AlphaBeater

AlphaBeater is an autonomous trading agent built with LLM and Alpaca. It addresses a common problem with AI trading systems: language models can generate convincing ideas, but those ideas should not be trusted without deterministic testing and strict risk controls. The agent reads current market data (SPY, QQQ, and IWM currently) from Alpaca, summarizes price, volatility, and volume conditions, and asks an agent to produce falsifiable market hypotheses. Another agent then converts each hypothesis into interpretable expressions using AlphaBeater's constrained factor language. Regular Python code validates and calculates every expression without executing model-generated code. Candidates are evaluated using chronological training, validation, and test periods with transaction costs. The agent can reject every candidate and abstain from trading when results are inconsistent. Research-qualified strategies must pass performance, drawdown, liquidity, account, exposure, and maximum-loss checks. AlphaBeater also provides an explicitly labeled forward paper-experiment mode. This mode allows unvalidated candidates to collect genuine paper-trading evidence while keeping financial and operational safeguards active. During our end-to-end demonstration, AlphaBeater selected an IWM options contract, passed its applicable safety checks, submitted the order through Alpaca's official MCP server, received a fill, and monitored its live paper P&L. Every hypothesis, factor, backtest, decision, rejected check, order, and position update is stored in an inspectable audit trail. The result is not a black-box trading bot, but a transparent agent that can research, test, execute, monitor, and, importantly, decide whether to trade.