def test_mean_reversion_5day_sector_neutral_smoothed(fn):
column_name = 'Mean_Reversion_5Day_Sector_Neutral_Smoothed'
start_date_str = '2015-01-05'
end_date_str = '2015-01-07'
# Build engine
trading_calendar = get_calendar('NYSE')
bundle_data = bundles.load(project_helper.EOD_BUNDLE_NAME)
engine = project_helper.build_pipeline_engine(bundle_data,
trading_calendar)
# Build pipeline
universe_window_length = 2
universe_asset_count = 4
universe = AverageDollarVolume(
window_length=universe_window_length).top(universe_asset_count)
pipeline = Pipeline(screen=universe)
run_pipeline_args = {
'pipeline': pipeline,
'start_date': pd.Timestamp(start_date_str, tz='utc'),
'end_date': pd.Timestamp(end_date_str, tz='utc')
}
fn_inputs = {
'window_length': 3,
'universe': universe,
'sector': project_helper.Sector()
}
fn_correct_outputs = OrderedDict([
('pipline_out',
pd.DataFrame([
0.44721360, 1.34164079, -1.34164079, -0.44721360, 1.34164079,
0.44721360, -1.34164079, -0.44721360, 0.44721360, 1.34164079,
-1.34164079, -0.44721360
],
engine.run_pipeline(**run_pipeline_args).index,
[column_name]))
])
print('Running Integration Test on pipeline:')
print('> start_dat = pd.Timestamp(\'{}\', tz=\'utc\')'.format(
start_date_str))
print('> end_date = pd.Timestamp(\'{}\', tz=\'utc\')'.format(end_date_str))
print('> universe = AverageDollarVolume(window_length={}).top({})'.format(
universe_window_length, universe_asset_count))
print('> factor = {}('.format(fn.__name__))
print(' window_length={},'.format(fn_inputs['window_length']))
print(' universe=universe,')
print(' sector=project_helper.Sector())')
print('> pipeline.add(factor, \'{}\')'.format(column_name))
print('> engine.run_pipeline(pipeline, start_dat, end_date)')
print('')
pipeline.add(fn(**fn_inputs), column_name)
assert_output(engine.run_pipeline,
run_pipeline_args,
fn_correct_outputs,
check_parameter_changes=False)
return factor * -1
project_tests.test_mean_reversion_5day_sector_neutral(mean_reversion_5day_sector_neutral)
# ### View Data
# Let's see what some of the factor data looks like. For calculating factors, we'll be looking back 2 years.
#
# **Note:** _Going back 2 years falls on a day when the market is closed. Pipeline package doesn't handle start or end dates that don't fall on days when the market is open. To fix this, we went back 2 extra days to fall on the next day when the market is open._
# In[28]:
factor_start_date = universe_end_date - pd.DateOffset(years=2, days=2)
sector = project_helper.Sector()
window_length = 5
pipeline = Pipeline(screen=universe)
pipeline.add(
mean_reversion_5day_sector_neutral(window_length, universe, sector),
'Mean_Reversion_5Day_Sector_Neutral')
engine.run_pipeline(pipeline, factor_start_date, universe_end_date)
# ## Mean Reversion 5 Day Sector Neutral Smoothed Factor
# Taking the output of the previous factor, let's create a smoothed version. Implement `mean_reversion_5day_sector_neutral_smoothed` to generate a mean reversion 5 fay sector neutral smoothed factor. Call the `mean_reversion_5day_sector_neutral` function to get the unsmoothed factor, then use `SimpleMovingAverage` function to smooth it. You'll have to apply rank and zscore again.
# In[29]:
def test_mean_reversion_5day_sector_neutral_smoothed(fn):
column_name = "Mean_Reversion_5Day_Sector_Neutral_Smoothed"
start_date_str = "2015-01-05"
end_date_str = "2015-01-07"
# Build engine
trading_calendar = get_calendar("NYSE")
bundle_data = bundles.load(project_helper.EOD_BUNDLE_NAME)
engine = project_helper.build_pipeline_engine(bundle_data,
trading_calendar)
# Build pipeline
universe_window_length = 2
universe_asset_count = 4
universe = AverageDollarVolume(
window_length=universe_window_length).top(universe_asset_count)
pipeline = Pipeline(screen=universe)
run_pipeline_args = {
"pipeline": pipeline,
"start_date": pd.Timestamp(start_date_str, tz="utc"),
"end_date": pd.Timestamp(end_date_str, tz="utc"),
}
fn_inputs = {
"window_length": 3,
"universe": universe,
"sector": project_helper.Sector(),
}
fn_correct_outputs = OrderedDict([(
"pipline_out",
pd.DataFrame(
[
0.44721360,
1.34164079,
-1.34164079,
-0.44721360,
1.34164079,
0.44721360,
-1.34164079,
-0.44721360,
0.44721360,
1.34164079,
-1.34164079,
-0.44721360,
],
engine.run_pipeline(**run_pipeline_args).index,
[column_name],
),
)])
print("Running Integration Test on pipeline:")
print("> start_dat = pd.Timestamp('{}', tz='utc')".format(start_date_str))
print("> end_date = pd.Timestamp('{}', tz='utc')".format(end_date_str))
print("> universe = AverageDollarVolume(window_length={}).top({})".format(
universe_window_length, universe_asset_count))
print("> factor = {}(".format(fn.__name__))
print(" window_length={},".format(fn_inputs["window_length"]))
print(" universe=universe,")
print(" sector=project_helper.Sector())")
print("> pipeline.add(factor, '{}')".format(column_name))
print("> engine.run_pipeline(pipeline, start_dat, end_date)")
print("")
pipeline.add(fn(**fn_inputs), column_name)
assert_output(
engine.run_pipeline,
run_pipeline_args,
fn_correct_outputs,
check_parameter_changes=False,
)
