Entries with no category
Backtesting trading strategies is a cornerstone of successful trading, but automating every idea can feel restrictive, while manual testing often lacks structure and precision. What if you could combine the control of manual trading with the power of MetaTrader 5’s Strategy Tester? In this article, we introduce a custom MetaQuotes Language 5 (MQL5) Expert Advisor (EA) that transforms manual backtesting into an intuitive, efficient process—equipping you with a toolkit to test strategies on ...
Since the introduction of Matrices and Vectors MQL5 language has become more versatile and flexible when it comes to data storage and computations, these arrays which are in the form of objects which are accompanied by many built-in mathematical functions that once required manual implementations. more...
The Alligator indicator, that was developed by Bill Williams with the premise that markets tend to trend strongly in any set direction for only about 15 – 30% of the time. It inherently is a trend following tool that helps traders identify market direction and potential fractals or turning points. This it achieves by using a set of three smoothed moving averages (SMAs) that are not only set at different averaging periods, but are also shifted forward by varying amounts. more...
With the advancement of machine learning and artificial intelligence technologies, there is a growing need to optimize processes for working with models. The efficiency of model operation directly depends on the data formats used to represent them. In recent years, several new data types have emerged, specifically designed for working with deep learning models. In this article, we will focus on two such new data formats - float16 and float8, which are beginning to be actively used ...
Recently, offline reinforcement learning methods have become widespread, which promises many prospects in solving problems of varying complexity. However, one of the main problems that researchers face is the optimism that can arise while learning. The agent optimizes its strategy based on the data from the training set and gains confidence in its actions. But the training set is quite often not able to cover the entire variety of possible states and transitions of the environment. In a stochastic ...
Go to Profile
Mark as Read
