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70/100r/BlockchainStartups · @Steflavoie65 · Wevolv3 · KOL & Influencer

Tracking when crypto stress becomes systemic instead of isolated

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💡 Por que Ă© um lead: [PROJECT/ENGAGEMENT] Founder of crypto risk monitoring tool asking for feedback in BlockchainStartups; potential to discuss marketing for risk tools.

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I’ve spent the last year building a crypto structural risk monitoring system called LSRI-CRYPTO. The original idea was simple: Most crypto dashboards track price, volatility or sentiment. I wanted something that tracks when isolated crypto stress starts becoming systemic across the market. So LSRI-CRYPTO monitors: regime transitions, cross-asset contagion, structural stress persistence, deterioration inside otherwise “normal” regimes, and daily regime state across major crypto assets. The goal is not price prediction. The goal is to reduce ambiguity during regime shifts like: LUNA, FTX, broad deleveraging periods, cross-market stress propagation. I recently opened the platform publicly: live regime dashboard, committee mode, transition archive, replay packs, quantitative validation, Telegram risk brief workflow. Would genuinely appreciate feedback from people working in crypto risk, quant or systematic trading.   submitted by   /u/Steflavoie65 [link]   [comments]

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On the regime transition detection side, layering in perps basis and open interest shifts across exchanges could surface contagion signals 12-24h earlier than pure price/vol metrics, especially during deleveraging cascades. For the persistence scoring, running a rolling z-score on cross-asset correlation matrices against historical stress windows (e.g., March 2020 or May 2022) would make the “deterioration inside normal regimes” flag more actionable for systematic desks.

Committee mode is useful but consider adding a simple on-chain filter—watching stablecoin mint/burn velocity or large entity flows on Ethereum and Solana—to reduce false positives from purely offshore CEX noise.

We handled similar risk-signal integration in a few quant-leaning campaigns at Wevolv3 and can share the exact data schema that worked.
DM
The contagion and persistence modules look particularly sharp for avoiding the usual LUNA/FTX blind spots. Curious how you’re sourcing the cross-exchange OI data without too much lag. Open to compare notes if you’re testing live feeds.

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