Yapay zekâ ajanlarımızın okuduğu kitap, makale, araştırma yazısı ve kodların Stratmill araştırma ajanı tarafından hazırlanan özetleri ve temel fikirleri. Her sayfada orijinal kaynağa bağlantı bulunur.
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The article shows how to extend RSI into a single indicator that supplies signals for both trend-following and range-oriented strategies. It develops the indicator in stages, adding buy and sell buffers, signal counters, more complete signal handling,…
The article explains rules for an indicator that draws inclined and horizontal support and resistance levels. For inclined lines, it uses Bill Williams fractals as candidate turning points: resistance starts from a prominent upper fractal and support from a…
This installment explains how a library of timeseries objects can supply data to MetaTrader indicators. Its central concern is that indicators run in a symbol-specific execution stream and must not block while historical data is being downloaded or…
This article outlines an Expert Advisor for trading breakouts from a recent consolidation range in MetaTrader 5. The range is defined by the highest and lowest prices over a chosen number of bars, optionally subject to criteria such as a minimum bar count…
The article presents an interface for changing indicator parameters directly on a MetaTrader chart. Its example is a trend strategy using fast and slow moving averages: the slow period is twice the fast period, and the trader searches for the smallest pair…
The document presents a method for aligning bar data across instruments before portfolio analysis or trading. It uses one instrument’s bar opening times as a reference, matches other symbols to those timestamps, fills missing bars, and trims excess history…
The article describes Fish School Search, a population-based metaheuristic for continuous optimization. Each candidate solution is represented as a fish with a weight reflecting search success. The method combines individual movement, a shared instinctive…
The document outlines an ARIMA training method implemented as an MQL5 class. It reviews autoregressive, moving-average, and differencing components, then describes fitting coefficients by generating predictions over known observations and minimizing their…
The document explains how to assess the path risk hidden by a single historical equity curve. It resamples realized trade profits and losses with replacement to create alternative sequences, then tracks equity paths, percentile bands, maximum drawdown,…
In this interview, an Automated Trading Championship participant describes a Forex Expert Advisor based on breakouts of levels from the Folding Rule, a simplified wave-analysis method. The robot combines those levels with an ABC formation and money…
The article outlines a rules-based approach that combines order block zones with Fibonacci retracements. A bullish setup is defined by a bearish candle followed by an engulfing bullish candle and further bullish movement; a bearish setup reverses those…
This article describes the receiving side of a communication protocol between a MetaTrader 5 Chart Trade indicator and an Expert Advisor. Since the indicator cannot submit or manage orders, it sends custom chart events containing serialized trade details;…
The article explains Donchian Channels as rolling high and low boundaries whose breaks can signal trend entries. It describes variations that calculate the boundaries using highs, lows, opens, or closes, and notes that false breakouts make channel signals…
The document outlines a Python workflow for studying forex trends using historical data from MetaTrader 5. It identifies local highs and lows with a centered rolling window, alternates successive extremes to form uptrends and downtrends, and records each…
The Parafrac Oscillator combines Parabolic SAR with recent fractal highs and lows. Fractals mark local price extremes using a five-bar structure, while PSAR provides trend direction and a moving reference level. The oscillator divides the distance between…
The document describes an Expert Advisor that uses Parabolic SAR (PSAR) to detect possible reversals. A buy setup requires a bullish current bar with PSAR below its close, while the two preceding bars have PSAR above their closes; the sell setup reverses…
The document proposes a framework for checking whether familiar candlestick patterns still predict subsequent price movement. For each pattern, it counts occurrences over a chosen historical interval and timeframe, then measures whether price moves a…
This tutorial explains how to assemble MQL5 Standard Library components into an Expert Advisor, using an on-chart information panel as its working example. It recommends first examining a class’s purpose, inheritance, dependencies, and public methods; then…
The article defines the Pivot Mean (PM) as price divided by its cumulative moving average (CMA), making it a normalized measure of price’s distance from a running average. The Pivot Mean Oscillator (PMO) is the difference between moving averages of PM…
The article describes an implementation of HiSSD, a hierarchical skill-discovery framework, adapted for algorithmic trading. Its central design separates common skills that can apply across situations from task-specific skills derived from local…
The article introduces N-BEATS, a neural network architecture for time-series forecasting, and explains how stacked blocks iteratively produce forecasts and backcasts. Each block transforms a lookback window, estimates its contribution to the forecast, and…
This article sketches an MQL5 neural-network implementation for market data, combining a state-space model, transformer attention, multi-horizon context memories, and a verification module. It describes short-, medium-, and long-term memories alongside…
This article presents an MQL5 implementation of a trading model built around DA-CG-LSTM, which combines feature attention, temporal attention, and a modified recurrent block. The attention stages weight input variables and historical intervals; the recurrent…
The article introduces supervised decision trees and explains how the ID3 algorithm builds a classification tree. ID3 starts at the root and greedily selects a feature for each split, aiming to make the resulting groups more homogeneous. The tutorial focuses…