A game-theoretical approach for designing market trading strategies

Investors are always looking for good stock market trading strategies to maximize their profit. Under the technical school of thought trading rules are developed by studying historical market data to find trends that investors can exploit. These market trends tend to appear when certain features (narrow range, DOJI, etc.) appear in the historical data. Unfortunately, these features often appear only in partial form, which makes trend analysis challenging. In the paper we co-evolve fuzzy trading rules from market trend features. We show how fuzzy membership functions naturally handle partial form features in historical data. The co-evolutionary process is formulated as a zero-sum, competitive game to match how trading strategies are evaluated by brokerage firms. Our experimental results indicate the co-evolutionary process creates trading rule-bases that produce positive returns when evaluated using actual stock market data.