market-ingest

Original：🇺🇸 English

Translated

Ingest and normalize market data into OHLCV vectors with HNSW indexing

3installs

Sourceruvnet/ruflo

Added on2026-05-03

NPX Install

npx skill4agent add ruvnet/ruflo market-ingest

SKILL.md Content

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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

Fetch data -- retrieve OHLCV data for the symbol from the configured data source (REST API, CSV file, or manual input)
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
Vectorize -- encode each candle as a 64-dimension padded vector (5 normalized OHLCV values + padding)
Store -- call
```
mcp__claude-flow__agentdb_hierarchical-store
```
to persist normalized OHLCV data in the
```
market-data
```
namespace with symbol and date as keys
Index -- call
```
mcp__claude-flow__ruvllm_hnsw_add
```
to add vectors to the HNSW index for nearest-neighbor search
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"