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For CME group, you might want to pay attention the methodology. There is pre-liminary and final numbers provided for settlement: The disclaimer on the data from the source is: Disclaimer: Files are published periodically, initially as preliminary settlement prices, which are subject to change until final settlement prices are posted at approximately 6:00 p.m....


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For ICE, all the settlement times are in one document, which is here: https://www.theice.com/publicdocs/futures/Designated_Settlement_Periods_Volume_Thresholds.pdf On your Bloomberg data, how do you know if you have closing or settlement prices? This is determined by the user's settings in GFUT --- you can choose either last trade or settlement to be ...


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At 14:12:06 there was what is called an "intermarket-sweep". There were only about 12,300 trades in CTAS all day, but at 14:12:06 1716 trades occurred. This means that someone really, really, really, wanted to sell this stock. They kept selling and selling and cleaned out the entire book across all markets. This is a real trade and you could ...


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This could happen if somebody was stupid, set flags to indicate they did not want to get routed to the best orders because they wanted to trade on a certain exchange, and got terrible pricing as a result. This could also happen if that person sent a very aggressive IOC order. However, I doubt that is the case: Most smart order routers would not do this and ...


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Interactive brokers have it. https://interactivebrokers.github.io/tws-api/tick_types.html You need data subscription.


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You have not said whether the prices are sorted by date (your function requires it), and you have not said what the desired final data structure should be. But here is one way to do it. Start with your example dataset: df <- data.frame(date = as.Date(c("2019-11-29", "2019-12-31", "2014-01-31",...


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I do not have a R/dplyr at my hand right now, but the following should work: nyseamex %<>% group_by(name) %>% mutate(mon_return = adjprice/lag(adjprice)-1) %>% ungroup() The first operator %<>%is the re-assignment operator, effectively x=f(x), and when using any of the pipe operators (%>% and %<>%), the first argument to the ...


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It turns out Polygon.io provides the endpoint I'm looking for for $199/month, they just hadn't indicated in their docs that in addition to date ranges, one can also use timestamp ranges to find ticks intraday within specific intraday ranges.


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Barchart.com has such an API. It is not free (but I am not aware of any intraday data for free, except when sandboxed [on Quantopian, e.g.]


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Despite the initial reaction, this is actually an interesting question. A related question arises naturally in the context of filling in missing financial time series data or perhaps in back-testing path dependent strategies again with data limitations. An approach based on a Brownian bridge appears here.


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