mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 23:24:02 +08:00
Eddie Hebert
ccb05acf5c
Account for byte/string compatibility when consuming response from requests module.
137 lines
4.0 KiB
Python
137 lines
4.0 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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import pandas as pd
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from six import iteritems
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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 iteritems(_BENCHMARK_MAPPING)}
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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.finance.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.text.splitlines())
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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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date = pd.tseries.tools.normalize_date(data_point['date'])
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daily_return = DailyReturn(date=date, returns=returns)
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benchmark_returns.append(daily_return)
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return benchmark_returns
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