Prediction Market Traderv1.0.0

A user deploys this skill to automatically scan Kalshi tennis markets, compare prices to de-vigged bookmaker odds from Sofascore, and execute trades when a minimum 4% edge is detected. It uses quarter-Kelly sizing for risk management, focusing on qualifying and challenger matches where edges are highest, and monitors positions for profit targets.

A quantitative analyst integrates this toolkit to analyze mispricings between Kalshi prediction markets and true probabilities derived from bookmaker odds. They use it to calculate expected value, size positions with Kelly criterion, and build automated trading strategies for sports, weather, and political events, optimizing for high-frequency opportunities in live markets.

A fintech educator uses this skill package as a teaching tool to demonstrate concepts like de-vigging, Kelly criterion, and API-based trading. Students run scripts to scan markets, simulate trades, and learn risk management rules, applying lessons from the included references to avoid common mistakes in prediction market trading.

A sports betting operator employs this skill to monitor Kalshi prices against their own odds, identifying arbitrage opportunities or market inefficiencies. They use the de-vigging methodology to adjust their pricing models and ensure competitiveness, while leveraging the API for real-time data integration and order execution in fast-moving markets.

A developer builds a custom dashboard that uses this toolkit's scripts to fetch Kalshi market data, calculate edges, and visualize trading opportunities. They integrate it with additional data sources like weather forecasts or political polls to enhance decision-making, automating trades based on predefined risk rules and portfolio tracking.

Offer a service where users subscribe to receive real-time trading signals generated by the skill's scanning and edge detection capabilities. Provide alerts for high-probability trades in tennis or other sports markets, with detailed analysis and risk sizing recommendations, charging a monthly fee for access.

License the skill package to other companies or hedge funds looking to enter prediction market trading. Customize the toolkit with their branding and integrate it into their existing systems, providing ongoing support and updates for a licensing fee or revenue-sharing agreement based on trading profits.

Operate a managed account service where clients deposit funds, and you use the skill to execute trades on their behalf on Kalshi. Apply the quarter-Kelly sizing and risk management rules to grow their capital, charging a performance fee based on profits earned, similar to a hedge fund structure.