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12 trend following strategies for crypto, from the AlgoBrain wiki. Ride persistent moves with moving averages, channels and trailing exits — cut losers, let winners run. 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=trend-following.
An ATR trailing stop is an exit rule that places a stop a fixed multiple of the Average True Range (ATR) away from the most favorable price reached since entry, ratcheting in the direction of the trade but never against it.
Why it works: An exit rule, not an entry edge. It monetizes the persistence of trends by staying in a position while it runs and exiting only when price retraces by a volatility-scaled amount, while behaviorally enforcing discipline against the disposition effect (cutting winners early, holding losers).
Build the ATR Trailing Stop 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/atr-trailing-stop.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/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute 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 ATR Trailing Stop 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/atr-trailing-stop.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 quantitative arbitrage strategy that systematically shorts protocols whose TVL is growing rapidly while their audit recency is decaying or absent.
Why it works: Trail of Bits disclosed <20% of deployed contracts received human audit by end of 2025; AI-generated code has 40% vuln rate. Protocols growing TVL fast while audit cadence stays fixed accumulate exploit-risk that the market underprices because audit data is publicly available but not systematically scored. Counterparty: yield chasers who allocate to high-APR protocols without modeling audit-recenc
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/audit-recency-tvl-growth-short
AI-agent prompts
Build it with an AI agent
Build the Audit-Recency × TVL-Growth Systematic Short 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/audit-recency-tvl-growth-short.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 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 Audit-Recency × TVL-Growth Systematic Short strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=1d (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/arbitrage/audit-recency-tvl-growth-short.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.
Major Trend Reclaim / Rejection (Hyperliquid Basket) #
The Major Trend Reclaim / Rejection basket focuses on the most significant structural price levels in crypto — the 200-day moving average, weekly EMAs, and major historical support/resistance zones — and trades binary outcomes at those levels.
Why it works: Algorithmic and institutional participants cluster orders at the 200-day MA and major historical structure — a reclaim triggers forced short-covering and programmatic buy signals across many systems simultaneously, creating momentum disproportionate to the individual trade size; the basket front-runs this convergence.
Via API/api/v1/strategies/major-trend-reclaim-rejection
AI-agent prompts
Build it with an AI agent
Build the Major Trend Reclaim / Rejection (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/major-trend-reclaim-rejection.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/hyperliquid/l2-book
3. Compute 200-Day Moving Average, Support and Resistance, Open Interest, Funding Rate, Exponential 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 Major Trend Reclaim / Rejection (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/major-trend-reclaim-rejection.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 Moving Average Crossover is the canonical trend following strategy: enter long when a shorter-period moving average crosses above a longer-period one, exit (or reverse short) when it crosses back.
Why it works: Persistent trends arise from slow information diffusion, herding, and rebalancing inertia; trend-followers are paid by mean-reversion traders and impatient liquidity demanders
Via API/api/v1/strategies/moving-average-crossover
AI-agent prompts
Build it with an AI agent
Build the Moving Average Crossover 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/moving-average-crossover.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 Moving Averages, Simple Moving Average, Average True Range (ATR), Exponential Moving Average, 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 Moving Average Crossover 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/moving-average-crossover.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.
Narrative with trend confirmation is narrative trading — positioning into crypto assets that are the focus of a dominant, spreading market story — where a new entry requires price-structure confirmation: the asset must have broken above a key resistance level, or have formed a higher-low above a medium-term moving average, after the narrative has been identified.
Why it works: Narrative capital flows are predictable once a story achieves consensus, but pure narrative entries frequently trap early buyers in drawdowns before the flow arrives; requiring a price-structure confirmation (breakout or higher-low above a moving average) as a second trigger filters out the 'narrative value trap' regime and enters only after market structure agrees that the story is *currently* at
Via API/api/v1/strategies/narrative-with-trend-confirmation
AI-agent prompts
Build it with an AI agent
Build the Narrative with Trend Confirmation 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/narrative-with-trend-confirmation.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/sentiment/fear-greed
- 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 Moving Averages 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 Narrative with Trend Confirmation 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/narrative-with-trend-confirmation.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 OI-Confirmed Trend basket enters trend-following positions — long or short — only when price movement is confirmed by a simultaneous increase in Open Interest.
Why it works: Retail traders chase price on low-OI moves that mean-revert; genuine institutional or informed-money conviction is only observable when rising OI accompanies price — those moves persist, while OI-divergent moves fade, giving the OI filter a structural advantage over pure momentum.
Build the OI-Confirmed Trend (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/oi-confirmed-trend.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/hyperliquid/l2-book
3. Compute Open Interest, Average True Range (ATR), Funding Rate, Technical / Structural Regime, Volatility Regime Classification 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 OI-Confirmed Trend (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/oi-confirmed-trend.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.
1. Behavioural. Crypto trends persist because participants underreact to gradually-arriving information and herd into reflexive narratives (halving cycles, ETF adoption, an L1/L2 rotation, an AI-token theme).
Why it works: Crypto trends persist via slow information diffusion, reflexive narratives, and structural spot flow (ETF creations, treasury-company buying, stablecoin dry powder deploying); a pullback is a temporary, lower-risk entry into an intact trend as over-leveraged weak hands are shaken out — often via a stop-hunt or long-liquidation flush — before that slower flow resumes buying.
Build the Pullback 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/pullback-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 Trend, Moving Averages, Fibonacci Retracement, Liquidation, 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 Pullback Trading (Crypto) 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/pullback-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.
Systematic premium selling is the mechanical, rules-based implementation of options premium selling, re-scoped to crypto: 21-45 DTE deribit BTC/ETH strangles, 16-delta short strikes, mechanical entries gated by DVOL percentile, exits at 50% of max profit or the crypto gamma-zone time stop (whichever comes first), with an institutional risk-discipline overlay including a permanent long vol overlay
Why it works: Harvests the crypto variance risk premium (DVOL > realized RV) via mechanically executed 16-delta strangles; the systematic overlay reduces execution variance — the dominant real-world source of premium-seller underperformance — delivering the edge minus the discretionary mistakes.
Via API/api/v1/strategies/premium-selling-systematic
AI-agent prompts
Build it with an AI agent
Build the Systematic Premium Selling (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/premium-selling-systematic.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, Funding Rate, Open Interest, Realized Volatility, Gamma 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 Systematic Premium Selling (Crypto) 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/premium-selling-systematic.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 structural edge: crypto markets have two distinct crisis modes — slow trend-reversals (multi-week bear markets driven by rate tightening, liquidity withdrawal, or post-mania deleveraging) and sudden flash cascades (exchange insolvencies, stablecoin depegs, weekend liquidation spirals).
Why it works: Trend following generates crisis alpha in sustained downtrends via short perp positions, while the tail-hedge leg (Deribit OTM puts / long straddles) captures the sudden-onset flash crash that trend signals are too slow to detect; the combination costs less than either component alone because trend income offsets the put-buying bleed, and the counterparty is the leveraged-long perp holder liquidat
Build the Trend Following + Tail Risk 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/trend-plus-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, Funding Rate, Open Interest, Volatility Regime 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 Trend Following + Tail Risk Hedge strategy on CryptoDataAPI history before trusting it.
- Bars: GET https://cryptodataapi.com/api/v1/backtesting/klines?symbol=BTC&interval=1d (Pro)
- Funding: GET https://cryptodataapi.com/api/v1/backtesting/funding?symbol=BTC (Pro)
- Rules: from the playbook at /api/v1/algobrain/page?path=wiki/strategies/combinations/trend-plus-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.
The core behavioral source is the systematic under-reaction of price-insensitive participants — fundamental investors who average down, passive hedgers who must roll at scheduled dates, leveraged longs who exit only after forced margin calls. Their slow, predictable behaviour creates momentum that trend signals exploit.
Why it works: Trend followers profit from the systematic under-reaction and delayed adjustment of price-insensitive participants (fundamental investors, passive rebalancers, hedgers) who are slow to exit losing positions; the crowded side of a persistent trend keeps paying as late arrivals chase and early holders average down.
Build the Trend Following CTA 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/trend-following-cta.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 Moving Averages, Momentum, Average True Range (ATR), Simple Moving Average, Exponential Moving Average 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 Trend Following CTA 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/trend-following-cta.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.
Trend-aligned premium selling is a short-vol strategy that sells options on deribit (BTC and ETH) where the trend direction selects which wing to sell — puts in confirmed uptrends, calls in confirmed downtrends — rather than selling both wings symmetrically as in a standard strangle or iron condor.
Why it works: Options vol sellers who sell both wings symmetrically suffer systematic losses on the side that the trend is pressing into — the into-the-move options remain bid by directional flow and produce frequent delta losses; by selling only the wing the trend supports (puts in confirmed uptrends, calls in confirmed downtrends), the strategy harvests the structural VRP on the side where the trend-following
Via API/api/v1/strategies/trend-aligned-premium-selling
AI-agent prompts
Build it with an AI agent
Build the Trend-Aligned Premium Selling 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/trend-aligned-premium-selling.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 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-Aligned Premium Selling 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/trend-aligned-premium-selling.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-targeted trend following runs a momentum/trend signal on crypto perpetuals with volatility-targeted position sizing: each position's notional is scaled inversely to the asset's current realized volatility, so the strategy targets a constant risk contribution per signal rather than a constant notional per signal.
Why it works: Crypto trend following captures momentum persistence driven by underreaction and anchoring; volatility targeting adds a systematic risk-management overlay that reduces position sizes when realized vol is elevated (preventing over-exposure to whipsaws in high-vol regimes) and increases them when vol is compressed (capturing the full move in low-vol breakouts), improving the Sharpe and drawdown prof
Via API/api/v1/strategies/vol-targeted-trend-following
AI-agent prompts
Build it with an AI agent
Build the Vol-Targeted Trend Following 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-targeted-trend-following.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 Funding Rate, Realized 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 Vol-Targeted Trend Following 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/vol-targeted-trend-following.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 trend following crypto trading strategies?
Ride persistent moves with moving averages, channels and trailing exits — cut losers, let winners run.
How many trend following strategies are there?
12: ATR Trailing Stop, Audit-Recency × TVL-Growth Systematic Short, Major Trend Reclaim / Rejection (Hyperliquid Basket), Moving Average Crossover, Narrative with Trend Confirmation, OI-Confirmed Trend (Hyperliquid Basket), Pullback Trading (Crypto), Systematic Premium Selling (Crypto), Trend Following + Tail Risk Hedge, Trend Following CTA, Trend-Aligned Premium Selling, Vol-Targeted Trend Following.
Which indicators do trend following strategies use?
Most often Average True Range (ATR), Open Interest, Funding Rate, Moving Averages.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=trend-following 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.