JerBouma/FinanceToolkit v2.1.4 on GitHub

This is one of the larger feature releases to date. It roughly doubles the depth of three modules — Models, Performance, and Risk — and rounds out Technicals, Ratios, and Discovery with metrics that were previously missing.

Two capabilities now cut across nearly the whole toolkit:

rolling and trailing windows. Many metrics used to return one value per reporting period. They can now be computed over a sliding window (rolling=<n>) or a trailing sum/average (trailing=<n>), turning a snapshot into a proper time series.
standardize (Z-Score). Most get_* methods across Economics, Ratios, Technicals, Risk, Performance, Models, Options, and Fixed Income now accept standardize=True. It converts raw values into standard deviations from their own historical mean, so metrics on completely different scales become directly comparable — useful for ranking, scoring, or spotting an unusual reading at a glance.

On the fix side: swaption prices are discounted correctly now, a case-mismatch bug that was silently zeroing out diluted EPS is gone, and the new Forward P/E / Forward PEG ratios cache analyst estimates properly instead of double-fetching.

Volatility, Excess Return, and Excess Volatility are gone from toolkit.get_historical_data(). Use these instead:

toolkit.risk.get_volatility(period=...)
toolkit.risk.get_excess_volatility(period=...)
toolkit.risk.get_variance(period=...)
toolkit.performance.get_excess_return(risk_free_rate=..., cumulative=...)

Every method below hangs off the matching Toolkit sub-module (e.g. toolkit.models.get_graham_number()) or its standalone class (Discovery(...), Economics(...), FixedIncome(...)). Full runnable examples with real output live in the documentation — each section below links to its module's page.

New Features

Standardization (Z-Score)

standardize: bool = False is now available on nearly every controller — Economics, Ratios, Technicals, Risk, Performance, Models, Options, and Fixed Income, roughly 270 methods in total.

Z-Score = (value - mean) / standard deviation

Mean and standard deviation come from each series' own history: per ticker for entity-indexed data like ratios, per column for date-indexed data like economic indicators. With growth=True, it standardizes the growth values instead of the raw ones. Use it to compare metrics that live on incompatible scales, build a quick cross-sectional ranking straight from toolkit output, or spot when a current reading is an unusual number of standard deviations from its own norm.

Financial Models

Call these via toolkit.models.<method>(). Full examples: docs/models.

get_economic_value_added (EVA, economic profit) — whether a company earns more than its true cost of capital, something Net Income alone can't tell you.

NOPAT = EBIT × (1 − Effective Tax Rate)
Invested Capital = Total Equity + Total Debt (each smoothed as a 2-period average, or a trailing-period average, to soften a single noisy balance-sheet snapshot)
EVA = NOPAT − (WACC × Invested Capital)
Read it as: positive EVA creates value for capital providers; negative EVA destroys it, even in periods where net income is positive.

get_beneish_m_score (earnings manipulation score) — Messod Beneish's model for flagging likely earnings manipulation, a companion to the Altman Z-Score and Piotroski F-Score already in the toolkit.

M-Score = -4.84 + 0.92·DSRI + 0.528·GMI + 0.404·AQI + 0.892·SGI + 0.115·DEPI − 0.172·SGAI + 4.679·TATA − 0.327·LVGI
Each of the eight inputs is its own function in beneish_model.py: get_days_sales_in_receivables_index (DSRI — receivables outgrowing sales), get_gross_margin_index (GMI — margin deterioration), get_asset_quality_index (AQI — a rising share of non-core assets), get_sales_growth_index (SGI — fast growth raises manipulation pressure), get_depreciation_index (DEPI — a slowing depreciation rate flatters earnings), get_selling_general_and_administrative_expenses_index (SGAI), get_leverage_index (LVGI), and get_total_accruals_to_total_assets (TATA — the accrual share of earnings, a red flag across most accounting-fraud models).
Read it as: above roughly -1.78, manipulation is more likely; below it, less likely. It's probabilistic, not a verdict — pair it with real fundamental analysis. The first period is always NaN since every input needs a prior period to compare against.
Reference: Beneish, M. D. "The Detection of Earnings Manipulation." Financial Analysts Journal, Vol. 55, No. 5, 1999, pp. 24-36.

get_sustainable_growth_rate (SGR) — the fastest a company can grow revenue on internal funds alone, without new equity or added leverage.

SGR = Return on Equity × (1 − Dividend Payout Ratio)
Read it as: growing faster than SGR without outside financing means improving margins, cutting the payout, or taking on more debt — so actual growth versus SGR is a quick tell for how a company is funding its expansion.

get_internal_growth_rate (IGR) — a stricter version of SGR: growth funded from retained earnings only, with no debt added at all.

IGR = (ROA × Retention Ratio) / (1 − (ROA × Retention Ratio))
Read it as: IGR is always below SGR for a levered company. The gap between them is roughly how much of that company's "sustainable" growth actually depends on debt.

get_graham_number (Graham fair value) — Benjamin Graham's conservative ceiling on what a defensive investor should pay.

Graham Number = √(22.5 × EPS × Book Value per Share)
Read it as: most useful for stable, profitable companies with positive book value. Negative earnings or book value produce NaN (the square root of a negative number is undefined) — comparing the current price against this number is a quick margin-of-safety check.

Ratios

Call these via toolkit.ratios.<method>(). Full examples: docs/ratios.

Efficiency

get_research_and_development_ratio, get_selling_and_marketing_ratio, get_general_and_administrative_ratio — R&D, S&M, and G&A each as a share of Revenue. Most companies only report these lumped into SG&A; splitting them out lets you compare R&D intensity or customer-acquisition spend independent of admin overhead.
get_stock_based_compensation_ratio — SBC ÷ Revenue. A high or rising number is a quality-of-earnings flag, since SBC gets added back in cash flow from operations (flattering FCF) while still diluting existing shareholders.
get_deferred_revenue_ratio — Deferred Revenue ÷ Revenue. A leading indicator for subscription/SaaS businesses: a growing balance here signals revenue that hasn't hit the income statement yet.

Profitability

get_cash_tax_rate — taxes actually paid in cash ÷ Income Before Tax, versus the accrual-based Effective Tax Rate.
get_tax_rate_divergence — Cash Tax Rate minus Effective Tax Rate. Persistently positive means more cash tax paid than expensed (e.g. deferred liabilities unwinding); persistently negative can flag aggressive tax deferral.

Solvency

get_debt_to_capital_ratio — Total Debt / (Total Debt + Total Equity). Stays between 0 and 1, unlike Debt-to-Equity, which makes capital structure easier to compare across companies.
get_preferred_dividend_coverage_ratio — Net Income ÷ |Preferred Dividends|: how many times earnings cover the obligation to preferred shareholders.
get_interest_paid_to_expense_ratio — Interest Paid ÷ Interest Expense. Well below 1 suggests interest is being accrued rather than paid (e.g. payment-in-kind debt); well above 1 suggests catch-up payments or a cash/accrual timing gap.

Valuation

get_ev_to_free_cash_flow_ratio — Enterprise Value ÷ Free Cash Flow, a multiple that (unlike EV/EBITDA) accounts for capex and working capital.
get_buyback_yield — -(Repurchases + Issuances) / Market Cap. Positive means the company is a net repurchaser; negative means it's a net issuer, diluting shareholders.
get_shareholder_yield — Dividend Yield + Buyback Yield: total cash returned to shareholders through both channels.
get_sbc_adjusted_free_cash_flow — Free Cash Flow minus Stock-Based Compensation, treating SBC as the real cost it is instead of the non-cash add-back FCF normally lets it be.
get_forward_price_earnings_ratio (Forward P/E) — Stock Price / Estimated EPS Average, using the analyst consensus estimate for a future period instead of trailing EPS. Needs a Premium FMP subscription; estimates are fetched once per Toolkit instance and cached automatically, so there's no need to call get_analyst_estimates() first.
get_forward_price_earnings_growth_ratio (Forward PEG) — Forward P/E / (Estimated EPS Growth Rate × 100), where growth bridges the analyst estimate to the company's most recent actual EPS (get_estimated_eps_growth_rate, its own reusable function) rather than comparing two historical periods the way growth=True does everywhere else. Read it as: near or below 1 is the classic (simplistic) heuristic for fairly priced growth; well above 1 means the market is paying a premium for it.

Technical Indicators

Call these via toolkit.technicals.<method>(). Full examples: docs/technicals.

Overlap

get_weighted_moving_average (WMA) — weights recent prices more, so it tracks price more closely than a Simple Moving Average while staying smoother than an Exponential one.
get_hull_moving_average (HMA) — combines a half-window WMA, a full-window WMA, and a √window WMA specifically to cut moving-average lag without giving up smoothness.
get_volume_weighted_average_price (VWAP) — typical price weighted by volume over a rolling window; the benchmark most execution algorithms are measured against.
get_parabolic_sar — a trend-following stop-and-reverse level that flips sides when the trend reverses, with an acceleration factor (af_start/af_increment/af_max, defaults 0.02/0.02/0.2) that speeds convergence as a trend runs longer. Commonly used to trail stop-losses.
get_pivot_points — Pivot Point plus Resistance/Support 1-3, derived from the prior period's high/low/close to flag likely next-period levels.

Volatility

get_donchian_channels — highest high and lowest low over a rolling window, with the midpoint as the middle line; spots breakouts and shows how wide the recent range has been.
get_volatility_cone — the historical percentile spread (min through max, plus current) of annualized realized volatility across several lookback windows ([10, 20, 30, 60, 90, 120] by default). Shows at a glance whether current volatility is cheap or expensive versus its own history — often used before comparing to options-implied volatility.

Breadth (cross-sectional across tickers at a point in time, not per-ticker over time)

get_trin (Arms Index) — (Advancing Issues / Declining Issues) / (Advancing Volume / Declining Volume). Below 1 suggests volume conviction is outpacing the number of advancing names (bullish); above 1 suggests the reverse.
get_new_highs_new_lows — tickers at a window-period high minus tickers at a window-period low: a read on how broad a market move really is beneath the index-level headline.

Performance

Call these via toolkit.performance.<method>(). Full examples: docs/performance.

get_calmar_ratio — Return / |Maximum Drawdown|. Penalizes purely by the single worst peak-to-trough loss, not by volatility the way Sharpe/Sortino do.
get_sterling_ratio — Return / (|Average Drawdown| + adjustment) (adjustment defaults to 10%), using the average of all drawdowns rather than just the worst one — softer and less driven by a single event than Calmar.
get_burke_ratio — Excess Return / Burke Drawdown Measure, where the measure is √(Σ drawdown²) (new get_burke_drawdown_measure). Squaring before summing penalizes both frequency and depth of drawdowns, so two strategies sharing a max drawdown can still score very differently here.
get_upside_capture_ratio / get_downside_capture_ratio — the asset's average return versus the benchmark's, split across periods where the benchmark was positive or negative. Upside capture above 1 and downside capture below 1 is the profile most investors want: gaining more in rallies than losing in selloffs.
get_win_rate — the share of periods where the asset beat the benchmark. Simple, distribution-free complement to the capture ratios.
get_kappa_ratio (default order=3) — Sortino generalized to a higher-order lower partial moment; Sortino itself is the order=2 case. Higher orders punish larger losses more, making Kappa more tail-sensitive than Sortino.
get_omega_ratio (default minimum_acceptable_return=0.0) — gains above a minimum acceptable return divided by losses below it, using the full return distribution rather than just mean and variance — so it captures skew and fat tails that Sharpe/Sortino miss.
get_gain_to_pain_ratio — Σ all returns / Σ |losses|. A blunt, easy-to-explain measure of gain earned per unit of pain endured.
get_returns / get_excess_return now take a cumulative parameter (pulled out of get_historical_data, see Breaking Changes) — cumulative=True compounds returns into a running index instead of period-over-period values.
Rolling variants — get_rolling_alpha, get_rolling_sortino_ratio, get_rolling_m2_ratio, get_rolling_tracking_error, get_rolling_information_ratio, plus rolling support on Beta, CAPM, Jensen's Alpha, and Treynor Ratio. Pass rolling=<window> to see risk-adjusted performance evolve through time instead of getting one backward-looking number.

Risk

Call these via toolkit.risk.<method>(). Full examples: docs/risk.

get_conditional_drawdown_at_risk (CDaR) — extends VaR/CVaR from returns to drawdowns: DaR is the alpha-quantile of the drawdown distribution, CDaR is the average of drawdowns at least that severe. Where VaR/CVaR ask "how bad could one period be," CDaR asks "how bad could the cumulative peak-to-trough loss get."
get_tail_ratio — |percentile(1-alpha)| / |percentile(alpha)| of returns. Above 1 means best-case gains outsize worst-case losses — a quick complement to skewness that doesn't assume a distribution shape.
get_maximum_drawdown_duration — periods between the peak and trough of the single largest drawdown: how long the worst decline took to play out.
get_maximum_drawdown_recovery_time — periods from that trough until cumulative return hits a new high (NaN if it hasn't yet). Paired with duration, this gives the full timeline of a strategy's worst stretch.
get_downside_deviation (semi-deviation, default minimum_acceptable_return=0.0) — standard deviation of only the returns below a minimum acceptable return, so upside swings no longer count against the metric the way they do in plain standard deviation.
get_mean_absolute_deviation (MAD) — mean absolute distance from the mean return. No squaring, so a handful of outliers move it less than they'd move variance.
get_coefficient_of_variation (CV, relative standard deviation) — standard deviation / mean of returns, which lets you compare relative volatility across assets with very different average returns — something raw standard deviation can't do.
get_ewma_volatility (RiskMetrics volatility, default lambda_=0.94) — EWMA Variance(t) = λ·EWMA Variance(t-1) + (1-λ)·Return(t-1)². Weights recent observations more, so it reacts to a changing volatility regime faster than a fixed rolling window, while staying much simpler than a full GARCH fit.
get_autocorrelation (default lags=10) — correlation between a series and its own lagged values; usually the first thing checked before deciding how many AR/MA terms a series needs.
get_hurst_exponent (default max_lag=20) — estimated via rescaled range (R/S). H < 0.5 mean-reverting, H = 0.5 random walk, H > 0.5 trending — a single number for whether a series tends to trend or revert.
Extreme Value Theory tail modelling — get_value_at_risk(distribution="evt") now fits a Generalized Pareto Distribution to the worst losses via Peak-over-Threshold (new get_var_evt, plus a threshold_percentile parameter, default 0.95, for how much of the tail to fit on). Models the tail's actual shape instead of assuming the whole distribution (Gaussian, Cornish-Fisher, Student-t) represents extreme outcomes well.
get_volatility / get_variance / get_excess_volatility are now first-class Risk methods (pulled out of get_historical_data, see Breaking Changes).
Rolling variants — get_rolling_var_historic, get_rolling_cvar_historic, get_rolling_variance, get_rolling_volatility, get_rolling_excess_volatility, get_rolling_tail_ratio, get_rolling_conditional_drawdown_at_risk, get_rolling_skewness, get_rolling_kurtosis, get_rolling_downside_deviation, get_rolling_omega_ratio — same sliding-window idea as Performance, across Risk.

Economics

Call these via Economics(...).get_*(), or toolkit.economics.<method>() on a Toolkit instance. Full examples: docs/economics.

rolling and trailing parameters now cover essentially the whole module — GDP, CPI, inflation, trade, investment, consumption, confidence indices, government fiscal metrics, interest rates, unemployment, population, and more (45 methods). rolling=<n> smooths noisy monthly/quarterly prints with a simple moving average; trailing=<n> sums over a trailing window (e.g. a trailing 4-quarter sum to annualize a quarterly flow). Both apply before any growth/standardize step.

Discovery & News

Call these via Discovery(...).get_*() / search_*(), or toolkit.discovery.<method>() for the ticker-scoped ones. Full examples: docs/discovery.

On the Toolkit itself: get_stock_news(pages, limit) and get_press_releases(pages, limit) — ticker-scoped, filtered automatically to the Toolkit's start_date/end_date.
In Discovery, not ticker-scoped: get_stock_news, get_general_news (broad market news), get_press_releases, get_crypto_news, get_forex_news, plus free-text search via search_stock_news, search_press_releases, search_crypto_news, search_forex_news.
Corporate events & calendars: get_ipo_calendar, get_ipo_disclosures (SEC pre-IPO filings), get_ipo_prospectuses (S-1/424B4 pricing), get_stock_splits_calendar, get_mergers_acquisitions_latest (recent deals with acquirer/target and a link to the SEC filing).
Sector & industry data: get_sector_performance / get_industry_performance and get_sector_pe / get_industry_pe — pass date for a snapshot across everything, or sector/industry for one name's history. Good for checking whether a move was sector-wide or idiosyncratic, or whether a sector looks rich or cheap against its own P/E history.

Fixed Income

Call this via FixedIncome().get_derivative_price(...). Full examples: docs/fixedincome.

tenor and payment_frequency parameters on get_derivative_price — see Bug Fixes above; this is what makes swaption pricing account for the underlying swap's full payment schedule instead of treating it as a single-date payout.

Bug Fixes

Swaption pricing was missing the annuity leg. get_black_price and get_bachelier_price used to discount the option payoff with a single factor to the expiration date, as if exercising a swaption paid out once. It doesn't — it grants the right to enter a swap that pays out at every payment date over the swap's tenor. Both functions now multiply by an annuity factor (the sum of discount factors across those payment dates) via a new _get_annuity_factor() helper. Two new parameters, tenor and payment_frequency, let you set the underlying swap's length and payment schedule separately from the option's own expiration. Previously tenor had zero effect on price; now a 1-year option into a 5-year swap correctly prices roughly 5x higher than one into a 1-year swap, since it covers five times as many payment dates.
trailing rolled the wrong axis when combined with show_daily. get_enterprise_value, get_market_cap, and related valuation ratios rolled the trailing window across tickers instead of across dates whenever both parameters were active together.
trailing did nothing in get_market_cap. Its if/else branches were identical, so the parameter was silently ignored.
Cumulative return broke on gappy data. (1 + returns).cumprod() became (1 + returns.fillna(0)).cumprod() in historical_model.py and risk_model.py, with .replace([np.inf, -np.inf], np.nan) added before the product to stop infinities from propagating through the series.
Silenced a pandas FutureWarning. Added future_stack=True to stack() calls in the performance controller ahead of pandas' default-behaviour change.
Fixed a type annotation. pd.PeriodIndex corrected to pd.DatetimeIndex in a helpers.py signature.
Risk methods ignored the requested date range. get_value_at_risk, get_conditional_value_at_risk, get_entropic_value_at_risk, get_conditional_drawdown_at_risk, get_tail_ratio, get_maximum_drawdown, get_maximum_drawdown_duration, get_maximum_drawdown_recovery_time, get_ulcer_index, get_garch, get_skewness, get_kurtosis, and get_variance could silently return data outside start_date/end_date, especially with rolling or within_period set. All now truncate correctly.
A one-character typo zeroed out diluted EPS everywhere. financetoolkit/normalization/income.csv mapped FMP's field as epsdiluted (lowercase), but the stable endpoint actually returns epsDiluted (capital D). Normalization fills any expected-but-missing column with 0 before renaming, so this mismatch meant get_income_statement() always reported "EPS Diluted" as zero — the real column was silently dropped instead of renamed. It stayed invisible until the new Forward PEG ratio started reading that field directly, at which point it returned exactly 0.0 for every ticker, every period. Fixed by correcting the key to epsDiluted.
Forward-looking ratios fetched analyst estimates twice instead of caching once. get_forward_price_earnings_ratio() and get_forward_price_earnings_growth_ratio() are meant to fetch Premium-only analyst estimates once and reuse them. The cache lived on the Ratios instance, but toolkit.ratios builds a brand-new Ratios instance on every access — so calling both methods back to back (exactly what the docs show) fetched estimates twice, doubling Premium API usage for nothing. Fixed by sharing a mutable cache dict from the Toolkit instance into every Ratios instance it builds, so the fetch-once behaviour survives across calls for the life of the Toolkit object.
Log messages were mis-pluralized. "Obtaining %s data for %d tickers" now reads "...for %d ticker(s)", so single-ticker runs no longer say "for 1 tickers".
Local dev setup was missing MCP dependencies. The dev dependency group in pyproject.toml didn't pull in the project's own mcp extra, so a plain uv sync left fastmcp/pydantic and friends missing. Added "financetoolkit[mcp]" to dev plus a [tool.uv.sources] workspace entry.

Test Coverage

New coverage for the Risk module's new metrics (test_cvar_model.py, expanded test_risk_controller.py, test_risk_model.py, test_var_model.py), the new technical indicators (Donchian Channels, Hull Moving Average, New Highs/New Lows, Parabolic SAR, Pivot Points, TRIN, VWAP, Weighted Moving Average, Volatility Cone), and the internal helpers.py split (test_dataframe_model.py, test_requests_model.py, test_statistics_model.py). Added missing snapshot tests for collect_custom_ratios, get_cash_conversion_efficiency, get_effective_tax_rate, get_debt_service_coverage_ratio, get_weighted_dividend_yield, get_reinvestment_rate, get_ev_to_ebit, collect_all_metrics (performance + risk), get_compound_growth_rate, and get_estimated_eps_growth_rate. Updated snapshots for valuation methods affected by the trailing/show_daily axis fix. Added httpx2 as a dev dependency to resolve a StarletteDeprecationWarning in MCP OAuth tests.

MCP Server

Tool/category descriptions in config.yaml and parameter descriptions in registry_controller.py updated to cover all of the above: rolling/trailing on Economics, the new standardize/cumulative/threshold_percentile/minimum_acceptable_return params, the new Risk and Performance metrics, and the new Models functions.
The valuation tool group now explicitly lists get_forward_price_earnings_ratio and get_forward_price_earnings_growth_ratio via a method_override entry. They're deliberately excluded from the bulk collect_valuation_ratios scan — including them there would trigger a Premium-only analyst-estimates fetch on every bulk request, even for free-tier users — so normal method discovery can't find them on its own.
MCPB manifest and build script updated to match.
Removed an unused get_stock_quotes function from discovery_model.py — dead code, since fmp_model.get_quote already covers per-ticker real-time quotes.

Full comparison: v2.1.3...v2.1.4

JerBouma/FinanceToolkit v2.1.4 FinanceToolkit v2.1.4 on GitHub