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22 defi yield & on-chain strategies for crypto, from the AlgoBrain wiki. On-chain return streams: liquidity provision, staking and restaking, lending loops and on-chain flow signals. 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=defi-yield-onchain.
Trading the emerging Bitcoin staking ecosystem built on Babylon Labs' trustless BTC-staking protocol. Babylon enables Bitcoin holders to lock BTC in self-custodial Bitcoin scripts and use it as economic security for Proof-of-Stake chains — without bridging or custodial wrapping.
Why it works: Babylon (mainnet 2024) introduced trustless Bitcoin staking — BTC holders can stake to PoS chains (Cosmos zones, future modular L2s) without custodial bridges, earning yield. The ecosystem produced multiple Liquid Bitcoin Staking Tokens (LBTCs) — Lombard's LBTC, SolvBTC, pumpBTC, others — each with different custody, yield, and points-farming dynamics. Cross-LBTC triangulation and base-yield-vs-po
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/babylon-bitcoin-staking-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Babylon Bitcoin Staking 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/babylon-bitcoin-staking-arbitrage.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/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Babylon Bitcoin Staking 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/babylon-bitcoin-staking-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.
Cross-chain yield farming is the strategy of actively moving capital across multiple blockchains to capture the highest available yields.
Why it works: Captures yield dispersion across fragmented chain ecosystems from protocols that deliberately overpay for early TVL and from passive single-chain farmers who will not bear bridging friction and bridge risk
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/cross-chain-yield-farming
AI-agent prompts
Build it with an AI agent
Build the Cross-Chain Yield Farming 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/cross-chain-yield-farming.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/event/calendar
- 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 the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Cross-Chain Yield Farming 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/algorithmic/cross-chain-yield-farming.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 crypto yield stack layers multiple DeFi yield sources on top of the same base capital so that one unit of ETH earns staking issuance, restaking rewards, LP swap fees, lending interest, and points simultaneously.
Why it works: Each DeFi layer pays a protocol-native yield (issuance, fees, interest, incentive emissions) to whoever supplies the same base capital; you collect all layers at once and are paid to warehouse the stacked smart-contract, slashing, depeg, and impermanent-loss risk that conservative holders refuse to bear. The informational sliver is picking which points programs will actually pay.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Crypto Yield Stack 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/crypto-yield-stack.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Crypto Yield Stack 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/crypto-yield-stack.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 the largest, most-established DeFi protocol tokens with active Hyperliquid perpetuals.
Why it works: DeFi bluechip tokens share an on-chain revenue and TVL driver that separates them from pure-narrative altcoins; in DeFi-active regimes, protocol revenues create structural price support and within-sector dispersion tracks genuine product-market-fit differences, enabling cross-sectional harvesting of sector momentum.
Build the DeFi Bluechip 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/defi-bluechip-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 DeFi Bluechip 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/defi-bluechip-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.
DeFi yield / LP regime gate restricts liquidity provider capital deployment to low-volatility, range-bound regimes where LP fee income reliably exceeds Loss Versus Rebalancing (LVR) and gas costs, and withdraws or reduces LP exposure in trending or high-volatility regimes where LVR mathematically overwhelms fee income regardless of fee tier or range placement.
Why it works: LP positions are structurally short gamma (short a synthetic straddle on the pooled assets): fee income is roughly proportional to volatility while Loss Versus Rebalancing (LVR) — the continuous cost of being adversely selected by informed arbitrageurs — grows with the square of volatility. A vol-regime gate that restricts LP deployment to low-vol regimes (where fee income exceeds LVR and gas cost
Build the DeFi Yield / LP Regime 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/defi-yield-regime-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/market-intelligence/options
- GET https://cryptodataapi.com/api/v1/volatility/implied
- GET https://cryptodataapi.com/api/v1/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
3. Compute Realized Volatility 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 DeFi Yield / LP Regime Gate 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/defi-yield-regime-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.
DeFi yield / LP event calendar withdraws liquidity provider and farming positions before scheduled catalysts — token unlocks of the pool's underlying asset, points-program endings, pool-contract upgrades, major governance votes, or large airdrop distributions — and redeploys capital after the event-induced volatility has passed and a stable post-event fee environment has re-established.
Why it works: Scheduled token unlocks and protocol catalysts (token generation events, points-program endings, pool-contract upgrades, large governance votes) predictably spike the volatility and directional price risk of the LP's pooled assets and amplify LVR costs; withdrawing LP and farming positions before the event window and redeploying after avoids the worst LVR and impermanent-loss outcomes, while captu
Via API/api/v1/strategies/defi-yield-event-calendar
AI-agent prompts
Build it with an AI agent
Build the DeFi Yield / LP × Unlock/Event Calendar 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/defi-yield-event-calendar.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/market-intelligence/options
- GET https://cryptodataapi.com/api/v1/volatility/implied
- GET https://cryptodataapi.com/api/v1/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
- GET https://cryptodataapi.com/api/v1/supply/unlocks
- GET https://cryptodataapi.com/api/v1/event/calendar
3. Compute Realized Volatility 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 DeFi Yield / LP × Unlock/Event Calendar 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/defi-yield-event-calendar.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.
DeFi yield farming deploys crypto assets into decentralized protocols to earn a blend of trading fees, token emissions, and incentives — most commonly by providing liquidity to an AMM pool and staking the LP token for extra rewards.
Why it works: Protocols pay LPs a blend of real trading fees (structural — LPs are the counterparty to every swapper) plus token emissions and bribes (behavioral — protocols overpay to bootstrap TVL); the farmer is compensated for bearing impermanent-loss/LVR, smart-contract, and reward-token-depreciation risks that passive holders refuse to bear.
Build the DeFi Yield Farming 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/defi-yield-farming.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/dex/trending
- GET https://cryptodataapi.com/api/v1/dex/new-pools
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute Funding Rate 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 DeFi Yield Farming 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/algorithmic/defi-yield-farming.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.
Delta-neutral yield farming holds a yield-bearing crypto asset (a liquid-staking token, an LP position, or a restaked asset) and simultaneously shorts an equal-notional perp to cancel the price delta.
Why it works: Stakers and LPs earn a protocol-native yield on a volatile asset while leveraged perp longs pay funding; you hold the yield-bearing asset and short an equal-notional perp to strip out price risk, keeping staking + LP + funding income and being paid to bear the basis, depeg, smart-contract, and liquidation risk the yield-chaser will not.
Via API/api/v1/strategies/delta-neutral-yield-farming
AI-agent prompts
Build it with an AI agent
Build the Delta-Neutral Yield Farming 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/delta-neutral-yield-farming.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, Liquidation 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 Delta-Neutral Yield Farming 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/delta-neutral-yield-farming.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 directional short-only basket of Hyperliquid perp positions, activated exclusively during confirmed macro downtrends — not corrections, not pullbacks, but full bear-market conditions characterised by sustained lower highs and lower lows, deteriorating on-chain fundamentals, and broad derivatives fragility.
Why it works: In confirmed bear markets the structural tide is against leveraged longs — forced sellers (liquidations, token unlock pressure, ETF outflows) dominate; behavioural overconfidence among longs who 'buy the dip' provides sustained counterparty flow for disciplined shorts.
Build the Full Bear Short Book (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/full-bear-short-book.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/market-health/altcoin-breadth
3. Compute Open Interest, Funding Rate, Liquidation, 200-Day Moving Average 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 Full Bear Short Book (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/full-bear-short-book.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 HLP + Cascade Alongside Playbook is the integrated, four-leg strategy that a sophisticated retail or small-fund operator runs on Hyperliquid to occupy the documented "30-60% APR with Sharpe 1.5-2.5" sweet spot the wiki has flagged across hyperliquid hlp basis arbitrage, liquidation cascade arbitrage, liquidation cascade fade, and hyperliquid perp trading map.
Why it works: Stack HLP passive deposit (captures liquidation bonus + spread + funding flow) with active cascade-follower perp trading (captures the discretionary panic premium), so each cascade event pays you twice.
Via API/api/v1/strategies/hlp-cascade-alongside-playbook
AI-agent prompts
Build it with an AI agent
Build the HLP + Cascade Alongside Playbook 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/hlp-cascade-alongside-playbook.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/market-intelligence/liquidations
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Cascade Detection Signals 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 HLP + Cascade Alongside Playbook 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/hlp-cascade-alongside-playbook.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.
Trading strategies built around HLP (Hyperliquidity Provider) — Hyperliquid's protocol-native market-maker / liquidator vault.
Why it works: Hyperliquid's HLP (Hyperliquidity Provider) vault is the protocol's own market-maker + liquidator, capitalized by depositors. HLP profits from spreads, liquidation fees, and adverse-selection risk. By analyzing HLP's positioning, traders can: (1) deposit into HLP for ~30-100% APR yield, (2) front-run HLP's known liquidation behavior, or (3) trade against HLP's directional accumulation when off-sid
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/hyperliquid-hlp-basis-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Hyperliquid HLP Basis Arbitrage crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/arbitrage/hyperliquid-hlp-basis-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/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/hyperliquid/candles
3. Compute the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Hyperliquid HLP Basis 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/hyperliquid-hlp-basis-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.
A sector basket of liquid staking token and restaking protocol tokens with active Hyperliquid perpetuals. Captures the Ethereum staking yield and restaking narrative — the "yield on yield" story that emerged post-Merge and expanded through EigenLayer and competing restaking protocols.
Why it works: LST and restaking tokens share an Ethereum-staking yield narrative and compete for the same staked ETH capital; within-sector momentum tracks which protocols are winning the yield-maximisation race, creating cross-sectional dispersion while the sector as a whole co-moves with the Ethereum staking rate and re-staking narrative cycle.
Build the LST / Restaking 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/lst-restaking-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 LST / Restaking 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/lst-restaking-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.
NFT markets have no unified order book — each marketplace (OpenSea, Blur, X2Y2, Magic Eden, Sudoswap) holds its own listings independently. No automatic arbitrage mechanism forces prices to converge; only human or automated searchers do. The structural source is marketplace fragmentation itself.
Why it works: NFT marketplace fragmentation (no shared order book) creates persistent price dislocations; the counterparty is uninformed sellers who list below the clearing price on a low-traffic venue, and slow/manual buyers who do not monitor all venues simultaneously.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the NFT 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/nft-arbitrage.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute the signals described in the playbook on 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 NFT Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=15m (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/nft-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.
NFT trading, as a buildable strategy, is the systematic exploitation of pricing inefficiencies in non-fungible token markets on OpenSea, Blur, and Magic Eden through four repeatable playbooks: floor sweeping (accumulating the cheapest items of a collection ahead of a catalyst), rarity/trait sniping (buying rares listed below their trait-adjusted fair value), trait arbitrage (harvesting the spread
Why it works: Illiquid, unique assets with no continuous order book are systematically mispriced at the trait/rarity level and over-react to catalysts; you are paid an illiquidity/risk premium to be the standing bid (LP) or to buy the underpriced rare that a lazy lister dumped at floor, with the crowd of catalyst-chasers and floor-only listers on the other side.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the NFT 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/nft-trading.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/on-chain/exchange-flows/spike-alerts
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute the signals described in the playbook on 4h bars (pinned: interval=4h, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the NFT 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/nft-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.
This is the macro, basket-level member of the on-chain analytics family; contrast with the wallet-level on chain smart money tracking.
Why it works: Aggregate on-chain flows (exchange in/outflows, stablecoin balances, dormancy, whale wallets) lead price because they reveal supply hitting/leaving exchanges and large-holder intent before it prints on the tape; the edge is reading that leakage before slower participants price it in.
Build the On-Chain Flow 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/on-chain-flow-trading.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/on-chain/exchange-flows/spike-alerts
- GET https://cryptodataapi.com/api/v1/on-chain/whales
- GET https://cryptodataapi.com/api/v1/quant/whales
- GET https://cryptodataapi.com/api/v1/market-intelligence/etf/{asset}/flows
- GET https://cryptodataapi.com/api/v1/sentiment/stablecoins
- GET https://cryptodataapi.com/api/v1/regimes/current
3. Compute Exchange Net Flows 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 On-Chain Flow 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/on-chain-flow-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.
This strategy is one node of the broader on-chain analytics toolkit; see also the low-cap crypto trading map.
Why it works: High-PnL wallets disclose their entries on-chain in real time; following them transfers some of their information edge to copiers — until the wallet is too widely followed, at which point copiers themselves create the exit liquidity.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/on-chain-smart-money-tracking
AI-agent prompts
Build it with an AI agent
Build the On-Chain Smart Money Tracking 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/on-chain-smart-money-tracking.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/on-chain/whales
- GET https://cryptodataapi.com/api/v1/quant/whales
- GET https://cryptodataapi.com/api/v1/dex/trending
- GET https://cryptodataapi.com/api/v1/dex/new-pools
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute the signals described in the playbook on 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 On-Chain Smart Money Tracking 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/algorithmic/on-chain-smart-money-tracking.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.
Points farming is the practice of deploying capital and on-chain activity into pre-token crypto protocols to accumulate proprietary "points" that are expected to convert into a governance token at a future token generation event (TGE).
Why it works: Protocols hand out proprietary points to bootstrap TVL, liquidity, and attention before a token exists; the points farmer supplies that capital and activity in exchange for a lottery ticket on future token dilution, and is paid an expected-value premium for bearing conversion uncertainty, lockup, and Sybil-disqualification risk the protocol offloads onto early users.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Points Farming 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/points-farming.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/supply/unlocks
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Points Farming 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/algorithmic/points-farming.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.
Restaking takes already-staked ETH — or a liquid staking token like stETH/rETH — and re-pledges it through eigenlayer to secure additional protocols (Actively Validated Services, or AVSs) in exchange for a layered yield on the same capital.
Why it works: AVSs and LRT protocols pay a yield to whoever will pledge already-staked ETH as slashable economic security they cannot bootstrap themselves; the restaker is paid a risk premium for bearing slashing, LRT-depeg, withdrawal-queue, and stacked smart-contract risk that passive stakers refuse to hold.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Restaking Strategies 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/restaking-strategies.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Restaking Strategies 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/algorithmic/restaking-strategies.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 arbitrage strategy emerging from Ethereum restaking infrastructure: triangulating between ETH, Liquid Staking Tokens (LSTs) like stETH/cbETH/rETH, Liquid Restaking Tokens (LRTs) like eETH (Ether.fi), ezETH (Renzo), rsETH (Kelp), pufETH (Puffer), and the underlying AVS (Actively Validated Service) yields they accrue.
Why it works: EigenLayer's restaking creates a stack: ETH → LST (stETH/cbETH) → LRT (eETH/ezETH/rsETH/pufETH) → AVS yield. Each layer adds slashing risk and points-farming optionality. Layers depeg from each other when redemption rails differ in latency or when restaking-protocol incentives shift. Triangulation across the LRT stack was highly profitable for specialist desks in 2024-2025.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/restaking-token-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Restaking Token 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/restaking-token-arbitrage.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
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 Restaking Token Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/restaking-token-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.
Stablecoin yield is the practice of earning income on dollar-pegged tokens (USDC, USDT, DAI, etc.) without taking directional crypto exposure. Capital is deployed into lending markets, money-market/treasury-backed stablecoins, stablecoin-pair liquidity pools, or delta-neutral basis trades to harvest a dollar yield.
Why it works: Leveraged crypto longs and protocols persistently demand dollar liquidity (via perp funding, borrow markets, and money-market issuance), and the stablecoin holder is paid a real yield for supplying that dollar liquidity while bearing depeg, counterparty, and smart-contract risk.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Stablecoin Yield 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/stablecoin-yield.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/sentiment/stablecoins
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Stablecoin Yield 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/stablecoin-yield.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.
Staking yield arbitrage harvests the spread between what a liquid-staking token (LST) earns and what its underlying asset costs to borrow or hedge.
Why it works: Liquid-staking tokens yield the consensus/restaking reward, but ETH can be borrowed for less. The arbitrageur loops LST collateral against borrowed ETH to lever the staking-minus-borrow spread, or hedges LST spot with a short perp to isolate the yield. The counterparty is the leverage-hungry LST holder (who bids the borrow rate) and the market's refusal to bear LST depeg / liquidation / smart-cont
Build the Staking Yield 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/staking-yield-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/dex/trending
- GET https://cryptodataapi.com/api/v1/dex/new-pools
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute Funding Rate on 4h bars (pinned: interval=4h, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Staking Yield Arbitrage strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/staking-yield-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.
Validator delegation is the simplest Bittensor-native yield strategy: stake TAO with a validator on one or more subnets, and receive a cut of the validator's dividends plus the alpha tokens accruing on your stake.
Why it works: Validators earn 41% of the block emissions on each subnet they validate; stakers who delegate TAO to a validator earn a cut of those dividends. Choosing validators with high subnet exposure, good performance, and reasonable take rates produces yield in TAO plus alpha tokens -- a simple passive income stream backed by Bittensor emissions.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/tao-validator-delegation
AI-agent prompts
Build it with an AI agent
Build the TAO Validator Delegation 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/tao-validator-delegation.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute the signals described in the playbook on 1d bars (pinned: interval=1d, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the TAO Validator Delegation 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/tao-validator-delegation.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 defi yield & on-chain crypto trading strategies?
On-chain return streams: liquidity provision, staking and restaking, lending loops and on-chain flow signals.
How many defi yield & on-chain strategies are there?
Which indicators do defi yield & on-chain strategies use?
Most often Open Interest, Funding Rate, Realized Volatility, Liquidation.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=defi-yield-onchain 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.