With simple technical analysis tools and simple procedures we can build a successful trading strategy that will provide positive long-term cash flows and ensure zero probability of failure in the periods of non-flourishing markets and in the periods of unexpected market shocks as well. In order to consider financial time series heteroscedasticity we must include volatility, which represents the size of the price movements and a capital risk. How do we know that the built strategy will achieve great returns? Actually we do not, because we do not know the future price movement. What we can do is to backtest our rules on the historical data and develop a methodology for measuring performance. The outcomes represent historical trading profits, which serve for empirical efficiency assessment of the built strategy. When we are interpreting the outcomes we must be careful about assumptions. There are two very important assumptions - knowing the history and the assumption of zero operational costs.
Only strategies with technical analysis indicators and patterns identifiable by programming code can be backtested. For instance, simple algorithms like $k$-means clustering and kernel regression determine supports and resistances, while moving averages that are also a part of many indicators, define position sizing and entry signals.
Backtesting is also used for optimisation. Optimisation is an important process in strategy building and/or strategy analysis. Plenty of original rules' parameters can be slightly modified to provide much better outcomes.
In the end, all analysis results, current information, additional tools, common sense etc. are used for future expectation about strategy performance and therefore for trading opportunities.
|
