Quick one for anyone building trading bots or AI agents in here 👇 The problem with 99% of "crypto trading prompts": the model has no live data. It sounds smart and makes the whole thing up — stale funding, unknown open interest, no clue what regime we're in. So we made our prompt library free and open. The difference: **every prompt is wired to a live data endpoint**. You fetch real numbers, paste them in, and the model actually reasons over what the market is doing right now. 10 to start with — funding-rate extremes, market regime detection, open-interest divergence, whale positioning, an autonomous risk monitor, a signal generator, an MCP analyst, a Telegram alert agent, a volatility position sizer, and a regime-aware execution controller. Copy-paste, works with Claude / GPT / Gemini. Best part — one command connects your model to the data directly: `claude mcp add cryptodataapi` Then it fetches live crypto data itself. No copy-paste. Grab them free: https://cryptodataapi.com/prompts GitHub (PRs welcome): https://github.com/Crypto-Data-API/cryptodataapi-prompt-library If you want the full-universe quant/whale/per-coin feeds, first 10 signups here get **20% off with code `SOCIAL20`**. Free key runs most of the prompts. Not financial advice — it's data, structure, and risk framing. What prompt should I add next?

Most builders assume that multi-year Binance historical data — the kind you need to backtest a strategy or train a model — sits behind an expensive data vendor. It doesn't. Binance publishes its entire futures price and funding history as free, checksummed ZIP files on a public CDN, with no API key and no rate limits. This is the exact dataset we use to train our HMM market-regime model: USD⃋-M perpetual futures, 1-hour klines plus funding rates, going back to January 2020. Roughly 56,000 hourly candles per long-lived symbol. For how to get the data for yourself see our blog post here: https://cryptodataapi.com/blog/free-binance-historical-data-backtesting Rule of thumb: pull raw price and funding history from the free Binance archive, and use the API for the things you can't reconstruct from candles — our regime labels, health scores, and the point-in-time snapshot archive that captures what every signal read on a given day.

Just shipped: a Model Context Protocol (MCP) server for CryptoDataAPI. This means you can plug Claude (or any MCP-compatible AI client) directly into real-time Hyperliquid perps data — funding rates, order book depth, OI, liquidations, top trader positioning — and have it reason over live market data, not stale training knowledge. Setup takes 30 seconds, COPY 1 LINE BELOW TO YOUR AI AGENT: claude mcp add cryptodataapi -- npx -y cryptodataapi-mcp Add that to your Claude MCP config and you're live. What you can do with it: - Ask Claude to analyse current funding rate extremes across all Hyperliquid markets - Have it identify OI divergences from price action in real time - Use it as a research layer while you're building — query live data in plain English - Feed it into an AI agent pipeline for autonomous signal monitoring Start asking it real questions about real-time markets! Would love to see what people build with this. Drop your use case below if you give it a go 👇

Most traders watch price. Smart money traders watch who's positioned and how much conviction they have. Hyperliquid is unique in that every trade, every position, every wallet is fully on-chain — which means whale activity isn't hidden. It's just raw data most people don't know how to pull or use programmatically. Here's what's actually trackable and useful as a trading input: → Large position openings/closings When a whale opens a significant long or short, it shows up on-chain in real time. The size, leverage, and liquidation distance all tell a story about conviction level. → Top trader long/short skew Are the highest PnL wallets on the exchange net long or net short right now? This is one of the cleaner sentiment signals available — these accounts didn't get to the top of the leaderboard by accident. → Liquidation distance clustering Where are the largest positions sitting relative to their liquidation prices? Knowing where forced selling is likely to emerge before it happens changes how you think about targets and stop placement. → Position changes over time A whale quietly adding to a position across multiple hours is a very different signal to a whale opening full size in one block. The pattern of accumulation matters as much as the size. The challenge has always been getting this data in a clean, structured format that's actually usable in a trading system — not just a Telegram alert you manually react to. That's exactly what we built. The Hyperliquid Whale Activity API endpoint returns structured JSON: top trader positions, size, leverage, unrealized PnL, liquidation levels — ready to feed directly into your bot or AI agent as a signal input. Full breakdown here 👉 cryptodataapi.com/blog/hyperliquid-whale-activity-api Are you currently tracking whale activity at all? Manual or automated? Drop your approach below 👇

One of the most expensive mistakes in algo trading is running a trend-following strategy in a ranging market, or a mean-reversion strategy in a strong trend. The bot isn't broken. You just have the wrong tool for the regime. That's why we built the Daily Trend Radar — a multi-factor indicator that classifies the current market regime so your system knows what kind of market it's operating in before it decides what to do. What it measures: - Trend direction and strength (bull/bear, confidence score) - Whether OI is expanding or contracting with price - Funding rate bias (are longs or shorts paying?) - Breadth — are most assets moving together or diverging? The output isn't a buy/sell signal. It's a regime label — something like "Strong Trend (Bear) — 96.6% confidence" with an approximate duration. Your strategy layer then decides what to do with that context. Why this matters for builders: Most bots hardcode their logic. The Trend Radar gives you a dynamic filter layer you can drop in front of any strategy: - Trend-following bot? Only run it when Radar confirms a strong trend - Mean-reversion bot? Gate it to ranging/neutral regimes only - Risk management? Tighten stops automatically when confidence is high on a bear regime It's available directly via the API — one endpoint call returns the current regime classification, confidence, and duration estimate. Full breakdown of how it works here 👉 cryptodataapi.com/blog/daily-trend-radar-indicator-crypto Members grab your discount code from the resources tab and give it a pull 👇 What regime filter (if any) are you currently using in your systems?