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)
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