mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 13:52:31 +08:00
Scott Sanderson
ef4f642e62
This patch lays the groundwork for a compute engine designed to facilitate construction of factor-based universe screening and portfolio allocation. It contains: A new module, `zipline.modelling`, containing entities that can be used to express computations as dependency graphs. Each node in such a graph is an instance of the base `Term` class, defined in `zipline.modelling.term`. Dependency graphs are executed by instances of `FFCEngine`, defined in `zipline.modelling.engine`. A new module, `zipline.data.ffc`, containing loaders and dataset definitions for inputs to the modelling API. New `TradingAlgorithm` api methods: `add_factor`, and `add_filter`. These methods can only be called from `initialize`, and are used to inform the algorithm that each day it should compute the given terms. Computed factor results are made available through a new attribute of the `data` object in `before_trading_start` and `handle_data`. Computed filter results control which assets are available in the factor matrix on each day.
57 lines
1.3 KiB
Python
57 lines
1.3 KiB
Python
"""
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Control flow utilities.
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"""
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from warnings import (
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catch_warnings,
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filterwarnings,
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)
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class nullctx(object):
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"""
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Null context manager. Useful for conditionally adding a contextmanager in
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a single line, e.g.:
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with SomeContextManager() if some_expr else nullctx():
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do_stuff()
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"""
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def __enter__(self):
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return self
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def __exit__(*args):
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return False
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class WarningContext(object):
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"""
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Re-entrant contextmanager for contextually managing warnings.
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"""
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def __init__(self, *warning_specs):
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self._warning_specs = warning_specs
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self._catchers = []
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def __enter__(self):
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catcher = catch_warnings()
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catcher.__enter__()
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self._catchers.append(catcher)
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for args, kwargs in self._warning_specs:
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filterwarnings(*args, **kwargs)
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return catcher
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def __exit__(self, *exc_info):
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catcher = self._catchers.pop()
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return catcher.__exit__(*exc_info)
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def ignore_nanwarnings():
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"""
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Helper for building a WarningContext that ignores warnings from numpy's
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nanfunctions.
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"""
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return WarningContext(
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(
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('ignore',),
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{'category': RuntimeWarning, 'module': 'numpy.lib.nanfunctions'},
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)
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)
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