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.

Security Scan

The skill's code broadly matches a crypto trading assistant, but the package metadata and SKILL.md omit important runtime requirements (notably LLM API keys and potential exchange credentials) and contain contradictory packaging/installation claims — these inconsistencies warrant caution before installing or running.

Purpose & Capability

The codebase (many trading, pattern-recognition, backtesting modules plus an LLM conversational layer) is coherent with the stated purpose of an AI cryptocurrency trading agent. However the registry metadata and SKILL.md claim 'Required env vars: none' while the code (llm_trading_assistant.py) clearly expects OPENAI_API_KEY or ANTHROPIC_API_KEY (or an explicit api_key argument). That omission is inconsistent — LLM integration is reasonable for this skill, but the metadata should declare those credentials.

Instruction Scope

SKILL.md instructs running CLI commands and to 'pip install -r requirements.txt', and requests an Internet connection and knowledge of user account balance. The runtime instructions do not mention reading environment variables for LLM providers or possibly exchange API keys, yet the code reads OPENAI_API_KEY/ANTHROPIC_API_KEY from the environment. The instructions are otherwise narrowly scoped to trading flows, but the omission of required env vars and lack of disclosure about network calls and external API usage is a scope/visibility problem.

Install Mechanism

No install spec was declared in the registry, yet the package contains a requirements.txt and the PRODUCTION_READY_SUMMARY includes unzip/cp installation instructions and a pip install -r requirements.txt step. There are no suspicious external download URLs in the provided manifest. The install approach relies on pip (requirements.txt) which is normal, but the registry metadata should reflect that and list the dependency file so users know what will be installed.

Credentials

The declared 'Required env vars: none' is inconsistent with code usage of LLM API keys (OPENAI_API_KEY / ANTHROPIC_API_KEY). The code will also perform network I/O for market data and may require exchange credentials (MarketDataProvider/ccxt-style exchanges) although exchange keys are not declared. Requesting or using LLM keys and potentially exchange API keys is proportionate to the skill's purpose, but the missing declaration is a red flag — users aren't being informed of what secrets will be needed or used.

Persistence & Privilege

The skill does not request always:true and does not declare system-wide config changes. Installation/install-spec (per PRODUCTION_READY_SUMMARY) writes files only into a user skill directory (~/.claude/skills/...), which is normal for a skill. There is no evidence in the provided files of attempts to modify other skills' configs or escalate privilege.

What to consider before installing

This package contains substantial source code and an LLM-powered interface; do not install blindly. Before running or installing: 1) Inspect requirements.txt to see which third-party packages will be installed. 2) Search the source (e.g., llm_trading_assistant.py, market/data_provider.py) for references to environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, BINANCE_API_KEY, SECRET, TOKEN, etc.) and endpoints the code contacts. 3) Confirm whether the market data code uses public REST endpoints or requires exchange API keys; if it requires exchange keys, provide them only in a controlled, minimal-permission account. 4) Run the code in an isolated environment (container or VM) and with dummy API keys first. 5) If you plan to use the LLM integration, prefer passing API keys explicitly to constructors rather than relying on environment variables so you know what is being used. 6) Do not connect real trading accounts or use real funds until you have reviewed tests, logs, and run dry runs/backtests. 7) If you need to trust this skill long-term, ask the publisher for source provenance and a signed release; currently the registry lists an unknown source and no homepage, increasing the need for manual review.

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