# Data mining of financial time series and pattern discovery - beyond technical analysis?

There are lot of works about pattern discovery in financial time series - but all of them (known to me) are connected with technical analysis and that is field (as said by many) that is not rigorous science. So - are there pattern discovery approaches that goes beyond technical analysis and that can be called science - e.g. ones that try to discover parameters of hidden markov model or that otherwise try to model the different financial regimes as responses to the macroeconomic events (e.g. country's position in the economic cycle) or as responses to the other events, e.g. by changing tax legislation, by monetary policy changes (interest rate etc.), by governments' stimulus efforts, etc.?

• If any of the answers were helpful your acceptance of one of them is highly appreciated in this community - Thank you – vonjd Jan 20 '17 at 9:16

I think it is important to understand the following: Technical analysis is not rigorous not because of the used data per se but because of the used methodology!

Classical technical analysis uses ill-defined graphical patterns and has no good quantitative methodology for testing the accuracy of the results.

Many of the same problems often haunt classical fundamental analysis which uses different metrics so the big difference to quantitative finance - as I understand it - is a mathematical systematization of those concepts and using the scientific method to find out what works and what doesn't.

On top of that with quantitative finance und especially machine learning (which is the term used nowadays) you could use the full construction kit of algorithms that can learn from and make predictions on data.

But even classical technical analysis can be made rigorous, see e.g. my answer here:
https://quant.stackexchange.com/a/8258/12

Here is my understanding of when technical analysis and mathematical techniques differ.

Technical analysis predominantly utilizes three things:

1. Price levels
2. Indicators on price levels
3. Trading using rules based on 1 and 2.

Anything outside of 1, 2 and 3 is beyond technical analysis. Naturally, your analysis can involved all 3, but once you use a technique outside those three, you are slowly inching towards statistical and mathematical techniques. Examples follow.

The typical technical analysis backtester is, pick an asset, pick an interval, define an indicator on OHLC bars in that interval and then buy when indicator > value and sell when indicator < value.

Suppose, now you have a trading systems defined by parameter set. And you perform cross validation to test its predictive power. While you have used technical analysis to devise the trading rules, cross validation is a statistical technique.

Suppose now that you've decided to perform you analysis. But instead of looking at price levels, you look at the returns of an asset instead of price levels. To me, using returns of an asset is more in the domain of mathematical analysis than technical analysis.

Finally, there is a host of other trading strategies that originates from the return series. I can convincingly say that these are NOT technical analysis.