nixtla-market-risk-analyzer
Analyze market risk with VaR, volatility, and position sizing using forecast data. Use when assessing investment risk. Trigger with 'analyze market risk' or 'calculate VaR'.
npx skills add majiayu000/claude-skill-registry --skill nixtla-market-risk-analyzer-intent-solutions-io-plugins-nixtla --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Market Risk Analyzer Calculates key market risk metrics and recommends optimal position sizes using historical data analysis. ## Overview This skill analyzes market risk using historical price data to calculate Value at Risk (VaR), volatility metrics, maximum drawdown, Sharpe ratios, and optimal position sizing. Provides actionable insights for managing investment risk and optimizing portfolio allocation. Uses four specialized Python scripts for data preparation, risk analysis, position sizing, and report generation. ## Prerequisites **Tools**: Read, Write, Bash, Glob, Grep **Environment**: Optional `NIXTLA_TIMEGPT_API_KEY` for volatility forecasting (not required for core analysis) **Packages**: ```bash pip install pandas numpy scipy matplotlib # Optional for forecasting: pip install nixtla ``` **Input Data Format**: CSV file with columns: - Date column: `ds`, `date`, or `timestamp` - Price column: `y`, `price`, or `close` ## Instructions ### Step 1: Prepare Price Data Execute the data preparation script to load prices and calculate returns: ```bash python {baseDir}/scripts/prepare_data.py prices.csv --method log --output returns.csv ``` **Script**: `{baseDir}/scripts/prepare_da
- Overview
- Prerequisites
- Instructions
- Step 1: Prepare Price Data
- Step 2: Calculate Risk Metrics
- Step 3: Calculate Position Sizing
- Step 4: Generate Risk Report
- Output
- Error Handling
- Examples
- Example 1: Analyze Stock Risk
- Example 2: Prediction Market Contract Analysis
- Example 3: Custom Risk Parameters
- Resources
pip install pandas numpy scipy matplotlib
Optional for forecasting:
pip install nixtla
python {baseDir}/scripts/prepare_data.py prices.csv --method log --output returns.csv
python {baseDir}/scripts/risk_metrics.py prices.csv --output risk_metrics.json --risk-free-rate 0.05
python {baseDir}/scripts/position_sizing.py \
python {baseDir}/scripts/generate_report.py prices.csv \
Complete workflow for stock analysis
python {baseDir}/scripts/prepare_data.py AAPL_prices.csv
python {baseDir}/scripts/risk_metrics.py AAPL_prices.csvWhat does the nixtla-market-risk-analyzer skill do?
Analyze market risk with VaR, volatility, and position sizing using forecast data. Use when assessing investment risk. Trigger with 'analyze market risk' or 'calculate VaR'.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill nixtla-market-risk-analyzer-intent-solutions-io-plugins-nixtla --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.