mirror of
https://github.com/wassname/catalyst.git
synced 2026-08-03 12:40:47 +08:00
Eddie Hebert
31d214d8ff
Instead of having two leverage functions, whose differences were the parameter names, add a `calc_leverage` function, with the calling code determining whether it is gross or net by the type of exposure passed in.
474 lines
18 KiB
Python
474 lines
18 KiB
Python
#
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# Copyright 2014 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 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 assets 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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| 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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from __future__ import division
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import logbook
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import numpy as np
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from collections import namedtuple
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from zipline.assets import Future
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try:
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# optional cython based OrderedDict
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from cyordereddict import OrderedDict
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except ImportError:
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from collections import OrderedDict
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from six import itervalues, iteritems
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import zipline.protocol as zp
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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 zipline.finance.performance.position_tracker import calc_position_stats
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log = logbook.Logger('Performance')
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TRADE_TYPE = zp.DATASOURCE_TYPE.TRADE
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PeriodStats = namedtuple('PeriodStats',
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['net_liquidation',
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'gross_leverage',
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'net_leverage'])
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def calc_net_liquidation(ending_cash, long_value, short_value):
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return ending_cash + long_value + short_value
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def calc_leverage(exposure, net_liq):
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if net_liq != 0:
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return exposure / net_liq
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return np.inf
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def calc_period_stats(pos_stats, ending_cash):
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net_liq = calc_net_liquidation(ending_cash,
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pos_stats.long_value,
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pos_stats.short_value)
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gross_leverage = calc_leverage(pos_stats.gross_exposure, net_liq)
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net_leverage = calc_leverage(pos_stats.net_exposure, net_liq)
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return PeriodStats(
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net_liquidation=net_liq,
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gross_leverage=gross_leverage,
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net_leverage=net_leverage)
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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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asset_finder,
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period_open=None,
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period_close=None,
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keep_transactions=True,
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keep_orders=False,
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serialize_positions=True):
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self.asset_finder = asset_finder
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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.ending_exposure = 0.0
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self.period_cash_flow = 0.0
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self.pnl = 0.0
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self.ending_cash = starting_cash
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# rollover initializes a number of self's attributes:
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self.rollover()
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self.keep_transactions = keep_transactions
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self.keep_orders = keep_orders
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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._account_store = zp.Account()
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self.serialize_positions = serialize_positions
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# This dict contains the known cash flow multipliers for sids and is
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# keyed on sid
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self._execution_cash_flow_multipliers = {}
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_position_tracker = None
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@property
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def position_tracker(self):
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return self._position_tracker
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@position_tracker.setter
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def position_tracker(self, obj):
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if obj is None:
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raise ValueError("position_tracker can not be None")
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self._position_tracker = obj
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# we only calculate perf once we inject PositionTracker
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self.calculate_performance()
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def rollover(self):
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self.starting_value = self.ending_value
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self.starting_exposure = self.ending_exposure
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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.orders_by_modified = {}
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self.orders_by_id = OrderedDict()
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def handle_dividends_paid(self, net_cash_payment):
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if net_cash_payment:
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self.handle_cash_payment(net_cash_payment)
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self.calculate_performance()
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def handle_cash_payment(self, payment_amount):
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self.adjust_cash(payment_amount)
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def handle_commission(self, commission):
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# Deduct from our total cash pool.
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self.adjust_cash(-commission.cost)
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def adjust_cash(self, amount):
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self.period_cash_flow += amount
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def adjust_field(self, field, value):
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setattr(self, field, value)
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def calculate_performance(self):
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pt = self.position_tracker
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pos_stats = calc_position_stats(pt)
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self.ending_value = pos_stats.net_value
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self.ending_exposure = pos_stats.net_exposure
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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_cash_flow
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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 record_order(self, order):
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if self.keep_orders:
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try:
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dt_orders = self.orders_by_modified[order.dt]
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if order.id in dt_orders:
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del dt_orders[order.id]
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except KeyError:
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self.orders_by_modified[order.dt] = dt_orders = OrderedDict()
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dt_orders[order.id] = order
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# to preserve the order of the orders by modified date
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# we delete and add back. (ordered dictionary is sorted by
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# first insertion date).
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if order.id in self.orders_by_id:
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del self.orders_by_id[order.id]
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self.orders_by_id[order.id] = order
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def handle_execution(self, txn):
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self.period_cash_flow += self._calculate_execution_cash_flow(txn)
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if self.keep_transactions:
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try:
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self.processed_transactions[txn.dt].append(txn)
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except KeyError:
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self.processed_transactions[txn.dt] = [txn]
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def _calculate_execution_cash_flow(self, txn):
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"""
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Calculates the cash flow from executing the given transaction
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"""
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# Check if the multiplier is cached. If it is not, look up the asset
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# and cache the multiplier.
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try:
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multiplier = self._execution_cash_flow_multipliers[txn.sid]
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except KeyError:
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asset = self.asset_finder.retrieve_asset(txn.sid)
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# Futures experience no cash flow on transactions
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if isinstance(asset, Future):
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multiplier = 0
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else:
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multiplier = 1
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self._execution_cash_flow_multipliers[txn.sid] = multiplier
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# Calculate and return the cash flow given the multiplier
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return -1 * txn.price * txn.amount * multiplier
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# backwards compat. TODO: remove?
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@property
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def positions(self):
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return self.position_tracker.positions
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@property
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def position_amounts(self):
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return self.position_tracker.position_amounts
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def __core_dict(self):
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pos_stats = calc_position_stats(self.position_tracker)
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period_stats = calc_period_stats(pos_stats, self.ending_cash)
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rval = {
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'ending_value': self.ending_value,
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'ending_exposure': self.ending_exposure,
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# this field is renamed to capital_used for backward
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# compatibility.
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'capital_used': self.period_cash_flow,
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'starting_value': self.starting_value,
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'starting_exposure': self.starting_exposure,
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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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'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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'gross_leverage': period_stats.gross_leverage,
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'net_leverage': period_stats.net_leverage,
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'short_exposure': pos_stats.short_exposure,
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'long_exposure': pos_stats.long_exposure,
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'short_value': pos_stats.short_value,
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'long_value': pos_stats.long_value,
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'longs_count': pos_stats.longs_count,
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'shorts_count': pos_stats.shorts_count,
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}
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return rval
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def to_dict(self, dt=None):
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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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Kwargs:
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dt (datetime): If present, only return transactions for the dt.
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"""
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rval = self.__core_dict()
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if self.serialize_positions:
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positions = self.position_tracker.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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if dt:
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# Only include transactions for given dt
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try:
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transactions = [x.to_dict()
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for x in self.processed_transactions[dt]]
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except KeyError:
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transactions = []
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else:
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transactions = \
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[y.to_dict()
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for x in itervalues(self.processed_transactions)
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for y in x]
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rval['transactions'] = transactions
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if self.keep_orders:
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if dt:
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# only include orders modified as of the given dt.
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try:
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orders = [x.to_dict()
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for x in itervalues(self.orders_by_modified[dt])]
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except KeyError:
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orders = []
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else:
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orders = [x.to_dict() for x in itervalues(self.orders_by_id)]
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rval['orders'] = orders
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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
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PerformancePeriod, and in this method we rename some
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fields for usability and remove extraneous fields.
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"""
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# Recycles containing objects' Portfolio object
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# which is used for returning values.
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# as_portfolio is called in an inner loop,
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# so repeated object creation becomes too expensive
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portfolio = self._portfolio_store
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# maintaining the old name for the portfolio field for
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# backward compatibility
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portfolio.capital_used = self.period_cash_flow
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portfolio.starting_cash = self.starting_cash
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portfolio.portfolio_value = self.ending_cash + self.ending_value
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portfolio.pnl = self.pnl
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portfolio.returns = self.returns
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portfolio.cash = self.ending_cash
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portfolio.start_date = self.period_open
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portfolio.positions = self.position_tracker.get_positions()
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portfolio.positions_value = self.ending_value
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portfolio.positions_exposure = self.ending_exposure
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return portfolio
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def as_account(self):
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account = self._account_store
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pt = self.position_tracker
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pos_stats = calc_position_stats(pt)
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period_stats = calc_period_stats(pos_stats, self.ending_cash)
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# If no attribute is found on the PerformancePeriod resort to the
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# following default values. If an attribute is found use the existing
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# value. For instance, a broker may provide updates to these
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# attributes. In this case we do not want to over write the broker
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# values with the default values.
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account.settled_cash = \
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getattr(self, 'settled_cash', self.ending_cash)
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account.accrued_interest = \
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getattr(self, 'accrued_interest', 0.0)
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account.buying_power = \
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getattr(self, 'buying_power', float('inf'))
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account.equity_with_loan = \
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getattr(self, 'equity_with_loan',
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self.ending_cash + self.ending_value)
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account.total_positions_value = \
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getattr(self, 'total_positions_value', self.ending_value)
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account.total_positions_value = \
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getattr(self, 'total_positions_exposure', self.ending_exposure)
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account.regt_equity = \
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getattr(self, 'regt_equity', self.ending_cash)
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account.regt_margin = \
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getattr(self, 'regt_margin', float('inf'))
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account.initial_margin_requirement = \
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getattr(self, 'initial_margin_requirement', 0.0)
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account.maintenance_margin_requirement = \
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getattr(self, 'maintenance_margin_requirement', 0.0)
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account.available_funds = \
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getattr(self, 'available_funds', self.ending_cash)
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account.excess_liquidity = \
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getattr(self, 'excess_liquidity', self.ending_cash)
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account.cushion = \
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getattr(self, 'cushion',
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self.ending_cash / (self.ending_cash + self.ending_value))
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account.day_trades_remaining = \
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getattr(self, 'day_trades_remaining', float('inf'))
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account.leverage = getattr(self, 'leverage',
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period_stats.gross_leverage)
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account.net_leverage = period_stats.net_leverage
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account.net_liquidation = getattr(self, 'net_liquidation',
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period_stats.net_liquidation)
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return account
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def __getstate__(self):
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state_dict = {k: v for k, v in iteritems(self.__dict__)
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if not k.startswith('_')}
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state_dict['_portfolio_store'] = self._portfolio_store
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state_dict['_account_store'] = self._account_store
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state_dict['processed_transactions'] = \
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dict(self.processed_transactions)
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state_dict['orders_by_id'] = \
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dict(self.orders_by_id)
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state_dict['orders_by_modified'] = \
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dict(self.orders_by_modified)
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STATE_VERSION = 3
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state_dict[VERSION_LABEL] = STATE_VERSION
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return state_dict
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def __setstate__(self, state):
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OLDEST_SUPPORTED_STATE = 3
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version = state.pop(VERSION_LABEL)
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if version < OLDEST_SUPPORTED_STATE:
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raise BaseException("PerformancePeriod saved state is too old.")
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processed_transactions = {}
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processed_transactions.update(state.pop('processed_transactions'))
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orders_by_id = OrderedDict()
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orders_by_id.update(state.pop('orders_by_id'))
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orders_by_modified = {}
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orders_by_modified.update(state.pop('orders_by_modified'))
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self.processed_transactions = processed_transactions
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self.orders_by_id = orders_by_id
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self.orders_by_modified = orders_by_modified
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self._execution_cash_flow_multipliers = {}
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self.__dict__.update(state)
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