Question

请注意：此问题已在下面成功解答ptrj。我还在我的博客上写了一篇关于我对zipline的体验的博客文章，你可以在这里找到：https://financialzipline.wordpress.com

我的总部设在南非，我正试图将南非股票加载到数据框中，以便它可以提供带有股价信息的zipline。让我们来看看我在JSE（约翰内斯堡证券交易所）上市的 AdCorp控股有限公司：

Google财经向我提供了历史价格信息：

https://www.google.com/finance/historical?q=JSE%3AADR&ei=5G6OV4ibBIi8UcP-nfgB

Yahoo Finance 没有关于该公司的信息。

https://finance.yahoo.com/quote/adcorp?ltr=1

在iPython Notebook中输入以下代码，可以获取Google财经信息的数据框：

start = datetime.datetime(2016,7,1)
end = datetime.datetime(2016,7,18)    
f = web.DataReader('JSE:ADR', 'google',start,end)

如果我显示f，我看到该信息实际上也与Google财经信息相对应：

这就是Google财经的价格，您可以在Google财经网站上看到2016-07-18的信息与我的数据框完全匹配。

但是，我不确定如何加载此数据框，以便zipline可以将其用作数据包。

如果查看为buyapple.py给出的示例，您可以看到它只是从摄取的数据包quantopian-quandl中提取苹果共享（APPL）的数据。这里的挑战是将APPL替换为JSE:ADR，以便每天订购10 JSE:ADR份来自数据框而不是数据包quantopian-quandl，并将其绘制在图表。

有谁知道怎么做？网上几乎没有关于这个问题的例子......

这是zipline＆amp;示例文件夹中提供的buyapple.py代码：

from zipline.api import order, record, symbol


def initialize(context):
    pass


def handle_data(context, data):
    order(symbol('AAPL'), 10)
    record(AAPL=data.current(symbol('AAPL'), 'price'))


# Note: this function can be removed if running
# this algorithm on quantopian.com
def analyze(context=None, results=None):
    import matplotlib.pyplot as plt
    # Plot the portfolio and asset data.
    ax1 = plt.subplot(211)
    results.portfolio_value.plot(ax=ax1)
    ax1.set_ylabel('Portfolio value (USD)')
    ax2 = plt.subplot(212, sharex=ax1)
    results.AAPL.plot(ax=ax2)
    ax2.set_ylabel('AAPL price (USD)')

    # Show the plot.
    plt.gcf().set_size_inches(18, 8)
    plt.show()


def _test_args():
    """Extra arguments to use when zipline's automated tests run this example.
    """
    import pandas as pd

    return {
        'start': pd.Timestamp('2014-01-01', tz='utc'),
        'end': pd.Timestamp('2014-11-01', tz='utc'),
    }

修改

我查看了从雅虎财经中提取数据的代码，并对其进行了一些修改以使其获取Google财经数据。可以在此处找到Yahoo Finance的代码：http://www.zipline.io/_modules/zipline/data/bundles/yahoo.html。

这是我的摄取Google财经的代码 - 遗憾的是它无效。更精通python的人可以帮助我吗？：

import os

import numpy as np
import pandas as pd
from pandas_datareader.data import DataReader
import requests

from zipline.utils.cli import maybe_show_progress


def _cachpath(symbol, type_):
    return '-'.join((symbol.replace(os.path.sep, '_'), type_))


def google_equities(symbols, start=None, end=None):
    """Create a data bundle ingest function from a set of symbols loaded from
    yahoo.

    Parameters
    ----------
    symbols : iterable[str]
        The ticker symbols to load data for.
    start : datetime, optional
        The start date to query for. By default this pulls the full history
        for the calendar.
    end : datetime, optional
        The end date to query for. By default this pulls the full history
        for the calendar.

    Returns
    -------
    ingest : callable
        The bundle ingest function for the given set of symbols.

    Examples
    --------
    This code should be added to ~/.zipline/extension.py

    .. code-block:: python

       from zipline.data.bundles import yahoo_equities, register

       symbols = (
           'AAPL',
           'IBM',
           'MSFT',
       )
       register('my_bundle', yahoo_equities(symbols))

    Notes
    -----
    The sids for each symbol will be the index into the symbols sequence.
    """
    # strict this in memory so that we can reiterate over it
    symbols = tuple(symbols)

    def ingest(environ,
               asset_db_writer,
               minute_bar_writer,  # unused
               daily_bar_writer,
               adjustment_writer,
               calendar,
               cache,
               show_progress,
               output_dir,
               # pass these as defaults to make them 'nonlocal' in py2
               start=start,
               end=end):
        if start is None:
            start = calendar[0]
        if end is None:
            end = None

        metadata = pd.DataFrame(np.empty(len(symbols), dtype=[
            ('start_date', 'datetime64[ns]'),
            ('end_date', 'datetime64[ns]'),
            ('auto_close_date', 'datetime64[ns]'),
            ('symbol', 'object'),
        ]))

        def _pricing_iter():
            sid = 0
            with maybe_show_progress(
                    symbols,
                    show_progress,
                    label='Downloading Google pricing data: ') as it, \
                    requests.Session() as session:
                for symbol in it:
                    path = _cachpath(symbol, 'ohlcv')
                    try:
                        df = cache[path]
                    except KeyError:
                        df = cache[path] = DataReader(
                            symbol,
                            'google',
                            start,
                            end,
                            session=session,
                        ).sort_index()

                    # the start date is the date of the first trade and
                    # the end date is the date of the last trade
                    start_date = df.index[0]
                    end_date = df.index[-1]
                    # The auto_close date is the day after the last trade.
                    ac_date = end_date + pd.Timedelta(days=1)
                    metadata.iloc[sid] = start_date, end_date, ac_date, symbol

                    df.rename(
                        columns={
                            'Open': 'open',
                            'High': 'high',
                            'Low': 'low',
                            'Close': 'close',
                            'Volume': 'volume',
                        },
                        inplace=True,
                    )
                    yield sid, df
                    sid += 1

        daily_bar_writer.write(_pricing_iter(), show_progress=True)

        symbol_map = pd.Series(metadata.symbol.index, metadata.symbol)
        asset_db_writer.write(equities=metadata)

        adjustment_writer.write(splits=pd.DataFrame(), dividends=pd.DataFrame())
        # adjustments = []
        # with maybe_show_progress(
        #         symbols,
        #         show_progress,
        #         label='Downloading Google adjustment data: ') as it, \
        #         requests.Session() as session:
        #     for symbol in it:
        #         path = _cachpath(symbol, 'adjustment')
        #         try:
        #             df = cache[path]
        #         except KeyError:
        #             df = cache[path] = DataReader(
        #                 symbol,
        #                 'google-actions',
        #                 start,
        #                 end,
        #                 session=session,
        #             ).sort_index()

        #         df['sid'] = symbol_map[symbol]
        #         adjustments.append(df)

        # adj_df = pd.concat(adjustments)
        # adj_df.index.name = 'date'
        # adj_df.reset_index(inplace=True)

        # splits = adj_df[adj_df.action == 'SPLIT']
        # splits = splits.rename(
        #     columns={'value': 'ratio', 'date': 'effective_date'},
        # )
        # splits.drop('action', axis=1, inplace=True)

        # dividends = adj_df[adj_df.action == 'DIVIDEND']
        # dividends = dividends.rename(
        #     columns={'value': 'amount', 'date': 'ex_date'},
        # )
        # dividends.drop('action', axis=1, inplace=True)
        # # we do not have this data in the yahoo dataset
        # dividends['record_date'] = pd.NaT
        # dividends['declared_date'] = pd.NaT
        # dividends['pay_date'] = pd.NaT

        # adjustment_writer.write(splits=splits, dividends=dividends)

    return ingest

Answer 1

我按照http://www.zipline.io/上的教程进行了操作，并按照以下步骤操作：

为谷歌股票准备一个摄取功能。

您粘贴的相同代码（基于文件yahoo.py），并进行了以下修改：
```
# Replace line
# adjustment_writer.write(splits=pd.DataFrame(), dividends=pd.DataFrame())
# with line
adjustment_writer.write()
```
我将文件命名为google.py并将其复制到zipline安装目录的子目录zipline/data/bundle。（它可以放在python路径上的任何位置。或者您可以修改zipline/data/bundle/__init__.py以便能够像yahoo_equities一样调用它。）
摄取（参见http://www.zipline.io/bundles.html）

将以下行添加到主目录中的文件.zipline/extension.py - 主目录是Windows上的用户目录（C：\ Users \ your username）。 .zipline文件夹是一个隐藏文件夹，您必须取消隐藏文件才能看到它。
```
from zipline.data.bundles import register

from zipline.data.bundles.google import google_equities

equities2 = {
    'JSE:ADR',
}

register(
    'my-google-equities-bundle',  # name this whatever you like
    google_equities(equities2),
)
```
然后运行
```
zipline ingest -b my-google-equities-bundle
```

测试（如http://www.zipline.io/beginner-tutorial.html）

我采用了一个示例文件zipline/examples/buyapple.py（与您粘贴的相同），将'AAPL'符号'JSE:ADR'替换为buyadcorp.py，重命名为python -m zipline run -f buyadcorp.py --bundle my-google-equities-bundle --start 2000-1-1 --end 2014-1-1并运行

import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from ipywidgets.widgets import Button
from IPython.display import display

class Test(object):
    def __init__(self):
        self.figure = plt.figure()
        self.ax = self.figure.gca()
        self.button = Button(description = "Draw new points.")
        display(self.button)
        self.button.on_click(self.button_clicked)
        self.button2 = Button(description = "Draw more points.")
        display(self.button2)
        self.button2.on_click(self.button_clicked2)

    def button_clicked(self, event):
        self.ax.scatter([1,2,8], [6,5,4])
        self.figure.canvas.draw()
        plt.show()

    def button_clicked2(self, event):
        self.ax.scatter([1,0,5], [3,8,3])
        self.figure.canvas.draw()
        plt.show()

test = Test()

结果与直接从Google财经下载的数据一致。

Zipline：使用pandas-datareader为非美国金融市场提供Google财经数据框

1 个答案: