openseries

This is a project with tools to analyze financial timeseries of a single asset or a group of assets. It is solely made for daily or less frequent data.

Basic Usage

To install:

pip install openseries

or:

conda install -c conda-forge openseries

An example of how to make use of the OpenTimeSeries is shown below. The design aligns with how we within our fund company's code base have a subclass of the OpenTimeSeries with class methods for our different data sources. Combined with some additional tools it allows us to efficiently present investment cases to clients.

The code snippet can be pasted into a Python console to run it. Install openseries and yfinance first.

from openseries import OpenTimeSeries
import yfinance as yf

msft=yf.Ticker("MSFT")
history=msft.history(period="max")
series=OpenTimeSeries.from_df(history.loc[:, "Close"])
_=series.value_to_log().set_new_label("Microsoft Log Returns of Close Prices")
_,_=series.plot_series()

Sample output using the OpenFrame.all_properties() method:

                       Scilla Global Equity C (simulation+fund) Global Low Volatility index, SEK
                                                ValueType.PRICE                  ValueType.PRICE
Total return                                           3.641282                         1.946319
Arithmetic return                                      0.096271                         0.069636
Geometric return                                       0.093057                          0.06464
Volatility                                             0.120279                         0.117866
Return vol ratio                                       0.800396                          0.59081
Downside deviation                                     0.085956                         0.086723
Sortino ratio                                          1.119993                         0.802975
Positive share                                         0.541783                         0.551996
Worst                                                 -0.071616                        -0.089415
Worst month                                           -0.122503                        -0.154485
Max drawdown                                          -0.309849                        -0.435444
Max drawdown in cal yr                                -0.309849                        -0.348681
Max drawdown dates                                   2020-03-23                       2009-03-09
CVaR 95.0%                                             -0.01793                        -0.018429
VaR 95.0%                                             -0.011365                        -0.010807
Imp vol from VaR 95%                                   0.109204                         0.103834
Z-score                                                0.587905                         0.103241
Skew                                                  -0.650782                        -0.888109
Kurtosis                                               8.511166                        17.527367
observations                                               4309                             4309
span of days                                               6301                             6301
first indices                                        2006-01-03                       2006-01-03
last indices                                         2023-04-05                       2023-04-05

Usage example on Jupyter Nbviewer

Development Instructions

These instructions assume that you have a compatible Python version installed on your machine and that you are OK to install this project in a virtual environment.

The OpenTimeSeries and OpenFrame classes are both subclasses of the Pydantic BaseModel. Please refer to its documentation for information on any attributes or methods inherited from this model.

Windows Powershell

git clone https://github.com/CaptorAB/openseries.git
cd openseries
./make.ps1 make

Mac Terminal/Linux

git clone https://github.com/CaptorAB/openseries.git
cd openseries
make
source source_me
make install

Testing and Linting / Type-checking

Ruff and Mypy checking is embedded in the pre-commit hook. Both are also used in the project's GitHub workflows and are run when the lint alternative is chosen in the below commands. Any silenced error codes can be found in the pyproject.toml file or in in-line comments.

Windows Powershell

./make.ps1 test
./make.ps1 lint

Mac Terminal/Linux

make test
make lint

Table of Contents

• Basic Usage
• Development Instructions
• Testing and Linting / Type-checking
• On some files in the project
• Class methods used to construct an OpenTimeSeries
• OpenTimeSeries non-numerical properties
• OpenFrame non-numerical properties
• Non-numerical properties for both classes
• OpenTimeSeries only methods
• OpenFrame only methods
• Methods for both classes
• Numerical properties for both classes
• Numerical methods with period arguments for both classes

On some files in the project

File	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. The module also defines functions to simulate, optimize, and plot portfolios.
simulation.py	Defines the class ReturnSimulation to create simulated financial timeseries. Used in the project's test suite

Class methods used to construct objects.

Method	Applies to	Description
from_arrays	OpenTimeSeries	Class method to create an OpenTimeSeries object from a list of date strings and a list of values.
from_df	OpenTimeSeries	Class method to create an OpenTimeSeries object from a pandas.DataFrame or pandas.Series.
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.

Non-numerical or "helper" properties that apply only to the OpenTimeSeries class.

Property	type	Applies to	Description
timeseries_id	str	OpenTimeSeries	Placeholder for database identifier for the timeseries. Can be left as empty string.
instrument_id	str	OpenTimeSeries	Placeholder for database identifier for the instrument associated with the timeseries. Can be left as empty string.
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.
countries	list or str	OpenTimeSeries	(List of) country code(s) according to ISO 3166-1 alpha-2 used to generate business days.
valuetype	ValueType	OpenTimeSeries	Field identifies the type of values in the series. ValueType is an Enum.

Non-numerical or "helper" properties that apply only to the OpenFrame class.

Property	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.

Non-numerical or "helper" properties that apply to both the OpenTimeSeries and the OpenFrame class.

Property	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.

Methods that apply only to the OpenTimeSeries class.

Method	Applies to	Description
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.
from_1d_rate_to_cumret	OpenTimeSeries	Converts a series of 1-day rates into a cumulative valueseries.

                                                                       |

Methods that apply only to the OpenFrame class.

Method	Applies to	Description
merge_series	OpenFrame	Merges the Pandas Dataframes of the constituent OpenTimeSeries.
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 based on the series and weights. Weights can be provided as a list, or a weight strategy can be set as equal weights or inverted volatility.
ord_least_squares_fit	OpenFrame	Performs a regression and an Ordinary Least Squares fit.
beta	OpenFrame	Calculates Beta of an asset relative a market.
jensen_alpha	OpenFrame	Calculates Jensen's Alpha 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.
correl_matrix	OpenFrame	Returns a pandas.DataFrame with a correlation matrix.
ewma_risk	OpenFrame	Returns a pandas.DataFrame with volatility and correlation based on Exponentially Weighted Moving Average.

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.
resample_to_business_period_ends	OpenTimeSeries, OpenFrame	Resamples the series to month-end dates with monthly, quarterly or annual 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.
to_json	OpenTimeSeries, OpenFrame	Method to export object data to a json file.
to_xlsx	OpenTimeSeries, OpenFrame	Method to save the data in the .tsdf DataFrame to an Excel file.
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 Scatter plot of the series in a browser window.
plot_bars	OpenTimeSeries, OpenFrame	Opens a HTML Plotly Bar plot of the series in a browser window.
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 numerical methods and not meant to be used independently.

Numerical properties available for individual OpenTimeSeries or on all series in an OpenFrame.

Property	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.
omega_ratio	float, pandas.Series	OpenTimeSeries, OpenFrame	The Omega Ratio compares returns above a certain target level (MAR) to the total downside risk below MAR.
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.

Methods below are identical to the Numerical 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.
omega_ratio_func	float, pandas.Series	OpenTimeSeries, OpenFrame	The Omega Ratio compares returns above a certain target level (MAR) to the total downside risk below MAR.
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.