All-Weather Strategy Research Team

Nautilus – from ancient Greek “sailor” and naus “ship.”

The nautilus shell consists of modular chambers with a growth factor that approximates a logarithmic spiral. This geometry informs the aesthetics and architecture of our research systems.

To address the non-stationarity and high-noise characteristics of order flow in highly liquid cryptocurrency assets (BTC/USDT), this study proposes a multi-scale signal generation framework integrating Continuous Wavelet Transform (CWT), an LSTM–ARIMA hybrid model, and Relative Strength Index (RSI). By decomposing 2–7 days of historical order-flow data (price, volume, order-book depth) using CWT, mid-to-short-term components (1h–6h) are extracted to suppress high-frequency noise, while low-frequency components (>6h) are input into an ARIMA model to analyze mean reversion and seasonal fluctuations. High-frequency components (<1h) are processed by an LSTM network to capture nonlinear dynamics such as market sentiment shifts and liquidity pulses. The RSI indicator optimizes signal trigger thresholds, and cointegration error filtering (BTC–BNB spread portfolio) eliminates anomalous signals. Empirical analysis on 2023 BTC/USDT high-frequency data from Binance shows a Sharpe ratio of 3.72 (26.4% improvement over the standalone LSTM model), maximum drawdown of 1.9%, and a win rate of 58.7%, validating the model’s effectiveness in noise reduction, dynamic response, and risk control.

Model Architecture and Empirical Analysis Multi-scale decomposition uses the Morlet wavelet basis to isolate high-frequency noise (<1h), mid-term trends (1h–6h), and long-term cycles (>6h). Low-frequency components are modeled by ARIMA for linear statistical patterns, while high-frequency components are fed into an LSTM network to capture nonlinear dynamics such as liquidity pulses and whale-address anomalies.

Signal Fusion and Optimization A dynamic weighting mechanism integrates LSTM (nonlinear responses) and ARIMA (steady-state trends), augmented by a 14-period RSI to filter overbought/oversold signals and reduce false trading frequency. Cointegration filtering constructs a BTC–BNB spread portfolio, leveraging the Johansen test to establish long-term equilibrium relationships and dynamically discard signals deviating from the cointegration space.