Financial machine learning
The goal of the fml package is to learning about the emerging field of financial
machine learning and its applications in computer age econometrics. This package contains templates for reports, and functions and workshops
using in Algorithmic trading and
investment) taught by Barry
Quinn in Queen’s Management School.
The package os inspired by the fantastic work of Marcos López de Prado, and his books entitled Advances in Financial Machine Learning and Machine learning for Asset Managers.
Installation
Install/or reinstall the package from GitHub using the following.
remove.packages('fml')
.rs.restartR()
remotes::install_github("quinfer/fml")Example
This is a basic example which shows you how to solve a common problem:
library(fml)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
#> ✓ tibble 3.1.6 ✓ dplyr 1.0.7
#> ✓ tidyr 1.1.4 ✓ stringr 1.4.0
#> ✓ readr 2.1.1 ✓ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
## basic example code
fml::daily_factors %>% summary()
#> date rm rf
#> Min. :1988-10-03 Min. :-0.0834130 Min. :0.0000000
#> 1st Qu.:1996-01-23 1st Qu.:-0.0046057 1st Qu.:0.0000210
#> Median :2003-05-19 Median : 0.0006790 Median :0.0001825
#> Mean :2003-05-15 Mean : 0.0003942 Mean :0.0001765
#> 3rd Qu.:2010-09-06 3rd Qu.: 0.0055839 3rd Qu.:0.0002231
#> Max. :2017-12-29 Max. : 0.0921039 Max. :0.0005430
#> rmrf smb hml
#> Min. :-0.0835837 Min. :-6.301e-02 Min. :-4.187e-02
#> 1st Qu.:-0.0047718 1st Qu.:-3.755e-03 1st Qu.:-2.963e-03
#> Median : 0.0005000 Median : 1.062e-04 Median :-7.734e-05
#> Mean : 0.0002178 Mean :-2.793e-05 Mean : 6.069e-05
#> 3rd Qu.: 0.0053843 3rd Qu.: 3.883e-03 3rd Qu.: 2.888e-03
#> Max. : 0.0920217 Max. : 3.561e-02 Max. : 5.784e-02
#> umd
#> Min. :-0.0813362
#> 1st Qu.:-0.0034154
#> Median :-0.0001573
#> Mean :-0.0003336
#> 3rd Qu.: 0.0030267
#> Max. : 0.0599399Function test
?fml::estRMT()Tutorials
The tutorials can be run on a local machine only. You can start the tutorials in one of two ways. First, in RStudio 1.3 or later, you will find the ATI tutorials listed in the “Tutorial” tab in the top-right pane (by default). Find a tutorial and click “Run Tutorial” to get started. Second, you can run any tutorial from the R console by typing the following line:
learnr::run_tutorial("Workshop2","fml")This should bring up a tutorial in your default web browser. You can see the full list of tutorials by running:
learnr::run_tutorial(package = "fml")Critical Essay
This package also includes a RMarkdown template for use in the critical
essay assessment. Go to File>New>R Markdown… and choose from
From Template then Report.
Datasets
FTSE 350 data
The package includes point in time FTSE350 data from 2016-2020, downloaded from Refinitiv Datastream for teaching purposes only. The data has been used to create two return series 1. A point in time Top 25 by average market value returns series 2. A current Top 30 by market capitalisation returns series
fml::ftse350
fml::ftse30_returns_mthly
fml::ftse25_rtns_mthlyDaily uk asset pricing risk factors
These are created by Essex university business school and downloaded from UK Data Service API.
fml::daily_factors"
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