Merge pull request #247 from quantopian/test_tears

Test tearsheets

Author: twiecki

.travis.yml

@@ -20,9 +20,10 @@ before_install:
   - conda update -q conda
   # Useful for debugging any issues with conda
   - conda info -a
+  - cp pyfolio/tests/matplotlibrc .
 
 install:
-  - conda create -q -n testenv --yes python=$TRAVIS_PYTHON_VERSION ipython pyzmq numpy scipy nose matplotlib pandas Cython patsy statsmodels flake8 scikit-learn seaborn runipy pytables networkx pandas-datareader
+  - conda create -q -n testenv --yes python=$TRAVIS_PYTHON_VERSION ipython pyzmq numpy scipy nose matplotlib=1.4.1 pandas Cython patsy statsmodels flake8 scikit-learn seaborn runipy pytables networkx pandas-datareader
   - source activate testenv
   - pip install nose_parameterized contextlib2 logbook==0.10.1
   - pip install --no-deps git+https://github.com/quantopian/zipline

pyfolio/tears.py

@@ -453,6 +453,11 @@ def create_txn_tear_sheet(returns, positions, transactions,
     transactions : pd.DataFrame
         Prices and amounts of executed trades. One row per trade.
          - See full explanation in create_full_tear_sheet.
+    unadjusted_returns : pd.Series, optional
+        Daily unadjusted returns of the strategy, noncumulative.
+        Will plot additional swippage sweep analysis.
+         - See pyfolio.plotting.plot_swippage_sleep and
+           pyfolio.plotting.plot_slippage_sensitivity
     return_fig : boolean, optional
         If True, returns the figure that was plotted on.
     """

pyfolio/tests/matplotlibrc

@@ -0,0 +1 @@
+backend : Agg

pyfolio/tests/test_tears.py

@@ -0,0 +1,135 @@
+from matplotlib.testing.decorators import cleanup
+
+from unittest import TestCase
+from nose_parameterized import parameterized
+
+import os
+import gzip
+
+from pandas import read_csv
+
+from pyfolio.tears import (create_full_tear_sheet,
+                           create_returns_tear_sheet,
+                           create_position_tear_sheet,
+                           create_txn_tear_sheet,
+                           create_round_trip_tear_sheet,
+                           create_interesting_times_tear_sheet,
+                           create_bayesian_tear_sheet)
+
+
+def to_utc(df):
+    try:
+        df.index = df.index.tz_localize('UTC')
+    except TypeError:
+        df.index = df.index.tz_convert('UTC')
+
+    return df
+
+
+def to_series(df):
+    return df[df.columns[0]]
+
+
+class PositionsTestCase(TestCase):
+    __location__ = os.path.realpath(
+        os.path.join(os.getcwd(), os.path.dirname(__file__)))
+
+    test_returns = read_csv(
+        gzip.open(
+            __location__ + '/test_data/test_returns.csv.gz'),
+        index_col=0, parse_dates=0)
+    test_returns = to_series(to_utc(test_returns))
+    test_gross_lev = read_csv(
+        gzip.open(
+            __location__ + '/test_data/test_gross_lev.csv.gz'),
+        index_col=0, parse_dates=0)
+    test_gross_lev = to_series(to_utc(test_gross_lev))
+    test_txn = to_utc(read_csv(
+        gzip.open(
+            __location__ + '/test_data/test_txn.csv.gz'),
+        index_col=0, parse_dates=0))
+    test_pos = to_utc(read_csv(
+        gzip.open(__location__ + '/test_data/test_pos.csv.gz'),
+        index_col=0, parse_dates=0))
+
+    @parameterized.expand([({},),
+                           ({'slippage': 1},),
+                           ({'live_start_date':
+                             test_returns.index[-20]},),
+                           ({'round_trips': True},),
+                           ({'hide_positions': True},),
+                           ({'cone_std': 1},),
+                           ])
+    @cleanup
+    def test_create_full_tear_sheet_breakdown(self, kwargs):
+        create_full_tear_sheet(self.test_returns,
+                               positions=self.test_pos,
+                               transactions=self.test_txn,
+                               gross_lev=self.test_gross_lev,
+                               **kwargs
+                               )
+
+    @parameterized.expand([({},),
+                           ({'live_start_date':
+                             test_returns.index[-20]},),
+                           ({'cone_std': 1},),
+                           ])
+    @cleanup
+    def test_create_returns_tear_sheet_breakdown(self, kwargs):
+        create_returns_tear_sheet(self.test_returns,
+                                  **kwargs
+                                  )
+
+    @parameterized.expand([({},),
+                           ({'hide_positions': True},),
+                           ({'show_and_plot_top_pos': 0},),
+                           ({'show_and_plot_top_pos': 1},),
+                           ])
+    @cleanup
+    def test_create_position_tear_sheet_breakdown(self, kwargs):
+        create_position_tear_sheet(self.test_returns,
+                                   self.test_pos,
+                                   gross_lev=self.test_gross_lev,
+                                   **kwargs
+                                   )
+
+    @parameterized.expand([({},),
+                           ({'unadjusted_returns': test_returns},),
+                           ])
+    @cleanup
+    def test_create_txn_tear_sheet_breakdown(self, kwargs):
+        create_txn_tear_sheet(self.test_returns,
+                              self.test_pos,
+                              self.test_txn,
+                              **kwargs
+                              )
+
+    @parameterized.expand([({},),
+                           ({'sector_mappings': {}},),
+                           ])
+    @cleanup
+    def test_create_round_trip_tear_sheet_breakdown(self, kwargs):
+        create_round_trip_tear_sheet(self.test_pos,
+                                     self.test_txn,
+                                     **kwargs
+                                     )
+
+    @parameterized.expand([({},),
+                           ({'legend_loc': 1},),
+                           ])
+    @cleanup
+    def test_create_interesting_times_tear_sheet_breakdown(self,
+                                                           kwargs):
+        create_interesting_times_tear_sheet(self.test_returns,
+                                            **kwargs
+                                            )
+
+    @parameterized.expand([({},),
+                           ({'stoch_vol': True},),
+                           ])
+    @cleanup
+    def test_create_bayesian_tear_sheet_breakdown(self, kwargs):
+        create_bayesian_tear_sheet(
+            self.test_returns,
+            live_start_date=self.test_returns.index[-20],
+            **kwargs)

pyfolio/tests/test_timeseries.py

@@ -369,7 +369,7 @@ def test_bootstrap_cone_against_linear_cone_normal_returns(self):
         random_seed = 100
         np.random.seed(random_seed)
         days_forward = 200
-        cone_stdevs = [1, 1.5, 2]
+        cone_stdevs = (1., 1.5, 2.)
         mu = .005
         sigma = .002
         rets = pd.Series(np.random.normal(mu, sigma, 10000))

pyfolio/timeseries.py

@@ -964,7 +964,7 @@ def rolling_sharpe(returns, rolling_sharpe_window):
         * np.sqrt(APPROX_BDAYS_PER_YEAR)
 
 
-def forecast_cone_bootstrap(is_returns, num_days, cone_std=[1, 1.5, 2],
+def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.),
                             starting_value=1, num_samples=1000,
                             random_seed=None):
     """