mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-04 20:27:34 +08:00
Scott Sanderson
466 lines
19 KiB
Python
466 lines
19 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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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 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.protocol as zp
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import zipline.finance.risk as risk
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from zipline.finance import trading
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from . period import PerformancePeriod
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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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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_day = self.sim_params.first_open
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self.market_open, self.market_close = \
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trading.environment.get_open_and_close(first_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 = trading.environment.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.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.intraday_risk_metrics = None
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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.intraday_risk_metrics = \
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risk.RiskMetricsCumulative(self.sim_params)
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self.cumulative_risk_metrics = \
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risk.RiskMetricsCumulative(self.sim_params,
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returns_frequency='daily',
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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.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.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.perf_periods.append(self.todays_performance)
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self.saved_dt = self.period_start
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self.returns = pd.Series(index=self.trading_days)
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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.event_count = 0
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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 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):
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self.update_performance()
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return self.cumulative_performance.as_portfolio()
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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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if not emission_type:
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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.update({'daily_perf': self.todays_performance.to_dict()})
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elif emission_type == 'minute':
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_dict.update({
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'intraday_risk_metrics': self.intraday_risk_metrics.to_dict(),
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'minute_perf': self.todays_performance.to_dict(self.saved_dt)
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})
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return _dict
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def process_event(self, event):
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self.event_count += 1
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if event.type == zp.DATASOURCE_TYPE.TRADE:
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# update last sale
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for perf_period in self.perf_periods:
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perf_period.update_last_sale(event)
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elif event.type == zp.DATASOURCE_TYPE.TRANSACTION:
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# Trade simulation always follows a transaction with the
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# TRADE event that was used to simulate it, so we don't
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# check for end of day rollover messages here.
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self.txn_count += 1
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for perf_period in self.perf_periods:
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perf_period.execute_transaction(event)
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elif event.type == zp.DATASOURCE_TYPE.DIVIDEND:
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log.info("Ignoring DIVIDEND event.")
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elif event.type == zp.DATASOURCE_TYPE.SPLIT:
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for perf_period in self.perf_periods:
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perf_period.handle_split(event)
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elif event.type == zp.DATASOURCE_TYPE.ORDER:
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for perf_period in self.perf_periods:
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perf_period.record_order(event)
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elif event.type == zp.DATASOURCE_TYPE.COMMISSION:
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for perf_period in self.perf_periods:
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perf_period.handle_commission(event)
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elif event.type == zp.DATASOURCE_TYPE.CUSTOM:
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pass
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elif event.type == zp.DATASOURCE_TYPE.BENCHMARK:
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if (
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self.sim_params.data_frequency == 'minute'
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and
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self.sim_params.emission_rate == 'daily'
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):
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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 midgnight.
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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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self.all_benchmark_returns[midnight] = event.returns
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def check_upcoming_dividends(self, midnight_of_date_that_just_ended):
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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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next_trading_day_idx = self.trading_days.get_loc(
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midnight_of_date_that_just_ended,
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) + 1
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if next_trading_day_idx < len(self.trading_days):
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next_trading_day = self.trading_days[next_trading_day_idx]
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else:
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# Bail if the next trading day is outside our trading range, since
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# we won't simulate the next day.
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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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for period in self.perf_periods:
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# TODO SS: There's no reason we should have to duplicate this
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# computation, but we do it currently because each perf
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# period maintains its own separate positiondict. We
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# should eventually remove this duplication and give each
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# period a (preferably read-only) DataFrame of positions.
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if len(dividends_earnable):
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period.earn_dividends(dividends_earnable)
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if len(dividends_payable):
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period.pay_dividends(dividends_payable)
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def handle_minute_close(self, dt):
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self.update_performance()
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todays_date = normalize_date(dt)
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minute_returns = self.minute_performance.returns
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self.minute_performance.rollover()
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# the intraday risk is calculated on top of minute performance
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# returns for the bench and the algo
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self.intraday_risk_metrics.update(dt,
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minute_returns,
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self.all_benchmark_returns[dt])
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bench_since_open = \
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self.intraday_risk_metrics.benchmark_cumulative_returns[dt]
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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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# if this is the close, save the returns objects for cumulative risk
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# calculations and update dividends for the next day.
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if dt == self.market_close:
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self.check_upcoming_dividends(todays_date)
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self.returns[todays_date] = self.todays_performance.returns
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def handle_intraday_market_close(self, new_mkt_open, new_mkt_close):
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"""
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Function called at market close only when emitting at minutely
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frequency.
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"""
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# update_performance should have been called in handle_minute_close
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# so it is not repeated here.
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self.intraday_risk_metrics = \
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risk.RiskMetricsCumulative(self.sim_params)
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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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self.market_open = new_mkt_open
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self.market_close = new_mkt_close
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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 = normalize_date(self.market_close)
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# add the return results from today to the returns series
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self.returns[completed_date] = self.todays_performance.returns
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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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# increment the day counter before we move markers forward.
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self.day_count += 1.0
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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()
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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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self.market_open, self.market_close = \
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trading.environment.next_open_and_close(self.market_open)
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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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self.check_upcoming_dividends(completed_date)
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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.day_count), m=self.total_days))
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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 = self.cumulative_risk_metrics.benchmark_returns
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ars = self.cumulative_risk_metrics.algorithm_returns
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self.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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risk_dict = self.risk_report.to_dict()
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return risk_dict
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