katsustats 0.2.2

A modernized backtest report module powered by Polars

Katsustats

A simple backtest tool for your return series, inspired by quantstats.

Put your return series, and you get backtest results with visualizations and key metrics.

How to use

Installation

pip install katsustats

Or with uv:

uv add katsustats

Data format

katsustats accepts either a Polars or pandas DataFrame with two required columns:

column	type	description
date	pl.Date	Trading date
pnl	pl.Float64	Daily return (e.g. 0.01 = +1%)

When a pandas DataFrame is passed, katsustats converts it to Polars at the start of processing.

Basic usage

import polars as pl
import katsustats

# Build your return series
pnl = pl.DataFrame({
    "date": pl.date_range(pl.date(2020, 1, 1), pl.date(2023, 12, 31), "1d", eager=True),
    "pnl": your_daily_returns,   # list / numpy array of floats
})

# Generate the full report (prints metrics + shows all plots)
results = katsustats.reports.full(pnl)

Pandas inputs work too:

import pandas as pd

pnl = pd.DataFrame({
    "date": dates,
    "pnl": your_daily_returns,
})

results = katsustats.reports.full(pnl)

results is a dict with the following keys:

key	type	description
metrics	pl.DataFrame	Summary metrics table
drawdowns	pl.DataFrame	Top-5 drawdown periods
dow_stats	pl.DataFrame	Day-of-week statistics
figures	dict[str, Figure]	All matplotlib figures

With a benchmark

benchmark = pl.DataFrame({
    "date": pl.date_range(pl.date(2020, 1, 1), pl.date(2023, 12, 31), "1d", eager=True),
    "pnl": benchmark_daily_returns,
})

results = katsustats.reports.full(pnl, base_pnl=benchmark)

When a benchmark is provided, the metrics table also includes Alpha, Beta, Correlation, Information Ratio, and Excess Return.

Advanced options

results = katsustats.reports.full(
    pnl,
    base_pnl=benchmark,
    rf=0.04,          # annualized risk-free rate (default 0.0)
    periods=252,      # trading days per year (default 252)
    show=False,       # suppress inline plot display
)

HTML report

Generate a self-contained HTML report (similar to qs.reports.html()):

# Save to file
katsustats.reports.html(pnl, base_pnl=benchmark, title="My Strategy", output="report.html")

# Or get HTML string
html_str = katsustats.reports.html(pnl, title="My Strategy")

The report includes headline metric cards, performance tables, drawdown analysis, day-of-week statistics, and all 8 charts embedded as images — all in a single .html file that works offline.

Using individual modules

import katsustats

# --- Stats ---
katsustats.stats.total_return(pnl)
katsustats.stats.cagr(pnl)
katsustats.stats.sharpe(pnl, rf=0.0)
katsustats.stats.sortino(pnl)
katsustats.stats.max_drawdown(pnl)
katsustats.stats.calmar(pnl)
katsustats.stats.volatility(pnl)
katsustats.stats.win_rate(pnl)
katsustats.stats.profit_factor(pnl)
katsustats.stats.value_at_risk(pnl, alpha=0.05)

katsustats.stats.drawdown_details(pnl, top_n=5)      # pl.DataFrame
katsustats.stats.day_of_week_stats(pnl)              # pl.DataFrame
katsustats.stats.summary_metrics(pnl, base_pnl)     # pl.DataFrame

# --- Plots ---
katsustats.plots.plot_cumulative_returns(pnl, base_pnl)
katsustats.plots.plot_drawdown(pnl)
katsustats.plots.plot_monthly_heatmap(pnl)
katsustats.plots.plot_yearly_returns(pnl, base_pnl)
katsustats.plots.plot_return_distribution(pnl, base_pnl)
katsustats.plots.plot_rolling_sharpe(pnl, base_pnl)
katsustats.plots.plot_rolling_volatility(pnl, base_pnl)
katsustats.plots.plot_dow_returns(pnl)

Metrics produced

metric	description
Total Return	Compounded return over the full period
CAGR	Compound Annual Growth Rate
Sharpe Ratio	Annualized risk-adjusted return
Sortino Ratio	Sharpe using only downside deviation
Max Drawdown	Largest peak-to-trough decline
Calmar Ratio	CAGR / |Max Drawdown|
Volatility (ann.)	Annualized standard deviation
Win Rate	% of days with positive returns
Profit Factor	Gross profit / gross loss
Best / Worst Day	Largest single-day gain / loss
Avg Win / Avg Loss	Mean return on winning / losing days
Daily VaR (95%)	5th-percentile daily return
Recovery Factor	Total return / |Max Drawdown|
Skewness / Kurtosis	Distribution shape statistics