mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-05 03:37:55 +08:00
Eddie Hebert
7a1a6ddb37
Instead of using the pandas.Series datetime index for every single vector, get the index at the beginning of the update loop based on the dt and then use that index to set the values. Also, since the dt lookup is no longer needed, store the values as numpy arrays, which are more lightweight. Locally, this patch cuts out about 60% of the time spent in the update method.
344 lines
11 KiB
Python
344 lines
11 KiB
Python
#
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# Copyright 2013 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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import unittest
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import datetime
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import pytz
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import numpy as np
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from zipline.finance.trading import SimulationParameters
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from zipline.finance import trading
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from zipline.algorithm import TradingAlgorithm
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from zipline.protocol import (
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Event,
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DATASOURCE_TYPE
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)
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class BuyAndHoldAlgorithm(TradingAlgorithm):
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SID_TO_BUY_AND_HOLD = 1
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def initialize(self):
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self.holding = False
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def handle_data(self, data):
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if not self.holding:
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self.order(self.sid(self.SID_TO_BUY_AND_HOLD), 100)
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self.holding = True
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class TestEventsThroughRisk(unittest.TestCase):
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def test_daily_buy_and_hold(self):
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start_date = datetime.datetime(
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year=2006,
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month=1,
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day=3,
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hour=0,
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minute=0,
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tzinfo=pytz.utc)
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end_date = datetime.datetime(
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year=2006,
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month=1,
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day=5,
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hour=0,
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minute=0,
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tzinfo=pytz.utc)
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sim_params = SimulationParameters(
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period_start=start_date,
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period_end=end_date,
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data_frequency='daily',
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emission_rate='daily'
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)
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algo = BuyAndHoldAlgorithm(
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identifiers=[1],
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sim_params=sim_params)
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first_date = datetime.datetime(2006, 1, 3, tzinfo=pytz.utc)
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second_date = datetime.datetime(2006, 1, 4, tzinfo=pytz.utc)
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third_date = datetime.datetime(2006, 1, 5, tzinfo=pytz.utc)
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trade_bar_data = [
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Event({
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'open_price': 10,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': first_date,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 15,
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'close_price': 20,
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'price': 20,
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'volume': 2000,
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'sid': 1,
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'dt': second_date,
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'source_id': 'test_list',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 20,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': third_date,
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'source_id': 'test_list',
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'type': DATASOURCE_TYPE.TRADE
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}),
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]
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benchmark_data = [
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Event({
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'returns': 0.1,
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'dt': first_date,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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Event({
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'returns': 0.2,
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'dt': second_date,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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Event({
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'returns': 0.4,
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'dt': third_date,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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]
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algo.benchmark_return_source = benchmark_data
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algo.set_sources(list([trade_bar_data]))
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gen = algo._create_generator(sim_params)
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# TODO: Hand derive these results.
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# Currently, the output from the time of this writing to
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# at least be an early warning against changes.
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expected_algorithm_returns = {
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first_date: 0.0,
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second_date: -0.000350,
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third_date: -0.050018
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}
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# TODO: Hand derive these results.
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# Currently, the output from the time of this writing to
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# at least be an early warning against changes.
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expected_sharpe = {
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first_date: np.nan,
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second_date: -22.322677,
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third_date: -9.353741
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}
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for bar in gen:
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current_dt = algo.datetime
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crm = algo.perf_tracker.cumulative_risk_metrics
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dt_loc = crm.cont_index.get_loc(current_dt)
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np.testing.assert_almost_equal(
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crm.algorithm_returns[dt_loc],
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expected_algorithm_returns[current_dt],
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decimal=6)
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np.testing.assert_almost_equal(
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crm.metrics.sharpe[current_dt],
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expected_sharpe[current_dt],
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decimal=6,
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err_msg="Mismatch at %s" % (current_dt,))
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def test_minute_buy_and_hold(self):
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with trading.TradingEnvironment():
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start_date = datetime.datetime(
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year=2006,
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month=1,
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day=3,
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hour=0,
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minute=0,
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tzinfo=pytz.utc)
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end_date = datetime.datetime(
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year=2006,
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month=1,
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day=5,
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hour=0,
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minute=0,
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tzinfo=pytz.utc)
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sim_params = SimulationParameters(
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period_start=start_date,
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period_end=end_date,
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emission_rate='daily',
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data_frequency='minute')
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algo = BuyAndHoldAlgorithm(
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identifiers=[1],
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sim_params=sim_params)
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first_date = datetime.datetime(2006, 1, 3, tzinfo=pytz.utc)
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first_open, first_close = \
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trading.environment.get_open_and_close(first_date)
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second_date = datetime.datetime(2006, 1, 4, tzinfo=pytz.utc)
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second_open, second_close = \
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trading.environment.get_open_and_close(second_date)
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third_date = datetime.datetime(2006, 1, 5, tzinfo=pytz.utc)
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third_open, third_close = \
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trading.environment.get_open_and_close(third_date)
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benchmark_data = [
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Event({
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'returns': 0.1,
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'dt': first_close,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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Event({
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'returns': 0.2,
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'dt': second_close,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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Event({
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'returns': 0.4,
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'dt': third_close,
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'source_id': 'test-benchmark-source',
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'type': DATASOURCE_TYPE.BENCHMARK
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}),
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]
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trade_bar_data = [
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Event({
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'open_price': 10,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': first_open,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 10,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': first_open + datetime.timedelta(minutes=10),
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 15,
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'close_price': 20,
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'price': 20,
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'volume': 2000,
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'sid': 1,
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'dt': second_open,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 15,
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'close_price': 20,
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'price': 20,
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'volume': 2000,
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'sid': 1,
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'dt': second_open + datetime.timedelta(minutes=10),
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 20,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': third_open,
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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Event({
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'open_price': 20,
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'close_price': 15,
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'price': 15,
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'volume': 1000,
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'sid': 1,
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'dt': third_open + datetime.timedelta(minutes=10),
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'source_id': 'test-trade-source',
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'type': DATASOURCE_TYPE.TRADE
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}),
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]
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algo.benchmark_return_source = benchmark_data
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algo.set_sources(list([trade_bar_data]))
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gen = algo._create_generator(sim_params)
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crm = algo.perf_tracker.cumulative_risk_metrics
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first_msg = next(gen)
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self.assertIsNotNone(first_msg,
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"There should be a message emitted.")
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# Protects against bug where the positions appeared to be
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# a day late, because benchmarks were triggering
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# calculations before the events for the day were
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# processed.
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self.assertEqual(1, len(algo.portfolio.positions), "There should "
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"be one position after the first day.")
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self.assertEquals(
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0,
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crm.metrics.algorithm_volatility[algo.datetime.date()],
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"On the first day algorithm volatility does not exist.")
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second_msg = next(gen)
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self.assertIsNotNone(second_msg, "There should be a message "
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"emitted.")
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self.assertEqual(1, len(algo.portfolio.positions),
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"Number of positions should stay the same.")
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# TODO: Hand derive. Current value is just a canary to
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# detect changes.
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np.testing.assert_almost_equal(
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0.050022510129558301,
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crm.algorithm_returns[-1],
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decimal=6)
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third_msg = next(gen)
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self.assertEqual(1, len(algo.portfolio.positions),
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"Number of positions should stay the same.")
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self.assertIsNotNone(third_msg, "There should be a message "
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"emitted.")
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# TODO: Hand derive. Current value is just a canary to
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# detect changes.
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np.testing.assert_almost_equal(
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-0.047639464532418657,
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crm.algorithm_returns[-1],
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decimal=6)
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