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https://github.com/wassname/catalyst.git
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Eddie Hebert
7cc24cec1f
The calculations that are expected to change are:
- cumulative.beta
- cumulative.alpha
- cumulative.information
- cumulative.sharpe
- period.sortino
* Explanation of how risk calculations are changing
** Risk Fixes for Both Period and Cumulative
*** Downside Risk
Use sample instead of population for standard deviation.
Add a rounding factor, so that if the two values are close for a given
dt, that they do not count as a downside value, which would throw off
the denominator of the standard deviation of the downside diffs.
*** Standard Deviation Type
Across the board the standard deviation has been standardized to using
a 'sample' calculation, whereas before cumulative risk was monstly using
'population'. Using `ddof=1` with `np.std` calculates as if the values
are a sample.
** Cumulative Risk Fixes
*** Beta
Use the daily algorithm returns and benchmarks instead of annualized
mean returns.
*** Volatility
Use sample instead of population with standard deviation.
The volatility is an input to other calculations so this change affects
Sharpe and Information ratio calculations.
*** Information Ratio
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
*** Alpha
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
** Period Risk Fixes
*** Sortino
Use the downside risk of the daily return vs. the mean algorithm returns
for the minimum acceptable return instead of the treasury return.
The above required adding the calculation of the mean algorithm returns
for period risk.
Also, use algorithm_period_returns and tresaury_period_return as the
cumulative Sortino does, instead of using algorithm returns for both
inputs into the Sortino calculation.
* Other Supporting Changes
** answer_key
Add new mappings for downside risk and Sortino as well as
re-address the index mappings because of changes to the answer key
spread sheet.
** test_risk_cumulative
Change the decimal precision to expect higher precision.
The calculations are now more aligned with the answer key, so we can
expect higher precision. In particular now that the standard deviation
type matches everywhere in both the Python implementation and the answer
sheet, the precision of the first value no longer has to be glossed over.
** test_events_through_risk
Change the results which are used as a canary for risk changes,
since we do expect Sharpe to change with this change..
30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
#
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# Copyright 2014 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from unittest import TestCase
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from zipline.test_algorithms import NoopAlgorithm
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from zipline.utils import factory
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class TestTradeSimulation(TestCase):
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def test_minutely_emissions_generate_performance_stats_for_last_day(self):
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params = factory.create_simulation_parameters(num_days=1)
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params.data_frequency = 'minute'
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params.emission_rate = 'minute'
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algo = NoopAlgorithm()
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algo.run(source=[], sim_params=params)
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self.assertEqual(algo.perf_tracker.day_count, 1.0)
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