| name | factor-mining |
| shortcut | factor |
| description | Guide user through factor research and mining. Trigger when user asks to "find effective factors", "mine factors", "research alpha factors", "help me design a factor", "factor analysis", or similar requests about discovering new trading factors. |
| description_zh | 引导用户进行因子研究与挖掘，包括假设生成、表达式构建、有效性评估和因子保存。 |
Factor Mining Workflow
Guide the user through discovering, testing, and saving effective trading factors.
Pre-requisites (MUST confirm)
- Which exchange to analyze (Hyperliquid or Binance)
- Which symbol(s) to focus on (e.g., BTC, ETH)
- What trading style or hypothesis they have in mind (optional)
Workflow
Phase 1: Survey Existing Factors
Use query_factors to check what's already computed:
- Show top factors ranked by |ICIR| for the target symbol
- Highlight factors with |ICIR| > 1.5 (strong) or |IC| > 0.05 (meaningful)
- Note which categories are well-covered vs under-explored
[CHECKPOINT] Present existing factor landscape. Ask user:
- Any patterns they notice?
- Which direction to explore (momentum, volatility, microstructure, custom)?
Phase 2: Hypothesis Generation
IMPORTANT: Call get_factor_functions first to get the full list of available functions and their signatures. Do NOT guess function names or signatures — always check the registry.
Based on user's interest, generate 2-3 factor hypotheses:
Approach A: Expression-based (no web search needed)
- Combine existing indicators in new ways
- Common patterns: ratio (EMA7/EMA21-1), acceleration (ROC3-ROC10), normalized deviation ((close-SMA20)/STDDEV(close,20))
- Cross-category combinations (momentum + volatility, trend + volume)
Approach B: Research-driven (use web_search + fetch_url)
- If user wants inspiration from academic research, known factor libraries, or quant blogs
- Step 1: Search for sources — prioritize academic and code repositories:
- For known factor sets (e.g., WorldQuant 101 Alphas):
site:github.com WorldQuant alpha101 formula or site:arxiv.org 101 Formulaic Alphas
- For specific factor numbers:
site:github.com "Alpha#101" formula
- For general quant research:
site:arxiv.org cryptocurrency momentum factor
- Step 2: Fetch full content — use
fetch_url on the most promising URL to read the actual formula/paper
- Step 3: Translate to expression — convert the retrieved formula into a factor expression compatible with our system
- Do NOT repeatedly search with different keywords hoping snippets contain the answer
[CHECKPOINT] Present hypotheses with rationale. Let user pick which to test.
Phase 3: Test & Evaluate
For each chosen hypothesis:
- Use
evaluate_factor with the expression + target symbol
- Interpret results:
- ICIR > 2.0: Very strong predictive power
- ICIR 1.0-2.0: Moderate, worth exploring
- ICIR < 0.5: Weak, likely noise
- Win rate > 55%: Directionally useful
- Check across forward periods (1h/4h/12h/24h) for decay pattern
- Compare with existing built-in factors — is the new factor adding value?
[CHECKPOINT] Present evaluation results in a comparison table. Recommend which factors to keep.
Phase 4: Save & Compute
For factors that pass evaluation:
- Use
save_factor with a descriptive name and clear description
- Use
compute_factor to run full evaluation across all watchlist symbols
- Suggest the user visit Factor Library to view results
[CHECKPOINT] Summarize what was saved. Suggest next steps:
- Test more variations of successful factors
- Consider how to integrate into trading strategy (Phase 3-4 of factor system)
- Set up periodic re-evaluation
Tips for the AI
- Always call
get_factor_functions before writing any expression
- Always use English for web search queries (better results)
- When comparing factors, use a markdown table for clarity
- Explain IC/ICIR/win_rate in plain language for less experienced users
- If a factor has high IC but low ICIR, explain it means inconsistent signal
- Suggest testing both the factor and its negation (multiply by -1)
