Directional Movement Index + Fisher Price Action With LabelsDIRECTIONAL MOVEMENT INDEX + FISHER PRICE ACTION WITH LABELS
Directional Movement Index shows buy and sell pressure.
Fisher transform shows price action trending bullish or bearish.
Caution dots notify you of conflicting trends.
***HOW TO USE***
The top lines are the fisher transform showing you the price action trend.
The bottom lines filled with color shows the DMI directional movement index.
The yellow dots at the bottom tell you if these two indicators are currently giving conflicting signals.
DMI
If the green line is above the red line and the background is colored green, there is more market buying than selling.
If the red line is above the green line and the background is colored red, there is more market selling than buying.
FISHER TRANSFORM
If the lines are painted green, the price action is trending up.
If the lines are painted red, the price action is trending down.
CAUTION DOTS
If a yellow dot shows up at the bottom of the chart, it is notifying you that the DMI and Fisher Transform are currently giving opposite signals…. so use caution.
***BULLISH/BEARISH LABEL***
There is also a label on the right side that tells you whether there is more buying or selling. This table updates in real time and changes colors so you can get an easy, quick interpretation of the current buy/sell pressure without having to look at the indicator data so you can make faster decisions on whether to enter or exit a trade.
Green means more market buying than selling.
Red means more market selling than buying.
Blue means an equal amount of market buying and selling.
If buying pressure is bullish but below the 20 level, a second label will show up in purple letting you know there is weak buying pressure so use caution.
If selling pressure is bearish but below the 20 level, a second label will show up in purple letting you know there is weak selling pressure so use caution.
There is a third label showing the current trend of the fisher transform. Green means bullish price action. Red means bearish price action.
The fourth label is orange and only shows up when the DMI and Fisher Transform are currently giving opposite signals, so make sure you use caution during those times.
***MARKETS***
This indicator can be used as a signal on all markets, including stocks, crypto, futures and forex.
***TIMEFRAMES***
This directional movement index + fisher transform indicator can be used on all timeframes.
***TIPS***
Try using numerous indicators of ours on your chart so you can instantly see the bullish or bearish trend of multiple indicators in real time without having to analyze the data. Some of our favorites are our Auto Fibonacci, Volume Profile, Momentum, Auto Support And Resistance and Money Flow Index in combination with this Directional Movement Index + Fisher Transform. They all have real time Bullish and Bearish labels as well so you can immediately understand each indicator's trend.
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Multifactor Inverse Fisher Strategy (ps4)Best for higher time frames - 30m, 1H, 2H, 3H, 4H, D this strategy uses several factors that are pushed through an Inverse Fisher Transform (IFT). The higher the TF, the better the performance, up to 98%, but the number of deals tends to drop). Middle time frames (5m, 15m) look viable with Scaled Price (Scaled %P) and MFI factors. The factor list can be extended to include cci, stoch, rsi_stoch, emo, macd, cog, dpo, roc, accdist, cctbb, mom, awesome, tva, etc. Some of them need to be rescaled to a 0..100 interval. The IFT produces a value in the -1..1 interval (see: www.mesasoftware.com). This indicator does NOT repaint. Strategy
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Fisher Least Squares Moving AverageIntroduction
I already estimated the least-squares moving average numerous times, one of the most elegant ways was by rescaling a linear function to the price by using the z-score, today i will propose a new smoother (FLSMA) based on the line rescaling approach and the inverse fisher transform of a scaled moving average error with the goal to provide an alternative least-squares smoother, the indicator won't use the correlation coefficient and will try to adresses problems such as overshoots and lag reduction.
Line Rescaling Method
For those who did not see my least squares moving average estimation using the line rescaling method here is a resume, we want to fit a polynomial function of degree 1 to the price by reducing the sum of squares between the price and the filter, squares is a term meaning the squared difference between the price and its estimation. The line rescaling technique work as follow :
1 - get the z-score of a line.
2 - multiply this z-score with the correlation between the price and a line.
3 - multiply the precedent result with the standard deviation of the price, then sum that to a simple moving average.
This process is shorter than the classical least-squares moving average method.
Z-Score Derivation And The Inverse Fisher Transform
The FLSMA will use a similar approach to the line rescaling technique but instead of using the correlation during step 2 we will use an alternative calculated from the error between the estimate and the price.
In order to do so we must use the inverse fisher transform, the inverse fisher transform can take a z-score and scale it in a range of (1,-1), it is possible to estimate the correlation with it. First lets create our modified z-score in the form of : Z = ma((y - Y)/e) where y is the price, Y our output estimate and e the moving average absolute error between the price and Y and lets call it scaled smoothed error , then apply the inverse fisher transform : r = IFT(Z) = tanh(Z) , we then multiply the z-score of the line with it.
Performance
The FLSMA greatly reduce the overshoots, this mean that the maximas of abs(r) are lower than the maxima's of the absolute correlation, such case is not "bad" but we can see that the filter is not closer to the price than the LSMA during trending periods, we can assume the filter don't reduce least-squares as well as the LSMA.
The image above is the running mean of the absolute error of each the FLSMA (in red) and the LSMA (in blue), we could fix this problem by multiplying the smooth scaled error by p where p can be any number, for example :
z = sma(src - nz(b ,src),length)/e * p where p = 2
In red the FLSMA and in blue the FLSMA with p = 2 , the greater p is the less lag the FLSMA will have.
Conclusion
It could be possible to get better results than the LSMA with such design, the presented indicator use its own correlation replacement but it is possible to use anything in a range of (1,-1) to multiply the line z-score. Although the proposed filter only reduce overshoots without keeping the accuracy of the LSMA i believe the code can be useful for others.
Thanks for reading.
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Inverse Fisher Fast Z-scoreIntroduction
The fast z-score is a modification of the classic z-score that allow for smoother and faster results by using two least squares moving averages, however oscillators of this kind can be hard to read and modifying its shape to allow a better interpretation can be an interesting thing to do.
The Indicator
I already talked about the fisher transform, this statistical transform is originally applied to the correlation coefficient, the normal transform allow to get a result similar to a smooth z-score if applied to the correlation coefficient, the inverse transform allow to take the z-score and rescale it in a range of (1,-1), therefore the inverse fisher transform of the fast z-score can rescale it in a range of (1,-1).
inverse = (exp(k*fz) - 1)/(exp(k*fz) + 1)
Here k will control the squareness of the output, an higher k will return heavy side step shapes while a lower k will preserve the smoothness of the output.
Conclusion
The fisher transform sure is useful to kinda filter visual information, it also allow to draw levels since the rescaling is in a specific range, i encourage you to use it.
Notes
During those almost 2 weeks i was even lazier and sadder than ever before, so i think its no use to leave, i also have papers to publish and i need tv for that.
Thanks for reading ! 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.
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