lumibot
Python framework for algorithmic trading: backtesting and live deployment for stocks, options, crypto, futures, and forex. Same code for backtest and live trading.
Decision gist · record as of 2026-08-14
Yes, if you are building algorithmic trading strategies or AI trading agents. Lumibot is actively maintained, permissively licensed, and solves the core problem of code reuse across backtest and live trading. The 50 runtime dependencies are substantial but reflect the breadth of brokers and data sources it supports; install friction is low. No known vulnerabilities. Start with backtest and paper trading to validate your logic before going live.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Broker paper/live trading requires valid API credentials (e.g., Alpaca key/secret) set as environment variables.
- Installation is low-friction; the package ships as a wheel and is actively maintained with a recent release.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute Lumibot freely in commercial and private projects without restriction, though you must retain the license notice.
last release 2026-08-05 (9 days) · last repo commit 2026-08-13 · 1,924 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 106,381 downloads/mo, #12,654 on PyPI
Alternatives
Verify before relying
pip install lumibot
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:
order = self.create_order("AAPL", 10, "buy")
self.submit_order(order)
MyStrategy.backtest(
YahooDataBacktesting,
datetime(2023, 1, 1),
datetime(2024, 1, 1),
)- Whether the 50 runtime dependencies are all required for basic backtest use or if many are optional broker/data-provider integrations.
- Performance characteristics and scalability limits for large strategy universes or high-frequency backtests.
- Whether AI agent functionality requires external LLM API keys and what the associated costs are.
What it is and what it does
Lumibot is a framework that lets you write a single Python strategy class and run it in three modes: backtest against historical data, paper trade through a broker's simulation, or execute live orders—without rewriting your logic. It abstracts away broker APIs (Alpaca, Interactive Brokers, Tradier, Schwab, and others) and data sources (Yahoo Finance, Polygon, Alpha Vantage, Quandl) so you focus on strategy rules, position sizing, and risk controls.
The framework also includes a built-in AI agent runtime where you can define multiple agents with different roles—researcher, bull, bear, trader—that reason through market data, filings, indicators, and macro context before submitting orders. You can mix deterministic Python logic with AI reasoning, or use either approach alone. Backtests produce inspectable artifacts (orders, fills, equity curves), and the same strategy code transitions directly to paper or live trading once you swap the broker configuration.
Use it for
- Backtest a multi-asset strategy (stocks, options, crypto, futures) against historical data to validate logic before risking capital.
- Paper-trade a strategy through Alpaca or another broker to test execution and slippage in real market conditions without live money.
- Deploy a deterministic trading bot that runs on a schedule, checking technical indicators and submitting orders through a live broker connection.
- Build an AI trading team where multiple agents debate market evidence and a trader agent decides whether to execute, all within the same Lumibot loop.
- Transition a backtest-validated strategy to live trading by changing only the broker configuration, keeping strategy code identical.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building algorithmic trading strategies or AI trading agents.
Lumibot is actively maintained, permissively licensed, and solves the core problem of code reuse across backtest and live trading. The 50 runtime dependencies are substantial but reflect the breadth of brokers and data sources it supports; install friction is low. No known vulnerabilities. Start with backtest and paper trading to validate your logic before going live.
Install
lumibot on PyPI
Before you install
Installation is low-friction; the package ships as a wheel and is actively maintained with a recent release. The 50 runtime dependencies span data providers (yfinance, polygon-api-client, alpha_vantage), brokers (alpaca-py, ibapi), analysis tools (pandas, numpy, scipy, quantstats-lumi), and scheduling (apscheduler), which is typical for a multi-asset trading platform but adds complexity to your environment.
Requires Python 3.10 or later. Broker paper/live trading requires valid API credentials (e.g., Alpaca key/secret) set as environment variables.
License in practice
MIT license is permissive; you can use, modify, and distribute Lumibot freely in commercial and private projects without restriction, though you must retain the license notice.
Quickstart
pip install lumibot
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:
order = self.create_order("AAPL", 10, "buy")
self.submit_order(order)
MyStrategy.backtest(
YahooDataBacktesting,
datetime(2023, 1, 1),
datetime(2024, 1, 1),
)Verify before relying
- Whether the 50 runtime dependencies are all required for basic backtest use or if many are optional broker/data-provider integrations.
- Performance characteristics and scalability limits for large strategy universes or high-frequency backtests.
- Whether AI agent functionality requires external LLM API keys and what the associated costs are.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 50 packagespolygon-api-clientalpaca-pyalpha_vantageibapiyfinancematplotlibquandlnumpypandaspolarspandas_market_calendarspandas-ta-classicplotlysqlalchemybcryptpytestyappiscipyquantstats-lumipython-dotenvccxttermcolorjsonpickleapschedulerappdirspyarrowtqdmlumiwealth-tradierpy-clob-client-v2pytz |
| Maintenance | Actively maintained 9 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 106,381 / month, #12,654 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Office/Business :: FinancialTopic :: Office/Business :: Financial :: InvestmentTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Python Modules |
Evidence: lumibot-4.5.83-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “algorithmic trading framework python”
- lumibotLumibot is a Python framework for building, backtesting, and running…
- alpaca-pyAlpaca-py is the official Python SDK for interacting with Alpaca's…
- nautilus_traderNautilusTrader is a Rust-native event-driven trading engine that runs…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also backtesting · freqtrade · backtrader · investor-agent · finlab · alpaca-py · nautilus_trader · vectorbt · metatrader5 · pandas-ta