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If the only purpose is to backtest with the data, the primary (in some cases, only) access pattern is to seek to a start time and read all of the data serially through to an end time. Then, there is a strong argument for storing it in plain, flat files with binary encoding, i.e. dumping the data structs in their in-memory layout straight to disk. Storing it ...


2

Appendix I of Thompson (2007, "The tulipmania: Fact or artifact?") mentions the following dates and respective tulip price index values. In R: tms <- as.Date(c("1636-11-10", "1636-11-12", "1636-11-25", "1636-12-01", "1636-12-12", "1637-02-01", "1637-02-03", &...


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Iceberg order detection requires access to the full order log. Last trade and Level 1 (top of book) is not sufficient. Trading venues never translate iceberg order flag or non-displayed size in real-time feeds. This information is often obfuscated even in archive products. Generally, there are two criteria to detect icebergs from the order feed: a) traded ...


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In general you cannot determine this information from the public data feeds- the purpose of Iceberg orders in particular is to be hidden and difficult to detect. Also, there isn't really a standardized definition of an Iceberg, so there would be no consistent way of performing this labeling. If you could reliably identify them though, there is good money in ...


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I am not 100% sure what you need. Have you tried on Damodaran? He has data divided by regions and industries. What in particular do you need?


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There are two Zipline forks worth checking: zipline-reloaded enter link description here I have no affiliation with neither of the two, but personally I like zipline-reloaded the most, because it runs on Python 3.9 while zipline-trader only recently added support for Python 3.6. However zipline-trader supports live trading with Alpaca or IB, so you should ...


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