import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go

path = '/home/v-gazh/PycharmProjects/us_data/limit_code.csv'

df = pd.read_csv(path)
df.set_index(['code'], inplace=True)

# ST 占比
total_count = len(df)
st_count = len(df[df['isST']==1])
print(f'禁投池總數(shù)：{total_count}')
print(f'禁投池中ST個(gè)數(shù)：{st_count}') # f'禁投池中ST個(gè)數(shù)：{}'

# 成分股占比
sz50_count = len(df[df['isSz50']==1])
print(f'禁投池中上證50個(gè)數(shù)：{sz50_count}')
hs300_count = len(df[df['isHs300']==1])
print(f'禁投池中滬深300個(gè)數(shù)：{hs300_count}')
zz500_count = len(df[df['isZz500']==1])
print(f'禁投池中中證500個(gè)數(shù)：{zz500_count}')

# 退市占比
outdate_count = len(df['outDate'].dropna())
print(f'禁投池中退市股票個(gè)數(shù)：{outdate_count}')

# 非股票
not_stock = len(df[df['type']!=1])
print(f'禁投池中非股票個(gè)數(shù)：{not_stock} 【SZ006415 為基金：F006415 | SZ000000 代碼錯(cuò)誤】')

# 次新股
delta_df = pd.DataFrame((pd.to_datetime(df['date']) - pd.to_datetime(df['ipoDate'])))
new_stock = len(delta_df[delta_df[0] < pd.Timedelta('365 days')]) # 上市不滿一年為次新股 
print(f'禁投池中次新股個(gè)數(shù)：{new_stock}')

# 市值小于30億的股票
maketValue = len(df[df['maketValue'] < 3000000000])
print(f'市值小于30億股票個(gè)數(shù)：{maketValue}')

# 畫圖
labels = ['股票總數(shù)', 'ST股票', '深證50', '滬深300', '中證500', '退市股票', '非股票', '次新股', '小市值']
values = [total_count, st_count, sz50_count, hs300_count, zz500_count, outdate_count, not_stock, new_stock, maketValue]

trace = go.Pie(labels=labels, values=values,textfont=dict(size=15),)
py.iplot([trace], filename='basic_pie_chart')

# 畫圖
labels = ['股票總數(shù)', 'ST股票', '深證50', '滬深300', '中證500', '退市股票', '非股票', '次新股', '小市值']
values = [total_count, st_count, sz50_count, hs300_count, zz500_count, outdate_count, not_stock, new_stock, maketValue]

trace = go.Pie(labels=labels, values=values,textfont=dict(size=15),)
py.iplot([trace], filename='basic_pie_chart')

values = [total_count, st_count, sz50_count, hs300_count, zz500_count, outdate_count, not_stock, new_stock, maketValue]