Quantitative DeFi analysis, productized.
PhD-grade liquidity-position analysis powered by defipy — our open-source AMM analytics library with 55,000+ downloads. Fixed-price reports. Operator sign-off. Methodology you can verify.
Three productized services. One scoped engagement path for everything else.
Productized services are fixed-price and fixed-scope. Custom engagements are scoped on a discovery call. All engagements contracted through DeFiMind Inc.
LP Position Audit
Forensic analysis of a single liquidity position. PnL attribution, impermanent loss exposure, exit slippage, depeg risk, and 1–3 concrete recommendations.
Covers Uniswap V2, V3, Balancer weighted pools, Curve stableswaps.
- Deliverable
- 6–10 page PDF
- Turnaround
- 5 business days
- Price
- $2,500
DAO Treasury Review
Full review of your DAO’s LP position book. Per-position analysis, cross-position risk, named stress scenarios, prioritized recommendations — written for governance forum publication.
Up to 10 positions. Larger books quoted on request.
- Deliverable
- PDF + markdown, 20–35 pages
- Turnaround
- 2–3 weeks
- Price
- From $10,000
Pool Health & Rug Risk
Pre-entry due diligence on a pool you’re considering. TVL trend, fee/volume health, liquidity stability, rug signal screen, and slippage curves at your deployment size.
Ends with an explicit go/no-go recommendation.
- Deliverable
- 4–6 page PDF
- Turnaround
- 3 business days
- Price
- From $1,500
DeFi Quant Consulting
Scoped engagements for work that doesn’t fit the productized services above — recurring monitoring, agent-augmented analytics, advisory retainers, custom risk modeling, or larger reviews. Discovery call first; scope, timeline, and price agreed before any work begins.
Common shapes: monthly treasury monitoring, agent strategy review, custom AMM modeling, fund-level due diligence, expert-witness or advisory retainer.
- Deliverable
- Scoped on call
- Turnaround
- Per scope
- Price
- Quoted
The math is open. The reports are paid.
DeFiMind analysis is powered by defipy — our open-source DeFi analytics library, maintained on GitHub with 55,000+ downloads across two years. Full coverage of Uniswap V2 and V3, Balancer weighted pools, and Curve-style stableswaps.
Buyers don’t pay for proprietary math. They pay for the analysis: the right position selected, the data pulled cleanly, the model run correctly the first time, and the recommendation written by the operator behind the methodology.
Every report cites the defipy functions used, links to the source, and is reproducible by anyone who wants to verify it.
PnL attribution
Decompose a position's return into fee income, impermanent loss, price exposure, swap costs, and gas. Powered by AnalyzePosition for V2/V3, AnalyzeBalancerPosition for weighted pools, AnalyzeStableswapPosition for Curve.
Risk decomposition
Quantify depeg exposure for stableswap LPs (AssessDepegRisk), IL trajectory under named price scenarios (SimulatePriceMove and variants), and exit slippage at the position's size.
Pool diagnostics
TVL trend, fee/volume ratio, liquidity stability, depth at notional sizes, and rug-signal screens (CheckPoolHealth, DetectRugSignals). Captures the operating health of the host pool, not just the position.
Slippage and price impact
Decompose slippage into pool-state impact and execution cost across V2/V3 (CalculateSlippage). Useful pre-entry, pre-exit, and for sizing rebalances.
Five years of peer-style research underpinning the practice.
Preprints on the math behind on-chain registries, provenance, agentic DeFi substrates, and gas-cost dynamics — published since 2021. The methodology in DeFiMind engagements draws from this work; the work draws from the engagements.
- May 2021arXiv 2105.03521
Stochastic Properties of EIP-1559 Basefees
Ian C. Moore, Jagdeep Sidhu
Mathematical foundations for analyzing EIP-1559 base-fee dynamics as a stochastic process. Identifies the conditions under which gas-price outcomes behave as a stationary process and characterizes the regime boundaries.
Read on arXiv - April 2026arXiv 2604.03434
Trustless Provenance Trees: A Game-Theoretic Framework for Operator-Gated Blockchain Registries
Ian C. Moore
A formal treatment of provenance trees with operator-gated registration. Introduces a dual-layer cryptographic commitment scheme that makes false attribution a strictly dominated strategy, with honest behavior as the unique Nash equilibrium. Deployed on Base as AnchorRegistry.
Read on arXiv - May 2026arXiv 2605.11522
State Twins: An Off-Chain Substrate for Agentic Reasoning over Decentralized Finance Protocols
Ian C. Moore
Introduces the State Twin: a typed, in-memory, replayable off-chain substrate for agentic reasoning over AMM protocols. Decouples reasoning from chain time and admits operations on-chain state cannot — forking, replay, counterfactual rollout. Ships in DeFiPy v2 with a reference Model Context Protocol server exposing typed analytical primitives as LLM tools; the same primitive serves a notebook quant, a backtest, and an LLM agent without modification.
Read on arXiv - June 2026SoonForthcoming
Parent-Hash DAG: A Cost Analysis of Constant-Time Append for On-Chain Registries
Ian C. Moore, Fernando Paredes García
Formal and stochastic cost analysis of the two dominant append-only registry primitives. Establishes O(1) gas complexity for parent-hash DAG, derives closed-form moments for incremental Merkle tree per-insert cost, and locates the empirical crossover at depth 5–6 across deployments on Base.
Available shortly.
Ian Moore, PhD
Principal, DeFiMind
DeFiMind is a quantitative DeFi research practice. Engagements range from productized work — LP audits, treasury reviews, pool diagnostics — through scoped advisory for teams whose problems don’t fit a fixed-scope SKU. The methodology is grounded in defipy, the open-source AMM analytics library Ian founded and maintains.
PhD in Engineering Mathematics from Queen’s University, with a specialization in time-series analysis. Four years as Research Scientist at the Syscoin Foundation (2021–2025) — first on Bitcoin L2 architecture, then on AMM mechanics across Uniswap V2/V3, Balancer, and Curve. Five years of peer-style preprints on EIP-1559 dynamics, on-chain provenance, and the cost analysis of append-only registry primitives. Selected speaker at ETH Denver 2024 on protocol-level liquidity research.
Prior to DeFi, ten years as a senior data scientist and applied mathematician. Senior Data Scientist at GE Digital working on the Predix industrial IoT platform; Forecasting Specialist at the Government of Ontario, building statistical and time-series models that informed over $200M in long-term capital allocation. Peer-reviewed publications in applied harmonic analysis (over 170 citations), biostatistics, and anesthesiology. Adjunct Lecturer in Biostatistics at the University of Toronto, 2010–2014.
Also founder of AnchorRegistry, a separate venture building provenance infrastructure for the agentic economy (arXiv:2604.03434).
Based in British Columbia. Engagements contracted through DeFiMind Inc.
Building toward agent-augmented analytics with operator sign-off — open methodology, human accountability, faster delivery. More on the direction in the arXiv paper and on Medium.
Two paths in.
Productized SKUs route to email; scoped engagements start with a 30-minute call.
LP Audits & Pool Health
Email imoore@defimind.ai with the position or pool address and your inputs. Reply within 24 hours.
imoore@defimind.ai →Treasury Reviews & Scoped Work
Book a 30-minute scoping call. We’ll cover scope, inputs, timeline, and price before any commitment.
Book a call →