Factor Mining

Factor Mining Workflow

Guide the user through discovering, testing, and saving effective trading factors.

Pre-requisites (MUST confirm)

1. Which exchange to analyze (Hyperliquid or Binance)
2. Which symbol(s) to focus on (e.g., BTC, ETH)
3. 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:

1. Use evaluate_factor with the expression + target symbol
2. 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
3. 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:

1. Use save_factor with a descriptive name and clear description
2. Use compute_factor to run full evaluation across all watchlist symbols
3. 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)