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marketcalls·vlatest·data

Analyzes market data to suggest portfolio adjustments maximizing returns based on user-defined risk tolerance.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add marketcalls/vectorbt-backtesting-skills --skill optimize -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

marketcalls

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned marketcalls/vectorbt-backtesting-skills at 05d9e8b12f.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
05d9e8b12f
Manifest
.claude/skills/optimize/SKILL.md
Scanned
1 file, 64 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to optimize trading strategy parameters using VectorBT and backtesting techniques. It automatically generates Python scripts that test various parameter combinations for a given trading strategy, symbol, exchange, and interval. The skill leverages OpenAlgo for market data retrieval (or DuckDB as an alternative) and TA-Lib for indicator calculations, providing performance heatmaps and comparisons against benchmark indices like NIFTY.

When to use it

  • You want to improve the performance of a VectorBT trading strategy by finding optimal parameter settings.
  • You need to quickly evaluate different parameter combinations for a specific stock or index.
  • You're looking for a way to compare your strategy’s performance against a market benchmark.

Key capabilities

  • Automated Script Generation: Creates Python scripts for VectorBT backtesting optimization based on user input.
  • Parameter Optimization: Tests parameter ranges (e.g., EMA periods) to maximize returns and Sharpe ratio.
  • Data Integration: Loads market data from OpenAlgo or DuckDB.
  • Indicator Calculation: Uses TA-Lib for standard indicators and openalgo.ta for specialized ones like Supertrend and Donchian.
  • Performance Visualization: Generates Plotly heatmaps of total return and Sharpe ratio.
  • Benchmark Comparison: Compares strategy performance against a NIFTY benchmark, providing plain-language results.
  • Cost Awareness: Accounts for Indian delivery fees and futures lot sizes.

Example prompts

  • "Optimize the 'ema-crossover' strategy for SBIN on NSE with a daily interval."
  • "Create an optimization script for the 'rsi' strategy using RELIANCE on NFO, hourly data."
  • "Generate a backtesting script to optimize a Donchian channel strategy for NIFTY."

Tips & gotchas

  • Familiarity with VectorBT and Python is helpful. Refer to the vectorbt-expert skill rules for reference patterns.
  • The skill relies on OpenAlgo or DuckDB for data; ensure you have appropriate configuration set up (e.g., .env file).
  • TA-Lib must be installed for indicator calculations.