# Tag Info

4

I think storing in UTC format is good practice. Here couple ideas that may motivate someone to deviate from that: Some markets are subject to day light saving time shifts and thus it introduces additional computations to convert back and forth, having to keep track of the 2 times a year the shifts occur. Some only limit themselves to an individual market, ...

3

In the dot.com era the Internet was considered a-winner-takes-it-all market, new tech start-ups (like Netscape, Amazon.com and the famous Pets.com) was measured by how much the capital they where able to chew through, the logic being that the more they spend the more aggressive they were (at least in the investors' eyes), conquering this new market known as ...

1

The average would be called the mid-price, not the best in my opinion, but that depends on your modeling. Another strategy is to weight the bid and offer prices according to size, also called the micro-price or bid-offer weighted price. This has the advantage of moving your calculated price closer to where it is traded as volume is depleted from whatever ...

1

In the set of an index where all insturments are traded in the same time zone I would agree that vola pa from say weekly returns is lower than from daily returns. Besides this, the distribution of weekly returns should look "more" Gaussian than the one of daily returns. This is called aggregational Gaussianity e.g. in the paper by Rogers and Zhang. The term ...

1

It is most common to use the "square root of time" method to scale volatility (i.e. standard deviation of returns) to a year (annualize it) if needed, i.e. if the estimate is based on a sample with higher frequency (daily, weekly,..). Mathematically this requires the underlying stochastic process $(X_t)_{t\in T}$ (I've omitted some technical prerequisites ...

1

If you are familiar with programming (which is required to deal with HF data), I would strongly recommend you to use the "HighFrequency" R package. It includes a lot of procedures to clean HF data and to estimate volatility. You can find here a very good tutorial about the package. If needed you can find here some good tutorials for R. Credits: The ...

1

It is not entirely clear what you're after, since Method 1 from the question is a statistical model, while Method 2 is a statistical test. From the initial question, I'm going to make the assumption that what you're actually after is some number that summarises "momentum" on a given day. If this is the case, I would weakly prefer the Ljung-Box test ...

1

Assuming that we are talking about volatility as the standard deviation of uncorrelated random variables (in this case this would mean no autocorrelation) the variance is additive, which means that we get $\sqrt{.15^2+.2^2}=.25=25\%$. You can illustrate this result by simulation in R: > sd(rnorm(1e7,sd=.15)+rnorm(1e7,sd=.2)) [1] 0.2500001 If you want ...

1

"Burn rate" is a measure of "spend rate" relative to cash on hand. So if you have $10 million dollars, and you spend$1 million dollars a month, you will "burn through" your cash in ten months, at which time your company will either "take off," get new financing, or go under. Strategies that rely on "burn rate" are risky ones. Nevertheless, they are ...

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This kind of question is exactly why spot FX takes T + 2 to settle. Exactly why you are looking for a convention is, I would say, because the whole thing depends on the conventions your trading partners use. You need to refer to the provider from whom you are getting the data. For me, the trade date is most likely the time of the provider pricing engine ...

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no, you should use your original variables, no truncating, normalizing or whatever. And remember that you need Johansen only in case of more than one independent variable.

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Interactive Brokers provides it as a field called count. In this page of the IB API Reference Guide count is described as follows When TRADES historical data is returned, represents the number of trades that occurred during the time period the bar covers.

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Its not available for free anywhere that I know of. Your only option is to purchase tick data for the instruments you're interested in and then count the ticks per trading session (or whichever timeframe you want to use). EDIT: This link might be interesting for you regarding how to get market data. http://www.quantshare.com/sa-426-6-ways-to-download-free-...

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