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You could try Arctic. Other open source column-oriented databases that you may not have considered include LucidDB and C-Store.


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"The Art of R Programming (A Tour of Statistical Software Design)" by Norman Matloff. It has quite high marks on Amazon. Moreover, you can find a legal version of this book on the Internet.


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Introduction to R for Quantitative Finance received a favorable review here: http://www.thertrader.com/category/book-review/ Besides finance-specific books, perhaps 'R Cookbook'?


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Suppose at the close of August 27 the price exceeds EMA200 for the first time, then as long as you look at the probability of up down on August 28 (and following days) there is no statistical bias. Similarly when it goes below on September 13 close, you must count that day as "above EMA200", but the next day will be in the "below EMA200" category. In other ...


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Regarding the relationship between white noise and a random walk, I would put it this way: a random walk is integrated white noise. [And vice versa we get a white noise when we differentiate/difference a random walk]. Or to put it in quant finance terms: white noise is like the daily changes in the S&P in points, a random walk is the S&P daily level ...


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I will assume a white noise is a process $(\varepsilon_t)$ with zero mean, no autocorrelation and constant variance $\sigma^2 > 0$ while a random walk is a process $(x_t)$ defined by $$ x_{t+1} = x_t + \varepsilon_{t+1} $$ where $\varepsilon$ is a white noise. 1) No since $Var(x_{t+1}) = Var(x_t) + Var(\varepsilon_{t+1})$ is stricly increasing while ...


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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 ...


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In no particular order here are some ideas to get you started. Liquidity (ADV, # of shares, etc) Cost Basis (Cost to put on a trade) Back test / Cross validation P-values


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Instead of wild guesses about R's/python's future in the community, here some facts: The following query on StackExchange Data Explorer counts the number of questions that have <r> or <python> tags. If you scroll down on one of the three webpages provided below, you can see a graph with data on a monthly basis. You can easily run this query on ...


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The major advantage of Python (w/ pandas) over R is that Python supports OOP (object-oriented programming). It makes sense to organize a large code base using a hierarchy of classes. Python also supports the notion of polymorphism so that we can use well-known design patterns (e.g., Strategy, Observer, etc.) in our code.


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There are a few points to address here. First, I'll start with the theoretical stuff. Realised variance is usually used to refer to a sum of squared intraday returns over a span no longer than a day, i.e. $\sum_i^n r_{t,i}^2$, where $i = 1, ..., n$ denotes the $n$ intraday returns from day $t$. This is important, because all the statistical properties that ...


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This link has a worthwhile discussion of two possible approaches: the Nearby approach (paragraph 6.6.1) and the Constant Maturity approach (para 6.6.2). http://www.value-at-risk.net/futures-prices/ . With the pluses and minuses of each. Ultimately it is going to come down to your judgement of what is best in your situation.


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There are ways to get a continuous time series from switching futures prices. These include: 1) Taking return of a leading future contract (max open interest) on every date, and 2) Taking a weighted average return among a group of leading contracts, with weights based on open interest of each contract. For example: R_average = (OI1*R1 + OI2*R2)/(OI1 + OI2) ...



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