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from __future__ import print_function |
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from zipline.assets import AssetFinder |
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from .classifier import Classifier |
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from .engine import SimplePipelineEngine |
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from .factors import Factor, CustomFactor |
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from .filters import Filter |
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from .term import Term |
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from .graph import TermGraph |
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from .pipeline import Pipeline |
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from .loaders import USEquityPricingLoader |
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def engine_from_files(daily_bar_path, |
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adjustments_path, |
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asset_db_path, |
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calendar, |
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warmup_assets=False): |
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""" |
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Construct a SimplePipelineEngine from local filesystem resources. |
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Parameters |
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---------- |
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daily_bar_path : str |
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Path to pass to `BcolzDailyBarReader`. |
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adjustments_path : str |
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Path to pass to SQLiteAdjustmentReader. |
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asset_db_path : str |
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Path to pass to `AssetFinder`. |
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calendar : pd.DatetimeIndex |
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Calendar to use for the loader. |
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warmup_assets : bool, optional |
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Whether or not to populate AssetFinder caches. This can speed up |
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initial latency on subsequent pipeline runs, at the cost of extra |
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memory consumption. Default is False |
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""" |
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loader = USEquityPricingLoader.from_files(daily_bar_path, adjustments_path) |
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if not asset_db_path.startswith("sqlite:"): |
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asset_db_path = "sqlite:///" + asset_db_path |
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asset_finder = AssetFinder(asset_db_path) |
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if warmup_assets: |
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results = asset_finder.retrieve_all(asset_finder.sids) |
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print("Warmed up %d assets." % len(results)) |
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return SimplePipelineEngine( |
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lambda _: loader, |
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calendar, |
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asset_finder, |
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) |
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__all__ = ( |
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'Classifier', |
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'CustomFactor', |
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'engine_from_files', |
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'Factor', |
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'Filter', |
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'Pipeline', |
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'SimplePipelineEngine', |
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'Term', |
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'TermGraph', |
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) |
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quantopian /
zipline
