risk-metrics-calculation

Overview of risk-metrics-calculation skill

The risk-metrics-calculation skill is a finance-focused analysis skill for computing portfolio and strategy risk measures such as volatility, beta, Value at Risk (VaR), Conditional VaR / Expected Shortfall, drawdowns, Sharpe ratio, Sortino ratio, and related risk-adjusted performance views. It is best suited to users who already have return series, position history, or portfolio performance data and want a repeatable way to turn that data into defensible risk reporting.

Who this skill is for

This skill fits:

• portfolio managers and analysts building risk dashboards
• quantitative traders validating strategy risk
• finance teams setting limits or monitoring exposures
• developers who want the model to generate or adapt risk-metric code quickly

It is less useful if you do not yet have usable return data, a defined time horizon, or clarity on whether you need portfolio-level, strategy-level, or asset-level risk measurement.

What job the risk-metrics-calculation skill helps you do

Most users are not looking for a textbook definition of VaR or Sharpe. They need to answer practical questions like:

• How risky is this portfolio over a chosen horizon?
• What is the worst expected loss under normal or tail conditions?
• Is performance attractive after adjusting for downside risk?
• How severe and persistent were drawdowns?
• Which metrics should be used for monitoring, limits, or reporting?

The risk-metrics-calculation skill is useful because it organizes those calculations into a coherent workflow instead of leaving you to ask for each metric separately.

What makes this skill different from a generic finance prompt

A normal prompt may return a few formulas. The risk-metrics-calculation skill is more install-worthy when you need:

• a bundled set of metrics, not one-off answers
• implementation patterns in Python for actual calculation
• guidance across metric categories: volatility, tail risk, drawdown, and risk-adjusted return
• explicit attention to time horizon, which materially changes interpretation

That structure reduces guesswork when converting raw returns into usable risk output.

What to verify before adopting it

Before using this risk-metrics-calculation skill in production work, confirm:

• your return series frequency is consistent
• your benchmark is defined if you need beta or relative risk
• your risk-free rate assumption matches your market and period
• you know whether historical, parametric, or scenario-style risk estimates are required
• you can explain the limitations of each metric to stakeholders

This is a calculation aid, not a substitute for market data quality control or risk governance.

How to Use risk-metrics-calculation skill

Install context for risk-metrics-calculation

This repository is part of the wshobson/agents skill collection. A common install pattern is:

npx skills add https://github.com/wshobson/agents --skill risk-metrics-calculation

If your environment uses a different skill loader, use the GitHub path directly:

https://github.com/wshobson/agents/tree/main/plugins/quantitative-trading/skills/risk-metrics-calculation

Because this skill appears to be contained mainly in SKILL.md, adoption is straightforward: you are primarily installing a structured calculation guide rather than a package with helper scripts.

Read this file first

Start with:

• SKILL.md

Since the repository evidence shows no extra resources/, references/, rules/, or helper scripts for this skill, SKILL.md is the main source of truth. That matters for installation decisions: there is less hidden machinery, but also less automation and fewer baked-in validation rules.

What input the skill needs

The risk-metrics-calculation skill works best when you provide:

• a return series, ideally as periodic decimal returns
• portfolio weights or position-level returns if computing portfolio risk
• time frequency: intraday, daily, weekly, monthly, or annual
• lookback window, such as 60, 252, or 756 observations
• confidence level for tail metrics, such as 95% or 99%
• risk-free rate assumption for Sharpe-style metrics
• benchmark returns if asking for beta or relative risk
• any constraints, such as regulatory reporting, capital limits, or downside-only analysis

Without these inputs, the model may still produce formulas, but not a reliable risk workflow.

Strong prompt structure for risk-metrics-calculation usage

A weak prompt:

• “Calculate portfolio risk.”

A stronger prompt:

• “Use the risk-metrics-calculation skill to compute daily volatility, 95% historical VaR, 95% CVaR, max drawdown, Sharpe, and Sortino for this strategy. Assume daily returns, annual risk-free rate of 3%, 252 trading days, and explain any annualization choices. Return both Python code and a short interpretation.”

Why this is better:

• it names the metrics
• it defines frequency and annualization basis
• it removes ambiguity around confidence level
• it tells the model whether code, explanation, or both are required

Example inputs that improve output quality

Provide returns like this:

• asset or portfolio return series
• date index
• frequency
• missing data handling preference
• benchmark series if needed

Example prompt framing:

“Use the risk-metrics-calculation skill for Finance. I have 3 years of daily portfolio returns in a pandas Series. Compute annualized volatility, Sharpe, Sortino, 99% historical VaR, 99% CVaR, rolling 60-day max drawdown, and Calmar ratio. Flag assumptions, edge cases, and whether the sample size is weak for tail estimates.”

That prompt yields better output than asking for “risk metrics” because it specifies scope and quality checks.

Typical workflow in practice

A practical risk-metrics-calculation guide looks like this:

1. Clean and align return data.
2. Confirm frequency and lookback.
3. Choose metric families relevant to the decision.
4. Compute core statistics.
5. Annualize only where appropriate.
6. Interpret results in portfolio context.
7. Compare against limits, benchmarks, or prior periods.

This matters because many bad outputs come from skipping step 1 or mixing incompatible frequencies.

Metrics this skill is best at bundling

The source skill clearly centers on four categories:

• volatility metrics: standard deviation, beta
• tail risk metrics: VaR, CVaR
• drawdown metrics: max drawdown, Calmar
• risk-adjusted metrics: Sharpe, Sortino

That makes it especially useful when you want a balanced risk report rather than over-relying on one headline number.

Time-horizon choices change the answer

One of the most important details in the source material is time horizon. The same portfolio can look safe or dangerous depending on whether you measure:

• intraday risk for active trading
• daily risk for standard monitoring
• weekly or monthly risk for rebalancing
• annualized risk for strategic reporting

If you use the risk-metrics-calculation skill without stating horizon, you risk mismatched assumptions and misleading annualization.

When to ask for code vs interpretation

Ask for code when you need:

• reproducible calculations
• integration into notebooks or dashboards
• transparent formulas and assumptions

Ask for interpretation when you need:

• explanation of why Sharpe and Sortino differ
• discussion of tail loss severity
• commentary on whether drawdowns are acceptable for mandate limits

Best results usually come from requesting both in one pass.

Practical cautions before using output

The skill is useful, but you should explicitly guard against:

• non-stationary return series
• very short samples for VaR/CVaR
• mixing arithmetic and log returns without saying so
• annualizing metrics from sparse or irregular data
• using Sharpe where returns are highly skewed
• treating historical VaR as forward truth

These are not minor details; they are common reasons finance teams reject model-generated risk analysis.

risk-metrics-calculation skill FAQ

Is the risk-metrics-calculation skill good for beginners?

Yes, if you already understand basic return series and portfolio concepts. It is not a full finance primer. Beginners can use it to generate code and structured explanations, but they still need to understand what each metric means and when it breaks down.

What is the main advantage over a normal LLM prompt?

The main benefit of the risk-metrics-calculation skill is scoped structure. It pushes the model toward a complete risk toolkit instead of drifting into generic finance commentary or giving only one metric such as volatility.

Does this skill cover regulatory-grade risk reporting?

Not by itself. It can help draft calculations used in reporting, but regulatory workflows usually need:

• approved methodologies
• documented data lineage
• exception handling
• model validation
• governance sign-off

Use it as an accelerator, not as a compliance system.

Is risk-metrics-calculation usage limited to portfolios?

No. You can use it for:

• single-asset return streams
• trading strategies
• factor sleeves
• portfolio aggregates

The key requirement is having a coherent return series and a clear interpretation target.

When should I not use this skill?

Skip this skill if:

• you need option Greeks or derivatives-specific risk engines
• you need Monte Carlo infrastructure beyond simple prompting
• your data is incomplete or not yet normalized
• you need real-time production risk services, not analysis guidance

In those cases, a dedicated quant stack is a better fit.

Does risk-metrics-calculation for Finance require Python?

No, but the upstream skill includes Python implementation patterns, so Python is the natural fit. If your stack is R, SQL, or another environment, ask the model to translate the calculations while preserving assumptions.

How to Improve risk-metrics-calculation skill

Give better data, not just more data

The fastest way to improve risk-metrics-calculation output is to provide cleaner inputs:

• clearly labeled periodic returns
• known frequency
• explicit benchmark
• known handling of missing days
• statement of whether returns are net or gross of fees

Messy inputs create false precision.

Specify the exact metric definitions you want

Many “wrong” outputs are actually definition mismatches. Improve quality by stating:

• historical vs parametric VaR
• confidence level
• downside threshold for Sortino
• rolling window length
• simple vs log returns
• annualization basis such as 252 trading days

This avoids silent assumption drift.

Ask for a complete output shape

A strong request for the risk-metrics-calculation skill includes:

• formulas or method names
• implementation code
• assumptions
• edge-case warnings
• plain-English interpretation
• tabular summary of metrics

That structure makes the first answer more usable and easier to review.

Iterate on outliers and bad-looking numbers

If the first output shows suspicious VaR, Sharpe, or drawdown results, ask follow-up questions like:

• “Recalculate after removing NaNs and checking duplicate dates.”
• “Show rolling metrics to detect regime change.”
• “Compare historical and parametric VaR.”
• “Explain why Sortino exceeds Sharpe by this amount.”
• “Check whether annualization is being applied correctly.”

This is a better improvement loop than simply saying “try again.”

Use comparison prompts to improve decision value

The skill becomes more actionable when you compare:

• current portfolio vs benchmark
• current month vs trailing year
• strategy A vs strategy B
• equal-weight vs actual-weight portfolio
• pre-hedge vs post-hedge risk

Comparison prompts turn raw metrics into decisions.

Watch the common failure modes

Most weak outputs from the risk-metrics-calculation skill come from:

• missing time horizon
• no confidence level for VaR/CVaR
• no benchmark for beta
• return series too short for tail inference
• code that computes metrics but does not interpret them
• outputs that ignore downside asymmetry or drawdown path

If you check those items first, results improve quickly.

Make the model explain tradeoffs, not just numbers

For stronger risk-metrics-calculation usage, ask:

• which metric best matches my use case?
• what are the blind spots of VaR here?
• why is drawdown more decision-relevant than volatility?
• when should I prefer Sortino over Sharpe?

This helps turn the skill from a calculator into a better finance analysis assistant.

Pair this skill with your own review checklist

Before trusting the output, review:

• data frequency
• annualization assumptions
• metric definitions
• sample length adequacy
• benchmark selection
• interpretation quality
• whether the output answers the actual portfolio decision

That final review is where most real-world quality gains happen.