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 presents a chart-based interpretation of the Murray system, derived from Gann-related ideas. It divides a chosen price range into eight equal parts, called an octave, then subdivides those intervals into smaller octaves for closer analysis. The…
This article proposes balancing position sizes across instruments using each symbol’s tick value and average daily volatility. It combines those inputs into an estimate of risk per standard contract, then scales position volume relative to the highest…
The article tests three simple forecasts for the next candle: repeat the previous candle’s direction, predict the opposite direction, or switch between those rules after a forecast error. Candle direction is defined by whether its close is above, below, or…
The article explains how MQL5 services run independently of charts and how to manage them in MetaTrader 5. A service starts through its OnStart handler; it can perform a one-time task and exit or remain active in a loop until stopped. The examples show…
The article presents two approaches to Asian-session forex trading. For quieter pairs such as EURUSD, it discusses treating the session as a range and using short-period Bollinger Bands to identify potential reversals near the band edges. For more active yen…
The article explains a forecasting approach that converts market observations into discrete states and estimates the probabilities of transitions between them. It reviews the Markov property, transition matrices, multi-step matrix forecasts, and stationary…
This tutorial shows how to use MATLAB Simulink and Stateflow to model a simple automated trading system, test it on MetaTrader 5 historical prices, and translate its logic into an MQL5 Expert Advisor. It explains the visual modeling tools, discrete solver…
The article compares learning rates for a neural network trained to detect chart fractals. In the first experiment, separate trading programs use fixed rates or reduce the rate over time; a second experiment continues training from shared weights to control…
This article describes a Smart Money Concept approach that turns three-candle fair value gaps into automated trades. It identifies an imbalance around a large central candle when the first and third candles leave a gap between their wicks. The gap is drawn…
The article outlines an Expert Advisor strategy that uses scheduled economic releases to prepare for a potential volatility breakout. A few minutes before a high-impact event, the EA places a buy stop above the market and a sell stop below it. If one order…
This notebook applies TabM, a tabular neural-network approach, to foreign-exchange pair prediction rows without treating the data as a sequence. It uses shared runner infrastructure to fit preprocessing within each training fold, save declared weight…
This notebook builds a daily feature panel for a long-short ranking strategy across twenty FX pairs. It aggregates four-hour spot bars into sessions ending at the New York 5 PM rollover, then constructs trailing return, channel, volatility, drawdown, range,…
This notebook explains how double machine learning (DML) estimates whether FX momentum affects future returns after adjusting for configured confounders. It distinguishes this intervention question from prediction: predictive models are compared by…
This notebook studies how position sizing affects FX backtests after the model has already selected which currency pairs to trade. It preserves the winning baseline’s predictions, signal mapping, costs, and execution settings, then varies allocation rules.…
This notebook describes a TabM workflow for foreign-exchange pair models. TabM applies a small neural network to each decision row rather than treating the observations as a sequence. The notebook takes architecture and checkpoint schedules from…
This notebook tests whether gradient-boosted trees improve cross-sectional ranking across currency pairs beyond a penalized linear model. The FX universe contains pairs sharing currencies, so observations are dependent: a move in one currency affects…
This notebook checks whether four-hour spot FX data can support a daily cross-sectional strategy that ranks currency pairs using momentum and carry. It tests whether the declared instruments have prices at each decision point, whether the universe represents…
This notebook describes a double machine learning analysis of the effect associated with an FX momentum treatment after adjustment for configured confounders. Flexible nuisance models estimate the outcome and treatment from those confounders; cross-fitting…
This notebook configures an NLinear forecasting run for FX pairs. The model uses a fixed consecutive lookback and subtracts the last observed level, directing its fit toward changes over that window. It resolves the lookback, normalization, device, folds,…
This notebook audits the complete set of canonical validation predictions for an FX pairs study. It checks model identity, artifact availability, completeness, configured-label coverage, and whether the assembled configurations match the declared menu.…
This notebook audits the registered validation predictions for an FX pairs study. It checks that the catalog includes the configured model population, complete prediction sets, available artifacts, and current model identities. It then summarizes predictive…
This notebook presents correctness and runtime comparisons for VectorBT Pro and VectorBT OSS against ML4T on supported case-study strategies. It covers ETF allocation, USD-quoted foreign exchange, and a US equity panel for both editions; VectorBT Pro also…
This notebook describes a shared workflow for fitting temporal convolutional networks to daily FX pair data. Instead of treating each date as an isolated feature row, the model uses a fixed lookback of consecutive observations. The runner determines eligible…
This case study evaluates daily momentum and carry signals across a small, correlated universe of G10 spot currency pairs. Its workflow moves from feasibility checks and forward-return labels through engineered and model-based features, cross-validation,…