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ENH: Adds futures trading and asset management logic to TradingAlgorithm and performance classes

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This commit is contained in:
jfkirk
2015-06-11 11:35:49 -04:00
parent 035bfbd514
commit b84ac01cbf
25 changed files with 844 additions and 304 deletions
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zipline/algorithm.py
+115 -42
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@@ -39,9 +39,10 @@ from zipline.errors import (
UnsupportedCommissionModel,
UnsupportedOrderParameters,
UnsupportedSlippageModel,
SidNotFound,
)
from zipline.finance import trading
from zipline.finance.trading import TradingEnvironment
from zipline.finance.blotter import Blotter
from zipline.finance.commission import PerShare, PerTrade, PerDollar
from zipline.finance.controls import (
@@ -64,6 +65,7 @@ from zipline.finance.slippage import (
SlippageModel,
transact_partial
)
from zipline.assets import Asset, Future
from zipline.gens.composites import date_sorted_sources
from zipline.gens.tradesimulation import AlgorithmSimulator
from zipline.sources import DataFrameSource, DataPanelSource
@@ -133,8 +135,27 @@ class TradingAlgorithm(object):
How much capital to start with.
instant_fill : bool <default: False>
Whether to fill orders immediately or on next bar.
environment : str <default: 'zipline'>
The environment that this algorithm is running in.
asset_finder : An AssetFinder object
A new AssetFinder object to be used in this TradingEnvironment
asset_metadata: can be either:
- dict
- pandas.DataFrame
- object with 'read' property
If dict is provided, it must have the following structure:
* keys are the identifiers
* values are dicts containing the metadata, with the metadata
field name as the key
If pandas.DataFrame is provided, it must have the
following structure:
* column names must be the metadata fields
* index must be the different asset identifiers
* array contents should be the metadata value
If an object with a 'read' property is provided, 'read' must
return rows containing at least one of 'sid' or 'symbol' along
with the other metadata fields.
identifiers : List
Any asset identifiers that are not provided in the
asset_metadata, but will be traded by this TradingAlgorithm
"""
self.datetime = None
@@ -167,10 +188,23 @@ class TradingAlgorithm(object):
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params is None:
self.sim_params = create_simulation_parameters(
capital_base=self.capital_base
capital_base=self.capital_base,
start=kwargs.pop('start', None),
end=kwargs.pop('end', None)
)
self.perf_tracker = PerformanceTracker(self.sim_params)
# Update the TradingEnvironment with the provided asset metadata
self.trading_environment = kwargs.pop('env',
TradingEnvironment.instance())
self.trading_environment.update_asset_finder(
asset_finder=kwargs.pop('asset_finder', None),
asset_metadata=kwargs.pop('asset_metadata', None),
identifiers=kwargs.pop('identifiers', None)
)
# Pull in the environment's new AssetFinder for quick reference
self.asset_finder = self.trading_environment.asset_finder
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
@@ -322,11 +356,10 @@ class TradingAlgorithm(object):
sim_params = self.sim_params
if self.benchmark_return_source is None:
env = trading.environment
if sim_params.data_frequency == 'minute' or \
sim_params.emission_rate == 'minute':
def update_time(date):
return env.get_open_and_close(date)[1]
return self.trading_environment.get_open_and_close(date)[1]
else:
def update_time(date):
return date
@@ -336,7 +369,7 @@ class TradingAlgorithm(object):
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in
trading.environment.benchmark_returns.iteritems()
self.trading_environment.benchmark_returns.iteritems()
if dt.date() >= sim_params.period_start.date() and
dt.date() <= sim_params.period_end.date()
]
@@ -410,8 +443,7 @@ class TradingAlgorithm(object):
If pandas.DataFrame is provided, it must have the
following structure:
* column names must consist of ints representing the
different sids
* column names must be the different asset identifiers
* index must be DatetimeIndex
* array contents should be price info.
@@ -420,9 +452,11 @@ class TradingAlgorithm(object):
Daily performance metrics such as returns, alpha etc.
"""
# Ensure that source is a DataSource object
if isinstance(source, list):
if overwrite_sim_params:
warnings.warn("""List of sources passed, will not attempt to extract sids, and start and end
warnings.warn("""List of sources passed, will not attempt to extract start and end
dates. Make sure to set the correct fields in sim_params passed to
__init__().""", UserWarning)
overwrite_sim_params = False
@@ -443,8 +477,19 @@ class TradingAlgorithm(object):
self.sim_params.period_start = source.start
if hasattr(source, 'end'):
self.sim_params.period_end = source.end
# The sids field of the source is the canonical reference for
# sids in this run
all_sids = [sid for s in self.sources for sid in s.sids]
self.sim_params.sids = set(all_sids)
# Check that all sids from the source are accounted for in
# the AssetFinder
for sid in self.sim_params.sids:
try:
self.asset_finder.retrieve_asset(sid)
except SidNotFound:
warnings.warn("No Asset found for sid '%s'. Make sure "
"that the correct identifiers and asset "
"metadata are passed to __init__()." % sid)
# Changing period_start and period_close might require updating
# of first_open and last_close.
self.sim_params._update_internal()
@@ -604,17 +649,52 @@ class TradingAlgorithm(object):
def symbol(self, symbol_str):
"""
Default symbol lookup for any source that directly maps the
symbol to the identifier (e.g. yahoo finance).
symbol to the Asset (e.g. yahoo finance).
"""
return symbol_str
asset, _ = self.asset_finder.lookup_generic(
asset_convertible_or_iterable=symbol_str,
as_of_date=self.datetime,
)
return asset
@api_method
def symbols(self, *args):
"""
Default symbols lookup for any source that directly maps the
symbol to the identifier (e.g. yahoo finance).
symbol to the Asset (e.g. yahoo finance).
"""
return args
return [self.symbol(identifier) for identifier in args]
@api_method
def sid(self, a_sid):
"""
Default sid lookup for any source that directly maps the integer sid
to the Asset.
"""
return self.asset_finder.retrieve_asset(a_sid)
def _calculate_order_value_amount(self, asset, value):
"""
Calculates how many shares/contracts to order based on the type of
asset being ordered.
"""
last_price = self.trading_client.current_data[asset].price
if tolerant_equals(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=asset
)
if self.logger:
self.logger.debug(zero_message)
# Don't place any order
return 0
if isinstance(asset, Future):
value_multiplier = asset.contract_multiplier
else:
value_multiplier = 1
return value / (last_price * value_multiplier)
@api_method
def order(self, sid, amount,
@@ -655,7 +735,7 @@ class TradingAlgorithm(object):
return self.blotter.order(sid, amount, style)
def validate_order_params(self,
sid,
asset,
amount,
limit_price,
stop_price,
@@ -682,8 +762,14 @@ class TradingAlgorithm(object):
msg="Passing both stop_price and style is not supported."
)
if not isinstance(asset, Asset):
raise UnsupportedOrderParameters(
msg="Passing non-Asset argument to 'order()' is not supported."
" Use 'sid()' or 'symbol()' methods to look up an Asset."
)
for control in self.trading_controls:
control.validate(sid,
control.validate(asset,
amount,
self.updated_portfolio(),
self.get_datetime(),
@@ -718,6 +804,8 @@ class TradingAlgorithm(object):
Place an order by desired value rather than desired number of shares.
If the requested sid is found in the universe, the requested value is
divided by its price to imply the number of shares to transact.
If the Asset being ordered is a Future, the 'value' calculated
is actually the exposure, as Futures have no 'value'.
value > 0 :: Buy/Cover
value < 0 :: Sell/Short
@@ -726,21 +814,11 @@ class TradingAlgorithm(object):
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
last_price = self.trading_client.current_data[sid].price
if tolerant_equals(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=sid
)
if self.logger:
self.logger.debug(zero_message)
# Don't place any order
return
else:
amount = value / last_price
return self.order(sid, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
amount = self._calculate_order_value_amount(sid, value)
return self.order(sid, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@property
def recorded_vars(self):
@@ -855,7 +933,7 @@ class TradingAlgorithm(object):
def order_percent(self, sid, percent,
limit_price=None, stop_price=None, style=None):
"""
Place an order in the specified security corresponding to the given
Place an order in the specified asset corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
@@ -898,15 +976,10 @@ class TradingAlgorithm(object):
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 the Asset being ordered is a Future, the 'target value' calculated
is actually the target exposure, as Futures have no 'value'.
"""
last_price = self.trading_client.current_data[sid].price
if tolerant_equals(last_price, 0):
# Don't place an order
if self.logger:
zero_message = "Price of 0 for {psid}; can't infer value"
self.logger.debug(zero_message.format(psid=sid))
return
target_amount = target / last_price
target_amount = self._calculate_order_value_amount(sid, target)
return self.order_target(sid, target_amount,
limit_price=limit_price,
stop_price=stop_price,
@@ -1066,7 +1139,7 @@ class TradingAlgorithm(object):
increasing the absolute value of shares/dollar value exceeding one of
these limits, raise a TradingControlException.
"""
control = MaxPositionSize(sid=sid,
control = MaxPositionSize(asset=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)
@@ -1081,7 +1154,7 @@ class TradingAlgorithm(object):
If an algorithm attempts to place an order that would result in
exceeding one of these limits, raise a TradingControlException.
"""
control = MaxOrderSize(sid=sid,
control = MaxOrderSize(asset=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)