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10 portfolio & risk management strategies for crypto, from the AlgoBrain wiki. The layer every strategy needs: sizing, hedging, rebalancing and drawdown control. 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=portfolio-risk.
The safe bucket earns the risk-free rate; the convex bucket exploits the fact that most participants are structurally unable to hold through repeated small losses.
Why it works: The safe bucket earns risk-free carry while the convex bucket is an out-of-the-money bet; the counterparty is the market participant who sells convexity cheaply (options writers, early-stage token issuers) and the concentrated investor who has no safe bucket to survive a tail event.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Asymmetric Barbell 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/combinations/asymmetric-barbell.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/options
- GET https://cryptodataapi.com/api/v1/volatility/implied
- GET https://cryptodataapi.com/api/v1/sentiment/stablecoins
- GET https://cryptodataapi.com/api/v1/dex/trending
3. Compute the signals described in the playbook 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 Asymmetric Barbell 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/combinations/asymmetric-barbell.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.
ATR position sizing is a risk-management overlay that sets position size so that a fixed fraction of account equity is at risk on each trade, using the Average True Range (ATR) as the measure of expected per-unit price movement.
Why it works: Not a return-generating edge by itself — it is a risk-normalization overlay. By equalizing dollar risk per trade across instruments of different volatility, it prevents a handful of high-volatility positions from dominating portfolio P&L and improves the geometric (compounded) growth rate of any underlying signal.
Build the ATR Position Sizing 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-position-sizing.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 Position Sizing 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-position-sizing.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 for buying bounded-cost convex protection during periods of cheap implied volatility and holding it through multi-year carry phases until tail events deliver multi-bagger payoffs.
Why it works: Markets cyclically underprice tail probability. Implied volatility / spreads / option premium are systematically too cheap relative to actual tail outcomes during periods of complacency. The trade is to systematically buy bounded-cost convex protection during cheap-vol regimes, hold through carry phases, and exit at multi-bagger gains when tail events materialize. Counterparty: vol-suppression sel
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/convex-tail-hedge-arbitrage
AI-agent prompts
Build it with an AI agent
Build the Convex Tail-Hedge Arbitrage crypto trading strategy using the CryptoDataAPI (X-API-Key header, base https://cryptodataapi.com).
1. Read the playbook first: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/strategies/arbitrage/convex-tail-hedge-arbitrage.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/market-intelligence/options
- GET https://cryptodataapi.com/api/v1/volatility/implied
- 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 Convex Tail-Hedge Arbitrage 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/convex-tail-hedge-arbitrage.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
A cheap-to-carry tail-risk hedge basket sized to capture cross chain contagion cascades when major exploits land. Sub-strategy under ai amplified exploit arbitrage with structural similarity to crisis alpha and tail risk hedging — accept negative carry between events; capture asymmetric upside on hits.
Why it works: KelpDAO (Apr 2026) demonstrated a measurable contagion multiplier: $290M direct loss → $15B TVL drain across DeFi within 48 hours, ~50× cascade. Each major exploit on a composable LRT or major bridge now triggers measurable cascade across LRTs, lending protocols, and stablecoin markets. A cheap-to-carry hedge basket sized to (expected exploit-frequency × 50× contagion multiplier) provides protecti
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/cross-chain-contagion-hedge
AI-agent prompts
Build it with an AI agent
Build the Cross-Chain Contagion 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/arbitrage/cross-chain-contagion-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/news/market-moving
- 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 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 Cross-Chain Contagion 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/arbitrage/cross-chain-contagion-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.
Standard dollar cost averaging (DCA) invests a fixed amount at fixed intervals regardless of price. It is disciplined but blind -- you buy at tops and bottoms equally. The DCA-Technical hybrid adds simple technical analysis filters to skew entries toward better prices.
Why it works: Retail DCA investors buy indiscriminately at every price level; this strategy exploits mean-reversion signals to accumulate at below-average prices while maintaining the discipline of regular investment
Build the DCA + Technical Analysis Hybrid 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/dca-technical-hybrid.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/indicators/technical
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute Moving Averages, Relative Strength Index (RSI), Support and Resistance, Volume Analysis, Candlestick Patterns 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 DCA + Technical Analysis Hybrid 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/dca-technical-hybrid.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.
Dollar-cost averaging (DCA) is an investment strategy where a fixed dollar amount is invested at regular intervals (e.g., weekly or monthly) regardless of the asset's price.
Why it works: No market edge — DCA is a behavioral commitment device that guarantees participation in the equity risk premium for investors who would otherwise stay in cash or mistime entries
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Dollar-Cost Averaging 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/dca-strategy.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 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 Dollar-Cost Averaging 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/dca-strategy.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.
Mandate-constrained funds must execute at specific calendar dates regardless of price. The edge is knowing in advance that forced flow will hit and positioning with or against it.
Why it works: Calendar-mandated flows (quarterly futures rolls, crypto Deribit monthly OpEx, passive-fund rebalancing) are non-discretionary and publicly known; the counterparty is the mandate-constrained fund that must execute regardless of price, and the silo-focused trader unaware of the calendar effect.
Via API/api/v1/strategies/expiration-and-rebalancing-flows
AI-agent prompts
Build it with an AI agent
Build the Expiration & Rebalancing Flows 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/expiration-and-rebalancing-flows.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 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 Expiration & Rebalancing Flows 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/expiration-and-rebalancing-flows.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.
Build the Risk Reversal 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/risk-reversal.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/market-intelligence/options
- GET https://cryptodataapi.com/api/v1/volatility/implied
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
3. Compute Funding Rate, Delta, Vega, Theta, 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 Risk Reversal 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/risk-reversal.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.
Tail hedging is a risk management discipline of buying convex protection against rare, large drawdowns — the left tail of the return distribution — typically via deep out-of-the-money (OTM) BTC/ETH puts on deribit, long DVOL-linked volatility exposure (straddles/strangles and variance), or defined-cost put spreads.
Why it works: The tail hedger accepts negative expected carry (paying the variance risk premium to vol sellers) in exchange for convex portfolio-level protection; the counterparty is the premium-selling desk that is structurally short the tail, and the edge materializes when that counterparty is forced to cover during a cascade — providing the hedger with liquidity at advantageous prices.
Build the Tail Hedging 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/tail-hedging.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, Gamma, Implied Volatility, 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 Tail Hedging 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/tail-hedging.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-scaled carry sizing is a risk-budget framework for carry books — both funding carry (short perp / long spot, collecting positive funding) and basis/cash-and-carry (long spot / short dated futures, collecting the basis premium) — that sizes total book notional to a constant realized P&L volatility target rather than a fixed capital allocation.
Why it works: Carry books (funding carry and basis/cash-and-carry) accumulate maximum notional exactly when realized P&L volatility is highest — which is precisely when the funding rate is most elevated and the short-squeeze / basis-blowout risk is at its greatest; vol-scaling the carry book to a constant daily-risk budget forces the book to shrink during its most dangerous regimes and expand when carry is quie
Build the Vol-Scaled Carry Sizing 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-scaled-carry-sizing.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 Realized Volatility, Funding Rate, Open Interest 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 Vol-Scaled Carry Sizing 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/vol-scaled-carry-sizing.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 portfolio & risk management crypto trading strategies?
The layer every strategy needs: sizing, hedging, rebalancing and drawdown control.
How many portfolio & risk management strategies are there?
Which indicators do portfolio & risk management strategies use?
Most often Open Interest, Funding Rate, Gamma, Realized Volatility.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=portfolio-risk 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.