Jiritsu Network
ProjectAutomated Token-Issuer Screening Infrastructure with Scalable ETL, Ranking Models & Counterparty Shortlisting
- Representative example: re-implemented GSIA negative screening and best-in-class selection as a two-stage “screen out bad, then select the best” workflow, moving from 18,000 CoinGecko tokens to an 800-issuer universe and a 30-issuer shortlist for commercial, risk, and governance review
- Engineered a Python ETL with Prefect, DuckDB, and Parquet to ingest, standardize, and refresh large-scale token data for issuer analysis
- Built negative screens covering liquidity and pricing validity, data completeness, and basic governance and disclosure red flags to remove unworkable names early
- Added business relevance filters to focus on RWA, stablecoin, serious DeFi, and infrastructure issuers, excluding meme, gambling, and NSFW categories
- De-duplicated multi-chain and wrapped assets and mapped tokens to issuer-level entities to produce a partnership-ready counterparty list
- Trained a LightGBM pairwise ranker for “go for good,” using uncertainty sampling to concentrate labeling on the hardest boundary decisions
- Used Leiden clustering to surface look-alike issuers and improve coverage and diversity beyond the top-ranked candidates
- Led governance and disclosure engagements with shortlisted issuers, aligning on reporting cadence, reserve transparency, and inputs needed for on-chain verification
- Structured 6 tokenization pilots with lower-risk counterparties, tying selection to clear disclosure commitments and verification readiness
- Made the pipeline repeatable and monitorable with scheduled runs, retries on failure, and Parquet-based outputs for fast reruns and backfills