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12 mev & on-chain execution strategies for crypto, from the AlgoBrain wiki. Strategies that earn from blockspace itself: mempool ordering, sniping new pools and launches, and airdrop farming. 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=mev-onchain-execution.
Airdrop farming is a crypto-native strategy of positioning wallets and on-chain activity across pre-token protocols to qualify for token airdrops — retroactive distributions that VC-funded protocols use to decentralise ownership and bootstrap network effects.
Why it works: VC-funded protocols retroactively reward genuine early usage with token distributions to decentralise ownership and bootstrap network effects; the farmer supplies that early activity and is paid a claim on the token, bearing the eligibility, Sybil-disqualification, and no-token risk the protocol offloads onto users.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Airdrop 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/airdrop-farming.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/supply/unlocks
- 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 Airdrop 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/airdrop-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.
Trading the MEV opportunities on Solana via the Jito Labs infrastructure stack — the dominant MEV system on Solana, parallel to Flashbots on Ethereum.
Why it works: Solana's 400ms block times and high throughput create a parallel MEV ecosystem distinct from Ethereum's 12s. Jito Labs operates the dominant MEV infrastructure: validator clients with ShredStream (private order flow) + bundle auctions + Jito Block Engine. Triangulation arb, sandwich attacks, and liquidation extraction work fundamentally differently on Solana — and at much higher frequencies.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/jito-solana-mev-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Jito / Solana MEV 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/jito-solana-mev-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 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 Jito / Solana MEV 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/jito-solana-mev-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.
There is no informational or analytical edge. This page describes the execution layer that other Solana snipers must use to extract their alpha without leaking it to MEV bots.
Why it works: On Solana, the open path from RPC to leader is racy and exposes naive memecoin buys to sandwich attacks (a frontrun by a sniper bot followed by a backrun that sells into the victim's slippage). Jito bundles are atomic, ordered groups of transactions auctioned to the current Jito-validator leader: bundles either land together in the specified order or not at all, and they bypass the public propagat
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Jito Bundle Sniping 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/jito-bundle-sniping.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/dex/trending
- GET https://cryptodataapi.com/api/v1/dex/new-pools
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute 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 Jito Bundle Sniping 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/jito-bundle-sniping.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.
Liquidity sniping is a bot-driven algorithmic strategy that buys a token in the same block (or within milliseconds) that its initial liquidity is added on a decentralized exchange — Uniswap on Ethereum/Base, Raydium and Pump.fun on Solana, PancakeSwap on BSC.
Why it works: You buy in the same block that initial liquidity is added, at the pool's opening price, before the crowd that discovers the token on DEX Screener/Telegram minutes later can bid it up — a pure speed-and-position-in-block race against other bots and the retail buyers on the other side.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Liquidity Sniping 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/liquidity-sniping.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/hyperliquid/l2-book
- GET https://cryptodataapi.com/api/v1/liquidity/depth
- 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 Liquidity Sniping 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/liquidity-sniping.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.
Memecoin sniping is the practice of using automated bots to buy newly launched meme coins within seconds of liquidity being added to a DEX. The strategy targets the explosive price appreciation that occurs when memecoins launch on platforms like Pump.fun (Solana), Raydium, or Base DEXs and attract viral attention.
Why it works: Late retail buyers, FOMO traders and slower bots are on the other side; they pay snipers a premium for the right to buy after price discovery has begun.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Memecoin Sniping 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/memecoin-sniping.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 Memecoin Sniping 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/memecoin-sniping.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.
MEV session density filter is an operational scheduling overlay on top of any MEV strategy family (DEX-to-DEX arbitrage, sandwich backrunning, JIT liquidity provision, liquidation MEV) that concentrates searcher infrastructure spend — builder bids, failed-transaction gas burn, and co-location compute — on the hours of the day where gross extractable value per block is historically highest.
Why it works: MEV opportunity density (cross-exchange DEX arb, liquidation MEV, sandwich backrun) is demonstrably non-uniform across the 24-hour clock — it peaks at major CEX-session opens (Asia, Europe, New York) when fresh directional order flow arrives and moves spot prices faster than AMM pools can rebalance, and during elevated-volatility windows that produce liquidation cascades. A session-density filter
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the MEV Session Density 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/mev-session-density.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/market-intelligence/liquidations
- 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 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 MEV Session Density Filter 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/mev-session-density.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.
Maximal Extractable Value (MEV) is the profit extractable by controlling the ordering, insertion, or censoring of transactions within a block. Originally "Miner Extractable Value" on proof-of-work ethereum, it was renamed after The Merge once validators inherited block-production rights.
Why it works: Block-ordering rights are auctioned to whoever pays the builder most; a searcher with faster detection, atomic execution, and private orderflow captures price discrepancies and ordering value that appear and vanish inside a single block, at the expense of slower searchers and the users whose transactions move the state.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the MEV 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/mev-strategies.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/market-intelligence/liquidations
- GET https://cryptodataapi.com/api/v1/hyperliquid/trade-flow
- GET https://cryptodataapi.com/api/v1/market-intelligence/taker-buy-sell
- 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 MEV Strategies 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/mev-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.
Arbitrage executed against the private order-flow auctions (OFAs) that have replaced the public mempool as the primary venue for retail and institutional swap flow on Ethereum (and increasingly on L2s).
Why it works: Private order-flow auction venues (Flashbots Protect RPC, MEV-Share, BloXroute Private Tx, Eden Network) sell hashed/partial transaction information to whitelisted searchers. Subscribers see incoming user trades before public mempool, can construct backruns/bundles, share kickback with the user.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/private-mempool-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Private Mempool 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/private-mempool-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 Private Mempool 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/private-mempool-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.
Trading the Pump.fun launchpad — a Solana protocol that allows anyone to launch a memecoin in seconds via a deterministic bonding curve.
Why it works: Pump.fun launches Solana memecoins on a bonding curve where buys progressively lift the price along a deterministic curve. After ~$69k in buys, the token 'graduates' to Raydium with a $12k LP seed. Snipers buy at the curve's flat early portion and sell at the price impact peak before the LP seed dilutes them.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/pump-fun-bonding-curve-sniping
AI-agent prompts
Build it with an AI agent
Build the Pump.fun Bonding Curve Sniping 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/pump-fun-bonding-curve-sniping.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 Pump.fun Bonding Curve Sniping 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/pump-fun-bonding-curve-sniping.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.
Telegram bot trading uses chat-based interfaces within Telegram to execute on-chain trades without directly interacting with DEX frontends or signing complex wallet transactions.
Why it works: Bots compress execution time vs. manual DEX trading and surface alpha (new pools, copied wallets, limit fills) faster than the user could find unaided.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Telegram Bot 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/telegram-bot-trading.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 Telegram Bot Trading strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=15m (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/algorithmic/telegram-bot-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.
There is no informational edge in the source-document sense — the migration trigger is publicly observable on-chain. The edge is purely who acts on it first.
Why it works: When a Pump.fun token's bonding curve completes (~$69k MC cumulative buys), the token 'graduates' and migration to Raydium (or PumpSwap) is triggered. Migration creates a discrete liquidity event: a fresh AMM pool is seeded, the price discovery regime changes from a deterministic curve to a CPMM, and a wave of FOMO buyers arrives at the new venue. Volatility on the listing block and minute is dram
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Token Migration Sniping 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/token-migration-sniping.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 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 Token Migration Sniping 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/token-migration-sniping.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
An emerging arbitrage strategy class combining off-chain machine-learning prediction with zero-knowledge proofs to enable conditional MEV extraction. A model trained on historical pool data, oracle feeds, and mempool flow predicts likely state changes (e.g.
Why it works: Off-chain ML models predict pool-state changes (oracle updates, large incoming swaps, cross-chain arrivals) milliseconds-to-seconds in advance. ZK proofs let the model's output be verified on-chain without exposing the model itself, enabling conditional MEV bundles that only execute if the prediction holds.
Build the ZKML Predictive MEV 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/zkml-predictive-mev.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 Liquidation, Funding Rate, Basis 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 ZKML Predictive MEV 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/zkml-predictive-mev.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 mev & on-chain execution crypto trading strategies?
Strategies that earn from blockspace itself: mempool ordering, sniping new pools and launches, and airdrop farming.
How many mev & on-chain execution strategies are there?
Which indicators do mev & on-chain execution strategies use?
Most often Liquidation, Funding Rate, Basis.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=mev-onchain-execution 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.