mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-08 07:43:58 +08:00
Eddie Hebert
0617e53d69
Also, removes flake8 ignores, since the warnings that were at odds with eachother now work.
582 lines
23 KiB
Python
582 lines
23 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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| capital_used | the net capital consumed (positive means spent) by |
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| | buying and selling securities in the period |
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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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from zipline.utils.protocol_utils import ndict
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import zipline.protocol as zp
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import zipline.finance.risk as risk
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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, trading_environment):
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self.trading_environment = trading_environment
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self.trading_day = datetime.timedelta(hours=6, minutes=30)
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self.calendar_day = datetime.timedelta(hours=24)
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self.started_at = datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
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self.period_start = self.trading_environment.period_start
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self.period_end = self.trading_environment.period_end
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self.market_open = self.trading_environment.first_open
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self.market_close = self.market_open + self.trading_day
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self.progress = 0.0
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self.total_days = self.trading_environment.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.trading_environment.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 = risk.RiskMetricsIterative(
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self.period_start, self.trading_environment)
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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 positions are empty
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positiondict(),
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# initial portfolio positions have zero value
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0,
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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 positions are empty
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positiondict(),
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# initial portfolio positions have zero value
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0,
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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 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 event in stream_in:
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if event.dt == "DONE":
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event.perf_message = self.handle_simulation_end()
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del event['TRANSACTION']
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yield event
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else:
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event.perf_message = self.process_event(event)
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event.portfolio = self.get_portfolio()
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del event['TRANSACTION']
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yield event
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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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message = None
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assert isinstance(event, ndict)
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self.event_count += 1
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if(event.dt >= self.market_close):
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message = 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(event.TRANSACTION)
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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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#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 message
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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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todays_return_obj = risk.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.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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#move the market day markers forward
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self.market_open = self.market_open + self.calendar_day
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while not self.trading_environment.is_trading_day(self.market_open):
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if self.market_open > self.trading_environment.trading_days[-1]:
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raise Exception(
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"Attempt to backtest beyond available history.")
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self.market_open = self.market_open + self.calendar_day
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self.market_close = self.market_open + self.trading_day
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# Roll over positions to current day.
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self.todays_performance = PerformancePeriod(
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self.todays_performance.positions,
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self.todays_performance.ending_value,
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self.todays_performance.ending_cash,
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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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)
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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=self.day_count, m=self.total_days))
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log.info("first open: {d}".format(
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d=self.trading_environment.first_open))
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# the stream will end on the last trading day, but will not trigger
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# an end of day, so we trigger the final market close here.
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self.handle_market_close()
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self.risk_report = risk.RiskReport(
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self.returns,
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self.trading_environment
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)
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risk_dict = self.risk_report.to_dict()
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return 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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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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initial_positions,
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starting_value,
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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_capital_used = 0.0
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self.pnl = 0.0
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#sid => position object
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if not isinstance(initial_positions, positiondict):
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self.positions = positiondict()
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self.positions.update(initial_positions)
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else:
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self.positions = initial_positions
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self.starting_value = starting_value
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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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self.calculate_performance()
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def calculate_performance(self):
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self.ending_value = self.calculate_positions_value()
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total_at_start = self.starting_cash + self.starting_value
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self.ending_cash = self.starting_cash + self.period_capital_used
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total_at_end = self.ending_cash + self.ending_value
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self.pnl = total_at_end - total_at_start
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if total_at_start != 0:
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self.returns = self.pnl / total_at_start
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else:
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self.returns = 0.0
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def execute_transaction(self, txn):
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# Update Position
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# ----------------
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self.positions[txn.sid].update(txn)
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self.period_capital_used += -1 * txn.price * txn.amount
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# Max Leverage
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# ---------------
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# Calculate the maximum capital used and maximum leverage
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transaction_cost = txn.price * txn.amount
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self.cumulative_capital_used += transaction_cost
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if math.fabs(self.cumulative_capital_used) > self.max_capital_used:
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self.max_capital_used = math.fabs(self.cumulative_capital_used)
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# We want to conveye a level, rather than a precise figure.
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# round to the nearest 5,000 to keep the number easy on the eyes
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self.max_capital_used = self.round_to_nearest(
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self.max_capital_used,
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base=5000
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)
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# we're adding a 10% cushion to the capital used.
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self.max_leverage = 1.1 * \
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self.max_capital_used / self.starting_cash
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# add transaction to the list of processed transactions
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if self.keep_transactions:
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self.processed_transactions.append(txn)
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def round_to_nearest(self, x, base=5):
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return int(base * round(float(x) / base))
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def calculate_positions_value(self):
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mktValue = 0.0
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for key, pos in self.positions.iteritems():
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mktValue += pos.currentValue()
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return mktValue
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def update_last_sale(self, event):
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is_trade = event.type == zp.DATASOURCE_TYPE.TRADE
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if event.sid in self.positions and is_trade:
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self.positions[event.sid].last_sale_price = event.price
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self.positions[event.sid].last_sale_date = event.dt
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def __core_dict(self):
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rval = {
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'ending_value': self.ending_value,
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'capital_used': self.period_capital_used,
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'starting_value': self.starting_value,
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'starting_cash': self.starting_cash,
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'ending_cash': self.ending_cash,
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'portfolio_value': self.ending_cash + self.ending_value,
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'cumulative_capital_used': self.cumulative_capital_used,
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'max_capital_used': self.max_capital_used,
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'max_leverage': self.max_leverage,
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'pnl': self.pnl,
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'returns': self.returns,
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'period_open': self.period_open,
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'period_close': self.period_close
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}
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return rval
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def to_dict(self):
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"""
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Creates a dictionary representing the state of this performance
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period. See header comments for a detailed description.
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"""
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rval = self.__core_dict()
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positions = self.get_positions_list()
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rval['positions'] = positions
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# we want the key to be absent, not just empty
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if self.keep_transactions:
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transactions = [x.as_dict() for x in self.processed_transactions]
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rval['transactions'] = transactions
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return rval
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def as_portfolio(self):
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"""
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The purpose of this method is to provide a portfolio
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object to algorithms running inside the same trading
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client. The data needed is captured raw in a
|
|
PerformancePeriod, and in this method we rename some
|
|
fields for usability and remove extraneous fields.
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"""
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portfolio = self.__core_dict()
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# rename:
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# ending_cash -> cash
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|
# period_open -> backtest_start
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|
#
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|
# remove:
|
|
# period_close, starting_value,
|
|
# cumulative_capital_used, max_leverage, max_capital_used
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|
portfolio['cash'] = portfolio['ending_cash']
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|
portfolio['start_date'] = portfolio['period_open']
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|
portfolio['positions_value'] = portfolio['ending_value']
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|
|
|
del(portfolio['ending_cash'])
|
|
del(portfolio['period_open'])
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|
del(portfolio['period_close'])
|
|
del(portfolio['starting_value'])
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|
del(portfolio['ending_value'])
|
|
del(portfolio['cumulative_capital_used'])
|
|
del(portfolio['max_leverage'])
|
|
del(portfolio['max_capital_used'])
|
|
|
|
portfolio['positions'] = self.get_positions()
|
|
|
|
return ndict(portfolio)
|
|
|
|
def get_positions(self):
|
|
|
|
positions = ndict(internal=position_ndict())
|
|
|
|
for sid, pos in self.positions.iteritems():
|
|
cur = pos.to_dict()
|
|
positions[sid] = ndict(cur)
|
|
|
|
return positions
|
|
|
|
def get_positions_list(self):
|
|
positions = []
|
|
for sid, pos in self.positions.iteritems():
|
|
cur = pos.to_dict()
|
|
positions.append(cur)
|
|
return positions
|
|
|
|
|
|
class positiondict(dict):
|
|
|
|
def __missing__(self, key):
|
|
pos = Position(key)
|
|
self[key] = pos
|
|
return pos
|
|
|
|
|
|
class position_ndict(dict):
|
|
|
|
def __missing__(self, key):
|
|
pos = Position(key)
|
|
self[key] = ndict(pos.to_dict())
|
|
return pos
|