separated utility functions and exceptions in standalone files
This commit is contained in:
parent
ef2973a1d1
commit
7cac5cc307
116
fincal/core.py
116
fincal/core.py
@ -2,13 +2,9 @@ import datetime
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from collections import UserDict, UserList
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from dataclasses import dataclass
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from numbers import Number
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from typing import Iterable, List, Literal, Mapping, Sequence, Tuple, Union
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from typing import Iterable, List, Literal, Sequence
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@dataclass
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class FincalOptions:
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date_format: str = "%Y-%m-%d"
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closest: str = "before" # after
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from .utils import _parse_date, _preprocess_timeseries
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@dataclass(frozen=True)
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@ -29,114 +25,6 @@ class AllFrequencies:
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Y = Frequency("annual", "years", 1, 365, "Y")
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class DateNotFoundError(Exception):
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"""Exception to be raised when date is not found"""
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def __init__(self, message, date):
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message = f"{message}: {date}"
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super().__init__(message)
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def _parse_date(date: str, date_format: str = None):
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"""Parses date and handles errors"""
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if isinstance(date, (datetime.datetime, datetime.date)):
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return datetime.datetime.fromordinal(date.toordinal())
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if date_format is None:
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date_format = FincalOptions.date_format
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try:
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date = datetime.datetime.strptime(date, date_format)
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except TypeError:
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raise ValueError("Date does not seem to be valid date-like string")
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except ValueError:
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raise ValueError("Date could not be parsed. Have you set the correct date format in FincalOptions.date_format?")
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return date
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def _preprocess_timeseries(
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data: Union[
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Sequence[Iterable[Union[str, datetime.datetime, float]]],
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Sequence[Mapping[str, Union[float, datetime.datetime]]],
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Sequence[Mapping[Union[str, datetime.datetime], float]],
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Mapping[Union[str, datetime.datetime], float],
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],
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date_format: str,
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) -> List[Tuple[datetime.datetime, float]]:
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"""Converts any type of list to the correct type"""
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if isinstance(data, Mapping):
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current_data = [(k, v) for k, v in data.items()]
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return _preprocess_timeseries(current_data, date_format)
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if not isinstance(data, Sequence):
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raise TypeError("Could not parse the data")
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if isinstance(data[0], Sequence):
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return sorted([(_parse_date(i, date_format), j) for i, j in data])
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if not isinstance(data[0], Mapping):
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raise TypeError("Could not parse the data")
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if len(data[0]) == 1:
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current_data = [tuple(*i.items()) for i in data]
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elif len(data[0]) == 2:
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current_data = [tuple(i.values()) for i in data]
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else:
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raise TypeError("Could not parse the data")
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return _preprocess_timeseries(current_data, date_format)
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def _preprocess_match_options(as_on_match: str, prior_match: str, closest: str) -> datetime.timedelta:
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"""Checks the arguments and returns appropriate timedelta objects"""
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deltas = {"exact": 0, "previous": -1, "next": 1}
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if closest not in deltas.keys():
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raise ValueError(f"Invalid argument for closest: {closest}")
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as_on_match = closest if as_on_match == "closest" else as_on_match
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prior_match = closest if prior_match == "closest" else prior_match
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if as_on_match in deltas.keys():
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as_on_delta = datetime.timedelta(days=deltas[as_on_match])
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else:
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raise ValueError(f"Invalid as_on_match argument: {as_on_match}")
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if prior_match in deltas.keys():
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prior_delta = datetime.timedelta(days=deltas[prior_match])
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else:
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raise ValueError(f"Invalid prior_match argument: {prior_match}")
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return as_on_delta, prior_delta
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def _find_closest_date(data, date, delta, if_not_found):
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"""Helper function to find data for the closest available date"""
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row = data.get(date, None)
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if row is not None:
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return date, row
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if delta:
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return _find_closest_date(data, date + delta, delta, if_not_found)
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if if_not_found == "fail":
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raise DateNotFoundError("Data not found for date", date)
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if if_not_found == "nan":
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return date, float("NaN")
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raise ValueError(f"Invalid argument for if_not_found: {if_not_found}")
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def _interval_to_years(interval_type: Literal["years", "months", "day"], interval_value: int) -> int:
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"""Converts any time period to years for use with compounding functions"""
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year_conversion_factor = {"years": 1, "months": 12, "days": 365}
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years = interval_value / year_conversion_factor[interval_type]
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return years
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class _IndexSlicer:
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"""Class to create a slice using iloc in TimeSeriesCore"""
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6
fincal/exceptions.py
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6
fincal/exceptions.py
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@ -0,0 +1,6 @@
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class DateNotFoundError(Exception):
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"""Exception to be raised when date is not found"""
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def __init__(self, message, date):
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message = f"{message}: {date}"
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super().__init__(message)
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@ -5,9 +5,8 @@ from typing import List, Literal, Union
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from dateutil.relativedelta import relativedelta
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from .core import (
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AllFrequencies,
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TimeSeriesCore,
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from .core import AllFrequencies, TimeSeriesCore
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from .utils import (
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_find_closest_date,
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_interval_to_years,
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_parse_date,
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111
fincal/utils.py
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111
fincal/utils.py
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@ -0,0 +1,111 @@
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import datetime
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from dataclasses import dataclass
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from typing import Iterable, List, Literal, Mapping, Sequence, Tuple, Union
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from .exceptions import DateNotFoundError
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@dataclass
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class FincalOptions:
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date_format: str = "%Y-%m-%d"
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closest: str = "before" # after
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def _parse_date(date: str, date_format: str = None):
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"""Parses date and handles errors"""
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if isinstance(date, (datetime.datetime, datetime.date)):
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return datetime.datetime.fromordinal(date.toordinal())
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if date_format is None:
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date_format = FincalOptions.date_format
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try:
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date = datetime.datetime.strptime(date, date_format)
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except TypeError:
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raise ValueError("Date does not seem to be valid date-like string")
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except ValueError:
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raise ValueError("Date could not be parsed. Have you set the correct date format in FincalOptions.date_format?")
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return date
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def _preprocess_timeseries(
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data: Union[
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Sequence[Iterable[Union[str, datetime.datetime, float]]],
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Sequence[Mapping[str, Union[float, datetime.datetime]]],
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Sequence[Mapping[Union[str, datetime.datetime], float]],
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Mapping[Union[str, datetime.datetime], float],
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],
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date_format: str,
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) -> List[Tuple[datetime.datetime, float]]:
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"""Converts any type of list to the correct type"""
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if isinstance(data, Mapping):
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current_data = [(k, v) for k, v in data.items()]
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return _preprocess_timeseries(current_data, date_format)
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if not isinstance(data, Sequence):
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raise TypeError("Could not parse the data")
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if isinstance(data[0], Sequence):
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return sorted([(_parse_date(i, date_format), j) for i, j in data])
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if not isinstance(data[0], Mapping):
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raise TypeError("Could not parse the data")
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if len(data[0]) == 1:
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current_data = [tuple(*i.items()) for i in data]
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elif len(data[0]) == 2:
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current_data = [tuple(i.values()) for i in data]
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else:
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raise TypeError("Could not parse the data")
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return _preprocess_timeseries(current_data, date_format)
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def _preprocess_match_options(as_on_match: str, prior_match: str, closest: str) -> datetime.timedelta:
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"""Checks the arguments and returns appropriate timedelta objects"""
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deltas = {"exact": 0, "previous": -1, "next": 1}
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if closest not in deltas.keys():
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raise ValueError(f"Invalid argument for closest: {closest}")
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as_on_match = closest if as_on_match == "closest" else as_on_match
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prior_match = closest if prior_match == "closest" else prior_match
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if as_on_match in deltas.keys():
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as_on_delta = datetime.timedelta(days=deltas[as_on_match])
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else:
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raise ValueError(f"Invalid as_on_match argument: {as_on_match}")
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if prior_match in deltas.keys():
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prior_delta = datetime.timedelta(days=deltas[prior_match])
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else:
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raise ValueError(f"Invalid prior_match argument: {prior_match}")
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return as_on_delta, prior_delta
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def _find_closest_date(data, date, delta, if_not_found):
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"""Helper function to find data for the closest available date"""
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row = data.get(date, None)
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if row is not None:
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return date, row
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if delta:
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return _find_closest_date(data, date + delta, delta, if_not_found)
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if if_not_found == "fail":
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raise DateNotFoundError("Data not found for date", date)
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if if_not_found == "nan":
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return date, float("NaN")
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raise ValueError(f"Invalid argument for if_not_found: {if_not_found}")
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def _interval_to_years(interval_type: Literal["years", "months", "day"], interval_value: int) -> int:
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"""Converts any time period to years for use with compounding functions"""
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year_conversion_factor = {"years": 1, "months": 12, "days": 365}
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years = interval_value / year_conversion_factor[interval_type]
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return years
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111
testing.ipynb
111
testing.ipynb
@ -20,11 +20,19 @@
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"id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: total: 125 ms\n",
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"Wall time: 99 ms\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[(datetime.datetime(2022, 1, 31, 0, 0), 310.980011),\n",
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" (datetime.datetime(2021, 5, 28, 0, 0), 249.679993)]"
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" (datetime.datetime(2021, 5, 28, 0, 0), 249.67999300000002)]"
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]
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},
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"execution_count": 2,
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@ -33,6 +41,7 @@
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}
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],
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"source": [
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"%%time\n",
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"dfd = pd.read_csv('test_files/msft.csv')\n",
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"# dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)\n",
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"ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')\n",
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@ -40,6 +49,27 @@
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"ts[['2022-01-31', '2021-05-28']]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "ffd9665d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(datetime.datetime(2022, 1, 31, 0, 0), 310.980011)"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ts['2022-01-31']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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@ -50,7 +80,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wall time: 17 ms\n"
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"CPU times: total: 15.6 ms\n",
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"Wall time: 16 ms\n"
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]
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},
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{
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@ -86,7 +117,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wall time: 5.97 ms\n"
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"CPU times: total: 15.6 ms\n",
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"Wall time: 4 ms\n"
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]
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},
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{
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@ -95,7 +127,7 @@
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"[(datetime.datetime(1992, 2, 19, 0, 0), 2.398438),\n",
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" (datetime.datetime(1992, 2, 20, 0, 0), 2.447917),\n",
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" (datetime.datetime(1992, 2, 21, 0, 0), 2.385417),\n",
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" (datetime.datetime(1992, 2, 24, 0, 0), 2.393229),\n",
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" (datetime.datetime(1992, 2, 24, 0, 0), 2.3932290000000003),\n",
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" (datetime.datetime(1992, 2, 25, 0, 0), 2.411458),\n",
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" (datetime.datetime(1992, 2, 26, 0, 0), 2.541667),\n",
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" (datetime.datetime(1992, 2, 27, 0, 0), 2.601563),\n",
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@ -116,7 +148,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 11,
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"id": "dc469722-c816-4b57-8d91-7a3b865f86be",
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"metadata": {
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"tags": []
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@ -126,7 +158,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wall time: 311 ms\n"
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"CPU times: total: 297 ms\n",
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"Wall time: 290 ms\n"
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]
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}
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],
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@ -186,6 +219,70 @@
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"sr = Series([1, 2, 3, 4, 5], 'number')\n",
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"sr"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "adceda69",
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"metadata": {},
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"outputs": [],
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"source": [
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"from fincal.fincal import TimeSeries\n",
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"import datetime\n",
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"ts = TimeSeries(data = [('2021-01-01', 220), ('2021-02-01', 230), ('2021-03-01', 240)], frequency='M')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "68cf9f8c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(datetime.datetime(2021, 2, 1, 0, 0), 0.045454545454545414)"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ts.calculate_returns('2021-02-05', interval_type='months', interval_value=1, compounding=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "a583347f",
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"metadata": {},
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"outputs": [],
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"source": [
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"D = {'a': 1, 'b': 2}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "f79ac787",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"dict_keys(['a', 'b'])"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"D.keys()"
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]
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}
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],
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"metadata": {
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@ -204,7 +301,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.2"
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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@ -4,8 +4,10 @@ import random
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from typing import Literal, Sequence
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import pytest
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from fincal.core import DateNotFoundError, FincalOptions, Frequency, Series
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from fincal.core import Frequency, Series
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from fincal.exceptions import DateNotFoundError
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from fincal.fincal import TimeSeries, create_date_series
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from fincal.utils import FincalOptions
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THIS_DIR = os.path.dirname(os.path.abspath(__file__))
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sample_data_path = os.path.join(THIS_DIR, "data")
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