single-factor-model 0.2.1

factor model

This programme is built for back-testing factors.

Dependencies

• python 3.5

• pandas 0.23.0

• numba 0.38.0

• empyrical 0.5.0

• data_box

• pickle

• multiprocessing

Example

Data Box: pre-process

from data_box import data_box
db=data_box()\
    .load_indestry(ind)\
    .load_indexWeight(ind_weight)\
    .calc_indweight()\
    .load_suspend(sus)\
    .load_adjPrice(price)\
    .add_factor('factor0',factor0)\
    .add_factor('factor1',factor1)\
    .set_lag(freq='d',day_lag=1)\
    .align_data()
# freq can be 'd' or 'm', for detail please refer to db.set_lag doc.

Where price,ind,ind_weight,sus,factor0,factor1 are all dataframes with index as date (yyyymmdd,int) and column as tickers. You can save and load this data box object by db.save('path') and db.load('path'). You can find more in data_box project.

Back Test

from single_factor_model import run_back_test

single process

Value,Turnover=run_back_test(data_box=db,back_end=None,n=5,weight_path=None,double_side_cost=0.003)

multi process

Value,Turnover=run_back_test(data_box=db,back_end='loky',n=5,weight_path=None,verbose=50)

or

with __name__=='__main__':
    Value,Turnover=run_back_test(data_box=db,back_end='multiprocessing',n=5,weight_path=None)

To check detailed position of each portfolio each day, just assign weight_path.

Summary and Plot

calculate return including long short portfolio(and reverse)

from single_factor_model import calc_return
Return = calc_return(Value,Turnover,long_short,double_side_cost=0.003)

summary

from single_factor_model import summary
S=summary(Return)

plot

run_plot(Return,show=True)