Publication: Automatic generation of trading rules adjusted to the market state
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2019-06
Defense date
2019-06-20
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Abstract
The search for profitable trading strategies has been driving research efforts for
decades. Among the many different approaches that can be used to design trading
rules (such as Particle Swarm Optimization, Genetic Algorithms, Artificial Neural
Networks and Fuzzy Methods), there are some based on evolutionary computation
that are especially interesting due to three key features: the process of rule generation
is automatic, the resulting rules are interpretable, and their structure is flexible.
Since Allen and Karjalainen published their seminal piece on evolution of trading
rules using Genetic Programming (GP), many authors have made related contributions
either based on the same technique, or Grammatical Evolution (GE).
Most of these contributions generate investment rules based on a combination
of raw market data and technical indicators and, unlike related approaches that
use genetic algorithms or evolution strategies to optimize predefined rules, these
have the advantage of creating flexible structures automatically.
A common limitation is that it is often the case that the approaches are static
and do not take into account the structural changes of the state of the market. Given
that this phenomenon is very prevalent in financial time series, the decision rules
are commonly derived from market environments that do not hold in test periods.
The problem of adjusting to structural changes is that we must choose between
two opposite extremes: keeping the same model over time, or updating it
constantly. Even though the second might seem, at least in principle, more appropriate,
there is a possibility that the constant change in the model will have undesirable
consequences due to transaction costs. The evolutionary process of GP/GE
considers commissions throughout the period as part of the fitness function, and
that makes it select rules that generate a limited number of signals. However, it is possible that a constant model update interferes with that endogenous control
mechanism of the number of purchase and sale orders.
This Tesis tackles with dynamic trading system solutions based on the use of
ensembles and GE. The approach combines the possibility of changing the model
as a reaction to changes in the price generation mechanism, with an inertia component
that mitigates the consequences of overtrading. We also work with a different
approach that is not based on ensembles but on a system that takes advantage of
an internal hysteresis mechanisms that is part of the own models.
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Keywords
Trading rules, Evolutionary computation, Market state, Reglas de inversión, Estado de mercado, Generación automática