mirror of
https://github.com/wassname/catalyst.git
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Dale Jung
7892a6943f
on the number of per-tick update that occur since they were duplicated per each PerformancePeriod. Also opens up the path to cythonizing the entire object
114 lines
3.7 KiB
Python
114 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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func = lambda case: 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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