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market2000.xyz Backtest — Historical Analog Forward-Return Distribution
x402
market2000.xyz Backtest — Historical Analog Forward-Return Distribution is a paid API for AI agents from market2000.xyz, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-16).
Finds historical price-pattern analogs for a ticker's recent shape and returns the forward-return distribution (win rate, median, tails, sample size) over a specified horizon.
Price / call$0.1
Success—
StatusUnknown
How this service works
Backtest — historical-analog forward-return distribution for a ticker's recent shape. HOW TO CALL: GET /api/backtest?ticker=BTC&lookback_days=20&forward_days=30 PARAMETERS: ticker — asset symbol (required) lookback_days — pattern window, 5–60 (default 20) forward_days — forward horizon, 7–90 (default 30) min_return / max_return — optional analog filters (fraction) RESPONSE: win rate, median, tails, sample size over the forward horizon. PRICING: $0.10 per call.
Output
Returns a statistical summary of historical forward-return analogs: win rate (fraction of positive outcomes), median return, tail percentiles (e.g. 10th/90th), and the number of matching historical analog periods over the specified forward horizon.
Example use cases
Pre-trade conviction check for crypto
“Before I buy ETH, can you backtest its current 20-day price shape against history and tell me how often it went up over the next 30 days, what the median return was, and what the downside tail looks like?”
Equity pattern analysis before earnings
“Run a 14-day lookback backtest on NVDA and show me the historical forward-return distribution for the next 21 days — I want to know the win rate and the 10th/90th percentile outcomes.”
Filtered analog search for strong setups
“Find historical BTC analogs matching the last 30 days of price action, but only keep analogs where the forward 60-day return was at least 10%, and tell me the sample size and median return.”
Technical details
Endpoint
GEThttps://market2000.xyz/api/backtestWhen to prefer
Use this endpoint when you need a data-driven, historically grounded probability distribution for a ticker's near-term returns based on pattern similarity — not a point forecast or a sentiment signal. It is especially useful for quantifying conviction before a trade, sizing a position, or filtering for historically favorable setups. Prefer it over generic forecast APIs when the user wants win rates, tail risks, and sample-size transparency from real historical analogs.
Side effects
- Invalid or unrecognized ticker symbol returns an error or empty analog set
- lookback_days or forward_days outside allowed ranges (5–60 and 7–90 respectively) may return a validation error
- min_return/max_return filters too restrictive may result in a very small or zero sample size
- Insufficient historical data for a recently listed asset yields low or zero sample count
- Payment failure (x402) returns 402 status before any data is returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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