Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition
The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. The presented paper proposes a new approach, based on Intelligent Computation, in particular genetic algorithms, which aims to manage a financial portfolio by using technical analysis indicators (EMA, HMA, ROC, RSI, MACD, TSI, OBV). In order to validate the developed solution an extensive evaluation was performed, comparing the designed strategy against the market itself and several other investment methodologies, such as Buy and Hold and a purely random strategy. The time span (20032009) employed to test the approach allowed the performance evaluation under distinct market conditions, culminating with the most recent financial crash. The results are promising since the approach clearly beats the remaining approaches during the recent market crash.
optimization technical analysis portfolio composition stock trading evolutionary computation