In this post, we explain how to use SQL to extend back ETF price data with total return data from mutual funds or indexes. On Zero to Robot Master Bootcamp, we teach how to build a portfolio of three automated systematic trading strategies. One of them is a long term Risk Premia Harvesting strategy […]
Read more...Nearly everyone starts trading with unrealistic expectations. “If I make a 0.5% returns every day I can make over $100k in a year on 20k of starting capital.” 0.5% return every day sounds realistic, right? Wrong. Let’s sense check that… If we could make 0.5% returns every day… then our 20k would be worth: $123,000 […]
Read more...In the Robot Wealth Pro Community, we’ve started doing weekend “quant-teasers” where we discuss the solutions to quant problems. Here is a recent one… Why aren’t calls more expensive than puts for an asset which is more likely to go up than down? We have an asset trading at $100 for which the distribution of […]
Read more...Here’s a round-up of our new articles this week. They cover crash protection, sloppy, noisy regressions, and data-munging skills. Finding Options for Effective Crash Protection Large capital losses can be devastating to your trading account. A couple of weeks ago, we explained how you can use SPY put options to protect your portfolio against severe market […]
Read more...When you’re working with large universes of stock data you’ll come across a lot of challenges: Stocks pay dividends and other distributions that have to be accounted for. Stocks are subject to splits and other corporate actions which also have to be accounted for. New stocks are listed all the time – you won’t have […]
Read more...One way we can quantify a stock’s movement relative to the market index is by calculating its “beta” to the market. To calculate the beta of MSFT to SPY (for example) we: calculate daily MSFT returns and daily SPY returns align the returns with one another regress MSFT returns against SPY returns. This shows the […]
Read more...Here’s a round-up of our new articles this week. They cover options trading, digital signal processing, data munging and Kris’s luxurious moustache… Trading Insanity! Every new trader tries out a few insane trading ideas! In a new series on the blog, Kris explores three insane trading strategies that tempted him back when he didn’t know any […]
Read more...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 […]
Read more...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 […]
Read more...If you want to make money trading, you’re going to need a way to identify when an asset is likely to be cheap and when it is likely to be expensive. You want to be a net buyer of the cheap stuff and a net seller of the expensive stuff. Thanks, Capitain Obvious. You’re welcome. […]
Read more...