Machine Learning at the Boundary: There is nothing new in the fact that machine learning models can outperform traditional econometric models but I want to show as part of my research why and how some models make given predictions or in this instance classifications. I wanted to show the decision ...

Introduction Using Machine Learning (ML) and past price data to predict the next periods price or direction in the stock market is not new, neither does it produce any meaningful predictions. In this post I collapse down a series of asset time series data into a simple classification problem and ...

Introduction: The literature in portfolio optimisation has been around for decades. In this post I cover a number of traditional portfolio optimisation models. The general aim is to select a portfolio of assets out of a set of all possible portfolios being considered with a defined objective function. The data: ...

Strategies Introduction: Note: This post is unfinished. This post goes through a number of the technical trading functions in the TTR package here. I add definitions and show examples of how the functions work.

library(dplyr)
library(quantmod)
library(tidyquant)
library(TTR)
library(timetk)
library(tidyr)
library(ggplot2) 
library(directlabels)
library(data.table)
library(quantstrat)
library(purrr)
library(quantstrat)

We can download some data using the quantmod package which stores the different assets into ...

The most popular models for analysing the risk of portfolios are factor models, since stocks have a tendency to move together. The principal component of securities often explains a large share of it’s variance. Since we are mostly concerned with multiple assets which form a portfolio we need to ...