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Compare options history with the risks of current positions

Research and backtest quantitative strategies in notebooks and code

Develop Python strategies with graphical backtesting and market gateways

Choose Python research and backtesting engines for the task

Test entry and exit rules against historical price bars

Replay historical markets across charts and depth-of-market views

Backtest portfolio allocations with cash flows, benchmarks and simulations

Study an exploratory Python engine for historical equities tests

Develop and compare trading strategies in a Windows backtesting workspace

Screen options, test explicit rules and inspect each simulated trade

Build, test, and automate options trading plans

Research options in a browser or build on licensed historical datasets

Revisit options positions with five-minute history and tracked adjustments

Build custom market scans and test chart-based strategies

Replay recorded executions alongside a trading journal

Build historical market simulations with configurable orders and costs

Maintain event-driven Python backtests with the Zipline research model

Test sentiment and market rules, then inspect the individual outcomes

Scan markets and test explicit rules in desktop software

AI trading copilot for market research, strategy and execution.

Build event-driven market simulations with a shared strategy engine

Calculate returns, portfolio statistics and allocation weights in Python

Screen fundamentals, visualize metrics and export financial data

Research trading decisions with reinforcement-learning experiments