You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Population optimization algorithms: Stochastic Diffusion Search (SDS)
Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction
In the previous article, we completed the implementation of a CSV file management class for storing and retrieving data related to financial markets. Having created the infrastructure, we are now ready to use this data to build and train a machine learning model.
Our task in this article is to implement a regression model that can predict the closing price of a financial asset within a week. This forecast will allow us to analyze market behavior and make informed decisions when trading financial assets.
Population optimization algorithms: Charged System Search (CSS) algorithm
Charged System Search (CSS) was first proposed by A. Kaveh and S. Talatahari in 2010.
Optimization is an important and integral part of solving problems of mathematical modeling and machine learning. Metaheuristic algorithms are an effective and popular class of optimization methods. Metaheuristics can be understood as an algorithm that stochastically searches for possible solutions to a problem that are close to optimal until a certain condition is met or a given number of iterations is reached.
In the scientific literature, metaheuristics are considered to combine basic heuristic methods into higher-level algorithmic schemes that allow more efficient exploration of search spaces and decision making. This usually requires less work than developing new specialized heuristics. The challenge is to adapt general metaheuristic schemes to solve difficult optimization problems. In addition, an effective implementation of metaheuristics can ensure that a solution close to the optimal one is found in an acceptable time. Various approaches to understanding metaheuristics make it possible to formulate some fundamental properties that characterize them. In recent years, the use of metaheuristic methods has increased, and efforts have been made to increase the power of algorithms and reduce optimization time.
Optimisation allows you to see profit where there is none.
A good optimiser allows you to switch to real, spend extra and enter new optimisations faster.
brute force optimisation algorithms improves brute force skills :-) In extreme cases promotes dieting
Population optimization algorithms: Intelligent Water Drops (IWD) algorithm
1. Introduction
2. Algorithm
3. Modified version of SDSm
4. Test results
Population optimization algorithms: Spiral Dynamics Optimization (SDO) algorithm
Population optimization algorithms: Differential Evolution (DE)
1. Introduction2. Algorithm
3. Test results
Differential evolution (DE) is one of the metaheuristic optimization methods. It differs from other methods in its simplicity and efficiency. DE uses a population of vectors that mutate and crossbreed to create new solutions. It does not require knowledge of the gradient and is capable of finding global optima.
The DE algorithm was developed in the 90s by Storn and Price (published in "Differential Evolution - A Simple and Efficient Heuristic for global Optimization over Continuous Spaces"), and has since become one of the most popular optimization methods that uses a population of parameter vectors to find the optimal solution.
Population optimization algorithms: Nelder–Mead, or simplex search (NM) method
1. Introduction2. Algorithm
3. Test results
The Nelder-Mead method was called the "simplex method" and was published in the article "A Simplex Method for Function Minimization" in The Computer Journal in 1965. This method has been accepted by the scientific community and has become widely used in various fields requiring function optimization.
A simplex is a set of points forming a polyhedron, where each point is a set of parameter values of the function being optimized. The idea is to change and move the simplex in the parameter space to find the optimal value of the function.
The Nelder-Mead method (Nelder-Mead simplex method) belongs to the class of unconditional optimization algorithms. It is a deterministic algorithm that does not require the use of function derivatives and can work with functions that have multiple local minimums.
Population optimization algorithms: Simulated Annealing (SA) algorithm. Part I
Population optimization algorithms: Simulated Isotropic Annealing (SIA) algorithm. Part II