def run_pipeline(print_algo=True, **kwargs):
"""Runs a full zipline pipeline given configuration keyword
arguments.
1. Load data (start and end dates can be provided a strings as
well as the source and symobls).
2. Instantiate algorithm (supply either algo_text or algofile
kwargs containing initialize() and handle_data() functions). If
algofile is supplied, will try to look for algofile_analyze.py and
append it.
3. Run algorithm (supply capital_base as float).
4. Return performance dataframe.
:Arguments:
* print_algo : bool <default=True>
Whether to print the algorithm to command line. Will use
pygments syntax coloring if pygments is found.
"""
start = kwargs['start']
end = kwargs['end']
# Compare against None because strings/timestamps may have been given
if start is not None:
start = pd.Timestamp(start, tz='UTC')
if end is not None:
end = pd.Timestamp(end, tz='UTC')
# Fail out if only one bound is provided
if ((start is None) or (end is None)) and (start != end):
raise PipelineDateError(start=start, end=end)
# Check if start and end are provided, and if the sim_params need to read
# a start and end from the DataSource
if start is None:
overwrite_sim_params = True
else:
overwrite_sim_params = False
symbols = kwargs['symbols'].split(',')
asset_identifier = kwargs['metadata_index']
# Pull asset metadata
asset_metadata = kwargs.get('asset_metadata', None)
asset_metadata_path = kwargs['metadata_path']
# Read in a CSV file, if applicable
if asset_metadata_path is not None:
if os.path.isfile(asset_metadata_path):
asset_metadata = pd.read_csv(asset_metadata_path,
index_col=asset_identifier)
source_arg = kwargs['source']
source_time_column = kwargs['source_time_column']
if source_arg is None:
raise NoSourceError()
elif source_arg == 'yahoo':
source = zipline.data.load_bars_from_yahoo(stocks=symbols,
start=start,
end=end)
elif os.path.isfile(source_arg):
source = zipline.data.load_prices_from_csv(
filepath=source_arg, identifier_col=source_time_column)
elif os.path.isdir(source_arg):
source = zipline.data.load_prices_from_csv_folder(
folderpath=source_arg, identifier_col=source_time_column)
else:
raise NotImplementedError('Source %s not implemented.' %
kwargs['source'])
algo_text = kwargs.get('algo_text', None)
if algo_text is None:
# Expect algofile to be set
algo_fname = kwargs['algofile']
with open(algo_fname, 'r') as fd:
algo_text = fd.read()
if print_algo:
if PYGMENTS:
highlight(algo_text,
PythonLexer(),
TerminalFormatter(),
outfile=sys.stdout)
else:
print_(algo_text)
algo = zipline.TradingAlgorithm(script=algo_text,
namespace=kwargs.get('namespace', {}),
capital_base=float(kwargs['capital_base']),
algo_filename=kwargs.get('algofile'),
equities_metadata=asset_metadata,
start=start,
end=end)
perf = algo.run(source, overwrite_sim_params=overwrite_sim_params)
output_fname = kwargs.get('output', None)
if output_fname is not None:
perf.to_pickle(output_fname)
return perf
def run_pipeline(print_algo=True, **kwargs):
"""Runs a full zipline pipeline given configuration keyword
arguments.
1. Load data (start and end dates can be provided a strings as
well as the source and symobls).
2. Instantiate algorithm (supply either algo_text or algofile
kwargs containing initialize() and handle_data() functions). If
algofile is supplied, will try to look for algofile_analyze.py and
append it.
3. Run algorithm (supply capital_base as float).
4. Return performance dataframe.
:Arguments:
* print_algo : bool <default=True>
Whether to print the algorithm to command line. Will use
pygments syntax coloring if pygments is found.
"""
start = pd.Timestamp(kwargs['start'], tz='UTC')
end = pd.Timestamp(kwargs['end'], tz='UTC')
symbols = kwargs['symbols'].split(',')
if kwargs['source'] == 'yahoo':
source = zipline.data.load_bars_from_yahoo(stocks=symbols,
start=start,
end=end)
else:
raise NotImplementedError('Source %s not implemented.' %
kwargs['source'])
algo_text = kwargs.get('algo_text', None)
if algo_text is None:
# Expect algofile to be set
algo_fname = kwargs['algofile']
with open(algo_fname, 'r') as fd:
algo_text = fd.read()
analyze_fname = os.path.splitext(algo_fname)[0] + '_analyze.py'
if os.path.exists(analyze_fname):
with open(analyze_fname, 'r') as fd:
# Simply append
algo_text += fd.read()
if print_algo:
if PYGMENTS:
highlight(algo_text,
PythonLexer(),
TerminalFormatter(),
outfile=sys.stdout)
else:
print_(algo_text)
algo = zipline.TradingAlgorithm(script=algo_text,
namespace=kwargs.get('namespace', {}),
capital_base=float(kwargs['capital_base']),
algo_filename=kwargs.get('algofile'))
perf = algo.run(source)
output_fname = kwargs.get('output', None)
if output_fname is not None:
perf.to_pickle(output_fname)
return perf