polars-ta 0.5.17

polars expressions

polars_ta

Technical Indicator Operators Rewritten in polars.

We provide wrappers for some functions (like TA-Lib) that are not pl.Expr alike.

How to Install

Using pip

pip install -i https://pypi.org/simple --upgrade polars_ta
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade polars_ta  # Mirror in China

Build from Source

git clone --depth=1 https://github.com/wukan1986/polars_ta.git
cd polars_ta
python -m build
cd dist
pip install polars_ta-0.1.2-py3-none-any.whl

How to Install TA-Lib

Non-official TA-Lib wheels can be downloaded from https://github.com/cgohlke/talib-build/releases

Usage

See examples folder.

# We need to modify the function name by prefixing `ts_` before using them in `expr_coodegen`
from polars_ta.prefix.tdx import *
# Import functions from `wq`
from polars_ta.prefix.wq import *

# Example
df = df.with_columns([
    # Load from `wq`
    *[ts_returns(CLOSE, i).alias(f'ROCP_{i:03d}') for i in (1, 3, 5, 10, 20, 60, 120)],
    *[ts_mean(CLOSE, i).alias(f'SMA_{i:03d}') for i in (5, 10, 20, 60, 120)],
    *[ts_std_dev(CLOSE, i).alias(f'STD_{i:03d}') for i in (5, 10, 20, 60, 120)],
    *[ts_max(HIGH, i).alias(f'HHV_{i:03d}') for i in (5, 10, 20, 60, 120)],
    *[ts_min(LOW, i).alias(f'LLV_{i:03d}') for i in (5, 10, 20, 60, 120)],

    # Load from `tdx`
    *[ts_RSI(CLOSE, i).alias(f'RSI_{i:03d}') for i in (6, 12, 24)],
])

When both min_samples and MIN_SAMPLES are set, min_samples takes precedence. default value is None.

import polars_ta

# Global settings. Priority Low
polars_ta.MIN_SAMPLES = 1

# High priority
ts_mean(CLOSE, 10, min_samples=1)

How We Designed This

1. We use Expr instead of Series to avoid using Series in the calculation. Functions are no longer methods of class.
2. Use wq first. It mimics WorldQuant Alpha and strives to be consistent with them.
3. Use ta otherwise. It is a polars-style version of TA-Lib. It tries to reuse functions from wq.
4. Use tdx last. It also tries to import functions from wq and ta.
5. We keep the same signature and parameters as the original TA-Lib in talib.
6. If there is a naming conflict, we suggest calling wq, ta, tdx, talib in order. The higher the priority, the closer the implementation is to Expr.

Comparison of Our Indicators and Others

See compare

Handling Null/NaN Values

See nan_to_null

Debugging

git clone --depth=1 https://github.com/wukan1986/polars_ta.git
cd polars_ta
pip install -e .

Notice: If you have added some functions in ta or tdx, please run prefix_ta.py or prefix_tdx.py inside the tools folder to generate the corrected Python script (with the prefix added). This is required to use in expr_codegen.

Reference

• https://github.com/pola-rs/polars
• https://github.com/TA-Lib/ta-lib
• https://github.com/twopirllc/pandas-ta
• https://github.com/bukosabino/ta
• https://github.com/peerchemist/finta
• https://github.com/wukan1986/ta_cn
• https://support.worldquantbrain.com/hc/en-us/community/posts/20278408956439-从价量看技术指标总结-Technical-Indicator-
• https://platform.worldquantbrain.com/learn/operators/operators

polars_ta

基于polars的算子库。实现量化投研中常用的技术指标、数据处理等函数。对于不易翻译成Expr的库（如：TA-Lib）也提供了函数式调用的封装

安装

在线安装

pip install -i https://pypi.org/simple --upgrade polars_ta  # 官方源
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade polars_ta  # 国内镜像源

源码安装

git clone --depth=1 https://github.com/wukan1986/polars_ta.git
cd polars_ta
python -m build
cd dist
pip install polars_ta-0.1.2-py3-none-any.whl

TA-Lib安装

Windows用户不会安装可从https://github.com/cgohlke/talib-build/releases 下载对应版本whl文件

使用方法

参考examples目录即可，例如：

# 如果需要在`expr_codegen`中使用，需要有`ts_`等前权，这里导入提供了前缀
from polars_ta.prefix.tdx import *
# 导入wq公式
from polars_ta.prefix.wq import *

# 演示生成大量指标
df = df.with_columns([
    # 从wq中导入指标
    *[ts_returns(CLOSE, i).alias(f'ROCP_{i:03d}') for i in (1, 3, 5, 10, 20, 60, 120)],
    *[ts_mean(CLOSE, i).alias(f'SMA_{i:03d}') for i in (5, 10, 20, 60, 120)],
    *[ts_std_dev(CLOSE, i).alias(f'STD_{i:03d}') for i in (5, 10, 20, 60, 120)],
    *[ts_max(HIGH, i).alias(f'HHV_{i:03d}') for i in (5, 10, 20, 60, 120)],
    *[ts_min(LOW, i).alias(f'LLV_{i:03d}') for i in (5, 10, 20, 60, 120)],

    # 从tdx中导入指标
    *[ts_RSI(CLOSE, i).alias(f'RSI_{i:03d}') for i in (6, 12, 24)],
])

当min_samples和MIN_SAMPLES都设置时，以min_samples为准，默认值为None

import polars_ta

# 全局设置。优先级低
polars_ta.MIN_SAMPLES = 1

# 指定函数。优先级高
ts_mean(CLOSE, 10, min_samples=1)

设计原则

1. 调用方法由成员函数换成独立函数。输入输出使用Expr，避免使用Series
2. 优先实现wq公式，它仿WorldQuant Alpha公式，与官网尽量保持一致。如果部分功能实现在此更合适将放在此处
3. 其次实现ta公式，它相当于TA-Lib的polars风格的版本。优先从wq中导入更名
4. 最后实现tdx公式，它也是优先从wq和ta中导入
5. talib的函数名与参数与原版TA-Lib完全一致
6. 如果出现了命名冲突，建议调用优先级为wq、ta、tdx、talib。因为优先级越高，实现方案越接近于Expr

指标区别

请参考compare

空值处理

请参考nan_to_null

开发调试

git clone --depth=1 https://github.com/wukan1986/polars_ta.git
cd polars_ta
pip install -e .

注意：如果你在ta或tdx中添加了新的函数，请再运行tools下的prefix_ta.py或prefix_tdx.py，用于生成对应的前缀文件。前缀文件方便在expr_codegen中使用

文档生成

pip install -r requirements-docs.txt
mkdocs build

文档生成在site目录下，其中的llms-full.txt可以作为大语言模型的知识库导入。

也可以通过以下链接导入： https://polars-ta.readthedocs.io/en/latest/llms-full.txt

提示词

由于llms-full.txt信息不适合做提示词，所以tools/prompt.py提供了生成更简洁算子清单的功能。

用户也可以直接使用prompt.txt(欢迎提示词工程专家帮忙改进，做的更准确)

参考

• https://github.com/pola-rs/polars
• https://github.com/TA-Lib/ta-lib
• https://github.com/twopirllc/pandas-ta
• https://github.com/bukosabino/ta
• https://github.com/peerchemist/finta
• https://github.com/wukan1986/ta_cn
• https://support.worldquantbrain.com/hc/en-us/community/posts/20278408956439-从价量看技术指标总结-Technical-Indicator-