Markets oscillate from periods of low volatility to high volatility
and back. The author`s research indicates that after periods of
extremely low volatility, volatility tends to increase and price
may move sharply. This increase in volatility tends to correlate
with the beginning of short- to intermediate-term moves in price.
They have found that we can identify which markets are about to make
such a move by measuring the historical volatility and the application
of pattern recognition.
The indicator is calculating as the standard deviation of day-to-day
logarithmic closing price changes expressed as an annualized percentage.
and back. The author`s research indicates that after periods of
extremely low volatility, volatility tends to increase and price
may move sharply. This increase in volatility tends to correlate
with the beginning of short- to intermediate-term moves in price.
They have found that we can identify which markets are about to make
such a move by measuring the historical volatility and the application
of pattern recognition.
The indicator is calculating as the standard deviation of day-to-day
logarithmic closing price changes expressed as an annualized percentage.
//////////////////////////////////////////////////////////// // Copyright by HPotter v1.0 16/07/2014 // Markets oscillate from periods of low volatility to high volatility // and back. The author`s research indicates that after periods of // extremely low volatility, volatility tends to increase and price // may move sharply. This increase in volatility tends to correlate // with the beginning of short- to intermediate-term moves in price. // They have found that we can identify which markets are about to make // such a move by measuring the historical volatility and the application // of pattern recognition. // The indicator is calculating as the standard deviation of day-to-day // logarithmic closing price changes expressed as an annualized percentage. //////////////////////////////////////////////////////////// study(title="Historical Volatility") LookBack = input(20, minval=1) Annual = input(365, minval=1) hline(0, color=purple, linestyle=dashed) xPrice = log(close / close[1]) nPer = iff(isintraday or isdaily, 1, 7) xPriceAvg = sma(xPrice, LookBack) xStdDev = stdev(xPrice, LookBack) HVol = (xStdDev * sqrt(Annual / nPer)) * 100 plot(HVol, color=blue, title="Historical Volatility")