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.
6 documents
This report presents a short backtest of a strategy labeled “momentum-news-generic” against SPY, using Yahoo data. Over the stated period, the strategy had a slightly negative total return, negative annualized return, negative Sharpe and Sortino ratios, and…
This code outlines a daily trading workflow in which separate AI agents research a universe of leveraged exchange-traded funds, argue bullish and bearish cases, and pass their summaries to a trading judge. The universe includes leveraged long and inverse…
This proposed Chinese-equity screen combines three initial conditions: market capitalization below 10 billion yuan, no reported losses, and a daily increase in position share above five percent. It also uses the product of price change and large-order net…
This strategy uses a research agent to rank leveraged ETFs from recent prices and trends, then has bull and bear agents assess the same research. A judge and trading agent selects a side for each index, allocates the account among chosen funds, and revisits…
This guide outlines six compact trading bot demos, each built around a single AI agent using plain-language instructions and built-in data tools. The examples include discretionary stock selection, market news, news sentiment, trend following, a…
This reference explains a price-bars data object that stores a time-indexed DataFrame with open, high, low, close, volume, dividend, and stock-split fields. It identifies metadata such as the data source and symbol, and describes helpers for retrieving the…