Things I've learned this past year

So it's been about a year since I left the quantitative analyst job to trade full time, here are some things I've learned.

• It's pretty easy to find inefficiencies for profitable trading, it just takes a ton of work finding the MOST profitable trades. 

• A good trade must always start with robust theory, therefore when the discovered inefficiency erodes, it would be simple and cheap to stop investing capital into it.

• Back testing historical data has only 1 real purpose: Estimating transaction costs, explicit and implied, to determine whether the fundamental edge is large enough.

• About code writing for automation. It more often adds another layer of liabilities than value; therefore unless the trade absolutely requires it, it's better to do everything by hand.

• Commodity futures/options are the way to go as a self employed trader due to leverage available.

• Even with profitable trading, life still gives you plenty of other challenges. It never gets easy.

• There's still a ton of stuff I have to learn.

Porsche's influence on VW in 2008 (Financial Documentary)

During the crisis of 2008, Volkswagon was the only car company whose stock price seemed way out of line compared to other industry participants, prompting many traders to sell VW short, and lose money. This documents attempts to take a deeper look into the incident, and Porsche's role.

Moral of the story: if everybody can see it, there's probably no money to be made trying to exploit it.

Enjoy the documentary.

Part 1/3




Part 2/3




Part 3/3

S&P500 Daily Return Distribution

Came across a Cook Pine Capital report off my daily reading, and noticed some interesting S&P500 daily return statistics. This not only supports the notion that equity index return distributions are heavily fat tailed, but also that Central Limit Theorem does not always apply in real life.

Description Statistics for S&P Daily Return Data (1927 ~ 2008)
Sample Size: 20,319
Mean: 0.026%
Standard Deviation: 1.182%
Kurtosis: 18.347(!)
Skewness: -0.098

Actual vs. Normal Distributions