This algorithm puts in place a strategy called Breakout Trading. In correspondence with the formation of supports and resistances, specific pending orders are issued. When the price breaks the level of support or resistance, it generates a strong push, which allows you to make profits with a moderate risk.

The Expert is based on an efficient and simple strategy, but sensitive to slippage and spread increases. Precisely for this reason, only pending orders are used and the inputs to the market are filtered, with a continuous control on the spread, which removes the orders before execution if necessary.

All orders are protected by Stop Loss, profits are managed and maximized through an Adaptive Trailing Stop. Pending orders are not left open during the weekend, in order to avoid the Gap that usually occur at the opening of the markets. Previously canceled orders are subsequently relocated.

The Expert is optimized for the following currency pairs: EURUSD, GBPUSD, and USDJPY on the Timeframe M30. You can also choose from three different models for money management.

1) Risk allocation in percentage for each trade

This model calculates the number of lots to be reinvested on each individual trade, putting a percentage of the capital at risk.

It represents a conservative solution for money management, which reduces the lots invested in the phase of negative excursions of capital and increases the lots in the event of an increase in capital.

2) Progressive increase of lots for consecutive winning trades

This model gradually increases the lots invested in the trade, in relation to a series of consecutive victories and following a specific plan.

If the previous position has been lost or if a predetermined number of cycles has been reached, the lot size will return to the specified minimum. This model is very effective in stabilizing the Equity curves, fixing profits and minimizing any negative capital excursions.

3) Fusion of the two previous models

In this configuration, the two money management models previously described are used together. The configuration is made by specifying the percentage of risk with which the number of initial lots will be calculated and the number of cycles for consecutive winning positions. This model of money management is very effective in stabilizing the Equity curves, but above all in increasing Total net profit.

Parameters

• Operativity of the Expert: Defines whether the Expert must be active or not.
• Adaptive Trailing Type: Defines Adaptive Trailing. More_Tight, Less_Tight.
• Stealth Trailing Stop: Hides the trailing stop work to the broker.
• Maximun Spread Allowed in Pips: Maximum allowed spread to enter the market.
• Fixed Magic Number identification of EA: Magic number of the EA can not be changed.
• Money Management Models: Type of Money Management Model to use.
• Start Lots size for progressive lots increase: Defines the number of initial lots for the model Progressive Increment for winning trades.
• Percentage Allocation of the Risk for the Other Models: Defines the risk percentages for the model Allocation of risk in percent for each trade and Fusion of the two previous models.
• Number of Cycle: Defines the number of cycles to construct a trade plan (allowed is from 1 to 10).
• Show Information Panel: Select the visibility of the Information Panel.
• Color Combination of Template: Two color combinations for graphs and displayed objects, Clear and Dark Templates.
• Enable Vocal Warnings: Activate the Vocal Warnings.
• Write a Log File: Write reporting services information.

The expert make a double test on the input parameters. A first test on the Percentage of Risk, or initial Lots, in relation to the selected Money Management model, which determines whether the values entered can be Safe or Dangerous, considering the maximum Drow Down per contract, obtained in long-term backtest. A second check verifies the maximum exposure of the Lots present in the programmed upper Cycle, considering the free margin of the symbol which must not be less than 50% of the balance.

For the creation of the strategy, accurate historical data were used, which generated Back-test with 99.9% modeling quality.