Robot James You’ve probably noticed that there’s a US election on the horizon. This is an event of known uncertainty: a “known unknown” in the now immortal language of Donald Rumsfeld. In trading, we sometimes observe marginal pricing inefficiencies around these “known unknowns”. For example, ahead of stock earnings announcements or significant economic or policy announcements, we tend …
This is a review of Positional Option Trading by Euan Sinclair. Trading books set a low bar for the reviewer. 99% are full of facile feel-good advice (don’t fight the trend, always use a protective stop). The 1% that are useful tend to either be dry technical treatments (quants who don’t trade), or sporadically helpful …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
