Welcome to AssetMatrix— the next-generation AI-driven DeFi asset management solution that bridges on-chain automation with off-chain analytics to maximize returns while maintaining transparency and security.
1. Problem Statement
Retail and institutional DeFi investors currently juggle isolated single-strategy products, manual rebalancing, and opaque analytics. This fragmentation yields suboptimal returns, heightened risk exposure, and sluggish adaptation to changing market regimes. A unified, on-chain system for multi-strategy selection, verifiable backtesting, and automated execution is critically missing.
2. Solution Overview
AssetMatrix empowers users to design, simulate, and deploy AI-orchestrated, multi-strategy portfolios via a single programmable token. It leverages:
How does federated learning work in AssetMatrix? Are user data and model updates completely private, or partially shared across DAO-approved aggregators?
Are there insurance mechanisms, circuit breakers, or treasury buffers to protect HSIT holders in the event of underperformance or strategy collapse?
What kind of AI models are currently in use ? And how are they audited for fairness and risk management?
How does AssetMatrix reconcile the need for proprietary AI model performance (often a black box) with the DeFi ethos of verifiability and openness. especially when strategy selection and backtesting occur off-chain?
1. Federated Learning Privacy
Federated learning in AssetMatrix X means individual strategy contracts train local model parameters (e.g., gradient updates) on their own backtest data. Only the encrypted parameter deltas (not raw price or user-specific data) are sent to DAO-approved aggregator nodes. These aggregators perform a secure weighted average (e.g., FedAvg) and publish the updated global model on-chain. In this setup, no user’s raw portfolio history or private weight vectors ever leave their local environment—only masked gradient updates are shared.
2. Downside Protections for HSIT Holders
To safeguard HSIT holders against severe drawdowns or strategy failures, AssetMatrix X incorporates:
Circuit Breakers: If on-chain anomaly detectors (e.g., sudden oracle discrepancies, extreme volatility spikes) exceed predefined thresholds, the strategy contract auto-pauses new rebalances and locks in position until manual DAO review.
Treasury Buffer: A small portion of protocol fees is funneled into a reserve treasury that can purchase underperforming strategy tokens in secondary markets, providing interim liquidity to HSIT holders.
Parametric Insurance Pools: Users can opt into a side-pool funded by insurance premiums; if a strategy’s drawdown exceeds a DAO-set CVaR threshold, eligible HSIT holders receive a proportional compensation from that pool.
3. AI Models & Auditing
Current Models:
Reinforcement-Learning Agents: Lightweight Q-learning variants running on-chain (implemented in WASM via Hyperion), tuned to optimize a discounted reward function E[∑γtrt], where rt is incremental PnL.
Anomaly Detectors: On-chain one-dimensional convolutional detectors that flag oracle price deviations beyond a statistical threshold δ\delta.
Meta-Learner (off-chain): A federated-trained gradient-boosted decision tree that recommends strategy weight allocations based on macro volatility regimes.
Fairness & Risk Audits:
On-Chain Verifiability: All model weights and inference logic reside in transparent contracts or open IPFS manifests.
Third-Party Reviews: Before each major model release, independent auditors verify (a) absence of adversarial bias (e.g., overfitting to whale wallet patterns) and (b) correct implementation of risk metrics (Sharpe, CVaR, drawdown flags).
Continuous Monitoring: DAO-run “AI Auditor” bots sample model inferences on random historical windows to ensure no unexplained performance divergence.
AssetMatrix X balances proprietary AI with on-chain transparency through a three-layer approach:
Off-Chain Training & Proof Anchoring
• Strategies and model parameters are tuned privately off-chain.
• Rather than exposing raw data or weight matrices, we publish a succinct cryptographic commitment (e.g., a Merkle root or zk-SNARK) of backtest results and optimized parameters. Anyone can verify that the on-chain deployment matches those proofs without seeing the “black-box” internals.
On-Chain Parameter Commitment & Inference Logic
• Only vetted parameter vectors (target weight allocations, rebalance thresholds, model hyperparameters) are committed to the EIP-7702 factory. Their hashes are fully auditable on-chain.
• The actual inference code (e.g., Q-learning or anomaly detectors) is published in open-source or WASM bytecode on IPFS. While training remains proprietary, the on-chain execution path is deterministic and transparent.
Community Auditing & Governance
• A public registry holds backtest proofs, allowing anyone to recompute or audit historical performance using the same data sources (e.g., Pyth snapshots).
• DAO-appointed auditors can spot-check random historical segments, confirming that published parameters align with off-chain results.
• Federated learning shares only encrypted gradient deltas—protecting IP—while letting the community vote on model updates.
Thank you for being so interested in our project. We will publish the full technical details on our blog once our concept advances to the next round. To answer your questions at a high level:
1. On-Chain Q-Learning (High-Level)
We compress each portfolio snapshot into a state summary (current allocations plus recent price/volatility signals). Actions correspond to predefined rebalance steps (e.g., shift 5 % from Token A to Token B).
After executing an action, the contract measures net portfolio value change (including trading costs) and uses that as a reward signal. While we share this framework, the exact feature-engineering “recipe” remains proprietary to preserve our competitive edge.
2. NAV Updates & Oracle Risk Mitigation
Whenever HSIT is minted, burned, or an AI-driven rebalance occurs, the contract immediately recalculates the fund’s total value and adjusts HSIT’s on-chain price. This ensures all trades use the most current holdings.
For oracle safety, we pull multiple recent price ticks from Pyth and compute a simple median or average. Any tick that deviates beyond a safe threshold is rejected. If Pyth data goes stale or experiences an abnormal spike, a circuit breaker pauses mint/burn and rebalance functions until a DAO member confirms valid prices.
3. Off-Chain Backtesting Integrity
Historical prices come from both Pyth’s archived snapshots and The Graph’s indexed events. When one source has gaps, we backfill using the other.
To avoid survivorship bias, we include tokens that are no longer trading today, capturing a complete historical record.
We remove duplicate or zero-value data points, interpolate very short gaps, and flag longer gaps or known oracle glitches for manual audit.
Once backtests finish off-chain, we generate a Merkle root over timestamped snapshots of rebalance weights and NAV. Only that root is published on-chain. Anyone can download the leaf data, rerun the same backtest, and verify the on-chain root matches—ensuring full transparency without exposing internal model details.
Interesting, but what are the trade-offs of using Q-learning in an on-chain environment, especially when state and action spaces are simplified due to gas and computation limits? Could this limit the strategy’s ability to generalize in volatile markets?
On-chain Q-learning must simplify states and actions to limit gas—e.g., using a few price/volatility bins and coarse rebalance steps. This inevitably reduces sensitivity to nuanced market moves, so the agent can miss rapid regime shifts and react conservatively in extreme volatility.
Learning is also slower, since updates only happen on transactions or scheduled calls. In fast-moving markets, the policy lags until sufficient on-chain experience accumulates. A compact Q-table or lightweight approximation can’t cover every scenario, so when conditions fall outside its trained range, the agent defaults to “safe” actions, foregoing upside.
Mitigations:
Perform full-scale training off-chain (with richer states/actions) and distill distilled policies for on-chain use.
Periodically inject off-chain retrained models via federated updates so the on-chain agent gradually incorporates broader market patterns without blowing up gas costs.
Frequency: We plan scheduled retraining every quarter, with additional off-cycle updates if market conditions change materially (e.g., a prolonged volatility regime shift).
Community Notification: Each retraining cycle is announced on our DAO forum and Discord one week in advance. After retraining, we publish a summary report (highlighting performance improvements, key parameter shifts, and backtest outcomes) on GitHub and our blog. HSIT holders can also opt into email or on-chain alerts when a new model version is deployed.
Transparency of Data & Methodology
Phase-One Plan: Initially, we’ll share high-level summaries of data sources (e.g., “we used two years of Pyth and The Graph historical feeds across five assets”) and aggregate performance metrics, without exposing raw data tables or proprietary feature-engineering scripts.
Long-Term Goal: As AssetMatrix matures, we intend to open-source most of our data-cleaning pipelines and publish sanitized training samples that don’t compromise user privacy. Methodology write-ups (including pseudo-code for state encoding and reward calculations) will live on GitHub under a Creative Commons license. Full raw datasets won’t be disclosed (to protect third-party license agreements and user confidentiality), but we’ll provide clear replication steps so anyone can independently rebuild our training inputs.
AssetMatrix guarantees reliability and transparency by:
Clean, Verifiable Data: We ingest historical prices from multiple sources (Pyth snapshots and The Graph indices), automatically remove duplicates or erroneous ticks, and flag any gaps or oracle glitches. This ensures backtests run on complete, unbiased datasets—including delisted tokens.
Cryptographic Proof Anchoring: Each off-chain backtest generates a Merkle root (or zk-SNARK) over timestamped weight and NAV snapshots. Only that root is stored on-chain, so anyone can download the exact leaf data, rerun the simulation, and confirm the on-chain anchor matches.
Open Inference Logic & Community Audits: The deployed Q-learning and anomaly-detection contracts are open-source (WASM bytecode on IPFS), making every decision path auditable. DAO-appointed auditors routinely spot-check random historical segments against published proofs to validate consistency between off-chain backtests and on-chain parameters.
Let me know if you have more questions, I am happy to answer.
Thank you for the clear and detailed explanation the combination of cryptographic anchoring and open inference logic is especially impressive.
Here’s a question:
How do you handle evolving market conditions or new asset listings in real-time without compromising the integrity of your historical models?
