atr-adaptive-laguerre 0.1.0

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 online computation (talipp pattern)
• ✅ Multi-interval: Supports 1s-1d timeframes
• ✅ Validated: Information coefficient > 0.03 on k-step-ahead returns

Installation

uv add atr-adaptive-laguerre

Quick Start

from atr_adaptive_laguerre import ATRAdaptiveLaguerreRSI, ATRAdaptiveLaguerreRSIConfig
from atr_adaptive_laguerre.data import BinanceAdapter

# Fetch data
adapter = BinanceAdapter()
df = adapter.fetch("BTCUSDT", "1h", "2024-01-01", "2024-06-30")

# Create feature
config = ATRAdaptiveLaguerreRSIConfig(atr_period=32, smoothing_period=5)
feature = ATRAdaptiveLaguerreRSI(config)

# Transform (non-anticipative)
rsi_series = feature.fit_transform(df)

Documentation

• Algorithm Details - MQL5 → Python implementation mapping
• API Reference - Complete API documentation
• Validation Methodology - Quality proxies and testing
• Seq2Seq Integration - Downstream usage guide

License

MIT License - Eon Labs Ltd.