---
name: market-ingest-ca741be3588a
description: Ingest and normalize market data into OHLCV vectors with HNSW indexing
argument-hint: "<symbol> [--source api]"
allowed-tools: Bash mcp__claude-flow__memory_store mcp__claude-flow__memory_search mcp__claude-flow__ruvllm_hnsw_create mcp__claude-flow__ruvllm_hnsw_add mcp__claude-flow__embeddings_generate
---

# Market Ingest

Fetch market data for a symbol, normalize to OHLCV vectors, and store with HNSW indexing for fast pattern search.

## When to use

When you need to ingest raw market data (price and volume) for a symbol and prepare it for pattern detection and similarity search. This is the first step before running pattern detection or comparison.

## Steps

1. **Fetch data** -- retrieve OHLCV data for the symbol from the configured data source (REST API, CSV file, or manual input)
2. **Normalize** -- convert raw prices to relative values:
   - Open: `(open - prev_close) / prev_close`
   - High: `(high - open) / open`
   - Low: `(low - open) / open`
   - Close: `(close - open) / open`
   - Volume: Z-score against rolling mean/std
3. **Vectorize** -- encode each candle as a 64-dimension padded vector (5 normalized OHLCV values + padding). For semantic embeddings of pattern descriptions, use `mcp__claude-flow__embeddings_generate` (NOT `embeddings_embed` — that tool name does not exist).
4. **Store** -- call `mcp__claude-flow__memory_store --namespace market-data` to persist normalized OHLCV data with symbol+date keys. The `memory_*` tool family routes by namespace; the `agentdb_hierarchical-*` family routes by tier (`working|episodic|semantic`) and ignores namespace strings, so use `memory_*` here.
5. **Index** -- call `mcp__claude-flow__ruvllm_hnsw_add` to add vectors to the HNSW index for nearest-neighbor search.
6. **Report** -- summarize: candles ingested, date range, price range, average volume

## CLI alternative

```bash
npx @claude-flow/cli@latest memory store --namespace market-data --key "symbol-SYMBOL-DATE" --value "OHLCV_JSON"
```


---
Source attribution: digitalcrest01
Original project: https://github.com/digitalcrest01/ruflow/blob/8f78f238e1775aedf7faa6026b5f20c9aa8cbd12/plugins/ruflo-market-data/skills/market-ingest/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2024-2026 ruvnet

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: digitalcrest01
Original project: https://github.com/digitalcrest01/ruflow/blob/8f78f238e1775aedf7faa6026b5f20c9aa8cbd12/plugins/ruflo-market-data/skills/market-ingest/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Analyst, Market Research Analyst, ML / AI Engineer
Specialties: API, Database
Tools: Claude Flow
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: c9d4b1246c3f8d21206444c6e399156844dbf15cb15616a862f4f8dd85a7bfe0