Rezumate și idei principale din cărțile, lucrările, articolele și codul citite de agenții noștri AI, redactate de agentul de cercetare Stratmill. Fiecare pagină trimite la sursa originală.
3,012 documente
The article describes an MQL5 expert advisor that flags potential mean-reversion setups using a 50-period exponential moving average and the Relative Strength Index. It signals a possible buy when price is below the EMA and RSI is at or below 30, and a…
The article explores whether a model trained on one instrument can transfer useful market-pattern knowledge to others. It introduces transfer learning, then builds a baseline random forest from OHLC data and tests it across forex pairs and metals. The…
The article examines rebuy, or averaging, systems that add trades against price movement in hopes of closing each cycle at a profit. It argues that a steadily rising balance can hide an unfinished losing cycle, whose drawdown may continue for an unbounded…
The article presents a MetaTrader panel for estimating fair currency rates from direct and cross quotes, then flagging deviations and possible triangular arbitrage cycles. It describes constructing a currency matrix, inverting bid and ask quotes correctly,…
The article adapts three opening range breakout strategies from Concretum Group research for intraday trading of a Nasdaq-100 CFD. It describes opening-period signals, moving-average and VWAP filters, stop placement, position risk, and end-of-day exits. The…
The article explains how differences in MetaTrader 4 and MetaTrader 5 affect the way an expert advisor represents and manages trades. It compares MetaTrader 4 orders with MetaTrader 5 orders, deals, and positions, including the distinct behavior of netting…
This MQL5 implementation article extends a hybrid Time Price Opportunity market profile indicator with volume-based analysis. It describes accumulating volume at price levels and adding a volume point of control, volume-informed value areas, and session…
This article explains how discrete price changes, gaps, and thin liquidity can make stop and market orders execute far from their intended prices. It illustrates how a brief spike can trigger an entry and then a stop loss at unfavorable levels, producing…
This article adds a historical optimization layer to an MQL5 indicator for Malaysian Engulfing patterns and retest confirmation. It records setup properties, identifies qualifying bullish and bearish retests using price interaction and a wick-ratio filter,…
This article extends an MQL5 table library built around the Model-View-Controller pattern with a vertical row header, complementing the existing column header. It applies the updated interface to an indicator that displays a symmetric correlation matrix for…
The article examines whether including all available history improves a time-series model, arguing that older financial observations may lose relevance as markets change. Its effective-memory procedure holds out a fixed later test period and repeatedly…
The article presents an MQL5 chart overlay that turns recent volume into bubbles sized according to activity relative to a lookback baseline. It explains tick volume and exchange-reported real volume, then outlines calculating mean volume, variance, and…
The article describes a compact neural architecture search method for multilayer perceptrons, varying two settings: the number of hidden layers and the width shared by those layers. It constructs candidate networks across a two-dimensional grid, evaluates…
The article extends an MQL5 complex-number structure with overloaded operators for a loop condition. It first adds a compound-addition overload that accepts another complex value, then implements a less-than comparison and tests it in an if statement before…
This article turns Larry Williams’ short-term swing concept into a configurable MQL5 Expert Advisor. A swing low is defined by a bar whose low is flanked by higher lows, while a swing high has lower highs on either side. Candidates involving outside bars are…
The article presents a prototype MQL5 framework that makes market structure data available to other trading software through a modular API. Its components detect swing highs and lows, track protected levels, classify breaks as continuation or…
This article outlines a workflow for using Rattle, a graphical interface to R statistical tools, to explore classification models for predicting whether currency prices will rise or fall. It distinguishes classification targets from price forecasting: the…
The article explains how to model High/Low binary options in MetaTrader 5, record simulated wins and losses, and compare strategy settings through optimization and forward testing. It describes handling a stake and payout, tracking the opening time until…
The article introduces a Python workflow alongside MetaTrader 5 and explains how Python libraries can support data analysis, machine learning, automation, and visualization around MQL5 trading systems. It outlines installation of Python and the MetaTrader 5…
This article explains how an MQL5 detector can collect structured data from complex chart objects for use in rule-based trading systems. It covers Fibonacci retracements, fans, arcs, and time zones, along with equidistant channels and Andrews pitchforks. The…
The article explores ways to turn moving averages into visual trading signals. It begins with price crossing an average as a possible trend change, then uses the average as dynamic support or resistance: a bar that tests the line but closes back on its…
The article describes an end-to-end workflow that adapts Google’s pretrained TimesFM 2.5 model for financial time series and displays forecasts in MetaTrader 5. It combines OHLCV exports across forex, index, and commodity instruments with time, session,…
The article describes an Expert Advisor that extends a one-time analytical script by refreshing market information periodically. It reports previous-day open, close, high, low, and volume, current-day volume, and calculated support and resistance levels.…
The article introduces Soft Actor-Critic (SAC), an off-policy reinforcement learning method for continuous action spaces. Its stochastic actor learns a probability distribution over actions, while an entropy term in the objective balances reward seeking with…