gourav/PyFacts
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Setup package and tested with tox

Browse Source
This commit is contained in:
Gourav Kumar 2022-02-17 16:20:48 +05:30
parent c9ead1a561
commit 115e667bde
11 changed files with 229 additions and 77 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
.gitignore vendored Normal file
View File

@ -0,0 +1,5 @@
.tox
.eggs
.env
*egg-info
__pycache__

24
LICENSE Normal file
View File

@ -0,0 +1,24 @@
This is free and unencumbered software released into the public domain.
Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.
In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit
of the public at large and to the detriment of our heirs and
successors. We intend this dedication to be an overt act of
relinquishment in perpetuity of all present and future rights to this
software under copyright law.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
For more information, please refer to <http://unlicense.org>

15
README.md Normal file
View File

@ -0,0 +1,15 @@
# Fincal
This module simplified handling of time-series data
## The problem
Time series data often have missing data points. These missing points mess things up when you are trying to do a comparison between two sections of a time series.
To make things worse, most libraries don't allow comparison based on dates. Month to Month and year to year comparisons become difficult as they cannot be translated into number of days. However, these are commonly used metrics while looking at financial data.
## The Solution
Fincal aims to simplify things by allowing you to:
* Compare time-series data based on dates
* Easy way to work around missing dates by taking the closest data points
* Completing series with missing data points using forward fill and backward fill
## Examples

1
VERSION Normal file
View File

@ -0,0 +1 @@
0.0.1

1
fincal/__init__.py Normal file
View File

@ -0,0 +1 @@
from fincal import *

20
fincal/__main__.py Normal file
View File

@ -0,0 +1,20 @@
import sys
def main(args=None):
"""The main routine."""
if args is None:
args = sys.argv[1:]
print("This is the main routine.")
print("It should do something interesting.")
print("This is the name of the script: ", sys.argv[0])
print("Number of arguments: ", len(sys.argv))
print("The arguments are: ", str(sys.argv))
# Do argument parsing here with argparse
if __name__ == "__main__":
main()

191
fincal/fincal.py
View File

@ -1,122 +1,159 @@
import datetime
import pandas as pd
from typing import Union, Dict, List, Iterable, Any
from typing import Any, Dict, Iterable, List, Union
from dateutil.relativedelta import relativedelta
class TimeSeries:
def __init__(
self,
data=List[tuple],
date_format: str = '%Y-%m-%d',
frequency='infer' # D, W, M, Q, H, Y
):
self.time_series = [(datetime.datetime.strptime(i[0], date_format), i[1]) for i in data]
self.dates = {i[0] for i in self.time_series}
"""Container for TimeSeries objects"""
def __init__(self, data: List[tuple], date_format: str = "%Y-%m-%d", frequency="infer"):
"""Instantiate a TimeSeries object
Parameters
----------
data : List[tuple]
Time Series data in the form of list of tuples.
The first element of each tuple should be a date and second element should be a value.
date_format : str, optional, default "%Y-%m-%d"
Specify the format of the date
Required only if the first argument of tuples is a string. Otherwise ignored.
frequency : str, optional, default "infer"
The frequency of the time series. Default is infer.
The class will try to infer the frequency automatically and adjust to the closest member.
Note that inferring frequencies can fail if the data is too irregular.
Valid values are {D, W, M, Q, H, Y}
"""
time_series = [(datetime.datetime.strptime(i[0], date_format), i[1]) for i in data]
time_series.sort()
self.time_series = dict(time_series)
self.dates = set(list(self.time_series))
if len(self.dates) != len(time_series):
print("Warning: The input data contains duplicate dates which have been ignored.")
self.start_date = list(self.time_series)[0]
self.end_date = list(self.time_series)[-1]
# def infer_frequency(self):
# sample_dates = [i[0] for i in self.time_series[:10]]
# for i in sample_dates
def __repr__(self):
if len(self.time_series) > 6:
printable_data_1 = self.time_series[:3]
printable_data_2 = self.time_series[-3:]
printable_data_1 = list(self.time_series)[:3]
printable_data_2 = list(self.time_series)[-3:]
printable_str = "TimeSeries([{}\n\t...\n\t{}])".format(
',\n\t'.join([str(i) for i in printable_data_1]),
',\n\t'.join([str(i) for i in printable_data_2])
)
',\n\t'.join([str({i: self.time_series[i]}) for i in printable_data_1]),
',\n\t'.join([str({i: self.time_series[i]}) for i in printable_data_2])
)
else:
printable_data = self.time_series
printable_str = "TimeSeries([{}])".format(',\n\t'.join([str(i) for i in printable_data]))
printable_str = "TimeSeries([{}])".format(',\n\t'.join(
[str({i: self.time_series[i]}) for i in printable_data]))
return printable_str
def __str__(self):
if len(self.time_series) > 6:
printable_data_1 = self.time_series[:3]
printable_data_2 = self.time_series[-3:]
printable_data_1 = list(self.time_series)[:3]
printable_data_2 = list(self.time_series)[-3:]
printable_str = "[{}\n ...\n {}]".format(
',\n '.join([str(i) for i in printable_data_1]),
',\n '.join([str(i) for i in printable_data_2])
)
',\n '.join([str({i: self.time_series[i]}) for i in printable_data_1]),
',\n '.join([str({i: self.time_series[i]}) for i in printable_data_2])
)
else:
printable_data = self.time_series
printable_str = "[{}]".format(',\n '.join([str(i) for i in printable_data]))
printable_str = "[{}]".format(',\n '.join([str({i: self.time_series[i]}) for i in printable_data]))
return printable_str
def ffill(self):
new_ts = []
for dt, val in self.time_series:
if dt == self.time_series[0][0]:
new_ts.append((dt, val))
else:
diff = (dt - prev_date).days
if diff != 1:
for k in range(1, diff):
new_ts.append((prev_date + datetime.timedelta(days=k), prev_val))
new_ts.append((dt, val))
prev_date = dt
prev_val = val
self.ffilled_time_series = new_ts
return self.ffilled_time_series
def info(self):
"""Summary info about the TimeSeries object"""
def bfill(self):
new_ts = []
for dt, val in self.time_series[::-1]:
if dt == self.time_series[-1][0]:
new_ts.append((dt, val))
else:
diff = (prev_date - dt).days
if diff != 1:
for k in range(1, diff):
new_ts.append((prev_date - datetime.timedelta(days=k), prev_val))
new_ts.append((dt, val))
prev_date = dt
prev_val = val
self.ffilled_time_series = new_ts[::-1]
return self.ffilled_time_series
total_dates = len(self.time_series.keys())
res_string = "First date: {}\nLast date: {}\nNumber of rows: {}"
return res_string.format(self.start_date, self.end_date, total_dates)
def ffill(self, inplace=False):
num_days = (self.end_date - self.start_date).days + 1
new_ts = dict()
for i in range(num_days):
cur_date = self.start_date + datetime.timedelta(days=i)
try:
cur_val = self.time_series[cur_date]
except KeyError:
pass
new_ts.update({cur_date: cur_val})
if inplace:
self.time_series = new_ts
return None
return new_ts
def bfill(self, inplace=False):
num_days = (self.end_date - self.start_date).days + 1
new_ts = dict()
for i in range(num_days):
cur_date = self.end_date - datetime.timedelta(days=i)
try:
cur_val = self.time_series[cur_date]
except KeyError:
pass
new_ts.update({cur_date: cur_val})
if inplace:
self.time_series = new_ts
return None
return dict(reversed(new_ts.items()))
def calculate_returns(
self,
as_on: datetime.date,
closest: str = 'previous',
compounding: bool = True,
years: int = 1
self, as_on: datetime.datetime, closest: str = "previous", compounding: bool = True, years: int = 1
) -> int:
"""Method to calculate returns for a certain time-period as on a particular date
>>> calculate_returns(datetime.date(2020, 1, 1), years=1)
>>> calculate_returns(datetime.date(2020, 1, 1), years=1)
"""
current = [(dt, val) for dt, val in self.time_series if dt == as_on][0]
if not current:
try:
current = self.time_series[as_on]
except KeyError:
raise ValueError("As on date not found")
prev_date = as_on.replace(year=as_on.year-years)
if closest == 'previous':
previous = [(dt, val) for dt, val in self.time_series if dt <= prev_date][-1]
elif closest == 'next':
previous = [(dt, val) for dt, val in self.time_series if dt >= prev_date][0]
# print(current, previous)
prev_date = as_on - relativedelta(years=years)
if closest == "previous":
delta = -1
elif closest == "next":
delta = 1
else:
raise ValueError(f"Invalid value for closes parameter: {closest}")
returns = current[1]/previous[1]
while True:
try:
previous = self.time_series[prev_date]
break
except KeyError:
prev_date = prev_date + relativedelta(days=delta)
returns = current / previous
if compounding:
returns = returns ** (1/years)
returns = returns ** (1 / years)
return returns - 1
def calculate_rolling_returns(
self,
from_date: datetime.date,
to_date: datetime.date,
frequency: str = 'd',
closest: str = 'previous',
frequency: str = "d",
closest: str = "previous",
compounding: bool = True,
years: int = 1
years: int = 1,
) -> List[tuple]:
"""Calculates the rolling return"""
datediff = (to_date - from_date).days
dates = []
all_dates = set()
for i in range(datediff):
if from_date + datetime.timedelta(days=i) in self.dates:
dates.append(from_date + datetime.timedelta(days=i))
all_dates.add(from_date + datetime.timedelta(days=i))
dates = all_dates.intersection(self.dates)
rolling_returns = []
for i in dates:

22
setup.py Normal file
View File

@ -0,0 +1,22 @@
from setuptools import find_packages, setup
license = open("LICENSE").read().strip()
setup(
name="Fincal",
version='0.0.1',
license=license,
author="Gourav Kumar",
author_email="gouravkr@outlook.in",
url="https://gouravkumar.com",
description="A library which makes handling time series data easier",
long_description=open("README.md").read().strip(),
packages=find_packages(),
install_requires=["python-dateutil"],
test_suite="tests",
entry_points={
"console_scripts": [
"fincal=fincal.__main__:main",
]
},
)

0
tests/__init__.py Normal file
View File

21
tests/test_fincal.py Normal file
View File

@ -0,0 +1,21 @@
import unittest
from fincal.fincal import TimeSeries
class TestFincal(unittest.TestCase):
def test_ts(self):
data = [
('2020-01-01', 23),
('2020-01-02', 24),
('2020-01-03', 25),
('2020-01-06', 26),
('2020-01-07', 27),
('2020-01-08', 28),
('2020-01-10', 29),
('2020-01-11', 30)
]
time_series = TimeSeries(data)
time_series.ffill(inplace=True)
self.assertEqual(len(time_series.time_series), 11)

6
tox.ini Normal file
View File

@ -0,0 +1,6 @@
[tox]
envlist = py39
[testenv]
deps = pytest
commands = pytest