Projects

A focused set of systems across backtesting, perp trading infrastructure, fund research tooling, DeFi intelligence, and onchain games.

Watson

Backtesting engine for TradingView power users

Research system

Backtesting engine for people who already live in TradingView and need to stop eyeballing charts. It turns exported signals and market data into replayable tests, candidate strategies, walk-forward validation, and guardrails against fake edge.

TradingViewBacktestingValidationTradingData
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Rialto

Near-zero-latency perp trading engine

Trading infrastructure

Perp trading engine written in Rust for Hyperliquid-style execution: low-latency signal handling, deterministic risk controls, order/execution state, and a clean boundary between market interpretation and trade management.

RustPerpsHyperliquidLow LatencyRisk Engine
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vc-analyzer

Graph/provenance workflows for fund research

11K+ deals/funds indexed

Research system built on 11,000+ indexed crypto deals and funds. Maps deals, investors, co-investment edges, provenance, and research workflows so VC diligence can move from scattered spreadsheets to repeatable graph-backed analysis.

Graph DBPythonTypeScriptDataVC
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DeFi Scanner

Wallet intelligence + LP opportunities

Capital allocation tooling

Combined DeFi Scanner + Shikamaru direction: wallet intelligence, smart-money signals, LP opportunity discovery, yield/liquidity/risk scoring, and candidate wallets for liquid deals and capital allocation.

Wallet IntelLPDeFiPostgresAnalytics

Warpacks

Onchain collectible pack experience on Starknet

Grant funded

Founder-led onchain game/product work: game mechanics, economy design, and Cairo smart contracts for a collectible pack experience on Starknet.

GamingStarknetCairoGame Design
GitHub →

Vara Arena

Onchain PvP game on Vara

Grant funded

Founder-led PvP game built around onchain mechanics and Rust smart contracts on Vara/Gear, from game design through implementation and grants.

GamingVaraRustPvP
GitHub →

Case studies

Proof Notes

Watson

Research layer for Rialto: dataset alignment, feature work, replay, and validation

101,311 Bybit rows

Problem

Trading research is easy to fake by accident: misaligned datasets, future leakage, cherry-picked cohorts, and candidates that look strong only because the validation loop is weak. Watson exists to make Rialto research harder to fool.

Approach

Built a research layer around canonical dataset alignment: 101,311 Bybit 1m rows, 3,377 CSV overlay rows, and 286 reconstructed events. On top: feature registry, cohort mining, candidate generation, walk-forward validation, Time Machine replay, no-leakage checks, and promotion guardrails.

Why it matters

The system changes the question from “does this signal look good?” to “does this signal survive clean data, realistic replay, and promotion criteria?” That is the difference between a demo and decision infrastructure.

Case Study

Rialto

Autonomous trading system with LLM reasoning and Rust risk controls

Live in production

Problem

Manual crypto trading loses money because humans react late and trade on emotion. I wanted to test whether an LLM reasoning layer inside a disciplined system could make better decisions — not as a black box, but as one component with clear boundaries.

Approach

TradingView alerts flow into market/context analysis, LLM signal evaluation, and a Rust risk engine that owns sizing, exposure, execution, and guardrails. Watson now strengthens the research side by testing which candidates deserve promotion before they reach production.

Key decision

Separate reasoning from execution. The model can interpret noisy signals; deterministic software enforces risk. One component should not do both.

Case Study

vc-analyzer

Graph/provenance workflows for crypto fund research

11K+ deals/funds indexed

Problem

Evaluating a crypto deal means understanding investors, co-investors, provenance, outcomes, and market context. A spreadsheet stores the facts, but it does not make the network or source trail easy to interrogate.

Approach

Indexed 11K+ crypto deals and funds into graph/provenance-oriented workflows so research could move through deals, investors, fund clusters, cap tables, and supporting sources without rebuilding the context manually each time.

Outcome

Used for DD and deal-flow triage: faster first-pass research, clearer investor structure, and less time wasted stitching together source material before the real judgment work begins.

Let's talk

If you're hiring for product, research, or a builder-analyst role, book 30 minutes and I'll come prepared.

Funds → trial DD Product teams → free teardown
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