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MAINT: Split performance module into submodules.

Browse Source 
So that when searching code for `returns` and `update`, it is
easier to discern which performance class is affected.

Should be no functional changes.
This commit is contained in:
Eddie Hebert
2013-09-26 13:00:58 -04:00
parent a29e0c40b6
commit 9dd52be73b
5 changed files with 1011 additions and 938 deletions
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zipline/finance/performance.py
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#
# Copyright 2013 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 Tracking
====================
+-----------------+----------------------------------------------------+
| key | value |
+=================+====================================================+
| period_start | The beginning of the period to be tracked. datetime|
| | in pytz.utc timezone. Will always be 0:00 on the |
| | date in UTC. The fact that the time may be on the |
| | prior day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| period_end | The end of the period to be tracked. datetime |
| | in pytz.utc timezone. Will always be 23:59 on the |
| | date in UTC. The fact that the time may be on the |
| | next day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| progress | percentage of test completed |
+-----------------+----------------------------------------------------+
| capital_base | The initial capital assumed for this tracker. |
+-----------------+----------------------------------------------------+
| cumulative_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
+-----------------+----------------------------------------------------+
| todays_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker with datetime stamps between last_open|
| | and last_close. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
| | TODO: adding this because we calculate it. May be |
| | overkill. |
+-----------------+----------------------------------------------------+
| cumulative_risk | A dictionary representing the risk metrics |
| _metrics | calculated based on the positions aggregated |
| | through all the events delivered to this tracker. |
| | For details look at the comments for |
| | :py:meth:`zipline.finance.risk.RiskMetrics.to_dict`|
+-----------------+----------------------------------------------------+
Position Tracking
=================
+-----------------+----------------------------------------------------+
| key | value |
+=================+====================================================+
| sid | the identifier for the security held in this |
| | position. |
+-----------------+----------------------------------------------------+
| amount | whole number of shares in the position |
+-----------------+----------------------------------------------------+
| last_sale_price | price at last sale of the security on the exchange |
+-----------------+----------------------------------------------------+
| cost_basis | the volume weighted average price paid per share |
+-----------------+----------------------------------------------------+
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 securities 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. |
+---------------+------------------------------------------------------+
| max_leverage | The maximum leverage used during the 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 math
import numpy as np
import pandas as pd
from collections import OrderedDict, defaultdict
import zipline.protocol as zp
import zipline.finance.risk as risk
import zipline.finance.trading as trading
log = logbook.Logger('Performance')
class PerformanceTracker(object):
"""
Tracks the performance of the algorithm.
"""
def __init__(self, sim_params):
self.sim_params = sim_params
self.period_start = self.sim_params.period_start
self.period_end = self.sim_params.period_end
self.last_close = self.sim_params.last_close
first_day = self.sim_params.first_open
self.market_open, self.market_close = \
trading.environment.get_open_and_close(first_day)
self.total_days = self.sim_params.days_in_period
self.capital_base = self.sim_params.capital_base
self.emission_rate = sim_params.emission_rate
self.emission_rate = sim_params.emission_rate
self.perf_periods = []
if self.emission_rate == 'daily':
self.all_benchmark_returns = pd.Series(
index=trading.environment.trading_days)
self.intraday_risk_metrics = None
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params)
elif self.emission_rate == 'minute':
self.all_benchmark_returns = pd.Series(index=pd.date_range(
self.sim_params.first_open, self.sim_params.last_close,
freq='Min'))
self.intraday_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params)
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params,
returns_frequency='daily')
self.minute_performance = PerformancePeriod(
# initial cash is your capital base.
self.capital_base,
# the cumulative period will be calculated over the
# entire test.
self.period_start,
self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumualtive period
serialize_positions=False
)
self.perf_periods.append(self.minute_performance)
# this performance period will span the entire simulation from
# inception.
self.cumulative_performance = PerformancePeriod(
# initial cash is your capital base.
self.capital_base,
# the cumulative period will be calculated over the entire test.
self.period_start,
self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumualtive period
serialize_positions=False
)
self.perf_periods.append(self.cumulative_performance)
# this performance period will span just the current market day
self.todays_performance = PerformancePeriod(
# initial cash is your capital base.
self.capital_base,
# the daily period will be calculated for the market day
self.market_open,
self.market_close,
keep_transactions=True,
keep_orders=True,
serialize_positions=True
)
self.perf_periods.append(self.todays_performance)
self.saved_dt = self.period_start
self.returns = []
# one indexed so that we reach 100%
self.day_count = 0.0
self.txn_count = 0
self.event_count = 0
def __repr__(self):
return "%s(%r)" % (
self.__class__.__name__,
{'simulation parameters': self.sim_params})
@property
def progress(self):
if self.emission_rate == 'minute':
# Fake a value
return 1.0
elif self.emission_rate == 'daily':
return self.day_count / self.total_days
def set_date(self, date):
if self.emission_rate == 'minute':
self.saved_dt = date
self.todays_performance.period_close = self.saved_dt
def get_portfolio(self):
return self.cumulative_performance.as_portfolio()
def to_dict(self, emission_type=None):
"""
Creates a dictionary representing the state of this tracker.
Returns a dict object of the form described in header comments.
"""
if not emission_type:
emission_type = self.emission_rate
_dict = {
'period_start': self.period_start,
'period_end': self.period_end,
'capital_base': self.capital_base,
'cumulative_perf': self.cumulative_performance.to_dict(),
'progress': self.progress,
'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict()
}
if emission_type == 'daily':
_dict.update({'daily_perf': self.todays_performance.to_dict()})
elif emission_type == 'minute':
_dict.update({
'intraday_risk_metrics': self.intraday_risk_metrics.to_dict(),
'minute_perf': self.todays_performance.to_dict(self.saved_dt)
})
return _dict
def process_event(self, event):
self.event_count += 1
if event.type == zp.DATASOURCE_TYPE.TRADE:
# update last sale
for perf_period in self.perf_periods:
perf_period.update_last_sale(event)
elif event.type == zp.DATASOURCE_TYPE.TRANSACTION:
# Trade simulation always follows a transaction with the
# TRADE event that was used to simulate it, so we don't
# check for end of day rollover messages here.
self.txn_count += 1
for perf_period in self.perf_periods:
perf_period.execute_transaction(event)
elif event.type == zp.DATASOURCE_TYPE.DIVIDEND:
for perf_period in self.perf_periods:
perf_period.add_dividend(event)
elif event.type == zp.DATASOURCE_TYPE.SPLIT:
for perf_period in self.perf_periods:
perf_period.handle_split(event)
elif event.type == zp.DATASOURCE_TYPE.ORDER:
for perf_period in self.perf_periods:
perf_period.record_order(event)
elif event.type == zp.DATASOURCE_TYPE.COMMISSION:
for perf_period in self.perf_periods:
perf_period.handle_commission(event)
elif event.type == zp.DATASOURCE_TYPE.CUSTOM:
pass
elif event.type == zp.DATASOURCE_TYPE.BENCHMARK:
if (
self.sim_params.data_frequency == 'minute'
and
self.sim_params.emission_rate == 'daily'
):
# Minute data benchmarks should have a timestamp of market
# close, so that calculations are triggered at the right time.
# However, risk module uses midnight as the 'day'
# marker for returns, so adjust back to midgnight.
midnight = event.dt.replace(
hour=0,
minute=0,
second=0,
microsecond=0)
else:
midnight = event.dt
self.all_benchmark_returns[midnight] = event.returns
# calculate performance as of last trade
for perf_period in self.perf_periods:
perf_period.calculate_performance()
def handle_minute_close(self, dt):
todays_date = dt.replace(hour=0, minute=0, second=0, microsecond=0)
minute_returns = self.minute_performance.returns
self.minute_performance.rollover()
algo_minute_returns = pd.Series({dt: minute_returns})
bench_minute_returns = pd.Series({dt: self.all_benchmark_returns[dt]})
# the intraday risk is calculated on top of minute performance
# returns for the bench and the algo
self.intraday_risk_metrics.update(dt,
algo_minute_returns,
bench_minute_returns)
bench_since_open = \
self.intraday_risk_metrics.benchmark_period_returns[dt]
benchmark_returns = pd.Series({todays_date: bench_since_open})
# if we've reached market close, check on dividends
if dt == self.market_close:
for perf_period in self.perf_periods:
perf_period.update_dividends(todays_date)
algorithm_returns = pd.Series({
todays_date: self.todays_performance.returns
})
self.cumulative_risk_metrics.update(todays_date,
algorithm_returns,
benchmark_returns)
# if this is the close, save the returns objects for cumulative
# risk calculations
if dt == self.market_close:
todays_return_obj = zp.DailyReturn(
todays_date,
self.todays_performance.returns
)
self.returns.append(todays_return_obj)
def handle_intraday_close(self):
self.intraday_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params)
# increment the day counter before we move markers forward.
self.day_count += 1.0
# move the market day markers forward
if self.market_close < trading.environment.last_trading_day:
self.market_open, self.market_close = \
trading.environment.next_open_and_close(self.market_open)
else:
self.market_close = self.sim_params.last_close
def handle_market_close(self):
# add the return results from today to the list of DailyReturn objects.
todays_date = self.market_close.replace(hour=0, minute=0, second=0,
microsecond=0)
self.cumulative_performance.update_dividends(todays_date)
self.todays_performance.update_dividends(todays_date)
todays_return_obj = zp.DailyReturn(
todays_date,
self.todays_performance.returns
)
self.returns.append(todays_return_obj)
# update risk metrics for cumulative performance
self.cumulative_risk_metrics.update(
todays_return_obj.date,
todays_return_obj.returns,
self.all_benchmark_returns[todays_return_obj.date])
# increment the day counter before we move markers forward.
self.day_count += 1.0
# Take a snapshot of our current performance to return to the
# browser.
daily_update = self.to_dict()
# On the last day of the test, don't create tomorrow's performance
# period. We may not be able to find the next trading day if we're
# at the end of our historical data
if self.market_close >= self.last_close:
return daily_update
# move the market day markers forward
self.market_open, self.market_close = \
trading.environment.next_open_and_close(self.market_open)
# Roll over positions to current day.
self.todays_performance.rollover()
self.todays_performance.period_open = self.market_open
self.todays_performance.period_close = self.market_close
# The dividend calculation for the daily needs to be made
# after the rollover. midnight_between is the last midnight
# hour between the close of markets and the next open. To
# make sure midnight_between matches identically with
# dividend data dates, it is in UTC.
midnight_between = self.market_open.replace(hour=0, minute=0, second=0,
microsecond=0)
self.cumulative_performance.update_dividends(midnight_between)
self.todays_performance.update_dividends(midnight_between)
return daily_update
def handle_simulation_end(self):
"""
When the simulation is complete, run the full period risk report
and send it out on the results socket.
"""
log_msg = "Simulated {n} trading days out of {m}."
log.info(log_msg.format(n=int(self.day_count), m=self.total_days))
log.info("first open: {d}".format(
d=self.sim_params.first_open))
log.info("last close: {d}".format(
d=self.sim_params.last_close))
bms = self.cumulative_risk_metrics.benchmark_returns
ars = self.cumulative_risk_metrics.algorithm_returns
self.risk_report = risk.RiskReport(
ars,
self.sim_params,
benchmark_returns=bms)
risk_dict = self.risk_report.to_dict()
return risk_dict
class Position(object):
def __init__(self, sid, amount=0, cost_basis=0.0,
last_sale_price=0.0, last_sale_date=0.0,
dividends=None):
self.sid = sid
self.amount = amount
self.cost_basis = cost_basis # per share
self.last_sale_price = last_sale_price
self.last_sale_date = last_sale_date
self.dividends = dividends or []
def update_dividends(self, midnight_utc):
"""
midnight_utc is the 0 hour for the current (not yet open) trading day.
This method will be invoked at the end of the market
close handling, before the next market open.
"""
payment = 0.0
unpaid_dividends = []
for dividend in self.dividends:
if midnight_utc == dividend.ex_date:
# if we own shares at midnight of the div_ex date
# we are entitled to the dividend.
dividend.amount_on_ex_date = self.amount
if dividend.net_amount:
dividend.payment = self.amount * dividend.net_amount
else:
dividend.payment = self.amount * dividend.gross_amount
if midnight_utc == dividend.pay_date:
# if it is the payment date, include this
# dividend's actual payment (calculated on
# ex_date)
payment += dividend.payment
else:
unpaid_dividends.append(dividend)
self.dividends = unpaid_dividends
return payment
def add_dividend(self, dividend):
self.dividends.append(dividend)
# Update the position by the split ratio, and return the
# resulting fractional share that will be converted into cash.
# Returns the unused cash.
def handle_split(self, split):
if (self.sid != split.sid):
raise NameError("updating split with the wrong sid!")
ratio = split.ratio
log.info("handling split for sid = " + str(split.sid) +
", ratio = " + str(split.ratio))
log.info("before split: " + str(self))
# adjust the # of shares by the ratio
# (if we had 100 shares, and the ratio is 3,
# we now have 33 shares)
# (old_share_count / ratio = new_share_count)
# (old_price * ratio = new_price)
# ie, 33.333
raw_share_count = self.amount / float(ratio)
# ie, 33
full_share_count = math.floor(raw_share_count)
# ie, 0.333
fractional_share_count = raw_share_count - full_share_count
# adjust the cost basis to the nearest cent, ie, 60.0
new_cost_basis = round(self.cost_basis * ratio, 2)
# adjust the last sale price
new_last_sale_price = round(self.last_sale_price * ratio, 2)
self.cost_basis = new_cost_basis
self.last_sale_price = new_last_sale_price
self.amount = full_share_count
return_cash = round(float(fractional_share_count * new_cost_basis), 2)
log.info("after split: " + str(self))
log.info("returning cash: " + str(return_cash))
# return the leftover cash, which will be converted into cash
# (rounded to the nearest cent)
return return_cash
def update(self, txn):
if(self.sid != txn.sid):
raise Exception('updating position with txn for a '
'different sid')
# we're covering a short or closing a position
if(self.amount + txn.amount == 0):
self.cost_basis = 0.0
self.amount = 0
else:
prev_cost = self.cost_basis * self.amount
txn_cost = txn.amount * txn.price
total_cost = prev_cost + txn_cost
total_shares = self.amount + txn.amount
self.cost_basis = total_cost / total_shares
self.amount = total_shares
def adjust_commission_cost_basis(self, commission):
"""
A note about cost-basis in zipline: all positions are considered
to share a cost basis, even if they were executed in different
transactions with different commission costs, different prices, etc.
Due to limitations about how zipline handles positions, zipline will
currently spread an externally-delivered commission charge across
all shares in a position.
"""
if commission.sid != self.sid:
raise Exception('Updating a commission for a different sid?')
if commission.cost == 0.0:
return
# If we no longer hold this position, there is no cost basis to
# adjust.
if self.amount == 0:
return
prev_cost = self.cost_basis * self.amount
new_cost = prev_cost + commission.cost
self.cost_basis = new_cost / self.amount
def __repr__(self):
template = "sid: {sid}, amount: {amount}, cost_basis: {cost_basis}, \
last_sale_price: {last_sale_price}"
return template.format(
sid=self.sid,
amount=self.amount,
cost_basis=self.cost_basis,
last_sale_price=self.last_sale_price
)
def to_dict(self):
"""
Creates a dictionary representing the state of this position.
Returns a dict object of the form:
"""
return {
'sid': self.sid,
'amount': self.amount,
'cost_basis': self.cost_basis,
'last_sale_price': self.last_sale_price
}
class PerformancePeriod(object):
def __init__(
self,
starting_cash,
period_open=None,
period_close=None,
keep_transactions=True,
keep_orders=False,
serialize_positions=True):
self.period_open = period_open
self.period_close = period_close
self.ending_value = 0.0
self.period_cash_flow = 0.0
self.pnl = 0.0
# sid => position object
self.positions = positiondict()
self.ending_cash = starting_cash
# rollover initializes a number of self's attributes:
self.rollover()
self.keep_transactions = keep_transactions
self.keep_orders = keep_orders
# Maps position to following array indexes
self._position_index_map = {}
# Arrays for quick calculations of positions value
self._position_amounts = np.array([])
self._position_last_sale_prices = np.array([])
self.calculate_performance()
# 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._positions_store = zp.Positions()
self.serialize_positions = serialize_positions
def rollover(self):
self.starting_value = self.ending_value
self.starting_cash = self.ending_cash
self.period_cash_flow = 0.0
self.pnl = 0.0
self.processed_transactions = defaultdict(list)
self.orders_by_modified = defaultdict(OrderedDict)
self.orders_by_id = OrderedDict()
self.cumulative_capital_used = 0.0
self.max_capital_used = 0.0
self.max_leverage = 0.0
def index_for_position(self, sid):
try:
index = self._position_index_map[sid]
except KeyError:
index = len(self._position_index_map)
self._position_index_map[sid] = index
self._position_amounts = np.append(self._position_amounts, [0])
self._position_last_sale_prices = np.append(
self._position_last_sale_prices, [0])
return index
def add_dividend(self, div):
# The dividend is received on midnight of the dividend
# declared date. We calculate the dividends based on the amount of
# stock owned on midnight of the ex dividend date. However, the cash
# is not dispersed until the payment date, which is
# included in the event.
self.positions[div.sid].add_dividend(div)
def handle_split(self, split):
if split.sid in self.positions:
# Make the position object handle the split. It returns the
# leftover cash from a fractional share, if there is any.
leftover_cash = self.positions[split.sid].handle_split(split)
if leftover_cash > 0:
self.handle_cash_payment(leftover_cash)
def update_dividends(self, todays_date):
"""
Check the payment date and ex date against today's date
to determine if we are owed a dividend payment or if the
payment has been disbursed.
"""
cash_payments = 0.0
for sid, pos in self.positions.iteritems():
cash_payments += pos.update_dividends(todays_date)
# credit our cash balance with the dividend payments, or
# if we are short, debit our cash balance with the
# payments.
# debit our cumulative cash spent with the dividend
# payments, or credit our cumulative cash spent if we are
# short the stock.
self.handle_cash_payment(cash_payments)
# recalculate performance, including the dividend
# payments
self.calculate_performance()
def handle_cash_payment(self, payment_amount):
self.adjust_cash(payment_amount)
def handle_commission(self, commission):
# Deduct from our total cash pool.
self.adjust_cash(-commission.cost)
# Adjust the cost basis of the stock if we own it
if commission.sid in self.positions:
self.positions[commission.sid].\
adjust_commission_cost_basis(commission)
def adjust_cash(self, amount):
self.period_cash_flow += amount
self.cumulative_capital_used -= amount
def calculate_performance(self):
self.ending_value = self.calculate_positions_value()
total_at_start = self.starting_cash + self.starting_value
self.ending_cash = self.starting_cash + self.period_cash_flow
total_at_end = self.ending_cash + self.ending_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:
dt_orders = self.orders_by_modified[order.dt]
if order.id in dt_orders:
del dt_orders[order.id]
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 execute_transaction(self, txn):
# Update Position
# ----------------
position = self.positions[txn.sid]
position.update(txn)
index = self.index_for_position(txn.sid)
self._position_amounts[index] = position.amount
self.period_cash_flow -= txn.price * txn.amount
# Max Leverage
# ---------------
# Calculate the maximum capital used and maximum leverage
transaction_cost = txn.price * txn.amount
self.cumulative_capital_used += transaction_cost
if math.fabs(self.cumulative_capital_used) > self.max_capital_used:
self.max_capital_used = math.fabs(self.cumulative_capital_used)
# We want to conveye a level, rather than a precise figure.
# round to the nearest 5,000 to keep the number easy on the eyes
self.max_capital_used = self.round_to_nearest(
self.max_capital_used,
base=5000
)
# we're adding a 10% cushion to the capital used.
self.max_leverage = 1.1 * \
self.max_capital_used / self.starting_cash
# add transaction to the list of processed transactions
if self.keep_transactions:
self.processed_transactions[txn.dt].append(txn)
def round_to_nearest(self, x, base=5):
return int(base * round(float(x) / base))
def calculate_positions_value(self):
return np.dot(self._position_amounts, self._position_last_sale_prices)
def update_last_sale(self, event):
is_trade = event.type == zp.DATASOURCE_TYPE.TRADE
has_price = not np.isnan(event.price)
# isnan check will keep the last price if its not present
if (event.sid in self.positions) and is_trade and has_price:
self.positions[event.sid].last_sale_price = event.price
index = self.index_for_position(event.sid)
self._position_last_sale_prices[index] = event.price
self.positions[event.sid].last_sale_date = event.dt
def __core_dict(self):
rval = {
'ending_value': self.ending_value,
# this field is renamed to capital_used for backward
# compatibility.
'capital_used': self.period_cash_flow,
'starting_value': self.starting_value,
'starting_cash': self.starting_cash,
'ending_cash': self.ending_cash,
'portfolio_value': self.ending_cash + self.ending_value,
'cumulative_capital_used': self.cumulative_capital_used,
'max_capital_used': self.max_capital_used,
'max_leverage': self.max_leverage,
'pnl': self.pnl,
'returns': self.returns,
'period_open': self.period_open,
'period_close': self.period_close
}
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.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
transactions = [x.to_dict()
for x in self.processed_transactions[dt]]
else:
transactions = \
[y.to_dict()
for x in self.processed_transactions.itervalues()
for y in x]
rval['transactions'] = transactions
if self.keep_orders:
if dt:
# only include orders modified as of the given dt.
orders = [x.to_dict()
for x in self.orders_by_modified[dt].itervalues()]
else:
orders = [x.to_dict() for x in self.orders_by_id.itervalues()]
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.period_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.get_positions()
portfolio.positions_value = self.ending_value
return portfolio
def get_positions(self):
positions = self._positions_store
for sid, pos in self.positions.iteritems():
if sid not in positions:
positions[sid] = zp.Position(sid)
position = positions[sid]
position.amount = pos.amount
position.cost_basis = pos.cost_basis
position.last_sale_price = pos.last_sale_price
return positions
def get_positions_list(self):
positions = []
for sid, pos in self.positions.iteritems():
if pos.amount != 0:
positions.append(pos.to_dict())
return positions
class positiondict(dict):
def __missing__(self, key):
pos = Position(key)
self[key] = pos
return pos
zipline/finance/performance/__init__.py
+24
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#
# Copyright 2013 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 . tracker import PerformanceTracker
from . period import PerformancePeriod
from . position import Position
__all__ = [
'PerformanceTracker',
'PerformancePeriod',
'Position',
]
zipline/finance/performance/period.py
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#
# Copyright 2013 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 securities 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. |
+---------------+------------------------------------------------------+
| max_leverage | The maximum leverage used during the 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 math
import numpy as np
from collections import OrderedDict, defaultdict
import zipline.protocol as zp
from . position import positiondict
log = logbook.Logger('Performance')
class PerformancePeriod(object):
def __init__(
self,
starting_cash,
period_open=None,
period_close=None,
keep_transactions=True,
keep_orders=False,
serialize_positions=True):
self.period_open = period_open
self.period_close = period_close
self.ending_value = 0.0
self.period_cash_flow = 0.0
self.pnl = 0.0
# sid => position object
self.positions = positiondict()
self.ending_cash = starting_cash
# rollover initializes a number of self's attributes:
self.rollover()
self.keep_transactions = keep_transactions
self.keep_orders = keep_orders
# Maps position to following array indexes
self._position_index_map = {}
# Arrays for quick calculations of positions value
self._position_amounts = np.array([])
self._position_last_sale_prices = np.array([])
self.calculate_performance()
# 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._positions_store = zp.Positions()
self.serialize_positions = serialize_positions
def rollover(self):
self.starting_value = self.ending_value
self.starting_cash = self.ending_cash
self.period_cash_flow = 0.0
self.pnl = 0.0
self.processed_transactions = defaultdict(list)
self.orders_by_modified = defaultdict(OrderedDict)
self.orders_by_id = OrderedDict()
self.cumulative_capital_used = 0.0
self.max_capital_used = 0.0
self.max_leverage = 0.0
def index_for_position(self, sid):
try:
index = self._position_index_map[sid]
except KeyError:
index = len(self._position_index_map)
self._position_index_map[sid] = index
self._position_amounts = np.append(self._position_amounts, [0])
self._position_last_sale_prices = np.append(
self._position_last_sale_prices, [0])
return index
def add_dividend(self, div):
# The dividend is received on midnight of the dividend
# declared date. We calculate the dividends based on the amount of
# stock owned on midnight of the ex dividend date. However, the cash
# is not dispersed until the payment date, which is
# included in the event.
self.positions[div.sid].add_dividend(div)
def handle_split(self, split):
if split.sid in self.positions:
# Make the position object handle the split. It returns the
# leftover cash from a fractional share, if there is any.
leftover_cash = self.positions[split.sid].handle_split(split)
if leftover_cash > 0:
self.handle_cash_payment(leftover_cash)
def update_dividends(self, todays_date):
"""
Check the payment date and ex date against today's date
to determine if we are owed a dividend payment or if the
payment has been disbursed.
"""
cash_payments = 0.0
for sid, pos in self.positions.iteritems():
cash_payments += pos.update_dividends(todays_date)
# credit our cash balance with the dividend payments, or
# if we are short, debit our cash balance with the
# payments.
# debit our cumulative cash spent with the dividend
# payments, or credit our cumulative cash spent if we are
# short the stock.
self.handle_cash_payment(cash_payments)
# recalculate performance, including the dividend
# payments
self.calculate_performance()
def handle_cash_payment(self, payment_amount):
self.adjust_cash(payment_amount)
def handle_commission(self, commission):
# Deduct from our total cash pool.
self.adjust_cash(-commission.cost)
# Adjust the cost basis of the stock if we own it
if commission.sid in self.positions:
self.positions[commission.sid].\
adjust_commission_cost_basis(commission)
def adjust_cash(self, amount):
self.period_cash_flow += amount
self.cumulative_capital_used -= amount
def calculate_performance(self):
self.ending_value = self.calculate_positions_value()
total_at_start = self.starting_cash + self.starting_value
self.ending_cash = self.starting_cash + self.period_cash_flow
total_at_end = self.ending_cash + self.ending_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:
dt_orders = self.orders_by_modified[order.dt]
if order.id in dt_orders:
del dt_orders[order.id]
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 execute_transaction(self, txn):
# Update Position
# ----------------
position = self.positions[txn.sid]
position.update(txn)
index = self.index_for_position(txn.sid)
self._position_amounts[index] = position.amount
self.period_cash_flow -= txn.price * txn.amount
# Max Leverage
# ---------------
# Calculate the maximum capital used and maximum leverage
transaction_cost = txn.price * txn.amount
self.cumulative_capital_used += transaction_cost
if math.fabs(self.cumulative_capital_used) > self.max_capital_used:
self.max_capital_used = math.fabs(self.cumulative_capital_used)
# We want to conveye a level, rather than a precise figure.
# round to the nearest 5,000 to keep the number easy on the eyes
self.max_capital_used = self.round_to_nearest(
self.max_capital_used,
base=5000
)
# we're adding a 10% cushion to the capital used.
self.max_leverage = 1.1 * \
self.max_capital_used / self.starting_cash
# add transaction to the list of processed transactions
if self.keep_transactions:
self.processed_transactions[txn.dt].append(txn)
def round_to_nearest(self, x, base=5):
return int(base * round(float(x) / base))
def calculate_positions_value(self):
return np.dot(self._position_amounts, self._position_last_sale_prices)
def update_last_sale(self, event):
is_trade = event.type == zp.DATASOURCE_TYPE.TRADE
has_price = not np.isnan(event.price)
# isnan check will keep the last price if its not present
if (event.sid in self.positions) and is_trade and has_price:
self.positions[event.sid].last_sale_price = event.price
index = self.index_for_position(event.sid)
self._position_last_sale_prices[index] = event.price
self.positions[event.sid].last_sale_date = event.dt
def __core_dict(self):
rval = {
'ending_value': self.ending_value,
# this field is renamed to capital_used for backward
# compatibility.
'capital_used': self.period_cash_flow,
'starting_value': self.starting_value,
'starting_cash': self.starting_cash,
'ending_cash': self.ending_cash,
'portfolio_value': self.ending_cash + self.ending_value,
'cumulative_capital_used': self.cumulative_capital_used,
'max_capital_used': self.max_capital_used,
'max_leverage': self.max_leverage,
'pnl': self.pnl,
'returns': self.returns,
'period_open': self.period_open,
'period_close': self.period_close
}
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.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
transactions = [x.to_dict()
for x in self.processed_transactions[dt]]
else:
transactions = \
[y.to_dict()
for x in self.processed_transactions.itervalues()
for y in x]
rval['transactions'] = transactions
if self.keep_orders:
if dt:
# only include orders modified as of the given dt.
orders = [x.to_dict()
for x in self.orders_by_modified[dt].itervalues()]
else:
orders = [x.to_dict() for x in self.orders_by_id.itervalues()]
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.period_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.get_positions()
portfolio.positions_value = self.ending_value
return portfolio
def get_positions(self):
positions = self._positions_store
for sid, pos in self.positions.iteritems():
if sid not in positions:
positions[sid] = zp.Position(sid)
position = positions[sid]
position.amount = pos.amount
position.cost_basis = pos.cost_basis
position.last_sale_price = pos.last_sale_price
return positions
def get_positions_list(self):
positions = []
for sid, pos in self.positions.iteritems():
if pos.amount != 0:
positions.append(pos.to_dict())
return positions
zipline/finance/performance/position.py
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#
# Copyright 2013 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.
"""
Position Tracking
=================
+-----------------+----------------------------------------------------+
| key | value |
+=================+====================================================+
| sid | the identifier for the security held in this |
| | position. |
+-----------------+----------------------------------------------------+
| amount | whole number of shares in the position |
+-----------------+----------------------------------------------------+
| last_sale_price | price at last sale of the security on the exchange |
+-----------------+----------------------------------------------------+
| cost_basis | the volume weighted average price paid per share |
+-----------------+----------------------------------------------------+
"""
from __future__ import division
import logbook
import math
log = logbook.Logger('Performance')
class Position(object):
def __init__(self, sid, amount=0, cost_basis=0.0,
last_sale_price=0.0, last_sale_date=0.0,
dividends=None):
self.sid = sid
self.amount = amount
self.cost_basis = cost_basis # per share
self.last_sale_price = last_sale_price
self.last_sale_date = last_sale_date
self.dividends = dividends or []
def update_dividends(self, midnight_utc):
"""
midnight_utc is the 0 hour for the current (not yet open) trading day.
This method will be invoked at the end of the market
close handling, before the next market open.
"""
payment = 0.0
unpaid_dividends = []
for dividend in self.dividends:
if midnight_utc == dividend.ex_date:
# if we own shares at midnight of the div_ex date
# we are entitled to the dividend.
dividend.amount_on_ex_date = self.amount
if dividend.net_amount:
dividend.payment = self.amount * dividend.net_amount
else:
dividend.payment = self.amount * dividend.gross_amount
if midnight_utc == dividend.pay_date:
# if it is the payment date, include this
# dividend's actual payment (calculated on
# ex_date)
payment += dividend.payment
else:
unpaid_dividends.append(dividend)
self.dividends = unpaid_dividends
return payment
def add_dividend(self, dividend):
self.dividends.append(dividend)
# Update the position by the split ratio, and return the
# resulting fractional share that will be converted into cash.
# Returns the unused cash.
def handle_split(self, split):
if (self.sid != split.sid):
raise NameError("updating split with the wrong sid!")
ratio = split.ratio
log.info("handling split for sid = " + str(split.sid) +
", ratio = " + str(split.ratio))
log.info("before split: " + str(self))
# adjust the # of shares by the ratio
# (if we had 100 shares, and the ratio is 3,
# we now have 33 shares)
# (old_share_count / ratio = new_share_count)
# (old_price * ratio = new_price)
# ie, 33.333
raw_share_count = self.amount / float(ratio)
# ie, 33
full_share_count = math.floor(raw_share_count)
# ie, 0.333
fractional_share_count = raw_share_count - full_share_count
# adjust the cost basis to the nearest cent, ie, 60.0
new_cost_basis = round(self.cost_basis * ratio, 2)
# adjust the last sale price
new_last_sale_price = round(self.last_sale_price * ratio, 2)
self.cost_basis = new_cost_basis
self.last_sale_price = new_last_sale_price
self.amount = full_share_count
return_cash = round(float(fractional_share_count * new_cost_basis), 2)
log.info("after split: " + str(self))
log.info("returning cash: " + str(return_cash))
# return the leftover cash, which will be converted into cash
# (rounded to the nearest cent)
return return_cash
def update(self, txn):
if(self.sid != txn.sid):
raise Exception('updating position with txn for a '
'different sid')
# we're covering a short or closing a position
if(self.amount + txn.amount == 0):
self.cost_basis = 0.0
self.amount = 0
else:
prev_cost = self.cost_basis * self.amount
txn_cost = txn.amount * txn.price
total_cost = prev_cost + txn_cost
total_shares = self.amount + txn.amount
self.cost_basis = total_cost / total_shares
self.amount = total_shares
def adjust_commission_cost_basis(self, commission):
"""
A note about cost-basis in zipline: all positions are considered
to share a cost basis, even if they were executed in different
transactions with different commission costs, different prices, etc.
Due to limitations about how zipline handles positions, zipline will
currently spread an externally-delivered commission charge across
all shares in a position.
"""
if commission.sid != self.sid:
raise Exception('Updating a commission for a different sid?')
if commission.cost == 0.0:
return
# If we no longer hold this position, there is no cost basis to
# adjust.
if self.amount == 0:
return
prev_cost = self.cost_basis * self.amount
new_cost = prev_cost + commission.cost
self.cost_basis = new_cost / self.amount
def __repr__(self):
template = "sid: {sid}, amount: {amount}, cost_basis: {cost_basis}, \
last_sale_price: {last_sale_price}"
return template.format(
sid=self.sid,
amount=self.amount,
cost_basis=self.cost_basis,
last_sale_price=self.last_sale_price
)
def to_dict(self):
"""
Creates a dictionary representing the state of this position.
Returns a dict object of the form:
"""
return {
'sid': self.sid,
'amount': self.amount,
'cost_basis': self.cost_basis,
'last_sale_price': self.last_sale_price
}
class positiondict(dict):
def __missing__(self, key):
pos = Position(key)
self[key] = pos
return pos
zipline/finance/performance/tracker.py
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#
# Copyright 2013 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 Tracking
====================
+-----------------+----------------------------------------------------+
| key | value |
+=================+====================================================+
| period_start | The beginning of the period to be tracked. datetime|
| | in pytz.utc timezone. Will always be 0:00 on the |
| | date in UTC. The fact that the time may be on the |
| | prior day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| period_end | The end of the period to be tracked. datetime |
| | in pytz.utc timezone. Will always be 23:59 on the |
| | date in UTC. The fact that the time may be on the |
| | next day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| progress | percentage of test completed |
+-----------------+----------------------------------------------------+
| capital_base | The initial capital assumed for this tracker. |
+-----------------+----------------------------------------------------+
| cumulative_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
+-----------------+----------------------------------------------------+
| todays_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker with datetime stamps between last_open|
| | and last_close. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
| | TODO: adding this because we calculate it. May be |
| | overkill. |
+-----------------+----------------------------------------------------+
| cumulative_risk | A dictionary representing the risk metrics |
| _metrics | calculated based on the positions aggregated |
| | through all the events delivered to this tracker. |
| | For details look at the comments for |
| | :py:meth:`zipline.finance.risk.RiskMetrics.to_dict`|
+-----------------+----------------------------------------------------+
"""
from __future__ import division
import logbook
import pandas as pd
import zipline.protocol as zp
import zipline.finance.risk as risk
import zipline.finance.trading as trading
from . period import PerformancePeriod
log = logbook.Logger('Performance')
class PerformanceTracker(object):
"""
Tracks the performance of the algorithm.
"""
def __init__(self, sim_params):
self.sim_params = sim_params
self.period_start = self.sim_params.period_start
self.period_end = self.sim_params.period_end
self.last_close = self.sim_params.last_close
first_day = self.sim_params.first_open
self.market_open, self.market_close = \
trading.environment.get_open_and_close(first_day)
self.total_days = self.sim_params.days_in_period
self.capital_base = self.sim_params.capital_base
self.emission_rate = sim_params.emission_rate
self.emission_rate = sim_params.emission_rate
self.perf_periods = []
if self.emission_rate == 'daily':
self.all_benchmark_returns = pd.Series(
index=trading.environment.trading_days)
self.intraday_risk_metrics = None
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params)
elif self.emission_rate == 'minute':
self.all_benchmark_returns = pd.Series(index=pd.date_range(
self.sim_params.first_open, self.sim_params.last_close,
freq='Min'))
self.intraday_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params)
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params,
returns_frequency='daily')
self.minute_performance = PerformancePeriod(
# initial cash is your capital base.
self.capital_base,
# the cumulative period will be calculated over the
# entire test.
self.period_start,
self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumualtive period
serialize_positions=False
)
self.perf_periods.append(self.minute_performance)
# this performance period will span the entire simulation from
# inception.
self.cumulative_performance = PerformancePeriod(
# initial cash is your capital base.
self.capital_base,
# the cumulative period will be calculated over the entire test.
self.period_start,
self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumualtive period
serialize_positions=False
)
self.perf_periods.append(self.cumulative_performance)
# this performance period will span just the current market day
self.todays_performance = PerformancePeriod(
# initial cash is your capital base.
self.capital_base,
# the daily period will be calculated for the market day
self.market_open,
self.market_close,
keep_transactions=True,
keep_orders=True,
serialize_positions=True
)
self.perf_periods.append(self.todays_performance)
self.saved_dt = self.period_start
self.returns = []
# one indexed so that we reach 100%
self.day_count = 0.0
self.txn_count = 0
self.event_count = 0
def __repr__(self):
return "%s(%r)" % (
self.__class__.__name__,
{'simulation parameters': self.sim_params})
@property
def progress(self):
if self.emission_rate == 'minute':
# Fake a value
return 1.0
elif self.emission_rate == 'daily':
return self.day_count / self.total_days
def set_date(self, date):
if self.emission_rate == 'minute':
self.saved_dt = date
self.todays_performance.period_close = self.saved_dt
def get_portfolio(self):
return self.cumulative_performance.as_portfolio()
def to_dict(self, emission_type=None):
"""
Creates a dictionary representing the state of this tracker.
Returns a dict object of the form described in header comments.
"""
if not emission_type:
emission_type = self.emission_rate
_dict = {
'period_start': self.period_start,
'period_end': self.period_end,
'capital_base': self.capital_base,
'cumulative_perf': self.cumulative_performance.to_dict(),
'progress': self.progress,
'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict()
}
if emission_type == 'daily':
_dict.update({'daily_perf': self.todays_performance.to_dict()})
elif emission_type == 'minute':
_dict.update({
'intraday_risk_metrics': self.intraday_risk_metrics.to_dict(),
'minute_perf': self.todays_performance.to_dict(self.saved_dt)
})
return _dict
def process_event(self, event):
self.event_count += 1
if event.type == zp.DATASOURCE_TYPE.TRADE:
# update last sale
for perf_period in self.perf_periods:
perf_period.update_last_sale(event)
elif event.type == zp.DATASOURCE_TYPE.TRANSACTION:
# Trade simulation always follows a transaction with the
# TRADE event that was used to simulate it, so we don't
# check for end of day rollover messages here.
self.txn_count += 1
for perf_period in self.perf_periods:
perf_period.execute_transaction(event)
elif event.type == zp.DATASOURCE_TYPE.DIVIDEND:
for perf_period in self.perf_periods:
perf_period.add_dividend(event)
elif event.type == zp.DATASOURCE_TYPE.SPLIT:
for perf_period in self.perf_periods:
perf_period.handle_split(event)
elif event.type == zp.DATASOURCE_TYPE.ORDER:
for perf_period in self.perf_periods:
perf_period.record_order(event)
elif event.type == zp.DATASOURCE_TYPE.COMMISSION:
for perf_period in self.perf_periods:
perf_period.handle_commission(event)
elif event.type == zp.DATASOURCE_TYPE.CUSTOM:
pass
elif event.type == zp.DATASOURCE_TYPE.BENCHMARK:
if (
self.sim_params.data_frequency == 'minute'
and
self.sim_params.emission_rate == 'daily'
):
# Minute data benchmarks should have a timestamp of market
# close, so that calculations are triggered at the right time.
# However, risk module uses midnight as the 'day'
# marker for returns, so adjust back to midgnight.
midnight = event.dt.replace(
hour=0,
minute=0,
second=0,
microsecond=0)
else:
midnight = event.dt
self.all_benchmark_returns[midnight] = event.returns
# calculate performance as of last trade
for perf_period in self.perf_periods:
perf_period.calculate_performance()
def handle_minute_close(self, dt):
todays_date = dt.replace(hour=0, minute=0, second=0, microsecond=0)
minute_returns = self.minute_performance.returns
self.minute_performance.rollover()
algo_minute_returns = pd.Series({dt: minute_returns})
bench_minute_returns = pd.Series({dt: self.all_benchmark_returns[dt]})
# the intraday risk is calculated on top of minute performance
# returns for the bench and the algo
self.intraday_risk_metrics.update(dt,
algo_minute_returns,
bench_minute_returns)
bench_since_open = \
self.intraday_risk_metrics.benchmark_period_returns[dt]
benchmark_returns = pd.Series({todays_date: bench_since_open})
# if we've reached market close, check on dividends
if dt == self.market_close:
for perf_period in self.perf_periods:
perf_period.update_dividends(todays_date)
algorithm_returns = pd.Series({
todays_date: self.todays_performance.returns
})
self.cumulative_risk_metrics.update(todays_date,
algorithm_returns,
benchmark_returns)
# if this is the close, save the returns objects for cumulative
# risk calculations
if dt == self.market_close:
todays_return_obj = zp.DailyReturn(
todays_date,
self.todays_performance.returns
)
self.returns.append(todays_return_obj)
def handle_intraday_close(self):
self.intraday_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params)
# increment the day counter before we move markers forward.
self.day_count += 1.0
# move the market day markers forward
if self.market_close < trading.environment.last_trading_day:
self.market_open, self.market_close = \
trading.environment.next_open_and_close(self.market_open)
else:
self.market_close = self.sim_params.last_close
def handle_market_close(self):
# add the return results from today to the list of DailyReturn objects.
todays_date = self.market_close.replace(hour=0, minute=0, second=0,
microsecond=0)
self.cumulative_performance.update_dividends(todays_date)
self.todays_performance.update_dividends(todays_date)
todays_return_obj = zp.DailyReturn(
todays_date,
self.todays_performance.returns
)
self.returns.append(todays_return_obj)
# update risk metrics for cumulative performance
self.cumulative_risk_metrics.update(
todays_return_obj.date,
todays_return_obj.returns,
self.all_benchmark_returns[todays_return_obj.date])
# increment the day counter before we move markers forward.
self.day_count += 1.0
# Take a snapshot of our current performance to return to the
# browser.
daily_update = self.to_dict()
# On the last day of the test, don't create tomorrow's performance
# period. We may not be able to find the next trading day if we're
# at the end of our historical data
if self.market_close >= self.last_close:
return daily_update
# move the market day markers forward
self.market_open, self.market_close = \
trading.environment.next_open_and_close(self.market_open)
# Roll over positions to current day.
self.todays_performance.rollover()
self.todays_performance.period_open = self.market_open
self.todays_performance.period_close = self.market_close
# The dividend calculation for the daily needs to be made
# after the rollover. midnight_between is the last midnight
# hour between the close of markets and the next open. To
# make sure midnight_between matches identically with
# dividend data dates, it is in UTC.
midnight_between = self.market_open.replace(hour=0, minute=0, second=0,
microsecond=0)
self.cumulative_performance.update_dividends(midnight_between)
self.todays_performance.update_dividends(midnight_between)
return daily_update
def handle_simulation_end(self):
"""
When the simulation is complete, run the full period risk report
and send it out on the results socket.
"""
log_msg = "Simulated {n} trading days out of {m}."
log.info(log_msg.format(n=int(self.day_count), m=self.total_days))
log.info("first open: {d}".format(
d=self.sim_params.first_open))
log.info("last close: {d}".format(
d=self.sim_params.last_close))
bms = self.cumulative_risk_metrics.benchmark_returns
ars = self.cumulative_risk_metrics.algorithm_returns
self.risk_report = risk.RiskReport(
ars,
self.sim_params,
benchmark_returns=bms)
risk_dict = self.risk_report.to_dict()
return risk_dict