Python Data API - retrieving time series data for FUNDS - Performance
I am attempting to pull data < 1month, preferably daily timeseries performance data for funds and etfs ie: for
LP68467397 & LP68402195. Kindly help!
Best Answer
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@J13513, If you need daily performance for funds (Lipper data), you need to check with your account manager on other products. Lipper do have desktop and data feeds products able to provide daily performance.
If you insist on using Eikon API, the best I can find is weekly performance up to 1 year. You can use the function under "Lipper Funds":
"TR.FundRollingPerformance(RollTimeFrame=1Y Interval=W)"
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Answers
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Hi @J13513
To get time series data for funds it is advised to use NAV values.
Here is the example:
df, err=ek.get_data(['LP68467397','LP68402195'],['TR.NETASSETVAL.date','TR.NETASSETVAL'], {'SDate':'-1AM', 'Edate':'0D'})
df0 -
The fund prices (e.g. NAV) from Lipper is the raw price reported by fund houses, it may not reflect the total return performance of the fund. For example, the NAV is not adjusted for distributions paid out nor events like unit split / merge.
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Thank-you both for your input @marcin.bunkowski & @Wan Po.Lee. @Wan Po.Lee @marcin.bunkowski would you be able to suggest how I might find time-series data that matches that on Eikon for these funds - I'm looking for the same 'Cumulative Performance' via the API except, more frequently than just the discrete 1month-3month-6month. Daily would be preferable - I've attached a cumulative-fund-perf.png , data-item-browser.pngscreenshot for clarity.
Your help is much appreciated.
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Hi @Wan Po.Lee
Currently there is no possibility to retrieve that data for a custom interval using Eikon, only predefined periods are available.
However you can calculate it using one of our other products - Lipper for Investment Management. For more details please reach out to your Account Manager.
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Thank-you once again @Wan Po.Lee - this is very useful. I do indeed have access to the 'Lipper for Investment Management' - will check this out also.
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Thanks @marcin.bunkowski
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