← All projects
Sentiment Market MM monitoring dashboard

Sentiment Market MM

Updated Mar 2026

Git Repo
PythonFastAPIPostgreSQLRedisReactTypeScriptDocker

Sentiment Market MM is a full-stack market-making system for prediction markets. It ingests real-time sentiment signals, prices two-sided quotes with dynamic spread adjustment, manages inventory exposure, and executes across multiple venues through a unified order management layer.

Sentiment-weighted spread pricing that adjusts bid-ask width based on news flow, social volume, and order book imbalance signals

Inventory risk engine with position limits, Greeks-inspired exposure tracking, and dynamic skew to reduce directional risk

Multi-venue execution layer with order lifecycle management across Kalshi and Polymarket via unified abstraction

What I built

  • FastAPI backend with sentiment ingestion, spread pricing engine, order management, and position tracking endpoints
  • Postgres + Redis schema for markets, quotes, orders, fills, positions, and real-time sentiment state
  • React monitoring dashboard for live P&L, position exposure, spread visualization, and order flow

How it works

  1. 1Ingest sentiment signals from news APIs, social feeds, and order book data into a scoring pipeline
  2. 2Price two-sided quotes with spread width driven by sentiment confidence, volatility, and current inventory skew
  3. 3Execute and manage order lifecycle across venues with fill tracking and position reconciliation

Results

  • ✓End-to-end market-making loop from signal ingestion through quote pricing to execution and risk tracking
  • ✓Provides a working research environment for testing spread strategies against real prediction market dynamics

Next steps

  • Add backtesting harness to evaluate spread strategies against historical order book snapshots
  • Implement adaptive position sizing based on realized P&L and drawdown thresholds