strategy-compare — agent-skills vectorbt-backtesting-skills, community, agent-skills, ide skills, backtesting, monte-carlo, optimization, python, quantstats, robustness

v1.0.0

About this Skill

Perfect for Trading Agents needing advanced backtesting and strategy comparison capabilities with VectorBT Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.

# Core Topics

marketcalls marketcalls
[93]
[25]
Updated: 3/11/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Locale and body language aligned
Review Score
7/11
Quality Score
46
Canonical Locale
en
Detected Body Locale
en

Perfect for Trading Agents needing advanced backtesting and strategy comparison capabilities with VectorBT Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.

Core Value

Empowers agents to backtest and compare trading strategies using VectorBT, supporting Indian, US, and Crypto markets, with realistic transaction cost modeling and QuantStats tearsheets, enabling data-driven decision making with libraries like VectorBT and QuantStats

Ideal Agent Persona

Perfect for Trading Agents needing advanced backtesting and strategy comparison capabilities with VectorBT

Capabilities Granted for strategy-compare

Backtesting trading strategies with VectorBT
Comparing performance of different trading strategies
Optimizing trading strategies with realistic transaction cost modeling

! Prerequisites & Limits

  • Requires VectorBT and QuantStats libraries
  • Limited to Indian, US, and Crypto markets
  • Python environment required

Why this page is reference-only

  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is strategy-compare?

Perfect for Trading Agents needing advanced backtesting and strategy comparison capabilities with VectorBT Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.

How do I install strategy-compare?

Run the command: npx killer-skills add marketcalls/vectorbt-backtesting-skills/strategy-compare. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for strategy-compare?

Key use cases include: Backtesting trading strategies with VectorBT, Comparing performance of different trading strategies, Optimizing trading strategies with realistic transaction cost modeling.

Which IDEs are compatible with strategy-compare?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for strategy-compare?

Requires VectorBT and QuantStats libraries. Limited to Indian, US, and Crypto markets. Python environment required.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add marketcalls/vectorbt-backtesting-skills/strategy-compare. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use strategy-compare immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

strategy-compare

Install strategy-compare, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

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

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