# Alpha Factor Mining

> Compute momentum, fundamental quality, and news sentiment factor signals in parallel to generate a fused target portfolio.

- Skill: `knuckles-team/alpha-factor-mining` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add knuckles-team/alpha-factor-mining`
- Raw SKILL.md: https://api.skillmd.com/api/skills/knuckles-team/alpha-factor-mining/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Knuckles-Team (https://skillmd.com/u/knuckles-team)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/knuckles-team/alpha-factor-mining

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# Alpha Factor Mining Workflow

**CONCEPT:EE-011**

Compute momentum, fundamental quality, and news sentiment factor signals in parallel to generate a fused target portfolio.

## Steps

### Step 1: Technical Alpha
**Agent**: `data-fetcher`
**Tools**: `graph_query, sx_search`

Compute rolling standard momentum, RSI, mean-reversion metrics, and volume-weighted indicators from high-frequency market tick logs.
Expected: `technical-factors`

### Step 2: Fundamental Alpha [depends_on: none]
**Agent**: `compute-engine`
**Tools**: `graph_analyze`

Extract historical and recent financial filing data, calculating PE, debt-to-equity ratios, and gross margin momentum.
Expected: `fundamental-factors`

### Step 3: Sentiment Alpha [depends_on: none]
**Agent**: `risk-assessor`
**Tools**: `graph_query, graph_analyze`

Perform natural language sentiment extraction from recent financial news stories, earnings call transcripts, and social media feeds.
Expected: `sentiment-factors`

### Step 4: Factor Fusion [depends_on: technical-alpha, fundamental-alpha, sentiment-alpha]
**Agent**: `report-generator`
**Tools**: `graph_write, document_tools`

Synthesize the technical, fundamental, and sentiment signals, perform correlation testing to remove multi-collinearity, and run a risk-budgeted mean-variance optimization.
Expected: `optimized-portfolio-weights`

### Step 5: KG Persistence [depends_on: Factor Fusion]
**Agent**: `report-generator`
**Tools**: `graph_write`

Persist workflow results as nodes and edges in the Knowledge Graph.
Create appropriate typed nodes with metadata and link to existing domain entities.

## Output
- Alpha Factor Mining results persisted in KG
- Structured report (MD/PDF)
- Audit trail with timestamps and agent attributions

## Execution

Run this workflow as a dependency-ordered DAG. Steps with no unmet `depends_on` run in parallel; dependents run after their prerequisites complete.

- **Run first (in parallel):** Step 1 — Technical Alpha; Step 2 — Fundamental Alpha; Step 3 — Sentiment Alpha
- **After level 0:** Step 4 — Factor Fusion
- **After level 1:** Step 5 — KG Persistence

**Execution:** If graph-os is reachable, offload the whole DAG via `graph_orchestrate action=execute_workflow` (or the `kg-delegate` skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet `depends_on` in parallel, then their dependents.