标签：add 第一步 test format from sqlite bundle -o mat
A data bundle is a collection of pricing data, adjustment data, and an asset database. Bundles allow us to preload all of the data we will need to run backtests and store the data for future runs.
数据包是定价数据，调整数据和资产数据库的集合。 Bundles允许我们预先加载所有需要运行回测的数据并存储数据以供将来运行。
Zipline comes with a few bundles by default as well as the ability to register new bundles. To see which bundles we have available, we may run the bundles command, for example:
Zipline默认带有几个捆绑包以及注册新捆绑包的功能。 要查看我们可用的捆绑包，我们可以运行捆绑命令，例如：
$ zipline bundles my-custom-bundle 2016-05-05 20:35:19.809398 my-custom-bundle 2016-05-05 20:34:53.654082 my-custom-bundle 2016-05-05 20:34:48.401767 quandl <no ingestions> quantopian-quandl 2016-05-05 20:06:40.894956
The output here shows that there are 3 bundles available: 这里的输出显示有3个可用包：
my-custom-bundle (added by the user)quandl (provided by zipline)quantopian-quandl (provided by zipline)The dates and times next to the name show the times when the data for this bundle was ingested. We have run three different ingestions for my-custom-bundle. We have never ingested any data for the quandl bundle so it just shows <no ingestions> instead. Finally, there is only one ingestion for quantopian-quandl.
名称旁边的日期和时间显示摄取这个包的数据的时间。对 my-custom-bundle 有三种不同的提示。我们从来没有摄入任何quandl包的数据，所以它只是显示<no ingestions>。最后，Quantopian-quandl只有一次摄取。
The first step to using a data bundle is to ingest the data. The ingestion process will invoke some custom bundle command and then write the data to a standard location that zipline can find. By default the location where ingested data will be written is $ZIPLINE_ROOT/data/<bundle> where by default ZIPLINE_ROOT=~/.zipline. The ingestion step may take some time as it could involve downloading and processing a lot of data. You’ll need a Quandl API key to ingest the default bundle. This can be run with:
使用数据包的第一步是摄取数据。摄入过程将调用一些自定义捆绑命令，然后将数据写入zipline可以找到的标准位置。默认情况下，写入数据的位置是$ ZIPLINE_ROOT / data / <bundle>，默认情况下ZIPLINE_ROOT =?/ .zipline。摄取步骤可能需要一些时间，因为它可能涉及下载和处理大量数据。您需要一个Quandl API密钥来获取默认包。这可以通过以下方式运行：
$ 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。
When the ingest command is used it will write the new data to a subdirectory of $ZIPLINE_ROOT/data/<bundle> which is named with the current date. This makes it possible to look at older data or even run backtests with the older copies. Running a backtest with an old ingestion makes it easier to reproduce backtest results later.
当使用ingest 命令时，它会将新数据写入到以当前日期命名的$ ZIPLINE_ROOT / data / <bundle>的子目录中。这使得可以查看较旧的数据，甚至可以使用较旧的副本进行反向测试。使用旧的服务运行回测可以更容易地在以后重新生成回测结果。
One drawback of saving all of the data by default is that the data directory may grow quite large even if you do not want to use the data. As shown earlier, we can list all of the ingestions with the bundles command. To solve the problem of leaking old data there is another command: clean, which will clear data bundles based on some time constraints.
默认情况下保存所有数据的一个缺点是，即使您不想使用数据，数据目录也可能变得非常大。如前所示，我们可以使用bundles命令列出所有的提示。为了解决泄露旧数据的问题，还有另外一个命令：clean，它将根据一些时间限制清除数据包。
For example:
# clean everything older than <date> $ zipline clean [-b <bundle>] --before <date> # clean everything newer than <date> $ zipline clean [-b <bundle>] --after <date> # keep everything in the range of [before, after] and delete the rest $ zipline clean [-b <bundle>] --before <date> --after <after> # clean all but the last <int> runs $ zipline clean [-b <bundle>] --keep-last <int>
Now that the data has been ingested we can use it to run backtests with the run command. The bundle to use can be specified with the --bundle option like:
现在数据已被摄入，我们可以使用它来运行run命令。可以使用--bundle选项指定要使用的软件包，如下所示：
$ zipline run --bundle <bundle> --algofile algo.py ...
We may also specify the date to use to look up the bundle data with the --bundle-timestamp option. Setting the --bundle-timestamp will cause run to use the most recent bundle ingestion that is less than or equal to the bundle-timestamp. This is how we can run backtests with older data.bundle-timestamp uses a less-than-or-equal-to relationship so that we can specify the date that we ran an old backtest and get the same data that would have been available to us on that date. The bundle-timestamp defaults to the current day to use the most recent data.
By default zipline comes with the quandl data bundle which uses quandl’s WIKI dataset. The quandl data bundle includes daily pricing data, splits, cash dividends, and asset metadata. To ingest the quandl data bundle we recommend creating an account on quandl.com to get an API key to be able to make more API requests per day. Once we have an API key we may run:
$ QUANDL_API_KEY=<api-key> zipline ingest -b quandl
though we may still run ingest as an anonymous quandl user (with no API key). We may also set the QUANDL_DOWNLOAD_ATTEMPTS environment variable to an integer which is the number of attempts that should be made to download data from quandls servers. By default QUANDL_DOWNLOAD_ATTEMPTS will be 5, meaning that we will retry each attempt 5 times.
Note
QUANDL_DOWNLOAD_ATTEMPTS is not the total number of allowed failures, just the number of allowed failures per request. The quandl loader will make one request per 100 equities for the metadata followed by one request per equity.
Data bundles exist to make it easy to use different data sources with zipline. To add a new bundle, one must implement an ingest function.
The ingest function is responsible for loading the data into memory and passing it to a set of writer objects provided by zipline to convert the data to zipline’s internal format. The ingest function may work by downloading data from a remote location like the quandl bundle or it may just load files that are already on the machine. The function is provided with writers that will write the data to the correct location transactionally. If an ingestion fails part way through the bundle will not be written in an incomplete state.
The signature of the ingest function should be:
ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir)
environenviron is a mapping representing the environment variables to use. This is where any custom arguments needed for the ingestion should be passed, for example: the quandl bundle uses the enviornment to pass the API key and the download retry attempt count.
asset_db_writerasset_db_writer is an instance of AssetDBWriter. This is the writer for the asset metadata which provides the asset lifetimes and the symbol to asset id (sid) mapping. This may also contain the asset name, exchange and a few other columns. To write data, invoke write() with dataframes for the various pieces of metadata. More information about the format of the data exists in the docs for write.
minute_bar_writerminute_bar_writer is an instance of BcolzMinuteBarWriter. This writer is used to convert data to zipline’s internal bcolz format to later be read by a BcolzMinuteBarReader. If minute data is provided, users should call write() with an iterable of (sid, dataframe) tuples. The show_progress argument should also be forwarded to this method. If the data source does not provide minute level data, then there is no need to call the write method. It is also acceptable to pass an empty iterator to write() to signal that there is no minutely data.
Note
The data passed to write() may be a lazy iterator or generator to avoid loading all of the minute data into memory at a single time. A given sid may also appear multiple times in the data as long as the dates are strictly increasing.
daily_bar_writerdaily_bar_writer is an instance of BcolzDailyBarWriter. This writer is used to convert data into zipline’s internal bcolz format to later be read by a BcolzDailyBarReader. If daily data is provided, users should call write() with an iterable of (sid dataframe) tuples. The show_progress argument should also be forwarded to this method. If the data shource does not provide daily data, then there is no need to call the write method. It is also acceptable to pass an empty iterable to write() to signal that there is no daily data. If no daily data is provided but minute data is provided, a daily rollup will happen to service daily history requests.
Note
Like the minute_bar_writer, the data passed to write() may be a lazy iterable or generator to avoid loading all of the data into memory at once. Unlike the minute_bar_writer, a sid may only appear once in the data iterable.
adjustment_writeradjustment_writer is an instance of SQLiteAdjustmentWriter. This writer is used to store splits, mergers, dividends, and stock dividends. The data should be provided as dataframes and passed to write(). Each of these fields are optional, but the writer can accept as much of the data as you have.
calendarcalendar is an instance of zipline.utils.calendars.TradingCalendar. The calendar is provided to help some bundles generate queries for the days needed.
start_sessionstart_session is a pandas.Timestamp object indicating the first day that the bundle should load data for.
end_sessionend_session is a pandas.Timestamp object indicating the last day that the bundle should load data for.
cachecache is an instance of dataframe_cache. This object is a mapping from strings to dataframes. This object is provided in case an ingestion crashes part way through. The idea is that the ingest function should check the cache for raw data, if it doesn’t exist in the cache, it should acquire it and then store it in the cache. Then it can parse and write the data. The cache will be cleared only after a successful load, this prevents the ingest function from needing to redownload all the data if there is some bug in the parsing. If it is very fast to get the data, for example if it is coming from another local file, then there is no need to use this cache.
show_progressshow_progress is a boolean indicating that the user would like to receive feedback about the ingest function’s progress fetching and writing the data. Some examples for where to show how many files you have downloaded out of the total needed, or how far into some data conversion the ingest function is. One tool that may help with implementing show_progress for a loop ismaybe_show_progress. This argument should always be forwarded to minute_bar_writer.write and daily_bar_writer.write.
output_diroutput_dir is a string representing the file path where all the data will be written. output_dir will be some subdirectory of $ZIPLINE_ROOT and will contain the time of the start of the current ingestion. This can be used to directly move resources here if for some reason your ingest function can produce it’s own outputs without the writers. For example, the quantopian:quandl bundle uses this to directly untar the bundle into the output_dir.
Zipline provides a bundle called csvdir, which allows users to ingest data from .csv files. The format of the files should be in OHLCV format, with dates, dividends, and splits. A sample is provided below. There are other samples for testing purposes in zipline/tests/resources/csvdir_samples.
date,open,high,low,close,volume,dividend,split
2012-01-03,58.485714,58.92857,58.42857,58.747143,75555200,0.0,1.0
2012-01-04,58.57143,59.240002,58.468571,59.062859,65005500,0.0,1.0
2012-01-05,59.278572,59.792858,58.952858,59.718571,67817400,0.0,1.0
2012-01-06,59.967144,60.392857,59.888573,60.342857,79573200,0.0,1.0
2012-01-09,60.785713,61.107143,60.192856,60.247143,98506100,0.0,1.0
2012-01-10,60.844284,60.857143,60.214287,60.462856,64549100,0.0,1.0
2012-01-11,60.382858,60.407143,59.901428,60.364285,53771200,0.0,1.0
Once you have your data in the correct format, you can edit your extension.py file in~/.zipline/extension.py and import the csvdir bundle, along with pandas.
import pandas as pd
from zipline.data.bundles import register
from zipline.data.bundles.csvdir import csvdir_equities
We’ll then want to specify the start and end sessions of our bundle data:
start_session = pd.Timestamp(‘2016-1-1‘, tz=‘utc‘)
end_session = pd.Timestamp(‘2018-1-1‘, tz=‘utc‘)
And then we can register() our bundle, and pass the location of the directory in which our .csvfiles exist:
register(
‘custom-csvdir-bundle‘,
csvdir_equities(
[‘daily‘],
‘/path/to/your/csvs‘,
),
calendar_name=‘NYSE‘, # US equities
start_session=start_session,
end_session=end_session
)
To finally ingest our data, we can run:
$ zipline ingest -b custom-csvdir-bundle
Loading custom pricing data: [############------------------------] 33% | FAKE: sid 0
Loading custom pricing data: [########################------------] 66% | FAKE1: sid 1
Loading custom pricing data: [####################################] 100% | FAKE2: sid 2
Loading custom pricing data: [####################################] 100%
Merging daily equity files: [####################################]
# optionally, we can pass the location of our csvs via the command line
$ CSVDIR=/path/to/your/csvs zipline ingest -b custom-csvdir-bundle
If you would like to use equities that are not in the NYSE calendar, or the existing zipline calendars, you can look at the Trading Calendar Tutorial to build a custom trading calendar that you can then pass the name of to register().
标签：add 第一步 test format from sqlite bundle -o mat
原文地址：https://www.cnblogs.com/fangbei/p/Zipline-Data-Bundles.html
