finance calcs

Standard financial calculations

Overview

finance-calcs provides composable Polars expression metrics plus a smaller set of explicitly eager statistical, preprocessing, post-trade, and native kernel helpers. It is designed for lazy execution where the algorithm permits it, namespace-style ergonomics, and direct interoperability with the rest of the finance-* stack.

The public API follows a few rules:

• every expression metric accepts and returns pl.Expr

• metrics are exposed once, with optional window= and period= controls rather than separate rolling, monthly, and annual variants

• functions are also available through the .fcalcs namespace on both pl.Expr and pl.Series

• examples use synthetic but realistic fixtures from finance-datagen

Return, risk, and tail expression metrics accept periodic returns rather than prices. Convert prices first with simple_returns or log_returns. These metrics treat floating-point NaN and Polars null values as missing. Compound returns treat missing observations as neutral, while statistical aggregations exclude them.

Materializing helpers are not available through .fcalcs: native ADX, SAR, and GARCH kernels accept numeric sequences and return NumPy arrays; GPD fits and several statistical helpers accept pl.Series; preprocessing and post-trade summaries accept concrete pl.DataFrame inputs.

Implemented coverage

Topic	Functions
Returns and periods	period_bucket, simple_returns, log_returns, cumulative_returns, cumulative_return, annualized_return, annualized_volatility
Risk and drawdown	sharpe, sortino, calmar, downside_deviation, drawdown_series, max_drawdown, drawdown_details, and historical, parametric, and generalized-Pareto tail risk
Report metrics	Best/worst returns, average wins/losses, gain-to-pain, recovery factor, and Kelly criterion
Technical indicators	Moving averages, Bollinger/Donchian channels, momentum oscillators, range volatility, and volume indicators
Alpha and quantiles	Forward returns, conditional/horizon IC, IC decay, IC summaries, quantile assignment, signal normalization, quantile returns, turnover, and long/short spreads
Factor and benchmark metrics	Alpha, beta, benchmark R-squared, up/down capture, batting average, tracking error, and information ratio
Distribution and tail risk	Higher moments, Sharpe significance helpers, tail ratio, ulcer index, omega ratio, GPD VaR, and GPD CVaR
Portfolio and post-trade	Exposure, concentration, active share, transaction costs/volume/attribution, slippage, turnover, round trips, MAE/MFE, and trade-quality metrics

See the Examples page for workflows with generated data and the API page for a complete grouped reference for every public function.

Quick start

Generate a deterministic daily equity path with finance-datagen, then compute return and risk metrics as Polars expressions.

import polars as pl
from finance_datagen import generate_prices

import finance_calcs as fc

prices = generate_prices(symbol="ACME", seed=7)

out = prices.with_columns(
    pl.col("price").fcalcs.simple_returns().alias("ret"),
).select(
    fc.cumulative_return(pl.col("ret")).alias("total_return"),
    pl.col("ret").fcalcs.annualized_return().alias("annualized_return"),
    pl.col("ret").fcalcs.annualized_volatility().alias("annualized_volatility"),
    pl.col("ret").fcalcs.sharpe().alias("sharpe"),
    pl.col("ret").fcalcs.max_drawdown().alias("max_drawdown"),
)

Use finance-datagen.ohlc_from_close when calculations need OHLCV bars:

from finance_datagen import ohlc_from_close

bars = ohlc_from_close(prices["price"], symbol="ACME", seed=7)

features = bars.with_columns(
    pl.col("close").fcalcs.sma(20).alias("sma_20"),
    pl.col("close").fcalcs.rsi(14).alias("rsi_14"),
    fc.atr(pl.col("high"), pl.col("low"), pl.col("close")).alias("atr_14"),
    fc.obv(pl.col("close"), pl.col("volume")).alias("obv"),
)

Period and frequency slices

Use period= for calendar-style slices and keep window= for rolling row-count windows. A period can be a finance_enums.Frequency, any alias accepted by finance_enums.to_frequency(), any Polars dt.truncate() duration string, or a precomputed bucket expression.

import polars as pl
from finance_enums import Frequency

monthly = prices.with_columns(
    pl.col("price").fcalcs.simple_returns().alias("ret"),
).with_columns(
    fc.period_bucket(pl.col("timestamp"), Frequency.Month).alias("month"),
    pl.col("ret").fcalcs.cumulative_return(period="month", date=pl.col("timestamp")).alias("month_return"),
    pl.col("ret").fcalcs.sharpe(period="1q", date=pl.col("timestamp")).alias("quarter_sharpe"),
)

For fiscal periods, strategy regimes, or exchange-calendar grids built upstream, pass the bucket expression directly:

bucketed = prices.with_columns(
    pl.col("price").fcalcs.simple_returns().alias("ret"),
    pl.col("timestamp").dt.year().alias("fiscal_year"),
).with_columns(
    fc.cumulative_return(pl.col("ret"), period=pl.col("fiscal_year")).alias("fiscal_return"),
)

Stack integration

finance-calcs is intended to pair with:

• finance-datagen for synthetic fixtures and test inputs

• finance-dates for calendar-aware date handling upstream

• finance-enums for shared enum-backed trading semantics upstream

That keeps calculations focused on typed expressions instead of schema cleanup, string parsing, or calendar repair.