Where do Algorithmic Trading Strategies Come From?

Analysis!  Whenever you see someone describing the performance of their fund/system/algorithm, you'll see the standard qualifier "past performance is not an indicator of future results". Actually this is an over-done legal statement.  It's not exactly true, otherwise people would not even bother showing you their past performance. Past performance can be an indicator for future results (unless it has been faked - for a well-known example look at the behaviour of Bernard Madoff, but that's another story). It's just a very weak indicator. However, given a choice between no indicator, and a weak indicator, you would probably choose the weak one. This is the basis behind algorithmic trading strategy development. By analyzing past price history, and particularly patterns therein, people aim to find means of predicting future behaviour. Amazingly, this actually works. However, there are many pitfalls and it takes experience to be able to do this without finding false positives. I won't list any secrets here, but will point out a few of the well-known things that you need to be careful of when looking for patterns. Finding patterns is where analysis comes in.

Technical Analysis versus Statistical Modeling

There are many ways you can think about finding patterns. Some people like to do this visually. However, this generally doesn't lead to good codeable algorithms. Perhaps the industry standard for some time has been technical analysis. People do use this successfully.  Very loosely speaking, technical analysis is a system for looking at price history plots ("charts") and using them to decide when to enter and exit trades.  While this is a perfectly reasonable thing to do, and many people do this successfully, I avoid this approach because I have a strong preference (because of my background) for statistical modeling.  I use a mixture of regression and time series analysis, along with a few of my own custom tools, to analyze prices and develop strategies.   Interestingly, the results are not always different.  There are many cases where the signals generated by regression models are approximately aligned with signals that could be obtained using methods from technical analysis.  

The key difference between the two approaches is the following.   While technical analysis looks for patterns that will tell you if the price is about to go up or down, statistical models make statements about the probability distributions of price movements. In other words, technical analysis tells you when to go long or short, but it doesn't make any statement beyond that. Statistical modeling has at least two advantages:

Statistical modeling also has a disadvantage that is worth noting.  That is,

There are many nonlinearities apparent in markets.  I won't explain the ones I use (trade secrets).  But to summarize, technical analysis indirectly provides ways of using important nonlinearities, while the common statistical models are not well-suited to capture these nonlinearities.

Ideally, one would aim for the best of both worlds - that is to have good nonlinear statistical models that capture some of the things that technical analysis makes use of.

On a side note, the lack of probabilistic content in technical analysis is recognized.  The book Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals, by David Aronson, appears to be a nice effort to address this problem.

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