ck - Crypto Correlation IndicatorA simple Correlation Indicator initially configured for Crypto Trader use (but other markets can use this too).
It plots the correlation between the current chart (say BTCUSD ) versus 4 user-definable indices, currency pairs, stocks etc.
By default, the indicator is preconfigured for:
GOLD (Oz/$),
Dow Jones Index (DJI),
Standard & Poor 500 Index (SPX) ,
Dollar Index ( DXY )
You can set the period (currently 1D resolution) in the "Period" box in the settings, valid inputs are:
minutes (number), days (1D, 2D, 3D etc), weeks (1W, 2W etc), months (1M, 2M etc)
Length is the lagging period/smoothing applied - default is 14
When changing comparison instruments/tickers, you may find it useful to prefix the exchange with the instrument's ticker, for example:
Binance:BTCUSDT, NYSE:GOOG etc
*** Idea originally from the brilliant Backtest Rookies - backtest-rookies.com *** Indicator
Inverse Fisher Z-Score Introduction
The inverse fisher transform or hyperbolic tangent function is a type os sigmoid function (sometime called squashing function) , those types of functions can rescale a result in a certain range and are widely used in artificial intelligence. More in depth the fisher transform can make the correlation coefficient of a time series normally distributed, in practice if you apply the fisher transform to the correlation coefficient between a time series and a linear function you will end up with an estimate of the z-score of the time series. The inverse transform however can do the contrary, it can take the z-score and transform it into a rough estimate of the correlation coefficient, if your z-score is not smooth then you will have a non-smooth estimate of the correlation coefficient, that's quite nice no ?
The Indicator
The inverse fisher transform of the z-score will produce results in a range of 1/-1, here however i will rescale in a range of 100/0 because its a standard range for oscillators in technical analysis. Values over 80 indicate an overbought market, under 20 an oversold market. The smooth option in the indicator settings will make the indicator use a linearly weighted moving average as input thus resulting in a smoother result.
The indicator with smooth option.
Conclusion
I presented a new oscillator indicator who use the inverse fisher transform of a z-score. Using the fisher transform and its inverse can give a new shape to your indicator, make sure to control the scale of your indicator before applying the fisher transform, the inverse transform should be applied to values in range of 1/-1 but you can use higher limits (2/-2,3/-3...) , however remember that higher limits will approximate an heavy side step function (square shape) . I hope you will find an use to this indicator.
Thanks for reading ! Indicator
Function To Candles - Another way to see indicatorsIntroduction
There are different and better way's to see price data, a candlestick chart is one of the best way to see the price since you have access to the open/high/low/close information, this is really efficient and can allow for naked non parametric trading strategies (candlesticks patterns) . But what about making candles out of indicators ? There are tons of studies about candlesticks patterns in price data but none (?) about candlestick patterns using indicator data, therefore i made this script in order to show candles from various indicators, i also made an heikin-ashi mode.
Rsi To Candles
All the indicators are use the open/high/low/close price as input in order to return candles. length control the indicator period.
Stochastic To Candles
The stochastic oscillator is restrained in a range of 0/100, therefore when equal to 0 or 100 the candles can be flat.
Rate Of Change To Candles
The rate of change don't distort price as heavily as other indicators since its based on differencing.
Center Of Gravity To Candles
The center of gravity (cog) is defined from pulsewire as "an indicator based on statistics and the Fibonacci golden ratio", its not an indicator i'am familiar with and i don't know if its the same proposed by Elhers. The candles are smooth, high length can flatten the candles heavily making them hard to see.
Correlation Oscillator
In a range of -1/1 this indicator is quite smooth and can also flatten candles.
Patterns And Heikin-Ashi
There are tons of patterns that can be generated from candlesticks, they can be applied to this indicator as well.
The indicator can show an heikin-ashi mode, heikin-ashi candlestick use averaging to plot candles, this is why they appear smoother, some signals generated from heikin-ashi candles are :
Bullish body with no lower shadows = Strong Uptrend
Bearish body with no higher shadows = Strong Downtrend
High range and small body = Indecision/Risk of reversal
Conclusion
I made an indicator able to draw candles from other indicators, those candles contain various information that can generate decision from patterns. I hope you find a use to it, if its the case share your findings with me, maybe that you will even be able find a new candlestick pattern :)
Thanks for reading !
Indicator
Japanese Correlation CoefficientIntroduction
This indicator was asked and named by a trading meetup participant in Sevilla. The original question was "How to estimate the correlation between the price and a line as easy as possible", a question who got little attention. I previously proposed a correlation estimate using a modification of the standard score (see at the end of the post) for the estimation of a Savitzky-Golay moving average (LSMA) of order 1, however something faster could maybe be done and this is why i accepted the challenge.
Japanese Correlation
Correlation is defined as the linear relationship between two variables x and y , if x and y follow the same direction then the correlation increase else decrease. The correlation coefficient is always equal or below 1 and equal or above -1, it also have to be taken into account that this coefficient is quite smooth. Smoothing is not a problem, scaling however require more attention, high price > closing price > low price, therefore scaling can be done. First we smooth the closing/high/low price with a simple moving average of period p/2 , then we take the difference of the smoothed close with the smoothed close p/2 bars back, this result is then divided by the difference between the highest smoothed high's with the lowest smoothed low's over period p/2 .
Since we use information provided by candlesticks (close/high/low) i have been asked to publish this estimator with the name Japanese correlation coefficient , this name don't imply the use of data from Japanese markets, "Japanese" is used because of the candlestick method coming from Japan.
Comparison
I compare this estimation with the correlation coefficient provided in pinescript by the correlation function.
The estimation in orange with the original correlation coefficient using n as independent variable in blue with both length = 50.
comparison with length = 200.
Conclusion
I have shown that it is possible to roughly estimate the correlation coefficient between price and a linear function by using different price information. Correlation can be further estimated by using homogeneous bridge OHLC volatility estimators thus making able the use of different independent variables. I really hope you like this indicator and thanks to the meetup participant asking the question, i had a lot of fun making the indicator.
An alternative method
Indicator
Light LSMAEstimating the LSMA Without Classics Parameters
I already mentioned various methods in order to estimate the LSMA in the idea i published. The parameter who still appeared on both the previous estimation and the classic LSMA was the sample correlation coefficient. This indicator will use an estimate of the correlation coefficient using the standard score thus providing a totally different approach in the estimation of the LSMA. My motivation for such indicator was to provide a different way to estimate a LSMA.
Standardization
The standard score is a statistical tool used to measure at how many standard deviations o a data point is bellow or above its mean. It can also be used to rescale variables, this conversion process is called standardizing or normalizing and it will be the basis of our estimation.
Calculation : (x - x̄)/o where x̄ is the moving average of x and o the standard deviation.
Estimating the Correlation Coefficient
We will use standardization to estimate the correlation coefficient r . 1 > r > -1 so in (y - x̄)/o we want to find y such that y is always above or below 1 standard deviation of x̄ , i had for first idea to pass the price through a band-stop filter but i found it was better to just use a moving average of period/2 .
Estimating the LSMA
We finally rescale a line through the price like mentioned in my previous idea, for that we standardize a line and we multiply the result by our correlation estimation, next we multiply the previous calculation by the price standard deviation, then we sum this calculation to the price moving average.
Comparison of our estimate in white with a LSMA in red with both period 50 :
Working With Different Independents Variables
Here the independent variable is a line n (which represent the number of data point and thus create a straight line) but a classic LSMA can work with other independent variables, for exemple if a LSMA use the volume as independent variable we need to change our correlation estimate with (ȳ - x̄)/ô where ȳ is the moving average of period length/2 of y, y is equal to : change(close,length)*change(volume,length) , x̄ is the moving average of y of period length , and ô is the standard deviation of y. This is quite rudimentary and if our goal is to provide a easier way to calculate correlation then the product-moment correlation coefficient would be more adapted (but less reactive than the sample correlation) .
Conclusion
I showed a way to estimate the correlation coefficient, of course some tweaking could provide a better estimate but i find the result still quite close to the LSMA.
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