For a deeper look into our Eikon Data API, look into:
Overview | Quickstart | Documentation | Downloads | Tutorials | Articles
39.2k
●75 ●11 ●27
To create timeseries in 30 minute interval you'd need to retrieve 1 minute bars and summarize them into 30 minute bars.
Thanks ,after i get data via
df1=ek.get_timeseries(s1,fields=["Open","High","Low","Close","Volume"], start_date = "2017-11-13T01:00:00",end_date = "2017-11-13T07:04:05",interval='minute')
Do u have any high effect ways to handle this data into 5mins bar?
There's nothing from Thomson Reuters that would facilitate this operation, since Python has very rich capabilities for all kinds of data transformation, summarization and statistical analysis.
Alex, thanks for your answer. Of course You are right since Pyhon has very rich libs for calculation, but my requirement is to get 3000 RICs' 5mins bar in real-time, do you have any efficient way to handle this? thanks again :)
4.3k
●2 ●4 ●5
Hi,
If you wonder if get_timeseries function accepts other interval type than 'tick', 'daily', ... , the response is no.
4.3k
●2 ●4 ●5
This a simple example to extract a 30 minute interval timeseries from a minute interval result:
import eikon as ek
from datetime import timedelta
interval = 30
def ohlcsum(df):
return [df['OPEN'][0],df['HIGH'].max(),df['LOW'].min(),
df['CLOSE'][-1],int(df['VOLUME'].sum())]
data_1min = ek.get_timeseries('PEUP.PA', interval='minute')
timestamps=[]
result=[]
start_slice = data_1min.index[0]
while True:
end_slice = start_slice + timedelta(minutes=interval-1)
interval_slice = data_1min.loc[start_slice : end_slice]
try:
data = ohlcsum(interval_slice)
except IndexError:
break
timestamps.append(interval_slice.index[-1])
result.append(data)
start_slice = start_slice + timedelta(minutes=interval)
if start_slice > data_1min.index[-1]:
break
labels = ['OPEN','HIGH','LOW','CLOSE','VOLUME']
data_new_interval = pd.DataFrame.from_records(result, index=timestamps, columns=labels)
I don't think u got the right data for 5 min
for every 5 min data, it should be calculated by the following function
def ohlcsum(df):
df = df.sort()
return {
'Open': df['Open'][0],
'High': df['High'].max(),
'Low': df['Low'].min(),
'Close': df['Close'][-1],
'Volume': df['Volume'].sum()
}
