Vibetrading Code Gen

VibeTrading Code Generator

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.

Quick Start

Basic Usage

# Generate a simple RSI strategy
python scripts/strategy_generator.py "Generate a BTC RSI strategy, buy below 30, sell above 70"

# Generate a grid trading strategy
python scripts/strategy_generator.py "BTC grid trading 50000-60000 10 grids 0.01 BTC per grid"

# Generate a signal-following strategy
python scripts/strategy_generator.py "ETH trading strategy based on VibeTrading signals, buy on bullish signals, sell on bearish signals"

Output Structure

The generator creates:

1. Strategy Python file - Complete trading strategy class
2. Configuration file - Strategy parameters and settings
3. Usage instructions - How to run and monitor the strategy
4. Requirements file - Python dependencies

Code Validation System

Automatic Code Validation

All generated code is automatically validated and fixed using the built-in validation system:

# Validate generated code
python scripts/code_validator.py generated_strategy.py

# Validate and fix automatically
python scripts/code_validator.py generated_strategy.py --fix

# Validate entire directory
python scripts/code_validator.py strategy_directory/

Validation Steps

The validation system performs these checks:

1. Syntax Validation - Python syntax checking
2. Import Validation - Module import verification
3. Compatibility Checks - Python 3.5+ compatibility
4. Common Issue Detection - Missing imports, encoding issues, etc.

Automatic Fixes

When validation fails, the system automatically fixes common issues:

1. Add missing imports - Add typing imports if type annotations are used
2. Fix encoding declaration - Add # -*- coding: utf-8 -*- if missing
3. Remove incompatible syntax - Remove f-strings and type annotations for Python 3.5 compatibility
4. Fix import paths - Add sys.path modifications for API wrappers
5. Fix logger initialization order - Ensure logger is initialized before API client
6. Remove pathlib usage - Replace with os.path for Python 3.4 compatibility
7. Fix string formatting - Convert f-strings to .format() method

Validation Configuration

The validation system can be configured via command-line arguments:

# Basic validation
python scripts/code_validator.py strategy.py

# Validate and fix automatically
python scripts/code_validator.py strategy.py --fix

# Use specific Python executable
python scripts/code_validator.py strategy.py --python python3.6

# Validate directory with all files
python scripts/code_validator.py strategies/ --fix

# Maximum 5 fix iterations
python scripts/code_validator.py strategy.py --fix --max-iterations 5

Validation Rules

The system enforces these rules for generated code:

1. Python 3.5+ Compatibility

   • No f-strings (use .format() or % formatting)
   • No type annotations (remove or use comments)
   • No pathlib (use os.path instead)
   • No typing module imports

2. Code Quality

   • Proper encoding declaration (# -*- coding: utf-8 -*-)
   • Logger initialized before API client
   • All imports are resolvable
   • No syntax errors

3. Security

   • API keys loaded from environment variables
   • No hardcoded credentials
   • Proper error handling for API calls

4. Performance

   • Reasonable check intervals (not too frequent)
   • Efficient data fetching
   • Proper resource cleanup

Validation Workflow

User Prompt → Code Generation → Validation → Fixes → Final Code
                    ↓
              If validation fails
                    ↓
            Apply automatic fixes
                    ↓
          Re-validate until success
                    ↓
          Deliver validated code

Validation Failure Handling

When validation fails, the system automatically updates the code with these steps:

1. Error Analysis - Identify the specific validation errors
2. Fix Application - Apply appropriate fixes based on error type
3. Re-validation - Validate again after fixes
4. Iterative Repair - Repeat until code is valid (max 3 iterations)
5. Fallback Strategy - If automatic fixes fail, provide detailed error report and manual fix instructions

Automatic Fix Examples

Fix 1: Missing Imports

# Before (error: NameError: name 'List' is not defined)
def calculate_prices(prices: List[float]) -> List[float]:

# After (automatic fix)
from typing import List, Dict, Optional
def calculate_prices(prices):

Fix 2: Encoding Issues

# Before (error: SyntaxError: Non-ASCII character)
# Strategy description: Grid trading

# After (automatic fix)
# -*- coding: utf-8 -*-
# Strategy description: Grid trading

Fix 3: Python 3.5 Incompatibility

# Before (error: SyntaxError in Python 3.5)
price = f"Current price: {current_price}"

# After (automatic fix)
price = "Current price: {}".format(current_price)

Fix 4: Import Path Issues

# Before (error: ImportError: No module named 'hyperliquid_api')
from hyperliquid_api import HyperliquidClient

# After (automatic fix)
import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "api_wrappers"))
from hyperliquid_api import HyperliquidClient

Supported Strategy Types

1. Technical Indicator Strategies

• RSI-based: Oversold/overbought trading
• MACD-based: Trend following with MACD crossovers
• Moving Average: SMA/EMA cross strategies
• Bollinger Bands: Mean reversion strategies

2. Advanced Trading Strategies

• Grid Trading: Price range trading with multiple orders
• Mean Reversion: Statistical arbitrage strategies
• Trend Following: Momentum-based strategies
• Arbitrage: Spot-perp or cross-exchange arbitrage

3. Signal-Driven Strategies

• VibeTrading Integration: Follow AI-generated trading signals
• News-based: React to market news and sentiment
• Whale Activity: Track large wallet movements
• Funding Rate: Funding rate arbitrage strategies

How It Works

Step 1: Prompt Analysis

The generator analyzes your natural language prompt to identify:

• Trading symbol (BTC, ETH, SOL, etc.)
• Strategy type (grid, RSI, signal-based, etc.)
• Key parameters (price ranges, grid counts, indicator values)
• Risk management preferences

Step 2: Template Selection

Based on the analysis, the system selects the most appropriate template from:

• templates/grid_trading.py - Grid trading strategy template

Step 3: Code Generation

The generator:

1. Fills template parameters with your values
2. Adds proper error handling and logging
3. Includes configuration management
4. Generates complete runnable code

Step 4: Code Validation

The generated code is automatically validated and fixed:

1. Syntax checking - Ensure valid Python syntax
2. Import verification - Check all imports are resolvable
3. Compatibility testing - Verify Python 3.5+ compatibility
4. Automatic fixes - Apply fixes for common issues
5. Re-validation - Validate again after fixes
6. Error reporting - If fixes fail, provide detailed error report

Validation Failure Handling

If validation fails after automatic fixes:

1. Error Analysis Report - Detailed breakdown of remaining issues
2. Manual Fix Instructions - Step-by-step guidance for manual fixes
3. Fallback Template - Option to use a simpler, validated template
4. Support Contact - Instructions for getting help

Step 5: Output Delivery

You receive validated, runnable code including:

1. Validated Python strategy file - Fully tested and fixed
2. Configuration template - Strategy parameters and settings
3. Validation report - Summary of validation results and fixes applied
4. Usage instructions - How to run and monitor the strategy
5. Troubleshooting guide - Common issues and solutions
6. Risk warnings - Important safety information

API Integration

The generated code uses mature Hyperliquid API implementations that support:

Trading Operations

• Spot trading (buy/sell with limit/market orders)
• Perpetual contracts (long/short with leverage)
• Order management (cancel, modify, query)
• Position management (reduce, hedge)

Market Data

• Real-time prices and OHLCV data
• Funding rates and open interest
• Order book depth
• Historical data access

Account Management

• Balance queries (spot and futures)
• Position tracking
• PNL calculation
• Risk metrics

Template System

Template Structure

Each template includes:

• Strategy class with initialization and main logic
• Configuration section for easy parameter tuning
• Error handling with comprehensive logging
• Risk management features
• Monitoring loop for continuous operation

Available Templates

Grid Trading Template

• grid_trading.py - Grid trading within price ranges (Python 3.5+ compatible)
  • No f-strings
  • No type annotations
  • Proper encoding declaration
  • Logger initialized before API client

Configuration Management

Strategy Configuration

Generated strategies include configurable parameters:

STRATEGY_CONFIG = {
    "symbol": "BTC",
    "timeframe": "1h",
    "parameters": {
        "rsi_period": 14,
        "oversold": 30,
        "overbought": 70
    },
    "risk_management": {
        "position_size": 0.01,
        "stop_loss": 0.05,
        "take_profit": 0.10,
        "max_drawdown": 0.20
    }
}

Environment Setup

# Required environment variables
export HYPERLIQUID_API_KEY="your_api_key_here"
export HYPERLIQUID_ACCOUNT_ADDRESS="your_address_here"
export TELEGRAM_BOT_TOKEN="optional_for_alerts"

Risk Management Features

All generated strategies include:

1. Position Sizing

• Fixed percentage of portfolio
• Dynamic position sizing based on volatility
• Maximum position limits

2. Stop Loss Mechanisms

• Percentage-based stop loss
• Trailing stops
• Time-based exits

3. Risk Controls

• Maximum daily loss limits
• Drawdown protection
• Correlation checks
• Market condition filters

4. Monitoring & Alerts

• Real-time position tracking
• Telegram/Slack notifications
• Performance reporting
• Error alerts and recovery

Integration with VibeTrading Signals

Generated strategies can integrate with VibeTrading Global Signals:

from vibetrading import get_latest_signals

# Get AI-generated signals
signals = get_latest_signals("BTC,ETH")

# Use signals in trading logic
if signals["BTC"]["sentiment"] == "BULLISH":
    strategy.execute_buy("BTC", amount=0.01)

Usage Examples

Example 1: Simple RSI Strategy

Prompt: "Generate a BTC RSI strategy, buy 0.01 BTC when RSI below 30, sell when above 70"

Generated Code Features:

• RSI calculation with 14-period default
• Configurable oversold/overbought thresholds
• Proper error handling for API calls
• Logging for all trading actions
• 1-hour check interval

Example 2: Grid Trading Strategy

Prompt: "ETH grid trading strategy, price range 3000-4000, 20 grids, 0.1 ETH per grid"

Generated Code Features:

• Automatic grid price calculation
• Order placement and management
• Grid rebalancing logic
• Price monitoring and adjustment
• Comprehensive logging

Example 3: Signal-Based Strategy

Prompt: "SOL trading strategy based on VibeTrading signals, buy on bullish signals, sell on bearish signals, 10 SOL per trade"

Generated Code Features:

• VibeTrading API integration
• Signal polling and parsing
• Trade execution based on sentiment
• Position management
• Performance tracking

Best Practices

1. Start with Paper Trading

• Always test strategies in simulation mode first
• Use small position sizes initially
• Monitor performance for at least 1-2 weeks

2. Risk Management

• Never risk more than 1-2% per trade
• Use stop losses on all positions
• Diversify across multiple strategies
• Monitor correlation between strategies

3. Monitoring & Maintenance

• Regularly review strategy performance
• Adjust parameters based on market conditions
• Keep logs for audit and analysis
• Set up alerts for critical events

4. Security

• Store API keys securely (environment variables)
• Use separate accounts for different strategies
• Regularly rotate API keys
• Monitor for unauthorized access