The SdePy package provides tools to state and numerically integrate Ito Stochastic Differential Equations (SDEs), including equations with time-dependent parameters, time-dependent correlations, and stochastic jumps, and to compute with, and extract statistics from, their realized paths.
Several preset processes are provided, including lognormal, Ornstein-Uhlenbeck, Hull-White n-factor, Heston, and jump-diffusion processes.
Computations are fully vectorized across paths, via NumPy and SciPy, making live sessions with 100000 paths reasonably fluent on single cpu hardware.
This package came out of practical need, so expect a flexible tool that gets real-life things done. On the other hand, not every part of it is clean and polished, so expect rough edges, and the occasional bug (please report!).
Developers are committed to the stability of the public API, here again out of practical need to safeguard dependencies.
Start here
Installation: pip install sdepy
Quick Guide (as notebook)
Documentation (as pdf)
1 maintainer
Metadata
1 maintainer
Release files for sdepy 1.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sdepy-1.2.0.tar.gz | 99.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sdepy-1.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 204.6 kB
Release files / sdepy-1.2.0.tar.gz
| Download URL | sdepy-1.2.0.tar.gz |
|---|---|
| Size | 99.3 kB |
| Tags | Source |
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Release files / sdepy-1.2.0-py3-none-any.whl
| Download URL | sdepy-1.2.0-py3-none-any.whl |
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| Size | 105.3 kB |
| Tags | Python 3 |
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