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[ENH] Switch regressions to use the pandas implementation #63

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a35347a
[ENH] Switch regressions to use the pandas implementation
Jul 15, 2015
9152923
[TST] AlmostEqualified some of the tests.
Jul 23, 2015
9a30668
[ENH] Make regressions use pd.ols
Jul 23, 2015
29742da
[DOC] Switch to numpy style docstrings
Jul 27, 2015
cbbe4e6
[MAINT] Removes return_ols_model kwargs
Jul 27, 2015
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12 changes: 6 additions & 6 deletions pyfolio/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -90,20 +90,20 @@ def plot_rolling_risk_factors(
rolling_risk_multifactor = timeseries.rolling_multifactor_beta(
returns,
risk_factors.loc[:, ['SMB', 'HML', 'UMD']],
rolling_window=rolling_beta_window)
window=rolling_beta_window)

rolling_beta_SMB = timeseries.rolling_beta(
returns,
risk_factors['SMB'],
rolling_window=rolling_beta_window)
window=rolling_beta_window)
rolling_beta_HML = timeseries.rolling_beta(
returns,
risk_factors['HML'],
rolling_window=rolling_beta_window)
window=rolling_beta_window)
rolling_beta_UMD = timeseries.rolling_beta(
returns,
risk_factors['UMD'],
rolling_window=rolling_beta_window)
window=rolling_beta_window)

rolling_beta_SMB.plot(color='steelblue', alpha=0.7, ax=ax, **kwargs)
rolling_beta_HML.plot(color='orangered', alpha=0.7, ax=ax, **kwargs)
Expand Down Expand Up @@ -608,10 +608,10 @@ def plot_rolling_beta(returns, benchmark_rets,
ax.set_title("Rolling Portfolio Beta to S&P 500")
ax.set_ylabel('Beta')
rb_1 = timeseries.rolling_beta(
returns, benchmark_rets, rolling_window=rolling_beta_window * 2)
returns, benchmark_rets, window=rolling_beta_window * 2)['x']
rb_1.plot(color='steelblue', lw=3, alpha=0.6, ax=ax, **kwargs)
rb_2 = timeseries.rolling_beta(
returns, benchmark_rets, rolling_window=rolling_beta_window * 3)
returns, benchmark_rets, window=rolling_beta_window * 3)['x']
rb_2.plot(color='grey', lw=3, alpha=0.4, ax=ax, **kwargs)
ax.set_ylim((-2.5, 2.5))
ax.axhline(rb_1.mean(), color='steelblue', linestyle='--', lw=3)
Expand Down
43 changes: 22 additions & 21 deletions pyfolio/tests/test_timeseries.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
from unittest import TestCase
import unittest
from nose_parameterized import parameterized

import numpy as np
Expand All @@ -7,8 +7,9 @@

from .. import timeseries

DECIMAL_PLACES = 9

class TestDrawdown(TestCase):
class TestDrawdown(unittest.TestCase):
px_list_1 = np.array(
[100, 120, 100, 80, 70, 110, 180, 150]) / 100. # Simple
px_list_2 = np.array(
Expand Down Expand Up @@ -106,13 +107,13 @@ def test_gen_drawdown_table(
(pd.Series(px_list_1 - 1, index=dt), -0.44000000000000011)
])
def test_max_drawdown(self, df_rets, expected):
self.assertEqual(timeseries.max_drawdown(df_rets), expected)
self.assertAlmostEqual(timeseries.max_drawdown(df_rets), expected, DECIMAL_PLACES)

@parameterized.expand([
(pd.Series(px_list_1 - 1, index=dt), -0.44000000000000011)
])
def test_max_drawdown_underwater(self, underwater, expected):
self.assertEqual(timeseries.max_drawdown(underwater), expected)
self.assertAlmostEqual(timeseries.max_drawdown(underwater), expected, DECIMAL_PLACES)

@parameterized.expand([
(pd.Series(px_list_1,
Expand All @@ -130,7 +131,7 @@ def test_top_drawdowns(self, df_rets, top, expected):
expected)


class TestCumReturns(TestCase):
class TestCumReturns(unittest.TestCase):
dt = pd.date_range('2000-1-3', periods=3, freq='D')

@parameterized.expand([
Expand All @@ -144,7 +145,7 @@ def test_expected_result(self, input, expected, starting_value):
pdt.assert_series_equal(output, expected)


class TestVariance(TestCase):
class TestVariance(unittest.TestCase):

@parameterized.expand([
(1e7, 0.5, 1, 1, -10000000.0)
Expand All @@ -159,7 +160,7 @@ def test_var_cov_var_normal(self, P, c, mu, sigma, expected):
expected)


class TestNormalize(TestCase):
class TestNormalize(unittest.TestCase):
dt = pd.date_range('2000-1-3', periods=8, freq='D')
px_list = [1.0, 1.2, 1.0, 0.8, 0.7, 0.8, 0.8, 0.8]

Expand All @@ -171,7 +172,7 @@ def test_normalize(self, df, expected):
self.assertTrue(timeseries.normalize(df).equals(expected))


class TestAggregateReturns(TestCase):
class TestAggregateReturns(unittest.TestCase):
simple_rets = pd.Series(
[0.1] * 3 + [0] * 497,
pd.date_range(
Expand All @@ -192,7 +193,7 @@ def test_aggregate_rets(self, df_rets, convert_to, expected):
expected)


class TestStats(TestCase):
class TestStats(unittest.TestCase):
simple_rets = pd.Series(
[0.1] * 3 + [0] * 497,
pd.date_range(
Expand Down Expand Up @@ -224,18 +225,18 @@ def test_annual_ret(self, df_rets, style, expected):
(simple_rets, 0.12271674212427248)
])
def test_annual_volatility(self, df_rets, expected):
self.assertEqual(timeseries.annual_volatility(df_rets), expected)
self.assertAlmostEqual(timeseries.annual_volatility(df_rets), expected, DECIMAL_PLACES)

@parameterized.expand([
(simple_rets, 'calendar', 1.7112579454508172),
(simple_rets, 'compound', 1.3297007080039505)
])
def test_sharpe(self, df_rets, returns_style, expected):
self.assertEqual(
self.assertAlmostEqual(
timeseries.sharpe_ratio(
df_rets,
returns_style=returns_style),
expected)
expected, DECIMAL_PLACES)

@parameterized.expand([
(simple_rets[:5], 2, '[nan, inf, inf, 11.224972160321828, inf]')
Expand All @@ -248,25 +249,25 @@ def test_sharpe_2(self, df_rets, rolling_sharpe_window, expected):
(simple_rets, True, 0.010766923838471554)
])
def test_stability_of_timeseries(self, df_rets, logValue, expected):
self.assertEqual(
self.assertAlmostEqual(
timeseries.stability_of_timeseries(
df_rets,
logValue=logValue),
expected)
expected, DECIMAL_PLACES)

@parameterized.expand([
(simple_rets[:5], simple_benchmark[:5], 2, 8.024708101613483e-32)
])
def test_beta(self, df_rets, benchmark_rets, rolling_window, expected):
def test_beta(self, df_rets, benchmark_rets, window, expected):
self.assertEqual(
timeseries.rolling_beta(
df_rets,
benchmark_rets,
rolling_window=rolling_window).values.tolist()[2],
window=window).values.tolist()[2],
expected)


class TestMultifactor(TestCase):
class TestMultifactor(unittest.TestCase):
simple_rets = pd.Series(
[0.1] * 3 + [0] * 497,
pd.date_range(
Expand Down Expand Up @@ -300,16 +301,16 @@ def test_calc_multifactor(self, df_rets, factors, expected):
0.002997302427814967])
])
def test_multifactor_beta(
self, df_rets, benchmark_df, rolling_window, expected):
self, df_rets, benchmark_df, window, expected):
self.assertEqual(
timeseries.rolling_multifactor_beta(
df_rets,
benchmark_df,
rolling_window=rolling_window).values.tolist()[2],
window=window).values.tolist()[2],
expected)


class TestPerfStats(TestCase):
class TestPerfStats(unittest.TestCase):
simple_rets = pd.Series(
[0.1] * 3 + [0] * 497,
pd.date_range(
Expand All @@ -335,4 +336,4 @@ def test_perf_stats(
self.assertEqual(timeseries.perf_stats(df_rets,
returns_style=returns_style,
return_as_dict=return_as_dict).values.tolist()[-2:],
expected)
expected)
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