Indicator
VWMA/SMA Breakout and Divergence DetectorThis indicator compares four different values :
-Fast Simple Moving Average(SMA)
-Fast Volume Weighted Moving Average(VWMA)
-Slow SMA
-Slow VWMA
Comparing SMA's and VWMA's of the same length is a common trading tactic. Since volume is not taken into consideration when calculating Simple Moving Averages, we can gain valuable insights from the difference between the two lines.
Since volume should be increasing along with an upwards price movement, the VWMA should be greater than the SMA during a volume-supported uptrend. Thus, we can confirm an uptrend if the VWMA remains greater than the SMA. If the VWMA falls under the SMA in the midst of an upwards price movement, however, that indicates bearish divergence. The opposite is true for downtrends. If price is decreasing and volume is decreasing at the same time (as it should), then we can confirm the downtrend.
Interpreting the Graph:
If the slow SMA is greater than the slow VWMA, then the area representing the difference between the two lines is filled in red. If the slow VWMA is greater than the slow SMA, however, the area between the two is filled green.
If the fast SMA is greater than the fast VWMA, then the area between the two dotted lines is filled in red. On the other hand, the area will be filled green if the fast VWMA is greater than the slow SMA.
In addition to spotting divergences and confirming trends, the four lines can be used to spot breakouts. Typically, a VWMA crossover will precede the SMA crossover. When the fast VWMA crosses over the slow VWMA and then a SMA crossover follows shortly after, then it is a hint that a bullish trend is beginning to form.
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Autonomous Recursive Moving AverageIntroduction
People often ask me what is my best indicators, i can't really respond to this question with a straight answer but i would say you to check this indicator. The Autonomous Recursive Moving Average (ARMA) is an adaptive moving average that try to minimize the sum of squares thanks to a ternary operator, this choice can seem surprising since most of the adaptive moving averages adapt to a smoothing variable thanks to exponential averaging, but there are lot of downsides to this method, i really wanted to have a flat filter during flat markets and this is what i achieved.
The Indicator
length control the amount of smoothing during trending periods, gamma is the trend sensitivity threshold, higher values of gamma will make an overall flat filter, adjust gamma to skip ranging markets.
gamma = 2, we can adjust to 3 while preserving smoothing reactivity with trading periods.
gamma = 3
low length and higher gamma create more boxy result, the filter add overshoots directly in the output, its unfortunate.
The Zero-Lag option can reduce the lag as well as getting additional flat results without changing gamma.
Conclusion
The indicator need work, but i can't leave without publishing it, the overshoots are a big problems, changing sma for another stable filter can help. I hope you find an use to it, i really like this indicator.
Thanks for reading Indicator
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Smoothed Delta's Ratio OscillatorIntroduction
Scaled and smoothed oscillators can provide easy to read/use information regarding price, therefore i will introduce a new oscillator who create smooth results and use a fast and practical scaling method. In order to allow for even more smoothness the option to smooth the input with a lsma has been added.
Scaling Using Changes
In this indicator scaling in a range of (1,-1) is achieved through the following calculations :
a = sma(abs(change(src,length)),length)
b = change(sma(src,length),length)
c = b/a
where src is our input. The two elements a and b are quite similar, a smooth the absolute change of the input over length period while b calculate the change of the smoothed input over length period, this make a > b and able us to perform scaling in a range of (1,-1).
The Indicator Parameters
Length control the differencing/smoothing period of the indicator, greater values create smoother and less volatile results, this mean that the oscillator will tend to be equal to 1 or -1 in a longer period of time if length is high. The smooth option allow for even smoother results by enabling the input to be smoothed by a lsma of length period.
Conclusions
I presented a smooth oscillator using a new rescaling technique. Parameters can be separated to provide different results, i believe the code is simple enough for everyone to modify it in order to provide interesting creations.
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