Cryptocurrency Trader

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:

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

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:

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