403 lines
14 KiB
Python
403 lines
14 KiB
Python
from __future__ import annotations
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import datetime
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from typing import Iterable, List, Literal, Mapping, Union
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from dateutil.relativedelta import relativedelta
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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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_preprocess_match_options,
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)
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def create_date_series(
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start_date: Union[str, datetime.datetime],
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end_date: Union[str, datetime.datetime],
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frequency: Literal["D", "W", "M", "Q", "H", "Y"],
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eomonth: bool = False,
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) -> List[datetime.datetime]:
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"""Create a date series with a specified frequency
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Parameters
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----------
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start_date : str | datetime.datetime
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Date series will always start at this date
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end_date : str | datetime.datetime
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The date till which the series should extend
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Depending on the other parameters, this date may or may not be present
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in the final date series
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frequency : D | W | M | Q | H | Y
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Frequency of the date series.
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The gap between each successive date will be equivalent to this frequency
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eomonth : bool, optional
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Speacifies if the dates in the series should be end of month dates.
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Can only be used if the frequency is Monthly or lower.
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Returns
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-------
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List[datetime.datetime]
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Returns the series as a list of datetime objects
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Raises
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------
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ValueError
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If eomonth is True and frequency is higher than monthly
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"""
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frequency = getattr(AllFrequencies, frequency)
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if eomonth and frequency.days < AllFrequencies.M.days:
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raise ValueError(f"eomonth cannot be set to True if frequency is higher than {AllFrequencies.M.name}")
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start_date = _parse_date(start_date)
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end_date = _parse_date(end_date)
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datediff = (end_date - start_date).days / frequency.days + 1
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dates = []
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for i in range(0, int(datediff)):
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diff = {frequency.freq_type: frequency.value * i}
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date = start_date + relativedelta(**diff)
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if eomonth:
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next_month = 1 if date.month == 12 else date.month + 1
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date = date.replace(day=1).replace(month=next_month) - relativedelta(days=1)
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if date <= end_date:
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dates.append(date)
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return dates
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class TimeSeries(TimeSeriesCore):
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"""1-Dimensional Time Series object
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Parameters
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----------
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data : List[Iterable] | Mapping
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Time Series data in the form of list of tuples.
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The first element of each tuple should be a date and second element should be a value.
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The following types of objects can be passed to create a TimeSeries object:
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* List of tuples containing date & value
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* List of lists containing date & value
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* List of dictionaries containing key: value pair of date and value
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* List of dictionaries with 2 keys, first representing date & second representing value
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* Dictionary of key: value pairs
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date_format : str, optional, default "%Y-%m-%d"
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Specify the format of the date
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Required only if the first argument of tuples is a string. Otherwise ignored.
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frequency : str, optional, default "infer"
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The frequency of the time series. Default is infer.
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The class will try to infer the frequency automatically and adjust to the closest member.
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Note that inferring frequencies can fail if the data is too irregular.
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Valid values are {D, W, M, Q, H, Y}
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"""
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def __init__(
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self,
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data: Union[List[Iterable], Mapping],
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frequency: Literal["D", "W", "M", "Q", "H", "Y"],
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date_format: str = "%Y-%m-%d",
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):
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"""Instantiate a TimeSeriesCore object"""
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super().__init__(data, frequency, date_format)
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def info(self):
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"""Summary info about the TimeSeries object"""
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total_dates = len(self.data.keys())
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res_string = "First date: {}\nLast date: {}\nNumber of rows: {}"
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return res_string.format(self.start_date, self.end_date, total_dates)
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def ffill(self, inplace: bool = False, limit: int = None) -> Union[TimeSeries, None]:
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"""Forward fill missing dates in the time series
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Parameters
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----------
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inplace : bool
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Modify the time-series data in place and return None.
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limit : int, optional
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Maximum number of periods to forward fill
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Returns
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-------
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Returns a TimeSeries object if inplace is False, otherwise None
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"""
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eomonth = True if self.frequency.days >= AllFrequencies.M.days else False
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dates_to_fill = create_date_series(self.start_date, self.end_date, self.frequency.symbol, eomonth)
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new_ts = dict()
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for cur_date in dates_to_fill:
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try:
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cur_val = self.data[cur_date]
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except KeyError:
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pass
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new_ts.update({cur_date: cur_val})
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if inplace:
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self.data = new_ts
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return None
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return self.__class__(new_ts, frequency=self.frequency.symbol)
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def bfill(self, inplace: bool = False, limit: int = None) -> Union[TimeSeries, None]:
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"""Backward fill missing dates in the time series
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Parameters
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----------
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inplace : bool
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Modify the time-series data in place and return None.
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limit : int, optional
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Maximum number of periods to back fill
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Returns
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-------
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Returns a TimeSeries object if inplace is False, otherwise None
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"""
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eomonth = True if self.frequency.days >= AllFrequencies.M.days else False
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dates_to_fill = create_date_series(self.start_date, self.end_date, self.frequency.symbol, eomonth)
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dates_to_fill.append(self.end_date)
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bfill_ts = dict()
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for cur_date in reversed(dates_to_fill):
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try:
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cur_val = self.data[cur_date]
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except KeyError:
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pass
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bfill_ts.update({cur_date: cur_val})
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new_ts = {k: bfill_ts[k] for k in reversed(bfill_ts)}
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if inplace:
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self.data = new_ts
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return None
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return self.__class__(new_ts, frequency=self.frequency.symbol)
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def calculate_returns(
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self,
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as_on: Union[str, datetime.datetime],
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return_actual_date: bool = True,
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as_on_match: str = "closest",
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prior_match: str = "closest",
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closest: Literal["previous", "next", "exact"] = "previous",
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closest_max_days: int = -1,
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if_not_found: Literal["fail", "nan"] = "fail",
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compounding: bool = True,
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interval_type: Literal["years", "months", "days"] = "years",
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interval_value: int = 1,
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date_format: str = None,
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) -> float:
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"""Method to calculate returns for a certain time-period as on a particular date
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Parameters
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----------
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as_on : datetime.datetime
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The date as on which the return is to be calculated.
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return_actual_date : bool, default True
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If true, the output will contain the actual date based on which the return was calculated.
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Set to False to return the date passed in the as_on argument.
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as_on_match : str, optional
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The mode of matching the as_on_date. Refer closest.
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prior_match : str, optional
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The mode of matching the prior_date. Refer closest.
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closest : str, optional
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The mode of matching the closest date.
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Valid values are 'exact', 'previous', 'next' and next.
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closest_max_days: int, default -1
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The maximum acceptable gap between the provided date arguments and actual date.
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Pass -1 for no limit.
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Note: There's a hard max limit of 1000 days due to Python's limits on recursion.
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This can be overridden by importing the sys module.
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if_not_found : 'fail' | 'nan'
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What to do when required date is not found:
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* fail: Raise a ValueError
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* nan: Return nan as the value
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compounding : bool, optional
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Whether the return should be compounded annually.
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interval_type : 'years', 'months', 'days'
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The type of time period to use for return calculation.
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interval_value : int
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The value of the specified interval type over which returns needs to be calculated.
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date_format: str
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The date format to use for this operation.
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Should be passed as a datetime library compatible string.
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Sets the date format only for this operation. To set it globally, use FincalOptions.date_format
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Returns
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-------
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A tuple containing the date and float value of the returns.
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Raises
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------
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ValueError
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* If match mode for any of the dates is exact and the exact match is not found
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* If the arguments passsed for closest, as_on_match, and prior_match are invalid
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Example
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--------
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>>> calculate_returns(datetime.date(2020, 1, 1), years=1)
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(datetime.datetime(2020, 1, 1, 0, 0), .0567)
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"""
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as_on = _parse_date(as_on, date_format)
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as_on_delta, prior_delta = _preprocess_match_options(as_on_match, prior_match, closest)
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prev_date = as_on - relativedelta(**{interval_type: interval_value})
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current = _find_closest_date(self.data, as_on, closest_max_days, as_on_delta, if_not_found)
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previous = _find_closest_date(self.data, prev_date, closest_max_days, prior_delta, if_not_found)
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if current[1] == str("nan") or previous[1] == str("nan"):
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return as_on, float("NaN")
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returns = current[1] / previous[1]
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if compounding:
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years = _interval_to_years(interval_type, interval_value)
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returns = returns ** (1 / years)
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return (current[0] if return_actual_date else as_on), returns - 1
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def calculate_rolling_returns(
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self,
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from_date: Union[datetime.date, str],
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to_date: Union[datetime.date, str],
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frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
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as_on_match: str = "closest",
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prior_match: str = "closest",
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closest: Literal["previous", "next", "exact"] = "previous",
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if_not_found: Literal["fail", "nan"] = "fail",
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compounding: bool = True,
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interval_type: Literal["years", "months", "days"] = "years",
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interval_value: int = 1,
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date_format: str = None,
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) -> TimeSeries:
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"""Calculate the returns on a rolling basis.
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This is a wrapper function around the calculate_returns function.
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Parameters
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----------
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from_date : datetime.date | str
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Start date for the return calculation.
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to_date : datetime.date | str
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End date for the returns calculation.
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frequency : str, optional
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Frequency at which the returns should be calcualated.
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Valid values are {D, W, M, Q, H, Y}
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as_on_match : str, optional
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The match mode to be used for the as on date.
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If not specified, the value for the closes parameter will be used.
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prior_match : str, optional
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The match mode to be used for the prior date, i.e., the date against which the return will be calculated.
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If not specified, the value for the closes parameter will be used.
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closest : previous | next | exact
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The default match mode for dates.
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* Previous: look for the immediate previous available date
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* Next: look for the immediate next available date
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* Exact: Only look for the exact date passed in the input
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if_not_found : fail | nan
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Specifies what should be done if the date is not found.
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* fail: raise a DateNotFoundError.
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* nan: return nan as the value.
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Note, this will return float('NaN') and not 'nan' as string.
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Note, this function will always raise an error if it is not possible to find a matching date.`
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For instance, if the input date is before the starting of the first date of the time series,
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but match mode is set to previous. A DateOutOfRangeError will be raised in such cases.
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compounding : bool, optional
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Should the returns be compounded annually.
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interval_type : years | month | days
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The interval for the return calculation.
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interval_value : int, optional
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The value of the interval for return calculation.
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date_format : str, optional
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A datetime library compatible format string.
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If not specified, will use the setting in FincalOptions.date_format.
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Returns
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-------
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Returs the rolling returns as a TimeSeries object.
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Raises
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------
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ValueError
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- If an invalid argument is passed for frequency parameter.
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See also
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--------
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TimeSeries.calculate_returns
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"""
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from_date = _parse_date(from_date, date_format)
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to_date = _parse_date(to_date, date_format)
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if frequency is None:
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frequency = self.frequency
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else:
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try:
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frequency = getattr(AllFrequencies, frequency)
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except AttributeError:
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raise ValueError(f"Invalid argument for frequency {frequency}")
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dates = create_date_series(from_date, to_date, frequency.symbol)
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if frequency == AllFrequencies.D:
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dates = [i for i in dates if i in self.data]
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rolling_returns = []
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for i in dates:
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returns = self.calculate_returns(
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as_on=i,
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compounding=compounding,
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interval_type=interval_type,
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interval_value=interval_value,
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as_on_match=as_on_match,
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prior_match=prior_match,
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closest=closest,
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if_not_found=if_not_found,
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)
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rolling_returns.append(returns)
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rolling_returns.sort()
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return self.__class__(rolling_returns, self.frequency.symbol)
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if __name__ == "__main__":
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date_series = [
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datetime.datetime(2020, 1, 11),
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datetime.datetime(2020, 1, 12),
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datetime.datetime(2020, 1, 13),
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datetime.datetime(2020, 1, 14),
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datetime.datetime(2020, 1, 17),
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datetime.datetime(2020, 1, 18),
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datetime.datetime(2020, 1, 19),
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datetime.datetime(2020, 1, 20),
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datetime.datetime(2020, 1, 22),
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]
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