mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-29 09:37:54 +08:00
Eddie Hebert
97cbea2514
The recent change to the creation of the data panel ended up with a panel with the dtype of 'object', which was causing numpy ufuncs like `log` to crash out on an `AttributeError`. This forces all frames in the panel to use a dtype of 'float', we may want to look at seeting a dtype on a frame by frame basis, e.g. 'volume' may more accurately be 'int'.
432 lines
13 KiB
Python
432 lines
13 KiB
Python
#
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# Copyright 2012 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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"""
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Algorithm Protocol
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===================
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For a class to be passed as a trading algorithm to the
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:py:class:`zipline.lines.SimulatedTrading` zipline it must follow an
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implementation protocol. Examples of this algorithm protocol are provided
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below.
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The algorithm must expose methods:
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- initialize: method that takes no args, no returns. Simply called to
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enable the algorithm to set any internal state needed.
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- get_sid_filter: method that takes no args, and returns a list of valid
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sids. List must have a length between 1 and 10. If None is returned the
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filter will block all events.
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- handle_data: method that accepts a :py:class:`zipline.protocol_utils.ndict`
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of the current state of the simulation universe. An example data ndict:
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.. This outputs the table as an HTML table but for some reason there
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is no bounding box. Make the previous paragraph ending colon a
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double-colon to turn this back into blockquoted table in ASCII art.
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+-----------------+--------------+----------------+-------------------+
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| | sid(133) | sid(134) | sid(135) |
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+=================+==============+================+===================+
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| price | $10.10 | $22.50 | $13.37 |
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+-----------------+--------------+----------------+-------------------+
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| volume | 10,000 | 5,000 | 50,000 |
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+-----------------+--------------+----------------+-------------------+
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| mvg_avg_30 | $9.97 | $22.61 | $13.37 |
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+-----------------+--------------+----------------+-------------------+
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| dt | 6/30/2012 | 6/30/2011 | 6/29/2012 |
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+-----------------+--------------+----------------+-------------------+
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- set_order: method that accepts a callable. Will be set as the value of the
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order method of trading_client. An algorithm can then place orders with a
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valid sid and a number of shares::
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self.order(sid(133), share_count)
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- set_performance: property which can be set equal to the
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cumulative_trading_performance property of the trading_client. An
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algorithm can then check position information with the
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Portfolio object::
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self.Portfolio[sid(133)]['cost_basis']
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- set_transact_setter: method that accepts a callable. Will
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be set as the value of the set_transact_setter method of
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the trading_client. This allows an algorithm to change the
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slippage model used to predict transactions based on orders
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and trade events.
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"""
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from copy import deepcopy
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import numpy as np
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from zipline.algorithm import TradingAlgorithm
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from zipline.finance.slippage import FixedSlippage
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class TestAlgorithm(TradingAlgorithm):
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"""
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This algorithm will send a specified number of orders, to allow unit tests
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to verify the orders sent/received, transactions created, and positions
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at the close of a simulation.
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"""
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def initialize(self, sid, amount, order_count, sid_filter=None):
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self.count = order_count
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self.sid = sid
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self.amount = amount
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self.incr = 0
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if sid_filter:
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self.sid_filter = sid_filter
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else:
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self.sid_filter = [self.sid]
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def handle_data(self, data):
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self.frame_count += 1
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#place an order for 100 shares of sid
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if self.incr < self.count:
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self.order(self.sid, self.amount)
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self.incr += 1
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class HeavyBuyAlgorithm(TradingAlgorithm):
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"""
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This algorithm will send a specified number of orders, to allow unit tests
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to verify the orders sent/received, transactions created, and positions
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at the close of a simulation.
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"""
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def initialize(self, sid, amount):
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self.sid = sid
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self.amount = amount
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self.incr = 0
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def handle_data(self, data):
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self.frame_count += 1
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#place an order for 100 shares of sid
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self.order(self.sid, self.amount)
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self.incr += 1
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class NoopAlgorithm(TradingAlgorithm):
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"""
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Dolce fa niente.
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"""
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def get_sid_filter(self):
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return []
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def set_transact_setter(self, txn_sim_callable):
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pass
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class ExceptionAlgorithm(TradingAlgorithm):
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"""
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Throw an exception from the method name specified in the
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constructor.
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"""
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def initialize(self, throw_from, sid):
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self.throw_from = throw_from
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self.sid = sid
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if self.throw_from == "initialize":
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raise Exception("Algo exception in initialize")
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else:
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pass
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def set_order(self, order_callable):
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if self.throw_from == "set_order":
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raise Exception("Algo exception in set_order")
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else:
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pass
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def set_portfolio(self, portfolio):
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if self.throw_from == "set_portfolio":
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raise Exception("Algo exception in set_portfolio")
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else:
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pass
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def handle_data(self, data):
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if self.throw_from == "handle_data":
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raise Exception("Algo exception in handle_data")
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else:
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pass
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def get_sid_filter(self):
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if self.throw_from == "get_sid_filter":
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raise Exception("Algo exception in get_sid_filter")
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else:
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return [self.sid]
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def set_transact_setter(self, txn_sim_callable):
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pass
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class DivByZeroAlgorithm(TradingAlgorithm):
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def initialize(self, sid):
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self.sid = sid
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self.incr = 0
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def handle_data(self, data):
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self.incr += 1
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if self.incr > 4:
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5 / 0
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pass
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class TooMuchProcessingAlgorithm(TradingAlgorithm):
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def initialize(self, sid):
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self.sid = sid
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def handle_data(self, data):
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# Unless we're running on some sort of
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# supercomputer this will hit timeout.
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for i in xrange(1000000000):
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self.foo = i
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class TimeoutAlgorithm(TradingAlgorithm):
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def initialize(self, sid):
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self.sid = sid
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self.incr = 0
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def handle_data(self, data):
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if self.incr > 4:
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import time
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time.sleep(100)
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pass
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class RecordAlgorithm(TradingAlgorithm):
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def initialize(self):
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self.incr = 0
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self.record_variables(['incr'])
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def handle_data(self, data):
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self.incr += 1
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from zipline.algorithm import TradingAlgorithm
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from zipline.transforms import BatchTransform, batch_transform
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from zipline.transforms import MovingAverage
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class TestRegisterTransformAlgorithm(TradingAlgorithm):
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def initialize(self, *args, **kwargs):
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self.add_transform(MovingAverage, 'mavg', ['price'],
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market_aware=True,
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window_length=2)
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self.set_slippage(FixedSlippage())
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def handle_data(self, data):
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pass
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##########################################
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# Algorithm using simple batch transforms
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class ReturnPriceBatchTransform(BatchTransform):
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def get_value(self, data):
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assert data.shape[1] == self.window_length, \
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"data shape={0} does not equal window_length={1} for data={2}".\
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format(data.shape[1], self.window_length, data)
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return data.price
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@batch_transform
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def return_price_batch_decorator(data):
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return data.price
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@batch_transform
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def return_args_batch_decorator(data, *args, **kwargs):
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return args, kwargs
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@batch_transform
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def return_data(data, *args, **kwargs):
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return data
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@batch_transform
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def uses_ufunc(data, *args, **kwargs):
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# ufuncs like np.log should not crash
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return np.log(data)
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class BatchTransformAlgorithm(TradingAlgorithm):
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def initialize(self, *args, **kwargs):
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self.refresh_period = kwargs.pop('refresh_period', 1)
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self.window_length = kwargs.pop('window_length', 3)
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self.args = args
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self.kwargs = kwargs
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self.history_return_price_class = []
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self.history_return_price_decorator = []
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self.history_return_args = []
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self.history_return_arbitrary_fields = []
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self.history_return_nan = []
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self.history_return_sid_filter = []
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self.history_return_field_filter = []
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self.history_return_field_no_filter = []
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self.history_return_ticks = []
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self.history_return_not_full = []
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self.return_price_class = ReturnPriceBatchTransform(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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clean_nans=False
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)
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self.return_price_decorator = return_price_batch_decorator(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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clean_nans=False
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)
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self.return_args_batch = return_args_batch_decorator(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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clean_nans=False
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)
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self.return_arbitrary_fields = return_data(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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clean_nans=False
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)
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self.return_nan = return_price_batch_decorator(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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clean_nans=True
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)
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self.return_sid_filter = return_price_batch_decorator(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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clean_nans=True,
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sids=[0]
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)
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self.return_field_filter = return_data(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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clean_nans=True,
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fields=['price']
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)
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self.return_field_no_filter = return_data(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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clean_nans=True
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)
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self.return_ticks = return_data(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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create_panel=False
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)
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self.return_not_full = return_data(
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refresh_period=0,
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window_length=self.window_length,
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compute_only_full=False
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)
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self.uses_ufunc = uses_ufunc(
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refresh_period=self.refresh_period,
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window_length=self.window_length,
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clean_nans=False
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)
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self.iter = 0
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self.set_slippage(FixedSlippage())
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def handle_data(self, data):
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self.history_return_price_class.append(
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self.return_price_class.handle_data(data))
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self.history_return_price_decorator.append(
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self.return_price_decorator.handle_data(data))
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self.history_return_args.append(
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self.return_args_batch.handle_data(
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data, *self.args, **self.kwargs))
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self.history_return_ticks.append(
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self.return_ticks.handle_data(data))
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self.history_return_not_full.append(
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self.return_not_full.handle_data(data))
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self.uses_ufunc.handle_data(data)
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new_data = deepcopy(data)
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for sid in new_data:
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new_data[sid]['arbitrary'] = 123
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self.history_return_arbitrary_fields.append(
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self.return_arbitrary_fields.handle_data(new_data))
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# nan every second event price
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if self.iter % 2 == 0:
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self.history_return_nan.append(
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self.return_nan.handle_data(data))
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else:
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nan_data = deepcopy(data)
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for sid in nan_data.iterkeys():
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nan_data[sid].price = np.nan
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self.history_return_nan.append(
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self.return_nan.handle_data(nan_data))
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self.iter += 1
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# Add a new sid to check that it does not get included
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extra_sid_data = deepcopy(data)
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extra_sid_data[1] = extra_sid_data[0]
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self.history_return_sid_filter.append(
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self.return_sid_filter.handle_data(extra_sid_data)
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)
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# Add a field to check that it does not get included
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extra_field_data = deepcopy(data)
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extra_field_data[0]['ignore'] = extra_sid_data[0]['price']
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self.history_return_field_filter.append(
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self.return_field_filter.handle_data(extra_field_data)
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)
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self.history_return_field_no_filter.append(
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self.return_field_no_filter.handle_data(extra_field_data)
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)
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class SetPortfolioAlgorithm(TradingAlgorithm):
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"""
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An algorithm that tries to set the portfolio directly.
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The portfolio should be treated as a read-only object
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within the algorithm.
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"""
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def initialize(self, *args, **kwargs):
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pass
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def handle_data(self, data):
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self.portfolio = 3
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