# market2000.xyz Playbook — Time Series Pattern Matching API

> market2000.xyz Playbook — Time Series Pattern Matching API is a paid API for AI agents from market2000.xyz, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-15).

Finds historical analog periods for a given stock ticker and returns forward-looking return statistics for the ticker and its peers based on pattern matching.

## Facts

- Endpoint: GET https://market2000.xyz/playbook
- Price: $0.1/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-playbook-time-series-pattern-matching-api-53a60dfe
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_LY--UdiJ8ViZJvSeVXJv5

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-playbook-time-series-pattern-matching-api-53a60dfe
```

Example prompt: Run a playbook analysis on NVDA using its price pattern from November 1 2025 to January 15 2026, project 30 days forward, and tell me how NVDA plus peers like MSFT and AMD have historically performed after similar setups.

## When to prefer this

Use this endpoint when you need quantitative, historically-grounded forward return expectations for a specific stock based on pattern similarity — especially when you want peer comparison alongside the primary ticker. Prefer this over generic sentiment or news-based analysis when the user wants a base-rate, data-driven view of what the market has done after similar price setups.

## Known failure modes

- Invalid or unrecognized ticker symbol returns an error or empty analog set
- Query window too short to find meaningful analogs results in low analog count
- Payment failure via x402 on Base blocks the call entirely
- Future or malformed date range causes a validation error
- Peer tickers not found in the dataset return empty or partial peer_playbook entries

## 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

Returns a JSON object with the primary ticker, the query window dates, the number of historical analogs matched, the query window's own return percentage, a forward projection horizon in days, and a peer_playbook breakdown per peer ticker showing analog count, percent of positive outcomes, and average forward return percentage.

## 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",
      "peers"
     ],
     "properties": {
      "k": {
       "type": "integer",
       "description": "Number of analogs to average (default 10)"
      },
      "peers": {
       "type": "string",
       "description": "Comma-separated peer tickers e.g. MSFT,AMD,QQQ"
      },
      "ticker": {
       "type": "string",
       "description": "Primary ticker whose pattern to analyze"
      },
      "end_date": {
       "type": "string",
       "description": "Pattern window end (YYYY-MM-DD)"
      },
      "start_date": {
       "type": "string",
       "description": "Pattern window start (YYYY-MM-DD)"
      },
      "forward_days": {
       "type": "integer",
       "description": "Forward horizon days (default 30)"
      }
     }
    }
   }
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object",
     "properties": {
      "peers": {
       "type": "array"
      },
      "analogs_used": {
       "type": "integer"
      },
      "forward_days": {
       "type": "integer"
      },
      "generated_at": {
       "type": "string"
      },
      "query_window": {
       "type": "object"
      },
      "peer_playbook": {
       "type": "object"
      },
      "primary_ticker": {
       "type": "string"
      },
      "query_return_pct": {
       "type": "number"
      }
     }
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "peers": [
   "MSFT",
   "AMD",
   "QQQ"
  ],
  "analogs_used": 8,
  "forward_days": 30,
  "query_window": {
   "end": "2026-01-15",
   "start": "2025-11-01"
  },
  "peer_playbook": {
   "AMD": {
    "count": 8,
    "pct_positive": 75,
    "avg_return_pct": 6.8
   },
   "QQQ": {
    "count": 8,
    "pct_positive": 62.5,
    "avg_return_pct": 3.1
   },
   "MSFT": {
    "count": 8,
    "pct_positive": 62.5,
    "avg_return_pct": 4.2
   }
  },
  "primary_ticker": "NVDA",
  "query_return_pct": 12.4
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/market2000-xyz-playbook-time-series-pattern-matching-api-53a60dfe/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)