fin-eda 1.0.0

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 rich, colour-coded tearsheet covering returns, risk, drawdowns, benchmark comparison, volatility, liquidity, and more — all in one call.

Installation

pip install fin-eda

Quick Start

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)

Output

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

A header panel shows the current trend snapshot, followed by one table per section.

Metrics Covered

Section	Key Metrics
Core Return & Risk	Cumulative return, arithmetic & geometric mean, median, std dev, annualized volatility & variance — across 1M, 3M, 6M, 1Y, 3Y, 5Y, 10Y, YTD
Risk-Adjusted Performance	Sharpe ratio, Sortino ratio, downside deviation, semi-variance, profit factor — from 6M+
Drawdown & Capital Destruction	Max drawdown, average drawdown, time to recovery, max consecutive loss days — from 3M+
Trend Structure & Price Health	50/100/200D moving averages, price vs 200D MA, golden/death cross spread, trend persistence, 52W high distance
Relative Performance vs Benchmark	Excess return, information ratio — from 6M+
Beta, Correlation & Market Dependence	Beta, correlation vs benchmark — from 3M+
Capture Ratios	Up-market and down-market capture (annualized) — from 6M+
Return Distribution & Non-Normality	Skewness, kurtosis, Jarque-Bera statistic — from 3M+
Tail Risk & Stress	Historical VaR (95% & 99%), Expected Shortfall/CVaR, worst daily/weekly/monthly return, extreme loss frequency
Regime & Time-Series Behavior	Return autocorrelation at 1D, 5D, 21D lags
Volatility Metrics	Parkinson volatility (21D/63D/126D), rolling vol percentile, volatility of volatility, current vs 1Y vol ratio
Liquidity Metrics	Average daily volume (30D/90D), volume trend, Amihud illiquidity ratio (30D/90D/full)

Parameters

Parameter	Type	Default	Description
ticker_or_prices	str or pd.Series	—	Yahoo Finance ticker or a Series of close prices
benchmark_ticker	str	'SPY'	Benchmark symbol. Pass None to skip
risk_free_rate	float	0.0	Annual risk-free rate (e.g. 0.05 for 5%)
period	str	'10y'	yfinance history period (ignored if start/end provided)
start	str	None	Start date YYYY-MM-DD
end	str	None	End date YYYY-MM-DD
return_results	bool	False	Return the metrics dict instead of printing
quiet	bool	False	Suppress all printed output

Dependencies

• pandas
• numpy
• scipy
• yfinance
• rich

License

MIT