Warren Buffett Value Trading Team

This example is a long-term business-quality flow. It asks one agent to find the best business, one agent to require valuation discipline, and one final portfolio manager to trade only if the idea survives.

Agent flow

• annual_report_reader looks for business quality, cash flow, balance-sheet strength, and durability.

• valuation_skeptic challenges valuation and requires a margin of safety.

• portfolio_manager buys the best long-term compounder with trading permission.

Example code

"""Warren Buffett-inspired annual-report value AI trading team example.

This example is inspired by public Berkshire Hathaway shareholder letters and
value-investing principles. It is not affiliated with or endorsed by Warren
Buffett, Berkshire Hathaway, or related companies.

Set GEMINI_API_KEY, then run:
    python ai_trading_team_warren_buffett_value.py
"""

import os
from datetime import datetime

from lumibot.strategies.strategy import Strategy


class AITradingTeamWarrenBuffettValueStrategy(Strategy):
    parameters = {
        "universe": ["AAPL", "MSFT", "GOOGL", "COST", "V", "MA", "KO", "AXP", "JPM", "PG"],
    }

    def initialize(self):
        self.sleeptime = "1D"
        model = os.environ.get("AI_TRADING_TEAM_MODEL", "gemini-3.1-flash-lite")
        self.agents.create(
            name="annual_report_reader",
            model=model,
            allow_trading=False,
            system_prompt="Find the best business quality from filings, fundamentals, cash flow, balance sheet strength, and durability.",
        )
        self.agents.create(
            name="valuation_skeptic",
            model=model,
            allow_trading=False,
            system_prompt="Challenge the business-quality case. Require a margin of safety and reject weak or overpriced ideas.",
        )
        self.agents.create(
            name="portfolio_manager",
            model=model,
            allow_trading=True,
            system_prompt="Buy the best long-term compounder from the universe when quality and margin of safety are acceptable. Use nearly all cash.",
        )

    def on_trading_iteration(self):
        context = {
            "date": self.get_datetime().date().isoformat(),
            "universe": self.parameters["universe"],
        }
        report = self.agents["annual_report_reader"].run(task_prompt="Pick the highest-quality business.", context=context)
        skeptic = self.agents["valuation_skeptic"].run(
            task_prompt="Challenge the valuation and business-quality case. Require margin of safety.",
            context={**context, "report": report.summary},
        )
        self.agents["portfolio_manager"].run(
            task_prompt="Sell anything that is not the best long-term compounder, then buy the best stock with nearly all available cash.",
            context={**context, "report": report.summary, "skeptic": skeptic.summary},
        )


if __name__ == "__main__":
    from lumibot.backtesting import YahooDataBacktesting

    AITradingTeamWarrenBuffettValueStrategy.backtest(
        YahooDataBacktesting,
        datetime(2026, 4, 7),
        datetime(2026, 5, 22),
    )