Embedded Markets, Inc., a financial technology firm, announced its official public launch following ten months of research and development. The company builds AI agent-based simulation and risk management tools that let professional traders stress-test trading strategies across thousands of alternate market histories, rather than the single realized path that history actually took.

Traditional backtesting methods fall short in today's markets. Walk-forward tests replay one realized historical path, while Monte Carlo approaches resample returns but treat market participants as passive noise rather than adaptive agents who learn, react, imitate, and adjust leverage endogenously. Meanwhile, autonomous AI trading agents are introducing new forms of feedback loops, herding, model monoculture, and liquidity shocks that traditional backtests do not capture.

"If you've ever watched a trading strategy get destroyed by a four-sigma event or sold just before a large move up, you know what's missing from the trading software marketplace," said Jonathan Haynes, Ph.D., Founder & CEO of Embedded Markets. "This is why our mission is to create the premier risk management platform for trading any type of asset."

Haynes holds a Ph.D. in Sociology from Stanford University and completed postdoctoral research at the Northwestern Institute on Complex Systems and the Kellogg School of Management. He led data science teams for over a decade and later led a global forecasting, pricing, and analytics team at a pharmaceutical firm before founding Embedded Markets.

Initial products include LeveeBacktest™ Engine and emPortfolioAnalyzer™, both targeted for beta release in Q2 2027. LeveeBacktest™ Engine, a backtester powered by AI agents, runs strategies through a multiverse of heterogeneous agent-driven histories with network contagion and threshold cascades. Unlike agent-based models governed by fixed rules, or off-the-shelf LLMs handed a trading prompt, this approach uses LoRA fine-tuning at the model level, so collective behaviors emerge from training rather than role-play. emPortfolioAnalyzer™ enables regime-specific correlation analyses and hypothetical position sizing aligned with return targets, drawdown tolerance, and risk budgets.