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.
11 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 report presents a brief backtest of a market-news trading bot against SPY, covering January 4–15, 2026. It lists return, drawdown, risk, correlation, and other performance statistics, along with model-call and data-source details. The strategy reports a…
The script describes a daily SPY allocation strategy driven by CNN’s Fear and Greed Index. A research agent retrieves the latest score from a prior day, while a separate trading agent maps score ranges to target allocations: higher equity exposure at low…
This page catalogs trading bot examples built around AI agents, ranging from copying reported investor or insider holdings to sentiment signals, agent debates, options strategies, intraday rules, and macro or sector portfolio discussions. It outlines…
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 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 example describes a daily rule that uses CNN’s Fear & Greed Index to set exposure to SPY. A research agent retrieves a recent score, and a trading agent assigns one of five SPY allocations: full exposure at extreme fear, progressively smaller…
This example describes a watchlist strategy that uses public SEC Form 4 filings to adjust portfolio weights. A research agent retrieves recent filings and keeps open-market purchases and discretionary open-market sales, while excluding grants, gifts, option…
The document presents a QuantStats tear sheet for a strategy named fear-greed-plain-v2, compared with SPY over a brief January 2026 test period. It reports returns, drawdowns, risk-adjusted metrics, benchmark correlation, time in the market, and daily gains…
The strategy uses a fixed equity watchlist and a daily agent workflow to review SEC Form 4 filings available as of each decision time. Its research step filters recent filings, opens the source documents, and focuses on non-derivative open-market purchases…
This document presents a QuantStats tear sheet for a strategy labeled news-sentiment-generic, compared with SPY over January 4–15, 2026. It reports a 1% total return for both, while the strategy has higher annualized return and volatility, a lower Sharpe…