Over at Abnormal Returns is an interview that Tadas Viskanta had with Dr. Wesley Gray of Turnkey Analyst about his book, Quantitative Value. It makes a great case for systematic investing (i.e. investing based on rules, not intuition) and why removing emotion from the investing process is really the best way to success in the investment world. However, he goes a step farther by differentiating between investing based on logic and based on empirical data. The argument focuses on value investing, but I think the case presented is applicable to all styles of investment.
Jay Kaeppel at Optionetics.com has a semi-humorous post about a new algorithmic trading system that he invented today (appropriately called JK Today). On the surface, at least, it appears to be a viable system with good profit potential. In short, he uses the 4% Zweig swing as a directional indicator, and semiconductor to inverse Nasdaq-100 ratio as an overbought/oversold indicator. Again, see the post for the details and his calculations. However, the real point of his post is to show some of the thought process involved in developing a set of rules for a trading system, and what is required of the trader to ensure that the trading system is a success. Here are the performance stats, just to entice you to read it all.
Matt Radtke at Tradingmarkets.com has a nice 6 part series on how to build a trading strategy, from conceptualization to implementation. This is a great place to start for those just beginning to learn about the world of algorithmic trading. Make sure to try out the techniques detailed by writing down your trading rules or testing them on data in Excel.
- Central Thesis
- Defining a Universe
- Additional filters
- Exit methods and stop losses
You can download historical stock data from Yahoo Finance to get started.
I recently came across a great blog run by fellow Collective2 system developer Sanz Prophet with a treasure trove of information for those just starting out with algorithmic trading. Check out the following posts:
- A quick survey of backtesting tools, including Amibroker, Quantshare, Multicharts, and IQBroker.
- Backtesting a multi-strategy algorithm in Quantopian.
- Running Quantshare in the cloud (Amazon EC2).
- An intro to Amibroker.
A great resource. Visit his blog for more.
Jev Kuznetsov at tradingwithpython.blogspot.nl has announced that he will be starting an online course to teach the programming language Python to traders starting in April. Python is one of the more popular programming languages used by quants for trading (e.g. Quantopian allows traders to code and backtest systems in Python online).