AI SummaryCompares multiple trading strategies (long, short, or both) on the same symbol and generates side-by-side performance stats tables. Useful for quantitative traders and backtesting analysts who need to evaluate strategy performance variations quickly.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "strategy-compare" skill in my project. Please run this command in my terminal: # Install skill into your project mkdir -p .claude/skills/strategy-compare && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/strategy-compare/SKILL.md "https://raw.githubusercontent.com/marketcalls/vectorbt-backtesting-skills/master/.claude/skills/strategy-compare/SKILL.md" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
Example Usage
/strategy-compare RELIANCE ema-crossover rsi donchian /strategy-compare SBIN long-vs-short ema-crossover
Arguments
Parse $ARGUMENTS as: symbol followed by strategy names • $0 = symbol (e.g., SBIN, RELIANCE, NIFTY) • Remaining args = strategies to compare (e.g., ema-crossover rsi donchian) If only a symbol is given with no strategies, compare: ema-crossover, rsi, donchian, supertrend. If "long-vs-short" is one of the strategies, compare longonly vs shortonly vs both for the first real strategy.
Instructions
• Read the vectorbt-expert skill rules for reference patterns • Create backtesting/strategy_comparison/ directory if it doesn't exist (on-demand) • Create a .py file in backtesting/strategy_comparison/ named {symbol}_strategy_comparison.py • The script must: • Fetch data once via OpenAlgo • If user provides a DuckDB path, load data directly via duckdb.connect(path, read_only=True). See vectorbt-expert rules/duckdb-data.md. • If openalgo.ta is not importable (standalone DuckDB), use inline exrem() fallback. • Use TA-Lib for ALL indicators (never VectorBT built-in) • Use OpenAlgo ta for specialty indicators (Supertrend, Donchian, etc.) • Clean signals with ta.exrem() (always .fillna(False) before exrem) • Run each strategy on the same data • Indian delivery fees: fees=0.00111, fixed_fees=20 for delivery equity • Collect key metrics from each into a side-by-side DataFrame • Include NIFTY benchmark in the comparison table (via OpenAlgo NSE_INDEX) • Print Strategy vs Benchmark comparison table: Total Return, Sharpe, Sortino, Max DD, Win Rate, Trades, Profit Factor • Explain results in plain language - which strategy performed best and why • Plot overlaid equity curves for all strategies using Plotly (template="plotly_dark") • Save comparison to CSV • Never use icons/emojis in code or logger output
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