Strategy Backtest

Quantitative strategy backtesting—implement, run, and tune trading rules on historical data; performance metrics (CAGR, max drawdown, Sharpe, win rate) and s...

This skill is inconsistent: the description and SKILL.md promise a functioning backtest/optimization engine, but the shipped Python file is only a stub that logs commands to a local JSON file and returns canned success messages. Before installing or running: 1) Do not assume it performs real backtests — inspect or grep the code for actual strategy logic (indicators, trade generation, P&L calculations). 2) Note the SKILL.md example paths do not match the real script path; verify invocation paths. 3) If you expect full backtesting, either obtain a real implementation or replace the stub with trusted code. 4) Run any untrusted Python in a sandbox or VM and review what files it writes (the script writes data/strategy_backtest_data.json). 5) Don’t rely on this tool for investment decisions — it does not implement the analytics it advertises. Providing the maintainer/source or an updated implementation would change the assessment to benign if the code truly performed the claimed tasks and paths matched the documentation.

Type: OpenClaw Skill Name: strategy-backtest Version: 1.0.0 The strategy-backtest skill bundle is a legitimate tool for logging and managing quantitative trading strategy workflows. The primary script, strategy_backtest_tool.py, functions as a CLI wrapper that records command execution (backtest, optimize, report) into a local JSON file (strategy_backtest_data.json). No evidence of data exfiltration, malicious execution, or prompt injection was found; the code and instructions are consistent with the stated purpose of financial backtesting.