be quite interesting, lot of indicators can be made using it, i only posted a fraction of what can be done with it, which make the recursive bands indicator one of the best indicators i ever made in my opinion.
The proposed indicator is stable, and don't require nightmarish manipulations (unlike the linear channels indicator), its ability to detect possible support and resistances points, although subjective, remain a feature of the indicator. The use of recursion make the indicator efficient. I hope the indicator find some use in the community.
Thanks for reading !
Linear version.
Note
Respect the house rules, always request permission before publishing open source code. This is an original work, requesting permission is the least you can do.
I apologize for any grammatical/orthographic error in this post.
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