Timeseries Package

Algebra

abs_(x)	Absolute value of each element in series
add(x, y[, method])	Add two series or scalars
and_(*series)	Logical "and" of two or more boolean series.
ceil(x[, value])	Cap series at maximum value
divide(x, y[, method])	Divide two series or scalars
exp(x)	Exponential of series
filter_(x[, operator, value])	Removes values where comparison with the operator and value combination results in true, defaults to removing missing values from the series
filter_dates(x[, operator, dates])	Removes dates where comparison with the operator and dates combination results in true, defaults to removing missing values from the series
floor(x[, value])	Floor series at minimum value
floordiv(x, y[, method])	Floor divide two series or scalars
if_(flags, x, y)	Returns a series s.
log(x)	Natural logarithm of series
multiply(x, y[, method])	Multiply two series or scalars
not_(series)	Logical negation of a single boolean series.
or_(*series)	Logical "or" of two or more boolean series.
power(x[, y])	Raise each element in series to power
sqrt(x)	Square root of (a) each element in a series or (b) a real number
subtract(x, y[, method])	Add two series or scalars
weighted_sum(series, weights)	Calculate a weighted sum.

Analysis

diff(x[, obs])	Diff observations with given lag
first(x)	First value of series
last(x)	Last value of series (as a series)
last_value(x)	Last value of series (as a scalar)
count(x)	Count observations in series
lag(x[, obs, mode])	Lag timeseries by a number of observations or a relative date.

Backtesting

basket_series(series[, weights, costs, ...])

Calculates a basket return series.

Basket(stocks[, weights, rebal_freq])

Construct a basket of stocks

Date / Time

align(x, y[, method])	Align dates of two series or scalars
interpolate(x[, dates, method])	Interpolate over specified dates or times
value(x, date[, method])	Value at specified date or time
day(x)	Day of each value in series
weekday(x)	Weekday of each value in series
month(x)	Month of each value in series
year(x)	Year of each value in series
quarter(x)	Quarter of each value in series
date_range(x, start_date, end_date[, ...])	Create a time series from a (sub-)range of dates in an existing time series.
prepend(x)	Prepend data series
union(x)	Fill in missing dates or times of one series with another
bucketize(series, aggregate_function, period)	Bucketize a series and apply aggregate function to each bucket

Window([w, r])

Create a Window with size and ramp up to use.

Econometrics

annualize(x)	Annualize series based on sample observation frequency
beta(x, b[, w, prices])	Rolling beta of price series and benchmark
change(x)	Arithmetic series normalization
correlation(x, y[, w, type_])	Rolling correlation of two price series
excess_returns_(price_series[, currency])	Calculate excess returns
index(x[, initial])	Geometric series normalization
max_drawdown(x[, w])	Compute the maximum peak to trough drawdown over a rolling window as a ratio.
prices(series[, initial, type])	Calculate price levels from returns series
returns(series[, obs, type])	Calculate returns from price series
sharpe_ratio(series[, currency, w, ...])	Calculate Sharpe ratio
volatility(x[, w, returns_type])	Realized volatility of price series

Statistics

cov(x, y[, w])	Rolling co-variance of series over given window
exponential_std(x[, beta])	Exponentially weighted standard deviation
generate_series(length[, direction])	Generate sample timeseries
max_(x[, w])	Maximum value of series over given window
mean(x[, w])	Arithmetic mean of series over given window
median(x[, w])	Median value of series over given window
min_(x[, w])	Minimum value of series over given window
mode(x[, w])	Most common value in series over given window
percentile(x, n[, w])	Returns the nth percentile of a series.
percentiles(x[, y, w])	Rolling percentiles over given window
product(x[, w])	Rolling product of series over given window
range_(x[, w])	Range of series over given window
std(x[, w])	Rolling standard deviation of series over given window
sum_(x[, w])	Rolling sum of series over given window
var(x[, w])	Rolling variance of series over given window
winsorize(x[, limit, w])	Limit extreme values in series
zscores(x[, w])	Rolling z-scores over a given window

LinearRegression(X, y[, fit_intercept])	Fit an Ordinary least squares (OLS) linear regression model.
RollingLinearRegression(X, y, w[, fit_intercept])	Fit a rolling ordinary least squares (OLS) linear regression model.
SIRModel([beta, gamma, s, i, r, n, fit, ...])	SIR Compartmental model for transmission of infectious disease
SEIRModel([beta, gamma, sigma, s, e, i, r, ...])	SEIR Compartmental model for transmission of infectious disease

Technical Analysis

bollinger_bands(x[, w, k])	Bollinger bands with given window and width
moving_average(x[, w])	Moving average over specified window
exponential_moving_average(x[, beta])	Exponentially weighted moving average
exponential_volatility(x[, beta])	Exponentially weighted volatility
exponential_spread_volatility(x[, beta])	Exponentially weighted spread volatility
smoothed_moving_average(x[, w])	Smoothed moving average over specified window
relative_strength_index(x[, w])	Relative Strength Index
macd(x[, m, n, s])	Moving average convergence divergence (MACD).