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catalyst/zipline/finance/performance/period.py
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Andrew Liang 98f3fc9326 MAINT: Refactor application of capital changes 
Previously, on the dt of a capital change, we use the un-updated
prices to find the ending performance of the previous subperiod and
then got the new prices to determine the portfolio value used to
calculate the delta, without actually updating the performance
before applying the capital change. This logic is confusing and
unintuitive. Instead, save the ending performance as we do previously,
but have temp values for the starting current subperiod value.
Update those temp values after processing the capital change
2016-08-01 11:51:45 -04:00

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#
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Performance Period
==================
Performance Periods are updated with every trade. When calling
code needs a portfolio object that fulfills the algorithm
protocol, use the PerformancePeriod.as_portfolio method. See that
method for comments on the specific fields provided (and
omitted).
+---------------+------------------------------------------------------+
| key | value |
+===============+======================================================+
| ending_value | the total market value of the positions held at the |
| | end of the period |
+---------------+------------------------------------------------------+
| cash_flow | the cash flow in the period (negative means spent) |
| | from buying and selling assets in the period. |
| | Includes dividend payments in the period as well. |
+---------------+------------------------------------------------------+
| starting_value| the total market value of the positions held at the |
| | start of the period |
+---------------+------------------------------------------------------+
| starting_cash | cash on hand at the beginning of the period |
+---------------+------------------------------------------------------+
| ending_cash | cash on hand at the end of the period |
+---------------+------------------------------------------------------+
| positions | a list of dicts representing positions, see |
| | :py:meth:`Position.to_dict()` |
| | for details on the contents of the dict |
+---------------+------------------------------------------------------+
| pnl | Dollar value profit and loss, for both realized and |
| | unrealized gains. |
+---------------+------------------------------------------------------+
| returns | percentage returns for the entire portfolio over the |
| | period |
+---------------+------------------------------------------------------+
| cumulative\ | The net capital used (positive is spent) during |
| _capital_used | the period |
+---------------+------------------------------------------------------+
| max_capital\ | The maximum amount of capital deployed during the |
| _used | period. |
+---------------+------------------------------------------------------+
| period_close | The last close of the market in period. datetime in |
| | pytz.utc timezone. |
+---------------+------------------------------------------------------+
| period_open | The first open of the market in period. datetime in |
| | pytz.utc timezone. |
+---------------+------------------------------------------------------+
| transactions | all the transactions that were acrued during this |
| | period. Unset/missing for cumulative periods. |
+---------------+------------------------------------------------------+
"""
from __future__ import division
import logbook
import numpy as np
from collections import namedtuple
from zipline.assets import Future
try:
# optional cython based OrderedDict
from cyordereddict import OrderedDict
except ImportError:
from collections import OrderedDict
from six import itervalues, iteritems
import zipline.protocol as zp
log = logbook.Logger('Performance')
TRADE_TYPE = zp.DATASOURCE_TYPE.TRADE
PeriodStats = namedtuple('PeriodStats',
['net_liquidation',
'gross_leverage',
'net_leverage'])
PrevSubPeriodStats = namedtuple(
'PrevSubPeriodStats', ['returns', 'pnl', 'cash_flow']
)
CurrSubPeriodStats = namedtuple(
'CurrSubPeriodStats', ['starting_value', 'starting_cash']
)
def calc_net_liquidation(ending_cash, long_value, short_value):
return ending_cash + long_value + short_value
def calc_leverage(exposure, net_liq):
if net_liq != 0:
return exposure / net_liq
return np.inf
def calc_period_stats(pos_stats, ending_cash):
net_liq = calc_net_liquidation(ending_cash,
pos_stats.long_value,
pos_stats.short_value)
gross_leverage = calc_leverage(pos_stats.gross_exposure, net_liq)
net_leverage = calc_leverage(pos_stats.net_exposure, net_liq)
return PeriodStats(
net_liquidation=net_liq,
gross_leverage=gross_leverage,
net_leverage=net_leverage)
def calc_payout(multiplier, amount, old_price, price):
return (price - old_price) * multiplier * amount
class PerformancePeriod(object):
def __init__(
self,
starting_cash,
asset_finder,
data_frequency,
period_open=None,
period_close=None,
keep_transactions=True,
keep_orders=False,
serialize_positions=True,
name=None):
self.asset_finder = asset_finder
self.data_frequency = data_frequency
# Start and end of the entire period
self.period_open = period_open
self.period_close = period_close
self.initialize(starting_cash=starting_cash,
starting_value=0.0,
starting_exposure=0.0)
self.ending_value = 0.0
self.ending_exposure = 0.0
self.ending_cash = starting_cash
self.subperiod_divider = None
# Keyed by asset, the previous last sale price of positions with
# payouts on price differences, e.g. Futures.
#
# This dt is not the previous minute to the minute for which the
# calculation is done, but the last sale price either before the period
# start, or when the price at execution.
self._payout_last_sale_prices = {}
self.keep_transactions = keep_transactions
self.keep_orders = keep_orders
self.name = name
# An object to recycle via assigning new values
# when returning portfolio information.
# So as not to avoid creating a new object for each event
self._portfolio_store = zp.Portfolio()
self._account_store = zp.Account()
self.serialize_positions = serialize_positions
# This dict contains the known cash flow multipliers for sids and is
# keyed on sid
self._execution_cash_flow_multipliers = {}
_position_tracker = None
def initialize(self, starting_cash, starting_value, starting_exposure):
# Performance stats for the entire period, returned externally
self.pnl = 0.0
self.returns = 0.0
self.cash_flow = 0.0
self.starting_value = starting_value
self.starting_exposure = starting_exposure
self.starting_cash = starting_cash
# The cumulative capital change occurred within the period
self._total_intraperiod_capital_change = 0.0
self.processed_transactions = {}
self.orders_by_modified = {}
self.orders_by_id = OrderedDict()
@property
def position_tracker(self):
return self._position_tracker
@position_tracker.setter
def position_tracker(self, obj):
if obj is None:
raise ValueError("position_tracker can not be None")
self._position_tracker = obj
# we only calculate perf once we inject PositionTracker
self.calculate_performance()
def adjust_period_starting_capital(self, capital_change):
self.ending_cash += capital_change
self.starting_cash += capital_change
def rollover(self):
# We are starting a new period
self.initialize(starting_cash=self.ending_cash,
starting_value=self.ending_value,
starting_exposure=self.ending_exposure)
self.subperiod_divider = None
payout_assets = self._payout_last_sale_prices.keys()
for asset in payout_assets:
if asset in self._payout_last_sale_prices:
self._payout_last_sale_prices[asset] = \
self.position_tracker.positions[asset].last_sale_price
else:
del self._payout_last_sale_prices[asset]
def initialize_subperiod_divider(self):
self.calculate_performance()
# Initialize a subperiod divider to stash the current performance
# values. Current period starting values are set to equal ending values
# of the previous subperiod
self.subperiod_divider = SubPeriodDivider(
prev_returns=self.returns,
prev_pnl=self.pnl,
prev_cash_flow=self.cash_flow,
curr_starting_value=self.ending_value,
curr_starting_cash=self.ending_cash
)
def set_current_subperiod_starting_values(self, capital_change):
# Apply the capital change to the ending cash
self.ending_cash += capital_change
# Increment the total capital change occurred within the period
self._total_intraperiod_capital_change += capital_change
# Update the current subperiod starting cash to reflect the capital
# change
starting_value = self.subperiod_divider.curr_subperiod.starting_value
self.subperiod_divider.curr_subperiod = CurrSubPeriodStats(
starting_value=starting_value,
starting_cash=self.ending_cash)
def handle_dividends_paid(self, net_cash_payment):
if net_cash_payment:
self.handle_cash_payment(net_cash_payment)
self.calculate_performance()
def handle_cash_payment(self, payment_amount):
self.adjust_cash(payment_amount)
def handle_commission(self, cost):
# Deduct from our total cash pool.
self.adjust_cash(-cost)
def adjust_cash(self, amount):
self.cash_flow += amount
def adjust_field(self, field, value):
setattr(self, field, value)
def _get_payout_total(self, positions):
payouts = []
for asset, old_price in iteritems(self._payout_last_sale_prices):
pos = positions[asset]
amount = pos.amount
payout = calc_payout(
asset.multiplier,
amount,
old_price,
pos.last_sale_price)
payouts.append(payout)
return sum(payouts)
def calculate_performance(self):
pt = self.position_tracker
pos_stats = pt.stats()
self.ending_value = pos_stats.net_value
self.ending_exposure = pos_stats.net_exposure
payout = self._get_payout_total(pt.positions)
self.ending_cash = self.starting_cash + self.cash_flow + \
self._total_intraperiod_capital_change + payout
total_at_end = self.ending_cash + self.ending_value
# If there is a previous subperiod, the performance is calculated
# from the previous and current subperiods. Otherwise, the performance
# is calculated based on the start and end values of the whole period
if self.subperiod_divider:
starting_cash = self.subperiod_divider.curr_subperiod.starting_cash
total_at_start = starting_cash + \
self.subperiod_divider.curr_subperiod.starting_value
# Performance for this subperiod
pnl = total_at_end - total_at_start
if total_at_start != 0:
returns = pnl / total_at_start
else:
returns = 0.0
# Performance for this whole period
self.pnl = self.subperiod_divider.prev_subperiod.pnl + pnl
self.returns = \
(1 + self.subperiod_divider.prev_subperiod.returns) * \
(1 + returns) - 1
else:
total_at_start = self.starting_cash + self.starting_value
self.pnl = total_at_end - total_at_start
if total_at_start != 0:
self.returns = self.pnl / total_at_start
else:
self.returns = 0.0
def record_order(self, order):
if self.keep_orders:
try:
dt_orders = self.orders_by_modified[order.dt]
if order.id in dt_orders:
del dt_orders[order.id]
except KeyError:
self.orders_by_modified[order.dt] = dt_orders = OrderedDict()
dt_orders[order.id] = order
# to preserve the order of the orders by modified date
# we delete and add back. (ordered dictionary is sorted by
# first insertion date).
if order.id in self.orders_by_id:
del self.orders_by_id[order.id]
self.orders_by_id[order.id] = order
def handle_execution(self, txn):
self.cash_flow += self._calculate_execution_cash_flow(txn)
asset = self.asset_finder.retrieve_asset(txn.sid)
if isinstance(asset, Future):
try:
old_price = self._payout_last_sale_prices[asset]
pos = self.position_tracker.positions[asset]
amount = pos.amount
price = txn.price
cash_adj = calc_payout(
asset.multiplier, amount, old_price, price)
self.adjust_cash(cash_adj)
if amount + txn.amount == 0:
del self._payout_last_sale_prices[asset]
else:
self._payout_last_sale_prices[asset] = price
except KeyError:
self._payout_last_sale_prices[asset] = txn.price
if self.keep_transactions:
try:
self.processed_transactions[txn.dt].append(txn)
except KeyError:
self.processed_transactions[txn.dt] = [txn]
def _calculate_execution_cash_flow(self, txn):
"""
Calculates the cash flow from executing the given transaction
"""
# Check if the multiplier is cached. If it is not, look up the asset
# and cache the multiplier.
try:
multiplier = self._execution_cash_flow_multipliers[txn.sid]
except KeyError:
asset = self.asset_finder.retrieve_asset(txn.sid)
# Futures experience no cash flow on transactions
if isinstance(asset, Future):
multiplier = 0
else:
multiplier = 1
self._execution_cash_flow_multipliers[txn.sid] = multiplier
# Calculate and return the cash flow given the multiplier
return -1 * txn.price * txn.amount * multiplier
# backwards compat. TODO: remove?
@property
def positions(self):
return self.position_tracker.positions
@property
def position_amounts(self):
return self.position_tracker.position_amounts
def __core_dict(self):
pos_stats = self.position_tracker.stats()
period_stats = calc_period_stats(pos_stats, self.ending_cash)
rval = {
'ending_value': self.ending_value,
'ending_exposure': self.ending_exposure,
# this field is renamed to capital_used for backward
# compatibility.
'capital_used': self.cash_flow,
'starting_value': self.starting_value,
'starting_exposure': self.starting_exposure,
'starting_cash': self.starting_cash,
'ending_cash': self.ending_cash,
'portfolio_value': self.ending_cash + self.ending_value,
'pnl': self.pnl,
'returns': self.returns,
'period_open': self.period_open,
'period_close': self.period_close,
'gross_leverage': period_stats.gross_leverage,
'net_leverage': period_stats.net_leverage,
'short_exposure': pos_stats.short_exposure,
'long_exposure': pos_stats.long_exposure,
'short_value': pos_stats.short_value,
'long_value': pos_stats.long_value,
'longs_count': pos_stats.longs_count,
'shorts_count': pos_stats.shorts_count,
}
return rval
def to_dict(self, dt=None):
"""
Creates a dictionary representing the state of this performance
period. See header comments for a detailed description.
Kwargs:
dt (datetime): If present, only return transactions for the dt.
"""
rval = self.__core_dict()
if self.serialize_positions:
positions = self.position_tracker.get_positions_list()
rval['positions'] = positions
# we want the key to be absent, not just empty
if self.keep_transactions:
if dt:
# Only include transactions for given dt
try:
transactions = [x.to_dict()
for x in self.processed_transactions[dt]]
except KeyError:
transactions = []
else:
transactions = \
[y.to_dict()
for x in itervalues(self.processed_transactions)
for y in x]
rval['transactions'] = transactions
if self.keep_orders:
if dt:
# only include orders modified as of the given dt.
try:
orders = [x.to_dict()
for x in itervalues(self.orders_by_modified[dt])]
except KeyError:
orders = []
else:
orders = [x.to_dict() for x in itervalues(self.orders_by_id)]
rval['orders'] = orders
return rval
def as_portfolio(self):
"""
The purpose of this method is to provide a portfolio
object to algorithms running inside the same trading
client. The data needed is captured raw in a
PerformancePeriod, and in this method we rename some
fields for usability and remove extraneous fields.
"""
# Recycles containing objects' Portfolio object
# which is used for returning values.
# as_portfolio is called in an inner loop,
# so repeated object creation becomes too expensive
portfolio = self._portfolio_store
# maintaining the old name for the portfolio field for
# backward compatibility
portfolio.capital_used = self.cash_flow
portfolio.starting_cash = self.starting_cash
portfolio.portfolio_value = self.ending_cash + self.ending_value
portfolio.pnl = self.pnl
portfolio.returns = self.returns
portfolio.cash = self.ending_cash
portfolio.start_date = self.period_open
portfolio.positions = self.position_tracker.get_positions()
portfolio.positions_value = self.ending_value
portfolio.positions_exposure = self.ending_exposure
return portfolio
def as_account(self):
account = self._account_store
pt = self.position_tracker
pos_stats = pt.stats()
period_stats = calc_period_stats(pos_stats, self.ending_cash)
# If no attribute is found on the PerformancePeriod resort to the
# following default values. If an attribute is found use the existing
# value. For instance, a broker may provide updates to these
# attributes. In this case we do not want to over write the broker
# values with the default values.
account.settled_cash = \
getattr(self, 'settled_cash', self.ending_cash)
account.accrued_interest = \
getattr(self, 'accrued_interest', 0.0)
account.buying_power = \
getattr(self, 'buying_power', float('inf'))
account.equity_with_loan = \
getattr(self, 'equity_with_loan',
self.ending_cash + self.ending_value)
account.total_positions_value = \
getattr(self, 'total_positions_value', self.ending_value)
account.total_positions_exposure = \
getattr(self, 'total_positions_exposure', self.ending_exposure)
account.regt_equity = \
getattr(self, 'regt_equity', self.ending_cash)
account.regt_margin = \
getattr(self, 'regt_margin', float('inf'))
account.initial_margin_requirement = \
getattr(self, 'initial_margin_requirement', 0.0)
account.maintenance_margin_requirement = \
getattr(self, 'maintenance_margin_requirement', 0.0)
account.available_funds = \
getattr(self, 'available_funds', self.ending_cash)
account.excess_liquidity = \
getattr(self, 'excess_liquidity', self.ending_cash)
account.cushion = \
getattr(self, 'cushion',
self.ending_cash / (self.ending_cash + self.ending_value))
account.day_trades_remaining = \
getattr(self, 'day_trades_remaining', float('inf'))
account.leverage = getattr(self, 'leverage',
period_stats.gross_leverage)
account.net_leverage = getattr(self, 'net_leverage',
period_stats.net_leverage)
account.net_liquidation = getattr(self, 'net_liquidation',
period_stats.net_liquidation)
return account
class SubPeriodDivider(object):
"""
A marker for subdividing the period at the latest intraperiod capital
change. prev_subperiod and curr_subperiod hold information respective to
the previous and current subperiods.
"""
def __init__(self, prev_returns, prev_pnl, prev_cash_flow,
curr_starting_value, curr_starting_cash):
self.prev_subperiod = PrevSubPeriodStats(
returns=prev_returns,
pnl=prev_pnl,
cash_flow=prev_cash_flow)
self.curr_subperiod = CurrSubPeriodStats(
starting_value=curr_starting_value,
starting_cash=curr_starting_cash)
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