{"uid":"cap_9aH7EZD634Q648nxEmo0f","slug":"api-carbon-cashmere-de-5b02322e","name":"Carbon Cashmere Per-Coin ML Feature Vector","description":"Per-coin ML feature vector (46 fields, 6h-delayed) — price returns, volatility, volume, RSI/MACD, Fear & Greed, funding, OI, options, orderbook, on-chain (whale, mempool). Standard TA + market microstructure. AI agent API for feature engineering and research.","url":"https://api.carbon-cashmere.de/v1/features/:coin","method":"GET","headers":{},"bodySchema":null,"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"down","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_kSBA54GyfW7lSSriFqCUT","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Returns a 46-field ML feature vector for a specified cryptocurrency, including price returns, volatility, volume, RSI/MACD, Fear & Greed index, funding rates, open interest, options data, orderbook microstructure, and on-chain signals — with a 6-hour delay.","exampleAgentPrompt":"Pull the full 46-field ML feature vector for BTC from Carbon Cashmere — I need all the TA indicators, funding rate, open interest, orderbook microstructure, and on-chain whale signals so I can feed it into my prediction model.","exampleUseCases":[{"title":"Train crypto price prediction model","prompt":"Grab me the complete ML feature vector for Ethereum from Carbon Cashmere — I want the RSI, MACD, volatility, funding rate, open interest, and whale flow signals all in one shot so I can plug them straight into my XGBoost price prediction pipeline."},{"title":"Backtest algo strategy with microstructure","prompt":"Pull the 46-field feature vector for Solana from Carbon Cashmere, including the orderbook bid/ask imbalance, spread, funding rates, and open interest — I'm running a backtesting study and need a standardized feature set without building my own data pipeline from scratch."},{"title":"Research sentiment and on-chain features","prompt":"Fetch the full Carbon Cashmere feature vector for Dogecoin — I specifically need the Fear and Greed index score alongside the mempool signals and whale flow data so I can analyze how on-chain sentiment features correlate with short-term price moves in my research notebook."}],"resultDescription":"A JSON object containing 46 ML-ready feature fields for the requested coin: price return metrics, realized and implied volatility, volume statistics, RSI and MACD values, Fear & Greed index score, perpetual funding rates, open interest, options market data, L2 orderbook bid/ask imbalance and spread, on-chain whale flow, and mempool signals. Data is sourced from standard technical analysis plus market microstructure and is delayed by 6 hours.","failureModes":["Unsupported coin ticker returns 404 or empty result","6-hour data delay means real-time signals are not available","Payment failure via x402 protocol results in 402 response","Coin with insufficient liquidity may have missing or null fields for options/orderbook features","Rate limiting or quota exceeded returns 429 error"],"whenToPreferThis":"Prefer this endpoint when you need a comprehensive, pre-computed ML feature vector for a single cryptocurrency that spans technical analysis, market microstructure, and on-chain signals in one call. Ideal for feature engineering pipelines, backtesting, and training supervised ML models where 6-hour latency is acceptable. Use over building your own feature pipeline when you want a standardized 46-field representation covering RSI/MACD, funding, OI, whale activity, and mempool in a single request.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T13:08:48.864Z","isFirstParty":false}