def create_index_figure_column(code, dtype, start_date, end_date):
delta_days = (end_date - start_date).days
if delta_days <= 0: return None
start_date = start_date.strftime('%Y-%m-%d')
end_date = end_date.strftime('%Y-%m-%d')
if dtype == 'K线图':
obj = CIndex(code)
df = obj.get_k_data_in_range(start_date, end_date)
source = ColumnDataSource(df)
mapper = linear_cmap(field_name='pchange', palette=['red', 'green'], low=0, high=0, low_color = 'green', high_color = 'red')
p = figure(plot_height=500, plot_width=1200, tools="", toolbar_location=None, sizing_mode="scale_both", x_range=(0, len(df)))
p.xaxis.axis_label = "时间"
p.yaxis.axis_label = "点数"
p.xaxis.major_label_overrides = {i: mdate for i, mdate in enumerate(df['date'])}
p.segment(x0='index', y0='low', x1='date', y1='high', line_width=2, color='black', source=source)
p.vbar(x='index', bottom='open', top='close', width = 50 / delta_days, color=mapper, source=source)
p.add_tools(HoverTool(tooltips=[("数量", "@close"), ("时间", "@date")]))
volume_p = figure(plot_height=150, plot_width=1200, tools="", toolbar_location=None, sizing_mode="scale_both")
volume_p.x_range = p.x_range
volume_p.vbar(x='date', top='volume', width = 50 / delta_days, color=mapper, source=source)
return column(p, volume_p)
else:
obj = BullStockRatio(code)
df = obj.get_k_data_between(start_date, end_date)
source = ColumnDataSource(df)
p = figure(plot_height=500, plot_width=1200, tools="", toolbar_location=None, sizing_mode="scale_both")
p.xaxis.axis_label = "时间"
p.yaxis.axis_label = "比例"
p.line(x = 'index', y = 'ratio', line_width = 3, line_alpha = 1.0, source = source)
p.xaxis.major_label_overrides = {i: mdate for i, mdate in enumerate(df['date'])}
p.add_tools(HoverTool(tooltips=[("比例", "@ratio"), ("时间", "@date")]))
return column(p)
def get_index_data(start_date, end_date, index_code):
iobj = CIndex(index_code, dbinfo=ct.OUT_DB_INFO, redis_host='127.0.0.1')
i_data = iobj.get_k_data_in_range(start_date, end_date)
i_data = i_data[[
'open', 'high', 'low', 'close', 'volume', 'amount', 'date'
]]
return i_data
def choose_plate(edate = '2016-10-11', ndays = 90):
rindustry_info_client = RIndexIndustryInfo(redis_host='127.0.0.1')
today_industry_df = rindustry_info_client.get_k_data(edate)
pchange_df = today_industry_df.sort_values(by = 'pchange', ascending = False).head(3)
mchange_df = today_industry_df.sort_values(by = 'mchange', ascending = False).head(3)
plate_code_list = list(set(pchange_df.code.tolist()).intersection(pchange_df.code.tolist()))
if len(plate_code_list) == 0:
logger.info("no interested plate for date:%s" % edate)
return list()
sdate = get_day_nday_ago(edate, ndays, '%Y-%m-%d')
#get sh index data
sh_index_obj = CIndex('000001', redis_host='127.0.0.1')
sh_index_info = sh_index_obj.get_k_data_in_range(sdate, edate)
sh_index_pchange = 100 * (sh_index_info.loc[len(sh_index_info) - 1, 'close'] - sh_index_info.loc[0, 'preclose']) / sh_index_info.loc[0, 'preclose']
#get industry data
all_industry_df = rindustry_info_client.get_k_data_in_range(sdate, edate)
all_industry_df = all_industry_df.loc[all_industry_df.code.isin(plate_code_list)]
industry_static_info = DataFrame(columns={'code', 'sai', 'pchange', ct.KL, ct.QL, ct.JL, ct.FL})
#choose better industry
redisobj = create_redis_obj("127.0.0.1")
today_industry_info = IndustryInfo.get(redisobj)
for code, industry in all_industry_df.groupby('code'):
industry = industry.reset_index(drop = True)
industry['sri'] = 0
industry['sri'] = industry['pchange'] - sh_index_info['pchange']
industry['sai'] = 0
industry.at[(industry.pchange > 0) & (sh_index_info.pchange < 0), 'sai'] = industry.loc[(industry.pchange > 0) & (sh_index_info.pchange < 0), 'sri']
industry_sai = len(industry.loc[industry.sai > 0])
industry_pchange = 100 * (industry.loc[len(industry) - 1, 'close'] - industry.loc[0, 'preclose']) / industry.loc[0, 'preclose']
code_list = json.loads(today_industry_info.loc[today_industry_info.code == code, 'content'].values[0])
info_dict, good_code_list = choose_stock(code_list, sdate, edate)
industry_static_info = industry_static_info.append(DataFrame([[code, industry_sai, industry_pchange, info_dict[ct.KL], info_dict[ct.QL], info_dict[ct.JL], info_dict[ct.FL]]], columns = ['code', 'sai', 'pchange', ct.KL, ct.QL, ct.JL, ct.FL]), sort = 'True')
industry_static_info = industry_static_info.reset_index(drop = True)
industry_static_info = industry_static_info.sort_values(by=['pchange'], ascending=False)
return good_code_list
def get_data(code, start_date, end_date):
cstock_obj = CIndex(code, dbinfo=ct.OUT_DB_INFO, redis_host='127.0.0.1')
data = cstock_obj.get_k_data_in_range(start_date, end_date)
data = data.set_index('date')
if is_df_has_unexpected_data(data): return None
data.index = pd.to_datetime(data.index)
data = data.dropna(how='any')
return data
def get_index_df(self, code, start_date, end_date):
cindex_client = CIndex(code)
df = cindex_client.get_k_data_in_range(start_date, end_date)
df['time'] = df.index.tolist()
df = df[[
'time', 'open', 'high', 'low', 'close', 'volume', 'amount', 'date'
]]
return df
def get_index_data(self, start_date, end_date, index_code):
iobj = CIndex(index_code, dbinfo = self.dbinfo, redis_host = self.redis_host)
i_data = iobj.get_k_data_in_range(start_date, end_date)
i_data = i_data.sort_values(by=['date'], ascending=True)
i_data = i_data.reset_index(drop = True)
i_data['time'] = i_data.index.tolist()
i_data = i_data[['time', 'open', 'high', 'low', 'close', 'volume', 'amount', 'date']]
return i_data
def get_data(self, start_date, end_date, index_code):
df = self.ris.get_k_data_in_range(start_date, end_date)
iobj = CIndex(index_code,
dbinfo=ct.OUT_DB_INFO,
redis_host='127.0.0.1')
i_data = iobj.get_k_data_in_range(start_date, end_date)
i_data['time'] = i_data.index.tolist()
i_data = i_data[[
'time', 'open', 'high', 'low', 'close', 'volume', 'amount', 'date'
]]
return df, i_data
base_color = '#e6daa6'
name = "shanghai_%s_image" % cdate
i_data = i_data[[
'date', 'open', 'high', 'low', 'close', 'volume', 'amount'
]]
fig, index_ax = plt.subplots(figsize=(16, 10))
fig.subplots_adjust(bottom=0.2)
candlestick2_ochl(index_ax,
i_data['open'],
i_data['close'],
i_data['high'],
i_data['low'],
width=1.0,
colorup='r',
colordown='g')
index_ax.set_ylabel(name)
index_ax.set_xticks(range(0, len(i_data['date']), 10))
plt.plot()
fig.autofmt_xdate()
plt.show()
index_obj = CIndex('000001', dbinfo=ct.OUT_DB_INFO, redis_host='127.0.0.1')
df = index_obj.get_k_data()
cdates = df[(df.pchange > 4) & (df.date > '1999-01-01')].date.tolist()
for mdate in cdates:
start_date = mdate
end_date = get_day_nday_after(start_date, num=40, dformat="%Y-%m-%d")
index_data = index_obj.get_k_data_in_range(start_date, end_date)
plot_index(start_date, index_data)
