A financial trading system with optimized indicator setting, trading rule definition, and signal aggregation through Particle Swarm Optimization

Algorithmic trading, a widespread practice in the financial industry, is based on the automatic signal generation based on trading rules of one or more technical analysis indicators. Generally, the parameters for computing the indicators (such as the time windows), the trading rules (converting the indicator into a trading signal) and the weights for signal aggregation (for combining the signals from a plurality of indicators) are established by the trader based on her experience and are treated as fixed inputs of the trading algorithm. In recent literature, simple optimization systems are introduced by varying only one category of parameters at a time, that is only the indicators setting, only the trading rules definition, or only the signal aggregation while keeping the remaining parameters fixed. Our research goes further and proposes an automated trading system based on simultaneous optimization of the three categories of parameters. More precisely, we consider four technical indicators widely used in financial practice, the Exponential Moving Average, the Relative Strength Index, the Moving Average Convergence/Divergence, and the Bollinger Bands and we determine the optimal signal aggregation, trading rule definition and indicator setting using the Particle Swarm Optimization metaheuristic over a commonly used fitness function, that is the net capital at the end of the trading period. We apply our trading system to the Italian index FTSE MIB and to a set of financial stocks belonging to the FTSE MIB over a multi-year period for training and testing. We generally achieve superior performance both in sample and out of sample, using a standard technical analysis system as a benchmark. Furthermore, we successfully verify the ability of the optimized trading system to accurately classify the stock price trends.