A professional-grade toolkit for building, backtesting, and optimizing quantitative trading strategies across crypto and equity markets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bytesagain-quant-trader
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install bytesagain-quant-trader using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Quant Trader?
Quant Trader is a comprehensive technical framework designed to streamline the lifecycle of quantitative strategy development. By integrating with Openclaw Skills, this tool enables developers and traders to automate the generation of trading logic, analyze market signals, and perform rigorous backtesting without manual overhead. Whether you are focusing on momentum-based crypto trading or mean-reversion in equities, this skill provides the mathematical foundation needed for disciplined market participation.
The skill emphasizes a data-driven approach to finance, offering specific modules for signal analysis and portfolio optimization. As a part of the broader Openclaw Skills ecosystem, it allows for high-level abstraction of complex financial calculations, making it easier to build autonomous trading agents or sophisticated research pipelines.
Quant Trader Use Cases
- Designing and validating momentum, mean-reversion, or breakout strategies for various assets.
- Automating technical screening to identify assets based on specific RSI or moving average criteria.
- Calculating optimal position sizing using the Kelly criterion to manage portfolio risk effectively.
- Analyzing correlation and allocation weights across a multi-asset crypto or stock portfolio.
How Quant Trader Works
- Select a trading style and asset to generate a core strategy framework.
- Input real-time or historical price data to generate precise entry and exit signals based on technical indicators.
- Execute backtesting commands to simulate performance over historical periods and retrieve Sharpe ratios and drawdowns.
- Apply the risk module to determine the safest capital allocation based on entry prices and stop-loss levels.
- Use the portfolio and screener tools to monitor market-wide opportunities and aggregate risk across all holdings.
Quant Trader Setup
To get started with Quant Trader in your environment, ensure you have the necessary script permissions. Use the following commands to initialize the Openclaw Skills component:
# Navigate to the skill directory
cd bytesagain-quant-trader
# Ensure the execution script is runnable
chmod +x scripts/script.sh
# Verify installation by viewing help
bash scripts/script.sh help
Quant Trader Data Schema & Taxonomy
Quant Trader organizes its output into actionable financial datasets. The following table describes the primary data structures generated:
| Category | Key Metrics / Data Points |
|---|---|
| Strategy Logic | Style (momentum, breakout, trend), Entry/Exit Rules |
| Performance | Sharpe Ratio, Max Drawdown, Win Rate, Profit Factor |
| Risk Management | Kelly Criterion, Fixed Fractional Sizing, Risk/Reward Ratio |
| Technicals | RSI, Moving Averages (MA20/50/200), Volume Profile |
Quant Trader Advanced Features
- Multi-market screening capabilities for filtering assets by complex technical criteria.
- Integrated risk-to-reward calculation for every generated signal.
- Support for advanced strategy styles including pairs trading and trend-following.
- Seamless integration into automated AI agent workflows via the Openclaw Skills standardized command interface.
SKILL.md
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