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Market Making & Microstructure Crypto Trading Strategies
11 market making & microstructure strategies for crypto, from the AlgoBrain wiki. Short-horizon edges from the order book: quoting both sides, grids, order-flow reads and execution alpha. 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=market-making-microstructure.
ATR-scaled grid is a grid trading strategy that continuously adjusts its grid spacing and per-level size to a multiple of the current Average True Range, so the grid geometry tracks the asset's actual oscillation amplitude rather than remaining fixed at the spacing calibrated at deployment.
Why it works: A fixed-spacing grid earns optimally only when its spacing is close to the asset's current oscillation amplitude; in high-vol regimes, too-tight spacing produces excessive churn (all fills are taker, no maker profit per cycle), while in low-vol regimes, too-wide spacing misses fills entirely; ATR-scaling the grid geometry continuously matches the spacing to the actual oscillation amplitude, keepin
Build the ATR-Scaled Grid 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/atr-scaled-grid.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
- GET https://cryptodataapi.com/api/v1/regimes/current
- GET https://cryptodataapi.com/api/v1/quant/market
- GET https://cryptodataapi.com/api/v1/indicators/technical
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute Average True Range (ATR), Volatility Regime 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 ATR-Scaled Grid 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/atr-scaled-grid.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 funding-skewed grid is grid trading on crypto perpetuals where the inventory allocation is biased toward the funding-receiver side: if funding is positive (longs pay shorts), the grid holds a net short skew — more capital at sell levels than buy levels — to collect the 8h funding settlement while still quoting on both sides.
Why it works: A symmetric grid on perpetuals earns spread capture but carries no funding direction; by skewing the inventory allocation toward the funding-receiver side (more capital on the side that collects the 8h settlement), the grid earns both the oscillation-harvest premium and a structural carry — while the rebalancing logic keeps the skew aligned with the prevailing funding direction, ensuring the carry
Build the Funding-Skewed Grid 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-skewed-grid.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 Funding Rate, Open Interest, Average Directional Index (ADX), Average True Range (ATR) 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-Skewed Grid 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-skewed-grid.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.
Grid trading is a systematic, no-directional-view strategy that places a ladder of buy and sell limit orders at fixed price intervals around a reference price.
Why it works: Within range-bound regimes, you are paid the bid-ask spread and volatility premium for absorbing oscillating flow; the entire edge is conditional on the regime filter being correct
Build the Grid 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/quantitative/grid-trading.md
2. Pull the inputs:
- 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 Average Directional Index (ADX), Bollinger Bands, Average True Range (ATR), Funding Rate, Vol Regime Detection 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 Grid 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/quantitative/grid-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.
A grid-with-tail-hedge overlays a budgeted OTM put position — financed entirely from grid income — onto an otherwise standard grid (market-making) strategy.
Why it works: A grid earns small oscillation profits from continuous mean-reversion in a range but carries gap risk that can wipe accumulated gains in a single directional move or cascade; OTM put options purchased with a budget drawn from grid income cap the maximum loss from that gap risk, converting the grid from an unbounded-downside strategy to one with a defined maximum loss per deployment cycle — the tai
Build the Grid 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/grid-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 DVOL — Deribit Volatility Index, Implied Volatility, Open Interest, 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 Grid with Tail Hedge 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/grid-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.
mev strategies describes what MEV (Maximal Extractable Value) is and the strategies that capture it. This page covers how to actually execute — Flashbots bundle construction, gas bidding strategy, MEV protection, mempool monitoring, and the operational infrastructure for running MEV-aware arbitrage.
Why it works: Block-ordering rights are auctioned to whoever pays builders the most. A searcher with faster detection, atomic execution, and private orderflow captures price discrepancies that arrive and vanish within a single block.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the MEV Execution Guide 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/mev-execution-guide.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 MEV Execution Guide 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/mev-execution-guide.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.
OI-aware grid is a grid trading strategy that pauses or de-sizes when open interest is building rapidly, and resumes at full size when OI stabilises back into the range-bound regime.
Why it works: Grids generate income from oscillation in range-bound, low-leverage regimes; when OI builds rapidly — signalling that fresh directional leverage is entering the market and creating breakout fuel — the grid is paused or de-sized before the trend runs through it; the OI-build signal identifies the regime transition earlier than realized-vol measures because OI accumulates before the breakout occurs,
Build the OI-Aware Grid 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/oi-aware-grid.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/derivatives/open-interest
- GET https://cryptodataapi.com/api/v1/hyperliquid/open-interest
- GET https://cryptodataapi.com/api/v1/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
3. Compute Open Interest, Funding Rate, Average True Range (ATR) 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 OI-Aware Grid 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/oi-aware-grid.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.
Order flow scalping reads the live aggressor tape of a crypto perpetual — who is hitting the bid vs lifting the offer, and whether resting liquidity absorbs or gives way — to enter tiny, seconds-to-minutes trades aligned with the dominant aggressive flow.
Why it works: You read the aggressor imbalance (CVD) and the absorption of forced/impatient flow a beat before price adjusts; the counterparties are momentum-chasing takers and liquidation engines dumping non-economic supply — but co-located market makers see the same book faster, so the retail edge is thin and fee-sensitive.
Build the Order Flow Scalping (Crypto Perps) 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/day-trading/order-flow-scalping.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/hyperliquid/l2-book
- GET https://cryptodataapi.com/api/v1/liquidity/depth
- GET https://cryptodataapi.com/api/v1/hyperliquid/trade-flow
3. Compute Cumulative Volume Delta (CVD), Footprint Charts, Liquidation, Funding Rate, Tape Reading 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 Order Flow Scalping (Crypto Perps) 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/day-trading/order-flow-scalping.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 regime-gated grid is grid trading with an explicit regime classification layer that activates the grid only in confirmed low-volatility, range-bound periods and issues a hard kill order the moment the regime flips to trend or high-volatility.
Why it works: In confirmed low-volatility, range-bound regimes the grid mechanically harvests bid-ask spread and oscillation; the regime gate removes the known catastrophic failure mode of inventory accumulation in trending markets by killing the grid at the first sign of a regime flip, leaving only the regime where the structural spread-capture edge actually works.
Build the Regime-Gated Grid 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/regime-gated-grid.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
- GET https://cryptodataapi.com/api/v1/regimes/current
- GET https://cryptodataapi.com/api/v1/quant/market
3. Compute Vol Regime Detection, Average Directional Index (ADX), Average True Range (ATR), Bollinger Bands, Volatility Regime 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 Regime-Gated Grid 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/regime-gated-grid.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.
Scalping extracts many small profits (5-20 bps) from brief price movements on crypto perps, holding positions for seconds to a few minutes.
Why it works: Capture the bid-ask spread and micro mean-reversion by resting maker orders that earn rebates while impatient takers cross the spread into you; funding, fees, and fill quality — not the price view — decide whether the pennies net positive.
Build the Scalping (Crypto Perps) 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/day-trading/scalping.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/hyperliquid/trade-flow
- GET https://cryptodataapi.com/api/v1/market-intelligence/taker-buy-sell
3. Compute Funding Rate, Cumulative Volume Delta (CVD), VPIN (Volume-Synchronized Probability of Informed Trading) 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 Scalping (Crypto Perps) 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/day-trading/scalping.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 pairs on-chain smart-money tracking — following wallets with a persistent, out-of-sample profitable record (primarily hyperliquid perp traders whose positions are public, plus large on-chain accumulators) — with live order-flow confirmation (CVD, taker buy/sell imbalance, and L2 book absorption).
Why it works: Crypto's public ledgers and Hyperliquid's public perp positions expose what persistently-profitable wallets are doing before price fully reflects it; order-flow (CVD, taker imbalance, book absorption) confirms the crowd is on the wrong side. You trade with the informed wallet against late retail copy-traders and momentum chasers who fill you at worse prices.
Via API/api/v1/strategies/smart-money-orderflow-combo
AI-agent prompts
Build it with an AI agent
Build the Smart-Money + Order-Flow Combo 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/smart-money-orderflow-combo.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/hyperliquid/trade-flow
- GET https://cryptodataapi.com/api/v1/market-intelligence/taker-buy-sell
- GET https://cryptodataapi.com/api/v1/on-chain/whales
- GET https://cryptodataapi.com/api/v1/quant/whales
3. Compute Cumulative Volume Delta (CVD), Funding Rate, Tape Reading 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 Smart-Money + Order-Flow Combo 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/smart-money-orderflow-combo.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 Volume-Weighted Average Price (vwap) is the average price an instrument has traded at over a window, weighted by volume — the intraday fair-value and execution benchmark used by funds and market-maker desks to judge fill quality (buy below VWAP = good fill; above = overpaid).
Why it works: Funds and market-maker desks slice large orders around VWAP as an execution benchmark, creating gravitation toward it; price extended from an anchored VWAP on thin conviction reverts as passive execution refills the mean. Weaker in crypto than equities — there is no closing auction or Reg-NMS benchmark forcing the flow.
Build the VWAP Trading (Crypto Intraday) 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/day-trading/vwap-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/hyperliquid/trade-flow
- GET https://cryptodataapi.com/api/v1/market-intelligence/taker-buy-sell
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/indicators/technical
3. Compute VWAP (Volume Weighted Average Price), TWAP (Time-Weighted Average Price), Liquidation, Funding Rate, Average True Range (ATR) 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 VWAP Trading (Crypto Intraday) 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/day-trading/vwap-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.
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 market making & microstructure crypto trading strategies?
Short-horizon edges from the order book: quoting both sides, grids, order-flow reads and execution alpha.
How many market making & microstructure strategies are there?
Which indicators do market making & microstructure strategies use?
Most often Funding Rate, Average True Range (ATR), Open Interest, Average Directional Index (ADX).
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=market-making-microstructure 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.