JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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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. - Otherwise, an AmbiguousDomain erro...
Given a date, align it to the calendar of the pipeline's domain. Parameters ---------- dt : pd.Timestamp Returns ------- pd.Timestamp def roll_forward(self, dt): """ Given a date, align it to the calendar of the pipeline's domain. Parameters ...
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.array[datetime64[ns]] The days in these da...
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] The index of the first date with non-nan values, f...
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 mapping each OHLCV field to a dataframe with a row for each dat...
Parameters ---------- country_code : str The ISO 3166 alpha-2 country code for this country. data : iterable[tuple[int, pandas.DataFrame]] The data chunks to write. Each chunk should be a tuple of sid and the data for that asset. scaling_factors : dict...
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_file(cls, h5_file, country_code): """ Con...
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 from_path(cls, path, country_code): """ Cons...
Parameters ---------- columns : list of str 'open', 'high', 'low', 'close', or 'volume' start_date: Timestamp Beginning of the window range. end_date: Timestamp End of the window range. assets : list of int The asset identifiers in the ...
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] Index array containing the index in ``self.sids`` ...
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 provided asset identifiers are not cont...
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 name for the desired data point. Returns -...
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 last traded day. dt : pd.Timestamp The dt a...
Construct from an h5py.File. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file. def from_file(cls, h5_file): """ Construct from an h5py.File. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file...
Parameters ---------- columns : list of str 'open', 'high', 'low', 'close', or 'volume' start_date: Timestamp Beginning of the window range. end_date: Timestamp End of the window range. assets : list of int The asset identifiers in the ...
Returns ------- sessions : DatetimeIndex All session labels (unioning the range for all assets) which the reader can provide. def sessions(self): """ Returns ------- sessions : DatetimeIndex All session labels (unioning the range for all ...
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 name for the desired data point. Returns -...
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 last traded day. dt : pd.Timestamp The dt a...
Update dataframes in place to set indentifier columns as indices. For each input frame, if the frame has a column with the same name as its associated index column, set that column as the index. Otherwise, assume the index already contains identifiers. If frames are passed as None, they're ignored. ...
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-delimited symbol to be split Returns ------- ...
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, that contains the user's input metadata for the asset type being ...
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 The exchanges table. asset_exchange : pd.Series A serie...
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_info : pd.DataFrame The asset info with one ...
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 _dt_to_epoch_ns(dt_series): """Convert...
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 expected_version : int The expected version of the asset database Rai...
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_value : int The version to write in to the database def write_...
Write asset metadata to a sqlite database in the format that it is stored in the assets db. Parameters ---------- equities : pd.DataFrame, optional The equity metadata. The columns for this dataframe are: symbol : str The ticker symbol for th...
Write asset metadata to a sqlite database. Parameters ---------- equities : pd.DataFrame, optional The equity metadata. The columns for this dataframe are: symbol : str The ticker symbol for this equity. asset_name : str ...
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 tables are present, otherwise False. def _all_tables_present...
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 ------- metadata : sa.Me...
Returns a standard set of pandas.DataFrames: equities, futures, exchanges, root_symbols def _load_data(self, equities, futures, exchanges, root_symbols, equity_supplementary_mappings): """ Returns a s...
Given an expression representing data to load, perform normalization and forward-filling and return the data, materialized. Only accepts data with a `sid` field. Parameters ---------- assets : pd.int64index the assets to load data for. data_query_cutoff_times : pd.DatetimeIndex ...
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 tuple length is not 2 or 3. de...
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 : tuple or range A tuple to pass t...
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. def _check_steps(a, b): """Check that the steps of ``a`...
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 != 1. def overlap(a, b): ...
Merge two ranges with step == 1. Parameters ---------- a : range The first range. b : range The second range. def merge(a, b): """Merge two ranges with step == 1. Parameters ---------- a : range The first range. b : range The second range. """ ...
helper for ``_group_ranges`` 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 ...
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 ``ranges``. Examples -------- >>>...
Returns a handle to data file. Creates containing directory, if needed. 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.pat...
Does `series_or_df` have data on or before first_date and on or after last_date? 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.DatetimeI...
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 Bond rates and are downloaded from 'www.federalreserve.gov' by default. For Canadian exchanges, a loader for Canadian b...
Ensure we have benchmark data for `symbol` from `first_date` to `last_date` Parameters ---------- symbol : str The symbol for the benchmark to load. first_date : pd.Timestamp First required date for the cache. last_date : pd.Timestamp Last required date for the cache. no...
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.Timestamp First date required to be in the cache. last_date : pd.Timestamp ...
Specialize a term if it's loadable. def maybe_specialize(term, domain): """Specialize a term if it's loadable. """ if isinstance(term, LoadableTerm): return term.specialize(domain) return term
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. def _add_to_graph(self, term, parents): """ Add a term and all its children to ``graph``. ``parents`` is the...
Return a topologically-sorted iterator over the terms in ``self`` which need to be computed. 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( ...
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 equal to its outdegree, and output nodes get one extra ...
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_recursive(self, term, refcounts, garbage): """ Decr...
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 refcounts. Return ------ garbage : set[Term] ...
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 ``input`` before using it. We compu...
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 how many additional rows of `term`'s inpu...
Ensure that we're going to compute at least N extra rows of `term`. 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))
Load mask and mask row labels for term. Parameters ---------- term : Term The term to load the mask and labels for. root_mask_term : Term The term that represents the root asset exists mask. workspace : dict[Term, any] The values that have bee...
Make sure that we've specialized all loadable terms in the graph. 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): ass...
Make an extension for an AdjustedArrayWindow specialization. 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)], ...
Read a requirements.txt file, expressed as a path relative to Zipline root. Returns requirements with the pinned versions as lower bounds if `strict_bounds` is falsey. def read_requirements(path, strict_bounds, conda_format=False, filter_names=...
Normalize a time. If the time is tz-naive, assume it is UTC. 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)
Builds the offset argument for event rules. 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)) ...
Builds the date argument for event rules. 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: ...
Builds the time argument for event rules. 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) ...
A preprocessor that coerces integral floats to ints. Receipt of non-integral floats raises a TypeError. 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(ar...
Constructs an event rule from the factory api. 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...
Adds an event to the manager. 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)
Calls the callable only when the rule is triggered. 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)
Composes the two rules with a lazy composer. 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 )
Given a date, find that day's open and period end (open + offset). 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)...
Given a dt, find that day's close and period start (close - offset). 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), )[...
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 the bounds of a uint32. *columns : iterable[str] The names of t...
Parameters ---------- data : iterable[tuple[int, pandas.DataFrame or bcolz.ctable]] The data chunks to write. Each chunk should be a tuple of sid and the data for that asset. assets : set[int], optional The assets that should be in ``data``. If this is provide...
Read CSVs as DataFrames from our asset map. Parameters ---------- asset_map : dict[int -> str] A mapping from asset id to file path with the CSV data for that asset show_progress : bool Whether or not to show a progress bar while writing. inva...
Internal implementation of write. `iterator` should be an iterator yielding pairs of (asset, ctable). def _write_internal(self, iterator, assets): """ Internal implementation of write. `iterator` should be an iterator yielding pairs of (asset, ctable). """ total_rows =...
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_idx : int Index of last date for which we want data. as...
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 (uint32) Full read array of the carray i...
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 given sid and day. Raises a NoDataOnDate exce...
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', 'volume') Returns ------- floa...
Construct and store a PipelineEngine from loader. If get_loader is None, constructs an ExplodingPipelineEngine def init_engine(self, get_loader): """ Construct and store a PipelineEngine from loader. If get_loader is None, constructs an ExplodingPipelineEngine """ if g...
Call self._initialize with `self` made available to Zipline API functions. def initialize(self, args, kwargs): """ Call self._initialize with `self` made available to Zipline API functions. """ with ZiplineAPI(self): self._initialize(self, args, **kwargs)
If the clock property is not set, then create one based on frequency. 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 =...
Compute any pipelines attached with eager=True. 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)
Run the algorithm. 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: ...
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 target value, the change will be computed on the portfolio value according to prices at the given dt `portfolio_value_adjustment`, if spe...
Query the execution environment. Parameters ---------- field : {'platform', 'arena', 'data_frequency', 'start', 'end', 'capital_base', 'platform', '*'} The field to query. The options have the following meanings: arena : str The arena...
Fetch a csv from a remote url and register the data so that it is queryable from the ``data`` object. Parameters ---------- url : str The url of the csv file to load. pre_func : callable[pd.DataFrame -> pd.DataFrame], optional A callback to allow preproce...
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 rule is triggered. def add_event(self, rule, c...
Schedules a function to be called according to some timed rules. Parameters ---------- func : callable[(context, data) -> None] The function to execute when the rule is triggered. date_rule : EventRule, optional The rule for the dates to execute this function. ...
Create a specifier for a continuous contract. Parameters ---------- root_symbol_str : str The root symbol for the future chain. offset : int, optional The distance from the primary contract. Default is 0. roll_style : str, optional How rolls...
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. Returns ------- equity : Equity ...
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 ------- equities : list[Equity] ...
Calculates how many shares/contracts to order based on the type of asset being ordered. 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, an...
Place an order. Parameters ---------- asset : Asset The asset that this order is for. amount : int The amount of shares to order. If ``amount`` is positive, this is the number of shares to buy or cover. If ``amount`` is negative, this is t...
Helper method for validating parameters to the order API function. Raises an UnsupportedOrderParameters if invalid arguments are found. def validate_order_params(self, asset, amount, limit_price, ...
Helper method for converting deprecated limit_price and stop_price arguments into ExecutionStyle instances. This function assumes that either style == None or (limit_price, stop_price) == (None, None). def __convert_order_params_for_blotter(asset, lim...
Place an order by desired value rather than desired number of shares. Parameters ---------- asset : Asset The asset that this order is for. value : float If the requested asset exists, the requested value is divided by its price to imply the n...
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 calls in the same bar are cheap. def _sync_la...
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. def on_dt_changed(self, dt): """ Callback triggered by the simulation loop whenever the current dt ...
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 current simulation datetime converted to ``tz``. def get_...
Set the slippage models for the simulation. Parameters ---------- us_equities : EquitySlippageModel The slippage model to use for trading US equities. us_futures : FutureSlippageModel The slippage model to use for trading US futures. See Also ---...
Sets the commission models for the simulation. Parameters ---------- us_equities : EquityCommissionModel The commission model to use for trading US equities. us_futures : FutureCommissionModel The commission model to use for trading US futures. See Also ...
Sets the order cancellation policy for the simulation. Parameters ---------- cancel_policy : CancelPolicy The cancellation policy to use. See Also -------- :class:`zipline.api.EODCancel` :class:`zipline.api.NeverCancel` def set_cancel_policy(self, c...

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. - Otherwise, an AmbiguousDomain erro...

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

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.array[datetime64[ns]] The days in these da...

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] The index of the first date with non-nan values, f...

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 mapping each OHLCV field to a dataframe with a row for each dat...

Parameters ---------- country_code : str The ISO 3166 alpha-2 country code for this country. data : iterable[tuple[int, pandas.DataFrame]] The data chunks to write. Each chunk should be a tuple of sid and the data for that asset. scaling_factors : dict...

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_file(cls, h5_file, country_code): """ Con...

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 from_path(cls, path, country_code): """ Cons...

Parameters ---------- columns : list of str 'open', 'high', 'low', 'close', or 'volume' start_date: Timestamp Beginning of the window range. end_date: Timestamp End of the window range. assets : list of int The asset identifiers in the ...

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] Index array containing the index in ``self.sids`` ...

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 provided asset identifiers are not cont...

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 name for the desired data point. Returns -...

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 last traded day. dt : pd.Timestamp The dt a...

Construct from an h5py.File. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file. def from_file(cls, h5_file): """ Construct from an h5py.File. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file...

Parameters ---------- columns : list of str 'open', 'high', 'low', 'close', or 'volume' start_date: Timestamp Beginning of the window range. end_date: Timestamp End of the window range. assets : list of int The asset identifiers in the ...

Returns ------- sessions : DatetimeIndex All session labels (unioning the range for all assets) which the reader can provide. def sessions(self): """ Returns ------- sessions : DatetimeIndex All session labels (unioning the range for all ...

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 name for the desired data point. Returns -...

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 last traded day. dt : pd.Timestamp The dt a...

Update dataframes in place to set indentifier columns as indices. For each input frame, if the frame has a column with the same name as its associated index column, set that column as the index. Otherwise, assume the index already contains identifiers. If frames are passed as None, they're ignored. ...

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-delimited symbol to be split Returns ------- ...

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, that contains the user's input metadata for the asset type being ...

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 The exchanges table. asset_exchange : pd.Series A serie...

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_info : pd.DataFrame The asset info with one ...

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 _dt_to_epoch_ns(dt_series): """Convert...

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 expected_version : int The expected version of the asset database Rai...

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_value : int The version to write in to the database def write_...

Write asset metadata to a sqlite database in the format that it is stored in the assets db. Parameters ---------- equities : pd.DataFrame, optional The equity metadata. The columns for this dataframe are: symbol : str The ticker symbol for th...

Write asset metadata to a sqlite database. Parameters ---------- equities : pd.DataFrame, optional The equity metadata. The columns for this dataframe are: symbol : str The ticker symbol for this equity. asset_name : str ...

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 tables are present, otherwise False. def _all_tables_present...

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 ------- metadata : sa.Me...

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

Given an expression representing data to load, perform normalization and forward-filling and return the data, materialized. Only accepts data with a `sid` field. Parameters ---------- assets : pd.int64index the assets to load data for. data_query_cutoff_times : pd.DatetimeIndex ...

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 tuple length is not 2 or 3. de...

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 : tuple or range A tuple to pass t...

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. def _check_steps(a, b): """Check that the steps of ``a`...

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 != 1. def overlap(a, b): ...

Merge two ranges with step == 1. Parameters ---------- a : range The first range. b : range The second range. def merge(a, b): """Merge two ranges with step == 1. Parameters ---------- a : range The first range. b : range The second range. """ ...

helper for ``_group_ranges`` 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 ...

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 ``ranges``. Examples -------- >>>...

Returns a handle to data file. Creates containing directory, if needed. 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.pat...

Does `series_or_df` have data on or before first_date and on or after last_date? 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.DatetimeI...

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 Bond rates and are downloaded from 'www.federalreserve.gov' by default. For Canadian exchanges, a loader for Canadian b...

Ensure we have benchmark data for `symbol` from `first_date` to `last_date` Parameters ---------- symbol : str The symbol for the benchmark to load. first_date : pd.Timestamp First required date for the cache. last_date : pd.Timestamp Last required date for the cache. no...

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.Timestamp First date required to be in the cache. last_date : pd.Timestamp ...

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

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. def _add_to_graph(self, term, parents): """ Add a term and all its children to ``graph``. ``parents`` is the...

Return a topologically-sorted iterator over the terms in ``self`` which need to be computed. 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( ...

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 equal to its outdegree, and output nodes get one extra ...

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_recursive(self, term, refcounts, garbage): """ Decr...

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 refcounts. Return ------ garbage : set[Term] ...

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 ``input`` before using it. We compu...

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 how many additional rows of `term`'s inpu...

Ensure that we're going to compute at least N extra rows of `term`. 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))

Load mask and mask row labels for term. Parameters ---------- term : Term The term to load the mask and labels for. root_mask_term : Term The term that represents the root asset exists mask. workspace : dict[Term, any] The values that have bee...

Make sure that we've specialized all loadable terms in the graph. 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): ass...

Make an extension for an AdjustedArrayWindow specialization. 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)], ...

Read a requirements.txt file, expressed as a path relative to Zipline root. Returns requirements with the pinned versions as lower bounds if `strict_bounds` is falsey. def read_requirements(path, strict_bounds, conda_format=False, filter_names=...

Normalize a time. If the time is tz-naive, assume it is UTC. 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)

Builds the offset argument for event rules. 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)) ...

Builds the date argument for event rules. 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: ...

Builds the time argument for event rules. 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) ...

A preprocessor that coerces integral floats to ints. Receipt of non-integral floats raises a TypeError. 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(ar...

Constructs an event rule from the factory api. 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...

Adds an event to the manager. 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)

Calls the callable only when the rule is triggered. 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)

Composes the two rules with a lazy composer. 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 )

Given a date, find that day's open and period end (open + offset). 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)...

Given a dt, find that day's close and period start (close - offset). 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), )[...

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 the bounds of a uint32. *columns : iterable[str] The names of t...

Parameters ---------- data : iterable[tuple[int, pandas.DataFrame or bcolz.ctable]] The data chunks to write. Each chunk should be a tuple of sid and the data for that asset. assets : set[int], optional The assets that should be in ``data``. If this is provide...

Read CSVs as DataFrames from our asset map. Parameters ---------- asset_map : dict[int -> str] A mapping from asset id to file path with the CSV data for that asset show_progress : bool Whether or not to show a progress bar while writing. inva...

Internal implementation of write. `iterator` should be an iterator yielding pairs of (asset, ctable). def _write_internal(self, iterator, assets): """ Internal implementation of write. `iterator` should be an iterator yielding pairs of (asset, ctable). """ total_rows =...

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_idx : int Index of last date for which we want data. as...

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 (uint32) Full read array of the carray i...

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 given sid and day. Raises a NoDataOnDate exce...

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', 'volume') Returns ------- floa...

Construct and store a PipelineEngine from loader. If get_loader is None, constructs an ExplodingPipelineEngine def init_engine(self, get_loader): """ Construct and store a PipelineEngine from loader. If get_loader is None, constructs an ExplodingPipelineEngine """ if g...

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

If the clock property is not set, then create one based on frequency. 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 =...

Compute any pipelines attached with eager=True. 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)

Run the algorithm. 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: ...

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 target value, the change will be computed on the portfolio value according to prices at the given dt `portfolio_value_adjustment`, if spe...

Query the execution environment. Parameters ---------- field : {'platform', 'arena', 'data_frequency', 'start', 'end', 'capital_base', 'platform', '*'} The field to query. The options have the following meanings: arena : str The arena...

Fetch a csv from a remote url and register the data so that it is queryable from the ``data`` object. Parameters ---------- url : str The url of the csv file to load. pre_func : callable[pd.DataFrame -> pd.DataFrame], optional A callback to allow preproce...

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 rule is triggered. def add_event(self, rule, c...

Schedules a function to be called according to some timed rules. Parameters ---------- func : callable[(context, data) -> None] The function to execute when the rule is triggered. date_rule : EventRule, optional The rule for the dates to execute this function. ...

Create a specifier for a continuous contract. Parameters ---------- root_symbol_str : str The root symbol for the future chain. offset : int, optional The distance from the primary contract. Default is 0. roll_style : str, optional How rolls...

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. Returns ------- equity : Equity ...

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 ------- equities : list[Equity] ...

Calculates how many shares/contracts to order based on the type of asset being ordered. 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, an...

Place an order. Parameters ---------- asset : Asset The asset that this order is for. amount : int The amount of shares to order. If ``amount`` is positive, this is the number of shares to buy or cover. If ``amount`` is negative, this is t...

Helper method for validating parameters to the order API function. Raises an UnsupportedOrderParameters if invalid arguments are found. def validate_order_params(self, asset, amount, limit_price, ...

Helper method for converting deprecated limit_price and stop_price arguments into ExecutionStyle instances. This function assumes that either style == None or (limit_price, stop_price) == (None, None). def __convert_order_params_for_blotter(asset, lim...

Place an order by desired value rather than desired number of shares. Parameters ---------- asset : Asset The asset that this order is for. value : float If the requested asset exists, the requested value is divided by its price to imply the n...

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 calls in the same bar are cheap. def _sync_la...

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. def on_dt_changed(self, dt): """ Callback triggered by the simulation loop whenever the current dt ...

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 current simulation datetime converted to ``tz``. def get_...

Set the slippage models for the simulation. Parameters ---------- us_equities : EquitySlippageModel The slippage model to use for trading US equities. us_futures : FutureSlippageModel The slippage model to use for trading US futures. See Also ---...

Sets the commission models for the simulation. Parameters ---------- us_equities : EquityCommissionModel The commission model to use for trading US equities. us_futures : FutureCommissionModel The commission model to use for trading US futures. See Also ...

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