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JerBouma

Finance Toolkit

by JerBouma

performance

Read-onlyIdempotent

Calculate risk-adjusted performance metrics like Sharpe ratio, Sortino, Alpha, Beta, and more for any ticker or portfolio, with support for rolling windows and cumulative returns.

Instructions

Pre-computed risk-adjusted performance metrics (Sharpe ratio incl. standard/adjusted/probabilistic/deflated methods, Sortino ratio, Alpha, Jensen's Alpha, Beta, CAPM, Treynor ratio, M2 ratio, Tracking Error, Information Ratio, Fama-French factors, period Returns, Excess Returns — Returns/Excess Returns support cumulative=true for a compounded growth index rebased to 1). Beta, CAPM, Alpha, Jensen's Alpha, Treynor, Sortino, M2, Tracking Error and Information Ratio 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_alpha, get_appraisal_ratio, get_beta, get_burke_ratio, get_calmar_ratio, get_capital_asset_pricing_model, get_carhart_four_factor_model, get_compound_growth_rate, get_correlation_matrix, get_covariance_matrix, get_downside_capture_ratio, get_excess_return, get_factor_asset_correlations, get_factor_correlations, get_fama_and_french_model, get_fama_decomposition, get_gain_to_pain_ratio, get_henriksson_merton_model, get_information_ratio, get_jensens_alpha, get_kappa_ratio, get_m2_ratio, get_omega_ratio, get_rachev_ratio, get_returns, get_sharpe_ratio, get_sortino_ratio, get_starr_ratio, get_sterling_ratio, get_tracking_error, get_treynor_mazuy_model, get_treynor_ratio, g

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lagNoNumber of periods to lag when computing growth rates.
alphaNoValue for alpha.
orderNoValue for order.
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 'multi' for get_fama_and_french_model; 'standard' for get_sharpe_ratio.
periodNoObservation frequency, e.g. 'monthly', 'quarterly', or 'annual'.
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_alpha, get_appraisal_ratio, get_beta, get_capital_asset_pricing_model, get_fama_decomposition, get_information_ratio, get_jensens_alpha, get_m2_ratio, get_omega_ratio, get_sharpe_ratio, get_sortino_ratio, get_tracking_error, get_treynor_ratio; 14 for get_ulcer_performance_index.
tickersNoComma-separated ticker symbols, e.g. 'AAPL,MSFT,GOOGL'.
end_dateNoEnd of the date range in YYYY-MM-DD format.2026-08-19
n_trialsNoValue for n_trials.
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.
adjustmentNoValue for adjustment.
cumulativeNoReturn the cumulative value compounded over time instead of the discrete value per period. Always rebased to start at 1 at the beginning of the selected date range.
start_dateNoStart of the date range in YYYY-MM-DD format.2021-08-20
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.
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.
trials_windowNoValue for trials_window.
within_periodNoValue for within_period.
benchmark_tickerNoTicker used as the market benchmark, e.g. 'SPY' or '^GSPC'.SPY
exclude_risk_freeNoValue for exclude_risk_free.
show_full_resultsNoValue for show_full_results.
factors_to_calculateNoComma-separated factor names to include in the calculation.
benchmark_sharpe_ratioNoValue for benchmark_sharpe_ratio.
include_daily_residualsNoValue for include_daily_residuals.
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?

The description adds useful behavioral detail beyond the readOnly/idempotent annotations: cumulative=true returns a compounded growth index rebased to 1, rolling=N changes output to a rolling N-period value, and period='daily' is explicitly rejected. This gives the agent practical expectations about output shape and supported frequencies without contradicting the annotations.

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

Conciseness3/5

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

The core sentences are dense and informative, front-loading the main metrics and key parameter behaviors. However, the long 'Available indicators' list duplicates the input schema enum and appears truncated mid-word with 'g', which is redundant and structurally messy.

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 is an indicator hub with 26 parameters and an output schema, the description covers the most important decision-relevant behavior: available metric families, cumulative/rolling semantics, required tickers, and unsupported daily periods. It does not explain benchmark_ticker defaults or factor-specific parameters, but those are already documented in the schema and the description provides enough orienting context for a complex tool.

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

Parameters4/5

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

Schema description coverage is 100%, so the schema already documents all 26 parameters. The description adds meaningful semantics for high-leverage parameters by explaining cumulative=true, rolling=N, period options, tickers formatting, and required indicator behavior. This goes beyond the schema's generic per-parameter descriptions, especially for rolling and cumulative combinations.

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

Purpose4/5

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

The description identifies the resource as 'Pre-computed risk-adjusted performance metrics' and enumerates specific metrics (Sharpe, Sortino, Alpha, Beta, CAPM, etc.), which distinguishes it from sibling tools like momentum, volatility, and risk. However, it lacks an explicit action verb such as 'calculates' or 'returns', and the broad tool name 'performance' makes the purpose slightly less crisp than a direct command.

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

Usage Guidelines3/5

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

The description gives concrete usage constraints: tickers must be supplied like 'AAPL', comma-separated for multiple tickers, and period='daily' is unsupported with explicit alternatives ('use weekly, monthly, quarterly, or yearly'). It does not, however, explain when to use this tool instead of sibling tools such as risk or volatility, or when to choose one indicator family over another.

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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