Market Language Models,
not Large Language Models

AI-native | Domain-specific | Small, not Large

M-Cube Technologies is an AI-first lab founded on the ethos that markets speak their own language. This market is capacity-constrained and its native language is nonlinear. Our approach is fundamentally different from the process of querying an LLM for market insights, which goes through an "English language filter" (and is then tokenized anyway). Our market models learn from domain-native representations rather than forcing our interpretations through an English translation. We believe we can develop better models of market behavior with this approach, ultimately leading to higher alpha.

Market visualization

The M-Cube Engine, Trading Live Capital

Under the hood, M-Cube has built a fundamental market grammar representation, on structured market data with strict point-in-time discipline. Our agents live with their predictions, just as real capital must.

Discovers Grammar

Learns latent syntax of price, volume, fundamentals, and cross-asset dynamics without succumbing to the widely-prevalent, oversimplification of linear factor models.

Algorithmic Innovation

Inventing optimization algorithms is one of our specialties, and we have developed our very own algorithm, derived from first principles, to train our models, that simultaneously reduces model size for better interpretability and inference.

Live Trading

M-Cube's engine has been running live capital since 2023 with returns running more than 900 bps above the S&P 500 on an annualized basis, announced on LinkedIn. These results come from a GIPS-compliant reporting tool developed by Interactive Brokers.

The Next Frontier

We're now exploring the next set of strategies that are designed to have low correlation with the markets while generating similar returns. This is an exciting new frontier that combines computation, strategy design, and AI/optimization algorithms, and fills a gap that typical long-only or long/short strategies cannot.

The Founder

Indraneel Das, Ph.D.

Indraneel Das, Ph.D.

Computational Mathematician, Inventor & Former Global Equity Portfolio Manager

Computational and applied mathematician with heavily cited research across engineering disciplines. He has studied markets for two decades, more than half of that as a global equity portfolio manager. His earlier work in nonlinear optimization and his resulting inventions run aircraft engines; his later work in fundamental stock-picking and AI powers M-Cube's AI-native market language models.

We've built M-Cube with internal and founder-adjacent capital. We're open to external partners who resonate with our vision and can help us scale and build faster.

"We started building our codebase well before ChatGPT was released, and do not use any large language model, or off-the-shelf AI agents, within our machinery. We're not bolting AI onto legacy factor models, which derive from linear regression - this is our own mathematical formulation that captures nonlinear relationships. We're building a new layer that addresses financial markets, and, in the process, developing technology that can be applied across domains."

We're building to interpret and model financial markets, and possibly a lot more

We’re building AI from first principles, not from language models. If you believe the next wave of AI will come from new mathematical formulations that generalize across domains - beyond just language - we’d love to chat.

Get in Touch