Show HN: Pre-computed market context for agents

TickerDB launched a pre-computed market context API for AI agents that reduces token usage and improves reasoning by delivering categorical market data instead of raw price bars. The service provides technical, fundamental, and analyst intelligence in a single response, along with watchlist monitoring and historical query capabilities.

Market context for agents. Connect your agent to pre-computed market context that improves reasoning and reduces token usage. No credit card required bash $ curl "https://api.tickerdb.com/v1/summary/NVDA" -H "Authorization: Bearer tdb your api key" { "ticker": "NVDA", "trend": { "direction": "strong uptrend", "duration days": 34, "ma alignment": "aligned bullish", "volume confirmation": "confirmed" }, "momentum": { "rsi zone": "overbought", "macd state": "expanding positive", "divergence detected": true }, "extremes": { "condition": "overbought", "condition rarity": "rare", "condition percentile": 7.3 }, "resistance level": { "status": "approaching", "distance band": "very close", "touch count": 4 }, "fundamentals": { "valuation zone": "undervalued", "growth zone": "high growth", "analyst consensus": "strong buy" } } Improve reasoning with actionable context. Before your agent can reason about an asset, it needs proper market context. TickerDB computes the context for you. { "date": "2024-01-15", "open": 182.16, "high": 184.26, "low": 180.93, "close": 183.63, "volume": 65234100 }, // ... 200 more rows - LLM has to compute raw data - Uses more tokens - Not designed for agents - Inconsistent reasoning { "trend": { "direction": "uptrend", "ma alignment": "aligned bullish" }, "momentum": { "rsi zone": "neutral high" }, "volatility": { "regime": "normal" } } - LLM reads categorical bands it understands - Lower token usage - Designed for agents - Consistent reasoning The context your agent needs. Pre-computed Our data is computed after market close and cached. Zero latency on your request - no indicator math, no delays. Categorical vocabulary Responses use terms like oversold , uptrend , and deeply overvalued - the same vocabulary LLMs use to reason about markets. Fewer wasted tokens A full asset summary is a fraction of the tokens you'd need to pass raw OHLCV. Your model gets more context, not more noise. Multi-source intelligence Technical, fundamental, and analyst data in one response. No stitching together multiple providers. Per-asset behavioral context Historical streaks, medians, and percentiles specific to each asset. Your agent knows what's unusual for this ticker. 5 years of queryable history Every categorical field, every day, for 5 years. Just connect and query. Built for workflows agents struggle with. TickerDB is strongest when your agent needs actionable market context, historical precedent, or watchlist diffs instead of raw price bars. Watchlist monitoring Track saved assets and pull only what changed with /v1/watchlist/changes . Ideal for daily alerts, portfolio briefings, and autonomous monitoring. Market scanning Use /v1/search to find oversold names, strong uptrends, rare conditions, or valuation mismatches across the full universe. Historical precedent Query /v1/summary with field and band to see when a setup last appeared and what happened after. Track state changes effortlessly. TickerDB monitors your watchlist. When something meaningful changes, a structured diff is generated. Pull it on demand or get it pushed via webhooks. /v1/watchlist/changes { "timeframe": "daily", "run date": "2026-03-28", "changes": { "AAPL": { "field": "rsi zone", "from": "neutral", "to": "oversold" }, { "field": "divergence detected", "from": false, "to": true } , "TSLA": { "field": "macd state", "from": "contracting negative", "to": "expanding positive" } , "BTCUSD": { "field": "squeeze active", "from": false, "to": true } }, "tickers checked": 12, "tickers changed": 3 } { "timeframe": "daily", "run date": "2026-03-28", "changes": { "AAPL": { "field": "rsi zone", "from": "neutral", "to": "oversold" }, { "field": "divergence detected", "from": false, "to": true } }, "tickers checked": 12, "tickers changed": 1 } Add tickers to your watchlist Track the assets you care about. Stocks, crypto, or both. TickerDB computes daily diffs After each pipeline run, every tracked field is compared against the prior day. Only assets with at least one change are included. Your agent reads only what changed No full snapshots to diff yourself. No wasted tokens on data that hasn't moved. Just the fields that shifted, with from and to values your agent can act on. Integrate in minutes. Make your first call in minutes. Drop in our SDK or make direct HTTP calls. bash Get a full market summary for AAPL $ curl "https://api.tickerdb.com/v1/summary/AAPL" \ -H "Authorization: Bearer YOUR API KEY" Search for oversold stocks $ curl -G "https://api.tickerdb.com/v1/search" \ --data-urlencode 'filters= {"field":"momentum rsi zone","op":"eq","value":"oversold"} ' \ -H "Authorization: Bearer YOUR API KEY" Get state changes on your watchlist $ curl https://api.tickerdb.com/v1/watchlist/changes \ -H "Authorization: Bearer YOUR API KEY" python import requests One call. Full market context. res = requests.get "https://api.tickerdb.com/v1/summary/AAPL", headers={"Authorization": "Bearer YOUR API KEY"} data = res.json Hand it directly to your LLM prompt = f""" Analyze AAPL based on this market data: {data} Is this a good entry point? """ Ready for your LLM js import { TickerDB } from 'tickerdb'; // One call. Full market context. const client = new TickerDB { apiKey: 'YOUR API KEY' } ; const { data } = await client.summary 'AAPL' ; // Hand it directly to your LLM const prompt = Analyze AAPL based on this market data: $${JSON.stringify data $} Is this a good entry point? ; // Ready for your LLM import "context" import "github.com/tickerdb/tickerdb-go" // One call. Full market context. client := tickerdb.NewClient "YOUR API KEY" resp, := client.Summary context.Background , "AAPL", nil // Raw JSON plus rate limits fmt.Println string resp.Data fmt.Println resp.RateLimits.RequestsRemaining // claude desktop config.json { "mcpServers": { "tickerdb": { "command": "npx", "args": "tickerdb-mcp" , "env": { "TICKERDB KEY": "YOUR API KEY" } } } } Once connected, Claude can call get summary , get search , get watchlist changes , and more - directly from the chat. all MCP tools → /docs bash Install the TickerDB skill $ clawhub install tickerdb Then just ask your agent "How's AAPL looking? Give me the full summary." "Add NVDA, AAPL, and BTCUSD to my watchlist and flag anything that changed." "When was NVDA last deep oversold, and what happened after?" One install. Your agent gets summaries, search, watchlists, and schema - no config needed. OpenClaw integration guide → /openclaw Compatible with everything. MCP, OpenClaw, SDKs or plain HTTP. If it can make a GET request, it works with TickerDB. MCP Server Plug TickerDB into Claude Desktop or any MCP client. Your AI assistant pulls EOD market context from the chat. setup guide /docs Any agent framework LangChain, LlamaIndex, AutoGen, CrewAI - it's just HTTP. If it can make a GET request, it works. API reference /docs Python, Node.js & Go SDKs Official SDKs with typed responses. Or just use fetch - the API is simple enough. get your API key /docs/authentication One database. Infinite ways to query. Over 140 queryable fields across 10,000+ assets.