mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-25 13:10:33 +08:00
Eddie Hebert
37c56b9aa4
Remove the lists of DailyReturn objects in favor of using pd.Series to store the return values. Should make it easier to inspect the values when stepping through, make the windowing of data to a certain range more facile by using, and have some performance increases due to removing object creation and member access.
141 lines
6.1 KiB
Python
141 lines
6.1 KiB
Python
#
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# Copyright 2013 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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"""
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Risk Report
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===========
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+-----------------+----------------------------------------------------+
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| key | value |
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+=================+====================================================+
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| trading_days | The number of trading days between self.start_date |
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| | and self.end_date |
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+-----------------+----------------------------------------------------+
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| benchmark_volat\| The volatility of the benchmark between |
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| ility | self.start_date and self.end_date. |
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+-----------------+----------------------------------------------------+
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| algo_volatility | The volatility of the algo between self.start_date |
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| | and self.end_date. |
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+-----------------+----------------------------------------------------+
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| treasury_period\| The return of treasuries over the period. Treasury |
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| _return | maturity is chosen to match the duration of the |
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| | test period. |
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+-----------------+----------------------------------------------------+
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| sharpe | The sharpe ratio based on the _algorithm_ (rather |
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| | than the static portfolio) returns. |
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+-----------------+----------------------------------------------------+
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| information | The information ratio based on the _algorithm_ |
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| | (rather than the static portfolio) returns. |
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+-----------------+----------------------------------------------------+
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| beta | The _algorithm_ beta to the benchmark. |
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+-----------------+----------------------------------------------------+
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| alpha | The _algorithm_ alpha to the benchmark. |
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+-----------------+----------------------------------------------------+
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| excess_return | The excess return of the algorithm over the |
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| | treasuries. |
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+-----------------+----------------------------------------------------+
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| max_drawdown | The largest relative peak to relative trough move |
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| | for the portfolio returns between self.start_date |
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| | and self.end_date. |
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+-----------------+----------------------------------------------------+
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"""
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import logbook
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import datetime
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from dateutil.relativedelta import relativedelta
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from . period import RiskMetricsPeriod
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log = logbook.Logger('Risk Report')
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class RiskReport(object):
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def __init__(self, algorithm_returns, sim_params, benchmark_returns=None):
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"""
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algorithm_returns needs to be a list of daily_return objects
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sorted in date ascending order
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"""
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self.algorithm_returns = algorithm_returns
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self.sim_params = sim_params
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self.benchmark_returns = benchmark_returns
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if len(self.algorithm_returns) == 0:
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start_date = self.sim_params.period_start
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end_date = self.sim_params.period_end
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else:
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start_date = self.algorithm_returns.index[0]
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end_date = self.algorithm_returns.index[-1]
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self.month_periods = self.periods_in_range(1, start_date, end_date)
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self.three_month_periods = self.periods_in_range(3, start_date,
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end_date)
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self.six_month_periods = self.periods_in_range(6, start_date, end_date)
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self.year_periods = self.periods_in_range(12, start_date, end_date)
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def to_dict(self):
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"""
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RiskMetrics are calculated for rolling windows in four lengths::
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- 1_month
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- 3_month
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- 6_month
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- 12_month
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The return value of this funciton is a dictionary keyed by the above
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list of durations. The value of each entry is a list of RiskMetric
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dicts of the same duration as denoted by the top_level key.
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See :py:meth:`RiskMetrics.to_dict` for the detailed list of fields
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provided for each period.
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"""
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return {
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'one_month': [x.to_dict() for x in self.month_periods],
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'three_month': [x.to_dict() for x in self.three_month_periods],
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'six_month': [x.to_dict() for x in self.six_month_periods],
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'twelve_month': [x.to_dict() for x in self.year_periods],
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}
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def periods_in_range(self, months_per, start, end):
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one_day = datetime.timedelta(days=1)
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ends = []
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cur_start = start.replace(day=1)
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# in edge cases (all sids filtered out, start/end are adjacent)
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# a test will not generate any returns data
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if len(self.algorithm_returns) == 0:
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return ends
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# ensure that we have an end at the end of a calendar month, in case
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# the return series ends mid-month...
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the_end = end.replace(day=1) + relativedelta(months=1) - one_day
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while True:
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cur_end = cur_start + relativedelta(months=months_per) - one_day
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if(cur_end > the_end):
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break
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cur_period_metrics = RiskMetricsPeriod(
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start_date=cur_start,
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end_date=cur_end,
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returns=self.algorithm_returns,
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benchmark_returns=self.benchmark_returns
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)
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ends.append(cur_period_metrics)
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cur_start = cur_start + relativedelta(months=1)
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return ends
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