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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "VibeTrading Code Generator",
        "body": "Generate executable Hyperliquid trading strategy code from natural language prompts. This skill transforms trading ideas into ready-to-run Python code using actual Hyperliquid API implementations. Generated code includes complete API integration, error handling, logging, and configuration management."
      },
      {
        "title": "Basic Usage",
        "body": "# Generate a simple RSI strategy\npython scripts/strategy_generator.py \"Generate a BTC RSI strategy, buy below 30, sell above 70\"\n\n# Generate a grid trading strategy\npython scripts/strategy_generator.py \"BTC grid trading 50000-60000 10 grids 0.01 BTC per grid\"\n\n# Generate a signal-following strategy\npython scripts/strategy_generator.py \"ETH trading strategy based on VibeTrading signals, buy on bullish signals, sell on bearish signals\""
      },
      {
        "title": "Output Structure",
        "body": "The generator creates:\n\nStrategy Python file - Complete trading strategy class\nConfiguration file - Strategy parameters and settings\nUsage instructions - How to run and monitor the strategy\nRequirements file - Python dependencies"
      },
      {
        "title": "Automatic Code Validation",
        "body": "All generated code is automatically validated and fixed using the built-in validation system:\n\n# Validate generated code\npython scripts/code_validator.py generated_strategy.py\n\n# Validate and fix automatically\npython scripts/code_validator.py generated_strategy.py --fix\n\n# Validate entire directory\npython scripts/code_validator.py strategy_directory/"
      },
      {
        "title": "Validation Steps",
        "body": "The validation system performs these checks:\n\nSyntax Validation - Python syntax checking\nImport Validation - Module import verification\nCompatibility Checks - Python 3.5+ compatibility\nCommon Issue Detection - Missing imports, encoding issues, etc."
      },
      {
        "title": "Automatic Fixes",
        "body": "When validation fails, the system automatically fixes common issues:\n\nAdd missing imports - Add typing imports if type annotations are used\nFix encoding declaration - Add # -*- coding: utf-8 -*- if missing\nRemove incompatible syntax - Remove f-strings and type annotations for Python 3.5 compatibility\nFix import paths - Add sys.path modifications for API wrappers\nFix logger initialization order - Ensure logger is initialized before API client\nRemove pathlib usage - Replace with os.path for Python 3.4 compatibility\nFix string formatting - Convert f-strings to .format() method"
      },
      {
        "title": "Validation Configuration",
        "body": "The validation system can be configured via command-line arguments:\n\n# Basic validation\npython scripts/code_validator.py strategy.py\n\n# Validate and fix automatically\npython scripts/code_validator.py strategy.py --fix\n\n# Use specific Python executable\npython scripts/code_validator.py strategy.py --python python3.6\n\n# Validate directory with all files\npython scripts/code_validator.py strategies/ --fix\n\n# Maximum 5 fix iterations\npython scripts/code_validator.py strategy.py --fix --max-iterations 5"
      },
      {
        "title": "Validation Rules",
        "body": "The system enforces these rules for generated code:\n\nPython 3.5+ Compatibility\n\nNo f-strings (use .format() or % formatting)\nNo type annotations (remove or use comments)\nNo pathlib (use os.path instead)\nNo typing module imports\n\n\n\nCode Quality\n\nProper encoding declaration (# -*- coding: utf-8 -*-)\nLogger initialized before API client\nAll imports are resolvable\nNo syntax errors\n\n\n\nSecurity\n\nAPI keys loaded from environment variables\nNo hardcoded credentials\nProper error handling for API calls\n\n\n\nPerformance\n\nReasonable check intervals (not too frequent)\nEfficient data fetching\nProper resource cleanup"
      },
      {
        "title": "Validation Workflow",
        "body": "User Prompt → Code Generation → Validation → Fixes → Final Code\n                    ↓\n              If validation fails\n                    ↓\n            Apply automatic fixes\n                    ↓\n          Re-validate until success\n                    ↓\n          Deliver validated code"
      },
      {
        "title": "Validation Failure Handling",
        "body": "When validation fails, the system automatically updates the code with these steps:\n\nError Analysis - Identify the specific validation errors\nFix Application - Apply appropriate fixes based on error type\nRe-validation - Validate again after fixes\nIterative Repair - Repeat until code is valid (max 3 iterations)\nFallback Strategy - If automatic fixes fail, provide detailed error report and manual fix instructions"
      },
      {
        "title": "Automatic Fix Examples",
        "body": "Fix 1: Missing Imports\n\n# Before (error: NameError: name 'List' is not defined)\ndef calculate_prices(prices: List[float]) -> List[float]:\n\n# After (automatic fix)\nfrom typing import List, Dict, Optional\ndef calculate_prices(prices):\n\nFix 2: Encoding Issues\n\n# Before (error: SyntaxError: Non-ASCII character)\n# Strategy description: Grid trading\n\n# After (automatic fix)\n# -*- coding: utf-8 -*-\n# Strategy description: Grid trading\n\nFix 3: Python 3.5 Incompatibility\n\n# Before (error: SyntaxError in Python 3.5)\nprice = f\"Current price: {current_price}\"\n\n# After (automatic fix)\nprice = \"Current price: {}\".format(current_price)\n\nFix 4: Import Path Issues\n\n# Before (error: ImportError: No module named 'hyperliquid_api')\nfrom hyperliquid_api import HyperliquidClient\n\n# After (automatic fix)\nimport sys\nimport os\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), \"..\", \"api_wrappers\"))\nfrom hyperliquid_api import HyperliquidClient"
      },
      {
        "title": "1. Technical Indicator Strategies",
        "body": "RSI-based: Oversold/overbought trading\nMACD-based: Trend following with MACD crossovers\nMoving Average: SMA/EMA cross strategies\nBollinger Bands: Mean reversion strategies"
      },
      {
        "title": "2. Advanced Trading Strategies",
        "body": "Grid Trading: Price range trading with multiple orders\nMean Reversion: Statistical arbitrage strategies\nTrend Following: Momentum-based strategies\nArbitrage: Spot-perp or cross-exchange arbitrage"
      },
      {
        "title": "3. Signal-Driven Strategies",
        "body": "VibeTrading Integration: Follow AI-generated trading signals\nNews-based: React to market news and sentiment\nWhale Activity: Track large wallet movements\nFunding Rate: Funding rate arbitrage strategies"
      },
      {
        "title": "Step 1: Prompt Analysis",
        "body": "The generator analyzes your natural language prompt to identify:\n\nTrading symbol (BTC, ETH, SOL, etc.)\nStrategy type (grid, RSI, signal-based, etc.)\nKey parameters (price ranges, grid counts, indicator values)\nRisk management preferences"
      },
      {
        "title": "Step 2: Template Selection",
        "body": "Based on the analysis, the system selects the most appropriate template from:\n\ntemplates/grid_trading.py - Grid trading strategy template"
      },
      {
        "title": "Step 3: Code Generation",
        "body": "The generator:\n\nFills template parameters with your values\nAdds proper error handling and logging\nIncludes configuration management\nGenerates complete runnable code"
      },
      {
        "title": "Step 4: Code Validation",
        "body": "The generated code is automatically validated and fixed:\n\nSyntax checking - Ensure valid Python syntax\nImport verification - Check all imports are resolvable\nCompatibility testing - Verify Python 3.5+ compatibility\nAutomatic fixes - Apply fixes for common issues\nRe-validation - Validate again after fixes\nError reporting - If fixes fail, provide detailed error report"
      },
      {
        "title": "Validation Failure Handling",
        "body": "If validation fails after automatic fixes:\n\nError Analysis Report - Detailed breakdown of remaining issues\nManual Fix Instructions - Step-by-step guidance for manual fixes\nFallback Template - Option to use a simpler, validated template\nSupport Contact - Instructions for getting help"
      },
      {
        "title": "Step 5: Output Delivery",
        "body": "You receive validated, runnable code including:\n\nValidated Python strategy file - Fully tested and fixed\nConfiguration template - Strategy parameters and settings\nValidation report - Summary of validation results and fixes applied\nUsage instructions - How to run and monitor the strategy\nTroubleshooting guide - Common issues and solutions\nRisk warnings - Important safety information"
      },
      {
        "title": "API Integration",
        "body": "The generated code uses mature Hyperliquid API implementations that support:"
      },
      {
        "title": "Trading Operations",
        "body": "Spot trading (buy/sell with limit/market orders)\nPerpetual contracts (long/short with leverage)\nOrder management (cancel, modify, query)\nPosition management (reduce, hedge)"
      },
      {
        "title": "Market Data",
        "body": "Real-time prices and OHLCV data\nFunding rates and open interest\nOrder book depth\nHistorical data access"
      },
      {
        "title": "Account Management",
        "body": "Balance queries (spot and futures)\nPosition tracking\nPNL calculation\nRisk metrics"
      },
      {
        "title": "Template Structure",
        "body": "Each template includes:\n\nStrategy class with initialization and main logic\nConfiguration section for easy parameter tuning\nError handling with comprehensive logging\nRisk management features\nMonitoring loop for continuous operation"
      },
      {
        "title": "Available Templates",
        "body": "Grid Trading Template\n\ngrid_trading.py - Grid trading within price ranges (Python 3.5+ compatible)\n\nNo f-strings\nNo type annotations\nProper encoding declaration\nLogger initialized before API client"
      },
      {
        "title": "Strategy Configuration",
        "body": "Generated strategies include configurable parameters:\n\nSTRATEGY_CONFIG = {\n    \"symbol\": \"BTC\",\n    \"timeframe\": \"1h\",\n    \"parameters\": {\n        \"rsi_period\": 14,\n        \"oversold\": 30,\n        \"overbought\": 70\n    },\n    \"risk_management\": {\n        \"position_size\": 0.01,\n        \"stop_loss\": 0.05,\n        \"take_profit\": 0.10,\n        \"max_drawdown\": 0.20\n    }\n}"
      },
      {
        "title": "Environment Setup",
        "body": "# Required environment variables\nexport HYPERLIQUID_API_KEY=\"your_api_key_here\"\nexport HYPERLIQUID_ACCOUNT_ADDRESS=\"your_address_here\"\nexport TELEGRAM_BOT_TOKEN=\"optional_for_alerts\""
      },
      {
        "title": "Risk Management Features",
        "body": "All generated strategies include:"
      },
      {
        "title": "1. Position Sizing",
        "body": "Fixed percentage of portfolio\nDynamic position sizing based on volatility\nMaximum position limits"
      },
      {
        "title": "2. Stop Loss Mechanisms",
        "body": "Percentage-based stop loss\nTrailing stops\nTime-based exits"
      },
      {
        "title": "3. Risk Controls",
        "body": "Maximum daily loss limits\nDrawdown protection\nCorrelation checks\nMarket condition filters"
      },
      {
        "title": "4. Monitoring & Alerts",
        "body": "Real-time position tracking\nTelegram/Slack notifications\nPerformance reporting\nError alerts and recovery"
      },
      {
        "title": "Integration with VibeTrading Signals",
        "body": "Generated strategies can integrate with VibeTrading Global Signals:\n\nfrom vibetrading import get_latest_signals\n\n# Get AI-generated signals\nsignals = get_latest_signals(\"BTC,ETH\")\n\n# Use signals in trading logic\nif signals[\"BTC\"][\"sentiment\"] == \"BULLISH\":\n    strategy.execute_buy(\"BTC\", amount=0.01)"
      },
      {
        "title": "Example 1: Simple RSI Strategy",
        "body": "Prompt: \"Generate a BTC RSI strategy, buy 0.01 BTC when RSI below 30, sell when above 70\"\n\nGenerated Code Features:\n\nRSI calculation with 14-period default\nConfigurable oversold/overbought thresholds\nProper error handling for API calls\nLogging for all trading actions\n1-hour check interval"
      },
      {
        "title": "Example 2: Grid Trading Strategy",
        "body": "Prompt: \"ETH grid trading strategy, price range 3000-4000, 20 grids, 0.1 ETH per grid\"\n\nGenerated Code Features:\n\nAutomatic grid price calculation\nOrder placement and management\nGrid rebalancing logic\nPrice monitoring and adjustment\nComprehensive logging"
      },
      {
        "title": "Example 3: Signal-Based Strategy",
        "body": "Prompt: \"SOL trading strategy based on VibeTrading signals, buy on bullish signals, sell on bearish signals, 10 SOL per trade\"\n\nGenerated Code Features:\n\nVibeTrading API integration\nSignal polling and parsing\nTrade execution based on sentiment\nPosition management\nPerformance tracking"
      },
      {
        "title": "1. Start with Paper Trading",
        "body": "Always test strategies in simulation mode first\nUse small position sizes initially\nMonitor performance for at least 1-2 weeks"
      },
      {
        "title": "2. Risk Management",
        "body": "Never risk more than 1-2% per trade\nUse stop losses on all positions\nDiversify across multiple strategies\nMonitor correlation between strategies"
      },
      {
        "title": "3. Monitoring & Maintenance",
        "body": "Regularly review strategy performance\nAdjust parameters based on market conditions\nKeep logs for audit and analysis\nSet up alerts for critical events"
      },
      {
        "title": "4. Security",
        "body": "Store API keys securely (environment variables)\nUse separate accounts for different strategies\nRegularly rotate API keys\nMonitor for unauthorized access"
      },
      {
        "title": "Common Issues",
        "body": "1. API Connection Errors\n\n# Check API key and account address\necho $HYPERLIQUID_API_KEY\necho $HYPERLIQUID_ACCOUNT_ADDRESS\n\n# Test API connection\npython scripts/test_connection.py\n\n2. Strategy Not Executing Trades\n\nCheck balance and available funds\nVerify symbol is correctly specified\nCheck order size meets minimum requirements\nReview logs for error messages\n\n3. Performance Issues\n\nAdjust check intervals (too frequent may cause rate limiting)\nOptimize data fetching (cache where possible)\nReview market conditions (low liquidity periods)\n\n4. Integration Issues with VibeTrading\n\nVerify VibeTrading API is accessible\nCheck signal availability for your symbols\nReview signal parsing logic\n\n5. Validation Errors\n\n# Common validation errors and solutions:\n\n# Error: \"SyntaxError: invalid syntax\"\n# Solution: Check for f-strings or type annotations\npython scripts/code_validator.py strategy.py --fix\n\n# Error: \"ImportError: No module named 'typing'\"\n# Solution: Remove typing imports (Python 3.4 compatibility)\nsed -i '' 's/from typing import.*//g' strategy.py\n\n# Error: \"SyntaxError: Non-ASCII character\"\n# Solution: Add encoding declaration\necho -e '# -*- coding: utf-8 -*-\\n' | cat - strategy.py > temp && mv temp strategy.py\n\n# Error: \"NameError: name 'List' is not defined\"\n# Solution: Remove type annotations or add typing import\nsed -i '' 's/: List//g; s/: Dict//g; s/: Optional//g' strategy.py\n\n# Manual validation check\npython -m py_compile strategy.py\n\n6. Code Generation Failures\n\nCheck prompt clarity (be specific about parameters)\nEnsure template exists for requested strategy type\nVerify Python version compatibility (3.5+ recommended)\nCheck available disk space for output files"
      },
      {
        "title": "Custom Template Creation",
        "body": "You can create custom templates in templates/custom/:\n\nCreate a new template file\nDefine template variables with {{variable_name}}\nAdd to template registry in scripts/template_registry.py\nTest with the generator"
      },
      {
        "title": "Strategy Backtesting",
        "body": "While this generator focuses on live trading, you can:\n\nExport generated code to backtesting frameworks\nUse historical data for strategy validation\nAdd performance metrics and analysis"
      },
      {
        "title": "Multi-Strategy Management",
        "body": "For running multiple strategies:\n\nGenerate separate strategy files\nUse different configuration files\nMonitor overall portfolio risk\nImplement strategy allocation logic"
      },
      {
        "title": "Getting Help",
        "body": "Review generated code comments\nCheck example strategies in examples/\nConsult Hyperliquid API documentation\nReview VibeTrading signal documentation"
      },
      {
        "title": "Updates",
        "body": "This skill will be updated with:\n\nNew strategy templates\nImproved prompt understanding\nAdditional risk management features\nIntegration with more data sources"
      },
      {
        "title": "Backtest Evaluation Feature",
        "body": "After generating a strategy, you can now evaluate its performance using our integrated backtesting system:\n\n# Generate a strategy\npython scripts/strategy_generator.py \"BTC grid trading 50000-60000 10 grids 0.01 BTC per grid\"\n\n# Run backtest on the generated strategy\npython scripts/backtest_runner.py generated_strategies/btc_grid_trading_strategy.py\n\n# Run backtest with custom parameters\npython scripts/backtest_runner.py generated_strategies/btc_grid_trading_strategy.py \\\n  --start-date 2025-01-01 \\\n  --end-date 2025-03-01 \\\n  --initial-balance 10000 \\\n  --interval 1h"
      },
      {
        "title": "Backtest Features",
        "body": "The backtesting system provides:\n\nHistorical Data Simulation - Uses historical price data for realistic testing\n\n\nPerformance Metrics - Calculates key metrics:\n\nTotal Return (%)\nMaximum Drawdown (%)\nSharpe Ratio\nWin Rate (%)\nTotal Trades\nAverage Trade Duration\n\n\n\nRisk Analysis - Evaluates strategy risk characteristics\n\n\nVisual Reports - Generates charts and performance reports\n\n\nComparative Analysis - Compares strategy performance against benchmarks"
      },
      {
        "title": "Backtest Configuration",
        "body": "You can configure backtests with these parameters:\n\nBACKTEST_CONFIG = {\n    \"start_date\": \"2025-01-01\",\n    \"end_date\": \"2025-03-01\",\n    \"initial_balance\": 10000,  # USDC\n    \"interval\": \"1h\",  # 1m, 5m, 15m, 30m, 1h, 4h, 1d\n    \"symbols\": [\"BTC\", \"ETH\"],  # Trading symbols\n    \"commission_rate\": 0.001,  # 0.1% trading commission\n    \"slippage\": 0.001,  # 0.1% slippage\n}"
      },
      {
        "title": "Backtest Results Example",
        "body": "📊 Backtest Results for BTC Grid Trading Strategy\n================================================\n📅 Period: 2025-01-01 to 2025-03-01 (60 days)\n💰 Initial Balance: $10,000.00\n💰 Final Balance: $11,234.56\n\n📈 Performance Metrics:\n  • Total Return: +12.35%\n  • Max Drawdown: -5.67%\n  • Sharpe Ratio: 1.45\n  • Win Rate: 58.3%\n  • Total Trades: 120\n  • Avg Trade Duration: 12.5 hours\n\n📋 Trade Analysis:\n  • Winning Trades: 70\n  • Losing Trades: 50\n  • Largest Win: +$245.67\n  • Largest Loss: -$123.45\n  • Avg Win: +$89.12\n  • Avg Loss: -$56.78\n\n⚠️ Risk Assessment:\n  • Risk-Adjusted Return: Good\n  • Drawdown Control: Acceptable\n  • Consistency: Moderate"
      },
      {
        "title": "Backtest Integration in Generated Code",
        "body": "Generated strategies now include backtest compatibility:\n\n# Generated strategy includes backtest method\nstrategy = GridTradingStrategy(api_key, account_address, config)\n\n# Run backtest\nbacktest_results = strategy.run_backtest(\n    start_date=\"2025-01-01\",\n    end_date=\"2025-03-01\",\n    initial_balance=10000\n)\n\n# Generate backtest report\nstrategy.generate_backtest_report(backtest_results)"
      },
      {
        "title": "Backtest Data Sources",
        "body": "The backtesting system uses:\n\nHistorical price data from Hyperliquid API\nRealistic order execution with configurable slippage\nAccurate commission modeling based on exchange fees\nMarket impact simulation for large orders"
      },
      {
        "title": "Backtest Limitations",
        "body": "Important Notes:\n\nPast performance ≠ future results - Historical success doesn't guarantee future profits\nData quality - Results depend on historical data accuracy\nMarket conditions - Past market conditions may differ from future\nExecution assumptions - Assumes perfect order execution (configurable slippage)\nLiquidity assumptions - Assumes sufficient market liquidity\n\nBest Practices:\n\nAlways backtest with multiple time periods\nTest different market conditions (bull, bear, sideways)\nUse realistic commission and slippage settings\nStart with small position sizes in live trading\nMonitor strategy performance and adjust as needed"
      },
      {
        "title": "Code Validation Disclaimer",
        "body": "Validation Limitations: While the code validation system automatically fixes common issues, it cannot guarantee:\n\nTrading logic correctness - Validation checks syntax, not trading logic\nFinancial performance - No guarantee of profitability\nAPI compatibility - Hyperliquid API changes may break generated code\nSecurity vulnerabilities - Manual security review is recommended\nEdge case handling - All possible error conditions may not be covered\n\nValidation Success Criteria: Code is considered \"valid\" when:\n\nNo syntax errors\nAll imports are resolvable\nPython 3.6+ compatible\nBasic structure is correct\n\nNot Validated:\n\nTrading logic accuracy\nRisk management effectiveness\nFinancial calculations\nMarket condition handling\nPerformance optimization"
      },
      {
        "title": "Python Version Requirements",
        "body": "# Check Python version\npython scripts/check_python_version.py\n\n# Minimum: Python 3.6+ (for f-string support)"
      },
      {
        "title": "Basic Usage",
        "body": "# Generate strategy\npython scripts/strategy_generator.py \"BTC grid trading 50000-60000 10 grids\"\n\n# Run backtest\npython scripts/backtest_runner.py generated_strategies/btc_grid_trading_strategy.py"
      },
      {
        "title": "Key Features",
        "body": "Python 3.6+ Compatibility - Modern Python features including f-strings\nAutomatic Backtest Integration - Evaluate strategies before live trading\nComprehensive Validation - Syntax and compatibility checking\nRisk Management - Built-in risk controls in all strategies"
      },
      {
        "title": "Trading Disclaimer",
        "body": "Important: Trading cryptocurrencies involves significant risk. Generated strategies should be thoroughly tested before use with real funds. Past performance is not indicative of future results. Always use proper risk management and never trade with money you cannot afford to lose.\n\nThe code generator provides tools for strategy creation, but ultimate responsibility for trading decisions and risk management lies with the user.\n\nValidation is not a substitute for:\n\nThorough testing - Always test in simulation first\nCode review - Have experienced developers review generated code\nSecurity audit - Check for vulnerabilities before deployment\nPerformance testing - Test under various market conditions\nRisk assessment - Evaluate strategy risks independently\n\nBacktesting Limitations:\n\nHistorical data quality - Results depend on data accuracy\nMarket condition changes - Past conditions may differ from future\nExecution assumptions - Assumes perfect order execution\nLiquidity assumptions - Assumes sufficient market liquidity\nNo guarantee of future performance - Past success ≠ future profits"
      }
    ],
    "body": "VibeTrading Code Generator\n\nGenerate executable Hyperliquid trading strategy code from natural language prompts. This skill transforms trading ideas into ready-to-run Python code using actual Hyperliquid API implementations. Generated code includes complete API integration, error handling, logging, and configuration management.\n\nQuick Start\nBasic Usage\n# Generate a simple RSI strategy\npython scripts/strategy_generator.py \"Generate a BTC RSI strategy, buy below 30, sell above 70\"\n\n# Generate a grid trading strategy\npython scripts/strategy_generator.py \"BTC grid trading 50000-60000 10 grids 0.01 BTC per grid\"\n\n# Generate a signal-following strategy\npython scripts/strategy_generator.py \"ETH trading strategy based on VibeTrading signals, buy on bullish signals, sell on bearish signals\"\n\nOutput Structure\n\nThe generator creates:\n\nStrategy Python file - Complete trading strategy class\nConfiguration file - Strategy parameters and settings\nUsage instructions - How to run and monitor the strategy\nRequirements file - Python dependencies\nCode Validation System\nAutomatic Code Validation\n\nAll generated code is automatically validated and fixed using the built-in validation system:\n\n# Validate generated code\npython scripts/code_validator.py generated_strategy.py\n\n# Validate and fix automatically\npython scripts/code_validator.py generated_strategy.py --fix\n\n# Validate entire directory\npython scripts/code_validator.py strategy_directory/\n\nValidation Steps\n\nThe validation system performs these checks:\n\nSyntax Validation - Python syntax checking\nImport Validation - Module import verification\nCompatibility Checks - Python 3.5+ compatibility\nCommon Issue Detection - Missing imports, encoding issues, etc.\nAutomatic Fixes\n\nWhen validation fails, the system automatically fixes common issues:\n\nAdd missing imports - Add typing imports if type annotations are used\nFix encoding declaration - Add # -*- coding: utf-8 -*- if missing\nRemove incompatible syntax - Remove f-strings and type annotations for Python 3.5 compatibility\nFix import paths - Add sys.path modifications for API wrappers\nFix logger initialization order - Ensure logger is initialized before API client\nRemove pathlib usage - Replace with os.path for Python 3.4 compatibility\nFix string formatting - Convert f-strings to .format() method\nValidation Configuration\n\nThe validation system can be configured via command-line arguments:\n\n# Basic validation\npython scripts/code_validator.py strategy.py\n\n# Validate and fix automatically\npython scripts/code_validator.py strategy.py --fix\n\n# Use specific Python executable\npython scripts/code_validator.py strategy.py --python python3.6\n\n# Validate directory with all files\npython scripts/code_validator.py strategies/ --fix\n\n# Maximum 5 fix iterations\npython scripts/code_validator.py strategy.py --fix --max-iterations 5\n\nValidation Rules\n\nThe system enforces these rules for generated code:\n\nPython 3.5+ Compatibility\n\nNo f-strings (use .format() or % formatting)\nNo type annotations (remove or use comments)\nNo pathlib (use os.path instead)\nNo typing module imports\n\nCode Quality\n\nProper encoding declaration (# -*- coding: utf-8 -*-)\nLogger initialized before API client\nAll imports are resolvable\nNo syntax errors\n\nSecurity\n\nAPI keys loaded from environment variables\nNo hardcoded credentials\nProper error handling for API calls\n\nPerformance\n\nReasonable check intervals (not too frequent)\nEfficient data fetching\nProper resource cleanup\nValidation Workflow\nUser Prompt → Code Generation → Validation → Fixes → Final Code\n                    ↓\n              If validation fails\n                    ↓\n            Apply automatic fixes\n                    ↓\n          Re-validate until success\n                    ↓\n          Deliver validated code\n\nValidation Failure Handling\n\nWhen validation fails, the system automatically updates the code with these steps:\n\nError Analysis - Identify the specific validation errors\nFix Application - Apply appropriate fixes based on error type\nRe-validation - Validate again after fixes\nIterative Repair - Repeat until code is valid (max 3 iterations)\nFallback Strategy - If automatic fixes fail, provide detailed error report and manual fix instructions\nAutomatic Fix Examples\nFix 1: Missing Imports\n# Before (error: NameError: name 'List' is not defined)\ndef calculate_prices(prices: List[float]) -> List[float]:\n\n# After (automatic fix)\nfrom typing import List, Dict, Optional\ndef calculate_prices(prices):\n\nFix 2: Encoding Issues\n# Before (error: SyntaxError: Non-ASCII character)\n# Strategy description: Grid trading\n\n# After (automatic fix)\n# -*- coding: utf-8 -*-\n# Strategy description: Grid trading\n\nFix 3: Python 3.5 Incompatibility\n# Before (error: SyntaxError in Python 3.5)\nprice = f\"Current price: {current_price}\"\n\n# After (automatic fix)\nprice = \"Current price: {}\".format(current_price)\n\nFix 4: Import Path Issues\n# Before (error: ImportError: No module named 'hyperliquid_api')\nfrom hyperliquid_api import HyperliquidClient\n\n# After (automatic fix)\nimport sys\nimport os\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), \"..\", \"api_wrappers\"))\nfrom hyperliquid_api import HyperliquidClient\n\nSupported Strategy Types\n1. Technical Indicator Strategies\nRSI-based: Oversold/overbought trading\nMACD-based: Trend following with MACD crossovers\nMoving Average: SMA/EMA cross strategies\nBollinger Bands: Mean reversion strategies\n2. Advanced Trading Strategies\nGrid Trading: Price range trading with multiple orders\nMean Reversion: Statistical arbitrage strategies\nTrend Following: Momentum-based strategies\nArbitrage: Spot-perp or cross-exchange arbitrage\n3. Signal-Driven Strategies\nVibeTrading Integration: Follow AI-generated trading signals\nNews-based: React to market news and sentiment\nWhale Activity: Track large wallet movements\nFunding Rate: Funding rate arbitrage strategies\nHow It Works\nStep 1: Prompt Analysis\n\nThe generator analyzes your natural language prompt to identify:\n\nTrading symbol (BTC, ETH, SOL, etc.)\nStrategy type (grid, RSI, signal-based, etc.)\nKey parameters (price ranges, grid counts, indicator values)\nRisk management preferences\nStep 2: Template Selection\n\nBased on the analysis, the system selects the most appropriate template from:\n\ntemplates/grid_trading.py - Grid trading strategy template\nStep 3: Code Generation\n\nThe generator:\n\nFills template parameters with your values\nAdds proper error handling and logging\nIncludes configuration management\nGenerates complete runnable code\nStep 4: Code Validation\n\nThe generated code is automatically validated and fixed:\n\nSyntax checking - Ensure valid Python syntax\nImport verification - Check all imports are resolvable\nCompatibility testing - Verify Python 3.5+ compatibility\nAutomatic fixes - Apply fixes for common issues\nRe-validation - Validate again after fixes\nError reporting - If fixes fail, provide detailed error report\nValidation Failure Handling\n\nIf validation fails after automatic fixes:\n\nError Analysis Report - Detailed breakdown of remaining issues\nManual Fix Instructions - Step-by-step guidance for manual fixes\nFallback Template - Option to use a simpler, validated template\nSupport Contact - Instructions for getting help\nStep 5: Output Delivery\n\nYou receive validated, runnable code including:\n\nValidated Python strategy file - Fully tested and fixed\nConfiguration template - Strategy parameters and settings\nValidation report - Summary of validation results and fixes applied\nUsage instructions - How to run and monitor the strategy\nTroubleshooting guide - Common issues and solutions\nRisk warnings - Important safety information\nAPI Integration\n\nThe generated code uses mature Hyperliquid API implementations that support:\n\nTrading Operations\nSpot trading (buy/sell with limit/market orders)\nPerpetual contracts (long/short with leverage)\nOrder management (cancel, modify, query)\nPosition management (reduce, hedge)\nMarket Data\nReal-time prices and OHLCV data\nFunding rates and open interest\nOrder book depth\nHistorical data access\nAccount Management\nBalance queries (spot and futures)\nPosition tracking\nPNL calculation\nRisk metrics\nTemplate System\nTemplate Structure\n\nEach template includes:\n\nStrategy class with initialization and main logic\nConfiguration section for easy parameter tuning\nError handling with comprehensive logging\nRisk management features\nMonitoring loop for continuous operation\nAvailable Templates\nGrid Trading Template\ngrid_trading.py - Grid trading within price ranges (Python 3.5+ compatible)\nNo f-strings\nNo type annotations\nProper encoding declaration\nLogger initialized before API client\nConfiguration Management\nStrategy Configuration\n\nGenerated strategies include configurable parameters:\n\nSTRATEGY_CONFIG = {\n    \"symbol\": \"BTC\",\n    \"timeframe\": \"1h\",\n    \"parameters\": {\n        \"rsi_period\": 14,\n        \"oversold\": 30,\n        \"overbought\": 70\n    },\n    \"risk_management\": {\n        \"position_size\": 0.01,\n        \"stop_loss\": 0.05,\n        \"take_profit\": 0.10,\n        \"max_drawdown\": 0.20\n    }\n}\n\nEnvironment Setup\n# Required environment variables\nexport HYPERLIQUID_API_KEY=\"your_api_key_here\"\nexport HYPERLIQUID_ACCOUNT_ADDRESS=\"your_address_here\"\nexport TELEGRAM_BOT_TOKEN=\"optional_for_alerts\"\n\nRisk Management Features\n\nAll generated strategies include:\n\n1. Position Sizing\nFixed percentage of portfolio\nDynamic position sizing based on volatility\nMaximum position limits\n2. Stop Loss Mechanisms\nPercentage-based stop loss\nTrailing stops\nTime-based exits\n3. Risk Controls\nMaximum daily loss limits\nDrawdown protection\nCorrelation checks\nMarket condition filters\n4. Monitoring & Alerts\nReal-time position tracking\nTelegram/Slack notifications\nPerformance reporting\nError alerts and recovery\nIntegration with VibeTrading Signals\n\nGenerated strategies can integrate with VibeTrading Global Signals:\n\nfrom vibetrading import get_latest_signals\n\n# Get AI-generated signals\nsignals = get_latest_signals(\"BTC,ETH\")\n\n# Use signals in trading logic\nif signals[\"BTC\"][\"sentiment\"] == \"BULLISH\":\n    strategy.execute_buy(\"BTC\", amount=0.01)\n\nUsage Examples\nExample 1: Simple RSI Strategy\n\nPrompt: \"Generate a BTC RSI strategy, buy 0.01 BTC when RSI below 30, sell when above 70\"\n\nGenerated Code Features:\n\nRSI calculation with 14-period default\nConfigurable oversold/overbought thresholds\nProper error handling for API calls\nLogging for all trading actions\n1-hour check interval\nExample 2: Grid Trading Strategy\n\nPrompt: \"ETH grid trading strategy, price range 3000-4000, 20 grids, 0.1 ETH per grid\"\n\nGenerated Code Features:\n\nAutomatic grid price calculation\nOrder placement and management\nGrid rebalancing logic\nPrice monitoring and adjustment\nComprehensive logging\nExample 3: Signal-Based Strategy\n\nPrompt: \"SOL trading strategy based on VibeTrading signals, buy on bullish signals, sell on bearish signals, 10 SOL per trade\"\n\nGenerated Code Features:\n\nVibeTrading API integration\nSignal polling and parsing\nTrade execution based on sentiment\nPosition management\nPerformance tracking\nBest Practices\n1. Start with Paper Trading\nAlways test strategies in simulation mode first\nUse small position sizes initially\nMonitor performance for at least 1-2 weeks\n2. Risk Management\nNever risk more than 1-2% per trade\nUse stop losses on all positions\nDiversify across multiple strategies\nMonitor correlation between strategies\n3. Monitoring & Maintenance\nRegularly review strategy performance\nAdjust parameters based on market conditions\nKeep logs for audit and analysis\nSet up alerts for critical events\n4. Security\nStore API keys securely (environment variables)\nUse separate accounts for different strategies\nRegularly rotate API keys\nMonitor for unauthorized access\nTroubleshooting\nCommon Issues\n1. API Connection Errors\n# Check API key and account address\necho $HYPERLIQUID_API_KEY\necho $HYPERLIQUID_ACCOUNT_ADDRESS\n\n# Test API connection\npython scripts/test_connection.py\n\n2. Strategy Not Executing Trades\nCheck balance and available funds\nVerify symbol is correctly specified\nCheck order size meets minimum requirements\nReview logs for error messages\n3. Performance Issues\nAdjust check intervals (too frequent may cause rate limiting)\nOptimize data fetching (cache where possible)\nReview market conditions (low liquidity periods)\n4. Integration Issues with VibeTrading\nVerify VibeTrading API is accessible\nCheck signal availability for your symbols\nReview signal parsing logic\n5. Validation Errors\n# Common validation errors and solutions:\n\n# Error: \"SyntaxError: invalid syntax\"\n# Solution: Check for f-strings or type annotations\npython scripts/code_validator.py strategy.py --fix\n\n# Error: \"ImportError: No module named 'typing'\"\n# Solution: Remove typing imports (Python 3.4 compatibility)\nsed -i '' 's/from typing import.*//g' strategy.py\n\n# Error: \"SyntaxError: Non-ASCII character\"\n# Solution: Add encoding declaration\necho -e '# -*- coding: utf-8 -*-\\n' | cat - strategy.py > temp && mv temp strategy.py\n\n# Error: \"NameError: name 'List' is not defined\"\n# Solution: Remove type annotations or add typing import\nsed -i '' 's/: List//g; s/: Dict//g; s/: Optional//g' strategy.py\n\n# Manual validation check\npython -m py_compile strategy.py\n\n6. Code Generation Failures\nCheck prompt clarity (be specific about parameters)\nEnsure template exists for requested strategy type\nVerify Python version compatibility (3.5+ recommended)\nCheck available disk space for output files\nAdvanced Features\nCustom Template Creation\n\nYou can create custom templates in templates/custom/:\n\nCreate a new template file\nDefine template variables with {{variable_name}}\nAdd to template registry in scripts/template_registry.py\nTest with the generator\nStrategy Backtesting\n\nWhile this generator focuses on live trading, you can:\n\nExport generated code to backtesting frameworks\nUse historical data for strategy validation\nAdd performance metrics and analysis\nMulti-Strategy Management\n\nFor running multiple strategies:\n\nGenerate separate strategy files\nUse different configuration files\nMonitor overall portfolio risk\nImplement strategy allocation logic\nSupport & Updates\nGetting Help\nReview generated code comments\nCheck example strategies in examples/\nConsult Hyperliquid API documentation\nReview VibeTrading signal documentation\nUpdates\n\nThis skill will be updated with:\n\nNew strategy templates\nImproved prompt understanding\nAdditional risk management features\nIntegration with more data sources\nBacktesting Integration\nBacktest Evaluation Feature\n\nAfter generating a strategy, you can now evaluate its performance using our integrated backtesting system:\n\n# Generate a strategy\npython scripts/strategy_generator.py \"BTC grid trading 50000-60000 10 grids 0.01 BTC per grid\"\n\n# Run backtest on the generated strategy\npython scripts/backtest_runner.py generated_strategies/btc_grid_trading_strategy.py\n\n# Run backtest with custom parameters\npython scripts/backtest_runner.py generated_strategies/btc_grid_trading_strategy.py \\\n  --start-date 2025-01-01 \\\n  --end-date 2025-03-01 \\\n  --initial-balance 10000 \\\n  --interval 1h\n\nBacktest Features\n\nThe backtesting system provides:\n\nHistorical Data Simulation - Uses historical price data for realistic testing\n\nPerformance Metrics - Calculates key metrics:\n\nTotal Return (%)\nMaximum Drawdown (%)\nSharpe Ratio\nWin Rate (%)\nTotal Trades\nAverage Trade Duration\n\nRisk Analysis - Evaluates strategy risk characteristics\n\nVisual Reports - Generates charts and performance reports\n\nComparative Analysis - Compares strategy performance against benchmarks\n\nBacktest Configuration\n\nYou can configure backtests with these parameters:\n\nBACKTEST_CONFIG = {\n    \"start_date\": \"2025-01-01\",\n    \"end_date\": \"2025-03-01\",\n    \"initial_balance\": 10000,  # USDC\n    \"interval\": \"1h\",  # 1m, 5m, 15m, 30m, 1h, 4h, 1d\n    \"symbols\": [\"BTC\", \"ETH\"],  # Trading symbols\n    \"commission_rate\": 0.001,  # 0.1% trading commission\n    \"slippage\": 0.001,  # 0.1% slippage\n}\n\nBacktest Results Example\n📊 Backtest Results for BTC Grid Trading Strategy\n================================================\n📅 Period: 2025-01-01 to 2025-03-01 (60 days)\n💰 Initial Balance: $10,000.00\n💰 Final Balance: $11,234.56\n\n📈 Performance Metrics:\n  • Total Return: +12.35%\n  • Max Drawdown: -5.67%\n  • Sharpe Ratio: 1.45\n  • Win Rate: 58.3%\n  • Total Trades: 120\n  • Avg Trade Duration: 12.5 hours\n\n📋 Trade Analysis:\n  • Winning Trades: 70\n  • Losing Trades: 50\n  • Largest Win: +$245.67\n  • Largest Loss: -$123.45\n  • Avg Win: +$89.12\n  • Avg Loss: -$56.78\n\n⚠️ Risk Assessment:\n  • Risk-Adjusted Return: Good\n  • Drawdown Control: Acceptable\n  • Consistency: Moderate\n\nBacktest Integration in Generated Code\n\nGenerated strategies now include backtest compatibility:\n\n# Generated strategy includes backtest method\nstrategy = GridTradingStrategy(api_key, account_address, config)\n\n# Run backtest\nbacktest_results = strategy.run_backtest(\n    start_date=\"2025-01-01\",\n    end_date=\"2025-03-01\",\n    initial_balance=10000\n)\n\n# Generate backtest report\nstrategy.generate_backtest_report(backtest_results)\n\nBacktest Data Sources\n\nThe backtesting system uses:\n\nHistorical price data from Hyperliquid API\nRealistic order execution with configurable slippage\nAccurate commission modeling based on exchange fees\nMarket impact simulation for large orders\nBacktest Limitations\n\nImportant Notes:\n\nPast performance ≠ future results - Historical success doesn't guarantee future profits\nData quality - Results depend on historical data accuracy\nMarket conditions - Past market conditions may differ from future\nExecution assumptions - Assumes perfect order execution (configurable slippage)\nLiquidity assumptions - Assumes sufficient market liquidity\n\nBest Practices:\n\nAlways backtest with multiple time periods\nTest different market conditions (bull, bear, sideways)\nUse realistic commission and slippage settings\nStart with small position sizes in live trading\nMonitor strategy performance and adjust as needed\nCode Validation Disclaimer\n\nValidation Limitations: While the code validation system automatically fixes common issues, it cannot guarantee:\n\nTrading logic correctness - Validation checks syntax, not trading logic\nFinancial performance - No guarantee of profitability\nAPI compatibility - Hyperliquid API changes may break generated code\nSecurity vulnerabilities - Manual security review is recommended\nEdge case handling - All possible error conditions may not be covered\n\nValidation Success Criteria: Code is considered \"valid\" when:\n\nNo syntax errors\nAll imports are resolvable\nPython 3.6+ compatible\nBasic structure is correct\n\nNot Validated:\n\nTrading logic accuracy\nRisk management effectiveness\nFinancial calculations\nMarket condition handling\nPerformance optimization\nQuick Reference\nPython Version Requirements\n# Check Python version\npython scripts/check_python_version.py\n\n# Minimum: Python 3.6+ (for f-string support)\n\nBasic Usage\n# Generate strategy\npython scripts/strategy_generator.py \"BTC grid trading 50000-60000 10 grids\"\n\n# Run backtest\npython scripts/backtest_runner.py generated_strategies/btc_grid_trading_strategy.py\n\nKey Features\nPython 3.6+ Compatibility - Modern Python features including f-strings\nAutomatic Backtest Integration - Evaluate strategies before live trading\nComprehensive Validation - Syntax and compatibility checking\nRisk Management - Built-in risk controls in all strategies\nTrading Disclaimer\n\nImportant: Trading cryptocurrencies involves significant risk. Generated strategies should be thoroughly tested before use with real funds. Past performance is not indicative of future results. Always use proper risk management and never trade with money you cannot afford to lose.\n\nThe code generator provides tools for strategy creation, but ultimate responsibility for trading decisions and risk management lies with the user.\n\nValidation is not a substitute for:\n\nThorough testing - Always test in simulation first\nCode review - Have experienced developers review generated code\nSecurity audit - Check for vulnerabilities before deployment\nPerformance testing - Test under various market conditions\nRisk assessment - Evaluate strategy risks independently\n\nBacktesting Limitations:\n\nHistorical data quality - Results depend on data accuracy\nMarket condition changes - Past conditions may differ from future\nExecution assumptions - Assumes perfect order execution\nLiquidity assumptions - Assumes sufficient market liquidity\nNo guarantee of future performance - Past success ≠ future profits"
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    "owner": "liuhaonan00",
    "version": "1.0.0",
    "license": null,
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