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    "info": {
        "author": "Yili Peng",
        "author_email": "yili_peng@outlook.com",
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        "description": "This programme is used for statistical arbitrage with co integation\nmethod\n\nDependencies\n~~~~~~~~~~~~\n\n-  python 3.5\n-  pandas 0.22.0\n-  spyder 3.2.8\n-  joblib 0.12.3\n-  RNWS 0.1.2\n-  mini_exchange 0.0.7\n\nSample\n~~~~~~\n\nBack Test\n---------\n\nCalculate p_value of stationarity among all pairs along all history\n\n.. code:: bash\n\n   import pandas as pd\n   from CointArbitrage.pairing_period import find_pair,filter_tickers\n\n   price=pd.read('price.csv') # index:yyyymmdd(int),columns:tickers,values:adjusted price\n\n   past_date=60 # use last 60 days to calculate p_value\n   P=[]\n   for i in range(past_date,price.shape[0]):\n       # filter unavailable tickers, this step is optional\n       df=filter_tickers(price.iloc[(i-past_date):i])\n       # calculate p_value\n       des=find_pair(df,mul=True,n_jobs=-1)\n       dt=filter_tickers.index[i]\n       P.append(pd.Series(des.p_value.values,index=des['ticker1'].str.cat(des['ticker2'],'|').values,name=dt))\n   pairs=pd.concat(P,axis=1).T    \n\nMake signal dataframe\n\n.. code:: bash\n\n   from CointArbitrage.trading_period import zscore_log_w,zscore_w,sig,sig_cut_tail\n   window=20 # use 20 days to calculate zscore ( = normalize(stockPrice1/stockPrice2))\n   ratio,zs=zscore_w(price_df=price,pair_lst=pairs.columns,window=window)\n   # or use log zscore = normalize(log(stockPrice1/stockPrice2))\n   ratio,zs=zscore_log_w(price_df=price,pair_lst=pairs.columns,window=window)\n   # or use exponential moving to calculate zscore with function zscore_df and zscore_log_df\n\n   # generate signal\n   # k0: close position, int, float or pd.Series if need to specify different values for each pairs\n   # k1: open position\n   # k2: close out position\n   k0,k1,k2=1,2,4\n   sig_df=sig(zscore=zs,k0=k0,k1=k1,k2=k2)\n   # sig_df contains value Nan,-3,-2,-1,0,1,2,3 \n   # 3(-3) means close position and open another position in different direction\n   # 2(-2) means close position\n   # 1(-1) means open position\n   # keep signal when stationary (i.e. p_val<0.1), others would be kept in Nan\n   # and add a new signal 4(-4), which means reaching the end of stationarity period\n   sig_result=sig_cut_tail(sig_df,pairs<0.1,n_jobs=-1,new_signal=4)\n\nSimulate trade\n\n::\n\n   from CointArbitrage.trading_period import Trade\n   start=20140101\n   end=20180101\n   TT=Trade(price,start=start,end=end)\n   user_name='user01'\n   TT.add_user(user_name,sig_result,start_amount=1000)\n   # add signal 4 as close signal \n   # and leave close status as -1 (default close status is 0)\n   # more reference can be found in mini_exchange package\n   TT.add_close_signal(4,close_status=-1)\n   TT.add_close_signal(-4,close_status=-1)\n   # trade 10 dollars when opening position each time\n   uad=pd.DataFrame({'user_name':[user_name],'amt_type':0,'value':10})\n   TT.trade(uad)\n   print(TT.summary())\n   # to analysis in detail, get the account info and position info of user01\n   account,position=TT.get_user(user_name)\n   # more details can be found in mini_exchange package\n   account.plot_history(by_pct=True)\n   account.annual_return()\n   account.draw_down()\n   account.romad()\n   position.win_rate(dual=True)\n   position.log\n   # plot one pair\n   pair='0001.HK|0002.HK'\n   TT.plot_trade_pair(user_name,pair,k0=k0,k1=k1,k2=k2,window=window)\n\nInstant simulation in HK market with Wind Api\n---------------------------------------------\n\nFind New Pair\n\n.. code:: bash\n\n   # initialize\n   from CointArbitrage.instant_with_wind import init_log\n   init_log('log.csv')\n\n   # last t trading days\n   from WindPy import w\n   from CointArbitrage.instant_with_wind import trading_times\n   w.start()\n   times=trading_times(w,length=60,text=\"TradingCalendar=HKEX\")\n\n   # download adjusted close price up to yesterday\n   # price is kept in file price_yyyymmdd.csv with eachline as 'tickers,values'\n   # more can be found in RNWS package\n   from CointArbitrage.instant_with_wind import download_hist_price\n   tickers=['0001.HK','0002.HK','0003.HK'...]\n   download_hist_price(tickers,times,'price_path',w)\n\n   # read in history price\n   from RNWS import read_df\n   hist_price=read_df('price_path',file_pattern='price',dt_range=times)\n\n   # filter stationary pairs\n   from CointArbitrage.pairing_period import filter_pval\n   import pandas as pd\n   pairs=['0001.HK|0002.HK','0001.HK|0003.HK',...]\n   new_pairs=filter_pval(hist_price,pairs,n_jobs=-1)\n   new_tickers=pd.Series(new_pairs).str.split('|',expand=True).unstack().unique().tolist()\n   new_hist=hist_price[new_tickers]\n\n   # lotsize and shortability\n   ls=pd.DataFrame({'shortable':[0,0,1,...],'lotsize':[500,1000,500,...]},index=['0001.HK','0002.HK','0003.HK'...])\n\n   # find new pairs\n   from CointArbitrage.instant_with_wind import find_new_hk\n   params={'log_path':'log.csv'\n           ,'hist_price':hist_price\n           ,'hist_log': pd.read_csv('history_log.csv') #from back test\n           ,'pairs':new_pairs\n           ,'tickers':new_tickers\n           ,'zs_window':20\n           ,'zs_log':False \n           ,'w':w\n           ,'ls':ls\n           ,'potential_path':'potential_path.csv'\n           ,'potential_k':1.8\n           ,'k0':1\n           ,'k1':2\n           ,'k1':4\n           ,'match_max':50000\n           }\n\n   # update log.csv\n   sign=find_new_hk(**params)\n\nupdate file every 1800s at trading hour and refresh evrey 900s at lunch\nbreak and before trading start\n\n.. code:: bash\n\n   from CointArbitrage.instant_with_wind import time_sleep\n   time_sleep(sign={0:1800,1:900,2:'break',3:'break'})(find_new_hk)(**params)\n\nRefresh log and check close status\n\n.. code:: bash\n\n   params2={}\n   for key in ['log_path','hist_price','w','hist_log','k0','k2','plot_mark','potential_path','zs_log','zs_window']:\n       params2.update(params[key])\n   refresh_hk(**params2)\n   # to continue refresh every 1800s\n   time_sleep(sign={0:1800,1:900,2:'break',3:'break'})(refresh_hk)(**params2)\n\nCheck stationarity by using the price at last 10min of all trading hours\n\n.. code:: bash\n\n   param3={}\n   for key in ['log_path','hist_price','w','zs_window','zs_log','k0','k1']\n       param3.update(key)\n   time_sleep(sign={0:10,1:9000,2:'break',3:'break'})(last_hk)(**param3)\n\nNotice: After using time_sleep, sleep loops will start directly. Thus\n``find_new``, ``refresh`` and ``last`` should be running in 3 different\nconsules.\n\n\n",
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