An AI Macro Trading Team That Debates Economic Scenarios Before ETF Allocation
Summary
The document describes a daily macro trading process built around distinct research perspectives. Separate agents assess economic growth, inflation and interest rates, and debt, liquidity, currency, and central bank policy. A disagreement agent challenges those views and identifies a strongest idea, after which a trading agent constructs a diversified ETF portfolio and handles orders. The design is presented as an example inspired by open debate, not as a reproduction of a named institutional portfolio.
A brief backtest summary reports that a portfolio of broad equity, emerging market, gold, Treasury, and short Treasury ETFs gained about 1% from January 5 to 16, 2026, ending near its starting cash benchmark and roughly matching SPY. The document says the strategy has a paper trading record and cautions that a short backtest does not promise future returns. It gives little detail for independently assessing robustness, and its results depend on the stated model, sample period, and daily process.
Key ideas
- Three research agents analyze growth, inflation, and debt or liquidity from separate perspectives.
- A fourth agent challenges the analyses and selects the strongest argument.
- A trading agent turns the selected view into a diversified macro ETF portfolio and places orders.
- The described process repeats daily.
- The reported backtest covers a short period and is not evidence of durable future performance.
Tags
Full text
# agents example ray dalio idea meritocracy Ray Dalio Idea Meritocracy AI Trading Team ========================================== .. meta:: :description: An AI macro trading team inspired by Ray Dalio's idea meritocracy: growth, inflation, and debt agents argue before a trader builds an ETF portfolio. Free Python code. .. image:: ../docs/assets/ai-trading-team-workflows/ray-dalio-idea-meritocracy.png :alt: Growth, inflation, and debt agents feed a disagreement agent and a trader, then the trade order :width: 100% This AI team is inspired by Ray Dalio's public writing about "idea meritocracy": smart people with different views argue openly, and the best idea wins. Three agents each look at the economy through a different lens, a fourth agent makes them argue, and a trader builds a diversified ETF portfolio from the winning ideas. It is not a copy of Bridgewater's All Weather portfolio. How it works ------------ 1. **Growth agent** asks what wins if the economy speeds up or slows down. 2. **Inflation agent** asks what wins or loses if inflation and interest rates surprise. 3. **Debt and liquidity agent** argues from debt, money supply, currency, and central bank policy. 4. **Disagreement agent** challenges all three and names the strongest idea. 5. **Trading agent** builds a diversified macro ETF portfolio and is the only agent allowed to place orders. The team repeats this once a day. The copyable example now uses GPT-6 Luna with high reasoning, matching the Luna challenge version. A leveraged version holds leveraged ETFs instead. Earlier BotSpot paper observations used Gemini; changing the model does not change those historical results. Run it on BotSpot ----------------- Run this team on `BotSpot <https://botspot.trade/marketplace?utm_source=documentation&utm_medium=docs&utm_campaign=lumibot_ai_examples&utm_content=agents_example_ray_dalio_idea_meritocracy>`_ without installing anything. BotSpot runs LumiBot in the cloud, backtests it, and connects it to your broker. Backtest tear sheet ------------------- GPT-6 Luna, January 5 to 16, 2026, Yahoo daily prices, $100,000 start. The team built a macro mix of SPY, EEM, GLD, IEF, and SHV and ended at $101,483 (+1%), about even with SPY. Cash never went below $262. Earlier BotSpot paper observations used Gemini and are separate from these Luna backtest results and the current Luna example. .. image:: ../docs/assets/ai-bot-backtests/ray-dalio-idea-meritocracy.png :alt: Backtest tear sheet for the Ray Dalio Idea Meritocracy AI Trading Team :width: 100% :target: tearsheets/ray-dalio-idea-meritocracy.html `Open the full tear sheet <tearsheets/ray-dalio-idea-meritocracy.html>`__. A short backtest shows the team works as written. It is not a promise of future returns. The code -------- Regular ETFs: .. literalinclude:: ../lumibot/example_strategies/ai_trading_team_ray_dalio_idea_meritocracy.py :language: python Leveraged ETFs: .. literalinclude:: ../lumibot/example_strategies/ai_trading_team_ray_dalio_idea_meritocracy_leveraged.py :language: python Run it yourself --------------- .. code-block:: bash pip install lumibot python -m lumibot.example_strategies.ai_trading_team_ray_dalio_idea_meritocracy Add ``OPENAI_API_KEY`` for GPT-6 Luna and your broker keys (for example ``ALPACA_API_KEY``, ``ALPACA_API_SECRET``, and ``ALPACA_IS_PAPER=true``) to your ``.env`` file. This file trades by default. Set ``IS_BACKTESTING=true`` in your environment to backtest it instead. For macro data, add a free ``FRED_API_KEY`` (see :doc:`macro_data`). Good to know ------------ * Inspired by Ray Dalio's public writing. Not affiliated with or endorsed by Ray Dalio or Bridgewater, and not a copy of any real strategy. * The picture shows the three research agents side by side because none of them depends on another. The code calls them one after another. See :doc:`agents_examples` for more AI trading bots and :doc:`strategy_run_modes` for backtest and live runs.
Shown in full with attribution under the source's licence. Licence: GPL-3.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.