mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-27 11:20:45 +08:00
fawceandEddie Hebert
2c7355a0dc
Global state for the financial simulation environment is accessed through the
zipline.finance.trading module, which now contains a module variable:
environment.
Parameters are passed into an algorithm as a keyword argument, sim_params.
SimulationParameters creates a trading day index for the test period that
can be used to find trading days, calculate distance between trading days,
and other common operations. The sim params index is just selected from the
global state.
================
Details:
- adding delorean to the requirements.
- made index symbol a parameter for loading the benchmark data. changed
messagepack storage to be symbol specific.
- ported risk, performance, algorithm, transforms, batch transforms
and associated tests to use simulation parameters and global environment
- factory and sim factory use global state and sim params
- factory method parameter names now reflect the class expected
141 lines
3.9 KiB
Python
141 lines
3.9 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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import os
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from os.path import expanduser
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import msgpack
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from collections import OrderedDict
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from treasuries import get_treasury_data
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from benchmarks import get_benchmark_returns
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from zipline.utils.date_utils import tuple_to_date
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import zipline.finance.risk as risk
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from operator import attrgetter
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# TODO: Make this path customizable.
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DATA_PATH = os.path.join(
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expanduser("~"),
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'.zipline',
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'data'
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)
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def get_datafile(name, mode='r'):
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"""
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Returns a handle to data file.
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Creates containing directory, if needed.
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"""
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if not os.path.exists(DATA_PATH):
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os.makedirs(DATA_PATH)
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return open(os.path.join(DATA_PATH, name), mode)
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def dump_treasury_curves():
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"""
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Dumps data to be used with zipline.
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Puts source treasury and data into zipline.
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"""
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tr_data = []
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for curve in get_treasury_data():
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date_as_tuple = curve['date'].timetuple()[0:6] + \
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(curve['date'].microsecond,)
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# Not ideal but massaging data into expected format
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del curve['date']
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tr = (date_as_tuple, curve)
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tr_data.append(tr)
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with get_datafile('treasury_curves.msgpack', mode='wb') as tr_fp:
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tr_fp.write(msgpack.dumps(tr_data))
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def dump_benchmarks(symbol):
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"""
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Dumps data to be used with zipline.
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Puts source treasury and data into zipline.
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"""
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benchmark_data = []
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for daily_return in get_benchmark_returns(symbol):
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date_as_tuple = daily_return.date.timetuple()[0:6] + \
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(daily_return.date.microsecond,)
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# Not ideal but massaging data into expected format
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benchmark = (date_as_tuple, daily_return.returns)
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benchmark_data.append(benchmark)
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with get_datafile(get_benchmark_filename(symbol), mode='wb') as bmark_fp:
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bmark_fp.write(msgpack.dumps(benchmark_data))
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def get_benchmark_filename(symbol):
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return "%s_benchmark.msgpack" % symbol
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def load_market_data(bm_symbol='^GSPC'):
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try:
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fp_bm = get_datafile(get_benchmark_filename(bm_symbol), "rb")
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except IOError:
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print """
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data msgpacks aren't distribute with source.
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Fetching data from Yahoo Finance.
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""".strip()
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dump_benchmarks(bm_symbol)
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fp_bm = get_datafile(get_benchmark_filename(bm_symbol), "rb")
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bm_list = msgpack.loads(fp_bm.read())
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bm_returns = []
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for packed_date, returns in bm_list:
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event_dt = tuple_to_date(packed_date)
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daily_return = risk.DailyReturn(date=event_dt, returns=returns)
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bm_returns.append(daily_return)
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fp_bm.close()
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bm_returns = sorted(bm_returns, key=attrgetter('date'))
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try:
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fp_tr = get_datafile('treasury_curves.msgpack', "rb")
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except IOError:
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print """
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data msgpacks aren't distribute with source.
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Fetching data from data.treasury.gov
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""".strip()
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dump_treasury_curves()
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fp_tr = get_datafile('treasury_curves.msgpack', "rb")
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tr_list = msgpack.loads(fp_tr.read())
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tr_curves = {}
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for packed_date, curve in tr_list:
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tr_dt = tuple_to_date(packed_date)
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#tr_dt = tr_dt.replace(hour=0, minute=0, second=0, tzinfo=pytz.utc)
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tr_curves[tr_dt] = curve
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fp_tr.close()
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tr_curves = OrderedDict(sorted(
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((dt, c) for dt, c in tr_curves.iteritems()),
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key=lambda t: t[0]))
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return bm_returns, tr_curves
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