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7 special situations strategies for crypto, from the AlgoBrain wiki. One-off dislocations with a defined resolution: bankruptcy claims, post-hack recoveries, forks, governance fights and counterparty stress. Each one lists the indicators it uses, the Crypto Data API endpoints that feed it and copy-paste prompts for an AI agent to build and backtest it. All of them are in the API: GET /api/v1/strategies?group=special-situations.
A narrow but reliably-recurring arbitrage (directional short) strategy: short Compound-fork tokens during their first 6-12 months post-launch as a bet on the well-documented donation-attack pattern recurring.
Why it works: Compound v2 has a well-documented donation/empty-market vulnerability that has been exploited repeatedly: Hundred Finance (Apr 2023), Onyx Protocol (Nov 2023, again Sep 2024), Sonne Finance (May 2024, $20M), and recurring Venus Protocol incidents (Feb 2025 zkSync, Mar 2026 BNB Chain). Each fork inherits the vulnerability template; AI scanners catch the pattern reliably; teams continue shipping any
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/compound-fork-donation-short
AI-agent prompts
Build it with an AI agent
Build the Compound-Fork Donation-Attack Systematic Short crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/arbitrage/compound-fork-donation-short.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Compound-Fork Donation-Attack Systematic Short strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=1d (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/compound-fork-donation-short.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
A strategy that trades the price impact of forced unwinds by counterparties whose position size exceeds market-clearing liquidity and whose capital structure forces them to unwind on a defined timeline.
Why it works: When a counterparty's position size exceeds market-clearing liquidity AND their capital structure forces an unwind on a defined timeline, the unwind path is structurally predictable. The trade is to receive the price impact of that forced unwind. Counterparty: the forced seller, whose capital structure makes their marginal price non-fundamental. Examples: Arnold/Amaranth 2006, Soros/BoE 1992, 3AC
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/counterparty-stress-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Counterparty Stress Arbitrage crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/arbitrage/counterparty-stress-arbitrage.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/sentiment/stablecoins
- GET https://cryptodataapi.com/api/v1/event/calendar
- GET https://cryptodataapi.com/api/v1/news/market-moving
- GET https://cryptodataapi.com/api/v1/sentiment/macro
3. Compute the signals described in the playbook on 4h bars (pinned: interval=4h, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Counterparty Stress Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/counterparty-stress-arbitrage.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
Crypto bankruptcy claim arbitrage involves buying claims against failed crypto firms — Mt. Gox, Celsius, Voyager, FTX, BlockFi, Genesis — at a discount on secondary OTC markets, then waiting for the bankruptcy estate to distribute recoveries.
Why it works: Bankruptcy claims trade at a discount to ultimate recovery because most retail creditors lack patience, accreditation, or legal expertise; specialised funds underwrite the legal process and earn the spread.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/bankruptcy-claim-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Crypto Bankruptcy Claim Arbitrage crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/arbitrage/bankruptcy-claim-arbitrage.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Crypto Bankruptcy Claim Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=1d (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/bankruptcy-claim-arbitrage.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
The days-2-through-weeks-8 playbook for trading the governance-vote and restitution waterfall after major crypto exploits. Sub-strategy of ai amplified exploit arbitrage.
Why it works: Post-exploit governance windows create predictable trading patterns. Note: DeFi OTC claim markets are thin to non-existent compared to bankruptcy claim trading — recovery in DeFi typically happens fast (weeks) via direct treasury reimbursement or not at all. Real edges: (a) governance-vote uncertainty arb on protocol-native tokens during decision windows, (b) bridge-token discount arb when wrapped
IndicatorsPrice and volume only — see the playbook for the exact rules.
CDA endpoints/api/v1/hyperliquid/candles
Via API/api/v1/strategies/governance-restitution-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Governance & Restitution Arbitrage crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/arbitrage/governance-restitution-arbitrage.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute the signals described in the playbook on 4h bars (pinned: interval=4h, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Governance & Restitution Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/governance-restitution-arbitrage.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
Mythos Release-Window Exploit Short (Hyperliquid Perps) #
A directional, event-driven short basket on Hyperliquid perpetuals, pre-positioned cheap-to-carry and scaled into the Mythos public-release window (now live, June–September 2026).
Why it works: A frontier model (Mythos) materially stronger at offensive security than Opus 4.8 — which already found the four-year ZEC Orchard counterfeiting bug — has now entered public release (9 Jun 2026, as Claude Fable 5 + the Glasswing-restricted Claude Mythos 5). This compresses attacker (and white-hat-disclosure) cost-per-vulnerability faster than protocol patch cycles can respond, raising the near-ter
Via API/api/v1/strategies/mythos-release-window-exploit-short
AI-agent prompts
Build it with an AI agent
Build the Mythos Release-Window Exploit Short (Hyperliquid Perps) crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/mythos-release-window-exploit-short.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/derivatives/open-interest
- GET https://cryptodataapi.com/api/v1/hyperliquid/open-interest
- GET https://cryptodataapi.com/api/v1/hyperliquid/l2-book
- GET https://cryptodataapi.com/api/v1/liquidity/depth
3. Compute Funding Rate on 4h bars (pinned: interval=4h, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Mythos Release-Window Exploit Short (Hyperliquid Perps) strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/mythos-release-window-exploit-short.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
The 0-72 hour playbook for trading crypto exploits at the moment of disclosure. A member of the arbitrage family — specifically an event-driven convergence arbitrage where the "fair value" is the realised loss-to-TVL ratio and the convergence is the market re-pricing toward it once liquidity gates lift.
Why it works: First 0-72h post-disclosure: CEXs halt deposits/withdrawals while DEXs stay open → DEX-CEX price decouples. LSTs and stablecoins overshoot fundamental loss-to-TVL ratio. Perp funding spikes as longs liquidate. Ladder shorts on first credible report, cover on confirmation; long the LST/stable depeg overshoot.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/post-hack-incident-response-arb
AI-agent prompts
Build it with an AI agent
Build the Post-Hack Incident Response Arbitrage crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/arbitrage/post-hack-incident-response-arb.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/on-chain/exchange-flows/spike-alerts
- GET https://cryptodataapi.com/api/v1/sentiment/stablecoins
- GET https://cryptodataapi.com/api/v1/news/market-moving
- GET https://cryptodataapi.com/api/v1/dex/trending
3. Compute the signals described in the playbook on 15m bars (pinned: interval=15m, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Post-Hack Incident Response Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=15m (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/post-hack-incident-response-arb.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
Vampire attack arbitrage is the strategy of providing liquidity (or volume, or order flow) as a liquidity provider to a forked protocol that pays migrating users in inflationary governance tokens, capturing those rewards, and exiting before token emissions dilute the price.
Why it works: Forked protocols subsidise migration with inflationary governance tokens; early LPs capture rewards before sell-pressure dilutes them, while incumbents are slow to defend their moat.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/vampire-attack-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Vampire Attack Arbitrage crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/arbitrage/vampire-attack-arbitrage.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/on-chain/exchange-flows/spike-alerts
- GET https://cryptodataapi.com/api/v1/supply/unlocks
- GET https://cryptodataapi.com/api/v1/dex/trending
- GET https://cryptodataapi.com/api/v1/dex/new-pools
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute the signals described in the playbook on 4h bars (pinned: interval=4h, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Vampire Attack Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/vampire-attack-arbitrage.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
Any key works, Free included — mint one in a single call. The list endpoint returns summaries; the per-slug endpoint adds the prompts, edge mechanism and data inputs. Full playbooks come from /api/v1/algobrain/page?path=… using each entry's wiki_path. Or use the MCP server.
What are special situations crypto trading strategies?
One-off dislocations with a defined resolution: bankruptcy claims, post-hack recoveries, forks, governance fights and counterparty stress.
Which indicators do special situations strategies use?
Most often Funding Rate.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=special-situations lists them; GET /api/v1/strategies/{slug} returns the build and backtest prompts, and each prompt names the exact Crypto Data API endpoints to call. Any API key works, Free included.