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The article presents an extended study of seasonal characteristics: autocorrelation heat maps and scatter plots. The purpose of the article is to show that "market memory" is of seasonal nature, which is expressed through maximized correlation of increments of arbitrary order.
Step-by-step instructions of how to organize data transfer from Matlab to MetaTrader 4 using DDE.
The article contains considerations regarding creation of a DLL library - wrapper that will enable the interaction of MetaTrader 4 and the MATLAB mathematical desktop package. It describes "pitfalls" and ways to overcome them. The article is intended for prepared C/C++ programmers that use the Borland C++ Builder 6 compiler.
The article describes the methods of how to understand the tester optimization results better. It also gives some tips that help to avoid "harmful optimization".
The article dwells on the notion of "market intuition" and ways of developing it. The method described in the article is based on the modeling of financial betting in the form of a simple game.
In this article, we discuss the method of trading analysis by measuring angles in the MetaTrader 4 terminal. The article provides a general plan of using angles for trend movement analysis, as well as non-standard ways to the practical application of angle analysis in trading. The article also provides conclusions that can be useful for trading.
This article focuses on specifics of choice, preconditioning and evaluation of the input variables (predictors) for use in machine learning models. New approaches and opportunities of deep predictor analysis and their influence on possible overfitting of models will be considered. The overall result of using models largely depends on the result of this stage. We will analyze two packages offering new and original approaches to the selection of predictors.
Automation of trading strategies involving graphical patterns requires the ability to search for extreme points on the charts for further processing and interpretation. Existing tools do not always provide such an ability. The algorithms described in the article allow finding all extreme points on charts. The tools discussed here are equally efficient both during trends and flat movements. The obtained results are not strongly affected by a selected timeframe and are only defined by a specified scale.
In this article, we continue to dwell on reversing techniques. We will try to reduce the maximum balance drawdown till an acceptable level for the instruments considered earlier. We will see if the measures will reduce the profit. We will also check how the reversing method performs on other markets, including stock, commodity, index, ETF and agricultural markets. Attention, the article contains a lot of images!
The article considers three methods which can be used to increase the classification quality of bagging ensembles, and their efficiency is estimated. The effects of optimization of the ELM neural network hyperparameters and postprocessing parameters are evaluated.
There are multiple different approaches to market research and analysis. The main ones are technical and fundamental. In technical analysis, traders collect, process and analyze numerical data and parameters related to the market, including prices, volumes, etc. In fundamental analysis, traders analyze events and news affecting the markets directly or indirectly. The article deals with price velocity measurement methods and studies trading strategies based on that methods.
Based on universal tools designed for working with Kohonen networks, we construct the system of analyzing and selecting the optimal EA parameters and consider forecasting time series. In Part I, we corrected and improved the publicly available neural network classes, having added necessary algorithms. Now, it is time to apply them to practice.
Many traders speak about neural networks, but what they are and what they really can is known to few people. This article sheds some light on the world of artificial intelligence. It describes, how to prepare correctly the data for the network. Here you will also find an example of forecasting using means of the program Matlab.
Step-by-step instructions of how to organize data arrays exchange between MetaTrader 4 and Matlab via CSV files.