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Optimize

Community
marketcalls
optimize

Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.

Overview

Publishermarketcalls
Repositoryvectorbt-backtesting-skills
Skill nameoptimize
Stars
210
Forks
47
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by marketcalls on GitHub. Read the source before you install it.

Installation

Install the Optimize AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/marketcalls/vectorbt-backtesting-skills/tree/master/.claude/skills/optimize
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/marketcalls/vectorbt-backtesting-skills.git /tmp/vectorbt-backtesting-skills
mkdir -p .claude/skills
cp -r /tmp/vectorbt-backtesting-skills/.claude/skills/optimize .claude/skills/optimize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Optimize in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Optimize on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Optimize is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Create a parameter optimization script for a VectorBT strategy.

Arguments

Parse $ARGUMENTS as: strategy symbol exchange interval

  • $0 = strategy name (e.g., ema-crossover, rsi, donchian). Default: ema-crossover
  • $1 = symbol (e.g., SBIN, RELIANCE, NIFTY). Default: SBIN
  • $2 = exchange (e.g., NSE, NFO). Default: NSE
  • $3 = interval (e.g., D, 1h, 5m). Default: D

If no arguments, ask the user which strategy to optimize.

Instructions

  1. Read the vectorbt-expert skill rules for reference patterns
  2. Create backtesting/{strategy_name}/ directory if it doesn't exist (on-demand)
  3. Create a .py file in backtesting/{strategy_name}/ named {symbol}_{strategy}_optimize.py
  4. The script must:
    • Load .env from project root using find_dotenv() and fetch data via OpenAlgo client.history()
    • 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 OpenAlgo ta for ALL indicators by default (never VectorBT built-in). Only switch to TA-Lib if the user explicitly says "talib"/"TA-Lib"
    • Always use OpenAlgo ta for specialty indicators (Supertrend, Donchian, etc.) - no TA-Lib equivalent exists
    • Use ta.exrem() to clean signals (always .fillna(False) before exrem)
    • Define sensible parameter ranges for the chosen strategy
    • Use loop-based optimization to collect multiple metrics per combo
    • Track: total_return, sharpe_ratio, max_drawdown, trade_count for each combination
    • Use tqdm for progress bars
    • Indian delivery fees: fees=0.00111, fixed_fees=20 for delivery equity
    • Find best parameters by total return AND by Sharpe ratio
    • Print top 10 results for both criteria
    • Generate Plotly heatmap of total return across parameter grid (template="plotly_dark")
    • Generate Plotly heatmap of Sharpe ratio across parameter grid
    • Fetch NIFTY benchmark and compare best parameters vs benchmark
    • Print Strategy vs Benchmark comparison table
    • Explain results in plain language for normal traders
    • Save results to CSV
  5. Never use icons/emojis in code or logger output
  6. For futures symbols, use lot-size-aware sizing:
    • NIFTY: min_size=65, size_granularity=65
    • BANKNIFTY: min_size=30, size_granularity=30

Default Parameter Ranges

StrategyParameter 1Parameter 2
ema-crossoverfast EMA: 5-50slow EMA: 10-60
rsiwindow: 5-30oversold: 20-40
donchianperiod: 5-50-
supertrendperiod: 5-30multiplier: 1.0-5.0

Example Usage

/optimize ema-crossover RELIANCE NSE D /optimize rsi SBIN

Frequently asked questions

What does the Optimize AI skill do?

Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.

Why use Optimize on TypingMind?

Because you install it once and use it with any model. Optimize is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Optimize in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/marketcalls/vectorbt-backtesting-skills/tree/master/.claude/skills/optimize. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Optimize?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Optimize?

As many as you like. As long as a model supports skills, you can use Optimize with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Optimize AI skill free?

It is published on GitHub by marketcalls. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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