Real-time crypto prediction market trading system for Kalshi, built to monitor live prices, generate trading signals, and automate order execution through a web dashboard.
This project is a real-time crypto prediction market trading system built for Kalshi. It monitors live crypto prices from Coinbase, compares them against Kalshi market structures, calculates trading signals, and automates order execution through a web-based control dashboard. All pricing formulas, edge logic, and trading rules were provided by the client; my role was to design and implement the system around those requirements.
The system is designed to turn fast-moving crypto market data into actionable Kalshi trading decisions. Its scope includes live data ingestion, market matching, signal generation, fair-value comparison, order planning, execution monitoring, chart-based trade filtering, and risk-controlled automation.
The workflow streams live Coinbase trades, fetches active Kalshi crypto markets, computes trading metrics using the client's formulas, generates executable signals such as PROBE and SCALE entries, validates them against order book depth and filters, places aggressive limit orders, and monitors fills, retries, scaling, and stop conditions in real time.
The project follows a modular, event-driven trading architecture with separate data, analytics, execution, and control layers. It combines real-time Coinbase WebSocket data, Kalshi market data and order books, CF Benchmarks reference pricing, and the client's fair-value and edge formulas inside a dashboard-driven automation system.
Built for automated trading on Kalshi crypto prediction markets, real-time signal monitoring, execution research for event-driven strategies, and trading dashboards that support both automation and manual intervention.