By Hoi, Steven C. H.; Li, Bin
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The book’s contributing authors are one of the most sensible researchers in swarm intelligence. The booklet is meant to supply an summary of the topic to rookies, and to supply researchers an replace on fascinating contemporary advancements. Introductory chapters take care of the organic foundations, optimization, swarm robotics, and purposes in new-generation telecommunication networks, whereas the second one half comprises chapters on extra particular subject matters of swarm intelligence learn.
This publication constitutes the refereed lawsuits of the twelfth Portuguese convention on man made Intelligence, EPIA 2005, held in Covilhã, Portugal in December 2005 as 9 built-in workshops. The fifty eight revised complete papers provided have been rigorously reviewed and chosen from a complete of 167 submissions. in line with the 9 constituting workshops, the papers are geared up in topical sections on normal man made intelligence (GAIW 2005), affective computing (AC 2005), man made existence and evolutionary algorithms (ALEA 2005), development and utilizing ontologies for the semantic internet (BAOSW 2005), computational tools in bioinformatics (CMB 2005), extracting wisdom from databases and warehouses (EKDB&W 2005), clever robotics (IROBOT 2005), multi-agent structures: thought and functions (MASTA 2005), and textual content mining and purposes (TEMA 2005).
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If the stock price follows the GBM model, the regret round becomes O(m log Q), where Q is a quadratic variability calculated as n − 1 times the sample variance of price relative vectors. Since Q is typically much smaller than n, the regret bound is significantly improved from previous O(m log n). Besides the improved regret bound, the authors also discussed the relationship between their algorithm and trading frequency. The authors asserted that increasing the trading frequency would decrease the variance of minimum-variance CRP, while the regret stays the same.
Formally, a strategy’s drawdown (DD) at period t is defined as DD(t) = sup[0, supi∈(0,t) Si − St ]. Its maximum drawdown (MDD) is the maximum of drawdowns over all periods and can effectively measure a strategy’s downside risk. Formally, maximum drawdown for a horizon of n, MDD(n), is defined as MDD(n) = sup [DD(t)]. t∈(0,n) Moreover, practitioners also adopt the Calmar ratio (CR) (Young 1991) to measure a strategy’s drawdown risk-adjusted return: CR = APY . MDD The smaller the maximum drawdown, the more drawdown risk the strategy can tolerate.
Suppose we are locating the price relative vectors that are similar to the next vector xt+1 . The basic routine is to iterate all historic price relatives xi , i = w + 1, . . , t and count xi as one similar vector, if its i−1 t preceding market window xi−w is similar to the latest market window xt−w+1 . A set Ct contains the indexes of similar price relatives. Note that the market window is a w × m-matrix and the similarity is typically calculated on the concatenated w × m-vectors. 1 further illustrates the procedure.