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MAINT: PEP8 compliance

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This commit is contained in:
Victor Grau Serrat
2017-12-08 13:18:24 -07:00
parent eb5d55478d
commit ce085e01ec
70 changed files with 757 additions and 1359 deletions
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catalyst/data/bundles/__init__.py
-1
View File
@@ -1,6 +1,5 @@
# These imports are necessary to force module-scope register calls to happen.
from . import quandl # noqa
from . import poloniex
from .core import (
UnknownBundle,
bundles,
catalyst/data/bundles/base.py
+35 -36
View File
@@ -13,10 +13,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from itertools import count
import tarfile
from time import time, sleep
from time import sleep
from abc import abstractmethod, abstractproperty
import logbook
@@ -37,6 +36,7 @@ log = logbook.Logger(__name__, level=LOG_LEVEL)
DEFAULT_RETRIES = 5
class BaseBundle(object):
def __init__(self, asset_filter=[]):
self._asset_filter = asset_filter
@@ -104,11 +104,11 @@ class BaseBundle(object):
def post_process_symbol_metadata(self, metadata, data):
return metadata
@abstractmethod
def fetch_raw_symbol_frame(self, api_key, symbol, start_date, end_date):
raise NotImplementedError()
def ingest(self,
environ,
asset_db_writer,
@@ -128,7 +128,7 @@ class BaseBundle(object):
retries = environ.get('CATALYST_DOWNLOAD_ATTEMPTS', 5)
if is_compile:
# User has instructed local compilation and ingestion of bundle.
# User has instructed local compilation & ingestion of bundle.
# Fetch raw metadata for all symbols.
raw_metadata = self._fetch_metadata_frame(
api_key,
@@ -157,9 +157,9 @@ class BaseBundle(object):
show_progress=show_progress,
)
# Post-process metadata using cached symbol frames, and write to
# disk. This metadata must be written before any attempt to write
# minute data.
# Post-process metadata using cached symbol frames, and write
# to disk. This metadata must be written before any attempt
# to write minute data.
metadata = self._post_process_metadata(
raw_metadata,
cache,
@@ -184,10 +184,11 @@ class BaseBundle(object):
show_progress=show_progress,
)
# For legacy purposes, this call is required to ensure the database
# contains an appropriately initialized file structure. We don't
# forsee a usecase for adjustments at this time, but may later
# choose to expose this functionality in the future.
# For legacy purposes, this call is required to ensure the
# database contains an appropriately initialized file
# structure. We don't forsee a usecase for adjustments at
# this time, but may later choose to expose this functionality
# in the future.
adjustment_writer.write(
splits=(
pd.concat(self.splits, ignore_index=True)
@@ -232,12 +233,12 @@ class BaseBundle(object):
tar.extractall(output_dir)
def _fetch_metadata_frame(self,
api_key,
cache,
retries=DEFAULT_RETRIES,
environ=None,
show_progress=False):
api_key,
cache,
retries=DEFAULT_RETRIES,
environ=None,
show_progress=False):
# Setup raw metadata iterator to fetch pages if necessary.
raw_iter = self._fetch_metadata_iter(api_key, cache, retries, environ)
@@ -251,7 +252,7 @@ class BaseBundle(object):
show_percent=False,
) as blocks:
metadata = pd.concat(blocks, ignore_index=True)
return metadata
def _fetch_metadata_iter(self, api_key, cache, retries, environ):
@@ -269,21 +270,20 @@ class BaseBundle(object):
page_number,
)
break
except ValueError as e:
except ValueError:
raw = pd.DataFrame([])
break
except Exception as e:
except Exception:
log.exception(
'Failed to load metadata from {}. '
'Retrying.'.format(self.name)
)
)
else:
raise ValueError(
'Failed to download metadata page {} after {} '
'attempts.'.format(page_number, retries)
)
if raw.empty:
# Empty DataFrame signals completion.
break
@@ -305,7 +305,7 @@ class BaseBundle(object):
columns=self.md_column_names,
index=metadata.index,
)
# Iterate over the available symbols, loading the asset's raw symbol
# data from the cache. The final metadata is computed and recorded in
# the appropriate row depending on the asset's id.
@@ -318,22 +318,22 @@ class BaseBundle(object):
show_percent=False,
) as symbols_map:
for asset_id, symbol in symbols_map:
# Attempt to load data from disk, the cache should have an entry
# for each symbol at this point of the execution. If one does
# not exist, we should fail.
# Attempt to load data from disk, the cache should have an
# entry for each symbol at this point of the execution. If one
# does not exist, we should fail.
key = '{sym}.daily.frame'.format(sym=symbol)
try:
raw_data = cache[key]
except KeyError:
raise ValueError(
'Unable to find cached data for symbol: {0}'.format(symbol)
)
'Unable to find cached data for symbol:'
' {0}'.format(symbol))
# Perform and require post-processing of metadata.
final_symbol_metadata = self.post_process_symbol_metadata(
asset_id,
metadata.iloc[asset_id],
raw_data,
raw_data,
)
# Record symbol's final metadata.
@@ -363,8 +363,8 @@ class BaseBundle(object):
# returns the cached data unaltered. The `should_sleep` flag
# indicates that an API call was attempted, and that we should be
# ensure aren't exceeding our rate limit before proceeding to the
# next symbol. If the raw_data is updated, it is cached before being
# returned.
# next symbol. If the raw_data is updated, it is cached before
# being returned.
raw_data, should_sleep = self._maybe_update_symbol_frame(
start_time,
api_key,
@@ -414,7 +414,7 @@ class BaseBundle(object):
last = start_session
if raw_data is not None and len(raw_data) > 0:
last = raw_data.index[-1].tz_localize('UTC')
should_sleep = False
# Determine time at which cached data will be considered stale.
@@ -455,7 +455,7 @@ class BaseBundle(object):
retries=DEFAULT_RETRIES):
# Data for symbol is old enough to attempt an update or is not
# present in the cache. Fetch raw data for a single symbol
# present in the cache. Fetch raw data for a single symbol
# with requested intervals and frequency. Retry as necessary.
for _ in range(retries):
try:
@@ -468,7 +468,6 @@ class BaseBundle(object):
data_frequency,
)
raw_data.index = pd.to_datetime(raw_data.index, utc=True)
#raw_data.index = raw_data.index.tz_localize('UTC')
# Filter incoming data to fit start and end sessions.
raw_data = raw_data[
@@ -482,7 +481,7 @@ class BaseBundle(object):
return raw_data
except Exception as e:
except Exception:
log.exception(
'Exception raised fetching {name} data. Retrying.'
.format(name=self.name)
catalyst/data/bundles/base_pricing.py
+3
View File
@@ -16,6 +16,7 @@
from catalyst.data.bundles.base import BaseBundle
from catalyst.utils.memoize import lazyval
class BasePricingBundle(BaseBundle):
@lazyval
def md_dtypes(self):
@@ -38,6 +39,7 @@ class BasePricingBundle(BaseBundle):
('volume', 'float64'),
]
class BaseCryptoPricingBundle(BasePricingBundle):
@lazyval
def calendar_name(self):
@@ -55,6 +57,7 @@ class BaseCryptoPricingBundle(BasePricingBundle):
def dividends(self):
return []
class BaseEquityPricingBundle(BasePricingBundle):
@lazyval
def calendar_name(self):
catalyst/data/bundles/core.py
+4 -1
View File
@@ -37,6 +37,7 @@ from catalyst.utils.cli import maybe_show_progress
ONE_MEGABYTE = 1024 * 1024
def asset_db_path(bundle_name, timestr, environ=None, db_version=None):
return pth.data_path(
asset_db_relative(bundle_name, timestr, environ, db_version),
@@ -135,6 +136,7 @@ def ingestions_for_bundle(bundle, environ=None):
reverse=True,
)
def download_with_progress(url, chunk_size, **progress_kwargs):
"""
Download streaming data from a URL, printing progress information to the
@@ -705,4 +707,5 @@ def _make_bundle_core():
)
bundles, register_bundle, register, unregister, ingest, load, clean = _make_bundle_core()
bundles, register_bundle, register, unregister, ingest, load, clean = \
_make_bundle_core()
catalyst/data/bundles/poloniex.py
+16 -18
View File
@@ -14,19 +14,17 @@
# limitations under the License.
import sys
from datetime import datetime
from six.moves.urllib.parse import urlencode
import pandas as pd
from six.moves.urllib.parse import urlencode
from catalyst.data.bundles.core import register_bundle
from catalyst.data.bundles.base_pricing import BaseCryptoPricingBundle
from catalyst.utils.memoize import lazyval
from catalyst.curate.poloniex import PoloniexCurator
class PoloniexBundle(BaseCryptoPricingBundle):
@lazyval
def name(self):
@@ -46,7 +44,8 @@ class PoloniexBundle(BaseCryptoPricingBundle):
@lazyval
def tar_url(self):
return (
'https://s3.amazonaws.com/enigmaco/catalyst-bundles/poloniex/poloniex-bundle.tar.gz'
'https://s3.amazonaws.com/enigmaco/catalyst-bundles/'
'poloniex/poloniex-bundle.tar.gz'
)
@lazyval
@@ -67,12 +66,11 @@ class PoloniexBundle(BaseCryptoPricingBundle):
raw = raw.sort_index().reset_index()
raw.rename(
columns={'index':'symbol'},
columns={'index': 'symbol'},
inplace=True,
)
raw = raw[raw['isFrozen'] == 0]
return raw
def post_process_symbol_metadata(self, asset_id, sym_md, sym_data):
@@ -98,7 +96,8 @@ class PoloniexBundle(BaseCryptoPricingBundle):
frequency):
# TODO: replace this with direct exchange call
# The end date and frequency should be used to calculate the number of bars
# The end date and frequency should be used to
# calculate the number of bars
if(frequency == 'minute'):
pc = PoloniexCurator()
raw = pc.onemin_to_dataframe(symbol, start_date, end_date)
@@ -116,8 +115,9 @@ class PoloniexBundle(BaseCryptoPricingBundle):
)
raw.set_index('date', inplace=True)
# BcolzDailyBarReader introduces a 1/1000 factor in the way pricing is stored
# on disk, which we compensate here to get the right pricing amounts
# BcolzDailyBarReader introduces a 1/1000 factor in the way
# pricing is stored on disk, which we compensate here to get
# the right pricing amounts
# ref: data/us_equity_pricing.py
scale = 1
raw.loc[:, 'open'] /= scale
@@ -139,7 +139,6 @@ class PoloniexBundle(BaseCryptoPricingBundle):
return self._format_polo_query(query_params)
def _format_data_url(self,
api_key,
symbol,
@@ -162,27 +161,26 @@ class PoloniexBundle(BaseCryptoPricingBundle):
('end', end_date.value / 10**9),
('period', period),
]
return self._format_polo_query(query_params)
def _format_polo_query(self, query_params):
# TODO: got against the exchange object
return 'https://poloniex.com/public?{query}'.format(
query=urlencode(query_params),
)
'''
As a second parameter, you can pass an array of currency pairs
that will be processed as an asset_filter to only process that
'''
As a second parameter, you can pass an array of currency pairs
that will be processed as an asset_filter to only process that
subset of assets in the bundle, such as:
register_bundle(PoloniexBundle, ['USDT_BTC',])
For a production environment make sure to use (to bundle all pairs):
register_bundle(PoloniexBundle)
'''
if 'ingest' in sys.argv and '-c' in sys.argv:
register_bundle(PoloniexBundle)
else:
register_bundle(PoloniexBundle, create_writers=False)
catalyst/data/bundles/quandl.py
+8 -19
View File
@@ -16,7 +16,6 @@
from datetime import datetime
import pandas as pd
from six.moves.urllib.parse import urlencode
from catalyst.data.bundles.core import register_bundle
@@ -26,25 +25,16 @@ from catalyst.utils.memoize import lazyval
"""
Module for building a complete daily dataset from Quandl's WIKI dataset.
"""
from itertools import count
import tarfile
from time import time, sleep
from datetime import datetime
from logbook import Logger
import pandas as pd
from six.moves.urllib.parse import urlencode
from catalyst.utils.calendars import register_calendar_alias
from catalyst.utils.cli import maybe_show_progress
from . import core as bundles
from catalyst.constants import LOG_LEVEL
from catalyst.utils.calendars import register_calendar_alias
log = Logger(__name__, level=LOG_LEVEL)
seconds_per_call = (pd.Timedelta('10 minutes') / 2000).total_seconds()
class QuandlBundle(BaseEquityPricingBundle):
@lazyval
def name(self):
@@ -109,8 +99,8 @@ class QuandlBundle(BaseEquityPricingBundle):
# Filter out invalid symbols
raw = raw[~raw.symbol.isin(self._excluded_symbols)]
# cut out all the other stuff in the name column
# we need to escape the paren because it is actually splitting on a regex
# cut out all the other stuff in the name column. We need to
# escape the paren because it is actually splitting on a regex
raw.asset_name = raw.asset_name.str.split(r' \(', 1).str.get(0)
return raw
@@ -175,7 +165,6 @@ class QuandlBundle(BaseEquityPricingBundle):
df['sid'] = asset_id
self.splits.append(df)
def _update_dividends(self, asset_id, raw_data):
divs = raw_data.ex_dividend
df = pd.DataFrame({'amount': divs[divs != 0]})
@@ -186,7 +175,6 @@ class QuandlBundle(BaseEquityPricingBundle):
df['record_date'] = df['declared_date'] = df['pay_date'] = pd.NaT
self.dividends.append(df)
def _format_metadata_url(self, api_key, page_number):
"""Build the query RL for the quandl WIKI metadata.
"""
@@ -200,10 +188,10 @@ class QuandlBundle(BaseEquityPricingBundle):
query_params = [('api_key', api_key)] + query_params
return (
'https://www.quandl.com/api/v3/datasets.csv?' + urlencode(query_params)
'https://www.quandl.com/api/v3/datasets.csv?'
+ urlencode(query_params)
)
def _format_wiki_url(self,
api_key,
symbol,
@@ -229,5 +217,6 @@ class QuandlBundle(BaseEquityPricingBundle):
)
)
register_calendar_alias('QUANDL', 'NYSE')
register_bundle(QuandlBundle)
catalyst/data/data_portal.py
+6 -6
View File
@@ -656,11 +656,11 @@ class DataPortal(object):
return spot_value
def _get_minutely_spot_value(self,
asset,
column,
dt,
data_frequency,
ffill=False):
asset,
column,
dt,
data_frequency,
ffill=False):
reader = self._get_pricing_reader(data_frequency)
@@ -706,7 +706,7 @@ class DataPortal(object):
asset,
column,
dt,
ffill,
ffill,
'minute',
)
catalyst/data/dispatch_bar_reader.py
+2
View File
@@ -133,11 +133,13 @@ class AssetDispatchBarReader(with_metaclass(ABCMeta)):
return results
class AssetDispatchMinuteBarReader(AssetDispatchBarReader):
def _dt_window_size(self, start_dt, end_dt):
return len(self.trading_calendar.minutes_in_range(start_dt, end_dt))
class AssetDispatchSessionBarReader(AssetDispatchBarReader):
def _dt_window_size(self, start_dt, end_dt):
catalyst/data/loader.py
+19 -78
View File
@@ -12,7 +12,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import os
from collections import OrderedDict
@@ -129,11 +128,13 @@ def load_crypto_market_data(trading_day=None, trading_days=None,
# before this date.
'''
if(bundle_data):
# If we are using the bundle to retrieve the cryptobenchmark, find the last
# date for which there is trading data in the bundle
asset = bundle_data.asset_finder.lookup_symbol(symbol=bm_symbol,as_of_date=None)
# If we are using the bundle to retrieve the cryptobenchmark, find
# the last date for which there is trading data in the bundle
asset = bundle_data.asset_finder.lookup_symbol(
symbol=bm_symbol,as_of_date=None)
ix = bundle_data.daily_bar_reader._last_rows[asset.sid]
last_date = pd.to_datetime(bundle_data.daily_bar_reader._spot_col('day')[ix],unit='s')
last_date = pd.to_datetime(
bundle_data.daily_bar_reader._spot_col('day')[ix],unit='s')
else:
last_date = trading_days[trading_days.get_loc(now, method='ffill') - 2]
'''
@@ -164,8 +165,8 @@ def load_crypto_market_data(trading_day=None, trading_days=None,
br.loc[start_dt] = 0
br = br.sort_index()
# Override first_date for treasury data since we have it for many more years
# and is independent of crypto data
# Override first_date for treasury data since we have it for many more
# years and is independent of crypto data
first_date_treasury = pd.Timestamp('1990-01-02', tz='UTC')
tc = ensure_treasury_data(
bm_symbol,
@@ -301,14 +302,14 @@ def ensure_crypto_benchmark_data(symbol,
if (bundle == 'poloniex'):
'''
If we're using the Poloniex bundle, we'll get the benchmark from the bundle
instead of downloading it from Poloniex every time we need it.
Poloniex has a captcha for API queries originating from outside the US that
prevents users abroad from getting Catalyst to work
If we're using the Poloniex bundle, we'll get the benchmark from the
bundle instead of downloading it from Poloniex every time we need it.
Poloniex has a captcha for API queries originating from outside the US
that prevents users abroad from getting Catalyst to work
'''
logger.info(
(
'Retrieving benchmark data from bundle for {symbol!r} from {first_date} to {last_date}'),
('Retrieving benchmark data from bundle for {symbol!r}'
' from {first_date} to {last_date}'),
symbol=symbol, first_date=first_date, last_date=last_date)
asset = bundle_data.asset_finder.lookup_symbol(symbol=symbol,
@@ -330,11 +331,12 @@ def ensure_crypto_benchmark_data(symbol,
last_date)]
else:
# This is how it used to be: downloading the benchmark everytime.
# Leaving this code here to be repurposed in the future for other bundles.
# This is how it used to be: downloading the benchmark everytime.
# Leaving this code here to be repurposed in the future for
# other bundles.
logger.info(
(
'Downloading benchmark data for {symbol!r} from {first_date} to {last_date}'),
('Downloading benchmark data for {symbol!r}'
' from {first_date} to {last_date}'),
symbol=symbol, first_date=first_date, last_date=last_date)
raise DeprecationWarning('poloniex bundle deprecated')
@@ -431,67 +433,6 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
return data
def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
environ=None):
"""
Ensure we have benchmark data for `symbol` from `first_date` to `last_date`
Parameters
----------
symbol : str
The symbol for the benchmark to load.
first_date : pd.Timestamp
First required date for the cache.
last_date : pd.Timestamp
Last required date for the cache.
now : pd.Timestamp
The current time. This is used to prevent repeated attempts to
re-download data that isn't available due to scheduling quirks or other
failures.
trading_day : pd.CustomBusinessDay
A trading day delta. Used to find the day before first_date so we can
get the close of the day prior to first_date.
We attempt to download data unless we already have data stored at the data
cache for `symbol` whose first entry is before or on `first_date` and whose
last entry is on or after `last_date`.
If we perform a download and the cache criteria are not satisfied, we wait
at least one hour before attempting a redownload. This is determined by
comparing the current time to the result of os.path.getmtime on the cache
path.
"""
filename = get_benchmark_filename(symbol)
data = _load_cached_data(filename, first_date, last_date, now, 'benchmark',
environ)
if data is not None:
return data
# If no cached data was found or it was missing any dates then download the
# necessary data.
logger.info(
('Downloading benchmark data for {symbol!r} '
'from {first_date} to {last_date}'),
symbol=symbol,
first_date=first_date - trading_day,
last_date=last_date
)
try:
data = get_benchmark_returns(
symbol,
first_date - trading_day,
last_date,
)
data.to_csv(get_data_filepath(filename, environ))
except (OSError, IOError, HTTPError):
logger.exception('Failed to cache the new benchmark returns')
raise
if not has_data_for_dates(data, first_date, last_date):
logger.warn("Still don't have expected data after redownload!")
return data
def ensure_treasury_data(symbol, first_date, last_date, now, environ=None):
"""
Ensure we have treasury data from treasury module associated with
catalyst/data/minute_bars.py
+8 -11
View File
@@ -341,12 +341,10 @@ class BcolzMinuteBarMetadata(object):
'end_session': str(self.end_session.date()),
# Write these values for backwards compatibility
'first_trading_day': str(self.start_session.date()),
'market_opens': (
market_opens.values.astype('datetime64[m]').
astype(np.int64).tolist()),
'market_closes': (
market_closes.values.astype('datetime64[m]').
astype(np.int64).tolist()),
'market_opens': (market_opens.values.astype('datetime64[m]').
astype(np.int64).tolist()),
'market_closes': (market_closes.values.astype('datetime64[m]').
astype(np.int64).tolist()),
}
with open(self.metadata_path(rootdir), 'w+') as fp:
json.dump(metadata, fp)
@@ -1256,8 +1254,8 @@ class BcolzMinuteBarReader(MinuteBarReader):
values = carray[start_idx:end_idx + 1]
if indices_to_exclude is not None:
for excl_start, excl_stop in indices_to_exclude[::-1]:
excl_slice = np.s_[
excl_start - start_idx:excl_stop - start_idx + 1]
excl_slice = np.s_[excl_start - start_idx:excl_stop
- start_idx + 1]
values = np.delete(values, excl_slice)
where = values != 0
@@ -1320,9 +1318,8 @@ class H5MinuteBarUpdateWriter(object):
def __init__(self, path, complevel=None, complib=None):
self._complevel = complevel if complevel \
is not None else self._COMPLEVEL
self._complib = complib if complib \
is not None else self._COMPLIB
is not None else self._COMPLEVEL
self._complib = complib if complib is not None else self._COMPLIB
self._path = path
def write(self, frames):
catalyst/data/us_equity_pricing.py
+11 -7
View File
@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import division # Python2 req to have division of ints yield float
from __future__ import division # Python2 req for division of ints yield float
from errno import ENOENT
from functools import partial
@@ -120,7 +120,8 @@ SQLITE_STOCK_DIVIDEND_PAYOUT_COLUMN_DTYPES = {
UINT32_MAX = iinfo(uint32).max
UINT64_MAX = iinfo(uint64).max
PRICE_ADJUSTMENT_FACTOR = 1000000000 # Provides 9 decimals resolution. Also affects _equities.pyx L220
# Provides 9 decimals resolution. Also affects _equities.pyx L220
PRICE_ADJUSTMENT_FACTOR = 1000000000
def check_uint32_safe(value, colname):
@@ -130,6 +131,7 @@ def check_uint32_safe(value, colname):
"for uint32" % (value, colname)
)
def check_uint64_safe(value, colname):
if value >= UINT64_MAX:
raise ValueError(
@@ -322,8 +324,8 @@ class BcolzDailyBarWriter(object):
# Maps column name -> output carray.
columns = {
k: carray(array([], dtype=uint64))
if k in OHLCV
else carray(array([], dtype=uint32))
if k in OHLCV
else carray(array([], dtype=uint32))
for k in US_EQUITY_PRICING_BCOLZ_COLUMNS
}
@@ -439,11 +441,13 @@ class BcolzDailyBarWriter(object):
return raw_data
winsorise_uint64(raw_data, invalid_data_behavior, 'volume', *OHLC)
processed = (raw_data[list(OHLC)] * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
processed = (raw_data[list(OHLC)]
* PRICE_ADJUSTMENT_FACTOR).astype('uint64')
dates = raw_data.index.values.astype('datetime64[s]')
check_uint32_safe(dates.max().view(np.int64), 'day')
processed['day'] = dates.astype('uint32')
processed['volume'] = (raw_data.volume * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
processed['volume'] = (raw_data.volume
* PRICE_ADJUSTMENT_FACTOR).astype('uint64')
return ctable.fromdataframe(processed)
@@ -496,7 +500,7 @@ class BcolzDailyBarReader(SessionBarReader):
The data in these columns is interpreted as follows:
- Price columns ('open', 'high', 'low', 'close') and Volume are interpreted
- Price columns ('open', 'high', 'low', 'close') and Volume are interpreted
as 10^9 * as-traded dollar value.
- Day is interpreted as seconds since midnight UTC, Jan 1, 1970.
- Id is the asset id of the row.