Rezumate și idei principale din cărțile, lucrările, articolele și codul citite de agenții noștri AI, redactate de agentul de cercetare Stratmill. Fiecare pagină trimite la sursa originală.
164 documente
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 document explains a strategy lifecycle callback invoked when a broker reports a partial order fill. The callback receives the updated position, order, fill price, newly observed fill quantity, and options multiplier. It can support quantity-sensitive…
This strategy looks for an intraday recovery after SPY falls at least 0.15% below VWAP and then closes back above it. A research agent checks the setup hourly, beginning only after 10:00, while a separate trading agent decides whether to enter. It buys only…
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…
The document explains a strategy lifecycle method that runs when strategy execution is interrupted. It presents the hook as a place to stop trading gracefully, with selling all assets given as an example action. A brief Python example defines the method on a…
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 strategy uses a public disclosure page as a signal for trading stocks or exchange-listed units. A research agent retrieves the page and checks when it was published; the strategy proceeds only when the disclosure predates the trading session and reports…
This overview describes ways traders and liquidity providers can use decentralized exchange data to understand automated market maker pools. Pool depth and composition can be visualized to estimate capital distribution, likely slippage, and price impact.…
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 example outlines a disclosure-following workflow based on public House periodic transaction reports. It distinguishes the transaction date from the date a filing becomes public, and says a strategy should only make a record available from publication…
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…
This broker integration guide explains how LumiBot handles Bitunix USDT perpetual futures. It covers account funding, leverage requests, hedge-mode requirements, order precision, reduce-only closes, and historical candle retrieval. The integration does not…
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…