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https://github.com/wassname/catalyst.git
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jfkirk
648 lines
21 KiB
Python
648 lines
21 KiB
Python
"""
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Test case definitions for history tests.
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"""
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import pandas as pd
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import numpy as np
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from zipline.finance.trading import TradingEnvironment, noop_load
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from zipline.history.history import HistorySpec
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from zipline.protocol import BarData
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from zipline.utils.test_utils import to_utc
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_cases_env = TradingEnvironment(load=noop_load)
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def mixed_frequency_expected_index(count, frequency):
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"""
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Helper for enumerating expected indices for test_mixed_frequency.
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"""
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minute = MIXED_FREQUENCY_MINUTES[count]
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if frequency == '1d':
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return [_cases_env.previous_open_and_close(minute)[1], minute]
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elif frequency == '1m':
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return [_cases_env.previous_market_minute(minute), minute]
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def mixed_frequency_expected_data(count, frequency):
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"""
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Helper for enumerating expected data test_mixed_frequency.
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"""
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if frequency == '1d':
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# First day of this test is July 3rd, which is a half day.
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if count < 210:
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return [np.nan, count]
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else:
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return [209, count]
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elif frequency == '1m':
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if count == 0:
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return [np.nan, count]
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else:
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return [count - 1, count]
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MIXED_FREQUENCY_MINUTES = _cases_env.market_minute_window(
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to_utc('2013-07-03 9:31AM'), 600,
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)
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ONE_MINUTE_PRICE_ONLY_SPECS = [
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HistorySpec(1, '1m', 'price', True, _cases_env, data_frequency='minute'),
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]
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DAILY_OPEN_CLOSE_SPECS = [
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HistorySpec(3, '1d', 'open_price', False, _cases_env,
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data_frequency='minute'),
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HistorySpec(3, '1d', 'close_price', False, _cases_env,
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data_frequency='minute'),
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]
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ILLIQUID_PRICES_SPECS = [
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HistorySpec(3, '1m', 'price', False, _cases_env, data_frequency='minute'),
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HistorySpec(5, '1m', 'price', True, _cases_env, data_frequency='minute'),
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]
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MIXED_FREQUENCY_SPECS = [
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HistorySpec(1, '1m', 'price', False, _cases_env, data_frequency='minute'),
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HistorySpec(2, '1m', 'price', False, _cases_env, data_frequency='minute'),
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HistorySpec(2, '1d', 'price', False, _cases_env, data_frequency='minute'),
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]
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MIXED_FIELDS_SPECS = [
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HistorySpec(3, '1m', 'price', True, _cases_env, data_frequency='minute'),
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HistorySpec(3, '1m', 'open_price', True, _cases_env,
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data_frequency='minute'),
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HistorySpec(3, '1m', 'close_price', True, _cases_env,
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data_frequency='minute'),
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HistorySpec(3, '1m', 'high', True, _cases_env, data_frequency='minute'),
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HistorySpec(3, '1m', 'low', True, _cases_env, data_frequency='minute'),
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HistorySpec(3, '1m', 'volume', True, _cases_env, data_frequency='minute'),
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]
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HISTORY_CONTAINER_TEST_CASES = {
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# June 2013
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# Su Mo Tu We Th Fr Sa
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# 1
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# 2 3 4 5 6 7 8
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# 9 10 11 12 13 14 15
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# 16 17 18 19 20 21 22
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# 23 24 25 26 27 28 29
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# 30
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'test one minute price only': {
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# A list of HistorySpec objects.
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'specs': ONE_MINUTE_PRICE_ONLY_SPECS,
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# Sids for the test.
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'sids': [1],
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# Start date for test.
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'dt': to_utc('2013-06-21 9:31AM'),
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# Sequency of updates to the container
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'updates': [
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BarData(
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{
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1: {
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'price': 5,
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'dt': to_utc('2013-06-21 9:31AM'),
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},
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},
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),
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BarData(
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{
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1: {
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'price': 6,
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'dt': to_utc('2013-06-21 9:32AM'),
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},
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},
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),
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],
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# Expected results
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'expected': {
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ONE_MINUTE_PRICE_ONLY_SPECS[0].key_str: [
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pd.DataFrame(
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data={
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1: [5],
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},
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index=[
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to_utc('2013-06-21 9:31AM'),
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],
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),
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pd.DataFrame(
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data={
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1: [6],
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},
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index=[
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to_utc('2013-06-21 9:32AM'),
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],
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),
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],
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},
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},
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'test daily open close': {
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# A list of HistorySpec objects.
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'specs': DAILY_OPEN_CLOSE_SPECS,
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# Sids for the test.
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'sids': [1],
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# Start date for test.
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'dt': to_utc('2013-06-21 9:31AM'),
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# Sequence of updates to the container
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'updates': [
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BarData(
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{
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1: {
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'open_price': 10,
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'close_price': 11,
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'dt': to_utc('2013-06-21 10:00AM'),
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},
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},
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),
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BarData(
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{
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1: {
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'open_price': 12,
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'close_price': 13,
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'dt': to_utc('2013-06-21 3:30PM'),
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},
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},
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),
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BarData(
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{
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1: {
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'open_price': 14,
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'close_price': 15,
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# Wait a full market day before the next bar.
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# We should end up with nans for Monday the 24th.
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'dt': to_utc('2013-06-25 9:31AM'),
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},
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},
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),
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],
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# Dictionary mapping spec_key -> list of expected outputs
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'expected': {
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# open
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DAILY_OPEN_CLOSE_SPECS[0].key_str: [
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pd.DataFrame(
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data={
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1: [np.nan, np.nan, 10]
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},
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index=[
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to_utc('2013-06-19 4:00PM'),
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to_utc('2013-06-20 4:00PM'),
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to_utc('2013-06-21 10:00AM'),
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],
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),
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pd.DataFrame(
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data={
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1: [np.nan, np.nan, 10]
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},
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index=[
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to_utc('2013-06-19 4:00PM'),
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to_utc('2013-06-20 4:00PM'),
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to_utc('2013-06-21 3:30PM'),
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],
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),
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pd.DataFrame(
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data={
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1: [10, np.nan, 14]
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},
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index=[
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to_utc('2013-06-21 4:00PM'),
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to_utc('2013-06-24 4:00PM'),
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to_utc('2013-06-25 9:31AM'),
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],
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),
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],
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# close
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DAILY_OPEN_CLOSE_SPECS[1].key_str: [
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pd.DataFrame(
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data={
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1: [np.nan, np.nan, 11]
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},
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index=[
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to_utc('2013-06-19 4:00PM'),
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to_utc('2013-06-20 4:00PM'),
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to_utc('2013-06-21 10:00AM'),
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],
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),
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pd.DataFrame(
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data={
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1: [np.nan, np.nan, 13]
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},
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index=[
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to_utc('2013-06-19 4:00PM'),
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to_utc('2013-06-20 4:00PM'),
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to_utc('2013-06-21 3:30PM'),
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],
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),
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pd.DataFrame(
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data={
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1: [13, np.nan, 15]
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},
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index=[
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to_utc('2013-06-21 4:00PM'),
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to_utc('2013-06-24 4:00PM'),
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to_utc('2013-06-25 9:31AM'),
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],
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),
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],
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},
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},
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'test illiquid prices': {
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# A list of HistorySpec objects.
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'specs': ILLIQUID_PRICES_SPECS,
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# Sids for the test.
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'sids': [1],
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# Start date for test.
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'dt': to_utc('2013-06-28 9:31AM'),
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# Sequence of updates to the container
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'updates': [
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BarData(
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{
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1: {
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'price': 10,
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'dt': to_utc('2013-06-28 9:31AM'),
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},
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},
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),
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BarData(
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{
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1: {
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'price': 11,
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'dt': to_utc('2013-06-28 9:32AM'),
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},
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},
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),
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BarData(
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{
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1: {
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'price': 12,
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'dt': to_utc('2013-06-28 9:33AM'),
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},
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},
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),
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BarData(
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{
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1: {
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'price': 13,
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# Note: Skipping 9:34 to simulate illiquid bar/missing
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# data.
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'dt': to_utc('2013-06-28 9:35AM'),
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},
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},
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),
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],
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# Dictionary mapping spec_key -> list of expected outputs
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'expected': {
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ILLIQUID_PRICES_SPECS[0].key_str: [
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pd.DataFrame(
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data={
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1: [np.nan, np.nan, 10],
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},
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index=[
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to_utc('2013-06-27 3:59PM'),
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to_utc('2013-06-27 4:00PM'),
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to_utc('2013-06-28 9:31AM'),
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],
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),
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pd.DataFrame(
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data={
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1: [np.nan, 10, 11],
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},
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index=[
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to_utc('2013-06-27 4:00PM'),
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to_utc('2013-06-28 9:31AM'),
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to_utc('2013-06-28 9:32AM'),
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],
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),
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pd.DataFrame(
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data={
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1: [10, 11, 12],
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},
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index=[
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to_utc('2013-06-28 9:31AM'),
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to_utc('2013-06-28 9:32AM'),
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to_utc('2013-06-28 9:33AM'),
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],
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),
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# Since there's no update for 9:34, this is called at 9:35.
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pd.DataFrame(
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data={
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1: [12, np.nan, 13],
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},
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index=[
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to_utc('2013-06-28 9:33AM'),
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to_utc('2013-06-28 9:34AM'),
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to_utc('2013-06-28 9:35AM'),
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],
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),
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],
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ILLIQUID_PRICES_SPECS[1].key_str: [
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pd.DataFrame(
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data={
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1: [np.nan, np.nan, np.nan, np.nan, 10],
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},
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index=[
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to_utc('2013-06-27 3:57PM'),
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to_utc('2013-06-27 3:58PM'),
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to_utc('2013-06-27 3:59PM'),
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to_utc('2013-06-27 4:00PM'),
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to_utc('2013-06-28 9:31AM'),
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],
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),
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pd.DataFrame(
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data={
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1: [np.nan, np.nan, np.nan, 10, 11],
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},
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index=[
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to_utc('2013-06-27 3:58PM'),
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to_utc('2013-06-27 3:59PM'),
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to_utc('2013-06-27 4:00PM'),
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to_utc('2013-06-28 9:31AM'),
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to_utc('2013-06-28 9:32AM'),
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],
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),
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pd.DataFrame(
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data={
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1: [np.nan, np.nan, 10, 11, 12],
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},
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index=[
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to_utc('2013-06-27 3:59PM'),
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to_utc('2013-06-27 4:00PM'),
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to_utc('2013-06-28 9:31AM'),
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to_utc('2013-06-28 9:32AM'),
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to_utc('2013-06-28 9:33AM'),
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],
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),
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# Since there's no update for 9:34, this is called at 9:35.
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# The 12 value from 9:33 should be forward-filled.
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pd.DataFrame(
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data={
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1: [10, 11, 12, 12, 13],
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},
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index=[
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to_utc('2013-06-28 9:31AM'),
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to_utc('2013-06-28 9:32AM'),
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to_utc('2013-06-28 9:33AM'),
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to_utc('2013-06-28 9:34AM'),
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to_utc('2013-06-28 9:35AM'),
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],
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),
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],
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},
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},
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'test mixed frequencies': {
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# A list of HistorySpec objects.
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'specs': MIXED_FREQUENCY_SPECS,
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# Sids for the test.
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'sids': [1],
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# Start date for test.
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# July 2013
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# Su Mo Tu We Th Fr Sa
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# 1 2 3 4 5 6
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# 7 8 9 10 11 12 13
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# 14 15 16 17 18 19 20
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# 21 22 23 24 25 26 27
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# 28 29 30 31
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'dt': to_utc('2013-07-03 9:31AM'),
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# Sequence of updates to the container
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'updates': [
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BarData(
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{
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1: {
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'price': count,
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'dt': dt,
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}
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}
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)
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for count, dt in enumerate(MIXED_FREQUENCY_MINUTES)
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],
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# Dictionary mapping spec_key -> list of expected outputs.
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'expected': {
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MIXED_FREQUENCY_SPECS[0].key_str: [
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pd.DataFrame(
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data={
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1: [count],
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},
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index=[minute],
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)
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for count, minute in enumerate(MIXED_FREQUENCY_MINUTES)
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],
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MIXED_FREQUENCY_SPECS[1].key_str: [
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pd.DataFrame(
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data={
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1: mixed_frequency_expected_data(count, '1m'),
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},
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index=mixed_frequency_expected_index(count, '1m'),
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)
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for count in range(len(MIXED_FREQUENCY_MINUTES))
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],
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MIXED_FREQUENCY_SPECS[2].key_str: [
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pd.DataFrame(
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data={
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1: mixed_frequency_expected_data(count, '1d'),
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},
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index=mixed_frequency_expected_index(count, '1d'),
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)
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for count in range(len(MIXED_FREQUENCY_MINUTES))
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]
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},
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},
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'test multiple fields and sids': {
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# A list of HistorySpec objects.
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'specs': MIXED_FIELDS_SPECS,
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# Sids for the test.
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'sids': [1, 10],
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# Start date for test.
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'dt': to_utc('2013-06-28 9:31AM'),
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# Sequence of updates to the container
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'updates': [
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BarData(
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{
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1: {
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'dt': dt,
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'price': count,
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'open_price': count,
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'close_price': count,
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'high': count,
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'low': count,
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'volume': count,
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},
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10: {
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'dt': dt,
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'price': count * 10,
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'open_price': count * 10,
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'close_price': count * 10,
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'high': count * 10,
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'low': count * 10,
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'volume': count * 10,
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},
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},
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)
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for count, dt in enumerate([
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to_utc('2013-06-28 9:31AM'),
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to_utc('2013-06-28 9:32AM'),
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to_utc('2013-06-28 9:33AM'),
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# NOTE: No update for 9:34
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to_utc('2013-06-28 9:35AM'),
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])
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],
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# Dictionary mapping spec_key -> list of expected outputs
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'expected': dict(
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# Build a dict from a list of tuples. Doing it this way because
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# there are two distinct cases we want to test: forward-fillable
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# fields and non-forward-fillable fields.
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[
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(
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# Non forward-fill fields
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key,
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[
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pd.DataFrame(
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data={
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1: [np.nan, np.nan, 0],
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10: [np.nan, np.nan, 0],
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},
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index=[
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to_utc('2013-06-27 3:59PM'),
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to_utc('2013-06-27 4:00PM'),
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to_utc('2013-06-28 9:31AM'),
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],
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),
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pd.DataFrame(
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data={
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1: [np.nan, 0, 1],
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10: [np.nan, 0, 10],
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},
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index=[
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to_utc('2013-06-27 4:00PM'),
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to_utc('2013-06-28 9:31AM'),
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to_utc('2013-06-28 9:32AM'),
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],
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),
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pd.DataFrame(
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data={
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1: [0, 1, 2],
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10: [0, 10, 20],
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},
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index=[
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to_utc('2013-06-28 9:31AM'),
|
|
to_utc('2013-06-28 9:32AM'),
|
|
to_utc('2013-06-28 9:33AM'),
|
|
],
|
|
|
|
),
|
|
pd.DataFrame(
|
|
data={
|
|
1: [2, np.nan, 3],
|
|
10: [20, np.nan, 30],
|
|
},
|
|
index=[
|
|
to_utc('2013-06-28 9:33AM'),
|
|
to_utc('2013-06-28 9:34AM'),
|
|
to_utc('2013-06-28 9:35AM'),
|
|
],
|
|
# For volume, when we are missing data, we replace
|
|
# it with 0s to show that no trades occured.
|
|
).fillna(0 if 'volume' in key else np.nan),
|
|
],
|
|
)
|
|
for key in [spec.key_str for spec in MIXED_FIELDS_SPECS
|
|
if spec.field not in HistorySpec.FORWARD_FILLABLE]
|
|
] +
|
|
|
|
# Concatenate the expected results for non-ffillable with
|
|
# expected result for ffillable.
|
|
[
|
|
(
|
|
# Forward-fillable fields
|
|
key,
|
|
[
|
|
pd.DataFrame(
|
|
data={
|
|
1: [np.nan, np.nan, 0],
|
|
10: [np.nan, np.nan, 0],
|
|
},
|
|
index=[
|
|
to_utc('2013-06-27 3:59PM'),
|
|
to_utc('2013-06-27 4:00PM'),
|
|
to_utc('2013-06-28 9:31AM'),
|
|
],
|
|
),
|
|
|
|
pd.DataFrame(
|
|
data={
|
|
1: [np.nan, 0, 1],
|
|
10: [np.nan, 0, 10],
|
|
},
|
|
index=[
|
|
to_utc('2013-06-27 4:00PM'),
|
|
to_utc('2013-06-28 9:31AM'),
|
|
to_utc('2013-06-28 9:32AM'),
|
|
],
|
|
),
|
|
|
|
pd.DataFrame(
|
|
data={
|
|
1: [0, 1, 2],
|
|
10: [0, 10, 20],
|
|
},
|
|
index=[
|
|
to_utc('2013-06-28 9:31AM'),
|
|
to_utc('2013-06-28 9:32AM'),
|
|
to_utc('2013-06-28 9:33AM'),
|
|
],
|
|
),
|
|
|
|
pd.DataFrame(
|
|
data={
|
|
1: [2, 2, 3],
|
|
10: [20, 20, 30],
|
|
},
|
|
index=[
|
|
to_utc('2013-06-28 9:33AM'),
|
|
to_utc('2013-06-28 9:34AM'),
|
|
to_utc('2013-06-28 9:35AM'),
|
|
],
|
|
),
|
|
],
|
|
)
|
|
for key in [spec.key_str for spec in MIXED_FIELDS_SPECS
|
|
if spec.field in HistorySpec.FORWARD_FILLABLE]
|
|
]
|
|
),
|
|
},
|
|
}
|