alphabuilder-signal 0.1.1.21

Alpha signal library for quantitative finance research

AlphaBuilder-Signal

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.

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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

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Installation

Install directly from PyPI:

pip install alphabuilder-signal

Quickstart Example

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

# 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

Read more about the Signals AlphaBuilder-Signal

Learn more about AlphaBuilder Project AlphaBuilder