PolyHedg: Certainty-as-a-Service

Inspiration

We saw a massive, unsolved problem in corporate finance: paralysis in the face of uncertainty. When the threat of a new tariff loomed, we watched CFOs grapple with a terrible choice. They could act preemptively and lose customers, or do nothing and pray, risking a catastrophic hit to their margins.

This was an unhedgeable, non-financial event risk. We realized prediction markets held the mathematical key to solve it.

PolyHedg was born from that insight. We built the bridge between real-world chaos and the certainty businesses need.


What it Does

PolyHedg is an automated risk management platform that transforms uncertain, event-driven financial exposure into a fixed, budgetable cost. A CFO can define a risk, like a "$10M loss if a chip tariff passes," and our system executes a sophisticated hedging strategy to neutralize it.

Probabilistic Graph Hedging: To scale beyond single, illiquid events, PolyHedg generates a Bayesian risk graph that maps first- and second-degree probabilistic dependencies between correlated markets (e.g., tariffs → supply shocks → semiconductor prices). By inferring joint event probabilities across related prediction markets, we can construct a synthetic, composite hedge with sufficient liquidity even when individual markets are too thin. This allows PolyHedg to hedge complex, interconnected real-world risks through a network of correlated event markets — not just a single binary outcome.

The Mechanism: From Chaos to Certainty

  1. Define the Financial Instrument: A company faces a $10M loss (L) if a tariff is enacted. The goal is to acquire 10 million "Yes" contracts on a prediction market for that event.

  2. Execute a Dynamic Hedge: Our system monitors the market price (p), the real-time probability of the event, and dynamically acquires contracts to optimize the total cost. This total cost becomes the "insurance premium" (L * p).

  3. Neutralize the Outcome:

    • Scenario A: The Tariff Passes. The company takes the $10M operational hit. However, its 10 million prediction market contracts pay out $10M, perfectly offsetting the loss. The net result is a fixed, budgeted cost.
    • Scenario B: The Tariff Fails. The company has no operational loss. The contracts expire worthless. The net result is the same fixed, budgeted cost.

PolyHedg converts a catastrophic binary risk ($0 or -$10M) into a predictable expense. We make the financial impact of the future irrelevant.


How We Built It: The Tech Stack

Our architecture is designed for security, verifiability, and scalability, bridging enterprise-grade finance with decentralized trust.

  • Theoretical Foundation: The system is based on a peer-to-peer risk transfer model. Trust comes not from an intermediary, but from a "proof-of-capital" consensus, where the market price itself is a public record of collective belief.

  • Frontend: A React and Tremor dashboard gives CFOs a mission control interface. They can define exposures in natural language and visualize P&L scenarios, hedging progress, and the total cost of certainty in real-time.

  • Backend Architecture: We built a distributed system for security and performance.

    • Polymarket Data API: A FastAPI service processes, filters, and caches terabytes of event data from Polymarket.
    • Secure Order Proxy API: We engineered a secure, self-hosted API proxy for the Polymarket CLOB, deployed on a remote server in Germany. This keeps our private keys from ever being exposed to the client or any public-facing service.
  • Decentralized Computation: The EigenLayer AVS: The heart of our innovation is our hedging logic, designed as an Actively Validated Service (AVS) on EigenLayer.

    • Our proprietary hedging algorithms run as a verifiable, unstoppable agent.
    • This logic is housed within an EigenLayer Trusted Execution Environment (TEE), guaranteeing our financial models remain confidential while their execution is cryptographically verifiable on-chain.

Challenges We Ran Into

  • Geo-Distributed, Secure Infrastructure: Our primary challenge was ensuring secure, low-latency order execution. We engineered and deployed a dedicated order proxy server in Germany, navigating complex server configurations and network security protocols to handle batch order requests to the Polymarket CLOB.

  • EigenLayer TEE Integration: Deploying our proprietary financial models in a trusted execution environment while maintaining the low-latency responses needed for dynamic market-making was a significant engineering feat.

  • Financial Modeling Under Uncertainty: Accurately modeling the dynamic cost basis and potential slippage for large-volume hedges across a volatile probability curve required sophisticated financial engineering.


Accomplishments We're Proud Of

In under 9 hours, we designed and built a functional, full-stack DeFi application that bridges corporate risk management with cutting-edge Web3 infrastructure.

We are particularly proud of architecting a secure, remote proxy for interacting with a CLOB and designing our core logic as a verifiable EigenLayer AVS.

We've proven a powerful, practical application for trustless computation in enterprise finance and pushed prediction markets far beyond simple speculation.


What We Learned

  • EigenLayer & AVS Design: We gained deep, practical experience designing services for verifiable, decentralized execution and learned how TEEs can protect proprietary algorithms.
  • Prediction Market Microstructure: It was a masterclass in order book liquidity, price impact (slippage), and the mechanics of executing large-scale hedges on a Central Limit Order Book (CLOB).
  • Secure Financial Infrastructure: We got firsthand experience with the complexities of deploying and securing geo-distributed infrastructure for financial applications.
  • Financial Engineering in Practice: We moved from theory to practice, applying risk neutralization mathematics, cost-averaging strategies, and optimal execution algorithms to build a perfect hedge.

What's Next for PolyHedg

  1. Full-Scale Automated Execution: Implement autonomous trading bots that execute hedging strategies over weeks or months.
  2. Enterprise ERP Integration: Develop a dedicated API to connect with corporate ERP systems like SAP and Oracle to automate risk discovery.
  3. Advanced Hedging Products: Offer more nuanced strategies, including partial hedges, dynamic position rebalancing, and complex conditional triggers.
  4. Expansion to New Risk Categories: Move beyond geopolitical events to cover a wider range of risks like regulatory approvals (FDA decisions), legal rulings, and climate-related events.

Built With

  • eigenlayer
  • nextjs
  • polymarket
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