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Regression

BTCUSD positions for risk takers - very short termHi. First wanna note that I prefer dynamic analysis and updating projections rather than positions suggestions. As Bertrand Russell (British philosopher) says, nothing is more important than "facts" and in reality facts are evolving. One day you see the trade war is intensifying and the other day they say nice things and some cease fire. Seems positive sentiments are dominant this week. So here I wanna show you two of my medium (inner) to high (outer) risk positions. The green projection is the result of a method I'm testing and it has passed back-test and benchmark periods (vertical lines). Also there's an alternative negative projection that wasn't relevant so far but below red zone it may work (based on fundamentals). more detail about back-test (before vertical red line) and benchmark (red to green) here: Usually I update my ideas more on this two medium/long term charts that both are on target zones: Feel free to share your ideas. Wish you best trades.
COINBASE:BTCUSDLong
by DarkWaveHunter
3
BTCUSD - Strong Chance for Reversal... Break Regression?Theirs a good chance we will reverse soon, most likely around 9.6k - 9.9k zone. This reversal could determine if we are to break our 54-day negative regression and continue our 99-day regression upward. Another possibility is we continue our 99-day triangle, reversing at 9.8k. Most likely not breaking till the end of this month or into early September. A less possible outcome is we break our triangle support line dropping into the 8k zone.
COINBASE:BTCUSD
by stdafx_h
BCDUSD Target 190USD to 650 USDBitcoin diamond suffered badly during the correctionphase of the market. Having an ATH of 148 USD this coin has the highest potential among the top 50 coins. This is not a coin to hold. It should be sold when "enough" profit is made. The charttechnic used is an invention of me . I call it focal point regression method (FPRM). I believe that it is more usefull than traditional chart technics regarding to predigt max/min of the coin price. I also believe that it is also possible to write a sw for painting the regression lines automatically.
BLong
by hkmh
NAV regression channel convergence, 3:1In my experience, charts have a strong attraction to moving towards multiple pattern convergence areas. Why? Because they're zones where a lot of emotion is triggered, aka lots of people will be involved. Market makers need to get you involved. That's why things drop just below your favorite area of the chart, get you emotionally triggered, and _then_ reverse. Or go just past your buy stop limit and then plummet. They go hunting for these trigger areas. And I think the one circled is where NAV is headed next. Perhaps after some more accumulation / low side (sell for you, buy for them) triggering. Could be a week or even three before move happens. And I could be totally wrong, of course.
NLong
by some_guy
LSD+MDMA (Linear-regression Slope Divergence, McGinley Dynamic)Long XRP@8000: Divergence of M cGinley D ynamic MA Oscillator from L inear R egression S lope. On Chart - Left: Past incidents of divergence from regression slope - Right: Detailed set of oscillators used by the MDMA oscillator to calculate final normalized differential oscillator In theory the more differential oscillators diverge the stronger the signal. We also found that combining both would give sharper results under administered conditions. ------------- "In art he, Thoth, was often depicted as a man with the head of an ibis bird"
Long
by duot
selby_exchange - BTCUSD - Selby Bitcoin Price AnalysisNew price/time prediction system of extrapolation based on decoupled EMA's and a hybrid linear regression with sidechain MMAR Price targets $3240, $2919, $2620. $2170, $1627 and $1252, no timing on these however I could see most of these targets hit by April. Selby finding creative patterns in charts on Tradingview! Not advice for investing lol, but I am one to watch ;) Rebellion=Change=Future Humanity Love Inclusion Autonomy Respect Opportunity Truth Veganism Singularity
COINBASE:BTCUSD
by selby_exchange
1
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