BoghdadyJR/codesearch_python · Data Studio

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def infer_domain(terms): """ Infer the domain from a collection of terms. The algorithm for inferring domains is as follows: - If all input terms have a domain of GENERIC, the result is GENERIC. - If there is exactly one non-generic domain in the input terms, the result is that domain. ...
def roll_forward(self, dt): """ Given a date, align it to the calendar of the pipeline's domain. Parameters ---------- dt : pd.Timestamp Returns ------- pd.Timestamp """ dt = pd.Timestamp(dt, tz='UTC') trading_days = self.all_ses...
def days_and_sids_for_frames(frames): """ Returns the date index and sid columns shared by a list of dataframes, ensuring they all match. Parameters ---------- frames : list[pd.DataFrame] A list of dataframes indexed by day, with a column per sid. Returns ------- days : np....
def compute_asset_lifetimes(frames): """ Parameters ---------- frames : dict[str, pd.DataFrame] A dict mapping each OHLCV field to a dataframe with a row for each date and a column for each sid, as passed to write(). Returns ------- start_date_ixs : np.array[int64] T...
def write(self, country_code, frames, scaling_factors=None): """Write the OHLCV data for one country to the HDF5 file. Parameters ---------- country_code : str The ISO 3166 alpha-2 country code for this country. frames : dict[str, pd.DataFrame] A dict map...
def write_from_sid_df_pairs(self, country_code, data, scaling_factors=None): """ Parameters ---------- country_code : str The ISO 3166 alpha-2 country code for this country. ...
def from_file(cls, h5_file, country_code): """ Construct from an h5py.File and a country code. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file. country_code : str The ISO 3166 alpha-2 country code for the country to read. ...
def from_path(cls, path, country_code): """ Construct from a file path and a country code. Parameters ---------- path : str The path to an HDF5 daily pricing file. country_code : str The ISO 3166 alpha-2 country code for the country to read. ...
def load_raw_arrays(self, columns, start_date, end_date, assets): """ Parameters ---------- columns : list of str 'open', 'high', 'low', 'close', or 'volume' start_date: Tim...
def _make_sid_selector(self, assets): """ Build an indexer mapping ``self.sids`` to ``assets``. Parameters ---------- assets : list[int] List of assets requested by a caller of ``load_raw_arrays``. Returns ------- index : np.array[int64] ...
def _validate_assets(self, assets): """Validate that asset identifiers are contained in the daily bars. Parameters ---------- assets : array-like[int] The asset identifiers to validate. Raises ------ NoDataForSid If one or more of the prov...
def get_value(self, sid, dt, field): """ Retrieve the value at the given coordinates. Parameters ---------- sid : int The asset identifier. dt : pd.Timestamp The timestamp for the desired data point. field : string The OHLVC na...
def get_last_traded_dt(self, asset, dt): """ Get the latest day on or before ``dt`` in which ``asset`` traded. If there are no trades on or before ``dt``, returns ``pd.NaT``. Parameters ---------- asset : zipline.asset.Asset The asset for which to get the la...
def from_file(cls, h5_file): """ Construct from an h5py.File. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file. """ return cls({ country: HDF5DailyBarReader.from_file(h5_file, country) for country in h5_file...
def load_raw_arrays(self, columns, start_date, end_date, assets): """ Parameters ---------- columns : list of str 'open', 'high', 'low', 'close', or 'volume' start_date: Tim...
def sessions(self): """ Returns ------- sessions : DatetimeIndex All session labels (unioning the range for all assets) which the reader can provide. """ return pd.to_datetime( reduce( np.union1d, (reader.d...
def get_value(self, sid, dt, field): """ Retrieve the value at the given coordinates. Parameters ---------- sid : int The asset identifier. dt : pd.Timestamp The timestamp for the desired data point. field : string The OHLVC na...
def get_last_traded_dt(self, asset, dt): """ Get the latest day on or before ``dt`` in which ``asset`` traded. If there are no trades on or before ``dt``, returns ``pd.NaT``. Parameters ---------- asset : zipline.asset.Asset The asset for which to get the la...
def _normalize_index_columns_in_place(equities, equity_supplementary_mappings, futures, exchanges, root_symbols): """ Update dataframes in place to set indentif...
def split_delimited_symbol(symbol): """ Takes in a symbol that may be delimited and splits it in to a company symbol and share class symbol. Also returns the fuzzy symbol, which is the symbol without any fuzzy characters at all. Parameters ---------- symbol : str The possibly-delimi...
def _generate_output_dataframe(data_subset, defaults): """ Generates an output dataframe from the given subset of user-provided data, the given column names, and the given default values. Parameters ---------- data_subset : DataFrame A DataFrame, usually from an AssetData object, ...
def _check_symbol_mappings(df, exchanges, asset_exchange): """Check that there are no cases where multiple symbols resolve to the same asset at the same time in the same country. Parameters ---------- df : pd.DataFrame The equity symbol mappings table. exchanges : pd.DataFrame T...
def _split_symbol_mappings(df, exchanges): """Split out the symbol: sid mappings from the raw data. Parameters ---------- df : pd.DataFrame The dataframe with multiple rows for each symbol: sid pair. exchanges : pd.DataFrame The exchanges table. Returns ------- asset_in...
def _dt_to_epoch_ns(dt_series): """Convert a timeseries into an Int64Index of nanoseconds since the epoch. Parameters ---------- dt_series : pd.Series The timeseries to convert. Returns ------- idx : pd.Int64Index The index converted to nanoseconds since the epoch. """ ...
def check_version_info(conn, version_table, expected_version): """ Checks for a version value in the version table. Parameters ---------- conn : sa.Connection The connection to use to perform the check. version_table : sa.Table The version table of the asset database expecte...
def write_version_info(conn, version_table, version_value): """ Inserts the version value in to the version table. Parameters ---------- conn : sa.Connection The connection to use to execute the insert. version_table : sa.Table The version table of the asset database version...
def write_direct(self, equities=None, equity_symbol_mappings=None, equity_supplementary_mappings=None, futures=None, exchanges=None, root_symbols=None, chunk_size=DEFAULT_CH...
def write(self, equities=None, futures=None, exchanges=None, root_symbols=None, equity_supplementary_mappings=None, chunk_size=DEFAULT_CHUNK_SIZE): """Write asset metadata to a sqlite database. Parameters ------...
def _all_tables_present(self, txn): """ Checks if any tables are present in the current assets database. Parameters ---------- txn : Transaction The open transaction to check in. Returns ------- has_tables : bool True if any table...
def init_db(self, txn=None): """Connect to database and create tables. Parameters ---------- txn : sa.engine.Connection, optional The transaction to execute in. If this is not provided, a new transaction will be started with the engine provided. Returns ...
def _load_data(self, equities, futures, exchanges, root_symbols, equity_supplementary_mappings): """ Returns a standard set of pandas.DataFrames: equities, futures, exchanges, root_symbols """ ...
def load_raw_data(assets, data_query_cutoff_times, expr, odo_kwargs, checkpoints=None): """ Given an expression representing data to load, perform normalization and forward-filling and return the data, materialized. Only accepts data wi...
def from_tuple(tup): """Convert a tuple into a range with error handling. Parameters ---------- tup : tuple (len 2 or 3) The tuple to turn into a range. Returns ------- range : range The range from the tuple. Raises ------ ValueError Raised when the tup...
def maybe_from_tuple(tup_or_range): """Convert a tuple into a range but pass ranges through silently. This is useful to ensure that input is a range so that attributes may be accessed with `.start`, `.stop` or so that containment checks are constant time. Parameters ---------- tup_or_range...
def _check_steps(a, b): """Check that the steps of ``a`` and ``b`` are both 1. Parameters ---------- a : range The first range to check. b : range The second range to check. Raises ------ ValueError Raised when either step is not 1. """ if a.step != 1: ...
def overlap(a, b): """Check if two ranges overlap. Parameters ---------- a : range The first range. b : range The second range. Returns ------- overlaps : bool Do these ranges overlap. Notes ----- This function does not support ranges with step != ...
def merge(a, b): """Merge two ranges with step == 1. Parameters ---------- a : range The first range. b : range The second range. """ _check_steps(a, b) return range(min(a.start, b.start), max(a.stop, b.stop))
def _combine(n, rs): """helper for ``_group_ranges`` """ try: r, rs = peek(rs) except StopIteration: yield n return if overlap(n, r): yield merge(n, r) next(rs) for r in rs: yield r else: yield n for r in rs: ...
def intersecting_ranges(ranges): """Return any ranges that intersect. Parameters ---------- ranges : iterable[ranges] A sequence of ranges to check for intersections. Returns ------- intersections : iterable[ranges] A sequence of all of the ranges that intersected in ``rang...
def get_data_filepath(name, environ=None): """ Returns a handle to data file. Creates containing directory, if needed. """ dr = data_root(environ) if not os.path.exists(dr): os.makedirs(dr) return os.path.join(dr, name)
def has_data_for_dates(series_or_df, first_date, last_date): """ Does `series_or_df` have data on or before first_date and on or after last_date? """ dts = series_or_df.index if not isinstance(dts, pd.DatetimeIndex): raise TypeError("Expected a DatetimeIndex, but got %s." % type(dts)) ...
def load_market_data(trading_day=None, trading_days=None, bm_symbol='SPY', environ=None): """ Load benchmark returns and treasury yield curves for the given calendar and benchmark symbol. Benchmarks are downloaded as a Series from IEX Trading. Treasury curves are US Treasury B...
def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day, environ=None): """ Ensure we have benchmark data for `symbol` from `first_date` to `last_date` Parameters ---------- symbol : str The symbol for the benchmark to load. first_date : pd.Ti...
def ensure_treasury_data(symbol, first_date, last_date, now, environ=None): """ Ensure we have treasury data from treasury module associated with `symbol`. Parameters ---------- symbol : str Benchmark symbol for which we're loading associated treasury curves. first_date : pd.Timesta...
def maybe_specialize(term, domain): """Specialize a term if it's loadable. """ if isinstance(term, LoadableTerm): return term.specialize(domain) return term
def _add_to_graph(self, term, parents): """ Add a term and all its children to ``graph``. ``parents`` is the set of all the parents of ``term` that we've added so far. It is only used to detect dependency cycles. """ if self._frozen: raise ValueError( ...
def execution_order(self, refcounts): """ Return a topologically-sorted iterator over the terms in ``self`` which need to be computed. """ return iter(nx.topological_sort( self.graph.subgraph( {term for term, refcount in refcounts.items() if refcount >...
def initial_refcounts(self, initial_terms): """ Calculate initial refcounts for execution of this graph. Parameters ---------- initial_terms : iterable[Term] An iterable of terms that were pre-computed before graph execution. Each node starts with a refcount...
def _decref_dependencies_recursive(self, term, refcounts, garbage): """ Decrement terms recursively. Notes ----- This should only be used to build the initial workspace, after that we should use: :meth:`~zipline.pipeline.graph.TermGraph.decref_dependencies` ...
def decref_dependencies(self, term, refcounts): """ Decrement in-edges for ``term`` after computation. Parameters ---------- term : zipline.pipeline.Term The term whose parents should be decref'ed. refcounts : dict[Term -> int] Dictionary of refco...
def offset(self): """ For all pairs (term, input) such that `input` is an input to `term`, compute a mapping:: (term, input) -> offset(term, input) where ``offset(term, input)`` is the number of rows that ``term`` should truncate off the raw array produced for ``inp...
def extra_rows(self): """ A dict mapping `term` -> `# of extra rows to load/compute of `term`. Notes ---- This value depends on the other terms in the graph that require `term` as an input. This is not to be confused with `term.dependencies`, which describes...
def _ensure_extra_rows(self, term, N): """ Ensure that we're going to compute at least N extra rows of `term`. """ attrs = self.graph.node[term] attrs['extra_rows'] = max(N, attrs.get('extra_rows', 0))
def mask_and_dates_for_term(self, term, root_mask_term, workspace, all_dates): """ Load mask and mask row labels for term. Parameters ---------- term :...
def _assert_all_loadable_terms_specialized_to(self, domain): """Make sure that we've specialized all loadable terms in the graph. """ for term in self.graph.node: if isinstance(term, LoadableTerm): assert term.domain is domain
def window_specialization(typename): """Make an extension for an AdjustedArrayWindow specialization.""" return Extension( 'zipline.lib._{name}window'.format(name=typename), ['zipline/lib/_{name}window.pyx'.format(name=typename)], depends=['zipline/lib/_windowtemplate.pxi'], )
def read_requirements(path, strict_bounds, conda_format=False, filter_names=None): """ Read a requirements.txt file, expressed as a path relative to Zipline root. Returns requirements with the pinned versions as lower bounds if `strict_b...
def ensure_utc(time, tz='UTC'): """ Normalize a time. If the time is tz-naive, assume it is UTC. """ if not time.tzinfo: time = time.replace(tzinfo=pytz.timezone(tz)) return time.replace(tzinfo=pytz.utc)
def _build_offset(offset, kwargs, default): """ Builds the offset argument for event rules. """ if offset is None: if not kwargs: return default # use the default. else: return _td_check(datetime.timedelta(**kwargs)) elif kwargs: raise ValueError('Can...
def _build_date(date, kwargs): """ Builds the date argument for event rules. """ if date is None: if not kwargs: raise ValueError('Must pass a date or kwargs') else: return datetime.date(**kwargs) elif kwargs: raise ValueError('Cannot pass kwargs and ...
def _build_time(time, kwargs): """ Builds the time argument for event rules. """ tz = kwargs.pop('tz', 'UTC') if time: if kwargs: raise ValueError('Cannot pass kwargs and a time') else: return ensure_utc(time, tz) elif not kwargs: raise ValueError(...
def lossless_float_to_int(funcname, func, argname, arg): """ A preprocessor that coerces integral floats to ints. Receipt of non-integral floats raises a TypeError. """ if not isinstance(arg, float): return arg arg_as_int = int(arg) if arg == arg_as_int: warnings.warn( ...
def make_eventrule(date_rule, time_rule, cal, half_days=True): """ Constructs an event rule from the factory api. """ _check_if_not_called(date_rule) _check_if_not_called(time_rule) if half_days: inner_rule = date_rule & time_rule else: inner_rule = date_rule & time_rule & N...
def add_event(self, event, prepend=False): """ Adds an event to the manager. """ if prepend: self._events.insert(0, event) else: self._events.append(event)
def handle_data(self, context, data, dt): """ Calls the callable only when the rule is triggered. """ if self.rule.should_trigger(dt): self.callback(context, data)
def should_trigger(self, dt): """ Composes the two rules with a lazy composer. """ return self.composer( self.first.should_trigger, self.second.should_trigger, dt )
def calculate_dates(self, dt): """ Given a date, find that day's open and period end (open + offset). """ period_start, period_close = self.cal.open_and_close_for_session( self.cal.minute_to_session_label(dt), ) # Align the market open and close times here wi...
def calculate_dates(self, dt): """ Given a dt, find that day's close and period start (close - offset). """ period_end = self.cal.open_and_close_for_session( self.cal.minute_to_session_label(dt), )[1] # Align the market close time here with the execution time...
def winsorise_uint32(df, invalid_data_behavior, column, *columns): """Drops any record where a value would not fit into a uint32. Parameters ---------- df : pd.DataFrame The dataframe to winsorise. invalid_data_behavior : {'warn', 'raise', 'ignore'} What to do when data is outside t...
def write(self, data, assets=None, show_progress=False, invalid_data_behavior='warn'): """ Parameters ---------- data : iterable[tuple[int, pandas.DataFrame or bcolz.ctable]] The data chunks to write. Each chunk should b...
def write_csvs(self, asset_map, show_progress=False, invalid_data_behavior='warn'): """Read CSVs as DataFrames from our asset map. Parameters ---------- asset_map : dict[int -> str] A mapping from asset id to file path...
def _write_internal(self, iterator, assets): """ Internal implementation of write. `iterator` should be an iterator yielding pairs of (asset, ctable). """ total_rows = 0 first_row = {} last_row = {} calendar_offset = {} # Maps column name -> outp...
def _compute_slices(self, start_idx, end_idx, assets): """ Compute the raw row indices to load for each asset on a query for the given dates after applying a shift. Parameters ---------- start_idx : int Index of first date for which we want data. end_...
def _spot_col(self, colname): """ Get the colname from daily_bar_table and read all of it into memory, caching the result. Parameters ---------- colname : string A name of a OHLCV carray in the daily_bar_table Returns ------- array (u...
def sid_day_index(self, sid, day): """ Parameters ---------- sid : int The asset identifier. day : datetime64-like Midnight of the day for which data is requested. Returns ------- int Index into the data tape for the gi...
def get_value(self, sid, dt, field): """ Parameters ---------- sid : int The asset identifier. day : datetime64-like Midnight of the day for which data is requested. colname : string The price field. e.g. ('open', 'high', 'low', 'close'...
def init_engine(self, get_loader): """ Construct and store a PipelineEngine from loader. If get_loader is None, constructs an ExplodingPipelineEngine """ if get_loader is not None: self.engine = SimplePipelineEngine( get_loader, self.a...
def initialize(self, args, kwargs): """ Call self._initialize with `self` made available to Zipline API functions. """ with ZiplineAPI(self): self._initialize(self, args, **kwargs)
def _create_clock(self): """ If the clock property is not set, then create one based on frequency. """ trading_o_and_c = self.trading_calendar.schedule.ix[ self.sim_params.sessions] market_closes = trading_o_and_c['market_close'] minutely_emission = False ...
def compute_eager_pipelines(self): """ Compute any pipelines attached with eager=True. """ for name, pipe in self._pipelines.items(): if pipe.eager: self.pipeline_output(name)
def run(self, data_portal=None): """Run the algorithm. """ # HACK: I don't think we really want to support passing a data portal # this late in the long term, but this is needed for now for backwards # compat downstream. if data_portal is not None: self.data_p...
def calculate_capital_changes(self, dt, emission_rate, is_interday, portfolio_value_adjustment=0.0): """ If there is a capital change for a given dt, this means the the change occurs before `handle_data` on the given dt. In the case of the change being a...
def get_environment(self, field='platform'): """Query the execution environment. Parameters ---------- field : {'platform', 'arena', 'data_frequency', 'start', 'end', 'capital_base', 'platform', '*'} The field to query. The options have the following meaning...
def fetch_csv(self, url, pre_func=None, post_func=None, date_column='date', date_format=None, timezone=pytz.utc.zone, symbol=None, mask=True, symbol_column=No...
def add_event(self, rule, callback): """Adds an event to the algorithm's EventManager. Parameters ---------- rule : EventRule The rule for when the callback should be triggered. callback : callable[(context, data) -> None] The function to execute when the...
def schedule_function(self, func, date_rule=None, time_rule=None, half_days=True, calendar=None): """Schedules a function to be called according to some timed rules. Paramet...
def continuous_future(self, root_symbol_str, offset=0, roll='volume', adjustment='mul'): """Create a specifier for a continuous contract. Parameters ---------- root_symbol_str : str ...
def symbol(self, symbol_str, country_code=None): """Lookup an Equity by its ticker symbol. Parameters ---------- symbol_str : str The ticker symbol for the equity to lookup. country_code : str or None, optional A country to limit symbol searches to. ...
def symbols(self, args, kwargs): """Lookup multuple Equities as a list. Parameters ---------- args : iterable[str] The ticker symbols to lookup. country_code : str or None, optional A country to limit symbol searches to. Returns ---...
def _calculate_order_value_amount(self, asset, value): """ Calculates how many shares/contracts to order based on the type of asset being ordered. """ # Make sure the asset exists, and that there is a last price for it. # FIXME: we should use BarData's can_trade logic her...
def order(self, asset, amount, limit_price=None, stop_price=None, style=None): """Place an order. Parameters ---------- asset : Asset The asset that this order is for. amount : int The ...
def validate_order_params(self, asset, amount, limit_price, stop_price, style): """ Helper method for validating parameters to the order API function. ...
def __convert_order_params_for_blotter(asset, limit_price, stop_price, style): """ Helper method for converting deprecated limit_price and stop_price arguments into Ex...
def order_value(self, asset, value, limit_price=None, stop_price=None, style=None): """Place an order by desired value rather than desired number of shares. Parameters ---------- ...
def _sync_last_sale_prices(self, dt=None): """Sync the last sale prices on the metrics tracker to a given datetime. Parameters ---------- dt : datetime The time to sync the prices to. Notes ----- This call is cached by the datetime. Repeated ...
def on_dt_changed(self, dt): """ Callback triggered by the simulation loop whenever the current dt changes. Any logic that should happen exactly once at the start of each datetime group should happen here. """ self.datetime = dt self.blotter.set_date(dt)
def get_datetime(self, tz=None): """ Returns the current simulation datetime. Parameters ---------- tz : tzinfo or str, optional The timezone to return the datetime in. This defaults to utc. Returns ------- dt : datetime The curre...
def set_slippage(self, us_equities=None, us_futures=None): """Set the slippage models for the simulation. Parameters ---------- us_equities : EquitySlippageModel The slippage model to use for trading US equities. us_futures : FutureSlippageModel The slipp...
def set_commission(self, us_equities=None, us_futures=None): """Sets the commission models for the simulation. Parameters ---------- us_equities : EquityCommissionModel The commission model to use for trading US equities. us_futures : FutureCommissionModel ...
def set_cancel_policy(self, cancel_policy): """Sets the order cancellation policy for the simulation. Parameters ---------- cancel_policy : CancelPolicy The cancellation policy to use. See Also -------- :class:`zipline.api.EODCancel` :class:`...

def infer_domain(terms): """ Infer the domain from a collection of terms. The algorithm for inferring domains is as follows: - If all input terms have a domain of GENERIC, the result is GENERIC. - If there is exactly one non-generic domain in the input terms, the result is that domain. ...

def roll_forward(self, dt): """ Given a date, align it to the calendar of the pipeline's domain. Parameters ---------- dt : pd.Timestamp Returns ------- pd.Timestamp """ dt = pd.Timestamp(dt, tz='UTC') trading_days = self.all_ses...

def days_and_sids_for_frames(frames): """ Returns the date index and sid columns shared by a list of dataframes, ensuring they all match. Parameters ---------- frames : list[pd.DataFrame] A list of dataframes indexed by day, with a column per sid. Returns ------- days : np....

def compute_asset_lifetimes(frames): """ Parameters ---------- frames : dict[str, pd.DataFrame] A dict mapping each OHLCV field to a dataframe with a row for each date and a column for each sid, as passed to write(). Returns ------- start_date_ixs : np.array[int64] T...

def write(self, country_code, frames, scaling_factors=None): """Write the OHLCV data for one country to the HDF5 file. Parameters ---------- country_code : str The ISO 3166 alpha-2 country code for this country. frames : dict[str, pd.DataFrame] A dict map...

def write_from_sid_df_pairs(self, country_code, data, scaling_factors=None): """ Parameters ---------- country_code : str The ISO 3166 alpha-2 country code for this country. ...

def from_file(cls, h5_file, country_code): """ Construct from an h5py.File and a country code. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file. country_code : str The ISO 3166 alpha-2 country code for the country to read. ...

def from_path(cls, path, country_code): """ Construct from a file path and a country code. Parameters ---------- path : str The path to an HDF5 daily pricing file. country_code : str The ISO 3166 alpha-2 country code for the country to read. ...

def load_raw_arrays(self, columns, start_date, end_date, assets): """ Parameters ---------- columns : list of str 'open', 'high', 'low', 'close', or 'volume' start_date: Tim...

def _make_sid_selector(self, assets): """ Build an indexer mapping ``self.sids`` to ``assets``. Parameters ---------- assets : list[int] List of assets requested by a caller of ``load_raw_arrays``. Returns ------- index : np.array[int64] ...

def _validate_assets(self, assets): """Validate that asset identifiers are contained in the daily bars. Parameters ---------- assets : array-like[int] The asset identifiers to validate. Raises ------ NoDataForSid If one or more of the prov...

def get_value(self, sid, dt, field): """ Retrieve the value at the given coordinates. Parameters ---------- sid : int The asset identifier. dt : pd.Timestamp The timestamp for the desired data point. field : string The OHLVC na...

def get_last_traded_dt(self, asset, dt): """ Get the latest day on or before ``dt`` in which ``asset`` traded. If there are no trades on or before ``dt``, returns ``pd.NaT``. Parameters ---------- asset : zipline.asset.Asset The asset for which to get the la...

def from_file(cls, h5_file): """ Construct from an h5py.File. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file. """ return cls({ country: HDF5DailyBarReader.from_file(h5_file, country) for country in h5_file...

def load_raw_arrays(self, columns, start_date, end_date, assets): """ Parameters ---------- columns : list of str 'open', 'high', 'low', 'close', or 'volume' start_date: Tim...

def sessions(self): """ Returns ------- sessions : DatetimeIndex All session labels (unioning the range for all assets) which the reader can provide. """ return pd.to_datetime( reduce( np.union1d, (reader.d...

def get_value(self, sid, dt, field): """ Retrieve the value at the given coordinates. Parameters ---------- sid : int The asset identifier. dt : pd.Timestamp The timestamp for the desired data point. field : string The OHLVC na...

def get_last_traded_dt(self, asset, dt): """ Get the latest day on or before ``dt`` in which ``asset`` traded. If there are no trades on or before ``dt``, returns ``pd.NaT``. Parameters ---------- asset : zipline.asset.Asset The asset for which to get the la...

def _normalize_index_columns_in_place(equities, equity_supplementary_mappings, futures, exchanges, root_symbols): """ Update dataframes in place to set indentif...

def split_delimited_symbol(symbol): """ Takes in a symbol that may be delimited and splits it in to a company symbol and share class symbol. Also returns the fuzzy symbol, which is the symbol without any fuzzy characters at all. Parameters ---------- symbol : str The possibly-delimi...

def _generate_output_dataframe(data_subset, defaults): """ Generates an output dataframe from the given subset of user-provided data, the given column names, and the given default values. Parameters ---------- data_subset : DataFrame A DataFrame, usually from an AssetData object, ...

def _check_symbol_mappings(df, exchanges, asset_exchange): """Check that there are no cases where multiple symbols resolve to the same asset at the same time in the same country. Parameters ---------- df : pd.DataFrame The equity symbol mappings table. exchanges : pd.DataFrame T...

def _split_symbol_mappings(df, exchanges): """Split out the symbol: sid mappings from the raw data. Parameters ---------- df : pd.DataFrame The dataframe with multiple rows for each symbol: sid pair. exchanges : pd.DataFrame The exchanges table. Returns ------- asset_in...

def _dt_to_epoch_ns(dt_series): """Convert a timeseries into an Int64Index of nanoseconds since the epoch. Parameters ---------- dt_series : pd.Series The timeseries to convert. Returns ------- idx : pd.Int64Index The index converted to nanoseconds since the epoch. """ ...

def check_version_info(conn, version_table, expected_version): """ Checks for a version value in the version table. Parameters ---------- conn : sa.Connection The connection to use to perform the check. version_table : sa.Table The version table of the asset database expecte...

def write_version_info(conn, version_table, version_value): """ Inserts the version value in to the version table. Parameters ---------- conn : sa.Connection The connection to use to execute the insert. version_table : sa.Table The version table of the asset database version...

def write_direct(self, equities=None, equity_symbol_mappings=None, equity_supplementary_mappings=None, futures=None, exchanges=None, root_symbols=None, chunk_size=DEFAULT_CH...

def write(self, equities=None, futures=None, exchanges=None, root_symbols=None, equity_supplementary_mappings=None, chunk_size=DEFAULT_CHUNK_SIZE): """Write asset metadata to a sqlite database. Parameters ------...

def _all_tables_present(self, txn): """ Checks if any tables are present in the current assets database. Parameters ---------- txn : Transaction The open transaction to check in. Returns ------- has_tables : bool True if any table...

def init_db(self, txn=None): """Connect to database and create tables. Parameters ---------- txn : sa.engine.Connection, optional The transaction to execute in. If this is not provided, a new transaction will be started with the engine provided. Returns ...

def _load_data(self, equities, futures, exchanges, root_symbols, equity_supplementary_mappings): """ Returns a standard set of pandas.DataFrames: equities, futures, exchanges, root_symbols """ ...

def load_raw_data(assets, data_query_cutoff_times, expr, odo_kwargs, checkpoints=None): """ Given an expression representing data to load, perform normalization and forward-filling and return the data, materialized. Only accepts data wi...

def from_tuple(tup): """Convert a tuple into a range with error handling. Parameters ---------- tup : tuple (len 2 or 3) The tuple to turn into a range. Returns ------- range : range The range from the tuple. Raises ------ ValueError Raised when the tup...

def maybe_from_tuple(tup_or_range): """Convert a tuple into a range but pass ranges through silently. This is useful to ensure that input is a range so that attributes may be accessed with `.start`, `.stop` or so that containment checks are constant time. Parameters ---------- tup_or_range...

def _check_steps(a, b): """Check that the steps of ``a`` and ``b`` are both 1. Parameters ---------- a : range The first range to check. b : range The second range to check. Raises ------ ValueError Raised when either step is not 1. """ if a.step != 1: ...

def overlap(a, b): """Check if two ranges overlap. Parameters ---------- a : range The first range. b : range The second range. Returns ------- overlaps : bool Do these ranges overlap. Notes ----- This function does not support ranges with step != ...

def merge(a, b): """Merge two ranges with step == 1. Parameters ---------- a : range The first range. b : range The second range. """ _check_steps(a, b) return range(min(a.start, b.start), max(a.stop, b.stop))

def _combine(n, rs): """helper for ``_group_ranges`` """ try: r, rs = peek(rs) except StopIteration: yield n return if overlap(n, r): yield merge(n, r) next(rs) for r in rs: yield r else: yield n for r in rs: ...

def intersecting_ranges(ranges): """Return any ranges that intersect. Parameters ---------- ranges : iterable[ranges] A sequence of ranges to check for intersections. Returns ------- intersections : iterable[ranges] A sequence of all of the ranges that intersected in ``rang...

def get_data_filepath(name, environ=None): """ Returns a handle to data file. Creates containing directory, if needed. """ dr = data_root(environ) if not os.path.exists(dr): os.makedirs(dr) return os.path.join(dr, name)

def has_data_for_dates(series_or_df, first_date, last_date): """ Does `series_or_df` have data on or before first_date and on or after last_date? """ dts = series_or_df.index if not isinstance(dts, pd.DatetimeIndex): raise TypeError("Expected a DatetimeIndex, but got %s." % type(dts)) ...

def load_market_data(trading_day=None, trading_days=None, bm_symbol='SPY', environ=None): """ Load benchmark returns and treasury yield curves for the given calendar and benchmark symbol. Benchmarks are downloaded as a Series from IEX Trading. Treasury curves are US Treasury B...

def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day, environ=None): """ Ensure we have benchmark data for `symbol` from `first_date` to `last_date` Parameters ---------- symbol : str The symbol for the benchmark to load. first_date : pd.Ti...

def ensure_treasury_data(symbol, first_date, last_date, now, environ=None): """ Ensure we have treasury data from treasury module associated with `symbol`. Parameters ---------- symbol : str Benchmark symbol for which we're loading associated treasury curves. first_date : pd.Timesta...

def maybe_specialize(term, domain): """Specialize a term if it's loadable. """ if isinstance(term, LoadableTerm): return term.specialize(domain) return term

def _add_to_graph(self, term, parents): """ Add a term and all its children to ``graph``. ``parents`` is the set of all the parents of ``term` that we've added so far. It is only used to detect dependency cycles. """ if self._frozen: raise ValueError( ...

def execution_order(self, refcounts): """ Return a topologically-sorted iterator over the terms in ``self`` which need to be computed. """ return iter(nx.topological_sort( self.graph.subgraph( {term for term, refcount in refcounts.items() if refcount >...

def initial_refcounts(self, initial_terms): """ Calculate initial refcounts for execution of this graph. Parameters ---------- initial_terms : iterable[Term] An iterable of terms that were pre-computed before graph execution. Each node starts with a refcount...

def _decref_dependencies_recursive(self, term, refcounts, garbage): """ Decrement terms recursively. Notes ----- This should only be used to build the initial workspace, after that we should use: :meth:`~zipline.pipeline.graph.TermGraph.decref_dependencies` ...

def decref_dependencies(self, term, refcounts): """ Decrement in-edges for ``term`` after computation. Parameters ---------- term : zipline.pipeline.Term The term whose parents should be decref'ed. refcounts : dict[Term -> int] Dictionary of refco...

def offset(self): """ For all pairs (term, input) such that `input` is an input to `term`, compute a mapping:: (term, input) -> offset(term, input) where ``offset(term, input)`` is the number of rows that ``term`` should truncate off the raw array produced for ``inp...

def extra_rows(self): """ A dict mapping `term` -> `# of extra rows to load/compute of `term`. Notes ---- This value depends on the other terms in the graph that require `term` **as an input**. This is not to be confused with `term.dependencies`, which describes...

def _ensure_extra_rows(self, term, N): """ Ensure that we're going to compute at least N extra rows of `term`. """ attrs = self.graph.node[term] attrs['extra_rows'] = max(N, attrs.get('extra_rows', 0))

def mask_and_dates_for_term(self, term, root_mask_term, workspace, all_dates): """ Load mask and mask row labels for term. Parameters ---------- term :...

def _assert_all_loadable_terms_specialized_to(self, domain): """Make sure that we've specialized all loadable terms in the graph. """ for term in self.graph.node: if isinstance(term, LoadableTerm): assert term.domain is domain

def window_specialization(typename): """Make an extension for an AdjustedArrayWindow specialization.""" return Extension( 'zipline.lib._{name}window'.format(name=typename), ['zipline/lib/_{name}window.pyx'.format(name=typename)], depends=['zipline/lib/_windowtemplate.pxi'], )

def read_requirements(path, strict_bounds, conda_format=False, filter_names=None): """ Read a requirements.txt file, expressed as a path relative to Zipline root. Returns requirements with the pinned versions as lower bounds if `strict_b...

def ensure_utc(time, tz='UTC'): """ Normalize a time. If the time is tz-naive, assume it is UTC. """ if not time.tzinfo: time = time.replace(tzinfo=pytz.timezone(tz)) return time.replace(tzinfo=pytz.utc)

def _build_offset(offset, kwargs, default): """ Builds the offset argument for event rules. """ if offset is None: if not kwargs: return default # use the default. else: return _td_check(datetime.timedelta(**kwargs)) elif kwargs: raise ValueError('Can...

def _build_date(date, kwargs): """ Builds the date argument for event rules. """ if date is None: if not kwargs: raise ValueError('Must pass a date or kwargs') else: return datetime.date(**kwargs) elif kwargs: raise ValueError('Cannot pass kwargs and ...

def _build_time(time, kwargs): """ Builds the time argument for event rules. """ tz = kwargs.pop('tz', 'UTC') if time: if kwargs: raise ValueError('Cannot pass kwargs and a time') else: return ensure_utc(time, tz) elif not kwargs: raise ValueError(...

def lossless_float_to_int(funcname, func, argname, arg): """ A preprocessor that coerces integral floats to ints. Receipt of non-integral floats raises a TypeError. """ if not isinstance(arg, float): return arg arg_as_int = int(arg) if arg == arg_as_int: warnings.warn( ...

def make_eventrule(date_rule, time_rule, cal, half_days=True): """ Constructs an event rule from the factory api. """ _check_if_not_called(date_rule) _check_if_not_called(time_rule) if half_days: inner_rule = date_rule & time_rule else: inner_rule = date_rule & time_rule & N...

def add_event(self, event, prepend=False): """ Adds an event to the manager. """ if prepend: self._events.insert(0, event) else: self._events.append(event)

def handle_data(self, context, data, dt): """ Calls the callable only when the rule is triggered. """ if self.rule.should_trigger(dt): self.callback(context, data)

def should_trigger(self, dt): """ Composes the two rules with a lazy composer. """ return self.composer( self.first.should_trigger, self.second.should_trigger, dt )

def calculate_dates(self, dt): """ Given a date, find that day's open and period end (open + offset). """ period_start, period_close = self.cal.open_and_close_for_session( self.cal.minute_to_session_label(dt), ) # Align the market open and close times here wi...

def calculate_dates(self, dt): """ Given a dt, find that day's close and period start (close - offset). """ period_end = self.cal.open_and_close_for_session( self.cal.minute_to_session_label(dt), )[1] # Align the market close time here with the execution time...

def winsorise_uint32(df, invalid_data_behavior, column, *columns): """Drops any record where a value would not fit into a uint32. Parameters ---------- df : pd.DataFrame The dataframe to winsorise. invalid_data_behavior : {'warn', 'raise', 'ignore'} What to do when data is outside t...

def write(self, data, assets=None, show_progress=False, invalid_data_behavior='warn'): """ Parameters ---------- data : iterable[tuple[int, pandas.DataFrame or bcolz.ctable]] The data chunks to write. Each chunk should b...

def write_csvs(self, asset_map, show_progress=False, invalid_data_behavior='warn'): """Read CSVs as DataFrames from our asset map. Parameters ---------- asset_map : dict[int -> str] A mapping from asset id to file path...

def _write_internal(self, iterator, assets): """ Internal implementation of write. `iterator` should be an iterator yielding pairs of (asset, ctable). """ total_rows = 0 first_row = {} last_row = {} calendar_offset = {} # Maps column name -> outp...

def _compute_slices(self, start_idx, end_idx, assets): """ Compute the raw row indices to load for each asset on a query for the given dates after applying a shift. Parameters ---------- start_idx : int Index of first date for which we want data. end_...

def _spot_col(self, colname): """ Get the colname from daily_bar_table and read all of it into memory, caching the result. Parameters ---------- colname : string A name of a OHLCV carray in the daily_bar_table Returns ------- array (u...

def sid_day_index(self, sid, day): """ Parameters ---------- sid : int The asset identifier. day : datetime64-like Midnight of the day for which data is requested. Returns ------- int Index into the data tape for the gi...

def get_value(self, sid, dt, field): """ Parameters ---------- sid : int The asset identifier. day : datetime64-like Midnight of the day for which data is requested. colname : string The price field. e.g. ('open', 'high', 'low', 'close'...

def init_engine(self, get_loader): """ Construct and store a PipelineEngine from loader. If get_loader is None, constructs an ExplodingPipelineEngine """ if get_loader is not None: self.engine = SimplePipelineEngine( get_loader, self.a...

def initialize(self, *args, **kwargs): """ Call self._initialize with `self` made available to Zipline API functions. """ with ZiplineAPI(self): self._initialize(self, *args, **kwargs)

def _create_clock(self): """ If the clock property is not set, then create one based on frequency. """ trading_o_and_c = self.trading_calendar.schedule.ix[ self.sim_params.sessions] market_closes = trading_o_and_c['market_close'] minutely_emission = False ...

def compute_eager_pipelines(self): """ Compute any pipelines attached with eager=True. """ for name, pipe in self._pipelines.items(): if pipe.eager: self.pipeline_output(name)

def run(self, data_portal=None): """Run the algorithm. """ # HACK: I don't think we really want to support passing a data portal # this late in the long term, but this is needed for now for backwards # compat downstream. if data_portal is not None: self.data_p...

def calculate_capital_changes(self, dt, emission_rate, is_interday, portfolio_value_adjustment=0.0): """ If there is a capital change for a given dt, this means the the change occurs before `handle_data` on the given dt. In the case of the change being a...

def get_environment(self, field='platform'): """Query the execution environment. Parameters ---------- field : {'platform', 'arena', 'data_frequency', 'start', 'end', 'capital_base', 'platform', '*'} The field to query. The options have the following meaning...

def fetch_csv(self, url, pre_func=None, post_func=None, date_column='date', date_format=None, timezone=pytz.utc.zone, symbol=None, mask=True, symbol_column=No...

def add_event(self, rule, callback): """Adds an event to the algorithm's EventManager. Parameters ---------- rule : EventRule The rule for when the callback should be triggered. callback : callable[(context, data) -> None] The function to execute when the...

def schedule_function(self, func, date_rule=None, time_rule=None, half_days=True, calendar=None): """Schedules a function to be called according to some timed rules. Paramet...

def continuous_future(self, root_symbol_str, offset=0, roll='volume', adjustment='mul'): """Create a specifier for a continuous contract. Parameters ---------- root_symbol_str : str ...

def symbol(self, symbol_str, country_code=None): """Lookup an Equity by its ticker symbol. Parameters ---------- symbol_str : str The ticker symbol for the equity to lookup. country_code : str or None, optional A country to limit symbol searches to. ...

def symbols(self, *args, **kwargs): """Lookup multuple Equities as a list. Parameters ---------- *args : iterable[str] The ticker symbols to lookup. country_code : str or None, optional A country to limit symbol searches to. Returns ---...

def _calculate_order_value_amount(self, asset, value): """ Calculates how many shares/contracts to order based on the type of asset being ordered. """ # Make sure the asset exists, and that there is a last price for it. # FIXME: we should use BarData's can_trade logic her...

def order(self, asset, amount, limit_price=None, stop_price=None, style=None): """Place an order. Parameters ---------- asset : Asset The asset that this order is for. amount : int The ...

def validate_order_params(self, asset, amount, limit_price, stop_price, style): """ Helper method for validating parameters to the order API function. ...

def __convert_order_params_for_blotter(asset, limit_price, stop_price, style): """ Helper method for converting deprecated limit_price and stop_price arguments into Ex...

def order_value(self, asset, value, limit_price=None, stop_price=None, style=None): """Place an order by desired value rather than desired number of shares. Parameters ---------- ...

def _sync_last_sale_prices(self, dt=None): """Sync the last sale prices on the metrics tracker to a given datetime. Parameters ---------- dt : datetime The time to sync the prices to. Notes ----- This call is cached by the datetime. Repeated ...

def on_dt_changed(self, dt): """ Callback triggered by the simulation loop whenever the current dt changes. Any logic that should happen exactly once at the start of each datetime group should happen here. """ self.datetime = dt self.blotter.set_date(dt)

def get_datetime(self, tz=None): """ Returns the current simulation datetime. Parameters ---------- tz : tzinfo or str, optional The timezone to return the datetime in. This defaults to utc. Returns ------- dt : datetime The curre...

def set_slippage(self, us_equities=None, us_futures=None): """Set the slippage models for the simulation. Parameters ---------- us_equities : EquitySlippageModel The slippage model to use for trading US equities. us_futures : FutureSlippageModel The slipp...

def set_commission(self, us_equities=None, us_futures=None): """Sets the commission models for the simulation. Parameters ---------- us_equities : EquityCommissionModel The commission model to use for trading US equities. us_futures : FutureCommissionModel ...

def set_cancel_policy(self, cancel_policy): """Sets the order cancellation policy for the simulation. Parameters ---------- cancel_policy : CancelPolicy The cancellation policy to use. See Also -------- :class:`zipline.api.EODCancel` :class:`...