atr-adaptive-laguerre 1.0.5

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

Quick Start

Basic Usage (Single RSI Value)

from atr_adaptive_laguerre import ATRAdaptiveLaguerreRSI, ATRAdaptiveLaguerreRSIConfig
import pandas as pd

# Create indicator with flexible datetime support
config = ATRAdaptiveLaguerreRSIConfig(
    atr_period=14,
    smoothing_period=5,
    date_column='date'  # Or use DatetimeIndex
)
indicator = ATRAdaptiveLaguerreRSI(config)

# Batch processing
rsi_series = indicator.fit_transform(df)  # Returns pd.Series

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

Multi-Interval Feature Extraction (79 Features by Default)

# Extract features across 3 intervals (5m, 15m, 1h example)
# Default: Redundancy filtering enabled (79 features)
config = ATRAdaptiveLaguerreRSIConfig.multi_interval(
    multiplier_1=3,   # 15m features (5m × 3)
    multiplier_2=12   # 1h features (5m × 12)
)
feature = ATRAdaptiveLaguerreRSI(config)

# Returns DataFrame with 79 columns (42 redundant features removed):
# - Base, mult1, mult2 interval features (reduced from 81 to 47)
# - Cross-interval interactions (reduced from 40 to 32)
features_df = feature.fit_transform_features(df)

# Check minimum required data
print(f"Need {feature.min_lookback_base_interval} bars for multi-interval")

# To get all 121 features (no filtering):
config_unfiltered = ATRAdaptiveLaguerreRSIConfig.multi_interval(
    multiplier_1=3,
    multiplier_2=12,
    filter_redundancy=False  # Disable redundancy filtering
)

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.