mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-19 11:22:06 +08:00
Ana Ruelas
902865cf71
TST: Remove metric correctness testing from period and cumulative tests ENH: Removed answer key and related files ENH: Update qrisk version
162 lines
7.0 KiB
Python
162 lines
7.0 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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| max_leverage | The largest gross leverage 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, trading_calendar,
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treasury_curves, benchmark_returns,
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algorithm_leverages=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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account needs to be a list of account objects sorted in date
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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.trading_calendar = trading_calendar
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self.treasury_curves = treasury_curves
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self.benchmark_returns = benchmark_returns
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self.algorithm_leverages = algorithm_leverages
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if len(self.algorithm_returns) == 0:
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start_session = self.sim_params.start_session
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end_session = self.sim_params.end_session
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else:
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start_session = self.algorithm_returns.index[0]
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end_session = self.algorithm_returns.index[-1]
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self.month_periods = self.periods_in_range(
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1, start_session, end_session
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)
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self.three_month_periods = self.periods_in_range(
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3, start_session, end_session
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)
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self.six_month_periods = self.periods_in_range(
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6, start_session, end_session
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)
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self.year_periods = self.periods_in_range(
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12, start_session, end_session
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)
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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 function 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_session, end_session):
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one_day = datetime.timedelta(days=1)
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ends = []
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cur_start = start_session.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_session.replace(day=1) + relativedelta(months=1) - \
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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_session=cur_start,
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end_session=cur_end,
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returns=self.algorithm_returns,
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benchmark_returns=self.benchmark_returns,
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trading_calendar=self.trading_calendar,
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treasury_curves=self.treasury_curves,
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algorithm_leverages=self.algorithm_leverages,
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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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