# market2000.xyz Forward Distribution — Time Series Pattern Matching

> market2000.xyz Forward Distribution — Time Series Pattern Matching is a paid API for AI agents from market2000.xyz, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-15).

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.

## Facts

- Endpoint: GET https://market2000.xyz/forward_distribution
- Price: $0.05/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/market2000-xyz-forward-distribution-time-series-pattern-matching-d0d154c0
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Uk3PmaamqsVyJgwzQnuZX

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability market2000-xyz-forward-distribution-time-series-pattern-matching-d0d154c0
```

Example prompt: 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?

## When to prefer this

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.

## Known failure modes

- 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

## How this service works

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.

## Output

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.

## Request schema (JSON Schema)

```json
{
 "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"
      }
     }
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "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
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/market2000-xyz-forward-distribution-time-series-pattern-matching-d0d154c0/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market2000.xyz](https://www.zero.xyz/host/market2000.xyz/llms.txt)