3 new papers on trading ...

Discussion in 'Strategy Development' started by bhamadicharef, Feb 19, 2008.

  1. 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 “software robot” 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 “ZIP60,” 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 “ZIP8” 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 “hybrid”
    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 “hybrid” 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

  2. The problem is how much it costs before the "system" learns.

    Usually, it ruins the account, any size.

  3. huzhen


  4. cgar


  5. Corey


  6. pstwo


  7. ramora