★ 20 Ways to Generate Stock Investment Profits with AI - 18. Real-Time AI Market Making for Retail

 

18. Real-Time AI Market Making for Retail

Introduction
Market making—quoting bid/ask spreads to capture the spread—has traditionally been institutional. AI can help retail participants, but challenges remain.

Mechanics

• Quote Optimization: AI forecasts short-term mid-price movements and dynamically adjusts spreads.

• Inventory Management: Limits on net position (e.g., ±100 shares) to control directional risk.

• Latency: Retail connectivity is slower than co-located institutional systems.

Building a Simulator

1. Historical Tick Data: Use tick-level data to simulate quoting logic.

2. Model Training: Train a regression model to predict 1-minute price drift.

3. Strategy Logic: Tighten spreads when predicted drift is low; widen when volatility spikes.

4. Fee and Rebate Modeling: Incorporate exchange fees/rebates into profitability calculations.

Reality Check
Simulations show that, after fees and latency, retail market makers often face razor-thin margins—success hinges on ultra-low fees and fast execution.

Conclusion
While theoretically possible, retail AI market making requires sophisticated infrastructure and cost advantages to be truly profitable.