clean factor data
Project description
This project is to clean factor data and to prepare for back test.
Dependencies
- python 3.5
- pandas 0.22.0
- numpy 1.14.3
- pickle
- sklearn 0.19.1 (for pca only)
Example
from data_box import data_box db=data_box()\ .set_lag(freq='d',day_lag=0)\ .load_adjPrice(price)\ # 'price' is a pd.DataFrame with dates(20190101 int type) as its index and tickers as its column .load_indestry(ind)\ .load_suspend(sus)\ .load_indexWeight(index_weight)\ .calc_indweight()\ # calculate industry weight based on index weight and stocks' industry in this index .load_cap(cap)\ .add_factor('f1',factor1)\ .add_factor('f2',factor2)\ .add_factor('f3',factor3)\ .align_data()\ .factor_pca()\ .factor_ind_neutral()\ .factor_size_neutral()\ .factor_zscore() print(db.Factor) print(db.Price) print(db.Sus) print(db.Cap) # save and reload db.save(path) db2=databox().load(path)
Project details
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