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JerBouma

Finance Toolkit

by JerBouma

risk

Read-onlyIdempotent

Calculate financial risk metrics (VaR, CVaR, drawdown, volatility, and more) from raw price data. Supports rolling windows and multiple tickers to quantify downside exposure and portfolio risk.

Instructions

Pre-computed risk metrics (VaR incl. historic/gaussian/cf/studentt/evt distributions, CVaR, EVaR, GARCH volatility, max drawdown, drawdown duration, drawdown recovery time, Conditional Drawdown at Risk (CDaR), Tail Ratio, skewness, kurtosis, downside deviation, Variance, Volatility incl. close_to_close/parkinson/garman_klass/rogers_satchell/yang_zhang estimators, Excess Volatility). VaR, CVaR, skewness, kurtosis, CDaR, Tail Ratio, downside deviation, Variance, Volatility and Excess Volatility support rolling=N for a rolling N-period value spanning the full history instead of one value per period (e.g. period='monthly', rolling=6 for a rolling 6-month figure). Requires tickers='AAPL' — use comma-separated values for multiple tickers. Does NOT support period='daily'; use weekly, monthly, quarterly, or yearly instead.

Available indicators: get_acerbi_szekely_test, get_amihud_illiquidity, get_autocorrelation, get_best_fitting_copula, get_coefficient_of_variation, get_component_value_at_risk, get_conditional_drawdown_at_risk, get_conditional_value_at_risk, get_copula_parameters, get_copula_simulation, get_covar, get_downside_deviation, get_egarch, get_egarch_forecast, get_egarch_parameters, get_entropic_value_at_risk, get_ewma_volatility, get_excess_volatility, get_garch, get_garch_forecast, get_garch_parameters, get_gjr_garch, get_gjr_garch_forecast, get_gjr_garch_parameters, get_har_rv_forecast, get_hill_estimator, get_hurst_exponent, get_kurtosis, get_marginal_value_at_risk, g

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoValue for k.
qNoValue for q.
dofNoValue for dof.
lagNoNumber of periods to lag when computing growth rates.
lagsNoValue for lags.
tailNoValue for tail.left
testNoValue for test.both
alphaNoValue for alpha.
scaleNoValue for scale.
columnNoValue for column.Return
copulaNoValue for copula.gaussian
fisherNoValue for fisher.
growthNoReturn period-over-period growth rates instead of absolute values.
methodNoValue for method. Leave unset to use the default of the indicator you selected. Defaults are 'close_to_close' for get_volatility; 'empirical' for get_tail_dependence_coefficient.
periodNoObservation frequency, e.g. 'monthly', 'quarterly', or 'annual'.
tickerNoValue for ticker. Leave unset to use the default of the indicator you selected. Required by: get_covar.
horizonNoValue for horizon.
lambda_NoValue for lambda_.
max_lagNoValue for max_lag.
rollingNoRolling window size in number of periods. When set, the metric is computed over a smoothly overlapping trailing window across the full history (e.g. period='monthly' and rolling=6 gives a rolling 6-month value) instead of one value per period, or (for economics indicators) a simple moving average used to smooth the raw series. Leave unset to use the default of the indicator you selected. Defaults are None for get_conditional_drawdown_at_risk, get_conditional_value_at_risk, get_downside_deviation, get_excess_volatility, get_kurtosis, get_skewness, get_tail_ratio, get_value_at_risk, get_variance, get_volatility; 14 for get_ulcer_index.
tickersNoComma-separated ticker symbols, e.g. 'AAPL,MSFT,GOOGL'.
weightsNoValue for weights.
end_dateNoEnd of the date range in YYYY-MM-DD format.2026-08-19
ticker_aNoValue for ticker_a. Leave unset to use the default of the indicator you selected. Required by: get_tail_dependence_coefficient. Defaults are None for get_best_fitting_copula, get_copula_parameters, get_copula_simulation.
ticker_bNoValue for ticker_b. Leave unset to use the default of the indicator you selected. Required by: get_tail_dependence_coefficient. Defaults are None for get_best_fitting_copula, get_copula_parameters, get_copula_simulation.
estimatorNoValue for estimator.squared_return
indicatorYesName of the specific metric to calculate, e.g. 'get_asset_turnover_ratio'. Required — omitting it returns the list of available indicators.
quarterlyNoReturn quarterly data instead of annual when True.
start_dateNoStart of the date range in YYYY-MM-DD format.2021-08-20
time_stepsNoValue for time_steps. Leave unset to use the default of the indicator you selected. Defaults are 10 for get_egarch_forecast, get_garch_forecast, get_gjr_garch_forecast; None for get_egarch, get_garch, get_gjr_garch.
n_bootstrapNoValue for n_bootstrap.
standardizeNoReturn the Z-Score (standard score) instead of the raw values, i.e. how many standard deviations each value is from the mean of its own series. When combined with growth=True, the growth values are standardized instead of the raw values.
window_sizeNoValue for window_size.
distributionNoValue for distribution.historic
random_stateNoValue for random_state.
show_columnsNoComma-separated names to filter the output. For historical data use the key names visible in any response record (e.g. 'Close,Volume,Return'). For financial statements use the 'metric' field values from the response (e.g. 'Revenue,Net Income,EBITDA'). Call the tool once without this parameter to see all available names, then repeat with show_columns to reduce response size and token usage.
n_simulationsNoValue for n_simulations.
weekly_windowNoValue for weekly_window.
within_periodNoValue for within_period. Leave unset to use the default of the indicator you selected. Defaults differ between indicators.
monthly_windowNoValue for monthly_window.
optimization_tNoValue for optimization_t.
benchmark_tickerNoTicker used as the market benchmark, e.g. 'SPY' or '^GSPC'.SPY
empirical_marginsNoValue for empirical_margins.
show_full_resultsNoValue for show_full_results.
conditioning_tickerNoValue for conditioning_ticker. Leave unset to use the default of the indicator you selected. Required by: get_covar.
threshold_percentileNoOnly used when distribution='evt'. The percentile of losses above which the Generalized Pareto Distribution is fitted, e.g. 0.95 fits on the worst 5% of losses.
minimum_acceptable_returnNoThe minimum acceptable return (MAR) threshold below which returns are considered downside, e.g. 0.0 for downside relative to a zero return.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is clear from the structured data alone. The description adds value by explaining behavioral nuances beyond the schema, such as the `rolling` behavior for different indicators and the `method` parameter defaults for specific indicators. There's no contradiction, so no points deducted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long, but the complexity of the tool (47 parameters, 42 indicators) justifies it. The most critical information is front-loaded, and the 'Available indicators' list, while lengthy, improves usability since it can be referenced directly. One minor deduction for some redundancy with schema enums, but overall it's well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and 47 parameters, the description covers the most critical constraints and interactions well. It explains the date handling via `period`, the roll-up via `rolling`, and default behaviors for parameters like `method`. However, it doesn't discuss all parameter combinations, and some less-common parameters like `within_period` or `show_full_results` are left to the schema. Still, it covers the critical mass for an agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 100%, the description provides crucial semantic context that the schema lacks. For example, it explains how `moving` and `period` interact, clarifies the meaning of `tickers` (comma-separated), and details the `method` parameter's per-indicator defaults. It also explains the `threshold_percentile` parameter, which is not explained in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as providing pre-computed risk metrics, listing a comprehensive set of metrics (VaR, CVaR, EVaR, GARCH, etc.). It differentiates from siblings by focusing solely on risk-related indicators and providing a large list of 'Available indicators'. The scope is explicit and detailed.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage constraints: 'Requires tickers='AAPL'' and 'Does NOT support period='daily''. It also explains how to use `rolling` and `period` together, and notes the behavior when `indicator` is omitted. These details far exceed what's in the schema, giving clear guidance on when and how to use the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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