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fawce
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
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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from zipline.finance.risk import DailyReturn
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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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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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