Stock selection heuristics for performing frequent intraday trading with genetic programming
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- @Article{Loginov:GPEM,
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author = "Alexander Loginov and Malcolm Heywood and
Garnett Wilson",
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title = "Stock selection heuristics for performing frequent
intraday trading with genetic programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2021",
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volume = "22",
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number = "1",
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pages = "35--72",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Stock,
Trading, Intraday, Portfolio",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-020-09390-5",
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abstract = "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).",
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notes = "Faculty of Computer Science, Dalhousie University,
6050 University Avenue, Halifax, NS, Canada",
- }
Genetic Programming entries for
Alexander Loginov
Malcolm Heywood
Garnett Carl Wilson
Citations