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catalyst/tests/test_perf_tracking.py
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Joe Jevnik bc0b117dc9 MAINT: make the data loading apis more consistent. 
Changes BcolzDailyBarWriter to not be an abc, data is passed as an
iterator of (sid, dataframe) pairs to the write method.

Changes the AssetsDBWriter to be a single class which accepts an engine
at construction time and has a `write` method for writing dataframes for
the various tables. We no longer support writing the various other data
types, callers should coerce their data into a dataframe themselves. See
zipline.assets.synthetic for some helpers to do this.

Adds many new fixtures and updates some existing fixtures to use the new
ones:

WithDefaultDateBounds
  A fixture that provides the suite a START_DATE and END_DATE. This is
  meant to make it easy for other fixtures to synchronize their date
  ranges without depending on eachother in strange ways. For example,
  WithBcolzMinuteBarReader and WithBcolzDailyBarReader by default should
  both have data for the same dates, so they may use depend on
  WithDefaultDates without forcing a dependency between them.

WithTmpDir, WithInstanceTmpDir
  Provides the suite or individual test case a temporary directory.

WithBcolzDailyBarReader
  Provides the suite a BcolzDailyBarReader which reads from bcolz data
  written to a temporary directory. The data will be read from
  dataframes and then converted to bcolz files with
  BcolzDailyBarWriter.write

WithBcolzDailyBarReaderFromCSVs
  Provides the suite a BcolzDailyBarReader which reads from bcolz data
  written to a temporary directory. The data will be read from a
  collection of CSV files and then converted into the bcolz data through
  BcolzDailyBarWriter.write_csvs

WithBcolzMinuteBarReader
  Provides the suite a BcolzMinuteBarReader which reads from bcolz data
  written to a temporary directory. The data will be read from
  dataframes and then converted to bcolz files with
  BcolzMinuteBarWriter.write

WithAdjustmentReader
  Provides the suite a SQLiteAdjustmentReader which reads from an in
  memory sqlite database. The data will be read from dataframes and then
  converted into sqlite with SQLiteAdjustmentWriter.write

WithDataPortal
  Provides each test case a DataPortal object with data from temporary
  resources.
2016-04-15 23:46:10 -04:00

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#
# Copyright 2016 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import division
import copy
from datetime import (
datetime,
timedelta,
)
import logging
import nose.tools as nt
import pytz
import pandas as pd
import numpy as np
from six.moves import range, zip
from zipline.assets import Asset
from zipline.assets.synthetic import make_simple_equity_info
from zipline.data.us_equity_pricing import (
SQLiteAdjustmentWriter,
SQLiteAdjustmentReader,
)
import zipline.utils.factory as factory
import zipline.finance.performance as perf
from zipline.finance.transaction import create_transaction
import zipline.utils.math_utils as zp_math
from zipline.finance.blotter import Order
from zipline.finance.commission import PerShare, PerTrade, PerDollar
from zipline.finance.performance.position import Position
from zipline.utils.factory import create_simulation_parameters
from zipline.utils.serialization_utils import (
loads_with_persistent_ids, dumps_with_persistent_ids
)
from zipline.testing import (
MockDailyBarReader,
create_data_portal_from_trade_history,
create_empty_splits_mergers_frame,
tmp_trading_env,
)
from zipline.testing.fixtures import (
WithInstanceTmpDir,
WithSimParams,
WithTmpDir,
WithTradingEnvironment,
ZiplineTestCase,
)
logger = logging.getLogger('Test Perf Tracking')
oneday = timedelta(days=1)
tradingday = timedelta(hours=6, minutes=30)
# nose.tools changed name in python 3
if not hasattr(nt, 'assert_count_equal'):
nt.assert_count_equal = nt.assert_items_equal
def check_perf_period(pp,
gross_leverage,
net_leverage,
long_exposure,
longs_count,
short_exposure,
shorts_count):
perf_data = pp.to_dict()
np.testing.assert_allclose(
gross_leverage, perf_data['gross_leverage'], rtol=1e-3)
np.testing.assert_allclose(
net_leverage, perf_data['net_leverage'], rtol=1e-3)
np.testing.assert_allclose(
long_exposure, perf_data['long_exposure'], rtol=1e-3)
np.testing.assert_allclose(
longs_count, perf_data['longs_count'], rtol=1e-3)
np.testing.assert_allclose(
short_exposure, perf_data['short_exposure'], rtol=1e-3)
np.testing.assert_allclose(
shorts_count, perf_data['shorts_count'], rtol=1e-3)
def check_account(account,
settled_cash,
equity_with_loan,
total_positions_value,
total_positions_exposure,
regt_equity,
available_funds,
excess_liquidity,
cushion,
leverage,
net_leverage,
net_liquidation):
# this is a long only portfolio that is only partially invested
# so net and gross leverage are equal.
np.testing.assert_allclose(settled_cash,
account['settled_cash'], rtol=1e-3)
np.testing.assert_allclose(equity_with_loan,
account['equity_with_loan'], rtol=1e-3)
np.testing.assert_allclose(total_positions_value,
account['total_positions_value'], rtol=1e-3)
np.testing.assert_allclose(total_positions_exposure,
account['total_positions_exposure'], rtol=1e-3)
np.testing.assert_allclose(regt_equity,
account['regt_equity'], rtol=1e-3)
np.testing.assert_allclose(available_funds,
account['available_funds'], rtol=1e-3)
np.testing.assert_allclose(excess_liquidity,
account['excess_liquidity'], rtol=1e-3)
np.testing.assert_allclose(cushion,
account['cushion'], rtol=1e-3)
np.testing.assert_allclose(leverage, account['leverage'], rtol=1e-3)
np.testing.assert_allclose(net_leverage,
account['net_leverage'], rtol=1e-3)
np.testing.assert_allclose(net_liquidation,
account['net_liquidation'], rtol=1e-3)
def create_txn(asset, dt, price, amount):
"""
Create a fake transaction to be filled and processed prior to the execution
of a given trade event.
"""
if not isinstance(asset, Asset):
raise ValueError("pass an asset to create_txn")
mock_order = Order(dt, asset, amount, id=None)
return create_transaction(mock_order, dt, price, amount)
def calculate_results(sim_params,
env,
data_portal,
splits=None,
txns=None,
commissions=None):
"""
Run the given events through a stripped down version of the loop in
AlgorithmSimulator.transform.
IMPORTANT NOTE FOR TEST WRITERS/READERS:
This loop has some wonky logic for the order of event processing for
datasource types. This exists mostly to accommodate legacy tests that were
making assumptions about how events would be sorted.
In particular:
- Dividends passed for a given date are processed PRIOR to any events
for that date.
- Splits passed for a given date are process AFTER any events for that
date.
Tests that use this helper should not be considered useful guarantees of
the behavior of AlgorithmSimulator on a stream containing the same events
unless the subgroups have been explicitly re-sorted in this way.
"""
txns = txns or []
splits = splits or {}
commissions = commissions or {}
perf_tracker = perf.PerformanceTracker(sim_params, env)
results = []
for date in sim_params.trading_days:
for txn in filter(lambda txn: txn.dt == date, txns):
# Process txns for this date.
perf_tracker.process_transaction(txn)
try:
commissions_for_date = commissions[date]
for comm in commissions_for_date:
perf_tracker.process_commission(comm)
except KeyError:
pass
try:
splits_for_date = splits[date]
perf_tracker.handle_splits(splits_for_date)
except KeyError:
pass
msg = perf_tracker.handle_market_close_daily(date, data_portal)
perf_tracker.position_tracker.sync_last_sale_prices(
date, False, data_portal,
)
msg['account'] = perf_tracker.get_account(True)
results.append(copy.deepcopy(msg))
return results
def check_perf_tracker_serialization(perf_tracker):
scalar_keys = [
'emission_rate',
'txn_count',
'market_open',
'last_close',
'period_start',
'day_count',
'capital_base',
'market_close',
'saved_dt',
'period_end',
'total_days',
]
p_string = dumps_with_persistent_ids(perf_tracker)
test = loads_with_persistent_ids(p_string, env=perf_tracker.env)
for k in scalar_keys:
nt.assert_equal(getattr(test, k), getattr(perf_tracker, k), k)
perf_periods = (
test.cumulative_performance,
test.todays_performance
)
for period in perf_periods:
nt.assert_true(hasattr(period, '_position_tracker'))
def setup_env_data(env, sim_params, sids, futures_sids=[]):
data = {}
for sid in sids:
data[sid] = {
"start_date": sim_params.trading_days[0],
"end_date": env.next_trading_day(sim_params.trading_days[-1])
}
env.write_data(equities_data=data)
futures_data = {}
for future_sid in futures_sids:
futures_data[future_sid] = {
"start_date": sim_params.trading_days[0],
"end_date": env.next_trading_day(sim_params.trading_days[-1]),
"multiplier": 100
}
env.write_data(futures_data=futures_data)
class TestSplitPerformance(WithSimParams, WithTmpDir, ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-01-04', tz='utc')
SIM_PARAMS_CAPITAL_BASE = 10e3
ASSET_FINDER_EQUITY_SIDS = 1, 2
@classmethod
def init_class_fixtures(cls):
super(TestSplitPerformance, cls).init_class_fixtures()
cls.asset1 = cls.env.asset_finder.retrieve_asset(1)
def test_multiple_splits(self):
# if multiple positions all have splits at the same time, verify that
# the total leftover cash is correct
perf_tracker = perf.PerformanceTracker(self.sim_params, self.env)
asset1 = self.asset_finder.retrieve_asset(1)
asset2 = self.asset_finder.retrieve_asset(2)
perf_tracker.position_tracker.positions[1] = \
Position(asset1, amount=10, cost_basis=10, last_sale_price=11)
perf_tracker.position_tracker.positions[2] = \
Position(asset2, amount=10, cost_basis=10, last_sale_price=11)
leftover_cash = perf_tracker.position_tracker.handle_splits(
[(1, 0.333), (2, 0.333)]
)
# we used to have 10 shares that each cost us $10, total $100
# now we have 33 shares that each cost us $3.33, total $99.9
# each position returns $0.10 as leftover cash
self.assertEqual(0.2, leftover_cash)
def test_split_long_position(self):
events = factory.create_trade_history(
self.asset1,
# TODO: Should we provide adjusted prices in the tests, or provide
# raw prices and adjust via DataPortal?
[20, 60],
[100, 100],
oneday,
self.sim_params,
env=self.env
)
# set up a long position in sid 1
# 100 shares at $20 apiece = $2000 position
data_portal = create_data_portal_from_trade_history(
self.env,
self.tmpdir,
self.sim_params,
{1: events},
)
txns = [create_txn(self.asset1, events[0].dt, 20, 100)]
# set up a split with ratio 3 occurring at the start of the second
# day.
splits = {
events[1].dt: [(1, 3)]
}
results = calculate_results(self.sim_params,
self.env,
data_portal,
txns=txns,
splits=splits)
# should have 33 shares (at $60 apiece) and $20 in cash
self.assertEqual(2, len(results))
latest_positions = results[1]['daily_perf']['positions']
self.assertEqual(1, len(latest_positions))
# check the last position to make sure it's been updated
position = latest_positions[0]
self.assertEqual(1, position['sid'])
self.assertEqual(33, position['amount'])
self.assertEqual(60, position['cost_basis'])
self.assertEqual(60, position['last_sale_price'])
# since we started with $10000, and we spent $2000 on the
# position, but then got $20 back, we should have $8020
# (or close to it) in cash.
# we won't get exactly 8020 because sometimes a split is
# denoted as a ratio like 0.3333, and we lose some digits
# of precision. thus, make sure we're pretty close.
daily_perf = results[1]['daily_perf']
self.assertTrue(
zp_math.tolerant_equals(8020,
daily_perf['ending_cash'], 1),
"ending_cash was {0}".format(daily_perf['ending_cash']))
# Validate that the account attributes were updated.
account = results[1]['account']
self.assertEqual(float('inf'), account['day_trades_remaining'])
# this is a long only portfolio that is only partially invested
# so net and gross leverage are equal.
np.testing.assert_allclose(0.198, account['leverage'], rtol=1e-3)
np.testing.assert_allclose(0.198, account['net_leverage'], rtol=1e-3)
np.testing.assert_allclose(8020, account['regt_equity'], rtol=1e-3)
self.assertEqual(float('inf'), account['regt_margin'])
np.testing.assert_allclose(8020, account['available_funds'], rtol=1e-3)
self.assertEqual(0, account['maintenance_margin_requirement'])
np.testing.assert_allclose(10000,
account['equity_with_loan'], rtol=1e-3)
self.assertEqual(float('inf'), account['buying_power'])
self.assertEqual(0, account['initial_margin_requirement'])
np.testing.assert_allclose(8020, account['excess_liquidity'],
rtol=1e-3)
np.testing.assert_allclose(8020, account['settled_cash'], rtol=1e-3)
np.testing.assert_allclose(10000, account['net_liquidation'],
rtol=1e-3)
np.testing.assert_allclose(0.802, account['cushion'], rtol=1e-3)
np.testing.assert_allclose(1980, account['total_positions_value'],
rtol=1e-3)
self.assertEqual(0, account['accrued_interest'])
for i, result in enumerate(results):
for perf_kind in ('daily_perf', 'cumulative_perf'):
perf_result = result[perf_kind]
# prices aren't changing, so pnl and returns should be 0.0
self.assertEqual(0.0, perf_result['pnl'],
"day %s %s pnl %s instead of 0.0" %
(i, perf_kind, perf_result['pnl']))
self.assertEqual(0.0, perf_result['returns'],
"day %s %s returns %s instead of 0.0" %
(i, perf_kind, perf_result['returns']))
class TestCommissionEvents(WithSimParams, WithTmpDir, ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-01-09', tz='utc')
ASSET_FINDER_EQUITY_SIDS = 0, 1, 133
SIM_PARAMS_CAPITAL_BASE = 10e3
@classmethod
def init_class_fixtures(cls):
super(TestCommissionEvents, cls).init_class_fixtures()
cls.asset1 = cls.env.asset_finder.retrieve_asset(1)
def test_commission_event(self):
trade_events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
# Test commission models and validate result
# Expected commission amounts:
# PerShare commission: 1.00, 1.00, 1.50 = $3.50
# PerTrade commission: 5.00, 5.00, 5.00 = $15.00
# PerDollar commission: 1.50, 3.00, 4.50 = $9.00
# Total commission = $3.50 + $15.00 + $9.00 = $27.50
data_portal = create_data_portal_from_trade_history(
self.env,
self.tmpdir,
self.sim_params,
{1: trade_events},
)
# Create 3 transactions: 50, 100, 150 shares traded @ $20
first_trade = trade_events[0]
transactions = [create_txn(first_trade.sid, first_trade.dt, 20, i)
for i in [50, 100, 150]]
# Create commission models and validate that produce expected
# commissions.
models = [PerShare(cost=0.01, min_trade_cost=1.00),
PerTrade(cost=5.00),
PerDollar(cost=0.0015)]
expected_results = [3.50, 15.0, 9.0]
for model, expected in zip(models, expected_results):
total_commission = 0
for trade in transactions:
total_commission += model.calculate(trade)[1]
self.assertEqual(total_commission, expected)
# Verify that commission events are handled correctly by
# PerformanceTracker.
commissions = {}
cash_adj_dt = trade_events[0].dt
cash_adjustment = factory.create_commission(1, 300.0, cash_adj_dt)
commissions[cash_adj_dt] = [cash_adjustment]
# Insert a purchase order.
txns = [create_txn(first_trade.sid, first_trade.dt, 20, 1)]
results = calculate_results(self.sim_params,
self.env,
data_portal,
txns=txns,
commissions=commissions)
# Validate that we lost 320 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9680, "Should have lost 320 from cash pool.")
# Validate that the cost basis of our position changed.
self.assertEqual(results[-1]['daily_perf']['positions']
[0]['cost_basis'], 320.0)
# Validate that the account attributes were updated.
account = results[1]['account']
self.assertEqual(float('inf'), account['day_trades_remaining'])
np.testing.assert_allclose(0.001, account['leverage'], rtol=1e-3,
atol=1e-4)
np.testing.assert_allclose(9680, account['regt_equity'], rtol=1e-3)
self.assertEqual(float('inf'), account['regt_margin'])
np.testing.assert_allclose(9680, account['available_funds'],
rtol=1e-3)
self.assertEqual(0, account['maintenance_margin_requirement'])
np.testing.assert_allclose(9690,
account['equity_with_loan'], rtol=1e-3)
self.assertEqual(float('inf'), account['buying_power'])
self.assertEqual(0, account['initial_margin_requirement'])
np.testing.assert_allclose(9680, account['excess_liquidity'],
rtol=1e-3)
np.testing.assert_allclose(9680, account['settled_cash'],
rtol=1e-3)
np.testing.assert_allclose(9690, account['net_liquidation'],
rtol=1e-3)
np.testing.assert_allclose(0.999, account['cushion'], rtol=1e-3)
np.testing.assert_allclose(10, account['total_positions_value'],
rtol=1e-3)
self.assertEqual(0, account['accrued_interest'])
def test_commission_zero_position(self):
"""
Ensure no div-by-zero errors.
"""
events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
data_portal = create_data_portal_from_trade_history(
self.env,
self.tmpdir,
self.sim_params,
{1: events},
)
# Buy and sell the same sid so that we have a zero position by the
# time of events[3].
txns = [
create_txn(self.asset1, events[0].dt, 20, 1),
create_txn(self.asset1, events[0].dt, 20, -1)
]
# Add a cash adjustment at the time of event[3].
cash_adj_dt = events[3].dt
commissions = {}
cash_adjustment = factory.create_commission(1, 300.0, cash_adj_dt)
commissions[cash_adj_dt] = [cash_adjustment]
results = calculate_results(self.sim_params,
self.env,
data_portal,
txns=txns,
commissions=commissions)
# Validate that we lost 300 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9700)
def test_commission_no_position(self):
"""
Ensure no position-not-found or sid-not-found errors.
"""
events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
data_portal = create_data_portal_from_trade_history(
self.env,
self.tmpdir,
self.sim_params,
{1: events},
)
# Add a cash adjustment at the time of event[3].
cash_adj_dt = events[3].dt
commissions = {}
cash_adjustment = factory.create_commission(self.asset1,
300.0, cash_adj_dt)
commissions[cash_adj_dt] = [cash_adjustment]
results = calculate_results(self.sim_params,
self.env,
data_portal,
commissions=commissions)
# Validate that we lost 300 dollars from our cash pool.
self.assertEqual(results[-1]['cumulative_perf']['ending_cash'],
9700)
class TestDividendPerformance(WithSimParams,
WithInstanceTmpDir,
ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-01-10', tz='utc')
ASSET_FINDER_EQUITY_SIDS = 1, 2
SIM_PARAMS_CAPITAL_BASE = 10e3
@classmethod
def init_class_fixtures(cls):
super(TestDividendPerformance, cls).init_class_fixtures()
cls.asset1 = cls.asset_finder.retrieve_asset(1)
cls.asset2 = cls.asset_finder.retrieve_asset(2)
def test_market_hours_calculations(self):
# DST in US/Eastern began on Sunday March 14, 2010
before = datetime(2010, 3, 12, 14, 31, tzinfo=pytz.utc)
after = factory.get_next_trading_dt(
before,
timedelta(days=1),
self.env,
)
self.assertEqual(after.hour, 13)
def test_long_position_receives_dividend(self):
# post some trades in the market
events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(
dbpath,
MockDailyBarReader(),
self.env.trading_days,
)
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([1], dtype=np.uint32),
'amount': np.array([10.00], dtype=np.float64),
'declared_date': np.array([events[0].dt], dtype='datetime64[ns]'),
'ex_date': np.array([events[1].dt], dtype='datetime64[ns]'),
'record_date': np.array([events[1].dt], dtype='datetime64[ns]'),
'pay_date': np.array([events[2].dt], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
adjustment_reader = SQLiteAdjustmentReader(dbpath)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: events},
)
data_portal._adjustment_reader = adjustment_reader
# Simulate a transaction being filled prior to the ex_date.
txns = [create_txn(self.asset1, events[0].dt, 10.0, 100)]
results = calculate_results(
self.sim_params,
self.env,
data_portal,
txns=txns,
)
self.assertEqual(len(results), 6)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.1, 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, 0.0])
cash_flows = [event['daily_perf']['capital_used']
for event in results]
self.assertEqual(cash_flows, [-1000, 0, 1000, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [-1000, -1000, 0, 0, 0, 0])
cash_pos = \
[event['cumulative_perf']['ending_cash'] for event in results]
self.assertEqual(cash_pos, [9000, 9000, 10000, 10000, 10000, 10000])
def test_long_position_receives_stock_dividend(self):
# post some trades in the market
events = {}
for asset in [self.asset1, self.asset2]:
events[asset.sid] = factory.create_trade_history(
asset,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(
dbpath,
MockDailyBarReader(),
self.env.trading_days,
)
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([], dtype=np.uint32),
'amount': np.array([], dtype=np.float64),
'declared_date': np.array([], dtype='datetime64[ns]'),
'ex_date': np.array([], dtype='datetime64[ns]'),
'pay_date': np.array([], dtype='datetime64[ns]'),
'record_date': np.array([], dtype='datetime64[ns]'),
})
sid_1 = events[1]
stock_dividends = pd.DataFrame({
'sid': np.array([1], dtype=np.uint32),
'payment_sid': np.array([2], dtype=np.uint32),
'ratio': np.array([2], dtype=np.float64),
'declared_date': np.array([sid_1[0].dt], dtype='datetime64[ns]'),
'ex_date': np.array([sid_1[1].dt], dtype='datetime64[ns]'),
'record_date': np.array([sid_1[1].dt], dtype='datetime64[ns]'),
'pay_date': np.array([sid_1[2].dt], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends, stock_dividends)
adjustment_reader = SQLiteAdjustmentReader(dbpath)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
events,
)
data_portal._adjustment_reader = adjustment_reader
txns = [create_txn(self.asset1, events[1][0].dt, 10.0, 100)]
results = calculate_results(
self.sim_params,
self.env,
data_portal,
txns=txns,
)
self.assertEqual(len(results), 6)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0.0, 0.0, 0.2, 0.2, 0.2, 0.2])
daily_returns = [event['daily_perf']['returns']
for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.2, 0.0, 0.0, 0.0])
cash_flows = [event['daily_perf']['capital_used']
for event in results]
self.assertEqual(cash_flows, [-1000, 0, 0, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [-1000] * 6)
cash_pos = \
[event['cumulative_perf']['ending_cash'] for event in results]
self.assertEqual(cash_pos, [9000] * 6)
def test_long_position_purchased_on_ex_date_receives_no_dividend(self):
# post some trades in the market
events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(
dbpath,
MockDailyBarReader(),
self.env.trading_days,
)
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([1], dtype=np.uint32),
'amount': np.array([10.00], dtype=np.float64),
'declared_date': np.array([events[0].dt], dtype='datetime64[ns]'),
'ex_date': np.array([events[1].dt], dtype='datetime64[ns]'),
'record_date': np.array([events[1].dt], dtype='datetime64[ns]'),
'pay_date': np.array([events[2].dt], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
adjustment_reader = SQLiteAdjustmentReader(dbpath)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: events},
)
data_portal._adjustment_reader = adjustment_reader
# Simulate a transaction being filled on the ex_date.
txns = [create_txn(self.asset1, events[1].dt, 10.0, 100)]
results = calculate_results(
self.sim_params,
self.env,
data_portal,
txns=txns,
)
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, 0, 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, -1000])
def test_selling_before_dividend_payment_still_gets_paid(self):
# post some trades in the market
events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(
dbpath,
MockDailyBarReader(),
self.env.trading_days,
)
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([1], dtype=np.uint32),
'amount': np.array([10.00], dtype=np.float64),
'declared_date': np.array([events[0].dt], dtype='datetime64[ns]'),
'ex_date': np.array([events[1].dt], dtype='datetime64[ns]'),
'record_date': np.array([events[1].dt], dtype='datetime64[ns]'),
'pay_date': np.array([events[3].dt], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
adjustment_reader = SQLiteAdjustmentReader(dbpath)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: events},
)
data_portal._adjustment_reader = adjustment_reader
buy_txn = create_txn(self.asset1, events[0].dt, 10.0, 100)
sell_txn = create_txn(self.asset1, events[2].dt, 10.0, -100)
txns = [buy_txn, sell_txn]
results = calculate_results(
self.sim_params,
self.env,
data_portal,
txns=txns,
)
self.assertEqual(len(results), 6)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [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, 0.1, 0, 0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [-1000, 0, 1000, 1000, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows,
[-1000, -1000, 0, 1000, 1000, 1000])
def test_buy_and_sell_before_ex(self):
# post some trades in the market
events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(
dbpath,
MockDailyBarReader(),
self.env.trading_days,
)
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([1], dtype=np.uint32),
'amount': np.array([10.0], dtype=np.float64),
'declared_date': np.array([events[3].dt], dtype='datetime64[ns]'),
'ex_date': np.array([events[4].dt], dtype='datetime64[ns]'),
'pay_date': np.array([events[5].dt], dtype='datetime64[ns]'),
'record_date': np.array([events[4].dt], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
adjustment_reader = SQLiteAdjustmentReader(dbpath)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: events},
)
data_portal._adjustment_reader = adjustment_reader
buy_txn = create_txn(self.asset1, events[1].dt, 10.0, 100)
sell_txn = create_txn(self.asset1, events[2].dt, 10.0, -100)
txns = [buy_txn, sell_txn]
results = calculate_results(
self.sim_params,
self.env,
data_portal,
txns=txns,
)
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(
self.asset1,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
pay_date = self.sim_params.first_open
# find pay date that is much later.
for i in range(30):
pay_date = factory.get_next_trading_dt(pay_date, oneday, self.env)
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(
dbpath,
MockDailyBarReader(),
self.env.trading_days,
)
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([1], dtype=np.uint32),
'amount': np.array([10.00], dtype=np.float64),
'declared_date': np.array([events[0].dt], dtype='datetime64[ns]'),
'ex_date': np.array([events[0].dt], dtype='datetime64[ns]'),
'record_date': np.array([events[0].dt], dtype='datetime64[ns]'),
'pay_date': np.array([pay_date], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
adjustment_reader = SQLiteAdjustmentReader(dbpath)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: events},
)
data_portal._adjustment_reader = adjustment_reader
txns = [create_txn(self.asset1, events[1].dt, 10.0, 100)]
results = calculate_results(
self.sim_params,
self.env,
data_portal,
txns=txns,
)
self.assertEqual(len(results), 6)
cumulative_returns = \
[event['cumulative_perf']['returns'] for event in results]
self.assertEqual(cumulative_returns, [0, 0, 0, 0.0, 0.0, 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, 0, 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, -1000]
)
def test_short_position_pays_dividend(self):
# post some trades in the market
events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(
dbpath,
MockDailyBarReader(),
self.env.trading_days,
)
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([1], dtype=np.uint32),
'amount': np.array([10.00], dtype=np.float64),
'declared_date': np.array([events[0].dt], dtype='datetime64[ns]'),
'ex_date': np.array([events[2].dt], dtype='datetime64[ns]'),
'record_date': np.array([events[2].dt], dtype='datetime64[ns]'),
'pay_date': np.array([events[3].dt], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
adjustment_reader = SQLiteAdjustmentReader(dbpath)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: events},
)
data_portal._adjustment_reader = adjustment_reader
txns = [create_txn(self.asset1, events[1].dt, 10.0, -100)]
results = calculate_results(
self.sim_params,
self.env,
data_portal,
txns=txns,
)
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, -0.1, -0.1, -0.1])
daily_returns = [event['daily_perf']['returns'] for event in results]
self.assertEqual(daily_returns, [0.0, 0.0, 0.0, -0.1, 0.0, 0.0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, 1000, 0, -1000, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows, [0, 1000, 1000, 0, 0, 0])
def test_no_position_receives_no_dividend(self):
# post some trades in the market
events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10, 10],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(
dbpath,
MockDailyBarReader(),
self.env.trading_days,
)
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([1], dtype=np.uint32),
'amount': np.array([10.00], dtype=np.float64),
'declared_date': np.array([events[0].dt], dtype='datetime64[ns]'),
'ex_date': np.array([events[1].dt], dtype='datetime64[ns]'),
'pay_date': np.array([events[2].dt], dtype='datetime64[ns]'),
'record_date': np.array([events[2].dt], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
adjustment_reader = SQLiteAdjustmentReader(dbpath)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: events},
)
data_portal._adjustment_reader = adjustment_reader
results = calculate_results(
self.sim_params,
self.env,
data_portal,
)
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, 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, 0.0])
cash_flows = [event['daily_perf']['capital_used'] for event in results]
self.assertEqual(cash_flows, [0, 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, 0])
def test_no_dividend_at_simulation_end(self):
# post some trades in the market
events = factory.create_trade_history(
self.asset1,
[10, 10, 10, 10, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(
dbpath,
MockDailyBarReader(),
self.env.trading_days,
)
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([1], dtype=np.uint32),
'amount': np.array([10.00], dtype=np.float64),
'declared_date': np.array([events[-3].dt], dtype='datetime64[ns]'),
'ex_date': np.array([events[-2].dt], dtype='datetime64[ns]'),
'record_date': np.array([events[0].dt], dtype='datetime64[ns]'),
'pay_date': np.array([self.env.next_trading_day(events[-1].dt)],
dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
adjustment_reader = SQLiteAdjustmentReader(dbpath)
# Set the last day to be the last event
sim_params = create_simulation_parameters(
num_days=6,
capital_base=10e3,
start=self.sim_params.period_start,
end=self.sim_params.period_end
)
sim_params.period_end = events[-1].dt
sim_params.update_internal_from_env(self.env)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
sim_params,
{1: events},
)
data_portal._adjustment_reader = adjustment_reader
# Simulate a transaction being filled prior to the ex_date.
txns = [create_txn(self.asset1, events[0].dt, 10.0, 100)]
results = calculate_results(
sim_params,
self.env,
data_portal,
txns=txns,
)
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, [-1000, 0, 0, 0, 0])
cumulative_cash_flows = \
[event['cumulative_perf']['capital_used'] for event in results]
self.assertEqual(cumulative_cash_flows,
[-1000, -1000, -1000, -1000, -1000])
class TestDividendPerformanceHolidayStyle(TestDividendPerformance):
# The holiday tests begins the simulation on the day
# before Thanksgiving, so that the next trading day is
# two days ahead. Any tests that hard code events
# to be start + oneday will fail, since those events will
# be skipped by the simulation.
START_DATE = pd.Timestamp('2003-11-30', tz='utc')
END_DATE = pd.Timestamp('2003-12-08', tz='utc')
class TestPositionPerformance(WithInstanceTmpDir, ZiplineTestCase):
def create_environment_stuff(self,
num_days=4,
sids=[1, 2],
futures_sids=[3]):
start = pd.Timestamp('2006-01-01', tz='utc')
end = start + timedelta(days=num_days * 2)
equities = make_simple_equity_info(sids, start, end)
futures = pd.DataFrame.from_dict(
{
sid: {
'start_date': start,
'end_date': end,
'multiplier': 100,
}
for sid in futures_sids
},
orient='index',
)
self.env = self.enter_instance_context(tmp_trading_env(
equities=equities,
futures=futures,
))
self.sim_params = create_simulation_parameters(
start=start,
num_days=num_days,
)
self.finder = self.env.asset_finder
self.asset1 = self.env.asset_finder.retrieve_asset(1)
self.asset2 = self.env.asset_finder.retrieve_asset(2)
self.asset3 = self.env.asset_finder.retrieve_asset(3)
def test_long_short_positions(self):
"""
start with $1000
buy 100 stock1 shares at $10
sell short 100 stock2 shares at $10
stock1 then goes down to $9
stock2 goes to $11
"""
self.create_environment_stuff()
trades_1 = factory.create_trade_history(
self.asset1,
[10, 10, 10, 9],
[100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
trades_2 = factory.create_trade_history(
self.asset2,
[10, 10, 10, 11],
[100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: trades_1, 2: trades_2}
)
txn1 = create_txn(self.asset1, trades_1[0].dt, 10.0, 100)
txn2 = create_txn(self.asset2, trades_1[0].dt, 10.0, -100)
pt = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
pp.position_tracker = pt
pt.execute_transaction(txn1)
pp.handle_execution(txn1)
pt.execute_transaction(txn2)
pp.handle_execution(txn2)
dt = trades_1[-2].dt
pt.sync_last_sale_prices(dt, False, data_portal)
pp.calculate_performance()
check_perf_period(
pp,
gross_leverage=2.0,
net_leverage=0.0,
long_exposure=1000.0,
longs_count=1,
short_exposure=-1000.0,
shorts_count=1)
# Validate that the account attributes were updated.
account = pp.as_account()
check_account(account,
settled_cash=1000.0,
equity_with_loan=1000.0,
total_positions_value=0.0,
total_positions_exposure=0.0,
regt_equity=1000.0,
available_funds=1000.0,
excess_liquidity=1000.0,
cushion=1.0,
leverage=2.0,
net_leverage=0.0,
net_liquidation=1000.0)
dt = trades_1[-1].dt
pt.sync_last_sale_prices(dt, False, data_portal)
pp.calculate_performance()
# Validate that the account attributes were updated.
account = pp.as_account()
check_perf_period(
pp,
gross_leverage=2.5,
net_leverage=-0.25,
long_exposure=900.0,
longs_count=1,
short_exposure=-1100.0,
shorts_count=1)
check_account(account,
settled_cash=1000.0,
equity_with_loan=800.0,
total_positions_value=-200.0,
total_positions_exposure=-200.0,
regt_equity=1000.0,
available_funds=1000.0,
excess_liquidity=1000.0,
cushion=1.25,
leverage=2.5,
net_leverage=-0.25,
net_liquidation=800.0)
def test_levered_long_position(self):
"""
start with $1,000, then buy 1000 shares at $10.
price goes to $11
"""
# post some trades in the market
self.create_environment_stuff()
trades = factory.create_trade_history(
self.asset1,
[10, 10, 10, 11],
[100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: trades})
txn = create_txn(self.asset1, trades[1].dt, 10.0, 1000)
pt = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
pp.position_tracker = pt
pt.execute_transaction(txn)
pp.handle_execution(txn)
pp.calculate_performance()
check_perf_period(
pp,
gross_leverage=10.0,
net_leverage=10.0,
long_exposure=10000.0,
longs_count=1,
short_exposure=0.0,
shorts_count=0)
# Validate that the account attributes were updated.
pt.sync_last_sale_prices(trades[-2].dt, False, data_portal)
# Validate that the account attributes were updated.
account = pp.as_account()
check_account(account,
settled_cash=-9000.0,
equity_with_loan=1000.0,
total_positions_value=10000.0,
total_positions_exposure=10000.0,
regt_equity=-9000.0,
available_funds=-9000.0,
excess_liquidity=-9000.0,
cushion=-9.0,
leverage=10.0,
net_leverage=10.0,
net_liquidation=1000.0)
# now simulate a price jump to $11
pt.sync_last_sale_prices(trades[-1].dt, False, data_portal)
pp.calculate_performance()
check_perf_period(
pp,
gross_leverage=5.5,
net_leverage=5.5,
long_exposure=11000.0,
longs_count=1,
short_exposure=0.0,
shorts_count=0)
# Validate that the account attributes were updated.
account = pp.as_account()
check_account(account,
settled_cash=-9000.0,
equity_with_loan=2000.0,
total_positions_value=11000.0,
total_positions_exposure=11000.0,
regt_equity=-9000.0,
available_funds=-9000.0,
excess_liquidity=-9000.0,
cushion=-4.5,
leverage=5.5,
net_leverage=5.5,
net_liquidation=2000.0)
def test_long_position(self):
"""
verify that the performance period calculates properly for a
single buy transaction
"""
self.create_environment_stuff()
# post some trades in the market
trades = factory.create_trade_history(
self.asset1,
[10, 10, 10, 11],
[100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: trades})
txn = create_txn(self.asset1, trades[1].dt, 10.0, 100)
pt = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency,
period_open=self.sim_params.period_start,
period_close=self.sim_params.period_end)
pp.position_tracker = pt
pt.execute_transaction(txn)
pp.handle_execution(txn)
# This verifies that the last sale price is being correctly
# set in the positions. If this is not the case then returns can
# incorrectly show as sharply dipping if a transaction arrives
# before a trade. This is caused by returns being based on holding
# stocks with a last sale price of 0.
self.assertEqual(pp.positions[1].last_sale_price, 10.0)
pt.sync_last_sale_prices(trades[-1].dt, False, data_portal)
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")
check_perf_period(
pp,
gross_leverage=1.0,
net_leverage=1.0,
long_exposure=1100.0,
longs_count=1,
short_exposure=0.0,
shorts_count=0)
# Validate that the account attributes were updated.
account = pp.as_account()
check_account(account,
settled_cash=0.0,
equity_with_loan=1100.0,
total_positions_value=1100.0,
total_positions_exposure=1100.0,
regt_equity=0.0,
available_funds=0.0,
excess_liquidity=0.0,
cushion=0.0,
leverage=1.0,
net_leverage=1.0,
net_liquidation=1100.0)
def test_short_position(self):
"""verify that the performance period calculates properly for a \
single short-sale transaction"""
self.create_environment_stuff(num_days=6)
trades = factory.create_trade_history(
self.asset1,
[10, 10, 10, 11, 10, 9],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
trades_1 = trades[:-2]
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: trades})
txn = create_txn(self.asset1, trades[1].dt, 10.0, -100)
pt = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp = perf.PerformancePeriod(
1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
pp.position_tracker = pt
pt.execute_transaction(txn)
pp.handle_execution(txn)
pt.sync_last_sale_prices(trades_1[-1].dt, False, data_portal)
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()
pt.sync_last_sale_prices(trades[-1].dt, False, data_portal)
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.
ptTotal = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
ppTotal = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
ppTotal.position_tracker = pt
ptTotal.execute_transaction(txn)
ppTotal.handle_execution(txn)
ptTotal.sync_last_sale_prices(trades[-1].dt, False, data_portal)
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"
)
check_perf_period(
pp,
gross_leverage=0.8181,
net_leverage=-0.8181,
long_exposure=0.0,
longs_count=0,
short_exposure=-900.0,
shorts_count=1)
# Validate that the account attributes.
account = ppTotal.as_account()
check_account(account,
settled_cash=2000.0,
equity_with_loan=1100.0,
total_positions_value=-900.0,
total_positions_exposure=-900.0,
regt_equity=2000.0,
available_funds=2000.0,
excess_liquidity=2000.0,
cushion=1.8181,
leverage=0.8181,
net_leverage=-0.8181,
net_liquidation=1100.0)
def test_long_future_position(self):
"""
verify that the performance period calculates properly for a
single buy transaction
"""
self.create_environment_stuff()
sim_params = copy.copy(self.sim_params)
sim_params.data_frequency = 'minute'
# post some trades in the market
trades = factory.create_trade_history(
self.asset3,
[10, 10, 10, 11],
[100, 100, 100, 100],
oneday,
sim_params,
env=self.env
)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{3: trades}
)
txn = create_txn(self.asset3, trades[1].dt, 10.0, 1)
pt = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
pp.position_tracker = pt
pt.execute_transaction(txn)
pp.handle_execution(txn)
# This verifies that the last sale price is being correctly
# set in the positions. If this is not the case then returns can
# incorrectly show as sharply dipping if a transaction arrives
# before a trade. This is caused by returns being based on holding
# stocks with a last sale price of 0.
self.assertEqual(pp.positions[3].last_sale_price, 10.0)
pt.sync_last_sale_prices(trades[-1].dt, False, data_portal)
pp.calculate_performance()
self.assertEqual(
pp.period_cash_flow,
0,
"there should be no cash flow on a futures txn"
)
self.assertEqual(
len(pp.positions),
1,
"should be just one position")
self.assertEqual(
pp.positions[3].sid,
txn.sid,
"position should be in security with id 1")
self.assertEqual(
pp.positions[3].amount,
txn.amount,
"should have a position of {sharecount} shares".format(
sharecount=txn.amount
)
)
self.assertEqual(
pp.positions[3].cost_basis,
txn.price,
"should have a cost basis of 10"
)
self.assertEqual(
pp.positions[3].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[3].last_sale_price)
)
self.assertEqual(
pp.ending_value,
0,
"ending value should be 0 because only futures are held"
)
self.assertEqual(
pp.ending_exposure,
1100,
"ending exposure should be price of last trade times number of \
contracts in position")
self.assertEqual(pp.pnl, 100, "gain of 1 on 1 100x contract should be "
"100")
check_perf_period(
pp,
gross_leverage=1.0,
net_leverage=1.0,
long_exposure=1100.0,
longs_count=1,
short_exposure=0.0,
shorts_count=0)
# Validate that the account attributes were updated.
account = pp.as_account()
check_account(account,
settled_cash=1100.0,
equity_with_loan=1100.0,
total_positions_value=0.0,
total_positions_exposure=1100.0,
regt_equity=1100.0,
available_funds=1100.0,
excess_liquidity=1100.0,
cushion=1.0,
leverage=1.0,
net_leverage=1.0,
net_liquidation=1100.0)
def test_short_future_position(self):
"""verify that the performance period calculates properly for a \
single short-sale transaction"""
self.create_environment_stuff(num_days=6)
trades = factory.create_trade_history(
self.asset3,
[10, 10, 10, 11, 10, 9],
[100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{3: trades}
)
trades_1 = trades[:-2]
txn = create_txn(self.asset3, trades[0].dt, 10.0, -1)
pt = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
pp.position_tracker = pt
pt.execute_transaction(txn)
pp.handle_execution(txn)
pt.sync_last_sale_prices(trades[-3].dt, False, data_portal)
pp.calculate_performance()
self.assertEqual(
pp.period_cash_flow,
0,
"there should be no cash flow on a futures txn"
)
self.assertEqual(
len(pp.positions),
1,
"should be just one position")
self.assertEqual(
pp.positions[3].sid,
txn.sid,
"position should be in future from the transaction"
)
self.assertEqual(
pp.positions[3].amount,
-1,
"should have a position of -1 contract"
)
self.assertEqual(
pp.positions[3].cost_basis,
txn.price,
"should have a cost basis of 10"
)
self.assertEqual(
pp.positions[3].last_sale_price,
trades_1[-1]['price'],
"last sale should be price of last trade"
)
self.assertEqual(
pp.ending_value,
0,
"ending value should be 0 because only futures are held"
)
self.assertEqual(
pp.ending_exposure,
-1100,
"ending exposure should be price of last trade times number of \
contracts in position")
self.assertEqual(pp.pnl, -100, "gain of 1 on 1 100x contract 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()
pt.sync_last_sale_prices(trades_2[-1].dt, False, data_portal)
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[3].sid,
txn.sid,
"position should be in future from the transaction"
)
self.assertEqual(
pp.positions[3].amount,
-1,
"should have a position of -1 contract"
)
self.assertEqual(
pp.positions[3].cost_basis,
txn.price,
"should have a cost basis of 10"
)
self.assertEqual(
pp.positions[3].last_sale_price,
trades_2[-1].price,
"last sale should be price of last trade"
)
self.assertEqual(
pp.ending_value,
0,
"ending value should be 0 because only futures are held")
self.assertEqual(
pp.ending_exposure,
-900,
"ending exposure should be price of last trade times number of \
shares in position")
self.assertEqual(
pp.pnl,
200,
"drop of 2 on -1 100x contract should be 200"
)
# now run a performance period encompassing the entire trade sample.
ptTotal = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
ppTotal = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
ppTotal.position_tracker = ptTotal
for trade in trades_1:
ptTotal.sync_last_sale_prices(trade.dt, False, data_portal)
ptTotal.execute_transaction(txn)
ppTotal.handle_execution(txn)
for trade in trades_2:
ptTotal.sync_last_sale_prices(trade.dt, False, data_portal)
ppTotal.calculate_performance()
self.assertEqual(
ppTotal.period_cash_flow,
0,
"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[3].sid,
txn.sid,
"position should be in security from the transaction"
)
self.assertEqual(
ppTotal.positions[3].amount,
-1,
"should have a position of -1 contract"
)
self.assertEqual(
ppTotal.positions[3].cost_basis,
txn.price,
"should have a cost basis of 10"
)
self.assertEqual(
ppTotal.positions[3].last_sale_price,
trades_2[-1].price,
"last sale should be price of last trade"
)
self.assertEqual(
pp.ending_value,
0,
"ending value should be 0 because only futures are held")
self.assertEqual(
pp.ending_exposure,
-900,
"ending exposure should be price of last trade times number of \
shares in position")
self.assertEqual(
ppTotal.pnl,
100,
"drop of 1 on -1 100x contract should be 100"
)
check_perf_period(
pp,
gross_leverage=0.8181,
net_leverage=-0.8181,
long_exposure=0.0,
longs_count=0,
short_exposure=-900.0,
shorts_count=1)
# Validate that the account attributes.
account = ppTotal.as_account()
check_account(account,
settled_cash=1100.0,
equity_with_loan=1100.0,
total_positions_value=0.0,
total_positions_exposure=-900.0,
regt_equity=1100.0,
available_funds=1100.0,
excess_liquidity=1100.0,
cushion=1.0,
leverage=0.8181,
net_leverage=-0.8181,
net_liquidation=1100.0)
def test_covering_short(self):
"""verify performance where short is bought and covered, and shares \
trade after cover"""
self.create_environment_stuff(num_days=10)
trades = factory.create_trade_history(
self.asset1,
[10, 10, 10, 11, 9, 8, 7, 8, 9, 10],
[100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
oneday,
self.sim_params,
env=self.env
)
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: trades})
short_txn = create_txn(self.asset1, trades[1].dt, 10.0, -100)
cover_txn = create_txn(self.asset1, trades[6].dt, 7.0, 100)
pt = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
pp.position_tracker = pt
pt.execute_transaction(short_txn)
pp.handle_execution(short_txn)
pt.execute_transaction(cover_txn)
pp.handle_execution(cover_txn)
pt.sync_last_sale_prices(trades[-1].dt, False, data_portal)
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),
0,
"should be zero positions"
)
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"
)
check_perf_period(
pp,
gross_leverage=0.0,
net_leverage=0.0,
long_exposure=0.0,
longs_count=0,
short_exposure=0.0,
shorts_count=0)
account = pp.as_account()
check_account(account,
settled_cash=1300.0,
equity_with_loan=1300.0,
total_positions_value=0.0,
total_positions_exposure=0.0,
regt_equity=1300.0,
available_funds=1300.0,
excess_liquidity=1300.0,
cushion=1.0,
leverage=0.0,
net_leverage=0.0,
net_liquidation=1300.0)
def test_cost_basis_calc(self):
self.create_environment_stuff(num_days=5)
history_args = (
self.asset1,
[10, 11, 11, 12, 10],
[100, 100, 100, 100, 100],
oneday,
self.sim_params,
self.env
)
trades = factory.create_trade_history(*history_args)
transactions = factory.create_txn_history(*history_args)[:4]
data_portal = create_data_portal_from_trade_history(
self.env,
self.instance_tmpdir,
self.sim_params,
{1: trades})
pt = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp = perf.PerformancePeriod(
1000.0,
self.env.asset_finder,
self.sim_params.data_frequency,
period_open=self.sim_params.period_start,
period_close=self.sim_params.trading_days[-1]
)
pp.position_tracker = pt
average_cost = 0
for i, txn in enumerate(transactions):
pt.execute_transaction(txn)
pp.handle_execution(txn)
average_cost = (average_cost * i + txn.price) / (i + 1)
self.assertEqual(pt.positions[1].cost_basis, average_cost)
dt = trades[-2].dt
self.assertEqual(
pt.positions[1].last_sale_price,
trades[-2].price,
"should have a last sale of 12, got {val}".format(
val=pt.positions[1].last_sale_price)
)
self.assertEqual(
pt.positions[1].cost_basis,
11,
"should have a cost basis of 11"
)
pt.sync_last_sale_prices(dt, False, data_portal)
pp.calculate_performance()
self.assertEqual(
pp.pnl,
400
)
down_tick = trades[-1]
sale_txn = create_txn(self.asset1, down_tick.dt, 10.0, -100)
pp.rollover()
pt.execute_transaction(sale_txn)
pp.handle_execution(sale_txn)
dt = down_tick.dt
pt.sync_last_sale_prices(dt, False, data_portal)
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(
pp.positions[1].cost_basis,
11,
"should have a cost basis of 11"
)
self.assertEqual(pp.pnl, -800, "this period goes from +400 to -400")
pt3 = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp3 = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
pp3.position_tracker = pt3
average_cost = 0
for i, txn in enumerate(transactions):
pt3.execute_transaction(txn)
pp3.handle_execution(txn)
average_cost = (average_cost * i + txn.price) / (i + 1)
self.assertEqual(pp3.positions[1].cost_basis, average_cost)
pt3.execute_transaction(sale_txn)
pp3.handle_execution(sale_txn)
trades.append(down_tick)
pt3.sync_last_sale_prices(trades[-1].dt, False, data_portal)
pp3.calculate_performance()
self.assertEqual(
pp3.positions[1].last_sale_price,
10,
"should have a last sale of 10"
)
self.assertEqual(
pp3.positions[1].cost_basis,
11,
"should have a cost basis of 11"
)
self.assertEqual(
pp3.pnl,
-400,
"should be -400 for all trades and transactions in period"
)
def test_cost_basis_calc_close_pos(self):
self.create_environment_stuff(num_days=8)
history_args = (
1,
[10, 9, 11, 8, 9, 12, 13, 14],
[200, -100, -100, 100, -300, 100, 500, 400],
oneday,
self.sim_params,
self.env
)
cost_bases = [10, 10, 0, 8, 9, 9, 13, 13.5]
transactions = factory.create_txn_history(*history_args)
pt = perf.PositionTracker(self.env.asset_finder,
self.sim_params.data_frequency)
pp = perf.PerformancePeriod(1000.0, self.env.asset_finder,
self.sim_params.data_frequency)
pp.position_tracker = pt
for idx, (txn, cb) in enumerate(zip(transactions, cost_bases)):
pt.execute_transaction(txn)
pp.handle_execution(txn)
if idx == 2:
# buy 200, sell 100, sell 100 = 0 shares = no position
self.assertNotIn(1, pp.positions)
else:
self.assertEqual(pp.positions[1].cost_basis, cb)
pp.calculate_performance()
self.assertEqual(pp.positions[1].cost_basis, cost_bases[-1])
class TestPositionTracker(WithTradingEnvironment,
WithInstanceTmpDir,
ZiplineTestCase):
ASSET_FINDER_EQUITY_SIDS = 1, 2
@classmethod
def make_futures_info(cls):
return pd.DataFrame.from_dict(
{
3: {'multiplier': 1000},
4: {'multiplier': 1000},
1032201401: {'multiplier': 50},
},
orient='index',
)
def test_empty_positions(self):
"""
make sure all the empty position stats return a numeric 0
Originally this bug was due to np.dot([], []) returning
np.bool_(False)
"""
sim_params = factory.create_simulation_parameters(
num_days=4, env=self.env
)
pt = perf.PositionTracker(self.env.asset_finder,
sim_params.data_frequency)
pos_stats = pt.stats()
stats = [
'net_value',
'net_exposure',
'gross_value',
'gross_exposure',
'short_value',
'short_exposure',
'shorts_count',
'long_value',
'long_exposure',
'longs_count',
]
for name in stats:
val = getattr(pos_stats, name)
self.assertEquals(val, 0)
self.assertNotIsInstance(val, (bool, np.bool_))
def test_position_values_and_exposures(self):
pt = perf.PositionTracker(self.env.asset_finder, None)
dt = pd.Timestamp("1984/03/06 3:00PM")
pos1 = perf.Position(1, amount=np.float64(10.0),
last_sale_date=dt, last_sale_price=10)
pos2 = perf.Position(2, amount=np.float64(-20.0),
last_sale_date=dt, last_sale_price=10)
pos3 = perf.Position(3, amount=np.float64(30.0),
last_sale_date=dt, last_sale_price=10)
pos4 = perf.Position(4, amount=np.float64(-40.0),
last_sale_date=dt, last_sale_price=10)
pt.update_positions({1: pos1, 2: pos2, 3: pos3, 4: pos4})
# Test long-only methods
pos_stats = pt.stats()
self.assertEqual(100, pos_stats.long_value)
self.assertEqual(100 + 300000, pos_stats.long_exposure)
self.assertEqual(2, pos_stats.longs_count)
# Test short-only methods
self.assertEqual(-200, pos_stats.short_value)
self.assertEqual(-200 - 400000, pos_stats.short_exposure)
self.assertEqual(2, pos_stats.shorts_count)
# Test gross and net values
self.assertEqual(100 + 200, pos_stats.gross_value)
self.assertEqual(100 - 200, pos_stats.net_value)
# Test gross and net exposures
self.assertEqual(100 + 200 + 300000 + 400000, pos_stats.gross_exposure)
self.assertEqual(100 - 200 + 300000 - 400000, pos_stats.net_exposure)
def test_update_positions(self):
pt = perf.PositionTracker(self.env.asset_finder, None)
dt = pd.Timestamp("2014/01/01 3:00PM")
pos1 = perf.Position(1, amount=np.float64(10.0),
last_sale_date=dt, last_sale_price=10)
pos2 = perf.Position(2, amount=np.float64(-20.0),
last_sale_date=dt, last_sale_price=10)
pos3 = perf.Position(1032201401, amount=np.float64(30.0),
last_sale_date=dt, last_sale_price=100)
# Call update_positions twice. When the second call is made,
# self.positions will already contain data. The order of this data
# needs to be preserved so that it is consistent with the order of the
# data stored in the multipliers OrderedDict()'s. If self.positions
# were to be stored as a dict, then its order could change in arbitrary
# ways when the second update_positions call is made. Hence we also
# store it as an OrderedDict.
pt.update_positions({1: pos1, 1032201401: pos3})
pt.update_positions({2: pos2})
pos_stats = pt.stats()
# Test long-only methods
self.assertEqual(100, pos_stats.long_value)
# 150,000 = 30 * 100 * 50 (amount * last_sale_price * multiplier)
self.assertEqual(100 + 150000, pos_stats.long_exposure)
self.assertEqual(2, pos_stats.longs_count)
# Test short-only methods
self.assertEqual(-200, pos_stats.short_value)
self.assertEqual(-200, pos_stats.short_exposure)
self.assertEqual(1, pos_stats.shorts_count)
# Test gross and net values
self.assertEqual(100 + 200, pos_stats.gross_value)
self.assertEqual(100 - 200, pos_stats.net_value)
# Test gross and net exposures
self.assertEqual(100 + 150000 + 200, pos_stats.gross_exposure)
self.assertEqual(100 + 150000 - 200, pos_stats.net_exposure)
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