Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.
3,012 dokumenter
The article explains how to identify bullish and bearish outside bars from two completed candles and translate those patterns into an automated Forex strategy. For a bullish signal, the outside bar must exceed the prior candle’s high and low, close above its…
This article explains how to configure multi-timeframe and multi-symbol signals in Expert Advisors assembled with the MQL5 Wizard. Its main implementation point is that a signal’s symbol and timeframe are set during initialization, so separate signal…
This tutorial explains how to make an on-chart MQL5 trade panel respond to user input. It uses the chart event handler to detect clicks, removes and restores the interface through helper functions, and connects controls such as order-type selectors and…
This tutorial explains how to turn a candlestick trend indicator and a manually applied risk/reward tool into a standalone MetaTrader 5 Expert Advisor. It outlines the EA lifecycle, including initialization, tick processing, trade events, tester callbacks,…
This guide walks through diagnosing MQL5 compilation errors and using the Strategy Tester to evaluate and optimize an Expert Advisor. Its debugging examples cover common issues such as mismatched types, missing punctuation or braces, incorrectly declared…
The article presents a MQL5 implementation of the Attraos approach to time-series forecasting, combining phase-space reconstruction, neural-network components, and adaptive state-space modeling. It explains how lagged observations are represented through the…
This article describes the design of a reusable channel indicator whose center line and width are configured independently. The center can use several moving-average variants or the midpoint of a price range. Borders can be based on average true range,…
The article introduces AdaBoost as an ensemble classification method that combines weak learners, commonly decision stumps or shallow trees. It explains that training examples receive changing weights: misclassified cases gain emphasis in later rounds, while…
This article explains how to structure one MQL5 Expert Advisor to trade multiple instruments from a single chart. Its example uses a Triple Exponential Moving Average (TEMA) trend signal, with independent symbol, trade permission, indicator period, stop…
This article introduces cointegration as a way to identify long-run relationships among stocks for statistical arbitrage, moving beyond simple correlation and two-asset pairs. It describes applying the Engle-Granger test to a pair and the Johansen method to…
This article presents a MetaTrader template that runs separate virtual experts for different symbol and timeframe combinations, with each configuration stored in text files. A supervising EA reads those files, creates or removes virtual charts and experts as…
This concluding article describes an implementation of a Spatial-Sequential Convolutional Neural Network for financial time-series forecasting in MQL5. Its central subject is the encoder, which separates long-term trend, seasonal, short-term, and spatial…
The article describes a chart-based tool for quickly examining how indicator parameters affect historical trade signals and cumulative results. It adds an equity or points balance curve to an earlier visual signal tester and introduces draggable triangle…
The article explains how to build an MQL5 Expert Advisor around Bollinger Bands. It describes the bands as a moving average with upper and lower boundaries based on standard deviations, then presents a basic reversal approach: buy when price falls below the…
This introduction explains portfolio trading as a synthetic position formed from multiple instruments, with their directions and lot sizes managed together. It distinguishes this from simply running independent systems on several symbols. Portfolio…
The article describes an MQL5 trading system organized around memory units, individual agents called brains, and a collective decision layer. Each memory unit associates an encoded market state with an action and tracks reward, estimated value, activations,…
This article develops ten candidate signal patterns that combine TRIX, a triple-smoothed momentum oscillator, with Williams Percent Range (WPR), which locates price within its recent high-low range. The examples use zero-line crossings, oscillator extremes,…
This installment describes changes to a multi-currency MetaTrader 5 expert advisor so it can recognize broker-specific prefixes or suffixes on forex and metal symbols, and limit trading to selected sessions. It outlines a fixed universe of 30 forex and metal…
The article demonstrates a data pipeline for retrieving BTCUSDT candles from Binance and preparing them for use in MetaTrader 5. An expert adviser makes a public API request for the latest ten 30-minute candles, checking for a new local chart bar before…
The article presents a workflow for investigating a trading strategy’s entries and finding indicator conditions that may help filter losing trades. It starts from a Kalman filter based expert adviser that showed late exits and losses during sideways markets,…
This article explains how to build two MQL5 indicators from incoming quote data: a tick chart that plots bid and ask prices, and tick candles that aggregate a chosen number of ticks per candle. The indicators store received prices in text files so data can…
This article extends a base risk manager into a subclass for algorithmic trading systems. It describes an interface that lets an Expert Advisor check whether new trades are permitted, and adds controls for order slippage and spread when opening positions.…
This article describes an MQL5 cache for symbol contract metadata and weekly trading sessions. A cache class loads stable properties such as point size, lot limits, contract size, currencies, and trade mode during initialization, then serves reads from…
This article develops the foundational structure of an MQL5 risk management class. It organizes risk settings into enumerations and a shared loss and profit structure, with options for fixed monetary limits or percentages applied to account measures such as…