Timeseries

Functions

Ad

Ad(df: DataFrame) -> pd.Series

Filter adj close price

Cl

Cl(df: DataFrame) -> pd.Series

Filter close price

Gap

Gap(df: DataFrame) -> pd.Series

Measure Gap up / down in percentage

Hi

Hi(df: DataFrame) -> pd.Series

Filter high price

HiCl

HiCl(df: DataFrame) -> pd.Series

High to Close difference

HiLo

HiLo(df: DataFrame) -> pd.Series

High to Low difference

Lo

Lo(df: DataFrame) -> pd.Series

Filter low price

LoCl

LoCl(df: DataFrame) -> pd.Series

Low to Close difference

Op

Op(df: DataFrame) -> pd.Series

Filter open price

OpCl

OpCl(df: DataFrame) -> pd.Series

Open to Close difference

OpHi

OpHi(df: DataFrame) -> pd.Series

Open to High difference

OpLo

OpLo(df: DataFrame) -> pd.Series

Open to Low difference

Vo

Vo(df: DataFrame) -> pd.Series

Filter volume

allReturn

allReturn(data: DataFrame | Series) -> pd.DataFrame | pd.Series

Calculates annual returns for the specified inputs.

annualReturn

annualReturn(data: DataFrame | Series) -> pd.DataFrame | pd.Series

Calculates annual returns for the specified inputs.

cagr

cagr(returns: Series, intra_period: int = 1, is_log: bool = False) -> float

Compounded Annual Growth Rate (CAGR) is the annual rate of return

Parameters:

Name	Type	Description	Default
returns	Series	price series	required
intra_period	int	period of intra-period returns, defaults to 1 for annual timeframe	1
is_log	bool	defaults to False if its simple return	False

Returns:

Type	Description
float	returns CAGR for the specified period

Notes

CAGR = (Ending Value / Starting Value)^(1/n) - 1

    Ending Value = Begging Value
    Starting Value = Ending Value
    n = period of intra-period returns

calmar

calmar(returns: Series, is_log: bool = False) -> float

Ratio of compounded annual growth rate and maximum drawdown. It is a measure of risk adjusted return. Lower the ratio, the worse the performance on a risk-adjusted basis.

Parameters:

Name	Type	Description	Default
returns	Series	price series	required
is_log	bool	defaults to False if its simple return	False

Returns:

Type	Description
float	returns calmar ratio

Notes

Calmar Ratio = CAGR / MDD

CAGR = (Ending Value / Starting Value)^(1/n) - 1
MDD = (Peak Value - Lowest Value) - Peak Value

dailyReturn

dailyReturn(data: DataFrame | Series) -> pd.DataFrame | pd.Series

Calculates daily returns for the specified inputs.

first

first(series: Series) -> float

Return first value of input series

lag

lag(series: Series, period: int = 1) -> pd.Series

Return previous value of input series

last

last(series: Series) -> float

Return last value of input series

lead

lead(series: Series, period: int = 1) -> pd.Series

Return next value of input series

maxdd

maxdd(returns: Series, is_log: bool = False) -> float

A maximum drawdown (MDD) is an indicator of downside risk and measures the largest percentage drop of the cumulative return over a specified time period.

Parameters:

Name	Type	Description	Default
returns	Series	price series	required
is_log	bool	defaults to False if its simple return	False

Returns:

Type	Description
float	returns MDD for the specified period in percentage

Notes

It observes the maximum loss from a peak to a trough of a portfolio before
a new peak is attained.

MDD = (Peak Value - Lowest Value) / Peak Value

    Peak Value = Highest Value of the cumulative return
    Lowest Value = Lowest Value of the cumulative return

monthlyReturn

monthlyReturn(data: DataFrame | Series) -> pd.DataFrame | pd.Series

Calculates monthly returns for the specified inputs.

periodReturn

periodReturn(data: DataFrame | Series, period: str = None) -> pd.DataFrame | pd.Series

Calculates periodic returns for the specified inputs

Parameters:

Name	Type	Description	Default
data	DataFrame | Series	price data	required
period	str	None, defaults to daily frequency Sepcifiy W, M, Q and Y for weekly, monthly, quarterly and annual frequency	None

Returns:

Type	Description
DataFrame | Series	resampled dataframe series of log returns

quarterlyReturn

quarterlyReturn(data: DataFrame | Series) -> pd.DataFrame | pd.Series

Calculates quarterly returns for the specified inputs.

seriesHi

seriesHi(series: Series) -> pd.DataFrame

Return the high of a given series.

seriesLo

seriesLo(series: Series) -> pd.DataFrame

Return the low of a given series.

sharpe

sharpe(returns: Series, is_log: bool = False, rf: float = 0.0) -> float

Sharpe ratio is the average return earned in excess of the risk free return for every unit of volatility. This is one of the most widely used meausre of risk adjusted return. Sharpe ration greater than 1 is considered to be good.

Parameters:

Name	Type	Description	Default
returns	Series	price series	required
is_log	bool	defaults to False if its simple return	False
rf	float	RiskFree rate of return, defaults to 0.	0.0

Returns:

Type	Description
float	returns sharpe ratio

Notes

Sharpe Ratio = (Expected Return - RiskFree Return) / Volatility of Returns

trend_score

trend_score(df: Union[DataFrame, Series]) -> float

Calculate trend score, a statistical measure to identify the strength of the time series. Range between -1 to +1.

volatility

volatility(returns: Series, intra_period: int = 1) -> float

Annualized volatility is key risk metrics

Parameters:

Name	Type	Description	Default
returns	Series	price series	required
intra_period	int	period of intra-period returns, defaults to 1 for annual timeframe	1

Returns:

Type	Description
float	returns annualized volatility

Notes

Annualization is achieved by multiplying volatility with square root of
a) 252 to annualize daily volatility
b) 52 to annualize weekly volatility
c) 12 to annualize monthly volatility

weeklyReturn

weeklyReturn(data: DataFrame | Series) -> pd.DataFrame | pd.Series

Calculates weekly returns for the specified inputs.