MQL5 PROJECTS: Allimacao Cost Averaging/Grid scalping system

Declaring global parameters shows how to set up an expert advisor in an mgcl file. It covers input parameters for MACD, alligator, and awesome oscillator, plus indicator handles and buffers.

Configure arrays for series with reversed order to prioritize latest data, then obtain indicator handles for MACD, Awesome Oscillator, and Alligator using current symbol and period, applying closing price.

Learn to fetch MACD, Awesome Oscillator, and Alligator values with copy buffer—from the latest price to the fifth candle—and store closing prices for signal generation.

Use the allocator indicator to detect uptrends (lips above teeth above jaws) and downtrends, then trigger buy/sell signals with MACD alpha and the awesome oscillator for a cost-averaging grid system.

Test indicator signals by visually validating uptrends and downtrends. Confirm buy and sell signals with alligator, MACD, and awesome oscillator momentum to ensure trades align with price action.

Prepare an MQL5 expert advisor for trade entry by configuring the trade class, setting input parameters for slippage, money management, and profit target, and initializing deviation and magic number.

Learn to prepare an expert advisor to enter trades, use buy and sell signals, and count positions to ensure only your trades enter per symbol, with filtering by magic number.

code a take-profit function that closes buys and sells when the profit target is met, iterating positions by ticket, filtering by expert advisor and symbol, and calculating profits and commissions.

Test conventional trade operations by simulating long and short exits, reviewing exit values and profits on both buy and sell trades, and assess balance vs equity dynamics.

Learn to implement a cost averaging grid that adds buys as prices fall, using a lot multiplier, defined trade limits, and adjustable grid distance and size to rescue losing trades.

Modify the expert advisor to enable cost averaging and grid trades by initializing point-valued variables, calculating next buy and sell prices, and using last trade prices.

Learn to implement cost averaging and grid trading in MQL5 by building create buy and create sell routines, with next buy price, next sell price, lot sizing, and trade-count control.

Filter entries to enter only in high momentum uptrends or downtrends. Create buy and sell signals as filters using momentum and trend confirmations to reduce risk in the grid system.

Develop a mechanism and implement a void function that closes losing trades on reversal to prevent large equity and balance divergence.

Improve a MQL5 expert advisor by implementing a new bar present check to detect new bars, run non-critical code once per bar, and optimize testing in the strategy tester.

Test and validate an mql5 grid scalping expert advisor by printing next buy price and distance, checking sell distances, and organizing inputs into grouped folders for a clean, professional interface.

The lecture shows testing the expert advisor in visual mode and long-term backtests, verifying entries with histograms, macd, and alligator signals, and refining trade limits to ensure rule-based performance.

The instructor wraps up the course by inviting learners to share cost averaging strategies for MQL5 expert advisors, explore grid scalping ideas, and create courses to develop and quote ideas.