Compare commits
No commits in common. "793d5b1ad7bffbc975e8de458f9886a36067f500" and "17b3e348a241b9279b3b56ddef92e67df952d5f8" have entirely different histories.
793d5b1ad7
...
17b3e348a2
@ -1,8 +1,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import datetime
|
||||
import math
|
||||
import statistics
|
||||
from typing import Iterable, List, Literal, Mapping, Union
|
||||
|
||||
from dateutil.relativedelta import relativedelta
|
||||
@ -192,7 +190,7 @@ class TimeSeries(TimeSeriesCore):
|
||||
closest: Literal["previous", "next", "exact"] = "previous",
|
||||
closest_max_days: int = -1,
|
||||
if_not_found: Literal["fail", "nan"] = "fail",
|
||||
annual_compounded_returns: bool = True,
|
||||
compounding: bool = True,
|
||||
interval_type: Literal["years", "months", "days"] = "years",
|
||||
interval_value: int = 1,
|
||||
date_format: str = None,
|
||||
@ -270,7 +268,7 @@ class TimeSeries(TimeSeriesCore):
|
||||
return as_on, float("NaN")
|
||||
|
||||
returns = current[1] / previous[1]
|
||||
if annual_compounded_returns:
|
||||
if compounding:
|
||||
years = _interval_to_years(interval_type, interval_value)
|
||||
returns = returns ** (1 / years)
|
||||
return (current[0] if return_actual_date else as_on), returns - 1
|
||||
@ -285,7 +283,7 @@ class TimeSeries(TimeSeriesCore):
|
||||
prior_match: str = "closest",
|
||||
closest: Literal["previous", "next", "exact"] = "previous",
|
||||
if_not_found: Literal["fail", "nan"] = "fail",
|
||||
annual_compounded_returns: bool = True,
|
||||
compounding: bool = True,
|
||||
interval_type: Literal["years", "months", "days"] = "years",
|
||||
interval_value: int = 1,
|
||||
date_format: str = None,
|
||||
@ -372,7 +370,7 @@ class TimeSeries(TimeSeriesCore):
|
||||
for i in dates:
|
||||
returns = self.calculate_returns(
|
||||
as_on=i,
|
||||
annual_compounded_returns=annual_compounded_returns,
|
||||
compounding=compounding,
|
||||
interval_type=interval_type,
|
||||
interval_value=interval_value,
|
||||
as_on_match=as_on_match,
|
||||
@ -384,60 +382,6 @@ class TimeSeries(TimeSeriesCore):
|
||||
rolling_returns.sort()
|
||||
return self.__class__(rolling_returns, self.frequency.symbol)
|
||||
|
||||
@date_parser(1, 2)
|
||||
def volatility(
|
||||
self,
|
||||
from_date: Union[datetime.date, str],
|
||||
to_date: Union[datetime.date, str],
|
||||
frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
|
||||
as_on_match: str = "closest",
|
||||
prior_match: str = "closest",
|
||||
closest: Literal["previous", "next", "exact"] = "previous",
|
||||
if_not_found: Literal["fail", "nan"] = "fail",
|
||||
annual_compounded_returns: bool = None,
|
||||
interval_type: Literal["years", "months", "days"] = "days",
|
||||
interval_value: int = 1,
|
||||
date_format: str = None,
|
||||
annualize_volatility: bool = True,
|
||||
):
|
||||
"""Calculates the volatility of the time series.add()
|
||||
|
||||
The volatility is calculated as the standard deviaion of periodic returns.
|
||||
The periodicity of returns is based on the periodicity of underlying data.
|
||||
"""
|
||||
|
||||
if frequency is None:
|
||||
frequency = self.frequency
|
||||
else:
|
||||
try:
|
||||
frequency = getattr(AllFrequencies, frequency)
|
||||
except AttributeError:
|
||||
raise ValueError(f"Invalid argument for frequency {frequency}")
|
||||
|
||||
if annual_compounded_returns is None:
|
||||
annual_compounded_returns = False if frequency.days <= 366 else True
|
||||
|
||||
rolling_returns = self.calculate_rolling_returns(
|
||||
from_date=from_date,
|
||||
to_date=to_date,
|
||||
frequency=frequency.symbol,
|
||||
as_on_match=as_on_match,
|
||||
prior_match=prior_match,
|
||||
closest=closest,
|
||||
if_not_found=if_not_found,
|
||||
annual_compounded_returns=annual_compounded_returns,
|
||||
interval_type=interval_type,
|
||||
interval_value=interval_value,
|
||||
)
|
||||
sd = statistics.stdev(rolling_returns.values)
|
||||
if annualize_volatility:
|
||||
if interval_type == "months":
|
||||
sd *= math.sqrt(12)
|
||||
elif interval_type == "days":
|
||||
sd *= math.sqrt(252)
|
||||
|
||||
return sd
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
date_series = [
|
||||
|
||||
81
test2.py
81
test2.py
@ -1,58 +1,37 @@
|
||||
import pandas as pd
|
||||
# type: ignore
|
||||
|
||||
from fincal.fincal import TimeSeries, create_date_series
|
||||
if __name__ == "__main__":
|
||||
|
||||
dfd = pd.read_csv("test_files/nav_history_daily - Copy.csv")
|
||||
dfd = dfd[dfd["amfi_code"] == 118825].reset_index(drop=True)
|
||||
ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency="D")
|
||||
repr(ts)
|
||||
# print(ts[['2022-01-31', '2021-05-28']])
|
||||
import datetime
|
||||
import time
|
||||
|
||||
# rr = ts.calculate_rolling_returns(from_date='2021-01-01', to_date='2022-01-01', frequency='D', interval_type='days', interval_value=30, compounding=False)
|
||||
import pandas as pd
|
||||
|
||||
from fincal.fincal import TimeSeries
|
||||
|
||||
# data = [
|
||||
# ("2020-01-01", 10),
|
||||
# ("2020-02-01", 12),
|
||||
# ("2020-03-01", 14),
|
||||
# ("2020-04-01", 16),
|
||||
# ("2020-05-01", 18),
|
||||
# ("2020-06-01", 20),
|
||||
# ("2020-07-01", 22),
|
||||
# ("2020-08-01", 24),
|
||||
# ("2020-09-01", 26),
|
||||
# ("2020-10-01", 28),
|
||||
# ("2020-11-01", 30),
|
||||
# ("2020-12-01", 32),
|
||||
# ("2021-01-01", 34),
|
||||
# ]
|
||||
df = pd.read_csv('test_files/msft.csv')
|
||||
df = df.sort_values(by='Date') # type: ignore
|
||||
data_list = [(i.Date, i.Close) for i in df.itertuples()]
|
||||
|
||||
# ts = TimeSeries(data, frequency="M")
|
||||
# rr = ts.calculate_rolling_returns(
|
||||
# "2020-02-01",
|
||||
# "2021-01-01",
|
||||
# if_not_found="nan",
|
||||
# compounding=False,
|
||||
# interval_type="months",
|
||||
# interval_value=1,
|
||||
# as_on_match="exact",
|
||||
# )
|
||||
start = time.time()
|
||||
ts_data = TimeSeries(data_list, frequency='D', date_format='%d-%m-%Y')
|
||||
print(f"Instantiation took {round((time.time() - start)*1000, 2)} ms")
|
||||
# ts_data.fill_missing_days()
|
||||
start = time.time()
|
||||
# ts_data.calculate_returns(as_on=datetime.datetime(2022, 1, 4), closest='next', years=1)
|
||||
rr = ts_data.calculate_rolling_returns(datetime.datetime(1994, 1, 1),
|
||||
datetime.datetime(2022, 2, 17),
|
||||
frequency='D',
|
||||
as_on_match='next',
|
||||
prior_match='previous',
|
||||
closest='previous',
|
||||
years=1)
|
||||
|
||||
# for i in rr:
|
||||
# print(i)
|
||||
|
||||
# returns = ts.calculate_returns(
|
||||
# "2020-04-25",
|
||||
# return_actual_date=True,
|
||||
# closest_max_days=15,
|
||||
# compounding=True,
|
||||
# interval_type="days",
|
||||
# interval_value=90,
|
||||
# closest="previous",
|
||||
# if_not_found="fail",
|
||||
# )
|
||||
|
||||
# print(returns)
|
||||
|
||||
volatility = ts.volatility(start_date="2018-01-01", end_date="2021-01-01")
|
||||
print(volatility)
|
||||
# ffill_data = ts_data.bfill()
|
||||
print(f"Calculation took {round((time.time() - start)*1000, 2)} ms")
|
||||
rr.sort()
|
||||
for i in rr[:10]:
|
||||
print(i)
|
||||
# print(ffill_data)
|
||||
# print(ts_data)
|
||||
# print(repr(ts_data))
|
||||
|
||||
@ -199,17 +199,17 @@ class TestReturns:
|
||||
|
||||
def test_returns_calc(self):
|
||||
ts = TimeSeries(self.data, frequency="M")
|
||||
returns = ts.calculate_returns("2021-01-01", annual_compounded_returns=False, interval_type="years", interval_value=1)
|
||||
returns = ts.calculate_returns("2021-01-01", compounding=False, interval_type="years", interval_value=1)
|
||||
assert returns[1] == 2.4
|
||||
returns = ts.calculate_returns("2020-04-01", annual_compounded_returns=False, interval_type="months", interval_value=3)
|
||||
returns = ts.calculate_returns("2020-04-01", compounding=False, interval_type="months", interval_value=3)
|
||||
assert round(returns[1], 4) == 0.6
|
||||
returns = ts.calculate_returns("2020-04-01", annual_compounded_returns=True, interval_type="months", interval_value=3)
|
||||
returns = ts.calculate_returns("2020-04-01", compounding=True, interval_type="months", interval_value=3)
|
||||
assert round(returns[1], 4) == 5.5536
|
||||
returns = ts.calculate_returns("2020-04-01", annual_compounded_returns=False, interval_type="days", interval_value=90)
|
||||
returns = ts.calculate_returns("2020-04-01", compounding=False, interval_type="days", interval_value=90)
|
||||
assert round(returns[1], 4) == 0.6
|
||||
returns = ts.calculate_returns("2020-04-01", annual_compounded_returns=True, interval_type="days", interval_value=90)
|
||||
returns = ts.calculate_returns("2020-04-01", compounding=True, interval_type="days", interval_value=90)
|
||||
assert round(returns[1], 4) == 5.727
|
||||
returns = ts.calculate_returns("2020-04-10", annual_compounded_returns=True, interval_type="days", interval_value=90)
|
||||
returns = ts.calculate_returns("2020-04-10", compounding=True, interval_type="days", interval_value=90)
|
||||
assert round(returns[1], 4) == 5.727
|
||||
with pytest.raises(DateNotFoundError):
|
||||
ts.calculate_returns("2020-04-10", interval_type="days", interval_value=90, as_on_match="exact")
|
||||
@ -220,7 +220,7 @@ class TestReturns:
|
||||
ts = TimeSeries(self.data, frequency="M")
|
||||
FincalOptions.date_format = "%d-%m-%Y"
|
||||
with pytest.raises(ValueError):
|
||||
ts.calculate_returns("2020-04-10", annual_compounded_returns=True, interval_type="days", interval_value=90)
|
||||
ts.calculate_returns("2020-04-10", compounding=True, interval_type="days", interval_value=90)
|
||||
|
||||
returns1 = ts.calculate_returns("2020-04-10", interval_type="days", interval_value=90, date_format="%Y-%m-%d")
|
||||
returns2 = ts.calculate_returns("10-04-2020", interval_type="days", interval_value=90)
|
||||
@ -228,7 +228,7 @@ class TestReturns:
|
||||
|
||||
FincalOptions.date_format = "%m-%d-%Y"
|
||||
with pytest.raises(ValueError):
|
||||
ts.calculate_returns("2020-04-10", annual_compounded_returns=True, interval_type="days", interval_value=90)
|
||||
ts.calculate_returns("2020-04-10", compounding=True, interval_type="days", interval_value=90)
|
||||
|
||||
returns1 = ts.calculate_returns("2020-04-10", interval_type="days", interval_value=90, date_format="%Y-%m-%d")
|
||||
returns2 = ts.calculate_returns("04-10-2020", interval_type="days", interval_value=90)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user