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.
127 documents
This strategy organizes research and trading for same-day-expiration bear call spreads through separate agents. A researcher gathers account and market information, checks the listed expiration, contract Greeks, and bid-ask quality, then identifies a short…
The document describes TradingSlippage as an execution cost applied during backtesting to SMART_LIMIT fills. It says slippage can be supplied at the strategy level, with separate lists for buy and sell orders. This lets a researcher model an assumed cost on…
This guide describes ways to organize AI agents inside a trading strategy, from a single analyst to specialist research teams, opposing bull and bear views, and sequential debate. It distinguishes deterministic strategies, agent-led decisions, and hybrid…
This example describes a concentrated long-only stock portfolio built through a sequence of AI agents. A research agent ranks companies for understandable businesses, cash generation, and attractive prices. A second agent challenges each idea by examining…
The document contrasts an educational AI investing project, which organizes investor-style agents to debate ideas, with a framework centered on the trading strategy lifecycle. It describes a workflow in which agent decisions are tested on historical data,…
The document contrasts OpenAlice, presented as an AI agent for researching and managing trades across a full lifecycle, with LumiBot, a Python framework for building trading strategies. LumiBot can support deterministic strategies, individual AI agents, or…
This example organizes daily decisions across leveraged sector and broad-market ETFs using separate AI agents for technology, financials, healthcare, energy, and consumer-related groups. Each sector pod is instructed to consult recent news and macroeconomic…
This repository overview describes a Python framework for building rule-based strategies, AI-assisted decision systems, and combinations of the two. Its central workflow is to test strategy decisions on historical data, inspect simulated orders and reports,…
This Korean-language project overview describes LumiBot, a Python framework for building trading strategies that can use ordinary rules, AI agents, or a combination. It presents a workflow that begins with a sample strategy and historical-data backtest, then…
This documentation explains how Lumibot represents cash flows separately from trading activity in strategy backtests and live broker data. It distinguishes deposits and withdrawals from performance while accounting for financing, dividends, fees, interest,…
This bot aims to mirror a named member of Congress’s reported stock holdings. A research agent checks House disclosure filings, using annual reports as the starting portfolio and applying later trade reports to update it. It excludes options, real estate,…
This report presents a short backtest of a large-cap stock strategy attributed to a multi-agent AI trading bot and compares it with SPY. The stated test ran from January 4 to January 15, 2026, using Yahoo data and a universe of large technology and other…
This document explains how to connect to BitMEX through Lumibot’s CCXT broker interface using an explicit exchange configuration and API credentials. It notes that BitMEX is not among the globally auto-detected credential paths, and identifies the exchange…
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 FAQ describes LumiBot, a Python framework for backtesting and live algorithmic trading across several asset classes and brokers. It outlines the shared strategy workflow, data-source requirements, and common operations such as handling fills, tracking…
This document describes a command-line tool for creating, backtesting, and running editable LumiBot strategies. Its ordinary Python template demonstrates a long-only moving-average rule: retrieve recent daily prices, compare the latest close with a rolling…
The script demonstrates a classic allocation strategy that holds a portfolio with a target mix of 60% stocks and 40% bonds. It uses a drift rebalancer: when asset weights move away from their targets by a configured threshold, the strategy sells assets that…
This document describes how to configure Lumibot’s CCXT broker for KuCoin. KuCoin is not presented as a globally auto-detected credential route, so the guide uses an explicit broker configuration with the exchange identifier and API key, secret, and…
This guide explains a strategy-level indicator accessor for calculating technical indicators using only market data available at the strategy’s current time. It describes built-in single- and multi-column indicators, Fibonacci retracement levels, and custom…
The document explains how to use ThetaData as a historical data source for LumiBot backtests covering stocks and options, as well as other asset types. It supports minute and daily bars directly; hourly bars can be built from minute data. Downloaded data is…
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…
This documentation entry directs coding agents to start from complete LumiBot examples for either AI-based or ordinary Python strategies. It describes the strategy lifecycle at a high level: create agents during initialization and invoke them during each…
This comparison surveys AI-oriented trading and research projects by their agent workflows, ability to replay or backtest decisions, broker paths, deterministic strategy support, and hosting or monitoring features. It distinguishes research-focused tools…
This documentation explains how a trading strategy can represent and submit orders, from basic market orders to limit, stop, stop-limit, and trailing-stop orders. It also describes a smart limit approach that moves through the bid–ask spread on a timed…