finmlsim 0.4.0

Simulation toolkit for financial machine learning: scholarly market, microstructure, and execution simulators for teaching and research.

finmlsim

Simulation and analysis toolkit for the FIN 70xx — Financial Machine Learning & AI course.

The course is simulation-driven: before confronting messy empirical data, we generate return series under controlled assumptions, turn each stylized fact of markets on and off, and watch the effect. finmlsim provides those generators and the tools to measure what they produce.

import finmlsim as fms
r = fms.simulate.garch(n=2000, dist="t", seed=0)   # volatility clustering + fat tails
fms.stylized.summary(r)                            # measure the stylized facts
prices = fms.stylized.to_prices(r)                 # log returns -> price path for plotting

Install

pip install -e .          # from the repo root (editable)
# core needs only numpy; figures/analysis also use pandas, scipy, matplotlib, statsmodels

Modules

• simulate — generators grouped by family:
  • Return series (1-D log returns): gaussian, student_t, arma, garch, regime_switching, jump_diffusion, cont_bouchaud.
  • Cross-sectional panel: panel (2-D, common-factor structure).
  • Continuous-time price processes: ornstein_uhlenbeck / vasicek, heston (1993), bates (1996), sabr (Hagan et al. 2002), rbergomi (Bayer-Friz-Gatheral 2016), fbm / fgn (Hurst-parametrized).
  • Microstructure and execution: hawkes (1971 self-exciting), roll_bounce (1984), glosten_milgrom (1985), kyle (1985), limit_order_book (Cont-Stoikov style), almgren_chriss (2000 optimal execution). Every generator takes a seed for reproducibility.
• stylized — diagnostics: acf, excess_kurtosis, vol_clustering, summary, to_prices.
• metrics — strategy evaluation: sharpe, max_drawdown, rank_ic, ic_ir, turnover, deflated_sharpe, min_track_record_length (used in Chapters 15 and 16).
• resample — block bootstraps for dependent series: stationary_bootstrap (Politis-Romano 1994), circular_block_bootstrap (Politis-Romano 1992). Pairs with the multiple-testing diagnostics in metrics.
• data — loaders for the bundled empirical datasets (Ken French daily factors, momentum, and 12 industry portfolios) used for the book's real-data "empirical companion" figures: ff_factors, ff_momentum, industries, market_return. Offline; see datasets/famafrench/.

The stylized facts each return-series generator produces

Generator	Fat tails	Vol clustering	Mean autocorrelation
gaussian	–	–	–
student_t	✓	–	–
arma	–	–	✓ (by design)
garch	mild	✓	–
garch(dist="t")	✓	✓	–
regime_switching	✓ (mixed)	✓	–
jump_diffusion	✓	–	–
cont_bouchaud	✓ (endogenous)	(weak)	–

What the continuous-time and microstructure models give you

Generator	Returns	Key parameter	Use case
ornstein_uhlenbeck / vasicek	path	kappa (mean-reversion rate)	pairs, short rates, vol state
heston	(S, v)	xi, rho, kappa	stochastic vol; smile/skew
bates	(S, v)	+ jump_intensity	stoch-vol with crash risk
sabr	(F, alpha)	beta, nu, rho	FX / rates vol surfaces
rbergomi	(S, xi)	H < 0.5	rough volatility
fbm / fgn	path / increments	H	Hurst-parameterized paths
hawkes	event times	alpha, beta	order-flow clustering, contagion
roll_bounce	tx-price log returns	half_spread	bid-ask bounce, negative AC(1)
glosten_milgrom	dict (ask/bid/spread/belief)	alpha (informed prob)	adverse-selection spread
kyle	dict (v, u, x, y, price)	sigma_v / sigma_u	strategic informed trading
limit_order_book	dict (mid, spread, depth)	lam0, theta, mu_mkt	LOB dynamics, depth, impact
almgren_chriss	dict (path, costs)	lam (risk aversion)	execution trajectories

These are teaching models, not calibrations. Planned additions: a cost-aware backtest and leak-free features helpers. The package will evolve with the course.