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This article describes the machine learning technique applied to grid and martingale trading. Surprisingly, this approach has little to no coverage in the global network. After reading the article, you will be able to create your own trading bots.
The article describes a method of fast optimization using the particle swarm algorithm. It also presents the method implementation in MQL, which is ready for use both in single-threaded mode inside an Expert Advisor and in a parallel multi-threaded mode as an add-on that runs on local tester agents.
The article suggests a technology helping everyone to create custom trading strategies by assembling an individual indicator set, as well as to develop custom market entry signals.
The article dwells on the development of an application for selecting the best optimization passes using several possible options. The application is able to sort out the optimization results by a variety of factors. Optimization passes are always written to a database, therefore you can always select new robot parameters without re-optimization. Besides, you are able to see all optimization passes on a single chart, calculate parametric VaR ratios and build the graph of the normal distribution of passes and trading results of a certain ratio set. Besides, the graphs of some calculated ratios are built dynamically beginning with the optimization start (or from a selected date to another selected date).
The article describes the way to create a custom strategy tester and a custom analyzer of the optimization passes. After reading it, you will understand how the math calculations mode and the mechanism of so-called frames work, how to prepare and load custom data for calculations and use effective algorithms for their compression. This article will also be interesting to those interested in ways of storing custom information within an expert.
In this article, we consider yet another custom trading strategy optimization criterion based on the balance graph analysis. The linear regression is calculated using the function from the ALGLIB library.
It is essential to detect whether a market is flat or not for many strategies. Using the well known ADX we demonstrate how we can use the Strategy Tester not only to optimize this indicator for our specific purpose, but as well we can decide whether this indicator will meet our needs and get to know the average range of the flat and trend markets which might be quite important to determine stops and targets of the markets.
A cross - track indicator that visually shows the current bullish/bearish trend of the market through the midline (mid track), main line (upper track), and sub-lines (lower track). Intersections or upper rail turns can be used as buying and selling positions. Parameter description: period：10 price： WEIGHTED //HIGH //LOW //OPEN //CLOSE //MEDIAN //TYPICAL //AVERAGE //WEIGHTED method：Linear weighted //Simple //Exponential //Smoothed // Linear weighted main offset：1.5 sub offset：1 base：2 counts add
根据高开低收价格确定反转点位，画出的极点折线。 The pole polyline is drawn according to the high opening and low closing price to determine the reversal point. 参数(Args)： 1.周期数(Period)。参与界限计算的K柱数量。 2.界限(Divide)。计算方式包括： t1,顺高低价 (Buy min H,Sell max L) t2,逆高低价 (Buy min L,Sell max H) t3,收盘价 (Buy min C,Sell max C) t4,开盘价 (Buy min O,Sell max O) t5,高开低收价的一半