zipline-benchmarks.py文件改写

改写原因：在这个模块中的 get_benchmark_returns() 方法回去谷歌财经下载对应SPY（类似于上证指数）的数据，但是Google上下载的数据在最后写入Io操作的时候会报一个恶心的编码的错误，很烦人，时好时坏的那种，就是图下这种报错。

　　　　　　　　　　　　　　　　　　　　　

改写方式：

　　1.首先去雅虎财经下载SPY.csv文件，然后把这个文件放到你对应的目录下

　　2.具体代码如下

#
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pandas as pd
import pytz
from datetime import datetimeimport pandas_datareader.data as pd_readerdef get_benchmark_returns(symbol, first_date, last_date):"""Get a Series of benchmark returns from Google associated with `symbol`.Default is `SPY`.Parameters----------symbol : strBenchmark symbol for which we're getting the returns.first_date : pd.TimestampFirst date for which we want to get data.last_date : pd.TimestampLast date for which we want to get data.The furthest date that Google goes back to is 1993-02-01. It has missingdata for 2008-12-15, 2009-08-11, and 2012-02-02, so we add data for thedates for which Google is missing data.We're also limited to 4000 days worth of data per request. If we make arequest for data that extends past 4000 trading days, we'll still onlyreceive 4000 days of data.first_date is **not** included because we need the close from day N - 1 tocompute the returns for day N."""# 源码# data = pd_reader.DataReader(#     symbol,#     'google',#     first_date,#     last_date# )## data = data['Close']## data[pd.Timestamp('2008-12-15')] = np.nan# data[pd.Timestamp('2009-08-11')] = np.nan# data[pd.Timestamp('2012-02-02')] = np.nan## data = data.fillna(method='ffill')# return data.sort_index().tz_localize('UTC').pct_change(1).iloc[1:]# 自己写的代码# parse = lambda x: pytz.utc.localize(datetime.strptime(x, '%Y-%m-%d'))# data = pd.read_csv("SPY.csv", parse_dates=['Date'], index_col=0, date_parser=parse)# data = data['Close']# data = data.fillna(method='ffill')# return data.sort_index().pct_change(1).iloc[0:]

总结：

　　1.这次报错后，我习惯性的找到最底层也就是最后两行错误，但是只能知道是编码的错误，但是解决不了。所以以后碰到类似的第三方包的错误，不要急着从最底层开始改，应该适当的想想，我在最开始出错的地方可不可以成功的避免掉，这也是一种思路。

　　2.Google财经的SPY数据不全，所以他在这个方法中定义了三列是因为这三天的数据他没有。