A multi-agent stock-market simulation where autonomous LLM agents trade on synthetic news in real time.
A fictional financial market populated by autonomous trading agents and a narrator model that produces synthetic news. A global narrator drives market sentiment, while three agents react with distinct trading strategies. Michael Chen trades on momentum, Victoria Shaw is a value trader with contrarian tendencies, and Hannah Park shifts focus from position execution to tail-hedging. All trades made by agents feed back into the market state.
The front-end is powered by a React dashboard, which streams prices, headlines, and the agent ledger over WebSockets.
As I built this platform while studying these trading strategies, and the stock market as a whole, I implemented a “Professor Mode” which educates on the definitions of the Greeks (Delta, Gamma, Theta, Vega) and the Black–Scholes pricing model.
The simulation only runs while at least one observer is connected, keeping inference cost near zero during development.