mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-07 21:00:17 +08:00
Eddie Hebert
962347318d
In preparation for the incoming changes which no longer push every bar through the tradesimulation, remove the adjustment of the period's cash on every pricing change of a held futures asset. Instead hold the last sale price for each held future either: - At the end of each peformance period update the last sale prices of all held futures, so that the pnl for the next period uses values derived from the cash difference between the end of the two periods. - When a transaction is processed for the Future, so that the correct amount is applied to each cash adjustment. (i.e. the cash adjustment is reset on every change of amount of the Future being held, so that multiple size and prices do not need to be tracked for the same asset.) Also, remove now unused dict of payout calculation modifier, since new calculation reads the value directly off of the asset. Remove update_last_sale test, since the method no longer returns a cash value.
574 lines
22 KiB
Python
574 lines
22 KiB
Python
#
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# Copyright 2015 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Performance Tracking
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====================
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+-----------------+----------------------------------------------------+
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| key | value |
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+=================+====================================================+
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| period_start | The beginning of the period to be tracked. datetime|
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| | in pytz.utc timezone. Will always be 0:00 on the |
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| | date in UTC. The fact that the time may be on the |
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| | prior day in the exchange's local time is ignored |
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+-----------------+----------------------------------------------------+
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| period_end | The end of the period to be tracked. datetime |
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| | in pytz.utc timezone. Will always be 23:59 on the |
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| | date in UTC. The fact that the time may be on the |
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| | next day in the exchange's local time is ignored |
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+-----------------+----------------------------------------------------+
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| progress | percentage of test completed |
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+-----------------+----------------------------------------------------+
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| capital_base | The initial capital assumed for this tracker. |
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+-----------------+----------------------------------------------------+
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| cumulative_perf | A dictionary representing the cumulative |
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| | performance through all the events delivered to |
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| | this tracker. For details see the comments on |
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| | :py:meth:`PerformancePeriod.to_dict` |
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+-----------------+----------------------------------------------------+
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| todays_perf | A dictionary representing the cumulative |
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| | performance through all the events delivered to |
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| | this tracker with datetime stamps between last_open|
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| | and last_close. For details see the comments on |
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| | :py:meth:`PerformancePeriod.to_dict` |
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| | TODO: adding this because we calculate it. May be |
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| | overkill. |
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+-----------------+----------------------------------------------------+
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| cumulative_risk | A dictionary representing the risk metrics |
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| _metrics | calculated based on the positions aggregated |
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| | through all the events delivered to this tracker. |
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| | For details look at the comments for |
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| | :py:meth:`zipline.finance.risk.RiskMetrics.to_dict`|
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+-----------------+----------------------------------------------------+
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"""
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from __future__ import division
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import logbook
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import pickle
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from six import iteritems
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from datetime import datetime
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import numpy as np
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import pandas as pd
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from pandas.tseries.tools import normalize_date
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import zipline.finance.risk as risk
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from . period import PerformancePeriod
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from zipline.utils.serialization_utils import (
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VERSION_LABEL
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)
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from . position_tracker import PositionTracker
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log = logbook.Logger('Performance')
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class PerformanceTracker(object):
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"""
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Tracks the performance of the algorithm.
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"""
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def __init__(self, sim_params, env):
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self.sim_params = sim_params
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self.env = env
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self.period_start = self.sim_params.period_start
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self.period_end = self.sim_params.period_end
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self.last_close = self.sim_params.last_close
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first_open = self.sim_params.first_open.tz_convert(
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self.env.exchange_tz
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)
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self.day = pd.Timestamp(datetime(first_open.year, first_open.month,
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first_open.day), tz='UTC')
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self.market_open, self.market_close = env.get_open_and_close(self.day)
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self.total_days = self.sim_params.days_in_period
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self.capital_base = self.sim_params.capital_base
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self.emission_rate = sim_params.emission_rate
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all_trading_days = env.trading_days
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mask = ((all_trading_days >= normalize_date(self.period_start)) &
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(all_trading_days <= normalize_date(self.period_end)))
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self.trading_days = all_trading_days[mask]
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self.dividend_frame = pd.DataFrame()
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self._dividend_count = 0
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self.position_tracker = PositionTracker(asset_finder=env.asset_finder)
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if self.emission_rate == 'daily':
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self.all_benchmark_returns = pd.Series(
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index=self.trading_days)
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self.cumulative_risk_metrics = \
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risk.RiskMetricsCumulative(self.sim_params, self.env)
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elif self.emission_rate == 'minute':
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self.all_benchmark_returns = pd.Series(index=pd.date_range(
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self.sim_params.first_open, self.sim_params.last_close,
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freq='Min'))
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self.cumulative_risk_metrics = \
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risk.RiskMetricsCumulative(self.sim_params, self.env,
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create_first_day_stats=True)
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# this performance period will span the entire simulation from
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# inception.
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self.cumulative_performance = PerformancePeriod(
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# initial cash is your capital base.
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starting_cash=self.capital_base,
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# the cumulative period will be calculated over the entire test.
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period_open=self.period_start,
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period_close=self.period_end,
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# don't save the transactions for the cumulative
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# period
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keep_transactions=False,
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keep_orders=False,
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# don't serialize positions for cumulative period
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serialize_positions=False,
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asset_finder=self.env.asset_finder,
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)
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self.cumulative_performance.position_tracker = self.position_tracker
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# this performance period will span just the current market day
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self.todays_performance = PerformancePeriod(
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# initial cash is your capital base.
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starting_cash=self.capital_base,
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# the daily period will be calculated for the market day
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period_open=self.market_open,
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period_close=self.market_close,
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keep_transactions=True,
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keep_orders=True,
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serialize_positions=True,
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asset_finder=self.env.asset_finder,
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)
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self.todays_performance.position_tracker = self.position_tracker
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self.saved_dt = self.period_start
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# one indexed so that we reach 100%
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self.day_count = 0.0
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self.txn_count = 0
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self.account_needs_update = True
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self._account = None
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def __repr__(self):
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return "%s(%r)" % (
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self.__class__.__name__,
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{'simulation parameters': self.sim_params})
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@property
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def progress(self):
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if self.emission_rate == 'minute':
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# Fake a value
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return 1.0
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elif self.emission_rate == 'daily':
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return self.day_count / self.total_days
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def set_date(self, date):
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if self.emission_rate == 'minute':
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self.saved_dt = date
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self.todays_performance.period_close = self.saved_dt
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def update_dividends(self, new_dividends):
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"""
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Update our dividend frame with new dividends. @new_dividends should be
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a DataFrame with columns containing at least the entries in
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zipline.protocol.DIVIDEND_FIELDS.
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"""
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# Mark each new dividend with a unique integer id. This ensures that
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# we can differentiate dividends whose date/sid fields are otherwise
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# identical.
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new_dividends['id'] = np.arange(
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self._dividend_count,
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self._dividend_count + len(new_dividends),
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)
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self._dividend_count += len(new_dividends)
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self.dividend_frame = pd.concat(
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[self.dividend_frame, new_dividends]
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).sort(['pay_date', 'ex_date']).set_index('id', drop=False)
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def initialize_dividends_from_other(self, other):
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"""
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Helper for copying dividends to a new PerformanceTracker while
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preserving dividend count. Useful if a simulation needs to create a
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new PerformanceTracker mid-stream and wants to preserve stored dividend
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info.
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Note that this does not copy unpaid dividends.
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"""
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self.dividend_frame = other.dividend_frame
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self._dividend_count = other._dividend_count
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def handle_sid_removed_from_universe(self, sid):
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"""
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This method handles any behaviors that must occur when a SID leaves the
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universe of the TradingAlgorithm.
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Parameters
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__________
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sid : int
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The sid of the Asset being removed from the universe.
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"""
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# Drop any dividends for the sid from the dividends frame
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self.dividend_frame = self.dividend_frame[
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self.dividend_frame.sid != sid
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]
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def update_performance(self):
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# calculate performance as of last trade
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self.cumulative_performance.calculate_performance()
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self.todays_performance.calculate_performance()
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def get_portfolio(self, performance_needs_update):
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if performance_needs_update:
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self.update_performance()
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self.account_needs_update = True
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return self.cumulative_performance.as_portfolio()
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def get_account(self, performance_needs_update):
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if performance_needs_update:
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self.update_performance()
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self.account_needs_update = True
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if self.account_needs_update:
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self._update_account()
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return self._account
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def _update_account(self):
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self._account = self.cumulative_performance.as_account()
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self.account_needs_update = False
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def to_dict(self, emission_type=None):
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"""
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Creates a dictionary representing the state of this tracker.
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Returns a dict object of the form described in header comments.
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"""
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# Default to the emission rate of this tracker if no type is provided
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if emission_type is None:
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emission_type = self.emission_rate
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_dict = {
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'period_start': self.period_start,
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'period_end': self.period_end,
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'capital_base': self.capital_base,
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'cumulative_perf': self.cumulative_performance.to_dict(),
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'progress': self.progress,
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'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict()
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}
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if emission_type == 'daily':
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_dict['daily_perf'] = self.todays_performance.to_dict()
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elif emission_type == 'minute':
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_dict['minute_perf'] = self.todays_performance.to_dict(
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self.saved_dt)
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else:
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raise ValueError("Invalid emission type: %s" % emission_type)
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return _dict
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def _handle_event_price(self, event):
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self.position_tracker.update_last_sale(event)
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def process_trade(self, event):
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self._handle_event_price(event)
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def process_transaction(self, event):
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self._handle_event_price(event)
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self.txn_count += 1
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self.cumulative_performance.handle_execution(event)
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self.todays_performance.handle_execution(event)
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self.position_tracker.execute_transaction(event)
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def process_dividend(self, dividend):
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log.info("Ignoring DIVIDEND event.")
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def process_split(self, event):
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leftover_cash = self.position_tracker.handle_split(event)
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if leftover_cash > 0:
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self.cumulative_performance.handle_cash_payment(leftover_cash)
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self.todays_performance.handle_cash_payment(leftover_cash)
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def process_order(self, event):
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self.cumulative_performance.record_order(event)
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self.todays_performance.record_order(event)
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def process_commission(self, commission):
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sid = commission.sid
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cost = commission.cost
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self.position_tracker.handle_commission(sid, cost)
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self.cumulative_performance.handle_commission(cost)
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self.todays_performance.handle_commission(cost)
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def process_benchmark(self, event):
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if self.sim_params.data_frequency == 'minute' and \
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self.sim_params.emission_rate == 'daily':
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# Minute data benchmarks should have a timestamp of market
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# close, so that calculations are triggered at the right time.
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# However, risk module uses midnight as the 'day'
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# marker for returns, so adjust back to midnight.
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midnight = pd.tseries.tools.normalize_date(event.dt)
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else:
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midnight = event.dt
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if midnight not in self.all_benchmark_returns.index:
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raise AssertionError(
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("Date %s not allocated in all_benchmark_returns. "
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"Calendar seems to mismatch with benchmark. "
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"Benchmark container is=%s" %
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(midnight,
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self.all_benchmark_returns.index)))
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self.all_benchmark_returns[midnight] = event.returns
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def process_close_position(self, event):
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# CLOSE_POSITION events that contain prices that must be handled as
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# a final trade event
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if 'price' in event:
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self.process_trade(event)
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txn = self.position_tracker.\
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maybe_create_close_position_transaction(event)
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if txn:
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self.process_transaction(txn)
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def check_upcoming_dividends(self, next_trading_day):
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"""
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Check if we currently own any stocks with dividends whose ex_date is
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the next trading day. Track how much we should be payed on those
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dividends' pay dates.
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Then check if we are owed cash/stock for any dividends whose pay date
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is the next trading day. Apply all such benefits, then recalculate
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performance.
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"""
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if len(self.dividend_frame) == 0:
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# We don't currently know about any dividends for this simulation
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# period, so bail.
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return
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# Dividends whose ex_date is the next trading day. We need to check if
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# we own any of these stocks so we know to pay them out when the pay
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# date comes.
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ex_date_mask = (self.dividend_frame['ex_date'] == next_trading_day)
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dividends_earnable = self.dividend_frame[ex_date_mask]
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# Dividends whose pay date is the next trading day. If we held any of
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# these stocks on midnight before the ex_date, we need to pay these out
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# now.
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pay_date_mask = (self.dividend_frame['pay_date'] == next_trading_day)
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dividends_payable = self.dividend_frame[pay_date_mask]
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position_tracker = self.position_tracker
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if len(dividends_earnable):
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position_tracker.earn_dividends(dividends_earnable)
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if not len(dividends_payable):
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return
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net_cash_payment = position_tracker.pay_dividends(dividends_payable)
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self.cumulative_performance.handle_dividends_paid(net_cash_payment)
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self.todays_performance.handle_dividends_paid(net_cash_payment)
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def check_asset_auto_closes(self, next_trading_day):
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"""
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Check if the position tracker currently owns any Assets with an
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auto-close date that is the next trading day. Close those positions.
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Parameters
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----------
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next_trading_day : pandas.Timestamp
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The next trading day of the simulation
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"""
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auto_close_events = self.position_tracker.auto_close_position_events(
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next_trading_day=next_trading_day
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)
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for event in auto_close_events:
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self.process_close_position(event)
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def handle_minute_close(self, dt):
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"""
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Handles the close of the given minute. This includes handling
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market-close functions if the given minute is the end of the market
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day.
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Parameters
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__________
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dt : Timestamp
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The minute that is ending
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Returns
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_______
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(dict, dict/None)
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A tuple of the minute perf packet and daily perf packet.
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If the market day has not ended, the daily perf packet is None.
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"""
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self.update_performance()
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todays_date = normalize_date(dt)
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account = self.get_account(False)
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bench_returns = self.all_benchmark_returns.loc[todays_date:dt]
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# cumulative returns
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bench_since_open = (1. + bench_returns).prod() - 1
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self.cumulative_risk_metrics.update(todays_date,
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self.todays_performance.returns,
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bench_since_open,
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account.leverage)
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minute_packet = self.to_dict(emission_type='minute')
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# if this is the close, update dividends for the next day.
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# Return the performance tuple
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if dt == self.market_close:
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return (minute_packet, self._handle_market_close(todays_date))
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else:
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return (minute_packet, None)
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def handle_market_close_daily(self):
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"""
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Function called after handle_data when running with daily emission
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rate.
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"""
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self.update_performance()
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completed_date = self.day
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account = self.get_account(False)
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# update risk metrics for cumulative performance
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self.cumulative_risk_metrics.update(
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completed_date,
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self.todays_performance.returns,
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self.all_benchmark_returns[completed_date],
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account.leverage)
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return self._handle_market_close(completed_date)
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def _handle_market_close(self, completed_date):
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# increment the day counter before we move markers forward.
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self.day_count += 1.0
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# Get the next trading day and, if it is past the bounds of this
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# simulation, return the daily perf packet
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next_trading_day = self.env.next_trading_day(completed_date)
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# Check if any assets need to be auto-closed before generating today's
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# perf period
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if next_trading_day:
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self.check_asset_auto_closes(next_trading_day=next_trading_day)
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# Take a snapshot of our current performance to return to the
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# browser.
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daily_update = self.to_dict(emission_type='daily')
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# On the last day of the test, don't create tomorrow's performance
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# period. We may not be able to find the next trading day if we're at
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# the end of our historical data
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if self.market_close >= self.last_close:
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return daily_update
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# move the market day markers forward
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self.market_open, self.market_close = \
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self.env.next_open_and_close(self.day)
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self.day = self.env.next_trading_day(self.day)
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# Roll over positions to current day.
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|
self.todays_performance.rollover()
|
|
self.todays_performance.period_open = self.market_open
|
|
self.todays_performance.period_close = self.market_close
|
|
|
|
# If the next trading day is irrelevant, then return the daily packet
|
|
if (next_trading_day is None) or (next_trading_day >= self.last_close):
|
|
return daily_update
|
|
|
|
# Check for any dividends and auto-closes, then return the daily perf
|
|
# packet
|
|
self.check_upcoming_dividends(next_trading_day=next_trading_day)
|
|
return daily_update
|
|
|
|
def handle_simulation_end(self):
|
|
"""
|
|
When the simulation is complete, run the full period risk report
|
|
and send it out on the results socket.
|
|
"""
|
|
|
|
log_msg = "Simulated {n} trading days out of {m}."
|
|
log.info(log_msg.format(n=int(self.day_count), m=self.total_days))
|
|
log.info("first open: {d}".format(
|
|
d=self.sim_params.first_open))
|
|
log.info("last close: {d}".format(
|
|
d=self.sim_params.last_close))
|
|
|
|
bms = pd.Series(
|
|
index=self.cumulative_risk_metrics.cont_index,
|
|
data=self.cumulative_risk_metrics.benchmark_returns_cont)
|
|
ars = pd.Series(
|
|
index=self.cumulative_risk_metrics.cont_index,
|
|
data=self.cumulative_risk_metrics.algorithm_returns_cont)
|
|
acl = self.cumulative_risk_metrics.algorithm_cumulative_leverages
|
|
self.risk_report = risk.RiskReport(
|
|
ars,
|
|
self.sim_params,
|
|
benchmark_returns=bms,
|
|
algorithm_leverages=acl,
|
|
env=self.env)
|
|
|
|
risk_dict = self.risk_report.to_dict()
|
|
return risk_dict
|
|
|
|
def __getstate__(self):
|
|
state_dict = \
|
|
{k: v for k, v in iteritems(self.__dict__)
|
|
if not k.startswith('_')}
|
|
|
|
state_dict['dividend_frame'] = pickle.dumps(self.dividend_frame)
|
|
|
|
state_dict['_dividend_count'] = self._dividend_count
|
|
|
|
STATE_VERSION = 4
|
|
state_dict[VERSION_LABEL] = STATE_VERSION
|
|
|
|
return state_dict
|
|
|
|
def __setstate__(self, state):
|
|
|
|
OLDEST_SUPPORTED_STATE = 4
|
|
version = state.pop(VERSION_LABEL)
|
|
|
|
if version < OLDEST_SUPPORTED_STATE:
|
|
raise BaseException("PerformanceTracker saved state is too old.")
|
|
|
|
self.__dict__.update(state)
|
|
|
|
# Handle the dividend frame specially
|
|
self.dividend_frame = pickle.loads(state['dividend_frame'])
|
|
|
|
# properly setup the perf periods
|
|
p_types = ['cumulative', 'todays']
|
|
for p_type in p_types:
|
|
name = p_type + '_performance'
|
|
period = getattr(self, name, None)
|
|
if period is None:
|
|
continue
|
|
period._position_tracker = self.position_tracker
|