semvec/pypi-packages huggingface dataset:
alphabuilder-signal 0.1.1.21
Alpha signal library for quantitative finance research
Requires Python: >=3.8

# AlphaBuilder-Signal
<p align="center">
  <img src="https://github.com/Jeetmu/AlphaBuilder-Signal/raw/master/img/Logo_4.png" alt="AlphaBuilder-Signal Logo" width="300"/>
</p>

**AlphaBuilder-Signal** is an experimental Python package providing a curated library of trading signals for quantitative finance.  

> ⚠️ This package is currently in **early development**. More features, indicators, and signal types will be added soon.  

---

## Features

### Current
- **Technical Indicators**: RSI, MACD, ADX, %D, Bollinger Bands, Momentum, etc.  
- **Lagged Features**: Previous price, returns, and indicator values for time-series modeling  
- **Moving Averages**: SMA, EMA, HMA, and other variants  
- **Target Indicators**: Returns, peaks, troughs (regression and classification targets)  
- **Data Fetching**: Asset wrapper for seamless download from `yfinance`  

### Coming Soon
- Additional **technical and fundamental indicators**  
- **Handcrafted signals**: curated alpha factors and custom rules  
- **ML-based signals**: tree models, RNNs, transformers applied to time series  
- **Regime-aware signals**: HMMs, regime switching filters  
- **Backtesting utilities**: Sharpe ratio, hit ratio, performance metrics  
- **Statistical arbitrage signals**: Copula, Vine Copula, Correlation  
- **News-based signals**  

---

## Installation

Install directly from [PyPI](https://pypi.org/project/alphabuilder-signal/):

```bash
pip install alphabuilder-signal
```

## Quickstart Example

The following example demonstrates how to fetch historical asset data and compute basic technical indicators using AlphaBuilder-Signal.

```python
# Import
from alphabuilder_signal import (
    Fetch,
    MovingAverage,
    TechnicalIndicators,
    LaggedFeatures,
    TargetClassifier,
    TargetRegressor,
)

# Fetch historical data
fetch_data = Fetch(
    tickers=["AAPL", "MSFT", "META"],
    start_date="2010-01-01",
    verbose=True
)
data = fetch_data.get_asset_data(combined=False)
print(data.head())

# Compute technical indicators
TI = TechnicalIndicators(
    tickers=['AAPL', 'MSFT', 'META'],
    start_date='2020-01-01',
    verbose=True,
    combined=True
)
results = (
    TI.relative_strength_index(windows=[14, 7, 6], source='Close')
      .relative_strength_index(windows=[7, 4], method='simple', source='Open')
      .momentum(windows=[7, 8, 9, 4], source='High')
      .average_true_range(windows=[4, 7])
      .commodity_channel_index(windows=[3, 4])
      .average_directional_index(windows=[5, 7])
      .bollinger_bands(windows=[7, 8])
      .get_data()
)

print(results.head())
```

## Documentation

Full Signals Documentation [Docs](https://alphabuilder.xyz/docs)

Read more about the Signals [AlphaBuilder-Signal](https://alphabuilder.xyz/signals)

Learn more about AlphaBuilder Project [AlphaBuilder](https://alphabuilder.xyz/)
mkdocs-ezglossary-plugin 2.1.0
manage multiple glossaries in mkdocs
Keywords: mkdocs, glossary, plugin, references, links

# mkdocs-ezglossary-plugin

> A powerful glossary plugin for MkDocs

## Features

- Multiple glossary sections
- Case-sensitive and case-insensitive term matching
- Rich text formatting in definitions
- Reference tracking and linking
- Customizable summaries
- Unicode support
- PDF export support
- Custom templates via Jinja2
- Plural forms support (English, Spanish, French, German)

## Documentation

📚 Read the [full documentation](https://realtimeprojects.github.io/mkdocs-ezglossary)

## Prerequisites

This plugin requires one of:
- [Material for MkDocs definition lists](https://squidfunk.github.io/mkdocs-material/reference/lists/) (recommended)
- Any plugin that generates [HTML description lists](https://www.w3schools.com/HTML/html_lists.asp)

## Installation

```bash
pip install mkdocs-ezglossary-plugin
```

## Quick Start

### 1. Enable the Plugin

Add ezglossary to your mkdocs.yml:

```yaml
plugins:
  - search
  - ezglossary
```

### 2. Define Terms

Add glossary terms anywhere in your documentation:

```markdown
fruit:apple
:   A very delicious and healthy fruit growing on trees.
```

### 3. Link to Terms

Reference terms in your documentation:

```markdown
An <fruit:apple> a day keeps the doctor away.
```

### 4. Create a Summary

Generate a summary of all terms:

```markdown
# Glossary

<glossary::fruit>
```

## Configuration

Basic configuration options:

```yaml
plugins:
  - ezglossary:
      # Case-insensitive term matching
      ignore_case: true
      
      # Show references in definitions
      inline_refs: short  # none, short, list
      
      # Enable plural forms (English)
      plurals: en  # en, es, it, fr, de
      
      # Custom templates directory
      templates: docs/templates
```

See the [configuration documentation](https://realtimeprojects.github.io/mkdocs-ezglossary/configuration) for all options.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

`mkdocs-ezglossary-plugin` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
llama-index-llms-sarvam 0.2.1
llama-index llms sarvam integration
Requires Python: <4.0,>=3.9

# LlamaIndex Llms Integration: Sarvam

This is the Sarvam integration for LlamaIndex. Visit [Sarvam](https://docs.sarvam.ai/api-reference-docs/chat/completions) for information on how to get an API key and which models are supported.

## Installation

```bash
pip install llama-index-llms-sarvam
```

## Usage

```python
from llama_index.llms.sarvam import Sarvam

llm = Sarvam(model="sarvam-m", api_key="your-api-key")
```