How AI Trader Profiling Changes Execution Strategy

The problem with static routing

Traditional bridges route orders based on simple rules — symbol, volume, account group. But these static rules can't distinguish between a retail trader placing a genuine order and a toxic flow scalper exploiting your latency.

The result? You either over-hedge (costly) or under-hedge (risky).

Enter machine learning

ZeroMS's AI Intelligence Engine profiles every trading account using machine learning models that analyze:

• Trade frequency and patterns
• Holding periods
• Win/loss ratios and consistency
• Timing relative to news events
• Order-to-trade ratios
• Latency sensitivity

Each account receives a risk score from 0 to 100, updated continuously as new data flows in.

Trader archetypes

The AI engine classifies traders into several archetypes:

• Retail — normal trading behavior, low risk
• Scalper — very short holding periods, exploiting small price movements
• News trader — high activity around economic releases
• HFT — extremely high frequency, latency-sensitive
• Arbitrageur — exploiting price discrepancies between venues
• Toxic — consistently profitable against the house

Score-based routing

The real power comes when you connect the AI block to your execution flow. Instead of routing all orders the same way, you can:

• A-Book high-risk traders automatically to protect your exposure
• B-Book low-risk retail traders to capture the spread
• Add delay blocks for suspected latency arbitrageurs
• Apply stricter last-look for toxic flow accounts

All of this happens in real-time, with the model retraining hourly to adapt to changing behavior.

The bottom line

AI profiling isn't about penalizing traders — it's about making smarter decisions. When you know who's on the other side of each trade, you can optimize your execution strategy for both profitability and risk management.

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Want to see AI profiling in action? Book a demo and we'll show you how it works with your own data.