mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-04 02:26:28 +08:00
Jean Bredeche
6fb4923cc7
Instead of having separate ExchangeCalendar and TradingSchedule objects, we now just have TradingCalendar. The TradingCalendar keeps track of each session (defined as a contiguous set of minutes between an open and a close). It's also responsible for handling the grouping logic of any given minute to its containing session, or the next/previous session if it's not a market minute for the given calendar.
437 lines
14 KiB
Python
437 lines
14 KiB
Python
#
|
|
# Copyright 2016 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.
|
|
|
|
from __future__ import division
|
|
|
|
import logbook
|
|
import numpy as np
|
|
from collections import namedtuple
|
|
from math import isnan
|
|
from zipline.finance.performance.position import Position
|
|
from zipline.finance.transaction import Transaction
|
|
|
|
try:
|
|
# optional cython based OrderedDict
|
|
from cyordereddict import OrderedDict
|
|
except ImportError:
|
|
from collections import OrderedDict
|
|
from six import iteritems, itervalues
|
|
|
|
import zipline.protocol as zp
|
|
from zipline.assets import (
|
|
Equity, Future
|
|
)
|
|
from zipline.errors import PositionTrackerMissingAssetFinder
|
|
from . position import positiondict
|
|
|
|
log = logbook.Logger('Performance')
|
|
|
|
|
|
PositionStats = namedtuple('PositionStats',
|
|
['net_exposure',
|
|
'gross_value',
|
|
'gross_exposure',
|
|
'short_value',
|
|
'short_exposure',
|
|
'shorts_count',
|
|
'long_value',
|
|
'long_exposure',
|
|
'longs_count',
|
|
'net_value'])
|
|
|
|
|
|
def calc_position_values(amounts,
|
|
last_sale_prices,
|
|
value_multipliers):
|
|
iter_amount_price_multiplier = zip(
|
|
amounts,
|
|
last_sale_prices,
|
|
itervalues(value_multipliers),
|
|
)
|
|
return [
|
|
price * amount * multiplier for
|
|
price, amount, multiplier in iter_amount_price_multiplier
|
|
]
|
|
|
|
|
|
def calc_net(values):
|
|
# Returns 0.0 if there are no values.
|
|
return sum(values, np.float64())
|
|
|
|
|
|
def calc_position_exposures(amounts,
|
|
last_sale_prices,
|
|
exposure_multipliers):
|
|
iter_amount_price_multiplier = zip(
|
|
amounts,
|
|
last_sale_prices,
|
|
itervalues(exposure_multipliers),
|
|
)
|
|
return [
|
|
price * amount * multiplier for
|
|
price, amount, multiplier in iter_amount_price_multiplier
|
|
]
|
|
|
|
|
|
def calc_long_value(position_values):
|
|
return sum(i for i in position_values if i > 0)
|
|
|
|
|
|
def calc_short_value(position_values):
|
|
return sum(i for i in position_values if i < 0)
|
|
|
|
|
|
def calc_long_exposure(position_exposures):
|
|
return sum(i for i in position_exposures if i > 0)
|
|
|
|
|
|
def calc_short_exposure(position_exposures):
|
|
return sum(i for i in position_exposures if i < 0)
|
|
|
|
|
|
def calc_longs_count(position_exposures):
|
|
return sum(1 for i in position_exposures if i > 0)
|
|
|
|
|
|
def calc_shorts_count(position_exposures):
|
|
return sum(1 for i in position_exposures if i < 0)
|
|
|
|
|
|
def calc_gross_exposure(long_exposure, short_exposure):
|
|
return long_exposure + abs(short_exposure)
|
|
|
|
|
|
def calc_gross_value(long_value, short_value):
|
|
return long_value + abs(short_value)
|
|
|
|
|
|
class PositionTracker(object):
|
|
|
|
def __init__(self, asset_finder, data_frequency):
|
|
self.asset_finder = asset_finder
|
|
|
|
# sid => position object
|
|
self.positions = positiondict()
|
|
# Arrays for quick calculations of positions value
|
|
self._position_value_multipliers = OrderedDict()
|
|
self._position_exposure_multipliers = OrderedDict()
|
|
self._unpaid_dividends = {}
|
|
self._unpaid_stock_dividends = {}
|
|
self._positions_store = zp.Positions()
|
|
|
|
self.data_frequency = data_frequency
|
|
|
|
def _update_asset(self, sid):
|
|
try:
|
|
self._position_value_multipliers[sid]
|
|
self._position_exposure_multipliers[sid]
|
|
except KeyError:
|
|
# Check if there is an AssetFinder
|
|
if self.asset_finder is None:
|
|
raise PositionTrackerMissingAssetFinder()
|
|
|
|
# Collect the value multipliers from applicable sids
|
|
asset = self.asset_finder.retrieve_asset(sid)
|
|
if isinstance(asset, Equity):
|
|
self._position_value_multipliers[sid] = 1
|
|
self._position_exposure_multipliers[sid] = 1
|
|
if isinstance(asset, Future):
|
|
self._position_value_multipliers[sid] = 0
|
|
self._position_exposure_multipliers[sid] = asset.multiplier
|
|
|
|
def update_positions(self, positions):
|
|
# update positions in batch
|
|
self.positions.update(positions)
|
|
for sid, pos in iteritems(positions):
|
|
self._update_asset(sid)
|
|
|
|
def update_position(self, sid, amount=None, last_sale_price=None,
|
|
last_sale_date=None, cost_basis=None):
|
|
if sid not in self.positions:
|
|
position = Position(sid)
|
|
self.positions[sid] = position
|
|
else:
|
|
position = self.positions[sid]
|
|
|
|
if amount is not None:
|
|
position.amount = amount
|
|
self._update_asset(sid=sid)
|
|
if last_sale_price is not None:
|
|
position.last_sale_price = last_sale_price
|
|
if last_sale_date is not None:
|
|
position.last_sale_date = last_sale_date
|
|
if cost_basis is not None:
|
|
position.cost_basis = cost_basis
|
|
|
|
def execute_transaction(self, txn):
|
|
# Update Position
|
|
# ----------------
|
|
sid = txn.sid
|
|
|
|
if sid not in self.positions:
|
|
position = Position(sid)
|
|
self.positions[sid] = position
|
|
else:
|
|
position = self.positions[sid]
|
|
|
|
position.update(txn)
|
|
|
|
if position.amount == 0:
|
|
# if this position now has 0 shares, remove it from our internal
|
|
# bookkeeping.
|
|
del self.positions[sid]
|
|
|
|
try:
|
|
# if this position exists in our user-facing dictionary,
|
|
# remove it as well.
|
|
del self._positions_store[sid]
|
|
except KeyError:
|
|
pass
|
|
|
|
self._update_asset(sid)
|
|
|
|
def handle_commission(self, sid, cost):
|
|
# Adjust the cost basis of the stock if we own it
|
|
if sid in self.positions:
|
|
self.positions[sid].adjust_commission_cost_basis(sid, cost)
|
|
|
|
def handle_splits(self, splits):
|
|
"""
|
|
Processes a list of splits by modifying any positions as needed.
|
|
|
|
Parameters
|
|
----------
|
|
splits: list
|
|
A list of splits. Each split is a tuple of (sid, ratio).
|
|
|
|
Returns
|
|
-------
|
|
int: The leftover cash from fractional sahres after modifying each
|
|
position.
|
|
"""
|
|
total_leftover_cash = 0
|
|
|
|
for split in splits:
|
|
sid = split[0]
|
|
if sid in self.positions:
|
|
# Make the position object handle the split. It returns the
|
|
# leftover cash from a fractional share, if there is any.
|
|
position = self.positions[sid]
|
|
leftover_cash = position.handle_split(sid, split[1])
|
|
self._update_asset(split[0])
|
|
total_leftover_cash += leftover_cash
|
|
|
|
return total_leftover_cash
|
|
|
|
def earn_dividends(self, dividends, stock_dividends):
|
|
"""
|
|
Given a list of dividends whose ex_dates are all the next trading day,
|
|
calculate and store the cash and/or stock payments to be paid on each
|
|
dividend's pay date.
|
|
|
|
Parameters
|
|
----------
|
|
dividends: iterable of (asset, amount, pay_date) namedtuples
|
|
|
|
stock_dividends: iterable of (asset, payment_asset, ratio, pay_date)
|
|
namedtuples.
|
|
"""
|
|
for dividend in dividends:
|
|
# Store the earned dividends so that they can be paid on the
|
|
# dividends' pay_dates.
|
|
div_owed = self.positions[dividend.asset].earn_dividend(dividend)
|
|
try:
|
|
self._unpaid_dividends[dividend.pay_date].append(div_owed)
|
|
except KeyError:
|
|
self._unpaid_dividends[dividend.pay_date] = [div_owed]
|
|
|
|
for stock_dividend in stock_dividends:
|
|
div_owed = \
|
|
self.positions[stock_dividend.asset].earn_stock_dividend(
|
|
stock_dividend)
|
|
try:
|
|
self._unpaid_stock_dividends[stock_dividend.pay_date].\
|
|
append(div_owed)
|
|
except KeyError:
|
|
self._unpaid_stock_dividends[stock_dividend.pay_date] = \
|
|
[div_owed]
|
|
|
|
def pay_dividends(self, next_trading_day):
|
|
"""
|
|
Returns a cash payment based on the dividends that should be paid out
|
|
according to the accumulated bookkeeping of earned, unpaid, and stock
|
|
dividends.
|
|
"""
|
|
net_cash_payment = 0.0
|
|
|
|
try:
|
|
payments = self._unpaid_dividends[next_trading_day]
|
|
# Mark these dividends as paid by dropping them from our unpaid
|
|
del self._unpaid_dividends[next_trading_day]
|
|
except KeyError:
|
|
payments = []
|
|
|
|
# representing the fact that we're required to reimburse the owner of
|
|
# the stock for any dividends paid while borrowing.
|
|
for payment in payments:
|
|
net_cash_payment += payment['amount']
|
|
|
|
# Add stock for any stock dividends paid. Again, the values here may
|
|
# be negative in the case of short positions.
|
|
try:
|
|
stock_payments = self._unpaid_stock_dividends[next_trading_day]
|
|
except:
|
|
stock_payments = []
|
|
|
|
for stock_payment in stock_payments:
|
|
payment_asset = stock_payment['payment_asset']
|
|
share_count = stock_payment['share_count']
|
|
# note we create a Position for stock dividend if we don't
|
|
# already own the asset
|
|
if payment_asset in self.positions:
|
|
position = self.positions[payment_asset]
|
|
else:
|
|
position = self.positions[payment_asset] = \
|
|
Position(payment_asset)
|
|
|
|
position.amount += share_count
|
|
self._update_asset(payment_asset)
|
|
|
|
return net_cash_payment
|
|
|
|
def maybe_create_close_position_transaction(self, asset, dt, data_portal):
|
|
if not self.positions.get(asset):
|
|
return None
|
|
|
|
amount = self.positions.get(asset).amount
|
|
price = data_portal.get_spot_value(
|
|
asset, 'price', dt, self.data_frequency)
|
|
|
|
# Get the last traded price if price is no longer available
|
|
if isnan(price):
|
|
price = self.positions.get(asset).last_sale_price
|
|
|
|
txn = Transaction(
|
|
sid=asset,
|
|
amount=(-1 * amount),
|
|
dt=dt,
|
|
price=price,
|
|
commission=0,
|
|
order_id=None,
|
|
)
|
|
return txn
|
|
|
|
def get_positions(self):
|
|
|
|
positions = self._positions_store
|
|
|
|
for sid, pos in iteritems(self.positions):
|
|
|
|
if pos.amount == 0:
|
|
# Clear out the position if it has become empty since the last
|
|
# time get_positions was called. Catching the KeyError is
|
|
# faster than checking `if sid in positions`, and this can be
|
|
# potentially called in a tight inner loop.
|
|
try:
|
|
del positions[sid]
|
|
except KeyError:
|
|
pass
|
|
continue
|
|
|
|
position = zp.Position(sid)
|
|
position.amount = pos.amount
|
|
position.cost_basis = pos.cost_basis
|
|
position.last_sale_price = pos.last_sale_price
|
|
position.last_sale_date = pos.last_sale_date
|
|
|
|
# Adds the new position if we didn't have one before, or overwrite
|
|
# one we have currently
|
|
positions[sid] = position
|
|
|
|
return positions
|
|
|
|
def get_positions_list(self):
|
|
positions = []
|
|
for sid, pos in iteritems(self.positions):
|
|
if pos.amount != 0:
|
|
positions.append(pos.to_dict())
|
|
return positions
|
|
|
|
def sync_last_sale_prices(self, dt, handle_non_market_minutes,
|
|
data_portal):
|
|
if not handle_non_market_minutes:
|
|
for asset, position in iteritems(self.positions):
|
|
last_sale_price = data_portal.get_spot_value(
|
|
asset, 'price', dt, self.data_frequency
|
|
)
|
|
|
|
if not np.isnan(last_sale_price):
|
|
position.last_sale_price = last_sale_price
|
|
else:
|
|
for asset, position in iteritems(self.positions):
|
|
last_sale_price = data_portal.get_adjusted_value(
|
|
asset,
|
|
'price',
|
|
data_portal.trading_calendar.previous_minute(dt),
|
|
dt,
|
|
self.data_frequency
|
|
)
|
|
|
|
if not np.isnan(last_sale_price):
|
|
position.last_sale_price = last_sale_price
|
|
|
|
def stats(self):
|
|
amounts = []
|
|
last_sale_prices = []
|
|
for pos in itervalues(self.positions):
|
|
amounts.append(pos.amount)
|
|
last_sale_prices.append(pos.last_sale_price)
|
|
|
|
position_values = calc_position_values(
|
|
amounts,
|
|
last_sale_prices,
|
|
self._position_value_multipliers
|
|
)
|
|
|
|
position_exposures = calc_position_exposures(
|
|
amounts,
|
|
last_sale_prices,
|
|
self._position_exposure_multipliers
|
|
)
|
|
|
|
long_value = calc_long_value(position_values)
|
|
short_value = calc_short_value(position_values)
|
|
gross_value = calc_gross_value(long_value, short_value)
|
|
long_exposure = calc_long_exposure(position_exposures)
|
|
short_exposure = calc_short_exposure(position_exposures)
|
|
gross_exposure = calc_gross_exposure(long_exposure, short_exposure)
|
|
net_exposure = calc_net(position_exposures)
|
|
longs_count = calc_longs_count(position_exposures)
|
|
shorts_count = calc_shorts_count(position_exposures)
|
|
net_value = calc_net(position_values)
|
|
|
|
return PositionStats(
|
|
long_value=long_value,
|
|
gross_value=gross_value,
|
|
short_value=short_value,
|
|
long_exposure=long_exposure,
|
|
short_exposure=short_exposure,
|
|
gross_exposure=gross_exposure,
|
|
net_exposure=net_exposure,
|
|
longs_count=longs_count,
|
|
shorts_count=shorts_count,
|
|
net_value=net_value
|
|
)
|