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        "author": "Yili Peng",
        "author_email": "yili_peng@outlook.com",
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        "description": "This programme is to automatically generate alpha factors and filter\nrelatively good factors with back-testing methods. Time consuming parts\nare optimized with ``numba`` package.\n\nDependencies\n------------\n\n-  python >= 3.5\n-  pandas >= 0.22.0\n-  numpy >= 1.14.0\n-  RNWS >= 0.2.1\n-  numba >= 0.38.0\n-  single_factor_model>=0.3.0\n-  IPython 5.1.0\n-  empyrical\n-  alphalens\n\nNote: It is best to use the latest version of ``llvmlite`` in order to\nmake ``numba`` work properly. Otherwise it may couse a kernel-dies\nsituation.\n\nExample\n-------\n\nload packages and read in data\n------------------------------\n\n.. code:: python\n\n   from alpha_factory import generator_class,get_memory_use_pct,clean\n   from RNWS import read\n   import numpy as np\n   import pandas as pd\n   start=20180101\n   end=20180331\n   factor_path='.'\n   frame_path='.'\n\n   df=pd.read_csv(frame_path+'/frames.csv')\n\n   ## read in data\n\n   re=read.read_df('./re',file_pattern='re',start=start,end=end)\n   cap=read.read_df('./cap',file_pattern='cap',header=0,dat_col='cap',start=start,end=end)\n   open_price,close,vwap,adj,high,low,volume,sus=read.read_df('./mkt_data',file_pattern='mkt',start=start,end=end,header=0,dat_col=['open','close','vwap','adjfactor','high','low','volume','sus'])\n   ind1,ind2,ind3=read.read_df('./ind',file_pattern='ind',start=start,end=end,header=0,dat_col=['level1','level2','level3'])\n   inx_weight=read.read_df('./ZZ800_weight','Stk_ZZ800',start=start,end=end,header=None,inx_col=1,dat_col=3)\n\nNote:\\ ``frames`` contains columns as:\n``df_name,equation,dependency,type``, where ``type`` includes\n``df,cap,group``. In this case ``frames.csv`` have ``df_name``:\n``re,cap,open_price,close,vwap,high,low,volume,ind1,ind2,ind3``.\n\nYou can also read data by using ``pd.read_csv`` directly depending on\nhow you store your data.\n\nstart to generate\n-----------------\n\n.. code:: python\n\n   parms={'re':close.mul(adj).pct_change()\n          ,'cap':cap\n          ,'open_price':open_price\n          ,'close':close\n          ,'vwap':vwap\n          ,'high':high\n          ,'low':low\n          ,'volume':volume\n          ,'ind1':ind1\n          ,'ind2':ind2\n          ,'ind3':ind3}\n\n   with generator_class(df,factor_path,**parms) as gen:\n       gen.generator(batch_size=3,name_start='a')\n       gen.generator(batch_size=3,name_start='a')\n       gen.output_df(path=frame_path+'/frames_new.csv')\n\ncontinue to generate with existing frames and factors\n-----------------------------------------------------\n\n.. code:: python\n\n   with generator_class(df,factor_path,**parms) as gen:\n       gen.reload_df(path=frame_path+'/frames_new.csv')\n       gen.reload_factors(align=True)\n       clean()\n       for i in range(5):\n           gen.generator(batch_size=2,name_start='a')\n           print('step %d memory usage:\\t %.1f%% \\n'%(i,get_memory_use_pct()))\n           if get_memory_use_pct()>80:\n               break\n       gen.output_df(path=frame_path+'/frames_new2.csv')\n\nNote: It is very important to ``align`` all factors and initial\ndataframes before generating.\n\nyou can also choose how to store your factors by setting\n``store_method``\n\nbacktesting with stratified sampling approach and ic-ir meansure after generation\n---------------------------------------------------------------------------------\n\n.. code:: python\n\n   data_box_param={'ind':ind1\n               ,'price':vwap*adjfactor\n               ,'sus':sus\n               ,'ind_weight':inx_weight\n               ,'path':'./databox'\n               }\n\n   back_test_param={'sharpe_ratio_thresh':3\n                    ,'n':5\n                    ,'out_path':'.'\n                    ,'back_end':'loky'\n                    ,'n_jobs':6\n                    ,'detail_root_path':None\n                    ,'double_side_cost':0.003\n                    ,'rf':0.03\n                    }\n\n   icir_param={'ir_thresh':0.4\n               ,'out_path':'.'\n               ,'back_end':'loky'\n               ,'n_jobs':6\n               }\n\n   with generator_class(df,factor_path,**parms) as gen: \n       for i in range(5):\n           gen.generator(batch_size=2,name_start='a')\n           gen.output_df(path=frame_path+'/frames_new.csv')\n           gen.getOrCreate_databox(**data_box_param)\n           gen.back_test(**back_test_param)\n           gen.icir(**icir_param)\n           clean()\n           if get_memory_use_pct()>90:\n               print('Memory exceeded')\n               break\n\nTo temporarily save (and reload) factor data you can use\n``create_tmp_memory`` and ``reload_tmp_memory`` methods. This is usually\nused before ``back_test`` and ``icir`` to release more memory for\nparallel running.\n\ngenerate script of factors\n--------------------------\n\n.. code:: python\n\n   from alpha_factory import write_file\n   import pandas as pd\n   df2=pd.read_csv(frame_path+'/frames_new.csv')\n   write_file(df2,'script.py')\n\nlocate a factor\n---------------\n\n.. code:: python\n\n   from alpha_factory.utilise import get_factor_path\n   factor_name='a0'\n   path=get_factor_path(factor_path,factor_name)\n\nonly when ``storage_method='byTime'``\n\nuse your own functions\n----------------------\n\nTo use your own functions you need to append your code in class\n``functions`` from ``basic_functions.py`` in the sourse file and also\nappend the corresponding names in ``functions.csv`` from ``data`` file\nin the sourse file.\n\nAfter that you can set ``debug=True`` in ``generator`` function to check\nif there is any bug from all those functions. If indeed there is, a new\nembeded ipython would be activated to help you find out what is going on\nin the loop.\n\n\n",
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