mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 15:55:02 +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.
37 lines
913 B
Python
37 lines
913 B
Python
"""
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Quick and dirty script to generate test case inputs.
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"""
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from __future__ import print_function
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from os.path import (
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dirname,
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join,
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)
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from pandas.io.data import get_data_yahoo
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here = join(dirname(__file__))
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def main():
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symbols = ['AAPL', 'MSFT', 'BRK-A']
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# Specifically chosen to include the AAPL split on June 9, 2014.
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for symbol in symbols:
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data = get_data_yahoo(symbol, start='2014-03-01', end='2014-09-01')
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data.rename(
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columns={
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'Open': 'open',
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'High': 'high',
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'Low': 'low',
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'Close': 'close',
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'Volume': 'volume',
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},
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inplace=True,
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)
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del data['Adj Close']
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dest = join(here, symbol + '.csv')
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print("Writing %s -> %s" % (symbol, dest))
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data.to_csv(dest, index_label='day')
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if __name__ == '__main__':
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main()
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