Ingesting Data 导入数据

If you haven’t ingested the data, run:

$ zipline ingest [-b <bundle>]

where <bundle> is the name of the bundle to ingest, defaulting to quantopian-quandl.

you can check out the ingesting data section for more detail.

Command line interface

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):

$ 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 [minute|daily]
                                  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: quantopian-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: -]
  --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 the Quantopian Quandl WIKI Mirror. 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 --output flag 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).

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

zipline run -f ../../zipline/examples/buyapple.py --start 2000-1-1 --end 2014-1-1 -o buyapple_out.pickle

AAPL
[2015-11-04 22:45:32.820166] INFO: Performance: Simulated 3521 trading days out of 3521.
[2015-11-04 22:45:32.820314] INFO: Performance: first open: 2000-01-03 14:31:00+00:00
[2015-11-04 22:45:32.820401] INFO: Performance: last close: 2013-12-31 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).

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.

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 2000. 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.

%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 0x7ff5c6147f90>

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.

Working example: Dual Moving Average Cross-Over 双移动平均线交叉

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.

As we need to have access to previous prices to implement this strategy we need a new concept: 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\' for \'1m\' but note that you need to have minute-level data for using 1m). For a more detailed description history()‘s features, see the Quantopian docs. Let’s look at the strategy which should make this clear:

%%zipline --start 2000-1-1 --end 2012-1-1 -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).

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).

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 fore more details.