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        "author": "Yili Peng",
        "author_email": "yili_peng@outlook.com",
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        "description": "This programme is built for back-testing factors.\n\nDependencies\n------------\n\n-  python 3.5\n-  pandas 0.23.0\n-  numba 0.38.0\n-  empyrical 0.5.0\n-  data_box\n-  pickle\n-  multiprocessing\n-  joblib\n\nLogic\n-----\n\nBasic definitions\n-----------------\n\n#. v_t,s_t,c_t: total value, stock value and cash value at time t after\n   trading\n#. v^f_t,s^f_t,c^f_t: total value, stock value and cash value at time t\n   before trading\n#. ss,sv: suspended stock value and valid stock value\n#. r_t: return at time t\n#. cost_t: cost to trade at time t\n\nNote: s,ss,sv are all vectors while others are numbers\n\nEquations\n---------\n\n#. v_t = \\|s_t\\| + c_t\n#. s^f_t = s_{t-1} \\* (1 + r_t) = ss^f_t + sv^f_t = ss_t + sv^f_t\n#. ss_t <- suspend, s^f_t\n#. c_{t-1} + \\|sv^f_t\\| = \\|sv_t\\| + c_t + cost_t ( where c_t, cost_t >=\n   0 )\n#. cost_t =|sv_t - sv^f_t\\| \\* costRate\n#. weight_t <- factor_{t-1},industry_t,suspend_t ( \\|weight_t\\| = 1 or 0\n   if there is no valid stocks or factors or industries)\n#. define cost^f_t = (2|sv^f_t\\| + c_{t-1}) \\* costRate s.t. cost^f_t >=\n   cost_t, which is greater than the maximum cost we may have during the\n   trade\n#. define available_value^f_t = c_{t-1} + \\|sv^f_t\\| - cost^f_t, which\n   means the value ( = \\|sv_t\\| if weight_t != 0) we have in stocks\n   after trading\n#. let sv_t = weight_t \\* available_value^f_t s.t. c_t = c_{t-1} +\n   \\|sv^f_t\\| - \\|sv_t\\| - cost_t >=0\n\nThus to update v_t, we would start with calculating s^f_t, ss, sv^f_t,\nthen cost^f_t, available_value^f_t, then sv_t, cost_t and c_t, and\nfinally v_t\n\nExample\n-------\n\nData Box: pre-process\n---------------------\n\n.. code:: python\n\n   from data_box import data_box\n   db=data_box()\\\n       .load_indestry(ind)\\\n       .load_indexWeight(ind_weight)\\\n       .calc_indweight()\\\n       .load_suspend(sus)\\\n       .load_adjPrice(price)\\\n       .add_factor('factor0',factor0)\\\n       .add_factor('factor1',factor1)\\\n       .set_lag(freq='d',day_lag=1)\\\n       .align_data()\n   # freq can be 'd' or 'm', for detail please refer to db.set_lag doc. \n\nWhere ``price,ind,ind_weight,sus,factor0,factor1`` are all dataframes\nwith index as date (yyyymmdd,int) and column as tickers. You can save\nand load this data box object by ``db.save('path')`` and\n``db.load('path')``. You can find more in data_box project.\n\nBack Test\n=========\n\n.. code:: python\n\n   from single_factor_model import run_back_test\n\nSingle processor\n\n.. code:: python\n\n   Value,Turnover=run_back_test(data_box=db,back_end=None,n=5,out_path=None,double_side_cost=0.003)\n\nMulti processors\n\n.. code:: python\n\n   Value,Turnover=run_back_test(data_box=db,back_end='loky',n=5,n_jobs=-1,out_path=None,verbose=50)\n\nor\n\n.. code:: python\n\n   with __name__=='__main__':\n       Value,Turnover=run_back_test(data_box=db,back_end='multiprocessing',n=5,n_jobs=-1,out_path=None)\n\nTo check detailed positions of each portfolio every day, just assign\n``out_path``.\n\nBack test for specific industries\n\n.. code:: python\n\n   from single_factor_model import  run_back_test_by_industry\n   Value_list,Turnover_list=run_back_test_by_industry(db,industry_list=None,back_end='loky',n_jobs=-1,double_side_cost=0.003,verbose=50) \n\nSummary and Plot\n================\n\nCalculate return including long short portfolio(and reverse)\n\n.. code:: python\n\n   from single_factor_model import calc_return\n   Return = calc_return(Value,Turnover,long_short=True,double_side_cost=0.003)\n\nSummary\n\n.. code:: python\n\n   from single_factor_model import summary\n   S=summary(Return)\n\nPlot\n\n.. code:: python\n\n   from single_factor_model import run_plot\n   run_plot(Return,show=True)\n\n\n",
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