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