Documentation
Neural API Reference
Comprehensive documentation for the rlx-search engine telemetry and the MCP orchestration server.
Engine StatusOperational
Engine Overview
The rlx-search service exposes high-performance JSON endpoints designed for historical pattern matching, walk-forward backtesting, and reinforcement learning telemetry.
Production Layer
Secure
https://aipricepatterns.comInteractive Playground
Test pattern search and backtest logic directly in our high-fidelity sandbox. No setup required.
MCP Agent Protocol
Deploy autonomous agents that reason over pattern distributions using our Model Context Protocol server.
Core Telemetry Schema
| Parameter | Logic Type | Fallback | Description |
|---|---|---|---|
| symbol | string | BTCUSDT | Target trading pair for neural scanning. |
| interval | string | 1h | Data granularity (resolution). |
| q | integer | 40 | Query window length (Lookback window). |
| f | integer | 30 | Forecast horizon projection. |
| start | integer | latest | Anchor point in history (0 = inception). |
| limit | integer | 32 | Max match density per telemetry request. |
| sort | string | similarity | Metric prioritization (corr, rmse, similarity). |
| force | bool | false | Bypass telemetry cache for real-time recalculation. |
State Invalidation Notice
Dataset mutations (expand, pruning) automatically evict the in-memory pattern caches and ANN indices. Subsequent telemetry requests will rebuild state transparently, which may impact latencies for large (~1M+ bar) datasets.
Zero-to-One
1. Set Global Context
Shell Configuration
export BASE_URL="https://aipricepatterns.com"
2. Target Analogs (Neural Search)
curl -s -X POST "$BASE_URL/api/v1/patterns/search" \
-H "Content-Type: application/json" \
-d '{"symbol":"BTCUSDT","interval":"1h","q":40,"f":30,"limit":10}' | jq