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.
3,012 documents
The article surveys several ways to calculate pivot points and their support and resistance levels, including classic, Woodie, Camarilla, and DeMark variants. It explains that these levels use prior-period prices, commonly the high, low, and close, and shows…
The article explains temporal-difference (TD) learning as an incremental reinforcement-learning method that updates a state’s value using the immediate reward and the estimated value of the next state. It contrasts this state-value approach with SARSA’s…
This document explains ways to connect MetaTrader 5 with MATLAB for mathematical modeling and trading-system development. It describes how to map MQL5 numeric types to MATLAB arrays, handle strings with different encodings, and account for differences in…
This document explains how to send status updates and alerts from a MetaTrader Expert Advisor through Skype. It distinguishes routine notifications, such as order changes or indicator events, from actionable messages about errors or a missing heartbeat that…
This programming guide explains the structure of custom indicators in MetaTrader 5 and how it differs from earlier MQL4 practice. It walks through indicator properties and inputs, buffer setup, plot configuration, initialization, calculation, and cleanup. A…
The article proposes turning lunar and solar timing into features for a currency forecasting model. It describes calculating phase angles from fixed cycle lengths and currency-specific reference dates, encoding phases with sine and cosine harmonics, and…
The article introduces Dichotomy of Control (DoC), a reinforcement learning approach that separates effects a policy can influence from environmental randomness. It explains the motivation through Decision Transformers: conditioning an agent on a desired…
This installment of a candlestick trend constraint series explains how MQL5 indicator drawing styles can make signals and trends easier to interpret. It recaps a model that filters lower-timeframe signals according to the direction of the daily candle, with…
The article presents Behavior-Guided Actor-Critic (BAC) as an alternative to entropy-regularized Soft Actor-Critic for reinforcement learning with continuous actions. BAC uses an autoencoder trained on state-action pairs: its reconstruction error serves as a…
This article describes a pipeline for moving historical MetaTrader 5 bars into a Python service as preparation for machine-learning analysis of price spikes. Its implementation retrieves OHLC history, serializes timestamps and selected prices into JSON, and…
This article explains how to build an MQL parser in MQL, using a grammar to recognize source-code structure rather than searching raw text. It introduces terminals, non-terminals, and Backus–Naur-style production rules, then uses a simple assignment language…
The article explains CATCH, a neural network framework for detecting anomalies in multivariate market time series. It transforms normalized data with an FFT, divides the real and imaginary spectra into patches, and uses a channel-masked Transformer to learn…
The article outlines an automated trendline breakout system that finds swing highs and lows, connects them into candidate support or resistance lines, and checks those lines using touch counts, spacing, angle limits, and an R-squared fit criterion. Trades…
This article describes using the Generalized Hurst Exponent (GHE) to study time-series scaling and classify series as persistent, random-like, or mean reverting. Unlike the standard Hurst formulation, GHE varies the moment order q to examine different…
The article explains GinAR, an end-to-end neural architecture for forecasting multivariate time series when some variables have incomplete or entirely missing histories. It combines a simple recurrent unit with Interpolation Attention, which reconstructs…
This article adapts the Diversity Is All You Need approach to training trading agents. Its first phase learns a set of skills without an external task reward: a skill-conditioned agent acts from the observed market and account state, while a discriminator…
This implementation article examines memory use in composite MetaTrader indicators that call auxiliary indicators across several timeframes or instruments. Its example combines Ichimoku and price-channel signals across five timeframes. Since each auxiliary…
This research article proposes approximating a sequential strategy with many possible trade outcomes as a two-state process with equal-sized up and down steps. The reduced model is described by the probability of an up step and the average formation times…
This study tests a moving average crossover rule on a combined Williams Percent Range indicator representing currency baskets. Using a 20-period WPR and a 10-period moving average, it treats an upward crossover as a buy signal and a downward crossover as a…
The article introduces neural network architectures and learning approaches, then focuses on deep networks for classifying financial market data. It describes autoencoders and restricted Boltzmann machines as building blocks for stacked models, and outlines…
This article presents an MQL5 indicator that places RSI information beside the current market price on the main chart instead of in a separate subwindow. The panel displays the RSI value, an overbought, oversold, or neutral state, and a weak, strong, or…
The article describes preparing an MQL5 news filter for Strategy Tester use by storing economic calendar events in a static CSV resource. Static data addresses the tester’s lack of access to live calendar updates and limited historical event retention, while…
This guide explains how to rent and operate MetaTrader virtual hosting for Expert Advisors, indicators, and signal copying. It covers selecting a server and subscription, payment and renewal, preparing the local platform, and migrating the configured…
The document outlines an experimental EURUSD workflow that trains a CNN–LSTM model on scaled closing prices and exports it to ONNX for use in MetaTrader 5. It describes a historical-data split for training and evaluation, dropout and early stopping during…