Technical Indicator Operators Rewritten in polars.
We provide wrappers for some functions (like TA-Lib) that are not pl.Expr alike.
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
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
Non-official TA-Lib wheels can be downloaded from https://github.com/cgohlke/talib-build/releases
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)- We use
Exprinstead ofSeriesto avoid usingSeriesin the calculation. Functions are no longer methods of class. - Use
wqfirst. It mimicsWorldQuant Alphaand strives to be consistent with them. - Use
taotherwise. It is apolars-style version ofTA-Lib. It tries to reuse functions fromwq. - Use
tdxlast. It also tries to import functions fromwqandta. - We keep the same signature and parameters as the original
TA-Libintalib. - If there is a naming conflict, we suggest calling
wq,ta,tdx,talibin order. The higher the priority, the closer the implementation is toExpr.
See compare
See nan_to_null
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.
- 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
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
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
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
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
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)- 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 - 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 - 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 - 最后实现
tdx公式，它也是优先从wq和ta中导入 talib的函数名与参数与原版TA-Lib完全一致- 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
请参考compare
请参考nan_to_null
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
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
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)
- 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-