alpha-factory 0.0.6

generate alpha factors

sample:

from alpha_factory import factor_gen,df_gen
import pandas as pd
from RNWS import read

# 1
df_gen.generator(20,'.../frames_new.csv')

# 2
factor_path='.../factor_path_test'

df=pd.read_csv('.../frames_new.csv',index_col=0)
factor_gen.find_dependency(df)

exr=read.read_df('.../exr',file_pattern='exr',start=20140101,end=20140201)
cap=read.read_df('.../cap',file_pattern='cap',header=0,dat_col='cap',start=20140101,end=20140201)
open_price,close,vwap,high,low,volume=read.read_df(r'.../mkt_data',file_pattern='mkt',start=20140101,end=20140201,header=0,dat_col=['open','close','vwap','high','low','volume'])
ind1,ind2,ind3=read.read_df(r'.../ind',file_pattern='ind',start=20140101,end=20140201,header=0,dat_col=['level1','level2','level3'])
parms={'exr':exr
,'cap':cap
,'open':open_price
,'close':close
,'vwap':vwap
,'high':high
,'low':low
,'volume':volume
,'ind1':ind1
,'ind2':ind2
,'ind3':ind3}

#3 generate new factors
with factor_gen.generator_class(df=df,factor_path=factor_path,**parms) as gc:
gc=factor_gen.generator_class(df=df,factor_path=factor_path,**parms)
gc.generator(batch_size=3)
gc.generator(batch_size=5)

# update
with factor_gen.generator_class(df=df,factor_path=factor_path,**parms) as gc:
gc.reload_factors()
gc.generator(batch_size=3)