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
The article defines an inside bar as a candle whose high and low remain within the preceding mother bar's range. It treats the pattern as a possible continuation or reversal setup and recommends assessing its context, with higher timeframes, nearby support…
This article explains how to build a chart-based dashboard for viewing economic calendar events in MQL5. It proposes a table where each event has a name, scheduled time, affected currency, importance level, and brief description. Contrasting visual styles…
The article explains the Janus factor as a framework for interpreting market behavior through feedback between price changes and trader reactions. Positive feedback can sustain trends as participants add to rising or falling moves, while early profit-taking…
In this interview, Automated Trading Championship participant Alexandr Artapov discusses the design and operation of a multicurrency Expert Advisor. He describes applying a common indicator-based strategy across selected currency pairs, grouping symbols into…
This article turns Bill Williams’ trading framework into a MetaTrader Expert Advisor and describes signals from the Alligator, Fractals, Awesome Oscillator, and Accelerator Oscillator. Entries combine fractal breakouts filtered by the Alligator with…
In this interview, Automated Trading Championship participant Egidijus Bockus describes a simple trend-following Expert Advisor built for the contest. It uses open and close prices and volume rather than technical indicators, and the participant says this…
This article describes creating an SQLite database from MQL5 Economic Calendar data so that event information can be queried and later used in news-strategy testing. It discusses why stored calendar history is useful when the calendar itself is unavailable…
The article distinguishes the gambling use of martingale, which increases stakes after losses, from the mathematical martingale process, whose next value has a conditional expectation equal to its current value. It explains why doubling down can appear…
This article outlines a Python and MetaTrader 5 architecture in which agents analyze different instruments and share signals through a coordination layer. It describes role-based agents, shared portfolio context, and a service that sends structured signals…
This article presents PairPlot, a graphical tool for examining relationships among multiple trading time series. It arranges a histogram for each series and scatter plots for each pair in a cross-table layout, with trend lines as visual aids. The design uses…
This article describes an MQL5 Expert Advisor that turns Fibonacci retracement drawing into an interactive chart tool. Users enter swing low and high values in an on-chart panel; the EA calculates retracement and extension levels, labels them with ratios and…
This article adapts angle-based analysis to trading indicators and trend estimation. For comparing two currency instruments, it scales prices into comparable point units and interprets their relative values as an angle, with the indicator intended to show…
The article proposes a market prediction pipeline combining quantum circuit measurements, CatBoost, and a language model. It motivates the design with shortcomings of conventional indicators and reports that an LLM’s analysis changed when prompts or…
This article outlines a forex pair-trading system that compares two instruments through their price ratio. It calculates a Z-score to measure how far the ratio has moved from its historical mean, then uses the sign of the score to choose which instrument to…
This installment of an MQL5 tutorial extends a ChatGPT-connected trading assistant with longer multiline prompts, a sidebar for saved conversations, and initial signals informed by chart data. It describes modularizing the program, refining text display and…
This interview profiles a novice Automated Trading Championship participant and summarizes his GBPUSD Expert Advisor. The robot uses MACD to generate buy or sell signals, then applies manually estimated probabilities of subsequent price movement to decide…
This article introduces simple recurrent neural networks (RNNs) for forex time-series forecasting and explains how their hidden state carries information across sequential inputs. It outlines the recurrent computation and backpropagation through time,…
In this interview, Automated Trading Championship participant Evgeny Gnidko describes an Expert Advisor that trades EURUSD using pending orders at levels calculated with the standard MetaTrader ZigZag indicator. The robot also applies signal filtering and a…
This tutorial describes a single-position MQL4 Expert Advisor built around MACD crossovers, with an exponential moving average used as a directional filter. Long entries require MACD to be below zero and turning upward across its signal line; short entries…
This article outlines a workflow for running a local language model with Ollama and adapting it to produce structured trading signals. It describes downloading a small base model, defining trading rules and output fields in a model configuration, and testing…
The article explains a reinforcement learning approach that trains a neural network to choose trading actions directly. Unlike Deep Q-Learning, which estimates expected rewards for actions, the policy model outputs a probability distribution over actions.…
The article walks through retrieving historical XAU/USD data from MetaTrader 5, preparing it in pandas, and visualizing price, tick volume, and moving averages in Jupyter. It also describes further analysis and a broader workflow for combining Python data…
The article derives the Flower Volatility Index (FVI) from the Cartesian components of a Rose Curve and presents it as a bounded oscillator for trend analysis. It explains how the curve’s parameter ratio affects its petal geometry and oscillation period,…
The article proposes pairing a base time-series classifier with a metamodel that filters difficult or unfavorable trading examples. The metamodel uses a longer history window, labels candidate situations as useful or unsuitable, and guides relabeling and…