SystemSelf reportedCryptoPythonai-agentsalgorithmic-tradingalpacabacktestingbroker

lumibot

Backtestable AI trading agents and Python algorithmic trading strategies for stocks, options, crypto, futures, forex, SEC filings, FRED macro data, and real brokers.

System Structure

Data Used
암호화폐 거래소 시장 데이터가격/거래량 데이터
Rules / Strategy

저장소별 전략/실행 조건 확인 필요

Execution

거래소 API 기반 자동 주문 실행

Editor Summary

저장소 설명과 공개 메타데이터 기준으로 AI/LLM 또는 머신러닝 활용, Python 기반 구현, 암호화폐 거래 봇, 거래소 API/프레임워크 성격의 프로젝트로 파악했습니다. 확인 근거는 README, 저장소 토픽, 저장소 설명, GitHub 지표, 파이프라인 필드이며, 주요 데이터는 암호화폐 거래소 시장 데이터, 가격/거래량 데이터입니다. 전략/실행 조건은 저장소별 문서와 코드 확인이 필요하며, 실행 방식은 "거래소 API 기반 자동 주문 실행"라고 보수적으로 기록했습니다. GitHub 지표는 별 1796개, 포크 343개입니다.

RepositoryLumiwealth/lumibot
CreatorLumiwealth
Stars / Forks★ 1796 / 343
LicenseGPL-3.0
Last Updated2026-07-08
Snapshot Date2026-07-08 (README below is a copy from this date)

This is third-party open-source code. QuantField does not guarantee its behavior or safety. Review the code before installing or running it.

README

CI Status Coverage PyPI Python License: MIT

Lumibot: Backtestable AI Agents and Python Algorithmic Trading

Build deterministic trading strategies, AI trading agents, and AI trading teams for stocks, options, crypto, futures, forex, prediction markets, SEC filings, FRED macro data, technical indicators, and real brokers. Backtest, paper trade, or run live with the same Python code.

Full docs: lumibot.lumiwealth.com · Managed cloud: BotSpot.trade · MCP: BotSpot for AI coding agents

What You Can Build

  • Deterministic strategies: normal Python logic, indicators, if statements, scheduled rules, position sizing, and risk controls.
  • AI-agent strategies: one or more agents that reason through evidence, call tools, write memory, and optionally place orders.
  • Backtests: replay historical data and simulated orders with artifacts you can inspect.
  • Paper or live trading: reuse the same strategy code with real broker state and real order routing.

Start with the open-source docs, then deploy when you are ready: Lumibot documentation · Try a sample Lumibot strategy on BotSpot

Quick Start

Backtest a strategy

pip install lumibot

Save this as my_strategy.py:

from datetime import datetime
from lumibot.strategies import Strategy
from lumibot.backtesting import YahooDataBacktesting

class MyStrategy(Strategy):
    def on_trading_iteration(self):
        if self.first_iteration:
            aapl = self.create_order("AAPL", 10, "buy")
            self.submit_order(aapl)

MyStrategy.backtest(
    YahooDataBacktesting,
    datetime(2023, 1, 1),
    datetime(2024, 1, 1),
)
python my_strategy.py

Run the same strategy with a paper broker

After the backtest works, keep the MyStrategy class and replace the final MyStrategy.backtest(...) call with a broker runner. This example uses Alpaca paper trading:

export ALPACA_API_KEY='your-alpaca-key'
export ALPACA_API_SECRET='your-alpaca-secret'
export ALPACA_IS_PAPER=true
python my_strategy.py
import os
from lumibot.brokers import Alpaca
from lumibot.traders import Trader

ALPACA_CONFIG = {
    "API_KEY": os.environ["ALPACA_API_KEY