Trading Algorithms for the Masses - Outline
Trading Algorithms for the Masses
What is quantitative trading?
Quantitative trading is hard
- Quantitative trading is an extremely sophisticated area of finance
- It has four main components:
- Strategy Identification - Finding a strategy, exploiting an edge and deciding on trading frequency
- Strategy Backtesting - Obtaining data, analysing strategy performance and removing biases
- Execution System - Linking to a brokerage, automating the trading and minimizing transaction costs
- Risk Management - Optimal capital allocation, "bet size" criterion and trading psychology
The story of Long Term Capital Management
- When it comes down to it, a good trading algorithm generally has to be written in C/C++ (it's the only thing fast enough)
- How fast do these guys trade? In the 2000s one of the top quant funds was single-handedly responsible for 1/6 of all trading on the Nasdaq
- Quantitative trading relies heavily on what is known as financial signal processing, the history of which can be traced back to Isaac Newton
- The result? Newton lost the equivalent of $3.7 million investing in the South Sea Company
So why use quantitative trading at all?
- The first massive quant fund, which many thought to be infallible, was Long Term Capital Management (LTCM)
- LTCM's algorithm, which was written by two Nobel-prize-winning economists and a Japanese rocket scientist, is still used today to value derivatives (forwards, futures, options, etc.)
- LTCM returned 21% (after fees) in its first year, 43% in the second year and 41% in the third year, the equivalent of turing $1 million into $2.4 million after fees
- However, even the LTCM model finally failed because it had not accounted for the Asian Currency Crisis and Russian debt default in the late 90s
- Asian currency crisis hit in 1997, bringing huge losses, but they were confident the algorithm could work in all situations and so essentially doubled-down on all their bets
- In 1998 when Russia defaulted on its debt, the fund lost $4.6 billion over a single four-month period (GRAPH - Wiki)
- Ultimately the fund collapsed and had to be bailed out by an international consortium of companies, though it still owed over $100 billion to various creditors when it was dissolved
The good news - quantitative trading is possible for anyone
- One of the posterchildren for quant trading today is Renaissance Technologies
- They have been using "black box" algorithms to invest since 1988
- Their flagship fund (closed to new investors in 1993) returned an average of 35% per year from 1988-1999, the equivalent of turing $1 million into $36.6 million after fees
- Fun fact: Renaissance is currently hiring developers!
- In 2013 Harvard grad Christopher Ivey acquired $4.5 million in funding and launched a web-based platform called Rizm
- Rizm lets individual investors with no coding skills build computer programs that select and trade stocks automatically, similar to the trading programs used by quant funds and high-frequency trading firms
- For $99 per month investors get quick cloud access to sophisticated algorithm-building tools and the capability to back-test strategies
- You say what you want to try, have it automatically back-tested to see how it would've worked in the past, and (hopefully) start making money!
- LINK: Renaissance Tech page, Rizm