Exp_AdaptiveRenko_MMRec_Duplex - expert pour MetaTrader 5

Two identical trading systems (for long and short deals) based on the AdaptiveRenko indicator signals that can be configured in different ways within a single EA with an ability to change the volume of a forthcoming trade depending on the results of the previous trades for this trading system This EA is the further development of trading system Exp_AdaptiveRenko_Duplex, aimed at resizing the positions opened.

For this purpose, the blocks of the EA's inputs have been added:

input uint    L_TotalMMTriger=5;    //L number of the last Buy deals to calculate stop loss
input uint    L_LossMMTriger=3;     //L number of losing Buy deals to reduce MM
input double  L_SmallMM=0.01;       //L Share of a deposit in a deal in case of loss
input double  L_MM=0.1;             //L Share of a deposit in a deal during normal trading
input MarginMode L_MMMode=LOT;      //L lot setting mode

and

input uint    S_TotalMMTriger=5;    //S number of the last Sell deals to calculate stop loss
input uint    S_LossMMTriger=3;     //S number of losing Sell deals to reduce MM
input double  S_SmallMM=0.01;       //S Share of a deposit in a deal in case of loss
input double  S_MM=0.1;             //S Share of a deposit in a deal during normal trading
input MarginMode S_MMMode=LOT;      //S lot setting mode

In case of such inputs and when selecting from the last five trades, three of which are loss-making in a single direction, the EA opens the next trade in the same direction having the volume of 0.01 lot. If there are less than three loss-making trades out of the last five ones, the position volume is 0.1.

For the EA to operate, the compiled files of indicator AdaptiveRenko.ex5 should be in <terminal_data_directory>\MQL5\Indicators.

Default Expert Advisor's input parameters have been used during the tests shown below. Stop Loss and Take Profit have not been used during the tests.

Fig. 1. Sample deals on the chart with symmetrical settings.

Testing results for USDJPY H4 over the year 2017: