atr-adaptive-laguerre 1.0.10

ATR-adaptive Laguerre RSI for non-anticipative feature engineering in seq-2-seq forecasting

ATR-Adaptive Laguerre RSI

Non-anticipative volatility-adaptive momentum indicator for sequence-to-sequence forecasting.

Overview

This library implements the ATR-Adaptive Laguerre RSI indicator, designed for robust feature engineering in financial time series forecasting. The indicator combines:

• True Range (TR) - Volatility measurement including gaps
• ATR with Min/Max Tracking - Rolling volatility envelope
• Adaptive Coefficient - Volatility-normalized adaptation
• Laguerre 4-Stage Cascade - Low-lag smoothing filter
• Laguerre RSI - Momentum from filter stage differences

Key Features

• ✅ Non-anticipative: Guaranteed no lookahead bias
• ✅ O(1) Incremental: Efficient streaming updates with .update() method
• ✅ Multi-interval: Supports 1s-1d timeframes with 79-feature extraction (121 without filtering)
• ✅ Redundancy filtering: Optional 121→79 feature reduction (|ρ| > 0.9 removed)
• ✅ Flexible datetime: Works with DatetimeIndex, 'date' column, or custom column names
• ✅ Validated: Information coefficient > 0.03 on k-step-ahead returns

Installation

uv add atr-adaptive-laguerre

Feature Modes: Choose Your Use Case

This package supports two operational modes with very different capabilities:

Mode	Features	Lookback	Use Case
Multi-Interval (Recommended)	79	360 bars	Production ML pipelines - includes cross-timeframe analysis
Single-Interval	27	30 bars	Minimal data requirements or single-timeframe analysis

⚠️ Important: Multi-Interval Mode is Recommended

If you're building ML features, you want multi-interval mode (79 features), which includes:

• Base interval features (16)
• First multiplier interval features (15)
• Second multiplier interval features (17)
• Cross-interval analysis features (31) ← Unique to multi-interval mode!

Cross-interval features detect multi-timeframe patterns like:

• Regime alignment across timeframes
• Divergence detection
• Momentum cascades
• Gradient analysis
• Statistical stability metrics

Quick Start

Multi-Interval Mode (Recommended - 79 Features)

from atr_adaptive_laguerre import ATRAdaptiveLaguerreRSI, ATRAdaptiveLaguerreRSIConfig

# RECOMMENDED: Use multi-interval mode for full feature set
config = ATRAdaptiveLaguerreRSIConfig.multi_interval(
    multiplier_1=4,   # 4x base interval (e.g., 2h → 8h)
    multiplier_2=12   # 12x base interval (e.g., 2h → 24h)
)
indicator = ATRAdaptiveLaguerreRSI(config)

# Extract 79 features across 3 timeframes
features_df = indicator.fit_transform_features(df)

print(f"Features extracted: {indicator.n_features}")  # 79
print(f"Min data required: {indicator.min_lookback_base_interval} bars")  # 360

Single-Interval Mode (Minimal Lookback - 27 Features)

Use this mode only if you have limited historical data or need single-timeframe analysis:

from atr_adaptive_laguerre import ATRAdaptiveLaguerreRSI, ATRAdaptiveLaguerreRSIConfig

# Single-interval mode (WARNING: only 27 features, missing cross-timeframe analysis)
config = ATRAdaptiveLaguerreRSIConfig(
    atr_period=14,
    smoothing_period=5,
    date_column='date'  # Or use DatetimeIndex
)
indicator = ATRAdaptiveLaguerreRSI(config)

# Get single RSI value
rsi_series = indicator.fit_transform(df)  # Returns pd.Series (single RSI column)

# Or get 27 single-interval features
features_df = indicator.fit_transform_features(df)  # Returns DataFrame with 27 columns

print(f"Features extracted: {indicator.n_features}")  # 27
print(f"Min data required: {indicator.min_lookback} bars")  # 30

Advanced: Incremental Updates (O(1) Streaming)

Both modes support efficient incremental updates:

# After initial fit_transform
new_row = {'open': 100, 'high': 101, 'low': 99, 'close': 100.5, 'volume': 1000}
new_rsi = indicator.update(new_row)  # Returns float (O(1) complexity)

Disabling Redundancy Filtering (79 → 121 Features)

# Disable redundancy filtering to get all 121 features
config = ATRAdaptiveLaguerreRSIConfig.multi_interval(
    multiplier_1=4,
    multiplier_2=12,
    filter_redundancy=False  # Get all 121 features
)
feature = ATRAdaptiveLaguerreRSI(config)

# Returns DataFrame with 121 columns (all features, including 42 redundant ones)
features_df = feature.fit_transform_features(df)

# Verify feature count
print(f"Features: {feature.n_features}")  # 121 (79 by default)

# Redundancy filtering (enabled by default):
# - Data: 3 years of 2h OHLCV (BTCUSDT, ETHUSDT, SOLUSDT)
# - Threshold: |ρ| > 0.9 (perfect correlations and near-redundant features)
# - Removes: Base RSI values, redundant distance metrics, duplicate regime features
# - Retains: Rate-of-change, cross-interval, and temporal features
# - IC validation: +45.54% improvement (PASSED)

Documentation

• API Reference - Complete API documentation
• Examples - Runnable usage examples
• Changelog - Release notes and version history

License

MIT License - Eon Labs Ltd.