BrainBytesandClaude Opus 4.6
9a8fcfcaac
The complete trading and quantitative finance system for Claude Code. - 18 specialized agents (quant-researcher, algo-strategist, risk-manager, derivatives-analyst, etc.) - 82 domain skills across 10 categories (quant methods, strategies, risk, derivatives, portfolio, execution, data, crypto, macro, backtesting) - 20 slash commands for trading workflows - 5 always-active rules (risk limits, data integrity, model governance, regulatory compliance, ethical trading) - 4 hook scripts (risk check, backtest validation, data quality, session start) - 3 context modes (research, live-trading, backtesting) - 3 example configs (quant fund, crypto trader, retail algo) - 8 MCP server configurations - The Complete Guide (setup + advanced patterns) - Viral-ready README, CONTRIBUTING, CI, PR template Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
4.2 KiB
4.2 KiB
description
| description |
|---|
| Decompose portfolio returns by factor exposures and identify unintended bets |
/factor-analysis
What This Command Does
Performs factor exposure analysis on a portfolio, decomposing returns and risk into systematic factor contributions (market, size, value, momentum, quality, volatility, and custom factors). The quant-researcher and portfolio-manager agents collaborate to identify intended and unintended factor bets.
When to Use
- You want to understand what factors are driving your portfolio returns
- You need to check for unintended factor tilts or crowded exposures
- You are building a factor-neutral portfolio and need to verify neutrality
- You want to attribute past performance to factor vs alpha contributions
How It Works
- Portfolio mapping — Maps holdings to factor loadings using cross-sectional regression or characteristic-based assignment.
- Factor decomposition — Decomposes portfolio returns into factor and residual (alpha) components.
- Exposure analysis — Quantifies active factor tilts relative to the benchmark.
- Crowding assessment — Checks factor exposures against aggregate market positioning for crowding risk.
- Attribution — Attributes historical returns to each factor and the residual.
- Recommendations — Identifies factor tilts to adjust and hedging trades to neutralize unintended exposures.
Example Usage
Input:
/factor-analysis for a US equity long-short portfolio,
benchmark Russell 1000, check for factor crowding
Output:
## Factor Analysis — US Equity Long-Short
### Factor Exposures (Active vs Russell 1000)
| Factor | Long Book | Short Book | Net | Benchmark | Active |
|----------------|-----------|-----------|-------|-----------|--------|
| Market (beta) | 1.05 | 0.92 | 0.13 | 1.00 | +0.13 |
| Size (SMB) | 0.28 | -0.15 | 0.43 | 0.00 | +0.43 |
| Value (HML) | 0.35 | 0.02 | 0.33 | 0.00 | +0.33 |
| Momentum (UMD) | 0.18 | -0.42 | 0.60 | 0.00 | +0.60 |
| Quality (QMJ) | 0.22 | -0.31 | 0.53 | 0.00 | +0.53 |
| Low Vol (BAB) | -0.08 | 0.12 | -0.20 | 0.00 | -0.20 |
### Return Attribution (Last 12 Months)
| Component | Contribution | % of Total Return |
|----------------|-------------|-------------------|
| Market | +1.4% | 12% |
| Size | +2.1% | 18% |
| Value | +1.8% | 15% |
| Momentum | +3.6% | 31% |
| Quality | +2.4% | 20% |
| Low Vol | -0.3% | -3% |
| **Alpha** | **+0.8%** | **7%** |
| **Total** | **+11.8%** | **100%** |
### Crowding Assessment
| Factor | Portfolio Tilt | Hedge Fund Avg | Crowding Risk |
|-----------|---------------|----------------|---------------|
| Momentum | +0.60 | +0.52 | HIGH |
| Quality | +0.53 | +0.41 | MODERATE |
| Size | +0.43 | +0.18 | LOW |
| Value | +0.33 | +0.25 | LOW |
WARNING: Momentum exposure (+0.60) is at the 89th percentile of
hedge fund positioning. Risk of crowded unwind during momentum
reversal events.
### Findings
1. Only 7% of returns attributable to alpha — 93% is factor exposure
2. Momentum is the dominant driver and is potentially crowded
3. Net long beta (+0.13) introduces unintended directional risk
4. Quality tilt is appropriate for late-cycle positioning
### Recommendations
- Reduce momentum exposure by 0.20 to bring below crowding threshold
- Hedge residual beta with S&P 500 futures (short ~$2M notional)
- Consider adding low-vol names to long book for diversification
- Monitor momentum factor for reversal signals (momentum crash indicator)
Related Agents
- quant-researcher — Co-leads, performs factor regression and crowding analysis
- portfolio-manager — Co-leads, translates factor insights into portfolio adjustments
- risk-manager — Validates factor exposure limits and concentration
- algo-strategist — Designs factor-hedging overlay strategies