class DataPortal(object):
"""Interface to all of the data that a zipline simulation needs.
This is used by the simulation runner to answer questions about the data,
like getting the prices of assets on a given day or to service history
calls.
Parameters
----------
asset_finder : zipline.assets.assets.AssetFinder
The AssetFinder instance used to resolve assets.
trading_calendar: zipline.utils.calendar.exchange_calendar.TradingCalendar
The calendar instance used to provide minute->session information.
first_trading_day : pd.Timestamp
The first trading day for the simulation.
equity_daily_reader : BcolzDailyBarReader, optional
The daily bar reader for equities. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
equity_minute_reader : BcolzMinuteBarReader, optional
The minute bar reader for equities. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
future_daily_reader : BcolzDailyBarReader, optional
The daily bar ready for futures. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
future_minute_reader : BcolzFutureMinuteBarReader, optional
The minute bar reader for futures. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
adjustment_reader : SQLiteAdjustmentWriter, optional
The adjustment reader. This is used to apply splits, dividends, and
other adjustment data to the raw data from the readers.
"""
def __init__(self,
asset_finder,
trading_calendar,
first_trading_day,
equity_daily_reader=None,
equity_minute_reader=None,
future_daily_reader=None,
future_minute_reader=None,
adjustment_reader=None):
self.trading_calendar = trading_calendar
self.asset_finder = asset_finder
self._adjustment_reader = adjustment_reader
# caches of sid -> adjustment list
self._splits_dict = {}
self._mergers_dict = {}
self._dividends_dict = {}
# Cache of sid -> the first trading day of an asset.
self._asset_start_dates = {}
self._asset_end_dates = {}
# Handle extra sources, like Fetcher.
self._augmented_sources_map = {}
self._extra_source_df = None
self._first_trading_session = first_trading_day
_last_sessions = [r.last_available_dt
for r in [equity_daily_reader, future_daily_reader]
if r is not None]
if _last_sessions:
self._last_trading_session = min(_last_sessions)
else:
self._last_trading_session = None
aligned_equity_minute_reader = self._ensure_reader_aligned(
equity_minute_reader)
aligned_equity_session_reader = self._ensure_reader_aligned(
equity_daily_reader)
aligned_future_minute_reader = self._ensure_reader_aligned(
future_minute_reader)
aligned_future_session_reader = self._ensure_reader_aligned(
future_daily_reader)
aligned_minute_readers = {}
aligned_session_readers = {}
if aligned_equity_minute_reader is not None:
aligned_minute_readers[Equity] = aligned_equity_minute_reader
if aligned_equity_session_reader is not None:
aligned_session_readers[Equity] = aligned_equity_session_reader
if aligned_future_minute_reader is not None:
aligned_minute_readers[Future] = aligned_future_minute_reader
if aligned_future_session_reader is not None:
aligned_session_readers[Future] = aligned_future_session_reader
_dispatch_minute_reader = AssetDispatchMinuteBarReader(
self.trading_calendar,
self.asset_finder,
aligned_minute_readers,
)
_dispatch_session_reader = AssetDispatchSessionBarReader(
self.trading_calendar,
self.asset_finder,
aligned_session_readers,
)
self._pricing_readers = {
'minute': _dispatch_minute_reader,
'daily': _dispatch_session_reader,
}
self._daily_aggregator = DailyHistoryAggregator(
self.trading_calendar.schedule.market_open,
_dispatch_minute_reader,
self.trading_calendar
)
self._history_loader = DailyHistoryLoader(
self.trading_calendar,
_dispatch_session_reader,
self._adjustment_reader
)
self._minute_history_loader = MinuteHistoryLoader(
self.trading_calendar,
_dispatch_minute_reader,
self._adjustment_reader
)
self._first_trading_day = first_trading_day
# Get the first trading minute
self._first_trading_minute, _ = (
self.trading_calendar.open_and_close_for_session(
self._first_trading_day
)
if self._first_trading_day is not None else (None, None)
)
# Store the locs of the first day and first minute
self._first_trading_day_loc = (
self.trading_calendar.all_sessions.get_loc(self._first_trading_day)
if self._first_trading_day is not None else None
)
self._first_trading_minute_loc = (
self.trading_calendar.all_minutes.get_loc(
self._first_trading_minute
)
if self._first_trading_minute is not None else None
)
def _ensure_reader_aligned(self, reader):
if reader is None:
return
if reader.trading_calendar.name == self.trading_calendar.name:
return reader
elif reader.data_frequency == 'minute':
return ReindexMinuteBarReader(
self.trading_calendar,
reader,
self._first_trading_session,
self._last_trading_session
)
elif reader.data_frequency == 'session':
return ReindexSessionBarReader(
self.trading_calendar,
reader,
self._first_trading_session,
self._last_trading_session
)
def _reindex_extra_source(self, df, source_date_index):
return df.reindex(index=source_date_index, method='ffill')
def handle_extra_source(self, source_df, sim_params):
"""
Extra sources always have a sid column.
We expand the given data (by forward filling) to the full range of
the simulation dates, so that lookup is fast during simulation.
"""
if source_df is None:
return
# Normalize all the dates in the df
source_df.index = source_df.index.normalize()
# source_df's sid column can either consist of assets we know about
# (such as sid(24)) or of assets we don't know about (such as
# palladium).
#
# In both cases, we break up the dataframe into individual dfs
# that only contain a single asset's information. ie, if source_df
# has data for PALLADIUM and GOLD, we split source_df into two
# dataframes, one for each. (same applies if source_df has data for
# AAPL and IBM).
#
# We then take each child df and reindex it to the simulation's date
# range by forward-filling missing values. this makes reads simpler.
#
# Finally, we store the data. For each column, we store a mapping in
# self.augmented_sources_map from the column to a dictionary of
# asset -> df. In other words,
# self.augmented_sources_map['days_to_cover']['AAPL'] gives us the df
# holding that data.
source_date_index = self.trading_calendar.sessions_in_range(
sim_params.start_session,
sim_params.end_session
)
# Break the source_df up into one dataframe per sid. This lets
# us (more easily) calculate accurate start/end dates for each sid,
# de-dup data, and expand the data to fit the backtest start/end date.
grouped_by_sid = source_df.groupby(["sid"])
group_names = grouped_by_sid.groups.keys()
group_dict = {}
for group_name in group_names:
group_dict[group_name] = grouped_by_sid.get_group(group_name)
# This will be the dataframe which we query to get fetcher assets at
# any given time. Get's overwritten every time there's a new fetcher
# call
extra_source_df = pd.DataFrame()
for identifier, df in iteritems(group_dict):
# Before reindexing, save the earliest and latest dates
earliest_date = df.index[0]
latest_date = df.index[-1]
# Since we know this df only contains a single sid, we can safely
# de-dupe by the index (dt). If minute granularity, will take the
# last data point on any given day
df = df.groupby(level=0).last()
# Reindex the dataframe based on the backtest start/end date.
# This makes reads easier during the backtest.
df = self._reindex_extra_source(df, source_date_index)
if not isinstance(identifier, Asset):
# for fake assets we need to store a start/end date
self._asset_start_dates[identifier] = earliest_date
self._asset_end_dates[identifier] = latest_date
for col_name in df.columns.difference(['sid']):
if col_name not in self._augmented_sources_map:
self._augmented_sources_map[col_name] = {}
self._augmented_sources_map[col_name][identifier] = df
# Append to extra_source_df the reindexed dataframe for the single
# sid
extra_source_df = extra_source_df.append(df)
self._extra_source_df = extra_source_df
def _get_pricing_reader(self, data_frequency):
return self._pricing_readers[data_frequency]
def get_last_traded_dt(self, asset, dt, data_frequency):
"""
Given an asset and dt, returns the last traded dt from the viewpoint
of the given dt.
If there is a trade on the dt, the answer is dt provided.
"""
return self._get_pricing_reader(data_frequency).get_last_traded_dt(
asset, dt)
@staticmethod
def _is_extra_source(asset, field, map):
"""
Internal method that determines if this asset/field combination
represents a fetcher value or a regular OHLCVP lookup.
"""
# If we have an extra source with a column called "price", only look
# at it if it's on something like palladium and not AAPL (since our
# own price data always wins when dealing with assets).
return not (field in BASE_FIELDS and isinstance(asset, Asset))
def _get_fetcher_value(self, asset, field, dt):
day = normalize_date(dt)
try:
return \
self._augmented_sources_map[field][asset].loc[day, field]
except KeyError:
return np.NaN
def get_spot_value(self, asset, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
asset : Asset
The asset whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume',
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The spot value of ``field`` for ``asset`` The return type is based
on the ``field`` requested. If the field is one of 'open', 'high',
'low', 'close', or 'price', the value will be a float. If the
``field`` is 'volume' the value will be a int. If the ``field`` is
'last_traded' the value will be a Timestamp.
"""
if self._is_extra_source(asset, field, self._augmented_sources_map):
return self._get_fetcher_value(asset, field, dt)
if field not in BASE_FIELDS:
raise KeyError("Invalid column: " + str(field))
session_label = self.trading_calendar.minute_to_session_label(dt)
if dt < asset.start_date or \
(data_frequency == "daily" and
session_label > asset.end_date) or \
(data_frequency == "minute" and
session_label > asset.end_date):
if field == "volume":
return 0
elif field != "last_traded":
return np.NaN
if data_frequency == "daily":
return self._get_daily_data(asset, field, session_label)
else:
if field == "last_traded":
return self.get_last_traded_dt(asset, dt, 'minute')
elif field == "price":
return self._get_minute_spot_value(asset, "close", dt,
ffill=True)
else:
return self._get_minute_spot_value(asset, field, dt)
def get_adjustments(self, assets, field, dt, perspective_dt):
"""
Returns a list of adjustments between the dt and perspective_dt for the
given field and list of assets
Parameters
----------
assets : list of type Asset, or Asset
The asset, or assets whose adjustments are desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
adjustments : list[Adjustment]
The adjustments to that field.
"""
if isinstance(assets, Asset):
assets = [assets]
adjustment_ratios_per_asset = []
split_adj_factor = lambda x: x if field != 'volume' else 1.0 / x
for asset in assets:
adjustments_for_asset = []
split_adjustments = self._get_adjustment_list(
asset, self._splits_dict, "SPLITS"
)
for adj_dt, adj in split_adjustments:
if dt <= adj_dt <= perspective_dt:
adjustments_for_asset.append(split_adj_factor(adj))
elif adj_dt > perspective_dt:
break
if field != 'volume':
merger_adjustments = self._get_adjustment_list(
asset, self._mergers_dict, "MERGERS"
)
for adj_dt, adj in merger_adjustments:
if dt <= adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
dividend_adjustments = self._get_adjustment_list(
asset, self._dividends_dict, "DIVIDENDS",
)
for adj_dt, adj in dividend_adjustments:
if dt <= adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
ratio = reduce(mul, adjustments_for_asset, 1.0)
adjustment_ratios_per_asset.append(ratio)
return adjustment_ratios_per_asset
def get_adjusted_value(self, asset, field, dt,
perspective_dt,
data_frequency,
spot_value=None):
"""
Returns a scalar value representing the value
of the desired asset's field at the given dt with adjustments applied.
Parameters
----------
asset : Asset
The asset whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The value of the given ``field`` for ``asset`` at ``dt`` with any
adjustments known by ``perspective_dt`` applied. The return type is
based on the ``field`` requested. If the field is one of 'open',
'high', 'low', 'close', or 'price', the value will be a float. If
the ``field`` is 'volume' the value will be a int. If the ``field``
is 'last_traded' the value will be a Timestamp.
"""
if spot_value is None:
# if this a fetcher field, we want to use perspective_dt (not dt)
# because we want the new value as of midnight (fetcher only works
# on a daily basis, all timestamps are on midnight)
if self._is_extra_source(asset, field,
self._augmented_sources_map):
spot_value = self.get_spot_value(asset, field, perspective_dt,
data_frequency)
else:
spot_value = self.get_spot_value(asset, field, dt,
data_frequency)
if isinstance(asset, Equity):
ratio = self.get_adjustments(asset, field, dt, perspective_dt)[0]
spot_value *= ratio
return spot_value
def _get_minute_spot_value(self, asset, column, dt, ffill=False):
reader = self._get_pricing_reader('minute')
try:
result = reader.get_value(
asset.sid, dt, column
)
except NoDataOnDate:
if not ffill:
if column == 'volume':
return 0
else:
return np.nan
if not ffill:
return result
# we are looking for price, and didn't find one. have to go hunting.
last_traded_dt = reader.get_last_traded_dt(asset, dt)
if last_traded_dt is pd.NaT:
# no last traded dt, bail
if column == 'volume':
return 0
else:
return np.nan
# get the value as of the last traded dt
result = reader.get_value(
asset.sid,
last_traded_dt,
column
)
if np.isnan(result):
return np.nan
if dt == last_traded_dt or dt.date() == last_traded_dt.date():
return result
# the value we found came from a different day, so we have to adjust
# the data if there are any adjustments on that day barrier
return self.get_adjusted_value(
asset, column, last_traded_dt,
dt, "minute", spot_value=result
)
def _get_daily_data(self, asset, column, dt):
reader = self._get_pricing_reader('daily')
if column == "last_traded":
last_traded_dt = reader.get_last_traded_dt(asset, dt)
if pd.isnull(last_traded_dt):
return pd.NaT
else:
return last_traded_dt
elif column in OHLCV_FIELDS:
# don't forward fill
try:
val = reader.get_value(asset, dt, column)
if val == -1:
if column == "volume":
return 0
else:
return np.nan
else:
return val
except NoDataOnDate:
return np.nan
elif column == "price":
found_dt = dt
while True:
try:
value = reader.get_value(
asset, found_dt, "close"
)
if value != -1:
if dt == found_dt:
return value
else:
# adjust if needed
return self.get_adjusted_value(
asset, column, found_dt, dt, "minute",
spot_value=value
)
else:
found_dt -= self.trading_calendar.day
except NoDataOnDate:
return np.nan
@remember_last
def _get_days_for_window(self, end_date, bar_count):
tds = self.trading_calendar.all_sessions
end_loc = tds.get_loc(end_date)
start_loc = end_loc - bar_count + 1
if start_loc < self._first_trading_day_loc:
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day.date(),
bar_count=bar_count,
suggested_start_day=tds[
self._first_trading_day_loc + bar_count
].date(),
)
return tds[start_loc:end_loc + 1]
def _get_history_daily_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of daily frequency for the given sids.
"""
session = self.trading_calendar.minute_to_session_label(end_dt)
days_for_window = self._get_days_for_window(session, bar_count)
if len(assets) == 0:
return pd.DataFrame(None,
index=days_for_window,
columns=None)
data = self._get_history_daily_window_data(
assets, days_for_window, end_dt, field_to_use
)
return pd.DataFrame(
data,
index=days_for_window,
columns=assets
)
def _get_history_daily_window_data(
self, assets, days_for_window, end_dt, field_to_use):
ends_at_midnight = end_dt.hour == 0 and end_dt.minute == 0
if ends_at_midnight:
# two cases where we use daily data for the whole range:
# 1) the history window ends at midnight utc.
# 2) the last desired day of the window is after the
# last trading day, use daily data for the whole range.
return self._get_daily_window_for_sids(
assets,
field_to_use,
days_for_window,
extra_slot=False
)
else:
# minute mode, requesting '1d'
daily_data = self._get_daily_window_for_sids(
assets,
field_to_use,
days_for_window[0:-1]
)
if field_to_use == 'open':
minute_value = self._daily_aggregator.opens(
assets, end_dt)
elif field_to_use == 'high':
minute_value = self._daily_aggregator.highs(
assets, end_dt)
elif field_to_use == 'low':
minute_value = self._daily_aggregator.lows(
assets, end_dt)
elif field_to_use == 'close':
minute_value = self._daily_aggregator.closes(
assets, end_dt)
elif field_to_use == 'volume':
minute_value = self._daily_aggregator.volumes(
assets, end_dt)
# append the partial day.
daily_data[-1] = minute_value
return daily_data
def _handle_history_out_of_bounds(self, bar_count):
suggested_start_day = (
self.trading_calendar.all_minutes[
self._first_trading_minute_loc + bar_count
] + self.trading_calendar.day
).date()
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day.date(),
bar_count=bar_count,
suggested_start_day=suggested_start_day,
)
def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
minutes_for_window = self.trading_calendar.minutes_window(
end_dt, -bar_count
)
except KeyError:
self._handle_history_out_of_bounds(bar_count)
if minutes_for_window[0] < self._first_trading_minute:
self._handle_history_out_of_bounds(bar_count)
asset_minute_data = self._get_minute_window_for_assets(
assets,
field_to_use,
minutes_for_window,
)
return pd.DataFrame(
asset_minute_data,
index=minutes_for_window,
columns=assets
)
def get_history_window(self, assets, end_dt, bar_count, frequency, field,
ffill=True):
"""
Public API method that returns a dataframe containing the requested
history window. Data is fully adjusted.
Parameters
----------
assets : list of zipline.data.Asset objects
The assets whose data is desired.
bar_count: int
The number of bars desired.
frequency: string
"1d" or "1m"
field: string
The desired field of the asset.
ffill: boolean
Forward-fill missing values. Only has effect if field
is 'price'.
Returns
-------
A dataframe containing the requested data.
"""
if field not in OHLCVP_FIELDS:
raise ValueError("Invalid field: {0}".format(field))
if frequency == "1d":
if field == "price":
df = self._get_history_daily_window(assets, end_dt, bar_count,
"close")
else:
df = self._get_history_daily_window(assets, end_dt, bar_count,
field)
elif frequency == "1m":
if field == "price":
df = self._get_history_minute_window(assets, end_dt, bar_count,
"close")
else:
df = self._get_history_minute_window(assets, end_dt, bar_count,
field)
else:
raise ValueError("Invalid frequency: {0}".format(frequency))
# forward-fill price
if field == "price":
if frequency == "1m":
data_frequency = 'minute'
elif frequency == "1d":
data_frequency = 'daily'
else:
raise Exception(
"Only 1d and 1m are supported for forward-filling.")
dt_to_fill = df.index[0]
perspective_dt = df.index[-1]
assets_with_leading_nan = np.where(pd.isnull(df.iloc[0]))[0]
for missing_loc in assets_with_leading_nan:
asset = assets[missing_loc]
previous_dt = self.get_last_traded_dt(
asset, dt_to_fill, data_frequency)
if pd.isnull(previous_dt):
continue
previous_value = self.get_adjusted_value(
asset,
field,
previous_dt,
perspective_dt,
data_frequency,
)
df.iloc[0, missing_loc] = previous_value
df.fillna(method='ffill', inplace=True)
for asset in df.columns:
if df.index[-1] >= asset.end_date:
# if the window extends past the asset's end date, set
# all post-end-date values to NaN in that asset's series
series = df[asset]
series[series.index.normalize() > asset.end_date] = np.NaN
return df
def _get_minute_window_for_assets(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
and specified date range. Used to support the history API method for
minute bars.
Missing bars are filled with NaN.
Parameters
----------
asset : Asset
The asset whose data is desired.
field: string
The specific field to return. "open", "high", "close_price", etc.
minutes_for_window: pd.DateTimeIndex
The list of minutes representing the desired window. Each minute
is a pd.Timestamp.
Returns
-------
A numpy array with requested values.
"""
return self._get_minute_window_data(assets, field, minutes_for_window)
def _get_minute_window_data(
self, assets, field, minutes_for_window):
return self._minute_history_loader.history(assets,
minutes_for_window,
field,
False)
def _get_daily_window_for_sids(
self, assets, field, days_in_window, extra_slot=True):
"""
Internal method that gets a window of adjusted daily data for a sid
and specified date range. Used to support the history API method for
daily bars.
Parameters
----------
asset : Asset
The asset whose data is desired.
start_dt: pandas.Timestamp
The start of the desired window of data.
bar_count: int
The number of days of data to return.
field: string
The specific field to return. "open", "high", "close_price", etc.
extra_slot: boolean
Whether to allocate an extra slot in the returned numpy array.
This extra slot will hold the data for the last partial day. It's
much better to create it here than to create a copy of the array
later just to add a slot.
Returns
-------
A numpy array with requested values. Any missing slots filled with
nan.
"""
bar_count = len(days_in_window)
# create an np.array of size bar_count
if extra_slot:
return_array = np.zeros((bar_count + 1, len(assets)))
else:
return_array = np.zeros((bar_count, len(assets)))
if field != "volume":
# volumes default to 0, so we don't need to put NaNs in the array
return_array[:] = np.NAN
if bar_count != 0:
data = self._history_loader.history(assets,
days_in_window,
field,
extra_slot)
if extra_slot:
return_array[:len(return_array) - 1, :] = data
else:
return_array[:len(data)] = data
return return_array
def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
A dictionary of sid -> list that is used as a cache.
table_name: string
The table that contains this data in the adjustments db.
Returns
-------
adjustments: list
A list of [multiplier, pd.Timestamp], earliest first
"""
if self._adjustment_reader is None:
return []
sid = int(asset)
try:
adjustments = adjustments_dict[sid]
except KeyError:
adjustments = adjustments_dict[sid] = self._adjustment_reader.\
get_adjustments_for_sid(table_name, sid)
return adjustments
def _check_is_currently_alive(self, asset, dt):
sid = int(asset)
if sid not in self._asset_start_dates:
self._get_asset_start_date(asset)
start_date = self._asset_start_dates[sid]
if self._asset_start_dates[sid] > dt:
raise NoTradeDataAvailableTooEarly(
sid=sid,
dt=normalize_date(dt),
start_dt=start_date
)
end_date = self._asset_end_dates[sid]
if self._asset_end_dates[sid] < dt:
raise NoTradeDataAvailableTooLate(
sid=sid,
dt=normalize_date(dt),
end_dt=end_date
)
def _get_asset_start_date(self, asset):
self._ensure_asset_dates(asset)
return self._asset_start_dates[asset]
def _get_asset_end_date(self, asset):
self._ensure_asset_dates(asset)
return self._asset_end_dates[asset]
def _ensure_asset_dates(self, asset):
sid = int(asset)
if sid not in self._asset_start_dates:
if self._first_trading_day is not None:
self._asset_start_dates[sid] = \
max(asset.start_date, self._first_trading_day)
else:
self._asset_start_dates[sid] = asset.start_date
self._asset_end_dates[sid] = asset.end_date
def get_splits(self, sids, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
sids : container
Sids for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
expected to be midnight UTC.
Returns
-------
splits : list[(int, float)]
List of splits, where each split is a (sid, ratio) tuple.
"""
if self._adjustment_reader is None or not sids:
return {}
# convert dt to # of seconds since epoch, because that's what we use
# in the adjustments db
seconds = int(dt.value / 1e9)
splits = self._adjustment_reader.conn.execute(
"SELECT sid, ratio FROM SPLITS WHERE effective_date = ?",
(seconds,)).fetchall()
splits = [split for split in splits if split[0] in sids]
return splits
def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
trading_days: pd.DatetimeIndex
The trading range.
Returns
-------
list: A list of objects with all relevant attributes populated.
All timestamp fields are converted to pd.Timestamps.
"""
if self._adjustment_reader is None:
return []
if len(trading_days) == 0:
return []
start_dt = trading_days[0].value / 1e9
end_dt = trading_days[-1].value / 1e9
dividends = self._adjustment_reader.conn.execute(
"SELECT * FROM stock_dividend_payouts WHERE sid = ? AND "
"ex_date > ? AND pay_date < ?", (int(sid), start_dt, end_dt,)).\
fetchall()
dividend_info = []
for dividend_tuple in dividends:
dividend_info.append({
"declared_date": dividend_tuple[1],
"ex_date": pd.Timestamp(dividend_tuple[2], unit="s"),
"pay_date": pd.Timestamp(dividend_tuple[3], unit="s"),
"payment_sid": dividend_tuple[4],
"ratio": dividend_tuple[5],
"record_date": pd.Timestamp(dividend_tuple[6], unit="s"),
"sid": dividend_tuple[7]
})
return dividend_info
def contains(self, asset, field):
return field in BASE_FIELDS or \
(field in self._augmented_sources_map and
asset in self._augmented_sources_map[field])
def get_fetcher_assets(self, dt):
"""
Returns a list of assets for the current date, as defined by the
fetcher data.
Returns
-------
list: a list of Asset objects.
"""
# return a list of assets for the current date, as defined by the
# fetcher source
if self._extra_source_df is None:
return []
day = normalize_date(dt)
if day in self._extra_source_df.index:
assets = self._extra_source_df.loc[day]['sid']
else:
return []
if isinstance(assets, pd.Series):
return [x for x in assets if isinstance(x, Asset)]
else:
return [assets] if isinstance(assets, Asset) else []
# cache size picked somewhat loosely. this code exists purely to
# handle deprecated API.
@weak_lru_cache(20)
def _get_minute_count_for_transform(self, ending_minute, days_count):
# This function works in three steps.
# Step 1. Count the minutes from ``ending_minute`` to the start of its
# session.
# Step 2. Count the minutes from the prior ``days_count - 1`` sessions.
# Step 3. Return the sum of the results from steps (1) and (2).
# Example (NYSE Calendar)
# ending_minute = 2016-12-28 9:40 AM US/Eastern
# days_count = 3
# Step 1. Calculate that there are 10 minutes in the ending session.
# Step 2. Calculate that there are 390 + 210 = 600 minutes in the prior
# two sessions. (Prior sessions are 2015-12-23 and 2015-12-24.)
# 2015-12-24 is a half day.
# Step 3. Return 600 + 10 = 610.
cal = self.trading_calendar
ending_session = cal.minute_to_session_label(
ending_minute,
direction="none", # It's an error to pass a non-trading minute.
)
# Assume that calendar days are always full of contiguous minutes,
# which means we can just take 1 + (number of minutes between the last
# minute and the start of the session). We add one so that we include
# the ending minute in the total.
ending_session_minute_count = timedelta_to_integral_minutes(
ending_minute - cal.open_and_close_for_session(ending_session)[0]
) + 1
if days_count == 1:
# We just need sessions for the active day.
return ending_session_minute_count
# XXX: We're subtracting 2 here to account for two offsets:
# 1. We only want ``days_count - 1`` sessions, since we've already
# accounted for the ending session above.
# 2. The API of ``sessions_window`` is to return one more session than
# the requested number. I don't think any consumers actually want
# that behavior, but it's the tested and documented behavior right
# now, so we have to request one less session than we actually want.
completed_sessions = cal.sessions_window(
cal.previous_session_label(ending_session),
2 - days_count,
)
completed_sessions_minute_count = (
self.trading_calendar.minutes_count_for_sessions_in_range(
completed_sessions[0],
completed_sessions[-1]
)
)
return ending_session_minute_count + completed_sessions_minute_count
def get_simple_transform(self, asset, transform_name, dt, data_frequency,
bars=None):
if transform_name == "returns":
# returns is always calculated over the last 2 days, regardless
# of the simulation's data frequency.
hst = self.get_history_window(
[asset], dt, 2, "1d", "price", ffill=True
)[asset]
return (hst.iloc[-1] - hst.iloc[0]) / hst.iloc[0]
if bars is None:
raise ValueError("bars cannot be None!")
if data_frequency == "minute":
freq_str = "1m"
calculated_bar_count = int(self._get_minute_count_for_transform(
dt, bars
))
else:
freq_str = "1d"
calculated_bar_count = bars
price_arr = self.get_history_window(
[asset], dt, calculated_bar_count, freq_str, "price", ffill=True
)[asset]
if transform_name == "mavg":
return nanmean(price_arr)
elif transform_name == "stddev":
return nanstd(price_arr, ddof=1)
elif transform_name == "vwap":
volume_arr = self.get_history_window(
[asset], dt, calculated_bar_count, freq_str, "volume",
ffill=True
)[asset]
vol_sum = nansum(volume_arr)
try:
ret = nansum(price_arr * volume_arr) / vol_sum
except ZeroDivisionError:
ret = np.nan
return ret
class DataPortal(object):
"""Interface to all of the data that a zipline simulation needs.
This is used by the simulation runner to answer questions about the data,
like getting the prices of assets on a given day or to service history
calls.
Parameters
----------
asset_finder : zipline.assets.assets.AssetFinder
The AssetFinder instance used to resolve assets.
trading_calendar: zipline.utils.calendar.exchange_calendar.TradingCalendar
The calendar instance used to provide minute->session information.
first_trading_day : pd.Timestamp
The first trading day for the simulation.
equity_daily_reader : BcolzDailyBarReader, optional
The daily bar reader for equities. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
equity_minute_reader : BcolzMinuteBarReader, optional
The minute bar reader for equities. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
future_daily_reader : BcolzDailyBarReader, optional
The daily bar ready for futures. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
future_minute_reader : BcolzFutureMinuteBarReader, optional
The minute bar reader for futures. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
adjustment_reader : SQLiteAdjustmentWriter, optional
The adjustment reader. This is used to apply splits, dividends, and
other adjustment data to the raw data from the readers.
"""
def __init__(self,
asset_finder,
trading_calendar,
first_trading_day,
equity_daily_reader=None,
equity_minute_reader=None,
future_daily_reader=None,
future_minute_reader=None,
adjustment_reader=None):
self.trading_calendar = trading_calendar
self.asset_finder = asset_finder
self._adjustment_reader = adjustment_reader
# caches of sid -> adjustment list
self._splits_dict = {}
self._mergers_dict = {}
self._dividends_dict = {}
# Cache of sid -> the first trading day of an asset.
self._asset_start_dates = {}
self._asset_end_dates = {}
# Handle extra sources, like Fetcher.
self._augmented_sources_map = {}
self._extra_source_df = None
self._equity_daily_reader = equity_daily_reader
if self._equity_daily_reader is not None:
self._history_loader = DailyHistoryLoader(
self.trading_calendar, self._equity_daily_reader,
self._adjustment_reader)
self._equity_minute_reader = equity_minute_reader
self._future_daily_reader = future_daily_reader
self._future_minute_reader = future_minute_reader
self._pricing_readers = {
Equity: {
'minute': equity_minute_reader,
'daily': equity_daily_reader,
},
Future: {
'minute': future_minute_reader,
'daily': future_daily_reader
}
}
self._daily_aggregator = DailyHistoryAggregator(
self.trading_calendar.schedule.market_open,
self._equity_minute_reader, self.trading_calendar)
self._minute_history_loader = MinuteHistoryLoader(
self.trading_calendar, self._equity_minute_reader,
self._adjustment_reader)
self._first_trading_day = first_trading_day
# Get the first trading minute
self._first_trading_minute, _ = (
self.trading_calendar.open_and_close_for_session(
self._first_trading_day)
if self._first_trading_day is not None else (None, None))
# Store the locs of the first day and first minute
self._first_trading_day_loc = (
self.trading_calendar.all_sessions.get_loc(self._first_trading_day)
if self._first_trading_day is not None else None)
self._first_trading_minute_loc = (
self.trading_calendar.all_minutes.get_loc(
self._first_trading_minute)
if self._first_trading_minute is not None else None)
def _reindex_extra_source(self, df, source_date_index):
return df.reindex(index=source_date_index, method='ffill')
def handle_extra_source(self, source_df, sim_params):
"""
Extra sources always have a sid column.
We expand the given data (by forward filling) to the full range of
the simulation dates, so that lookup is fast during simulation.
"""
if source_df is None:
return
# Normalize all the dates in the df
source_df.index = source_df.index.normalize()
# source_df's sid column can either consist of assets we know about
# (such as sid(24)) or of assets we don't know about (such as
# palladium).
#
# In both cases, we break up the dataframe into individual dfs
# that only contain a single asset's information. ie, if source_df
# has data for PALLADIUM and GOLD, we split source_df into two
# dataframes, one for each. (same applies if source_df has data for
# AAPL and IBM).
#
# We then take each child df and reindex it to the simulation's date
# range by forward-filling missing values. this makes reads simpler.
#
# Finally, we store the data. For each column, we store a mapping in
# self.augmented_sources_map from the column to a dictionary of
# asset -> df. In other words,
# self.augmented_sources_map['days_to_cover']['AAPL'] gives us the df
# holding that data.
source_date_index = self.trading_calendar.sessions_in_range(
sim_params.start_session, sim_params.end_session)
# Break the source_df up into one dataframe per sid. This lets
# us (more easily) calculate accurate start/end dates for each sid,
# de-dup data, and expand the data to fit the backtest start/end date.
grouped_by_sid = source_df.groupby(["sid"])
group_names = grouped_by_sid.groups.keys()
group_dict = {}
for group_name in group_names:
group_dict[group_name] = grouped_by_sid.get_group(group_name)
# This will be the dataframe which we query to get fetcher assets at
# any given time. Get's overwritten every time there's a new fetcher
# call
extra_source_df = pd.DataFrame()
for identifier, df in iteritems(group_dict):
# Before reindexing, save the earliest and latest dates
earliest_date = df.index[0]
latest_date = df.index[-1]
# Since we know this df only contains a single sid, we can safely
# de-dupe by the index (dt). If minute granularity, will take the
# last data point on any given day
df = df.groupby(level=0).last()
# Reindex the dataframe based on the backtest start/end date.
# This makes reads easier during the backtest.
df = self._reindex_extra_source(df, source_date_index)
if not isinstance(identifier, Asset):
# for fake assets we need to store a start/end date
self._asset_start_dates[identifier] = earliest_date
self._asset_end_dates[identifier] = latest_date
for col_name in df.columns.difference(['sid']):
if col_name not in self._augmented_sources_map:
self._augmented_sources_map[col_name] = {}
self._augmented_sources_map[col_name][identifier] = df
# Append to extra_source_df the reindexed dataframe for the single
# sid
extra_source_df = extra_source_df.append(df)
self._extra_source_df = extra_source_df
def _get_pricing_reader(self, asset, data_frequency):
return self._pricing_readers[type(asset)][data_frequency]
def get_last_traded_dt(self, asset, dt, data_frequency):
"""
Given an asset and dt, returns the last traded dt from the viewpoint
of the given dt.
If there is a trade on the dt, the answer is dt provided.
"""
return self._get_pricing_reader(asset, data_frequency).\
get_last_traded_dt(asset, dt)
@staticmethod
def _is_extra_source(asset, field, map):
"""
Internal method that determines if this asset/field combination
represents a fetcher value or a regular OHLCVP lookup.
"""
# If we have an extra source with a column called "price", only look
# at it if it's on something like palladium and not AAPL (since our
# own price data always wins when dealing with assets).
return not (field in BASE_FIELDS and isinstance(asset, Asset))
def _get_fetcher_value(self, asset, field, dt):
day = normalize_date(dt)
try:
return \
self._augmented_sources_map[field][asset].loc[day, field]
except KeyError:
return np.NaN
def get_spot_value(self, asset, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
asset : Asset
The asset whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume',
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The spot value of ``field`` for ``asset`` The return type is based
on the ``field`` requested. If the field is one of 'open', 'high',
'low', 'close', or 'price', the value will be a float. If the
``field`` is 'volume' the value will be a int. If the ``field`` is
'last_traded' the value will be a Timestamp.
"""
if self._is_extra_source(asset, field, self._augmented_sources_map):
return self._get_fetcher_value(asset, field, dt)
if field not in BASE_FIELDS:
raise KeyError("Invalid column: " + str(field))
session_label = self.trading_calendar.minute_to_session_label(dt)
if dt < asset.start_date or \
(data_frequency == "daily" and
session_label > asset.end_date) or \
(data_frequency == "minute" and
session_label > asset.end_date):
if field == "volume":
return 0
elif field != "last_traded":
return np.NaN
if data_frequency == "daily":
return self._get_daily_data(asset, field, session_label)
else:
if field == "last_traded":
return self.get_last_traded_dt(asset, dt, 'minute')
elif field == "price":
return self._get_minute_spot_value(asset,
"close",
dt,
ffill=True)
else:
return self._get_minute_spot_value(asset, field, dt)
def get_adjustments(self, assets, field, dt, perspective_dt):
"""
Returns a list of adjustments between the dt and perspective_dt for the
given field and list of assets
Parameters
----------
assets : list of type Asset, or Asset
The asset, or assets whose adjustments are desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
adjustments : list[Adjustment]
The adjustments to that field.
"""
if isinstance(assets, Asset):
assets = [assets]
adjustment_ratios_per_asset = []
split_adj_factor = lambda x: x if field != 'volume' else 1.0 / x
for asset in assets:
adjustments_for_asset = []
split_adjustments = self._get_adjustment_list(
asset, self._splits_dict, "SPLITS")
for adj_dt, adj in split_adjustments:
if dt <= adj_dt <= perspective_dt:
adjustments_for_asset.append(split_adj_factor(adj))
elif adj_dt > perspective_dt:
break
if field != 'volume':
merger_adjustments = self._get_adjustment_list(
asset, self._mergers_dict, "MERGERS")
for adj_dt, adj in merger_adjustments:
if dt <= adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
dividend_adjustments = self._get_adjustment_list(
asset,
self._dividends_dict,
"DIVIDENDS",
)
for adj_dt, adj in dividend_adjustments:
if dt <= adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
ratio = reduce(mul, adjustments_for_asset, 1.0)
adjustment_ratios_per_asset.append(ratio)
return adjustment_ratios_per_asset
def get_adjusted_value(self,
asset,
field,
dt,
perspective_dt,
data_frequency,
spot_value=None):
"""
Returns a scalar value representing the value
of the desired asset's field at the given dt with adjustments applied.
Parameters
----------
asset : Asset
The asset whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The value of the given ``field`` for ``asset`` at ``dt`` with any
adjustments known by ``perspective_dt`` applied. The return type is
based on the ``field`` requested. If the field is one of 'open',
'high', 'low', 'close', or 'price', the value will be a float. If
the ``field`` is 'volume' the value will be a int. If the ``field``
is 'last_traded' the value will be a Timestamp.
"""
if spot_value is None:
# if this a fetcher field, we want to use perspective_dt (not dt)
# because we want the new value as of midnight (fetcher only works
# on a daily basis, all timestamps are on midnight)
if self._is_extra_source(asset, field,
self._augmented_sources_map):
spot_value = self.get_spot_value(asset, field, perspective_dt,
data_frequency)
else:
spot_value = self.get_spot_value(asset, field, dt,
data_frequency)
if isinstance(asset, Equity):
ratio = self.get_adjustments(asset, field, dt, perspective_dt)[0]
spot_value *= ratio
return spot_value
def _get_minute_spot_value(self, asset, column, dt, ffill=False):
reader = self._get_pricing_reader(asset, 'minute')
result = reader.get_value(asset.sid, dt, column)
if not ffill:
return result
# we are looking for price, and didn't find one. have to go hunting.
last_traded_dt = reader.get_last_traded_dt(asset, dt)
if last_traded_dt is pd.NaT:
# no last traded dt, bail
if column == 'volume':
return 0
else:
return np.nan
# get the value as of the last traded dt
result = reader.get_value(asset.sid, last_traded_dt, column)
if np.isnan(result):
return np.nan
if dt == last_traded_dt or dt.date() == last_traded_dt.date():
return result
# the value we found came from a different day, so we have to adjust
# the data if there are any adjustments on that day barrier
return self.get_adjusted_value(asset,
column,
last_traded_dt,
dt,
"minute",
spot_value=result)
def _get_daily_data(self, asset, column, dt):
reader = self._pricing_readers[type(asset)]['daily']
if column == "last_traded":
last_traded_dt = reader.get_last_traded_dt(asset, dt)
if pd.isnull(last_traded_dt):
return pd.NaT
else:
return last_traded_dt
elif column in OHLCV_FIELDS:
# don't forward fill
try:
val = reader.spot_price(asset, dt, column)
if val == -1:
if column == "volume":
return 0
else:
return np.nan
else:
return val
except NoDataOnDate:
return np.nan
elif column == "price":
found_dt = dt
while True:
try:
value = reader.spot_price(asset, found_dt, "close")
if value != -1:
if dt == found_dt:
return value
else:
# adjust if needed
return self.get_adjusted_value(asset,
column,
found_dt,
dt,
"minute",
spot_value=value)
else:
found_dt -= self.trading_calendar.day
except NoDataOnDate:
return np.nan
@remember_last
def _get_days_for_window(self, end_date, bar_count):
tds = self.trading_calendar.all_sessions
end_loc = tds.get_loc(end_date)
start_loc = end_loc - bar_count + 1
if start_loc < self._first_trading_day_loc:
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day.date(),
bar_count=bar_count,
suggested_start_day=tds[self._first_trading_day_loc +
bar_count].date(),
)
return tds[start_loc:end_loc + 1]
def _get_history_daily_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of daily frequency for the given sids.
"""
days_for_window = self._get_days_for_window(end_dt.date(), bar_count)
if len(assets) == 0:
return pd.DataFrame(None, index=days_for_window, columns=None)
data = self._get_history_daily_window_data(assets, days_for_window,
end_dt, field_to_use)
return pd.DataFrame(data, index=days_for_window, columns=assets)
def _get_history_daily_window_data(self, assets, days_for_window, end_dt,
field_to_use):
ends_at_midnight = end_dt.hour == 0 and end_dt.minute == 0
if ends_at_midnight:
# two cases where we use daily data for the whole range:
# 1) the history window ends at midnight utc.
# 2) the last desired day of the window is after the
# last trading day, use daily data for the whole range.
return self._get_daily_window_for_sids(assets,
field_to_use,
days_for_window,
extra_slot=False)
else:
# minute mode, requesting '1d'
daily_data = self._get_daily_window_for_sids(
assets, field_to_use, days_for_window[0:-1])
if field_to_use == 'open':
minute_value = self._daily_aggregator.opens(assets, end_dt)
elif field_to_use == 'high':
minute_value = self._daily_aggregator.highs(assets, end_dt)
elif field_to_use == 'low':
minute_value = self._daily_aggregator.lows(assets, end_dt)
elif field_to_use == 'close':
minute_value = self._daily_aggregator.closes(assets, end_dt)
elif field_to_use == 'volume':
minute_value = self._daily_aggregator.volumes(assets, end_dt)
# append the partial day.
daily_data[-1] = minute_value
return daily_data
def _handle_history_out_of_bounds(self, bar_count):
suggested_start_day = (
self.trading_calendar.all_minutes[self._first_trading_minute_loc +
bar_count] +
self.trading_calendar.day).date()
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day.date(),
bar_count=bar_count,
suggested_start_day=suggested_start_day,
)
def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
minutes_for_window = self.trading_calendar.minutes_window(
end_dt, -bar_count)
except KeyError:
self._handle_history_out_of_bounds(bar_count)
if minutes_for_window[0] < self._first_trading_minute:
self._handle_history_out_of_bounds(bar_count)
asset_minute_data = self._get_minute_window_for_assets(
assets,
field_to_use,
minutes_for_window,
)
return pd.DataFrame(asset_minute_data,
index=minutes_for_window,
columns=assets)
def get_history_window(self,
assets,
end_dt,
bar_count,
frequency,
field,
ffill=True):
"""
Public API method that returns a dataframe containing the requested
history window. Data is fully adjusted.
Parameters
----------
assets : list of zipline.data.Asset objects
The assets whose data is desired.
bar_count: int
The number of bars desired.
frequency: string
"1d" or "1m"
field: string
The desired field of the asset.
ffill: boolean
Forward-fill missing values. Only has effect if field
is 'price'.
Returns
-------
A dataframe containing the requested data.
"""
if field not in OHLCVP_FIELDS:
raise ValueError("Invalid field: {0}".format(field))
if frequency == "1d":
if field == "price":
df = self._get_history_daily_window(assets, end_dt, bar_count,
"close")
else:
df = self._get_history_daily_window(assets, end_dt, bar_count,
field)
elif frequency == "1m":
if field == "price":
df = self._get_history_minute_window(assets, end_dt, bar_count,
"close")
else:
df = self._get_history_minute_window(assets, end_dt, bar_count,
field)
else:
raise ValueError("Invalid frequency: {0}".format(frequency))
# forward-fill price
if field == "price":
if frequency == "1m":
data_frequency = 'minute'
elif frequency == "1d":
data_frequency = 'daily'
else:
raise Exception(
"Only 1d and 1m are supported for forward-filling.")
dt_to_fill = df.index[0]
perspective_dt = df.index[-1]
assets_with_leading_nan = np.where(pd.isnull(df.iloc[0]))[0]
for missing_loc in assets_with_leading_nan:
asset = assets[missing_loc]
previous_dt = self.get_last_traded_dt(asset, dt_to_fill,
data_frequency)
if pd.isnull(previous_dt):
continue
previous_value = self.get_adjusted_value(
asset,
field,
previous_dt,
perspective_dt,
data_frequency,
)
df.iloc[0, missing_loc] = previous_value
df.fillna(method='ffill', inplace=True)
for asset in df.columns:
if df.index[-1] >= asset.end_date:
# if the window extends past the asset's end date, set
# all post-end-date values to NaN in that asset's series
series = df[asset]
series[series.index.normalize() > asset.end_date] = np.NaN
return df
def _get_minute_window_for_assets(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
and specified date range. Used to support the history API method for
minute bars.
Missing bars are filled with NaN.
Parameters
----------
asset : Asset
The asset whose data is desired.
field: string
The specific field to return. "open", "high", "close_price", etc.
minutes_for_window: pd.DateTimeIndex
The list of minutes representing the desired window. Each minute
is a pd.Timestamp.
Returns
-------
A numpy array with requested values.
"""
return self._get_minute_window_data(assets, field, minutes_for_window)
def _get_minute_window_data(self, assets, field, minutes_for_window):
return self._minute_history_loader.history(assets, minutes_for_window,
field, False)
def _apply_all_adjustments(self,
data,
asset,
dts,
field,
price_adj_factor=1.0):
"""
Internal method that applies all the necessary adjustments on the
given data array.
The adjustments are:
- splits
- if field != "volume":
- mergers
- dividends
- * 0.001
- any zero fields replaced with NaN
- all values rounded to 3 digits after the decimal point.
Parameters
----------
data : np.array
The data to be adjusted.
asset: Asset
The asset whose data is being adjusted.
dts: pd.DateTimeIndex
The list of minutes or days representing the desired window.
field: string
The field whose values are in the data array.
price_adj_factor: float
Factor with which to adjust OHLC values.
Returns
-------
None. The data array is modified in place.
"""
self._apply_adjustments_to_window(
self._get_adjustment_list(asset, self._splits_dict, "SPLITS"),
data, dts, field != 'volume')
if field != 'volume':
self._apply_adjustments_to_window(
self._get_adjustment_list(asset, self._mergers_dict,
"MERGERS"), data, dts, True)
self._apply_adjustments_to_window(
self._get_adjustment_list(asset, self._dividends_dict,
"DIVIDENDS"), data, dts, True)
if price_adj_factor is not None:
data *= price_adj_factor
np.around(data, 3, out=data)
def _get_daily_window_for_sids(self,
assets,
field,
days_in_window,
extra_slot=True):
"""
Internal method that gets a window of adjusted daily data for a sid
and specified date range. Used to support the history API method for
daily bars.
Parameters
----------
asset : Asset
The asset whose data is desired.
start_dt: pandas.Timestamp
The start of the desired window of data.
bar_count: int
The number of days of data to return.
field: string
The specific field to return. "open", "high", "close_price", etc.
extra_slot: boolean
Whether to allocate an extra slot in the returned numpy array.
This extra slot will hold the data for the last partial day. It's
much better to create it here than to create a copy of the array
later just to add a slot.
Returns
-------
A numpy array with requested values. Any missing slots filled with
nan.
"""
bar_count = len(days_in_window)
# create an np.array of size bar_count
if extra_slot:
return_array = np.zeros((bar_count + 1, len(assets)))
else:
return_array = np.zeros((bar_count, len(assets)))
if field != "volume":
# volumes default to 0, so we don't need to put NaNs in the array
return_array[:] = np.NAN
if bar_count != 0:
data = self._history_loader.history(assets, days_in_window, field,
extra_slot)
if extra_slot:
return_array[:len(return_array) - 1, :] = data
else:
return_array[:len(data)] = data
return return_array
@staticmethod
def _apply_adjustments_to_window(adjustments_list, window_data,
dts_in_window, multiply):
if len(adjustments_list) == 0:
return
# advance idx to the correct spot in the adjustments list, based on
# when the window starts
idx = 0
while idx < len(adjustments_list) and dts_in_window[0] >\
adjustments_list[idx][0]:
idx += 1
# if we've advanced through all the adjustments, then there's nothing
# to do.
if idx == len(adjustments_list):
return
while idx < len(adjustments_list):
adjustment_to_apply = adjustments_list[idx]
if adjustment_to_apply[0] > dts_in_window[-1]:
break
range_end = dts_in_window.searchsorted(adjustment_to_apply[0])
if multiply:
window_data[0:range_end] *= adjustment_to_apply[1]
else:
window_data[0:range_end] /= adjustment_to_apply[1]
idx += 1
def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
A dictionary of sid -> list that is used as a cache.
table_name: string
The table that contains this data in the adjustments db.
Returns
-------
adjustments: list
A list of [multiplier, pd.Timestamp], earliest first
"""
if self._adjustment_reader is None:
return []
sid = int(asset)
try:
adjustments = adjustments_dict[sid]
except KeyError:
adjustments = adjustments_dict[sid] = self._adjustment_reader.\
get_adjustments_for_sid(table_name, sid)
return adjustments
def _check_is_currently_alive(self, asset, dt):
sid = int(asset)
if sid not in self._asset_start_dates:
self._get_asset_start_date(asset)
start_date = self._asset_start_dates[sid]
if self._asset_start_dates[sid] > dt:
raise NoTradeDataAvailableTooEarly(sid=sid,
dt=normalize_date(dt),
start_dt=start_date)
end_date = self._asset_end_dates[sid]
if self._asset_end_dates[sid] < dt:
raise NoTradeDataAvailableTooLate(sid=sid,
dt=normalize_date(dt),
end_dt=end_date)
def _get_asset_start_date(self, asset):
self._ensure_asset_dates(asset)
return self._asset_start_dates[asset]
def _get_asset_end_date(self, asset):
self._ensure_asset_dates(asset)
return self._asset_end_dates[asset]
def _ensure_asset_dates(self, asset):
sid = int(asset)
if sid not in self._asset_start_dates:
if self._first_trading_day is not None:
self._asset_start_dates[sid] = \
max(asset.start_date, self._first_trading_day)
else:
self._asset_start_dates[sid] = asset.start_date
self._asset_end_dates[sid] = asset.end_date
def get_splits(self, sids, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
sids : container
Sids for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
expected to be midnight UTC.
Returns
-------
splits : list[(int, float)]
List of splits, where each split is a (sid, ratio) tuple.
"""
if self._adjustment_reader is None or not sids:
return {}
# convert dt to # of seconds since epoch, because that's what we use
# in the adjustments db
seconds = int(dt.value / 1e9)
splits = self._adjustment_reader.conn.execute(
"SELECT sid, ratio FROM SPLITS WHERE effective_date = ?",
(seconds, )).fetchall()
splits = [split for split in splits if split[0] in sids]
return splits
def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
trading_days: pd.DatetimeIndex
The trading range.
Returns
-------
list: A list of objects with all relevant attributes populated.
All timestamp fields are converted to pd.Timestamps.
"""
if self._adjustment_reader is None:
return []
if len(trading_days) == 0:
return []
start_dt = trading_days[0].value / 1e9
end_dt = trading_days[-1].value / 1e9
dividends = self._adjustment_reader.conn.execute(
"SELECT * FROM stock_dividend_payouts WHERE sid = ? AND "
"ex_date > ? AND pay_date < ?", (int(sid), start_dt, end_dt,)).\
fetchall()
dividend_info = []
for dividend_tuple in dividends:
dividend_info.append({
"declared_date":
dividend_tuple[1],
"ex_date":
pd.Timestamp(dividend_tuple[2], unit="s"),
"pay_date":
pd.Timestamp(dividend_tuple[3], unit="s"),
"payment_sid":
dividend_tuple[4],
"ratio":
dividend_tuple[5],
"record_date":
pd.Timestamp(dividend_tuple[6], unit="s"),
"sid":
dividend_tuple[7]
})
return dividend_info
def contains(self, asset, field):
return field in BASE_FIELDS or \
(field in self._augmented_sources_map and
asset in self._augmented_sources_map[field])
def get_fetcher_assets(self, dt):
"""
Returns a list of assets for the current date, as defined by the
fetcher data.
Returns
-------
list: a list of Asset objects.
"""
# return a list of assets for the current date, as defined by the
# fetcher source
if self._extra_source_df is None:
return []
day = normalize_date(dt)
if day in self._extra_source_df.index:
assets = self._extra_source_df.loc[day]['sid']
else:
return []
if isinstance(assets, pd.Series):
return [x for x in assets if isinstance(x, Asset)]
else:
return [assets] if isinstance(assets, Asset) else []
@weak_lru_cache(20)
def _get_minute_count_for_transform(self, ending_minute, days_count):
# cache size picked somewhat loosely. this code exists purely to
# handle deprecated API.
# bars is the number of days desired. we have to translate that
# into the number of minutes we want.
# we get all the minutes for the last (bars - 1) days, then add
# all the minutes so far today. the +2 is to account for ignoring
# today, and the previous day, in doing the math.
session_for_minute = self.trading_calendar.minute_to_session_label(
ending_minute)
previous_session = self.trading_calendar.previous_session_label(
session_for_minute)
sessions = self.trading_calendar.sessions_in_range(
self.trading_calendar.sessions_window(previous_session,
-days_count + 2)[0],
previous_session,
)
minutes_count = sum(
len(self.trading_calendar.minutes_for_session(session))
for session in sessions)
# add the minutes for today
today_open = self.trading_calendar.open_and_close_for_session(
session_for_minute)[0]
minutes_count += \
((ending_minute - today_open).total_seconds() // 60) + 1
return minutes_count
def get_simple_transform(self,
asset,
transform_name,
dt,
data_frequency,
bars=None):
if transform_name == "returns":
# returns is always calculated over the last 2 days, regardless
# of the simulation's data frequency.
hst = self.get_history_window([asset],
dt,
2,
"1d",
"price",
ffill=True)[asset]
return (hst.iloc[-1] - hst.iloc[0]) / hst.iloc[0]
if bars is None:
raise ValueError("bars cannot be None!")
if data_frequency == "minute":
freq_str = "1m"
calculated_bar_count = self._get_minute_count_for_transform(
dt, bars)
else:
freq_str = "1d"
calculated_bar_count = bars
price_arr = self.get_history_window([asset],
dt,
calculated_bar_count,
freq_str,
"price",
ffill=True)[asset]
if transform_name == "mavg":
return nanmean(price_arr)
elif transform_name == "stddev":
return nanstd(price_arr, ddof=1)
elif transform_name == "vwap":
volume_arr = self.get_history_window([asset],
dt,
calculated_bar_count,
freq_str,
"volume",
ffill=True)[asset]
vol_sum = nansum(volume_arr)
try:
ret = nansum(price_arr * volume_arr) / vol_sum
except ZeroDivisionError:
ret = np.nan
return ret
class DataPortal(object):
"""Interface to all of the data that a zipline simulation needs.
This is used by the simulation runner to answer questions about the data,
like getting the prices of assets on a given day or to service history
calls.
Parameters
----------
asset_finder : zipline.assets.assets.AssetFinder
The AssetFinder instance used to resolve assets.
trading_calendar: zipline.utils.calendar.exchange_calendar.TradingCalendar
The calendar instance used to provide minute->session information.
first_trading_day : pd.Timestamp
The first trading day for the simulation.
equity_daily_reader : BcolzDailyBarReader, optional
The daily bar reader for equities. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
equity_minute_reader : BcolzMinuteBarReader, optional
The minute bar reader for equities. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
future_daily_reader : BcolzDailyBarReader, optional
The daily bar ready for futures. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
future_minute_reader : BcolzFutureMinuteBarReader, optional
The minute bar reader for futures. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
adjustment_reader : SQLiteAdjustmentWriter, optional
The adjustment reader. This is used to apply splits, dividends, and
other adjustment data to the raw data from the readers.
last_available_session : pd.Timestamp, optional
The last session to make available in session-level data.
last_available_minute : pd.Timestamp, optional
The last minute to make available in minute-level data.
"""
def __init__(self,
asset_finder,
trading_calendar,
first_trading_day,
equity_daily_reader=None,
equity_minute_reader=None,
future_daily_reader=None,
future_minute_reader=None,
adjustment_reader=None,
last_available_session=None,
last_available_minute=None,
minute_history_prefetch_length=_DEF_M_HIST_PREFETCH,
daily_history_prefetch_length=_DEF_D_HIST_PREFETCH):
self.trading_calendar = trading_calendar
self.asset_finder = asset_finder
self._adjustment_reader = adjustment_reader
# caches of sid -> adjustment list
self._splits_dict = {}
self._mergers_dict = {}
self._dividends_dict = {}
# Cache of sid -> the first trading day of an asset.
self._asset_start_dates = {}
self._asset_end_dates = {}
# Handle extra sources, like Fetcher.
self._augmented_sources_map = {}
self._extra_source_df = None
self._first_trading_session = first_trading_day
_last_sessions = [
r.last_available_dt
for r in [equity_daily_reader, future_daily_reader]
if r is not None
]
if _last_sessions:
self._last_trading_session = min(_last_sessions)
else:
self._last_trading_session = None
aligned_equity_minute_reader = self._ensure_reader_aligned(
equity_minute_reader)
aligned_equity_session_reader = self._ensure_reader_aligned(
equity_daily_reader)
aligned_future_minute_reader = self._ensure_reader_aligned(
future_minute_reader)
aligned_future_session_reader = self._ensure_reader_aligned(
future_daily_reader)
self._roll_finders = {
'calendar':
CalendarRollFinder(self.trading_calendar, self.asset_finder),
}
aligned_minute_readers = {}
aligned_session_readers = {}
if aligned_equity_minute_reader is not None:
aligned_minute_readers[Equity] = aligned_equity_minute_reader
if aligned_equity_session_reader is not None:
aligned_session_readers[Equity] = aligned_equity_session_reader
if aligned_future_minute_reader is not None:
aligned_minute_readers[Future] = aligned_future_minute_reader
aligned_minute_readers[ContinuousFuture] = \
ContinuousFutureMinuteBarReader(
aligned_future_minute_reader,
self._roll_finders,
)
if aligned_future_session_reader is not None:
aligned_session_readers[Future] = aligned_future_session_reader
self._roll_finders['volume'] = VolumeRollFinder(
self.trading_calendar,
self.asset_finder,
aligned_future_session_reader,
)
aligned_session_readers[ContinuousFuture] = \
ContinuousFutureSessionBarReader(
aligned_future_session_reader,
self._roll_finders,
)
_dispatch_minute_reader = AssetDispatchMinuteBarReader(
self.trading_calendar,
self.asset_finder,
aligned_minute_readers,
last_available_minute,
)
_dispatch_session_reader = AssetDispatchSessionBarReader(
self.trading_calendar,
self.asset_finder,
aligned_session_readers,
last_available_session,
)
self._pricing_readers = {
'minute': _dispatch_minute_reader,
'daily': _dispatch_session_reader,
}
self._daily_aggregator = DailyHistoryAggregator(
self.trading_calendar.schedule.market_open,
_dispatch_minute_reader, self.trading_calendar)
self._history_loader = DailyHistoryLoader(
self.trading_calendar,
_dispatch_session_reader,
self._adjustment_reader,
self.asset_finder,
self._roll_finders,
prefetch_length=daily_history_prefetch_length,
)
self._minute_history_loader = MinuteHistoryLoader(
self.trading_calendar,
_dispatch_minute_reader,
self._adjustment_reader,
self.asset_finder,
self._roll_finders,
prefetch_length=minute_history_prefetch_length,
)
self._first_trading_day = first_trading_day
# Get the first trading minute
self._first_trading_minute, _ = (
self.trading_calendar.open_and_close_for_session(
self._first_trading_day)
if self._first_trading_day is not None else (None, None))
# Store the locs of the first day and first minute
self._first_trading_day_loc = (
self.trading_calendar.all_sessions.get_loc(self._first_trading_day)
if self._first_trading_day is not None else None)
def _ensure_reader_aligned(self, reader):
if reader is None:
return
if reader.trading_calendar.name == self.trading_calendar.name:
return reader
elif reader.data_frequency == 'minute':
return ReindexMinuteBarReader(self.trading_calendar, reader,
self._first_trading_session,
self._last_trading_session)
elif reader.data_frequency == 'session':
return ReindexSessionBarReader(self.trading_calendar, reader,
self._first_trading_session,
self._last_trading_session)
def _reindex_extra_source(self, df, source_date_index):
return df.reindex(index=source_date_index, method='ffill')
def handle_extra_source(self, source_df, sim_params):
"""
Extra sources always have a sid column.
We expand the given data (by forward filling) to the full range of
the simulation dates, so that lookup is fast during simulation.
"""
if source_df is None:
return
# Normalize all the dates in the df
source_df.index = source_df.index.normalize()
# source_df's sid column can either consist of assets we know about
# (such as sid(24)) or of assets we don't know about (such as
# palladium).
#
# In both cases, we break up the dataframe into individual dfs
# that only contain a single asset's information. ie, if source_df
# has data for PALLADIUM and GOLD, we split source_df into two
# dataframes, one for each. (same applies if source_df has data for
# AAPL and IBM).
#
# We then take each child df and reindex it to the simulation's date
# range by forward-filling missing values. this makes reads simpler.
#
# Finally, we store the data. For each column, we store a mapping in
# self.augmented_sources_map from the column to a dictionary of
# asset -> df. In other words,
# self.augmented_sources_map['days_to_cover']['AAPL'] gives us the df
# holding that data.
source_date_index = self.trading_calendar.sessions_in_range(
sim_params.start_session, sim_params.end_session)
# Break the source_df up into one dataframe per sid. This lets
# us (more easily) calculate accurate start/end dates for each sid,
# de-dup data, and expand the data to fit the backtest start/end date.
grouped_by_sid = source_df.groupby(["sid"])
group_names = grouped_by_sid.groups.keys()
group_dict = {}
for group_name in group_names:
group_dict[group_name] = grouped_by_sid.get_group(group_name)
# This will be the dataframe which we query to get fetcher assets at
# any given time. Get's overwritten every time there's a new fetcher
# call
extra_source_df = pd.DataFrame()
for identifier, df in iteritems(group_dict):
# Before reindexing, save the earliest and latest dates
earliest_date = df.index[0]
latest_date = df.index[-1]
# Since we know this df only contains a single sid, we can safely
# de-dupe by the index (dt). If minute granularity, will take the
# last data point on any given day
df = df.groupby(level=0).last()
# Reindex the dataframe based on the backtest start/end date.
# This makes reads easier during the backtest.
df = self._reindex_extra_source(df, source_date_index)
if not isinstance(identifier, Asset):
# for fake assets we need to store a start/end date
self._asset_start_dates[identifier] = earliest_date
self._asset_end_dates[identifier] = latest_date
for col_name in df.columns.difference(['sid']):
if col_name not in self._augmented_sources_map:
self._augmented_sources_map[col_name] = {}
self._augmented_sources_map[col_name][identifier] = df
# Append to extra_source_df the reindexed dataframe for the single
# sid
extra_source_df = extra_source_df.append(df)
self._extra_source_df = extra_source_df
def _get_pricing_reader(self, data_frequency):
return self._pricing_readers[data_frequency]
def get_last_traded_dt(self, asset, dt, data_frequency):
"""
Given an asset and dt, returns the last traded dt from the viewpoint
of the given dt.
If there is a trade on the dt, the answer is dt provided.
"""
return self._get_pricing_reader(data_frequency).get_last_traded_dt(
asset, dt)
@staticmethod
def _is_extra_source(asset, field, map):
"""
Internal method that determines if this asset/field combination
represents a fetcher value or a regular OHLCVP lookup.
"""
# If we have an extra source with a column called "price", only look
# at it if it's on something like palladium and not AAPL (since our
# own price data always wins when dealing with assets).
return not (field in BASE_FIELDS and
(isinstance(asset, (Asset, ContinuousFuture))))
def _get_fetcher_value(self, asset, field, dt):
day = normalize_date(dt)
try:
return \
self._augmented_sources_map[field][asset].loc[day, field]
except KeyError:
return np.NaN
def get_spot_value(self, asset, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
asset : Asset
The asset whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume',
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The spot value of ``field`` for ``asset`` The return type is based
on the ``field`` requested. If the field is one of 'open', 'high',
'low', 'close', or 'price', the value will be a float. If the
``field`` is 'volume' the value will be a int. If the ``field`` is
'last_traded' the value will be a Timestamp.
"""
if self._is_extra_source(asset, field, self._augmented_sources_map):
return self._get_fetcher_value(asset, field, dt)
if field not in BASE_FIELDS:
raise KeyError("Invalid column: " + str(field))
session_label = self.trading_calendar.minute_to_session_label(dt)
if dt < asset.start_date or \
(data_frequency == "daily" and
session_label > asset.end_date) or \
(data_frequency == "minute" and
session_label > asset.end_date):
if field == "volume":
return 0
elif field != "last_traded":
return np.NaN
elif field == "contract":
return None
if data_frequency == "daily":
if field == "contract":
return self._get_current_contract(asset, session_label)
else:
return self._get_daily_spot_value(asset, field, session_label)
else:
if field == "last_traded":
return self.get_last_traded_dt(asset, dt, 'minute')
elif field == "price":
return self._get_minute_spot_value(asset,
"close",
dt,
ffill=True)
elif field == "contract":
return self._get_current_contract(asset, dt)
else:
return self._get_minute_spot_value(asset, field, dt)
def get_adjustments(self, assets, field, dt, perspective_dt):
"""
Returns a list of adjustments between the dt and perspective_dt for the
given field and list of assets
Parameters
----------
assets : list of type Asset, or Asset
The asset, or assets whose adjustments are desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
adjustments : list[Adjustment]
The adjustments to that field.
"""
if isinstance(assets, Asset):
assets = [assets]
adjustment_ratios_per_asset = []
split_adj_factor = lambda x: x if field != 'volume' else 1.0 / x
for asset in assets:
adjustments_for_asset = []
split_adjustments = self._get_adjustment_list(
asset, self._splits_dict, "SPLITS")
for adj_dt, adj in split_adjustments:
if dt <= adj_dt <= perspective_dt:
adjustments_for_asset.append(split_adj_factor(adj))
elif adj_dt > perspective_dt:
break
if field != 'volume':
merger_adjustments = self._get_adjustment_list(
asset, self._mergers_dict, "MERGERS")
for adj_dt, adj in merger_adjustments:
if dt <= adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
dividend_adjustments = self._get_adjustment_list(
asset,
self._dividends_dict,
"DIVIDENDS",
)
for adj_dt, adj in dividend_adjustments:
if dt <= adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
ratio = reduce(mul, adjustments_for_asset, 1.0)
adjustment_ratios_per_asset.append(ratio)
return adjustment_ratios_per_asset
def get_adjusted_value(self,
asset,
field,
dt,
perspective_dt,
data_frequency,
spot_value=None):
"""
Returns a scalar value representing the value
of the desired asset's field at the given dt with adjustments applied.
Parameters
----------
asset : Asset
The asset whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The value of the given ``field`` for ``asset`` at ``dt`` with any
adjustments known by ``perspective_dt`` applied. The return type is
based on the ``field`` requested. If the field is one of 'open',
'high', 'low', 'close', or 'price', the value will be a float. If
the ``field`` is 'volume' the value will be a int. If the ``field``
is 'last_traded' the value will be a Timestamp.
"""
if spot_value is None:
# if this a fetcher field, we want to use perspective_dt (not dt)
# because we want the new value as of midnight (fetcher only works
# on a daily basis, all timestamps are on midnight)
if self._is_extra_source(asset, field,
self._augmented_sources_map):
spot_value = self.get_spot_value(asset, field, perspective_dt,
data_frequency)
else:
spot_value = self.get_spot_value(asset, field, dt,
data_frequency)
if isinstance(asset, Equity):
ratio = self.get_adjustments(asset, field, dt, perspective_dt)[0]
spot_value *= ratio
return spot_value
def _get_minute_spot_value(self, asset, column, dt, ffill=False):
reader = self._get_pricing_reader('minute')
try:
result = reader.get_value(asset.sid, dt, column)
except NoDataOnDate:
if not ffill:
if column == 'volume':
return 0
else:
return np.nan
if not ffill:
return result
# we are looking for price, and didn't find one. have to go hunting.
last_traded_dt = reader.get_last_traded_dt(asset, dt)
if last_traded_dt is pd.NaT:
# no last traded dt, bail
if column == 'volume':
return 0
else:
return np.nan
# get the value as of the last traded dt
result = reader.get_value(asset.sid, last_traded_dt, column)
if np.isnan(result):
return np.nan
if dt == last_traded_dt or dt.date() == last_traded_dt.date():
return result
# the value we found came from a different day, so we have to adjust
# the data if there are any adjustments on that day barrier
return self.get_adjusted_value(asset,
column,
last_traded_dt,
dt,
"minute",
spot_value=result)
def _get_daily_spot_value(self, asset, column, dt):
reader = self._get_pricing_reader('daily')
if column == "last_traded":
last_traded_dt = reader.get_last_traded_dt(asset, dt)
if isnull(last_traded_dt):
return pd.NaT
else:
return last_traded_dt
elif column in OHLCV_FIELDS:
# don't forward fill
try:
return reader.get_value(asset, dt, column)
except NoDataOnDate:
return np.nan
elif column == "price":
found_dt = dt
while True:
try:
value = reader.get_value(asset, found_dt, "close")
if not isnull(value):
if dt == found_dt:
return value
else:
# adjust if needed
return self.get_adjusted_value(asset,
column,
found_dt,
dt,
"minute",
spot_value=value)
else:
found_dt -= self.trading_calendar.day
except NoDataOnDate:
return np.nan
@remember_last
def _get_days_for_window(self, end_date, bar_count):
tds = self.trading_calendar.all_sessions
end_loc = tds.get_loc(end_date)
start_loc = end_loc - bar_count + 1
if start_loc < self._first_trading_day_loc:
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day.date(),
bar_count=bar_count,
suggested_start_day=tds[self._first_trading_day_loc +
bar_count].date(),
)
return tds[start_loc:end_loc + 1]
def _get_history_daily_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of daily frequency for the given sids.
"""
session = self.trading_calendar.minute_to_session_label(end_dt)
days_for_window = self._get_days_for_window(session, bar_count)
if len(assets) == 0:
return pd.DataFrame(None, index=days_for_window, columns=None)
data = self._get_history_daily_window_data(assets, days_for_window,
end_dt, field_to_use)
return pd.DataFrame(data, index=days_for_window, columns=assets)
def _get_history_daily_window_data(self, assets, days_for_window, end_dt,
field_to_use):
ends_at_midnight = (end_dt.hour == end_dt.minute == 0)
if ends_at_midnight:
# two cases where we use daily data for the whole range:
# 1) the history window ends at midnight utc.
# 2) the last desired day of the window is after the
# last trading day, use daily data for the whole range.
return self._get_daily_window_data(assets,
field_to_use,
days_for_window,
extra_slot=False)
else:
# minute mode, requesting '1d'
daily_data = self._get_daily_window_data(assets, field_to_use,
days_for_window[0:-1])
if field_to_use == 'open':
minute_value = self._daily_aggregator.opens(assets, end_dt)
elif field_to_use == 'high':
minute_value = self._daily_aggregator.highs(assets, end_dt)
elif field_to_use == 'low':
minute_value = self._daily_aggregator.lows(assets, end_dt)
elif field_to_use == 'close':
minute_value = self._daily_aggregator.closes(assets, end_dt)
elif field_to_use == 'volume':
minute_value = self._daily_aggregator.volumes(assets, end_dt)
elif field_to_use == 'sid':
minute_value = [
int(self._get_current_contract(asset, end_dt))
for asset in assets
]
# append the partial day.
daily_data[-1] = minute_value
return daily_data
def _handle_minute_history_out_of_bounds(self, bar_count):
first_trading_minute_loc = (self.trading_calendar.all_minutes.get_loc(
self._first_trading_minute) if self._first_trading_minute
is not None else None)
suggested_start_day = (
self.trading_calendar.all_minutes[first_trading_minute_loc +
bar_count] +
self.trading_calendar.day).date()
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day.date(),
bar_count=bar_count,
suggested_start_day=suggested_start_day,
)
def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
minutes_for_window = self.trading_calendar.minutes_window(
end_dt, -bar_count)
except KeyError:
self._handle_minute_history_out_of_bounds(bar_count)
if minutes_for_window[0] < self._first_trading_minute:
self._handle_minute_history_out_of_bounds(bar_count)
asset_minute_data = self._get_minute_window_data(
assets,
field_to_use,
minutes_for_window,
)
return pd.DataFrame(asset_minute_data,
index=minutes_for_window,
columns=assets)
def get_history_window(self,
assets,
end_dt,
bar_count,
frequency,
field,
ffill=True):
"""
Public API method that returns a dataframe containing the requested
history window. Data is fully adjusted.
Parameters
----------
assets : list of zipline.data.Asset objects
The assets whose data is desired.
bar_count: int
The number of bars desired.
frequency: string
"1d" or "1m"
field: string
The desired field of the asset.
ffill: boolean
Forward-fill missing values. Only has effect if field
is 'price'.
Returns
-------
A dataframe containing the requested data.
"""
if field not in OHLCVP_FIELDS and field != 'sid':
raise ValueError("Invalid field: {0}".format(field))
if frequency == "1d":
if field == "price":
df = self._get_history_daily_window(assets, end_dt, bar_count,
"close")
else:
df = self._get_history_daily_window(assets, end_dt, bar_count,
field)
elif frequency == "1m":
if field == "price":
df = self._get_history_minute_window(assets, end_dt, bar_count,
"close")
else:
df = self._get_history_minute_window(assets, end_dt, bar_count,
field)
else:
raise ValueError("Invalid frequency: {0}".format(frequency))
# forward-fill price
if field == "price":
if frequency == "1m":
data_frequency = 'minute'
elif frequency == "1d":
data_frequency = 'daily'
else:
raise Exception(
"Only 1d and 1m are supported for forward-filling.")
assets_with_leading_nan = np.where(isnull(df.iloc[0]))[0]
history_start, history_end = df.index[[0, -1]]
initial_values = []
for asset in df.columns[assets_with_leading_nan]:
last_traded = self.get_last_traded_dt(
asset,
history_start,
data_frequency,
)
if isnull(last_traded):
initial_values.append(nan)
else:
initial_values.append(
self.get_adjusted_value(
asset,
field,
dt=last_traded,
perspective_dt=history_end,
data_frequency=data_frequency,
))
# Set leading values for assets that were missing data, then ffill.
df.ix[0, assets_with_leading_nan] = np.array(initial_values,
dtype=np.float64)
df.fillna(method='ffill', inplace=True)
# forward-filling will incorrectly produce values after the end of
# an asset's lifetime, so write NaNs back over the asset's
# end_date.
normed_index = df.index.normalize()
for asset in df.columns:
if history_end >= asset.end_date:
# if the window extends past the asset's end date, set
# all post-end-date values to NaN in that asset's series
df.loc[normed_index > asset.end_date, asset] = nan
return df
def _get_minute_window_data(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
and specified date range. Used to support the history API method for
minute bars.
Missing bars are filled with NaN.
Parameters
----------
assets : iterable[Asset]
The assets whose data is desired.
field: string
The specific field to return. "open", "high", "close_price", etc.
minutes_for_window: pd.DateTimeIndex
The list of minutes representing the desired window. Each minute
is a pd.Timestamp.
Returns
-------
A numpy array with requested values.
"""
return self._minute_history_loader.history(assets, minutes_for_window,
field, False)
def _get_daily_window_data(self,
assets,
field,
days_in_window,
extra_slot=True):
"""
Internal method that gets a window of adjusted daily data for a sid
and specified date range. Used to support the history API method for
daily bars.
Parameters
----------
asset : Asset
The asset whose data is desired.
start_dt: pandas.Timestamp
The start of the desired window of data.
bar_count: int
The number of days of data to return.
field: string
The specific field to return. "open", "high", "close_price", etc.
extra_slot: boolean
Whether to allocate an extra slot in the returned numpy array.
This extra slot will hold the data for the last partial day. It's
much better to create it here than to create a copy of the array
later just to add a slot.
Returns
-------
A numpy array with requested values. Any missing slots filled with
nan.
"""
bar_count = len(days_in_window)
# create an np.array of size bar_count
dtype = float64 if field != 'sid' else int64
if extra_slot:
return_array = np.zeros((bar_count + 1, len(assets)), dtype=dtype)
else:
return_array = np.zeros((bar_count, len(assets)), dtype=dtype)
if field != "volume":
# volumes default to 0, so we don't need to put NaNs in the array
return_array[:] = np.NAN
if bar_count != 0:
data = self._history_loader.history(assets, days_in_window, field,
extra_slot)
if extra_slot:
return_array[:len(return_array) - 1, :] = data
else:
return_array[:len(data)] = data
return return_array
def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
A dictionary of sid -> list that is used as a cache.
table_name: string
The table that contains this data in the adjustments db.
Returns
-------
adjustments: list
A list of [multiplier, pd.Timestamp], earliest first
"""
if self._adjustment_reader is None:
return []
sid = int(asset)
try:
adjustments = adjustments_dict[sid]
except KeyError:
adjustments = adjustments_dict[sid] = self._adjustment_reader.\
get_adjustments_for_sid(table_name, sid)
return adjustments
def _check_is_currently_alive(self, asset, dt):
sid = int(asset)
if sid not in self._asset_start_dates:
self._get_asset_start_date(asset)
start_date = self._asset_start_dates[sid]
if self._asset_start_dates[sid] > dt:
raise NoTradeDataAvailableTooEarly(sid=sid,
dt=normalize_date(dt),
start_dt=start_date)
end_date = self._asset_end_dates[sid]
if self._asset_end_dates[sid] < dt:
raise NoTradeDataAvailableTooLate(sid=sid,
dt=normalize_date(dt),
end_dt=end_date)
def _get_asset_start_date(self, asset):
self._ensure_asset_dates(asset)
return self._asset_start_dates[asset]
def _get_asset_end_date(self, asset):
self._ensure_asset_dates(asset)
return self._asset_end_dates[asset]
def _ensure_asset_dates(self, asset):
sid = int(asset)
if sid not in self._asset_start_dates:
if self._first_trading_day is not None:
self._asset_start_dates[sid] = \
max(asset.start_date, self._first_trading_day)
else:
self._asset_start_dates[sid] = asset.start_date
self._asset_end_dates[sid] = asset.end_date
def get_splits(self, sids, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
sids : container
Sids for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
expected to be midnight UTC.
Returns
-------
splits : list[(int, float)]
List of splits, where each split is a (sid, ratio) tuple.
"""
if self._adjustment_reader is None or not sids:
return {}
# convert dt to # of seconds since epoch, because that's what we use
# in the adjustments db
seconds = int(dt.value / 1e9)
splits = self._adjustment_reader.conn.execute(
"SELECT sid, ratio FROM SPLITS WHERE effective_date = ?",
(seconds, )).fetchall()
splits = [split for split in splits if split[0] in sids]
return splits
def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
trading_days: pd.DatetimeIndex
The trading range.
Returns
-------
list: A list of objects with all relevant attributes populated.
All timestamp fields are converted to pd.Timestamps.
"""
if self._adjustment_reader is None:
return []
if len(trading_days) == 0:
return []
start_dt = trading_days[0].value / 1e9
end_dt = trading_days[-1].value / 1e9
dividends = self._adjustment_reader.conn.execute(
"SELECT * FROM stock_dividend_payouts WHERE sid = ? AND "
"ex_date > ? AND pay_date < ?", (int(sid), start_dt, end_dt,)).\
fetchall()
dividend_info = []
for dividend_tuple in dividends:
dividend_info.append({
"declared_date":
dividend_tuple[1],
"ex_date":
pd.Timestamp(dividend_tuple[2], unit="s"),
"pay_date":
pd.Timestamp(dividend_tuple[3], unit="s"),
"payment_sid":
dividend_tuple[4],
"ratio":
dividend_tuple[5],
"record_date":
pd.Timestamp(dividend_tuple[6], unit="s"),
"sid":
dividend_tuple[7]
})
return dividend_info
def contains(self, asset, field):
return field in BASE_FIELDS or \
(field in self._augmented_sources_map and
asset in self._augmented_sources_map[field])
def get_fetcher_assets(self, dt):
"""
Returns a list of assets for the current date, as defined by the
fetcher data.
Returns
-------
list: a list of Asset objects.
"""
# return a list of assets for the current date, as defined by the
# fetcher source
if self._extra_source_df is None:
return []
day = normalize_date(dt)
if day in self._extra_source_df.index:
assets = self._extra_source_df.loc[day]['sid']
else:
return []
if isinstance(assets, pd.Series):
return [x for x in assets if isinstance(x, Asset)]
else:
return [assets] if isinstance(assets, Asset) else []
# cache size picked somewhat loosely. this code exists purely to
# handle deprecated API.
@weak_lru_cache(20)
def _get_minute_count_for_transform(self, ending_minute, days_count):
# This function works in three steps.
# Step 1. Count the minutes from ``ending_minute`` to the start of its
# session.
# Step 2. Count the minutes from the prior ``days_count - 1`` sessions.
# Step 3. Return the sum of the results from steps (1) and (2).
# Example (NYSE Calendar)
# ending_minute = 2016-12-28 9:40 AM US/Eastern
# days_count = 3
# Step 1. Calculate that there are 10 minutes in the ending session.
# Step 2. Calculate that there are 390 + 210 = 600 minutes in the prior
# two sessions. (Prior sessions are 2015-12-23 and 2015-12-24.)
# 2015-12-24 is a half day.
# Step 3. Return 600 + 10 = 610.
cal = self.trading_calendar
ending_session = cal.minute_to_session_label(
ending_minute,
direction="none", # It's an error to pass a non-trading minute.
)
# Assume that calendar days are always full of contiguous minutes,
# which means we can just take 1 + (number of minutes between the last
# minute and the start of the session). We add one so that we include
# the ending minute in the total.
ending_session_minute_count = timedelta_to_integral_minutes(
ending_minute -
cal.open_and_close_for_session(ending_session)[0]) + 1
if days_count == 1:
# We just need sessions for the active day.
return ending_session_minute_count
# XXX: We're subtracting 2 here to account for two offsets:
# 1. We only want ``days_count - 1`` sessions, since we've already
# accounted for the ending session above.
# 2. The API of ``sessions_window`` is to return one more session than
# the requested number. I don't think any consumers actually want
# that behavior, but it's the tested and documented behavior right
# now, so we have to request one less session than we actually want.
completed_sessions = cal.sessions_window(
cal.previous_session_label(ending_session),
2 - days_count,
)
completed_sessions_minute_count = (
self.trading_calendar.minutes_count_for_sessions_in_range(
completed_sessions[0], completed_sessions[-1]))
return ending_session_minute_count + completed_sessions_minute_count
def get_simple_transform(self,
asset,
transform_name,
dt,
data_frequency,
bars=None):
if transform_name == "returns":
# returns is always calculated over the last 2 days, regardless
# of the simulation's data frequency.
hst = self.get_history_window([asset],
dt,
2,
"1d",
"price",
ffill=True)[asset]
return (hst.iloc[-1] - hst.iloc[0]) / hst.iloc[0]
if bars is None:
raise ValueError("bars cannot be None!")
if data_frequency == "minute":
freq_str = "1m"
calculated_bar_count = int(
self._get_minute_count_for_transform(dt, bars))
else:
freq_str = "1d"
calculated_bar_count = bars
price_arr = self.get_history_window([asset],
dt,
calculated_bar_count,
freq_str,
"price",
ffill=True)[asset]
if transform_name == "mavg":
return nanmean(price_arr)
elif transform_name == "stddev":
return nanstd(price_arr, ddof=1)
elif transform_name == "vwap":
volume_arr = self.get_history_window([asset],
dt,
calculated_bar_count,
freq_str,
"volume",
ffill=True)[asset]
vol_sum = nansum(volume_arr)
try:
ret = nansum(price_arr * volume_arr) / vol_sum
except ZeroDivisionError:
ret = np.nan
return ret
def get_current_future_chain(self, continuous_future, dt):
"""
Retrieves the future chain for the contract at the given `dt` according
the `continuous_future` specification.
Returns:
future_chain : list[Future]
A list of active futures, where the first index is the current
contract specified by the continuous future definition, the second
is the next upcoming contract and so on.
"""
rf = self._roll_finders[continuous_future.roll_style]
session = self.trading_calendar.minute_to_session_label(dt)
contract_center = rf.get_contract_center(continuous_future.root_symbol,
session,
continuous_future.offset)
oc = self.asset_finder.get_ordered_contracts(
continuous_future.root_symbol)
chain = oc.active_chain(contract_center, session.value)
return self.asset_finder.retrieve_all(chain)
def _get_current_contract(self, continuous_future, dt):
rf = self._roll_finders[continuous_future.roll_style]
return self.asset_finder.retrieve_asset(
rf.get_contract_center(continuous_future.root_symbol, dt,
continuous_future.offset))
class DataPortal(object):
"""Interface to all of the data that a zipline simulation needs.
This is used by the simulation runner to answer questions about the data,
like getting the prices of assets on a given day or to service history
calls.
Parameters
----------
asset_finder : zipline.assets.assets.AssetFinder
The AssetFinder instance used to resolve assets.
trading_calendar: trading_calendars.TradingCalendar
The calendar instance used to provide minute->session information.
first_trading_day : pd.Timestamp
The first trading day for the simulation.
equity_daily_reader : BcolzDailyBarReader, optional
The daily bar reader for equities. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
equity_minute_reader : BcolzMinuteBarReader, optional
The minute bar reader for equities. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
future_daily_reader : BcolzDailyBarReader, optional
The daily bar ready for futures. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
future_minute_reader : BcolzFutureMinuteBarReader, optional
The minute bar reader for futures. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
adjustment_reader : SQLiteAdjustmentWriter, optional
The adjustment reader. This is used to apply splits, dividends, and
other adjustment data to the raw data from the readers.
last_available_session : pd.Timestamp, optional
The last session to make available in session-level data.
last_available_minute : pd.Timestamp, optional
The last minute to make available in minute-level data.
"""
def __init__(self,
asset_finder,
trading_calendar,
first_trading_day,
equity_daily_reader=None,
equity_minute_reader=None,
future_daily_reader=None,
future_minute_reader=None,
adjustment_reader=None,
last_available_session=None,
last_available_minute=None,
minute_history_prefetch_length=_DEF_M_HIST_PREFETCH,
daily_history_prefetch_length=_DEF_D_HIST_PREFETCH):
self.trading_calendar = trading_calendar
self.asset_finder = asset_finder
self._adjustment_reader = adjustment_reader
# caches of sid -> adjustment list
self._splits_dict = {}
self._mergers_dict = {}
self._dividends_dict = {}
self._first_available_session = first_trading_day
if last_available_session:
self._last_available_session = last_available_session
else:
# Infer the last session from the provided readers.
last_sessions = [
reader.last_available_dt
for reader in [equity_daily_reader, future_daily_reader]
if reader is not None
]
if last_sessions:
self._last_available_session = min(last_sessions)
else:
self._last_available_session = None
if last_available_minute:
self._last_available_minute = last_available_minute
else:
# Infer the last minute from the provided readers.
last_minutes = [
reader.last_available_dt
for reader in [equity_minute_reader, future_minute_reader]
if reader is not None
]
if last_minutes:
self._last_available_minute = max(last_minutes)
else:
self._last_available_minute = None
aligned_equity_minute_reader = self._ensure_reader_aligned(
equity_minute_reader)
aligned_equity_session_reader = self._ensure_reader_aligned(
equity_daily_reader)
aligned_future_minute_reader = self._ensure_reader_aligned(
future_minute_reader)
aligned_future_session_reader = self._ensure_reader_aligned(
future_daily_reader)
self._roll_finders = {
'calendar':
CalendarRollFinder(self.trading_calendar, self.asset_finder),
}
aligned_minute_readers = {}
aligned_session_readers = {}
if aligned_equity_minute_reader is not None:
aligned_minute_readers[Equity] = aligned_equity_minute_reader
if aligned_equity_session_reader is not None:
aligned_session_readers[Equity] = aligned_equity_session_reader
if aligned_future_minute_reader is not None:
aligned_minute_readers[Future] = aligned_future_minute_reader
aligned_minute_readers[ContinuousFuture] = \
ContinuousFutureMinuteBarReader(
aligned_future_minute_reader,
self._roll_finders,
)
if aligned_future_session_reader is not None:
aligned_session_readers[Future] = aligned_future_session_reader
self._roll_finders['volume'] = VolumeRollFinder(
self.trading_calendar,
self.asset_finder,
aligned_future_session_reader,
)
aligned_session_readers[ContinuousFuture] = \
ContinuousFutureSessionBarReader(
aligned_future_session_reader,
self._roll_finders,
)
_dispatch_minute_reader = AssetDispatchMinuteBarReader(
self.trading_calendar,
self.asset_finder,
aligned_minute_readers,
self._last_available_minute,
)
_dispatch_session_reader = AssetDispatchSessionBarReader(
self.trading_calendar,
self.asset_finder,
aligned_session_readers,
self._last_available_session,
)
self._pricing_readers = {
'minute': _dispatch_minute_reader,
'daily': _dispatch_session_reader,
}
self._daily_aggregator = DailyHistoryAggregator(
self.trading_calendar.schedule.market_open,
_dispatch_minute_reader, self.trading_calendar)
self._history_loader = DailyHistoryLoader(
self.trading_calendar,
_dispatch_session_reader,
self._adjustment_reader,
self.asset_finder,
self._roll_finders,
prefetch_length=daily_history_prefetch_length,
)
self._minute_history_loader = MinuteHistoryLoader(
self.trading_calendar,
_dispatch_minute_reader,
self._adjustment_reader,
self.asset_finder,
self._roll_finders,
prefetch_length=minute_history_prefetch_length,
)
self._first_trading_day = first_trading_day
# Get the first trading minute
self._first_trading_minute, _ = (
self.trading_calendar.open_and_close_for_session(
self._first_trading_day)
if self._first_trading_day is not None else (None, None))
# Store the locs of the first day and first minute
self._first_trading_day_loc = (
self.trading_calendar.all_sessions.get_loc(self._first_trading_day)
if self._first_trading_day is not None else None)
def _ensure_reader_aligned(self, reader):
if reader is None:
return
if reader.trading_calendar.name == self.trading_calendar.name:
return reader
elif reader.data_frequency == 'minute':
return ReindexMinuteBarReader(self.trading_calendar, reader,
self._first_available_session,
self._last_available_session)
elif reader.data_frequency == 'session':
return ReindexSessionBarReader(self.trading_calendar, reader,
self._first_available_session,
self._last_available_session)
def _get_pricing_reader(self, data_frequency):
return self._pricing_readers[data_frequency]
def get_last_traded_dt(self, asset, dt, data_frequency):
"""
Given an asset and dt, returns the last traded dt from the viewpoint
of the given dt.
If there is a trade on the dt, the answer is dt provided.
"""
return self._get_pricing_reader(data_frequency).get_last_traded_dt(
asset, dt)
def _get_single_asset_value(self, session_label, asset, field, dt,
data_frequency):
if field not in BASE_FIELDS:
raise KeyError("Invalid column: " + str(field))
if dt < asset.start_date or \
(data_frequency == "daily" and
session_label > asset.end_date) or \
(data_frequency == "minute" and
session_label > asset.end_date):
if field == "volume":
return 0
elif field == "contract":
return None
elif field != "last_traded":
return np.NaN
if data_frequency == "daily":
if field == "contract":
return self._get_current_contract(asset, session_label)
else:
return self._get_daily_spot_value(
asset,
field,
session_label,
)
else:
if field == "last_traded":
return self.get_last_traded_dt(asset, dt, 'minute')
elif field == "price":
return self._get_minute_spot_value(
asset,
"close",
dt,
ffill=True,
)
elif field == "contract":
return self._get_current_contract(asset, dt)
else:
return self._get_minute_spot_value(asset, field, dt)
def get_spot_value(self, assets, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
assets : Asset, ContinuousFuture, or iterable of same.
The asset or assets whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume',
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The spot value of ``field`` for ``asset`` The return type is based
on the ``field`` requested. If the field is one of 'open', 'high',
'low', 'close', or 'price', the value will be a float. If the
``field`` is 'volume' the value will be a int. If the ``field`` is
'last_traded' the value will be a Timestamp.
"""
assets_is_scalar = False
if isinstance(assets, (AssetConvertible, PricingDataAssociable)):
assets_is_scalar = True
else:
# If 'assets' was not one of the expected types then it should be
# an iterable.
try:
iter(assets)
except TypeError:
raise TypeError("Unexpected 'assets' value of type {}.".format(
type(assets)))
session_label = self.trading_calendar.minute_to_session_label(dt)
if assets_is_scalar:
return self._get_single_asset_value(
session_label,
assets,
field,
dt,
data_frequency,
)
else:
get_single_asset_value = self._get_single_asset_value
return [
get_single_asset_value(
session_label,
asset,
field,
dt,
data_frequency,
) for asset in assets
]
def get_scalar_asset_spot_value(self, asset, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
assets : Asset
The asset or assets whose data is desired. This cannot be
an arbitrary AssetConvertible.
field : {'open', 'high', 'low', 'close', 'volume',
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The spot value of ``field`` for ``asset`` The return type is based
on the ``field`` requested. If the field is one of 'open', 'high',
'low', 'close', or 'price', the value will be a float. If the
``field`` is 'volume' the value will be a int. If the ``field`` is
'last_traded' the value will be a Timestamp.
"""
return self._get_single_asset_value(
self.trading_calendar.minute_to_session_label(dt),
asset,
field,
dt,
data_frequency,
)
def get_adjustments(self, assets, field, dt, perspective_dt):
"""
Returns a list of adjustments between the dt and perspective_dt for the
given field and list of assets
Parameters
----------
assets : list of type Asset, or Asset
The asset, or assets whose adjustments are desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
Returns
-------
adjustments : list[Adjustment]
The adjustments to that field.
"""
if isinstance(assets, Asset):
assets = [assets]
adjustment_ratios_per_asset = []
def split_adj_factor(x):
return x if field != 'volume' else 1.0 / x
for asset in assets:
adjustments_for_asset = []
split_adjustments = self._get_adjustment_list(
asset, self._splits_dict, "SPLITS")
for adj_dt, adj in split_adjustments:
if dt < adj_dt <= perspective_dt:
adjustments_for_asset.append(split_adj_factor(adj))
elif adj_dt > perspective_dt:
break
if field != 'volume':
merger_adjustments = self._get_adjustment_list(
asset, self._mergers_dict, "MERGERS")
for adj_dt, adj in merger_adjustments:
if dt < adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
dividend_adjustments = self._get_adjustment_list(
asset,
self._dividends_dict,
"DIVIDENDS",
)
for adj_dt, adj in dividend_adjustments:
if dt < adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
ratio = reduce(mul, adjustments_for_asset, 1.0)
adjustment_ratios_per_asset.append(ratio)
return adjustment_ratios_per_asset
def get_adjusted_value(self,
asset,
field,
dt,
perspective_dt,
data_frequency,
spot_value=None):
"""
Returns a scalar value representing the value
of the desired asset's field at the given dt with adjustments applied.
Parameters
----------
asset : Asset
The asset whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The value of the given ``field`` for ``asset`` at ``dt`` with any
adjustments known by ``perspective_dt`` applied. The return type is
based on the ``field`` requested. If the field is one of 'open',
'high', 'low', 'close', or 'price', the value will be a float. If
the ``field`` is 'volume' the value will be a int. If the ``field``
is 'last_traded' the value will be a Timestamp.
"""
if spot_value is None:
spot_value = self.get_spot_value(asset, field, dt, data_frequency)
if isinstance(asset, Equity):
ratio = self.get_adjustments(asset, field, dt, perspective_dt)[0]
spot_value *= ratio
return spot_value
def _get_minute_spot_value(self, asset, column, dt, ffill=False):
reader = self._get_pricing_reader('minute')
if not ffill:
try:
return reader.get_value(asset.sid, dt, column)
except NoDataOnDate:
if column != 'volume':
return np.nan
else:
return 0
# At this point the pairing of column='close' and ffill=True is
# assumed.
try:
# Optimize the best case scenario of a liquid asset
# returning a valid price.
result = reader.get_value(asset.sid, dt, column)
if not pd.isnull(result):
return result
except NoDataOnDate:
# Handling of no data for the desired date is done by the
# forward filling logic.
# The last trade may occur on a previous day.
pass
# If forward filling, we want the last minute with values (up to
# and including dt).
query_dt = reader.get_last_traded_dt(asset, dt)
if pd.isnull(query_dt):
# no last traded dt, bail
return np.nan
result = reader.get_value(asset.sid, query_dt, column)
if (dt == query_dt) or (dt.date() == query_dt.date()):
return result
# the value we found came from a different day, so we have to
# adjust the data if there are any adjustments on that day barrier
return self.get_adjusted_value(asset,
column,
query_dt,
dt,
"minute",
spot_value=result)
def _get_daily_spot_value(self, asset, column, dt):
reader = self._get_pricing_reader('daily')
if column == "last_traded":
last_traded_dt = reader.get_last_traded_dt(asset, dt)
if isnull(last_traded_dt):
return pd.NaT
else:
return last_traded_dt
elif column in OHLCV_FIELDS:
# don't forward fill
try:
return reader.get_value(asset, dt, column)
except NoDataOnDate:
return np.nan
elif column == "price":
found_dt = dt
while True:
try:
value = reader.get_value(asset, found_dt, "close")
if not isnull(value):
if dt == found_dt:
return value
else:
# adjust if needed
return self.get_adjusted_value(asset,
column,
found_dt,
dt,
"minute",
spot_value=value)
else:
found_dt -= self.trading_calendar.day
except NoDataOnDate:
return np.nan
@remember_last
def _get_days_for_window(self, end_date, bar_count):
tds = self.trading_calendar.all_sessions
end_loc = tds.get_loc(end_date)
start_loc = end_loc - bar_count + 1
if start_loc < self._first_trading_day_loc:
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day.date(),
bar_count=bar_count,
suggested_start_day=tds[self._first_trading_day_loc +
bar_count].date(),
)
return tds[start_loc:end_loc + 1]
def _get_history_daily_window(self, assets, end_dt, bar_count,
field_to_use, data_frequency):
"""
Internal method that returns a dataframe containing history bars
of daily frequency for the given sids.
"""
session = self.trading_calendar.minute_to_session_label(end_dt)
days_for_window = self._get_days_for_window(session, bar_count)
if len(assets) == 0:
return pd.DataFrame(None, index=days_for_window, columns=None)
data = self._get_history_daily_window_data(assets, days_for_window,
end_dt, field_to_use,
data_frequency)
return pd.DataFrame(data, index=days_for_window, columns=assets)
def _get_history_daily_window_data(self, assets, days_for_window, end_dt,
field_to_use, data_frequency):
if data_frequency == 'daily':
# two cases where we use daily data for the whole range:
# 1) the history window ends at midnight utc.
# 2) the last desired day of the window is after the
# last trading day, use daily data for the whole range.
return self._get_daily_window_data(assets,
field_to_use,
days_for_window,
extra_slot=False)
else:
# minute mode, requesting '1d'
daily_data = self._get_daily_window_data(assets, field_to_use,
days_for_window[0:-1])
if field_to_use == 'open':
minute_value = self._daily_aggregator.opens(assets, end_dt)
elif field_to_use == 'high':
minute_value = self._daily_aggregator.highs(assets, end_dt)
elif field_to_use == 'low':
minute_value = self._daily_aggregator.lows(assets, end_dt)
elif field_to_use == 'close':
minute_value = self._daily_aggregator.closes(assets, end_dt)
elif field_to_use == 'volume':
minute_value = self._daily_aggregator.volumes(assets, end_dt)
elif field_to_use == 'sid':
minute_value = [
int(self._get_current_contract(asset, end_dt))
for asset in assets
]
# append the partial day.
daily_data[-1] = minute_value
return daily_data
def _handle_minute_history_out_of_bounds(self, bar_count):
cal = self.trading_calendar
first_trading_minute_loc = (cal.all_minutes.get_loc(
self._first_trading_minute) if self._first_trading_minute
is not None else None)
suggested_start_day = cal.minute_to_session_label(
cal.all_minutes[first_trading_minute_loc + bar_count] + cal.day)
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day.date(),
bar_count=bar_count,
suggested_start_day=suggested_start_day.date(),
)
def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
minutes_for_window = self.trading_calendar.minutes_window(
end_dt, -bar_count)
except KeyError:
self._handle_minute_history_out_of_bounds(bar_count)
if minutes_for_window[0] < self._first_trading_minute:
self._handle_minute_history_out_of_bounds(bar_count)
asset_minute_data = self._get_minute_window_data(
assets,
field_to_use,
minutes_for_window,
)
return pd.DataFrame(asset_minute_data,
index=minutes_for_window,
columns=assets)
def get_history_window(self,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency,
ffill=True):
"""
Public API method that returns a dataframe containing the requested
history window. Data is fully adjusted.
Parameters
----------
assets : list of zipline.data.Asset objects
The assets whose data is desired.
bar_count: int
The number of bars desired.
frequency: string
"1d" or "1m"
field: string
The desired field of the asset.
data_frequency: string
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars.
ffill: boolean
Forward-fill missing values. Only has effect if field
is 'price'.
Returns
-------
A dataframe containing the requested data.
"""
if field not in OHLCVP_FIELDS and field != 'sid':
raise ValueError("Invalid field: {0}".format(field))
if bar_count < 1:
raise ValueError(
"bar_count must be >= 1, but got {}".format(bar_count))
if frequency == "1d":
if field == "price":
df = self._get_history_daily_window(assets, end_dt, bar_count,
"close", data_frequency)
else:
df = self._get_history_daily_window(assets, end_dt, bar_count,
field, data_frequency)
elif frequency == "1m":
if field == "price":
df = self._get_history_minute_window(assets, end_dt, bar_count,
"close")
else:
df = self._get_history_minute_window(assets, end_dt, bar_count,
field)
else:
raise ValueError("Invalid frequency: {0}".format(frequency))
# forward-fill price
if field == "price":
if frequency == "1m":
ffill_data_frequency = 'minute'
elif frequency == "1d":
ffill_data_frequency = 'daily'
else:
raise Exception(
"Only 1d and 1m are supported for forward-filling.")
assets_with_leading_nan = np.where(isnull(df.iloc[0]))[0]
history_start, history_end = df.index[[0, -1]]
if ffill_data_frequency == 'daily' and data_frequency == 'minute':
# When we're looking for a daily value, but we haven't seen any
# volume in today's minute bars yet, we need to use the
# previous day's ffilled daily price. Using today's daily price
# could yield a value from later today.
history_start -= self.trading_calendar.day
initial_values = []
for asset in df.columns[assets_with_leading_nan]:
last_traded = self.get_last_traded_dt(
asset,
history_start,
ffill_data_frequency,
)
if isnull(last_traded):
initial_values.append(nan)
else:
initial_values.append(
self.get_adjusted_value(
asset,
field,
dt=last_traded,
perspective_dt=history_end,
data_frequency=ffill_data_frequency,
))
# Set leading values for assets that were missing data, then ffill.
df.iloc[0, assets_with_leading_nan] = np.array(initial_values,
dtype=np.float64)
df.fillna(method='ffill', inplace=True)
# forward-filling will incorrectly produce values after the end of
# an asset's lifetime, so write NaNs back over the asset's
# end_date.
normed_index = df.index.normalize()
for asset in df.columns:
if history_end >= asset.end_date:
# if the window extends past the asset's end date, set
# all post-end-date values to NaN in that asset's series
df.loc[normed_index > asset.end_date, asset] = nan
return df
def _get_minute_window_data(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
and specified date range. Used to support the history API method for
minute bars.
Missing bars are filled with NaN.
Parameters
----------
assets : iterable[Asset]
The assets whose data is desired.
field: string
The specific field to return. "open", "high", "close_price", etc.
minutes_for_window: pd.DateTimeIndex
The list of minutes representing the desired window. Each minute
is a pd.Timestamp.
Returns
-------
A numpy array with requested values.
"""
return self._minute_history_loader.history(assets, minutes_for_window,
field, False)
def _get_daily_window_data(self,
assets,
field,
days_in_window,
extra_slot=True):
"""
Internal method that gets a window of adjusted daily data for a sid
and specified date range. Used to support the history API method for
daily bars.
Parameters
----------
asset : Asset
The asset whose data is desired.
start_dt: pandas.Timestamp
The start of the desired window of data.
bar_count: int
The number of days of data to return.
field: string
The specific field to return. "open", "high", "close_price", etc.
extra_slot: boolean
Whether to allocate an extra slot in the returned numpy array.
This extra slot will hold the data for the last partial day. It's
much better to create it here than to create a copy of the array
later just to add a slot.
Returns
-------
A numpy array with requested values. Any missing slots filled with
nan.
"""
bar_count = len(days_in_window)
# create an np.array of size bar_count
dtype = float64 if field != 'sid' else int64
if extra_slot:
return_array = np.zeros((bar_count + 1, len(assets)), dtype=dtype)
else:
return_array = np.zeros((bar_count, len(assets)), dtype=dtype)
if field != "volume":
# volumes default to 0, so we don't need to put NaNs in the array
return_array[:] = np.NAN
if bar_count != 0:
data = self._history_loader.history(assets, days_in_window, field,
extra_slot)
if extra_slot:
return_array[:len(return_array) - 1, :] = data
else:
return_array[:len(data)] = data
return return_array
def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
A dictionary of sid -> list that is used as a cache.
table_name: string
The table that contains this data in the adjustments db.
Returns
-------
adjustments: list
A list of [multiplier, pd.Timestamp], earliest first
"""
if self._adjustment_reader is None:
return []
sid = int(asset)
try:
adjustments = adjustments_dict[sid]
except KeyError:
adjustments = adjustments_dict[sid] = self._adjustment_reader.\
get_adjustments_for_sid(table_name, sid)
return adjustments
def get_splits(self, assets, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
assets : container
Assets for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
expected to be midnight UTC.
Returns
-------
splits : list[(asset, float)]
List of splits, where each split is a (asset, ratio) tuple.
"""
if self._adjustment_reader is None or not assets:
return []
# convert dt to # of seconds since epoch, because that's what we use
# in the adjustments db
seconds = int(dt.value / 1e9)
splits = self._adjustment_reader.conn.execute(
"SELECT sid, ratio FROM SPLITS WHERE effective_date = ?",
(seconds, )).fetchall()
splits = [split for split in splits if split[0] in assets]
splits = [(self.asset_finder.retrieve_asset(split[0]), split[1])
for split in splits]
return splits
def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
trading_days: pd.DatetimeIndex
The trading range.
Returns
-------
list: A list of objects with all relevant attributes populated.
All timestamp fields are converted to pd.Timestamps.
"""
if self._adjustment_reader is None:
return []
if len(trading_days) == 0:
return []
start_dt = trading_days[0].value / 1e9
end_dt = trading_days[-1].value / 1e9
dividends = self._adjustment_reader.conn.execute(
"SELECT declared_date, ex_date, pay_date, payment_sid, ratio, "
"record_date, sid FROM stock_dividend_payouts "
"WHERE sid = ? AND ex_date > ? AND pay_date < ?", (
int(sid),
start_dt,
end_dt,
)).fetchall()
dividend_info = []
for dividend_tuple in dividends:
dividend_info.append({
"declared_date":
pd.Timestamp(dividend_tuple[0], unit="s"),
"ex_date":
pd.Timestamp(dividend_tuple[1], unit="s"),
"pay_date":
pd.Timestamp(dividend_tuple[2], unit="s"),
"payment_sid":
dividend_tuple[3],
"ratio":
dividend_tuple[4],
"record_date":
pd.Timestamp(dividend_tuple[5], unit="s"),
"sid":
dividend_tuple[6],
})
return dividend_info
def contains(self, asset, field):
return field in BASE_FIELDS
def get_current_future_chain(self, continuous_future, dt):
"""
Retrieves the future chain for the contract at the given `dt` according
the `continuous_future` specification.
Returns
-------
future_chain : list[Future]
A list of active futures, where the first index is the current
contract specified by the continuous future definition, the second
is the next upcoming contract and so on.
"""
rf = self._roll_finders[continuous_future.roll_style]
session = self.trading_calendar.minute_to_session_label(dt)
contract_center = rf.get_contract_center(continuous_future.root_symbol,
session,
continuous_future.offset)
oc = self.asset_finder.get_ordered_contracts(
continuous_future.root_symbol)
chain = oc.active_chain(contract_center, session.value)
return self.asset_finder.retrieve_all(chain)
def _get_current_contract(self, continuous_future, dt):
rf = self._roll_finders[continuous_future.roll_style]
contract_sid = rf.get_contract_center(continuous_future.root_symbol,
dt, continuous_future.offset)
if contract_sid is None:
return None
return self.asset_finder.retrieve_asset(contract_sid)
@property
def adjustment_reader(self):
return self._adjustment_reader