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20 funding, carry & basis strategies for crypto, from the AlgoBrain wiki. Harvest the premium leveraged longs pay: perp funding, dated-futures basis and term-structure carry. 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=funding-carry-basis.
Basis trading is a delta-neutral crypto strategy that captures the basis — the price gap between a spot asset and its futures contract — by buying the cheaper leg and shorting the richer one, then holding until the two prices converge.
Why it works: Leveraged crypto longs bid dated and perpetual futures to a premium over spot to obtain convex, capital-efficient bull exposure; the basis trader sells them that exposure, hedges with spot, and is paid the convergence spread for bearing counterparty, backwardation, and liquidation risk they refuse to hold.
Build the Basis 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/algorithmic/basis-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/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute Funding Rate, Open Interest, Basis, Time to Expiration (DTE) 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 Basis 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/algorithmic/basis-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.
Carry with tail hedge is a carry book (short perp / long spot, collecting positive funding) combined with a budgeted out-of-the-money convex overlay — typically OTM puts on BTC/ETH or long positions in crypto volatility instruments — where the hedge premium is sized as a fixed percentage (10-25%) of the expected annual carry income from the book.
Why it works: The carry book earns contractual funding income by being the hedge that leveraged perp longs need; the tail-hedge overlay — sized as a fixed fraction (10-25%) of expected carry income — reprices the negative-skew tail explicitly rather than leaving it as an uncompensated risk, so the combined book earns carry net of the tail-insurance cost and survives the occasional crash that would otherwise wip
Build the Carry with Tail Hedge 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/carry-with-tail-hedge.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 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 Carry with Tail Hedge 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/combinations/carry-with-tail-hedge.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.
Cash and carry arbitrage buys a crypto asset on the spot market and simultaneously shorts an equal-notional dated futures contract trading at a premium, then holds to expiry to collect the basis — the futures-minus-spot gap — as a near-market-neutral profit.
Why it works: Leveraged directional longs bid dated crypto futures above spot (contango); the arbitrageur buys spot, shorts the future at the premium, and is paid the basis at convergence. The counterparty is the leverage-hungry long who will not hold spot, and the risk the arb absorbs is counterparty failure, margin-call-on-the-short, and basis path risk that the long refuses to bear.
Build the Cash and Carry 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/cash-and-carry.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/etf/{asset}/flows
- GET https://cryptodataapi.com/api/v1/sentiment/macro
3. Compute Basis, Funding Rate, Open Interest, Time to Expiration (DTE) 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 Cash and Carry Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=1d (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/cash-and-carry.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.
When crypto assets rally sharply, retail and momentum traders pile into long perpetual positions expecting further upside. As the long book swells, funding turns significantly positive — meaning longs are paying shorts — signalling an overcrowded trade.
Why it works: Retail and momentum traders pile into long positions during bull runs; crowded longs paying positive funding signals an overcrowded trade — going short collects the funding carry and positions for mean-reversion or a long squeeze when sentiment reverses.
Via API/api/v1/strategies/crowded-long-funding-fade
AI-agent prompts
Build it with an AI agent
Build the Crowded-Long Funding Fade (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/crowded-long-funding-fade.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 Funding Rate, Open Interest, Support and Resistance, Liquidation, VWAP (Volume Weighted Average Price) 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 Crowded-Long Funding Fade (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/crowded-long-funding-fade.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.
When a crypto asset falls sharply, retail and momentum traders pile into short perp positions expecting further downside. As the short book swells, funding flips deeply negative — meaning shorts are paying longs — signalling an overcrowded trade.
Why it works: Retail and momentum traders pile into short positions after a price decline; the crowded short creates a funding payment from shorts to longs, and the mechanical pressure of those losing positions creates an asymmetric snap-back when sentiment reverses — the fade trader goes long, collects the funding, and rides the unwind.
Via API/api/v1/strategies/crowded-short-funding-fade
AI-agent prompts
Build it with an AI agent
Build the Crowded-Short Funding Fade (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/crowded-short-funding-fade.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 Funding Rate, Open Interest, Support and Resistance, 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 Crowded-Short Funding Fade (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/crowded-short-funding-fade.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.
Funding flush reversal is a mean reversion strategy that buys crypto dips on spot or perpetuals only after funding has flushed deeply negative — confirming that the preceding downmove has forced out the leveraged long side and that shorts are now the crowded party paying carry.
Why it works: Retail longs are forced out (funding flushes negative) precisely when sentiment and leverage are most extended against them; the strategy enters a mean-reversion long only after this capitulation has been confirmed by a sustained negative funding shift, buying the dip when shorts are now the crowded side paying carry, not longs — the structural setup has flipped in the buyer's favour.
Build the Funding Flush Reversal 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/funding-flush-reversal.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/macro
- GET https://cryptodataapi.com/api/v1/indicators/technical
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 Funding Flush Reversal 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/funding-flush-reversal.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.
Funding rate arbitrage is a delta-neutral strategy that captures the periodic funding payments exchanged between long and short holders of perpetual futures.
Why it works: Leveraged retail and trend-chasing longs persistently bid perpetual futures above spot; the arb provides the hedge they need and is paid funding to bear basis-blowup, exchange-failure, and oracle risks they refuse to bear.
Build the Funding Rate 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/funding-rate-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/derivatives/open-interest
- GET https://cryptodataapi.com/api/v1/hyperliquid/open-interest
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute Open Interest, Liquidation, Funding Rate, Basis 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 Funding Rate Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=1d (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/funding-rate-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.
Funding vs basis rotation is an allocation-layer strategy that holds a single carry book on BTC/ETH and switches its expression — either perp-funding carry (long spot, short perpetual) or dated-futures basis carry (long spot, short a quarterly/monthly future) — depending on which instrument is currently paying a higher annualised yield, net of venue and execution costs.
Why it works: Retail/leverage demand creates an excess structural premium in whichever carry instrument is currently in favour — perp funding when the crowd is long-perp, dated-futures basis when the calendar curve steepens; the rotation framework extracts the highest-available carry by switching between the two and avoids leaving premium on the table by sitting in the cheaper instrument while the better one is
Via API/api/v1/strategies/funding-vs-basis-rotation
AI-agent prompts
Build it with an AI agent
Build the Funding vs Basis 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/combinations/funding-vs-basis-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/derivatives/open-interest
- GET https://cryptodataapi.com/api/v1/hyperliquid/open-interest
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute Funding Rate, Open Interest, Basis, Time to Expiration (DTE) 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 Funding vs Basis Rotation 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/combinations/funding-vs-basis-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.
Funding window timing is a short-term, intraday combination strategy that overlays a session/time filter — the predictable 8-hourly (CEX) or hourly (Hyperliquid) funding settlement timestamps — on top of the funding carry primitive.
Why it works: Funding settlements are discrete, predictable events that create a directional incentive for leveraged participants to be on the receiving side at the snapshot — the pre-settlement drift is the crowd repositioning to capture one period's payment, and the post-settlement reversal is the same crowd unwinding that positioning; the strategy goes with the pre-settlement drift early and exits before the
Build the Funding Window 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/combinations/funding-window-timing.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/derivatives/binance/long-short-ratio
3. Compute Funding Rate, 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 Funding Window Timing 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/funding-window-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.
Funding-conditioned vol selling is crypto options volatility selling — the systematic sale of BTC/ETH options on deribit — where entry requires two simultaneous signals: the perp funding rate is elevated (≥ 0.03%/8h), confirming that a crowd of leveraged retail longs is driving the implied-vol richness, and DVOL exceeds trailing 30-day realized volatility by a minimum threshold.
Why it works: Elevated perp funding flags a crowd of leveraged retail longs whose demand for lottery-ticket calls and panic puts simultaneously inflates implied vol and skew above fair value; by entering short-vol only when funding is elevated AND IV exceeds realised vol by a threshold, the strategy sells options when the crowding-driven IV premium is confirmed at its richest and avoids the DVOL-percentile regi
Via API/api/v1/strategies/funding-conditioned-vol-selling
AI-agent prompts
Build it with an AI agent
Build the Funding-Conditioned Vol Selling 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/funding-conditioned-vol-selling.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, DVOL — Deribit Volatility Index, Realized Volatility, Open Interest, Gamma 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 Funding-Conditioned Vol Selling 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/funding-conditioned-vol-selling.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.
Funding-filtered momentum is a breakout and trend-following strategy on crypto perpetuals that uses the prevailing funding rate as a positioning filter: it blocks new longs when funding is stretched positive (the crowd is already long and paying for the privilege) and upweights entries when momentum is positive but funding is flat or negative (a non-consensus trend where institutional or informed
Why it works: Leveraged retail persistently bids perps when they are bullish, pushing funding positive and crowding out the long side; a momentum signal that only fires when funding is flat or negative finds the non-consensus trend — where the marginal buyer is not yet in — and avoids paying crowding costs and becoming a liquidation target when the crowd unwinds.
Via API/api/v1/strategies/funding-filtered-momentum
AI-agent prompts
Build it with an AI agent
Build the Funding-Filtered 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/funding-filtered-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/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
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 Funding-Filtered 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/funding-filtered-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 delta-neutral or near-neutral carry strategy that collects funding payments from the leveraged side of perpetual futures markets without taking significant directional price risk.
Why it works: Perpetual futures carry a persistent funding premium because speculative demand for leveraged long (or short) exposure exceeds the supply of counterparties willing to take the other side; the harvest captures this structural imbalance by running on the paid side of the trade with minimal directional exposure.
Build the Funding-Rate Harvest (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/funding-rate-harvest.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 Funding Rate, Open Interest, Basis, 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 Funding-Rate Harvest (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/funding-rate-harvest.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 — not a sector basket — that dynamically selects the highest-funding-rate perpetuals on Hyperliquid and goes short them (earning the positive funding as carry income), hedging BTC-beta with a proportional long in a low-funding major (BTC-PERP or ETH-PERP).
Why it works: Perps with persistently high positive funding rates have levered retail longs paying carry to shorts; by going short the highest-funding perps (earning carry) and hedging BTC-beta with a long on a low-funding major, the strategy earns the funding spread as a structural carry income while maintaining approximately market-neutral exposure to broad market direction.
Via API/api/v1/strategies/high-funding-carry-basket
AI-agent prompts
Build it with an AI agent
Build the High Funding Carry 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-funding-carry-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/derivatives/binance/long-short-ratio
- GET https://cryptodataapi.com/api/v1/regimes/current
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 Funding Carry 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-funding-carry-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 cross-venue funding-rate arbitrage that pairs a Hyperliquid perp leg against a Binance, Bybit, or OKX perp leg of equal notional and opposite direction.
Why it works: Hyperliquid's hourly funding and distinct retail base produces persistent funding-rate dislocations vs Binance/Bybit. Long the venue paying you funding, short the venue charging you funding, profit from the spread until convergence.
Via API/api/v1/strategies/hl-vs-cex-funding-divergence
AI-agent prompts
Build it with an AI agent
Build the HL vs CEX Funding Divergence 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/hl-vs-cex-funding-divergence.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/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Funding Rate, Open Interest, Basis, 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 HL vs CEX Funding Divergence 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/hl-vs-cex-funding-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.
Options relative-value funding filter uses the perp funding rate as a leading indicator for skew richness on the Deribit vol surface: stretched positive funding (leveraged longs crowded) predicts call skew richness and put skew cheapness; stretched negative funding (leveraged shorts crowded) predicts put skew richness and call skew cheapness.
Why it works: Stretched positive perp funding rates reflect an overcrowded leveraged-long crowd whose demand for call options simultaneously bids up call skew (puts become cheap relative to calls); conversely, stretched negative funding reflects an overcrowded short crowd that has bid put skew to richness (calls become cheap). The funding rate is a real-time revealed proxy for the derivative crowd's directional
Via API/api/v1/strategies/options-rv-funding-filter
AI-agent prompts
Build it with an AI agent
Build the Options Relative-Value × Funding 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/options-rv-funding-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/derivatives/binance/long-short-ratio
- GET https://cryptodataapi.com/api/v1/market-intelligence/options
3. Compute Implied Volatility, DVOL — Deribit Volatility Index, Funding Rate, Open Interest, 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 Options Relative-Value × Funding 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/options-rv-funding-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.
Pairs-with-funding-differential is a stat-arb/pairs strategy expressed through crypto perpetuals where the spread entry gate requires not only that the spread z-score is at an extreme, but also that the funding differential between the two perp legs agrees with the spread direction — meaning the strategy collects carry while holding the spread.
Why it works: Leveraged retail participants on the crowded leg of a relative-value spread persistently misprize the cost of holding that leg, creating a funding differential between the two perps that simultaneously agrees with the spread direction; the strategy earns both mean-reversion of the spread z-score and a structural funding carry for being on the less-crowded side, while the counterparty pays crowd-pr
Via API/api/v1/strategies/pairs-with-funding-differential
AI-agent prompts
Build it with an AI agent
Build the Pairs with Funding Differential 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/pairs-with-funding-differential.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 Cointegration, 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 Pairs with Funding Differential 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/pairs-with-funding-differential.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 Stock Perp Oracle Basis trade fades the price drift between an on-chain stock perpetual (e.g., AAPL-PERP, TSLA-PERP on AsterDEX) and its Pyth oracle reference price during hours when the NYSE is closed.
Why it works: When the NYSE is closed, the cash side of the basis cannot trade, so the on-chain perp drifts on speculator flow with no arbitrage anchor. The drift mean-reverts at market open as cash-side arbitrage capital returns and pulls the perp back to oracle / cash price.
Build the Stock Perp Oracle Basis 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/stock-perp-oracle-basis.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/event/calendar
- GET https://cryptodataapi.com/api/v1/indicators/technical
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute 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 Stock Perp Oracle Basis strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=15m (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/stock-perp-oracle-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.
Synthetic asset trading, as a buildable strategy, is the systematic harvesting of two structural inefficiencies in DeFi synthetic-perp venues — Synthetix Perps (Optimism/Base), GMX, and similar oracle-priced protocols: (1) the skew-funding differential those venues pay to whoever takes the underweight side of open interest, relative to the funding on the equivalent CEX perp; and (2) the oracle-upd
Why it works: Synthetic-perp venues (Synthetix, GMX) price fills off an oracle and pay/charge funding to balance skew; you harvest the funding differential vs CEX perps and the oracle-update-lag basis, getting paid by the debt-pool/LP counterparty to hold the side of the skew nobody else wants and to bear oracle, depeg, and smart-contract risk.
Build the Synthetic Asset 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/algorithmic/synthetic-asset-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/market-intelligence/options
- GET https://cryptodataapi.com/api/v1/volatility/implied
3. Compute Funding Rate, Open Interest, Cboe SKEW Index 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 Synthetic Asset 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/algorithmic/synthetic-asset-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.
Trend-aware carry is a carry book (short perp / long spot, collecting positive funding) that scales down or exits the short-perp leg when a strong directional trend is running against the carry structure — specifically, strong uptrends that compress or flip funding capture and mechanically widen the short-perp basis, or strong downtrends that raise depeg and venue-stress risk.
Why it works: Leveraged retail longs pay structural funding to the carry book; the trend overlay reduces or exits that book specifically when a strong directional trend makes the short-perp leg mechanically expensive — the counterparty is the carry operator who ignores trend context and absorbs the full basis-blowout and squeeze risk embedded in trending perp markets, while paying for insurance they did not nee
Build the Trend-Aware Carry 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/trend-aware-carry.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/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
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 Trend-Aware Carry 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/combinations/trend-aware-carry.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.
Whale copy-flow funding filter enters a directional position in BTC or ETH when two conditions hold simultaneously: (1) an on-chain whale / large-wallet accumulation signal fires (smart-money wallets are net-buying into weakness or accumulating at current prices), AND (2) the perp funding rate is flat or negative — confirming that the derivative crowd has NOT yet followed the whale's move.
Why it works: Whale / large-wallet on-chain accumulation signals (Nansen, Arkham, on-chain-smart-money-tracking) provide an informational edge when they fire before the leveraged derivatives crowd has noticed and followed the move. When the same whale accumulation signal fires while funding is already elevated (> +0.03%/8h), the derivative crowd has preceded the whale signal or is simultaneously piling in — the
Via API/api/v1/strategies/whale-copy-flow-funding-filter
AI-agent prompts
Build it with an AI agent
Build the Whale Copy-Flow Funding 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/whale-copy-flow-funding-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/derivatives/binance/long-short-ratio
- GET https://cryptodataapi.com/api/v1/on-chain/whales
3. Compute Exchange Net Flows, 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 Whale Copy-Flow Funding 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/whale-copy-flow-funding-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.
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 funding, carry & basis crypto trading strategies?
Harvest the premium leveraged longs pay: perp funding, dated-futures basis and term-structure carry.
How many funding, carry & basis strategies are there?
Which indicators do funding, carry & basis strategies use?
Most often Open Interest, Funding Rate, Basis, Liquidation.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=funding-carry-basis 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.