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

Impact of a Right Ventricular Impedance Sensor on the Cardiovascular Responses to Exercise in Pacemaker Dependent Patients

Background. The evaluation of the heart rate (HR) response to exercise is important for the assessment of the rate response algorithm of sensor-controlled pacemakers. This study examined the effects of a right ventricular impedance sensor driven pacemaker on the cardiovascular responses to incremental exercise in pacemaker dependent patients. Methods. Twelve patients (70.5 ± 9.5 years; 5 Females: 7 Males) implanted with an Inos2+ closed loop stimulation (CLS) pacemaker were compared to 12 healthy age and sex matched controls (70.6 ± 4.8 years). All subjects performed the chronotropic assessment exercise protocol (CAEP). Variables of interest included HR, cardiac output (Q), oxygen uptake (Vo2) and blood pressure (BP). Data were analyzed at rest, throughout exercise and during recovery. Furthermore, patient chronotropic responses were compared to a reference chronotropic response slope for aerobic exercise. Results. There were no differences between groups for HR or Q. response throughout exercise. At peak exercise, V.o2 (mL.kg-1.min-1) was higher for the controls (p < 0.05). The patient chronotropic response slope was comparable to the CAEP reference slope from rest to both the anaerobic threshold (AT) and peak exercise. During recovery, no differences were observed between the groups for any parameters or for the HR decay slopes. Conclusions. Up to the anaerobic threshold, the right ventricular impedance sensor driven pacemaker delivered a pacing rate that contributed to an overall cardiovascular response similar to that observed in healthy age matched subjects

Developing sustainable trading strategies using directional changes with high frequency data

Market prices are traditionally recorded in fixed time intervals. Directional Change is an alternative approach to summarize price movements in financial markets that is consistent with across all time scales. Unlike time series, directional change summarizes the big data in finance by focusing on the intrinsic time of the data. This captures deeper intrinsic data qualities and thus trading strategies based on directional change are more sustainable and less disruptive. In this paper, we propose four trading strategies using the concept of directional change and explore the combination with technical analysis. The trading strategies are tested using EUR/USD and GBP/USD high frequency FX market data. Empirical results show good performance of our trading strategies based on thresholds, and that combining with technical analysis brings further improvement

Backlash algorithm: A trading strategy based on directional change

Directional Change (DC) is a new way to summarize price movements in a financial market. Unlike time series, it samples data at irregular time intervals. According to the DC concept, the data is sampled only when the magnitude of price changes is significant according to the investor. In this paper, we propose a contrarian trading strategy which is based on the DC concept. We test our trading strategy using two currency pairs; namely EUR/CHF and EUR/USD. The results show that our proposed trading strategy is consistently profitable; it produce a profit of up to 145% within seven months; whereas the buy-and-hold approach incurred a loss of –14% during the same trading period

Intermittency in Two-Dimensional Turbulence with Drag

We consider the enstrophy cascade in forced two-dimensional turbulence with a linear drag force. In the presence of linear drag, the energy wavenumber spectrum drops with a power law faster than in the case without drag, and the vorticity field becomes intermittent, as shown by the anomalous scaling of the vorticity structure functions. Using a previous theory, we compare numerical simulation results with predictions for the power law exponent of the energy wavenumber spectrum and the scaling exponents of the vorticity structure functions $\zeta_{2q}$ obtained in terms of the distribution of finite time Lyapunov exponents. We also study, both by numerical experiment and theoretical analysis, the multifractal structure of the viscous enstrophy dissipation in terms of its R\'{e}nyi dimension spectrum $D_q$ and singularity spectrum $f(\alpha)$. We derive a relation between $D_q$ and $\zeta_{2q}$, and discuss its relevance to a version of the refined similarity hypothesis. In addition, we obtain and compare theoretically and numerically derived results for the dependence on separation $r$ of the probability distribution of \delta_{\V{r}}\omega, the difference between the vorticity at two points separated by a distance $r$. Our numerical simulations are done on a $4096 \times 4096$ grid.Comment: 18 pages, 17 figure