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import pandas as pd

import numpy as np

from scipy import stats

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

!free -h

# 以下代碼是為了顯示正文正常

import matplotlib as mpl

import matplotlib.font_manager as font_manager

path_eng = '/usr/share/fonts/chinese/REFSAN.TTF'

path_CHN = '/usr/share/fonts/chinese/simhei.ttf'

prop = font_manager.FontProperties(fname=path_CHN)  #Set the microsoft sans serief as default font family. if show chinese test, set path_CHN instead.

#prop.set_weight = 'light'

mpl.rcParams['font.family'] = prop.get_name()

Today = '2018-02-01'

def view_data():

   print('數(shù)據(jù)載入中...')

   sec_map = pd.read_hdf('uqer/sec_map.h5','map') # sec_map 包含了約2800多個(gè)指數(shù),實(shí)際指數(shù)約550只

   history = pd.read_hdf('uqer/uq_history.h5','history') #hisotry 包含了從2004年到2017年11月16日的指數(shù)數(shù)據(jù)( 大約86萬條數(shù)據(jù)).

   #history = history.sort_index(level=0)

   ntickers = history.index.levshape[0]

   nTradeDate =history.index.levshape[1]/250

   nRecords = history.shape[0]/10**4

   print('交易歷史數(shù)據(jù)庫包含了:\n\t{1:,.0f}萬條日交易數(shù)據(jù).\n\t{0}只指數(shù)，\n\t單只指數(shù)最長交易記錄為{2:.1f}年.'.format(ntickers,nRecords,nTradeDate))

   print('\n注 ： 數(shù)據(jù)源基于Uqer,作了初步校對和修正，后存儲(chǔ)在 History_fixed.h5 。例如：中證傳媒2017年12月~1月的市盈率數(shù)據(jù)修正為choice的數(shù)據(jù)。\n\n')

   print('歷史交易數(shù)據(jù)：\n   特征列表：{0}'.format(history.columns.tolist()))

   print('股指名稱數(shù)據(jù)：\n   特征列表：{0}'.format(sec_map.columns.tolist()))

view_data()

map_E2C = {'ticker':'指數(shù)代碼','secShortName':'指數(shù)名稱',

           'tradeDate':'日期', 'Close':'收盤價(jià)-Close',

            'PB1':'市凈率-PB', 'PE1':'市盈率-PE',

            'TurnoverValue':'成交額', 'TurnoverVol':'成交量','ROE':'凈資產(chǎn)回報(bào)率-ROE'}

print('\n本文使用的特征為：{0}'.format(list(map_E2C.values())))

def filter_history():

   def ticker_filter(x):

       ntradedays_annual = 250  # 假設(shè)一年交易日為250天

       nyear=3                 # 假設(shè)至少需要3年數(shù)據(jù)

       checkday=Today          # Checkday是設(shè)定有效的交易日期。默認(rèn)為Today, 全局變量

       mask_years    = x.shape[0]>(ntradedays_annual*nyear)

       mask_checkday = x.index.isin([checkday],level='tradeDate').any()

       mask = mask_years & mask_checkday

       return mask

   history = pd.read_hdf('uqer/uq_history.h5','history') #hisotry 包含了從2004年到2017年11月16日的指數(shù)數(shù)據(jù)( 大約86萬條數(shù)據(jù)).

   history= history.loc(axis=0)[:,:Today]

   history_filtered= history.groupby(level=0).filter(ticker_filter)

   ntickers = len(history_filtered.groupby(level=0).groups)

   print('{0}只指數(shù)交易時(shí)間超過了3年 且在{1}日有交易記錄'.format(ntickers,Today))

   return history_filtered

history =filter_history()

history['ROE'] = history['PB1']/history['PE1']

def Check_summary():

   def lastz(x):

       freedom = 1 # it is sample, so the sample std degree of freedome should not be 0 but 1

       Arry=x.values

       zscore = stats.zmap(Arry[-1],Arry,ddof=freedom)

       return zscore

   def recent_mean(x):

       mean = x.tail(250).sum()/250

       return mean

   grp = history.groupby(level=0)

   grp_last = grp.agg({'Close':'last','PE1':'last','PB1':'last','ROE':'last','TurnoverValue':'last'})

   grp_last['TurnoverValue'] =grp_last['TurnoverValue']

   grp_rank = grp.agg({'ROE':recent_mean,'TurnoverValue':recent_mean})

   grp_z = grp.agg({'Close':lastz,'PE1':lastz,'PB1':lastz,'ROE':lastz,'TurnoverValue':lastz})

   ROE_rank = pd.cut(grp_rank.ROE,bins=4,labels=['超低','低','中','高'])

   TV_rank = pd.cut(grp_rank.TurnoverValue,bins=5*np.logspace(7,11,5),labels=['5億','50億','500億','5000億'])

   ROE_rank.name = '凈資產(chǎn)回報(bào)率等級'

   TV_rank.name ='日交易額規(guī)模'    

   sec_map = pd.read_hdf('uqer/sec_map.h5','map') # sec_map 包含了約2800多個(gè)指數(shù),實(shí)際指數(shù)約550只{}

   #decimals = pd.Series([1,1,1,2,1 ], index=['Close','PE1','PB1','ROE','TurnoverValue'])

   combined = sec_map[['secShortName']].join(grp_last,how='right').join(ROE_rank,rsuffix='_R').join(TV_rank,rsuffix='_R')

   combined = combined.join(grp_z,rsuffix='_Z')

   return combined

checkday_summary = Check_summary()

checkday_summary.columns

tmp=sns.countplot('日交易額規(guī)模',data=checkday_summary)

checkday_summary.groupby(by='日交易額規(guī)模')[['ROE']].mean().rename(columns={'ROE':'平均凈資產(chǎn)回報(bào)率'})

col='Close'

fig,ax = plt.subplots(1,2,figsize=(10,5))

sns.boxplot(x='日交易額規(guī)模',y=col,ax=ax[0],data=checkday_summary)

ax[0].set_title('不同規(guī)模指數(shù) - {0}({1})箱體圖'.format(map_E2C[col],col))

sns.boxplot(x='日交易額規(guī)模',y=col+'_Z',ax=ax[1],data=checkday_summary)

ax[1].set_title('不同規(guī)模指數(shù) - {0}({1})-Z值 箱體圖'.format(map_E2C[col],col))

col='PE1'

fig,ax = plt.subplots(1,2,figsize=(10,5))

sns.boxplot(x='日交易額規(guī)模',y=col,ax=ax[0],data=checkday_summary)

ax[0].set_title('不同規(guī)模指數(shù) - {0}({1})箱體圖'.format(map_E2C[col],col))

sns.boxplot(x='日交易額規(guī)模',y=col+'_Z',ax=ax[1],data=checkday_summary)

ax[1].set_title('不同規(guī)模指數(shù) - {0}({1})-Z值 箱體圖'.format(map_E2C[col],col))

def show_min_PE_Z():

   col='PE1'

   def get_max(x):

       colz= col+'_Z'

       index =x[colz].idxmax()

       return x.loc[index][['secShortName',col,colz]]

   def get_min(x):

       colz= col+'_Z'

       index =x[colz].idxmin()

       return x.loc[index][['secShortName',col,colz]]

   print(checkday_summary.groupby('日交易額規(guī)模').apply(get_max).round(1))

   print(checkday_summary.groupby('日交易額規(guī)模').apply(get_min).round(1))

show_min_PE_Z()

tickers=['399417','399550']

sec_map = pd.read_hdf('uqer/sec_map.h5','map')

for ticker in tickers:

   print('{0} base day is {1}'.format(ticker,sec_map.loc[ticker].baseDate))

   fig,ax = plt.subplots(1,2,figsize=(6,3))

   fig.suptitle(ticker)

   PE1 = history.loc[ticker]['PE1']

   PE1_Z = history.loc[ticker][['PE1']].apply(stats.zscore)

   PE1.plot(ax=ax[0])

   sns.distplot(PE1_Z,ax=ax[1],vertical=True)

col='PB1'

fig,ax = plt.subplots(1,2,figsize=(10,5))

sns.boxplot(x='日交易額規(guī)模',y=col,ax=ax[0],data=checkday_summary)

ax[0].set_title('不同規(guī)模指數(shù) - {0}({1})箱體圖'.format(map_E2C[col],col))

sns.boxplot(x='日交易額規(guī)模',y=col+'_Z',ax=ax[1],data=checkday_summary)

ax[1].set_title('不同規(guī)模指數(shù) - {0}({1})-Z值 箱體圖'.format(map_E2C[col],col))

def show_min_PB_Z():

   col='PB1'

   def get_max(x):

       colz= col+'_Z'

       index =x[colz].idxmax()

       return x.loc[index][['secShortName',col,colz]]

   def get_min(x):

       colz= col+'_Z'

       index =x[colz].idxmin()

       return x.loc[index][['secShortName',col,colz]]

   print(checkday_summary.groupby('日交易額規(guī)模').apply(get_max).round(1))

   print(checkday_summary.groupby('日交易額規(guī)模').apply(get_min).round(1))

show_min_PB_Z()

col='ROE'

fig,ax = plt.subplots(1,2,figsize=(10,5))

sns.boxplot(x='日交易額規(guī)模',y=col,ax=ax[0],data=checkday_summary)

ax[0].set_title('不同規(guī)模指數(shù) - {0}({1})箱體圖'.format(map_E2C[col],col))

sns.boxplot(x='日交易額規(guī)模',y=col+'_Z',ax=ax[1],data=checkday_summary)

ax[1].set_title('不同規(guī)模指數(shù) - {0}({1})-Z值 箱體圖'.format(map_E2C[col],col))

mask_scale= checkday_summary['日交易額規(guī)模'].isin(['50億','500億','5000億'])

print('滿足流動(dòng)性規(guī)模的指數(shù)有{0}只'.format(mask_scale.sum()))

risk_zscore= -1.4  #注 z值小于 -1 的 指數(shù)太少，故改設(shè)為-0.3

mask_risk= (checkday_summary['PE1_Z']0) & (checkday_summary['PB1_Z']0)\

   &((checkday_summary['PE1_Z'] + checkday_summary['PB1_Z']<>

print('滿足低風(fēng)險(xiǎn)要求的指數(shù)有{0}只'.format(mask_risk.sum()))

roe_score =['中','高']

roe_zscore =[-2,2]

mask_roe = checkday_summary['凈資產(chǎn)回報(bào)率等級'].isin(roe_score) & checkday_summary['ROE_Z'].between(roe_zscore[0],roe_zscore[-1])

print('滿足收益率要求的指數(shù)有{0}只'.format(mask_roe.sum()))

mask_all = mask_scale & mask_risk & mask_roe

result =checkday_summary[mask_all].drop_duplicates(subset=['secShortName'])

print('滿足高流動(dòng)性，低風(fēng)險(xiǎn)，較好收益率要求的指數(shù)有{0}只'.format(result.shape[0]))

def strategy_tickers_today(strategy='customize',scale_score =None,risk_zscore =None,roe_score =None):

   if strategy =='customize':

       scale_score =['50億','500億','5000億']

       scale_zscore=(-1,1)

       risk_zscore= -1.4  #注 z值小于 -1 的 指數(shù)太少，故改設(shè)為-0.3

       roe_score =['中','高']

       roe_zscore =[-2,2]

   elif strategy =='ROE':

       scale_score =None

       risk_zscore= None

       roe_score =['高']

   elif strategy =='RISK':

        risk_zscore= -2 #PE & PB Z值小于-2， 如果是 正態(tài)分布，這風(fēng)險(xiǎn)小于95%

   elif strategy =='TURNOVER':

       scale_score =['5000億']

       scale_zscore=(-1,1)

   #交易規(guī)模過濾  

   if scale_score ==None:

       mask_scale =True

   else:

       mask_scale= (checkday_summary['日交易額規(guī)模'].isin(scale_score)) \

       & ( checkday_summary['TurnoverValue_Z'].between(scale_zscore[0],scale_zscore[-1]))

   #風(fēng)險(xiǎn)指標(biāo)過濾

   if risk_zscore ==None:

       mask_risk =True

   else:

       risk_zscore= -1.4

       mask_risk= (checkday_summary['PE1_Z']0) & (checkday_summary['PB1_Z']0)\

       &((checkday_summary['PE1_Z'] + checkday_summary['PB1_Z']<>

   #投資回報(bào)率過濾

   if roe_score ==None:

       mask_roe=True

   else:

       mask_roe = checkday_summary['凈資產(chǎn)回報(bào)率等級'].isin(roe_score)

   mask_all = mask_scale & mask_risk & mask_roe

   result =checkday_summary[mask_all].drop_duplicates(subset=['secShortName'])

   return result

def show_tickers_color(tickers= None):

   if (tickers is None) or (tickers.shape[0]==0):

       print('Pls prepare the checkday_summary dataframe')

       return

   else:

       html =tickers[['secShortName','Close', 'PE1', 'PB1', 'ROE','Close_Z', 'PE1_Z', 'PB1_Z', 'ROE_Z','凈資產(chǎn)回報(bào)率等級', '日交易額規(guī)模']]\

    .round(2).rename(columns=map_E2C).style.bar(subset=['Close_Z', 'PE1_Z', 'PB1_Z'],\

            align='zero', color=[ '#5fba7d','#d65f5f',],width=100/2)

       return html

tmp = strategy_tickers_today('RISK')

tmp['RISK'] = tmp['PE1_Z'] + tmp['PB1_Z']+tmp['Close_Z']*0.2

show_tickers_color(tmp.sort_values(by=['ROE'],ascending=False).head(5))