177 lines
6.3 KiB
Python
177 lines
6.3 KiB
Python
import datetime
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from dataclasses import dataclass
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from typing import List, Literal, Mapping, Sequence, Tuple
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from .exceptions import DateNotFoundError, DateOutOfRangeError
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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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traded_days: int = 365
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get_closest: str = "exact"
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def _parse_date(date: str, date_format: str = None) -> datetime.datetime:
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"""Parses date and handles errors
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Parameters:
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-----------
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date: str | datetime.date
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The date to be parsed.
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If the date passed is already a datetime object, it will return it unprocessed.
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date_format: str, default None
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The format of the date string in datetime.strftime friendly format.
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If format is None, format in FincalOptions.date_format will be used.
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Returns:
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--------
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Returns a datetime.datetime object.
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Raises:
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-------
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TypeError: If the is not a date-like string
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ValueError: If the date could not be parsed with the given format
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"""
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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: Sequence[Tuple[str | datetime.datetime, float]]
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| Sequence[Mapping[str | datetime.datetime, float]]
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| Mapping[str | datetime.datetime, float],
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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 TimeSeries friendly format.
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This function is internally called by the __init__ function of the TimeSeriesCore class
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The TimeSeries class can internally process a list of Tuples.
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However, users have the option of passing a variety of types.
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This function preprocesses the data and converts it into the relevant format.
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If the data is a dictionary, it will be converted using .items() iteration.
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If the data is not a dictionary or a list, it will raise an error.
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If the data is of list type:
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* If the first item is also of list type, it will be parsed as a list of lists
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* If the first item is a dictionary with one key, then key will be parsed as date
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* If the first item is a dictionary with two keys, then first key will be date and second will be value
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* If the first element is of another type, it will raise an error
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The final return value is sorted by date
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Parameters:
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-----------
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Data:
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The data for the time series. Can be a dictionary, a list of tuples, or a list of dictionaries.
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date_format: str
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The format of the date in strftime friendly format.
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Returns:
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-----------
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Returns a list of Tuples where the first element of each tuple is of datetime.datetime class
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and the second element is of float class
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Raises:
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--------
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TypeError: If the data is not in a format which can be parsed.
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"""
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if isinstance(data, Mapping):
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current_data: List[tuple] = [(k, v) for k, v in data.items()]
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return _preprocess_timeseries(current_data, date_format)
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# If data is not a dictionary or list, it cannot be parsed
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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), float(j)) for i, j in data])
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# If first element is not a dictionary or tuple, it cannot be parsed
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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: List[tuple] = [tuple(*i.items()) for i in data]
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elif len(data[0]) == 2:
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current_data: List[tuple] = [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) -> Tuple[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: str = closest if as_on_match == "closest" else as_on_match
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prior_match: str = 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 = 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 = 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(
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data: Mapping[datetime.datetime, float],
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date: datetime.datetime,
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limit_days: int,
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delta: datetime.timedelta,
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if_not_found: Literal["fail", "nan"],
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):
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"""Helper function to find data for the closest available date"""
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if delta.days < 0 and date < min(data):
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raise DateOutOfRangeError(date, "min")
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if delta.days > 0 and date > max(data):
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raise DateOutOfRangeError(date, "max")
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row: tuple = 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 and limit_days != 0:
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return _find_closest_date(data, date + delta, limit_days - 1, 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) -> float:
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"""Converts any time period to years for use with compounding functions"""
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year_conversion_factor: dict = {"years": 1, "months": 12, "days": 365}
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years: float = interval_value / year_conversion_factor[interval_type]
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return years
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