/polars_ta

Technical Analysis Indicators for polars

Primary LanguagePythonMIT LicenseMIT

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)],
])

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

Evolve of Our TA-Lib Wrappers

  1. Expr.map_batches can be used to call third-party libraries, such as TA-Lib, bottleneck. But because of the input and output format requirements, you need to wrap the third-party API with a function.
  • Both input and output can only be one column. If you want to support multiple columns, you need to convert them to pl.Struct. After that, you need to use unnest to split pl.Struct.
  • The output must be pl.Series
  1. Start to use register_expr_namespace to simplify the code
  • Implementation helper.py
  • Usage demo demo_ta1.py
  • Pros: Easy to use
  • Cons:
    • The member function call mode is not convenient for inputting into genetic algorithms for factor mining
    • __getattribute__ dynamic method call is very flexible, but loses IDE support.
  1. Prefix expression. Convert all member functions into formulas
  • Implementation wrapper.py
  • Usage demo demo_ta2.py
  • Pros: Can be input into our implementation of genetic algorithms
  • Cons: __getattribute__ dynamic method call is very flexible, but loses IDE support.
  1. Code generation.

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

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)],
])

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

TA-Lib封装的演化

  1. Expr.map_batches可以实现调用第三方库，如TA-Lib, bottleneck。但因为对输入与输出格式有要求，所以还需要用函数对第三方API封装一下。
    • 输入输出都只能是一列，如要支持多列需转换成pl.Struct。事后pl.Struct要拆分需使用unnest
    • 输出必须是pl.Series
  2. 参数多，代码长。开始使用register_expr_namespace来简化代码
    • 实现代码helper.py
    • 使用演示demo_ta1.py
    • 优点：使用简单
    • 不足：成员函数调用模式不便于输入到遗传算法中进行因子挖掘
    • 不足：__getattribute__动态方法调用非常灵活，但失去了IDE智能提示
  3. 前缀表达式。将所有的成员函数都转换成公式
    • 实现代码wrapper.py
    • 使用演示demo_ta2.py
    • 优点：可以输入到遗传算法
    • 不足：__getattribute__动态方法调用非常灵活，但失去了IDE智能提示
  4. 代码自动生成。

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

参考