Implementation of Autoregressive Integrated Moving Average (ARIMA) Model
Development of Autoregressive Integrated Moving Average (ARIMA) model within MATLAB environment with comprehensive algorithmic implementation
Time Series Forecasting Algorithms
Time series forecasting algorithms including 5 common methods such as moving average, nonlinear regression, exponential smoothing with implementation insights
Simulink-Based Algorithm for Signal Processing: Trigger Implementation
A MATLAB/Simulink-based algorithm called Trigger that performs noise reduction and peak detection using moving average and matched filters, while validating signal integrity. The algorithm is implemented in M-code and encapsulated into Simulink via S-functions, providing configurable parameters for diverse application scenarios.
Rebar Futures High-Frequency Trading Arbitrage Strategy
A MATLAB-implemented high-frequency trading arbitrage strategy for rebar futures, utilizing Moving Average (MA) and Relative Strength Index (RSI) technical indicators for trade decision-making. The core algorithm combines MA's strength in trending markets with RSI's effectiveness in oscillating conditions through signal fusion, achieving robust risk resistance. Backtesting with historical data demonstrates optimized parameters yielding 23.6% annualized return and 2.05 Sharpe ratio. Code implementation includes real-time data processing, multi-timeframe analysis, and automated execution modules.
Time Series Forecasting Algorithms
Time Series Forecasting Algorithms with Implementation Approaches