Basics
What is technical analysis?
Technical analysis is a method used in financial markets to forecast future price movements based on the analysis of historical price and volume data.
It is primarily focused on studying patterns, trends, and statistical indicators derived from market charts and graphs. The practice of technical analysis is based on the assumption that historical price data can provide insights into the future direction of a security's price, and that market trends tend to repeat over time.
History of technical analysis.
The origins of technical analysis can be traced back to the late 19th century. Charles Dow, one of the founders of Dow Jones & Company, developed the basis for technical analysis through his observations of market price behavior.
He introduced the Dow Theory, which emphasized the importance of analyzing price patterns, volume, and market trends to identify potential trading opportunities. Over time, other prominent technical analysts, such as Ralph Nelson Elliott and Richard W. Schabacker, further refined the discipline and contributed to the development of various technical indicators and charting techniques.
The crypto-currency market is relatively new compared to the stock market and traditional currencies. It is also always open and has no sessions. Therefore, many of the old principles of technical analysis have changed drastically to accommodate for this new frontier. In our opinion, historically-proven methods should be respected, but also tested and improved.
Shortcomings of Technical Analysis
Technical analysis is a good tool for extracting data from a given market and making predictions. In fact, technical analysis is data. It is the study of prices on the exchange, and nothing more than that. This, from a purely mathematical perspective is ideal for making statistical studies. It carries a few major risks, however. One of them is that it can be made to look perfect at first glance, but not work in reality.
Why? Over-optimization
One of the biggest issues with any trading strategy, automated or not, is its ability to perform well on unknown data, such as live trading. If a strategy looks good on a backtest (historical data) but stops performing well immediately when tested on unknown data, it is over-optimized. This is a major issue that is talked about, but not stressed enough in our view. Traders who do not come from a computer science background are often not aware of the risks posed by using algorithms to optimize strategies.
This is due to the unpredictable nature of the markets and the fact that systems optimized for one type of market conditions don't work well in all market conditions. For example, a strategy used in a bullish market is not expected to perform the same on a choppy or bearish market. Also, if a specific strategy uses moving averages of volume data, for example, this volume is subject to very unpredictable change and sometimes manipulation in the future.
One way to design a good system is to reduce the amount of fixed parameters it uses. At Sofex, we have found that using multiple adaptive indicators, which seek to adjust their parameters based on calculations taken from recent market prices, instead of relying on fixed values is a great practice. Based on our experience, these adaptive indicators are more reliable in the long term and can capture market movements more quickly.
Another way to design a good system is to be mindful of each indicator’s default values and possible ranges, and not to chase historical profits by using irrational parameters.
As mentioned above, using algorithms and software tools to optimize systems is good, but needs to be monitored and tested on unknown data periodically.