Metadata-Version: 2.4
Name: alphabuilder_signal
Version: 0.1.1.21
Summary: Alpha signal library for quantitative finance research
Home-page: https://alphabuilder.xyz/
Author: Geet Mukherjee
Author-email: mukherjeegeet3@gmail.com
Project-URL: Signals, https://alphabuilder.xyz/signals
Project-URL: Changelog, https://alphabuilder.xyz/changelog
Project-URL: Documentation, https://alphabuilder.xyz/docs
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: yfinance<0.3,>=0.2
Requires-Dist: pandas<3.0,>=1.3
Requires-Dist: finnhub-python>=2.4.24
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: project-url
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# 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/)
