MQL5 PROJECTS: Code a Quarters Theory Strategy in MQL5

Create a basic mql5 expert advisor template, declare general parameters (id, symbol, time frame, slippage), and customize the chart on load using on initialization and on tick logic.

Set the calculation interval for the Ontick function by using a new bar check driven by alt_num_pass to run code only when a new bar appears, improving efficiency.

Learn to calculate round numbers for support and resistance in a MQL5 strategy using whole, half, and quarter intervals, and implement functions to find the next level and visualize them.

Create a draw_line function in MQL5 to render round-number support and resistance as horizontal lines with price and color. Then use OnTick function to draw these levels after retrieving them.

Create objects of the CTrade, CPositionInfo, and related info classes by importing the trade.mk file; then initialize to set the magic number and deviation in points for all trades.

Learn to build MQL5 functions to count trades and pending orders, filtering by symbol, position type, magic number, and order comments for the quarters theory strategy.

Compute the position size from account balance and risk inputs using a balance-based lot size per trade. Round and clamp the lot within symbol limits with a lot size function.

Enter trades as buy and sell limit pending orders at support and resistance. Prepare trade comments and delete old pending orders before cost averaging to enter new trades.

Detects changes in support and resistance and updates pending orders to new levels, using boolean functions on alt support and alt resistance to flag updates in MQL5.

Modify pending buy and sell orders when support or resistance changes, using f buy modify and f cell modify to update new and current entry prices.

Implement trailing stop loss functions for pi trades and sell trades by setting trade management inputs, converting pips to points, and updating stop losses as price moves.

Develop functions to compute the next buy and next sell prices for cost averaging in MQL5, using open positions, price arrays, and distance in points.

Implement cost averaging trades by initializing two boolean switches, entering pending buy and sell orders at target prices, and adjusting lot sizes to capture retracements for profit.

Modify pending cost-averaging orders as support and resistance shift, using by limit modifications to adjust entry prices, with no take profit or stop loss and success or error alerts.

Construct a function to compute the average price of a specific trade type in MQL5 by weighting entry prices with volumes, summing totals, and normalizing the result.

Create two functions to trail take profit for all positions and sell trades, using a for loop to adjust take profit from average price and buy points, with error handling.

Develop boolean functions to detect when a pending buy or sell order is triggered in MQL5. Compare trade counts to update state and allow pending orders to be placed again.

Put everything together in onClick and Ontick logic by executing fpi and ftrace, applying cpi and csell modify, handling by limit orders, and resetting flags and trade counts on trigger.

Test the quotas theory expert advisor on the euro-dollar 1-hour chart from 2019 to 2024 with visual mode, two pending orders: buy limit and sell limit, and a $3,000 account.

Apply goddess theory to identify key price zones that act as natural support and resistance, empowering your manual and automated trading projects in MQL5 via the Quotas theorem.