finance plots

Matplotlib plots and performance tables for financial return series, price paths, and technical-indicator panels.

Overview

finance-plots is the presentation layer for the finance stack. It accepts Narwhals-compatible inputs such as pandas, Polars, numpy, and other supported series-like objects, then returns ordinary matplotlib figures or Great Tables objects that can be saved, embedded in notebooks, or composed into tearsheets.

The initial release focuses on a compact, useful surface:

• Return/risk plots for cumulative returns, rolling volatility, rolling Sharpe, rolling beta/correlation, benchmark scatter, drawdowns, and period-return views.
• Technical-indicator plots for price overlays, secondary-axis indicators, and indicator sub-panels.
• Performance summary tables backed by great-tables.
• Post-trade diagnostics for trading-cost breakdowns, MAE/MFE scatter, and execution-quality distributions.
• Alpha-analysis plots for IC, quantile returns, turnover, and cumulative factor returns.

Install

pip install finance-plots

The gallery and documentation examples use the released data/calculation stack:

pip install "finance-plots[examples]"

Quick Start

Generate deterministic prices with finance-datagen, compute returns with finance-calcs, and plot them with finance-plots.

from datetime import datetime, timezone

import polars as pl
from finance_datagen import generate_prices

import finance_calcs as fc
import finance_plots as fp

start_ms = int(datetime(2021, 1, 4, tzinfo=timezone.utc).timestamp() * 1000)
prices = generate_prices(symbol="ACME", seed=7, start_ms=start_ms)
returns = prices.with_columns(
    fc.simple_returns(pl.col("price")).alias("ret"),
).select("ret").drop_nulls()["ret"]

fig = fp.plot_rolling_returns(returns)

Current Plot Catalog

Current Table Catalog