mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-05 10:32:06 +08:00
fawce
a4a4d38a73
- added LSE reference rrules calendar (thanks to Edward Johns)
- added tests to verify LSE environment matches rrule calendar
- added a test to verify global environment behavior can be set.
- moved DailyReturn class to trading to eliminate circularity from
risk <-> trading.
- updated TradingEnvironment to be a context manager. This allows users
to run algorithms in individually isolated environments in one python
process. This is useful for managing multiple algorithms in a single
ipython notebook.
- added comments to explain behavior and useage of the global environment
688 lines
28 KiB
Python
688 lines
28 KiB
Python
#
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# Copyright 2012 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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| started_at | datetime in utc marking the start of this test |
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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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Position Tracking
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=================
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+-----------------+----------------------------------------------------+
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| key | value |
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+=================+====================================================+
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| sid | the identifier for the security held in this |
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| | position. |
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+-----------------+----------------------------------------------------+
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| amount | whole number of shares in the position |
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+-----------------+----------------------------------------------------+
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| last_sale_price | price at last sale of the security on the exchange |
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+-----------------+----------------------------------------------------+
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| cost_basis | the volume weighted average price paid per share |
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+-----------------+----------------------------------------------------+
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Performance Period
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==================
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Performance Periods are updated with every trade. When calling
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code needs a portfolio object that fulfills the algorithm
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protocol, use the PerformancePeriod.as_portfolio method. See that
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method for comments on the specific fields provided (and
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omitted).
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+---------------+------------------------------------------------------+
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| key | value |
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+===============+======================================================+
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| ending_value | the total market value of the positions held at the |
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| | end of the period |
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+---------------+------------------------------------------------------+
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| cash_flow | the cash flow in the period (negative means spent) |
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| | from buying and selling securities in the period. |
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| | Includes dividend payments in the period as well. |
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+---------------+------------------------------------------------------+
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| starting_value| the total market value of the positions held at the |
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| | start of the period |
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+---------------+------------------------------------------------------+
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| starting_cash | cash on hand at the beginning of the period |
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+---------------+------------------------------------------------------+
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| ending_cash | cash on hand at the end of the period |
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+---------------+------------------------------------------------------+
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| positions | a list of dicts representing positions, see |
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| | :py:meth:`Position.to_dict()` |
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| | for details on the contents of the dict |
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+---------------+------------------------------------------------------+
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| pnl | Dollar value profit and loss, for both realized and |
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| | unrealized gains. |
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+---------------+------------------------------------------------------+
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| returns | percentage returns for the entire portfolio over the |
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| | period |
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+---------------+------------------------------------------------------+
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| cumulative\ | The net capital used (positive is spent) during |
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| _capital_used | the period |
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+---------------+------------------------------------------------------+
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| max_capital\ | The maximum amount of capital deployed during the |
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| _used | period. |
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+---------------+------------------------------------------------------+
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| max_leverage | The maximum leverage used during the period. |
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+---------------+------------------------------------------------------+
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| period_close | The last close of the market in period. datetime in |
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| | pytz.utc timezone. |
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+---------------+------------------------------------------------------+
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| period_open | The first open of the market in period. datetime in |
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| | pytz.utc timezone. |
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+---------------+------------------------------------------------------+
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| transactions | all the transactions that were acrued during this |
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| | period. Unset/missing for cumulative periods. |
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+---------------+------------------------------------------------------+
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"""
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import logbook
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import datetime
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import pytz
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import math
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import numpy as np
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import zipline.protocol as zp
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import zipline.finance.risk as risk
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import zipline.finance.trading as trading
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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 zipline as it is running in
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the simulator, relays this out to the Deluge broker and then
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to the client. Visually:
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+--------------------+ Result Stream +--------+
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| PerformanceTracker | ----------------> | Deluge |
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+--------------------+ +--------+
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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.started_at = datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
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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.progress = 0.0
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self.total_days = self.sim_params.days_in_period
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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.capital_base = self.sim_params.capital_base
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self.returns = []
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self.txn_count = 0
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self.event_count = 0
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self.last_dict = None
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self.cumulative_risk_metrics = \
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risk.RiskMetricsIterative(self.period_start)
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# this performance period will span the entire simulation.
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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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)
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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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# save the transactions for the daily periods
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keep_transactions=True
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)
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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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def transform(self, stream_in):
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"""
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Main generator work loop.
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"""
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for date, snapshot in stream_in:
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new_snapshot = []
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for event in snapshot:
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messages = self.process_event(event)
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if messages is not None:
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event.perf_messages = messages
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event.portfolio = self.get_portfolio()
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new_snapshot.append(event)
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if len(new_snapshot) > 0:
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yield date, new_snapshot
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def get_portfolio(self):
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return self.cumulative_performance.as_portfolio()
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def to_dict(self):
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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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return {
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'started_at': self.started_at,
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'period_start': self.period_start,
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'period_end': self.period_end,
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'progress': self.progress,
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'capital_base': self.capital_base,
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'cumulative_perf': self.cumulative_performance.to_dict(),
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'daily_perf': self.todays_performance.to_dict(),
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'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict()
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}
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def process_event(self, event):
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messages = None
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self.event_count += 1
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if event.type == zp.DATASOURCE_TYPE.TRADE:
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messages = []
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while event.dt > self.market_close and event.dt < self.last_close:
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messages.append(self.handle_market_close())
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if event.TRANSACTION:
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self.txn_count += 1
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self.cumulative_performance.execute_transaction(
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event.TRANSACTION
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)
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self.todays_performance.execute_transaction(event.TRANSACTION)
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#update last sale
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self.cumulative_performance.update_last_sale(event)
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self.todays_performance.update_last_sale(event)
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del event['TRANSACTION']
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elif event.type == zp.DATASOURCE_TYPE.DIVIDEND:
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self.cumulative_performance.add_dividend(event)
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self.todays_performance.add_dividend(event)
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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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return messages
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def handle_market_close(self):
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# add the return results from today to the list of DailyReturn objects.
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todays_date = self.market_close.replace(hour=0, minute=0, second=0)
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self.cumulative_performance.update_dividends(todays_date)
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self.todays_performance.update_dividends(todays_date)
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todays_return_obj = trading.DailyReturn(
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todays_date,
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self.todays_performance.returns
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)
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self.returns.append(todays_return_obj)
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#update risk metrics for cumulative performance
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self.cumulative_risk_metrics.update(
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self.market_close,
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self.todays_performance.returns)
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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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# calculate progress of test
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self.progress = self.day_count / self.total_days
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# Take a snapshot of our current peformance 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
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# at 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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# The dividend calculation for the daily needs to be made
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# after the rollover. midnight_between is the last midnight
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# hour between the close of markets and the next open. To
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# make sure midnight_between matches identically with
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# dividend data dates, it is in UTC.
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midnight_between = self.market_open.replace(hour=0, minute=0, second=0)
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self.cumulative_performance.update_dividends(midnight_between)
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self.todays_performance.update_dividends(midnight_between)
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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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# the stream will end on the last trading day, but will
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# not trigger an end of day, so we trigger the final
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# market close(s) here
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perf_messages = []
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while self.last_close > self.market_close:
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perf_messages.append(self.handle_market_close())
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perf_messages.append(self.handle_market_close())
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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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self.risk_report = risk.RiskReport(self.returns, self.sim_params)
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risk_dict = self.risk_report.to_dict()
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return perf_messages, risk_dict
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class Position(object):
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def __init__(self, sid):
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self.sid = sid
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self.amount = 0
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self.cost_basis = 0.0 # per share
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self.last_sale_price = 0.0
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self.last_sale_date = 0.0
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self.dividends = []
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def update_dividends(self, midnight_utc):
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"""
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midnight_utc is the 0 hour for the current (not yet open) trading day.
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This method will be invoked at the end of the market
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close handling, before the next market open.
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"""
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payment = 0.0
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unpaid_dividends = []
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for dividend in self.dividends:
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if midnight_utc == dividend.ex_date:
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# if we own shares at midnight of the div_ex date
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# we are entitled to the dividend.
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dividend.amount_on_ex_date = self.amount
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if dividend.net_amount:
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dividend.payment = self.amount * dividend.net_amount
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else:
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dividend.payment = self.amount * dividend.gross_amount
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if midnight_utc == dividend.pay_date:
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# if it is the payment date, include this
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# dividend's actual payment (calculated on
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# ex_date)
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payment += dividend.payment
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else:
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unpaid_dividends.append(dividend)
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self.dividends = unpaid_dividends
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return payment
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def add_dividend(self, dividend):
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self.dividends.append(dividend)
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def update(self, txn):
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if(self.sid != txn.sid):
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raise NameError('updating position with txn for a different sid')
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#we're covering a short or closing a position
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if(self.amount + txn.amount == 0):
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self.cost_basis = 0.0
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self.amount = 0
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else:
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prev_cost = self.cost_basis * self.amount
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txn_cost = txn.amount * txn.price
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total_cost = prev_cost + txn_cost
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total_shares = self.amount + txn.amount
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self.cost_basis = total_cost / total_shares
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self.amount = self.amount + txn.amount
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def currentValue(self):
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return self.amount * self.last_sale_price
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def __repr__(self):
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template = "sid: {sid}, amount: {amount}, cost_basis: {cost_basis}, \
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last_sale_price: {last_sale_price}"
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return template.format(
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sid=self.sid,
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amount=self.amount,
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cost_basis=self.cost_basis,
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last_sale_price=self.last_sale_price
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)
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def to_dict(self):
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"""
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Creates a dictionary representing the state of this position.
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Returns a dict object of the form:
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"""
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return {
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'sid': self.sid,
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'amount': self.amount,
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'cost_basis': self.cost_basis,
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'last_sale_price': self.last_sale_price
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}
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class PerformancePeriod(object):
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def __init__(
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self,
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starting_cash,
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period_open=None,
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period_close=None,
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keep_transactions=False):
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self.period_open = period_open
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self.period_close = period_close
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self.ending_value = 0.0
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self.period_cash_flow = 0.0
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self.pnl = 0.0
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#sid => position object
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self.positions = positiondict()
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self.starting_value = 0.0
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#cash balance at start of period
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self.starting_cash = starting_cash
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self.ending_cash = starting_cash
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self.keep_transactions = keep_transactions
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self.processed_transactions = []
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self.cumulative_capital_used = 0.0
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self.max_capital_used = 0.0
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self.max_leverage = 0.0
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# Maps position to following array indexes
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self._position_index_map = {}
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# Arrays for quick calculations of positions value
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self._position_amounts = np.array([])
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self._position_last_sale_prices = np.array([])
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self.calculate_performance()
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# An object to recycle via assigning new values
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# when returning portfolio information.
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# So as not to avoid creating a new object for each event
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self._portfolio_store = zp.Portfolio()
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self._positions_store = zp.Positions()
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def rollover(self):
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self.starting_value = self.ending_value
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self.starting_cash = self.ending_cash
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self.period_cash_flow = 0.0
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self.pnl = 0.0
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self.processed_transactions = []
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self.cumulative_capital_used = 0.0
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self.max_capital_used = 0.0
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self.max_leverage = 0.0
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def index_for_position(self, sid):
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try:
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index = self._position_index_map[sid]
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except KeyError:
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index = len(self._position_index_map)
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self._position_index_map[sid] = index
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self._position_amounts = np.append(self._position_amounts, [0])
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self._position_last_sale_prices = np.append(
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self._position_last_sale_prices, [0])
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return index
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def add_dividend(self, div):
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# The dividend is received on midnight of the dividend
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# declared date. We calculate the dividends based on the amount of
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# stock owned on midnight of the ex dividend date. However, the cash
|
|
# is not dispersed until the payment date, which is
|
|
# included in the event.
|
|
self.positions[div.sid].add_dividend(div)
|
|
|
|
def update_dividends(self, todays_date):
|
|
"""
|
|
Check the payment date and ex date against today's date
|
|
to detrmine if we are owed a dividend payment or if the
|
|
payment has been disbursed.
|
|
"""
|
|
cash_payments = 0.0
|
|
for sid, pos in self.positions.iteritems():
|
|
cash_payments += pos.update_dividends(todays_date)
|
|
|
|
# credit our cash balance with the dividend payments, or
|
|
# if we are short, debit our cash balance with the
|
|
# payments.
|
|
self.period_cash_flow += cash_payments
|
|
# debit our cumulative cash spent with the dividend
|
|
# payments, or credit our cumulative cash spent if we are
|
|
# short the stock.
|
|
self.cumulative_capital_used -= cash_payments
|
|
|
|
# recalculate performance, including the dividend
|
|
# paymtents
|
|
self.calculate_performance()
|
|
|
|
def calculate_performance(self):
|
|
self.ending_value = self.calculate_positions_value()
|
|
|
|
total_at_start = self.starting_cash + self.starting_value
|
|
self.ending_cash = self.starting_cash + self.period_cash_flow
|
|
total_at_end = self.ending_cash + self.ending_value
|
|
|
|
self.pnl = total_at_end - total_at_start
|
|
if total_at_start != 0:
|
|
self.returns = self.pnl / total_at_start
|
|
else:
|
|
self.returns = 0.0
|
|
|
|
def execute_transaction(self, txn):
|
|
# Update Position
|
|
# ----------------
|
|
position = self.positions[txn.sid]
|
|
position.update(txn)
|
|
index = self.index_for_position(txn.sid)
|
|
self._position_amounts[index] = position.amount
|
|
|
|
self.period_cash_flow += -1 * txn.price * txn.amount
|
|
|
|
# Max Leverage
|
|
# ---------------
|
|
# Calculate the maximum capital used and maximum leverage
|
|
transaction_cost = txn.price * txn.amount
|
|
self.cumulative_capital_used += transaction_cost
|
|
|
|
if math.fabs(self.cumulative_capital_used) > self.max_capital_used:
|
|
self.max_capital_used = math.fabs(self.cumulative_capital_used)
|
|
|
|
# We want to conveye a level, rather than a precise figure.
|
|
# round to the nearest 5,000 to keep the number easy on the eyes
|
|
self.max_capital_used = self.round_to_nearest(
|
|
self.max_capital_used,
|
|
base=5000
|
|
)
|
|
|
|
# we're adding a 10% cushion to the capital used.
|
|
self.max_leverage = 1.1 * \
|
|
self.max_capital_used / self.starting_cash
|
|
|
|
# add transaction to the list of processed transactions
|
|
if self.keep_transactions:
|
|
self.processed_transactions.append(txn)
|
|
|
|
def round_to_nearest(self, x, base=5):
|
|
return int(base * round(float(x) / base))
|
|
|
|
def calculate_positions_value(self):
|
|
return np.dot(self._position_amounts, self._position_last_sale_prices)
|
|
|
|
def update_last_sale(self, event):
|
|
is_trade = event.type == zp.DATASOURCE_TYPE.TRADE
|
|
if event.sid in self.positions and is_trade:
|
|
self.positions[event.sid].last_sale_price = event.price
|
|
index = self.index_for_position(event.sid)
|
|
self._position_last_sale_prices[index] = event.price
|
|
|
|
self.positions[event.sid].last_sale_date = event.dt
|
|
|
|
def __core_dict(self):
|
|
rval = {
|
|
'ending_value': self.ending_value,
|
|
# this field is renamed to capital_used for backward
|
|
# compatibility.
|
|
'capital_used': self.period_cash_flow,
|
|
'starting_value': self.starting_value,
|
|
'starting_cash': self.starting_cash,
|
|
'ending_cash': self.ending_cash,
|
|
'portfolio_value': self.ending_cash + self.ending_value,
|
|
'cumulative_capital_used': self.cumulative_capital_used,
|
|
'max_capital_used': self.max_capital_used,
|
|
'max_leverage': self.max_leverage,
|
|
'pnl': self.pnl,
|
|
'returns': self.returns,
|
|
'period_open': self.period_open,
|
|
'period_close': self.period_close
|
|
}
|
|
|
|
return rval
|
|
|
|
def to_dict(self):
|
|
"""
|
|
Creates a dictionary representing the state of this performance
|
|
period. See header comments for a detailed description.
|
|
"""
|
|
rval = self.__core_dict()
|
|
positions = self.get_positions_list()
|
|
rval['positions'] = positions
|
|
|
|
# we want the key to be absent, not just empty
|
|
if self.keep_transactions:
|
|
transactions = [x.__dict__ for x in self.processed_transactions]
|
|
rval['transactions'] = transactions
|
|
|
|
return rval
|
|
|
|
def as_portfolio(self):
|
|
"""
|
|
The purpose of this method is to provide a portfolio
|
|
object to algorithms running inside the same trading
|
|
client. The data needed is captured raw in a
|
|
PerformancePeriod, and in this method we rename some
|
|
fields for usability and remove extraneous fields.
|
|
"""
|
|
# Recycles containing objects' Portfolio object
|
|
# which is used for returning values.
|
|
# as_portfolio is called in an inner loop,
|
|
# so repeated object creation becomes too expensive
|
|
portfolio = self._portfolio_store
|
|
# maintaining the old name for the portfolio field for
|
|
# backward compatibility
|
|
portfolio.capital_used = self.period_cash_flow
|
|
portfolio.starting_cash = self.starting_cash
|
|
portfolio.portfolio_value = self.ending_cash + self.ending_value
|
|
portfolio.pnl = self.pnl
|
|
portfolio.returns = self.returns
|
|
portfolio.cash = self.ending_cash
|
|
portfolio.start_date = self.period_open
|
|
portfolio.positions = self.get_positions()
|
|
portfolio.positions_value = self.ending_value
|
|
return portfolio
|
|
|
|
def get_positions(self):
|
|
|
|
positions = self._positions_store
|
|
|
|
for sid, pos in self.positions.iteritems():
|
|
|
|
if sid not in positions:
|
|
positions[sid] = zp.Position(sid)
|
|
position = positions[sid]
|
|
position.amount = pos.amount
|
|
position.cost_basis = pos.cost_basis
|
|
position.last_sale_price = pos.last_sale_price
|
|
|
|
return positions
|
|
|
|
def get_positions_list(self):
|
|
positions = []
|
|
for sid, pos in self.positions.iteritems():
|
|
if pos.amount != 0:
|
|
positions.append(pos.to_dict())
|
|
return positions
|
|
|
|
|
|
class positiondict(dict):
|
|
|
|
def __missing__(self, key):
|
|
pos = Position(key)
|
|
self[key] = pos
|
|
return pos
|