MQL5 MACHINE LEARNING: Linear Regression for Algo Trading

Create a linear regression indicator in meta editor by configuring on initialization and on calculate, setting buffers and plots, applying the closing price, and styling lines with specified colors.

Declare and initialize integer inputs and buffers to compute a regression line over 1000 data points, then plot one and two standard deviation bands on the chart.

Validate the regression period in the initialization function by correcting values below two to two and capping values at total paths minus one.

Bind indicator buffers to arrays with set index buffer, assign the regression line to buffer zero. Label plots for regression, overbought, oversold, upper extremes line, and lower extremes line.

Declare local double variables for x, y, x y, sum squares, and regression parameters a and p; check bar count and set the plotting start index using the used period.

Initialize all accumulators to zero and loop through 1000 bars of price data for linear regression. Compute sums x, y, x y, and x squared to derive gradient and intercept.

Compute regression line values and the standard deviation of residuals to plot a linear regression channel with overbought, oversold, and upper/lower extreme lines for mean reversion in MQL5.

Develop a mean reversion trading strategy using a linear regression model with standard deviation bands, entering buys at extreme deviations and sells near the regression line, with measured lot sizing.

Build a linear regression based expert advisor in meta editor, set general inputs (e metric, eurusd, m15, maximum slippage), and configure on initialization, on tick, and chart appearance.

Implement a function that detects new bars by comparing current bar count to a stored value, updating it, and executing ontick only on new bars to save cpu during optimization.

Create and initialize trade objects to access the C trade and C position info classes, set aspect metric number, deviation, and slippage, for streamlined trading functions.

Obtain linear regression indicator values to signal trades and manage closings. Set a regression period and initialize the regression handle to use buffers like regression line and price data.

Count the total open trades with a dynamic num_trades function to regulate the number of trades at any time, looping through positions and filtering by symbol and type.

Compute dynamic lot size per trade in MQL5 using volume increase and balance increase risk inputs, with lot max, min, and step bounds and proper rounding.

Generate and implement signals for entering pi trades, selling, and managing positions using oversold and overbought lines, close-by signals, and regression-line gradient checks.

Execute trades by defining trait management inputs, enforcing a ten-pip minimum distance between trades, and using gradient-based, oversold/overbought signals to set next buy and sell prices.

Develop an MQL5 function to calculate the average entry price for trades of a given type by summing open prices, counting trades, and returning the average normalized to digits.

Implement a void function to close all trades by iterating positions, filtering by metric and position type, and closing by ticket; also close pie and sell trades by signals.

Test the expert advisor's linear regression strategy on euro/dollar 15-minute chart from 2022 to 2024, starting with $3,000 and using every tick to monitor mean reversion around regression line.

Conclude by highlighting the testing of the linear regression model and its trading strategy, and how it enhances a portfolio of expert advisors for algorithmic trading.