Intraday trading attempts to obtain a profit from the microstructure implicit in price data. Intraday trading implies many more transactions per stock compared to long term buy-and-hold strategies. As a consequence, transaction costs will have a more significant impact on the profitability. Furthermore, the application of existing long term portfolio selection algorithms for intraday trading cannot guarantee optimal stock selection. This implies that intraday trading strategies may require a different approach to stock selection for daily portfolios. In this work, we assume a symbiotic genetic programming framework that simultaneously coevolves the decision trees and technical indicators to generate trading signals. We generalize this approach to identify specific stocks for intraday trading using stock ranking heuristics: Moving Sharpe ratio and a Moving Average of Daily Returns. Specifically, the trading scenario adopted by this work assumes that a bag of available stocks exist. Our agent then has to both identify which subset of stocks to trade in the next trading day, and the specific buy-hold-sell decisions for each selected stock during real-time trading for the duration of the intraday period. A benchmarking comparison of the proposed ranking heuristics with stock selection performed using the well known Kelly Criterion is conducted and a strong preference for the proposed Moving Sharpe ratio demonstrated. Moreover, portfolios ranked by both the Moving Sharpe ratio and a Moving Average of Daily Returns perform significantly better than any of the comparator methods (buy-and-hold strategy, investment in the full set of 86 stocks, portfolios built from random stock selection and Kelly Criterion).

中文翻译：

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用基因编程进行频繁日内交易的股票选择启发式

日内交易试图从价格数据中隐含的微观结构中获取利润。与长期买入并持有策略相比，日内交易意味着每只股票的交易量要多得多。因此，交易成本将对盈利能力产生更显着的影响。此外，将现有的长期投资组合选择算法应用于日内交易并不能保证最佳的股票选择。这意味着日内交易策略可能需要不同的方法来为日常投资组合选择股票。在这项工作中，我们假设了一个共生遗传编程框架，该框架同时共同进化决策树和技术指标以生成交易信号。我们将这种方法推广到使用股票排名启发式方法来识别日内交易的特定股票：移动夏普比率和每日回报的移动平均线。具体来说，这项工作采用的交易场景假设存在一袋可用股票。然后，我们的代理必须确定在下一个交易日要交易的股票子集，以及在盘中期间的实时交易期间对每只选定股票的具体买入-持有-卖出决策。对建议的排名启发式与使用众所周知的凯利准则进行的股票选择进行了基准比较，并证明了对建议的移动夏普比率的强烈偏好。此外，按移动夏普比率和每日回报移动平均线排名的投资组合的表现明显优于任何比较方法（买入并持有策略、投资于 86 只股票的全部组合、