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.
2,013 documents
The article describes a multi-symbol Expert Advisor that adjusts risk and trade parameters according to volatility. It uses ATR to inform stop placement and classify volatility into high, moderate, and low risk tiers, with position size derived from account…
The article presents an implementation of MASAAT, an attention-based multi-agent framework for portfolio optimization. Agents analyze price sequences at multiple scales, using directional movement thresholds to represent shifts of varying size.…
This article explains an MQL5 dashboard for examining pairwise relationships among a user-selected set of financial symbols. It computes correlations from price changes using Pearson for linear association, Spearman for rank-based monotonic association, or…
The article presents a spatio-temporal neural network for multi-step forecasting from short multivariate time series. Its central idea is a spatio-temporal information transformation that recasts relationships among input variables as temporal information…
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…
This article presents an integration pattern for storing and analyzing trading data from MQL4 or MQL5 Expert Advisors in Microsoft SQL Server. Since MetaTrader does not provide direct database-server access, the approach uses a C# class library that exports…
The article measures how MetaTrader 5 tester runtime and memory use change as the number of strategy instances in a multi-currency Expert Advisor grows. It compares several tick simulation modes, estimates cost per instance per test day, and separates EA…
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…
This document describes a daily ETF candidate universe covering equities, fixed income, commodities, and currencies. It outlines a workflow for downloading market data, loading it for analysis, inspecting coverage by symbol and category, and filtering by…
This document outlines a shared data system for quantitative trading research, cataloging datasets across equities, options, futures, crypto, foreign exchange, factors, macroeconomics, filings, positioning, news, and prediction markets. It describes the…
This case study compares predictive models for monthly cross-asset rotation across ETFs spanning equities, fixed income, commodities, currencies, and real estate. Its central lesson is that information coefficient (IC) and trading performance can rank models…
This notebook shows how to align macroeconomic observations with the dates traders could actually have known them. It distinguishes the period a value measures from its publication date, estimates release dates from period length and agency lag schedules,…
This notebook queries backtest registries across nine case studies and assembles comparable tables for downstream analysis. It organizes results by asset class and data frequency, then records performance across signal, allocation, cost, and risk stages,…
This notebook develops a financial feature matrix for a cross-asset ETF momentum hypothesis: assets with stronger relative performance may continue to outperform over the following month. It combines trailing returns at several horizons, risk-adjusted…
This document presents a cross-market inventory of model-based feature artifacts from nine case studies. It reads parquet schemas rather than loading their rows, excludes identifier columns, counts feature columns, and groups names by tokens associated with…
This document explains how to build and inspect a feature matrix for a cross-sectional ETF momentum and rotation hypothesis. It defines each feature’s lookback and information lag, then constructs trailing returns, risk-adjusted returns, volatility, trend…
This utility module supports deep learning workflows for financial time series across multiple assets. It resolves dataset aliases and loads canonical case study data, then creates sliding-window sequences independently for each symbol. The sequence…
This chapter presents strategy research as the design and evaluation of an executable decision process, from the initial economic idea through position sizing, constraints, costs, and live-like testing. It recommends classifying strategy families and…
This notebook checks whether historical ETF data can support a monthly ranking strategy before fitting a model or making forecasts. It tests the tradable universe using prior-year liquidity, counts eligible funds on rebalance dates, converts per-share…
This notebook compares six long-only ETF allocation methods designed to reduce reliance on unstable estimates. It applies Ledoit-Wolf covariance shrinkage to estimators that use covariance, and contrasts mean-variance maximum Sharpe with minimum variance,…
This notebook compares single-objective hyperparameter tuning with a multiobjective search for LightGBM prediction models. The baseline maximizes validation information coefficient (IC). The NSGA-II search instead maximizes IC while minimizing normalized…
This shared analysis module describes ways to estimate trading frictions across asset classes. It includes high-low and return-autocovariance estimators for bid-ask spreads, rolling average volume measures, and regression approaches for calibrating…
This notebook develops a unit-aware framework for estimating trading costs across equities, crypto perpetuals, futures, ETFs, and foreign exchange. It distinguishes share volume, contract volume, base-asset volume, and price-update counts, converting…
This notebook compares reference ETF portfolios under historical crises, hand-defined simultaneous asset shocks, and Monte Carlo loss scenarios. Historical windows include major equity and rate-driven selloffs, with portfolio returns compounded over explicit…