Trading Strategies for All Stock Programs

 Market traders buy and sell volatile assets frequently, with a goal to maximize their total return. There is usually a commission for each purchase and sale. Two such assets are gold and bitcoin. In order to solve the existing issues of purchases between gold and bitcoin, given that we have 1,000 USD, what strategies should we take to maximize our profits? In this article, the authors established seven models to predict the value of gold and bitcoins and how you should buy them, as the trends of value fluctuate, our models must be accurate enough to avoid being influenced. Targeted at that, the content is divided into three parts. For part 1: The authors selected several indicators that feature how the stock runs. For instance, price of gold and profit of gold to build first two models, which are the risk of investment model and the judgment on bull-or-bear market model. Then we use these models to evaluate whether it is safe to invest. The models are as follows: bear-bull market judgment model, risk of investment evaluation model, prediction model, trade model. For part 2: Based on the data concerned, the authors established the time series model to predict the way the market fluctuates. Meanwhile, the result of this model can be applied in correcting the results of former two models so as to make it more accurate. For part 3: The authors combined models above to give the best trading strategy. In addition, we improved the models by adding more indicators to make it more precise. We hope that by applying our models and strategies, you can successfully maximize your profit

Consensus Building by Committed Agents

One of the most striking features of our time is the polarization, nationally and globally, in politics and religion. How can a society achieve anything, let alone justice, when there are fundamental disagreements about what problems a society needs to address, about priorities among those problems, and no consensus on what constitutes justice itself? This paper explores a model for building social consensus in an ideologically divided community. Our model has three states: two of these represent ideological extremes while the third state designates a moderate position that blends aspects of the two extremes. Each individual in the community is in one of these three states. A constant fraction of individuals are committed agents dedicated to the third, moderate state, while all other moderates and those from either extreme are uncommitted. The states of the uncommitted may change as they interact, according to prescribed rules, at each time step with their neighbors; the committed agents, however, cannot be moved from their moderate position, although they can influence neighbors. Our main objective is to investigate how the proportion of committed agents affects the large-scale dynamics of the population: in other words, we examine the special role played by those committed to embracing both sides of an ideological divide. A secondary but equally important goal is to gently introduce important dynamical systems concepts in a natural setting. Finally, we briefly outline a model with different interaction rules, a fourth state representing those who loathe the other three states, and agents who may be committed to any one of the four states

Robusni algoritam praćenja mjerenjem smjera pomoću strukturiranog potpunog Kalmanovog filtra zasnovanog na metodi najmanjih kvadrata

A nonlinear approach called the robust structured total least squares kalman filter (RSTLS-KF) algorithm is proposed for solving tracking inaccuracy caused by outliers in bearings-only multi-station passive tracking. In that regard, the robust extremal function is introduced to the weighted structured total least squares (WSTLS) location criterion, and then the improved Danish equivalent weight function is built on the basis, which can identify outliers automatically and reduce the weight of the polluted data. Finally, the observation equation is linearized according to the RSTLS location result with the structured total least norm (STLN) solution. Hence location and velocity of the target can be given by the Kalman filter. Simulation results show that tracking performance of the RSTLS-KF is comparable or better than that of conventional algorithms. Furthermore, when outliers appear, the RSTLS-KF is accurate and robust, whereas the conventional algorithms become distort seriously.U ovome radu predložen je nelinearni pristup za rješavanje netočnosti uzrokovanih netipčnim vrijednostima kod praćenja mjerenjem smjera pasivnim senzorima s više stanica. Pristup je zasnovan na robusnom strukturiranom potpunom Kalmanovom filtru zasnovanom na metodi najmanjih kvadrata. Pomoću predložene metode moguće je estimirati položaj i brzinu praćenog objekta. Simulacijski rezultati pokazuju da je učinkovitost predloženog algoritma jednaka ili bolja od konvencionalnih algoritama. Nadalje, u prisustvu netipčnih vrijednosti mjerenja, predloženi algoritam zadržava točnost i robusnost, dok konvencionalni algoritmi pokazuju pogreške u estimaciji