• timco

Bubbles, Booms, and Busts

My first bread crumb.

Where I began. A natural starting place for my adoption of computer science would be in quantitative finance. The two styles of investing are fundamental and technical. One old-school and one new-school. Quantitative finance falls under the second category and, as you can tell, has a very technical side to it.

For this, I found The site allows everyday traders to build there own altos that essentially trade for them once you code up a financial methodology. Fun thing to dip the toes with! Thankfully, I had a plethora of financial theories to think about. Also have to thank Jared Broad the founder for helping me get situated when starting out. A founder willing to help me out? No need to say much more.

Theoretically speaking, I leveraged a macroeconomic indicator (Cape ratio) to identify when my microeconomic triggers (MACD, RSI) to trade in this bubble environment since early 2000's. As we all know, the dot com and financial crashes have made this era in finance quite unprecedented. You'll see in the code I backtested against technology, finance, and social media stocks to see if I could generate good returns with these volatile types in volatile times. The idea was to understand when markets were booming and when they were crashing to their own dooms. If I know that, I can use the microeconomic triggers to buy on the boom and actually short shell on the bust. So I'd be profiting from these ridiculous sways in the markets no matter the direction.

My results I posted though were trading the S&P index to verify performance since I could have a base performance to score against. You'll see how I don't lose out during the down markets of the dot com and financial crash! Using this algo, I ended up beating the Sharpe Ratio of the S&P by 4x during the same time period. Not too shabby.

github repo

discussion on quantconnect

My results!

#breadcrumbs #random