Quant / Research / Software · Learning exercise
Brownian Motion Lab
Seeded Brownian-path simulations with terminal mean and variance checks.
- My role
- Independent learning project
- Period
- 2026
- Source
- Public repository
The question
Does the simulation behave like the stochastic process it is meant to illustrate?
Python · NumPy · Matplotlib · JupyterMy contribution
Implemented seeded path simulations, corrected the time grid, and added a terminal mean/variance sanity check.
01 / work
Path simulation
The notebook constructs Brownian increments in one, two, and three dimensions and plots individual paths and ensembles. A seeded generator makes the examples reproducible, while an explicit zero starting point keeps the path and time grid aligned.
02 / evaluation
Mean and variance checks
A terminal mean/variance sanity check across many sampled paths tests the scale of the simulated process. It is a useful implementation check, not a proof of the model or a claim that a financial price follows Brownian motion.
03 / limitations
Scope and limitations
- The repository currently has one implemented notebook, not a strategy library or backtester.
- The paths are observed on a finite time grid, rather than being the complete continuous-time process.
- The notebook credits assistance with its initial 3D plotting setup.
Source
Selected public files, with commit references and links to GitHub.
Public overview reviewed 8 September 2026. Repository commit: 2026-08-31.