Ray Dalio Idea Meritocracy AI Trading Team

This strategy is inspired by Ray Dalio’s public writing about idea meritocracy and thoughtful disagreement. It is not an “All Weather” clone. The important idea is the operating system: independent thinkers argue from different models of the world, the disagreement is explicit, and the final decision should be stronger because weak assumptions were challenged.

In Lumibot, that turns into a macro trading team. One agent argues from growth, one argues from inflation and rates, one argues from debt, liquidity, currency, and policy pressure, then a disagreement agent stress-tests all three before the trader picks one ETF.

See this strategy running live on BotSpot

How the team works

• growth_agent asks what wins if growth improves.

• inflation_agent asks what wins or loses if inflation and rates surprise.

• debt_liquidity_agent argues from debt, liquidity, currency, and policy pressure.

• thoughtful_disagreement challenges the other agents and names the strongest idea.

• trader buys the best macro ETF idea and is the only agent allowed to trade.

Backtest snapshot

Run it with a broker

The file defaults to broker-connected execution. With Alpaca, it runs in paper mode unless you set ALPACA_IS_PAPER=false.

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 lumibot/example_strategies/ai_trading_team_ray_dalio_idea_meritocracy.py

Backtest it

Use the same strategy class and change IS_BACKTESTING = False to IS_BACKTESTING = True in the runner:

export GEMINI_API_KEY='your-key-here'
python lumibot/example_strategies/ai_trading_team_ray_dalio_idea_meritocracy.py

Example code

"""Ray Dalio / Bridgewater-inspired idea-meritocracy AI trading team example.

Set GEMINI_API_KEY plus Alpaca credentials, then run paper trading:
    python ai_trading_team_ray_dalio_idea_meritocracy.py

Set IS_BACKTESTING=True in the runner to run the historical example instead.
"""

import os
from datetime import datetime

from lumibot.strategies.strategy import Strategy


class AITradingTeamRayDalioIdeaMeritocracyStrategy(Strategy):
    parameters = {
        "universe": ["SPY", "QQQ", "IWM", "TLT", "IEF", "TIP", "GLD", "DBC", "VNQ", "UUP", "FXI", "EEM"],
    }

    def initialize(self):
        self.sleeptime = "1D"
        model = os.environ.get("AI_TRADING_TEAM_MODEL", "gemini-3.1-flash-lite")
        self.agents.create(
            name="growth_agent",
            model=model,
            allow_trading=False,
            system_prompt="Argue which ETFs win if growth improves. Be direct and expose weak assumptions.",
        )
        self.agents.create(
            name="inflation_agent",
            model=model,
            allow_trading=False,
            system_prompt="Argue which ETFs win or lose if inflation and rates surprise. Be direct.",
        )
        self.agents.create(
            name="debt_liquidity_agent",
            model=model,
            allow_trading=False,
            system_prompt="Argue from debt, liquidity, currency, and policy pressure. Be direct.",
        )
        self.agents.create(
            name="thoughtful_disagreement",
            model=model,
            allow_trading=False,
            system_prompt="Challenge all views with thoughtful disagreement. Identify the best idea after stress testing.",
        )
        self.agents.create(
            name="trader",
            model=model,
            allow_trading=True,
            system_prompt="Choose the best macro idea after the disagreement. Buy one ETF aggressively with nearly all cash.",
        )

    def on_trading_iteration(self):
        context = {
            "date": self.get_datetime().date().isoformat(),
            "universe": self.parameters["universe"],
        }
        growth = self.agents["growth_agent"].run(task_prompt="Pick the strongest ETF from a growth-regime view.", context=context)
        inflation = self.agents["inflation_agent"].run(task_prompt="Pick the strongest ETF from an inflation-and-rates view.", context=context)
        liquidity = self.agents["debt_liquidity_agent"].run(task_prompt="Pick the strongest ETF from a debt-and-liquidity view.", context=context)
        disagreement = self.agents["thoughtful_disagreement"].run(
            task_prompt="Challenge the growth, inflation, and liquidity views. Pick the best idea.",
            context={**context, "growth": growth.summary, "inflation": inflation.summary, "liquidity": liquidity.summary},
        )
        self.agents["trader"].run(
            task_prompt="Sell anything that is not the best idea, then buy the best ETF with nearly all available cash.",
            context={**context, "growth": growth.summary, "inflation": inflation.summary, "liquidity": liquidity.summary, "disagreement": disagreement.summary},
        )


if __name__ == "__main__":
    IS_BACKTESTING = False

    if IS_BACKTESTING:
        from lumibot.backtesting import YahooDataBacktesting

        AITradingTeamRayDalioIdeaMeritocracyStrategy.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 = AITradingTeamRayDalioIdeaMeritocracyStrategy(broker=broker)

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