Skill: veeramanikandanr48/cryptocurrency-trader-skill (version 0.1.0)

--- 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 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`

path: __main__.py
content: #!/usr/bin/env python3
"""
AI Trading Skill - Module Entry Point

Allows the skill to be invoked as a module:
    python -m cryptocurrency_trader_skill analyze BTC/USDT --balance 10000
"""

from skill import main

if __name__ == '__main__':
    main()


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)