institutional-crypto

Institutional Crypto Analysis

Analyze institutional and TradFi exposure to crypto by combining corporate holdings data, ETF flow data, and price context.

Workflow

Step 1 - Corporate and institutional holdings

Fetch public companies and institutions that hold crypto on their balance sheets, including market-to-NAV ratios.

defillama:get_dat_holdings
  include_mnav: true

Key fields: institution_slug, token, amount, holding_usd_value, mNAV.

Sort by value to show the largest holders. Note any entities trading at a significant premium or discount to NAV (mNAV far from 1.0).

Step 2 - Bitcoin ETF flows

Get recent BTC ETF inflow and outflow data.

defillama:get_etf_flows
  token: "bitcoin"

Positive flows = net buying. Negative flows = net selling.

Step 3 - Ethereum ETF flows

Get recent ETH ETF inflow and outflow data.

defillama:get_etf_flows
  token: "ethereum"

Step 4 - Price context

Fetch current BTC and ETH prices to contextualize the flows.

defillama:get_token_prices
  token: ["coingecko:bitcoin", "coingecko:ethereum"]

Output Format

Present the report with these sections in order:

1. Institutional Holdings - Top holders ranked by USD value. Include entity name, token held, amount, value, and mNAV. Call out any notable changes or premium/discount to NAV.
2. Bitcoin ETF Flows - Recent daily/weekly flows, cumulative AUM, and trend direction. Name the top ETFs by flow.
3. Ethereum ETF Flows - Same structure as BTC ETFs.
4. Price Context - Current BTC and ETH prices, helping the user understand the dollar magnitude of flows.
5. Key Takeaways - Summarize whether institutional appetite is growing or shrinking, and any notable patterns.

Tips

• Sustained positive ETF flows alongside rising price = strong institutional demand.
• An entity with mNAV >> 1 is trading at a premium to its crypto holdings (e.g., MicroStrategy often trades above NAV).
• Compare weekly ETF flows to previous weeks to identify acceleration or deceleration in institutional buying.
• Negative ETF flows during price dips may indicate profit-taking rather than loss of conviction if flows resume quickly.
• Use start_date / end_date for custom date ranges when analyzing specific periods (e.g., start_date: "2025-01-01", end_date: "2025-03-31").