openseries 0.3.1

Package for simple financial time series analysis.

OpenSeries

This is a project where we keep tools to perform timeseries analysis on a single asset or a group of assets.

To install:

pip install openseries

To construct an OpenTimeSeries object from a "raw" dictionary:

data = {'_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)'}

series =  = OpenTimeSeries(data)

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

capirisc = 'SE0009807308'
bonds = OpenTimeSeries.from_open_nav(isin=capirisc)

And print its geometric annual return and volatility:

print(f'Return    : {bonds.geo_ret:.2%}\nVolatility: {bonds.vol:.2%}.')

To compare timeseries an OpenFrame is constructed as below. The names of the timeseries must be unique.

basket = OpenFrame([series, bonds])

The helper methods to work with the timeseries can be chained like this:

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

And 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)

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

basket.plot_series(tick_fmt='.1%')

To make use of some of the 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

• Modules described
• Methods to construct OpenTimeSeries
• OpenTimeSeries non-numeric properties
• OpenFrame non-numeric properties
• Non-numeric properties for both classes
• OpenTimeSeries only methods
• OpenFrame only methods
• Methods for both classes
• Numeric properties for both classes
• Numeric methods with period arguments for both classes

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.
captor_open_api_sdk.py	A Python SDK to interact with the Captor Open API.
datefixer.py	A module with date utilities.
helpfilewriter.py	A module that allows printing a description of a Python class.
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 Captor API endpoint.
from_open_nav	OpenTimeSeries	Class method to create an OpenTimeSeries object from a Captor API endpoint.
from_open_fundinfo	OpenTimeSeries	Class method to create an OpenTimeSeries object from a Captor 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	Captor database identifier for the timeseries.
instrumentId	str	OpenTimeSeries	Captor 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.
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.
sweden	SwedenHolidayCalendar	OpenTimeSeries, OpenFrame	A calendar object used to generate business days.
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.
validate_vs_schema	OpenTimeSeries	Method to validate the OpenTimeSeries input against the openseries.json jsonschema.
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.

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.
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_corr	OpenFrame	Calculates and adds a series of rolling correlations between two other series.
ord_least_squares_fit	OpenFrame	Calculates the Beta and an Ordinary Least Squares fitted series from two others.

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 or resets a value series to start 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	Arithmetic annualized log return.
geo_ret	float, pandas.Series	OpenTimeSeries, OpenFrame	Geometric annualized return (CAGR).
twr_ret	float, pandas.Series	OpenTimeSeries, OpenFrame	Annualized time weighted return.
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	Annualized volatility of all negative return observations. Minimum Accepted Return (MAR) set to zero.
ret_vol_ratio	float, pandas.Series	OpenTimeSeries, OpenFrame	Ratio of geometric return and annualized volatility. Riskfree rate set to zero.
sortino_ratio	float, pandas.Series	OpenTimeSeries, OpenFrame	Ratio of geometric return and downside deviation.
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.
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	Arithmetic annualized log return.
geo_ret_func	float, pandas.Series	OpenTimeSeries, OpenFrame	Geometric annualized return.
twr_ret_func	float, pandas.Series	OpenTimeSeries, OpenFrame	Annualized time weighted return.
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	Annualized volatility of all negative return observations. MAR and riskfree rate can be set.
ret_vol_ratio_func	float, pandas.Series	OpenTimeSeries, OpenFrame	Ratio of geometric return and annualized volatility. 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	Ratio of geometric return and 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 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 Conditional Value At Risk, "CVaR". Default is 95% confidence level.
worst_func	float, pandas.Series	OpenTimeSeries, OpenFrame	Most negative percentage change.
max_drawdown_func	float, pandas.Series	OpenTimeSeries, OpenFrame	Most negative month.
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.

Below are the steps used to publish the package on pypi. First update the version in setup.py and do git commands.

git add --all
git commit -m "changes made"
git push

And then the packaging steps from here:

pip install --upgrade pip
pip install --upgrade build
pip install --upgrade twine
py -m build
py -m twine upload --repository pypi dist/*

Future proofing

I have run the test suite with Python version 3.10.0 using nose2 and the module versions below. Everything works except the warnings and test failure outlined below.

C:\Python310> py -3.10 -m pip list
Package            Version
------------------ ---------
attrs              21.2.0
certifi            2021.10.8
charset-normalizer 2.0.9
coverage           6.2     # updated
coverage-badge     1.1.0   # updated
idna               3.3
jsonschema         4.3.2   # updated
nose2              0.10.0
numpy              1.21.5  # updated
pandas             1.3.5   # updated
patsy              0.5.2
pip                21.3.1
plotly             5.5.0   # updated
pyrsistent         0.18.0
python-dateutil    2.8.2
pytz               2021.3
requests           2.26.0  # updated
scipy              1.7.3   # updated
setuptools         60.1.0
six                1.16.0
statsmodels        0.13.1  # updated
tenacity           8.0.1
testfixtures       6.18.3
urllib3            1.26.7
wheel              0.37.1

Pandas==1.3.5 causes the test test_openframe_rolling_corr to fail and it warns as below on the other relevant tests:

FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior.
In a future version these will be considered non-comparable.Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.