def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
self.data_gen = self._create_data_generator(source_filter, sim_params)
# if live execution is active instantiate perf_tracker with
# the portfolio downloaded from IB
if self.asset_type == self.asset_types.EQUITY:
self.perf_tracker = PerformanceTracker(sim_params)
elif self.asset_type == self.asset_types.FUTURES:
self.perf_tracker = FuturesPerformanceTracker(sim_params)
else:
self.perf_tracker = PerformanceTracker(sim_params)
if self.live_execution:
self._init_positions()
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
self.data_gen = self._create_data_generator(source_filter,
sim_params)
# if live execution is active instantiate perf_tracker with
# the portfolio downloaded from IB
if self.asset_type == self.asset_types.EQUITY:
self.perf_tracker = PerformanceTracker(sim_params)
elif self.asset_type == self.asset_types.FUTURES:
self.perf_tracker = FuturesPerformanceTracker(sim_params)
else:
self.perf_tracker = PerformanceTracker(sim_params)
if self.live_execution:
self._init_positions()
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
class MyAlgo(TradingAlgorithm):
def initialize(self, sids, amount):
self.sids = sids
self.amount = amount
def handle_data(self, data):
sid = self.sids[0]
amount = self.amount
self.order(sid, amount)
```
To then to run this algorithm:
my_algo = MyAlgo([0], 100) # first argument has to be list of sids
stats = my_algo.run(data)
"""
asset_types = AssetTypeEnum()
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
data_frequency : str (daily, hourly or minutely)
The duration of the bars.
annualizer : int <optional>
Which constant to use for annualizing risk metrics.
If not provided, will extract from data_frequency.
capital_base : float <default: 1.0e5>
How much capital to start with.
"""
self._portfolio = None
self.datetime = None
# can be reset in a subclass's initialize() method
self.asset_type = self.asset_types.EQUITY
self.registered_transforms = {}
self.transforms = []
self.sources = []
self._recorded_vars = {}
self.logger = None
self.benchmark_return_source = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
if 'data_frequency' in kwargs:
self.set_data_frequency(kwargs.pop('data_frequency'))
else:
self.data_frequency = None
self.instant_fill = kwargs.pop('instant_fill', False)
# Override annualizer if set
if 'annualizer' in kwargs:
self.annualizer = kwargs['annualizer']
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params:
self.sim_params.data_frequency = self.data_frequency
self.live_execution = kwargs.pop('live_execution', False)
if self.live_execution:
# Only import and instantiate an IB Execution instance
# If it is explicitly requested in kwargs
# Todo: [BUG FIX] object instantiates on import and
# therefore connects to the IB API
from alephnull.live.broker import LiveExecution
self.live_execution = LiveExecution(call_msg=False)
# reconcile algo with InteractiveBrokers
self.capital_base = self.live_execution.total_cash()
self._portfolio = self.live_execution.ib_portfolio()
self._portfolio.cash = self._portfolio.starting_cash = self.capital_base
self._portfolio.portfolio_value = self._portfolio.cash + \
self._portfolio.positions_value
kwargs['blotter'] = self.live_execution.blotter
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
# an algorithm subclass needs to set initialized to True when
# it is fully initialized.
self.initialized = False
# call to user-defined constructor method
self.initialize(*args, **kwargs)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _init_positions(self):
for sid, pos in self._portfolio.positions.iteritems():
for perf_period in self.perf_tracker.perf_periods:
perf_period.update_position(
sid=sid,
contract=pos.contract
if hasattr(pos, 'contract') else None,
amount=pos.amount,
last_sale_price=pos.last_sale_price,
last_sale_date=None,
cost_basis=pos.cost_basis)
def _create_data_generator(self, source_filter, sim_params):
"""
Create a merged data generator using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if self.benchmark_return_source is None:
benchmark_return_source = [
Event({
'dt': dt,
'returns': ret,
'type': alephnull.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'
})
for dt, ret in trading.environment.benchmark_returns.iterkv()
if dt.date() >= sim_params.period_start.date()
and dt.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = ifilter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted, *self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
self.data_gen = self._create_data_generator(source_filter, sim_params)
# if live execution is active instantiate perf_tracker with
# the portfolio downloaded from IB
if self.asset_type == self.asset_types.EQUITY:
self.perf_tracker = PerformanceTracker(sim_params)
elif self.asset_type == self.asset_types.FUTURES:
self.perf_tracker = FuturesPerformanceTracker(sim_params)
else:
self.perf_tracker = PerformanceTracker(sim_params)
if self.live_execution:
self._init_positions()
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
def initialize(self, *args, **kwargs):
pass
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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 self.registered_transforms.iteritems():
sf = StatefulTransform(trans_descr['class'], *trans_descr['args'],
**trans_descr['kwargs'])
sf.namestring = namestring
self.transforms.append(sf)
# create transforms and zipline
self.gen = self._create_generator(sim_params)
# 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)
return daily_stats
def _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars'))
daily_perfs.append(perf['daily_perf'])
else:
self.risk_report = perf
daily_dts = [
np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs
]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
def add_transform(self, transform_class, tag, *args, **kwargs):
"""Add a single-sid, sequential transform to the model.
:Arguments:
transform_class : class
Which transform to use. E.g. mavg.
tag : str
How to name the transform. Can later be access via:
data[sid].tag()
Extra args and kwargs will be forwarded to the transform
instantiation.
"""
self.registered_transforms[tag] = {
'class': transform_class,
'args': args,
'kwargs': kwargs
}
def record(self, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
for name, value in kwargs.items():
self._recorded_vars[name] = value
def order(self, sid, amount, limit_price=None, stop_price=None):
self.blotter.update_account(self.portfolio)
return self.blotter.order(sid, amount, limit_price, stop_price)
def order_value(self, sid, value, limit_price=None, stop_price=None):
last_price = self.trading_client.current_data[sid].price
return self.blotter.order_value(sid,
value,
last_price,
limit_price=limit_price,
stop_price=stop_price)
def get_open_orders(self, sid=None):
"""
Return open order events
"""
if sid is None:
return {
key: [order.to_api_obj() for order in orders]
for key, orders in self.blotter.open_orders.iteritems()
}
if sid in self.blotter.open_orders:
orders = self.blotter.open_orders[sid]
return [order.to_api_obj() for order in orders]
return []
def get_orders(self, sid=None):
"""
Return order events
"""
orders = {
id_: {
(self.blotter.orders[id_].__dict__['sid'], self.blotter.orders[id_].__dict__['contract']):
self.blotter.orders[id_].__dict__
}
for id_ in self.blotter.orders.keys()
}
orders = [{sym: {
key: v
}
for sym, v in orders[key].iteritems()}
for key in orders.keys()]
orders_flat = {}
for d in orders:
orders_flat.update(d)
if sid:
return orders_flat[sid]
else:
return orders_flat
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self._portfolio
def set_portfolio(self, portfolio):
self._portfolio = portfolio
def set_logger(self, logger):
self.logger = logger
def set_datetime(self, dt):
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
def get_datetime(self):
"""
Returns a copy of the datetime.
"""
date_copy = copy(self.datetime)
assert date_copy.tzinfo == pytz.utc, \
"Algorithm should have a utc datetime"
return date_copy
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
def set_transforms(self, transforms):
assert isinstance(transforms, list)
self.transforms = transforms
def set_data_frequency(self, data_frequency):
assert data_frequency in ('daily', 'minute')
self.data_frequency = data_frequency
self.annualizer = ANNUALIZER[self.data_frequency]
def order_percent(self, sid, percent, limit_price=None, stop_price=None):
"""
Place an order in the specified security corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
"""
value = self.portfolio.portfolio_value * percent
return self.order_value(sid, value, limit_price, stop_price)
def target(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target number of shares. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target number of shares and the
current number of shares.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
req_shares = target - current_position
return self.order(sid, req_shares, limit_price, stop_price)
else:
return self.order(sid, target, limit_price, stop_price)
def target_value(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target value. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target value and the
current value.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
req_value = target - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
else:
return self.order_value(sid, target, limit_price, stop_price)
def target_percent(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Note that target must expressed as a decimal (0.50 means 50\%).
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
else:
current_value = 0
target_value = self.portfolio.portfolio_value * target
req_value = target_value - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
class MyAlgo(TradingAlgorithm):
def initialize(self, sids, amount):
self.sids = sids
self.amount = amount
def handle_data(self, data):
sid = self.sids[0]
amount = self.amount
self.order(sid, amount)
```
To then to run this algorithm:
my_algo = MyAlgo([0], 100) # first argument has to be list of sids
stats = my_algo.run(data)
"""
asset_types = AssetTypeEnum()
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
data_frequency : str (daily, hourly or minutely)
The duration of the bars.
annualizer : int <optional>
Which constant to use for annualizing risk metrics.
If not provided, will extract from data_frequency.
capital_base : float <default: 1.0e5>
How much capital to start with.
"""
self._portfolio = None
self.datetime = None
# can be reset in a subclass's initialize() method
self.asset_type = self.asset_types.EQUITY
self.registered_transforms = {}
self.transforms = []
self.sources = []
self._recorded_vars = {}
self.logger = None
self.benchmark_return_source = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
if 'data_frequency' in kwargs:
self.set_data_frequency(kwargs.pop('data_frequency'))
else:
self.data_frequency = None
self.instant_fill = kwargs.pop('instant_fill', False)
# Override annualizer if set
if 'annualizer' in kwargs:
self.annualizer = kwargs['annualizer']
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params:
self.sim_params.data_frequency = self.data_frequency
self.live_execution = kwargs.pop('live_execution', False)
if self.live_execution:
# Only import and instantiate an IB Execution instance
# If it is explicitly requested in kwargs
# Todo: [BUG FIX] object instantiates on import and
# therefore connects to the IB API
from alephnull.live.broker import LiveExecution
self.live_execution = LiveExecution(call_msg=False)
# reconcile algo with InteractiveBrokers
self.capital_base = self.live_execution.total_cash()
self._portfolio = self.live_execution.ib_portfolio()
self._portfolio.cash = self._portfolio.starting_cash = self.capital_base
self._portfolio.portfolio_value = self._portfolio.cash + \
self._portfolio.positions_value
kwargs['blotter'] = self.live_execution.blotter
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
# an algorithm subclass needs to set initialized to True when
# it is fully initialized.
self.initialized = False
# call to user-defined constructor method
self.initialize(*args, **kwargs)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _init_positions(self):
for sid, pos in self._portfolio.positions.iteritems():
for perf_period in self.perf_tracker.perf_periods:
perf_period.update_position(
sid=sid,
contract=pos.contract if hasattr(pos, 'contract') else None,
amount=pos.amount,
last_sale_price=pos.last_sale_price,
last_sale_date=None,
cost_basis=pos.cost_basis)
def _create_data_generator(self, source_filter, sim_params):
"""
Create a merged data generator using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if self.benchmark_return_source is None:
benchmark_return_source = [
Event({'dt': dt,
'returns': ret,
'type': alephnull.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in trading.environment.benchmark_returns.iterkv()
if dt.date() >= sim_params.period_start.date()
and dt.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = ifilter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
self.data_gen = self._create_data_generator(source_filter,
sim_params)
# if live execution is active instantiate perf_tracker with
# the portfolio downloaded from IB
if self.asset_type == self.asset_types.EQUITY:
self.perf_tracker = PerformanceTracker(sim_params)
elif self.asset_type == self.asset_types.FUTURES:
self.perf_tracker = FuturesPerformanceTracker(sim_params)
else:
self.perf_tracker = PerformanceTracker(sim_params)
if self.live_execution:
self._init_positions()
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
def initialize(self, *args, **kwargs):
pass
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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 self.registered_transforms.iteritems():
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
# create transforms and zipline
self.gen = self._create_generator(sim_params)
# 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)
return daily_stats
def _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars')
)
daily_perfs.append(perf['daily_perf'])
else:
self.risk_report = perf
daily_dts = [np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
def add_transform(self, transform_class, tag, *args, **kwargs):
"""Add a single-sid, sequential transform to the model.
:Arguments:
transform_class : class
Which transform to use. E.g. mavg.
tag : str
How to name the transform. Can later be access via:
data[sid].tag()
Extra args and kwargs will be forwarded to the transform
instantiation.
"""
self.registered_transforms[tag] = {'class': transform_class,
'args': args,
'kwargs': kwargs}
def record(self, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
for name, value in kwargs.items():
self._recorded_vars[name] = value
def order(self, sid, amount, limit_price=None, stop_price=None):
self.blotter.update_account(self.portfolio)
return self.blotter.order(sid, amount, limit_price, stop_price)
def order_value(self, sid, value, limit_price=None, stop_price=None):
last_price = self.trading_client.current_data[sid].price
return self.blotter.order_value(sid, value, last_price,
limit_price=limit_price,
stop_price=stop_price)
def get_open_orders(self, sid=None):
"""
Return open order events
"""
if sid is None:
return {key: [order.to_api_obj() for order in orders]
for key, orders
in self.blotter.open_orders.iteritems()}
if sid in self.blotter.open_orders:
orders = self.blotter.open_orders[sid]
return [order.to_api_obj() for order in orders]
return []
def get_orders(self, sid=None):
"""
Return order events
"""
orders = {id_: {(self.blotter.orders[id_].__dict__['sid'],
self.blotter.orders[id_].__dict__['contract']):
self.blotter.orders[id_].__dict__} for id_ in
self.blotter.orders.keys()}
orders = [{sym: {key: v} for sym, v in orders[key].iteritems()} for key in orders.keys()]
orders_flat = {}
for d in orders:
orders_flat.update(d)
if sid:
return orders_flat[sid]
else:
return orders_flat
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self._portfolio
def set_portfolio(self, portfolio):
self._portfolio = portfolio
def set_logger(self, logger):
self.logger = logger
def set_datetime(self, dt):
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
def get_datetime(self):
"""
Returns a copy of the datetime.
"""
date_copy = copy(self.datetime)
assert date_copy.tzinfo == pytz.utc, \
"Algorithm should have a utc datetime"
return date_copy
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
def set_transforms(self, transforms):
assert isinstance(transforms, list)
self.transforms = transforms
def set_data_frequency(self, data_frequency):
assert data_frequency in ('daily', 'minute')
self.data_frequency = data_frequency
self.annualizer = ANNUALIZER[self.data_frequency]
def order_percent(self, sid, percent, limit_price=None, stop_price=None):
"""
Place an order in the specified security corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
"""
value = self.portfolio.portfolio_value * percent
return self.order_value(sid, value, limit_price, stop_price)
def target(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target number of shares. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target number of shares and the
current number of shares.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
req_shares = target - current_position
return self.order(sid, req_shares, limit_price, stop_price)
else:
return self.order(sid, target, limit_price, stop_price)
def target_value(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target value. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target value and the
current value.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
req_value = target - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
else:
return self.order_value(sid, target, limit_price, stop_price)
def target_percent(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Note that target must expressed as a decimal (0.50 means 50\%).
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
else:
current_value = 0
target_value = self.portfolio.portfolio_value * target
req_value = target_value - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
