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What a daily market risk dashboard should show
A compact dashboard is most useful when it makes the portfolio’s performance and risk assumptions visible alongside the results. A public Python project demonstrates a workflow for equity and ETF portfolios using user-selected tickers, weights, date ranges, and a benchmark. Its project description is an example, not an independent validation of the calculations: project overview.
- Portfolio and benchmark performance: show cumulative equity curves and identify the tickers, weights, and benchmark used.
- Volatility: report daily and annualized volatility, with the return period and annualization convention stated. The example does not establish one specific convention.
- Maximum drawdown: show the largest peak-to-trough decline over the selected observation window.
- Tail-loss estimates: label the VaR or Expected Shortfall (ES) confidence level, time horizon, sample window, and calculation method.
- Rolling correlation: show how the portfolio’s historical relationship with the selected benchmark changes over time; it is not a guarantee of how they will move together in the future.
- Stress illustrations: make clear that a one-day shock or historical crisis window is a what-if calculation based on stated assumptions, not a forecast.
How to build the free prototype
The example’s stack is Python, pandas, NumPy, SciPy, yfinance, and Streamlit. Its project description outlines a straightforward pipeline: fetch prices, calculate returns, combine returns using portfolio weights, calculate risk statistics, and render charts. It suggests installing dependencies manually, so do not assume that its environment is pinned or reproducible without checking the project files.
- Choose inputs: collect the tickers, portfolio weights, date range, and benchmark the user wants to analyze.
- Fetch price history: the example uses yfinance to retrieve historical OHLCV data from Yahoo Finance.
- Calculate daily returns: derive each asset’s returns from the selected price series, then combine them using the portfolio weights.
- Calculate and label risk metrics: compute volatility, maximum drawdown, VaR, and ES. Include the method, confidence level, horizon, and lookback window wherever a tail-loss figure appears.
- Render the results: use Streamlit for inputs and charts, including portfolio and benchmark performance, rolling correlation, and clearly defined stress scenarios.
Using yfinance in this example establishes what the project uses; it does not establish data licensing, guaranteed uptime, commercial-use suitability, or an official Yahoo Finance API commitment. Check the relevant provider terms and data suitability before relying on an input for more than a prototype.
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How VaR, Expected Shortfall, and stress tests differ
A dashboard should not present these measures as interchangeable. The project describes historical VaR, normal-distribution parametric VaR, and historical ES/CVaR.
| Measure | What it represents | What to label |
|---|---|---|
| Historical VaR | A loss threshold estimated from the portfolio’s observed historical return distribution. | Confidence level, horizon, and historical sample window. |
| Parametric VaR | A threshold estimated using a normal-distribution assumption. | Confidence level, horizon, sample inputs, and the parametric method. |
| Historical ES / CVaR | The average loss beyond the VaR threshold in the historical tail. | Confidence level, horizon, and historical sample window. |
| Stress scenario | A what-if estimate under a selected one-day shock or defined historical crisis period. | The shock or crisis dates and assumptions used. |
These figures depend on their method and chosen observation window. They describe estimates under those assumptions; they should not be presented as a prediction of the next loss.
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What “free” market data means in practice
The example uses yfinance for historical equity and ETF price data, but the project description does not establish its data rights or service guarantees. Another option, XOOMAR, advertises 31 datasets, with JSON access and mostly CSV access, and says users can start without an API key. According to its own page, checked on October 4, 2026, it allows 10 requests per minute per IP without a key or 30 requests per minute with a free account key, and offers at least six months of history. Its advertised categories include SEC filings and ownership, short-interest and FINRA data, macroeconomic and interest-rate data, positioning and flows, crypto derivatives, and government data. These are provider-published claims, not an independent audit; check current coverage, quotas, terms, attribution requirements, and data quality before building around the service: XOOMAR data access.
For context, the Federal Reserve describes FR VV-1 as a supervisory reporting regime for covered trading desks that collects daily risk, performance, and customer-facing activity data, including VaR and profit-and-loss attribution. It is not a template that a personal dashboard must follow. The Federal Reserve page says the current form applies to covered firms and lists a last update of January 2, 2024: Federal Reserve FR VV-1 information.
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Why curl behaves differently in Windows PowerShell
In Windows PowerShell 5.1, curl is a built-in alias for Invoke-WebRequest. That alias takes different parameters from the curl executable and can shadow it. Microsoft documents the behavior and notes that PowerShell 7 and later do not define the alias by default: Microsoft Learn: curl on Windows.
Run the actual executable
Call curl.exe explicitly so PowerShell selects the executable rather than the alias:
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curl.exe -X GET "https://example.com/api"For a file download, Microsoft’s example is:
curl.exe -O https://example.com/file.zipRemove the alias for the current session
If you prefer to type curl, remove the alias in the current PowerShell session:
Remove-Item Alias:curlThis removes the alias for that session. If the command is still not found, check which shell and version you are running; the alias behavior above is specific to Windows PowerShell 5.1, not a universal Windows rule.
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