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$SLP is turning resistence into support on daily 1D frameNASDAQ:SLP Smooth Love Potion is holding nicely over several suports It is holding above 200MA 1D, above 200EMA 1D and above long term fib level And it is retesting as a support the anterior blue resistence! Bollinger Band is narrowing (gray) and there is also strong support from Visible Range Volume past trades (horizontal bars on right) As the target of the down wedge has already been achieved (pink arrow), it may be forming a kind of a triangle on these last 20 days
by CCCLopes
1
Stock PersonalityIt is super important to make a NOTE of what the personality of the stock looks like. You should choose a stock with a smooth personality. Avoid stocks with schizophrenic noisy personalities. There is someone who wants to get in or get out. This someone has a plan and trades the stock according to his plan. This someone doesn't change his plan every day, since this will be time and effort-consuming and confusing even to himself. People want to be consistent. So if this someone decides I will make my life easy and sell sell sell and buy buy buy which makes the stock looks smooth. This is someone you will have a much better trading experience with. THE GOAL: is to search where you can make your money easily - egoless. Follow to tune in to more of this material.
NASDAQ:BDTXEducation
by ZoharCho
2
Approximating A Least Square Moving Average In PineLeast Squares Moving Average, Running Least Squares, Regression Line or even Running Line, this method is among the most popular ones in statistics and technical analysis. The LSMA is extremely useful, it approximate the price pretty well and can be considered as one of the best low-lagging filters out there. Knowing how this filter is made can be really interesting. May the methods i share below inspire you to create great indicators or start coding in pine :) A Least Squares Moving Average is defined by Tradingview as : A line that best fits the prices specified over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where length is the y argument, offset is the z argument, intercept and slope are the values calculated with the least squares method on source series (x argument). Alright, we wont use the offset parameter for our approximations, so how to calculate a least squares moving average ? If you find the mathematical formula of it you will certainly ask yourself "what are all of those maths" . But its ok, in the Pinescript you can just use the linreg() function, or you could calculate it like that : slope = correlation(close,n,length) * (stdev(close,length)/stdev(n,length)) intercept = sma(close,length) - slope*sma(n,length) linreg = slope*n + intercept Ok, but can we use different estimation methods ? Certainly, the key of the LSMA is only the correlation coefficient after all, all the other parameters can be estimated. Standard Score Or Rescaling A Line To The Price Rescaling a line to the price is easy to do, it will give a similar result as the LSMA but it is faster to write, here the code : A = (n - sma(n,length))/stdev(n,length) * correlation(close,n,length) B = sma(close,length) + A*stdev(close,length) Easier no ? We first standardized a line (n) and multiplied it by its correlation with the price, our first parameter A is dimensionless . Then we rescaled the result to the price by multiplying our parameter with the price standard deviation and summing this result to the price moving average. here the difference between our method and the classic LSMA of both period 100 If you put both together you wont see any difference. Overshoots can be reduced by modifying the standard deviation size. Correlation Rescaling The correlation coefficient is the core of a LSMA, if we rescale it we can approximate a LSMA, here the code : a = (correlation(close,n,length) + 1)/2 b = sma(close,length) + stdev(close,length)*1.7 c = sma(close,length) - stdev(close,length)*1.7 k = c + a*(b-c) The correlation coefficient oscillate in a range of 1/-1, we first scale it in a range of 1/0. Then you may have recognized the b and c formulas, they are the one used in bollinger bands, the standard deviation is multiplied by 1.7 because it was the number who best approximated a LSMA, but it could be any number defined by the user, something interesting is that this method to can fix overshoots in a classic LSMA using lower multiplier. Since our correlation is in a range of 1/0 we can rescale it to the price thanks to the method used in k. In red our method, in blue the LSMA of both period 100. Here the standard deviation is not multiplied by a number, this result in less overshoot. In order to have even more manipulation over the LSMA i will try to estimate the correlation coefficient the best i can :) So here you go, i hope you will find a use for it.
Education
by alexgrover
6
Triangular Moving Average (TRIMA) IndicatorThe TRIMA is simply the SMA of the SMA -- a double-smoothed simple moving average. The end effect of the double smoothing is that greater weight is placed on values near the middle of the lookback period. It therefore reacts relatively slowly to price changes compared to most moving averages. But why would I want more lag? One potential use of this moving average that I've found is that it can allow price to run for a bit after crossing the TRIMA before catching up and creating an opposing signal. It therefore creates the chance for the price to "run its course" so to speak, which can make whipsaw signals less common.
NYMEX:CL1!
by JacobAmos
1