The reasons for moving an indicator code to an Expert Advisor may vary. How to assess the pros and cons of this approach? The article describes implementing an indicator code into an EA. Several experiments are conducted to assess the speed of the EA's operation.
Developing the oscillator-based ZigZag indicator. Example of executing a requirements specification
The article demonstrates the development of the ZigZag indicator in accordance with one of the sample specifications described in the article "How to prepare Requirements Specification when ordering an indicator". The indicator is built by extreme values defined using an oscillator. There is an ability to use one of five oscillators: WPR, CCI, Chaikin, RSI or Stochastic Oscillator.
Trade Operations in MQL5 - It's Easy
Almost all traders come to market to make money but some traders also enjoy the process itself. However, it is not only manual trading that can provide you with an exciting experience. Automated trading systems development can also be quite absorbing. Creating a trading robot can be as interesting as reading a good mystery novel.
Random Decision Forest in Reinforcement learning
Random Forest (RF) with the use of bagging is one of the most powerful machine learning methods, which is slightly inferior to gradient boosting. This article attempts to develop a self-learning trading system that makes decisions based on the experience gained from interaction with the market.
The article explores the advantages and disadvantages of trading in flat periods. The ten strategies created and tested within this article are based on the tracking of price movements inside a channel. Each strategy is provided with a filtering mechanism, which is aimed at avoiding false market entry signals.
How to analyze the trades of the Signal selected in the chart
The trade Signals service develops in leaps and bounds. Trusting our funds to a signal provider, we would like to minimize the risk of losing our deposit. So how to puzzle out in this forest of trade signals? How to find the one that would produce profits? This paper proposes to create a tool for visually analyzing the history of trades on trade signals in a symbol chart.
Deep Neural Networks (Part VI). Ensemble of neural network classifiers: bagging
The article discusses the methods for building and training ensembles of neural networks with bagging structure. It also determines the peculiarities of hyperparameter optimization for individual neural network classifiers that make up the ensemble. The quality of the optimized neural network obtained in the previous article of the series is compared with the quality of the created ensemble of neural networks. Possibilities of further improving the quality of the ensemble's classification are considered.
Applying the Monte Carlo method for optimizing trading strategies
Before launching a robot on a trading account, we usually test and optimize it on quotes history. However, a reasonable question arises: how can past results help us in the future? The article describes applying the Monte Carlo method to construct custom criteria for trading strategy optimization. In addition, the EA stability criteria are considered.
Trade Operations in MQL5 - It's Easy
Almost all traders come to market to make money but some traders also enjoy the process itself. However, it is not only manual trading that can provide you with an exciting experience. Automated trading systems development can also be quite absorbing. Creating a trading robot can be as interesting as reading a good mystery novel.
Developing the oscillator-based ZigZag indicator. Example of executing a requirements specification
The article demonstrates the development of the ZigZag indicator in accordance with one of the sample specifications described in the article "How to prepare Requirements Specification when ordering an indicator". The indicator is built by extreme values defined using an oscillator. There is an ability to use one of five oscillators: WPR, CCI, Chaikin, RSI or Stochastic Oscillator.
Random Decision Forest in Reinforcement learning
Random Forest (RF) with the use of bagging is one of the most powerful machine learning methods, which is slightly inferior to gradient boosting. This article attempts to develop a self-learning trading system that makes decisions based on the experience gained from interaction with the market.
Before launching a robot on a trading account, we usually test and optimize it on quotes history. However, a reasonable question arises: how can past results help us in the future? The article describes applying the Monte Carlo method to construct custom criteria for trading strategy optimization. In addition, the EA stability criteria are considered.
