generate alpha factors
Project description
'''
multiprocessing is deprecated due to memory sharing issue
'''
from alpha_factory import generator_class,get_memory_use_pct,clean
from RNWS import read
import numpy as np
import pandas as pd
factor_path='.'
frame_path='.'
df=pd.read_csv(frame_path+'/frames.csv')
exr=read.read_df(r'.\exr',file_pattern='exr',start=20160101,end=20170201)
cap=read.read_df(r'.\cap',file_pattern='cap',header=0,dat_col='cap',start=20160101,end=20170201)
open_price,close,vwap,high,low,volume=read.read_df(r'.\mkt_data',file_pattern='mkt',start=20160101,end=20170201,header=0,dat_col=['open','close','vwap','high','low','volume'])
ind1,ind2,ind3=read.read_df(r'.\ind',file_pattern='ind',start=20160101,end=20170201,header=0,dat_col=['level1','level2','level3'])
parms={'exr':exr
,'cap':cap
,'open_price':open_price
,'close':close
,'vwap':vwap
,'high':high
,'low':low
,'volume':volume
,'ind1':ind1
,'ind2':ind2
,'ind3':ind3}
# generate starting:
gc=generator_class(df,factor_path,**parms)
gc.generator(batch_size=3)
gc.generator(batch_size=3)
gc.output_df(path=frame_path+'/frames_new.csv')
# generate continue:
with generator_class(df,factor_path,**parms) as gc:
gc.reload_df(path=frame_path+'/frames_new.csv')
gc.reload_factors()
clean()
for i in range(5):
gc.generator(batch_size=2)
print('step %d memory usage:\t %.1f%% \n'%(i,get_memory_use_pct()))
if get_memory_use_pct()>65:
break
gc.output_df(path=frame_path+'/frames_new2.csv')
multiprocessing is deprecated due to memory sharing issue
'''
from alpha_factory import generator_class,get_memory_use_pct,clean
from RNWS import read
import numpy as np
import pandas as pd
factor_path='.'
frame_path='.'
df=pd.read_csv(frame_path+'/frames.csv')
exr=read.read_df(r'.\exr',file_pattern='exr',start=20160101,end=20170201)
cap=read.read_df(r'.\cap',file_pattern='cap',header=0,dat_col='cap',start=20160101,end=20170201)
open_price,close,vwap,high,low,volume=read.read_df(r'.\mkt_data',file_pattern='mkt',start=20160101,end=20170201,header=0,dat_col=['open','close','vwap','high','low','volume'])
ind1,ind2,ind3=read.read_df(r'.\ind',file_pattern='ind',start=20160101,end=20170201,header=0,dat_col=['level1','level2','level3'])
parms={'exr':exr
,'cap':cap
,'open_price':open_price
,'close':close
,'vwap':vwap
,'high':high
,'low':low
,'volume':volume
,'ind1':ind1
,'ind2':ind2
,'ind3':ind3}
# generate starting:
gc=generator_class(df,factor_path,**parms)
gc.generator(batch_size=3)
gc.generator(batch_size=3)
gc.output_df(path=frame_path+'/frames_new.csv')
# generate continue:
with generator_class(df,factor_path,**parms) as gc:
gc.reload_df(path=frame_path+'/frames_new.csv')
gc.reload_factors()
clean()
for i in range(5):
gc.generator(batch_size=2)
print('step %d memory usage:\t %.1f%% \n'%(i,get_memory_use_pct()))
if get_memory_use_pct()>65:
break
gc.output_df(path=frame_path+'/frames_new2.csv')
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