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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

Track stock-market events with live alerts and ranked screeners

Assemble portfolio backtests from reusable Python rules

Search public trading disclosures and compare the dates behind them

Compare strategy experiments across assets, parameters and historical periods

Build trading tools and examine cBot behaviour on historical data

Turn portfolio returns into charts and risk-analysis summaries

Build formula screens and examine their historical matches

Compare historical portfolios and model allocation scenarios

Build trading algorithms with AI, backtest them, then execute on one platform.