Financial Portfolio Risk Metrics
This dataset provides detailed, simulated and real portfolio-level risk metrics, including volatility, risk scores, drawdowns, and asset class allocations for asset managers and fintech teams. It supports robust risk analysis, performance benchmarking, and scenario modeling, making it ideal for financial analytics, compliance reporting, and investment strategy development.
Sample rows
preview · 8 of 200 rows · all 18 columns| portfolio_idstring | currencystring | volatility_3yfloat | is_simulatedboolean | portfolio_namestring | manager_idstring | as_of_datedate | total_assets_usdfloat | risk_scorefloat | volatility_1yfloat | sharpe_ratio_1yfloat | max_drawdown_1yfloat | value_at_risk_95float | expected_shortfall_95float | annual_return_1yfloat | annual_return_3yfloat | notesstring | asset_class_breakdownstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PF000001 | USD | 3.7 | false | US Conservative Bond Income | MGR001 | 2024-02-29 | 35680000.32 | 22.5 | 3.1 | 0.68 | -1.7 | 0.8 | 1.1 | 3.6 | 3.2 | Retiree mandate | bonds:80,cash:15,equities:5 |
| PF000002 | USD | 18.3 | false | Global Growth Equity Fund | MGR002 | 2023-12-31 | 87400000.1 | 59.4 | 16.8 | 1.09 | -13.2 | 7.6 | 10.2 | 13.2 | 11.2 | blank | equities:70,bonds:15,cash:10,alternatives:5 |
| PF000003 | EUR | 10.1 | false | Eurozone ESG Balanced | MGR003 | 2024-02-29 | 15320000 | 39.7 | 9.2 | 1.21 | -6.1 | 4.2 | 6.3 | 8.7 | 8.1 | Balanced, ESG focus | equities:40,bonds:35,cash:15,alternatives:10 |
| PF000004 | USD | 0.3 | false | Pension Cash Reserve | MGR004 | 2024-02-29 | 18450 | 3.2 | 0.2 | 0.06 | 0 | 0 | 0 | 0.2 | 0.2 | Liquidity only | cash:100 |
| PF000005 | JPY | 19.6 | false | Asia Pacific Opportunities | MGR005 | 2023-11-30 | 23890000.12 | 64.2 | 18.8 | 1.12 | -14.7 | 9.3 | 12.7 | 17.1 | 14.5 | blank | equities:80,bonds:10,cash:5,alternatives:5 |
| PF000006 | GBP | 2.8 | false | UK Gilt & Cash Conservative | MGR006 | 2024-01-31 | 909000 | 14.8 | 2.3 | 0.52 | -0.7 | 0.4 | 0.5 | 2.7 | 3.4 | blank | bonds:70,cash:30 |
| PF000007 | USD | 99.2 | true | Simulated Stress Test Crypto | MGR007 | 2024-02-29 | 20500 | 99.6 | 98.5 | -8.3 | -99.8 | 96.3 | 99 | -94.2 | -84.5 | Scenario: Crypto market crash | cryptocurrency:100 |
| PF000008 | USD | 9.3 | false | North America Real Estate | MGR008 | 2023-12-31 | 52000000 | 34.5 | 8.1 | 1.16 | -3.9 | 2.7 | 4.4 | 8.9 | 8.7 | blank | real_estate:40,equities:35,bonds:15,cash:10 |
| PF000009 | CHF | 6.2 | false | Swiss Multi-Asset Diversified | MGR009 | 2024-01-31 | 18900000 | 28.8 | 5.7 | 0.78 | -2.2 | 1.3 | 1.7 | 4.2 | 4.6 | blank | bonds:65,cash:20,equities:10,alternatives:5 |
| PF000010 | CAD | 11.6 | false | Canadian Balanced Mandate | MGR010 | 2024-02-29 | 5900000 | 51 | 10.5 | 1.02 | -7.2 | 5.5 | 7.9 | 8.7 | 7.4 | blank | equities:55,bonds:25,cash:10,real_estate:10 |
| PF000011 | USD | 100 | true | Simulated Black Swan Event | MGR011 | 2023-12-31 | 12000 | 100 | 100 | -10 | -100 | 100 | 100 | -99.9 | -99.5 | Scenario: Global financial panic | alternatives:50,cryptocurrency:50 |
| PF000012 | JPY | 3.2 | false | Japan Conservative Income | MGR012 | 2024-02-29 | 4600000 | 16.1 | 2.8 | 0.65 | -1 | 0.6 | 0.8 | 2.4 | 2.8 | blank | bonds:90,cash:10 |
| PF000013 | USD | 94.9 | true | Simulated High Return Scenario | MGR013 | 2024-01-31 | 25000 | 97.5 | 94.3 | 3.9 | -92.1 | 93.2 | 96.4 | 50 | 38.4 | Scenario: Crypto bull run | cryptocurrency:90,alternatives:10 |
| PF000014 | EUR | 5.4 | false | Euro Diversified Income | MGR014 | 2023-11-30 | 33400000 | 23.9 | 4.3 | 0.72 | -2.3 | 1.2 | 1.6 | 3.9 | 4.1 | blank | bonds:75,cash:20,equities:5 |
| PF000015 | AUD | 12.9 | false | Aussie Balanced Growth | MGR015 | 2024-02-29 | 8000000 | 48.1 | 11.5 | 1.23 | -6.8 | 5.9 | 7.2 | 9.3 | 8.8 | blank | equities:50,bonds:30,cash:10,commodities:10 |
| PF000016 | USD | 13.7 | false | US Ultra High Net Worth | MGR016 | 2024-01-31 | 327000000 | 44.2 | 12.9 | 1.15 | -7.7 | 6.1 | 8 | 10.7 | 9.6 | blank | equities:55,bonds:20,cash:10,alternatives:15 |
| PF000017 | USD | 0 | false | Simulated 100% Cash Portfolio | MGR017 | 2024-02-29 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Inactive, closed | blank |
| PF000018 | CHF | 4 | false | Swiss Franc Defensive | MGR018 | 2023-12-31 | 11200000 | 18.7 | 3.4 | 0.74 | -1.2 | 0.8 | 1.2 | 3.1 | 3.6 | blank | bonds:85,cash:15 |
| PF000019 | USD | 76.9 | true | Simulated Multi-Strategy | MGR019 | 2024-01-31 | 50000 | 82.1 | 73.2 | -3.2 | -58.3 | 63.1 | 77.2 | -44.2 | -39.7 | Scenario: Multi-asset drawdown | futures:40,equities:30,alternatives:30 |
| PF000020 | CAD | 0.2 | false | Canadian Liquidity Reserve | MGR020 | 2024-02-29 | 34000 | 1.9 | 0.1 | 0.02 | 0 | 0 | 0 | 0.1 | 0.2 | blank | cash:100 |
What the 200 rows show
from the 200-row sampleUSD currency stands out: mean volatility_
- 21%is_
simulated = true - 10.3median volatility_
3y - 50managers
- 12,200,000median total_
assets_ usd - 36.2median risk_
score - 9.2median volatility_
1y
Median 10.3, from 0.0 to 100.0.
- string 6
- float 10
- date 1
- boolean 1
Columns
18 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 6 columns | |||
portfolio_id | string | Unique identifier for the investment portfoliounique | PF000001 |
portfolio_name | string | Descriptive name of the investment portfolio | Eurozone ESG Balanced |
manager_id | string | Unique identifier for the portfolio manager or asset manager | MGR001 |
asset_class_breakdown | string | JSON object with asset class names as keys and their percentage weights as values (e.g., {"equities": 60, "bonds": 30, "cash": 10})optional | cash:100 |
currency | string | Primary reporting currency for the portfolio (ISO 4217 code, e.g., USD, EUR)7 currencies | USD |
notes | string | Free-text notes or comments about the portfolio or simulation scenariooptional | Retiree mandate |
| Numbers 10 columns | |||
total_assets_usd | float | Total market value of assets in the portfolio, in US dollars0 or more | 35680000.32 |
risk_score | float | Composite risk score for the portfolio (typically 0-100, higher is riskier)0 to 100 | 22.5 |
volatility_1y | float | Annualized volatility of the portfolio over the past 1 year (as a percentage, e.g., 12.5 for 12.5%)0 or more | 3.1 |
volatility_3y | float | Annualized volatility of the portfolio over the past 3 years (as a percentage)0 or more · optional | 3.7 |
sharpe_ratio_1y | float | Sharpe ratio of the portfolio over the past 1 yearoptional | 0.68 |
max_drawdown_1y | float | Maximum observed loss from a peak to a trough over the past 1 year (as a negative percentage, e.g., -15.2 for -15.2%)up to 0 · optional | -1.7 |
value_at_risk_95 | float | Value at Risk (VaR) at 95% confidence level, expressed as a percentage of portfolio value0 or more · optional | 0.8 |
expected_shortfall_95 | float | Expected Shortfall (ES) at 95% confidence level, expressed as a percentage of portfolio value0 or more · optional | 1.1 |
annual_return_1y | float | Annualized return of the portfolio over the past 1 year (as a percentage)optional | 3.6 |
annual_return_3y | float | Annualized return of the portfolio over the past 3 years (as a percentage)optional | 3.2 |
| Dates and times 1 column | |||
as_of_date | date | Date for which the risk metrics and performance are reported | 2024-02-29 |
| True or false 1 column | |||
is_simulated | boolean | Indicates if the portfolio performance/risk metrics are simulated (true) or real (false) | false |
Use it for
A finance dashboard
The is_
simulated rate and volatility_ 3y by currency. Excel, Power BI or Tableau. Why do the 110 USD rows have a mean volatility_
3y of 40.5? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Portfolios200PF0000013.7USDPF00000799.2USDPF000011100USD
A software demo
Believable portfolios with portfolio_
name, manager_ id and as_ of_ date to fill a screen in front of a buyer.
This dataset200 rows18 columns
Yours1,000 rows18 columns
blueprint · financial-portfolio-risk-metrics
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 18 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each row represents a portfolio snapshot and risk metrics.
- Assets classified across standard asset classes.
- Portfolio allocations and weights detailed.
- Risk calculated using VAR and Sharpe ratio.
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- financial-portfolio-risk-metrics