Skill-Details

cryptocurrency-trader

Analyzes crypto markets and generates risk-managed trading signals.

ÜbereinstimmungDirektGeprüft für trading
Quellesundial-org/awesome-openclaw-skillsExterne Quelle
Gemeldete Installationen80Nur Popularitätssignal

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SKILL.md

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---
name: cryptocurrency-trader
description: Production-grade AI trading agent for cryptocurrency markets with advanced mathematical modeling, multi-layer validation, probabilistic analysis, and zero-hallucination tolerance. Implements Bayesian inference, Monte Carlo simulations, advanced risk metrics (VaR, CVaR, Sharpe), chart pattern recognition, and comprehensive cross-verification for real-world trading application.
---

# Cryptocurrency Trading Agent Skill

## Purpose

Provide production-grade cryptocurrency trading analysis with mathematical rigor, multi-layer validation, and comprehensive risk assessment. Designed for real-world trading application with zero-hallucination tolerance through 6-stage validation pipeline.

## When to Use This Skill

Use this skill when users request:
- Analysis of specific cryptocurrency trading pairs (e.g., BTC/USDT, ETH/USDT)
- Market scanning to find best trading opportunities
- Comprehensive risk assessment with probabilistic modeling
- Trading signals with advanced pattern recognition
- Professional risk metrics (VaR, CVaR, Sharpe, Sortino)
- Monte Carlo simulations for scenario analysis
- Bayesian probability calculations for signal confidence

## Core Capabilities

### Validation & Accuracy
- 6-stage validation pipeline with zero-hallucination tolerance
- Statistical anomaly detection (Z-score, IQR, Benford's Law)
- Cross-verification across multiple timeframes
- 14 circuit breakers to prevent invalid signals

### Analysis Methods
- Bayesian inference for probability calculations
- Monte Carlo simulations (10,000 scenarios)
- GARCH volatility forecasting
- Advanced chart pattern recognition
- Multi-timeframe consensus (15m, 1h, 4h)

### Risk Management
- Value at Risk (VaR) and Conditional VaR (CVaR)
- Risk-adjusted metrics (Sharpe, Sortino, Calmar)
- Kelly Criterion position sizing
- Automated stop-loss and take-profit calculation

**Detailed capabilities:** See `references/advanced-capabilities.md`

## Prerequisites

Ensure the following before using this skill:
1. Python 3.8+ environment available
2. Internet connection for real-time market data
3. Required packages installed: `pip install -r requirements.txt`
4. User's account balance known for position sizing

## How to Use This Skill

### Quick Start Commands

**Analyze a specific cryptocurrency:**
```bash
python skill.py analyze BTC/USDT --balance 10000
```

**Scan market for best opportunities:**
```bash
python skill.py scan --top 5 --balance 10000
```

**Interactive mode for exploration:**
```bash
python skill.py interactive --balance 10000
```

### Default Parameters

- **Balance:** If not specified by user, use `--balance 10000`
- **Timeframes:** 15m, 1h, 4h (automatically analyzed)
- **Risk per trade:** 2% of balance (enforced by default)
- **Minimum risk/reward:** 1.5:1 (validated by circuit breakers)

### Common Trading Pairs

Major: BTC/USDT, ETH/USDT, BNB/USDT, SOL/USDT, XRP/USDT
AI Tokens: RENDER/USDT, FET/USDT, AGIX/USDT
Layer 1: ADA/USDT, AVAX/USDT, DOT/USDT
Layer 2: MATIC/USDT, ARB/USDT, OP/USDT
DeFi: UNI/USDT, AAVE/USDT, LINK/USDT
Meme: DOGE/USDT, SHIB/USDT, PEPE/USDT

### Workflow

1. **Gather Information**
   - Ask user for trading pair (if analyzing specific symbol)
   - Ask for account balance (or use default $10,000)
   - Confirm user wants production-grade analysis

2. **Execute Analysis**
   - Run appropriate command (analyze, scan, or interactive)
   - Wait for comprehensive analysis to complete
   - System automatically validates through 6 stages

3. **Present Results**
   - Display trading signal (LONG/SHORT/NO_TRADE)
   - Show confidence level and execution readiness
   - Explain entry, stop-loss, and take-profit prices
   - Present risk metrics and position sizing
   - Highlight validation status (6/6 passed = execution ready)

4. **Interpret Output**
   - Reference `references/output-interpretation.md` for detailed guidance
   - Translate technical metrics into user-friendly language
   - Explain ris
Vollständige Quelle auf GitHub lesen (öffnet externe Seite)
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