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:

Python 3.8+ environment available
Internet connection for real-time market data
Required packages installed: pip install -r requirements.txt
User's account balance known for position sizing

How to Use This Skill

Quick Start Commands

Analyze a specific cryptocurrency:

python skill.py analyze BTC/USDT --balance 10000

Scan market for best opportunities:

python skill.py scan --top 5 --balance 10000

Interactive mode for exploration:

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

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

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:

# 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:

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

Cryptocurrency Trader

Description