Name
Abstract | ||
|---|---|---|
Automated Trading is the activity of buying and selling financial instruments for the purpose of gaining a profit, through the use of automated trading rules. This work presents an evolutionary approach for the design and optimization of artificial neural networks to the discovery of profitable automated trading rules. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns. |
Year | Venue | Keywords |
|---|---|---|
2008 | EvoWorkshops | input data,significant return,financial instrument,automated trading rule,evolutionary single-position automated trading,profitable automated trading rule,artificial neural network,evolutionary approach,automated trading,trading strategy,profitability |
Field | DocType | Volume |
Trading strategy,Downside risk,Microeconomics,Technical indicator,Alternative trading system,Financial instrument,Artificial neural network,Algorithmic trading,Business | Conference | 4974 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-78760-7 | 1 |
PageRank | References | Authors |
0.43 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonia Azzini | 1 | 119 | 20.38 |
| Andrea Tettamanzi | 2 | 667 | 84.56 |