Use LumiBot in another Python project

You can use selected research components without running a trading strategy. The examples below are network reads, not backtests or broker connections.

Read macro data

import os
from lumibot.macro import FREDMacroData

macro = FREDMacroData(api_key=os.environ["FRED_API_KEY"])
result = macro.get_series(
    "UNRATE", start="2024-01-01", end="2024-12-31", as_of="2025-01-15"
)
print(result)

Set FRED_API_KEY in your environment. as_of requests the historical information vintage; observation date and publication date are different. Inspect the returned data and metadata rather than assuming missing values are zero. The helper uses its cache and rate pacing; see FRED Macro Data for the full response and historical-data contract.

Read SEC submissions

import os
from lumibot.fundamentals import SECFundamentals

sec = SECFundamentals(user_agent=os.environ["LUMIBOT_SEC_USER_AGENT"])
submissions = sec.get_submissions("AAPL")
print(submissions)

SEC requests need a descriptive User-Agent with your contact information. The submissions response contains filing metadata; it is not a reconstructed historical portfolio. Inspect filing/publication dates before using it in a historical decision. See SEC Fundamentals for caching and error behavior. Keep provider exceptions visible so callers can distinguish failed research from an empty result.

Reuse an indicator function

A normal Python function can be shared between notebooks and strategies:

def completed_close_average(closes, length=20):
    """Average exactly the last length completed closes, oldest to newest."""
    import math
    if length <= 0 or len(closes) < length:
        raise ValueError("Supply enough completed closes and a positive length")
    values = [float(value) for value in closes[-length:]]
    if not all(math.isfinite(value) for value in values):
        raise ValueError("Closes must be finite")
    return sum(values) / length

The caller owns timestamp ordering, completed-bar selection, and timezone. Do not pass future rows or the still-forming bar. See Indicators for LumiBot’s existing indicator tools and Run your first AI backtest for @agent_tool wrappers. A new plugin registry is not required to reuse code.

Use the same function through the existing custom-indicator API when you need strategy-time history and memoization. Save this reusable function in your own my_indicators.py:

def rolling_close_average(df, length=20):
    return df["close"].rolling(length, min_periods=length).mean()

Then call it from a strategy lifecycle method:

from my_indicators import rolling_close_average
from lumibot.entities import Asset

result = self.indicators.custom(
    "rolling_close_average", rolling_close_average,
    Asset("SPY"), timestep="day", length=20,
)

custom accepts a function returning a pandas Series or DataFrame and uses history available as of strategy time. Keep the indicator name stable and pass its parameters explicitly. See Indicators for the returned result API.

Execution components have a lifecycle

Strategy and its AgentManager own simulated time, account state, and execution. Broker objects may start threads or streams; they are not all stateless REST clients. Use Strategy API Overview and Trading Brokers Supported by LumiBot when embedding trading execution, and preserve their startup/shutdown lifecycle.

Learn to build a complete strategy

Explore the AI Trading Bootcamp with Rob Grzesik for guided training.