Forecasting stock prices using Genetic Programming and Chance Discovery

In recent years the computers have shown to be a powerful tool in financial forecasting. Many machine learning techniques have been utilized to predict movements in financial markets. Machine learning classifiers involve extending the past experiences into the future. However the rareness of some events makes difficult to create a model that detect them. For example bubbles burst and crashes are rare cases, however their detection is crucial since they have a significant impact on the investment. One of the main problems for any machine learning classifier is to deal with unbalanced classes. Specifically Genetic Programming has limitation to deal with unbalanced environments. In a previous work we described the Repository Method, it is a technique that analyses decision trees produced by Genetic Programming to discover classification rules. The aim of that work was to forecast future opportunities in financial stock markets on situations where positive instances are rare. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. The objective of the present work is to find out the factors that work in favour of Repository Method, for that purpose a series of experiments was performed.Forecasting, Chance discovery, Genetic programming, machine learning

An Efficient Extension of Network Simplex Algorithm

In this paper, an efficient extension of network simplex algorithm is presented. In static scheduling problem, where there is no change in situation, the challenge is that the large problems can be solved in a short time. In this paper, the Static Scheduling problem of Automated Guided Vehicles in container terminal is solved by Network Simplex Algorithm (NSA) and NSA+, which extended the standard NSA. The algorithms are based on graph model and their performances are at least 100 times faster than traditional simplex algorithm for Linear Programs. Many random data are generated and fed to the model for 50 vehicles. We compared results of NSA and NSA+ for the static automated vehicle scheduling problem. The results show that NSA+ is significantly more efficient than NSA. It is found that, in practice, NSA and NSA+ take polynomial time to solve problems in this application

TSFDC: A Trading strategy based on forecasting directional change

Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market’s trend under the DC context. We examine the profitability, risk and risk-adjusted return of TSFDC in the FX market using eight currency pairs. We argue that TSFDC outperforms another DC-based trading strategy

Operations Research Meets Constraint Programming: Some Achievements So Far

This paper reports promising algorithms that have been built on top of both operations research (OR) and constraint programming (CP) research. First we describe Guided Local Search (GLS), a penalty-based meta-heuristic algorithm that sits on top of local search algorithms and helps them to escape local optima. Then we describe Fast Local Search (FLS), a strategy that reduces the size of the neighbourhood. GLS and FLS have been demonstrated to be highly successful in a number of non-trivial problems, including commercial applications. We shall also, in this paper, report selected promising research by other groups in combining OR and CP ideas -- namely (a) Lagrangian Method and (b) fine-grain interaction between mixed integer programming (MIP) and finite domain constraint propagation. The main message that we want to convey in this paper is: there is no real boundary between OR and CP. In fact, OR has already become an important part of CP and a great deal can be gained by cross-fertil..

A Family of Stochastic Methods For Constraint Satisfaction and Optimisation

Constraint satisfaction and optimisation is NP-complete by nature. The combinatorial explosion problem prevents complete constraint programming methods from solving many real-life constraint problems. In many situations, stochastic search methods, many of which sacrifice completeness for efficiency, are needed. This paper reports a family of stochastic algorithms for constraint satisfaction and optimisation. Developed with hardware implementation in mind, GENET is a class of computation models for constraint satisfaction. Genet is a connectionist approach. A problem is represented by a network with inhibitory connections. The network is designed to converge, in a fashion that resembles the min-conflict repair method. Reinforcement learning is used to bring GENET out of local optima. Building upon GENET as well as ideas from operations research, Guided Local Search (GLS) and Fast Local Search are novel meta-heuristic search methods for constraint optimisation. GLS sits on top of other l..

Novel constraints satisfaction models for optimization problems in container terminals

The growth of containerization and transporting goods in containers has created many problems for ports. In this paper, we systematically survey a literature over problems in container terminals. The operations that take place in container terminals are described and then the problems are classified into five scheduling decisions. For each of the decisions, an overview of the literature is presented. After that, each of the decisions is formulated as Constraint Satisfaction and Optimization Problems (CSOPs). The literature also includes simulations and performance in container terminals. To evaluate any solution methods to the decisions and to measure its efficiency, several indicators are suggested. © 2012 Elsevier Inc

Search for gravitational waves from Scorpius X-1 with a hidden Markov model in O3 LIGO data

Results are presented for a semicoherent search for continuous gravitational waves from the low-mass x-ray binary Scorpius X-1, using a hidden Markov model (HMM) to allow for spin wandering. This search improves on previous HMM-based searches of Laser Interferometer Gravitational-Wave Observatory data by including the orbital period in the search template grid, and by analyzing data from the latest (third) observing run. In the frequency range searched, from 60 to 500 Hz, we find no evidence of gravitational radiation. This is the most sensitive search for Scorpius X-1 using a HMM to date. For the most sensitive subband, starting at 256.06 Hz, we report an upper limit on gravitational wave strain (at 95% confidence) of h 95 % 0 = 6.16 × 10 − 26 , assuming the orbital inclination angle takes its electromagnetically restricted value ι = 4 4 ° . The upper limits on gravitational wave strain reported here are on average a factor of ∼ 3 lower than in the second observing run HMM search. This is the first Scorpius X-1 HMM search with upper limits that reach below the indirect torque-balance limit for certain subbands, assuming ι = 4 4 °

Search for intermediate-mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo

International audienceIntermediate-mass black holes (IMBHs) span the approximate mass range 100−105 M⊙, between black holes (BHs) that formed by stellar collapse and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries are the most energetic gravitational-wave sources accessible by the terrestrial detector network. Searches of the first two observing runs of Advanced LIGO and Advanced Virgo did not yield any significant IMBH binary signals. In the third observing run (O3), the increased network sensitivity enabled the detection of GW190521, a signal consistent with a binary merger of mass ∼150 M⊙ providing direct evidence of IMBH formation. Here, we report on a dedicated search of O3 data for further IMBH binary mergers, combining both modeled (matched filter) and model-independent search methods. We find some marginal candidates, but none are sufficiently significant to indicate detection of further IMBH mergers. We quantify the sensitivity of the individual search methods and of the combined search using a suite of IMBH binary signals obtained via numerical relativity, including the effects of spins misaligned with the binary orbital axis, and present the resulting upper limits on astrophysical merger rates. Our most stringent limit is for equal mass and aligned spin BH binary of total mass 200 M⊙ and effective aligned spin 0.8 at 0.056 Gpc−3 yr−1 (90% confidence), a factor of 3.5 more constraining than previous LIGO-Virgo limits. We also update the estimated rate of mergers similar to GW190521 to 0.08 Gpc−3 yr−1.Key words: gravitational waves / stars: black holes / black hole physicsCorresponding author: W. Del Pozzo, e-mail: [email protected]† Deceased, August 2020