trading-strategies

To backtest prediction market trading strategies in Python, you can use this framework to simulate historical performance across markets using sample MarketState data. It provides a Backtester component to evaluate strategy quality and calculate performance metrics.

Yes, you can implement risk management for prediction market strategies using built-in utilities to enforce position limits and drawdown constraints. These tools allow you to control exposure and manage risk during backtesting and deployment.

Supported trading strategies for prediction markets include Arbitrage, Copy Trading, Momentum, and Mean Reversion. You can extend a BaseStrategy class to implement custom logic and dynamically calculate position sizes for YES/NO markets.

To build a custom signal generation strategy, extend the BaseStrategy class and implement the analyze and calculate_position_size methods. This allows you to define custom signals and logic without starting from scratch.

Yes, this solution requires a Python environment to run prediction market backtesting and deploy trading strategies. The framework is designed to be extended through configuration and custom strategy implementations within your Python setup.

The best way to evaluate mean reversion strategy performance is by running the Backtester with sample MarketState data to simulate historical performance. The framework generates backtester results and metrics to assess strategy quality and risk.