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clawquant-trader

Executes batch and single Metamask wallet sweeps and transfers via CLI with JSON output.

86|21|Updated Mar 4, 2026
Search Tags:#trading#report#backtest#sweep#radar#data-pull#clawquant
One-click install
npx skills add https://github.com/duolaAmengweb3/clawquant-trader --skill clawquant-trader
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clawquant-trader
Source: https://github.com/duolaAmengweb3/clawquant-trader/tree/main/clawquant/skills
Command: npx skills add https://github.com/duolaAmengweb3/clawquant-trader --skill clawquant-trader

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables researchers and traders to run repeatable, large-scale quantitative research workflows from data acquisition to backtesting, parameter optimization, signal scanning, and report generation without manual CLI orchestration.

Core Features & Use Cases

  • Data Pull & Caching: Fetch OHLCV data from exchanges and store as Parquet for fast reuse and quality checks.
  • Backtest & Batch Comparison: Run single and batch backtests across multiple strategies and symbols, rank by return and Sharpe, and support walk-forward validation.
  • Parameter Sweep & Reporting: Perform grid/random sweeps to optimize parameters, compare to baseline, and generate JSON/Markdown/charts reports; useful for research, strategy selection, and pre-deployment validation.
  • Safe Deployment Workflow: Support paper trading and gated live deployment requiring explicit confirmation to avoid accidental real-money execution.

Quick Start

Run a batch backtest comparing dca, ma_crossover, and grid on BTC/USDT and ETH/USDT for 30 days with JSON output using the clawquant CLI.

Frequently Asked Questions about clawquant-trader

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run batch backtests across multiple trading symbols?▼

Run batch backtests by executing the clawquant CLI across multiple symbols and strategies. The tool ranks performance by return and Sharpe ratio, supporting walk-forward validation and outputting results as structured JSON for direct comparison.

What is parameter sweep optimization in quantitative trading?▼

Parameter sweep optimization tests strategy robustness by running grid or random parameter variations. This Skill executes sweeps via the clawquant CLI, compares outcomes against a baseline, and generates JSON, Markdown, and chart reports for strategy selection.

Can I pull and cache OHLCV data for backtesting?▼

Yes, you can pull and cache OHLCV data for backtesting. The Skill fetches exchange data and stores it as Parquet files, enabling fast reuse, parameter sweeps, and quality checks across multiple quantitative workflows without redundant API calls.

How do I prevent accidental live deployment when testing strategies?▼

To prevent accidental live deployment, the Skill enforces a safe deployment workflow requiring explicit confirmation before real-money execution. It supports paper trading and gated deployment to ensure strategies are validated via backtests and scans first.

Does the clawquant CLI support walk-forward validation?▼

Yes, the clawquant CLI supports walk-forward validation during batch backtesting. This evaluates strategy performance across multiple symbols, ranks results by return and Sharpe ratio, and validates parameter stability over segmented time periods before generating reports.

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