Market Making in Python
Avellaneda-Stoikov high-frequency market-making model computing optimal inventory-risk adjusted reservation prices and spread quotes.
Avellaneda-Stoikov high-frequency market-making model computing optimal inventory-risk adjusted reservation prices and spread quotes.
Python implementation of a stochastic-control market-making model with inventory risk, optimal bid/ask quotes, P&L simulations, sensitivity analysis and Coinbase BTC-USD calibration.
Avellaneda-Stoikov market making implemented faithfully, then stress-tested under adverse selection: four quoting strategies head-to-head against informed flow.
High-frequency market-making research system implementing the Avellaneda-Stoikov framework with dynamic spread adjustment based on real-time volatility and inventory risk. Features a low-latency core, kill switch, and real-time inventory/PnL visualization.
To associate your repository with the inventory-risk topic, visit your repo's landing page and select "manage topics."