I use the following code to download a csv of the previous 1 year's data for the instruments in my list and for the fields specified. My understanding is that using this method I am retrieving all of the data (unconstrained by dates) and then modifying that data so that a subset, constrained by 'TR.RNSAnnouncedDate', is downloaded as a csv. Is there a way I can only download the data for the previous year to make the request more efficient? In other words, can I apply a date parameter for 'TR.RNSAnnouncedDate' in my initial retreival?

The code:

#import packages

import eikon as ek # the Eikon Python wrapper package

import pandas as pd

import numpy as np

import datetime

from datetime import timedelta, date, datetime

from pandas.tseries.offsets import BDay

#connects to Bill's Eikon terminal

ek.set_app_key('72d2821f21064a0b8890860db39e375eacd87e24')

#retreive the RICs from Eikon

df_rics,e = ek.get_data("lists('Inv Trust List')","TR.RIC")

#convert that into a list and set as an object

ric_list = df_rics['Instrument'].tolist()

#Slice, loop and concatonate the retreival request - must be done in order to avoid a backend request timeout which

#happens when we use too many RICs. n can be toggled below.

n = 50

df = pd.DataFrame()

for ric_chunk in [ric_list[i:i + n]

for i in range(0, len(ric_list), n)]:

tmp_df, e = ek.get_data(ric_chunk,

['TR.RNSFilerName',

'TR.RNSAnnouncedDate',

'TR.RNSTransactionType',

'TR.RNSARNumShrsTransacted',

'TR.RNSARPctOSTransacted',

'TR.RNSARTransactionPrice',

'TR.RNSARMktValTransaction',

'TR.RNSARTotShrsPostTrans',

'TR.RNSARPctOSPostTrans'])

df = tmp_df.append(df)

#set the dates for the csv file we wish to download

end_date = date.today()

start_date = end_date - timedelta(days=365)

end_date_str = datetime.strftime(end_date, "%Y-%m-%d")

start_date_str = datetime.strftime(start_date, "%Y-%m-%d")

df['RNS Announced Date'] = pd.to_datetime(df['RNS Announced Date'])

mask = (df['RNS Announced Date'] > start_date_str) & (df['RNS Announced Date'] <= end_date_str)

df = df.loc[mask]

df.rename(columns={'RNS AR Price (at Transaction) - £': 'RNS AR Price (at Transaction) GBP',

'RNS AR Market Value of Transaction - £': 'RNS AR Market Value of Transaction - GBP'},

inplace=True)

#create file name and export as CSV

todays_date = date.today()

todays_date_str = datetime.strftime(todays_date, "%Y%m%d")

df.to_csv('Daily API Download_' + todays_date_str + '.csv')