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Let me start with a general point: Why do you want to use these datapoints if it is so hard to understand how they are constructed? First of all 4) I am not familiar with testing momentum strategies but you should be aware that the datapoints given are not normal assets you can invest in at the end of the day because at the end of each time period they are ...


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Assuming you are at a decent university, you are better off seeing if you can get this data from Bloomberg or from a university subscription to Compustat.


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As background, Floating point precision is a way of storing numbers such that the precision is relative to the largest digit. For instance, the number $0.00123$ stored in fixed precision needs 6 digits of precision (3 zeros and the 3 non-zero numbers). However, this same number stored as floating point precision $1.23 \cdot 10^{-3}$ needs only 3 ...


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I've run into this issue as well. I can't find any official documentation, but the issue arises because the SEC makes up CUSIPs for all of the options on the list. It does this by taking the first 6 digits of the underlying equity (which makes sense, as this represents the issuer, which should be the same for the stock and the option), and then for the 7th ...


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The Harvard Bankrupcy Data Project tries to do exactly that, they seem to have data for the specified time frame, but unfortunately I cannot vouch for its accuracy. http://bdp.law.harvard.edu/filingsdb.cfm edit: They don't seem to offer the full data any more, so that indeed won't work for you. You could instead try this one, one of the sources for the ...


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Recently I found a book on earnings trading but did not have time to read thoroughly. Trading on Corporate Earnings News - John Shon I also had spent some time to see earnings surprise effects and it is a quite interesting but not easy to use topic. There is certainly a jump if the estimates and announced earnings have a large mismatch but the magnitude ...


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For any data that is not strictly tabular and unchanging in schema, you should rule out SQL solutions. Option pricing fits that description in my experience, because high-liquidity stocks, currencies, or bonds, will have a far bigger set of strikes and maturities than lower liquidity instruments. Thus in a relational database you will have to have columns ...


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Lots of people focus on data storage ability and compare KDB with other SQL/query-based databases. Such comparison is like considering "Is a Ferrari good for running a bus route?" KDB+ is capable of manipulating and querying large data set. The performance is fast (in comparison to most RDBMS) due to its column based storage, but it's not her strongest ...


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Cassandra is the obvious choice. With MongoDB or any RDBMS, you will hold all ticks in a table (collection in Mongo-speak) and index by ticker. This means that when you want to retrieve data for a ticker, the data will not be contiguously stored, and you will have a massive usage of index and random reads. Even with SSDs this is slow. For 500k ticks into ...



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