mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-20 12:20:29 +08:00
Eddie Hebert
37c56b9aa4
Remove the lists of DailyReturn objects in favor of using pd.Series to store the return values. Should make it easier to inspect the values when stepping through, make the windowing of data to a certain range more facile by using, and have some performance increases due to removing object creation and member access.
133 lines
3.9 KiB
Python
133 lines
3.9 KiB
Python
#
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# Copyright 2013 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 collections
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from datetime import datetime
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import csv
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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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Mapping
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)
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DailyReturn = collections.namedtuple('DailyReturn', ['date', 'returns'])
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class BenchmarkDataNotFoundError(Exception):
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pass
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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 = collections.OrderedDict((
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('s', symbol),
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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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# 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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))
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res = requests.get('http://ichart.yahoo.com/table.csv',
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params=params, stream=True)
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if not res.ok:
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raise BenchmarkDataNotFoundError("""
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No benchmark data found for date range.
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start_date={start_date}, end_date={end_date}, url={url}""".strip().
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format(start_date=start_date,
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end_date=end_date,
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url=res.url))
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return csv.DictReader(res.iter_lines())
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def get_benchmark_data(symbol, start_date=None, end_date=None):
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"""
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Benchmarks from Yahoo.
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"""
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if start_date is None:
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start_date = datetime(year=1950, month=1, day=3)
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if end_date is None:
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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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mappings = benchmark_mappings()
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return source_to_records(mappings, raw_benchmark_data)
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def get_benchmark_returns(symbol, start_date=None, end_date=None):
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"""
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Returns a list of return percentages in chronological order.
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"""
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if start_date is None:
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start_date = datetime(year=1950, month=1, day=3)
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if end_date is None:
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end_date = datetime.utcnow()
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# Get the benchmark data and convert it to a list in chronological order.
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data_points = list(get_benchmark_data(symbol, start_date, end_date))
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data_points.reverse()
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# Calculate the return percentages.
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benchmark_returns = []
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for i, data_point in enumerate(data_points):
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if i == 0:
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curr_open = data_points[i]['open']
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returns = (data_points[i]['close'] - curr_open) / curr_open
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else:
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prev_close = data_points[i - 1]['close']
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returns = (data_point['close'] - prev_close) / prev_close
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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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