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4 multi-strategy combinations strategies for crypto, from the AlgoBrain wiki. Stacks of signals combined into one book — confirmations, filters and ensembles across the groups above. 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=multi-strategy.
In crypto, the most actionable alt-data sources are on-chain (whale accumulation, exchange net-outflows, MVRV-Z, SOPR) and social (LunarCrush engagement scores, Santiment dev-activity, Reddit/X volume spikes).
Why it works: Traders using faster or broader data sources know a material signal (exchange flows, social-engagement surge, on-chain accumulation) before the consensus has processed it; the counterparty is momentum investors and algorithmic traders who react only after the price move is already visible on-chain or in exchange data.
Build the Alternative Data Alpha 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/alternative-data-alpha.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/sentiment/fear-greed
- GET https://cryptodataapi.com/api/v1/on-chain/exchange-flows/spike-alerts
- GET https://cryptodataapi.com/api/v1/on-chain/whales
- GET https://cryptodataapi.com/api/v1/quant/whales
- GET https://cryptodataapi.com/api/v1/dex/trending
- GET https://cryptodataapi.com/api/v1/dex/new-pools
3. Compute Support and Resistance, Moving Averages, Bullish Engulfing, Fibonacci Retracement 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 Alternative Data Alpha 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/alternative-data-alpha.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 single book that runs four crypto return sleeves — perp-carry, momentum, on-chain, and memecoin — funded from a stablecoin treasury, sized by regime, and constrained by whole-book BTC-beta heat and per-venue counterparty limits.
Why it works: Four crypto edges with different drivers — perp-carry (risk-bearing/structural), momentum (behavioral), on-chain (informational), and memecoin convexity (behavioral/lottery) — are individually mediocre and individually fragile, but combine into a steadier book because their return drivers differ in calm regimes. The residual risk is that all four are long crypto risk-appetite and re-couple in a cr
Via API/api/v1/strategies/multi-strategy-crypto-portfolio
AI-agent prompts
Build it with an AI agent
Build the Multi-Strategy Crypto Portfolio 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/multi-strategy-crypto-portfolio.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/on-chain/exchange-flows/spike-alerts
- GET https://cryptodataapi.com/api/v1/sentiment/stablecoins
3. Compute Funding Rate, Momentum, MVRV Ratio, 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 Multi-Strategy Crypto Portfolio 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/multi-strategy-crypto-portfolio.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.
Most retail participants trade on a single timeframe — a 15-minute chart or a daily chart — and get whipsawed by moves that would have been obvious noise or contra-trend on the higher timeframe. Multi-timeframe confluence filters out those low-probability setups by requiring alignment across at least two timeframes.
Why it works: Retail participants trade single-timeframe signals; multi-timeframe filtering concentrates entries where the daily zone, weekly trend, and hourly momentum all agree, reducing the probability of entering against a higher-timeframe participant — institutional or large-holder — already positioned in the opposite direction.
Via API/api/v1/strategies/multi-timeframe-confluence
AI-agent prompts
Build it with an AI agent
Build the Multi-Timeframe Confluence 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/multi-timeframe-confluence.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/hyperliquid/candles
3. Compute Support and Resistance, Moving Averages, Fibonacci Retracement, Volume Analysis, Volume Profile 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 Multi-Timeframe Confluence 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/multi-timeframe-confluence.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 Wyckoff Method, developed by Richard D. Wyckoff in the early 1900s, is a framework for understanding how large institutional operators -- what Wyckoff called the "Composite Man" -- accumulate and distribute positions in the market.
Why it works: Read the footprints of large operators ('composite man') accumulating and distributing inventory; trade with the absorbed supply/demand rather than against it, exploiting the predictable behavior of trapped weak hands.
Build the Wyckoff Method 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/wyckoff-method.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
- GET https://cryptodataapi.com/api/v1/market-data/klines
3. Compute Volume, Accumulation/Distribution Line, Support and Resistance on 4h bars (pinned: interval=4h, lookback=500 bars, universe=BTC,ETH,SOL unless the playbook says otherwise).
4. Emit entry/exit rules, position size (risk 1% of equity per trade) and a stop, as JSON: {"symbol","side","entry","stop","target","size_pct","reason"}.
5. State which regime the rules are valid in (GET /api/v1/regimes/current) and stand aside outside it. Research only — do not place orders.
Backtest it
Backtest the Wyckoff Method 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/wyckoff-method.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 multi-strategy combinations crypto trading strategies?
Stacks of signals combined into one book — confirmations, filters and ensembles across the groups above.
How many multi-strategy combinations strategies are there?
4: Alternative Data Alpha, Multi-Strategy Crypto Portfolio, Multi-Timeframe Confluence, Wyckoff Method.
Which indicators do multi-strategy combinations strategies use?
Most often Support and Resistance, Moving Averages, Fibonacci Retracement, Bullish Engulfing.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=multi-strategy 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.