LumiBot AI Trading
Build AI trading agents. Backtest them. Trade with the same strategy.
LumiBot is a free, open-source Python framework for building trading bots.
Write your own trading rules, let AI agents make decisions, or combine both.
Backtest on historical data, inspect trades and results, and use the same
strategy class with a supported broker for paper or live trading.
Full docs: lumibot.lumiwealth.com · Managed cloud: BotSpot.trade · MCP: BotSpot for AI coding agents
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Why LumiBot?
- Native AI trading: agents research, reason and place orders from your prompts.
- Backtest before trading: evaluate AI decisions and Python rules on historical data, with charts, trades and logs.
- Same strategy for backtesting and trading: keep your strategy class; change the data source and broker runner.
- Multiple asset classes: stocks, options, futures, forex, crypto and prediction markets through supported brokers.
- The decision reaches a broker. Inspect the orders, fills and decision records. How execution works.
Connect your AI assistant
Build faster with BotSpot MCP. Claude, ChatGPT, Codex, Cursor, VS Code or
another MCP-compatible assistant can help you write and test LumiBot strategies.
Your coding assistant builds the code; the agents inside an AI-native
strategy make its trading decisions while it runs.
Connect the free BotSpot MCP for:
- Skills: build LumiBot strategies.
- Free data: news, economics and SEC filings.
- Backtests: run tests and inspect charts, trades and logs.
- Cloud hosting: run bots on a schedule.
- Community: share ideas and learn from other builders.
- Marketplace: discover and publish strategies.
- Broker linking: connect accounts through BotSpot.
Connect your AI assistant
Connecting is free; hosted usage depends on your plan.
For local coding help, install the free LumiBot trading skills.
Your running strategy's agents can get research tools too:
give them BotSpot MCP superpowers.
Three ways to build a strategy
Run your first AI backtest
The trading rules go in the prompt. Edit system_prompt below to change
what the agent does. initialize() creates it once; on_trading_iteration()
calls it on every iteration. This example has one agent that makes the trading
decisions and places orders.
Use Python 3.10+. This example uses Yahoo daily prices and an OpenAI model;
model calls incur charges.
pip install lumibot
export OPENAI_API_KEY="your-openai-api-key"
Save as my_ai_strategy.py:
from datetime import datetime
from lumibot.backtesting import YahooDataBacktesting
from lumibot.strategies import Strategy
class MyAIStrategy(Strategy):
def initialize(self):
self.sleeptime = "1D"
self.agents.create(
name="trader",
default_model="openai/gpt-6-luna",
reasoning_effort="medium",
allow_trading=True,
system_prompt=(
"Trade SPY using its last 20 completed daily bars. "
"Buy when the latest completed close is above their average; "
"sell an existing position when it is below. Otherwise hold. "
"Keep the position at or below 10% of portfolio value. "
"Check cash, positions and open orders before acting. "
"No shorting or leverage. Do not duplicate pending orders. "
"Use only information available as of the strategy time."
),
)
def on_trading_iteration(self):
self.agents["trader"].run(
task_prompt="Review SPY, apply your rules and verify any orders.",
context={"as_of": self.get_datetime().isoformat()},
)
if __name__ == "__main__":
MyAIStrategy.backtest(
YahooDataBacktesting,
datetime(2026, 4, 6),
datetime(2026, 4, 11),
budget=100_000,
benchmark_asset="SPY",
)
python my_ai_strategy.py
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