Zipline Beginner Tutorial

Zipline Beginner Tutorial

Basics

Zipline is an open-source algorithmic trading simulator written in Python .Zipline是一个用Python编写的开源算法交易模拟器。

The source can be found at: https://github.com/quantopian/zipline 源码地址在

Some benefits include: 一些优势包括：

• Realistic: slippage, transaction costs, order delays. 实时：滑点，交易成本，订单延迟。
• Stream-based: Process each event individually, avoids look-ahead bias. 基于流：分别处理每个事件，避免预见偏差。
• Batteries included: Common transforms (moving average) as well as common risk calculations (Sharpe). 电池包括：常见转换（移动平均值）以及常见风险计算（夏普）。
• Developed and continuously updated by Quantopian which provides an easy-to-use web-interface to Zipline, 10 years of minute-resolution historical US stock data, and live-trading capabilities. This tutorial is directed at users wishing to use Zipline without using Quantopian. If you instead want to get started on Quantopian, see here.由Quantopian开发并不断更新，为Zipline提供简单易用的网络界面，10年分钟级历史美国股票数据和实时交易功能。本教程面向希望在不使用Quantopian的情况下使用Zipline的用户。如果您想开始使用Quantopian，请参阅此处。

This tutorial assumes that you have zipline correctly installed, see the installation instructions if you haven’t set up zipline yet.本教程假定您已正确安装了zipline，如果尚未设置zipline，请参阅安装说明。

Every zipline algorithm consists of two functions you have to define: 每个zipline算法由两个你必须定义的函数组成：

• initialize(context)
• handle_data(context, data)

Before the start of the algorithm, zipline calls the initialize() function and passes in a context variable. context is a persistent namespace for you to store variables you need to access from one algorithm iteration to the next.

在算法开始之前，zipline调用initialize（）函数并传入一个context变量。context 是一个持久的名称空间，用于存储您需要从一个算法迭代访问下一个变量的变量。

After the algorithm has been initialized, zipline calls the handle_data() function once for each event. At every call, it passes the same context variable and an event-frame called data containing the current trading bar with open, high, low, and close (OHLC) prices as well as volume for each stock in your universe. For more information on these functions, see the relevant part of the Quantopian docs.

算法初始化后，zipline为每个事件调用一次handle_data（）函数。每次调用时，它都会传递相同的context变量和一个名为data的事件框架，其中包含当前交易栏的开盘价，最高价，最低价和收盘价（OHLC）以及您的全球每只股票的交易量。 有关这些功能的更多信息，请参阅Quantopian文档的相关部分。

My First Algorithm我的第一个算法

Lets take a look at a very simple algorithm from the examples directory, buyapple.py: 让我们看看examples目录中的一个非常简单的算法, buyapple.py

from zipline.examples import buyapple

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'))

As you can see, we first have to import some functions we would like to use. All functions commonly used in your algorithm can be found in zipline.api. Here we are using order() which takes two arguments: a security object, and a number specifying how many stocks you would like to order (if negative, order() will sell/short stocks). In this case we want to order 10 shares of Apple at each iteration. For more documentation on order(), see the Quantopian docs.

正如你所看到的，我们首先必须导入一些我们想使用的函数。您的算法中常用的所有函数都可以在zipline.api中找到。在这里我们使用了order（），它带有两个参数：一个安全对象和一个数字，该数字用于指定要订购多少股票（如果为负数，order（）将卖出/卖空股票）。在这种情况下，我们希望在每次迭代中购买10股苹果股票。有关order（）的更多文档，请参阅Quantopian文档。

Finally, the record() function allows you to save the value of a variable at each iteration. You provide it with a name for the variable together with the variable itself: varname=var. After the algorithm finished running you will have access to each variable value you tracked with record() under the name you provided (we will see this further below). You also see how we can access the current price data of the AAPL stock in the data event frame (for more information see here).

最后，record（）函数允许您在每次迭代中保存一个变量的值。您为变量提供变量名称和变量本身：varname = var。算法完成运行后，您将可以使用您提供的名称（我们将在下面进一步了解）使用record（）跟踪每个变量值。您还会看到我们如何在数据事件框架中访问AAPL股票的当前价格数据。

Running the Algorithm 运行算法

To now test this algorithm on financial data, zipline provides three interfaces: A command-line interface, IPython Notebook magic, and run_algorithm(). 为了现在在金融数据上测试这个算法，zipline提供了三个接口：命令行界面，IPython Notebook magic和run_algorithm（）。

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

run first calls the initialize() function, and then streams the historical stock price day-by-day through handle_data(). After each call to handle_data() we instruct zipline to order 10 stocks of AAPL. After the call of the order() function, zipline enters the ordered stock and amount in the order book. After the handle_data() function has finished, zipline looks for any open orders and tries to fill them. If the trading volume is high enough for this stock, the order is executed after adding the commission and applying the slippage model which models the influence of your order on the stock price, so your algorithm will be charged more than just the stock price * 10. (Note, that you can also change the commission and slippage model that zipline uses, see the Quantopian docs for more information).

run先调用initialize（）函数，然后通过handle_data（）逐日传输历史股票价格。在每次调用handle_data（）之后，我们会指示zipline买入AAPL的10股股票。在order（）函数的调用之后，zipline输入购买股票和买单中的金额。 handle_data（）函数完成后，zipline会查找任何未完成的订单并尝试填充它们。如果该股票的交易量足够高，则在添加佣金并应用模拟您的定单对股票价格的影响的滑点模型后执行定单，因此您的算法将被收取的不仅仅是股票价格* 10 （请注意，您也可以更改zipline使用的佣金和滑点模型，有关更多信息，请参阅Quantopian文档）。

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()

	AAPL	algo_volatility	algorithm_period_return	alpha	benchmark_period_return	benchmark_volatility	beta	capital_used	ending_cash	ending_exposure	ending_value	excess_return	gross_leverage	long_exposure	long_value	longs_count	max_drawdown	max_leverage	net_leverage	orders	period_close	period_label	period_open	pnl	portfolio_value	positions	returns	sharpe	short_exposure	short_value	shorts_count	sortino	starting_cash	starting_exposure	starting_value	trading_days	transactions	treasury_period_return
2016-01-04 21:00:00+00:00	105.35	NaN	0.000000e+00	NaN	-0.013983	NaN	NaN	0.0	10000000.0	0.0	0.0	0.0	0.000000	0.0	0.0	0	0.000000e+00	0.0	0.000000	[{\'dt\': 2016-01-04 21:00:00+00:00, \'reason\': N...	2016-01-04 21:00:00+00:00	2016-01	2016-01-04 14:31:00+00:00	0.0	10000000.0	[]	0.000000e+00	NaN	0	0	0	NaN	10000000.0	0.0	0.0	1	[]	0.0
2016-01-05 21:00:00+00:00	102.71	0.000001	-1.000000e-07	-0.000022	-0.012312	0.175994	-0.000006	-1028.1	9998971.9	1027.1	1027.1	0.0	0.000103	1027.1	1027.1	1	-1.000000e-07	0.0	0.000103	[{\'dt\': 2016-01-05 21:00:00+00:00, \'reason\': N...	2016-01-05 21:00:00+00:00	2016-01	2016-01-05 14:31:00+00:00	-1.0	9999999.0	[{\'sid\': Equity(8 [AAPL]), \'last_sale_price\': ...	-1.000000e-07	-11.224972	0	0	0	-11.224972	10000000.0	0.0	0.0	2	[{\'order_id\': \'4011063b5c094e82a5391527044098b...	0.0
2016-01-06 21:00:00+00:00	100.70	0.000019	-2.210000e-06	-0.000073	-0.024771	0.137853	0.000054	-1008.0	9997963.9	2014.0	2014.0	0.0	0.000201	2014.0	2014.0	1	-2.210000e-06	0.0	0.000201	[{\'dt\': 2016-01-06 21:00:00+00:00, \'reason\': N...	2016-01-06 21:00:00+00:00	2016-01	2016-01-06 14:31:00+00:00	-21.1	9999977.9	[{\'sid\': Equity(8 [AAPL]), \'last_sale_price\': ...	-2.110000e-06	-9.823839	0	0	0	-9.588756	9998971.9	1027.1	1027.1	3	[{\'order_id\': \'3bf9fe20cc46468d99f741474226c03...	0.0
2016-01-07 21:00:00+00:00	96.45	0.000064	-1.081000e-05	0.000243	-0.048168	0.167868	0.000300	-965.5	9996998.4	2893.5	2893.5	0.0	0.000289	2893.5	2893.5	1	-1.081000e-05	0.0	0.000289	[{\'dt\': 2016-01-07 21:00:00+00:00, \'reason\': N...	2016-01-07 21:00:00+00:00	2016-01	2016-01-07 14:31:00+00:00	-86.0	9999891.9	[{\'sid\': Equity(8 [AAPL]), \'last_sale_price\': ...	-8.600019e-06	-10.592737	0	0	0	-9.688947	9997963.9	2014.0	2014.0	4	[{\'order_id\': \'6af6aed9fbb44a6bba17e802051b94d...	0.0
2016-01-08 21:00:00+00:00	96.96	0.000063	-9.380000e-06	0.000466	-0.058601	0.145654	0.000311	-970.6	9996027.8	3878.4	3878.4	0.0	0.000388	3878.4	3878.4	1	-1.081000e-05	0.0	0.000388	[{\'dt\': 2016-01-08 21:00:00+00:00, \'reason\': N...	2016-01-08 21:00:00+00:00	2016-01	2016-01-08 14:31:00+00:00	14.3	9999906.2	[{\'sid\': Equity(8 [AAPL]), \'last_sale_price\': ...	1.430015e-06	-7.511729	0	0	0	-7.519659	9996998.4	2893.5	2893.5	5	[{\'order_id\': \'18f64975732449a18fca06e9c69bf5c...	0.0

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。

IPython Notebook

The IPython Notebook is a very powerful browser-based interface to a Python interpreter (this tutorial was written in it). As it is already the de-facto interface for most quantitative researchers zipline provides an easy way to run your algorithm inside the Notebook without requiring you to use the CLI.

IPython Notebook是一个非常强大的基于浏览器的Python解释器界面（本教程写在其中）。由于它已经是大多数定量研究人员的事实上的接口，zipline提供了一种在Notebook内运行算法的简单方法，无需您使用CLI。

To use it you have to write your algorithm in a cell and let zipline know that it is supposed to run this algorithm. This is done via the %%zipline IPython magic command that is available after youimport zipline from within the IPython Notebook. This magic takes the same arguments as the command line interface described above. Thus to run the algorithm from above with the same parameters we just have to execute the following cell after importing zipline to register the magic.

要使用它，你必须在单元格中编写你的算法，让zipline知道它应该运行这个算法。这是通过在IPython Notebook内 import zipline之后，使用 %% zipline这一IPython magic命令完成的。这种魔法与上述命令行界面具有相同的参数。因此，要使用相同的参数从上面运行算法，我们只需在导入zipline注册魔法后执行下面的单元格即可。

%load_ext zipline

%%zipline --start 2016-1-1 --end 2018-1-1
from zipline.api import symbol, order, record

def initialize(context):
    pass

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

Note that we did not have to specify an input file as above since the magic will use the contents of the cell and look for your algorithm functions there. Also, instead of defining an output file we are specifying a variable name with -o that will be created in the name space and contain the performance DataFrame we looked at above.

请注意，我们不需要像上面那样指定输入文件，因为魔术将使用单元格的内容并在那里查找您的算法函数。另外，我们不是定义一个输出文件，而是使用-o来指定一个变量名称，它将在名称空间中创建并包含我们在上面查看的变量DataFrame中。

_.head()

	AAPL	algo_volatility	algorithm_period_return	alpha	benchmark_period_return	benchmark_volatility	beta	capital_used	ending_cash	ending_exposure	ending_value	excess_return	gross_leverage	long_exposure	long_value	longs_count	max_drawdown	max_leverage	net_leverage	orders	period_close	period_label	period_open	pnl	portfolio_value	positions	returns	sharpe	short_exposure	short_value	shorts_count	sortino	starting_cash	starting_exposure	starting_value	trading_days	transactions	treasury_period_return
2016-01-04 21:00:00+00:00	105.35	NaN	0.000000e+00	NaN	-0.013983	NaN	NaN	0.00	10000000.00	0.0	0.0	0.0	0.000000	0.0	0.0	0	0.000000e+00	0.0	0.000000	[{\'created\': 2016-01-04 21:00:00+00:00, \'reaso...	2016-01-04 21:00:00+00:00	2016-01	2016-01-04 14:31:00+00:00	0.00	10000000.00	[]	0.000000e+00	NaN	0	0	0	NaN	10000000.00	0.0	0.0	1	[]	0.0
2016-01-05 21:00:00+00:00	102.71	1.122497e-08	-1.000000e-09	-2.247510e-07	-0.012312	0.175994	-6.378047e-08	-1027.11	9998972.89	1027.1	1027.1	0.0	0.000103	1027.1	1027.1	1	-9.999999e-10	0.0	0.000103	[{\'created\': 2016-01-04 21:00:00+00:00, \'reaso...	2016-01-05 21:00:00+00:00	2016-01	2016-01-05 14:31:00+00:00	-0.01	9999999.99	[{\'amount\': 10, \'cost_basis\': 102.711000000000...	-1.000000e-09	-11.224972	0	0	0	-11.224972	10000000.00	0.0	0.0	2	[{\'dt\': 2016-01-05 21:00:00+00:00, \'order_id\':...	0.0
2016-01-06 21:00:00+00:00	100.70	1.842654e-05	-2.012000e-06	-4.883861e-05	-0.024771	0.137853	5.744807e-05	-1007.01	9997965.88	2014.0	2014.0	0.0	0.000201	2014.0	2014.0	1	-2.012000e-06	0.0	0.000201	[{\'created\': 2016-01-05 21:00:00+00:00, \'reaso...	2016-01-06 21:00:00+00:00	2016-01	2016-01-06 14:31:00+00:00	-20.11	9999979.88	[{\'amount\': 20, \'cost_basis\': 101.706000000000...	-2.011000e-06	-9.171989	0	0	0	-9.169708	9998972.89	1027.1	1027.1	3	[{\'dt\': 2016-01-06 21:00:00+00:00, \'order_id\':...	0.0
2016-01-07 21:00:00+00:00	96.45	6.394658e-05	-1.051300e-05	2.633450e-04	-0.048168	0.167868	3.005102e-04	-964.51	9997001.37	2893.5	2893.5	0.0	0.000289	2893.5	2893.5	1	-1.051300e-05	0.0	0.000289	[{\'created\': 2016-01-06 21:00:00+00:00, \'reaso...	2016-01-07 21:00:00+00:00	2016-01	2016-01-07 14:31:00+00:00	-85.01	9999894.87	[{\'amount\': 30, \'cost_basis\': 99.9543333333335...	-8.501017e-06	-10.357397	0	0	0	-9.552189	9997965.88	2014.0	2014.0	4	[{\'dt\': 2016-01-07 21:00:00+00:00, \'order_id\':...	0.0
2016-01-08 21:00:00+00:00	96.96	6.275294e-05	-8.984000e-06	4.879306e-04	-0.058601	0.145654	3.118401e-04	-969.61	9996031.76	3878.4	3878.4	0.0	0.000388	3878.4	3878.4	1	-1.051300e-05	0.0	0.000388	[{\'created\': 2016-01-07 21:00:00+00:00, \'reaso...	2016-01-08 21:00:00+00:00	2016-01	2016-01-08 14:31:00+00:00	15.29	9999910.16	[{\'amount\': 40, \'cost_basis\': 99.2060000000002...	1.529016e-06	-7.215497	0	0	0	-7.301134	9997001.37	2893.5	2893.5	5	[{\'dt\': 2016-01-08 21:00:00+00:00, \'order_id\':...	0.0

Access to Previous Prices Using history 使用history获取以前的价格


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。

Conclusions结论

We hope that this tutorial gave you a little insight into the architecture, API, and features of zipline. For next steps, check out some of the examples.

我们希望本教程能够让您深入了解zipline的体系结构，API和功能。有关后续步骤，请查看一些示例。

Feel free to ask questions on our mailing list, report problems on our GitHub issue tracker, get involved, and checkout Quantopian.