kchodorow

Adventures in modern web programming

At this point, I’ve fallen so far behind of where JS developers are that I don’t think I’ll ever be able to figure out what’s going on. However, Vercel is a portfolio company of GV’s, so I decided to give it a valiant effort. Thus, I started at vercel.com. I went through their deploy flowContinue reading “Adventures in modern web programming”

Hassle-free LaTeX with Overleaf

There is something delightful about LaTeX. However, the last time I bothered with it was in college, since I don’t have much call for PDFs in day-to-day life. I recently came across Overleaf, which is an online LaTeX editor. The nice part is that it live-renders your work and you can right-click->Save as an PNG.Continue reading “Hassle-free LaTeX with Overleaf”

Risking it all

Sorry to keep posting financial stuff, but whatever, it’s my blog. It’s interesting how the amount of investment risk that a human can put up with is very relevant to how much they have invested, and it isn’t linear. Let’s take the case of three investors, all of whom currently can invest $1k/month and needContinue reading “Risking it all”

Part 4: compounding returns

As Einstein (maybe) said, compounding interest is the eighth wonder of the world. In the previous posts in this series, we used a very linear benchmark: 4% off of the amount contributed forever. However, this is a weird way to benchmark results. Imagine you and friend (call him Baelish) are both investing and comparing results.Continue reading “Part 4: compounding returns”

A multi-stock portfolio: comparing returns

The last posts have discussed portfolio performance with a very boring portfolio: one stock! Things get more interesting when there’s more than one stock to compare. Let’s say we have a two stock portfolio now: SPY (as before) and DOCU (Docusign). We’ll combine the two tickers into one dataframe: Now that things are getting moreContinue reading “A multi-stock portfolio: comparing returns”

Show me the money: tracking returns

Last post went over building a very simple portfolio tracker to show a portfolio’s performance over time. However, it would be easy to trick myself: “My portfolio value is going up over time, I’m doing great!” But I’m also adding money to my portfolio over time, so that money shouldn’t “count” in terms of performance.Continue reading “Show me the money: tracking returns”

Building a (very simple) portfolio tracker with pandas

All of these graphs were created in Colab. I’ve actually never found a commercial product that does everything I want, so I figured I’d build one up in a series of blog posts. We’ll see how many I get through! 🧵👇 First, we’ll get SPY’s stock history with pandas_datareader. This returns a dataframe that looksContinue reading “Building a (very simple) portfolio tracker with pandas”

The stock market is going down

Not the stocks, mind you, but the market itself. There are less than 4,000 companies listed, and new companies have less and less appetite for going public. Conversely, there are over 8,000 private-equity-backed companies in the US, and growing. There are a couple of problems with the stock market mouldering. One is that this isContinue reading “The stock market is going down”

Losing money sucks – the mathematics of loss aversion

There’s a lot of research on loss aversion: how bad people feel when they lose $50 vs. how good they feel good about gaining $50. This research is kind of taking an absolute value of emotion, positive or negative. We can represent this with emoji equations: 😀 == 😫 (large-magnitude happiness or sadness) 🙂 ==Continue reading “Losing money sucks – the mathematics of loss aversion”

The surprisingly complex math behind startup equity and taxes

Taxes for employees at startups are weird and can vastly change the amount you make. To illustrate why, let’s take a simple example. Suppose we have a group of early employees at a startup, we’ll call them the Unicorn Inc. Mafia. They’re all fresh out of college and managed to get through it without anyContinue reading “The surprisingly complex math behind startup equity and taxes”