Polars TA

https://github.com/wukan1986/polars_ta
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polars_ta provides technical indicator operators rewritten in polars, offering wrappers for...
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### Install TA-Lib Wheels

Source: https://github.com/wukan1986/polars_ta/blob/main/README.md

Guides users on how to install TA-Lib, specifically mentioning that non-official wheels can be downloaded from a provided GitHub releases page. This is a prerequisite for certain functionalities if TA-Lib is used as a backend.

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

--------------------------------

### Polars TA WQ Factor Examples

Source: https://github.com/wukan1986/polars_ta/blob/main/prompt.txt

Illustrative examples of quantitative factor implementations using the polars_ta.wq module's operators and basic factors.

```Python
alpha_004=-ts_rank(cs_rank(LOW),9)
alpha_006=-ts_corr(OPEN,VOLUME,10)
alpha_040=-cs_rank(ts_std_dev(HIGH,10))*ts_corr(HIGH,VOLUME,10)
alpha_041=power(HIGH*LOW,0.5)-VWAP
alpha_042=cs_rank(VWAP-CLOSE)/cs_rank(VWAP+CLOSE)
alpha_053=-ts_delta((CLOSE-LOW-(HIGH-CLOSE))/(CLOSE-LOW),9)
alpha_057=-((CLOSE-VWAP)/ts_decay_linear(cs_rank(ts_arg_max(CLOSE,30)),2))
alpha_101=(CLOSE-OPEN)/(HIGH-LOW+0.001)
```

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### Install polars_ta for Debugging

Source: https://github.com/wukan1986/polars_ta/blob/main/README.md

Provides commands to clone the repository and install the package in editable mode (`pip install -e .`). This setup is typically used for debugging or contributing to the library, allowing changes to be reflected immediately.

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

```

--------------------------------

### Install polars_ta using pip

Source: https://github.com/wukan1986/polars_ta/blob/main/README.md

Installs the polars_ta library using pip, with options for the official PyPI repository or a mirror in China. This is the standard method for installing the package.

```commandline
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 and Install polars_ta from Source

Source: https://github.com/wukan1986/polars_ta/blob/main/README.md

Provides instructions to clone the polars_ta repository, build the package using 'python -m build', and then install the generated wheel file. This method is useful for development or when the latest unreleased version is needed.

```commandline
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
```

--------------------------------

### Apply Technical Indicators using polars_ta

Source: https://github.com/wukan1986/polars_ta/blob/main/README.md

Demonstrates how to use polars_ta to add multiple technical indicators to a polars DataFrame. It shows importing functions from different prefixes (tdx, wq) and applying them using polars expressions, aliasing the results for clarity.

```python
# 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)],
])

```

--------------------------------

### Python None vs NaN Comparison

Source: https://github.com/wukan1986/polars_ta/blob/main/nan_to_null.md

Demonstrates the difference in equality comparison between Python's `None` and NumPy's `np.nan`. Highlights the need for `is_null` or `is_nan` functions for reliable checks.

```python
None == None  # True # (It should be `None is None`)
np.nan == np.nan  # False
```

--------------------------------

### Polars Series with nan_to_null

Source: https://github.com/wukan1986/polars_ta/blob/main/nan_to_null.md

Shows how to create a Polars Series while ensuring NaN values are converted to null. This is often necessary when interfacing with libraries that expect nulls or when converting from NumPy arrays.

```python
pl.Series(, nan_to_null=True)
```

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### Configure Global and Function-Specific Sample Counts

Source: https://github.com/wukan1986/polars_ta/blob/main/README.md

Illustrates how to set the minimum number of samples required for indicator calculations. It shows both a global setting using `polars_ta.MIN_SAMPLES` and a function-specific override using the `min_samples` parameter, with the latter taking precedence.

```python
import polars_ta

# Global settings. Priority Low
polars_ta.MIN_SAMPLES = 1

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

```
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