Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Cryptocurrency Trader
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.
skill openclawclawhub Free
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.
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- Tencent SkillHub
- What's included
- PRODUCTION_READY_SUMMARY.md, SKILL.md, __main__.py, change_log.md, config.yaml, example_usage.py
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Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
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- Publisher
- Veeramanikandanr48
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Documentation
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
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
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
Language Guidelines
Use beginner-friendly explanations: "LONG" β "Buy now, sell higher later" "SHORT" β "Sell now, buy back cheaper later" "Stop Loss" β "Automatic exit to limit loss if wrong" "Confidence %" β "How certain we are (higher = better)" "Risk/Reward" β "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
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: π’ 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
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs2 Scripts1 Config
- SKILL.md
- change_log.md
- PRODUCTION_READY_SUMMARY.md
- __main__.py
- example_usage.py
- config.yaml