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Dokumentu skaits: 164
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 documentation explains the strategy initialization lifecycle in Lumibot. The initialize method runs once when a strategy starts and can set operating parameters such as iteration interval and how long before the close trading should stop. It can also…
This code describes a deterministic replay process for trading on congressional disclosures. It uses each disclosure’s public publication time to decide whether the information was available, explicitly avoiding the transaction date as the signal timestamp.…
This documentation explains how to use Polygon as a historical price-data source for LumiBot backtests across stocks, options, forex, and cryptocurrencies. It describes supplying an API key, selecting a backtest date range, and running a simple example…
This framework overview explains lifecycle methods: functions the trading engine calls at defined points to initialize and run a strategy. A user-defined strategy must implement the trading-iteration method, which the engine calls repeatedly and which is…
This reference explains two strategy lifecycle hooks for handling setup before trading begins. The before-market-open hook runs each day before the market opens; an example use is canceling outstanding orders. If a strategy launches after the market has…
The document outlines an intraday SPY strategy that buys after price dips at least 0.15% below VWAP and then returns above it. A research agent checks minute bars hourly beginning at 10:00 ET, while a trading agent enters when the bounce is identified and no…
This example describes an AI-assisted value-investing workflow inspired by Warren Buffett’s public approach. One agent reviews filings and assesses business quality, cash generation, balance-sheet strength, and durability. A second challenges the valuation…
This guide catalogs implementation mistakes that can distort trading decisions or break a Lumibot strategy. It explains why backtests should use simulated time and completed candles, why persistent assets belong in strategy variables, and how to handle…
This example describes an AI trading team modeled on concentrated investing. A quality researcher selects a high-quality large-cap company, an activist bull develops the case for catalysts and value creation, and a short-seller challenges the thesis on…
This documentation explains why a trading strategy may need its own view of the current date and time. The strategy's clock reflects the simulated point in time during a backtest and the relevant time during live trading. This matters when historical logic…
This example describes a daily SPY strategy that assigns market analysis and order decisions to separate AI agents. The research agent compares the latest completed daily close with its 20-bar average and reports the date, observed prices, evidence for and…
This document is a QuantStats tear sheet comparing a strategy labeled “vwap-plain” with SPY over January 4–9, 2026. It reports a 0% total return for the strategy, a 0.12% maximum drawdown, a 0.76 Sharpe ratio, and 50% time in the market. The benchmark’s…
This guide explains how to inspect an AI agent’s decisions during backtests and live or paper trading. It describes per-run Parquet records, per-call JSON traces, summary logs, and machine-readable artifacts. These records expose prompts, tool calls and…
The document describes Lumibot as a Python framework for creating rule-based strategies, AI-assisted trading systems, and hybrid approaches. Conventional Python logic can handle indicators, schedules, position sizing, and risk controls, while AI agents can…
The document explains how to use selected LumiBot components in standalone scripts or notebooks without constructing a trading strategy. Examples cover querying FRED macroeconomic series with a historical information vintage, retrieving price bars through…
This document presents a QuantStats tear sheet for an automated strategy labeled “orb-plain,” compared with SPY over January 4–9, 2026. It reports return and risk statistics, including a 0% total return for the strategy, a 0.67% maximum drawdown, a 0.55…
The document explains what LumiBot’s HTML backtest tear sheet and companion machine-readable metrics file contain. It lists return and risk measures such as annualized and total return, Sharpe and Sortino ratios, return over maximum drawdown, maximum…
This document is a QuantStats tear sheet comparing an AI trading strategy with SPY over a brief January 2026 backtest, using Yahoo data. It reports a 1% total return for each, with the strategy showing a higher annualized return estimate but also a larger…
This report compares an AI-driven portfolio built from a named set of large-company stocks with SPY over a very short backtest window. It presents standard performance and risk measures, including returns, drawdown, Sharpe and Sortino ratios, benchmark…
This example demonstrates a daily-iteration stock strategy that submits limit buy and sell orders alongside two trailing stop sell orders. The orders target the same symbol, while the trailing exits use either a percentage retracement or a fixed price…
This documentation explains the built-in tools available to LumiBot agents for market research, account inspection, trading, memory, and notifications. It separates research agents from agents allowed to place or change orders: disabling trading removes…
The document explains what strategy trade exports contain and how to use them when reviewing a backtest. HTML and tabular files report order timing and prices, the traded asset, cash balances, raw portfolio value, and a cash-adjusted equity series intended…
The document describes a daily macro trading process built around distinct research perspectives. Separate agents assess economic growth, inflation and interest rates, and debt, liquidity, currency, and central bank policy. A disagreement agent challenges…