wassname/catalyst
1
0
Fork 0
mirror of https://github.com/wassname/catalyst.git synced 2026-07-11 11:05:56 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
817ed88e38abb4c3334bf142adc153567eeb20ef
catalyst/tests/test_perf_tracking.py
T
fawce 817ed88e38 Adds dividends to performance tracking. 
Algorithm returns and the risk calculations that depend on them now include
cash dividends. This commit does _not_ provide an API for user algorithms to
access dividends.

PerformanceTracker expects the dividend data to arrive as events, similar to
the way that Trades arrive. Dividends are expected to have adjusted payment
amounts that are inline with adjusted trades.

PerformanceTracker maintains state of all the unpaid dividends in the position
objects held in PerformancePeriod. Dividend objects contain all the relevant
dates (declared, ex, payment) as well as net and gross amounts. Dividends are
removed from the list as they are paid. Cash flow is not incremented until the
payment day. This creates the possibility of a dividend being owed but not
paid or realized before the end of a test. For example, a dividend with an
ex_date of today may have a pay date 2 weeks in the future. Right now the
algorithm does not receive any credit for unpaid dividends.

Tests cover buying/selling around the ex_date and payment_date, and checking
that the performance calculated is as expected.
2013-02-06 16:39:39 -05:00

1020 lines
32 KiB
Python
Raw Blame History

#
# Copyright 2012 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.
import collections
import unittest
from nose_parameterized import parameterized
import datetime
import pytz
import itertools
from operator import attrgetter
import zipline.utils.factory as factory
import zipline.finance.performance as perf
from zipline.utils.protocol_utils import ndict
from zipline.gens.composites import date_sorted_sources
from zipline.finance.trading import TradingEnvironment
from zipline.utils.factory import create_random_trading_environment
onesec = datetime.timedelta(seconds=1)
oneday = datetime.timedelta(days=1)
tradingday = datetime.timedelta(hours=6, minutes=30)
class TestDividendPerformance(unittest.TestCase):
def setUp(self):
self.trading_environment, self.dt, self.end_dt = \
create_random_trading_environment()
self.trading_environment.capital_base = 10e3
def test_long_position_receives_dividend(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
events.insert(1, dividend)
txn = factory.create_txn(1, 10.0, 100, self.dt+oneday)
events[2].TRANSACTION = txn
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(results[0]['daily_perf']['period_open'], events[0].dt)
self.assertEqual(
results[-1]['daily_perf']['period_open'],
events[-1].dt
)
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.1, 0.1, 0.1])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.10, 0.0, 0.0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, -1000, 1000, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, -1000, 0, 0, 0])
def test_post_ex_long_position_receives_no_dividend(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
events.insert(1, dividend)
txn = factory.create_txn(1, 10.0, 100, events[3].dt)
events[3].TRANSACTION = txn
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0, 0, 0, 0, 0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0, 0, 0, 0, 0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, 0, -1000, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, 0, -1000, -1000, -1000])
def test_selling_before_dividend_payment_still_gets_paid(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[3].dt
)
buy_txn = factory.create_txn(1, 10.0, 100, events[1].dt)
events[1].TRANSACTION = buy_txn
sell_txn = factory.create_txn(1, 10.0, -100, events[2].dt)
events[2].TRANSACTION = sell_txn
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0, 0, 0, 0.1, 0.1])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0, 0, 0, 0.1, 0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, -1000, 1000, 1000, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, -1000, 0, 1000, 1000])
def test_buy_and_sell_before_ex(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[3].dt,
events[4].dt,
events[5].dt
)
buy_txn = factory.create_txn(1, 10.0, 100, events[1].dt)
events[1].TRANSACTION = buy_txn
sell_txn = factory.create_txn(1, 10.0, -100, events[2].dt)
events[2].TRANSACTION = sell_txn
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 6)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0, 0, 0, 0, 0, 0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0, 0, 0, 0, 0, 0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, -1000, 1000, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, -1000, 0, 0, 0, 0])
def test_ending_before_pay_date(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[-1].dt + 10*oneday
)
buy_txn = factory.create_txn(1, 10.0, 100, events[1].dt)
events[1].TRANSACTION = buy_txn
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0, 0, 0, 0.0, 0.0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0, 0, 0, 0, 0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, -1000, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(
cumulative_cash_flows,
[0, -1000, -1000, -1000, -1000]
)
def test_short_position_receives_no_dividend(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
events.insert(1, dividend)
txn = factory.create_txn(1, 10.0, -100, self.dt+oneday)
events[2].TRANSACTION = txn
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.0, 0.0, 0.0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.0, 0.0, 0.0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, 1000, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, 1000, 1000, 1000, 1000])
def test_no_position_receives_no_dividend(self):
#post some trades in the market
events = factory.create_trade_history(
1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.trading_environment
)
dividend = factory.create_dividend(
1,
10.00,
events[0].dt,
events[1].dt,
events[2].dt
)
events.insert(1, dividend)
perf_tracker = perf.PerformanceTracker(self.trading_environment)
transformed_events = list(perf_tracker.transform(
((event.dt, [event]) for event in events))
)
#flatten the list of events
results = []
for te in transformed_events:
for event in te[1]:
for message in event.perf_messages:
results.append(message)
perf_messages, risk = perf_tracker.handle_simulation_end()
results.append(perf_messages[0])
self.assertEqual(len(results), 5)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.0, 0.0, 0.0])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.0, 0.0, 0.0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, 0, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, 0, 0, 0, 0])
class TestPositionPerformance(unittest.TestCase):
def setUp(self):
self.trading_environment, self.dt, self.end_dt = \
create_random_trading_environment()
def test_long_position(self):
"""
verify that the performance period calculates properly for a
single buy transaction
"""
#post some trades in the market
trades = factory.create_trade_history(
1,
[10, 10, 10, 11],
[100, 100, 100, 100],
onesec,
self.trading_environment
)
txn = factory.create_txn(1, 10.0, 100, self.dt + onesec)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(txn)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
self.assertEqual(
pp.period_cash_flow,
-1 * txn.price * txn.amount,
"capital used should be equal to the opposite of the transaction \
cost of sole txn in test"
)
self.assertEqual(
len(pp.positions),
1,
"should be just one position")
self.assertEqual(
pp.positions[1].sid,
txn.sid,
"position should be in security with id 1")
self.assertEqual(
pp.positions[1].amount,
txn.amount,
"should have a position of {sharecount} shares".format(
sharecount=txn.amount
)
)
self.assertEqual(
pp.positions[1].cost_basis,
txn.price,
"should have a cost basis of 10"
)
self.assertEqual(
pp.positions[1].last_sale_price,
trades[-1]['price'],
"last sale should be same as last trade. \
expected {exp} actual {act}".format(
exp=trades[-1]['price'],
act=pp.positions[1].last_sale_price)
)
self.assertEqual(
pp.ending_value,
1100,
"ending value should be price of last trade times number of \
shares in position"
)
self.assertEqual(pp.pnl, 100, "gain of 1 on 100 shares should be 100")
def test_short_position(self):
"""verify that the performance period calculates properly for a \
single short-sale transaction"""
trades = factory.create_trade_history(
1,
[10, 10, 10, 11, 10, 9],
[100, 100, 100, 100, 100, 100],
onesec,
self.trading_environment
)
trades_1 = trades[:-2]
txn = factory.create_txn(1, 10.0, -100, self.dt + onesec)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(txn)
for trade in trades_1:
pp.update_last_sale(trade)
pp.calculate_performance()
self.assertEqual(
pp.period_cash_flow,
-1 * txn.price * txn.amount,
"capital used should be equal to the opposite of the transaction\
cost of sole txn in test"
)
self.assertEqual(
len(pp.positions),
1,
"should be just one position")
self.assertEqual(
pp.positions[1].sid,
txn.sid,
"position should be in security from the transaction"
)
self.assertEqual(
pp.positions[1].amount,
-100,
"should have a position of -100 shares"
)
self.assertEqual(
pp.positions[1].cost_basis,
txn.price,
"should have a cost basis of 10"
)
self.assertEqual(
pp.positions[1].last_sale_price,
trades_1[-1]['price'],
"last sale should be price of last trade"
)
self.assertEqual(
pp.ending_value,
-1100,
"ending value should be price of last trade times number of \
shares in position"
)
self.assertEqual(pp.pnl, -100, "gain of 1 on 100 shares should be 100")
# simulate additional trades, and ensure that the position value
# reflects the new price
trades_2 = trades[-2:]
#simulate a rollover to a new period
pp.rollover()
for trade in trades_2:
pp.update_last_sale(trade)
pp.calculate_performance()
self.assertEqual(
pp.period_cash_flow,
0,
"capital used should be zero, there were no transactions in \
performance period"
)
self.assertEqual(
len(pp.positions),
1,
"should be just one position"
)
self.assertEqual(
pp.positions[1].sid,
txn.sid,
"position should be in security from the transaction"
)
self.assertEqual(
pp.positions[1].amount,
-100,
"should have a position of -100 shares"
)
self.assertEqual(
pp.positions[1].cost_basis,
txn.price,
"should have a cost basis of 10"
)
self.assertEqual(
pp.positions[1].last_sale_price,
trades_2[-1].price,
"last sale should be price of last trade"
)
self.assertEqual(
pp.ending_value,
-900,
"ending value should be price of last trade times number of \
shares in position")
self.assertEqual(
pp.pnl,
200,
"drop of 2 on -100 shares should be 200"
)
#now run a performance period encompassing the entire trade sample.
ppTotal = perf.PerformancePeriod(1000.0)
for trade in trades_1:
ppTotal.update_last_sale(trade)
ppTotal.execute_transaction(txn)
for trade in trades_2:
ppTotal.update_last_sale(trade)
ppTotal.calculate_performance()
self.assertEqual(
ppTotal.period_cash_flow,
-1 * txn.price * txn.amount,
"capital used should be equal to the opposite of the transaction \
cost of sole txn in test"
)
self.assertEqual(
len(ppTotal.positions),
1,
"should be just one position"
)
self.assertEqual(
ppTotal.positions[1].sid,
txn.sid,
"position should be in security from the transaction"
)
self.assertEqual(
ppTotal.positions[1].amount,
-100,
"should have a position of -100 shares"
)
self.assertEqual(
ppTotal.positions[1].cost_basis,
txn.price,
"should have a cost basis of 10"
)
self.assertEqual(
ppTotal.positions[1].last_sale_price,
trades_2[-1].price,
"last sale should be price of last trade"
)
self.assertEqual(
ppTotal.ending_value,
-900,
"ending value should be price of last trade times number of \
shares in position")
self.assertEqual(
ppTotal.pnl,
100,
"drop of 1 on -100 shares should be 100"
)
def test_covering_short(self):
"""verify performance where short is bought and covered, and shares \
trade after cover"""
trades = factory.create_trade_history(
1,
[10, 10, 10, 11, 9, 8, 7, 8, 9, 10],
[100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
onesec,
self.trading_environment
)
short_txn = factory.create_txn(
1,
10.0,
-100,
self.dt + onesec
)
cover_txn = factory.create_txn(1, 7.0, 100, self.dt + onesec * 6)
pp = perf.PerformancePeriod(1000.0)
pp.execute_transaction(short_txn)
pp.execute_transaction(cover_txn)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
short_txn_cost = short_txn.price * short_txn.amount
cover_txn_cost = cover_txn.price * cover_txn.amount
self.assertEqual(
pp.period_cash_flow,
-1 * short_txn_cost - cover_txn_cost,
"capital used should be equal to the net transaction costs"
)
self.assertEqual(
len(pp.positions),
1,
"should be just one position"
)
self.assertEqual(
pp.positions[1].sid,
short_txn.sid,
"position should be in security from the transaction"
)
self.assertEqual(
pp.positions[1].amount,
0,
"should have a position of -100 shares"
)
self.assertEqual(
pp.positions[1].cost_basis,
0,
"a covered position should have a cost basis of 0"
)
self.assertEqual(
pp.positions[1].last_sale_price,
trades[-1].price,
"last sale should be price of last trade"
)
self.assertEqual(
pp.ending_value,
0,
"ending value should be price of last trade times number of \
shares in position"
)
self.assertEqual(
pp.pnl,
300,
"gain of 1 on 100 shares should be 300"
)
def test_cost_basis_calc(self):
trades = factory.create_trade_history(
1,
[10, 11, 11, 12],
[100, 100, 100, 100],
onesec,
self.trading_environment
)
transactions = factory.create_txn_history(
1,
[10, 11, 11, 12],
[100, 100, 100, 100],
onesec,
self.trading_environment
)
pp = perf.PerformancePeriod(1000.0)
for txn in transactions:
pp.execute_transaction(txn)
for trade in trades:
pp.update_last_sale(trade)
pp.calculate_performance()
self.assertEqual(
pp.positions[1].last_sale_price,
trades[-1].price,
"should have a last sale of 12, got {val}".format(
val=pp.positions[1].last_sale_price)
)
self.assertEqual(
pp.positions[1].cost_basis,
11,
"should have a cost basis of 11"
)
self.assertEqual(
pp.pnl,
400
)
saleTxn = factory.create_txn(
1,
10.0,
-100,
self.dt + onesec * 4)
down_tick = factory.create_trade(
1,
10.0,
100,
trades[-1].dt + onesec)
pp.rollover()
pp.execute_transaction(saleTxn)
pp.update_last_sale(down_tick)
pp.calculate_performance()
self.assertEqual(
pp.positions[1].last_sale_price,
10,
"should have a last sale of 10, was {val}".format(
val=pp.positions[1].last_sale_price)
)
self.assertEqual(
round(pp.positions[1].cost_basis, 2),
11.33,
"should have a cost basis of 11.33"
)
#print "second period pnl is {pnl}".format(pnl=pp2.pnl)
self.assertEqual(pp.pnl, -800, "this period goes from +400 to -400")
pp3 = perf.PerformancePeriod(1000.0)
transactions.append(saleTxn)
for txn in transactions:
pp3.execute_transaction(txn)
trades.append(down_tick)
for trade in trades:
pp3.update_last_sale(trade)
pp3.calculate_performance()
self.assertEqual(
pp3.positions[1].last_sale_price,
10,
"should have a last sale of 10"
)
self.assertEqual(
round(pp3.positions[1].cost_basis, 2),
11.33,
"should have a cost basis of 11.33"
)
self.assertEqual(
pp3.pnl,
-400,
"should be -400 for all trades and transactions in period"
)
class TestPerformanceTracker(unittest.TestCase):
NumDaysToDelete = collections.namedtuple(
'NumDaysToDelete', ('start', 'middle', 'end'))
@parameterized.expand([
("Don't delete any events",
NumDaysToDelete(start=0, middle=0, end=0)),
("Delete first day of events",
NumDaysToDelete(start=1, middle=0, end=0)),
("Delete first two days of events",
NumDaysToDelete(start=2, middle=0, end=0)),
("Delete one day of events from the middle",
NumDaysToDelete(start=0, middle=1, end=0)),
("Delete two events from the middle",
NumDaysToDelete(start=0, middle=2, end=0)),
("Delete last day of events",
NumDaysToDelete(start=0, middle=0, end=1)),
("Delete last two days of events",
NumDaysToDelete(start=0, middle=0, end=2)),
("Delete all but one event.",
NumDaysToDelete(start=2, middle=1, end=2)),
])
def test_tracker(self, parameter_comment, days_to_delete):
"""
@days_to_delete - configures which days in the data set we should
remove, used for ensuring that we still return performance messages
even when there is no data.
"""
# This date range covers Columbus day,
# however Columbus day is not a market holiday
#
# October 2008
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
start_dt = datetime.datetime(year=2008,
month=10,
day=9,
tzinfo=pytz.utc)
end_dt = datetime.datetime(year=2008,
month=10,
day=16,
tzinfo=pytz.utc)
trade_count = 6
sid = 133
price = 10.1
price_list = [price] * trade_count
volume = [100] * trade_count
trade_time_increment = datetime.timedelta(days=1)
benchmark_returns, treasury_curves = \
factory.load_market_data()
trading_environment = TradingEnvironment(
benchmark_returns,
treasury_curves,
period_start=start_dt,
period_end=end_dt
)
trade_history = factory.create_trade_history(
sid,
price_list,
volume,
trade_time_increment,
trading_environment,
source_id="factory1"
)
sid2 = 134
price2 = 12.12
price2_list = [price2] * trade_count
trade_history2 = factory.create_trade_history(
sid2,
price2_list,
volume,
trade_time_increment,
trading_environment,
source_id="factory2"
)
# 'middle' start of 3 depends on number of days == 7
middle = 3
# First delete from middle
if days_to_delete.middle:
del trade_history[middle:(middle + days_to_delete.middle)]
del trade_history2[middle:(middle + days_to_delete.middle)]
# Delete start
if days_to_delete.start:
del trade_history[:days_to_delete.start]
del trade_history2[:days_to_delete.start]
# Delete from end
if days_to_delete.end:
del trade_history[-days_to_delete.end:]
del trade_history2[-days_to_delete.end:]
trading_environment.first_open = \
trading_environment.calculate_first_open()
trading_environment.last_close = \
trading_environment.calculate_last_close()
trading_environment.capital_base = 1000.0
trading_environment.frame_index = [
'sid',
'volume',
'dt',
'price',
'changed']
perf_tracker = perf.PerformanceTracker(
trading_environment
)
events = date_sorted_sources(trade_history, trade_history2)
events = [self.event_with_txn(event, trade_history[0].dt)
for event in events]
# Extract events with transactions to use for verification.
events_with_txns = [event for event in events if event.TRANSACTION]
perf_messages = \
[msg for date, snapshot in
perf_tracker.transform(
itertools.groupby(events, attrgetter('dt')))
for event in snapshot
for msg in event.perf_messages]
end_perf_messages, risk_message = perf_tracker.handle_simulation_end()
perf_messages.extend(end_perf_messages)
#we skip two trades, to test case of None transaction
self.assertEqual(perf_tracker.txn_count, len(events_with_txns))
cumulative_pos = perf_tracker.cumulative_performance.positions[sid]
expected_size = len(events_with_txns) / 2 * -25
self.assertEqual(cumulative_pos.amount, expected_size)
self.assertEqual(perf_tracker.last_close,
perf_tracker.cumulative_risk_metrics.end_date)
self.assertEqual(len(perf_messages),
trading_environment.days_in_period)
def event_with_txn(self, event, no_txn_dt):
#create a transaction for all but
#first trade in each sid, to simulate None transaction
if event.dt != no_txn_dt:
txn = ndict({
'sid': event.sid,
'amount': -25,
'dt': event.dt,
'price': 10.0,
'commission': 0.50
})
else:
txn = None
event['TRANSACTION'] = txn
return event
Reference in New Issue View Git Blame Copy Permalink