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Develop Python strategies with graphical backtesting and market gateways

Research and backtest quantitative strategies in notebooks and code

Compare options history with the risks of current positions

Choose Python research and backtesting engines for the task

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

Build custom market scans and test chart-based strategies

Test sentiment and market rules, then inspect the individual outcomes

Test entry and exit rules against historical price bars

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

AI trading copilot for market research, strategy and execution.

Scan markets and test explicit rules in desktop software

Build historical market simulations with configurable orders and costs

Replay recorded executions alongside a trading journal

Screen options, test explicit rules and inspect each simulated trade

Research options in a browser or build on licensed historical datasets

Maintain event-driven Python backtests with the Zipline research model

Revisit options positions with five-minute history and tracked adjustments

Track stock-market events with live alerts and ranked screeners

Build, test, and automate options trading plans

See cryptocurrency size and price movement together on a heatmap

Arrange crypto market charts and set indicator-based alerts

Crypto market tables, funding rounds and token-release calendars