🚀 AlloraNetwork, HyperLiquid & DeepSeek AI Auto Trade Bot

Welcome to the AlloraNetwork HyperLiquid Auto Trading Bot! By default, the bot uses a volatility-based strategy, but you can define your own custom strategy to suit your trading preferences.

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⚙️ Configuration Details

• PRICE_GAP: Percentage difference between Allora's prediction and the current price required to trigger a trade.
• ALLOWED_AMOUNT_PER_TRADE: 💵 Maximum amount (in USD) for each trade, excluding leverage.
• MAX_LEVERAGE: Maximum leverage multiplier (e.g., 1 for 1x leverage).
• CHECK_FOR_TRADES: Time interval (in seconds) for the bot to check for trading opportunities.
• VOLATILITY_THRESHOLD: Minimum volatility level required before a trade executes.
• BTC_TOPIC_ID and ETH_TOPIC_ID: Mapping of tradable tokens to their Allora prediction topic IDs.

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🧠 Default Volatility Strategy

By default, the bot employs a volatility-based strategy to analyze market conditions and Allora's predictions to execute trades. The strategy is implemented in the volatility_strategy module.

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🛠️ Custom Strategy

Modify custom_strategy.py to create a personalized trading strategy.

Example Custom Strategy

def custom_strategy(token, price=None, allora_signal=None, allora_prediction=None):
    """
    Custom strategy that trades against Allora’s predictions in high-volatility conditions.
    """
    if price is None or allora_signal is None or allora_prediction is None:
        return None
    
    return volatility_strategy.execute(token, price, allora_signal, allora_prediction)

How to Use

1. Open strategy/custom_strategy.py.
2. Modify custom_strategy to implement your own logic.

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🤖 DeepSeek AI Trade Reviewer

The bot integrates DeepSeek AI for trade validation. It assesses trade risk, confidence, and reasoning before execution.

How It Works

1. Trade Analysis:

   • Current price vs. Allora prediction
   • Market volatility
   • Trade direction (BUY/SELL)
   • Historical performance

2. Decision Metrics:

   • approval (true/false): Trade approval status
   • confidence (0-100): Confidence level in the trade
   • reasoning: Explanation of the trade decision
   • risk_score (1-10): Trade risk assessment

3. Trade Execution Criteria:

   • Confidence score ≥ 70%
   • DeepSeek AI approval ✅
   • Risk score logged for tracking

Example Output

Trade Review by DeepSeek AI:
  Confidence: 85%
  Reasoning: "Allora prediction aligns with market trends.
              Volatility stable. Historical success rate: 70%."
  Risk Score: 3/10

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🔄 Example Workflow

1. Allora Prediction: "BUY" signal detected for BTC.
2. Custom Strategy: Evaluates trade feasibility.
3. DeepSeek AI Review: Validates trade based on risk, confidence, and reasoning.
4. Bot Action:
   • ✅ If Allora, Custom Strategy, and DeepSeek AI agree → Trade executed.
   • ❌ If any disagree → Trade rejected.

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💬 Support

For questions, reach out via GitHub. If this project helps you, consider giving it a ⭐!

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🤝 Contribute

This project is open source! Feel free to fork the repo, improve the bot, and submit a pull request. 🌟

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Happy Trading! 🚀📈