market-ingest
Ingest and normalize market data into OHLCV vectors with HNSW indexing
Best for data pipelinesWorks with GitHub
⌘source
- author
@ruvnet- repo
- ruvnet/ruflo
- language
- TypeScript
★46kstars
5.1kforks
May 5, 2026
✦overview.md
Key Features
- ·Fetches OHLCV data from REST API, CSV, or manual input
- ·Normalizes prices to relative values
- ·Computes volume Z-score
- ·Encodes candles as 64-dim vectors
- ·Stores data with HNSW indexing for fast pattern search
- ·Uses memory tools for persistent storage
Use Cases
- →Ingesting daily stock prices to find similar historical patterns
- →Preprocessing crypto market data before running anomaly detection
- →Building a searchable database of normalized market vectors
Best for
- ✓data pipelines
- ✓market analysis
- ✓quantitative research
Not ideal for
- !real-time high-frequency trading
- !non-financial time series
FAQs
name
market-ingest
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
- 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
- Open:
- 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(NOTembeddings_embed— that tool name does not exist).
$install
1-click copynpx skills add ruvnet/ruflo --skill market-ingestSafety assessment
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4/ 5
very goodClear and well structured, with only minor parts that might need a second read.
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4/ 5
highMostly actionable with clear steps; only a few small gaps remain.