buyapple.py example: TypeError: 'zipline._protocol.BarData' object is not subscriptable

[kmeans27]

Dear Zipline Maintainers,

Before I tell you about my issue, let me describe my environment:

Environment

name: ml4t channels:

• fastai
• bashtage
• ml4t
• conda-forge
• defaults
• anaconda dependencies:
• _py-xgboost-mutex=2.0
• _pytorch_select=0.1
• _tflow_select=2.3.0
• abseil-cpp=20210324.2
• absl-py=0.13.0
• aiohttp=3.7.4.post0
• alembic=1.7.1
• alphalens-reloaded=0.4.2
• anyio=3.3.0
• appdirs=1.4.4
• arch=4.15
• argon2-cffi=20.1.0
• arrow-cpp=5.0.0
• arviz=0.11.2
• astor=0.8.1
• astunparse=1.6.3
• async-timeout=3.0.1
• attrs=21.2.0
• automat=20.2.0
• autopep8=1.5.7
• aws-c-cal=0.5.11
• aws-c-common=0.6.2
• aws-c-event-stream=0.2.7
• aws-c-io=0.10.5
• aws-checksums=0.1.11
• aws-sdk-cpp=1.8.186
• babel=2.9.1
• backcall=0.2.0
• backports=1.0
• backports.functools_lru_cache=1.6.4
• bcolz-zipline=1.2.4
• bcrypt=3.2.0
• beautifulsoup4=4.10.0
• blas=1.0
• bleach=4.1.0
• blinker=1.4
• blosc=1.21.0
• bokeh=2.3.3
• bottleneck=1.3.2
• box2d-py=2.3.8
• bqplot=0.12.30
• brotli=1.0.9
• brotli-bin=1.0.9
• brotlipy=0.7.0
• bzip2=1.0.8
• c-ares=1.17.2
• ca-certificates=2021.5.30
• cachetools=4.2.2
• cairo=1.16.0
• catalogue=2.0.5
• catboost=0.26.1
• certifi=2021.5.30
• cffi=1.14.6
• cfitsio=3.470
• cftime=1.5.0
• chardet=4.0.0
• charls=2.2.0
• charset-normalizer=2.0.0
• click=7.1.2
• cloudpickle=1.6.0
• colorama=0.4.4
• colorlover=0.3.0
• conda=4.10.3
• conda-package-handling=1.7.3
• constantly=15.1.0
• cryptography=3.4.7
• cssselect=1.1.0
• curl=7.78.0
• cycler=0.10.0
• cymem=2.0.5
• cython=0.29.24
• cython-blis=0.7.4
• cytoolz=0.11.0
• dask-core=2021.9.0
• dataclasses=0.8
• debugpy=1.4.1
• decorator=4.4.2
• defusedxml=0.7.1
• dill=0.3.4
• empyrical-reloaded=0.5.8
• entrypoints=0.3
• expat=2.4.1
• fastprogress=1.0.0
• ffmpeg=4.3.1
• filelock=3.0.12
• font-ttf-dejavu-sans-mono=2.37
• font-ttf-inconsolata=3.000
• font-ttf-source-code-pro=2.038
• font-ttf-ubuntu=0.83
• fontconfig=2.13.1
• fonts-conda-ecosystem=1
• fonts-conda-forge=1
• freetype=2.10.4
• fribidi=1.0.10
• fsspec=2021.8.1
• funcy=1.16
• future=0.18.2
• gast=0.4.0
• gensim=3.8.3
• getopt-win32=0.1
• gettext=0.19.8.1
• gflags=2.2.2
• giflib=5.2.1
• glog=0.5.0
• google-auth=1.35.0
• google-auth-oauthlib=0.4.6
• google-pasta=0.2.0
• googleapis-common-protos=1.53.0
• graphite2=1.3.13
• graphviz=2.49.0
• greenlet=1.1.1
• grpc-cpp=1.39.1
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• gym=0.19.0
• gym-box2d=0.19.0
• h2=3.2.0
• h5py=2.10.0
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• idna=3.1
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• importlib_resources=5.2.2
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• inflection=0.5.1
• intel-openmp=2019.4
• intervaltree=3.0.2
• ipydatawidgets=4.2.0
• ipykernel=6.3.1
• ipython=7.27.0
• ipython_genutils=0.2.0
• ipyvolume=0.6.0a8
• ipywebrtc=0.6.0
• ipywidgets=7.6.4
• iso3166=1.0.1
• iso4217=1.6.20180829
• itemadapter=0.4.0
• itemloaders=1.0.4
• jbig=2.1
• jedi=0.18.0
• jellyfish=0.8.2
• jinja2=3.0.1
• jmespath=0.10.0
• joblib=1.0.1
• jpeg=9d
• json5=0.9.5
• jsonschema=3.2.0
• jupyter=1.0.0
• jupyter_client=7.0.2
• jupyter_console=6.4.0
• jupyter_contrib_core=0.3.3
• jupyter_contrib_nbextensions=0.5.1
• jupyter_core=4.7.1
• jupyter_highlight_selected_word=0.2.0
• jupyter_latex_envs=1.4.6
• jupyter_nbextensions_configurator=0.4.1
• jupyter_server=1.10.2
• jupyterlab=3.1.11
• jupyterlab_server=2.8.1
• jupyterlab_widgets=1.0.1
• jxrlib=1.1
• keras-applications=1.0.8
• keras-preprocessing=1.1.2
• kiwisolver=1.3.2
• krb5=1.19.2
• lcms2=2.12
• lerc=2.2.1
• libaec=1.0.5
• libblas=3.8.0
• libbrotlicommon=1.0.9
• libbrotlidec=1.0.9
• libbrotlienc=1.0.9
• libcblas=3.8.0
• libclang=11.1.0
• libcurl=7.78.0
• libdeflate=1.7
• libffi=3.3
• libgd=2.3.2
• libglib=2.68.4
• libgpuarray=0.7.6
• libiconv=1.16
• liblapack=3.8.0
• libmklml=2019.0.5
• libnetcdf=4.8.1
• libpng=1.6.37
• libprotobuf=3.16.0
• libpython=2.0
• libsodium=1.0.18
• libssh2=1.10.0
• libthrift=0.14.2
• libtiff=4.3.0
• libutf8proc=2.6.1
• libwebp=1.2.1
• libwebp-base=1.2.1
• libxcb=1.13
• libxgboost=1.4.0
• libxml2=2.9.12
• libxslt=1.1.33
• libzip=1.8.0
• libzopfli=1.0.3
• lightgbm=3.2.1
• linearmodels=4.24
• llvmlite=0.36.0
• locket=0.2.0
• logbook=1.5.3
• lru-dict=1.1.7
• lxml=4.6.3
• lz4-c=1.9.3
• m2w64-binutils=2.25.1
• m2w64-bzip2=1.0.6
• m2w64-crt-git=5.0.0.4636.2595836
• m2w64-gcc=5.3.0
• m2w64-gcc-ada=5.3.0
• m2w64-gcc-fortran=5.3.0
• m2w64-gcc-libgfortran=5.3.0
• m2w64-gcc-libs=5.3.0
• m2w64-gcc-libs-core=5.3.0
• m2w64-gcc-objc=5.3.0
• m2w64-gmp=6.1.0
• m2w64-headers-git=5.0.0.4636.c0ad18a
• m2w64-isl=0.16.1
• m2w64-libiconv=1.14
• m2w64-libmangle-git=5.0.0.4509.2e5a9a2
• m2w64-libwinpthread-git=5.0.0.4634.697f757
• m2w64-make=4.1.2351.a80a8b8
• m2w64-mpc=1.0.3
• m2w64-mpfr=3.1.4
• m2w64-pkg-config=0.29.1
• m2w64-toolchain=5.3.0
• m2w64-tools-git=5.0.0.4592.90b8472
• m2w64-windows-default-manifest=6.4
• m2w64-winpthreads-git=5.0.0.4634.697f757
• m2w64-zlib=1.2.8
• mako=1.1.5
• markdown=3.3.4
• markupsafe=2.0.1
• matplotlib=3.4.3
• matplotlib-base=3.4.3
• matplotlib-inline=0.1.3
• menuinst=1.4.17
• mistune=0.8.4
• mkl=2019.4
• mkl-service=2.3.0
• mock=4.0.3
• more-itertools=8.9.0
• mplfinance=0.12.7a17
• mpmath=1.2.1
• msys2-conda-epoch=20160418
• multidict=5.1.0
• multipledispatch=0.6.0
• multitasking=0.0.9
• murmurhash=1.0.5
• mypy_extensions=0.4.3
• nb_conda=2.2.1
• nb_conda_kernels=2.3.1
• nbclassic=0.3.1
• nbconvert=5.6.1
• nbformat=5.1.3
• nest-asyncio=1.5.1
• netcdf4=1.5.7
• networkx=2.6.2
• ninja=1.10.2
• nltk=3.6.2
• notebook=6.4.3
• numba=0.53.1
• numexpr=2.7.3
• numpy=1.21.2
• oauthlib=3.1.1
• olefile=0.46
• opencv-python-headless=4.5.3.56
• openjpeg=2.4.0
• openssl=1.1.1l
• opt_einsum=3.3.0
• packaging=21.0
• pandas=1.2.5
• pandas-datareader=0.10.0
• pandoc=2.14.2
• pandocfilters=1.4.2
• pango=1.48.9
• parquet-cpp=1.5.1
• parsel=1.6.0
• parso=0.8.2
• partd=1.2.0
• pathy=0.6.0
• patsy=0.5.1
• pcre=8.45
• pickleshare=0.7.5
• pillow=8.3.2
• pip=21.2.4
• pixman=0.40.0
• plotly=5.3.1
• pooch=1.5.1
• preshed=3.0.5
• priority=1.3.0
• prometheus_client=0.11.0
• promise=2.3
• prompt-toolkit=3.0.20
• prompt_toolkit=3.0.20
• property-cached=1.6.4
• property_cached=1.6.4
• protego=0.1.16
• protobuf=3.16.0
• pthread-stubs=0.4
• py-xgboost=1.4.0
• pyarrow=5.0.0
• pyasn1=0.4.8
• pyasn1-modules=0.2.7
• pycodestyle=2.7.0
• pycosat=0.6.3
• pycparser=2.20
• pydantic=1.8.2
• pydispatcher=2.0.5
• pydot=1.4.2
• pyfolio-reloaded=0.9.4
• pyglet=1.5.16
• pygments=2.10.0
• pygpu=0.7.6
• pyjwt=2.1.0
• pykalman=0.9.5
• pyldavis=3.3.1
• pymc3=3.11.4
• pynndescent=0.5.4
• pyopenssl=20.0.1
• pyparsing=2.4.7
• pyphen=0.11.0
• pyqt=5.12.3
• pyqt-impl=5.12.3
• pyqt5-sip=4.19.18
• pyqtchart=5.12
• pyqtwebengine=5.12.1
• pyreadline=2.1
• pyrsistent=0.17.3
• pysocks=1.7.1
• pytables=3.6.1
• python=3.8.10
• python-dateutil=2.8.2
• python-graphviz=0.17
• python-interface=1.6.0
• python_abi=3.8
• pythreejs=2.3.0
• pytorch=1.6.0
• pytz=2021.1
• pyu2f=0.1.5
• pywavelets=1.1.1
• pywin32=301
• pywinpty=1.1.4
• pyyaml=5.4.1
• pyzmq=22.2.1
• qt=5.12.9
• qtconsole=5.1.1
• qtpy=1.11.0
• quandl=3.4.6
• queuelib=1.6.2
• re2=2021.09.01
• regex=2021.8.28
• requests=2.26.0
• requests-oauthlib=1.3.0
• requests-unixsocket=0.2.0
• rsa=4.7.2
• ruamel_yaml=0.15.80
• scikit-image=0.18.3
• scikit-learn=0.24.2
• scipy=1.7.1
• scrapy=2.5.0
• seaborn=0.11.2
• seaborn-base=0.11.2
• semver=2.13.0
• send2trash=1.8.0
• service_identity=18.1.0
• setuptools=58.0.3
• shap=0.39.0
• shellingham=1.4.0
• six=1.15.0
• slicer=0.0.7
• smart_open=5.2.1
• snappy=1.1.8
• sniffio=1.2.0
• sortedcontainers=2.4.0
• soupsieve=2.0.1
• spacy=3.1.2
• spacy-legacy=3.0.8
• sqlalchemy=1.4.23
• sqlite=3.36.0
• srsly=2.4.1
• statsmodels=0.12.2
• sympy=1.8
• ta-lib=0.4.19
• tbb=2020.2
• tenacity=8.0.1
• tensorboard=2.6.0
• tensorboard-data-server=0.6.0
• tensorboard-plugin-wit=1.8.0
• tensorflow=2.3.0
• tensorflow-base=2.3.0
• tensorflow-datasets=4.3.0
• tensorflow-estimator=2.5.0
• tensorflow-metadata=0.14.0
• termcolor=1.1.0
• terminado=0.12.1
• testpath=0.5.0
• textacy=0.11.0
• textblob=0.15.3
• theano-pymc=1.1.2
• thinc=8.0.10
• threadpoolctl=2.2.0
• tifffile=2021.7.2
• tk=8.6.11
• toml=0.10.2
• toolz=0.11.1
• tornado=6.1
• tqdm=4.62.2
• trading-calendars=2.1.1
• traitlets=5.1.0
• traittypes=0.2.1
• twisted=21.7.0
• twisted-iocpsupport=1.0.1
• typer=0.3.2
• typing-extensions=3.10.0.0
• typing_extensions=3.10.0.0
• ucrt=10.0.20348.0
• umap-learn=0.5.1
• urllib3=1.26.6
• vc=14.2
• vs2015_runtime=14.29.30037
• vs2017_win-64=19.16.27038
• vswhere=2.8.4
• w3lib=1.22.0
• wasabi=0.8.2
• wcwidth=0.2.5
• webencodings=0.5.1
• websocket-client=0.57.0
• werkzeug=2.0.1
• wheel=0.37.0
• widgetsnbextension=3.5.1
• win_inet_pton=1.1.0
• winpty=0.4.3
• wordcloud=1.8.1
• wrapt=1.12.1
• xarray=0.19.0
• xgboost=1.4.0
• xlrd=2.0.1
• xorg-kbproto=1.0.7
• xorg-libice=1.0.10
• xorg-libsm=1.2.3
• xorg-libx11=1.7.2
• xorg-libxau=1.0.9
• xorg-libxdmcp=1.1.3
• xorg-libxext=1.3.4
• xorg-libxpm=3.5.13
• xorg-libxt=1.2.1
• xorg-xextproto=7.3.0
• xorg-xproto=7.0.31
• xz=5.2.5
• yaml=0.2.5
• yarl=1.6.3
• yellowbrick=1.3.post1
• yfinance=0.1.63
• zeromq=4.3.4
• zfp=0.5.5
• zipline-reloaded=2.1.1
• zipp=3.5.0
• zlib=1.2.11
• zope.interface=5.4.0
• zstd=1.5.0
• pip:
  • backtrader==1.9.76.123
  • cvxpy==1.1.15
  • ecos==2.0.7.post1
  • livelossplot==0.5.4
  • osqp==0.6.2.post0
  • pymdptoolbox==4.0b3
  • pyportfolioopt==1.4.2
  • qdldl==0.1.5.post0
  • scs==2.1.2

• Operating System: (Windows Version or $ uname --all)
• Python Version: $ python --version
• Python Bitness: $ python -c 'import math, sys;print(int(math.log(sys.maxsize + 1, 2) + 1))'
• How did you install Zipline: (pip, conda, or other (please explain))
• Python packages: $ pip freeze or $ conda list

When i try to run the basic zipline example from https://zipline.ml4trading.io/beginner-tutorial.html

%load_ext zipline

%%zipline --start 2016-1-1 --end 2018-1-1 -o buyapple_out.pickle --no-benchmark 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)

I get a TypeError: "zipline._protocol.BarData' object is not subscriptable" I set my api key & ingested the quandl data.

What am I missing out here?

[mike576]

I have the same issue. I have the feeling the tutorial is not for the newest version of zipline 2.2.0. But it is just a guess.

[mike576]

so data[] got deprecated in 2.0 then it removed for 2.2.0 As said the doc is not updated. Here is a working version with 2.2.0:

from zipline import run_algorithm
import pandas as pd
import pandas_datareader.data as web
from zipline.assets import Equity
from zipline.api import symbol, order, record

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


def handle_data(context, data):
    order(symbol('AAPL'), 1.0)
    #record(AAPL=data[symbol('AAPL')].price)
    record(data.current(symbol('AAPL'),"close"))
    

start = pd.Timestamp('2014')
end = pd.Timestamp('2018')

sp500 = web.DataReader('SP500', 'fred', start, end).SP500
benchmark_returns = sp500.pct_change()

result = run_algorithm(start=start.tz_localize('UTC'),
                       end=end.tz_localize('UTC'),
                       initialize=initialize,
                       handle_data=handle_data,
                       capital_base=100000,
                       benchmark_returns=benchmark_returns,
                       bundle='quandl',
                       data_frequency='daily')

[russtoku]

Confirming that @mike576's fix:

record(data.current(symbol('AAPL'),"close"))

does work in a notebook cell.

The code in the tutorial for what to put in a notebook cell is incorrect.

record(AAPL=data[symbol('AAPL')].price)

The code in the section A simple example is the similar to @mike576's.

record(AAPL=data.current(symbol('AAPL'), 'price'))

I didn't read the tutorial carefully enough and should have compared the code in both sections.

Thanks @mike576 !

[andycwang]

@.***邮箱联系我，谢谢！