Lumibot

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

From Lumiwealth·Updated June 15, 2026·View on GitHub·

**Build deterministic trading strategies, AI trading agents, and AI trading teams for stocks, options, crypto, futures, forex, SEC filings, FRED macro data, technical indicators, and real brokers.** Backtest, paper trade, or run live with the same Python code. The project is written primarily in Python, distributed under the GNU General Public License v3.0 license, first published in 2020. It has gained significant community traction with 1,669 stars and 321 forks on GitHub. Key topics include: ai-agents, algorithmic-trading, alpaca, backtesting, broker.

Latest release: v4.5.49

June 12, 2026View Changelog →

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, 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

bash
pip install lumibot

Save this as my_strategy.py:

python
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),
)

bash
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:

bash
export ALPACA_API_KEY='your-alpaca-key'
export ALPACA_API_SECRET='your-alpaca-secret'
export ALPACA_IS_PAPER=true
python my_strategy.py

python
import os
from lumibot.brokers import Alpaca
from lumibot.traders import Trader

ALPACA_CONFIG = {
    "API_KEY": os.environ["ALPACA_API_KEY"],
    "API_SECRET": os.environ["ALPACA_API_SECRET"],
    "PAPER": os.environ.get("ALPACA_IS_PAPER", "true").lower() != "false",
}

broker = Alpaca(ALPACA_CONFIG)
strategy = MyStrategy(broker=broker)

trader = Trader()
trader.add_strategy(strategy)
trader.run_all()

Start with paper trading. When you are ready for live trading, use the same strategy class and switch your broker account/configuration intentionally.

For full setup guides, broker tutorials, AI-agent docs, examples, and deployment notes, use the Lumibot documentation.

AI Trading Team

Lumibot now includes a built-in AI agent runtime for financial research, reasoning, debate, risk review, and trade execution. Agents can inspect market data, read filings, query indicators, search memory, compare macro context, and submit orders through the same Lumibot strategy loop used by normal backtests and live trading.

Classic Python strategies are still first-class. Lumibot lets you choose the right level of intelligence: fixed rules, AI agents, or a hybrid where Python handles the hard gates and agents reason through evidence.

Built-in AI agent tools include market/account state, order inspection, DuckDB queries, documentation search, Alpaca news when credentials exist, technical indicators, SEC fundamentals and filings, FRED macro data, local memory, and Telegram notifications.

Design Your AI Trading Team

An AI trading team is just a group of agents with different jobs inside the same Lumibot strategy. You can build a single-agent strategy, a specialist research flow, bull/bear/neutral teams, model-vs-model debates, deterministic execution gates, or agent reviewers layered on top of normal Python logic.

Example: Research, Bull, Bear, and Trader Agents

Here is one example pattern: a researcher gathers evidence, bull and bear agents debate the trade, and a trader agent decides what to buy or sell.

In this pattern, each agent has a job:

Research Agent: builds the evidence pack from market data, filings, fundamentals, news, macro data, and indicators.
Bull Agent: turns that evidence into the strongest long thesis.
Bear Agent: challenges the thesis, looks for risk, and argues for avoiding, delaying, or reducing the trade.
Trader / Portfolio Manager Agent: checks cash, positions, open orders, and risk limits, then decides whether to trade.

The copy-paste example below implements that exact team. It uses Gemini Flash Lite because it is fast and inexpensive for experiments.

To run it with a broker in paper mode, set your AI and Alpaca credentials and run the file:

bash
export GEMINI_API_KEY='your-key-here'
export ALPACA_API_KEY='your-alpaca-key'
export ALPACA_API_SECRET='your-alpaca-secret'
export ALPACA_IS_PAPER=true
python ai_trading_team_bull_bear_leveraged_etf.py

To backtest the same strategy instead, change IS_BACKTESTING = False to IS_BACKTESTING = True in the runner:

bash
export GEMINI_API_KEY='your-key-here'
python ai_trading_team_bull_bear_leveraged_etf.py

Save this as ai_trading_team_bull_bear_leveraged_etf.py. If an AI key is missing or invalid, Lumibot stops and prints a clear provider key error with a link to create a key.

python
import os
from datetime import datetime

from lumibot.strategies.strategy import Strategy


class AITradingTeamBullBearLeveragedETFStrategy(Strategy):
    parameters = {
        "universe": ["TQQQ", "SQQQ", "UPRO", "SPXU", "UDOW", "SDOW", "TNA", "TZA", "TECL", "TECS", "SOXL", "SOXS", "WEBL", "WEBS", "FAS", "FAZ", "LABU", "LABD", "ERX", "ERY", "GUSH", "DRIP", "DRN", "DRV", "TMF", "TMV", "NUGT", "DUST"],
    }

    def initialize(self):
        self.sleeptime = "1D"
        model = os.environ.get("AI_TRADING_TEAM_MODEL", "gemini-3.1-flash-lite")
        # The first three agents are read-only. They can reason, but cannot trade.
        self.agents.create(
            name="researcher",
            model=model,
            allow_trading=False,
            system_prompt="Rank the ETFs by upside. Be direct.",
        )
        self.agents.create(
            name="bull",
            model=model,
            allow_trading=False,
            system_prompt="Argue for the strongest money-making trade.",
        )
        self.agents.create(
            name="bear",
            model=model,
            allow_trading=False,
            system_prompt="Point out the biggest risk, briefly.",
        )
        # Only this final agent can submit orders through Lumibot.
        self.agents.create(
            name="trader",
            model=model,
            allow_trading=True,
            system_prompt="Buy one ETF from the universe aggressively. Use nearly all cash.",
        )

    def on_trading_iteration(self):
        # Each trading day, pass the same market context through the team.
        context = {
            "date": self.get_datetime().date().isoformat(),
            "universe": self.parameters["universe"],
        }
        research = self.agents["researcher"].run(
            task_prompt="Pick the strongest ETF.",
            context=context,
        )
        bull = self.agents["bull"].run(
            task_prompt="Make the bull case.",
            context={**context, "research": research.summary},
        )
        bear = self.agents["bear"].run(
            task_prompt="Make the bear case.",
            context={**context, "research": research.summary, "bull": bull.summary},
        )
        self.agents["trader"].run(
            task_prompt="Sell anything that is not the pick, then buy the best ETF with nearly all available cash.",
            context={**context, "research": research.summary, "bull": bull.summary, "bear": bear.summary},
        )


if __name__ == "__main__":
    IS_BACKTESTING = False

    if IS_BACKTESTING:
        from lumibot.backtesting import YahooDataBacktesting

        AITradingTeamBullBearLeveragedETFStrategy.backtest(
            YahooDataBacktesting,
            datetime(2026, 4, 7),
            datetime(2026, 5, 22),
        )
    else:
        from lumibot.brokers import Alpaca
        from lumibot.traders import Trader

        ALPACA_CONFIG = {
            "API_KEY": os.environ["ALPACA_API_KEY"],
            "API_SECRET": os.environ["ALPACA_API_SECRET"],
            "PAPER": os.environ.get("ALPACA_IS_PAPER", "true").lower() != "false",
        }

        broker = Alpaca(ALPACA_CONFIG)
        strategy = AITradingTeamBullBearLeveragedETFStrategy(broker=broker)

        trader = Trader()
        trader.add_strategy(strategy)
        trader.run_all()

Example backtest artifact from this sample strategy:

Backtests are not expected future performance. The point is that the full AI trading team runs inside Lumibot's normal broker and backtest loops, so the decisions, orders, and artifacts are inspectable before you connect real money.

See this AI trading team running live on BotSpot

More AI Trading Team Examples

These examples show different ways to organize an AI trading team. Each page explains the inspiration, the agent flow, how to run it with a broker in paper mode, and how to backtest it.

Citadel sector pods AI trading team: inspired by the pod-style structure associated with Ken Griffin's Citadel: sector specialists pitch their best ideas, a risk manager challenges crowding and drawdown risk, and a portfolio manager rotates into the strongest sector ETF. Watch it live on BotSpot. Source: ai_trading_team_citadel_sector_pods.py.
Warren Buffett value AI trading team: uses AI agents like a patient value-investing desk: one agent digs into business quality and annual reports, one demands valuation discipline, and the portfolio manager only buys the best long-term compounder. Watch it live on BotSpot. Source: ai_trading_team_warren_buffett_value.py.
Ray Dalio idea meritocracy AI trading team: turns Bridgewater-style thoughtful disagreement into a macro ETF workflow, with growth, inflation, liquidity, and disagreement agents arguing before the trader acts. Watch it live on BotSpot. Source: ai_trading_team_ray_dalio_idea_meritocracy.py.
Bill Ackman concentrated AI trading team: inspired by Pershing Square-style concentrated investing: find one great business, make the activist bull case, attack it like a short seller, then let the portfolio manager take a focused position if the thesis survives. Watch it live on BotSpot. Source: ai_trading_team_bill_ackman_concentrated.py.
Bull/bear leveraged ETF AI trading team: a fast, aggressive demo where bull and bear agents debate leveraged long and inverse ETFs before the trader rotates into one high-conviction ETF. Watch it live on BotSpot. Source: ai_trading_team_bull_bear_leveraged_etf.py.
Bull/bear large-cap stocks AI trading team: the same debate structure applied to familiar large-cap stocks, which makes it easier to inspect each agent's reasoning before using more volatile instruments. Watch it live on BotSpot. Source: ai_trading_team_bull_bear_large_cap_stocks.py.

Run Lumibot Without Managing Servers

BotSpot is the managed cloud built around Lumibot. It makes Lumibot easier and cheaper to run because the data, backtesting workers, broker connections, scheduling, monitoring, logs, alerts, and kill switches are already wired together.

BotSpot is not a generic chatbot bolted onto a broker account. Its AI workflows, prompts, MCP tools, backtest setup, broker paths, and deployment flow are built for Lumibot.

Backtesting data included. Use hosted stock, futures, options, FRED macro, SEC filing, and other supported data without wrangling every feed and API key yourself. Some data is included; premium data can be much cheaper than buying direct subscriptions for occasional experiments.
Cheaper deployment at scale. Scheduled and periodic bots should not need a full always-on server per strategy. BotSpot runs Lumibot bots on managed infrastructure built for this workflow, with monitoring and controls included.
Lumibot-tuned AI. Generic coding tools can write Python, but BotSpot is tuned for Lumibot strategy structure, broker setup, backtests, artifacts, and deployment.
MCP for coding agents. Connect BotSpot to Codex, Claude Code, Cursor, and other MCP clients so your coding agent can run backtests, inspect artifacts, compare results, and prepare deployment instead of only generating code.
Work from anywhere. Use the same strategy workspace from the web, your phone, Telegram, Discord, Claude, ChatGPT, and coding tools. Start in one place and continue in another.
Marketplace and strategy library. Browse existing strategy code, clone and adapt strategies, run strategies where available, and publish your own strategies when you are ready.
Observability and control. Inspect why a bot bought or sold, review charts, logs, decisions, orders, notifications, audit history, and kill-switch controls in one place.

Why Lumibot?

AI trading projects have proved that people want agentic trading workflows. Lumibot's edge is that those workflows run inside a real Python trading framework: you can backtest the agent decisions, inspect artifacts, add Python guardrails, paper trade, and connect to brokers without rewriting the strategy.

That matters because an AI trading demo is not the same thing as a trading system. Without backtests and broker-aware strategy code, you are mostly trusting prompts. Lumibot lets you iterate faster: test the agent flow on historical data, see what it would have bought or sold, tighten the Python risk checks, then run the same lifecycle in paper or live trading.

Compared with AI trading agent projects

Project	Main angle	AI agents / teams	Backtest agent decisions	Paper/live broker path	Deterministic Python strategies	Hosted data/deploy/monitoring
Lumibot + BotSpot	Python strategies, flexible AI trading teams, hybrid guardrails, backtests, brokers, hosted deployment	Flexible teams, debates, specialist desks, and deterministic gates	Replayable decisions, orders, traces, artifacts, charts, logs	Yes: Alpaca, IBKR, Tradier, Schwab, Tradovate, ProjectX, Bitunix, selected CCXT	Yes	Hosted data, parallel backtests, deployment, monitoring, MCP, alerts, kill switches
TradingAgents	Multi-agent LLM trading research framework	Yes, with a specific research/debate structure	Research/demo oriented	Not the main focus	Limited	No
ai-hedge-fund	Educational AI hedge fund with named investor-style agents	Yes, with investor-style personas	Demo/backtest oriented	Not the main focus	Limited	No
OpenAlice	One-person Wall Street agent concept	Yes, end-to-end agent concept	Emerging/experimental	Local/self-run focus	Limited	No
QuantDinger	Self-hosted AI quant operating system	Yes	Yes	Crypto, IBKR, MT5, Alpaca	Yes	Self-hosted
Vibe-Trading	Personal trading agent	Yes	Yes	Agent trading platform focus	Limited	Platform-specific
AI-Trader	Agent-native trading platform	Yes	Platform focus	Platform focus	Limited	Platform-specific
OpenBB	Financial data platform for analysts, quants, and AI agents	Tooling for agents	Not a strategy backtester	No broker execution framework	No	OpenBB workspace/platform
Qlib	AI-oriented quant research platform	Research/ML agents	Quant research backtests	Limited live focus	Research pipelines	No

See the docs comparison pages for more detail: Lumibot vs TradingAgents, Lumibot vs ai-hedge-fund, Lumibot vs OpenAlice, and Lumibot vs QuantDinger.

Compared with backtesting libraries

Feature	Lumibot	Backtrader	Freqtrade	Zipline	Backtesting.py	Jesse	vectorbt	NautilusTrader	Hummingbot
Same code: backtest + live	Yes	Yes	Yes (crypto)	No	No	Yes (paid)	No	Yes	Yes (crypto)
Stocks	Yes	Yes	No	Yes	Yes	No	Yes	Yes	No
Options	Yes	No	No	No	No	No	No	Limited	No
Crypto	Yes	Limited	Yes	No	Yes	Yes	Yes	Yes	Yes
Futures	Yes	Limited	Crypto only	Partial	Yes	Crypto only	Yes	Yes	Perpetuals/crypto venues
Forex	Yes	Outdated	No	No	Yes	No	Yes	Yes	No
AI agent runtime	Built-in	No	FreqAI (ML)	No	No	ML pipeline	No	No	Scripts/controllers
Broker execution	Alpaca, IBKR, Tradier, Schwab, Tradovate, TopstepX (via ProjectX), Bitunix, selected CCXT	IB only (outdated)	Crypto exchanges	None	None	Crypto exchanges	No	Exchange adapters	Crypto exchanges
Hosted deployment path	BotSpot	No	No	No	No	Paid cloud	No	No	Hummingbot Foundation/enterprise ecosystem
License	MIT	GPL-3.0	GPL-3.0	Apache-2.0	AGPL-3.0	MIT	Apache-2.0	LGPL-3.0	Apache-2.0

Switching from Backtrader? See our migration guide for a side-by-side comparison with code examples.

Deploy Live

Option A: BotSpot (managed cloud)

BotSpot is the managed path for taking a Lumibot strategy from idea to backtest to paper or live trading. It handles the expensive and fragile parts around the strategy: hosted data setup for supported backtests, parallel backtest runs, broker connections, scheduling, logs, alerts, monitoring, audit history, and kill-switch controls.

This is especially useful when your strategy only needs to run daily or periodically. You get the same Lumibot code path without paying for always-on infrastructure, maintaining a scheduler, hand-wiring broker secrets, or building your own log and alerting stack.

Option B: Self-hosted (full control)

Run Lumibot on your own machine with any supported broker:

python
from lumibot.brokers import Alpaca
from lumibot.traders import Trader

ALPACA_CONFIG = {
    "API_KEY": "your-key",
    "API_SECRET": "your-secret",
    "PAPER": True,
}

broker = Alpaca(ALPACA_CONFIG)
strategy = MyStrategy(broker=broker)

trader = Trader()
trader.add_strategy(strategy)
trader.run_all()

Supported Brokers

Lumibot supports stocks, options, crypto, futures, forex, and indexes across several broker integrations:

Alpaca
Interactive Brokers and Interactive Brokers REST
Tradier
Schwab
Tradovate
TopstepX futures (via ProjectX)
Bitunix
Selected CCXT crypto paths. Coinbase, Kraken, and WEEX have auto-detected credential paths; KuCoin, Binance, and BitMEX have documented manual CCXT setup paths; Kraken, Binance, KuCoin, BitMEX, Bybit, and OKX have documented backtesting examples. Lumibot does not claim blanket support for every CCXT exchange.

Select Backtesting Data Sources

Lumibot can backtest from free daily data, broker data, premium market data, and your own files:

Yahoo Finance
Alpaca
Interactive Brokers REST
ThetaData
Polygon/Massive
DataBento
Tradier
Schwab
CCXT backtesting examples: Kraken, Binance, KuCoin, BitMEX, Bybit, and OKX
Pandas/CSV dataframes

Recommended Data Provider

For the deepest historical coverage (stocks, options, futures, indexes), we recommend ThetaData. Use promo code BotSpot10 for 10% off your first order.

AI Trading Agents

Lumibot includes a built-in AI trading agent runtime. Build agents that run identically in backtests and live trading.

Create agents with self.agents.create(...)
Use a different model per agent with model="openai/gpt-5.5" or any LiteLLM/ADK-supported provider string
Make research agents read-only with allow_trading=False
Give agents built-in SEC fundamentals, filings, FRED macro data, indicators, memory, and notifications
Use DuckDB for time-series analysis instead of dumping raw bars into prompts
Mount external MCP servers for news, macro data, filings, or any domain-specific tools
Replay identical agent decisions in backtests without paying for another model call

Use BotSpot MCP when you want an AI coding agent to generate Lumibot strategies, launch backtests, inspect artifacts, and iterate without leaving your editor.

Start here:

Memory and Traceability

AI strategies can record proposals, risk notes, actual trading decisions, submitted orders, lessons, open theses, tool calls, and run artifacts as local SQLite and Parquet files. This makes an AI backtest reviewable instead of a black box: you can inspect why the agent traded, which tools it used, what memory it retrieved, and what it remembered for later iterations. Memory events include agent/model-call provenance when they come from agent tools.

Community Strategies

Browse and contribute open-source strategies: lumibot-strategies. For hosted strategy discovery with performance, descriptions, visuals, and deploy flows, use the BotSpot marketplace.

Example Strategies

Lumibot includes 25+ example strategies covering stocks, options, crypto, futures, and forex:

bash
# Run a simple buy-and-hold backtest
python -m lumibot.example_strategies.stock_buy_and_hold

# Or explore all examples
ls lumibot/example_strategies/

Browse all examples: example_strategies/

External example repo: stock_example_algo shows a minimal strategy repository you can run yourself or adapt inside BotSpot.

Backtesting Data Sources

Select a data source via environment variable (overrides code):

bash
export BACKTESTING_DATA_SOURCE=thetadata   # or yahoo, ibkr, polygon

Multi-provider routing by asset type:

bash
export BACKTESTING_DATA_SOURCE='{"default":"thetadata","option":"thetadata","crypto":"ibkr","crypto_future":"ibkr","future":"ibkr","cont_future":"ibkr"}'

Crypto futures/perpetual backtests can route Asset.AssetType.CRYPTO_FUTURE through spot crypto history. USDT symbols such as BTCUSDT, ETHUSDT, and SOLUSDT use the matching USD spot proxy for prices.

Data source comparison

Data Source	OHLCV	Split Adjusted	Dividends	Dividend Adjusted Returns
Yahoo	Yes	Yes	Yes	Yes
Alpaca	Yes	Yes	No	No
Polygon	Yes	Yes	No	No
Tradier	Yes	Yes	No	No
Pandas*	Yes	Yes	Yes	Yes

*Pandas loads CSV files in Yahoo dataframe format, which can contain dividends.

Learn More

Documentation: lumibot.lumiwealth.com
Blog: lumiwealth.com/blog
AI strategy builder and hosted deployment: BotSpot.trade
BotSpot MCP for AI coding agents: botspot.trade/agents
Strategy marketplace: botspot.trade/marketplace
YouTube strategy builds: Lumiwealth on YouTube

Project Growth

AI Bootcamp

Learn to build, backtest, and deploy trading strategies using AI. Join 2,400+ traders.

Contributing

We welcome contributions! Here's a video to help you get started: Watch The Video

Steps:

Clone the repository
Create a new branch: git switch -c my-feature
Install dev dependencies: pip install -r requirements_dev.txt && pip install -e .
Make your changes
Run tests: pytest
Create a pull request

Running Tests

bash
pytest                          # Run all tests
pytest tests/test_asset.py      # Run a specific test file
coverage run; coverage report   # Show code coverage

Remote Cache Configuration

Lumibot can mirror its local parquet caches to AWS S3. See docs/remote_cache.md for configuration.

Architecture Documentation

Backtesting Architecture - Data flow diagrams for Yahoo, ThetaData, Polygon
Acceptance Backtests - End-to-end acceptance suite
Environment Variables - All configurable env vars
Changelog - Release notes
AI Assistant Guide - Instructions for AI coding assistants
Production Safety - ThetaData and production rules

Disclaimer

This software is provided for educational and informational purposes only. It is not financial advice and does not constitute a recommendation to buy or sell any security. Lumibot and BotSpot are not registered broker-dealers or financial advisors. Algorithmic trading involves substantial risk of loss, including the possibility of losses greater than your initial investment. Software bugs and errors can lead to rapid financial losses. Past backtest performance does not guarantee future results. Use this software at your own risk. You are solely responsible for compliance with all applicable laws and regulations regarding the assets you choose to trade.

Affiliate disclosure: some provider links or promo codes, including ThetaData, may support continued Lumibot development.