Quantum Capital

Multi-Modal Intelligence

Markets are influenced by numbers AND language: news, tweets, earnings calls, Fed statements. We train hybrid systems that fuse a time-series foundation model (long-context, probabilistic, regime-aware) with a retrieval-grounded language model that turns unstructured text into structured, tradeable signals.

A trading agent that reads a Fed statement AND adjusts strategy accordingly — with sourced evidence, calibrated uncertainty, and an explicit "no-trade" option when the edge isn't there.

Reinforcement Learning in Market Simulators

Deep RL trains agents by "learning through trading" — but with modern, production-grade constraints: offline + constrained RL on real historical trajectories, then robustness training in calibrated multi-agent simulators where liquidity, slippage, and market impact are endogenous (not assumed).

Agents don't just "self-play." They train against adversarial market conditions: regime shifts, volatility spikes, liquidity droughts, and adversarial flow — optimizing risk-adjusted returns under turnover, drawdown, and exposure limits.

AlphaGo for markets — execution-aware and stress-tested, with stochasticity and adversarial scenario generation built into training for robustness.

Large-Scale Foundation Models

Finance GPT variants trained on SEC filings, news, social sentiment — paired with time-series foundation backbones for prices, liquidity, and cross-asset structure, and mixture-of-experts routing that learns when each modality is predictive.

Models that digest information AND generate hypotheses with natural language interfaces — with post-training that enforces evidence discipline, calibration, and abstention rather than confident hallucination.

Explainable agent reasoning. Not a black box.

Every position comes with a decision trace: sources → extracted signals → forecast distributions → portfolio action → constraint checks → risk contributions → scenario sensitivity.

Continuous Adaptation

Unlike static algorithms, our AI continuously learns from new data — but never blindly. It detects drift, runs shadow evaluations, and triggers gated retraining with canary deployment, rollback, and hard risk limits.

An AI that updates itself like an evolving organism.