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Package for simple financial time series analysis.

Project description

OpenSeries

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OpenSeries is a project with tools to perform timeseries analysis on a single asset or a group of assets. It is solely made for daily or less frequent data.

Changelog

To install:

pip install openseries

Import statements

from openseries.frame import OpenFrame
from openseries.series import OpenTimeSeries, TimeSerie

To construct an OpenTimeSeries object from raw data in a TypedDict:

data = TimeSerie(
    _id="",
    currency="SEK",
    dates=["2020-09-03", "2020-09-04", "2020-09-07", "2020-09-08", "2020-09-09"],
    instrumentId="",
    local_ccy=True,
    name="Timeseries",
    values=[114.9965, 114.8355, 114.8694, 115.1131, 114.8643],
    valuetype="Price(Close)",
)

Instantiate OpenTimeSeries object:

series = OpenTimeSeries(data)

To construct using the class method designed to get a NAV timeseries for a Captor Fund:

capirisc = "SE0009807308"
scillagc = "SE0011670843"
bonds = OpenTimeSeries.from_open_nav(isin=capirisc)
equities = OpenTimeSeries.from_open_nav(isin=scillagc)

To compare assets an OpenFrame is constructed as below.

basket = OpenFrame([bonds, equities])

The data cleaning helper methods can be chained like this:

basket.trunc_frame().value_nan_handle().to_cumret()

A new portfolio timeseries can be constructed from an OpenFrame like this:

basket.weights = [0.6, 0.4]
portfolio = OpenTimeSeries.from_df(basket.make_portfolio("porfolio"))
basket.add_timeseries(portfolio)

To print return and volatility:

data = basket.all_properties(properties=["arithmetic_ret", "vol"]).T
data = data.applymap(lambda x: f"{x:.2%}")
print(data)

Finally, plotting is simple. This will plot the timeseries in a browser window:

basket.plot_series(tick_fmt=".2%")

To make use of some tools available in the Pandas library the OpenTimeSeries and OpenFrame classes have an attribute tsdf which is a DataFrame constructed from the raw data in the lists dates and values.

Table of Contents

These are the files / modules described.

Module Description
series.py Defines the class OpenTimeSeries for managing and analyzing a single timeseries. The module also defines a function timeseries_chain that can be used to chain two timeseries objects together.
frame.py Defines the class OpenFrame for managing a group of timeseries, and e.g. calculate a portfolio timeseries from a rebalancing strategy between timeseries.
frenkla_open_api_sdk.py A Python SDK to interact with the Frenkla Open API.
datefixer.py A module with date utilities.
openseries.json The jsonschema of the OpenTimeSeries class.
plotly_layouts.json A module setting Plotly defaults used in the plot_series methods.
plotly_captor_logo.json A module with a link to the Captor logo used in the plot_series methods.
risk.py Module with methods used to calculate VaR, CVaR and drawdowns.
sim_price.py Module to simulate OpenTimeSeries from different stochastic processes.
stoch_processes.py Module to generate stochastic processes used in the sim_price.py module.
sweden_holidays.py Module that defines a Swedish business calendar.

Below are the class methods used to construct an OpenTimeSeries object.

Method Applies to Description
from_open_api OpenTimeSeries Class method to create an OpenTimeSeries object from a Frenkla API endpoint.
from_open_nav OpenTimeSeries Class method to create an OpenTimeSeries object from a Frenkla API endpoint.
from_open_fundinfo OpenTimeSeries Class method to create an OpenTimeSeries object from a Frenkla API endpoint.
from_df OpenTimeSeries Class method to create an OpenTimeSeries object from a pandas.DataFrame column.
from_frame OpenTimeSeries Class method to create a new OpenTimeSeries object from a series within an OpenFrame.
from_fixed_rate OpenTimeSeries Class method to create an OpenTimeSeries object from a fixed rate, number of days and an end date.
from_deepcopy OpenTimeSeries, OpenFrame Creates a copy of an OpenTimeSeries object.

In this table are the non-numeric or "helper" properties that apply only to the OpenTimeSeries class.

Attribute type Applies to Description
_id str OpenTimeSeries Frenkla database identifier for the timeseries.
instrumentId str OpenTimeSeries Frenkla database identifier for the instrument associated with the timeseries.
dates List[str] OpenTimeSeries Dates of the timeseries. Not edited by any method to allow reversion to original.
values List[float] OpenTimeSeries Values of the timeseries. Not edited by any method to allow reversion to original.
currency str OpenTimeSeries Currency of the timeseries. Only used if conversion/hedging methods are added.
domestic str OpenTimeSeries Domestic currency of the user / investor. Only used if conversion/hedging methods are added.
local_ccy bool OpenTimeSeries Indicates if series should be in its local currency or the domestic currency of the user. Only used if conversion/hedging methods are added.
name str OpenTimeSeries An identifier field.
isin str OpenTimeSeries ISIN code of the associated instrument. If any.
label str OpenTimeSeries Field used in outputs. Derived from name as default.
sweden SwedenHolidayCalendar OpenTimeSeries A calendar object used to generate business days.
valuetype str OpenTimeSeries Field identifies a series of values, "Price(Close)", or a series of returns, "Return(Total)".

In this table are the non-numeric or "helper" properties that apply only to the OpenFrame class.

Attribute type Applies to Description
constituents List[OpenTimeSeries] OpenFrame A list of the OpenTimeSeries that make up an OpenFrame.
columns_lvl_zero list OpenFrame A list of the level zero column names in the OpenFrame pandas.DataFrame.
columns_lvl_one list OpenFrame A list of the level one column names in the OpenFrame pandas.DataFrame.
item_count int OpenFrame Number of columns in the OpenFrame pandas.DataFrame.
weights List[float] OpenFrame Weights used in the method make_portfolio.
first_indices pandas.Series OpenFrame First dates of all the series in the OpenFrame.
last_indices pandas.Series OpenFrame Last dates of all the series in the OpenFrame.
lengths_of_items pandas.Series OpenFrame Number of items in each of the series in the OpenFrame.
span_of_days_all pandas.Series OpenFrame Number of days from the first to the last in each of the series.

In this table are the non-numeric or "helper" properties that apply to both the OpenTimeSeries and the OpenFrame class.

Attribute type Applies to Description
first_idx datetime.date OpenTimeSeries, OpenFrame First date of the series.
last_idx datetime.date OpenTimeSeries, OpenFrame Last date of the series.
length int OpenTimeSeries, OpenFrame Number of items in the series.
span_of_days int OpenTimeSeries, OpenFrame Number of days from the first to the last date in the series.
tsdf pandas.DataFrame OpenTimeSeries, OpenFrame The Pandas DataFrame which gets edited by the class methods.
max_drawdown_date datetime.date, pandas.Series OpenTimeSeries, OpenFrame Date when the maximum drawdown occurred.
periods_in_a_year float OpenTimeSeries, OpenFrame The number of observations in an average year for all days in the data.
yearfrac float OpenTimeSeries, OpenFrame Length of timeseries expressed as np.float64 fraction of a year with 365.25 days.

In this table are the methods that apply only to the OpenTimeSeries class.

Method Applies to Description
setup_class OpenTimeSeries Class method that defines the domestic attribute and a sweden business day calendar.
to_json OpenTimeSeries Method to export the OpenTimeSeries __dict__ to a json file.
pandas_df OpenTimeSeries Method to create the tsdf pandas.DataFrame from the dates and values.
set_new_label OpenTimeSeries Method to change the pandas.DataFrame column MultiIndex.
running_adjustment OpenTimeSeries Adjusts the series performance with a float factor.
ewma_vol_func OpenTimeSeries Returns a pandas.Series with volatility based on Exponentially Weighted Moving Average.

In this table are the methods that apply only to the OpenFrame class.

Method Applies to Description
trunc_frame OpenFrame Truncates the OpenFrame to a common period.
add_timeseries OpenFrame Adds a given OpenTimeSeries to the OpenFrame.
delete_timeseries OpenFrame Deletes an OpenTimeSeries from the OpenFrame.
relative OpenFrame Calculates a new series that is the relative performance of two others.
make_portfolio OpenFrame Calculates a portfolio timeseries from series and weights.
ord_least_squares_fit OpenFrame Performs a regression and an Ordinary Least Squares fit.
beta OpenFrame Calculates Beta of an asset relative a market.
tracking_error_func OpenFrame Calculates the tracking errors relative to a selected series in the OpenFrame.
info_ratio_func OpenFrame Calculates the information ratios relative to a selected series in the OpenFrame.
capture_ratio_func OpenFrame Calculates up, down and up/down capture ratios relative to a selected series.
rolling_info_ratio OpenFrame Returns a pandas.DataFrame with the rolling information ratio between two series.
rolling_beta OpenFrame Returns a pandas.DataFrame with the rolling Beta of an asset relative a market.
rolling_corr OpenFrame Calculates and adds a series of rolling correlations between two other series.
ewma_risk OpenFrame Returns a pandas.DataFrame with volatility and correlation based on Exponentially Weighted Moving Average.

In this table are the methods that apply to both the OpenTimeSeries and the OpenFrame class.

Method Applies to Description
align_index_to_local_cdays OpenTimeSeries, OpenFrame Aligns the series dates to a business calendar. Defaults to Sweden.
resample OpenTimeSeries, OpenFrame Resamples the series to a specific frequency.
value_nan_handle OpenTimeSeries, OpenFrame Fills Nan in a value series with the preceding non-Nan value.
return_nan_handle OpenTimeSeries, OpenFrame Replaces Nan in a return series with a 0.0 float.
to_cumret OpenTimeSeries, OpenFrame Converts a return series into a value series and/or resets a value series to be rebased from 1.0.
value_to_ret OpenTimeSeries, OpenFrame Converts a value series into a percentage return series.
value_to_diff OpenTimeSeries, OpenFrame Converts a value series into a series of differences.
value_to_log OpenTimeSeries, OpenFrame Converts a value series into a logarithmic return series.
value_ret_calendar_period OpenTimeSeries, OpenFrame Returns the series simple return for a specific calendar period.
plot_series OpenTimeSeries, OpenFrame Opens a HTML Plotly plot of the series in a browser window.
drawdown_details OpenTimeSeries, OpenFrame Returns detailed drawdown characteristics.
to_drawdown_series OpenTimeSeries, OpenFrame Converts the series into drawdown series.
rolling_return OpenTimeSeries, OpenFrame Returns a pandas.DataFrame with rolling returns.
rolling_vol OpenTimeSeries, OpenFrame Returns a pandas.DataFrame with rolling volatilities.
rolling_var_down OpenTimeSeries, OpenFrame Returns a pandas.DataFrame with rolling VaR figures.
rolling_cvar_down OpenTimeSeries, OpenFrame Returns a pandas.DataFrame with rolling CVaR figures.
calc_range OpenTimeSeries, OpenFrame Returns the start and end dates of a range from specific period definitions. Used by the below numeric methods and not meant to be used independently.

Below are the numeric properties available for individual OpenTimeSeries or on all series in an OpenFrame.

Attribute type Applies to Description
all_properties pandas.DataFrame OpenTimeSeries, OpenFrame Returns most of the properties in one go.
arithmetic_ret float, pandas.Series OpenTimeSeries, OpenFrame Annualized arithmetic mean of returns.
geo_ret float, pandas.Series OpenTimeSeries, OpenFrame Compound Annual Growth Rate(CAGR), a specific implementation of geometric mean.
value_ret float, pandas.Series OpenTimeSeries, OpenFrame Simple return from first to last observation.
vol float, pandas.Series OpenTimeSeries, OpenFrame Annualized volatility. Pandas .std() is the equivalent of stdev.s([...]) in MS excel.
downside_deviation float, pandas.Series OpenTimeSeries, OpenFrame Downside deviation is the volatility of all negative return observations. Minimum Accepted Return (MAR) set to zero.
ret_vol_ratio float, pandas.Series OpenTimeSeries, OpenFrame Ratio of arithmetic mean return and annualized volatility. It is the Sharpe Ratio with the riskfree rate set to zero.
sortino_ratio float, pandas.Series OpenTimeSeries, OpenFrame The Sortino Ratio is the arithmetic mean return divided by the downside deviation. This attribute assumes that the riskfree rate and the MAR are both zero.
var_down float, pandas.Series OpenTimeSeries, OpenFrame Downside 95% Value At Risk, "VaR". The equivalent of percentile.inc([...], 1-level) over returns in MS Excel. For other confidence levels use the corresponding method.
cvar_down float, pandas.Series OpenTimeSeries, OpenFrame Downside 95% Conditional Value At Risk, "CVaR". For other confidence levels use the corresponding method.
worst float, pandas.Series OpenTimeSeries, OpenFrame Most negative percentage change of a single observation.
worst_month float, pandas.Series OpenTimeSeries, OpenFrame Most negative month.
max_drawdown float, pandas.Series OpenTimeSeries, OpenFrame Maximum drawdown.
max_drawdown_cal_year float, pandas.Series OpenTimeSeries, OpenFrame Max drawdown in a single calendar year.
positive_share float, pandas.Series OpenTimeSeries, OpenFrame The share of percentage changes that are positive.
vol_from_var float, pandas.Series OpenTimeSeries, OpenFrame Implied annualized volatility from the Downside VaR using the assumption that returns are normally distributed.
skew float, pandas.Series OpenTimeSeries, OpenFrame Skew of the return distribution.
kurtosis float, pandas.Series OpenTimeSeries, OpenFrame Kurtosis of the return distribution.
z_score float, pandas.Series OpenTimeSeries, OpenFrame Z-score as (last return - mean return) / standard deviation of returns.
correl_matrix pandas.DataFrame OpenFrame A correlation matrix.

The methods below are identical to the numeric properties above.

They are simply methods that take different date or length inputs to return the properties for subset periods.

Method type Applies to Description
arithmetic_ret_func float, pandas.Series OpenTimeSeries, OpenFrame Annualized arithmetic mean of returns.
geo_ret_func float, pandas.Series OpenTimeSeries, OpenFrame Compound Annual Growth Rate(CAGR), a specific implementation of geometric mean.
value_ret_func float, pandas.Series OpenTimeSeries, OpenFrame Simple return from first to last observation.
vol_func float, pandas.Series OpenTimeSeries, OpenFrame Annualized volatility. Pandas .std() is the equivalent of stdev.s([...]) in MS excel.
downside_deviation_func float, pandas.Series OpenTimeSeries, OpenFrame Downside deviation is the volatility of all negative return observations. MAR and riskfree rate can be set.
ret_vol_ratio_func float, pandas.Series OpenTimeSeries, OpenFrame Ratio of arithmetic mean return and annualized volatility. It is the Sharpe Ratio with the riskfree rate set to zero. A riskfree rate can be set as a float or a series chosen for the frame function.
sortino_ratio_func float, pandas.Series OpenTimeSeries, OpenFrame The Sortino Ratio is the arithmetic mean return divided by the downside deviation. A riskfree rate can be set as a float or a series chosen for the frame function. MAR is set to zero.
var_down_func float, pandas.Series OpenTimeSeries, OpenFrame Downside 95% Value At Risk, "VaR". The equivalent of percentile.inc([...], 1-level) over returns in MS Excel. Default is 95% confidence level.
cvar_down_func float, pandas.Series OpenTimeSeries, OpenFrame Downside 95% Conditional Value At Risk, "CVaR". Default is 95% confidence level.
worst_func float, pandas.Series OpenTimeSeries, OpenFrame Most negative percentage change for a given number of observations (default=1).
max_drawdown_func float, pandas.Series OpenTimeSeries, OpenFrame Maximum drawdown.
positive_share_func float, pandas.Series OpenTimeSeries, OpenFrame The share of percentage changes that are positive.
vol_from_var_func float, pandas.Series OpenTimeSeries, OpenFrame Implied annualized volatility from the Downside VaR using the assumption that returns are normally distributed.
skew_func float, pandas.Series OpenTimeSeries, OpenFrame Skew of the return distribution.
kurtosis_func float, pandas.Series OpenTimeSeries, OpenFrame Kurtosis of the return distribution.
z_score_func float, pandas.Series OpenTimeSeries, OpenFrame Z-score as (last return - mean return) / standard deviation of returns.
target_weight_from_var float, pandas.Series OpenTimeSeries, OpenFrame A position target weight from the ratio between a VaR implied volatility and a given target volatility.

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