gs-quant-backtesting

To backtest a quantitative trading strategy with GS Quant, define a Strategy with initial portfolios, triggers, and actions, select a backtest engine like GenericEngine, and run the simulation to view PnL and risk statistics.

Available backtesting engines include GenericEngine, EquityVolEngine, and PredefinedAssetEngine, providing flexible backtesting capabilities across multiple asset classes within the GS Quant framework.

Yes, you can extract risk and PnL time series from a backtest run. The framework provides access to backtest summaries, risk time series, and trade ledgers for comprehensive performance analysis across dates.

Triggers and actions work in strategy simulation by scheduling specific events that execute predefined actions, allowing the framework to simulate execution and handle risk, cash, and transaction costs across historical dates.

The best way to analyze transaction costs in a backtest is to run the strategy simulation and extract results from the trade ledger, which handles comprehensive risk, cash, and transaction cost data across dates.