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14 event-driven & news strategies for crypto, from the AlgoBrain wiki. Position around known catalysts: token unlocks, listings, launches, macro prints and breaking news. 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=event-driven.
Bitcoin Halving Cycle Timing is a long-horizon position strategy that uses on-chain valuation to identify cycle accumulation (bottom) and distribution (top) zones, overlaid on the halving supply-issuance clock.
Why it works: On-chain cost-basis metrics (MVRV, MVRV-Z, NUPL, realized-price bands) reveal the aggregate unrealized profit/loss of all Bitcoin holders; extremes in that gauge — heavy unrealized profit near cycle tops, deep unrealized loss near bottoms — front-run the distribution and capitulation that recency-biased holders execute late, and the halving supply-issuance clock provides a coarse timing overlay fo
Via API/api/v1/strategies/bitcoin-halving-cycle-timing
AI-agent prompts
Build it with an AI agent
Build the Bitcoin Halving Cycle Timing 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/position-trading/bitcoin-halving-cycle-timing.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/event/calendar
- GET https://cryptodataapi.com/api/v1/regimes/current
- GET https://cryptodataapi.com/api/v1/quant/market
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute MVRV Ratio, MVRV Z-Score, Net Unrealized Profit/Loss (NUPL), Realized Price & Realized Cap, Spent Output Profit Ratio (SOPR) 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 Bitcoin Halving Cycle Timing 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/position-trading/bitcoin-halving-cycle-timing.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 Policy Shock Trading is a directional, event-driven crypto strategy that takes a biased position around policy, regulatory, and geopolitical shocks — executive orders, exchange enforcement actions, country bans or adoptions, tariff/trade-war escalations, and central-bank rate signals.
Why it works: Crypto reacts to policy and geopolitical shocks (pro-crypto orders, bans, tariffs, rate decisions) with distinct, partly repeatable signatures — euphoric OI build vs risk-off cascade vs fade-within-days; the edge is reading the policy signature and the crowd's over/under-reaction faster and more soberly than headline-chasers.
Via API/api/v1/strategies/crypto-policy-shock-trading
AI-agent prompts
Build it with an AI agent
Build the Crypto Policy Shock Trading 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/fundamental-analysis/crypto-policy-shock-trading.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/event/calendar
- GET https://cryptodataapi.com/api/v1/news/market-moving
3. Compute Funding Rate, Open Interest 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 Crypto Policy Shock Trading 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/fundamental-analysis/crypto-policy-shock-trading.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.
Event calendar risk gating is a risk-management framework that systematically pauses or de-sizes mechanical/passive strategies — grids, market-making books, funding carry books, and short-volatility books — around scheduled binary events.
Why it works: Passive and mechanical strategies (grids, market-making, carry books, short-vol books) assume a continuous mean-reverting or carry-harvesting regime; scheduled binary events (major unlocks, protocol upgrades, macro data releases, regulatory decisions) temporarily suspend this regime assumption and introduce non-random, directional, vol-expansion risk that these strategies are structurally ill-equi
Via API/api/v1/strategies/event-calendar-risk-gating
AI-agent prompts
Build it with an AI agent
Build the Event Calendar Risk Gating 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/combinations/event-calendar-risk-gating.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/market-intelligence/options
- GET https://cryptodataapi.com/api/v1/volatility/implied
3. Compute Open Interest 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 Event Calendar Risk Gating 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/combinations/event-calendar-risk-gating.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.
An event-driven basket that captures the predictable price impact of new exchange listings (pump) and delistings (dump) by entering perpetual positions on Hyperliquid immediately upon a confirmed announcement, targeting the initial speculation surge on the long side and the panic-selling cascade on the short side.
Why it works: Retail and momentum chasers systematically bid confirmed listings before the event resolves, creating a predictable pump-then-fade cycle that a fast-entry short-to-neutral book can harvest; on the delisting side, panic selling by holders unable to bear the regulatory / liquidity loss is the forced counterparty.
Via API/api/v1/strategies/exchange-listing-delisting
AI-agent prompts
Build it with an AI agent
Build the Exchange Listing / Delisting (Hyperliquid Basket) 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/hyperliquid-baskets/exchange-listing-delisting.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 Open Interest, Funding Rate on 5m bars (pinned: interval=5m, 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 Exchange Listing / Delisting (Hyperliquid Basket) 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/hyperliquid-baskets/exchange-listing-delisting.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 strategy of triangulating between a parent asset, the to-be-airdropped/forked asset, and exchange-specific exposure to capture the fork or airdrop value with minimal directional exposure.
Why it works: When a blockchain hard-forks or a protocol airdrops tokens, holders of the parent asset receive new tokens free. Different exchanges credit at different times (or not at all). Implied parent-vs-fork pricing across venues creates triangular arbitrage from before the snapshot through several weeks post-distribution.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/fork-airdrop-triangulation
AI-agent prompts
Build it with an AI agent
Build the Fork & Airdrop Triangulation 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/fork-airdrop-triangulation.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 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 Fork & Airdrop Triangulation 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/fork-airdrop-triangulation.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 strategy of shorting pre-fork IOU futures (or pre-launch perpetuals) against an expected delivery of the forked / airdropped asset at the snapshot.
Why it works: Pre-fork IOU futures markets price the expected airdrop value before the snapshot. Retail and narrative-driven traders systematically over-price these IOUs vs. realized post-fork distribution value, allowing sophisticated participants to short pre-fork IOUs and deliver the airdropped asset at distribution.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Fork Futures / Spot IOU Basis 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/fork-futures-spot-basis.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 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 Fork Futures / Spot IOU Basis 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/fork-futures-spot-basis.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.
An event-driven basket that positions ahead of or immediately after known macro catalysts: FOMC decisions, CPI prints, spot Bitcoin ETF flow data releases, and major regulatory announcements.
Why it works: Consensus expectations for macro events are known pre-event; retail and systematic traders anchor to the expected outcome and over-react in the first minutes post-print, creating a buy-the-rumour/sell-the-news pattern or a mean-reversion opportunity in the initial overshoot that an informed, pre-positioned strategy can exploit.
Build the Macro-Event Pump (Hyperliquid Basket) 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/hyperliquid-baskets/macro-event-pump.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/market-intelligence/options
- GET https://cryptodataapi.com/api/v1/volatility/implied
3. Compute Funding Rate, Open Interest, Support and Resistance, Liquidation 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 Macro-Event Pump (Hyperliquid Basket) 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/hyperliquid-baskets/macro-event-pump.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.
Tracks the rotation of speculative capital into meme-coin perpetuals, which occurs in discrete, intense cycles typically near crypto bull-market peaks.
Why it works: Meme-coin cycles are driven by a discrete wave of speculative capital rotating from majors into low-cap narrative tokens near bull-market peaks; the edge is entering the long side early in the euphoria phase (before funding extremes and BTC-dominance reversal signal the cycle peak) and exiting or reversing before the violent mean-reversion that ends every cycle.
Build the Meme-Coin Cycle (Hyperliquid Basket) 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/hyperliquid-baskets/meme-coin-cycle.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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
3. Compute Funding Rate, Open Interest, Liquidation 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 Meme-Coin Cycle (Hyperliquid Basket) 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/hyperliquid-baskets/meme-coin-cycle.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.
An event-driven basket that trades the post-listing price pattern of newly added Hyperliquid perpetuals.
Why it works: Newly listed Hyperliquid perpetuals exhibit a predictable 3–14 day post-listing price pattern: an initial euphoria pump (liquidity vacuum + unhedged demand) followed by a mean-reversion; the information edge is detecting the listing before it is fully priced (monitoring HL meta feed) and entering the reversion rather than the pump, or fading the euphoria with size limits appropriate for thin oracl
Build the New Listing Basket (Hyperliquid Basket) 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/hyperliquid-baskets/new-listing-basket.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/regimes/current
- GET https://cryptodataapi.com/api/v1/quant/market
3. Compute Open Interest 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 New Listing Basket (Hyperliquid Basket) 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/hyperliquid-baskets/new-listing-basket.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 informational edge: markets do not fully price a large fundamental surprise in the first seconds; the first-mover who correctly reads the direction of a significant deviation from consensus and executes before the broader market repositions captures the initial price discovery move.
Why it works: Markets under-react to large fundamental surprises in the first seconds and over-react in the first minutes; the news trader either exploits the first-mover advantage on an unambiguous directional surprise (momentum) or fades the overreaction once the informed flow has dissipated (reversal), with crypto adding a second mechanism: macro releases now directly move BTC/ETH perp funding and open inter
Build the News Trading 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/fundamental-analysis/news-trading.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/market-intelligence/liquidations
- GET https://cryptodataapi.com/api/v1/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
- GET https://cryptodataapi.com/api/v1/event/calendar
3. Compute Volatility, DVOL — Deribit Volatility Index, Funding Rate 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 News Trading 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/fundamental-analysis/news-trading.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.
Monitors on-chain vesting schedules and known token unlock calendars to position short perpetual futures ahead of large cliff unlock events, harvesting the sell-side pressure from insiders and early investors whose cost basis is far below market price.
Why it works: Large scheduled token unlocks (cliff vesting events releasing > 5% of circulating supply) create predictable sell-side pressure from insiders and early investors who acquired tokens at a fraction of market price; the structural overhang depresses price in the 7–30 day window around the event, exploitable via a short position on the Hyperliquid perpetual.
Via API/api/v1/strategies/token-unlock-supply-event
AI-agent prompts
Build it with an AI agent
Build the Token Unlock / Supply Event (Hyperliquid Basket) 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/hyperliquid-baskets/token-unlock-supply-event.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/sentiment/fear-greed
- GET https://cryptodataapi.com/api/v1/supply/unlocks
3. Compute Funding Rate, Open Interest, Liquidation 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 Token Unlock / Supply Event (Hyperliquid Basket) 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/hyperliquid-baskets/token-unlock-supply-event.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.
Token unlock arbitrage exploits the predictable supply shocks created when locked tokens — typically held by VCs, founders, advisors, and early team — vest into circulating supply on contractually fixed schedules.
Why it works: Insider/VC vesting schedules are publicly disclosed but supply shocks are systematically underpriced; perp shorts allow capturing the front-run while spot reverts post-cliff.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Token Unlock 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/token-unlock-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/supply/unlocks
- GET https://cryptodataapi.com/api/v1/news/market-moving
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/indicators/technical
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 Token Unlock 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/token-unlock-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.
An unlock short with crowding gate is a token unlock supply event short on a perp — shorting a crypto asset ahead of a large, scheduled cliff unlock — filtered through a crowding gate that only permits entry when the short side is not already crowded.
Why it works: Token cliff unlocks create a predictable forced-supply overhang; the crowding gate (funding not deeply negative, OI not already spiked short) filters for the subset where the trade is non-consensus — entering before the short side is crowded avoids paying to be the last short in a position that is already squeezable, and captures the trade at its highest expected value.
Via API/api/v1/strategies/unlock-short-with-crowding-gate
AI-agent prompts
Build it with an AI agent
Build the Unlock Short with Crowding Gate 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/combinations/unlock-short-with-crowding-gate.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/supply/unlocks
- GET https://cryptodataapi.com/api/v1/sentiment/macro
3. Compute Funding Rate, Open Interest 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 Unlock Short with Crowding Gate 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/combinations/unlock-short-with-crowding-gate.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.
An event-driven basket that systematically shorts Hyperliquid perpetuals of tokens with imminent large cliff unlocks, exploiting the predictable supply-shock headwind in the 7–14 day pre-unlock window. The basket is short-only by design — it does not hold long positions in unlock-event tokens.
Why it works: Tokens with imminent large cliff-unlocks (≥ 5% of circulating supply releasing within 7–14 days) face a predictable supply-shock headwind that is observable in advance via unlock calendars; the basket systematically shorts the highest-unlock-concentration tokens in the 7–14 days before unlock execution, where historical average token price underperformance vs. sector peers has been −5% to −15% in
Build the Unlock-Heavy Basket (Hyperliquid Basket) 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/hyperliquid-baskets/unlock-heavy-basket.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/supply/unlocks
- GET https://cryptodataapi.com/api/v1/sentiment/macro
3. Compute Open Interest 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 Unlock-Heavy Basket (Hyperliquid Basket) 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/hyperliquid-baskets/unlock-heavy-basket.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 event-driven & news crypto trading strategies?
Position around known catalysts: token unlocks, listings, launches, macro prints and breaking news.
How many event-driven & news strategies are there?
Which indicators do event-driven & news strategies use?
Most often Open Interest, Funding Rate, Liquidation, MVRV Ratio.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=event-driven 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.