We're building the most comprehensive crypto data API we can, and it's still growing — there's always a coin, a field or an endpoint we haven't got to yet.
Please tell us about any data you think Crypto Data API should nail but doesn't. We're data nerds who love to geek out about market data, trading bots, AI agents, or how to make your trading stack work in general. Prefer a form? Send feedback.
9 mean reversion strategies for crypto, from the AlgoBrain wiki. Buy stretched-down, sell stretched-up: bets that price snaps back toward its average. 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=mean-reversion.
Bollinger Bands, created by John Bollinger in the 1980s, are a 20-period SMA (middle band) with upper and lower bands at 2 standard deviations.
Why it works: Overreacting, leveraged crypto flow pushes price to a volatility-scaled extreme (the band); the reversion trader fades the overshoot back toward the moving-average mean, paid to provide liquidity when the band touch coincides with exhausted flow — but only in ranging regimes.
Via API/api/v1/strategies/bollinger-band-reversion
AI-agent prompts
Build it with an AI agent
Build the Bollinger Band Reversion 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/bollinger-band-reversion.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 Simple Moving Average, Relative Strength Index (RSI), Average Directional Index (ADX), Volatility, Average True Range (ATR) 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 Bollinger Band Reversion strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/quantitative/bollinger-band-reversion.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.
Distribution / Post-Peak Short Book (Hyperliquid Basket) #
A selective short book targeting assets that have already peaked and are entering the distribution phase — where early-cycle or institutional holders sell into retail buying near all-time highs or major resistance.
Why it works: Late retail buyers absorb distribution from institutional / early-cycle holders who are selling into strength at resistance; the retail crowd anchors to the prior peak as a price target while the smart-money exit has already structurally weakened the book, leaving latecomers holding a deteriorating position as distribution resolves into a bear trend.
Via API/api/v1/strategies/distribution-post-peak-short-book
AI-agent prompts
Build it with an AI agent
Build the Distribution / Post-Peak 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/distribution-post-peak-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/on-chain/exchange-flows/spike-alerts
3. Compute Funding Rate, Divergence, Evening Star, Average True Range (ATR), Support and Resistance 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 Distribution / Post-Peak 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/distribution-post-peak-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.
A systematic strategy that bets crypto prices return to a short-window average after an outsized deviation. The signal is usually a z-score, rsi(2), or a Bollinger band touch on 1h–1d bars; the trade is a fade of the overshoot back toward the mean.
Why it works: Leveraged, forced, and narrative-chasing crypto flow overshoots fair value when it demands immediate liquidity into thin 24/7 books; the reversion trader is paid to absorb that flow and hold until price snaps back to a short-window mean.
Build the Mean Reversion 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/mean-reversion.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/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
- GET https://cryptodataapi.com/api/v1/regimes/current
3. Compute Relative Strength Index (RSI), Momentum, Liquidation, Funding Rate, Volatility on 4h bars (pinned: interval=4h, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Mean Reversion strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/quantitative/mean-reversion.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.
Buys at the lower boundary and sells (or shorts) at the upper boundary of an established range, on the assumption that the range will hold until price evidence proves otherwise.
Why it works: In a bounded range, market participants anchored to the prior equilibrium zone bid the lower boundary and offer the upper boundary, creating a persistent mean-reversion pull; the strategy earns by systematically selling this over-extension and buying the under-extension, until the range breaks — which is exactly when the range-breakout-breakdown basket takes over.
Build the Range Mean Reversion (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/range-mean-reversion.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 Bollinger Bands, Relative Strength Index (RSI), Volatility Regime Classification, 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 Range Mean Reversion (Hyperliquid Basket) strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=15m (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/hyperliquid-baskets/range-mean-reversion.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.
Under the null, RSI divergence is pattern-fitting on past price data — any higher high on price with a lower high on RSI is retrospectively identifiable on any price series, including random walks, because RSI is a smoothed function of price.
Why it works: Behavioral: RSI divergence identifies exhaustion — the crowd is still positioned for the prior trend, but the rate of new highs/lows is decelerating, signaling that the momentum buyers/sellers are running out of firepower. The divergence acts as a leading warning before price reverses. Analytical: combining divergence with a key support/resistance level creates a structural cluster of both technic
Build the RSI Divergence Strategy 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/technical-analysis/rsi-divergence.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- 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 Relative Strength Index (RSI), Momentum, Support and Resistance, Evening Star, Chandelier Exit 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 RSI Divergence Strategy 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/technical-analysis/rsi-divergence.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
The RSI Mean Reversion strategy buys assets that have suffered short, sharp pullbacks inside an established uptrend, betting that the panic-driven selling will reverse within a few days.
Why it works: Short-term oversold extremes in uptrends are caused by overreactive selling that the broader market quickly arbitrages away
Build the RSI Mean Reversion 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/rsi-mean-reversion.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/sentiment/fear-greed
- GET https://cryptodataapi.com/api/v1/dex/trending
- GET https://cryptodataapi.com/api/v1/dex/new-pools
- 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 Relative Strength Index (RSI), 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 RSI Mean Reversion strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/quantitative/rsi-mean-reversion.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
Session-aware mean reversion is a mean reversion strategy on crypto perpetuals and spot markets that conditions entry timing, parameter calibration, and position sizing on the current trading session.
Why it works: Thin-book overnight and weekend sessions produce systematic drift-and-revert patterns — price is pushed by low-liquidity momentum to an extreme that discretionary participants then fade at major session opens; conditioning mean-reversion entries on session structure (requiring entries during or after confirmed thin-book overshoot, sized and parameterized for the current liquidity window) extracts
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/session-aware-mean-reversion
AI-agent prompts
Build it with an AI agent
Build the Session-Aware Mean Reversion 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/session-aware-mean-reversion.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/l2-book
- GET https://cryptodataapi.com/api/v1/liquidity/depth
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/indicators/technical
3. Compute the signals described in the playbook on 15m bars (pinned: interval=15m, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Session-Aware Mean Reversion 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/session-aware-mean-reversion.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 Trend Pullback / Rally Fade basket trades with the primary trend by entering on counter-trend moves. In an uptrend, it buys weakness — entering long positions when price pulls back to a key level (Fibonacci retracement, EMA support, session VWAP) and pullback momentum fades.
Why it works: Trend-following participants who missed the primary move chase strength; their late entries at trend extremes push price away from value, creating the pullback that disciplined traders fade back into the trend — the late-chaser is the counterparty, systematically entering at worse prices.
Via API/api/v1/strategies/trend-pullback-rally-fade
AI-agent prompts
Build it with an AI agent
Build the Trend Pullback / Rally Fade (Hyperliquid Basket) crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/hyperliquid-baskets/trend-pullback-rally-fade.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/derivatives/open-interest
- GET https://cryptodataapi.com/api/v1/hyperliquid/open-interest
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/indicators/technical
3. Compute Fibonacci Retracement, Exponential Moving Average, VWAP (Volume Weighted Average Price), Relative Strength Index (RSI), Technical / Structural Regime 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 Trend Pullback / Rally Fade (Hyperliquid Basket) strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=4h (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/hyperliquid-baskets/trend-pullback-rally-fade.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
Vol-gated mean reversion is a mean-reversion strategy that replaces naive volatility-targeting (scale down positions in high-vol) with a conditional volatility framework: it distinguishes high-volatility periods that indicate a high-quality reversion setup (panic overshoot, forced liquidation overshoot, funding/OI flush — mean-reverting by mechanism) from high-volatility periods that indicate a st
Why it works: Mean-reversion edge is empirically strongest at the moments of highest realized volatility — exactly the moments that naive vol targeting would de-size most aggressively; the correct response is not to scale down uniformly on vol but to apply a signal-quality gate: distinguish high-vol that is high-quality reversion setup (panic overshoot, funding flush, OI flush — mean-reverting driver) from high
Via API/api/v1/strategies/vol-gated-mean-reversion
AI-agent prompts
Build it with an AI agent
Build the Vol-Gated Mean Reversion 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/vol-gated-mean-reversion.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/derivatives/open-interest
- GET https://cryptodataapi.com/api/v1/hyperliquid/open-interest
- GET https://cryptodataapi.com/api/v1/derivatives/binance/long-short-ratio
- GET https://cryptodataapi.com/api/v1/volatility/regime
3. Compute Funding Rate, Open Interest, Realized Volatility, 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 Vol-Gated Mean Reversion 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/vol-gated-mean-reversion.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 mean reversion crypto trading strategies?
Buy stretched-down, sell stretched-up: bets that price snaps back toward its average.
How many mean reversion strategies are there?
9: Bollinger Band Reversion, Distribution / Post-Peak Short Book (Hyperliquid Basket), Mean Reversion, Range Mean Reversion (Hyperliquid Basket), RSI Divergence Strategy, RSI Mean Reversion, Session-Aware Mean Reversion, Trend Pullback / Rally Fade (Hyperliquid Basket), Vol-Gated Mean Reversion.
Which indicators do mean reversion strategies use?
Most often Relative Strength Index (RSI), Average True Range (ATR), Funding Rate, Open Interest.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=mean-reversion 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.