finance-datagen 0.1.1

Standard financial data generation

finance datagen

Standard financial data generation

Overview

finance-datagen produces synthetic financial time series for testing, demos, and benchmarking the rest of the finance-* stack without relying on real market data. The numerical core is implemented in Rust and emits Apache Arrow RecordBatch values; the Python layer wraps each generator so the public API returns polars.DataFrame objects.

All public generator classes inherit from DataGenerator, a pydantic base model that validates typed parameters on construction. Use .generate() for the table output, or next(generator) for one-shot iterator-style use. Convenience functions such as generate_gbm(...) and generate_signal(...) instantiate the matching model and return .generate().

Generators

Price models (Rust core)

Symbol	Model	Output columns
GBMGenerator	Geometric Brownian Motion (log-Euler)	timestamp, symbol, price
HestonGenerator	Heston (1993) stochastic volatility (full-truncation Euler)	timestamp, symbol, price, variance
GARCHGenerator	GARCH(1,1) returns	timestamp, symbol, price, return, sigma
ohlc_from_close	OHLCV synthesis from any close series	timestamp, symbol, open, high, low, close, volume

Price-path convenience wrappers are also exported as generate_gbm, generate_heston, and generate_garch.

Python generators

Symbol	Output
SignalGenerator	Long-form [date, symbol, signal, fwd_returns] with target Pearson IC
FactorLoadingsGenerator	Wide [symbol, market, value, momentum, size, quality] Barra-style loadings
BenchmarkGenerator	[date, benchmark] Gaussian benchmark return series
PositionsGenerator	Long-form position panel [date, symbol, price, quantity, market_value, weight]
TransactionsGenerator	Transaction log with enum-backed side/position-effect labels and explicit costs
MultiAssetGBMGenerator	Correlated multi-asset GBM panel [timestamp, symbol, price, return]
RegimeSwitchingGenerator	Markov regime-switching price path [timestamp, symbol, price, return, regime]
MarketImpactCurveGenerator	Participation-rate impact curves with temporary, permanent, and total impact in bps
StatisticalRiskModelGenerator	PCA-style factor loadings, factor returns, and specific variance
FundamentalRiskModelGenerator	Barra-style enum-backed sector/style loadings plus specific variance
FactorCovarianceGenerator	Symmetric positive semidefinite factor covariance matrix
SpecificVarianceGenerator	Positive idiosyncratic variance vector

Every Python generator has a matching generate_* convenience wrapper, including the legacy generate_signal, generate_factor_loadings, and generate_benchmark functions.

All Rust generators accept an optional seed: int for bit-reproducible output across platforms (ChaCha8 RNG); the Python generators accept a seed for numpy.random.default_rng.

Quick start

from finance_datagen import GBMGenerator, generate_signal, ohlc_from_close

closes = GBMGenerator(s0=100.0, mu=0.07, sigma=0.25, seed=0).generate()
bars   = ohlc_from_close(closes["price"], seed=0)
signal = generate_signal(n_dates=20, n_assets=50, seed=0)

See the Data page for model math, parameter ranges, and output schemas, and the API page for a complete function-level reference.

Architecture

The Rust core (rust/src/) is polars-free: every generator builds an arrow_array::RecordBatch and returns it through the Arrow C Data Interface PyCapsule via pyo3-arrow. The Python wrappers call polars.from_arrow(batch) on the receiving end. This keeps the polars-rs and polars-py codebases on opposite sides of a stable ABI boundary, avoiding the binary-incompatibility issues that come with linking polars from both Rust and CPython.

[!NOTE] This library was generated using copier from the Base Python Project Template repository.