Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
4,574 documents
This article walks through a feed-forward neural network in MQL, from the weighted sums and biases of individual neurons to a network with two hidden layers. It uses hyperbolic tangent activations in the hidden layers and softmax outputs for three trading…
This article describes a cross-market trend-following method that combines each market’s own momentum with momentum from markets that tend to lead it. It first scales price changes by estimated volatility, then forms moving-average crossover oscillators at…
This article explains FEDformer, a Transformer architecture for long-horizon time-series forecasting that combines seasonal-trend decomposition with frequency-domain processing. Its Fourier version randomly selects a subset of frequency components, while its…
This article describes using the Mantis time-series architecture as a market representation module within a trading agent. It processes separate market-data channels and their first differences, extracts local features with convolutions, pools them into…
Combination Scalping proposes filtering an existing trading system by analyzing its historical outcomes across different trade expiration times and profit targets. For each setting, the author records candle measurements from the three bars before entry:…
The article applies a secondary machine-learning classifier to RSI crossover signals. RSI remains responsible for trade direction, while a Random Forest estimates whether each signal is likely to succeed using 27 contextual features measured at the signal…
The article explains Deep Q-Networks (DQN) as a reinforcement learning method that uses neural networks to estimate action values across market states. It outlines the agent, environment, state, action, and reward concepts, then describes a trading setup…
The article walks through a demonstration pipeline for forecasting financial prices: obtain EURUSD hourly data, scale closing prices, form rolling sequences, train a one-dimensional convolutional and LSTM network, export it to ONNX, and load the model in an…
The document describes a manual workflow for creating labeled time-series datasets from market charts. Instead of splitting a price series into separate files by trend direction, it adds a trend-group field to each observation so that the original…
The article presents a multi-symbol Expert Advisor that estimates a separate volatility signature for each instrument and uses it to adjust stop-loss distance. It collects historical hourly bars and derives features such as candle ranges, wick sizes, return…
The article outlines a remote risk manager for MetaTrader 5 accounts, focusing on automated limits intended to constrain losses and reduce decisions made under pressure. It describes tracking drawdown on balance and equity, enforcing daily and weekly loss…
This interview presents one programmer-trader’s experience building Expert Advisors for forex and competing in an automated trading championship. He favors relatively simple systems, argues that strategy-specific money and risk management require careful…
The article describes a machine learning workflow for searching for time-based patterns in forex trading. A filter selects bars by hour or weekday, and the labeling process keeps only examples that meet that condition. The same filter restricts trade entries…
The article describes MQL5 components for managing stop orders around news events. It explains a straddle approach: place buy-stop and sell-stop orders around the current price so a position can open when price breaks in either direction. The article…
The article introduces object-oriented programming as a way to organize automated trading systems in MQL5. It explains how classes and abstract data types can support code reuse, flexibility, reliability, and maintenance, and discusses UML as one option for…
The article presents a simple way to test whether a MetaTrader Expert Advisor depends on trades that may fail in live execution. It notes that the MT4 Strategy Tester builds ticks from one-minute bars and does not model broker requotes, which can make…
The article outlines an early data-preparation stage for a machine-learning trading project using EURUSD hourly data retrieved through MetaTrader 5. It describes constructing price and volume features such as moving averages, returns, rolling dispersion,…
The article introduces graphs as a way to represent the steps and connections in an MQL5 trading system. It distinguishes graph structure, which records vertices and arrows between them, from category theory’s emphasis on mappings and transformations. A…
The article presents an MQL5 indicator that maps higher-timeframe candles onto a lower-timeframe chart. It displays each selected candle’s range, body, and wicks, and can add pattern arrows and entry, stop-loss, and take-profit lines. Its central…
The article describes a pipeline for combining MetaTrader economic-calendar data with price bars to train and deploy a LightGBM model for news-aware forex trading. It collects event names, sectors, importance, actual, forecast, and previous values, filters…
The document introduces Ichimoku as a multi-line system for viewing trend, momentum, and potential support and resistance. It explains the roles of the Tenkan-sen, Kijun-sen, leading spans, lagging span, and cloud, then describes how an Expert Advisor can…
The article describes an MQL5 Expert Advisor that identifies bullish and bearish Butterfly harmonic patterns from five swing points, X, A, B, C, and D. It defines the target geometry using Fibonacci relationships: B near a 78.6% retracement of XA, BC within…
The article introduces the Kalman filter as a way to smooth noisy price observations and estimate an underlying market movement. It explains the filter’s two stages: predicting a state from a process model, then updating that estimate using the latest…
The article explains how generative adversarial networks can augment limited financial histories with synthetic price series, including scenarios such as volatility spikes or market declines. It outlines cleaning and scaling historical exchange-rate data,…