Algorithmic Alpha Generation

This application area focuses on designing, testing, and deploying systematic trading strategies that seek to generate excess returns (alpha) over market benchmarks, using advanced data‑driven methods. Instead of relying solely on traditional factor models or simple rule‑based systems, it leverages complex relationships across assets, time horizons, and market regimes to identify tradeable signals that persist in live conditions. In the highlighted use cases, language models and multi‑agent systems are used both to generate trading signals and to evaluate them realistically. Benchmarks like LiveTradeBench aim to close the gap between backtest performance and real‑world execution by incorporating slippage, liquidity constraints, and risk into standardized live‑like evaluations. Multi‑agent, market‑aware communication architectures attempt to uncover weak, distributed signals by allowing many specialized agents to coordinate based on current market conditions, with the goal of more robust, regime‑adaptable alpha generation that can survive production deployment.