rvi#

rvi(close, high=None, low=None, length=None, scalar=None, refined=None, thirds=None, mamode=None, drift=None, offset=None, **kwargs)[source]#

Relative Volatility Index (RVI)

The Relative Volatility Index (RVI) was created in 1993 and revised in 1995. Instead of adding up price changes like RSI based on price direction, the RVI adds up standard deviations based on price direction.

Sources:

https://www.tradingview.com/wiki/Keltner_Channels_(KC)

Calculation:

Default Inputs:: length=14, scalar=100, refined=None, thirds=None

EMA = Exponential Moving Average STDEV = Standard Deviation

UP = STDEV(src, length) IF src.diff() > 0 ELSE 0 DOWN = STDEV(src, length) IF src.diff() <= 0 ELSE 0

UPSUM = EMA(UP, length) DOWNSUM = EMA(DOWN, length

RVI = scalar * (UPSUM / (UPSUM + DOWNSUM))

Args:

high (pd.Series): Series of ‘high’s low (pd.Series): Series of ‘low’s close (pd.Series): Series of ‘close’s length (int): The short period. Default: 14 scalar (float): A positive float to scale the bands. Default: 100 refined (bool): Use ‘refined’ calculation which is the average of

RVI(high) and RVI(low) instead of RVI(close). Default: False

thirds (bool): Average of high, low and close. Default: False mamode (str): See `help(ta.ma)`. Default: ‘ema’ offset (int): How many periods to offset the result. Default: 0

Kwargs:

fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method

Returns:

pd.DataFrame: lower, basis, upper columns.