TimeSynth
stingray
| TimeSynth | stingray | |
|---|---|---|
| 1 | 1 | |
| 326 | 163 | |
| 0.6% | 0.6% | |
| 0.0 | 9.8 | |
| 6 months ago | 8 days ago | |
| Jupyter Notebook | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
TimeSynth
-
What is the best way to generate synthetic OHLC data?
I have the same question so I cant give a direct answer. However, I've been thinking of using SDV and TimeSynth python packages to produce synthetic data for backtesting.
stingray
What are some alternatives?
tsfresh - Automatic extraction of relevant features from time series:
SDV - Synthetic data generation for tabular data
timemachines - Predict time-series with one line of code.
ta - Technical Analysis Library using Pandas and Numpy
PyCBC-Tutorials - Learn how to use PyCBC to analyze gravitational-wave data and do parameter inference.
tempo - API for manipulating time series on top of Apache Spark: lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, downsampling, and interpolation
lama - 🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
pycaret - An open-source, low-code machine learning library in Python
priwo - I/O for common pulsar data formats.
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
tslearn - The machine learning toolkit for time series analysis in Python