def setUp(self): self.constants = { # Every day, assume every stock starts at 2, goes down to 1, # goes up to 4, and finishes at 3. USEquityPricing.low: 1, USEquityPricing.open: 2, USEquityPricing.close: 3, USEquityPricing.high: 4, } self.asset_ids = [1, 2, 3, 4] self.dates = date_range('2014-01', '2014-03', freq='D', tz='UTC') self.loader = PrecomputedLoader( constants=self.constants, dates=self.dates, sids=self.asset_ids, ) self.asset_info = make_simple_equity_info( self.asset_ids, start_date=self.dates[0], end_date=self.dates[-1], ) environment = TradingEnvironment() environment.write_data(equities_df=self.asset_info) self.asset_finder = environment.asset_finder self.assets = self.asset_finder.retrieve_all(self.asset_ids)
def setUpClass(cls): cls.AAPL = 1 cls.MSFT = 2 cls.BRK_A = 3 cls.assets = [cls.AAPL, cls.MSFT, cls.BRK_A] asset_info = make_simple_equity_info( cls.assets, Timestamp('2014'), Timestamp('2015'), ['AAPL', 'MSFT', 'BRK_A'], ) cls.env = trading.TradingEnvironment() cls.env.write_data(equities_df=asset_info) cls.tempdir = tempdir = TempDirectory() tempdir.create() try: cls.raw_data, bar_reader = cls.create_bar_reader(tempdir) adj_reader = cls.create_adjustment_reader(tempdir) cls.pipeline_loader = USEquityPricingLoader( bar_reader, adj_reader ) except: cls.tempdir.cleanup() raise cls.dates = cls.raw_data[cls.AAPL].index.tz_localize('UTC') cls.AAPL_split_date = Timestamp("2014-06-09", tz='UTC')
def setUpClass(cls): cls.AAPL = 1 cls.MSFT = 2 cls.BRK_A = 3 cls.assets = [cls.AAPL, cls.MSFT, cls.BRK_A] asset_info = make_simple_equity_info( cls.assets, Timestamp('2014'), Timestamp('2015'), ['AAPL', 'MSFT', 'BRK_A'], ) cls.env = trading.TradingEnvironment() cls.env.write_data(equities_df=asset_info) cls.tempdir = tempdir = TempDirectory() tempdir.create() try: cls.raw_data, bar_reader = cls.create_bar_reader(tempdir) adj_reader = cls.create_adjustment_reader(tempdir) cls.pipeline_loader = USEquityPricingLoader(bar_reader, adj_reader) except: cls.tempdir.cleanup() raise cls.dates = cls.raw_data[cls.AAPL].index.tz_localize('UTC') cls.AAPL_split_date = Timestamp("2014-06-09", tz='UTC')
def setUpClass(cls): cls.__calendar = date_range('2014', '2015', freq=trading_day) cls.__assets = assets = Int64Index(arange(1, 20)) cls.__tmp_finder_ctx = tmp_asset_finder( equities=make_simple_equity_info( assets, cls.__calendar[0], cls.__calendar[-1], )) cls.__finder = cls.__tmp_finder_ctx.__enter__() cls.__mask = cls.__finder.lifetimes( cls.__calendar[-30:], include_start_date=False, )
def setUpClass(cls): cls.__calendar = date_range('2014', '2015', freq=trading_day) cls.__assets = assets = Int64Index(arange(1, 20)) cls.__tmp_finder_ctx = tmp_asset_finder( equities=make_simple_equity_info( assets, cls.__calendar[0], cls.__calendar[-1], ) ) cls.__finder = cls.__tmp_finder_ctx.__enter__() cls.__mask = cls.__finder.lifetimes( cls.__calendar[-30:], include_start_date=False, )
def setUpClass(cls): cls.env = TradingEnvironment() day = cls.env.trading_day cls.sids = sids = Int64Index([1, 2, 3]) cls.dates = dates = date_range( '2015-02-01', '2015-02-28', freq=day, tz='UTC', ) asset_info = make_simple_equity_info( cls.sids, start_date=Timestamp('2015-01-31', tz='UTC'), end_date=Timestamp('2015-03-01', tz='UTC'), ) cls.env.write_data(equities_df=asset_info) cls.asset_finder = cls.env.asset_finder cls.raw_data = DataFrame( data=arange(len(dates) * len(sids), dtype=float).reshape( len(dates), len(sids), ), index=dates, columns=cls.asset_finder.retrieve_all(sids), ) close_loader = DataFrameLoader(USEquityPricing.close, cls.raw_data) volume_loader = DataFrameLoader( USEquityPricing.volume, cls.raw_data * 2, ) cls.engine = SimplePipelineEngine( { USEquityPricing.close: close_loader, USEquityPricing.volume: volume_loader, }.__getitem__, cls.dates, cls.asset_finder, )
def setUpClass(cls): cls.env = TradingEnvironment() day = cls.env.trading_day cls.asset_ids = [1, 2, 3] cls.dates = date_range( '2015-01-01', '2015-01-31', freq=day, tz='UTC', ) asset_info = make_simple_equity_info( cls.asset_ids, start_date=cls.dates[0], end_date=cls.dates[-1], ) cls.env.write_data(equities_df=asset_info) cls.asset_finder = cls.env.asset_finder cls.assets = cls.asset_finder.retrieve_all(cls.asset_ids)
def get_equity_info(cls): return make_simple_equity_info( cls.get_sids(), start_date=pd.Timestamp('2013-01-01', tz='UTC'), end_date=pd.Timestamp('2015-01-01', tz='UTC'), )
from zipline.utils.numpy_utils import ( float64_dtype, int64_dtype, repeat_last_axis, ) from zipline.testing import ( tmp_asset_finder, make_simple_equity_info, ) nameof = op.attrgetter('name') dtypeof = op.attrgetter('dtype') asset_infos = ( (make_simple_equity_info( tuple(map(ord, 'ABC')), pd.Timestamp(0), pd.Timestamp('2015'), ), ), (make_simple_equity_info( tuple(map(ord, 'ABCD')), pd.Timestamp(0), pd.Timestamp('2015'), ), ), ) with_extra_sid = parameterized.expand(asset_infos) with_ignore_sid = parameterized.expand( product(chain.from_iterable(asset_infos), [True, False])) def _utc_localize_index_level_0(df): """``tz_localize`` the first level of a multiindexed dataframe to utc.
from zipline.utils.numpy_utils import ( float64_dtype, int64_dtype, repeat_last_axis, ) from zipline.testing import ( tmp_asset_finder, make_simple_equity_info, ) nameof = op.attrgetter('name') dtypeof = op.attrgetter('dtype') asset_infos = ( (make_simple_equity_info( tuple(map(ord, 'ABC')), pd.Timestamp(0), pd.Timestamp('2015'), ),), (make_simple_equity_info( tuple(map(ord, 'ABCD')), pd.Timestamp(0), pd.Timestamp('2015'), ),), ) with_extra_sid = parameterized.expand(asset_infos) with_ignore_sid = parameterized.expand( product(chain.from_iterable(asset_infos), [True, False]) ) def _utc_localize_index_level_0(df):