pyttkl 0.1.1

Some Python tools

pyttkl

Links

• English Docs
• 中文文档

Description

Some Python tools

kits.py

• This module contains utility functions for handling strings, lists, and other data types.
• Extensions related to logging
• Extensions related to argparse

tmat.py

• A Trivial Matrix concept implementation that can read and write pd.DataFrame.
• Although internally it is stored as numpy-compatible data, considering pd.DataFrame(ndarray), it essentially wraps an ndarray or adds row and column names.
• load_tmat(filename, mmap_mode='c'): Automatically determines the payload type based on the meta information in the file and reads it. If the payload is mmap, you can specify mmap_mode.
• save_tmat(filename, tmat, compress='zstd'): Allows compressing the payload with zstd. There are more options in the code. It can also save as mmap.

Installation

You can install this package using pip:

pip install .

Design

tmat format:

• Design Goals:
  • 1. Compatible with numpy's ndarray
  • 2. Compatible with pandas' DataFrame
  • 3. Supports compression
  • 4. Supports mmap
  • 5. Supports multiple data types
  • 6. Native support for both C++ and Python
  • 7. Understandable by quantitative researchers by reading the code
• Example: TMT\n{header_length: 954}\n{"columns": ["a", "b", "c"], "rows": [1, 2, 3], "dtype": "int32", "compress": "zstd"}\n{payload}
• Header: 4-byte magic number, TMT\n
• JSON string {"header_length": 954}\n. If header_length > 0, the following meta is a payload compressed with lz4 of length header_length. Otherwise, it is a plain JSON string.
  • Trick: Use head -2 KLINE/OPEN/202401.tmt to view the file header.
• Meta: JSON string {"columns": ["a", "b", "c"], "rows": [1, 2, 3], "dtype": "int32", "compress": "zstd"}\n
• Payload: Binary data. The specific format depends on the meta content.
  • If {"compress": "zstd"}, it is zstd-compressed binary data. It can also be lz4 or zstd.
  • Otherwise, it is plain binary data, which can be directly loaded with mmap. You can specify the loading mode with mmap_mode.
    • Trick: You can use pandas and numpy's inplace operations to modify the file content directly. Be sure to check ndarray.flags when using it.

TODOS or NO-TODOS

There are many things that can be done, but to keep the project simple and at a level understandable by quantitative researchers, many TODOs become NO TODOs. Of course, if anyone thinks something can be improved, feel free to create an issue.

• TODOs

  • Code comments
  • Code documentation
  • Restrict exposed APIs

• NO TODOs

  • Multithreading

Requirements

• Python 3.11 or higher