364 search results for "quantmod"

Overnight vs. Intraday ETF Returns

January 25, 2014
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I haven’t done much “googling” before posting, so this topic might have been covered elsewhere but I think it’s  really worth sharing or repeating anyway. A lot has been written about the source of  ETF returns (some insights might be found here). In a nutshell some analysis found that the bulk of the return is made

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Playing Financial Data Series(1)

January 24, 2014
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Playing Financial Data Series(1)

These days I became interested in financial data, such as stock price, exchange rate and so on. Obviously there are a lot of available models to fit, analyze and predict these types of data. For instance, basic time series model arima(p,d,q), Garch model, and multivariate time series model such  as VARX model, state space models. … Continue reading...

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Solving Quadratic Progams with R’s quadprog package

January 13, 2014
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Solving Quadratic Progams with R’s quadprog package

In this post, we'll explore a special type of nonlinear constrained optimization problems called quadratic programs. Quadratic programs appear in many practical applications, including portfolio optimization and in solving support vector machine (SVM) classification problems. There are several packages available to solve quadratic programs in R. Here, we'll work with the quadprog package. Before we dive...

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Quantitative Finance Applications in R – 2

January 9, 2014
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Quantitative Finance Applications in R – 2

by Daniel Hanson QA Data Scientist, Revolution Analytics Some Applications of the xts Time Series Package In our previous discussion, we looked at accessing financial data using the quantmod and Quandl R packages. As noted there, the data series returned by quantmod comes in the form of an xts time series object, and Quandl provides a parameter that sets...

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2013 Summary

January 6, 2014
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2013 Summary

2013 was a tough year. Trading was tough, with one of my strategies experiencing a significant drawdown. Research was tough – wasted a lot of time on machine learing techneques, without much to show for it. Also made some expensive mistakes, so all in all – it was a year I’d prefer I had avoided.

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Quantitative Finance Applications in R

December 27, 2013
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Quantitative Finance Applications in R

by Daniel Hanson, QA Data Scientist, Revolution Analytics Extracting Financial Data from Internet Source Using R (first in a series) Earlier this month, a colleague and I attended a presentation on Computational Finance in R, given by Guy Yollin of the University of Washington Applied Mathematics faculty, at a meeting of the Seattle useR Group. The first among several...

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Financial Data Accessible from R – part IV

December 13, 2013
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DataMarket is the latest data source of financial data accessible from R I came across. A good tutorial can be found here. I updated the table and the descriptions below. Source R Package Free Access Available on CRAN Provider url Yahoo, FRED, Oanda, Google Quantmod Yes Yes Quantmod Quandl Quandl Yes Yes Quandl TrueFX TFX

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Weekend Reading: F-Squared

December 6, 2013
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Weekend Reading: F-Squared

Mebane Faber posted another interesting blog post: Building a Simple Sector Rotation on Momentum and Trend that caught my interest. Today I want to show how you can test such strategy using the Systematic Investor Toolbox: Mebane thank you very much for sharing your great ideas. I would encourage readers to play with this strategy

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Averaged Input Assumptions and Momentum

December 4, 2013
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Averaged Input Assumptions and Momentum

Today I want to share another interesting idea contributed by Pierre Chretien. Pierre suggested using Averaged Input Assumptions and Momentum to create reasonably quiet strategy. The averaging techniques are used to avoid over-fitting any particular frequency. To create Averaged Input Assumptions we combine returns over different look-back periods, giving more weight to the recent returns,

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Analyzing the DVI Indicator

November 30, 2013
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Analyzing the DVI Indicator

The DVI indicator is a well-known indicator, created by David Varadi from CSS Analytics. It was introduced in 2009 as a good predictor for the S&P 500 over the past 30 years. Its performance on the S&P 500 has been studied in the blogosphere comprehensively. None of these studies, however, contained everything I was looking

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