single-factor-model 0.1.4

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

• pickle

• multiprocessing

Example

Data Box: pre-process

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

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').

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,double_side_cost=0.003)

or

with __name__=='__main__':
    Value,Turnover=run_back_test(data_box=db,back_end='multiprocessing',n=5,weight_path=None,double_side_cost=0.003)

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

Summary and Plot

summary by month

from single_factor_model import summary
S=summary(Value)

summary whole time period only

from single_factor_model import summary_total
S=summary_total(Value)

plot

from single_factor_model import run_plot,run_plot_turnover
run_plot(Value,show=True)
run_plot_turnover(Turnover,show=True)