mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-03 06:34:39 +08:00
John Ricklefs
401 lines
13 KiB
Python
401 lines
13 KiB
Python
#
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# Copyright 2014 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import abc
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import pandas as pd
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from six import with_metaclass
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from zipline.errors import (
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AccountControlViolation,
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TradingControlViolation,
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)
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class TradingControl(with_metaclass(abc.ABCMeta)):
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"""
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Abstract base class representing a fail-safe control on the behavior of any
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algorithm.
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"""
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def __init__(self, **kwargs):
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"""
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Track any arguments that should be printed in the error message
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generated by self.fail.
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"""
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self.__fail_args = kwargs
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@abc.abstractmethod
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def validate(self,
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asset,
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amount,
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portfolio,
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algo_datetime,
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algo_current_data):
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"""
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Before any order is executed by TradingAlgorithm, this method should be
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called *exactly once* on each registered TradingControl object.
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If the specified asset and amount do not violate this TradingControl's
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restraint given the information in `portfolio`, this method should
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return None and have no externally-visible side-effects.
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If the desired order violates this TradingControl's contraint, this
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method should call self.fail(asset, amount).
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"""
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raise NotImplementedError
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def fail(self, asset, amount, datetime, metadata=None):
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"""
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Raise a TradingControlViolation with information about the failure.
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If dynamic information should be displayed as well, pass it in via
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`metadata`.
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"""
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constraint = repr(self)
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if metadata:
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constraint = "{constraint} (Metadata: {metadata})".format(
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constraint=constraint,
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metadata=metadata
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)
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raise TradingControlViolation(asset=asset,
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amount=amount,
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datetime=datetime,
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constraint=constraint)
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def __repr__(self):
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return "{name}({attrs})".format(name=self.__class__.__name__,
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attrs=self.__fail_args)
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class MaxOrderCount(TradingControl):
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"""
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TradingControl representing a limit on the number of orders that can be
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placed in a given trading day.
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"""
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def __init__(self, max_count):
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super(MaxOrderCount, self).__init__(max_count=max_count)
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self.orders_placed = 0
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self.max_count = max_count
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self.current_date = None
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def validate(self,
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asset,
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amount,
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_portfolio,
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algo_datetime,
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_algo_current_data):
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"""
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Fail if we've already placed self.max_count orders today.
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"""
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algo_date = algo_datetime.date()
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# Reset order count if it's a new day.
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if self.current_date and self.current_date != algo_date:
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self.orders_placed = 0
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self.current_date = algo_date
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if self.orders_placed >= self.max_count:
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self.fail(asset, amount, algo_datetime)
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self.orders_placed += 1
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class RestrictedListOrder(TradingControl):
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"""
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TradingControl representing a restricted list of assets that
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cannot be ordered by the algorithm.
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"""
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def __init__(self, restricted_list):
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"""
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restricted list can be an iterable or a
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container (implements __contains__) for dynamic
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restrictions.
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"""
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super(RestrictedListOrder, self).__init__()
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self.restricted_list = restricted_list
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def validate(self,
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asset,
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amount,
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_portfolio,
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_algo_datetime,
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_algo_current_data):
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"""
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Fail if the asset is in the restricted_list.
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"""
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if asset in self.restricted_list:
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self.fail(asset, amount, _algo_datetime)
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class MaxOrderSize(TradingControl):
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"""
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TradingControl representing a limit on the magnitude of any single order
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placed with the given asset. Can be specified by share or by dollar
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value.
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"""
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def __init__(self, asset=None, max_shares=None, max_notional=None):
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super(MaxOrderSize, self).__init__(asset=asset,
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max_shares=max_shares,
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max_notional=max_notional)
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self.asset = asset
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self.max_shares = max_shares
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self.max_notional = max_notional
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if max_shares is None and max_notional is None:
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raise ValueError(
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"Must supply at least one of max_shares and max_notional"
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)
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if max_shares and max_shares < 0:
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raise ValueError(
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"max_shares cannot be negative."
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)
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if max_notional and max_notional < 0:
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raise ValueError(
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"max_notional must be positive."
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)
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def validate(self,
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asset,
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amount,
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portfolio,
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_algo_datetime,
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algo_current_data):
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"""
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Fail if the magnitude of the given order exceeds either self.max_shares
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or self.max_notional.
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"""
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if self.asset is not None and self.asset != asset:
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return
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if self.max_shares is not None and abs(amount) > self.max_shares:
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self.fail(asset, amount, _algo_datetime)
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current_asset_price = algo_current_data[asset].price
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order_value = amount * current_asset_price
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too_much_value = (self.max_notional is not None and
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abs(order_value) > self.max_notional)
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if too_much_value:
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self.fail(asset, amount, _algo_datetime)
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class MaxPositionSize(TradingControl):
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"""
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TradingControl representing a limit on the maximum position size that can
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be held by an algo for a given asset.
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"""
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def __init__(self, asset=None, max_shares=None, max_notional=None):
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super(MaxPositionSize, self).__init__(asset=asset,
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max_shares=max_shares,
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max_notional=max_notional)
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self.asset = asset
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self.max_shares = max_shares
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self.max_notional = max_notional
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if max_shares is None and max_notional is None:
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raise ValueError(
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"Must supply at least one of max_shares and max_notional"
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)
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if max_shares and max_shares < 0:
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raise ValueError(
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"max_shares cannot be negative."
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)
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if max_notional and max_notional < 0:
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raise ValueError(
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"max_notional must be positive."
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)
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def validate(self,
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asset,
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amount,
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portfolio,
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algo_datetime,
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algo_current_data):
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"""
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Fail if the given order would cause the magnitude of our position to be
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greater in shares than self.max_shares or greater in dollar value than
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self.max_notional.
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"""
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if self.asset is not None and self.asset != asset:
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return
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current_share_count = portfolio.positions[asset].amount
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shares_post_order = current_share_count + amount
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too_many_shares = (self.max_shares is not None and
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abs(shares_post_order) > self.max_shares)
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if too_many_shares:
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self.fail(asset, amount, algo_datetime)
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current_price = algo_current_data[asset].price
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value_post_order = shares_post_order * current_price
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too_much_value = (self.max_notional is not None and
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abs(value_post_order) > self.max_notional)
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if too_much_value:
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self.fail(asset, amount, algo_datetime)
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class LongOnly(TradingControl):
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"""
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TradingControl representing a prohibition against holding short positions.
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"""
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def validate(self,
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asset,
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amount,
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portfolio,
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_algo_datetime,
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_algo_current_data):
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"""
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Fail if we would hold negative shares of asset after completing this
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order.
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"""
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if portfolio.positions[asset].amount + amount < 0:
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self.fail(asset, amount, _algo_datetime)
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class AssetDateBounds(TradingControl):
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"""
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TradingControl representing a prohibition against ordering an asset before
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its start_date, or after its end_date.
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"""
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def validate(self,
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asset,
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amount,
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portfolio,
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algo_datetime,
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algo_current_data):
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"""
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Fail if the algo has passed this Asset's end_date, or before the
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Asset's start date.
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"""
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# If the order is for 0 shares, then silently pass through.
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if amount == 0:
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return
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normalized_algo_dt = pd.Timestamp(algo_datetime).normalize()
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# Fail if the algo is before this Asset's start_date
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if asset.start_date:
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normalized_start = pd.Timestamp(asset.start_date).normalize()
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if normalized_algo_dt < normalized_start:
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metadata = {
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'asset_start_date': normalized_start
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}
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self.fail(asset, amount, algo_datetime, metadata=metadata)
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# Fail if the algo has passed this Asset's end_date
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if asset.end_date:
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normalized_end = pd.Timestamp(asset.end_date).normalize()
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if normalized_algo_dt > normalized_end:
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metadata = {
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'asset_end_date': normalized_end
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}
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self.fail(asset, amount, algo_datetime, metadata=metadata)
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class AccountControl(with_metaclass(abc.ABCMeta)):
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"""
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Abstract base class representing a fail-safe control on the behavior of any
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algorithm.
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"""
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def __init__(self, **kwargs):
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"""
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Track any arguments that should be printed in the error message
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generated by self.fail.
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"""
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self.__fail_args = kwargs
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@abc.abstractmethod
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def validate(self,
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_portfolio,
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_account,
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_algo_datetime,
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_algo_current_data):
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"""
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On each call to handle data by TradingAlgorithm, this method should be
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called *exactly once* on each registered AccountControl object.
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If the check does not violate this AccountControl's restraint given
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the information in `portfolio` and `account`, this method should
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return None and have no externally-visible side-effects.
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If the desired order violates this AccountControl's contraint, this
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method should call self.fail().
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"""
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raise NotImplementedError
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def fail(self):
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"""
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Raise an AccountControlViolation with information about the failure.
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"""
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raise AccountControlViolation(constraint=repr(self))
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def __repr__(self):
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return "{name}({attrs})".format(name=self.__class__.__name__,
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attrs=self.__fail_args)
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class MaxLeverage(AccountControl):
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"""
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AccountControl representing a limit on the maximum leverage allowed
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by the algorithm.
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"""
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def __init__(self, max_leverage):
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"""
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max_leverage is the gross leverage in decimal form. For example,
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2, limits an algorithm to trading at most double the account value.
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"""
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super(MaxLeverage, self).__init__(max_leverage=max_leverage)
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self.max_leverage = max_leverage
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if max_leverage is None:
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raise ValueError(
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"Must supply max_leverage"
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)
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if max_leverage < 0:
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raise ValueError(
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"max_leverage must be positive"
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)
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def validate(self,
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_portfolio,
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_account,
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_algo_datetime,
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_algo_current_data):
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"""
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Fail if the leverage is greater than the allowed leverage.
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"""
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if _account['leverage'] > self.max_leverage:
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self.fail()
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