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.

Instalar
$clawhub install cryptocurrency-trader-skill

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

Detalhes

Versão
v0.1.0
Downloads
2,459
Estrelas
6
ClawHub
Ver no ClawHub

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