def initialize(self, *args, **kwargs): # store algo reference in global space set_algo_instance(self) try: self._initialize(self) finally: set_algo_instance(None)
def test_volshare_slippage(self): # verify order -> transaction -> portfolio position. # -------------- test_algo = TradingAlgorithm( script=""" from zipline.api import * def initialize(context): model = slippage.VolumeShareSlippage( volume_limit=.3, price_impact=0.05 ) set_slippage(model) set_commission(commission.PerShare(0.02)) context.count = 2 context.incr = 0 def handle_data(context, data): if context.incr < context.count: # order small lots to be sure the # order will fill in a single transaction order(0, 5000) record(price=data[0].price) record(volume=data[0].volume) record(incr=context.incr) context.incr += 1 """, sim_params=self.sim_params, ) set_algo_instance(test_algo) self.zipline_test_config['algorithm'] = test_algo self.zipline_test_config['trade_count'] = 100 # 67 will be used inside assert_single_position # to confirm we have as many transactions as expected. # The algo places 2 trades of 5000 shares each. The trade # events have volume ranging from 100 to 950. The volume cap # of 0.3 limits the trade volume to a range of 30 - 316 shares. # The spreadsheet linked below calculates the total position # size over each bar, and predicts 67 txns will be required # to fill the two orders. The number of bars and transactions # differ because some bars result in multiple txns. See # spreadsheet for details: # https://www.dropbox.com/s/ulrk2qt0nrtrigb/Volume%20Share%20Worksheet.xlsx self.zipline_test_config['expected_transactions'] = 67 zipline = simfactory.create_test_zipline( **self.zipline_test_config) output, _ = assert_single_position(self, zipline) # confirm the slippage and commission on a sample # transaction per_share_commish = 0.02 perf = output[1] transaction = perf['daily_perf']['transactions'][0] commish = transaction['amount'] * per_share_commish self.assertEqual(commish, transaction['commission']) self.assertEqual(2.029, transaction['price'])
def test_fixed_slippage(self): # verify order -> transaction -> portfolio position. # -------------- test_algo = TradingAlgorithm( script=""" from zipline.api import (slippage, commission, set_slippage, set_commission, order, record) def initialize(context): model = slippage.FixedSlippage(spread=0.10) set_slippage(model) set_commission(commission.PerTrade(100.00)) context.count = 1 context.incr = 0 def handle_data(context, data): if context.incr < context.count: order(0, -1000) record(price=data[0].price) context.incr += 1""", sim_params=self.sim_params, ) set_algo_instance(test_algo) self.zipline_test_config['algorithm'] = test_algo self.zipline_test_config['trade_count'] = 200 # this matches the value in the algotext initialize # method, and will be used inside assert_single_position # to confirm we have as many transactions as orders we # placed. self.zipline_test_config['order_count'] = 1 # self.zipline_test_config['transforms'] = \ # test_algo.transform_visitor.transforms.values() zipline = simfactory.create_test_zipline( **self.zipline_test_config) output, _ = assert_single_position(self, zipline) # confirm the slippage and commission on a sample # transaction recorded_price = output[1]['daily_perf']['recorded_vars']['price'] transaction = output[1]['daily_perf']['transactions'][0] self.assertEqual(100.0, transaction['commission']) expected_spread = 0.05 expected_commish = 0.10 expected_price = recorded_price - expected_spread - expected_commish self.assertEqual(expected_price, transaction['price'])
def test_algo_record_allow_mock(self): """ Test that values from "MagicMock"ed methods can be passed to record. Relevant for our basic/validation and methods like history, which will end up returning a MagicMock instead of a DataFrame. """ test_algo = TradingAlgorithm(script=record_variables, sim_params=self.sim_params) set_algo_instance(test_algo) test_algo.record(foo=MagicMock())
def test_order_in_init(self): """ Test that calling order in initialize will raise an error. """ with self.assertRaises(OrderDuringInitialize): test_algo = TradingAlgorithm( script=call_order_in_init, sim_params=self.sim_params, ) set_algo_instance(test_algo)
def test_order_methods(self): """Only test that order methods can be called without error. Correct filling of orders is tested in zipline. """ test_algo = TradingAlgorithm(script=call_all_order_methods, sim_params=self.sim_params) set_algo_instance(test_algo) self.zipline_test_config["algorithm"] = test_algo self.zipline_test_config["trade_count"] = 200 zipline = simfactory.create_test_zipline(**self.zipline_test_config) output, _ = drain_zipline(self, zipline)
def _algo_record_float_magic_should_pass(self, var_type): test_algo = TradingAlgorithm(script=record_float_magic % var_type, sim_params=self.sim_params) set_algo_instance(test_algo) self.zipline_test_config["algorithm"] = test_algo self.zipline_test_config["trade_count"] = 200 zipline = simfactory.create_test_zipline(**self.zipline_test_config) output, _ = drain_zipline(self, zipline) self.assertEqual(len(output), 252) incr = [] for o in output[:200]: incr.append(o["daily_perf"]["recorded_vars"]["data"]) np.testing.assert_array_equal(incr, [np.nan] * 200)
def test_algo_record_vars(self): test_algo = TradingAlgorithm(script=record_variables, sim_params=self.sim_params) set_algo_instance(test_algo) self.zipline_test_config["algorithm"] = test_algo self.zipline_test_config["trade_count"] = 200 zipline = simfactory.create_test_zipline(**self.zipline_test_config) output, _ = drain_zipline(self, zipline) self.assertEqual(len(output), 252) incr = [] for o in output[:200]: incr.append(o["daily_perf"]["recorded_vars"]["incr"]) np.testing.assert_array_equal(incr, range(1, 201))
def test_account_in_init(self): """ Test that accessing account in init doesn't break. """ test_algo = TradingAlgorithm( script=access_account_in_init, sim_params=self.sim_params, ) set_algo_instance(test_algo) self.zipline_test_config['algorithm'] = test_algo self.zipline_test_config['trade_count'] = 1 zipline = simfactory.create_test_zipline( **self.zipline_test_config) output, _ = drain_zipline(self, zipline)
def test_portfolio_in_init(self): """ Test that accessing portfolio in init doesn't break. """ test_algo = TradingAlgorithm( script=access_portfolio_in_init, sim_params=self.sim_params, ) set_algo_instance(test_algo) self.zipline_test_config['algorithm'] = test_algo self.zipline_test_config['trade_count'] = 1 zipline = simfactory.create_test_zipline( **self.zipline_test_config) output, _ = drain_zipline(self, zipline)
def test_order_methods(self): """ Only test that order methods can be called without error. Correct filling of orders is tested in zipline. """ test_algo = TradingAlgorithm( script=call_all_order_methods, sim_params=self.sim_params, ) set_algo_instance(test_algo) self.zipline_test_config['algorithm'] = test_algo self.zipline_test_config['trade_count'] = 200 zipline = simfactory.create_test_zipline(**self.zipline_test_config) output, _ = drain_zipline(self, zipline)
def _algo_record_float_magic_should_pass(self, var_type): test_algo = TradingAlgorithm( script=record_float_magic % var_type, sim_params=self.sim_params, ) set_algo_instance(test_algo) self.zipline_test_config['algorithm'] = test_algo self.zipline_test_config['trade_count'] = 200 zipline = simfactory.create_test_zipline(**self.zipline_test_config) output, _ = drain_zipline(self, zipline) self.assertEqual(len(output), 252) incr = [] for o in output[:200]: incr.append(o['daily_perf']['recorded_vars']['data']) np.testing.assert_array_equal(incr, [np.nan] * 200)
def test_algo_record_vars(self): test_algo = TradingAlgorithm( script=record_variables, sim_params=self.sim_params, ) set_algo_instance(test_algo) self.zipline_test_config['algorithm'] = test_algo self.zipline_test_config['trade_count'] = 200 zipline = simfactory.create_test_zipline(**self.zipline_test_config) output, _ = drain_zipline(self, zipline) self.assertEqual(len(output), 252) incr = [] for o in output[:200]: incr.append(o['daily_perf']['recorded_vars']['incr']) np.testing.assert_array_equal(incr, range(1, 201))
def test_volshare_slippage(self): # verify order -> transaction -> portfolio position. # -------------- test_algo = TradingAlgorithm( script=""" from zipline.api import * def initialize(context): model = slippage.VolumeShareSlippage( volume_limit=.3, price_impact=0.05 ) set_slippage(model) set_commission(commission.PerShare(0.02)) context.count = 2 context.incr = 0 def handle_data(context, data): if context.incr < context.count: # order small lots to be sure the # order will fill in a single transaction order(0, 5000) record(price=data[0].price) record(volume=data[0].volume) record(incr=context.incr) context.incr += 1 """, sim_params=self.sim_params, ) set_algo_instance(test_algo) self.zipline_test_config['algorithm'] = test_algo self.zipline_test_config['trade_count'] = 100 # 67 will be used inside assert_single_position # to confirm we have as many transactions as expected. # The algo places 2 trades of 5000 shares each. The trade # events have volume ranging from 100 to 950. The volume cap # of 0.3 limits the trade volume to a range of 30 - 316 shares. # The spreadsheet linked below calculates the total position # size over each bar, and predicts 67 txns will be required # to fill the two orders. The number of bars and transactions # differ because some bars result in multiple txns. See # spreadsheet for details: # https://www.dropbox.com/s/ulrk2qt0nrtrigb/Volume%20Share%20Worksheet.xlsx self.zipline_test_config['expected_transactions'] = 67 # self.zipline_test_config['transforms'] = \ # test_algo.transform_visitor.transforms.values() zipline = simfactory.create_test_zipline( **self.zipline_test_config) output, _ = assert_single_position(self, zipline) # confirm the slippage and commission on a sample # transaction per_share_commish = 0.02 perf = output[1] transaction = perf['daily_perf']['transactions'][0] commish = transaction['amount'] * per_share_commish self.assertEqual(commish, transaction['commission']) self.assertEqual(2.029, transaction['price'])
def run(self, source, sim_params=None, benchmark_return_source=None): """Run the algorithm. :Arguments: source : can be either: - pandas.DataFrame - zipline source - list of zipline sources If pandas.DataFrame is provided, it must have the following structure: * column names must consist of ints representing the different sids * index must be DatetimeIndex * array contents should be price info. :Returns: daily_stats : pandas.DataFrame Daily performance metrics such as returns, alpha etc. """ if isinstance(source, (list, tuple)): assert self.sim_params is not None or sim_params is not None, \ """When providing a list of sources, \ sim_params have to be specified as a parameter or in the constructor.""" elif isinstance(source, pd.DataFrame): # if DataFrame provided, wrap in DataFrameSource source = DataFrameSource(source) elif isinstance(source, pd.Panel): source = DataPanelSource(source) if not isinstance(source, (list, tuple)): self.sources = [source] else: self.sources = source # Check for override of sim_params. # If it isn't passed to this function, # use the default params set with the algorithm. # Else, we create simulation parameters using the start and end of the # source provided. if not sim_params: if not self.sim_params: start = source.start end = source.end sim_params = create_simulation_parameters( start=start, end=end, capital_base=self.capital_base ) else: sim_params = self.sim_params # Create transforms by wrapping them into StatefulTransforms self.transforms = [] for namestring, trans_descr in iteritems(self.registered_transforms): sf = StatefulTransform( trans_descr['class'], *trans_descr['args'], **trans_descr['kwargs'] ) sf.namestring = namestring self.transforms.append(sf) # force a reset of the performance tracker, in case # this is a repeat run of the algorithm. self.perf_tracker = None # create transforms and zipline self.gen = self._create_generator(sim_params) # store algo reference in global space set_algo_instance(self) try: # loop through simulated_trading, each iteration returns a # perf dictionary perfs = [] for perf in self.gen: perfs.append(perf) # convert perf dict to pandas dataframe daily_stats = self._create_daily_stats(perfs) finally: # remove algo from global space set_algo_instance(None) return daily_stats
def run(self, source, sim_params=None, benchmark_return_source=None): """Run the algorithm. :Arguments: source : can be either: - pandas.DataFrame - zipline source - list of zipline sources If pandas.DataFrame is provided, it must have the following structure: * column names must consist of ints representing the different sids * index must be DatetimeIndex * array contents should be price info. :Returns: daily_stats : pandas.DataFrame Daily performance metrics such as returns, alpha etc. """ if isinstance(source, (list, tuple)): assert self.sim_params is not None or sim_params is not None, \ """When providing a list of sources, \ sim_params have to be specified as a parameter or in the constructor.""" elif isinstance(source, pd.DataFrame): # if DataFrame provided, wrap in DataFrameSource source = DataFrameSource(source) elif isinstance(source, pd.Panel): source = DataPanelSource(source) if not isinstance(source, (list, tuple)): self.sources = [source] else: self.sources = source # Check for override of sim_params. # If it isn't passed to this function, # use the default params set with the algorithm. # Else, we create simulation parameters using the start and end of the # source provided. if not sim_params: if not self.sim_params: start = source.start end = source.end sim_params = create_simulation_parameters( start=start, end=end, capital_base=self.capital_base) else: sim_params = self.sim_params # Create transforms by wrapping them into StatefulTransforms self.transforms = [] for namestring, trans_descr in iteritems(self.registered_transforms): sf = StatefulTransform(trans_descr['class'], *trans_descr['args'], **trans_descr['kwargs']) sf.namestring = namestring self.transforms.append(sf) # force a reset of the performance tracker, in case # this is a repeat run of the algorithm. self.perf_tracker = None # create transforms and zipline self.gen = self._create_generator(sim_params) # store algo reference in global space set_algo_instance(self) try: # loop through simulated_trading, each iteration returns a # perf dictionary perfs = [] for perf in self.gen: perfs.append(perf) # convert perf dict to pandas dataframe daily_stats = self._create_daily_stats(perfs) finally: # remove algo from global space set_algo_instance(None) return daily_stats