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Stock selection heuristics for performing frequent intraday trading with genetic programming - 2020

Research Area:  Machine Learning


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).

Author(s) Name:  Alexander Loginov, Malcolm Heywood & Garnett Wilson

Journal name:   Genetic Programming and Evolvable Machines

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s10710-020-09390-5

Volume Information:  volume 22, pages 35–72 (2021)