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13 breakout strategies for crypto, from the AlgoBrain wiki. Enter when price escapes a range or volatility squeeze, betting the expansion continues. 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=breakout.
A breakout is the event of price moving decisively beyond a well-defined level — typically a resistance ceiling, a support floor, the boundary of a consolidation range, or a chart-pattern line — usually accompanied by a surge in volume.
Why it works: Traders anchored to a defended price level are slow to re-rate when it breaks; clustered stop-loss orders just beyond the level provide forced counterparties that accelerate the move.
Build the Breakout 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/breakout.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Support and Resistance, Consolidation, Volume, Average True Range (ATR), Momentum 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 Breakout 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/breakout.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 confirmation-first breakout strategy that enters a perpetual-futures position only after price has broken a well-defined support or resistance level and returned to successfully retest that level as a new zone of support (or resistance).
Why it works: Retail traders anchored to the old range are slow to recognise the breakout as real; the retest shakes out weak longs (or shorts) who entered on the initial break, producing a second, higher-confidence entry precisely at the level where the structural edge — stop clusters now flipped to support — is strongest.
Build the Breakout and Retest (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/breakout-and-retest.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 Support and Resistance, Technical / Structural Regime, Average True Range (ATR), 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 Breakout and Retest (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/breakout-and-retest.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.
Breakout trading involves entering a position when price moves decisively above resistance or below support on increased volume. The thesis is that a breakout from a consolidation pattern or flat base signals the beginning of a new trend, as the balance between buyers and sellers has shifted.
Why it works: Traders anchored to old ranges are slow to recognize new trends; stop-loss clusters beyond the range provide forced counterparties when the level breaks.
Build the Breakout 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/technical-analysis/breakout-trading.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Support and Resistance, Volume, Consolidation, Flat Base, 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 Breakout 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/technical-analysis/breakout-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.
Under the null hypothesis, chart patterns are noise — the "channel" is a subjective drawing on random-walk price data, and breakouts are no more predictive of continued movement than a coin flip.
Why it works: Behavioral: price anchoring and herding behavior around geometric consolidation patterns causes participants to cluster stops and orders at the channel boundary; when the boundary breaks with conviction, the stop-cascade and breakout-chasing flow produces a sustained directional move. Analytical: the measured move technique provides an objective, pre-defined exit that removes discretion and exploi
Build the Channel Breakout 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/channel-breakout.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/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Volume, Fibonacci Extensions, Average True Range (ATR), Moving Averages, Bollinger Bands 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 Channel Breakout 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/channel-breakout.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 technical basket of Hyperliquid perp trades that explicitly fades breakouts and breakdowns that immediately reverse back into the range — the "trap" pattern.
Why it works: Breakout hunters and stop-cascade algorithms flush weak-handed positions at the boundary; when price snaps back inside the range, those same algorithms are now offside and must cover, driving a fast mean-reversion to range midpoint — the faded-breakout trader profits from the covering flow.
Via API/api/v1/strategies/failed-breakout-failed-breakdown
AI-agent prompts
Build it with an AI agent
Build the Failed Breakout / Failed Breakdown (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/failed-breakout-failed-breakdown.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 Support and Resistance, Technical / Structural Regime, Average True Range (ATR), Open Interest, Funding Rate on 15m bars (pinned: interval=15m, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Failed Breakout / Failed Breakdown (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/failed-breakout-failed-breakdown.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.
Mapping to the six categories in edge taxonomy, gap trading is a hybrid of three:
Why it works: When a lower-liquidity venue (CME futures over the weekend) closes while 24/7 spot keeps trading, price reopens at a distance from its prior close, leaving an untraded gap; that gap is a reference level clustered with resting orders and stops, and price is repeatedly drawn back to fill it (fade) unless a genuine catalyst carries it (continuation).
Build the Gap Trading (Crypto) 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/gap-trading.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/derivatives/funding-rates
- GET https://cryptodataapi.com/api/v1/hyperliquid/funding-rates
- GET https://cryptodataapi.com/api/v1/derivatives/open-interest
- GET https://cryptodataapi.com/api/v1/hyperliquid/open-interest
- GET https://cryptodataapi.com/api/v1/market-intelligence/liquidations
- GET https://cryptodataapi.com/api/v1/sentiment/macro
3. Compute Liquidation, Volume, Funding Rate, Open Interest, Support and Resistance 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 Gap Trading (Crypto) 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/technical-analysis/gap-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.
The London Breakout, adapted to crypto, is a session-based volatility breakout method that trades the directional expansion which tends to follow the quiet Asia-only hours.
Why it works: The Asia-only hours are crypto's thinnest, so BTC/ETH coil into a narrow range; when the European/London session opens and, more powerfully, when US macro and cash-equity flow arrives, deeper participation and higher volatility break that range in one direction — but obvious range edges also attract stop-hunts and liquidation wicks that fake the break before the real move.
Build the London Breakout Strategy (Crypto) 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/london-breakout.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/volatility/regime
3. Compute Volatility Regime, Liquidation, Average True Range (ATR), Open Interest, Funding Rate on 15m bars (pinned: interval=15m, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the London Breakout Strategy (Crypto) 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/technical-analysis/london-breakout.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.
For non-macro days, the ORB concept is weaker in crypto: without a genuine open, any 15-minute window can look like an ORB setup in hindsight. Validate that volume in the reference window is meaningfully above the 24-hour hourly average before treating it as an "opening range."
Why it works: Behavioral: participants psychologically anchor to the high/low of an early reference window; a break of that window triggers systematic stops and breakout-chasing entries, producing a sustained directional move. Structural: in crypto, the 08:00 UTC Deribit settlement creates a genuine daily 'pseudo-open' concentrating institutional flow into the post-settle window; additionally, FOMC/CPI macro pr
Build the Opening Range Breakout 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/opening-range-breakout.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 Volume, Average True Range (ATR), VWAP (Volume Weighted Average Price), Moving Averages, Funding Rate on 15m bars (pinned: interval=15m, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Opening Range Breakout Strategy 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/technical-analysis/opening-range-breakout.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.
Enters a perpetual futures position the moment price breaks cleanly above a well-defined range high or below a well-defined range low, without waiting for a retest.
Why it works: Stop-loss clusters stacked beyond well-defined range boundaries create a burst of forced market orders when the level breaks; this mechanical order flow extends the move before market makers can restore liquidity, giving an early directional entry a positive-expectancy edge over a cost-adjusted random entry.
Via API/api/v1/strategies/range-breakout-breakdown
AI-agent prompts
Build it with an AI agent
Build the Range Breakout / Breakdown (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-breakout-breakdown.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 Volatility Regime Classification, Technical / Structural Regime, Average True Range (ATR), Open Interest, Funding Rate on 15m bars (pinned: interval=15m, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Range Breakout / Breakdown (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-breakout-breakdown.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, support and resistance levels are arbitrary horizontal lines drawn on noise — price breaks through them at random and the apparent edge from "confirmed breakouts" is selection bias (only remembering the breakouts that worked).
Why it works: Behavioral: participants cluster stops and limit orders around previously tested horizontal levels; when the level breaks with conviction volume, a cascade of triggered stops plus breakout-chasing new entries produces a sustained directional move that is self-reinforcing until the next major level is reached. Structural: in crypto, the cascade is literal — leveraged perp long positions auto-liquid
Via API/api/v1/strategies/support-resistance-breakout
AI-agent prompts
Build it with an AI agent
Build the Support and Resistance Breakout 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/support-resistance-breakout.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 Support and Resistance, Volume, Moving Averages, Relative Strength Index (RSI), Candlestick Patterns 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 Support and Resistance Breakout 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/support-resistance-breakout.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 Turtle Trading system is one of the most famous systematic strategies in history and the canonical complete implementation of rule-based trend following. In 1983 commodities trader Richard Dennis bet his partner William Eckhardt (bill eckhardt) that trading could be taught rather than being an innate gift.
Why it works: Behavioral: trend-following profits from the crowd's tendency to anchor to prior prices and under-react to breakouts, creating momentum that the breakout-and-pyramid rules exploit. Analytical: ATR-normalized position sizing equalizes volatility contribution per unit and prevents any single market or regime from dominating P&L — this risk-equalization is the system's durable structural contribution
Build the Turtle 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/technical-analysis/turtle-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/market-intelligence/liquidations
- GET https://cryptodataapi.com/api/v1/sentiment/macro
- GET https://cryptodataapi.com/api/v1/indicators/technical
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute Average True Range (ATR), Funding Rate, Donchian Channels 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 Turtle Trading 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/technical-analysis/turtle-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.
Under the null, NR7 days occur at random and the subsequent day's range is drawn from the same distribution as any other day — narrow ranges are not followed by expansions more often than wide ranges are.
Why it works: Analytical: volatility is mean-reverting; NR7/BB-squeeze contraction identifies a low-vol regime that statistically precedes expansion with high reliability (~70% of cases). By entering just after the first expansion candle, the trade captures the bulk of the expansion move with a defined risk limited to the contraction range. Behavioral: after prolonged contraction, participants reduce position s
Build the Volatility Breakout 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/volatility-breakout.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 Average True Range (ATR), Bollinger Bands, Volume, Moving Averages, 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 Volatility Breakout 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/volatility-breakout.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.
Monitors Bollinger Band width, ATR, and historical volatility percentiles to identify periods of abnormally low volatility — compression. After prolonged compression, volatility typically expands sharply in one direction.
Why it works: Prolonged volatility compression concentrates stop-loss and breakout orders at a narrow price zone; when compression resolves, the burst of mechanical order flow is disproportionate to the range size, and a pre-positioned participant captures the initial expansion move before market makers can reprice the vol surface.
Via API/api/v1/strategies/volatility-compression-breakout
AI-agent prompts
Build it with an AI agent
Build the Volatility-Compression Breakout (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/volatility-compression-breakout.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 Bollinger Bands, Average True Range (ATR), Volatility Regime Classification, Exponential Moving Average, 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 Volatility-Compression Breakout (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/volatility-compression-breakout.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 breakout crypto trading strategies?
Enter when price escapes a range or volatility squeeze, betting the expansion continues.
How many breakout strategies are there?
13: Breakout, Breakout and Retest (Hyperliquid Basket), Breakout Trading, Channel Breakout Strategy, Failed Breakout / Failed Breakdown (Hyperliquid Basket), Gap Trading (Crypto), London Breakout Strategy (Crypto), Opening Range Breakout Strategy, Range Breakout / Breakdown (Hyperliquid Basket), Support and Resistance Breakout Strategy, Turtle Trading, Volatility Breakout Strategy…
Which indicators do breakout strategies use?
Most often Average True Range (ATR), Funding Rate, Volume, Support and Resistance.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=breakout 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.