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9 macro & fundamental strategies for crypto, from the AlgoBrain wiki. Top-down drivers — rates, liquidity, ETF flows and valuation — translated into crypto positioning. 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=macro-fundamental.
The ARC strategy is a three-step intraday method that trades reversals from a small set of institutional levels.
Why it works: Retail reversal trades at prior-day/swing levels where stop-order liquidity clusters; the counterparty is late breakout traders and swept stops.
Build the ARC (Area-Range-Candle) 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/arc-strategy.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 VWAP (Volume Weighted Average Price), Support and Resistance, Candlestick Patterns 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 ARC (Area-Range-Candle) 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/arc-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.
Buy and hold is a long-term, low-turnover strategy in which an investor purchases a diversified basket of assets — most commonly a broad equity index fund — and holds it for years or decades regardless of intervening market fluctuations, rather than attempting to time entries and exits.
Why it works: Harvests the equity risk premium: long-term holders are compensated for bearing market risk and providing patient capital, while avoiding the costs, taxes, and behavioral errors that erode the returns of active traders. The persistent counterparty is the active trader whose costs and timing mistakes transfer return to the patient holder.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Build the Buy and Hold 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/buy-and-hold.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 Buy and Hold 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/buy-and-hold.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 position-timeframe basket of Hyperliquid perpetual positions that tracks real-money institutional demand signals: spot Bitcoin and Ethereum ETF inflow/outflow data, the Coinbase premium/discount versus global exchanges, and custody-level on-chain flows from institutional-grade wallets.
Why it works: Spot ETF inflow/outflow data and Coinbase-premium signals reveal real-money institutional demand that is distinct from and leads leveraged crypto-native positioning; sustained institutional accumulation sets structural price floors that leveraged-long retail traders and on-chain metrics do not fully capture.
Via API/api/v1/strategies/etf-and-institutional-flow
AI-agent prompts
Build it with an AI agent
Build the ETF and Institutional Flow (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/etf-and-institutional-flow.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/on-chain/exchange-flows/spike-alerts
- GET https://cryptodataapi.com/api/v1/market-intelligence/etf/{asset}/flows
3. Compute Funding Rate, Open Interest, Coinbase Premium 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 ETF and Institutional Flow (Hyperliquid Basket) 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/hyperliquid-baskets/etf-and-institutional-flow.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.
ETF Flow Directional trades the direction of spot Bitcoin/Ethereum ETF net flow — not the creation/redemption arbitrage.
Why it works: Spot BTC/ETH ETF net creations force authorised participants to buy (or sell) real spot, an order-flow that is published daily, is strongly autocorrelated, and is price-impactful; the market underreacts to the persistence of that flow, so trading in the direction of the net-flow z-score front-runs the multi-day continuation the flow itself creates.
Build the ETF Flow Directional 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/etf-flow-directional.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/etf/{asset}/flows
- GET https://cryptodataapi.com/api/v1/market-intelligence/coinbase-premium
- GET https://cryptodataapi.com/api/v1/regimes/current
- GET https://cryptodataapi.com/api/v1/quant/market
3. Compute Coinbase Premium, Funding Rate, 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 ETF Flow Directional 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/etf-flow-directional.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, MACD crossovers are lagging indicators that signal past momentum, not future continuation. Because the signal fires after the move has already begun, a random-walk price series produces a significant number of valid-looking MACD crossovers that fail because the underlying move was noise.
Why it works: Behavioral: MACD crossovers identify early-stage momentum shifts when the crowd is still committed to the prior trend, capturing the lag between price reality and participant position adjustment. Analytical: the signal-line smoothing filters noise and confirms that the momentum shift is sustained rather than a single-candle spike. In crypto, momentum persistence is amplified by leveraged perp fund
Build the MACD Crossover 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/macd-crossover.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 Momentum, MACD (Moving Average Convergence Divergence), Moving Averages, Swing Low, Swing High 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 MACD Crossover 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/macd-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.
Strategies specific to prediction markets (Polymarket, Kalshi) that exploit the structural, behavioral, and informational characteristics unique to event-outcome trading.
Why it works: Prediction market participants overweight recent news and exciting outcomes, creating systematic mispricings that disciplined traders can exploit through arbitrage, bias fading, and information edge strategies.
Via API/api/v1/strategies/prediction-market-strategies
AI-agent prompts
Build it with an AI agent
Build the Prediction Market Trading Strategies 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/prediction-market-strategies.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/on-chain/whales
- GET https://cryptodataapi.com/api/v1/quant/whales
- GET https://cryptodataapi.com/api/v1/news/market-moving
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Federal Funds 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 Prediction Market Trading Strategies 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/prediction-market-strategies.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, ROC zero-line crossovers are random — a coin flip as to whether momentum will continue after the cross. The persistence of the null in practice is real: crypto's choppy, range-bound regimes generate frequent zero-line crossovers with no follow-through.
Why it works: Behavioral: ROC's zero-line crossover identifies the precise moment when current price surpasses the price from N periods ago, marking a definitive shift in medium-term momentum that most participants are slow to recognize. Analytical: ROC is a pure, un-smoothed price-change ratio — unlike MACD, it has no smoothing lag — making it a leading indicator of momentum shifts in crypto's fast-moving 24/7
Build the Rate of Change (ROC) 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/rate-of-change.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 Momentum, Oscillators, Divergence, Moving Averages, 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 Rate of Change (ROC) 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/rate-of-change.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 trading strategy basket is a group of related instruments traded together as a single position to express a thesis — for example, going long a basket of AI-infrastructure stocks, or long a basket of high-quality names while short a basket of low-quality names.
Why it works: A diversified basket isolates the intended thesis (theme, factor, or spread) while diversifying away idiosyncratic single-name noise, so the edge is expressed more reliably than via any one name.
Via API/api/v1/strategies/trading-strategy-baskets
AI-agent prompts
Build it with an AI agent
Build the Trading Strategy Baskets 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/trading-strategy-baskets.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 Beta 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 Trading Strategy Baskets 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/trading-strategy-baskets.md
Pinned: fees 4.5 bps taker per side, 2 bps slippage, funding applied every 8h, signals on bar close only (no lookahead), 70/30 in-sample/out-of-sample split. Report CAGR, Sharpe, max drawdown, trade count and out-of-sample vs in-sample decay.
The volume profile trading strategy is a family of discretionary (and increasingly semi-automated) setups that use the volume profile histogram — point of control (POC), value area (VAH/VAL), and high- and low-volume nodes — as the structural map for entries, exits, and stops.
Why it works: Volume clusters mark where two-sided trade was facilitated; price tends to rotate around those accepted levels and travel quickly through prices the auction rejected, giving statistical reference points for entries, targets and stops.
Via API/api/v1/strategies/volume-profile-trading-strategy
AI-agent prompts
Build it with an AI agent
Build the Volume Profile Trading 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/volume-profile-trading-strategy.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/hyperliquid/trade-flow
- GET https://cryptodataapi.com/api/v1/market-intelligence/taker-buy-sell
- GET https://cryptodataapi.com/api/v1/indicators/technical
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Volume Profile, Point of Control (POC), Volume Nodes (HVN, LVN, Single Prints), Market Profile, Value Area High and Low (VAH / VAL) 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 Volume Profile Trading 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/volume-profile-trading-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.
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 macro & fundamental crypto trading strategies?
Top-down drivers — rates, liquidity, ETF flows and valuation — translated into crypto positioning.
How many macro & fundamental strategies are there?
Which indicators do macro & fundamental strategies use?
Most often Momentum, Support and Resistance, Funding Rate, Coinbase Premium.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=macro-fundamental 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.