Smooth

Hamming Windowed Volume Weighted Moving Average Applying a window to the filter weights provides sometimes extra control over the characteristics of the filter.In this script an hamming window is applied to the volume before being used as a weight.In general this process smooth the frequency response of a filter. Lets compare the classic vwma with hamming windowed vwma Something i noticed is that windowed filters depending on their period ( high ones in general ) tend to make less bad crosses with the price ( at least with the hamming window ) Here are some data regarding number of crosses with period 50 with the hamming vwma in orange and the classic vwma in purple Feel free to use the hamming window when using weighted filter.

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Retrospective Candlestick Chart When i was in Japan with some traders colleagues we talked about traditional charting tools from this country and how they changed the way we look at our charts today. Then suddenly one of the japanese traders i have met earlier said "Why not making another charting tool ? Smoother than Heikin-Ashi and including all the information a trader may need but easier to interpret". So i had the idea of averaging the input and the output of the respective close / open / high and low price using a recursive exponential window functions, each values will be closer to their true value if they are volatile, if they are not then those values will look smoother, the length input represents the reactivity of the candles, high values represents smoother results but less reactive.The goal of those candles is to make all the information easier to interpret by a trader. 500 input length , the price look smoother, supports and resistances are easier to make. The interpretation of highs and lows are important, the Retrospective Candlestick Chart save you time by showing only huge movements.

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Logistic Correlation Logistic Correlation is a correlation oscillator using a logistic function. A Logistic Function is a Sigmoid Function who stabilize the variance of data.The logistic function have the same function as the inverse fisher transform but with an advantage over it, the k constant can control the steepness of the curve, lowers k's will preserve the original form of the data while highers one will transform it into a more square shaped form. 10 k 20 k

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Double Exponential Smoothing Single Exponential Smoothing ( ema ) does not excel in following the data when there is a trend. This situation can be improved by the introduction of a second equation with a second constant gamma . The gamma constant cant be lower than 0 and cant be greater than 1, higher values of gamma create less lag while preserving smoothness.Higher values of length must be followed by higher values of gamma in order to keep the lag low. The first smoothing part consist of a classic ema but we add s-s1 to the previous smoothed value, this will help decrease lag.The second smoothing part then updates the trend, which is expressed as the difference between the last two values.

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KT_Smooth_Stochastic I normally don't publish my indicators. However, I couldn't find a smoothed stochastic on PulseWire officially or unofficially. This is a standard implementation of a smoothed Fast Stochastic where %K and %D are calculated and then smoothed by n periods. This helps to reduce chop and gives better extreme signals. I have defaulted the indicator to use commonly used settings where %K is over 14 periods, %D is over 7 period, and the smoothing factor is 3 periods. I have also defaulted the extreme lines to an upper band of 80, mid band of 50, and lower band of 20. However, my favorite settings are %K = 10, %D = 10, Smooth = 3, upper band = 75, mid band = 50, and lower band = 25.

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