Machine learning at central banks

https://www.bankofengland.co.uk/working-paper/2017/machine-learning-at-central-banks

Published on 01 September 2017

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Optimal Portfolios

 The crash course on financial portfolio optimization, with application in R. The slides are available here, and R codes from there (in a Markdown). The first and second part is still online, here.

via Optimal Portfolios #2 — Freakonometrics

Optimal Portfolios #1 — Freakonometrics

 

Optimal Portfolios, or sort of…

Last week, we got our first class on portfolio optimization. We’ve seen Markowitz’s theory where expected returns and the covariance matrix are given, > download.file(url=”http://freakonometrics.free.fr/portfolio.r”,destfile = “portfolio.r”) > source(“portfolio.r”) > library(zoo) > library(FRAPO) > library(IntroCompFinR) > library(rrcov) > data( StockIndex ) > pzoo = zoo ( StockIndex , order.by = rownames ( StockIndex ) )…

via Optimal Portfolios, or sort of… — Freakonometrics

Descargar S&P 500 Stock Data desde Google/Quandl con R.

(This article was first published on R – Curtis Miller’s Personal Website, and kindly contributed to R-bloggers) DISCLAIMER: Any losses incurred based on the content of this post are the responsibility of the trader, not me. I, the author, neither take responsibility for the conduct of others nor offer any guarantees. None of this should…

via Downloading S

Stock Trading Analytics and Optimization in Python with PyFolio, R’s PerformanceAnalytics, and backtrader.

(This article was first published on R – Curtis Miller’s Personal Website, and kindly contributed to R-bloggers) Introduction Having figured out how to perform walk-forward analysis in Python with backtrader, I want to have a look at evaluating a strategy’s performance. So far, I have cared about only one metric: the final value of the…

via Stock Trading Analytics and Optimization in Python with PyFolio, R’s PerformanceAnalytics, and backtrader — R-bloggers

Data Science for Business – Time Series Forecasting Part 3: Forecasting with Facebook’s Prophet.

(This article was first published on Shirin’s playgRound, and kindly contributed to R-bloggers) In my last two posts (Part 1 and Part 2), I explored time series forecasting with the timekit package. In this post, I want to compare how Facebook’s prophet performs on the same dataset. Predicting future events/sales/etc. isn’t trivial for a number…

via Data Science for Business – Time Series Forecasting Part 3: Forecasting with Facebook’s Prophet — R-bloggers

Time Series Forecasting Part 2: Forecasting with timekit

(This article was first published on Shirin’s playgRound, and kindly contributed to R-bloggers) In my last post, I prepared and visually explored time series data. Now, I will use this data to test the timekit package for time series forecasting with machine learning. Forecasting In time series forecasting, we use models to predict future time…

via Data Science for Business – Time Series Forecasting Part 2: Forecasting with timekit — R-bloggers