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
This note summarizes when to use several portfolio allocation approaches. A naïve allocation is presented as a practical choice when the universe is broad and return forecasts are difficult. Risk parity and minimum variance are associated with settings where…
This description explains a MetaTrader 5 dashboard that classifies seven chart timeframes, from one minute through daily, as bullish, bearish, or neutral using an adjustable EMA-based model. It aggregates the individual classifications into a trend score and…
This research summary presents a macro timing framework for Chinese equities and bonds built around monetary conditions and credit growth. It treats policy shifts as potentially informative because monetary policy is described as delayed and constrained by…
WATR_HTF is a version of the WATR indicator with a timeframe selection input. The example sets the indicator period to four hours, indicating that users can request calculations from a chosen chart interval through its parameters. The description does not…
This document outlines a daily strategy for trading a set of assets using a cointegration vector estimated with the Johansen method on training data. It applies the vector to log prices to form a combined process, then sums its recent changes to determine…
The post raises a research question about obtaining daily exchange-rate data for several countries, including China, the United States, and Japan. Its author argues that broad equity-market timing should consider capital flows, distinguishing domestic flows…
The document outlines ways to set the changing asset allocation, or glide path, of a target-date fund. One approach uses an investor’s human capital and forecasts of future asset returns to choose a path that maximizes investor utility. Another bases the…
This reference guide describes Lumibot as a Python framework for creating, executing, and backtesting trading strategies across several asset classes and broker or data services. It outlines the strategy lifecycle, including initialization and a recurring…
The report describes an expanded Black-Litterman asset allocation model that combines subjective views with quantitative portfolio construction. It uses behavioral finance and historical trend information, including momentum and reversal effects, to form…
This Chinese market note proposes that China’s credit impulse may be turning upward and argues that it tends to lead A-share earnings growth. On that assumption, it recommends overweighting Chinese equities, favoring growth over value, and ranking…
This indicator compares momentum across as many as eight selected symbols. For each symbol, it calculates the difference between today’s moving average and the previous day’s moving average, using a configurable period, averaging method, and applied price. A…
This weekly market note responds to weakening Chinese equity sentiment and increased uncertainty by reducing A-share exposure, adding interest-rate bonds and secondary bond funds, and increasing exposure to low-valuation and dividend factors. Its rationale…
The document describes an MT5 dashboard indicator that scans selected symbols and timeframes for candles with prominent upper or lower wicks. Configurable rules include wick share of the candle range, minimum range and body size, a cap on the opposite wick,…
The document introduces financial trading as buying and selling assets for investment, hedging, or gains from price changes. It surveys equities, bonds, derivatives, foreign exchange, physical commodities, and cryptocurrencies, noting that each market has…
This Chinese language summary reports findings from a study of passive and active strategies across asset classes in the United States, United Kingdom, and Japan over a long historical period. It frames the analysis as useful because recent experience in…
The document describes a software framework for executing spread trades across multiple instruments. It tracks each leg’s orders, fills, costs, and positions, then estimates the spread’s completed volume and average fill price. For inverse contracts, it…
This document describes a live monitoring system in which users define named formulas over instrument prices. The system subscribes to the instruments referenced by each rule, reads their latest available prices when market updates arrive, evaluates the…
This document describes a graphical interface for defining and monitoring spread trades. Users can create standard or flexible spreads, specify leg instruments and directions, set a pricing formula, identify an active leg, and enter minimum trade volume. The…
This example outlines a multi-timeframe analysis workflow for Bitcoin-dollar price data. It loads minute history over a stated date range, configures a transaction-rate assumption and a rolling analysis window, and assigns technical indicators to several…
This document describes the data model and calculations behind a synthetic multi-leg spread. Each leg stores its market quotes, contract details, and position state. Configurable price multipliers define the spread price, while trading multipliers define how…
This guide explains how a Python script engine can connect to trading gateways, subscribe to market data, query account and instrument records, and submit or cancel orders. It describes both an interactive notebook workflow and a continuously running script…
This algorithm takes liquidity in the active leg of a multi-leg spread when the quoted spread reaches a configured limit. For a long spread, it checks whether the ask is at or below the target; for a short spread, it checks whether the bid is at or above it.…
The document surveys the components of a Python trading framework, from connections to market venues through strategy development and automated execution. It outlines event-driven infrastructure, data handling, graphical tools, and applications for…
The document explains how to simulate terminal prices for several assets whose returns are correlated, in order to value a multi-asset option by Monte Carlo. In a geometric Brownian motion model, dependence is specified through correlations among Brownian…