
optimize
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
- Installs
- 2.4K
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill optimize- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads optimize automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
- Read the vectorbt-expert skill rules for reference patterns
- Create
backtesting/{strategy_name}/directory if it doesn't exist (on-demand) - Create a
.pyfile inbacktesting/{strategy_name}/named{symbol}_{strategy}_optimize.py - The script must:
- Load
.envfrom project root usingfind_dotenv()and fetch data via OpenAlgoclient.history() - If user provides a DuckDB path, load data directly via
duckdb.connect(path, read_only=True). See vectorbt-expertrules/duckdb-data.md. - If
openalgo.tais not importable (standalone DuckDB), use inlineexrem()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
tqdmfor progress bars - Indian delivery fees:
fees=0.00111, fixed_fees=20for 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
- Load
- Never use icons/emojis in code or logger output
- For futures symbols, use lot-size-aware sizing:
- NIFTY:
min_size=65, size_granularity=65 - BANKNIFTY:
min_size=30, size_granularity=30
- NIFTY:
Default Parameter Ranges
| Strategy | Parameter 1 | Parameter 2 |
|---|---|---|
| ema-crossover | fast EMA: 5-50 | slow EMA: 10-60 |
| rsi | window: 5-30 | oversold: 20-40 |
| donchian | period: 5-50 | - |
| supertrend | period: 5-30 | multiplier: 1.0-5.0 |
Example Usage
/optimize ema-crossover RELIANCE NSE D
/optimize rsi SBIN
FAQ
What does the optimize skill do?
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
How do I install optimize?
Run npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill optimize in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/optimize/ (personal) or .claude/skills/optimize/ (project).
Which AI agents can use optimize?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is optimize safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains optimize?
It is published by marketcalls in marketcalls/vectorbt-backtesting-skills. It has 2,351 recorded installs.
More from marketcalls/vectorbt-backtesting-skills
backtest
marketcalls/vectorbt-backtesting-skills
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.
vectorbt-expert
marketcalls/vectorbt-backtesting-skills
VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
strategy-compare
marketcalls/vectorbt-backtesting-skills
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
quick-stats
marketcalls/vectorbt-backtesting-skills
Quickly fetch data and print key backtest stats for a symbol with a default EMA crossover strategy. No file creation needed - runs inline in a notebook cell or prints to console.
setup
marketcalls/vectorbt-backtesting-skills
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
Popular testing & qa skills
tdd
mattpocock/skills
Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.
grilling
mattpocock/skills
Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
migrate-to-shoehorn
mattpocock/skills
Migrate test files from as type assertions to @total-typescript/shoehorn. Use when user mentions shoehorn, wants to replace as in tests, or needs partial test data.
run-train
lllllllama/rigorpilot-skills
Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized trainoutputs/. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.
test-driven-development
obra/superpowers
Use when implementing any feature or bugfix, before writing implementation code
finishing-a-development-branch
obra/superpowers
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work