mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 14:47:08 +08:00
Jonathan Kamens
115 lines
3.7 KiB
Python
115 lines
3.7 KiB
Python
import datetime
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import pytz
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import nose.tools as nt
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import pandas.util.testing as tm
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import pandas as pd
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from zipline.finance.blotter import Blotter, Order
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from zipline.finance.commission import PerShare, PerTrade, PerDollar
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from zipline.finance.performance.period import PerformancePeriod
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from zipline.finance.performance.position import Position
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from zipline.finance.performance.tracker import PerformanceTracker
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from zipline.finance.performance.position_tracker import PositionTracker
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from zipline.finance.risk.cumulative import RiskMetricsCumulative
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from zipline.finance.risk.period import RiskMetricsPeriod
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from zipline.finance.risk.report import RiskReport
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from zipline.finance.slippage import (
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FixedSlippage,
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Transaction,
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VolumeShareSlippage
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)
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from zipline.protocol import Account
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from zipline.protocol import Portfolio
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from zipline.protocol import Position as ProtocolPosition
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from zipline.finance.trading import SimulationParameters
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from zipline.utils import factory
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def stringify_cases(cases, func=None):
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# get better test case names
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results = []
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if func is None:
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def func(case):
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return case[0].__name__
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for case in cases:
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new_case = list(case)
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key = func(case)
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new_case.insert(0, key)
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results.append(new_case)
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return results
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sim_params_daily = SimulationParameters(
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datetime.datetime(2013, 6, 19, tzinfo=pytz.UTC),
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datetime.datetime(2013, 6, 19, tzinfo=pytz.UTC),
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10000,
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emission_rate='daily')
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sim_params_minute = SimulationParameters(
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datetime.datetime(2013, 6, 19, tzinfo=pytz.UTC),
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datetime.datetime(2013, 6, 19, tzinfo=pytz.UTC),
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10000,
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emission_rate='minute')
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returns = factory.create_returns_from_list(
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[1.0], sim_params_daily)
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def object_serialization_cases(skip_daily=False):
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# Wrapped in a function to recreate DI objects.
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cases = [
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(Blotter, (), {}, 'repr'),
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(Order, (datetime.datetime(2013, 6, 19), 8554, 100), {}, 'dict'),
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(PerShare, (), {}, 'dict'),
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(PerTrade, (), {}, 'dict'),
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(PerDollar, (), {}, 'dict'),
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(PerformancePeriod,
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(10000,), {'position_tracker': PositionTracker()}, 'to_dict'),
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(Position, (8554,), {}, 'dict'),
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(PositionTracker, (), {}, 'dict'),
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(PerformanceTracker, (sim_params_minute,), {}, 'to_dict'),
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(RiskMetricsCumulative, (sim_params_minute,), {}, 'to_dict'),
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(RiskMetricsPeriod,
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(returns.index[0], returns.index[0], returns), {}, 'to_dict'),
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(RiskReport, (returns, sim_params_minute), {}, 'to_dict'),
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(FixedSlippage, (), {}, 'dict'),
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(Transaction,
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(8554, 10, datetime.datetime(2013, 6, 19), 100, "0000"), {},
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'dict'),
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(VolumeShareSlippage, (), {}, 'dict'),
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(Account, (), {}, 'dict'),
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(Portfolio, (), {}, 'dict'),
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(ProtocolPosition, (8554,), {}, 'dict')
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]
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if not skip_daily:
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cases.extend([
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(PerformanceTracker, (sim_params_daily,), {}, 'to_dict'),
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(RiskMetricsCumulative, (sim_params_daily,), {}, 'to_dict'),
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(RiskReport, (returns, sim_params_daily), {}, 'to_dict'),
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])
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return stringify_cases(cases)
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def assert_dict_equal(d1, d2):
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# check keys
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nt.assert_is_instance(d1, dict)
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nt.assert_is_instance(d2, dict)
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nt.assert_set_equal(set(d1.keys()), set(d2.keys()))
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for k in d1:
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v1 = d1[k]
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v2 = d2[k]
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asserter = nt.assert_equal
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if isinstance(v1, pd.DataFrame):
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asserter = tm.assert_frame_equal
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if isinstance(v1, pd.Series):
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asserter = tm.assert_series_equal
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try:
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asserter(v1, v2)
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except AssertionError:
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raise AssertionError('{k} is not equal'.format(k=k))
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