fin-eda 1.2.0

Comprehensive financial Exploratory Data Analysis for price series and tickers

fin-eda

Comprehensive financial Exploratory Data Analysis for any stock ticker or price series. Produces a numerical tearsheet (eda) and a visual tearsheet (eda_plot) covering returns, risk, drawdowns, benchmark comparison, volatility, liquidity, and more — all in one call.

Installation

# Numerical tearsheet only
pip install fin-eda

# Numerical + visual tearsheet
pip install fin-eda[plot]

Quick Start — Numerical (eda)

from fin_eda import eda

# Fetch data automatically via yfinance
eda("AAPL")

# Custom date range
eda("MSFT", start="2020-01-01", end="2024-01-01")

# Custom period
eda("TSLA", period="5y")

# Different benchmark
eda("QQQ", benchmark_ticker="SPY")

# Include a risk-free rate
eda("AAPL", risk_free_rate=0.05)

# Pass your own price series
import pandas as pd
prices = pd.Series(...)
eda(prices, benchmark_ticker="SPY")

# Return results as a dict (no print)
results = eda("AAPL", return_results=True, quiet=True)

Quick Start — Visual (eda_plot)

Requires pip install fin-eda[plot].

from fin_eda import eda_plot

# Full tearsheet
eda_plot("AAPL")

# Select specific panels
eda_plot("AAPL", panels=["price", "drawdown", "distribution", "heatmap"])

# Save to file (PNG, PDF, SVG — format inferred from extension)
eda_plot("MSFT", save_path="msft_tearsheet.png")

# Return the Figure for notebook embedding or further customisation
fig = eda_plot("TSLA", period="5y", return_fig=True)

# Custom benchmark and risk-free rate
eda_plot("QQQ", benchmark_ticker="^GSPC", risk_free_rate=0.05)

Available panels

Name	Description
price	Price history with 50D/200D moving averages and volume
cumulative	Cumulative return vs benchmark with shaded outperformance gap
drawdown	Underwater drawdown chart with top-3 trough annotations
heatmap	Monthly returns heatmap (year x month grid)
annual_returns	Year-by-year return bars (green/red) with benchmark overlay
distribution	Daily return histogram with normal overlay and VaR lines
beta_scatter	Return scatter vs benchmark with OLS regression line and beta/R-squared annotation
rolling_beta	Rolling 1Y (252D) beta vs benchmark, shaded above/below beta = 1
rolling_sharpe	Rolling 252D and 126D annualized Sharpe ratio
rolling_vol	Rolling 21D vs 252D volatility regime with elevated-vol shading

Benchmark-dependent panels (cumulative, beta_scatter, rolling_beta) are automatically skipped if no benchmark data is available.

Output

eda() renders in the terminal using Rich with colour-coded values (green = positive/good, yellow = neutral, red = negative/risk).

The header panel shows:

• Ticker or series label, current price, and the benchmark symbol actually used
• Data coverage: number of trading days and date range
• 50D/100D/200D moving average levels and the 50D-200D spread
• Price distance from the 200D MA and trend persistence (% days above 200D MA)
• 52W price range (low - high), days since the 52W high, and drawdown from the 52W high

Below the header, one table is printed per section. Period columns (e.g. 1M, 1Y, 5Y, 10Y) are shown only when the available data is sufficient to support them — longer columns appear automatically as data coverage grows, up to 30Y.

eda_plot() produces a single dark-themed matplotlib figure with up to 10 panels. Use save_path to export, or return_fig=True to get the Figure object.

Metrics Covered

Section	Key Metrics
Core Return & Risk	Cumulative return, CAGR, arithmetic and geometric mean daily return, median daily return, excess return over risk-free rate, standard deviation, annualized realized volatility, annualized variance — across 1M, 3M, 6M, 1Y, 3Y, 5Y, 10Y, 15Y, 20Y, 25Y, 30Y, YTD
Risk-Adjusted Performance	Sharpe ratio, Sortino ratio, Omega ratio, Calmar ratio, Treynor ratio, Jensen's alpha, downside deviation, semi-variance, profit factor, win rate, average return on up days, average return on down days, gain-loss ratio — from 6M+ for period ratios
Drawdown & Capital Destruction	Max drawdown, average drawdown, time to recovery, max consecutive loss days — from 3M+
Trend Structure & Price Health	50D/100D/200D moving average levels, price vs 200D MA, golden/death cross spread, trend persistence, 52W high price, 52W low price, time since 52W high, drawdown from 52W high
Relative Performance vs Benchmark	Geometric excess return vs benchmark, tracking error, information ratio — from 6M+
Beta, Correlation & Market Dependence	Beta vs market, correlation vs market, R-squared vs market — from 3M+
Capture Ratios	Up-market and down-market capture (annualized geometric) — from 6M+
Return Distribution & Non-Normality	Skewness, excess kurtosis — from 3M+
Tail Risk & Stress	Historical VaR (95% and 99%), Expected Shortfall/CVaR (95%) for 1Y+, best and worst daily/weekly/monthly return, extreme loss frequency beyond -2 sigma and -3 sigma
Volatility Metrics	Parkinson (high-low) volatility (21D/63D/126D), rolling volatility percentile, volatility of volatility, current 21D vs 1Y volatility ratio
Liquidity Metrics	Average daily volume (30D/90D), volume trend, Amihud illiquidity ratio (30D/90D/full history)
Annual Returns	Calendar-year return for each year in the data, partial current-year return, best and worst calendar year

Period columns scale automatically with available data. With 1Y of data the output caps at 1Y columns; with 30Y of data all columns through 30Y render.

Parameters

eda() — shared with eda_plot()

Parameter	Type	Default	Description
ticker_or_prices	str or pd.Series	—	Yahoo Finance ticker or a Series of close prices indexed by date
benchmark_ticker	str or None	'SPY'	Benchmark symbol. Falls back to ^GSPC only if the requested ticker fails to fetch or returns no data. Pass None to disable benchmark metrics entirely
risk_free_rate	float	0.0	Annual risk-free rate used in Sharpe, Sortino, Omega, Treynor, and Jensen's alpha (e.g. 0.05 for 5%)
period	str or None	'10y'	yfinance history period ('1y', '5y', '10y', 'max', etc.). Ignored when start/end are provided
start	str or None	None	Start date in YYYY-MM-DD format
end	str or None	None	End date in YYYY-MM-DD format
quiet	bool	False	Suppress benchmark fetch status messages

eda() only

Parameter	Type	Default	Description
return_results	bool	False	When True, return the full metrics dict in addition to (or instead of, if quiet=True) printing the report

eda_plot() only

Parameter	Type	Default	Description
panels	list[str] or None	None	Panels to render. None renders all available panels. See panel name table above
save_path	str or None	None	Save the figure to this path before displaying (e.g. 'out.png', 'report.pdf')
return_fig	bool	False	Return the matplotlib.Figure object instead of calling plt.show()

Calculation Notes

Returns and CAGR. Cumulative returns use log-sum form (expm1(sum(log1p(r)))) for numerical stability on long histories. CAGR is derived from the same log-sum: expm1(log_sum x 252 / n).

Excess return vs benchmark. Reported as the geometric excess: (1 + asset_return) / (1 + benchmark_return) - 1. This avoids the distortion of arithmetic differences over long compounding periods.

Sharpe ratio. Daily excess returns divided by their standard deviation, annualized by sqrt(252), per Sharpe (1994).

Sortino ratio. Uses sqrt(E[min(r - rf, 0)^2]) as the downside deviation denominator — all periods enter the average, with positive days contributing zero. This is the formulation from Sortino and Price (1994) and differs from implementations that take the standard deviation only of the negative tail.

Downside deviation and semi-variance. Same RMS-over-all-periods formula as the Sortino denominator, annualized.

Omega ratio. sum(max(r - rf, 0)) / sum(max(rf - r, 0)) — a full-distribution gain/loss ratio that does not assume normality.

Calmar ratio. CAGR divided by the absolute value of the maximum drawdown for the same period.

Treynor ratio. (CAGR - annual_rf) / beta.

Jensen's alpha. asset_CAGR - (rf + beta x (bench_CAGR - rf)) — CAPM-expected return removed from realized CAGR.

CVaR / Expected Shortfall. Mean of all daily returns at or below the historical 5th percentile (95% confidence). Reported for periods of 1Y and above.

Parkinson volatility. sqrt((1 / (4T ln 2)) x sum(ln(H/L)^2) x 252) per Parkinson (1980).

Amihud illiquidity. Mean of |return| / dollar_volume scaled by 10^6. Dollar volume is used (price x shares) rather than share volume alone, providing a size-neutral measure of price impact.

Dependencies

Core (installed automatically):

• pandas
• numpy
• scipy
• yfinance
• rich

Optional (for eda_plot, installed via pip install fin-eda[plot]):

• matplotlib >= 3.5

License

MIT

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Changelog

1.2.0

New metrics

• CAGR — annualized geometric return (expm1(log_sum x 252 / n)) added to Core Return & Risk for every period.
• Calmar ratio — CAGR divided by absolute max drawdown, per period.
• Treynor ratio — annualized excess return per unit of beta, per period (requires benchmark).
• Jensen's alpha — CAPM-adjusted outperformance (asset CAGR minus the CAPM-predicted return), per period (requires benchmark).
• Omega ratio — full-distribution gain/loss ratio above the risk-free threshold, per period.
• Win rate — percentage of trading days with a positive return (full history scalar).
• Average return on up days / down days — mean daily return on positive and negative days separately (full history scalars).
• Gain-loss ratio — average daily gain divided by the absolute average daily loss (full history scalar).
• Tracking error — annualized standard deviation of active (asset minus benchmark) daily returns, surfaced explicitly alongside the information ratio.
• R-squared vs market — square of the correlation with the benchmark, per period.
• Best daily / weekly / monthly return — counterpart to the existing worst-period metrics.
• 52W high price and 52W low price — absolute price levels added to Trend Structure & Price Health.
• Annual returns — new section with a calendar-year-by-year breakdown, partial current-year return, and best/worst calendar year summary.

Calculation corrections

• Sortino ratio and downside deviation — corrected to use sqrt(E[min(r - rf, 0)^2]) averaged over all periods (Sortino and Price, 1994). The previous implementation used std(negative_tail_returns), which divides by the count of negative days only and measures dispersion around the negative-tail mean rather than around the threshold — both incorrect.
• Excess return vs benchmark — changed from arithmetic (asset_cum - bench_cum) to geometric ((1 + asset_cum) / (1 + bench_cum) - 1). Arithmetic excess is misleading for periods beyond 2-3 years.
• 52W high date comparison — replaced is not pd.NaT with not pd.isna() for correct behaviour with timezone-aware timestamps returned by recent yfinance versions.

Removed metrics

• Jarque-Bera statistic — removed from Return Distribution & Non-Normality. For most financial return series the statistic is near-certain to reject normality regardless of sample size or economic significance; skewness and kurtosis convey the distributional shape more directly.
• Return autocorrelation — the Regime & Time-Series Behavior section (1D, 5D, 21D autocorrelation lags) has been removed from the numerical tearsheet.

Display improvements

• Header now shows: current price, the benchmark symbol actually used (including when the fallback to ^GSPC is triggered), and the full data coverage line (trading day count and date range).
• 52W section in the header now shows actual high and low prices alongside the existing days-since-high and drawdown figures.
• Period columns are suppressed automatically when all values for that column are NaN, so the output scales from the shortest available window up to 30Y without manual configuration. Long-period columns (15Y, 20Y, 25Y, 30Y) appear as data coverage grows.
• Each section table uses a descriptive row label (e.g. "Risk-Adjusted Metric", "Benchmark Comparison", "Calendar Year") instead of the generic "Metric".
• Best-period metrics (best daily, weekly, monthly, annual) are always coloured green; worst-period metrics remain always red.

1.1.1

eda_plot() — panel changes

• Added annual_returns: Year-by-year geometric return bars (green/red) with optional benchmark overlay. Annotates each bar when 15 or fewer years of data are shown.
• Added rolling_beta: Rolling 1Y (252D) beta vs benchmark computed as cov(asset, bench) / var(bench). Shaded above beta = 1, shaded below. Current beta annotated.
• Rolling Sharpe primary window clarified: 252D (1Y) is primary, 126D (6M) is the secondary overlay.

Bug fixes

• Benchmark fetch threshold — the previous check (len > 25 x 252 trading days) silently fell back to ^GSPC for any benchmark with less than roughly 25 years of history (e.g. GLD, sector ETFs, international funds). Now accepts any non-empty result; period='max' is used to retrieve full available history. Fallback to ^GSPC only triggers on a failed fetch.
• Distribution normal overlay — std now uses ddof=1 (sample standard deviation) to match the convention used by stats.skew and stats.kurtosis in the same panel.

1.1.0

New: eda_plot() — visual tearsheet

• 10-panel dark-themed matplotlib tearsheet mirroring all eda() inputs.
• Panels: price history, cumulative return vs benchmark, underwater drawdown, monthly returns heatmap, annual returns, return distribution, beta scatter, rolling beta, rolling Sharpe, and rolling volatility regime.
• panels=[...] parameter for rendering any subset of panels in one call.
• save_path parameter to export to PNG, PDF, SVG, or any matplotlib-supported format.
• return_fig=True for notebook embedding or programmatic figure composition.
• Benchmark-dependent panels degrade gracefully when data is unavailable.
• Style applied via mpl.rc_context — does not pollute the caller's global matplotlib state.
• Added as an optional dependency: pip install fin-eda[plot].

1.0.1

Bug fixes

• YTD period — corrected to use the current calendar year at runtime rather than the last year present in the data, which produced wrong results when analyzing historical series ending before the current year.
• Average drawdown — now returns no data when a period has no negative drawdown observations, instead of incorrectly showing 0.0.
• Time to recovery — now returns no data when a period has no drawdown to recover from, instead of showing 0 (which implied an instantaneous recovery had occurred).
• Volatility of volatility key name — internal key mismatch between the success and failure paths caused the metric to appear twice in the output under different names. Now consistently labeled.
• quiet=True not fully respected — benchmark data-fetch status messages were always printed to stdout regardless of the quiet flag. They now correctly respect quiet=True and route through the Rich console for consistent formatting.
• Monthly return resampling — added compatibility fallback for pandas < 2.2, where the 'ME' month-end alias was not yet available.

Numerical improvements

• Cumulative and geometric mean returns — switched from chained .prod() to log-sum form (np.expm1(np.log1p(r).sum())). Mathematically equivalent, but avoids floating-point overflow on very long return histories (20Y+) and resolves a pandas type-stub incompatibility with newer versions.
• Capture ratios — same log-sum refactor applied to up- and down-market annualization, eliminating potential overflow for assets with extreme up-market streaks.

Code quality

• period, start, end, and benchmark_ticker parameters now carry correct Optional[str] type annotations (previously typed as str despite accepting None).
• Period-pattern regex precompiled at module load time instead of on every report render.
• Removed stale internal comment referencing previously deleted metrics.
• Docstrings added to all internal helper functions.