mirror of
https://github.com/wassname/catalyst.git
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jfkirk
600 lines
23 KiB
Python
600 lines
23 KiB
Python
#
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# Copyright 2015 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:`zipline.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 pickle
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from six import iteritems
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from datetime import datetime
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import numpy as np
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import pandas as pd
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from pandas.tseries.tools import normalize_date
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import zipline.finance.risk as risk
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from zipline.finance.trading import TradingEnvironment
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from . period import PerformancePeriod
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from zipline.utils.serialization_utils import (
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VERSION_LABEL
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)
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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):
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self.sim_params = sim_params
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env = TradingEnvironment.instance()
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self.period_start = self.sim_params.period_start
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self.period_end = self.sim_params.period_end
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self.last_close = self.sim_params.last_close
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first_open = self.sim_params.first_open.tz_convert(env.exchange_tz)
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self.day = pd.Timestamp(datetime(first_open.year, first_open.month,
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first_open.day), tz='UTC')
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self.market_open, self.market_close = env.get_open_and_close(self.day)
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self.total_days = self.sim_params.days_in_period
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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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all_trading_days = env.trading_days
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mask = ((all_trading_days >= normalize_date(self.period_start)) &
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(all_trading_days <= normalize_date(self.period_end)))
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self.trading_days = all_trading_days[mask]
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self.dividend_frame = pd.DataFrame()
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self._dividend_count = 0
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self.position_tracker = PositionTracker()
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self.perf_periods = []
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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.trading_days)
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self.cumulative_risk_metrics = \
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risk.RiskMetricsCumulative(self.sim_params)
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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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self.cumulative_risk_metrics = \
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risk.RiskMetricsCumulative(self.sim_params,
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create_first_day_stats=True)
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self.minute_performance = PerformancePeriod(
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# initial cash is your capital base.
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self.capital_base,
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# the cumulative period will be calculated over the
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# entire test.
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self.period_start,
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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 cumualtive period
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serialize_positions=False
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)
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self.minute_performance.position_tracker = self.position_tracker
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self.perf_periods.append(self.minute_performance)
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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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self.capital_base,
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# the cumulative period will be calculated over the entire test.
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self.period_start,
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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 cumualtive period
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serialize_positions=False,
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)
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self.cumulative_performance.position_tracker = self.position_tracker
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self.perf_periods.append(self.cumulative_performance)
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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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self.capital_base,
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# the daily period will be calculated for the market day
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self.market_open,
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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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)
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self.todays_performance.position_tracker = self.position_tracker
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self.perf_periods.append(self.todays_performance)
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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.day_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.day_count / self.total_days
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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 update_dividends(self, new_dividends):
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"""
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Update our dividend frame with new dividends. @new_dividends should be
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a DataFrame with columns containing at least the entries in
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zipline.protocol.DIVIDEND_FIELDS.
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"""
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# Mark each new dividend with a unique integer id. This ensures that
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# we can differentiate dividends whose date/sid fields are otherwise
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# identical.
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new_dividends['id'] = np.arange(
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self._dividend_count,
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self._dividend_count + len(new_dividends),
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)
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self._dividend_count += len(new_dividends)
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self.dividend_frame = pd.concat(
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[self.dividend_frame, new_dividends]
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).sort(['pay_date', 'ex_date']).set_index('id', drop=False)
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def initialize_dividends_from_other(self, other):
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"""
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Helper for copying dividends to a new PerformanceTracker while
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preserving dividend count. Useful if a simulation needs to create a
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new PerformanceTracker mid-stream and wants to preserve stored dividend
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info.
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Note that this does not copy unpaid dividends.
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"""
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self.dividend_frame = other.dividend_frame
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self._dividend_count = other._dividend_count
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def handle_sid_removed_from_universe(self, sid):
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"""
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This method handles any behaviors that must occur when a SID leaves the
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universe of the TradingAlgorithm.
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Parameters
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__________
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sid : int
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The sid of the Asset being removed from the universe.
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"""
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# Drop any dividends for the sid from the dividends frame
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self.dividend_frame = self.dividend_frame[
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self.dividend_frame.sid != sid
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]
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def update_performance(self):
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# calculate performance as of last trade
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for perf_period in self.perf_periods:
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perf_period.calculate_performance()
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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 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 process_trade(self, event):
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# update last sale, and pay out a cash adjustment
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cash_adjustment = self.position_tracker.update_last_sale(event)
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if cash_adjustment != 0:
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for perf_period in self.perf_periods:
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perf_period.handle_cash_payment(cash_adjustment)
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def process_transaction(self, event):
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self.txn_count += 1
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self.position_tracker.execute_transaction(event)
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for perf_period in self.perf_periods:
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perf_period.handle_execution(event)
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def process_dividend(self, dividend):
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log.info("Ignoring DIVIDEND event.")
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def process_split(self, event):
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leftover_cash = self.position_tracker.handle_split(event)
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if leftover_cash > 0:
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for perf_period in self.perf_periods:
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perf_period.handle_cash_payment(leftover_cash)
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def process_order(self, event):
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for perf_period in self.perf_periods:
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perf_period.record_order(event)
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def process_commission(self, event):
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self.position_tracker.handle_commission(event)
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for perf_period in self.perf_periods:
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perf_period.handle_commission(event)
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def process_benchmark(self, event):
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if self.sim_params.data_frequency == 'minute' and \
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self.sim_params.emission_rate == 'daily':
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# Minute data benchmarks should have a timestamp of market
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# close, so that calculations are triggered at the right time.
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# However, risk module uses midnight as the 'day'
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# marker for returns, so adjust back to midnight.
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midnight = pd.tseries.tools.normalize_date(event.dt)
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else:
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midnight = event.dt
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if midnight not in self.all_benchmark_returns.index:
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raise AssertionError(
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("Date %s not allocated in all_benchmark_returns. "
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"Calendar seems to mismatch with benchmark. "
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"Benchmark container is=%s" %
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(midnight,
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self.all_benchmark_returns.index)))
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self.all_benchmark_returns[midnight] = event.returns
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def process_close_position(self, event):
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# CLOSE_POSITION events that contain prices that must be handled as
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# a final trade event
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if 'price' in event:
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self.process_trade(event)
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txn = self.position_tracker.\
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maybe_create_close_position_transaction(event)
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if txn:
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self.process_transaction(txn)
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def check_upcoming_dividends(self, next_trading_day):
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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 len(self.dividend_frame) == 0:
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# We don't currently know about any dividends for this simulation
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# period, so bail.
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return
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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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ex_date_mask = (self.dividend_frame['ex_date'] == next_trading_day)
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dividends_earnable = self.dividend_frame[ex_date_mask]
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# Dividends whose pay date is the next trading day. If we held any of
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# these stocks on midnight before the ex_date, we need to pay these out
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# now.
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pay_date_mask = (self.dividend_frame['pay_date'] == next_trading_day)
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dividends_payable = self.dividend_frame[pay_date_mask]
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position_tracker = self.position_tracker
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if len(dividends_earnable):
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position_tracker.earn_dividends(dividends_earnable)
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if not len(dividends_payable):
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return
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net_cash_payment = position_tracker.pay_dividends(dividends_payable)
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for period in self.perf_periods:
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# notify periods to update their stats
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period.handle_dividends_paid(net_cash_payment)
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def check_asset_auto_closes(self, next_trading_day):
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"""
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Check if the position tracker currently owns any Assets with an
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auto-close date that is the next trading day. Close those positions.
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Parameters
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----------
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next_trading_day : pandas.Timestamp
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The next trading day of the simulation
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"""
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auto_close_events = self.position_tracker.auto_close_position_events(
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next_trading_day=next_trading_day
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)
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for event in auto_close_events:
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self.process_close_position(event)
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def handle_minute_close(self, dt):
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"""
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Handles the close of the given minute. This includes handling
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market-close functions if the given minute is the end of the market
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day.
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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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(dict, dict/None)
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A tuple of the minute perf packet and daily perf packet.
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If the market day has not ended, the daily perf packet is None.
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"""
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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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self.minute_performance.rollover()
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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)
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minute_packet = self.to_dict(emission_type='minute')
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# if this is the close, update dividends for the next day.
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# Return the performance tuple
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if dt == self.market_close:
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return (minute_packet, self._handle_market_close(todays_date))
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else:
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return (minute_packet, None)
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def handle_market_close_daily(self):
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"""
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Function called after handle_data when running with daily emission
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rate.
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"""
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self.update_performance()
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completed_date = self.day
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account = self.get_account(False)
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# update risk metrics for cumulative performance
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self.cumulative_risk_metrics.update(
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completed_date,
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self.todays_performance.returns,
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self.all_benchmark_returns[completed_date],
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account)
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return self._handle_market_close(completed_date)
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def _handle_market_close(self, completed_date):
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# increment the day counter before we move markers forward.
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self.day_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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next_trading_day = TradingEnvironment.instance().\
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next_trading_day(completed_date)
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# Check if any assets need to be auto-closed before generating today's
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# perf period
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if next_trading_day:
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self.check_asset_auto_closes(next_trading_day=next_trading_day)
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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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# move the market day markers forward
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env = TradingEnvironment.instance()
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self.market_open, self.market_close = \
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env.next_open_and_close(self.day)
|
|
self.day = env.next_trading_day(self.day)
|
|
|
|
# Roll over positions to current day.
|
|
self.todays_performance.rollover()
|
|
self.todays_performance.period_open = self.market_open
|
|
self.todays_performance.period_close = self.market_close
|
|
|
|
# If the next trading day is irrelevant, then return the daily packet
|
|
if (next_trading_day is None) or (next_trading_day >= self.last_close):
|
|
return daily_update
|
|
|
|
# Check for any dividends and auto-closes, then return the daily perf
|
|
# packet
|
|
self.check_upcoming_dividends(next_trading_day=next_trading_day)
|
|
return daily_update
|
|
|
|
def handle_simulation_end(self):
|
|
"""
|
|
When the simulation is complete, run the full period risk report
|
|
and send it out on the results socket.
|
|
"""
|
|
|
|
log_msg = "Simulated {n} trading days out of {m}."
|
|
log.info(log_msg.format(n=int(self.day_count), m=self.total_days))
|
|
log.info("first open: {d}".format(
|
|
d=self.sim_params.first_open))
|
|
log.info("last close: {d}".format(
|
|
d=self.sim_params.last_close))
|
|
|
|
bms = pd.Series(
|
|
index=self.cumulative_risk_metrics.cont_index,
|
|
data=self.cumulative_risk_metrics.benchmark_returns_cont)
|
|
ars = pd.Series(
|
|
index=self.cumulative_risk_metrics.cont_index,
|
|
data=self.cumulative_risk_metrics.algorithm_returns_cont)
|
|
acl = self.cumulative_risk_metrics.algorithm_cumulative_leverages
|
|
self.risk_report = risk.RiskReport(
|
|
ars,
|
|
self.sim_params,
|
|
benchmark_returns=bms,
|
|
algorithm_leverages=acl)
|
|
|
|
risk_dict = self.risk_report.to_dict()
|
|
return risk_dict
|
|
|
|
def __getstate__(self):
|
|
state_dict = \
|
|
{k: v for k, v in iteritems(self.__dict__)
|
|
if not k.startswith('_')}
|
|
|
|
state_dict['dividend_frame'] = pickle.dumps(self.dividend_frame)
|
|
|
|
state_dict['_dividend_count'] = self._dividend_count
|
|
|
|
# we already store perf periods as attributes
|
|
del state_dict['perf_periods']
|
|
|
|
STATE_VERSION = 3
|
|
state_dict[VERSION_LABEL] = STATE_VERSION
|
|
|
|
return state_dict
|
|
|
|
def __setstate__(self, state):
|
|
|
|
OLDEST_SUPPORTED_STATE = 3
|
|
version = state.pop(VERSION_LABEL)
|
|
|
|
if version < OLDEST_SUPPORTED_STATE:
|
|
raise BaseException("PerformanceTracker saved state is too old.")
|
|
|
|
self.__dict__.update(state)
|
|
|
|
# Handle the dividend frame specially
|
|
self.dividend_frame = pickle.loads(state['dividend_frame'])
|
|
|
|
# properly setup the perf periods
|
|
self.perf_periods = []
|
|
p_types = ['cumulative', 'todays', 'minute']
|
|
for p_type in p_types:
|
|
name = p_type + '_performance'
|
|
period = getattr(self, name, None)
|
|
if period is None:
|
|
continue
|
|
period._position_tracker = self.position_tracker
|
|
self.perf_periods.append(period)
|