Trading Strategy Backtester is a finance claude skill built by Marketcalls. Best for: Quantitative traders evaluate strategy performance across Indian equities by comparing technical indicators (EMA, RSI, Donchian, Supertrend) with standardized metrics and NIFTY benchmark..

What it does
Compare multiple trading strategies side-by-side with backtesting metrics, benchmarks, and equity curve visualization.
Category
finance
Created by
Marketcalls
Last updated
Claude Skillfinance GitHub-backed CuratedadvancedClaude Code

Trading Strategy Backtester

Compare multiple trading strategies side-by-side with backtesting metrics, benchmarks, and equity curve visualization.

Skill instructions


name: strategy-compare description: Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table. argument-hint: "[symbol] [strategies...]" allowed-tools: Read, Write, Edit, Bash, Glob, Grep

Create a strategy comparison script.

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

  1. Read the vectorbt-expert skill rules for reference patterns
  2. Create backtesting/strategy_comparison/ directory if it doesn't exist (on-demand)
  3. Create a .py file in backtesting/strategy_comparison/ named {symbol}_strategy_comparison.py
  4. 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
  5. Never use icons/emojis in code or logger output

Example Usage

/strategy-compare RELIANCE ema-crossover rsi donchian /strategy-compare SBIN long-vs-short ema-crossover

Use this skill

Most skills are portable instruction packages. Claude Code supports SKILL.md directly. Other agents can use adapted files like AGENTS.md, .cursorrules, and GEMINI.md.

Claude Code

Save SKILL.md into your Claude Skills folder, then restart Claude Code.

mkdir -p ~/.claude/skills/trading-strategy-backtester && curl -L "https://raw.githubusercontent.com/marketcalls/vectorbt-backtesting-skills/3b16225272ec07b436d0b96216fb3bca1a566007/.claude/skills/strategy-compare/SKILL.md" -o ~/.claude/skills/trading-strategy-backtester/SKILL.md

Installs to ~/.claude/skills/trading-strategy-backtester/SKILL.md.

Use cases

Quantitative traders evaluate strategy performance across Indian equities by comparing technical indicators (EMA, RSI, Donchian, Supertrend) with standardized metrics and NIFTY benchmark.

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Stats

Installs0
GitHub Stars127
Forks32
UpdatedMar 23, 2026