Hi,
3 new papers on trading (to appears in IEEE Transactions on Evolutionary Computation)
Give your email if you want the PDF
=======================================================================
ZIP60: Further Explorations in the Evolutionary Design of Trader Agents and Online Auction-Market Mechanisms
D. Cliff (04444821.pdf)
Abstract The zero-intelligence plus (ZIP) adaptive automated trading algorithm has been demonstrated
to outperform human traders in experimental studies of continuous double auction (CDA) markets
populated by mixtures of human and âsoftware robotâ traders. Previous papers have shown that
values of the eight parameters governing behavior of ZIP traders can be automatically optimized
using a genetic algorithm (GA), and that markets populated by GA-optimized traders perform better
than those populated by ZIP traders with manually set parameter values. This paper introduces a
more sophisticated version of the ZIP algorithm, called âZIP60,â which requires the values of 60
parameters to be set correctly. ZIP60 is shown here to produce significantly better results in
comparison to the original ZIP algorithm (called âZIP8â hereafter) when a GA is used to search
the 60-dimensional parameter space. It is also demonstrated here that this works best when
the GA itself has control over the dimensionality of the search-space, allowing evolution to guide
the expansion of the search-space up from 8 parameters to 60 via intermediate steps. Principal
component analysis of the best evolved ZIP60 parameter-sets establishes that no ZIP8 solutions
are embedded in the 60-dimensional space. Moreover, some of the results and analysis presented
here cast doubt on previously published ZIP8 results concerning the evolution of new âhybridâ
auction mechanisms that appeared to be improvements on the CDA: it now seems likely that
those results were actually consequences of the relative lack of sophistication in the original
ZIP8 algorithm, because âhybridâ mechanisms occur much less frequently when ZIP60s are used.
=======================================================================
Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model
H. Huang, M. Pasquier, C. Quek (04444822.pdf)
Abstract Financial market prediction and trading presents a challenging task that attracts great interest
from researchers and investors because success may result in substantial rewards. This paper describes
the application of a hierarchical coevolutionary fuzzy system called HiCEFS for predicting financial time series.
A novel financial trading system using HiCEFS as a predictive model and employing a prudent trading
strategy based on the price percentage oscillator (PPO) is proposed. In order to construct an accurate
predictive model, a form of generic membership function named Irregular Shaped Membership Function
(ISMF) is employed and a hierarchical coevolutionary genetic algorithm (HCGA) is adopted to automatically
derive the ISMFs for each input feature in HiCEFS. With the accurate prediction from HiCEFS and the
prudent trading strategy, the proposed system outperforms the simple buy-and-hold strategy, the
trading system without prediction and the trading system with other predictive models (EFuNN,
DENFIS and RSPOP) on real-world financial data.
=======================================================================
Computational Intelligence for Evolving Trading Rules
A. Ghandar, Z. Michalewicz, M. Schmidt, T.-D. To, R. Zurbrugg (04444823.pdf)
Abstract This paper describes an adaptive computational intelligence system for learning trading rules.
The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary
process the system learns to form rules that can perform well in dynamic market conditions. A
comprehensive analysis of the results of applying the system for portfolio construction using portfolio
evaluation tools widely accepted by both the financial industry and academia is provided
=======================================================================
3 new papers on trading (to appears in IEEE Transactions on Evolutionary Computation)
Give your email if you want the PDF
=======================================================================
ZIP60: Further Explorations in the Evolutionary Design of Trader Agents and Online Auction-Market Mechanisms
D. Cliff (04444821.pdf)
Abstract The zero-intelligence plus (ZIP) adaptive automated trading algorithm has been demonstrated
to outperform human traders in experimental studies of continuous double auction (CDA) markets
populated by mixtures of human and âsoftware robotâ traders. Previous papers have shown that
values of the eight parameters governing behavior of ZIP traders can be automatically optimized
using a genetic algorithm (GA), and that markets populated by GA-optimized traders perform better
than those populated by ZIP traders with manually set parameter values. This paper introduces a
more sophisticated version of the ZIP algorithm, called âZIP60,â which requires the values of 60
parameters to be set correctly. ZIP60 is shown here to produce significantly better results in
comparison to the original ZIP algorithm (called âZIP8â hereafter) when a GA is used to search
the 60-dimensional parameter space. It is also demonstrated here that this works best when
the GA itself has control over the dimensionality of the search-space, allowing evolution to guide
the expansion of the search-space up from 8 parameters to 60 via intermediate steps. Principal
component analysis of the best evolved ZIP60 parameter-sets establishes that no ZIP8 solutions
are embedded in the 60-dimensional space. Moreover, some of the results and analysis presented
here cast doubt on previously published ZIP8 results concerning the evolution of new âhybridâ
auction mechanisms that appeared to be improvements on the CDA: it now seems likely that
those results were actually consequences of the relative lack of sophistication in the original
ZIP8 algorithm, because âhybridâ mechanisms occur much less frequently when ZIP60s are used.
=======================================================================
Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model
H. Huang, M. Pasquier, C. Quek (04444822.pdf)
Abstract Financial market prediction and trading presents a challenging task that attracts great interest
from researchers and investors because success may result in substantial rewards. This paper describes
the application of a hierarchical coevolutionary fuzzy system called HiCEFS for predicting financial time series.
A novel financial trading system using HiCEFS as a predictive model and employing a prudent trading
strategy based on the price percentage oscillator (PPO) is proposed. In order to construct an accurate
predictive model, a form of generic membership function named Irregular Shaped Membership Function
(ISMF) is employed and a hierarchical coevolutionary genetic algorithm (HCGA) is adopted to automatically
derive the ISMFs for each input feature in HiCEFS. With the accurate prediction from HiCEFS and the
prudent trading strategy, the proposed system outperforms the simple buy-and-hold strategy, the
trading system without prediction and the trading system with other predictive models (EFuNN,
DENFIS and RSPOP) on real-world financial data.
=======================================================================
Computational Intelligence for Evolving Trading Rules
A. Ghandar, Z. Michalewicz, M. Schmidt, T.-D. To, R. Zurbrugg (04444823.pdf)
Abstract This paper describes an adaptive computational intelligence system for learning trading rules.
The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary
process the system learns to form rules that can perform well in dynamic market conditions. A
comprehensive analysis of the results of applying the system for portfolio construction using portfolio
evaluation tools widely accepted by both the financial industry and academia is provided
=======================================================================