trading-strategies

The trading-strategies skill provides a professional-grade Python framework designed for developing, backtesting, and deploying algorithmic trading strategies on Polymarket. It is intended for quantitative developers and data analysts building autonomous trading agents that require high-performance signal generation, risk management, and order execution logic. By leveraging an object-oriented approach, this skill allows users to abstract complex market interactions into modular components, ensuring consistent behavior across different trading environments.

• Robust BaseStrategy architecture using abstract base classes for standardized strategy implementation.

• Built-in support for multiple strategy archetypes including Arbitrage (exploiting pricing inefficiencies), Copy Trading (mirroring professional trader activity), and Momentum-based strategies.

• Data structures for managing MarketState, including orderbook snapshots, pricing, volume, and open interest.

• Signal generation engine that produces actionable buy/sell/hold directives with confidence scores.

• Position sizing logic and risk management controls to protect portfolio capital.

• Integration capabilities with backtesting frameworks for validating strategies against historical data.

• Utilize the BaseStrategy interface to create custom logic by overriding analyze and calculate_position_size methods.

• Ensure all strategy configurations are passed as dictionaries to maintain flexibility across diverse market conditions.

• The skill assumes the user has access to real-time market feeds via WebSocket or REST API integrations.

• Inputs include MarketState data objects; outputs consist of Signal objects formatted for downstream execution modules.

• Strategies should be configured with thresholds for confidence levels and position sizing to mitigate market volatility risks.

• The architecture is compatible with existing CI/CD pipelines for automated testing and deployment, facilitating rapid iteration of trading models in production environments.