365 lines
12 KiB
Python
365 lines
12 KiB
Python
import datetime
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from dataclasses import dataclass
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from typing import Dict, Iterable, List, Literal, Tuple, Union
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from dateutil.relativedelta import relativedelta
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@dataclass
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class Options:
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date_format: str = '%Y-%m-%d'
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closest: str = 'before' # after
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@dataclass(frozen=True)
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class Frequency:
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name: str
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freq_type: str
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value: int
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days: int
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class AllFrequencies:
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D = Frequency('daily', 'days', 1, 1)
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W = Frequency('weekly', 'days', 7, 7)
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M = Frequency('monthly', 'months', 1, 30)
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Q = Frequency('quarterly', 'months', 3, 91)
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H = Frequency('half-yearly', 'months', 6, 182)
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Y = Frequency('annual', 'years', 1, 365)
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def create_date_series(
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start_date: datetime.datetime,
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end_date: datetime.datetime,
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frequency: Frequency
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) -> List[datetime.datetime]:
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"""Creates a date series using a frequency"""
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print(f"{start_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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dates.append(start_date + relativedelta(**diff))
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return dates
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def _preprocess_timeseries(
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data: Union[
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List[Iterable[Union[str, datetime.datetime, float]]],
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List[Dict[str, Union[float, datetime.datetime]]],
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List[Dict[Union[str, datetime.datetime], float]],
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Dict[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, list):
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if isinstance(data[0], dict):
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if len(data[0].keys()) == 2:
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current_data = [tuple(i.values()) for i in data]
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elif len(data[0].keys()) == 1:
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current_data = [tuple(*i.items()) for i in data]
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else:
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raise TypeError("Could not parse the data")
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current_data = _preprocess_timeseries(current_data, date_format)
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elif isinstance(data[0], Iterable):
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if isinstance(data[0][0], str):
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current_data = []
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for i in data:
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row = datetime.datetime.strptime(i[0], date_format), i[1]
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current_data.append(row)
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elif isinstance(data[0][0], datetime.datetime):
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current_data = [(i, j) for i, j in data]
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else:
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raise TypeError("Could not parse the data")
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else:
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raise TypeError("Could not parse the data")
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elif isinstance(data, dict):
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current_data = [(k, v) for k, v in data.items()]
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current_data = _preprocess_timeseries(current_data, date_format)
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else:
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raise TypeError("Could not parse the data")
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current_data.sort()
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return current_data
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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 closest argument: {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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class TimeSeriesCore:
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"""Defines the core building blocks of a TimeSeries object"""
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def __init__(
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self,
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data: List[Iterable],
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date_format: str = "%Y-%m-%d",
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frequency=Literal['D', 'W', 'M', 'Q', 'H', 'Y']
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):
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"""Instantiate a TimeSeries object
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Parameters
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----------
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data : List[tuple]
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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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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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data = _preprocess_timeseries(data, date_format=date_format)
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self.time_series = dict(data)
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self.dates = set(list(self.time_series))
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if len(self.dates) != len(data):
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print("Warning: The input data contains duplicate dates which have been ignored.")
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self.start_date = list(self.time_series)[0]
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self.end_date = list(self.time_series)[-1]
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self.frequency = getattr(AllFrequencies, frequency)
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def __repr__(self):
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if len(self.time_series) > 6:
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printable_data_1 = list(self.time_series)[:3]
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printable_data_2 = list(self.time_series)[-3:]
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printable_str = "TimeSeries([{}\n\t...\n\t{}])".format(
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',\n\t'.join([str((i, self.time_series[i])) for i in printable_data_1]),
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',\n\t'.join([str((i, self.time_series[i])) for i in printable_data_2])
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)
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else:
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printable_data = self.time_series
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printable_str = "TimeSeries([{}])".format(',\n\t'.join(
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[str((i, self.time_series[i])) for i in printable_data]))
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return printable_str
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def __str__(self):
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if len(self.time_series) > 6:
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printable_data_1 = list(self.time_series)[:3]
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printable_data_2 = list(self.time_series)[-3:]
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printable_str = "[{}\n ...\n {}]".format(
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',\n '.join([str((i, self.time_series[i])) for i in printable_data_1]),
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',\n '.join([str((i, self.time_series[i])) for i in printable_data_2])
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)
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else:
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printable_data = self.time_series
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printable_str = "[{}]".format(',\n '.join([str((i, self.time_series[i])) for i in printable_data]))
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return printable_str
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def __getitem__(self, n):
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all_keys = list(self.time_series.keys())
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if isinstance(n, int):
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keys = [all_keys[n]]
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else:
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keys = all_keys[n]
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item = [(key, self.time_series[key]) for key in keys]
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if len(item) == 1:
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return item[0]
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return item
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def __len__(self):
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return len(self.time_series.keys())
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def head(self, n: int = 6):
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keys = list(self.time_series.keys())
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keys = keys[:n]
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result = [(key, self.time_series[key]) for key in keys]
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return result
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def tail(self, n: int = 6):
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keys = list(self.time_series.keys())
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keys = keys[-n:]
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result = [(key, self.time_series[key]) for key in keys]
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return result
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class TimeSeries(TimeSeriesCore):
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"""Container for TimeSeries objects"""
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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.time_series.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=False):
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num_days = (self.end_date - self.start_date).days + 1
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new_ts = dict()
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for i in range(num_days):
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cur_date = self.start_date + datetime.timedelta(days=i)
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try:
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cur_val = self.time_series[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.time_series = new_ts
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return None
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return new_ts
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def bfill(self, inplace=False):
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num_days = (self.end_date - self.start_date).days + 1
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new_ts = dict()
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for i in range(num_days):
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cur_date = self.end_date - datetime.timedelta(days=i)
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try:
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cur_val = self.time_series[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.time_series = new_ts
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return None
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return dict(reversed(new_ts.items()))
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def calculate_returns(
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self,
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as_on: datetime.datetime,
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as_on_match: str = 'closest',
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prior_match: str = 'closest',
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closest: str = "previous",
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compounding: bool = True,
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years: int = 1
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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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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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compounding : bool, optional
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Whether the return should be compounded annually.
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years : int, optional
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number of years for which the returns should be calculated
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Returns
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-------
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The 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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"""
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as_on_delta, prior_delta = _preprocess_match_options(as_on_match, prior_match, closest)
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while True:
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current = self.time_series.get(as_on, None)
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if current is not None:
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break
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elif not as_on_delta:
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raise ValueError("As on date not found")
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as_on += as_on_delta
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prev_date = as_on - relativedelta(years=years)
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while True:
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previous = self.time_series.get(prev_date, None)
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if previous is not None:
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break
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elif not prior_delta:
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raise ValueError("Previous date not found")
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prev_date += prior_delta
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returns = current / previous
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if compounding:
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returns = returns ** (1 / years)
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return returns - 1
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def calculate_rolling_returns(
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self,
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from_date: datetime.date,
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to_date: datetime.date,
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frequency: str = "D",
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as_on_match: str = 'closest',
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prior_match: str = 'closest',
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closest: str = "previous",
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compounding: bool = True,
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years: int = 1,
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) -> List[tuple]:
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"""Calculates the rolling return"""
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all_dates = create_date_series(from_date, to_date, getattr(AllFrequencies, frequency))
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dates = set(all_dates)
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if frequency == AllFrequencies.D:
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dates = all_dates.intersection(self.dates)
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rolling_returns = []
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for i in dates:
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returns = self.calculate_returns(as_on=i, compounding=compounding, years=years, as_on_match=as_on_match,
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prior_match=prior_match, closest=closest)
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rolling_returns.append((i, returns))
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self.rolling_returns = rolling_returns
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return self.rolling_returns
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if __name__ == '__main__':
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date_series = [
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datetime.datetime(2020, 1, 1),
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datetime.datetime(2020, 1, 2),
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datetime.datetime(2020, 1, 3),
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datetime.datetime(2020, 1, 4),
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datetime.datetime(2020, 1, 7),
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datetime.datetime(2020, 1, 8),
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datetime.datetime(2020, 1, 9),
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datetime.datetime(2020, 1, 10),
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datetime.datetime(2020, 1, 12),
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]
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