Evolutionary optimization of trading strategies
- Publication Type:
- Thesis
- Issue Date:
- 2007
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 01Front.pdf | contents and abstract | 615.37 kB | |||
| 02Whole.pdf | thesis | 13.31 MB |
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NO FULL TEXT AVAILABLE. This thesis contains 3rd party copyright material. ----- A good many technical trading strategies have been proposed in financial
literatures and trading houses to support trading investment decisions. In
the stock market, a good trading strategy may make great profits while a bad
one could result in disastrous losses. Therefore, it is critical for stock traders
to find or tune trading strategies to maximize the profit and/or to minimize
the risk. Unfortunately, it is very challenging for financial professionals to
work out trading strategies dependable in the real world, not to mention
non-professional investors. This is due to many reasons, for instance, the
dynamic market environment, comprehensive constraints, huge quantities of
data, sensitive and inconstant strategy performance, and complicated computational
settings and development. Consequently, it is not practical for
traders to work out an 'optimal' trading strategies in a constrained market
by themselves. Therefore, the backtesting and optimization of trading
strategies are very significant for stock market participants. To this end, data
mining and related approaches have been demonstrated to play prominent
roles in optimizing trading strategies.
The existing data mining related research on the optimization of technical
trading strategies mainly concentrates on extracting interesting trading patterns
of statistical significance, demonstrating and pushing the use of specific
data mining algorithms. As a result, many interesting trading strategies are
found, while few of them are dependable in the market. The gap between
the academic findings and business expectations comes from a few reasons,
such as the over-simplification of optimization environment and evaluation
fitness. In particular, no work has been on providing a comprehensive analysis
and processing of trading strategy optimization, which consist of problem
definition, data. storage and pre-processing, parameter tuning, computational
efficiency, and system implementation, etc.
This thesis presents a systematic view of the above issues and corresponding
solutions in trading strategy optimization through developing new
and varying storage and evolutionary computing and system implementation
techniques. One of key characters of this work is its practicability of the
developed techniques and resulting findings, which have been trained and
tested in terms of huge amounts of market data, various organizational factors,
and realistic evaluation metrics. In particular, the following problems
have been addressed in this thesis, which more or less have not been analyzed
or tackled very well in the existing research and development.
First, the problem definition of trading strategy optimization is addressed
in terms of considering not only attributes enclosed in the optimization algorithms
and trading strategies, but also constraints in the target market where
the strategy to be identified and used. This presents a multi-attribute and
multi-objective constrained environment for trading strategy optimization.
Second, very detailed and enormous stock transaction data challenges efficient
data storage, access and preparation for trading strategy optimization.
This problem is answered by a new dynamic storage method which provides
a flexible mechanism to balance between storage space and access efficiency.
Third, genetic algorithm (GA) based evolutionary computing is used to
deal with the high dimension reduction of multi-attribute and multi-objective
trading strategy optimization. However, the parameter variations and discrete
fitness surfaces of GA-based optimization make it very time and computing
consuming. To this end, several techniques have been developed to
fine tune the GA-based optimization algorithms towards a more efficient and
effective way.
Fourth, the back testing and optimization of time consuming trading strategies
is very challenging. To address this challenge, parallel GA algorithms
have been developed, which can efficiently extract target trading strategies
in changing market conditions.
Fifth, the results given by GA are crisp and subject to the disturbance
of market dynamics and user preferences. This makes the findings unde-pendable
in the real markets. In order to obtain more stable solutions, an
enhanced CA algorithm with more robust deployment capability is proposed.
Finally, it is very difficult for a normal trader to build a system to deal
with the above challenges and support her/his optimization jobs. A multi-agent
system is proposed to address this issue. It integrates all the abovementioned
technologies, and can help users easily specify and execute optimization
jobs to their advantages.
In summary, this thesis explores in detail how evolutionary computing can
be effectively and efficiently applied to the optimization of technical trading
strategies. It provides not only a systematic processing of this optimization
problem, but also innovative and practical solutions to various impediments
that arise out of the optimization process.
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