Adverse Selection Classifier

To detect toxic flow, train a classifier on labeled historical trades to identify informed orders, then apply real-time classification to adjust spreads and kappa during quote generation to protect market-making profits.

Adverse selection occurs when informed traders exploit market makers, eroding profits. Real-time classification predicts trade toxicity, enabling dynamic adjustment of spreads and kappa to mitigate losses from informed orders during quote generation.

You adjust spreads and kappa by integrating a real-time toxicity classifier into your quote engine. The model predicts if incoming trades are informed or toxic, signaling the system to widen spreads and modify liquidation controls instantly.

Yes, you can use machine learning to detect liquidation cascades and arbitrage trades. The classifier implements labeling and feature engineering to categorize trades into Informed, Noise, Liquidation, and Arbitrage types for risk management.

You train the model by applying labeling strategies and feature engineering to historical trade data. Once trained, the classifier integrates into your risk management workflow to provide real-time toxicity predictions and validation metrics.