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29 momentum & rotation strategies for crypto, from the AlgoBrain wiki. Own what is outperforming: cross-sectional momentum, sector and altcoin rotation. 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=momentum.
Alt-season momentum gate conditions cross-sectional momentum deployment on the BTC-dominance regime: deploy alt-basket momentum only when BTC dominance is falling (alt-season active or beginning to form); shift to BTC-only or BTC-paired momentum entries when BTC dominance is rising (BTC capturing market share); sit flat or minimal in both when dominance is ambiguous (sideways).
Why it works: BTC dominance is a real-time regime indicator for where in the crypto cycle the momentum factor concentrates: when dominance is rising (BTC capturing market share), cross-sectional momentum on altcoins generates false signals (alts lose to BTC regardless of their relative price performance); when dominance is falling (alts outperforming BTC), cross-sectional momentum on alts is at its most product
Via API/api/v1/strategies/alt-season-momentum-gate
AI-agent prompts
Build it with an AI agent
Build the Alt-Season Momentum Gate (Dominance / Alt-Season 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/alt-season-momentum-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/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
- GET https://cryptodataapi.com/api/v1/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
3. Compute Market Breadth 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 Alt-Season Momentum Gate (Dominance / Alt-Season 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/alt-season-momentum-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.
Breadth and Momentum Divergence (Hyperliquid Basket) #
A regime-aware signal basket that measures the width of a crypto-market move — how many assets are genuinely participating — against the headline momentum of BTC or a broad index.
Why it works: Retail and momentum traders chase headline BTC/ETH price, missing breadth deterioration that precedes reversals; the divergence between index-level momentum and underlying altcoin participation is a structural leading indicator of regime change that consensus pricing does not immediately embed.
Via API/api/v1/strategies/breadth-and-momentum-divergence
AI-agent prompts
Build it with an AI agent
Build the Breadth and Momentum Divergence (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/breadth-and-momentum-divergence.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
- 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 Relative Strength, 200-Day Moving Average, Divergence, 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 Breadth and Momentum Divergence (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/breadth-and-momentum-divergence.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 sector basket of centralised exchange native tokens with active Hyperliquid perpetuals. Exchange tokens benefit from structural utility (trading fee discounts, launchpad access, burn mechanics tied to revenue) and co-move with overall crypto trading volume — the most direct "picks and shovels" crypto play.
Why it works: CEX tokens are directly linked to exchange trading volume and fee revenue; they co-move with the broad crypto market (more volume in bull markets) but also show within-sector dispersion based on exchange-specific events (listing announcements, token burns, regulatory actions, FTX-contagion-type events), creating cross-sectional opportunities between dominant and challenged exchange tokens.
Build the CEX Tokens 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/cex-tokens-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 CEX Tokens 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/cex-tokens-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.
A sector basket of Cosmos ecosystem and IBC-connected chain tokens with active Hyperliquid perpetuals. Cosmos is the "internet of blockchains" — a network of sovereign application-specific chains connected via IBC (Inter-Blockchain Communication).
Why it works: Cosmos ecosystem tokens share the IBC interoperability narrative and compete for the same sovereign-chain developer mindshare; ATOM acts as the economic center of the ecosystem while appchain tokens (OSMO, INJ, TIA, DYDX) move on sector-specific catalysts, creating both directional co-movement and within-sector cross-sectional dispersion.
Build the Cosmos / IBC 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/cosmos-ibc-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Cosmos / IBC 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/cosmos-ibc-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.
Crypto Beta Rotation is a risk-regime overlay that dials crypto-directional exposure up and down according to whether the macro tape is risk-on or risk-off.
Why it works: When crypto's rolling correlation to Nasdaq/equities is high, Bitcoin trades as a high-beta risk asset whose direction is set by the macro tape, not by the halving clock or on-chain flows; a strengthening DXY plus a risk-off correlation regime carries negative expected crypto returns, so systematically cutting or hedging crypto-directional beta in those windows sidesteps the macro-driven drawdowns
Build the Crypto Beta Rotation 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/quantitative/crypto-beta-rotation.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/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
- GET https://cryptodataapi.com/api/v1/sentiment/fear-greed
- GET https://cryptodataapi.com/api/v1/regimes/current
3. Compute VIX (CBOE Volatility Index), Funding Rate, Implied Volatility, Vol Regime Detection 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 Beta Rotation 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/quantitative/crypto-beta-rotation.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 Defensive Majors basket holds long perpetual positions in the highest-liquidity, highest-market-cap crypto assets — primarily BTC, ETH, and optionally SOL — with deliberately reduced leverage during periods of market uncertainty or elevated volatility.
Why it works: Retail and leveraged altcoin holders bear disproportionate drawdown during uncertainty; majors retain liquidity and ETF-flow support that thinner tokens lack, so the patient long earns the risk-premium without the idiosyncratic wipeout.
Build the Defensive Majors (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/defensive-majors.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/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
3. Compute Volatility Regime Classification, VWAP (Volume Weighted Average Price), Average True Range (ATR), Funding Rate, Open Interest 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 Defensive Majors (Hyperliquid Basket) 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/hyperliquid-baskets/defensive-majors.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 sector basket of Decentralised Physical Infrastructure Network tokens with active Hyperliquid perpetuals. DePIN covers wireless, compute, storage, energy, and mobility networks built on blockchain token incentives.
Why it works: DePIN tokens share a real-world adoption narrative (decentralised networks replacing traditional infrastructure) that creates correlated narrative cycles; within-sector dispersion tracks actual network metrics (active nodes, data transferred, compute hours sold), creating cross-sectional edges between adoption-leaders and laggards.
Build the DePIN 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/depin-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 DePIN 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/depin-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.
A sector basket of blockchain gaming and GameFi tokens with active Hyperliquid perpetuals. Captures the crypto gaming and metaverse narrative cycle — characteristically late-cycle, high-beta, and retail-driven. Deployed only in confirmed risk-on / alt-season regimes.
Why it works: GameFi tokens are driven by bull-market retail speculation and specific gaming-season narratives (major game launches, tournament events, platform milestones); the sector co-moves strongly during risk-on regimes and provides high-beta exposure to the crypto gaming cycle with concentrated within-sector dispersion driven by individual game-adoption metrics.
Build the Gaming / GameFi 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/gaming-gamefi-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Gaming / GameFi 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/gaming-gamefi-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.
A factor basket that dynamically selects the highest-BTC-beta altcoins on Hyperliquid for concentrated long exposure during confirmed alt-season and bull-market momentum regimes.
Why it works: High-beta altcoins outperform the broad crypto market by a multiple during confirmed bull-market and alt-season regimes, as retail capital rotates from majors into maximum-risk exposure seeking higher percentage returns; the basket captures this beta premium systematically using a factor screen on BTC-correlation-adjusted beta, deployed only when momentum and regime gates confirm the rotation is a
Build the High-Beta Alt 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/high-beta-alt-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/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
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 High-Beta Alt 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/high-beta-alt-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.
A basket of mid-to-large-cap crypto infrastructure tokens — oracles, data availability layers, identity, sequencers, and middleware — that serve multiple ecosystems and are less correlated with any single blockchain narrative.
Why it works: Infrastructure tokens (oracles, data layers, identity protocols, sequencers) provide services to multiple blockchain ecosystems and are less tied to a single narrative cycle than sector tokens; they co-move with broad DeFi activity but exhibit lower narrative volatility, making them suitable as a 'quality alt' position in late-bear/early-bull regimes when narrative alts are too risky.
Via API/api/v1/strategies/infrastructure-majors-basket
AI-agent prompts
Build it with an AI agent
Build the Infrastructure Majors 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/infrastructure-majors-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Infrastructure Majors 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/infrastructure-majors-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.
A sector basket of cross-chain interoperability and bridging protocol tokens with active Hyperliquid perpetuals.
Why it works: Interoperability tokens benefit from multi-chain activity cycles — the more chains are active simultaneously, the more bridging and messaging volume they capture; within-sector dispersion tracks which bridge is winning cross-chain volume and security credibility after the wave of bridge hacks that concentrated volume on the survivors.
Build the Interoperability 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/interoperability-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Interoperability 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/interoperability-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.
A sector basket of alternative Layer-1 smart-contract platform tokens with active Hyperliquid perpetuals. The basket covers the "Ethereum killers" and Ethereum-adjacent L1s — Solana, Avalanche, Near, Cosmos, etc. — that compete for developer mindshare and TVL in the smart-contract platform space.
Why it works: Alternative L1 tokens compete for developer activity and TVL in zero-sum competition with Ethereum; sector-relative momentum within L1s persists over 5–20 day windows as capital rotates between 'Ethereum killers' on narrative cycles (Solana season, Cosmos ecosystem, Move-VM), while all L1s share a common BTC/ETH beta that makes them a high-quality cointegrated universe for pairs and cross-sectiona
Build the L1 Blockchains 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/l1-blockchains-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 L1 Blockchains 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/l1-blockchains-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.
A sector basket of Layer-2 rollup perpetuals on Hyperliquid, providing concentrated exposure to the Ethereum scaling narrative.
Why it works: L2 rollup tokens co-move with Ethereum activity cycles and developer-adoption narratives; sector-relative momentum within L2s persists over 5–14 day windows as retail capital rotates between Optimistic and ZK-rollup narratives, creating harvestable dispersion for long-short or directional deployment against a confirmed alt-season gate.
Build the L2 Rollups 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/l2-rollups-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 L2 Rollups 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/l2-rollups-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.
A scalping basket that identifies moments when Hyperliquid's perpetual futures order book becomes thin on one side — a "liquidity vacuum" — and enters in the direction of the vacuum to capture the fast momentum move before liquidity re-enters.
Why it works: When the order book becomes thin on one side — a 'liquidity vacuum' — a small order-flow imbalance produces a disproportionately large price move; market makers reprice rather than absorb flow, creating a momentum cascade that persists until liquidity re-enters; the basket captures this by entering in the direction of the vacuum before the cascade fires and exiting before liquidity normalises.
Via API/api/v1/strategies/liquidity-vacuum-momentum
AI-agent prompts
Build it with an AI agent
Build the Liquidity-Vacuum Momentum (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/liquidity-vacuum-momentum.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/liquidations
- GET https://cryptodataapi.com/api/v1/hyperliquid/l2-book
3. Compute VWAP (Volume Weighted Average Price), Support and Resistance, Average True Range (ATR), 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 Liquidity-Vacuum Momentum (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/liquidity-vacuum-momentum.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 factor basket that selects the lowest-realised-volatility large-cap crypto perpetuals on Hyperliquid for long positioning.
Why it works: Low-realised-volatility crypto majors exhibit the low-volatility anomaly documented in equities: they consistently outperform on a risk-adjusted basis during risk-off periods and high-volatility regimes, because they are not forced-sold by leveraged participants facing liquidation cascades; holding the low-vol factor within the large-cap crypto universe earns the volatility risk premium with less
Build the Low-Vol Majors 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/low-vol-majors-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/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
3. Compute Open Interest 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 Low-Vol Majors Basket (Hyperliquid Basket) 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/hyperliquid-baskets/low-vol-majors-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.
A basket of the largest, most established memecoins with active Hyperliquid perpetuals — tokens with ≥ 6 months of market history, ≥ $500M peak market cap, and demonstrated multi-cycle survival.
Why it works: Established memecoins (> 6 months of market history, > $500M peak market cap) retain social-contagion momentum through multiple cycles with higher liquidity and lower oracle-manipulation risk than micro-cap memes; the basket captures meme-season upside at reduced single-name wipeout risk compared to the micro-cap meme-coin-cycle basket.
Build the Memecoin Majors 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/memecoin-majors-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Memecoin Majors 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/memecoin-majors-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.
Momentum investing is a strategy that buys assets that have performed well over a recent period (typically 3-12 months) and sells or avoids assets that have performed poorly.
Why it works: Investors underreact to news and sell winners too early (disposition effect), so prices drift toward fair value over months; momentum buyers harvest the drift while bearing crash risk at regime turns.
Build the Momentum Investing 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/momentum-investing.md
2. Pull the inputs:
- 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
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Momentum 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 Momentum Investing 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/momentum-investing.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.
Cross-sectional momentum rotation ranks a liquid universe of crypto assets by recent relative performance and rotates capital into the top performers (optionally shorting the bottom), rebalancing on a fixed cadence.
Why it works: Crypto narratives (L1 seasons, memecoin manias, sector rotations) draw reflexive retail flow that underreacts then chases, so recent relative winners keep winning for weeks. You buy the top cross-sectional performers and are paid by late trend-chasers who provide exit liquidity — until the reflexive flow reverses and momentum crashes.
Build the Momentum Rotation 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/quantitative/momentum-rotation.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/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
- GET https://cryptodataapi.com/api/v1/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
3. Compute Funding Rate, Momentum, Realized Volatility 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 Momentum Rotation 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/quantitative/momentum-rotation.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.
Narrative position vol targeting applies volatility-scaled position sizing to a narrative/memecoin book so that each individual trade contributes an equal amount of realized-volatility risk to the portfolio, regardless of how hot (high-vol) the narrative name is.
Why it works: Narrative and memecoin trading harvests capital-rotation and attention-driven price momentum; hot high-vol names dominate portfolio risk in a fixed-notional book because their realized vol is 3-10x that of BTC/ETH — vol targeting equalises each position's risk contribution so no single narrative can disproportionately blow up the book even if the narrative call is right, and the portfolio heat cap
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/narrative-position-vol-targeting
AI-agent prompts
Build it with an AI agent
Build the Narrative Position Vol Targeting 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/narrative-position-vol-targeting.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/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
- GET https://cryptodataapi.com/api/v1/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Narrative Position Vol Targeting 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/narrative-position-vol-targeting.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 sector basket of blockchain oracle protocol tokens with active Hyperliquid perpetuals. Oracle networks provide price feeds and external data to smart contracts — they are critical infrastructure for DeFi, RWA, and AI-agent applications.
Why it works: Oracle tokens share a critical infrastructure narrative — they power DeFi, RWA, and AI applications — that makes them co-move with broad DeFi activity cycles; within-sector dispersion tracks which oracle provider is winning market share (data feed counts, integrations), creating cross-sectional harvest between the dominant provider and challengers.
Build the Oracle 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/oracle-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Oracle 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/oracle-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.
A sector basket of crypto payments and settlement protocol tokens with active Hyperliquid perpetuals. Payments tokens target the use case of faster, cheaper cross-border payments, remittances, and merchant settlements — they carry real-world adoption narratives distinct from DeFi or speculative tokens.
Why it works: Payments tokens react to real-world adoption announcements (merchant partnerships, remittance corridor launches, CBDC integration news) and to regulatory environment shifts for cross-border payments; the sector co-moves on these shared catalysts while within-sector dispersion tracks which networks are winning payment volume.
Build the Payments 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/payments-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Payments 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/payments-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.
A sector basket of privacy-focused cryptocurrency tokens with active Hyperliquid perpetuals. Captures the privacy-coin narrative cycle — driven by regulatory developments, surveillance-state events, and demand for financial anonymity.
Why it works: Privacy tokens co-move on regulatory cycle events (exchange delistings, FATF guidance changes, OFAC actions) and on counter-surveillance demand spikes; the basket captures the regulatory-risk narrative cycle and within-sector momentum when privacy demand rises, while managing the elevated delisting risk that is the primary threat to this sector.
Build the Privacy 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/privacy-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Privacy 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/privacy-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.
A sector basket of Real World Asset tokenisation protocol tokens with active Hyperliquid perpetuals. Captures the institutional adoption of on-chain asset tokenisation — T-bills, private credit, real estate, and commodities issued as blockchain tokens.
Why it works: RWA tokens co-move on institutional adoption news (BlackRock tokenised fund announcements, T-bill on-chain yield expansions, real-estate tokenisation milestones) that create predictable narrative momentum; within-sector dispersion tracks which protocols are winning institutional mandate flow, creating cross-sectional harvest opportunities between leaders and laggards.
Build the RWA 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/rwa-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 RWA 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/rwa-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.
Session overlap momentum is an intraday crypto strategy that fades failed late-Asia breakouts into the London-NY overlap.
Why it works: Asian-session retail breakout buyers chase late-Asia momentum into thin late-Asia books; professional liquidity arriving with the London-NY overlap fades the false breakout against a measurable depth and funding backdrop.
Via API/api/v1/strategies/session-overlap-momentum
AI-agent prompts
Build it with an AI agent
Build the Session Overlap Momentum 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/quantitative/session-overlap-momentum.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/hyperliquid/l2-book
- GET https://cryptodataapi.com/api/v1/liquidity/depth
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/indicators/technical
3. Compute Funding Rate, Spot vs Derivatives Volume Ratio 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 Session Overlap Momentum 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/quantitative/session-overlap-momentum.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 sector basket of Solana-native protocol tokens with active Hyperliquid perpetuals. Deployed specifically during "Solana seasons" — periods when SOL outperforms ETH and the Solana DeFi ecosystem attracts narrative and capital rotation.
Why it works: Solana ecosystem tokens share the SOL price trend as their strongest common factor; during Solana-specific narrative cycles (DEX volume records, meme coin launches, mobile wallet adoption milestones), Solana-native DeFi and infrastructure tokens outperform the broad crypto market by 2–5× — creating concentrated sector momentum that the basket captures directionally while providing within-sector cr
Build the Solana Ecosystem 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/solana-ecosystem-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Solana Ecosystem 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/solana-ecosystem-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.
Spot-led momentum filter is a momentum/trend strategy on crypto perpetuals that restricts new entries to moves where the signal is generated by spot market capital inflow rather than leveraged perp speculation.
Why it works: Momentum moves where spot volume leads perp OI growth — confirmed by positive Coinbase premium and flat/normal funding — reflect genuine capital inflow from informed or real-money buyers; perp-led moves with stretched funding and OI growing faster than spot volume reflect leveraged speculation that historically mean-reverts rather than trends; restricting momentum entries to spot-led moves selects
Via API/api/v1/strategies/spot-led-momentum-filter
AI-agent prompts
Build it with an AI agent
Build the Spot-Led Momentum Filter 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/spot-led-momentum-filter.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/coinbase-premium
- GET https://cryptodataapi.com/api/v1/sentiment/macro
3. Compute Coinbase Premium, 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 Spot-Led Momentum Filter 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/spot-led-momentum-filter.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 sector basket of decentralised storage and compute tokens with active Hyperliquid perpetuals.
Why it works: Storage and compute tokens benefit from AI demand for decentralised infrastructure, co-moving on shared GPU-compute and data-storage narratives; within-sector dispersion tracks which protocol is gaining storage/compute utilisation, creating cross-sectional harvest as AI workloads grow unevenly across decentralised networks.
Build the Storage / Compute 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/storage-compute-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/market-health/altcoin-breadth
- GET https://cryptodataapi.com/api/v1/coins/top
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 Storage / Compute 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/storage-compute-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.
Swing trading is a style of trading that attempts to capture short-to-medium-term price moves over a holding period of days to weeks (typically 2-20 trading days). It sits between day trading (positions closed within a single session) and position trading (positions held for months).
Why it works: Captures multi-day continuation and overreaction swings created by investor under-reaction to news and overreaction at extremes; counterparties are impatient intraday traders selling winners early and slow-moving investors who reprice gradually.
Build the Swing 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/swing-trading.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/event/calendar
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Support and Resistance, Moving Averages, Relative Strength Index (RSI), MACD (Moving Average Convergence Divergence), Volume Analysis 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 Swing 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/swing-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.
Unlock-aware momentum is a momentum strategy on crypto altcoin perps and spot that systematically pauses new long entries and de-risks existing longs in the 5–10 days ahead of scheduled cliff unlocks and large linear emission events from the token's vesting calendar, and re-enters the momentum position after supply has been absorbed and price structure confirms continuation.
Why it works: Retail and institutional momentum participants ignore scheduled token supply events and hold through cliff unlocks that predictably apply sell pressure from insider/VC allocations; the strategy de-risks long positions ahead of those events and re-enters after supply has been absorbed, avoiding the structural headwind that kills the majority of crypto momentum blow-ups in altcoins.
Build the Unlock-Aware Momentum 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-aware-momentum.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/event/calendar
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-Aware Momentum 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-aware-momentum.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 momentum & rotation crypto trading strategies?
Own what is outperforming: cross-sectional momentum, sector and altcoin rotation.
How many momentum & rotation strategies are there?
Which indicators do momentum & rotation strategies use?
Most often Open Interest, Funding Rate, Average True Range (ATR), Momentum.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=momentum 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.