Financial Data Manipulation in dplyr for Quant Traders

In this post, we’re going to show how a quant trader can manipulate stock price data using the dplyr R package. Getting set up and loading data Load the dplyr package via the tidyverse package. if (!require(‘tidyverse’)) install.packages(‘tidyverse’) library(tidyverse) First, load some price data. energystockprices.RDS contains a data frame of daily price observations for 3 …

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Get Rich Quick Trading Strategies (and why they don’t work)

Every aspiring millionaire who comes to the markets armed with some programming ability has implemented a systematic Get Rich Quick (GRQ) trading strategy. Of course, they don’t work. Deep down even the greenest of newbies knows this. Yet, still, we are compelled to give them a try, just once, just for fun (or so we …

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How To Get Historical S&P 500 Constituents Data For Free

spx constituents historical mean return

In this post, we are going to construct snapshots of historic S&P 500 index constituents, from freely available data on the internet. Why? Well, one of the biggest challenges in looking for opportunities amongst a broad universe of stocks is choosing what stock “universe” to look at. One approach to dealing with this is to …

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How to Hedge a Portfolio with Put Options

There are 2 good reasons to buy put options: because you think they are cheap because you want downside protection. In the latter case, you are looking to use the skewed payoff profile of the put option to protect a portfolio against large downside moves without capping your upside too much. The first requires a …

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Pairs Trading Literature Review

This post summarises the key lessons of the academic literature that has been published on pairs trading.  The key themes are highlighted at the end of the page. Pair Trading Literature Review Gatev, Goetzmann, Rouwenhorst – “Pairs Trading: Performance of a Relative Value Arbitrage Strategy” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=141615 This is the first meaningful academic paper on pair …

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Efficiently Simulating Geometric Brownian Motion in R

For simulating stock prices, Geometric Brownian Motion (GBM) is the de-facto go-to model. It has some nice properties which are generally consistent with stock prices, such as being log-normally distributed (and hence bounded to the downside by zero), and that expected returns don’t depend on the magnitude of price. Of course, GBM is just a …

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