def plot_performance(self):
df_performance = jqdata.get_price('000300.XSHG',
start_date=self.start_date,
end_date=self.end_date)
df_performance = df_performance[[
'close'
]].rename(columns={'close': 'CSI 300'})
# convert to percentage
df_performance = df_performance / df_performance['CSI 300'][0] * 100
# get performance of portfolio
dates = df_performance.index
portfolio_performance = [self.calculate_pl(date) for date in dates]
df_performance['Small Cap'] = portfolio_performance
# plot graph
fig = go.Figure()
fig.add_trace(
go.Scatter(x=df_performance.index,
y=df_performance['CSI 300'],
mode='lines',
name='CSI 300'))
fig.add_trace(
go.Scatter(x=df_performance.index,
y=df_performance['Small Cap'],
mode='lines',
name='Small Cap'))
fig.show()
def generate_performance(self):
df_performance = jqdata.get_price('000300.XSHG',
start_date=self.start_date,
end_date=self.end_date)
df_performance = df_performance[[
'close'
]].rename(columns={'close': 'CSI 300'})
# convert to percentage
df_performance = df_performance / df_performance['CSI 300'][0] * 100
# get performance of portfolio
dates = df_performance.index
portfolio_performance = [self.calculate_pl(date) for date in dates]
df_performance['Small Cap'] = portfolio_performance
# df_performance.to_csv('performance.csv', index_label='date')
return df_performance
end_date = '{}-01-02'.format(year + 1)
for asset in indexlist:
print(
asset,
start_date,
end_date,
)
indexfile = os.path.join(
index_path, u'{}_{}_{}.pickle'.format(asset, year, frequence))
if os.path.isfile(indexfile):
print('文件已经存在，跳过 ', indexfile)
continue
try:
print(indexfile)
his = jqapi.get_price(asset,
start_date,
end_date,
frequency='60m')
export_metadata_to_pickle(index_path,
u'{}_{}'.format(asset, year),
metadata=his)
#data_day = QA.QA_fetch_stock_min_adv(codelist,
# start='2019-01-01',
# end='2020-08-28',
# frequence='60min')
#his = data_day.data
except Exception as e:
print(asset, e)
time.sleep(1)
for year in range(2017, 2005, -1):
start_date = '{}-01-01'.format(year)