Search results

A collaborative guide to implementing algorithms

Build trading tools and examine cBot behaviour on historical data

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

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

Programming explanations alongside algorithm practice

Replay recorded executions alongside a trading journal

Build historical market simulations with configurable orders and costs

Build custom market scans and test chart-based strategies

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

Test sentiment and market rules, then inspect the individual outcomes

Revisit options positions with five-minute history and tracked adjustments

Scan markets and test explicit rules in desktop software

Build, test, and automate options trading plans

AI trading copilot for market research, strategy and execution.

VFX software that propagates keyframe edits across video