Repeated Median Velocity Trading Strategy

The Repeated-Median-Velocity-Trading-Strategy is a quantitative trading strategy that utilizes the Alpaca Stock API to fetch market data and implements a repeated median velocity trading indicator. This project employs walk-forward optimization to identify optimal system parameters for intraday trading. The strategy is inspired by the research paper: The Robust Repeated Median Velocity System by Dennis Meyers.

Table of Contents

• Introduction
• Features
• Setup
• Usage
• Configuration

Introduction

The Repeated-Median Velocity is designed to capture trends efficiently while minimizing effects from outlier data by using a robust statistical method known as the repeated median regression (Siegel Slope).

This project provides a systematic approach to developing, backtesting, and optimizing trading strategies using vectorbt and the Alpaca API with Python.

Features

• Repeated Median Velocity Indicator: Implements the repeated median method
• Walk-Forward Optimization: Utilizes vectorbt walk-forward optimization techniques to determine the best strategy parameters for each trading week.
• Alpaca Stock API Integration: Fetches historical data from the Alpaca API (can be extended for real-time data).
• Intraday Trading Focus: Constructed for executing trades within a single trading day.
• Robust Backtesting: Uses vecortbt's tools to test strategy performance against historical data and highlight trading metrics.

Setup

To get started with the Repeated-Median-Velocity-Trading-Strategy, follow these steps:

Prerequisites

• Python with libraries found in requirements.txt
• Alpaca account with API key and secret

Clone the Repository

git clone https://github.com/0zean/Repeated-Median-Velocity-Trading-Strategy.git
cd Repeated-Median-Velocity-Trading-Strategy

Install dependencies

pip install -r requiremnts.txt