def setUp(self):
self.trading_environment = factory.create_trading_environment()
setup_logger(self)
trade_history = factory.create_trade_history(
133, [10.0, 10.0, 11.0, 11.0], [100, 100, 100, 300], timedelta(days=1), self.trading_environment
)
self.source = SpecificEquityTrades(event_list=trade_history)
def test_factory_daily(self):
trading_environment = factory.create_trading_environment()
trade_source = factory.create_daily_trade_source([133], 200,
trading_environment)
prev = None
for trade in trade_source:
if prev:
self.assertTrue(trade.dt > prev.dt)
prev = trade
def run(self, source=None, start=None, end=None):
"""Run the algorithm.
:Arguments:
source : can be either:
- pandas.Panel
- 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 source is None:
assert(self.sources)
else:
self.set_sources(source, start, end)
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in self.registered_transforms.iteritems():
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
environment = create_trading_environment(
start=self.start_datetime,
end=self.end_datetime,
capital_base=self.capital_base
)
# create transforms and zipline
self.gen = self._create_generator(environment)
# loop through simulated_trading, each iteration returns a
# perf ndict
perfs = list(self.gen)
# convert perf ndict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
return daily_stats
def setUp(self):
self.trading_environment = factory.create_trading_environment()
setup_logger(self)
trade_history = factory.create_trade_history(133,
[10.0, 10.0, 11.0, 11.0],
[100, 100, 100, 300],
timedelta(days=1),
self.trading_environment)
self.source = SpecificEquityTrades(event_list=trade_history)
def test_factory_daily(self):
trading_environment = factory.create_trading_environment()
trade_source = factory.create_daily_trade_source(
[133],
200,
trading_environment
)
prev = None
for trade in trade_source:
if prev:
self.assertTrue(trade.dt > prev.dt)
prev = trade
def test_generator_dates(self):
"""
Ensure the pipeline of generators are in sync, at least as far as
their current dates.
"""
algo = TestAlgo(self)
trading_environment = factory.create_trading_environment(
start=datetime(2012, 1, 3, tzinfo=pytz.utc),
end=datetime(2012, 7, 30, tzinfo=pytz.utc))
trade_source = factory.create_daily_trade_source([8229], 200,
trading_environment)
algo.set_sources([trade_source])
gen = algo.get_generator(trading_environment)
self.assertTrue(list(gen))
self.assertTrue(algo.slippage.latest_date)
self.assertTrue(algo.latest_date)
def test_generator_dates(self):
"""
Ensure the pipeline of generators are in sync, at least as far as
their current dates.
"""
algo = TestAlgo(self)
trading_environment = factory.create_trading_environment(
start=datetime(2011, 7, 30, tzinfo=pytz.utc),
end=datetime(2012, 7, 30, tzinfo=pytz.utc)
)
trade_source = factory.create_daily_trade_source(
[8229],
200,
trading_environment
)
algo.set_sources([trade_source])
gen = algo.get_generator(trading_environment)
self.assertTrue(list(gen))
self.assertTrue(algo.slippage.latest_date)
self.assertTrue(algo.latest_date)
def run(self, source, start=None, end=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 start is not None and end is not None, \
"""When providing a list of sources, \
start and end date have to be specified."""
elif isinstance(source, pd.DataFrame):
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
elif isinstance(source, pd.Panel):
source = DataPanelSource(source)
# If values not set, try to extract from source.
if start is None:
start = source.start
if end is None:
end = source.end
if not isinstance(source, (list, tuple)):
self.sources = [source]
else:
self.sources = source
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in self.registered_transforms.iteritems():
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
environment = create_trading_environment(
start=start,
end=end,
capital_base=self.capital_base
)
# create transforms and zipline
self.gen = self._create_generator(environment)
# loop through simulated_trading, each iteration returns a
# perf ndict
perfs = list(self.gen)
# convert perf ndict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
return daily_stats
def create_test_zipline(**config):
"""
:param config: A configuration object that is a dict with:
- environment - a \
:py:class:`zipline.finance.trading.TradingEnvironment`
- sid - an integer, which will be used as the security ID.
- order_count - the number of orders the test algo will place,
defaults to 100
- order_amount - the number of shares per order, defaults to 100
- trade_count - the number of trades to simulate, defaults to 101
to ensure all orders are processed.
- algorithm - optional parameter providing an algorithm. defaults
to :py:class:`zipline.test.algorithms.TestAlgorithm`
- trade_source - optional parameter to specify trades, if present.
If not present :py:class:`zipline.sources.SpecificEquityTrades`
is the source, with daily frequency in trades.
- slippage: optional parameter that configures the
:py:class:`zipline.gens.tradingsimulation.TransactionSimulator`.
Expects an object with a simulate mehod, such as
:py:class:`zipline.gens.tradingsimulation.FixedSlippage`.
:py:mod:`zipline.finance.trading`
- transforms: optional parameter that provides a list
of StatefulTransform objects.
"""
assert isinstance(config, dict)
sid_list = config.get('sid_list')
if not sid_list:
sid = config.get('sid')
sid_list = [sid]
concurrent_trades = config.get('concurrent_trades', False)
#--------------------
# Trading Environment
#--------------------
if 'environment' in config:
trading_environment = config['environment']
else:
trading_environment = factory.create_trading_environment()
if 'order_count' in config:
order_count = config['order_count']
else:
order_count = 100
if 'order_amount' in config:
order_amount = config['order_amount']
else:
order_amount = 100
if 'trade_count' in config:
trade_count = config['trade_count']
else:
# to ensure all orders are filled, we provide one more
# trade than order
trade_count = 101
#-------------------
# Create the Algo
#-------------------
if 'algorithm' in config:
test_algo = config['algorithm']
else:
test_algo = TestAlgorithm(
sid,
order_amount,
order_count
)
#-------------------
# Trade Source
#-------------------
if 'trade_source' in config:
trade_source = config['trade_source']
else:
trade_source = factory.create_daily_trade_source(
sid_list,
trade_count,
trading_environment,
concurrent=concurrent_trades
)
test_algo.set_sources([trade_source])
#-------------------
# Transforms
#-------------------
transforms = config.get('transforms', None)
if transforms is not None:
test_algo.set_transforms(transforms)
#-------------------
# Slippage
# ------------------
slippage = config.get('slippage', None)
if slippage is not None:
test_algo.set_slippage(slippage)
# ------------------
# generator/simulator
sim = test_algo.get_generator(trading_environment)
return sim
def run(self, source, start=None, end=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 start is not None and end is not None, \
"""When providing a list of sources, \
start and end date have to be specified."""
elif isinstance(source, pd.DataFrame):
assert isinstance(source.index, pd.tseries.index.DatetimeIndex)
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
# If values not set, try to extract from source.
if start is None:
start = source.start
if end is None:
end = source.end
if not isinstance(source, (list, tuple)):
self.sources = [source]
else:
self.sources = source
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in self.registered_transforms.iteritems():
sf = StatefulTransform(trans_descr['class'], *trans_descr['args'],
**trans_descr['kwargs'])
sf.namestring = namestring
self.transforms.append(sf)
environment = create_trading_environment(start=start, end=end)
# create transforms and zipline
self.gen = self._create_generator(environment)
# loop through simulated_trading, each iteration returns a
# perf ndict
perfs = list(self.gen)
# convert perf ndict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
return daily_stats
def transaction_sim(self, **params):
""" This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history"""
trade_count = params['trade_count']
trade_interval = params['trade_interval']
trade_delay = params.get('trade_delay')
order_count = params['order_count']
order_amount = params['order_amount']
order_interval = params['order_interval']
expected_txn_count = params['expected_txn_count']
expected_txn_volume = params['expected_txn_volume']
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get('alternate')
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get('complete_fill')
sid = 1
trading_environment = factory.create_trading_environment()
trade_sim = TransactionSimulator()
price = [10.1] * trade_count
volume = [100] * trade_count
start_date = trading_environment.first_open
generated_trades = factory.create_trade_history(
sid,
price,
volume,
trade_interval,
trading_environment
)
if alternate:
alternator = -1
else:
alternator = 1
order_date = start_date
for i in xrange(order_count):
order = ndict({
'sid': sid,
'amount': order_amount * alternator ** i,
'dt': order_date
})
trade_sim.place_order(order)
order_date = order_date + order_interval
# move after market orders to just after market next
# market open.
if order_date.hour >= 21:
if order_date.minute >= 00:
order_date = order_date + timedelta(days=1)
order_date = order_date.replace(hour=14, minute=30)
# there should now be one open order list stored under the sid
oo = trade_sim.open_orders
self.assertEqual(len(oo), 1)
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(order_count, len(order_list))
for i in xrange(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
self.assertEqual(order.amount, order_amount * alternator ** i)
tracker = PerformanceTracker(trading_environment)
# this approximates the loop inside TradingSimulationClient
transactions = []
for trade in generated_trades:
if trade_delay:
trade.dt = trade.dt + trade_delay
trade_sim.update(trade)
if trade.TRANSACTION:
transactions.append(trade.TRANSACTION)
tracker.process_event(trade)
if complete_fill:
self.assertEqual(len(transactions), len(order_list))
total_volume = 0
for i in xrange(len(transactions)):
txn = transactions[i]
total_volume += txn.amount
if complete_fill:
order = order_list[i]
self.assertEqual(order.amount, txn.amount)
self.assertEqual(total_volume, expected_txn_volume)
self.assertEqual(len(transactions), expected_txn_count)
cumulative_pos = tracker.cumulative_performance.positions[sid]
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should now be empty
oo = trade_sim.open_orders
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(0, len(order_list))
def transaction_sim(self, **params):
""" This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history"""
trade_count = params['trade_count']
trade_interval = params['trade_interval']
trade_delay = params.get('trade_delay')
order_count = params['order_count']
order_amount = params['order_amount']
order_interval = params['order_interval']
expected_txn_count = params['expected_txn_count']
expected_txn_volume = params['expected_txn_volume']
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get('alternate')
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get('complete_fill')
sid = 1
trading_environment = factory.create_trading_environment()
trade_sim = TransactionSimulator()
price = [10.1] * trade_count
volume = [100] * trade_count
start_date = trading_environment.first_open
generated_trades = factory.create_trade_history(
sid, price, volume, trade_interval, trading_environment)
if alternate:
alternator = -1
else:
alternator = 1
order_date = start_date
for i in xrange(order_count):
order = ndict({
'sid': sid,
'amount': order_amount * alternator**i,
'dt': order_date
})
trade_sim.place_order(order)
order_date = order_date + order_interval
# move after market orders to just after market next
# market open.
if order_date.hour >= 21:
if order_date.minute >= 00:
order_date = order_date + timedelta(days=1)
order_date = order_date.replace(hour=14, minute=30)
# there should now be one open order list stored under the sid
oo = trade_sim.open_orders
self.assertEqual(len(oo), 1)
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(order_count, len(order_list))
for i in xrange(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
self.assertEqual(order.amount, order_amount * alternator**i)
tracker = PerformanceTracker(trading_environment)
# this approximates the loop inside TradingSimulationClient
transactions = []
for trade in generated_trades:
if trade_delay:
trade.dt = trade.dt + trade_delay
trade_sim.update(trade)
if trade.TRANSACTION:
transactions.append(trade.TRANSACTION)
tracker.process_event(trade)
if complete_fill:
self.assertEqual(len(transactions), len(order_list))
total_volume = 0
for i in xrange(len(transactions)):
txn = transactions[i]
total_volume += txn.amount
if complete_fill:
order = order_list[i]
self.assertEqual(order.amount, txn.amount)
self.assertEqual(total_volume, expected_txn_volume)
self.assertEqual(len(transactions), expected_txn_count)
cumulative_pos = tracker.cumulative_performance.positions[sid]
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should now be empty
oo = trade_sim.open_orders
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(0, len(order_list))
def create_test_zipline(**config):
"""
:param config: A configuration object that is a dict with:
- environment - a \
:py:class:`zipline.finance.trading.TradingEnvironment`
- sid - an integer, which will be used as the security ID.
- order_count - the number of orders the test algo will place,
defaults to 100
- order_amount - the number of shares per order, defaults to 100
- trade_count - the number of trades to simulate, defaults to 101
to ensure all orders are processed.
- algorithm - optional parameter providing an algorithm. defaults
to :py:class:`zipline.test.algorithms.TestAlgorithm`
- trade_source - optional parameter to specify trades, if present.
If not present :py:class:`zipline.sources.SpecificEquityTrades`
is the source, with daily frequency in trades.
- slippage: optional parameter that configures the
:py:class:`zipline.gens.tradingsimulation.TransactionSimulator`.
Expects an object with a simulate mehod, such as
:py:class:`zipline.gens.tradingsimulation.FixedSlippage`.
:py:mod:`zipline.finance.trading`
- transforms: optional parameter that provides a list
of StatefulTransform objects.
"""
assert isinstance(config, dict)
sid_list = config.get('sid_list')
if not sid_list:
sid = config.get('sid')
sid_list = [sid]
concurrent_trades = config.get('concurrent_trades', False)
#--------------------
# Trading Environment
#--------------------
if 'environment' in config:
trading_environment = config['environment']
else:
trading_environment = factory.create_trading_environment()
if 'order_count' in config:
order_count = config['order_count']
else:
order_count = 100
if 'order_amount' in config:
order_amount = config['order_amount']
else:
order_amount = 100
if 'trade_count' in config:
trade_count = config['trade_count']
else:
# to ensure all orders are filled, we provide one more
# trade than order
trade_count = 101
#-------------------
# Create the Algo
#-------------------
if 'algorithm' in config:
test_algo = config['algorithm']
else:
test_algo = TestAlgorithm(sid, order_amount, order_count)
#-------------------
# Trade Source
#-------------------
if 'trade_source' in config:
trade_source = config['trade_source']
else:
trade_source = factory.create_daily_trade_source(
sid_list,
trade_count,
trading_environment,
concurrent=concurrent_trades)
test_algo.set_sources([trade_source])
#-------------------
# Transforms
#-------------------
transforms = config.get('transforms', None)
if transforms is not None:
test_algo.set_transforms(transforms)
#-------------------
# Slippage
# ------------------
slippage = config.get('slippage', None)
if slippage is not None:
test_algo.set_slippage(slippage)
# ------------------
# generator/simulator
sim = test_algo.get_generator(trading_environment)
return sim
