Polynomial-Regression-Fitted RSI [Loxx]Polynomial-Regression-Fitted RSI is an RSI indicator that is calculated using Polynomial Regression Analysis. For this one, we're just smoothing the signal this time. And we're using an odd moving average to do so: the 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). So we're trying to tease out some cycle information here as well, however, you can change this MA to whatever soothing method you wish. I may come back to this one and remove the point modifier and then add preliminary smoothing, but for now, just the signal gets the smoothing treatment.
What is Polynomial Regression?
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression .
Included
Alerts
Signals
Bar coloring
Loxx's Expanded Source Types
Loxx's Moving Averages
Other indicators in this series using Polynomial Regression Analysis.
Poly Cycle
PA-Adaptive Polynomial Regression Fitted Moving Average
Polynomial-Regression-Fitted Oscillator
Regressions
Polynomial-Regression-Fitted Oscillator [Loxx]Polynomial-Regression-Fitted Oscillator is an oscillator that is calculated using Polynomial Regression Analysis. This is an extremely accurate and processor intensive oscillator.
What is Polynomial Regression?
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression .
Things to know
You can select from 33 source types
The source is smoothed before being injected into the Polynomial fitting algorithm, there are 35+ moving averages to choose from for smoothing
This indicator is very processor heavy. so it will take some time load on the chart. Ideally the period input should allow for values from 1 to 200 or more, but due to processing restraints on Trading View, the max value is 80.
Included
Alerts
Signals
Bar coloring
Other indicators in this series using Polynomial Regression Analysis.
Poly Cycle
PA-Adaptive Polynomial Regression Fitted Moving Average
Operietur ⸗ Time Range BreakoutOur T.R.B ( Time Range Breakout ) indicator is very similar to the O.R.B ( Open Range Breakout ) indicator. This script plots the high/Low within a custom time-range which then extends that plot to end-of-day. A Fibonacci extension is then drawn from that range. The default settings of this indicator set the similarities to the ORB. This script only displays the last trading day.
Due to Tradingview's singular refresh rate for the larger timeframes("resolutions"); this indicator works on timeframes LESS than 60min. Additionally, the smaller the timeframe the more accurate the price range will be.
The movements within the specified period of time define the projected Fibonacci prices associated with the allotted time's price range.
• Custom Time Range
• Fibonacci Extensions
• Up to 5 PTs
• Customizable Multiplier
Additional script features allow for fully adjustable settings and configurations:
• Adjustable; PT Colors
• Adjustable; Range Color
• Adjustable; Toggles
Many Moving AveragesA smooth looking indicator created from a mix of ALMA and LRC curves. Includes alternative calculation for both which I came up with through trial and error so a variety of combinations work to varying degrees. Just something I was playing around with that looked pretty nice in the end.
Regression Channel Alternative MTF█ OVERVIEW
This indicator displays 3 timeframes of parallel channel using linear regression calculation to assist manual drawing of chart patterns.
This indicator is not true Multi Timeframe (MTF) but considered as Alternative MTF which calculate 100 bars for Primary MTF, can be refer from provided line helper.
The timeframe scenarios are defined based on Position, Swing and Intraday Trader.
█ INSPIRATIONS
These timeframe scenarios are defined based on Harmonic Trading : Volume Three written by Scott M Carney.
By applying channel on each timeframe, MW or ABCD patterns can be easily identified manually.
This can also be applied on other chart patterns.
█ CREDITS
Scott M Carney, Harmonic Trading : Volume Three (Reaction vs. Reversal)
█ TIMEFRAME EXPLAINED
Higher / Distal : The (next) longer or larger comparative timeframe after primary pattern has been identified.
Primary / Clear : Timeframe that possess the clearest pattern structure.
Lower / Proximate : The (next) shorter timeframe after primary pattern has been identified.
Lowest : Check primary timeframe as main reference.
█ EXAMPLE OF USAGE / EXPLAINATION
Mean Distance IndicatorThe Mean Distance Indicator
The distance indicator is a market regime technique that measures the relative distance between the market price and the moving average. To calculate the indicator we can follow these steps:
Calculate the difference between the market price and the current moving average value.
Calculate the RSI on the differenced values.
BTC - Novel RPPI IndicatorHey Everyone,
This is a collab effort between me (a statistician) and @Stein3d (A coder). So if you like this indicator, be sure to also give him the credit!
This a novel indicator theorized by me and applied by Stein3d. We are calling it the RPPI indicator, standing for Regression based Price Prediction Indicator.
This is specifically coded for BTC and cannot be used for alt coins or ETH.
This is pretty beta so your feedback and comments are encouraged!
I will keep it brief, but here is the run down:
What does it do:
The indicator does 3 main things:
1. Predicts bullish targets;
2. Predicts bearish targets;
3. Predicts close price
Who is it applicable for:
This is generally targeted to day trades, but it can have swing trade applications as well. Feel free to get creative with combining it with other indicators that you feel complement it well.
How does it work:
It uses statistical based regressive analysis of BTC to compare current price action to previous price action and determine where the natural high and lows will fall intra-day based on the current price action of the day.
How to use it:
This does not omit the need for technical analysis and chart interpretation; however, it sets realistic expectations of intra-day bullish and bearish price targets as well as its best guess of where the current day close is most likely to fall. Take a look at some of the images below:
The image is pretty self explanatory but you see that there are 2 bull and bear targets. The bull targets, of course, are listed in Green and the bear targets are listed in Red.
There is a dummy neutral support and resistance target which is listed in yellow and the close price is in the purple dotted line.
Of course these are all customizable.
I think that pretty much covers it in a nut shell but let us know if you have any other questions and also please provide feedback!
Thanks for checking it out!
[BUBBLENUKE] BOB The Reversal Trader Indicator=============================================================: BOB The Reversal Trader :=============================================================
COMPONENTS:
- VWAP Anchored at Friday CME close
- Bitcoin CME close
- Volume bars
DESCRIPTION:
BOB is a mean-reversion trading system focused in BTCUSDT asset in the 30M time frame. The system is divided into 2 types of entries:
WEEKENDS:
BOB will trigger his entry when the price of Bitcoin is at one of the two deviations from the VWAP anchored at Friday CME close
INTRA-WEEK:
BOB will trigger its entry when the price of Bitcoin is at one of the two deviations from the VWAP anchored at the Friday CME close or when a volume candle indicates a reversal
[BUBBLENUKE] BOB The Reversal Trader=============================================================: BOB The Reversal Trader :=============================================================
COMPONENTS:
- VWAP Anchored at Friday CME close
- Bitcoin CME close
- Volume bars
SETTINGS:
- Asset: BTCUSDTPERP
- Time frame: 30M
- Hard TP %: 1.5
- Hard SL %: 40
- Trading Session Start (UTC): 4
- Trading Session End (UTC): 17
DESCRIPTION:
BOB is a mean-reversion trading system focused in BTCUSDT asset in the 30M time frame. The system is divided into 2 types of entries:
WEEKENDS:
BOB will trigger his entry when the price of Bitcoin is at one of the two deviations from the VWAP anchored at Friday CME close and BOB will take your profits when the price returns to the VWAP. When BOB hits Sunday and the CME reopens, BOB will close all your open positions.
INTRA-WEEK:
BOB will trigger its entry when the price of Bitcoin is at one of the two deviations from the VWAP anchored at the Friday CME close or when a volume candle indicates a reversal. BOB will take your profits when the price returns to the VWAP or when the HARD TP % is reached (1.5% by default). When BOB hits Friday and the CME closes, BOB will close all your open positions.
RAS.V2 Strength Index OscillatorHeavily modified version of my previous "Relative Aggregate Strength Oscillator" -Added high/low lines, alma curves,, lrc bands, changed candle calculations + other small things. Replaces the standard RSI indicator with something a bit more insightful.
Credits to @wolneyyy - 'Mean Deviation Detector - Throw Out All Other Indicators ' And @algomojo - 'Responsive Coppock Curve'
And the default Relative Strength Index
The candles are the average of the MFI ,CCI ,MOM and RSI candles, they seemed similar enough in style to me so I created candles out of each and the took the sum of all the candle's OHLC values and divided by 4 to get an average, same as v1 but with some tweaks. Previous Peaks and Potholes visible with the blue horizontal lines which adjust when a new boundary is established. Toggle alma waves or smalrc curves or both to your liking. This indicator is great for calling out peaks and troughs in realtime, although is best when combined with other trusted indicators to get a consensus.
Polynomial Regression Extrapolation [LuxAlgo]This indicator fits a polynomial with a user set degree to the price using least squares and then extrapolates the result.
Settings
Length: Number of most recent price observations used to fit the model.
Extrapolate: Extrapolation horizon
Degree: Degree of the fitted polynomial
Src: Input source
Lock Fit: By default the fit and extrapolated result will readjust to any new price observation, enabling this setting allow the model to ignore new price observations, and extend the extrapolation to the most recent bar.
Usage
Polynomial regression is commonly used when a relationship between two variables can be described by a polynomial.
In technical analysis polynomial regression is commonly used to estimate underlying trends in the price as well as obtaining support/resistances. One common example being the linear regression which can be described as polynomial regression of degree 1.
Using polynomial regression for extrapolation can be considered when we assume that the underlying trend of a certain asset follows polynomial of a certain degree and that this assumption hold true for time t+1...,t+n . This is rarely the case but it can be of interest to certain users performing longer term analysis of assets such as Bitcoin.
The selection of the polynomial degree can be done considering the underlying trend of the observations we are trying to fit. In practice, it is rare to go over a degree of 3, as higher degree would tend to highlight more noisy variations.
Using a polynomial of degree 1 will return a line, and as such can be considered when the underlying trend is linear, but one could improve the fit by using an higher degree.
The chart above fits a polynomial of degree 2, this can be used to model more parabolic observations. We can see in the chart above that this improves the fit.
In the chart above a polynomial of degree 6 is used, we can see how more variations are highlighted. The extrapolation of higher degree polynomials can eventually highlight future turning points due to the nature of the polynomial, however there are no guarantee that these will reflect exact future reversals.
Details
A polynomial regression model y(t) of degree p is described by:
y(t) = β(0) + β(1)x(t) + β(2)x(t)^2 + ... + β(p)x(t)^p
The vector coefficients β are obtained such that the sum of squared error between the observations and y(t) is minimized. This can be achieved through specific iterative algorithms or directly by solving the system of equations:
β(0) + β(1)x(0) + β(2)x(0)^2 + ... + β(p)x(0)^p = y(0)
β(0) + β(1)x(1) + β(2)x(1)^2 + ... + β(p)x(1)^p = y(1)
...
β(0) + β(1)x(t-1) + β(2)x(t-1)^2 + ... + β(p)x(t-1)^p = y(t-1)
Note that solving this system of equations for higher degrees p with high x values can drastically affect the accuracy of the results. One method to circumvent this can be to subtract x by its mean.
ATR ChartATR Levels
Calculated by adding ATR to daily low and subtracting ATR from daily high.
Inputs can change ATR timeframe and range, defaults to 6 hr and daily.
Colorful RegressionColorful Regression is a trend indicator. The most important difference of it from other moving averages and regressions is that it can change color according to the momentum it has. so that users can have an idea about the direction, orientation and speed of the graph at the same time. This indicator contains 5 different colors. Black means extreme downtrend, red means downtrend, yellow means sideways trend, green means uptrend, and white means extremely uptrend. I recommend using it on the one hour chart. You can also use it in different time periods by changing the sensitivity settings.
TURK RSI+ICHIMOKUTypical 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.
TG:- @turk_shariq
Easy TrendCurrent script displays trend channel, which makes it easy to see reversal signals
Note:
- If price goes above the channel it might be an early sell signal
- If price falls from channel it might be a sell signal, better to enter position on retest
Plan for future development:
- Alerts
- Trend angle
Return & Drawdown
ReDraw script calculates the historical returns and drawdown for the given periods.
By default, the return of the linear regression trends is displayed (can be turned off in settings). In this mode, two linear regression trends are being computed for both long and short periods, and the percent value indicates the "return of the trend" for the corresponding period. Observing the dynamic of the linear regression trends can give a great hint if the trend is slowing down.
When the smoothing method is set to "none" or WMA3/5, the real asset return is shown for both periods, using the formula (LastPrice-FirstPrice)/FirstPrice
The script calculates the maximum drawdown for the long period using the formula (max(Price) - LastPrice) / max(Price).
The white line under the zero is the average maximum drawdown over the long period.
When the mode is set to Compare, ReDraw will display the difference in metrics between the current and selected symbol (SPY by default).
Super trend BThis indicator is a mix of 3 well known indicators
the buy point is based on linear regression
the sell points are based on mix of super trend and Bollinger
it try to find best point to sell and buy which are independent from each other
for each time frame you need to try to search for best setting
alerts included
Enio_LR_SlopeEnio_LR_Slope is the slope curve of a Linear Regression Line. As such, it describes whether the LRL is decreasing or vice versa.
Its crossing above the Zero line is considered a Buy signal, and vice versa. This signal can also be used to confirm signals from other indicators.
The default setting is:
Slope curve, 30 periods
Cut-Off signal, 7-periods (This is a simple moving average of the Slope curve).
Cut-Off signals can be used for early buy/sell positioning.
Regression Channel with projectionEXPERIMENTAL:
Auto adjusting regressive channel with projection.
Linear regression is a linear approach to modeling the relationship between a dependent variable and one or more independent variables.
In linear regression , the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data.
Disclaimer :
Success in trading is all about following your trading strategy and indicators should fit into your own strategy, and not be traded purely on.
This script is for informational and educational purposes only. Use of the script does not constitute professional and / or financial advice. You are solely responsible for evaluating the outcome of the script and the risks associated with using the script. In exchange for the use of the script, you agree not to hold monpotejulien TradingView user responsible for any possible claims for damages arising out of any decisions you make based on the use of the script.
Mocker Market Mean IndicatorThe objective of this indicator is to find the historical mean price of a market ( Intended for Indexes or ETF's). Based off of the concept that Benjamin Graham taught, that Mr. Market is a manic depressive forever oscillating between unjustified pessimism to extreme optimism. The intent of this indicator is to supplement a regular allocation strategy to an index or ETF , to increase that allocation when it's trading below its historical mean and decrease when trading above. It does this by using an exponential regression model to find the closest approximation of the current mean price based on past growth rate, and user defined lookback period.
Relative slopeRelative slope metric
Description:
I was in need to create a simple, naive and elegant metric that was able to tell how strong is the trend in a given rolling window. While abstaining from using more complicated and arguably more precise approaches, I’ve decided to use Linearly Weighted Linear Regression slope for this goal. Outright values are useful, but the problem was that I wasn’t able to use it in comparative analysis, i.e between different assets & different resolutions & different window sizes, because obviously the outputs are scale-variant.
Here is the asset-agnostic, resolution-agnostic and window size agnostic version of the metric.
I made it asset agnostic & resolution agnostic by including spread information to the formula. In our case it's weighted stdev over differenced data (otherwise we contaminate the spread with the trend info). And I made it window size agnostic by adding a non-linear relation of length to the output, so finally it will be aprox in (-1, 1) interval, by taking square root of length, nothing fancy. All these / 2 and * 2 in unexpected places all around the formula help us to return the data to it’s natural scale while keeping the transformations in place.
Peace TV
Trend Line wi 3-PointsHello, my friends. This is a new version of the trend line regression indicator, which always finds quantitive trend lines with three key points.
(1) Indicator description
This indicator finds a trend line with three key points on the historical K-line
Solving the problem of calculation timeout based on a faster trend line regression algorithm
Supports filtering unwanted trend lines by setting a trendline strength threshold
It's suitable for most markets and timeframes
(2) Key parameters
- Pivot High/Low Settings
Pivot Lookback Left: Number of K-lines to look back left from the pivot top/bottom
Pivot Lookback Right: Number of K-lines to look back right from the pivot top/bottom
- Trend Line Regression
Max of Lookback Forward: The maximum number of historical K-lines
Min Regression Strength: The minimum strength threshold for trend line regression
Multiply Regression Std: The width of the trend line to display on the chart
(3) Script description
Due to some circumstances that I don't want to see, subsequent scripts will not be open source, but you can still use the script for free. Thanks for your understanding and support!
If you have any suggestions or comments about the script, please feel free to leave your comments!
Happy trading, and enjoy your life!
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各位朋友大家好，这是一个全新的趋势线回归指标。该指标总是会找到在图表中具有3个关键点的合理趋势线
(1) 指标说明
该指标在历史K线上找到具有3个关键点的趋势线，并绘制于图表上
基于更快速的趋势线回归算法，解决了计算超时的问题
支持通过设置趋势线强度阈值过滤不需要的趋势线
该指标适用于大部分市场和时间周期
(2) 关键参数
- Pivot High/Low Settings
Pivot Lookback Left: 枢纽顶/底点往左回顾的 K线 数量
Pivot Lookback Right: 枢纽顶/底点往右回顾的 K线 数量
- Trend Line Regression
Max of Lookback Forward: 回顾历史 K线 的最大数量
Min Regression Strength: 趋势线回归的最小强度阈值
Multiply Regression Std: 趋势线的显示宽度
(3) 脚本说明
因为出现了一些我不希望看到的情况，后续的脚本将不再开源代码，但是您依然可以免费使用该脚本，感谢理解和支持！
如果您存在对于该脚本的使用建议或者意见，欢迎各位留言！
祝大家交易愉快
Compound strategyIn this strategy, I looked at how to manage the crypto I bought. Once we have a little understanding of how cryptocurrency is valued, we can manage the coins we have. For example, the most valuable coin in a coin is to sell when it is overvalued and re-buy when it is undervalued. Furthermore, I realised that buying from the right place and selling at the right time is very important to make a good profit. When it says sell, it's divided into several parts.
1. When the major uptrend is over and we are able to make the desired profit, we will sell our holdings outright.
2. Selling in the middle of a down trend and buying less than that amount again
3. When a small uptrend is over, sell the ones you bought at a lower price and make a small profit.
The other important thing is that the average cost is gradually reduced. Also, those who sell at a loss will reduce their profit (winning rate), so knowing that we will have a chance to calculate our loss and recover it. I used this to write a strategy in Trading View. I have put the link below it. From that we can see how this idea works. What I did was I made the signal by taking some technical indicators as I did in the previous one (all the indicators I got in this case were directional indicators, then I was able to get a good correlation and a standard deviation. I multiplied the correlation and the standard deviation by both and I took the signal as the time when the graph went through zero, and I connected it to the volume so that I could see some of the volume supported by it.)
Now let me tell you a little bit about what I see in this strategy. In this I used the compound effect. That is, the strategy, the profit he takes to reinvest. On the other hand, the strategy itself can put a separate stop loss value on each trade and avoid any major loss from that trade. I also added to this strategy the ability to do swing trading. That means we can take the small profits that come with going on a big up trend or a big down trend. Combined with Compound Effect, Stop Loss and Swing Trading, I was able to make a profit of 894% per annum (1,117.62% for 15 months) with a winning rate of 80%. Winning rate dropped to 80% because I added stop loss and swing trading. The other thing is that I applied DCA to this in both the up trend and the down trend (both). That was another reason for me to make a good profit. The orange line shows how to reduction of costly trade. The yellow line shows the profit and you can see that the profit line does not go down during the loss trades. That's because I want to absorb the loss from that trade.
