Polynomial Regression Bands w/ Extrapolation of Price [Loxx]Polynomial Regression Bands w/ Extrapolation of Price is a moving average built on Polynomial Regression. This indicator paints both a non-repainting moving average and also a projection forecast based on the Polynomial Regression. I've included 33 source types and 38 moving average types to smooth the price input before it's run through the Polynomial Regression algorithm. This indicator only paints X many bars back so as to increase on screen calculation speed. Make sure to read the tooltips to answer any questions you have.
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 .
Related indicators
Polynomial-Regression-Fitted Oscillator
Polynomial-Regression-Fitted RSI
PA-Adaptive Polynomial Regression Fitted Moving Average
Poly Cycle
Fourier Extrapolator of Price w/ Projection Forecast
Regressions
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
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
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
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!
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.
--------------------------------------------------------------------------------------------------------------------------------------------------------------
(+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
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
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
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.
Strategy LinReg ST@RLStrategy LinReg ST@RL
Strategy LinReg ST@RL is a visual trend following indicator.
It is compiled in PINE Script Version V5 language.
This indicator/strategy, based on Linear Regression Calculation, is intended to help beginners (and also the more experienced ones) to trade in the right direction of the market trend and test strategy. It allows you to avoid the mistakes of always trading against the trend.
Strategy based on an original idea of @KivancOzbilgic (SuperTrend) and DevLucem (@LucemAnb) (Lin Reg ++)
A special credit goes to - KivancOzbilgic and @LucemAnb which inspired me a lot to improve this indicator/Strategy.
This indicator can be configured to your liking,according to your needs or your tastes.
The indicator/Strategy works in multi time frame.
The settings (length, offset, deviation, smoothing) are identical for all time frames if “Conf Auto” is not checked.
In this case the default settings (time frame=H1 settings) apply for all time frames.
The choice of source setting is common for all time frames.
If “Auto Conf” is checked,
then the settings will be optimized for each selected time frame (1m-3m H2 H3 H1 H4 & Daily). Time frames, other than 1m-3m H2 H3 H1 H4 & Daily will be affected with the default settings corresponding to the H1 time frame and will therefore not be optimized! The default setting values of each time frame (1m-3m H2 H3 H1 H4 & Daily) can be configured differently and optimized by you.
REVERSAL mode: Signal Buy=Sell and Signal Sell=Buy.
This option may be better than the regular strategy. Default mode is Reversal option.
Note that only for 1m (1 minute) Time frame, the option REVERSAL is opposite as default choice in configuration. (If reversal option is checked, then option for time frame 1m is not reversal!)
Trend indications (potential sell or buy areas) are displayed as a background color (bullish: green or bearish: red), assume that the market is moving in one direction.
You can tune the input, style and visibility settings to match your own preferences or habits.
Label Info (Simple or Full) gives trend info for each Exit (or current trade)
The choice of indicator colors is suitable for a graph with a "dark" theme, which you will probably need to modify for visual comfort, if you are using a "Light" mode or a custom mode.
This script is an indicator that you can run on standard chart types. It also works on non-standard chart types but the results will be skewed and different.
Non-standard charts are:
• Heikin Ashi (HA)
• Renko
• Kagi
• Point & Figure
• Range
As a reminder: No indicator is capable of providing accurate signals 100% of the time. Every now and then, even the best will fail, leaving you with a losing deal. Whichever indicator you base yourself on, remember to follow the basic rules of risk management and capital allocation.
BINANCE:BTCUSDT
! Français !
Strategy LinReg ST@RL
Stratégie LinReg ST@RL est un indicateur visuel de suivi de tendance.
Il est compilé en langage PINE Script Version V5.
Stratégie basée sur une idée originale de @KivancOzbilgic (SuperTrend) et DevLucem (@LucemAnb) (Lin Reg ++) Un crédit spécial va à - KivancOzbilgic et @LucemAnb qui m'ont beaucoup inspiré pour améliorer cet indicateur/stratégie.
Cet indicateur/strategie, basé sur le calcul de régression linéaire, est destiné à aider les débutants (et aussi les plus expérimentés) à trader dans le bon sens de la tendance du marché et à tester la stratégie. Cela vous permet d'éviter les erreurs de toujours négocier à contre-courant.
Cet indicateur peut être configuré à votre guise, selon vos besoins ou vos goûts.
L'indicateur/Stratégie fonctionne sur plusieurs bases de temps.
Les réglages (longueur, décalage, déviation, lissage) sont identiques pour toutes les bases de temps si
« Conf Auto » n'est pas coché. Dans ce cas, les paramètres par défaut (intervalle de temps=paramètres H1) s'appliquent à toutes les bases de temps.
Le choix du réglage de la source est commun à toutes les bases de temps.
Si "Auto Conf" est coché, alors les paramètres seront optimisés pour chaque base de temps sélectionnée (1m-3m H2 H3 H1 H4 & Daily). Les bases de temps, autres que 1m-3m H2 H3 H1 H4 & Daily seront affectées par les paramètres par défaut correspondant à la base de temps H1 et ne seront donc pas optimisées ! Les valeurs de réglage par défaut de chaque période (1m-3m H2 H3 H1 H4 & Daily) peuvent être configurées différemment et optimisées par vous.
Mode REVERSAL : Signal Achat=Vente et Signal Vente=Achat. Cette option peut être meilleure que la stratégie habituelle. Le mode par défaut est l'option REVERSAL.
Notez que seulement pour la base de temps de 1m (1 minute), l'option REVERSAL est l’opposée du choix par défaut dans la configuration. (Si l'option REVERSAL est cochée, alors l'option pour la base de temps 1 m n'est pas REVERSAL !)
Les indications de tendance (zones potentielles de vente ou d'achat) sont affichées en couleur de fond (haussier : vert ou baissier : rouge), supposons que le marché évolue dans une direction. Vous pouvez ajuster les paramètres d'entrée, de style et de visibilité en fonction de vos propres préférences ou habitudes.
Les informations sur l'étiquette (simples ou complètes) donnent des informations sur de chaque clôture (ou position en cours)
Le choix des couleurs des indicateurs est adapté à un graphique avec un thème "sombre", qu'il vous faudra probablement modifier pour le confort visuel, si vous utilisez un mode "Clair" ou un mode personnalisé.
Ce script est un indicateur que vous pouvez exécuter sur des types de graphiques standard. Cela fonctionne également sur les types de graphiques non standard, mais les résultats seront faussés et différents.
Les graphiques non standard sont :
• Heikin Ashi (HA)
• Renko
• Kagi
• Point & Figure
• Range
Pour rappel : Aucun indicateur n'est capable de fournir des signaux précis 100% du temps. De temps en temps, même les meilleurs échoueront, vous laissant avec une affaire perdante. Quel que soit l'indicateur sur lequel vous vous basez, rappelez-vous de suivre les règles de base de la gestion des risques et de l'allocation du capital.
Trend Line RegressionThis is a fast trend line regressor based on least squares regression.
(1) Supports setting regression from the Nth candle
(2) Supports the minimum and maximum regression candle interval length
(3) Supports finding the optimal regression region based on the length step among the minimum and maximum regression region lengths
(4) Supports displaying the optimal regression level
(5) The size of the regression region is 0.5 times the standard deviation by default
(6) You can filter the trend line by setting minimum trend line regression level
(6) Please properly set the parameters to avoid calculation timeout
Enjoy!
这是一个基于最小二乘法回归的快速趋势线回归
(1) 支持从第N根蜡烛开始设置回归
(2) 支持最小和最大的回归蜡烛区间长度
(3) 支持在最小和最大回归区间长度的基础上寻找最佳回归区域
(4) 支持显示最佳回归水平
(5) 回归区域的大小默认为标准差的0.5倍
(6) 可以通过设置最小趋势线回归等级来过滤趋势线
(6) 请正确设置参数以避免计算超时
使用愉快!
Price Region RegressionThis is an optimized price range regressor based on least squares regression.
(1) Supports setting regression from the Nth candle
(2) Supports the minimum and maximum regression candle interval length
(3) Supports finding the optimal regression region based on the length step among the minimum and maximum regression region lengths
(4) Supports displaying the optimal regression level
(5) The size of the regression region is two times the standard deviation by default
这是一个基于最小二乘回归的价格区间回归指标
(1) 支持设置从第N个蜡烛开始回归
(2) 支持最小和最大回归蜡烛的区间长度
(3) 在最小和最大回归区间长度中，根据长度步进寻找最优的回归区间
(4) 支持显示最优回归等级
(5) 回归区间的大小默认为2倍标准差
StrengthA mathematically elegant, native & modern way how to measure velocity/ strength/ momentum. As you can see it looks like MACD, but !suddenly! has N times shorter code (disregard the functions), and only 1 parameter instead of 3. OMG HOW DID HE DO IT?!?
MACD: "Let's take one filter (1 parameter), than another filter (2 parameters), then let's take dem difference, then let's place another filter over the difference (3rd parameter + introduction of a nested calculation), and let's write a whole book about it, make thousands of multi-hours YouTube videos about it, and let's never mention about the amount of uncertainty being introduced by multiple parameters & introduction of the nested calculation."
Strength: "let's get real, let's drop a weighted linear regression & usual linear regression over the data of the same length, take dem slopes, then make the difference over these slopes, all good. And then share it with people w/o putting an ® sign".
Fyi, regressions were introduced centuries ago, maybe decades idk, the point is long time ago, and computational power enough to calculate what I'm saying is slightly more than required for macd.
Rationale.
Linearly weighted linear regression has steeper slope (W) than the usual linear regression slope (S) due to the fact that the recent datapoints got more weight. This alone is enough of a metric to measure velocity. But still I've recalled macd and decided to make smth like it cuz I knew it'll might make you happy. I realized that S can be used instead of smoothing the W, thus eliminating the nested calculation and keeping entropy & info loss in place. And see, what we get is natural, simple, makes sense and brings flex. I also wanna remind you that by applying regression we maximize the info gain by using all the data in the window, instead of taking difference between the first and the last datapoints.
This script is dedicated to my friend Fabien. Man, you were the light in the darkness in that company. You'll get your alien green Lambo if you'll really want it, no doubts on my side bout that.
Good hunting
End Point Moving Average [EPMA]The End Point Moving Average was introduced in the October 95 issue of Technical Analysis of Stocks &
Commodities in the article "The End Point Moving Average", by Patrick E. Lafferty.
The Time Series Forecast takes this value and the slope of the regression line to forecast the next day and then plots this forecasted price as today's value.
For interpretation refer to Mr. Lafferty's article.
Please note
From line 10 starts my personal experemental modifications to this script, all above is original formula by Patrick E. Lafferty.
Bitcoin Best Value CorridorHere is my interpretation of the "Best Time To Buy" Bitcoin over its lifetime using a logarithmic regression trendline. The upper and lower lines are 10% deviations from the centre line. I calculated the trendline in excel and then coded my results into pine script.
Top 40 High Low Strategy for SPY, 5minThis strategy is developed based on my High Low Index SPY Top 40 indicator
Notes:
- this strategy is only developed for SPY on the 5 min chart . It seems to work with QQQ as well, but it isn't optimized for it
- P/L shown is based on 10 SPY option contracts, call or put, with strike price closest to the entry SPY price and expiry of 0 to 1 day. This includes commissions (can be changed). This is only an estimate calculated using an arbitrary multiplier factor, this can be changed in the setting
- P/L is based on $5000 initial capital
- Works with both regular / extended trading session turned on/off. However, max drawdown is 1/2 with extended trading session ON
- there is still a bug that doesn't allow alert to be created due to calculation error, will update once fixed
This strategy combines signals from the following indicators to determine entry signals:
- High Low Index SPY Top 40
- MACD
- Linear Regression Slope
Entry signal is triggered when:
- High Low Index line crosses the EMA line
- MACD trending in the same direction
- Linear Regression slope is accelerating above a threshold in the same direction, indicating a strong trend
Profit target(PT) and stop loss(SL) are determined using ATR value, with 2:1 Reward to Risk ratio as default.
Exit signal may be triggered prior to PT or SL trigger when:
- High Low Index SPY Top 40 shows a reversal after overbought or oversold conditions (optional)
- Opposite entry signal is triggered
There are a number of optional settings:
- Turn on/off "option trading", P/L will be calculated using share price only without multiplication factor for trading option contracts
- # of options per trade, default to 10
- Reinvest with profit made
- Trade with trailing SL after PT hit
- Take profit early based on Top 40 overbought/oversold
- Trade 0/1 day expiry. This will signal exit by the end of the day on Mon/Wed/Fri, and only exits 1/2 of positions (if in profit) on Tues/Thurs
- Can reduce the SL level without impacting PT
- No entry between 10:05 - 10:20 (don't ask me why, but statistically it performs better)
Consider donating me some of your profit if you make $$$ hahaha~ ;)
Enjoy~~
