mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-29 12:22:42 +08:00
fawce
a4a4d38a73
- added LSE reference rrules calendar (thanks to Edward Johns)
- added tests to verify LSE environment matches rrule calendar
- added a test to verify global environment behavior can be set.
- moved DailyReturn class to trading to eliminate circularity from
risk <-> trading.
- updated TradingEnvironment to be a context manager. This allows users
to run algorithms in individually isolated environments in one python
process. This is useful for managing multiple algorithms in a single
ipython notebook.
- added comments to explain behavior and useage of the global environment
108 lines
2.8 KiB
Python
108 lines
2.8 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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from datetime import datetime
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import csv
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from StringIO import StringIO
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from functools import partial
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import requests
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from loader_utils import (
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date_conversion,
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source_to_records
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)
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from loader_utils import Mapping
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_BENCHMARK_MAPPING = {
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# Need to add 'symbol'
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'volume': (int, 'Volume'),
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'open': (float, 'Open'),
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'close': (float, 'Close'),
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'high': (float, 'High'),
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'low': (float, 'Low'),
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'adj_close': (float, 'Adj Close'),
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'date': (partial(date_conversion, date_pattern='%Y-%m-%d'), 'Date')
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}
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def benchmark_mappings():
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return {key: Mapping(*value)
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for key, value
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in _BENCHMARK_MAPPING.iteritems()}
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def get_raw_benchmark_data(start_date, end_date, symbol):
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# create benchmark files
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# ^GSPC 19500103
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params = {
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's': symbol,
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# end_date month, zero indexed
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'd': end_date.month - 1,
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# end_date day str(int(todate[6:8])) #day
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'e': end_date.day,
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# end_date year str(int(todate[0:4]))
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'f': end_date.year,
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# daily frequency
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'g': 'd',
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# start_date month, zero indexed
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'a': start_date.month - 1,
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# start_date day
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'b': start_date.day,
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# start_date year
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'c': start_date.year
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}
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res = requests.get('http://ichart.yahoo.com/table.csv',
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params=params)
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return csv.DictReader(StringIO(res.content))
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def get_benchmark_data(symbol):
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"""
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Benchmarks from Yahoo.
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"""
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start_date = datetime(year=1950, month=1, day=3)
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end_date = datetime.utcnow()
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raw_benchmark_data = get_raw_benchmark_data(start_date, end_date, symbol)
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# Reverse data so we can load it in reverse chron order.
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benchmarks_source = reversed(list(raw_benchmark_data))
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mappings = benchmark_mappings()
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return source_to_records(mappings, benchmarks_source)
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def get_benchmark_returns(symbol):
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from zipline.finance.trading import DailyReturn
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benchmark_returns = []
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for data_point in get_benchmark_data(symbol):
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returns = (data_point['close'] - data_point['open']) / \
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data_point['open']
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daily_return = DailyReturn(date=data_point['date'], returns=returns)
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benchmark_returns.append(daily_return)
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return benchmark_returns
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