নলেজ লাইব্রেরি

This example describes a daily AI-driven process for selecting sector ETFs. Five agents cover technology and communications, financials, healthcare, energy, and consumer sectors, each proposing an idea. A risk manager reviews the pitches for crowded…

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 example outlines a daily workflow that combines browser-based research, trade review, and optional publication of a trade receipt. A research agent visits a configured site, captures evidence and a screenshot, and returns claims, contradictions, and…

This Lumibot guide explains three futures asset choices: continuous contracts, specific-expiry contracts, and automatically selected expiries. It presents continuous futures as a convenient choice for multi-year backtests because they avoid manual expiration…

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 example demonstrates how to run a local backtest with synthetic minute-level prices and a simple strategy. The strategy waits for its first trading iteration and then submits a buy order for one share of a demonstration stock. The backtest uses a small,…

The document explains how to migrate a strategy from Backtrader to LumiBot by checking one behavior at a time: data timing, indicators, sizing, orders, and execution. It maps common lifecycle and broker concepts, then illustrates a simple allocation rule…

The document describes a two-agent bot that sells a same-day-expiring bear call spread on SPY. A research agent checks prices every 15 minutes and selects a short call near 0.20 delta plus a call five points higher. A trading agent opens one spread per day,…

This overview presents LumiBot as a Python framework for writing conventional rule-based strategies, AI-agent strategies, or combinations of the two. Its workflow supports running historical backtests before connecting to a broker, then running a strategy in…

The document explains how to connect Databento historical market data to Lumibot backtests. It covers API-key setup, asset definitions, timeframes, date-range configuration, caching, and handling common retrieval errors. Examples include stocks, continuous…

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 stock selection process in which a research agent ranks large US stocks using recent prices, trends, and news. Bull and bear agents assess the same research from opposing perspectives, then a judge and trading agent select the…

This overview explains how LumiBot strategies are organized. User strategies inherit from a common Strategy class, whose methods cover the bot lifecycle, strategy helpers, broker interactions, and market data access. It points readers to a copy-and-run…

This report compares a SPY 0DTE options strategy with SPY over a brief backtest covering January 4–6, 2026. It presents standard performance and risk measures, including returns, drawdown, Sharpe ratio, volatility, time in the market, and benchmark…

The document compares TradingAgents, presented as a framework for multi-agent financial research, with Lumibot, described as a Python trading framework that can place agent workflows inside a strategy lifecycle. It outlines how research and debate agents can…

This example shows how a Lumibot strategy configured for continuous crypto-market hours can submit market and limit orders, request recent price bars, and inspect price data. It demonstrates calculating RSI, MACD, and an exponential moving average from…

This bot outlines a disclosure-driven copy-trading process based on a member of Congress’s reported stock and call-option holdings. A research agent reads annual and transaction reports, reconstructs current holdings, and ignores filings dated after the…

This LumiBot example uses a team of agents to build a basket from leveraged long and inverse ETFs, with SHV as a cash-like fallback. Separate agents assess growth, inflation and rates, and debt and liquidity. A fourth challenges their conclusions, and a…

This document outlines an equity strategy in which a research agent calculates a point-in-time VWAP setup and assesses dip-and-reclaim evidence, while a separate trading and risk agent independently verifies the signal and handles orders. The design aims to…

The document describes an options workflow that separates research from trade execution. A non-trading researcher gathers market, account, option-chain, contract, Greeks, quote, and package-price information. A trading agent independently refreshes that…

The document explains Lumibot’s WEEX connection through its shared CCXT broker. The path is described as spot-oriented and auto-detected, with authentication requiring an API key, secret, and passphrase. It also states that WEEX does not offer a conventional…

This strategy allocates a portfolio across leveraged funds tied to US stock indexes, Treasury bonds, gold, and oil and gas companies. It assigns each holding a target weight, checks the portfolio daily, and rebalances every four days by comparing each target…

This example strategy demonstrates placing an entry market order followed by a one-cancels-the-other sell order. The OCO order pairs a take-profit limit price with a stop price, so execution of one exit is intended to cancel the other. The sample uses a…

This code example shows a scheduled strategy that checks for its first trading iteration, creates a forex asset using a configurable currency symbol, and submits a buy-to-open order for a fixed quantity. Its daily sleep interval means the strategy is…