An automated quantitative trading engine utilizing a four-strategy consensus mechanism for high-conviction cryptocurrency execution.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install quant-trading-system
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 quant-trading-system 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 Trading System?
The Quant Trading System is a robust automation framework designed to execute algorithmic trades across major cryptocurrency pairs like BTC, ETH, SOL, and XRP. As part of the Openclaw Skills ecosystem, this tool prioritizes risk management and signal accuracy by requiring a consensus among four distinct technical strategies before opening positions.
This skill is built for developers and traders who want to bridge the gap between technical analysis and automated execution. By leveraging Openclaw Skills for your trading infrastructure, you gain access to a system that handles position management, real-time market data processing, and automated exit strategies (SL/TP) out of the box.
Quant Trading System Use Cases
- Automating high-conviction crypto entries using a multi-strategy voting system.
- Testing trading algorithms in a risk-free environment with a $10,000 virtual paper trading account.
- Implementing disciplined risk management with automated 5% stop-loss and 10% take-profit triggers.
- Monitoring market health and strategy status through a streamlined CLI interface.
How Quant Trading System Works
- The system initializes by fetching real-time market data for supported assets (BTC, ETH, SOL, XRP).
- Four independent technical analysis modules (Momentum, Mean Reversion, MACD Cross, and Supertrend) evaluate the price action.
- A voting algorithm aggregates signals; an order is only triggered when strategies reach consensus.
- Upon entry, the system creates an automated position record and manages the lifecycle of the trade.
- The Risk Control module continuously monitors the PnL, automatically closing the position when the 5% stop-loss or 10% take-profit threshold is hit.
Quant Trading System Setup
To deploy this skill within your environment, ensure you have Python 3 installed and follow these steps:
# Check the current market status and strategy signals
python3 trading_system.py status
# Launch the automated trading lifecycle
python3 trading_system.py run
Quant Trading System Data Schema & Taxonomy
The Quant Trading System organizes its operational data and parameters based on the following structure:
| Parameter | Value / Description |
|---|---|
| Supported Assets | BTC, ETH, SOL, XRP |
| Virtual Capital | $10,000 for Paper Trading |
| Strategy Set | Momentum, Mean Reversion, MACD Cross, Supertrend |
| Risk Management | 5% Stop-Loss (SL) and 10% Take-Profit (TP) |
| Mode | Automated Execution |
| Data Source | Real-time Market Data Feed |
Quant Trading System Advanced Features
- Multi-Strategy Voting: Reduces false signals by requiring agreement between four distinct indicators.
- Automated Position Management: Fully handles the entry, monitoring, and exit phases without manual intervention.
- High-Density Risk Controls: Built-in safety nets ensure capital preservation through strict SL/TP rules.
- Real-Time Integration: Optimized for Openclaw Skills to process live market fluctuations with minimal latency.
- Paper Trading Engine: Provides a sandbox environment using real market data to validate strategies before deploying capital.
SKILL.md
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