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changed interval days calculation to math.ceil

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This is to avoid distorting round figure years
This commit is contained in:
Gourav Kumar 2022-06-26 20:40:03 +05:30
parent d229c9cf2d
commit 31abaa4052
2 changed files with 17 additions and 39 deletions
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10
pyfacts/statistics.py
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@ -90,7 +90,7 @@ def sharpe_ratio(
If risk free data or risk free rate is not provided. If risk free data or risk free rate is not provided.
""" """
interval_days = int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1) interval_days = math.ceil(_interval_to_years(return_period_unit, return_period_value) * 365)
if from_date is None: if from_date is None:
from_date = time_series_data.start_date + datetime.timedelta(days=interval_days) from_date = time_series_data.start_date + datetime.timedelta(days=interval_days)
@ -187,7 +187,7 @@ def beta(
The value of beta as a float. The value of beta as a float.
""" """
interval_years = _interval_to_years(return_period_unit, return_period_value) interval_years = _interval_to_years(return_period_unit, return_period_value)
interval_days = int(interval_years * 365 + 1) interval_days = math.ceil(interval_years * 365)
annual_compounded_returns = True if interval_years > 1 else False annual_compounded_returns = True if interval_years > 1 else False
@ -300,7 +300,7 @@ def jensens_alpha(
""" """
interval_years = _interval_to_years(return_period_unit, return_period_value) interval_years = _interval_to_years(return_period_unit, return_period_value)
interval_days = int(interval_years * 365 + 1) interval_days = math.ceil(interval_years * 365)
if from_date is None: if from_date is None:
from_date = asset_data.start_date + datetime.timedelta(days=interval_days) from_date = asset_data.start_date + datetime.timedelta(days=interval_days)
@ -423,7 +423,7 @@ def correlation(
* If both time series do not have data between the from date and to date * If both time series do not have data between the from date and to date
""" """
interval_years = _interval_to_years(return_period_unit, return_period_value) interval_years = _interval_to_years(return_period_unit, return_period_value)
interval_days = int(interval_years * 365 + 1) interval_days = math.ceil(interval_years * 365)
annual_compounded_returns = True if interval_years > 1 else False annual_compounded_returns = True if interval_years > 1 else False
@ -538,7 +538,7 @@ def sortino_ratio(
If risk free data or risk free rate is not provided. If risk free data or risk free rate is not provided.
""" """
interval_days = int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1) interval_days = math.ceil(_interval_to_years(return_period_unit, return_period_value) * 365)
if from_date is None: if from_date is None:
from_date = time_series_data.start_date + datetime.timedelta(days=interval_days) from_date = time_series_data.start_date + datetime.timedelta(days=interval_days)

46
tests/test_stats.py
View File

@ -86,47 +86,25 @@ class TestSharpe:
class TestSortino: class TestSortino:
def test_sortino_daily_freq(self, create_test_data): def test_sortino_daily_freq(self, create_test_data):
data = create_test_data(num=1305, frequency=pft.AllFrequencies.D, skip_weekends=True) data = create_test_data(num=3600, frequency=pft.AllFrequencies.D, mu=0.12, sigma=0.12)
ts = pft.TimeSeries(data, "D") ts = pft.TimeSeries(data, "D")
sortino_ratio = pft.sortino_ratio( sortino_ratio = pft.sortino_ratio(
ts, ts,
risk_free_rate=0.06, risk_free_rate=0.06 / 12,
from_date="2017-02-02", from_date="2017-02-02",
to_date="2021-12-31",
return_period_unit="months", return_period_unit="months",
return_period_value=1, return_period_value=1,
) )
assert round(sortino_ratio, 4) == 2.5377 assert round(sortino_ratio, 4) == 1.625
# sharpe_ratio = pft.sharpe_ratio( sortino_ratio = pft.sortino_ratio(
# ts, ts,
# risk_free_rate=0.06, risk_free_rate=0.06,
# from_date="2017-01-09", from_date="2018-01-02",
# to_date="2021-12-31", return_period_unit="years",
# return_period_unit="days", return_period_value=1,
# return_period_value=7, )
# ) assert round(sortino_ratio, 4) == 1.2564
# assert round(sharpe_ratio, 4) == 1.0701
# sharpe_ratio = pft.sharpe_ratio(
# ts,
# risk_free_rate=0.06,
# from_date="2018-01-02",
# to_date="2021-12-31",
# return_period_unit="years",
# return_period_value=1,
# )
# assert round(sharpe_ratio, 4) == 1.4374
# sharpe_ratio = pft.sharpe_ratio(
# ts,
# risk_free_rate=0.06,
# from_date="2017-07-03",
# to_date="2021-12-31",
# return_period_unit="months",
# return_period_value=6,
# )
# assert round(sharpe_ratio, 4) == 0.8401
# def test_sharpe_weekly_freq(self, create_test_data): # def test_sharpe_weekly_freq(self, create_test_data):
# data = create_test_data(num=261, frequency=pft.AllFrequencies.W, mu=0.6, sigma=0.7) # data = create_test_data(num=261, frequency=pft.AllFrequencies.W, mu=0.6, sigma=0.7)
@ -185,7 +163,7 @@ class TestBeta:
sts = pft.TimeSeries(stock_data, "D") sts = pft.TimeSeries(stock_data, "D")
mts = pft.TimeSeries(market_data, "D") mts = pft.TimeSeries(market_data, "D")
beta = pft.beta(sts, mts, frequency="M") beta = pft.beta(sts, mts, frequency="M")
assert round(beta, 4) == 1.6137 assert round(beta, 4) == 1.6131
def test_beta_monthly_freq_monthly_returns(self, create_test_data): def test_beta_monthly_freq_monthly_returns(self, create_test_data):
market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D) market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D)