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 risk/reward in simple terms
   • Always include risk warnings

5. Handle Edge Cases

   • If execution_ready = NO: Explain validation failures
   • If confidence <40%: Recommend waiting for better opportunity
   • If circuit breakers triggered: Explain specific issue
   • If network errors: Suggest retry with exponential backoff

Output Structure

Trading Signal: - Action: LONG/SHORT/NO_TRADE - Confidence: 0-95% (integer only, no false precision) - Entry Price: Recommended entry point - Stop Loss: Risk management exit (always required) - Take Profit: Profit target - Risk/Reward: Minimum 1.5:1 ratio

Probabilistic Analysis: - Bayesian probabilities (bullish/bearish) - Monte Carlo profit probability - Signal strength (WEAK/MODERATE/STRONG) - Pattern bias confirmation

Risk Assessment: - VaR and CVaR (Value at Risk metrics) - Sharpe/Sortino/Calmar ratios - Max drawdown and win rate - Profit factor

Position Sizing: - Standard (2% risk rule) - recommended - Kelly Conservative - mathematically optimal - Kelly Aggressive - higher risk/reward - Trading fees estimate

Validation Status: - Stages passed (must be 6/6 for execution ready) - Circuit breakers triggered (if any) - Warnings and critical failures

Detailed interpretation: See references/output-interpretation.md

Presenting Results to Users

Language Guidelines

Use beginner-friendly explanations: - "LONG" → "Buy now, sell higher later" - "SHORT" → "Sell now, buy back cheaper later" - "Stop Loss" → "Automatic exit to limit loss if wrong" - "Confidence %" → "How certain we are (higher = better)" - "Risk/Reward" → "For every $1 risked, potential $X profit"

Required Risk Warnings

ALWAYS include these reminders: - Markets are unpredictable - perfect analysis can still be wrong - Start with small amounts to learn - Never risk more than 2% per trade (enforced automatically) - Always use stop losses - This is analysis, NOT financial advice - Past performance does NOT guarantee future results - User is solely responsible for all trading decisions

When NOT to Trade

Advise users to avoid trading when: - Validation status <6/6 passed - Execution Ready flag = NO - Confidence <60% for moderate signals, <70% for strong - User doesn't understand the analysis - User can't afford potential loss - High emotional stress or fatigue

Advanced Usage

Programmatic Integration

For custom workflows, import directly: ```python from scripts.trading_agent_refactored import TradingAgent

agent = TradingAgent(balance=10000) analysis = agent.comprehensive_analysis('BTC/USDT') print(analysis['final_recommendation']) ```

See example_usage.py for 5 comprehensive examples.

Configuration

Customize behavior via config.yaml: - Validation strictness (strict vs normal mode) - Risk parameters (max risk, position limits) - Circuit breaker thresholds - Timeframe preferences

Testing

Verify installation and functionality: ```bash

Run compatibility test

./test_claude_code_compat.sh

Run comprehensive tests

python -m pytest tests/ ```

Reference Documentation

• references/advanced-capabilities.md - Detailed technical capabilities
• references/output-interpretation.md - Comprehensive output guide
• references/optimization.md - Trading optimization strategies
• references/protocol.md - Usage protocols and best practices
• references/psychology.md - Trading psychology principles
• references/user-guide.md - End-user documentation
• references/technical-docs/ - Implementation details and bug reports

Architecture

Core Modules: - scripts/trading_agent_refactored.py - Main trading agent (production) - scripts/advanced_validation.py - Multi-layer validation system - scripts/advanced_analytics.py - Probabilistic modeling engine - scripts/pattern_recognition_refactored.py - Chart pattern recognition - scripts/indicators/ - Technical indicator calculations - scripts/market/ - Data provider and market scanner - scripts/risk/ - Position sizing and risk management - scripts/signals/ - Signal generation and recommendation

Entry Points: - skill.py - Command-line interface (recommended) - __main__.py - Python module invocation - example_usage.py - Programmatic usage examples

Version

v2.0.1 - Production Hardened Edition

Recent improvements: - Fixed critical bugs (division by zero, import paths, NaN handling) - Enhanced network retry logic with exponential backoff - Improved logging infrastructure - Comprehensive input validation - UTC timezone consistency - Benford's Law threshold optimization

Status: 🟢 PRODUCTION READY

See references/technical-docs/FIXES_APPLIED.md for complete changelog.

Troubleshooting

Installation issues: bash pip install --upgrade pip pip install -r requirements.txt

Import errors: Ensure running from skill directory or using skill.py entry point.

Network failures: System automatically retries with exponential backoff (3 attempts).

Validation failures: Check validation report in output - explains which stage failed and why.

For detailed debugging: Enable logging in config.yaml or check references/technical-docs/BUG_ANALYSIS_REPORT.md