def aroon(s, n=25):
from pandas import DataFrame, Series
from pandas.stats import moments
s = pullStockData(s)
up = 100 * moments.rolling_apply(s['High'], n + 1, lambda x: x.argmax()) / n
dn = 100 * moments.rolling_apply(s['Low'], n + 1, lambda x: x.argmin()) / n
return DataFrame(dict(up=up, down=dn))
def test_rolling_apply_out_of_bounds(self):
# #1850
arr = np.arange(4)
# it works!
result = mom.rolling_apply(arr, 10, np.sum)
self.assertTrue(isnull(result).all())
result = mom.rolling_apply(arr, 10, np.sum, min_periods=1)
assert_almost_equal(result, result)
def test_rolling_apply_out_of_bounds(self):
# #1850
arr = np.arange(4)
# it works!
result = mom.rolling_apply(arr, 10, np.sum)
self.assert_(isnull(result).all())
result = mom.rolling_apply(arr, 10, np.sum, min_periods=1)
assert_almost_equal(result, result)
def roll_mean(x, window, min_periods=None, freq=None, center=False):
return mom.rolling_apply(x,
window,
lambda x: x[np.isfinite(x)].mean(),
min_periods=min_periods,
freq=freq,
center=center)
def cci(s, n=20, c=0.015):
if isinstance(s, DataFrame):
s = s[['high', 'low', 'close']].mean(axis=1)
mavg = moments.rolling_mean(s, n)
mdev = moments.rolling_apply(s, n, lambda x: np.fabs(x - x.mean()).mean())
return (s - mavg) / (c * mdev)
def cci(s, n=20, c=0.015):
if isinstance(s, DataFrame):
s = s[['high', 'low', 'close']].mean(axis=1)
mavg = moments.rolling_mean(s, n)
mdev = moments.rolling_apply(s, n, lambda x: np.fabs(x - x.mean()).mean())
return (s - mavg)/(c * mdev)
def test_rolling_apply(self):
ser = Series([])
assert_series_equal(ser, mom.rolling_apply(ser, 10, lambda x:x.mean()))
def roll_mean(x, window, min_periods=None, freq=None):
return mom.rolling_apply(x, window,
lambda x: x[np.isfinite(x)].mean(),
min_periods=min_periods,
freq=freq)
self._check_moment_func(roll_mean, np.mean)
def test_rolling_apply(self):
ser = Series([])
assert_series_equal(ser, mom.rolling_apply(ser, 10, lambda x:x.mean()))
def roll_mean(x, window, min_periods=None, freq=None):
return mom.rolling_apply(x, window,
lambda x: x[np.isfinite(x)].mean(),
min_periods=min_periods,
freq=freq)
self._check_moment_func(roll_mean, np.mean)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index,
major_axis=df.columns,
minor_axis=df.columns)
functions = [
lambda x: mom.rolling_cov(
x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(
x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(
x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(
x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df_result = f(df)
assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [
lambda x: mom.rolling_cov(
x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(
x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5
),
]
for f in functions:
df_result_panel = f(df)
assert_panel_equal(df_result_panel, df_expected_panel)
def safezone(s, position, nmean=10, npen=2.0, nagg=3):
if isinstance(s, DataFrame):
s = s.low if position == 'long' else s.high
sgn = -1.0 if position == 'long' else 1.0
# Compute the average upside/downside penetration
pen = moments.rolling_apply(
sgn * s.diff(), nmean, lambda x: x[x > 0].mean()
if (x > 0).any() else 0)
stop = s + sgn * npen * pen
return hhv(stop, nagg) if position == 'long' else llv(stop, nagg)
def safezone(s, position, nmean=10, npen=2.0, nagg=3):
if isinstance(s, DataFrame):
s = s.low if position == 'long' else s.high
sgn = -1.0 if position == 'long' else 1.0
# Compute the average upside/downside penetration
pen = moments.rolling_apply(
sgn*s.diff(), nmean,
lambda x: x[x > 0].mean() if (x > 0).any() else 0
)
stop = s + sgn*npen*pen
return hhv(stop, nagg) if position == 'long' else llv(stop, nagg)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1,5], [3, 2], [3,9], [-1,0]], columns=['A','B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index, major_axis=df.columns, minor_axis=df.columns)
functions = [lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df_result = f(df)
assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5),
]
for f in functions:
df_result_panel = f(df)
assert_panel_equal(df_result_panel, df_expected_panel)
def roll_mean(x, window, min_periods=None, freq=None, center=False):
return mom.rolling_apply(x, window,
lambda x: x[np.isfinite(x)].mean(),
min_periods=min_periods,
freq=freq,
center=center)
def aroon(s, n=25):
up = 100 * moments.rolling_apply(s.high, n + 1, lambda x: x.argmax()) / n
dn = 100 * moments.rolling_apply(s.low, n + 1, lambda x: x.argmin()) / n
return DataFrame(dict(up=up, down=dn))
def aroon(s, n=25):
up = 100 * moments.rolling_apply(s.high, n + 1, lambda x: x.argmax()) / n
dn = 100 * moments.rolling_apply(s.low, n + 1, lambda x: x.argmin()) / n
return DataFrame(dict(up=up, down=dn))
def roll_mean(x, window, min_periods=None, time_rule=None):
return moments.rolling_apply(x, window,
lambda x: x[np.isfinite(x)].mean(),
min_periods=min_periods,
time_rule=time_rule)
def roll_mean(x, window, min_periods=None, time_rule=None):
return mom.rolling_apply(x,
window,
lambda x: x[np.isfinite(x)].mean(),
min_periods=min_periods,
time_rule=time_rule)
def aroon(df, n=25):
up = 100 * moments.rolling_apply(df['High'], n + 1, lambda x: x.argmax()) / n
dn = 100 * moments.rolling_apply(df['Low'], n + 1, lambda x: x.argmin()) / n
return pd.DataFrame(dict(up=up, down=dn))
