dex-quant-skill加密货币量化交易 AI Skill。用自然语言描述交易规则 → 生成策略脚本 → 服务器回测 → 参数优化 → 实时监控。 支持 Binance/Hyperliquid 全币种,6 种优化算法(genetic/bayesian/grid/random/annealing/pso),异步进度推送。 Use when...
Install via ClawdBot CLI:
clawdbot install miyaosk/dex-quant-skillGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://github.com/Rohit24567/HyperLiquid-Claw.gitUses known external API (expected, informational)
raw.githubusercontent.comAudited Apr 16, 2026 · audit v1.0
Generated May 9, 2026
Users who lack trading strategy knowledge can receive pre-tested positive-return strategies (e.g., SOL RSI Momentum, BTC RSI Momentum) by simply asking for recommendations. The system responds with a fixed template featuring two backtested strategies and guides the user to select one for further backtesting or custom strategy creation.
Traders describe trading rules in natural language (e.g., 'buy when RSI crosses above 30 and volume spikes'), and the skill generates a complete Python strategy script. It handles parameter extraction and script generation using predefined templates, reducing manual coding effort.
Users can backtest any existing or newly created strategy against historical data to evaluate performance. The skill executes backtest code, prints comprehensive metrics (e.g., total return, Sharpe ratio, max drawdown), and generates an equity curve PNG for visual analysis.
Traders can optimize strategy parameters using six algorithms: genetic, bayesian, grid, random, simulated annealing, and particle swarm. The user selects an algorithm from a fixed menu, and the skill runs the optimization process to find the best parameter set for improved performance.
Once a strategy is finalized, users can deploy it for live monitoring and execution. The skill sets up a local monitoring environment (server monitoring interface not yet available) that continuously runs the strategy on schedule, enabling automated trading without manual intervention.
Offer curated, pre-optimized trading strategies as a subscription service. Users pay a monthly fee to access a library of backtested and optimized strategies, with the skill handling the creation, backtesting, and deployment processes.
Charge users for each parameter optimization run. Since optimization requires server-side computation, a per-request fee model can be applied, especially for compute-intensive algorithms like grid search or genetic algorithms.
Integrate with crypto exchanges to execute strategies on behalf of users and earn a performance-based commission. For each profitable trade executed by the deployed monitoring system, the platform takes a percentage of the profit.
💬 Integration Tip
The skill requires Python dependencies (httpx, loguru, matplotlib) and assumes Binance/Hyperliquid market data access. Ensure the base directory is writable for strategy scripts and output files.
Scored Jun 29, 2026
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