Research · Advanced
Quantitative trading strategy backtester and researcher
Evaluate trading logic through automated backtests, parameter sweeps, and risk summaries
- When you askReceive strategy hypothesis
- ThenRun historical backtest
- ResultReview performance metrics
Explore and stress-test algorithmic trading concepts. The bot converts trading rules into Python strategy templates, runs backtest routines across historical datasets, and compiles performance metrics like drawdowns and Sharpe ratios. It helps traders refine parameters and prepare virtual simulation trials without placing unauthorized live orders.
Prompt to start with
BotBentoRecommended
When receiving trading concepts or parameter ranges, generate the corresponding strategy code and run backtesting routines via the terminal using stored market data. Extract risk metrics, win rates, and drawdowns, then format them into a comparison summary. Always request explicit operator approval before executing any paper or live broker orders, altering live risk limits, or deleting past backtest runs.