import datetime import math import random from unittest import skip import pytest from dateutil.relativedelta import relativedelta from fincal.core import AllFrequencies, Frequency from fincal.exceptions import DateNotFoundError from fincal.fincal import MaxDrawdown, TimeSeries, create_date_series from fincal.utils import FincalOptions def create_prices(s0: float, mu: float, sigma: float, num_prices: int) -> list: """Generates a price following a geometric brownian motion process based on the input of the arguments. Since this function is used only to generate data for tests, the seed is fixed as 1234. Many of the tests rely on exact values generated using this seed. If the seed is changed, those tests will fail. Parameters: ------------ s0: float Asset inital price. mu: float Interest rate expressed annual terms. sigma: float Volatility expressed annual terms. num_prices: int number of prices to generate Returns: -------- Returns a list of values generated using GBM algorithm """ random.seed(1234) # WARNING! Changing the seed will cause most tests to fail all_values = [] for _ in range(num_prices): s0 *= math.exp( (mu - 0.5 * sigma**2) * (1.0 / 365.0) + sigma * math.sqrt(1.0 / 365.0) * random.gauss(mu=0, sigma=1) ) all_values.append(round(s0, 2)) return all_values def create_test_timeseries( frequency: Frequency, num: int = 1000, skip_weekends: bool = False, mu: float = 0.1, sigma: float = 0.05 ) -> TimeSeries: """Creates TimeSeries data Parameters: ----------- frequency: Frequency The frequency of the time series data to be generated. num: int Number of date: value pairs to be generated. skip_weekends: bool Whether weekends (saturday, sunday) should be skipped. Gets used only if the frequency is daily. mu: float Mean return for the values. sigma: float standard deviation of the values. Returns: -------- Returns a TimeSeries object """ start_date = datetime.datetime(2017, 1, 1) timedelta_dict = { frequency.freq_type: int( frequency.value * num * (7 / 5 if frequency == AllFrequencies.D and skip_weekends else 1) ) } end_date = start_date + relativedelta(**timedelta_dict) dates = create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends) values = create_prices(1000, mu, sigma, num) ts = TimeSeries(dict(zip(dates, values)), frequency=frequency.symbol) return ts class TestReturns: def test_returns_calc(self): ts = create_test_timeseries(AllFrequencies.D, skip_weekends=True) returns = ts.calculate_returns( "2020-01-01", annual_compounded_returns=False, return_period_unit="years", return_period_value=1 ) assert round(returns[1], 6) == 0.112913 returns = ts.calculate_returns( "2020-04-01", annual_compounded_returns=False, return_period_unit="months", return_period_value=3 ) assert round(returns[1], 6) == 0.015908 returns = ts.calculate_returns( "2020-04-01", annual_compounded_returns=True, return_period_unit="months", return_period_value=3 ) assert round(returns[1], 6) == 0.065167 returns = ts.calculate_returns( "2020-04-01", annual_compounded_returns=False, return_period_unit="days", return_period_value=90 ) assert round(returns[1], 6) == 0.017673 returns = ts.calculate_returns( "2020-04-01", annual_compounded_returns=True, return_period_unit="days", return_period_value=90 ) assert round(returns[1], 6) == 0.073632 with pytest.raises(DateNotFoundError): ts.calculate_returns("2020-04-04", return_period_unit="days", return_period_value=90, as_on_match="exact") with pytest.raises(DateNotFoundError): ts.calculate_returns("2020-04-04", return_period_unit="months", return_period_value=3, prior_match="exact") def test_date_formats(self): ts = create_test_timeseries(AllFrequencies.D, skip_weekends=True) FincalOptions.date_format = "%d-%m-%Y" with pytest.raises(ValueError): ts.calculate_returns( "2020-04-10", annual_compounded_returns=True, return_period_unit="days", return_period_value=90 ) returns1 = ts.calculate_returns( "2020-04-01", return_period_unit="days", return_period_value=90, date_format="%Y-%m-%d" ) returns2 = ts.calculate_returns("01-04-2020", return_period_unit="days", return_period_value=90) assert round(returns1[1], 6) == round(returns2[1], 6) == 0.073632 FincalOptions.date_format = "%m-%d-%Y" with pytest.raises(ValueError): ts.calculate_returns( "2020-04-01", annual_compounded_returns=True, return_period_unit="days", return_period_value=90 ) returns1 = ts.calculate_returns( "2020-04-01", return_period_unit="days", return_period_value=90, date_format="%Y-%m-%d" ) returns2 = ts.calculate_returns("04-01-2020", return_period_unit="days", return_period_value=90) assert round(returns1[1], 6) == round(returns2[1], 6) == 0.073632 def test_limits(self): FincalOptions.date_format = "%Y-%m-%d" ts = create_test_timeseries(AllFrequencies.D) with pytest.raises(DateNotFoundError): ts.calculate_returns("2020-11-25", return_period_unit="days", return_period_value=90, closest_max_days=10) class TestVolatility: def test_daily_ts(self): ts = create_test_timeseries(AllFrequencies.D) assert len(ts) == 1000 sd = ts.volatility(annualize_volatility=False) assert round(sd, 6) == 0.002622 sd = ts.volatility() assert round(sd, 6) == 0.050098 sd = ts.volatility(annual_compounded_returns=True) assert round(sd, 4) == 37.9329 sd = ts.volatility(return_period_unit="months", annual_compounded_returns=True) assert round(sd, 4) == 0.6778 sd = ts.volatility(return_period_unit="years") assert round(sd, 6) == 0.023164 sd = ts.volatility(from_date="2017-10-01", to_date="2019-08-31", annualize_volatility=True) assert round(sd, 6) == 0.050559 sd = ts.volatility(from_date="2017-02-01", frequency="M", return_period_unit="months") assert round(sd, 6) == 0.050884 sd = ts.volatility( frequency="M", return_period_unit="months", return_period_value=3, annualize_volatility=False, ) assert round(sd, 6) == 0.020547 class TestDrawdown: def test_daily_ts(self): ts = create_test_timeseries(AllFrequencies.D, skip_weekends=True) mdd = ts.max_drawdown() assert isinstance(mdd, dict) assert len(mdd) == 3 assert all(i in mdd for i in ["start_date", "end_date", "drawdown"]) expeced_response = { "start_date": datetime.datetime(2017, 6, 6, 0, 0), "end_date": datetime.datetime(2017, 7, 31, 0, 0), "drawdown": -0.028293686030751997, } assert mdd == expeced_response def test_weekly_ts(self): ts = create_test_timeseries(AllFrequencies.W, mu=1, sigma=0.5) mdd = ts.max_drawdown() assert isinstance(mdd, dict) assert len(mdd) == 3 assert all(i in mdd for i in ["start_date", "end_date", "drawdown"]) expeced_response = { "start_date": datetime.datetime(2019, 2, 17, 0, 0), "end_date": datetime.datetime(2019, 11, 17, 0, 0), "drawdown": -0.2584760499552089, } assert mdd == expeced_response