mirror of
https://github.com/wassname/catalyst.git
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Conner Fromknecht
2171 lines
76 KiB
Python
2171 lines
76 KiB
Python
#
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# Copyright 2016 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import division
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import copy
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from datetime import (
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datetime,
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timedelta,
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)
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import logging
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import nose.tools as nt
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import pytz
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import pandas as pd
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import numpy as np
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from six.moves import range, zip
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from catalyst.assets import Asset
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from catalyst.assets.synthetic import make_simple_equity_info
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from catalyst.data.us_equity_pricing import (
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SQLiteAdjustmentWriter,
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SQLiteAdjustmentReader,
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)
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import catalyst.utils.factory as factory
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import catalyst.finance.performance as perf
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from catalyst.finance.transaction import create_transaction
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import catalyst.utils.math_utils as zp_math
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from catalyst.finance.blotter import Order
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from catalyst.finance.performance.position import Position
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from catalyst.utils.factory import create_simulation_parameters
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from catalyst.utils.serialization_utils import (
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loads_with_persistent_ids, dumps_with_persistent_ids
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)
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from catalyst.testing import (
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MockDailyBarReader,
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create_data_portal_from_trade_history,
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create_empty_splits_mergers_frame,
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tmp_trading_env,
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)
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from catalyst.testing.fixtures import (
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WithInstanceTmpDir,
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WithSimParams,
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WithTmpDir,
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WithTradingEnvironment,
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ZiplineTestCase,
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)
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from catalyst.utils.calendars import get_calendar
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logger = logging.getLogger('Test Perf Tracking')
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oneday = timedelta(days=1)
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tradingday = timedelta(hours=6, minutes=30)
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# nose.tools changed name in python 3
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if not hasattr(nt, 'assert_count_equal'):
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nt.assert_count_equal = nt.assert_items_equal
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def check_perf_period(pp,
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gross_leverage,
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net_leverage,
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long_exposure,
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longs_count,
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short_exposure,
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shorts_count):
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perf_data = pp.to_dict()
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np.testing.assert_allclose(
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gross_leverage, perf_data['gross_leverage'], rtol=1e-3)
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np.testing.assert_allclose(
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net_leverage, perf_data['net_leverage'], rtol=1e-3)
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np.testing.assert_allclose(
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long_exposure, perf_data['long_exposure'], rtol=1e-3)
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np.testing.assert_allclose(
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longs_count, perf_data['longs_count'], rtol=1e-3)
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np.testing.assert_allclose(
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short_exposure, perf_data['short_exposure'], rtol=1e-3)
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np.testing.assert_allclose(
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shorts_count, perf_data['shorts_count'], rtol=1e-3)
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def check_account(account,
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settled_cash,
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equity_with_loan,
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total_positions_value,
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total_positions_exposure,
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regt_equity,
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available_funds,
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excess_liquidity,
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cushion,
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leverage,
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net_leverage,
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net_liquidation):
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# this is a long only portfolio that is only partially invested
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# so net and gross leverage are equal.
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np.testing.assert_allclose(settled_cash,
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account.settled_cash, rtol=1e-3)
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np.testing.assert_allclose(equity_with_loan,
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account.equity_with_loan, rtol=1e-3)
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np.testing.assert_allclose(total_positions_value,
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account.total_positions_value, rtol=1e-3)
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np.testing.assert_allclose(total_positions_exposure,
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account.total_positions_exposure, rtol=1e-3)
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np.testing.assert_allclose(regt_equity,
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account.regt_equity, rtol=1e-3)
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np.testing.assert_allclose(available_funds,
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account.available_funds, rtol=1e-3)
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np.testing.assert_allclose(excess_liquidity,
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account.excess_liquidity, rtol=1e-3)
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np.testing.assert_allclose(cushion,
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account.cushion, rtol=1e-3)
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np.testing.assert_allclose(leverage, account.leverage, rtol=1e-3)
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np.testing.assert_allclose(net_leverage,
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account.net_leverage, rtol=1e-3)
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np.testing.assert_allclose(net_liquidation,
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account.net_liquidation, rtol=1e-3)
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def create_txn(asset, dt, price, amount):
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"""
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Create a fake transaction to be filled and processed prior to the execution
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of a given trade event.
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"""
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if not isinstance(asset, Asset):
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raise ValueError("pass an asset to create_txn")
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mock_order = Order(dt, asset, amount, id=None)
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return create_transaction(mock_order, dt, price, amount)
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def calculate_results(sim_params,
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env,
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data_portal,
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splits=None,
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txns=None,
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commissions=None):
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"""
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Run the given events through a stripped down version of the loop in
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AlgorithmSimulator.transform.
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IMPORTANT NOTE FOR TEST WRITERS/READERS:
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This loop has some wonky logic for the order of event processing for
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datasource types. This exists mostly to accommodate legacy tests that were
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making assumptions about how events would be sorted.
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In particular:
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- Dividends passed for a given date are processed PRIOR to any events
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for that date.
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- Splits passed for a given date are process AFTER any events for that
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date.
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Tests that use this helper should not be considered useful guarantees of
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the behavior of AlgorithmSimulator on a stream containing the same events
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unless the subgroups have been explicitly re-sorted in this way.
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"""
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txns = txns or []
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splits = splits or {}
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commissions = commissions or {}
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perf_tracker = perf.PerformanceTracker(
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sim_params, get_calendar("NYSE"), env
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)
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results = []
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for date in sim_params.sessions:
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for txn in filter(lambda txn: txn.dt == date, txns):
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# Process txns for this date.
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perf_tracker.process_transaction(txn)
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try:
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commissions_for_date = commissions[date]
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for comm in commissions_for_date:
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perf_tracker.process_commission(comm)
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except KeyError:
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pass
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try:
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splits_for_date = splits[date]
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perf_tracker.handle_splits(splits_for_date)
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except KeyError:
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pass
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msg = perf_tracker.handle_market_close(date, data_portal)
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perf_tracker.position_tracker.sync_last_sale_prices(
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date, False, data_portal,
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)
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msg['account'] = perf_tracker.get_account(True)
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results.append(copy.deepcopy(msg))
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return results
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def check_perf_tracker_serialization(perf_tracker):
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scalar_keys = [
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'emission_rate',
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'txn_count',
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'market_open',
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'last_close',
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'start_session',
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'day_count',
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'capital_base',
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'market_close',
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'saved_dt',
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'period_end',
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'total_days',
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]
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p_string = dumps_with_persistent_ids(perf_tracker)
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test = loads_with_persistent_ids(p_string, env=perf_tracker.env)
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for k in scalar_keys:
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nt.assert_equal(getattr(test, k), getattr(perf_tracker, k), k)
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perf_periods = (
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test.cumulative_performance,
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test.todays_performance
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)
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for period in perf_periods:
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nt.assert_true(hasattr(period, '_position_tracker'))
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def setup_env_data(env, sim_params, sids, futures_sids=[]):
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data = {}
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for sid in sids:
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data[sid] = {
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"start_date": sim_params.sessions[0],
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"end_date": get_calendar("NYSE").next_session_label(
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sim_params.sessions[-1]
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)
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}
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env.write_data(equities_data=data)
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futures_data = {}
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for future_sid in futures_sids:
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futures_data[future_sid] = {
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"start_date": sim_params.sessions[0],
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# (obviously) FIXME once we have a future calendar
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"end_date": get_calendar("NYSE").next_session_label(
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sim_params.sessions[-1]
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),
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"multiplier": 100
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}
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env.write_data(futures_data=futures_data)
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class TestSplitPerformance(WithSimParams, WithTmpDir, ZiplineTestCase):
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START_DATE = pd.Timestamp('2006-01-03', tz='utc')
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END_DATE = pd.Timestamp('2006-01-04', tz='utc')
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SIM_PARAMS_CAPITAL_BASE = 10e3
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ASSET_FINDER_EQUITY_SIDS = 1, 2
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@classmethod
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def init_class_fixtures(cls):
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super(TestSplitPerformance, cls).init_class_fixtures()
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cls.asset1 = cls.env.asset_finder.retrieve_asset(1)
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cls.asset2 = cls.env.asset_finder.retrieve_asset(2)
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def test_multiple_splits(self):
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# if multiple positions all have splits at the same time, verify that
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# the total leftover cash is correct
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perf_tracker = perf.PerformanceTracker(self.sim_params,
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self.trading_calendar,
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self.env)
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perf_tracker.position_tracker.positions[1] = \
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Position(self.asset1, amount=10, cost_basis=10, last_sale_price=11)
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perf_tracker.position_tracker.positions[2] = \
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Position(self.asset2, amount=10, cost_basis=10, last_sale_price=11)
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leftover_cash = perf_tracker.position_tracker.handle_splits(
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[(self.asset1, 0.333), (self.asset2, 0.333)]
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)
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# we used to have 10 shares that each cost us $10, total $100
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# now we have 33 shares that each cost us $3.33, total $99.9
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# each position returns $0.10 as leftover cash
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self.assertEqual(0.2, leftover_cash)
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def test_split_long_position(self):
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events = factory.create_trade_history(
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self.asset1,
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# TODO: Should we provide adjusted prices in the tests, or provide
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# raw prices and adjust via DataPortal?
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[20, 60],
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[100, 100],
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oneday,
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self.sim_params,
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trading_calendar=self.trading_calendar,
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)
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# set up a long position in sid 1
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# 100 shares at $20 apiece = $2000 position
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data_portal = create_data_portal_from_trade_history(
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self.env.asset_finder,
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self.trading_calendar,
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self.tmpdir,
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self.sim_params,
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{1: events},
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)
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txns = [create_txn(self.asset1, events[0].dt, 20, 100)]
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# set up a split with ratio 3 occurring at the start of the second
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# day.
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splits = {
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events[1].dt: [(self.asset1, 3)]
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}
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results = calculate_results(self.sim_params,
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self.env,
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data_portal,
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txns=txns,
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splits=splits)
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# should have 33 shares (at $60 apiece) and $20 in cash
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self.assertEqual(2, len(results))
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latest_positions = results[1]['daily_perf']['positions']
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self.assertEqual(1, len(latest_positions))
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# check the last position to make sure it's been updated
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position = latest_positions[0]
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self.assertEqual(self.asset1, position['sid'])
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self.assertEqual(33, position['amount'])
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self.assertEqual(60, position['cost_basis'])
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self.assertEqual(60, position['last_sale_price'])
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# since we started with $10000, and we spent $2000 on the
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# position, but then got $20 back, we should have $8020
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# (or close to it) in cash.
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# we won't get exactly 8020 because sometimes a split is
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# denoted as a ratio like 0.3333, and we lose some digits
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# of precision. thus, make sure we're pretty close.
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daily_perf = results[1]['daily_perf']
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self.assertTrue(
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zp_math.tolerant_equals(8020,
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daily_perf['ending_cash'], 1),
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"ending_cash was {0}".format(daily_perf['ending_cash']))
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# Validate that the account attributes were updated.
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account = results[1]['account']
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self.assertEqual(float('inf'), account.day_trades_remaining)
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# this is a long only portfolio that is only partially invested
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# so net and gross leverage are equal.
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np.testing.assert_allclose(0.198, account.leverage, rtol=1e-3)
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np.testing.assert_allclose(0.198, account.net_leverage, rtol=1e-3)
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np.testing.assert_allclose(8020, account.regt_equity, rtol=1e-3)
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self.assertEqual(float('inf'), account.regt_margin)
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np.testing.assert_allclose(8020, account.available_funds, rtol=1e-3)
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self.assertEqual(0, account.maintenance_margin_requirement)
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np.testing.assert_allclose(10000,
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account.equity_with_loan, rtol=1e-3)
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self.assertEqual(float('inf'), account.buying_power)
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self.assertEqual(0, account.initial_margin_requirement)
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np.testing.assert_allclose(8020, account.excess_liquidity,
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rtol=1e-3)
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np.testing.assert_allclose(8020, account.settled_cash, rtol=1e-3)
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np.testing.assert_allclose(10000, account.net_liquidation,
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rtol=1e-3)
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np.testing.assert_allclose(0.802, account.cushion, rtol=1e-3)
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np.testing.assert_allclose(1980, account.total_positions_value,
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rtol=1e-3)
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self.assertEqual(0, account.accrued_interest)
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for i, result in enumerate(results):
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for perf_kind in ('daily_perf', 'cumulative_perf'):
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perf_result = result[perf_kind]
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# prices aren't changing, so pnl and returns should be 0.0
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self.assertEqual(0.0, perf_result['pnl'],
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"day %s %s pnl %s instead of 0.0" %
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(i, perf_kind, perf_result['pnl']))
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self.assertEqual(0.0, perf_result['returns'],
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"day %s %s returns %s instead of 0.0" %
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(i, perf_kind, perf_result['returns']))
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class TestDividendPerformance(WithSimParams,
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WithInstanceTmpDir,
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ZiplineTestCase):
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START_DATE = pd.Timestamp('2006-01-03', tz='utc')
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END_DATE = pd.Timestamp('2006-01-10', tz='utc')
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ASSET_FINDER_EQUITY_SIDS = 1, 2
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SIM_PARAMS_CAPITAL_BASE = 10e3
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@classmethod
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def init_class_fixtures(cls):
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super(TestDividendPerformance, cls).init_class_fixtures()
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cls.asset1 = cls.asset_finder.retrieve_asset(1)
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cls.asset2 = cls.asset_finder.retrieve_asset(2)
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def test_market_hours_calculations(self):
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# DST in US/Eastern began on Sunday March 14, 2010
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before = datetime(2010, 3, 12, 14, 31, tzinfo=pytz.utc)
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after = factory.get_next_trading_dt(
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before,
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timedelta(days=1),
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self.trading_calendar,
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)
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self.assertEqual(after.hour, 13)
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def test_long_position_receives_dividend(self):
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# post some trades in the market
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events = factory.create_trade_history(
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self.asset1,
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[10, 10, 10, 10, 10, 10],
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[100, 100, 100, 100, 100, 100],
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oneday,
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self.sim_params,
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trading_calendar=self.trading_calendar,
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)
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dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
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writer = SQLiteAdjustmentWriter(
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dbpath,
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MockDailyBarReader(),
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self.trading_calendar.all_sessions,
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)
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splits = mergers = create_empty_splits_mergers_frame()
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dividends = pd.DataFrame({
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'sid': np.array([1], dtype=np.uint32),
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'amount': np.array([10.00], dtype=np.float64),
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'declared_date': np.array([events[0].dt], dtype='datetime64[ns]'),
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'ex_date': np.array([events[1].dt], dtype='datetime64[ns]'),
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'record_date': np.array([events[1].dt], dtype='datetime64[ns]'),
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'pay_date': np.array([events[2].dt], dtype='datetime64[ns]'),
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})
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writer.write(splits, mergers, dividends)
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adjustment_reader = SQLiteAdjustmentReader(dbpath)
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data_portal = create_data_portal_from_trade_history(
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self.env.asset_finder,
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self.trading_calendar,
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self.instance_tmpdir,
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self.sim_params,
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{1: events},
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)
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data_portal._adjustment_reader = adjustment_reader
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# Simulate a transaction being filled prior to the ex_date.
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txns = [create_txn(self.asset1, events[0].dt, 10.0, 100)]
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results = calculate_results(
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self.sim_params,
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self.env,
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data_portal,
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txns=txns,
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)
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self.assertEqual(len(results), 6)
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cumulative_returns = \
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[event['cumulative_perf']['returns'] for event in results]
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self.assertEqual(cumulative_returns, [0.0, 0.0, 0.1, 0.1, 0.1, 0.1])
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daily_returns = [event['daily_perf']['returns']
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for event in results]
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self.assertEqual(daily_returns, [0.0, 0.0, 0.10, 0.0, 0.0, 0.0])
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cash_flows = [event['daily_perf']['capital_used']
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for event in results]
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self.assertEqual(cash_flows, [-1000, 0, 1000, 0, 0, 0])
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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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
|
|
|
|
writer = SQLiteAdjustmentWriter(
|
|
dbpath,
|
|
MockDailyBarReader(),
|
|
self.trading_calendar.all_sessions
|
|
)
|
|
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.asset_finder,
|
|
self.trading_calendar,
|
|
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,
|
|
trading_calendar=self.trading_calendar
|
|
)
|
|
|
|
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
|
|
|
|
writer = SQLiteAdjustmentWriter(
|
|
dbpath,
|
|
MockDailyBarReader(),
|
|
self.trading_calendar.all_sessions
|
|
)
|
|
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.asset_finder,
|
|
self.trading_calendar,
|
|
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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
|
|
|
|
writer = SQLiteAdjustmentWriter(
|
|
dbpath,
|
|
MockDailyBarReader(),
|
|
self.trading_calendar.all_sessions,
|
|
)
|
|
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.asset_finder,
|
|
self.trading_calendar,
|
|
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):
|
|
# need a six-day simparam
|
|
|
|
# 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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
|
|
|
|
writer = SQLiteAdjustmentWriter(
|
|
dbpath,
|
|
MockDailyBarReader(),
|
|
self.trading_calendar.all_sessions,
|
|
)
|
|
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.asset_finder,
|
|
self.trading_calendar,
|
|
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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
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.trading_calendar)
|
|
|
|
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
|
|
|
|
writer = SQLiteAdjustmentWriter(
|
|
dbpath,
|
|
MockDailyBarReader(),
|
|
self.trading_calendar.all_sessions,
|
|
)
|
|
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.asset_finder,
|
|
self.trading_calendar,
|
|
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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
|
|
|
|
writer = SQLiteAdjustmentWriter(
|
|
dbpath,
|
|
MockDailyBarReader(),
|
|
self.trading_calendar.all_sessions,
|
|
)
|
|
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.asset_finder,
|
|
self.trading_calendar,
|
|
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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
|
|
|
|
writer = SQLiteAdjustmentWriter(
|
|
dbpath,
|
|
MockDailyBarReader(),
|
|
self.trading_calendar.all_sessions,
|
|
)
|
|
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.asset_finder,
|
|
self.trading_calendar,
|
|
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, 10],
|
|
[100, 100, 100, 100, 100, 100],
|
|
oneday,
|
|
self.sim_params,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
dbpath = self.instance_tmpdir.getpath('adjustments.sqlite')
|
|
|
|
writer = SQLiteAdjustmentWriter(
|
|
dbpath,
|
|
MockDailyBarReader(),
|
|
self.trading_calendar.all_sessions,
|
|
)
|
|
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.trading_calendar.next_session_label(
|
|
self.trading_calendar.minute_to_session_label(
|
|
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.start_session,
|
|
end=self.sim_params.end_session
|
|
)
|
|
|
|
sim_params = sim_params.create_new(
|
|
sim_params.start_session,
|
|
events[-1].dt
|
|
)
|
|
|
|
data_portal = create_data_portal_from_trade_history(
|
|
self.env.asset_finder,
|
|
self.trading_calendar,
|
|
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), 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, [-1000, 0, 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, -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,
|
|
WithTradingEnvironment,
|
|
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,
|
|
'exchange': "TEST",
|
|
}
|
|
for sid in futures_sids
|
|
},
|
|
orient='index',
|
|
)
|
|
self.env = self.enter_instance_context(tmp_trading_env(
|
|
equities=equities,
|
|
futures=futures,
|
|
load=self.make_load_function(),
|
|
))
|
|
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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
trades_2 = factory.create_trade_history(
|
|
self.asset2,
|
|
[10, 10, 10, 11],
|
|
[100, 100, 100, 100],
|
|
oneday,
|
|
self.sim_params,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
data_portal = create_data_portal_from_trade_history(
|
|
self.env.asset_finder,
|
|
self.trading_calendar,
|
|
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.sim_params.data_frequency)
|
|
pp = perf.PerformancePeriod(1000.0, 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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
data_portal = create_data_portal_from_trade_history(
|
|
self.env.asset_finder,
|
|
self.trading_calendar,
|
|
self.instance_tmpdir,
|
|
self.sim_params,
|
|
{1: trades})
|
|
txn = create_txn(self.asset1, trades[1].dt, 10.0, 1000)
|
|
pt = perf.PositionTracker(self.sim_params.data_frequency)
|
|
pp = perf.PerformancePeriod(1000.0, 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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
data_portal = create_data_portal_from_trade_history(
|
|
self.env.asset_finder,
|
|
self.trading_calendar,
|
|
self.instance_tmpdir,
|
|
self.sim_params,
|
|
{1: trades})
|
|
txn = create_txn(self.asset1, trades[1].dt, 10.0, 100)
|
|
pt = perf.PositionTracker(self.sim_params.data_frequency)
|
|
pp = perf.PerformancePeriod(1000.0,
|
|
self.sim_params.data_frequency,
|
|
period_open=self.sim_params.start_session,
|
|
period_close=self.sim_params.end_session)
|
|
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.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].asset,
|
|
txn.asset,
|
|
"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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
trades_1 = trades[:-2]
|
|
|
|
data_portal = create_data_portal_from_trade_history(
|
|
self.env.asset_finder,
|
|
self.trading_calendar,
|
|
self.instance_tmpdir,
|
|
self.sim_params,
|
|
{1: trades})
|
|
|
|
txn = create_txn(self.asset1, trades[1].dt, 10.0, -100)
|
|
pt = perf.PositionTracker(self.sim_params.data_frequency)
|
|
pp = perf.PerformancePeriod(1000.0, 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.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].asset,
|
|
txn.asset,
|
|
"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.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].asset,
|
|
txn.asset,
|
|
"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.sim_params.data_frequency)
|
|
ppTotal = perf.PerformancePeriod(
|
|
1000.0, 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.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].asset,
|
|
txn.asset,
|
|
"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_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,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
data_portal = create_data_portal_from_trade_history(
|
|
self.env.asset_finder,
|
|
self.trading_calendar,
|
|
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.sim_params.data_frequency)
|
|
pp = perf.PerformancePeriod(1000.0, 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.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.trading_calendar,
|
|
)
|
|
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.asset_finder,
|
|
self.trading_calendar,
|
|
self.instance_tmpdir,
|
|
self.sim_params,
|
|
{1: trades})
|
|
|
|
pt = perf.PositionTracker(self.sim_params.data_frequency)
|
|
pp = perf.PerformancePeriod(
|
|
1000.0,
|
|
self.sim_params.data_frequency,
|
|
period_open=self.sim_params.start_session,
|
|
period_close=self.sim_params.sessions[-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.sim_params.data_frequency)
|
|
pp3 = perf.PerformancePeriod(1000.0, 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 = (
|
|
self.asset1,
|
|
[10, 9, 11, 8, 9, 12, 13, 14],
|
|
[200, -100, -100, 100, -300, 100, 500, 400],
|
|
oneday,
|
|
self.sim_params,
|
|
self.trading_calendar,
|
|
)
|
|
cost_bases = [10, 10, 0, 8, 9, 9, 13, 13.5]
|
|
|
|
transactions = factory.create_txn_history(*history_args)
|
|
|
|
pt = perf.PositionTracker(self.sim_params.data_frequency)
|
|
pp = perf.PerformancePeriod(1000.0, 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])
|
|
|
|
def test_capital_change_intra_period(self):
|
|
self.create_environment_stuff()
|
|
|
|
# post some trades in the market
|
|
trades = factory.create_trade_history(
|
|
self.asset1,
|
|
[10.0, 11.0, 12.0, 13.0],
|
|
[100, 100, 100, 100],
|
|
oneday,
|
|
self.sim_params,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
data_portal = create_data_portal_from_trade_history(
|
|
self.env.asset_finder,
|
|
self.trading_calendar,
|
|
self.instance_tmpdir,
|
|
self.sim_params,
|
|
{1: trades})
|
|
txn = create_txn(self.asset1, trades[0].dt, 10.0, 100)
|
|
pt = perf.PositionTracker(self.sim_params.data_frequency)
|
|
pp = perf.PerformancePeriod(1000.0,
|
|
self.sim_params.data_frequency,
|
|
period_open=self.sim_params.start_session,
|
|
period_close=self.sim_params.end_session)
|
|
pp.position_tracker = pt
|
|
|
|
pt.execute_transaction(txn)
|
|
pp.handle_execution(txn)
|
|
|
|
# sync prices before we introduce a capital change
|
|
pt.sync_last_sale_prices(trades[2].dt, False, data_portal)
|
|
|
|
pp.initialize_subperiod_divider()
|
|
pp.set_current_subperiod_starting_values(1000.0)
|
|
|
|
pt.sync_last_sale_prices(trades[-1].dt, False, data_portal)
|
|
pp.calculate_performance()
|
|
|
|
self.assertAlmostEqual(pp.returns, 1200/1000 * 2300/2200 - 1)
|
|
self.assertAlmostEqual(pp.pnl, 300)
|
|
self.assertAlmostEqual(pp.cash_flow, -1000)
|
|
|
|
def test_capital_change_inter_period(self):
|
|
self.create_environment_stuff()
|
|
|
|
# post some trades in the market
|
|
trades = factory.create_trade_history(
|
|
self.asset1,
|
|
[10.0, 11.0, 12.0, 13.0],
|
|
[100, 100, 100, 100],
|
|
oneday,
|
|
self.sim_params,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
data_portal = create_data_portal_from_trade_history(
|
|
self.env.asset_finder,
|
|
self.trading_calendar,
|
|
self.instance_tmpdir,
|
|
self.sim_params,
|
|
{1: trades})
|
|
txn = create_txn(self.asset1, trades[0].dt, 10.0, 100)
|
|
pt = perf.PositionTracker(self.sim_params.data_frequency)
|
|
pp = perf.PerformancePeriod(1000.0,
|
|
self.sim_params.data_frequency,
|
|
period_open=self.sim_params.start_session,
|
|
period_close=self.sim_params.end_session)
|
|
pp.position_tracker = pt
|
|
|
|
pt.execute_transaction(txn)
|
|
pp.handle_execution(txn)
|
|
pt.sync_last_sale_prices(trades[0].dt, False, data_portal)
|
|
pp.calculate_performance()
|
|
self.assertAlmostEqual(pp.returns, 0)
|
|
self.assertAlmostEqual(pp.pnl, 0)
|
|
self.assertAlmostEqual(pp.cash_flow, -1000)
|
|
pp.rollover()
|
|
|
|
pt.sync_last_sale_prices(trades[1].dt, False, data_portal)
|
|
pp.calculate_performance()
|
|
self.assertAlmostEqual(pp.returns, 1100.0/1000.0 - 1)
|
|
self.assertAlmostEqual(pp.pnl, 100)
|
|
self.assertAlmostEqual(pp.cash_flow, 0)
|
|
pp.rollover()
|
|
|
|
pp.adjust_period_starting_capital(1000)
|
|
pt.sync_last_sale_prices(trades[2].dt, False, data_portal)
|
|
pp.calculate_performance()
|
|
self.assertAlmostEqual(pp.returns, 2200.0/2100.0 - 1)
|
|
self.assertAlmostEqual(pp.pnl, 100)
|
|
self.assertAlmostEqual(pp.cash_flow, 0)
|
|
pp.rollover()
|
|
|
|
pt.sync_last_sale_prices(trades[3].dt, False, data_portal)
|
|
pp.calculate_performance()
|
|
self.assertAlmostEqual(pp.returns, 2300.0/2200.0 - 1)
|
|
self.assertAlmostEqual(pp.pnl, 100)
|
|
self.assertAlmostEqual(pp.cash_flow, 0)
|
|
|
|
|
|
class TestPositionTracker(WithTradingEnvironment,
|
|
WithInstanceTmpDir,
|
|
ZiplineTestCase):
|
|
ASSET_FINDER_EQUITY_SIDS = 1, 2
|
|
|
|
@classmethod
|
|
def init_class_fixtures(cls):
|
|
super(TestPositionTracker, cls).init_class_fixtures()
|
|
|
|
cls.EQUITY1 = cls.asset_finder.retrieve_asset(1)
|
|
cls.EQUITY2 = cls.asset_finder.retrieve_asset(2)
|
|
cls.FUTURE3 = cls.asset_finder.retrieve_asset(3)
|
|
cls.FUTURE4 = cls.asset_finder.retrieve_asset(4)
|
|
cls.FUTURE5 = cls.asset_finder.retrieve_asset(1032201401)
|
|
|
|
@classmethod
|
|
def make_futures_info(cls):
|
|
return pd.DataFrame.from_dict(
|
|
{
|
|
3: {'multiplier': 1000, 'exchange': 'TEST'},
|
|
4: {'multiplier': 1000, 'exchange': 'TEST'},
|
|
1032201401: {'multiplier': 50, 'exchange': 'TEST'},
|
|
},
|
|
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)
|
|
"""
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|
sim_params = factory.create_simulation_parameters(num_days=4)
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|
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pt = perf.PositionTracker(sim_params.data_frequency)
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|
pos_stats = pt.stats()
|
|
|
|
stats = [
|
|
'net_value',
|
|
'net_exposure',
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|
'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)
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|
self.assertEquals(val, 0)
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|
self.assertNotIsInstance(val, (bool, np.bool_))
|
|
|
|
def test_position_values_and_exposures(self):
|
|
pt = perf.PositionTracker(None)
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|
dt = pd.Timestamp("1984/03/06 3:00PM")
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|
pt.update_position(
|
|
self.EQUITY1, amount=np.float64(10.0),
|
|
last_sale_date=dt, last_sale_price=10
|
|
)
|
|
|
|
pt.update_position(
|
|
self.EQUITY2, amount=np.float64(-20.0),
|
|
last_sale_date=dt, last_sale_price=10
|
|
)
|
|
|
|
pt.update_position(
|
|
self.FUTURE3, amount=np.float64(30.0),
|
|
last_sale_date=dt, last_sale_price=10
|
|
)
|
|
|
|
pt.update_position(
|
|
self.FUTURE4, amount=np.float64(-40.0),
|
|
last_sale_date=dt, last_sale_price=10
|
|
)
|
|
|
|
# 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_cost_basis(self):
|
|
dt = pd.Timestamp("2015-12-10 15:00", tz='UTC')
|
|
|
|
equity_pos = perf.Position(
|
|
self.EQUITY1,
|
|
amount=10,
|
|
last_sale_date=dt,
|
|
cost_basis=10,
|
|
last_sale_price=11,
|
|
)
|
|
|
|
future_pos = perf.Position(
|
|
self.FUTURE3,
|
|
amount=10,
|
|
last_sale_date=dt,
|
|
cost_basis=10,
|
|
last_sale_price=11,
|
|
)
|
|
|
|
self.assertEqual(10, equity_pos.cost_basis)
|
|
|
|
# send a $5 commission to the equity position. Spread out over 10
|
|
# shares, that bumps the cost basis by $0.50.
|
|
equity_pos.adjust_commission_cost_basis(self.EQUITY1, 5)
|
|
self.assertEqual(10.5, equity_pos.cost_basis)
|
|
|
|
self.assertEqual(10, future_pos.cost_basis)
|
|
|
|
# send a $5k commission to the futures position. since self.FUTURE3
|
|
# has a contract size (multipler) of 1000, this should result in a
|
|
# $10.5 updated cost basis. (5000 / 1000 = $5, spread out over 10
|
|
# contracts, is $0.50 extra per contract).
|
|
future_pos.adjust_commission_cost_basis(self.FUTURE3, 5000)
|
|
self.assertEqual(10.5, future_pos.cost_basis)
|
|
|
|
def test_update_positions(self):
|
|
pt = perf.PositionTracker(None)
|
|
dt = pd.Timestamp("2014/01/01 3:00PM")
|
|
# pos1 = perf.Position(self.EQUITY1, amount=np.float64(10.0),
|
|
# last_sale_date=dt, last_sale_price=10)
|
|
# pos2 = perf.Position(self.EQUITY2, amount=np.float64(-20.0),
|
|
# last_sale_date=dt, last_sale_price=10)
|
|
# pos3 = perf.Position(self.FUTURE5, amount=np.float64(30.0),
|
|
# last_sale_date=dt, last_sale_price=100)
|
|
|
|
pt.update_position(
|
|
self.EQUITY1,
|
|
amount=np.float64(10.0),
|
|
last_sale_price=10,
|
|
last_sale_date=dt
|
|
)
|
|
|
|
pt.update_position(
|
|
self.EQUITY2,
|
|
amount=np.float64(-20.0),
|
|
last_sale_price=10,
|
|
last_sale_date=dt
|
|
)
|
|
|
|
pt.update_position(
|
|
self.FUTURE5,
|
|
amount=np.float64(30.0),
|
|
last_sale_price=100,
|
|
last_sale_date=dt
|
|
)
|
|
|
|
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)
|
|
|
|
def test_close_position(self):
|
|
pt = perf.PositionTracker(None)
|
|
dt = pd.Timestamp('2017/01/04 3:00PM')
|
|
|
|
pt.update_position(
|
|
asset=self.FUTURE5, amount=np.float64(30.0),
|
|
last_sale_date=dt, last_sale_price=100
|
|
)
|
|
|
|
pt.update_position(
|
|
asset=self.EQUITY1, amount=np.float64(10.0),
|
|
last_sale_date=dt, last_sale_price=10
|
|
)
|
|
|
|
txn = create_txn(self.FUTURE5, dt, 100, -30)
|
|
pt.execute_transaction(txn)
|
|
|
|
pos_stats = pt.stats()
|
|
|
|
# Test long-only methods.
|
|
self.assertEqual(100, pos_stats.long_value)
|
|
self.assertEqual(100, pos_stats.long_exposure)
|
|
self.assertEqual(1, pos_stats.longs_count)
|
|
|
|
# Test short-only methods.
|
|
self.assertEqual(0, pos_stats.short_value)
|
|
self.assertEqual(0, pos_stats.short_exposure)
|
|
self.assertEqual(0, pos_stats.shorts_count)
|
|
|
|
# Test gross and net values.
|
|
self.assertEqual(100, pos_stats.gross_value)
|
|
self.assertEqual(100, pos_stats.net_value)
|
|
|
|
# Test gross and net exposures.
|
|
self.assertEqual(100, pos_stats.gross_exposure)
|
|
self.assertEqual(100, pos_stats.net_exposure)
|