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Merge pull request #235 from quant12345/map
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Replacing the obsolete applymap with map.
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@timkpaine
timkpaine authored Jul 1, 2024
2 parents 0544289 + 60e4608 commit 32e6304
Showing 1 changed file with 31 additions and 19 deletions.
50 changes: 31 additions & 19 deletions tests/test_core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import numpy as np
from pytest import fixture
from numpy.testing import assert_almost_equal as aae
from packaging.version import Version


@fixture
Expand All @@ -16,6 +17,7 @@ def df():
raise (str(e2))
return df


@fixture
def ts(df):
return df["AAPL"].iloc[0:10]
Expand All @@ -25,7 +27,7 @@ def test_mtd_ytd(df):
data = df["AAPL"]

# Intramonth
prices = data[pd.to_datetime("2004-12-10") : pd.to_datetime("2004-12-25")]
prices = data[pd.to_datetime("2004-12-10"): pd.to_datetime("2004-12-25")]
dp = prices.resample("D").last().dropna()
mp = prices.resample(ffn.core._MonthEnd).last().dropna()
yp = prices.resample(ffn.core._YearEnd).last().dropna()
Expand All @@ -36,7 +38,7 @@ def test_mtd_ytd(df):
assert mtd_actual == ytd_actual

# Year change - first month
prices = data[pd.to_datetime("2004-12-10") : pd.to_datetime("2005-01-15")]
prices = data[pd.to_datetime("2004-12-10"): pd.to_datetime("2005-01-15")]
dp = prices.resample("D").last().dropna()
mp = prices.resample(ffn.core._MonthEnd).last().dropna()
yp = prices.resample(ffn.core._YearEnd).last().dropna()
Expand All @@ -47,7 +49,7 @@ def test_mtd_ytd(df):
assert mtd_actual == ytd_actual

# Year change - second month
prices = data[pd.to_datetime("2004-12-10") : pd.to_datetime("2005-02-15")]
prices = data[pd.to_datetime("2004-12-10"): pd.to_datetime("2005-02-15")]
dp = prices.resample("D").last().dropna()
mp = prices.resample(ffn.core._MonthEnd).last().dropna()
yp = prices.resample(ffn.core._YearEnd).last().dropna()
Expand Down Expand Up @@ -267,6 +269,7 @@ def test_calc_inv_vol_weights(df):
aae(actual["MSFT"], 0.464, 3)
aae(actual["C"], 0.318, 3)


def test_calc_inv_vol_weights_object_regression_204(df):
prc = df.iloc[0:11]
rets = prc.to_returns().dropna().astype(object)
Expand Down Expand Up @@ -442,6 +445,13 @@ def test_limit_weights():


def test_random_weights():
PANDAS_VERSION = Version(pd.__version__)
PANDAS_210 = PANDAS_VERSION >= Version("2.1.0")

select_map = "map"
if not PANDAS_210:
select_map = "applymap"

n = 10
bounds = (0.0, 1.0)
tot = 1.0000
Expand All @@ -452,7 +462,8 @@ def test_random_weights():
for i in df.index:
df.loc[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert df.applymap(lambda x: (x >= low and x <= high)).all().all()
assert (getattr(df, select_map)(lambda x: (x >= low and x <= high))
.all().all())

n = 4
bounds = (0.0, 0.25)
Expand All @@ -465,7 +476,8 @@ def test_random_weights():
df.loc[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert (
df.applymap(lambda x: (np.round(x, 2) >= low and np.round(x, 2) <= high))
getattr(df, select_map)(lambda x: (np.round(x, 2) >= low
and np.round(x, 2) <= high))
.all()
.all()
)
Expand All @@ -481,7 +493,8 @@ def test_random_weights():
df.loc[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert (
df.applymap(lambda x: (np.round(x, 2) >= low and np.round(x, 2) <= high))
getattr(df, select_map)(lambda x: (np.round(x, 2) >= low
and np.round(x, 2) <= high))
.all()
.all()
)
Expand All @@ -497,7 +510,8 @@ def test_random_weights():
df.loc[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert (
df.applymap(lambda x: (np.round(x, 2) >= low and np.round(x, 2) <= high))
getattr(df, select_map)(lambda x: (np.round(x, 2) >= low
and np.round(x, 2) <= high))
.all()
.all()
)
Expand Down Expand Up @@ -862,7 +876,6 @@ def test_resample_returns(df):


def test_monthly_returns():

dates = [
"31/12/2017",
"5/1/2018",
Expand Down Expand Up @@ -956,27 +969,26 @@ def test_drawdown_details(df):

def test_infer_nperiods():
daily = pd.DataFrame(np.random.randn(10),
index = pd.date_range(start='2018-01-01', periods = 10, freq = 'D'))
index=pd.date_range(start='2018-01-01', periods=10, freq='D'))
hourly = pd.DataFrame(np.random.randn(10),
index = pd.date_range(start='2018-01-01', periods = 10, freq = 'h'))
index=pd.date_range(start='2018-01-01', periods=10, freq='h'))
yearly = pd.DataFrame(np.random.randn(10),
index = pd.date_range(start='2018-01-01', periods = 10, freq = ffn.core._YearEnd))
index=pd.date_range(start='2018-01-01', periods=10, freq=ffn.core._YearEnd))
monthly = pd.DataFrame(np.random.randn(10),
index = pd.date_range(start='2018-01-01', periods = 10, freq = ffn.core._MonthEnd))
index=pd.date_range(start='2018-01-01', periods=10, freq=ffn.core._MonthEnd))
minutely = pd.DataFrame(np.random.randn(10),
index = pd.date_range(start='2018-01-01', periods = 10, freq = 'min'))
index=pd.date_range(start='2018-01-01', periods=10, freq='min'))
secondly = pd.DataFrame(np.random.randn(10),
index = pd.date_range(start='2018-01-01', periods = 10, freq = 's'))
index=pd.date_range(start='2018-01-01', periods=10, freq='s'))

minutely_30 = pd.DataFrame(np.random.randn(10),
index = pd.date_range(start='2018-01-01', periods = 10, freq = '30min'))

index=pd.date_range(start='2018-01-01', periods=10, freq='30min'))

not_known_vals = np.concatenate((pd.date_range(start='2018-01-01', periods = 5, freq = '1h').values,
pd.date_range(start='2018-01-02', periods = 5, freq = '5h').values))
not_known_vals = np.concatenate((pd.date_range(start='2018-01-01', periods=5, freq='1h').values,
pd.date_range(start='2018-01-02', periods=5, freq='5h').values))

not_known = pd.DataFrame(np.random.randn(10),
index = pd.DatetimeIndex(not_known_vals))
index=pd.DatetimeIndex(not_known_vals))

assert ffn.core.infer_nperiods(daily) == ffn.core.TRADING_DAYS_PER_YEAR
assert ffn.core.infer_nperiods(hourly) == ffn.core.TRADING_DAYS_PER_YEAR * 24
Expand Down

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