mirror of
https://github.com/wassname/catalyst.git
synced 2026-08-07 11:20:19 +08:00
Delaney Granizo-Mackenzie
0fd78cd54a
Previously the last sale price was not correctly being set on positions when the transaction arrived before the trade event. The last sale price was defaulted to zero and never updated. This resulted in one holding stocks that were bough >>0 and now had value 0 from the perspective of returns. The returns would display correctly again when the next trade of that security happened. For most securities trading is frequent enough that there's no issue, but for some illiquid ones it took hours to fix itself. Updated test_perf_tracking:TestPerformanceTracker.test_minute_tracker This test was based on assuming that last_sale_price was zero, allowing the sharpe ratio to be calculated. The sharpe ratio can no longer be calculated for this specific tested scenario and the test has been changed accordingly.
441 lines
18 KiB
Python
441 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 securities 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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import pandas as pd
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from collections import (
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defaultdict,
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OrderedDict,
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)
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from six import iteritems, itervalues
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import zipline.protocol as zp
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from . position import positiondict
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log = logbook.Logger('Performance')
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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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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.period_open = period_open
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self.period_close = period_close
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self.ending_value = 0.0
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self.period_cash_flow = 0.0
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self.pnl = 0.0
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# sid => position object
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self.positions = positiondict()
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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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# Arrays for quick calculations of positions value
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self._position_amounts = pd.Series()
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self._position_last_sale_prices = pd.Series()
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self.calculate_performance()
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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._positions_store = zp.Positions()
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self.serialize_positions = serialize_positions
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self._unpaid_dividends = pd.DataFrame(
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columns=zp.DIVIDEND_PAYMENT_FIELDS,
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)
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def rollover(self):
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self.starting_value = self.ending_value
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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 = defaultdict(list)
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self.orders_by_modified = defaultdict(OrderedDict)
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self.orders_by_id = OrderedDict()
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def ensure_position_index(self, sid):
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try:
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self._position_amounts[sid]
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self._position_last_sale_prices[sid]
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except (KeyError, IndexError):
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self._position_amounts = \
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self._position_amounts.append(pd.Series({sid: 0.0}))
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self._position_last_sale_prices = \
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self._position_last_sale_prices.append(pd.Series({sid: 0.0}))
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def handle_split(self, split):
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if split.sid in self.positions:
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# Make the position object handle the split. It returns the
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# leftover cash from a fractional share, if there is any.
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position = self.positions[split.sid]
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leftover_cash = position.handle_split(split)
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self._position_amounts[split.sid] = position.amount
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self._position_last_sale_prices[split.sid] = \
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position.last_sale_price
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if leftover_cash > 0:
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self.handle_cash_payment(leftover_cash)
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def earn_dividends(self, dividend_frame):
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"""
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Given a frame of dividends whose ex_dates are all the next trading day,
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calculate and store the cash and/or stock payments to be paid on each
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dividend's pay date.
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"""
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earned = dividend_frame.apply(self._maybe_earn_dividend, axis=1)\
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.dropna(how='all')
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if len(earned) > 0:
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# Store the earned dividends so that they can be paid on the
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# dividends' pay_dates.
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self._unpaid_dividends = pd.concat(
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[self._unpaid_dividends, earned],
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)
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def _maybe_earn_dividend(self, dividend):
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"""
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Take a historical dividend record and return a Series with fields in
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zipline.protocol.DIVIDEND_FIELDS (plus an 'id' field) representing
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the cash/stock amount we are owed when the dividend is paid.
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"""
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if dividend['sid'] in self.positions:
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return self.positions[dividend['sid']].earn_dividend(dividend)
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else:
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return zp.dividend_payment()
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def pay_dividends(self, dividend_frame):
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"""
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Given a frame of dividends whose pay_dates are all the next trading
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day, grant the cash and/or stock payments that were calculated on the
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given dividends' ex dates.
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"""
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payments = dividend_frame.apply(self._maybe_pay_dividend, axis=1)\
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.dropna(how='all')
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# Mark these dividends as paid by dropping them from our unpaid
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# table.
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self._unpaid_dividends.drop(payments.index)
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# Add cash equal to the net cash payed from all dividends. Note that
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# "negative cash" is effectively paid if we're short a security,
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# representing the fact that we're required to reimburse the owner of
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# the stock for any dividends paid while borrowing.
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net_cash_payment = payments['cash_amount'].fillna(0).sum()
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if net_cash_payment:
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self.handle_cash_payment(net_cash_payment)
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# Add stock for any stock dividends paid. Again, the values here may
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# be negative in the case of short positions.
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stock_payments = payments[payments['payment_sid'].notnull()]
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for _, row in stock_payments.iterrows():
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stock = row['payment_sid']
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share_count = row['share_count']
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position = self.positions[stock]
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position.amount += share_count
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self.ensure_position_index(stock)
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self._position_amounts[stock] = position.amount
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self._position_last_sale_prices[stock] = \
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position.last_sale_price
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# Recalculate performance after applying dividend benefits.
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self.calculate_performance()
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def _maybe_pay_dividend(self, dividend):
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"""
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Take a historical dividend record, look up any stored record of
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cash/stock we are owed for that dividend, and return a Series
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with fields drawn from zipline.protocol.DIVIDEND_PAYMENT_FIELDS.
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"""
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try:
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unpaid_dividend = self._unpaid_dividends.loc[dividend['id']]
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return unpaid_dividend
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except KeyError:
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return zp.dividend_payment()
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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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# Adjust the cost basis of the stock if we own it
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if commission.sid in self.positions:
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self.positions[commission.sid].\
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adjust_commission_cost_basis(commission)
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def adjust_cash(self, amount):
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self.period_cash_flow += amount
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def calculate_performance(self):
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self.ending_value = self.calculate_positions_value()
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total_at_start = self.starting_cash + self.starting_value
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self.ending_cash = self.starting_cash + self.period_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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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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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 update_position(self, sid, amount=None, last_sale_price=None,
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last_sale_date=None, cost_basis=None):
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pos = self.positions[sid]
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self.ensure_position_index(sid)
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if amount is not None:
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pos.amount = amount
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self._position_amounts[sid] = amount
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if last_sale_price is not None:
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pos.last_sale_price = last_sale_price
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self._position_last_sale_prices[sid] = last_sale_price
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if last_sale_date is not None:
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pos.last_sale_date = last_sale_date
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if cost_basis is not None:
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pos.cost_basis = cost_basis
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def execute_transaction(self, txn):
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# Update Position
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# ----------------
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# NOTE: self.positions has defaultdict semantics, so this will create
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# an empty position if one does not already exist.
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position = self.positions[txn.sid]
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position.update(txn)
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self.ensure_position_index(txn.sid)
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self._position_amounts[txn.sid] = position.amount
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self._position_last_sale_prices[txn.sid] = position.last_sale_price
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self.period_cash_flow -= txn.price * txn.amount
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if self.keep_transactions:
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self.processed_transactions[txn.dt].append(txn)
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def calculate_positions_value(self):
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return np.dot(self._position_amounts, self._position_last_sale_prices)
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def update_last_sale(self, event):
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if event.sid not in self.positions:
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return
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if event.type != zp.DATASOURCE_TYPE.TRADE:
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return
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if not pd.isnull(event.price):
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# isnan check will keep the last price if its not present
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self.update_position(event.sid, last_sale_price=event.price,
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last_sale_date=event.dt)
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def __core_dict(self):
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rval = {
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'ending_value': self.ending_value,
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# 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_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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}
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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.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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transactions = [x.to_dict()
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for x in self.processed_transactions[dt]]
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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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orders = [x.to_dict()
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for x in itervalues(self.orders_by_modified[dt])]
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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.get_positions()
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portfolio.positions_value = self.ending_value
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return portfolio
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def get_positions(self):
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positions = self._positions_store
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for sid, pos in iteritems(self.positions):
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if pos.amount == 0:
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# Clear out the position if it has become empty since the last
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# time get_positions was called. Catching the KeyError is
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# faster than checking `if sid in positions`, and this can be
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# potentially called in a tight inner loop.
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try:
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del positions[sid]
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except KeyError:
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pass
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continue
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# Note that this will create a position if we don't currently have
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# an entry
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position = positions[sid]
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position.amount = pos.amount
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position.cost_basis = pos.cost_basis
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position.last_sale_price = pos.last_sale_price
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return positions
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def get_positions_list(self):
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positions = []
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for sid, pos in iteritems(self.positions):
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if pos.amount != 0:
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positions.append(pos.to_dict())
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return positions
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