Ingesting Data 摄取数据

If you haven’t ingested the data, you’ll need a Quandl API key to ingest the default bundle. Then run: 如果您没有摄入数据，则需要使用Quandl API密钥来获取默认包。然后运行：

$ QUANDL_API_KEY=<yourkey> zipline ingest [-b <bundle>]

where <bundle> is the name of the bundle to ingest, defaulting to quandl. 其中<bundle>是要摄取的包的名称，默认为quandl。

you can check out the ingesting data section for more detail. 您可以查看获取数据部分了解更多详细信息。

Command Line Interface 命令行界面

After you installed zipline you should be able to execute the following from your command line (e.g. cmd.exe on Windows, or the Terminal app on OSX):

安装zipline后，您应该能够从命令行执行以下命令（例如Windows上的cmd.exe或OSX上的Terminal app）：

$ zipline run --help

Usage: zipline run [OPTIONS]

Run a backtest for the given algorithm.

Options:
 -f, --algofile FILENAME         The file that contains the algorithm to run.
 -t, --algotext TEXT             The algorithm script to run.
 -D, --define TEXT               Define a name to be bound in the namespace
                                 before executing the algotext. For example
                                 ‘-Dname=value‘. The value may be any python
                                 expression. These are evaluated in order so
                                 they may refer to previously defined names.
 --data-frequency [daily|minute]
                                 The data frequency of the simulation.
                                 [default: daily]
 --capital-base FLOAT            The starting capital for the simulation.
                                 [default: 10000000.0]
 -b, --bundle BUNDLE-NAME        The data bundle to use for the simulation.
                                 [default: quandl]
 --bundle-timestamp TIMESTAMP    The date to lookup data on or before.
                                 [default: <current-time>]
 -s, --start DATE                The start date of the simulation.
 -e, --end DATE                  The end date of the simulation.
 -o, --output FILENAME           The location to write the perf data. If this
                                 is ‘-‘ the perf will be written to stdout.
                                 [default: -]
 --trading-calendar TRADING-CALENDAR
                                 The calendar you want to use e.g. LSE. NYSE
                                 is the default.
 --print-algo / --no-print-algo  Print the algorithm to stdout.
 --help                          Show this message and exit.

As you can see there are a couple of flags that specify where to find your algorithm (-f) as well as parameters specifying which data to use, defaulting to quandl. There are also arguments for the date range to run the algorithm over (--start and --end). Finally, you’ll want to save the performance metrics of your algorithm so that you can analyze how it performed. This is done via the --outputflag and will cause it to write the performance DataFrame in the pickle Python file format. Note that you can also define a configuration file with these parameters that you can then conveniently pass to the -c option so that you don’t have to supply the command line args all the time (see the .conf files in the examples directory).

正如你所看到的，有几个标志指定在哪里找到你的算法（-f）以及指定使用哪些数据的参数，默认为quandl。还有一些参数用于运行算法的日期范围（--start和--end）。最后，您需要保存算法的性能指标，以便分析其执行方式。这是通过--outputflag完成的，并且会导致它以pickle Python文件格式写入性能数据帧。请注意，您也可以使用这些参数定义一个配置文件，然后您可以方便地将它们传递给-c选项，以便您不必始终提供命令行参数（请参阅示例目录中的.conf文件） 。

Thus, to execute our algorithm from above and save the results to buyapple_out.pickle we would call zipline run as follows:

因此，为了从上面执行我们的算法并将结果保存到buyapple_out.pickle，我们将调用zipline run，如下所示：

zipline run -f ../../zipline/examples/buyapple.py --start 2016-1-1 --end 2018-1-1 -o buyapple_out.pickle

AAPL
[2018-01-03 04:30:50.150039] WARNING: Loader: Refusing to download new benchmark data because a download succeeded at 2018-01-03 04:01:34+00:00.
[2018-01-03 04:30:50.191479] WARNING: Loader: Refusing to download new treasury data because a download succeeded at 2018-01-03 04:01:35+00:00.
[2018-01-03 04:30:51.843465] INFO: Performance: Simulated 503 trading days out of 503.
[2018-01-03 04:30:51.843598] INFO: Performance: first open: 2016-01-04 14:31:00+00:00
[2018-01-03 04:30:51.843672] INFO: Performance: last close: 2017-12-29 21:00:00+00:00

Lets take a quick look at the performance DataFrame. For this, we use pandas from inside the IPython Notebook and print the first ten rows. Note that zipline makes heavy usage of pandas, especially for data input and outputting so it’s worth spending some time to learn it.

让我们快速看一下DataFrame的性能。为此，我们使用IPython Notebook内的pandas并打印前十行。请注意，zipline大量使用pandas，特别是数据输入和输出，因此值得花一些时间来学习。

import pandas as pd
perf = pd.read_pickle(‘buyapple_out.pickle‘) # read in perf DataFrame
perf.head()

As you can see, there is a row for each trading day, starting on the first business day of 2016. In the columns you can find various information about the state of your algorithm. The very first columnAAPL was placed there by the record() function mentioned earlier and allows us to plot the price of apple. For example, we could easily examine now how our portfolio value changed over time compared to the AAPL stock price.

正如你所看到的，从2016年的第一个工作日开始，每个交易日都有一行。在这些列中你可以找到有关你的算法状态的各种信息。第一列AAPL被前面提到的record（）函数放在那里，并允许我们绘制苹果的价格。例如，我们现在可以轻松研究我们的投资组合与AAPL股票价格相比是如何随时间的变化而价值变动的。

%pylab inline
figsize(12, 12)
import matplotlib.pyplot as plt

ax1 = plt.subplot(211)
perf.portfolio_value.plot(ax=ax1)
ax1.set_ylabel(‘Portfolio Value‘)
ax2 = plt.subplot(212, sharex=ax1)
perf.AAPL.plot(ax=ax2)
ax2.set_ylabel(‘AAPL Stock Price‘)

Populating the interactive namespace from numpy and matplotlib

<matplotlib.text.Text at 0x10c48c198>

As you can see, our algorithm performance as assessed by the portfolio_value closely matches that of the AAPL stock price. This is not surprising as our algorithm only bought AAPL every chance it got.正如您所看到的，portfolio_value评估的算法性能与AAPL股票价格近似匹配。这并不奇怪，因为我们的算法只是在每次购买AAPL时都购买。

Working example: Dual Moving Average Cross-Over 工作示例：DMACD

The Dual Moving Average (DMA) is a classic momentum strategy. It’s probably not used by any serious trader anymore but is still very instructive. The basic idea is that we compute two rolling or moving averages (mavg) – one with a longer window that is supposed to capture long-term trends and one shorter window that is supposed to capture short-term trends. Once the short-mavg crosses the long-mavg from below we assume that the stock price has upwards momentum and long the stock. If the short-mavg crosses from above we exit the positions as we assume the stock to go down further.

双重移动平均线（DMA）是一个典型的动量策略。它可能不再被任何认真的交易者使用，但仍然非常有启发性。基本思想是我们计算两个滚动或移动平均数（mavg） - 一个窗口的长度应该能捕捉长期趋势，另一个窗口的长度应该能捕捉到短期趋势。一旦短线从下面穿过长线，我们就会假设股票价格上涨势头强劲。如果短线从上方穿过交叉长线，我们将认为股价走低。

As we need to have access to previous prices to implement this strategy we need a new concept: History

由于我们需要获得先前的价格来实施这一战略，我们需要一个新的概念：History

data.history() is a convenience function that keeps a rolling window of data for you. The first argument is the number of bars you want to collect, the second argument is the unit (either ‘1d‘ or ‘1m‘, but note that you need to have minute-level data for using 1m). For a more detailed description of history()‘s features, see the Quantopian docs. Let’s look at the strategy which should make this clear:

data.history（）是一个方便的函数，为您保留一个滚动的数据窗口。第一个参数是要收集的条数，第二个参数是单位（‘1d‘或‘1m‘，但请注意，您使用1m将获取分钟级数据）。有关history（）的更多详细信息，请参阅Quantopian文档。让我们看看应该明确这一点的策略：

%%zipline --start 2014-1-1 --end 2018-1-1 -o dma.pickle


from zipline.api import order_target, record, symbol

def initialize(context):
    context.i = 0
    context.asset = symbol(‘AAPL‘)


def handle_data(context, data):
    # Skip first 300 days to get full windows
    context.i += 1
    if context.i < 300:
        return

    # Compute averages
    # data.history() has to be called with the same params
    # from above and returns a pandas dataframe.
    short_mavg = data.history(context.asset, ‘price‘, bar_count=100, frequency="1d").mean()
    long_mavg = data.history(context.asset, ‘price‘, bar_count=300, frequency="1d").mean()

    # Trading logic
    if short_mavg > long_mavg:
        # order_target orders as many shares as needed to
        # achieve the desired number of shares.
        order_target(context.asset, 100)
    elif short_mavg < long_mavg:
        order_target(context.asset, 0)

    # Save values for later inspection
    record(AAPL=data.current(context.asset, ‘price‘),
           short_mavg=short_mavg,
           long_mavg=long_mavg)


def analyze(context, perf):
    fig = plt.figure()
    ax1 = fig.add_subplot(211)
    perf.portfolio_value.plot(ax=ax1)
    ax1.set_ylabel(‘portfolio value in $‘)

    ax2 = fig.add_subplot(212)
    perf[‘AAPL‘].plot(ax=ax2)
    perf[[‘short_mavg‘, ‘long_mavg‘]].plot(ax=ax2)

    perf_trans = perf.ix[[t != [] for t in perf.transactions]]
    buys = perf_trans.ix[[t[0][‘amount‘] > 0 for t in perf_trans.transactions]]
    sells = perf_trans.ix[
        [t[0][‘amount‘] < 0 for t in perf_trans.transactions]]
    ax2.plot(buys.index, perf.short_mavg.ix[buys.index],
             ‘^‘, markersize=10, color=‘m‘)
    ax2.plot(sells.index, perf.short_mavg.ix[sells.index],
             ‘v‘, markersize=10, color=‘k‘)
    ax2.set_ylabel(‘price in $‘)
    plt.legend(loc=0)
    plt.show()

Here we are explicitly defining an analyze() function that gets automatically called once the backtest is done (this is not possible on Quantopian currently).

这里我们明确地定义了一个analyze（）函数，一旦完成后测就自动调用（这在Quantopian目前是不可能的）。

Although it might not be directly apparent, the power of history() (pun intended) can not be under-estimated as most algorithms make use of prior market developments in one form or another. You could easily devise a strategy that trains a classifier with scikit-learn which tries to predict future market movements based on past prices (note, that most of the scikit-learn functions require numpy.ndarrays rather than pandas.DataFrames, so you can simply pass the underlying ndarray of a DataFrame via .values).

虽然它可能不是直接显而易见的，但由于大多数算法都以某种形式利用了先前的市场开发，所以history()（一语双关）的力量不能被低估。你可以很容易地设计一个策略，用scikit-learn来训练一个分类器，它试图根据过去的价格预测未来的市场走势（注意，大多数scikit-learn函数需要numpy.ndarrays而不是pandas.DataFrames，所以你可以简单地通过.values传递DataFrame的基础ndarray）。

We also used the order_target() function above. This and other functions like it can make order management and portfolio rebalancing much easier. See the Quantopian documentation on order functions for more details.

我们还使用了上面的order_target()函数。这种功能和其他功能可以使订单管理和投资组合重新平衡变得更加容易。有关订单功能的更多详细信息，请参阅Quantopian文档。