Time Series Analysis (TSA)

Mount Wilson, United States

Link to GitHub repository:

The Thalesians time series library is a heterogeneous collection of tools for facilitating efficient

  • data analysis and, more broadly,
  • data science; and
  • machine learning.

The originating developers’ primary applications are

  • quantitative finance and economics;
  • electronic trading, especially,
  • algorithmic trading, especially,
  • algorithmic market making;
  • high-frequency finance;
  • financial alpha generation;
  • client analysis;
  • risk analysis;
  • financial strategy backtesting.

However, since data science and machine learning are universal; it is hoped that this code will be useful in other areas. Therefore we are looking for contributors with the above background as well as

  • computer science,
  • engineering, especially mechanical, electrical, electronic, marine, aeronautical, and aerospace,
  • science, especially biochemistry and genetics, and
  • medicine.

Currently, the following functionality is implemented and is being expanded:

  • stochastic filtering, including Kalman and particle filtering approaches,
  • stochastic processes, including mean-reverting (Ornstein-Uhlenbeck) processes,
  • Gauss-Markov processes,
  • stochastic simulation, including Euler-Maruyama scheme,
  • interprocess communication via “pypes”,
  • online statistics,
  • visualisation, including interactive visualisation for Jupyter,
  • pre-, post-condition, and invariant checking,
  • utilities for dealing with Pandas dataframes, especially large ones,
  • native Python, NumPy, and Pandas type conversions,
  • interoperability with kdb+/q.