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import click | |
import os | |
import sys | |
import warnings | |
try: | |
from pygments import highlight | |
from pygments.lexers import PythonLexer | |
from pygments.formatters import TerminalFormatter |
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""" | |
Blaze integration with the Pipeline API. | |
For an overview of the blaze project, see blaze.pydata.org | |
The blaze loader for the Pipeline API is designed to allow us to load | |
data from arbitrary sources as long as we can execute the needed expressions | |
against the data with blaze. | |
Data Format |
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"""This algorithm is designed for validating risk model. | |
It can be configured to be: | |
* Momentum or Mean-reversion | |
* Sector-Neutral or not | |
""" | |
from quantopian.algorithm import attach_pipeline, pipeline_output, order_optimal_portfolio | |
from quantopian.pipeline import Pipeline | |
from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage, AverageDollarVolume, RollingLinearRegressionOfReturns, Returns | |
from quantopian.pipeline.data.builtin import USEquityPricing | |
from quantopian.pipeline.data import morningstar |
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import dask | |
import dask.array as da | |
import dask.dataframe as dd | |
import sparse | |
@dask.delayed(pure=True) | |
def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9): | |
return sparse.COO.from_numpy((np.dot(chunk1, chunk2.T) > corr_thresh)) | |
def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9): |
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