Hot answers tagged analysis
17
There is a very good reason why the ratio
$$\frac {mean(HIGH-LOW)}{mean(|CLOSE-OPEN|)} \approx 2$$
on various financial series. If the price of a security evolves according to a Wiener process beginning at the opening bell and throughout the day, and the drift is negligible for that period of time, i.e.$\mu=0$, then the denominator of the above ratio ...
2
If you just want to run some simplistic technical analysis on quotes, then select the last quote for each unique timestamp. That will ensure that you don't have duplicate timestamps. If you must have it evenly spaced (i.e. no gaps from one second to another), then you can reuse the previous quote to fill-in the missing value.
1
To help you understand why you need to follow recipes (like chrisaycock's) just have a look at your tick data. You will find ticks clustered at some points in time while they seem scarce at others.
If you proceed with your recipe 2, you will lose those clusters of activity and stretch them out. In periods of low activity you will condense the market.
...
1
Despite the rather unconventional terminology used I would say you are pretty much spot on with what you are doing and what you try to achieve. I would, however use log returns in order to get an identical percentage no matter whether you measure the distance from 100 -> 90 or 90 -> 100, for example. You can also standardize the value you capture by ...
1
As you said, this sort of financial data can be well aggregated using cubes; icCube is for example a fast in-memory OLAP server you can access via XMLA clients or JAVA or Javascript native API (on top of the MDX language and HTTP protocol).
[edit after comment] open/close/high/low are types of aggregation supported by icCube; so creating this kind of 'bars' ...
Only top voted, non community-wiki answers of a minimum length are eligible
