mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-27 18:58:48 +08:00
Victor Grau Serrat
472 lines
18 KiB
Python
472 lines
18 KiB
Python
#
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# Copyright 2016 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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Performance Tracking
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====================
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+-----------------+----------------------------------------------------+
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| key | value |
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+=================+====================================================+
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| period_start | The beginning of the period to be tracked. datetime|
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| | in pytz.utc timezone. Will always be 0:00 on the |
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| | date in UTC. The fact that the time may be on the |
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| | prior day in the exchange's local time is ignored |
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+-----------------+----------------------------------------------------+
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| period_end | The end of the period to be tracked. datetime |
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| | in pytz.utc timezone. Will always be 23:59 on the |
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| | date in UTC. The fact that the time may be on the |
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| | next day in the exchange's local time is ignored |
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+-----------------+----------------------------------------------------+
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| progress | percentage of test completed |
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+-----------------+----------------------------------------------------+
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| capital_base | The initial capital assumed for this tracker. |
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+-----------------+----------------------------------------------------+
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| cumulative_perf | A dictionary representing the cumulative |
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| | performance through all the events delivered to |
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| | this tracker. For details see the comments on |
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| | :py:meth:`PerformancePeriod.to_dict` |
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+-----------------+----------------------------------------------------+
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| todays_perf | A dictionary representing the cumulative |
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| | performance through all the events delivered to |
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| | this tracker with datetime stamps between last_open|
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| | and last_close. For details see the comments on |
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| | :py:meth:`PerformancePeriod.to_dict` |
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| | TODO: adding this because we calculate it. May be |
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| | overkill. |
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+-----------------+----------------------------------------------------+
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| cumulative_risk | A dictionary representing the risk metrics |
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| _metrics | calculated based on the positions aggregated |
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| | through all the events delivered to this tracker. |
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| | For details look at the comments for |
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| | :py:meth:`catalyst.finance.risk.RiskMetrics.to_dict`|
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+-----------------+----------------------------------------------------+
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"""
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from __future__ import division
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import logbook
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import pandas as pd
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from pandas.tseries.tools import normalize_date
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from catalyst.finance.performance.period import PerformancePeriod
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from catalyst.errors import NoFurtherDataError
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import catalyst.finance.risk as risk
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from . position_tracker import PositionTracker
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log = logbook.Logger('Performance')
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class PerformanceTracker(object):
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"""
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Tracks the performance of the algorithm.
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"""
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def __init__(self, sim_params, trading_calendar, env):
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self.sim_params = sim_params
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self.trading_calendar = trading_calendar
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self.asset_finder = env.asset_finder
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self.treasury_curves = env.treasury_curves
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self.period_start = self.sim_params.start_session
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self.period_end = self.sim_params.end_session
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self.last_close = self.sim_params.last_close
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self._current_session = self.sim_params.start_session
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self.market_open, self.market_close = \
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self.trading_calendar.open_and_close_for_session(
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self._current_session
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)
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self.total_session_count = len(self.sim_params.sessions)
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self.capital_base = self.sim_params.capital_base
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self.emission_rate = sim_params.emission_rate
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self.position_tracker = PositionTracker(
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data_frequency=self.sim_params.data_frequency
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)
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if self.emission_rate == 'daily':
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self.all_benchmark_returns = pd.Series(
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index=self.sim_params.sessions
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)
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self.cumulative_risk_metrics = \
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risk.RiskMetricsCumulative(
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self.sim_params,
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self.treasury_curves,
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self.trading_calendar
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)
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elif self.emission_rate == 'minute':
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self.all_benchmark_returns = pd.Series(index=pd.date_range(
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self.sim_params.first_open, self.sim_params.last_close,
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freq='Min')
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)
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self.cumulative_risk_metrics = \
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risk.RiskMetricsCumulative(
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self.sim_params,
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self.treasury_curves,
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self.trading_calendar,
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create_first_day_stats=True
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)
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# this performance period will span the entire simulation from
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# inception.
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self.cumulative_performance = PerformancePeriod(
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# initial cash is your capital base.
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starting_cash=self.capital_base,
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data_frequency=self.sim_params.data_frequency,
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# the cumulative period will be calculated over the entire test.
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period_open=self.period_start,
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period_close=self.period_end,
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# don't save the transactions for the cumulative
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# period
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keep_transactions=False,
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keep_orders=False,
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# don't serialize positions for cumulative period
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serialize_positions=False,
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name="Cumulative"
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)
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self.cumulative_performance.position_tracker = self.position_tracker
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# this performance period will span just the current market day
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self.todays_performance = PerformancePeriod(
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# initial cash is your capital base.
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starting_cash=self.capital_base,
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data_frequency=self.sim_params.data_frequency,
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# the daily period will be calculated for the market day
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period_open=self.market_open,
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period_close=self.market_close,
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keep_transactions=True,
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keep_orders=True,
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serialize_positions=True,
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name="Daily"
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)
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self.todays_performance.position_tracker = self.position_tracker
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self.saved_dt = self.period_start
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# one indexed so that we reach 100%
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self.session_count = 0.0
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self.txn_count = 0
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self.account_needs_update = True
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self._account = None
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def __repr__(self):
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return "%s(%r)" % (
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self.__class__.__name__,
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{'simulation parameters': self.sim_params})
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@property
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def progress(self):
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if self.emission_rate == 'minute':
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# Fake a value
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return 1.0
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elif self.emission_rate == 'daily':
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return self.session_count / self.total_session_count
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def set_date(self, date):
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if self.emission_rate == 'minute':
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self.saved_dt = date
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self.todays_performance.period_close = self.saved_dt
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def get_portfolio(self, performance_needs_update):
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if performance_needs_update:
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self.update_performance()
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self.account_needs_update = True
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return self.cumulative_performance.as_portfolio()
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def update_performance(self):
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# calculate performance as of last trade
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self.cumulative_performance.calculate_performance()
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self.todays_performance.calculate_performance()
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def get_account(self, performance_needs_update):
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if performance_needs_update:
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self.update_performance()
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self.account_needs_update = True
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if self.account_needs_update:
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self._update_account()
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return self._account
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def _update_account(self):
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self._account = self.cumulative_performance.as_account()
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self.account_needs_update = False
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def to_dict(self, emission_type=None):
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"""
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Creates a dictionary representing the state of this tracker.
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Returns a dict object of the form described in header comments.
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"""
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# Default to the emission rate of this tracker if no type is provided
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if emission_type is None:
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emission_type = self.emission_rate
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_dict = {
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'period_start': self.period_start,
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'period_end': self.period_end,
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'capital_base': self.capital_base,
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'cumulative_perf': self.cumulative_performance.to_dict(),
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'progress': self.progress,
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'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict()
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}
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if emission_type == 'daily':
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_dict['daily_perf'] = self.todays_performance.to_dict()
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elif emission_type == 'minute':
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_dict['minute_perf'] = self.todays_performance.to_dict(
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self.saved_dt)
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else:
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raise ValueError("Invalid emission type: %s" % emission_type)
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return _dict
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def prepare_capital_change(self, is_interday):
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self.cumulative_performance.initialize_subperiod_divider()
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if not is_interday:
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# Change comes in the middle of day
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self.todays_performance.initialize_subperiod_divider()
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def process_capital_change(self, capital_change_amount, is_interday):
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self.cumulative_performance.set_current_subperiod_starting_values(
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capital_change_amount)
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if is_interday:
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# Change comes between days
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self.todays_performance.adjust_period_starting_capital(
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capital_change_amount)
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else:
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# Change comes in the middle of day
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self.todays_performance.set_current_subperiod_starting_values(
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capital_change_amount)
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def process_transaction(self, transaction):
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self.txn_count += 1
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self.cumulative_performance.handle_execution(transaction)
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self.todays_performance.handle_execution(transaction)
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self.position_tracker.execute_transaction(transaction)
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def handle_splits(self, splits):
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leftover_cash = self.position_tracker.handle_splits(splits)
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if leftover_cash > 0:
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self.cumulative_performance.handle_cash_payment(leftover_cash)
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self.todays_performance.handle_cash_payment(leftover_cash)
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def process_order(self, event):
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self.cumulative_performance.record_order(event)
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self.todays_performance.record_order(event)
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def process_commission(self, commission):
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asset = commission['asset']
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cost = commission['cost']
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self.position_tracker.handle_commission(asset, cost)
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self.cumulative_performance.handle_commission(cost)
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self.todays_performance.handle_commission(cost)
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def process_close_position(self, asset, dt, data_portal):
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txn = self.position_tracker.\
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maybe_create_close_position_transaction(asset, dt, data_portal)
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if txn:
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self.process_transaction(txn)
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def check_upcoming_dividends(self, next_session, adjustment_reader):
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"""
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Check if we currently own any stocks with dividends whose ex_date is
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the next trading day. Track how much we should be payed on those
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dividends' pay dates.
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Then check if we are owed cash/stock for any dividends whose pay date
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is the next trading day. Apply all such benefits, then recalculate
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performance.
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"""
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if adjustment_reader is None:
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return
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position_tracker = self.position_tracker
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held_sids = set(position_tracker.positions)
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# Dividends whose ex_date is the next trading day. We need to check if
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# we own any of these stocks so we know to pay them out when the pay
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# date comes.
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if held_sids:
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cash_dividends = adjustment_reader.get_dividends_with_ex_date(
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held_sids,
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next_session,
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self.asset_finder
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)
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stock_dividends = adjustment_reader.\
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get_stock_dividends_with_ex_date(
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held_sids,
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next_session,
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self.asset_finder
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)
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position_tracker.earn_dividends(
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cash_dividends,
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stock_dividends
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)
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net_cash_payment = position_tracker.pay_dividends(next_session)
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if not net_cash_payment:
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return
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self.cumulative_performance.handle_dividends_paid(net_cash_payment)
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self.todays_performance.handle_dividends_paid(net_cash_payment)
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def handle_minute_close(self, dt, data_portal):
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"""
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Handles the close of the given minute in minute emission.
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Parameters
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__________
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dt : Timestamp
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The minute that is ending
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Returns
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_______
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A minute perf packet.
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"""
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self.position_tracker.sync_last_sale_prices(dt, False, data_portal)
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self.update_performance()
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todays_date = normalize_date(dt)
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account = self.get_account(False)
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bench_returns = self.all_benchmark_returns.loc[todays_date:dt]
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# cumulative returns
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bench_since_open = (1. + bench_returns).prod() - 1
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self.cumulative_risk_metrics.update(todays_date,
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self.todays_performance.returns,
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bench_since_open,
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account.leverage)
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minute_packet = self.to_dict(emission_type='minute')
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return minute_packet
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def handle_market_close(self, dt, data_portal):
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"""
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Handles the close of the given day, in both minute and daily emission.
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In daily emission, also updates performance, benchmark and risk metrics
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as it would in handle_minute_close if it were minute emission.
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Parameters
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__________
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dt : Timestamp
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The minute that is ending
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Returns
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_______
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A daily perf packet.
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"""
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completed_session = self._current_session
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if self.emission_rate == 'daily':
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# this method is called for both minutely and daily emissions, but
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# this chunk of code here only applies for daily emissions. (since
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# it's done every minute, elsewhere, for minutely emission).
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self.position_tracker.sync_last_sale_prices(dt, False, data_portal)
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self.update_performance()
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account = self.get_account(False)
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benchmark_value = self.all_benchmark_returns[completed_session]
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self.cumulative_risk_metrics.update(
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completed_session,
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self.todays_performance.returns,
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benchmark_value,
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account.leverage)
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# increment the day counter before we move markers forward.
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self.session_count += 1.0
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# Get the next trading day and, if it is past the bounds of this
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# simulation, return the daily perf packet
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try:
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next_session = self.trading_calendar.next_session_label(
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completed_session
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)
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except NoFurtherDataError:
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next_session = None
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# Take a snapshot of our current performance to return to the
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# browser.
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daily_update = self.to_dict(emission_type='daily')
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# On the last day of the test, don't create tomorrow's performance
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# period. We may not be able to find the next trading day if we're at
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# the end of our historical data
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if self.market_close >= self.last_close:
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return daily_update
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# If the next trading day is irrelevant, then return the daily packet
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if (next_session is None) or (next_session >= self.last_close):
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return daily_update
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# move the market day markers forward
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# TODO Is this redundant with next_trading_day above?
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self._current_session = next_session
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self.market_open, self.market_close = \
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self.trading_calendar.open_and_close_for_session(
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self._current_session
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)
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# Roll over positions to current day.
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self.todays_performance.rollover()
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self.todays_performance.period_open = self.market_open
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self.todays_performance.period_close = self.market_close
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# Check for any dividends, then return the daily perf packet
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self.check_upcoming_dividends(
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next_session=next_session,
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adjustment_reader=data_portal._adjustment_reader
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)
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return daily_update
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def handle_simulation_end(self):
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"""
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When the simulation is complete, run the full period risk report
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and send it out on the results socket.
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"""
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log_msg = "Simulated {n} trading days out of {m}."
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log.info(log_msg.format(n=int(self.session_count),
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m=self.total_session_count))
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log.info("first open: {d}".format(
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d=self.sim_params.first_open))
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log.info("last close: {d}".format(
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d=self.sim_params.last_close))
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bms = pd.Series(
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index=self.cumulative_risk_metrics.cont_index,
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data=self.cumulative_risk_metrics.benchmark_returns_cont)
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ars = pd.Series(
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index=self.cumulative_risk_metrics.cont_index,
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data=self.cumulative_risk_metrics.algorithm_returns_cont)
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acl = self.cumulative_risk_metrics.algorithm_cumulative_leverages
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risk_report = risk.RiskReport(
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ars,
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self.sim_params,
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benchmark_returns=bms,
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algorithm_leverages=acl,
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trading_calendar=self.trading_calendar,
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treasury_curves=self.treasury_curves,
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)
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return risk_report.to_dict()
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