Automated Market Making & Quantitative Trading

Overview

This project implements an automated market-making algorithm for a quantitative trading simulation. The goal is to dynamically adjust bid-offer spreads and optimize risk management strategies using Python.

Challenges & Solutions

• Challenge 1: Non-Skewed Market Making

  • Implemented a 2% non-skewed bid-offer spread around the reference price.
  • Processed real-time price requests and logged completed trades.
  • Visualized bid, offer, and reference prices using Matplotlib.

• Challenge 2: Skewed Market Making with Risk Management

  • Developed a dynamic pricing algorithm that skews bid-ask spreads based on risk exposure.
  • Implemented position-based adjustments (Axed Long, Axed Short).
  • Plotted price data for all tickers traded during the simulation.

Technologies Used

• 🐍 Python (OOP, Data Processing)
• 📊 Matplotlib (Data Visualization)
• 📈 Pandas (Data Manipulation)
• ⚡ AmplifyQuantTrading API (Simulated Market Data)