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def assert_datasource_protocol(event):
"""Assert that an event meets the protocol for datasource outputs.""" |
assert event.type in DATASOURCE_TYPE
# Done packets have no dt.
if not event.type == DATASOURCE_TYPE.DONE:
assert isinstance(event.dt, datetime)
assert event.dt.tzinfo == pytz.utc |
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def assert_trade_protocol(event):
"""Assert that an event meets the protocol for datasource TRADE outputs.""" |
assert_datasource_protocol(event)
assert event.type == DATASOURCE_TYPE.TRADE
assert isinstance(event.price, numbers.Real)
assert isinstance(event.volume, numbers.Integral)
assert isinstance(event.dt, datetime) |
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def date_sorted_sources(*sources):
""" Takes an iterable of sources, generating namestrings and piping their output into date_sort. """ |
sorted_stream = heapq.merge(*(_decorate_source(s) for s in sources))
# Strip out key decoration
for _, message in sorted_stream:
yield message |
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def create_daily_trade_source(sids, sim_params, asset_finder, trading_calendar):
""" creates trade_count trades for each sid in sids list. first trade will be on... |
return create_trade_source(
sids,
timedelta(days=1),
sim_params,
asset_finder,
trading_calendar=trading_calendar,
) |
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def load_data_table(file, index_col, show_progress=False):
""" Load data table from zip file provided by Quandl. """ |
with ZipFile(file) as zip_file:
file_names = zip_file.namelist()
assert len(file_names) == 1, "Expected a single file from Quandl."
wiki_prices = file_names.pop()
with zip_file.open(wiki_prices) as table_file:
if show_progress:
log.info('Parsing raw data.... |
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def fetch_data_table(api_key, show_progress, retries):
""" Fetch WIKI Prices data table from Quandl """ |
for _ in range(retries):
try:
if show_progress:
log.info('Downloading WIKI metadata.')
metadata = pd.read_csv(
format_metadata_url(api_key)
)
# Extract link from metadata and download zip file.
table_url = metadata... |
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def quandl_bundle(environ, asset_db_writer, minute_bar_writer, daily_bar_writer, adjustment_writer, calendar, start_session, end_session, cache, show_progress, ou... |
api_key = environ.get('QUANDL_API_KEY')
if api_key is None:
raise ValueError(
"Please set your QUANDL_API_KEY environment variable and retry."
)
raw_data = fetch_data_table(
api_key,
show_progress,
environ.get('QUANDL_DOWNLOAD_ATTEMPTS', 5)
)
ass... |
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def download_with_progress(url, chunk_size, **progress_kwargs):
""" Download streaming data from a URL, printing progress information to the terminal. Parameters... |
resp = requests.get(url, stream=True)
resp.raise_for_status()
total_size = int(resp.headers['content-length'])
data = BytesIO()
with progressbar(length=total_size, **progress_kwargs) as pbar:
for chunk in resp.iter_content(chunk_size=chunk_size):
data.write(chunk)
p... |
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def download_without_progress(url):
""" Download data from a URL, returning a BytesIO containing the loaded data. Parameters url : str A URL that can be understo... |
resp = requests.get(url)
resp.raise_for_status()
return BytesIO(resp.content) |
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def minute_frame_to_session_frame(minute_frame, calendar):
""" Resample a DataFrame with minute data into the frame expected by a BcolzDailyBarWriter. Parameters... |
how = OrderedDict((c, _MINUTE_TO_SESSION_OHCLV_HOW[c])
for c in minute_frame.columns)
labels = calendar.minute_index_to_session_labels(minute_frame.index)
return minute_frame.groupby(labels).agg(how) |
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def minute_to_session(column, close_locs, data, out):
""" Resample an array with minute data into an array with session data. This function assumes that the minu... |
if column == 'open':
_minute_to_session_open(close_locs, data, out)
elif column == 'high':
_minute_to_session_high(close_locs, data, out)
elif column == 'low':
_minute_to_session_low(close_locs, data, out)
elif column == 'close':
_minute_to_session_close(close_locs, data... |
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def opens(self, assets, dt):
""" The open field's aggregation returns the first value that occurs for the day, if there has been no data on or before the `dt` th... |
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'open')
opens = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
opens.append(np.NaN)
con... |
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def highs(self, assets, dt):
""" The high field's aggregation returns the largest high seen between the market open and the current dt. If there has been no data... |
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'high')
highs = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
highs.append(np.NaN)
con... |
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def lows(self, assets, dt):
""" The low field's aggregation returns the smallest low seen between the market open and the current dt. If there has been no data o... |
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'low')
lows = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
lows.append(np.NaN)
contin... |
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def closes(self, assets, dt):
""" The close field's aggregation returns the latest close at the given dt. If the close for the given dt is `nan`, the most recent... |
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'close')
closes = []
session_label = self._trading_calendar.minute_to_session_label(dt)
def _get_filled_close(asset):
"""
Returns the most recent non-nan close for the asset in this
session... |
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def volumes(self, assets, dt):
""" The volume field's aggregation returns the sum of all volumes between the market open and the `dt` If there has been no data o... |
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'volume')
volumes = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
volumes.append(0)
co... |
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def infer_domain(terms):
""" Infer the domain from a collection of terms. The algorithm for inferring domains is as follows: - If all input terms have a domain o... |
domains = {t.domain for t in terms}
num_domains = len(domains)
if num_domains == 0:
return GENERIC
elif num_domains == 1:
return domains.pop()
elif num_domains == 2 and GENERIC in domains:
domains.remove(GENERIC)
return domains.pop()
else:
# Remove GENER... |
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def roll_forward(self, dt):
""" Given a date, align it to the calendar of the pipeline's domain. Parameters dt : pd.Timestamp Returns ------- pd.Timestamp """ |
dt = pd.Timestamp(dt, tz='UTC')
trading_days = self.all_sessions()
try:
return trading_days[trading_days.searchsorted(dt)]
except IndexError:
raise ValueError(
"Date {} was past the last session for domain {}. "
"The last session ... |
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def days_and_sids_for_frames(frames):
""" Returns the date index and sid columns shared by a list of dataframes, ensuring they all match. Parameters frames : lis... |
if not frames:
days = np.array([], dtype='datetime64[ns]')
sids = np.array([], dtype='int64')
return days, sids
# Ensure the indices and columns all match.
check_indexes_all_same(
[frame.index for frame in frames],
message='Frames have mistmatched days.',
)
... |
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def write(self, country_code, frames, scaling_factors=None):
"""Write the OHLCV data for one country to the HDF5 file. Parameters country_code : str The ISO 3166... |
if scaling_factors is None:
scaling_factors = DEFAULT_SCALING_FACTORS
with self.h5_file(mode='a') as h5_file:
# ensure that the file version has been written
h5_file.attrs['version'] = VERSION
country_group = h5_file.create_group(country_code)
... |
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def from_file(cls, h5_file, country_code):
""" Construct from an h5py.File and a country code. Parameters h5_file : h5py.File An HDF5 daily pricing file. country... |
if h5_file.attrs['version'] != VERSION:
raise ValueError(
'mismatched version: file is of version %s, expected %s' % (
h5_file.attrs['version'],
VERSION,
),
)
return cls(h5_file[country_code]) |
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def from_path(cls, path, country_code):
""" Construct from a file path and a country code. Parameters path : str The path to an HDF5 daily pricing file. country_... |
return cls.from_file(h5py.File(path), country_code) |
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def _make_sid_selector(self, assets):
""" Build an indexer mapping ``self.sids`` to ``assets``. Parameters assets : list[int] List of assets requested by a calle... |
assets = np.array(assets)
sid_selector = self.sids.searchsorted(assets)
unknown = np.in1d(assets, self.sids, invert=True)
sid_selector[unknown] = -1
return sid_selector |
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def _validate_assets(self, assets):
"""Validate that asset identifiers are contained in the daily bars. Parameters assets : array-like[int] The asset identifiers... |
missing_sids = np.setdiff1d(assets, self.sids)
if len(missing_sids):
raise NoDataForSid(
'Assets not contained in daily pricing file: {}'.format(
missing_sids
)
) |
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def from_file(cls, h5_file):
""" Construct from an h5py.File. Parameters h5_file : h5py.File An HDF5 daily pricing file. """ |
return cls({
country: HDF5DailyBarReader.from_file(h5_file, country)
for country in h5_file.keys()
}) |
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def _normalize_index_columns_in_place(equities, equity_supplementary_mappings, futures, exchanges, root_symbols):
""" Update dataframes in place to set indentifi... |
for frame, column_name in ((equities, 'sid'),
(equity_supplementary_mappings, 'sid'),
(futures, 'sid'),
(exchanges, 'exchange'),
(root_symbols, 'root_symbol')):
if frame is not None a... |
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def split_delimited_symbol(symbol):
""" Takes in a symbol that may be delimited and splits it in to a company symbol and share class symbol. Also returns the fuz... |
# return blank strings for any bad fuzzy symbols, like NaN or None
if symbol in _delimited_symbol_default_triggers:
return '', ''
symbol = symbol.upper()
split_list = re.split(
pattern=_delimited_symbol_delimiters_regex,
string=symbol,
maxsplit=1,
)
# Break th... |
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def _generate_output_dataframe(data_subset, defaults):
""" Generates an output dataframe from the given subset of user-provided data, the given column names, and... |
# The columns provided.
cols = set(data_subset.columns)
desired_cols = set(defaults)
# Drop columns with unrecognised headers.
data_subset.drop(cols - desired_cols,
axis=1,
inplace=True)
# Get those columns which we need but
# for which no data ha... |
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def _check_symbol_mappings(df, exchanges, asset_exchange):
"""Check that there are no cases where multiple symbols resolve to the same asset at the same time in ... |
mappings = df.set_index('sid')[list(mapping_columns)].copy()
mappings['country_code'] = exchanges['country_code'][
asset_exchange.loc[df['sid']]
].values
ambigious = {}
def check_intersections(persymbol):
intersections = list(intersecting_ranges(map(
from_tuple,
... |
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def _dt_to_epoch_ns(dt_series):
"""Convert a timeseries into an Int64Index of nanoseconds since the epoch. Parameters dt_series : pd.Series The timeseries to con... |
index = pd.to_datetime(dt_series.values)
if index.tzinfo is None:
index = index.tz_localize('UTC')
else:
index = index.tz_convert('UTC')
return index.view(np.int64) |
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def check_version_info(conn, version_table, expected_version):
""" Checks for a version value in the version table. Parameters conn : sa.Connection The connectio... |
# Read the version out of the table
version_from_table = conn.execute(
sa.select((version_table.c.version,)),
).scalar()
# A db without a version is considered v0
if version_from_table is None:
version_from_table = 0
# Raise an error if the versions do not match
if (versi... |
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def write_version_info(conn, version_table, version_value):
""" Inserts the version value in to the version table. Parameters conn : sa.Connection The connection... |
conn.execute(sa.insert(version_table, values={'version': version_value})) |
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def write_direct(self, equities=None, equity_symbol_mappings=None, equity_supplementary_mappings=None, futures=None, exchanges=None, root_symbols=None, chunk_size... |
if equities is not None:
equities = _generate_output_dataframe(
equities,
_direct_equities_defaults,
)
if equity_symbol_mappings is None:
raise ValueError(
'equities provided with no symbol mapping data',
... |
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def write(self, equities=None, futures=None, exchanges=None, root_symbols=None, equity_supplementary_mappings=None, chunk_size=DEFAULT_CHUNK_SIZE):
"""Write asse... |
if exchanges is None:
exchange_names = [
df['exchange']
for df in (equities, futures, root_symbols)
if df is not None
]
if exchange_names:
exchanges = pd.DataFrame({
'exchange': pd.concat(exc... |
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def _all_tables_present(self, txn):
""" Checks if any tables are present in the current assets database. Parameters txn : Transaction The open transaction to che... |
conn = txn.connect()
for table_name in asset_db_table_names:
if txn.dialect.has_table(conn, table_name):
return True
return False |
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def init_db(self, txn=None):
"""Connect to database and create tables. Parameters txn : sa.engine.Connection, optional The transaction to execute in. If this is ... |
with ExitStack() as stack:
if txn is None:
txn = stack.enter_context(self.engine.begin())
tables_already_exist = self._all_tables_present(txn)
# Create the SQL tables if they do not already exist.
metadata.create_all(txn, checkfirst=True)
... |
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def load_raw_data(assets, data_query_cutoff_times, expr, odo_kwargs, checkpoints=None):
""" Given an expression representing data to load, perform normalization ... |
lower_dt, upper_dt = data_query_cutoff_times[[0, -1]]
raw = ffill_query_in_range(
expr,
lower_dt,
upper_dt,
checkpoints=checkpoints,
odo_kwargs=odo_kwargs,
)
sids = raw[SID_FIELD_NAME]
raw.drop(
sids[~sids.isin(assets)].index,
inplace=True
... |
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def from_tuple(tup):
"""Convert a tuple into a range with error handling. Parameters tup : tuple (len 2 or 3) The tuple to turn into a range. Returns ------- ran... |
if len(tup) not in (2, 3):
raise ValueError(
'tuple must contain 2 or 3 elements, not: %d (%r' % (
len(tup),
tup,
),
)
return range(*tup) |
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def maybe_from_tuple(tup_or_range):
"""Convert a tuple into a range but pass ranges through silently. This is useful to ensure that input is a range so that attr... |
if isinstance(tup_or_range, tuple):
return from_tuple(tup_or_range)
elif isinstance(tup_or_range, range):
return tup_or_range
raise ValueError(
'maybe_from_tuple expects a tuple or range, got %r: %r' % (
type(tup_or_range).__name__,
tup_or_range,
),
... |
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def _check_steps(a, b):
"""Check that the steps of ``a`` and ``b`` are both 1. Parameters a : range The first range to check. b : range The second range to check... |
if a.step != 1:
raise ValueError('a.step must be equal to 1, got: %s' % a.step)
if b.step != 1:
raise ValueError('b.step must be equal to 1, got: %s' % b.step) |
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def overlap(a, b):
"""Check if two ranges overlap. Parameters a : range The first range. b : range The second range. Returns ------- overlaps : bool Do these ran... |
_check_steps(a, b)
return a.stop >= b.start and b.stop >= a.start |
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def merge(a, b):
"""Merge two ranges with step == 1. Parameters a : range The first range. b : range The second range. """ |
_check_steps(a, b)
return range(min(a.start, b.start), max(a.stop, b.stop)) |
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def _combine(n, rs):
"""helper for ``_group_ranges`` """ |
try:
r, rs = peek(rs)
except StopIteration:
yield n
return
if overlap(n, r):
yield merge(n, r)
next(rs)
for r in rs:
yield r
else:
yield n
for r in rs:
yield r |
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def intersecting_ranges(ranges):
"""Return any ranges that intersect. Parameters ranges : iterable[ranges] A sequence of ranges to check for intersections. Retur... |
ranges = sorted(ranges, key=op.attrgetter('start'))
return sorted_diff(ranges, group_ranges(ranges)) |
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def get_data_filepath(name, environ=None):
""" Returns a handle to data file. Creates containing directory, if needed. """ |
dr = data_root(environ)
if not os.path.exists(dr):
os.makedirs(dr)
return os.path.join(dr, name) |
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def has_data_for_dates(series_or_df, first_date, last_date):
""" Does `series_or_df` have data on or before first_date and on or after last_date? """ |
dts = series_or_df.index
if not isinstance(dts, pd.DatetimeIndex):
raise TypeError("Expected a DatetimeIndex, but got %s." % type(dts))
first, last = dts[[0, -1]]
return (first <= first_date) and (last >= last_date) |
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def load_market_data(trading_day=None, trading_days=None, bm_symbol='SPY', environ=None):
""" Load benchmark returns and treasury yield curves for the given cale... |
if trading_day is None:
trading_day = get_calendar('XNYS').day
if trading_days is None:
trading_days = get_calendar('XNYS').all_sessions
first_date = trading_days[0]
now = pd.Timestamp.utcnow()
# we will fill missing benchmark data through latest trading date
last_date = tradi... |
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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 `la... |
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.
log... |
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def ensure_treasury_data(symbol, first_date, last_date, now, environ=None):
""" Ensure we have treasury data from treasury module associated with `symbol`. Param... |
loader_module, filename, source = INDEX_MAPPING.get(
symbol, INDEX_MAPPING['SPY'],
)
first_date = max(first_date, loader_module.earliest_possible_date())
data = _load_cached_data(filename, first_date, last_date, now, 'treasury',
environ)
if data is not None:
... |
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def maybe_specialize(term, domain):
"""Specialize a term if it's loadable. """ |
if isinstance(term, LoadableTerm):
return term.specialize(domain)
return term |
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def _add_to_graph(self, term, parents):
""" Add a term and all its children to ``graph``. ``parents`` is the set of all the parents of ``term` that we've added s... |
if self._frozen:
raise ValueError(
"Can't mutate %s after construction." % type(self).__name__
)
# If we've seen this node already as a parent of the current traversal,
# it means we have an unsatisifiable dependency. This should only be
# possi... |
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def execution_order(self, refcounts):
""" Return a topologically-sorted iterator over the terms in ``self`` which need to be computed. """ |
return iter(nx.topological_sort(
self.graph.subgraph(
{term for term, refcount in refcounts.items() if refcount > 0},
),
)) |
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def initial_refcounts(self, initial_terms):
""" Calculate initial refcounts for execution of this graph. Parameters initial_terms : iterable[Term] An iterable of... |
refcounts = self.graph.out_degree()
for t in self.outputs.values():
refcounts[t] += 1
for t in initial_terms:
self._decref_dependencies_recursive(t, refcounts, set())
return refcounts |
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def _decref_dependencies_recursive(self, term, refcounts, garbage):
""" Decrement terms recursively. Notes ----- This should only be used to build the initial wo... |
# Edges are tuple of (from, to).
for parent, _ in self.graph.in_edges([term]):
refcounts[parent] -= 1
# No one else depends on this term. Remove it from the
# workspace to conserve memory.
if refcounts[parent] == 0:
garbage.add(parent)
... |
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def decref_dependencies(self, term, refcounts):
""" Decrement in-edges for ``term`` after computation. Parameters term : zipline.pipeline.Term The term whose par... |
garbage = set()
# Edges are tuple of (from, to).
for parent, _ in self.graph.in_edges([term]):
refcounts[parent] -= 1
# No one else depends on this term. Remove it from the
# workspace to conserve memory.
if refcounts[parent] == 0:
... |
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def _ensure_extra_rows(self, term, N):
""" Ensure that we're going to compute at least N extra rows of `term`. """ |
attrs = self.graph.node[term]
attrs['extra_rows'] = max(N, attrs.get('extra_rows', 0)) |
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def mask_and_dates_for_term(self, term, root_mask_term, workspace, all_dates):
""" Load mask and mask row labels for term. Parameters term : Term The term to loa... |
mask = term.mask
mask_offset = self.extra_rows[mask] - self.extra_rows[term]
# This offset is computed against root_mask_term because that is what
# determines the shape of the top-level dates array.
dates_offset = (
self.extra_rows[root_mask_term] - self.extra_rows... |
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def _assert_all_loadable_terms_specialized_to(self, domain):
"""Make sure that we've specialized all loadable terms in the graph. """ |
for term in self.graph.node:
if isinstance(term, LoadableTerm):
assert term.domain is domain |
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def window_specialization(typename):
"""Make an extension for an AdjustedArrayWindow specialization.""" |
return Extension(
'zipline.lib._{name}window'.format(name=typename),
['zipline/lib/_{name}window.pyx'.format(name=typename)],
depends=['zipline/lib/_windowtemplate.pxi'],
) |
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def read_requirements(path, strict_bounds, conda_format=False, filter_names=None):
""" Read a requirements.txt file, expressed as a path relative to Zipline root... |
real_path = join(dirname(abspath(__file__)), path)
with open(real_path) as f:
reqs = _filter_requirements(f.readlines(), filter_names=filter_names,
filter_sys_version=not conda_format)
if not strict_bounds:
reqs = map(_with_bounds, reqs)
... |
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def ensure_utc(time, tz='UTC'):
""" Normalize a time. If the time is tz-naive, assume it is UTC. """ |
if not time.tzinfo:
time = time.replace(tzinfo=pytz.timezone(tz))
return time.replace(tzinfo=pytz.utc) |
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def _build_offset(offset, kwargs, default):
""" Builds the offset argument for event rules. """ |
if offset is None:
if not kwargs:
return default # use the default.
else:
return _td_check(datetime.timedelta(**kwargs))
elif kwargs:
raise ValueError('Cannot pass kwargs and an offset')
elif isinstance(offset, datetime.timedelta):
return _td_check(o... |
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def _build_date(date, kwargs):
""" Builds the date argument for event rules. """ |
if date is None:
if not kwargs:
raise ValueError('Must pass a date or kwargs')
else:
return datetime.date(**kwargs)
elif kwargs:
raise ValueError('Cannot pass kwargs and a date')
else:
return date |
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def _build_time(time, kwargs):
""" Builds the time argument for event rules. """ |
tz = kwargs.pop('tz', 'UTC')
if time:
if kwargs:
raise ValueError('Cannot pass kwargs and a time')
else:
return ensure_utc(time, tz)
elif not kwargs:
raise ValueError('Must pass a time or kwargs')
else:
return datetime.time(**kwargs) |
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def lossless_float_to_int(funcname, func, argname, arg):
""" A preprocessor that coerces integral floats to ints. Receipt of non-integral floats raises a TypeErr... |
if not isinstance(arg, float):
return arg
arg_as_int = int(arg)
if arg == arg_as_int:
warnings.warn(
"{f} expected an int for argument {name!r}, but got float {arg}."
" Coercing to int.".format(
f=funcname,
name=argname,
... |
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def add_event(self, event, prepend=False):
""" Adds an event to the manager. """ |
if prepend:
self._events.insert(0, event)
else:
self._events.append(event) |
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def handle_data(self, context, data, dt):
""" Calls the callable only when the rule is triggered. """ |
if self.rule.should_trigger(dt):
self.callback(context, data) |
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def should_trigger(self, dt):
""" Composes the two rules with a lazy composer. """ |
return self.composer(
self.first.should_trigger,
self.second.should_trigger,
dt
) |
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def winsorise_uint32(df, invalid_data_behavior, column, *columns):
"""Drops any record where a value would not fit into a uint32. Parameters df : pd.DataFrame Th... |
columns = list((column,) + columns)
mask = df[columns] > UINT32_MAX
if invalid_data_behavior != 'ignore':
mask |= df[columns].isnull()
else:
# we are not going to generate a warning or error for this so just use
# nan_to_num
df[columns] = np.nan_to_num(df[columns])
... |
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def write_csvs(self, asset_map, show_progress=False, invalid_data_behavior='warn'):
"""Read CSVs as DataFrames from our asset map. Parameters asset_map : dict[in... |
read = partial(
read_csv,
parse_dates=['day'],
index_col='day',
dtype=self._csv_dtypes,
)
return self.write(
((asset, read(path)) for asset, path in iteritems(asset_map)),
assets=viewkeys(asset_map),
show_progre... |
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def _compute_slices(self, start_idx, end_idx, assets):
""" Compute the raw row indices to load for each asset on a query for the given dates after applying a shi... |
# The core implementation of the logic here is implemented in Cython
# for efficiency.
return _compute_row_slices(
self._first_rows,
self._last_rows,
self._calendar_offsets,
start_idx,
end_idx,
assets,
) |
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def _spot_col(self, colname):
""" Get the colname from daily_bar_table and read all of it into memory, caching the result. Parameters colname : string A name of ... |
try:
col = self._spot_cols[colname]
except KeyError:
col = self._spot_cols[colname] = self._table[colname]
return col |
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def init_engine(self, get_loader):
""" Construct and store a PipelineEngine from loader. If get_loader is None, constructs an ExplodingPipelineEngine """ |
if get_loader is not None:
self.engine = SimplePipelineEngine(
get_loader,
self.asset_finder,
self.default_pipeline_domain(self.trading_calendar),
)
else:
self.engine = ExplodingPipelineEngine() |
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def initialize(self, *args, **kwargs):
""" Call self._initialize with `self` made available to Zipline API functions. """ |
with ZiplineAPI(self):
self._initialize(self, *args, **kwargs) |
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def _create_clock(self):
""" If the clock property is not set, then create one based on frequency. """ |
trading_o_and_c = self.trading_calendar.schedule.ix[
self.sim_params.sessions]
market_closes = trading_o_and_c['market_close']
minutely_emission = False
if self.sim_params.data_frequency == 'minute':
market_opens = trading_o_and_c['market_open']
minu... |
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def compute_eager_pipelines(self):
""" Compute any pipelines attached with eager=True. """ |
for name, pipe in self._pipelines.items():
if pipe.eager:
self.pipeline_output(name) |
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def run(self, data_portal=None):
"""Run the algorithm. """ |
# HACK: I don't think we really want to support passing a data portal
# this late in the long term, but this is needed for now for backwards
# compat downstream.
if data_portal is not None:
self.data_portal = data_portal
self.asset_finder = data_portal.asset_find... |
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def calculate_capital_changes(self, dt, emission_rate, is_interday, portfolio_value_adjustment=0.0):
""" If there is a capital change for a given dt, this means ... |
try:
capital_change = self.capital_changes[dt]
except KeyError:
return
self._sync_last_sale_prices()
if capital_change['type'] == 'target':
target = capital_change['value']
capital_change_amount = (
target -
... |
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def get_environment(self, field='platform'):
"""Query the execution environment. Parameters field : {'platform', 'arena', 'data_frequency', 'start', 'end', 'capi... |
env = {
'arena': self.sim_params.arena,
'data_frequency': self.sim_params.data_frequency,
'start': self.sim_params.first_open,
'end': self.sim_params.last_close,
'capital_base': self.sim_params.capital_base,
'platform': self._platform
... |
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def fetch_csv(self, url, pre_func=None, post_func=None, date_column='date', date_format=None, timezone=pytz.utc.zone, symbol=None, mask=True, symbol_column=None, ... |
if country_code is None:
country_code = self.default_fetch_csv_country_code(
self.trading_calendar,
)
# Show all the logs every time fetcher is used.
csv_data_source = PandasRequestsCSV(
url,
pre_func,
post_func,
... |
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def add_event(self, rule, callback):
"""Adds an event to the algorithm's EventManager. Parameters rule : EventRule The rule for when the callback should be trigg... |
self.event_manager.add_event(
zipline.utils.events.Event(rule, callback),
) |
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def schedule_function(self, func, date_rule=None, time_rule=None, half_days=True, calendar=None):
"""Schedules a function to be called according to some timed ru... |
# When the user calls schedule_function(func, <time_rule>), assume that
# the user meant to specify a time rule but no date rule, instead of
# a date rule and no time rule as the signature suggests
if isinstance(date_rule, (AfterOpen, BeforeClose)) and not time_rule:
warnin... |
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def continuous_future(self, root_symbol_str, offset=0, roll='volume', adjustment='mul'):
"""Create a specifier for a continuous contract. Parameters root_symbol_... |
return self.asset_finder.create_continuous_future(
root_symbol_str,
offset,
roll,
adjustment,
) |
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def symbol(self, symbol_str, country_code=None):
"""Lookup an Equity by its ticker symbol. Parameters symbol_str : str The ticker symbol for the equity to lookup... |
# If the user has not set the symbol lookup date,
# use the end_session as the date for symbol->sid resolution.
_lookup_date = self._symbol_lookup_date \
if self._symbol_lookup_date is not None \
else self.sim_params.end_session
return self.asset_finder.lookup_s... |
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def symbols(self, *args, **kwargs):
"""Lookup multuple Equities as a list. Parameters *args : iterable[str] The ticker symbols to lookup. country_code : str or N... |
return [self.symbol(identifier, **kwargs) for identifier in args] |
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def validate_order_params(self, asset, amount, limit_price, stop_price, style):
""" Helper method for validating parameters to the order API function. Raises an ... |
if not self.initialized:
raise OrderDuringInitialize(
msg="order() can only be called from within handle_data()"
)
if style:
if limit_price:
raise UnsupportedOrderParameters(
msg="Passing both limit_price and styl... |
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def __convert_order_params_for_blotter(asset, limit_price, stop_price, style):
""" Helper method for converting deprecated limit_price and stop_price arguments i... |
if style:
assert (limit_price, stop_price) == (None, None)
return style
if limit_price and stop_price:
return StopLimitOrder(limit_price, stop_price, asset=asset)
if limit_price:
return LimitOrder(limit_price, asset=asset)
if stop_price:
... |
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def order_value(self, asset, value, limit_price=None, stop_price=None, style=None):
"""Place an order by desired value rather than desired number of shares. Para... |
if not self._can_order_asset(asset):
return None
amount = self._calculate_order_value_amount(asset, value)
return self.order(asset, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style) |
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def _sync_last_sale_prices(self, dt=None):
"""Sync the last sale prices on the metrics tracker to a given datetime. Parameters dt : datetime The time to sync the... |
if dt is None:
dt = self.datetime
if dt != self._last_sync_time:
self.metrics_tracker.sync_last_sale_prices(
dt,
self.data_portal,
)
self._last_sync_time = dt |
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def on_dt_changed(self, dt):
""" Callback triggered by the simulation loop whenever the current dt changes. Any logic that should happen exactly once at the star... |
self.datetime = dt
self.blotter.set_date(dt) |
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def get_datetime(self, tz=None):
""" Returns the current simulation datetime. Parameters tz : tzinfo or str, optional The timezone to return the datetime in. Thi... |
dt = self.datetime
assert dt.tzinfo == pytz.utc, "Algorithm should have a utc datetime"
if tz is not None:
dt = dt.astimezone(tz)
return dt |
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def set_slippage(self, us_equities=None, us_futures=None):
"""Set the slippage models for the simulation. Parameters us_equities : EquitySlippageModel The slippa... |
if self.initialized:
raise SetSlippagePostInit()
if us_equities is not None:
if Equity not in us_equities.allowed_asset_types:
raise IncompatibleSlippageModel(
asset_type='equities',
given_model=us_equities,
... |
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def set_commission(self, us_equities=None, us_futures=None):
"""Sets the commission models for the simulation. Parameters us_equities : EquityCommissionModel The... |
if self.initialized:
raise SetCommissionPostInit()
if us_equities is not None:
if Equity not in us_equities.allowed_asset_types:
raise IncompatibleCommissionModel(
asset_type='equities',
given_model=us_equities,
... |
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def set_cancel_policy(self, cancel_policy):
"""Sets the order cancellation policy for the simulation. Parameters cancel_policy : CancelPolicy The cancellation po... |
if not isinstance(cancel_policy, CancelPolicy):
raise UnsupportedCancelPolicy()
if self.initialized:
raise SetCancelPolicyPostInit()
self.blotter.cancel_policy = cancel_policy |
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def order_percent(self, asset, percent, limit_price=None, stop_price=None, style=None):
"""Place an order in the specified asset corresponding to the given perce... |
if not self._can_order_asset(asset):
return None
amount = self._calculate_order_percent_amount(asset, percent)
return self.order(asset, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style) |
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def order_target(self, asset, target, limit_price=None, stop_price=None, style=None):
"""Place an order to adjust a position to a target number of shares. If the... |
if not self._can_order_asset(asset):
return None
amount = self._calculate_order_target_amount(asset, target)
return self.order(asset, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style) |
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def order_target_value(self, asset, target, limit_price=None, stop_price=None, style=None):
"""Place an order to adjust a position to a target value. If the posi... |
if not self._can_order_asset(asset):
return None
target_amount = self._calculate_order_value_amount(asset, target)
amount = self._calculate_order_target_amount(asset, target_amount)
return self.order(asset, amount,
limit_price=limit_price,
... |
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def order_target_percent(self, asset, target, limit_price=None, stop_price=None, style=None):
"""Place an order to adjust a position to a target percent of the c... |
if not self._can_order_asset(asset):
return None
amount = self._calculate_order_target_percent_amount(asset, target)
return self.order(asset, amount,
limit_price=limit_price,
stop_price=stop_price,
style=... |
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Description:
def batch_market_order(self, share_counts):
"""Place a batch market order for multiple assets. Parameters share_counts : pd.Series[Asset -> int] Map from asset t... |
style = MarketOrder()
order_args = [
(asset, amount, style)
for (asset, amount) in iteritems(share_counts)
if amount
]
return self.blotter.batch_order(order_args) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_open_orders(self, asset=None):
"""Retrieve all of the current open orders. Parameters asset : Asset If passed and not None, return only the open orders f... |
if asset is None:
return {
key: [order.to_api_obj() for order in orders]
for key, orders in iteritems(self.blotter.open_orders)
if orders
}
if asset in self.blotter.open_orders:
orders = self.blotter.open_orders[asset]
... |
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