{"uid":"cap_Uk3PmaamqsVyJgwzQnuZX","slug":"market2000-xyz-forward-distribution-time-series-pattern-matching-d0d154c0","name":"market2000.xyz Forward Distribution — Time Series Pattern Matching","description":"An origin the crawlers already index. We count what AI agents try to buy, what they are refused, and what the AI companies take without sending anyone back. Sold per call in USDC.","url":"https://market2000.xyz/forward_distribution","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","queryParams"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET","HEAD","DELETE"],"type":"string"},"queryParams":{"type":"object","required":["ticker","start_date","end_date"],"properties":{"k":{"type":"integer","description":"Number of analogs to sample (default 20)"},"metric":{"enum":["l1","l2"],"type":"string"},"ticker":{"type":"string","description":"Asset symbol"},"end_date":{"type":"string","description":"Query window end (YYYY-MM-DD)"},"start_date":{"type":"string","description":"Query window start (YYYY-MM-DD)"}}}}},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object","properties":{"ticker":{"type":"string"},"top_analogs":{"type":"array"},"analogs_used":{"type":"integer"},"generated_at":{"type":"string"},"query_window":{"type":"object"},"distributions":{"type":"object","description":"Keyed by horizon: '7d', '14d', '30d', '60d'"},"query_return_pct":{"type":"number"}}}}}}},"responseSchema":{"type":"json","example":{"ticker":"SPY","analogs_used":18,"query_window":{"end":"2026-06-01","start":"2026-04-01"},"distributions":{"30d":{"p10":-12.5,"p25":-5.1,"p50":2.3,"p75":8.4,"p90":14.2,"mean":1.8,"count":18,"stdev":9.1,"pct_positive":58.3}},"query_return_pct":-4.2}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.05","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"settled","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.05/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_YC8NHyxLVVDxSCUUEANGE","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.05","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Given a ticker and a recent price window, finds historical analogs and returns a forward return distribution (percentiles, mean, stdev, % positive) over the next 30 days.","exampleAgentPrompt":"Run a historical analog pattern match on SPY using the April 1 to June 1, 2026 price window and show me the 30-day forward return distribution — what does history say the likely range of outcomes looks like?","exampleUseCases":[{"title":"Options sizing for earnings volatility","prompt":"I'm holding QQQ calls into earnings next month. Can you find historical analogs for QQQ's price pattern over the last 60 days and show me what the 30-day forward return distribution looked like — I want to understand if I should be taking profits or holding based on what similar setups have historically returned."},{"title":"Risk scenario planning for portfolio","prompt":"Our tech-heavy portfolio is at an inflection point. Run pattern matching on the Nasdaq-100 using the last two-month price window and give me the full forward return distribution — specifically, what's the chance we see negative returns over the next month, and what does the downside look like at the 10th percentile?"},{"title":"Quantitative research on market regimes","prompt":"I'm trying to understand whether the current price setup in IWM is bullish or bearish relative to history. Match the past eight weeks of IWM's price action to historical analogs and show me the 30-day outcome distribution — what percentage of similar patterns led to positive returns, and what was the median gain?"}],"resultDescription":"A JSON object containing the ticker, number of historical analogs used, the query window dates, the query period's own return percentage, and a forward distribution object with p10/p25/p50/p75/p90 percentiles, mean, standard deviation, count, and percent of analogs that had positive returns over the next 30 days.","failureModes":["Unknown or invalid ticker symbol returns an error or empty analog set","Query window dates in an unsupported format or out of historical range may fail","Insufficient historical analogs for rare patterns may return low count or degenerate distribution","Payment failure via x402/USDC on Base will block the call","Malformed date range (start after end) will likely return a validation error"],"whenToPreferThis":"Use this endpoint when you need a probabilistic, historically-grounded forward return distribution for a security based on pattern matching rather than a point forecast. It is especially useful for quantitative research, options sizing, risk scenario planning, or any workflow where knowing the shape (percentiles, mean, stdev, % positive) of historical outcomes matters more than a single predicted price target.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T06:51:20.152Z","isFirstParty":false}