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11 sentiment & contrarian strategies for crypto, from the AlgoBrain wiki. Fade or follow the crowd's mood using fear & greed, social volume and positioning extremes. 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=sentiment-contrarian.
This is a behavioral + risk-bearing edge (see edge taxonomy). The behavioral component: persistent human fear/greed cycles push price away from fair value at extremes. The risk-bearing component: at peak panic, the contrarian is paid to supply liquidity and bear risk that the herd is desperate to offload.
Why it works: At sentiment extremes the pool of marginal sellers/buyers is exhausted; the counterparty is the herd that has already acted on emotion, leaving price dislocated from fair value
Build the Contrarian Extremes 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/contrarian-extremes.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
3. Compute Crypto Fear & Greed Index, Support and Resistance, Fibonacci Retracement, Volume Profile, 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 Contrarian Extremes 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/contrarian-extremes.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.
Contrarian trading is primarily a behavioral edge, often with a risk-bearing component (see edge taxonomy). Behaviorally, it harvests crowd overreaction: fear and greed cascade past the point fundamentals justify, and the contrarian profits as the emotion — and the price — normalizes.
Why it works: Crowds overreact to news and herd to sentiment extremes, pushing price away from value; the contrarian provides liquidity at the extreme and is paid as price mean-reverts and the crowd's emotion fades.
Build the Contrarian 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/contrarian-trading.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 Momentum, Relative Strength Index (RSI) 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 Contrarian 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/contrarian-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.
DeFi yield / LP sentiment-extreme filter deploys LP and yield-farming capital after fear extremes — when capital has already fled liquidity pools, pool depths are thin, and fee income per unit of LP is mechanically elevated — and de-risks at greed extremes — when TVL is crowded, yields are compressed to their minimum, and the LP holds assets near cycle highs with maximum impermanent-loss exposure
Why it works: DeFi LP yields are richest immediately after fear-driven capital flight from liquidity pools — pool TVL collapses as retail LPs panic-withdraw, compressing pool depth and elevating per-unit fee income; simultaneously, asset prices are depressed, providing a better IL-reference entry price. At greed extremes (TVL crowded, yields compressed), the LP is the marginal entrant earning the lowest per-uni
Via API/api/v1/strategies/defi-yield-sentiment-entry
AI-agent prompts
Build it with an AI agent
Build the DeFi Yield / LP × Sentiment-Extreme Filter 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/defi-yield-sentiment-entry.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/volatility/regime
- GET https://cryptodataapi.com/api/v1/volatility/index
3. Compute Crypto Fear & Greed Index, Realized Volatility 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 DeFi Yield / LP × Sentiment-Extreme Filter 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/defi-yield-sentiment-entry.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 miner capitulation bottom is a long-only, position-trading accumulation signal for Bitcoin that uses miner-stress metrics — Hash Ribbons (a 30-day vs 60-day hashrate moving-average cross), the Puell Multiple, and on-chain miner reserves — to time the late-bear-market bottoming zone.
Why it works: In deep bear markets, high-cost miners are forced to sell BTC to cover energy costs and eventually capitulate (turn off rigs); that forced, price-insensitive selling exhausts near cycle lows, and its end historically marks a bottoming zone — a structural supply event, not a forecast.
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/miner-capitulation-bottom
AI-agent prompts
Build it with an AI agent
Build the Miner Capitulation Bottom 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/quantitative/miner-capitulation-bottom.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/regimes/current
- GET https://cryptodataapi.com/api/v1/quant/market
- GET https://cryptodataapi.com/api/v1/on-chain/miners/hash-ribbon
- 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 Miner Capitulation Bottom 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/quantitative/miner-capitulation-bottom.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 crowding exit is a combination strategy that provides the EXIT discipline for any narrative-driven long position: ride the narrative while positioning is clean and the story has not yet become the consensus trade, then exit or trim when funding and OI data confirm that the narrative has been adopted by the crowd — signalling late-stage distribution risk.
Why it works: Narrative positions held past the consensus-adoption phase absorb late-cycle retail distribution risk — the smart-money crowd has already entered and the token's story has become the consensus trade; funding and OI data confirm when the narrative has transitioned from early-adopter accumulation to crowded-consensus positioning, allowing the strategy to exit or trim while late buyers are still ente
Build the Narrative Crowding Exit 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-crowding-exit.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/derivatives/binance/long-short-ratio
- GET https://cryptodataapi.com/api/v1/sentiment/macro
3. Compute Funding Rate, 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 Narrative Crowding Exit 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-crowding-exit.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 trading is the practice of positioning in assets that are the focus of a dominant, spreading market story — an AI boom, a Bitcoin-ETF approval cycle, a "soft landing," a memecoin mania — rather than (or ahead of) the underlying fundamentals.
Why it works: Capital and attention rotate into a dominant story faster than fundamentals can justify; early positioning in the prevailing narrative front-runs the herd that arrives once the story is consensus.
Build the Narrative 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/narrative-trading.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/sentiment/fear-greed
- 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 Momentum, Volume 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 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/narrative-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.
On-chain capitulation confluence is a bottom-fishing entry framework for BTC (and, by extension, ETH) that requires two simultaneous signals before initiating a position-trading long: (1) an on-chain capitulation signal — an exchange-inflow spike, a realized-loss spike (SOPR < 1 at a multi-month extreme), or a dormancy break (old coins moving into exchange wallets at a loss), confirming that holde
Why it works: At cycle bottoms, forced and emotionally exhausted sellers converge on exchanges (exchange inflow spike / realized-loss spike / dormancy break) at the same time that crowd sentiment reaches a multi-month fear floor; requiring BOTH signals reduces false-bottom entries that occur when only one trigger fires — either on-chain selling without sentiment extreme (still distributing, not yet exhausted) o
Via API/api/v1/strategies/onchain-capitulation-confluence
AI-agent prompts
Build it with an AI agent
Build the On-Chain Capitulation 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/onchain-capitulation-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/sentiment/fear-greed
- GET https://cryptodataapi.com/api/v1/on-chain/exchange-flows/spike-alerts
- GET https://cryptodataapi.com/api/v1/market-intelligence/etf/{asset}/flows
- GET https://cryptodataapi.com/api/v1/sentiment/macro
3. Compute Crypto Fear & Greed Index, Exchange Net Flows, MVRV Ratio, Spent Output Profit Ratio (SOPR) 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 On-Chain Capitulation Confluence 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/onchain-capitulation-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.
Crypto sentiment trading aggregates public emotion-and-positioning signals — the crypto Fear & Greed Index, perpetual funding rates, exchange flows, stablecoin dry powder, and social mention/polarity — into a single composite score, then trades it either contrarian at extremes (buy measured extreme fear, trim extreme greed) or momentum in the mid-range (ride accelerating sentiment).
Why it works: Crypto is retail-dominated and reflexive; crowd fear and greed overshoot fundamentals. At extremes the marginal price-setter is an emotional forced buyer/seller, so a systematic contrarian who buys measured extreme fear and trims extreme greed is paid by the herd's loss aversion. The informational sliver is aggregating dispersed sentiment (fear-greed, funding, social, flows) faster than the crowd
Build the Sentiment 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/quantitative/sentiment-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/sentiment/fear-greed
- GET https://cryptodataapi.com/api/v1/on-chain/exchange-flows/spike-alerts
- GET https://cryptodataapi.com/api/v1/sentiment/stablecoins
- GET https://cryptodataapi.com/api/v1/dex/trending
3. Compute Crypto Fear & Greed Index, Funding Rate, Vol Regime Detection, Exchange Net Flows 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 Sentiment 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/quantitative/sentiment-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.
Sentiment-positioning divergence is a contrarian swing strategy that trades the gap between stated sentiment — what people are publicly saying — and actual positioning — what the leveraged derivatives crowd is actually doing with money.
Why it works: Stated sentiment (Fear & Greed index, social media fear signals) and actual derivative positioning (funding rate, long/short ratio) measure two different groups — the vocal crowd vs the leveraged bet-placing crowd; when these diverge, the miscalibrated group is the counterparty: extreme fear with positive funding means longs are paying to stay long despite public fear rhetoric (capitulation is inc
Via API/api/v1/strategies/sentiment-positioning-divergence
AI-agent prompts
Build it with an AI agent
Build the Sentiment-Positioning Divergence 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/sentiment-positioning-divergence.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/derivatives/binance/long-short-ratio
- GET https://cryptodataapi.com/api/v1/sentiment/fear-greed
3. Compute Crypto Fear & Greed Index, Funding Rate, 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 Sentiment-Positioning Divergence 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/sentiment-positioning-divergence.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.
Smart-money vs crowd divergence enters a long position in BTC (or ETH) when two simultaneous and opposing signals are confirmed: (1) on-chain smart money — large non-exchange wallets, measured by whale accumulation score and exchange-outflow trends — is actively buying into weakness, AND (2) the leveraged perp crowd is positioned bearishly — negative or flat funding rates, short-biased long/short
Why it works: On-chain smart money (large non-exchange wallet accumulators) buys into weakness while the leveraged perp crowd is simultaneously positioned short (negative/flat funding, elevated short/long ratio) — when informed accumulation is confirmed on-chain while the crowd is bearishly positioned in derivatives, the setup is a squeeze in both dimensions: the on-chain bid absorbs spot supply as the short bo
Via API/api/v1/strategies/smart-money-vs-crowd-divergence
AI-agent prompts
Build it with an AI agent
Build the Smart-Money vs Crowd Divergence 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/smart-money-vs-crowd-divergence.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/derivatives/binance/long-short-ratio
- GET https://cryptodataapi.com/api/v1/on-chain/exchange-flows/spike-alerts
3. Compute Exchange Net Flows, Funding Rate, 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 Smart-Money vs Crowd Divergence 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/smart-money-vs-crowd-divergence.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.
Stablecoin depeg sentiment entry is a panic-driven mean-reversion strategy that buys a major overcollateralised or fiat-backed stablecoin at a discount to its $1.00 peg specifically when two simultaneous conditions are confirmed: (1) crypto-wide Fear & Greed is at a panic extreme (≤ 15 for 48+ hours), confirming that the depeg is driven by contagion fear rather than mechanism failure, AND (2) the
Why it works: Major stablecoin depegs divide into two types: panic-driven depegs (temporary, driven by fear contagion regardless of redemption mechanism health — e.g., USDC/SVB 2023 where the stablecoin depegged to $0.87 before recovering fully as the peg mechanism was intact) and structural depegs (permanent or semi-permanent collapse driven by actual mechanism failure — e.g., UST/LUNA 2022 where the algorithm
IndicatorsPrice and volume only — see the playbook for the exact rules.
Via API/api/v1/strategies/stablecoin-sentiment-depeg-entry
AI-agent prompts
Build it with an AI agent
Build the Stablecoin Depeg Sentiment Entry 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/stablecoin-sentiment-depeg-entry.md
2. Pull the inputs:
- GET https://cryptodataapi.com/api/v1/sentiment/fear-greed
- GET https://cryptodataapi.com/api/v1/sentiment/stablecoins
- GET https://cryptodataapi.com/api/v1/dex/trending
- GET https://cryptodataapi.com/api/v1/dex/new-pools
- GET https://cryptodataapi.com/api/v1/indicators/technical
- GET https://cryptodataapi.com/api/v1/hyperliquid/candles
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 Stablecoin Depeg Sentiment Entry 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/stablecoin-sentiment-depeg-entry.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 sentiment & contrarian crypto trading strategies?
Fade or follow the crowd's mood using fear & greed, social volume and positioning extremes.
How many sentiment & contrarian strategies are there?
Which indicators do sentiment & contrarian strategies use?
Most often Crypto Fear & Greed Index, Funding Rate, Open Interest, Exchange Net Flows.
Can an AI agent build these strategies from an API?
Yes. GET /api/v1/strategies?group=sentiment-contrarian 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.